United States Office of Research and EPA/625/R-00/010
Environmental Protection Development August 2000
Agency Cincinnati, OH 45268
v>EPA Environmental Problem Solving with
Geographic Information Systems
1994 and 1999 Conference Proceedings
About this CD:
This CD, EPA Document Number EPA/625/R-00/010, contains conference proceedings
documents from the 1994 and 1999 Environmental Problem Solving with Geographic
Information Systems conferences. The 1994 papers are also contained in a Seminar
Publication, EPA Document Number EPA/625/R-95/004, available from EPA.
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Contents of CD:
• 1999 Agenda.pdf- This file contains the agenda of the 1999 conference with links
to the available presentation papers. It is organized by date and time of presentation
at the conference.
• 1994 Contents.pdf- This file contains a list of presentations from the 1994
conference with links to the available presentation papers. It is organized by topic
area.
• 1999 Attendees.pdf- This file contains the list of attendees from the 1999
conference, with links to papers by attending authors.
-------
Papers - This folder contains PDF files of all of the available papers from the 1994
and 1999 conferences. Every paper in this folder may be accessed through the links
in the 1999 Agenda and 1994 Contents documents.
Readme.pdf - This file contains information about the CD contents, access, and
navigation. Start here when using this CD for the first time.
rs405eng.exe - This file contains the Adobe Acrobat 4.05 installation files.
Navigating between files:
The 1994 Contents, 1999 Agenda, and 1999 Attendees List may all be accessed from
this file. All the papers may be accessed by using the links from the 1994 Contents,
1999 Agenda, and 1999 Attendees List. When a link is followed to a paper, the
originating file is closed. To return to the contents or agenda file, click on the title of the
open paper. Every PDF document on this CD may also be accessed using your
Windows Explorer or File Manager.
For more information:
Contact the United States Environmental Protection Agency at: www.epa.gov
-------
Environmental Problem Solving
with Geographic Information Systems
September 21-23, 1994
Cincinnati, Ohio
GIS Concepts
GIS Uncertainty and Policy: Where Do We Draw the 25-Inch Line?
James E. Mitchell
Data Quality Issues Affecting GIS Use for Environmental Problem-Solving
Carol B. Griff in
You Can't Do That With These Data! Or: Uses and Abuses of Tap Water Monitoring
Analyses
Michael R. Schock and Jonathan A. Clement
Ground-Water Applications
Using GI5/GP5 in the Design and Operation of Minnesota's Ground Water
Monitoring and Assessment Program
Tom Clark, Yuan-Ming Hsu, Jennifer Schlotthauer, Don Jakes, and
Georgianna Myers
Use of GIS in Modeling Ground-Water Flow in the Memphis, Tennessee, Area
James Outlaw and Michael Clay Brown
MODRISI: A PC Approach to GIS and Ground-Water Modeling
Randall R. Ross and Milovan 5. Beljin
GIS in Statewide Ground-Water Vulnerability Evaluation to Pollution Potential
Navulur Kumar and Bernard A. Engel
Verification of Contaminant Flow Estimation With GIS and Aerial Photography
Thomas M. Williams
Geology of Will and Southern Cook Counties, Illinois
Edward Caldwell Smith
-------
Watershed Applications
The Watershed Assessment Project: Tools for Regional Problem Area
Identification
Christine Adamus
Watershed Stressors and Environmental Monitoring and Assessment Program
Estuarine Indicators for South Shore Rhode Island
John F. Paul and George E. Morrison
(515 Watershed Applications in the Analysis of Nonpoint Source Pollution
Thomas H. Cahill, Wesley R. Horner, and Joel 5. Mc(5uire
Using (515 To Examine Linkages Between Landscapes and Stream Ecosystems
Carl Richards, Lucinda Johnson, and George Host
Nonpoint Source Water Quality Impacts in an Urbanizing Watershed
Peter Coffin, Andrea Dorlester, and Julius Fabos
A (515 for the Ohio River Basin
Walter M. Grayman, Sudhir R. Kshirsagar, Richard M. Males, James A.
Goodrich, and Jason P. Heath
Nonpoint Source Pesticide Pollution of the Pequa Creek Watershed, Lancaster
County, Pennsylvania: An Approach Linking Probabilistic Transport Modeling and (515
Robert T. Paulsen and Allan Moose
Integration of (515 With the Agricultural Nonpoint Source Pollution Model: The
Effect of Resolution and Soils Data Sources on Model Input and Output
Suzanne R. Perlitsh
X(5RCWP, a Knowledge- and <5IS-Based System for Selection, Evaluation, and
Design of Water Quality Control Practices in Agricultural Watersheds
Runxuan Zhao, Michael A. Foster, Paul t>. Robillard, and David W. Lehning
Integration of EPA Mainframe Graphics and (515 in a UNIX Workstation
Environment To Solve Environmental Problems
William B. Samuels, Phillip Taylor, Paul Evenhouse, and Robert King
-------
Wetlands Applications
Wetlands Mapping and Assessment in Coastal North Carolina: A GIS-Based
Approach
Lori Sutter and James Wuenscher
Decision Support System for Multiobjective Riparian/Wetland Corridor Planning
Margaret A. Fast and Tina K. Rajala
Design of GI5 Analysis To Compare Wetland Impacts on Runoff in Upstream Basins
of the Mississippi and Volga Rivers
Tatiana B. Nawrocki
Water Quality Applications
Vulnerability Assessment of Missouri Drinking Water to Chemical Contamination
Christopher J. Barnett, Steven J. Vance, and Christopher L. Fulcher
Reach File 3 Hydrologic Network and the Development of GIS Water Quality Tools
Stephen Bevington
EPA's Reach Indexing Project: Using GI5 To Improve Water Quality Assessment
Jack Clifford, William D. Wheaton, and Ross J. Curry
Environmental Management Applications
Ecological Land Units, GIS, and Remote Sensing: Gap Analysis in the Central
Appalachians
Ree Brannon, Charles B. Yuill, and Sue A. Perry
A GIS Strategy for Lake Management Issues
Michael F. Troge
A Watershed-Oriented Database for Regional Cumulative Impact Assessment and
Land Use Planning
Steven J. Stichter
A GIS Demonstration for Greenbelt Land Use Analysis
Joanna J. Becker
-------
GIS as a Tool for Predicting Urban Growth Patterns and Risks From Accidental
Release of Industrial Toxins
Samuel V. Noe
Integration of GIS and Hydrologic Models for Nutrient Management Planning
Clyde W. Fraisse, Kenneth L. Campbell, James W. Jones, William &. Boggess,
and Babak Negahban
Other GIS Applications
Expedition of Water-Surface-Profile Computations Using GIS
Ralph J. Haefner, K. Scott Jackson, and James M. Sherwood
Small Is Beautiful: GIS and Small Native American Reservations—Approach,
Problems, Pitfalls, and Advantages
Jeff Besougloff
A GIS-Based Approach to Characterizing Chemical Compounds in Soil and Modeling
of Remedial System Design
Leslie L. Chau, Charles R. Comstock, and R. Frank Keyser
Polygon Development Improvement Techniques for Hazardous Waste Environmental
Impact Analysis
David A. Padgett
Comparing Experiences in the British and U.S. Virgin Islands in Implementing GIS
for Environmental Problem-Solving
Louis Potter and Bruce Potter
Application of GIS for Environmental Impact Analysis in a Traffic Relief Study
Bruce Stauffer and Xinhao Wang
-------
Environmental Problem Solving
With Geographic Information Systems
Tuesday, September 21, 1999
Pre-registration and Cash Bar Reception (5:00 PM - 8:00 PM)
Day 1 - Wednesday, September 22, 1999
Grand Ballroom A-B
PLENARY SESSION
7:30 - 9:00 Registration and Name Badge Pickup
9:00 - 9:15 Welcome & Overview
Sue Schock, USEPA, ORD, NRMRL
Daniel J. Murray, USEPA, ORD, NRMRL
9:15 - 10:00 New Directions in Environmental Problem Solving
Michael F. Goodchild, Ph. D., Chair
National Center for Geographic Information and Analysis, and
Department of Geography, University of California - Santa Barbara
10:00 - 10:20 BREAK
10:20 - 10:50 6IS Workgroup: An Overview 6IS - QA
George M.Brills, J.D., USEPA, ORD, NERL
10:50 - 11:30 Environmental Visioning with Geographic Information Systems
Sudhir R. Kshirsagar, Ph. D., Global Quality Corporation, and
Paul Koch, Pacific Environmental Services Inc.
11:30 - 1:00 LUNCH
-------
Day 1 - Wednesday, September 22, 1999 (continued)
Room
SESSION
Moderator
TIME
1:00 -
1:25
1:25 -
1:50
1:50 -
2:15
2:15 -
2:40
2:40 -
3:00
3:00 -
3:25
3:25 -
3:50
3:50 -
4:15
4:15 -
4:40
Grand Ballroom A
SESSION A
DIFFUSE SOURCE
Lyn Kirschner
Conservation Technology Informal-ion Center
PRESENTATION
&IS Watershed Delineation
Tools
Nonpoint Pollutant Loading
Application for ArcView GIS
Application of DEM and Land
Cover Data in Estimating
Atmospheric Deposition to the
Northeast and Mid-Atlantic
Regions: Model Development
and Applications
Assessing the Impact of
Landuse/Landcover on Stream
Chemistry in Maryland
SPEAKER(S)
James Goodrich,
Ph.D., USEPA,
ORD, NRMRL
Laurens van der
Tak, P.E.,
CH2MHJII
James A. Lynch,
Ph.D.,
Pennsylvania
State University
Gabriel Senay,
Ph.D., PAI/SAIC
.Grand Ballroom B
SESSION B
ASSESSMENT / REMEDIATION
Jill Neal, USEPA, ORt), NRMRL
PRESENTATION
GIS and GPS in Environmental
Remediation Oversight at
Federal Facilities in Ohio
The Impact of Spatial
Aggregation on Environmental
Modeling: A GIS Approach
Characterizing the Hydrogeology
of Acid Mine Discharges from
the Kempten Mine Complex,
West Virginia and Maryland
The GIS Connection to
Residential Yard Soil
Remediation
SPEAKER(S)
Kelly Kaletsky
and Bill Lohner,
Ohio EPA
Lin Liu, Ph.D.,
University of
Cincinnati
Benjamin R.
Hayes, Bucknell
University
Jennifer Deis,
Black & Veatch
BREAK
Assessing the Long-Term
Impact of Land Use Change On
Runoff and Non-Point Source
Pollution Using a GIS-NPS
Model
A Web- Based &IS Model for
Assessing the Long-Term
Hydrologic Impacts of Land
Use Change (L-THIA GIS
WWW): Motivation and
Development
Using a Geographic Information
System for Cost-Effective
Reductions in Nonpoint Source
Pollution: The Case of
Conservation Buffers
Putting Geospatial Information
Into the Hands of the "Real"
Natural Resource Managers:
Lessons from the NEMO
Project in Educating Local Land
Use Decision Makers
Budhendra
Bhaduri, Oak
Ridge National
Laboratory
Jon Harbor,
Purdue University
(Bernie Engel,
Ph.D.)
Mark S. Landry,
Virginia Tech
University
Joel Stacker,
Cooperative
Extension,
University of
Connecticut
GIS in the Confirmation Process
Using a Geographic Information
Systems Application to
Implement Risk Based Decisions
in Corrective Action
Determining the Accuracy of
Geographic Coordinates for
NPDES Permittees in the State
of Ohio
No presentation
Raymond E.
Bailey, Ph.D.,
MK-Ferguson
Lesley Hay
Wilson, P.E.,
The University
of Texas -
Austin
Bhagya
Subramanian,
USEPA, NERL
4:30 - 6:30 Reception (cash bar)
-------
Day 2 - Thursday, September 23, 1999
8:00 - 9:00 Registration and Name Badge Pickup
Room
SESSION
Moderator
TIME
8:30 -
8:55
8:55 -
9:20
9:20 -
9:45
9:45 -
10:10
10:10 -
10:30
10:30 -
10:55
10:55 -
11:20
11:20 -
11:45
11:45 -
1:30
Grand Ballroom A
SESSION C
APPLICATIONS
boug Grosse. USEPA, ORb, NRMRL
PRESENTATION
Evaluating Soil Erosion Parameter
Estimates from Different Data
Sources
A Planning Strategy for Siting
Animal Confinement Facilities: The
Integrated Use of &IS and
Digital Image Simulation
Technologies
Lake Superior Decision Support
Systems: GIS Databases and
Decision Support Systems for
Land Use Planning
Update of GIS Land Use
Attributes from Land Surface
Texture Information Using
SIR-C Images
SPEAKER(S)
Gabriel Senay,
Ph.D., PAI/SAIC
Thora Cartlidge,
AICP, ASLA,
University of
Minnesota
George E. Host,
Ph.D., Natural
Resources
Research
Institute
Francisco J.
Artigas, Ph.D.,
Rutgers
University
Grand Ballroom B
SESSION D
URBAN / BROWNFIELDS / COMMUNITY
Jim Kreissl, USEPA, CERI, TTB
PRESENTATION
Merging Transportation and
Enviromental Planning Using
GIS
Use of GIS Tools for
Conducting Community On-
Site Septic Management
Planning
Management and Reuse of
Contaminated Soil -- The
SoilTrak Method
Using GIS to Rank
Environmentally Sensitive Land
in Orange County, Florida
SPEAKER(S)
Elizabeth
Lanzer,
Washington
Dept. of
Transportation
David Healy,
Stone
Environmental,
Inc.
Edward
Rogers, Jr.,
BEM Systems,
Inc.
Michael J.
Gilbrook, HDR
Engineering,
Inc.
BREAK
Onsite Wastewater Management
Program in Hamilton County,
Ohio- -An Integrated Approach to
Improving Water Quality and
Preventing Disease
Modeling Combined Sewer
Overflow (CSO) Impact: The Use
of a Regional GIS in Facilities
Planning
Building a Shared and Integrated
GIS to Support Environmental
Regulatory Activities in South
Carolina
Timothy I.
Ingram, Hamilton
County General
Health District,
Ohio
Michael D.
Witwer, Metcalf
& Eddy, Inc.
Guang Zhao,
Ph.D., South
Carolina Dept. of
Health & Env.
Control
Use of GIS for the
Investigation and
Classification of Land Being
Redeveloped Under the Ohio
Voluntary Action Program
Assessing and Managing the
Impacts of Urban Sprawl on
Environmentally Critical
Areas: A Case Study of
Portage County, Ohio
Building a Brownfield Sitebank
With Internet Map Server
Technology
Andrew
Rawnsley,
Ravensfield
Geographic
Resources,
Ltd.
Jay Lee,
Ph.D., Kent
State
University
Alan Rao,
Ph.D.,
Vanasse
Hangen
Brustlin, Inc
LUNCH
-------
Day 2 - Thursday, September 23, 1999 (Continued)
Room
SESSION
Moderator
TIME
1:30 -
1:55
1:55 -
2:20
2:20 -
2:45
2:45 -
3:10
3:10 -
3:30
Grand Ballroom A
SESSION E
ECOLOGY / RESTORATION
Scott Minamyer, USEPA, CERI, TTB
PRESENTATION
Targeting the Knowledge
Assembly Process of the Flora of
North America (FNA): Biological
Resource Problem Solving Using
eis
GIS Standards for Environmental
Restoration and Compliance
Reporting on the Development of
an Environmental &IS Application
- Wetlands Restoration in the
Central Valley of California
Habitat Filters, GIS. and
Riverine Fish Assemblages:
Sifting Through the Relationships
Between Fishes and Their
Habitat
SPEAKER(S)
Leila M.
Shultz,Ph.D.,
Harvard
University and
Utah State
University
Bobby G.
Carpenter, P.E.,
Tri -Service
CADD/6IS
Tech. Center
David Hansen,
US Bureau of
Reclamation
Douglas A.
Nieman,
Normandeau
Associates
Grand Ballroom B
SESSION F
RISK / ENVIRONMENTAL JUSTICE /
EXPOSURE
Tom Brennan, USEPA, OPPT
PRESENTATION
Using GIS to Analyze the
Spatial Distribution of
Environmental, Human Health,
and Socio- Economic
Characteristics in Cincinnati
Public Participation GIS
Applications for Environmental
Justice Research and
Community Sustainability
Quantifying Risk in Watershed
Assessment Using GIS &
Stochastic Field-Scale
Modeling
Methodological Issues in GIS-
Based Environmental Justice
Research
SPEAKER(S)
Xinhao Wang,
Ph.D. and Chris
Auffrey,
University of
Cincinnati
David Padgett,
Ph.D.,
Tennessee
State
University
Conrad
Heatwole,
Ph.D., P.E.,
Virginia Tech
University
Jeremy Mennis,
Pennsylvania
State
University
BREAK
-------
Day 2 - Thursday, September 23, 1999 (Continued)
Room
SESSION
Moderator
TIME
3:30 -
3:55
3:55 -
4:20
4:20 -
4:45
Grand Ballroom A
SESSION E
ECOLOGY / RESTORATION
Scott Minamyer, USEPA, CERI, TTB
PRESENTATION
Using a GIS Model to Predict the
Extent of Common Reed
Encroachment into Two Tidal
Wetland Areas in Northeastern
New Jersey
The Application of GIS in the
Development of Regional
Restoration Goals for Wetland
Resources in the Greater Los
Angeles Drainage Area
Fractal Dimension as an Indicator
of Human Disturbance in Galveston
Bay, Texas
SPEAKER(S)
Karla Hyde and
Robin Dingle,
Northern
Ecological
Associates, Inc.
Charles Rairdan,
US Army Corps
of Engineers
Amy Liu,
PAI/SAIC
Grand Ballroom B
SESSION F
RISK / ENVIRONMENTAL JUSTICE /
EXPOSURE
Tom Brennan, USEPA, OPPT
PRESENTATION
Using GIS to Evaluate the
Effects of Flood Risk on
Residential Property Values
Environmental Justice in
Kentucky: Examining the
Relationships Between Low-
Income and Minority
Communities and the Location
of Landfills, and TSD
Facilities
Application of GIS to Address
Environmental Justice: Needs
and Issues
SPEAKER(S)
Alena
Bartosova and
David E.
Clark, Ph.D.,
Marquette
University
Larisa J.
Keith,
Northern
Kentucky Area
Planning
Commission
Babafemi A.
Adesanya,
Environmental
Equity
Information
Institute
4:30 - 6:30 Reception (cash bar)
-------
Day 3 - Friday, September 24, 1999
8:00 - 9:00 Registration and Name Badge Pickup
Room
SESSION
Moderator
TIME
8:30 -
8:55
8:55 -
9:20
9:20 -
9:45
9:45 -
10:10
10:10 -
10:30
10:30 -
10:55
10:55 -
11:20
11:20
Grand Ballroom A
SESSION &
WATERSHEDS.
Mike Troyer, Ph.D., USEPA, ORD, NRMRL
PRESENTATION
The National Hydrography
Dataset - Status and Applications
Sustainable Developments:
Definition, Location, and
Understanding
Development of a National
Watershed Boundaries Dataset
A Watershed -Based Approach to
Source Water Assessment and
Protection Utilizing GIS- Based
Inventories: A Case Study in
South Carolina
SPEAKER(S)
Thomas &. Dewald,
USEPA, and Keven
s. Roth, uses
Michael E. Troyer,
Ph.D., USEPA,
ORD, NRMRL
Alan Rea, US6S
James M. Rine,
Ph.D., Earth
Sciences Research
Institute
Grand Ballroom B
SESSION H
MODELS / SYSTEMS
Randall Ross, Ph.D., USEPA, ORD, NRMRL
PRESENTATION
Strategic Planning for
eis
No More 3 -Ring Binders!
Pollution Exposure Index
Model Measures Airborne
Pollutants in National
Forests
A GIS- Based Approach to
Predicting Wetland
Drainage & Wildlife
Habitat Loss in the
Prairie Pothole Region of
South-Central Canada
SPEAKER(S)
Parrish
Swear ingen,
Robins AFB
Margaret B.
Martin, P.E.,
US Army Corps
of Engineers
Michael V.
Miller,
CH2MHNI
David
Howerter,
Institute for
Wetland and
Waterfowl
Research
BREAK
Using an ARC/INFO 6IS to
Analyze Forest Patches for
Watershed -Based Conservation
and to Present Data on a Web
Site
Application of a Water Balance
Model and &IS for Sustainable
Watershed Management
Lonnie Darr,
Montgomery
County, MD, Dept.
of Env. Protection
Thomas H. Cahill,
P.E., and Susan
Pagano, Cahill
Associates
Application of a
Geographic Information
System for Containment
System Leak Detection
A High -Resolution
Hydrometeorological Data
System for Environmental
Modeling and Monitoring
Randall R.
Ross, Ph.D.,
USEPA, ORD,
NRMRL
David R.
Legates, Ph.D.,
University of
Delaware
CONFERENCE CONCLUDES
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Eric Adams
OFFICE: 614-644-2752
FAX: 614-644-2909
List of Attendees
Cincinnati, Ohio
Ohio EPA
P.O. Box 1049
Columbus OH
43216-1049
Babafemi A. Adesanya
OFFICE: 7578658950
FAX:
e2i2@eeii.org
Environmental Equity Information Institute
P.O.Box 189
Hampton VA 23669
USA
Larry Alber
OFFICE: 518-457-3143
FAX: 518-457-0738
lalber@gw. dec. state, ny. us
NYS Dept. of Environmental Conservation
50 Wolf Road
Albany NY 12233
Lora Alberto
OFFICE: 513-861-7666
FAX: 513-559-3155
mcrp@one.net
Mill Creek Restoration Project
42 Calhoun Street
Cincinnati OH 45219
Melanie Allamby
OFFICE: 216-881-6600x446
FAX: 216-881-2738
allambym@neorsd.org
N.E. Ohio Regional Sewer District
4415 Euclid Avenue
Cleveland OH 44103
Dr. James K. Andreasen (Jim)
OFFICE: 202-564-3293
FAX: 202-565-0076
andreasen.james@epa.gov
USEPA ORD Natl. Center for Env. Assessment
401 M Street, SW, Mail Code (8623D)
Washington DC 20460
Roger Anzzolin
OFFICE: 202-260-7282
FAX: 202-401-3041
anzzolin. roger@epa.gov
U.S. EPA OGWDW 4606
401 M. St. SW
Washington DC 20460
Gary Arnold
OFFICE: 541-686-7838x247
FAX: 541-686-7551
arnold.gary@deq. state, or. us
OREGON DEQ
1102 Lincoln
Eugene OR 97401
Francisco J. Artigas
OFFICE: 9733531069
FAX:
artigas@cimic. rutgers. edu
Center for Info. Mgmt., Integration & Connectivity
Rutgers University
180 University Avenue
Newark NJ 07102 USA
PAPER
Chris Auffrey
OFFICE: 513-556-0579
FAX: 513-556-1274
chris. auffrey@uc.edu
University of Cincinnati
P.O. Box210016
Cincinnati OH 45221-0016
PAPER
Margaret Ay cock
OFFICE: 409-880-8897
FAX: 409-880-1837
aycockma@hal. lamar.edu
Gulf Coast Hazardous Substance Research Center
P.O. Box 10671
Beaumont TX 77710-0671
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Russ Baier
OFFICE: 512-239-1746
FAX: 512-239-5687
rbaier@tnrcc. state, tx. us
List of Attendees
Cincinnati, Ohio
Texas Natural Resource Conservation Comm.
12100 Park 35 Circle
Austin TX 78753
Jeanne M. Bailey
OFFICE: 202-628-8303
FAX: 202-628-2846
jbailey@awwa. org
American Water Works Association
1401 New York Ave., NW#640
Washington DC 20005
Raymond E. Bailey
OFFICE: 6364418086
FAX:
ray_bailey@wssrap-host. wssrap. com
MK-Ferguson
7295 Highway 94 South
St. Charles MO 63304
PAPER
USA
Thomas E. Bailey
OFFICE: 513-287-2596
FAX: 513-287-3499
tbailey@cinergy. com
Cinergy Corp.
139 E. Fourth St., Room 552a
Cincinnati OH 45201
Kim Baker
OFFICE: 614-265-6411
FAX: 614-267-2981
kim. baker@dnr. state.oh. us
Ohio Dept. of Natural Resources
1952 Belcher Drive, Bldg. C-2
Columbus OH 43224-1386
M.C. Baldwin
OFFICE: 520-871-7690
FAX: 520-871-7599
mcb4gis@juno.com
Navajo Nation Environmental Protection Agency
Water Quality Program - PO Box 339
Window Rock AZ 86515
Todd Baldwin
OFFICE: 202-232-7933
FAX: 202-234-1328
tbaldwin@islandpress. org
Island Press
1718 Connecticut Avenue, NW, Suite 300
Washington DC 20009
Brian Balsley
OFFICE: 513-326-1500
FAX: 513-326-1550
bbalsley@bheenv.com
BHE Environmental, Inc.
11733ChesterdaleRd.
Cincinnati OH 45246
Robert Bamford
OFFICE: 775-687-4670
FAX: 775-687-6396
rbamford@ndep. carson-city. nv. us
Nevada Division of Environmental Protection
333 West Nye Lane
Carson City NV 89706
Quinn Barker
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
Chris Barnett
OFFICE: 573-882-9291
FAX: 573-884-2199
barnett@cares.missouri. edu
CARES - University of Missouri
130Mumford Road
Columbia MO 65211-6200
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Ralph P. Barr
OFFICE: 301-258-8651
FAX: 301-258-8679
barrR @ttnus. com
List of Attendees
Tetra Tech NUS, Inc.
910 Clopper Road, Suite 400
Gaithersburg MD 20878
Cincinnati, Ohio
Alena Bartosova
OFFICE: 4142885128
FAX:
5x99Bartosov@marquette.edu
Dept. of Civil & Env. Engineering
Marquette University
P.O. Box1881
Milwaukee Wl 53201-1881 USA
PAPER
Bruce Battles
OFFICE: 405-744-8974
FAX: 405-744-7008
bruce@seic. he. okstate.edu
Oklahoma State University
201 CITD
Stillwater OK 74078
Bruce Bauch
OFFICE: 502-493-1945
FAX:
bbauch@usgs.gov
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville KY 40299
Stephanie Beak
OFFICE: 614-644-4852
FAX: 614-728-1245
Stephanie. beak@epa. state.oh. us
Ohio EPA
122 South Front Street
Columbus OH 43215
Michael L. Bechdol
OFFICE: 214-665-7133
FAX: 214-665-2191
bechdol.michael@epamail. epa.gov
U.S. EPA - Region 6WQ-SG
1445 Ross Avenue
Dallas TX 75202
Glynn Beck
OFFICE: 270-827-3414
FAX: 270-827-1117
ebeck@kgs. mm. uky. edu
Kentucky Geological Survey
P.O. Box 653
Henderson KY 42419
Tara L. Beckman
OFFICE: 4129218358
FAX:
beckmant@ttnus. com
TetraTech NUS, Inc.
Foster Plaza 7, 661 Andersen Dr
Pittsburgh PA 15220
USA
Bob Bednar
OFFICE: 405-702-8197
FAX: 405-702-8101
bobby. bednar@deqmail.state.ok.us
Oklahoma Dept. of Environmental Quality
707 N. Robinson Ave.
Oklahoma City OK 73102
Brian Begley
OFFICE: 502-564-6716
FAX: 502-564-2705
brian. begley@mail.state.ky. us
Kentucky Division of Waste Management
14 Reilly Road
Frankfort KY 40601
Dick Behr
OFFICE: 207-287-6828
FAX: 207-287-7826
richard. s. behr@state.me. us
Maine Department of Environmental Protection
17 State House Station
Augusta ME 04333
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Milovan Beljin
OFFICE: 513-729-1602
FAX:
List of Attendees
M.S. Beljin and Assoc.
9416ShadyoakCt.
Cincinnati OH 45231
Cincinnati, Ohio
PAPER
Kevin Benck
OFFICE: 605-688-4776
FAX:
kbenck@brookings. net
Water Resources Institute-South Dakota St. Univ.
SDSUBox2120AE211
Brookings SD 57007
Malcolm Bender
OFFICE: 214-665-8378
FAX: 214-665-6660
bender.malcolm@epa.gov
U.S. EPA / SEE Program
1445 Ross Avenue
Dallas TX 75202
Clif Benoit
OFFICE: 801-625-5594
FAX: 801-625-5483
cbenoit/r4@fs.fed. us
US Forest Service
Federal Building
140 E. 3275 N.
N.Ogden UT 84414
Jerry Bernard
OFFICE: 202-720-5356
FAX: 202-720-0428
jerry, bernard@usda.gov
USDA-Natural Resource Conservation Service
P.O. Box 2890, Room 6123
Washington DC 20013
Budhendra Bhaduri
OFFICE: 423-241-9272
FAX: 423-241-6261
bhaduribl@ornl.gov
Oak Ridge National Laboratory
P.O. Box 2008, MS 6237
Oak Ridge TN 37831-6237
PAPER
Taher Bishr
OFFICE: 0020-12-2166800
FAX: 00203-4835337
20 Salah Salem Str. D. Toun
Alex - EGYPT
Ben Blaney
OFFICE: 513-569-7852
FAX: 513-569-7680
blaney. ben@epa.gov
USEPA /NRMRL
26 West Martin Luther King Dr.
Cincinnati OH 45268
Brian Bohl
OFFICE: 513-271-4182
FAX: 513-569-7160
bohl. brian@epamail. epa.gov
U.S. EPA
26W. Martin Luther King Drive
Cincinnati OH 45268
Steve Bolssen
OFFICE: 502-564-3410
FAX: 502-564-4245
Steven. bolssen@mail.state.ky. us
KY Dept. of Env. Protection, Division of Water
14 Reilly Road
Frankfort KY 40601-1189
Bill Boria
OFFICE: 716-753-4481
FAX: 716-753-4344
billb@health. co. chatauqua. ny. us
Chautauqua County Health Department
7 North Erie Street
Mayville NY 14757
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Joshua Bowen
OFFICE: 800-355-9429
FAX: 770-399-0535
jbowen@ftr. com
United Capitol Insurance Company
400 Perimeter Center Terrace, Suite 345
Atlanta GA 30346
Virgil Brack
OFFICE: 513-236-1163
FAX: 513-236-1178
vbrack@bheenv.com
BHE Environmental, Inc.
11733 Chesterdale Road
Cincinnati OH
John Bradley
OFFICE: 517-335-3146
FAX: 517-373-9657
bradlejn@state.mi. us
Michigan DEQ
300 South Washington Square
Lansing Ml 48933
Tom Brankamp
OFFICE: 606-431-8579
FAX: 606-431-8581
Tom Brennan
OFFICE: (202)260-3920
FAX:
brennan. thomas@epa.gov
Woolpert LLP
525 West Fifth Street, Suite 213
Covington KY 41011
Don Brannen
OFFICE: 513-681-8247
FAX: 513-681-1594
Cincinnati Recreation Commission
1655 Chase Avenue
Cincinnati OH 45206
U.S. EPA, OPPT
(MC7406)
Washington DC
20460
Timothy Bricker
OFFICE: 765-214-0088
FAX:
tjbricker@bsuvc. edu
Ball State Univeristy
3556 North Tillotson #205
Muncie IN 47304
James Bridges
OFFICE: 513-489-6611
FAX: 513-489-6619
jbridges@cin.pes. com
Pacific Environmental Services, Inc.
7209 E. Kemper Rd.
Cincinnati OH 45247
Jan W. Briede
OFFICE: 513-247-8000
FAX: 513-247-8010
jbriede@scientech. com
Scientech, NES, Inc.
11400 Grooms Road
Cincinnati OH 45242
George M. Brilis
OFFICE: 702-798-3128
FAX:
brilis.george@epamail.epa.gov
USEPA
P.O. Box 93478
Las Vegas NV 89193
Linda Briscoe
OFFICE: 513-641-3081
FAX: 513-641-0508
LBri938500@aol. com
Ohio/Cincinnati Women's Health Project
4860 Winneste Ave.
Cincinnati OH 45232
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Thomas M. Brody
OFFICE: 312-353-8340
FAX: 312-353-4755
brody.tom@epa.gov
List of Attendees
Region 5 Office of Information services
77 W. Jackson Blvd.
Chicago IL 60604
Cincinnati, Ohio
Joyce A. Broka
OFFICE: 517-686-8025x8371
FAX: 517-684-9799
brokaj@state.mi. us
Michigan Department of Environmental Quality
503 N. Euclid Ave.
Bay City Ml 48706
Hugh J. Brown
OFFICE: 765-285-5788
FAX: 765-285-2606
hbrown@gw. bsu. edu
Ball State University
2000 University
Muncie IN 47306
Mike Bruening
OFFICE: 513-3261500
FAX:
mbruening@bheevin.com
BHE Environmental
Cincinnati OH
Jason Buck
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
John Burckle
OFFICE: 513-569-7496
FAX: 513-569-7471
burckle.john@epamail. epa.gov
U.S. EPA / NRMRL
26 West Martin Luther King Drive
Cincinnati OH 45220
David Burden
OFFICE: 580-436-8606
FAX: 580-436-8614
burden, david@epa.gov
U.S. EPA/NRMRL/SPRD
P.O. Box 1198
ADA OK 74820
Tracy Burke
OFFICE: 717-236-3006
FAX: 717-233-0994
tabl51@vahoo.com
GTS Technologies
851 South 19th Street
Harrisburg PA 17104
David Butler
OFFICE: 502-564-6716x339
FAX: 502-564-2705
david. butler@mail.state.ky. us
Kentucky Division of Waste Management
14 Reilly Road
Frankfurt KY 40601
Neill Cade
OFFICE: 513-357-7211
FAX: 513-357-7262
neill. cade@chdburn. rcc. org
Cincinnati Health Dept.
3101 BurnetAve. Room 324
Cincinnati OH 45229
Thomas H. Cahill, P.E.
OFFICE: 610-696-4150
FAX: 610-696-8608
tcahill@thcahill. com
Cahill Associates
104 South High Street
West Chester PA 19382
USA
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Tom Cain
OFFICE: 304-636-1800x289
FAX: 304-636-1875
tcain/r9_monong@fs.fed. us
List of Attendees
Monongahela National Forest
200 Sycamore St.
Elkins WV 26241
Cincinnati, Ohio
Leslie M. Calderon
OFFICE: 817-608-2341
FAX: 817-695-9191
lcalder@dfwinfo. com
North Central Texas Council of Governments
616 Six Flags Drive, Suite 200, Centerpoint Two
Suite 200, Centerpoint Two
Arlington TX 76005-5888
Guy Cameron
OFFICE: 513-556-9740
FAX:
g.cameron@uc.edu
University of Cincinnati
Department of Biological Science
Cincinnati OH 45221
PAPER
John Capillo
OFFICE: 606-986-0868
FAX: 606-986-2695
kefcapil@acs. eku. edu
KY Environmental Foundation
PO Box 467
Berea KY 40403
Bobby G. Carpenter
OFFICE: 601-634-4572
FAX: 601-634-4584
carpenb@wes. army, mil
Tri-Service CADD/GIS Technology Center
USAGE Waterways Experiment Station, 3909 Halls Ferry Road
Vicksburg MS 39180-6199
USA
Thora Cartlidge
OFFICE: 612-624-9273
FAX: 612-624-1704
crd@tc. umn.edu
University of MN-Centerfor Rural Design
217-1518 Cleveland Avenue
St. Paul MN 55108
PAPER
Erman Caudill
OFFICE: 606-257-4093
FAX:
elcaudOO@pop. uky.edu
University of Kentucky
342 Waller Avenue #3C
Lexington KY 40504
Jean Caudill
OFFICE: 614-644-7181
FAX: 614-466-4556
jcaudill@gw. ohd. state, oh. us
Ohio Dept. of Health
P.O.Box 118
Columbus OH 43266-0118
Stephane Chalifoux
OFFICE: 514-287-8606
FAX: 514-287-8643
s. chalifoux@tecsult. com
TECSULT
85 St. Catherine Street West
Montreal, Quebec
CANADA H2X3P4
Yu-mei Chang
OFFICE: 513-558-2744
FAX:
changym@email.uc.edu
University of Cincinnati
202 Ruth Lyons Way, Suite #261
Cincinnati OH 45267-0840
James Chapman
OFFICE: 765-213-1269
FAX:
jdchapman31@hotmail. com
Delaware County Indiana
100 W. Main Room 206
Muncie IN 47305
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Jonathan W. Chapman
List of Attendees
Cincinnati, Ohio
OFFICE: 410-260-8514
FAX: 410-260-8595
jchapman@dnr. state, md. us
MD Dept. of Natural Resources-Forest Service
Tawes State Office Building E1 580 Taylor Avenue
Annapolis MD 21401
David Christenson
OFFICE: 303-312-3345
FAX: 303-312-6065
christenson. dave@epa.gov
USEPA Region VIM
999 18th Street, Suite 500
Denver CO 80202
David Christian
OFFICE: 613-787-3879
FAX: 613-787-3884
ATCO Frontec Corporation
100-170 Laurier Avenue West
Ottawa, Ontario K1P5V5
Kendra Cipollini
OFFICE: 312-886-1432
FAX: 312-886-9697
cipollini. kendra@epa. gov
USEPA Region 5, Critical Ecosystems Team
77 West Jackson Avenue T13J
Chicago IL 60604
John Ckmanec
OFFICE: 513-569-7481
FAX: 513-569-7585
ckmanec.john@epa.gov
U.S. EPA (MS G-75)
26 Martin Luther King Drive
Cincinnati OH 45268
Tara Clapp
OFFICE: 502-852-8152
FAX: 502-852-4558
tlclapp@rcf.usc. edu
University of Louisville
426 W. Bloom Street, 202
Louisville KY 40208
David E. Clark
OFFICE: 414-288-3339
FAX: 414-288-5757
clarkde@marquette. edu
Marquette University, Dept. of Economics
P.O. Box 1881
Milwaukee Wl 53201-1881
PAPER
Tim Clarke
OFFICE: 502-573-2886
FAX: 502-573-2355
tim.clarke@mail.state.ky.us
Kentucky State Nature Preserves Commision
801 Schenkel Lane
Frankfort KY 40601-1403
Richard Cochran
OFFICE: 615-532-0997
FAX: 615-532-0046
RCochran2@mail. state, tn.us
Tennessee Department of Environmental and Conservation
401 Church Street, 7th Floor L & C Annex, Div of Water Pollution Control
Nashville TN 37243
James C. Coleman II
OFFICE: 513-583-1249
FAX: 513-583-1250
jcoleman@environcorp. com
ENVIRON International Corp.
6443 Lewis Road
Loveland OH 45140
Jim Coon
OFFICE: 937-285-6038
FAX: 937-285-6404
jim.coon@epa. state.oh. us
Ohio EPA
401 E. Fifth St.
Dayton Oh 45402
PAPER
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Amy Covert
OFFICE: 502-573-2886
FAX: 502-573-2355
amy.covert@mail. state, ky. us
Kentucky State Nature Preserves Commission
801 Schenkel Lane
Frankfort KY 40601-1403
Christopher Cox
OFFICE: 309-782-2887
FAX: 309-782-5038
wildmanf@ria. army.mil
Rock Island Arsenal
510R1-AO, Bldg220
Rock Island IL 61299
Michael J. Cramer
OFFICE: 513-556-9740
FAX: 513-556-5299
michaeljcramer@hotmail. com
University of Cincinnati
P.O. Box210006
Cincinnati OH 45221-0006
Irene M. Crawford
OFFICE: 513-569-7167
FAX:
crawford.irene@epa.gov
SoBran, Inc.
26 West Martin Luther King Drive
Cincinnati OH 45268
Pat Curley
OFFICE: 770-673-3640
FAX: 770-396-9495
pcurley@brwncald. com
Brown and Caldwell
41 Perimeter Center East, Suite 400
Atlanta GA 30346
Darrin L. Curtis
OFFICE: 501-973-0760
FAX:
dlc2@engr. uark.edu
USAF
2335 East Yvonne Drive
Fayetteville AR 72703
Bernie Daniel
OFFICE: 513-569-7401
FAX: 513-569-7609
daniel. bernie@epa.gov
USEPA -NERL
26 West Martin Luther King Drive
Cincinnati OH 45242
Lonnie Darr
OFFICE: 2407777703
FAX:
darrl@co.mo.md. us
Watershed Management Div., Montgomery Cty Dept. of Env. Protection
Suite 120, 155 Rockville Pike
Rockville MD 20850
USA
Bruce De Young
OFFICE: 616-336-3234
FAX: 616-336-2436
el0771@iserv.net
Kent County Health Department
700 Fuller, N.E.
Grand Rapids Ml 49503
Kevin L. DeFosset
OFFICE: 606-624-4471
FAX:
studefok@acs. eku.edu
Ewers Water Consultants / Eastern KY University
326-10 Lancaster Avenue
Richmond KY 40475
Jennifer Deis, P.G.
OFFICE: 913-458-6585
FAX: 913-458-2934
deisj@bv. com
Black & Veatch Corporation
11401 Lamar Avenue
Overland Park KS 66211
PAPER
FINAL
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Ed Delisio
OFFICE: 312-886-1303
FAX: 312-353-5374
delisio. edward@epa. gov
List of Attendees
US EPA Region 5
77 West Jackson Blvd., B-19J
Chicago IL 60604
Cincinnati, Ohio
Phil Dennis
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
Tommy G. Dewald
OFFICE: 2022602488
FAX:
dewald.tommy@epamail.epa.gov
Office of Water
401 "M" Street, SW (4503F)
Washington DC 20460
USA
Martin Diaz-Zorita
OFFICE: 606-257-3655
FAX: 606-257-2185
mdzori2@pop. uky.edu
University of Kentucky - Agronomy Department
N122-Agric.Sci.Center North
Lexington KY 40546-0091
Robin Dingle
OFFICE: 2078799496
FAX:
Rdingle@neamaine.com
Northern Ecological Associates, Inc.
386 Fore St.
Portland OR 04101
USA
Harold B. Dirschl
OFFICE: 613-787-9611
FAX: 613-787-3884
hkdir@sympatico. ca
ATCO Frontec Corporation
100-170 Laurier Avenue West
Ottawa, Ontario K1P5V5
David Dixon
OFFICE: 513-247-8000
FAX: 513-247-8010
ddixon@scientech. com
Scientech, NES, Inc.
11400 Grooms Road
Cincinnati OH 45242
Mohsen Dkhili
OFFICE: 573-751-1300
FAX: 573-526-5797
nrdkhim@mail. dnr.state. mo. us
DNR/WPCP
P.O. Box 176
Jefferson City MO 65102
Doug Dobransky
OFFICE: 614-644-2752
FAX: 614-644-2909
Ohio EPA
P.O. Box1049
Columbus OH
43216-1049
Kevin Doniere
OFFICE: 513-281-2211
FAX: 513-281-2243
humannature@fuse.net
Human Nature
990 St. Paul Place
Cincinnati OH 45206
Damon Dougherty
OFFICE: 361-883-6016
FAX: 361-883-7417
damon@moorhousecc. com
Moorhouse Associates, Inc.
5826 Bear Lane
Corpus Christ! TX 78405
FINAL
10
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Joan Douglas
OFFICE: 352-378-6517
FAX: 352-338-8247
FACA@aol
List of Attendees
Florida Rural Community Assistance Project
6212 NW 43 Street, Suite A
Gainsville FL 32653
Cincinnati, Ohio
Mark E. Duewell
OFFICE: 573-526-5214
FAX: 573-751-6417
duewem@mail.health.state.mo.us
Missouri Department of Health
930 Wildwood, P.O. Box 570
Jefferson City MO 65102-0570
Jeffrey Duke
OFFICE: 216-881-6600x456
FAX: 216-881-2738
dukej@neodrsd. org
N. E. Ohio Regional Sewer District
4415 Euclid Avenue
Cleveland OH 44103
John Dunham R.S.
OFFICE: 513-564-1788
FAX: 513-564-1776
John.Dunham@igwmail. rcc. org
Cincinnati Health Dept.
3845 W.P. Dooley By-Pass
Cincinnati OH 42223
Scott Dyer
OFFICE: 513-627-1163
FAX: 513-627-1208
dyer. sd@pg. com
Procter & Gamble Co.
P.O. Box 538707
Cincinnati OH 45253-8707
Don Ebert
OFFICE: 702-798-2158
FAX: 702-798-2158
ebert. donald@epa.gov
U.S. EPA
944 E. Harmon Avenue
Las Vegas NV 89119
T. J. Edwards
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
James Eflin
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
Keith Egan
OFFICE: 513-576-0009
FAX: 513-574-9756
egank@srwenvironmental. com
SRW Environmental Services
55 West TechneCenter, Suite C
Milford OH 45150
Lisa E. Enderle
OFFICE: 703-645-6950
FAX: 703-698-6101
enderlel@saic. com
SAIC
2222 Gallows Road, Suite 300
Dunn Loring VA 22027
Bernie Engel
OFFICE: 765-494-1198
FAX: 765-496-1115
engelb@ecn.purdue.edu
Purdue University
1146 ABE
W. Lafayette IN 47907-1146
PAPER
FINAL
11
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Sendar Ertep
OFFICE: 404-562-9683
FAX: 404-562-9598
ertep.sendar@epamail. epa.gov
USEPA Region 4
61 Forsyth St., SW
Atlanta GA 30303
Alan Evereson
OFFICE: 513-569-7046
FAX:
U.S. EPA/ORD/NRMRL
26 W. Martin Luther King Drive
Cincinnati OH 45268
Dr. Ralph O. Ewers
OFFICE: 606-623-8464
FAX: 606-623-6464
glyewers@acs. eku. edu
Dept. of Earth Science Eastern KY University
160 Redwood
Richmond KY 40475
Susan Pagan
OFFICE: 202-260-9477
FAX: 202-260-2941
fagan.susan@epa.gov
U.S. EPA, Office of Water
401 -M Street, SW - Mailcode 4501F
Washington DC 20460
Todd Falter
OFFICE: 402-471-6571
FAX: 402-471-6436
tfalter@hhs. state, ne.us
NE Health and Human Services
P.O. Box 95007
Lincoln NE 68509
Ian Farrar
OFFICE: 304-545-4388
FAX:
ifarrar@columbiaenerorcnup.com
Collumbia Gas
1700 MacConkle Ave.
Charleston WV
Terry Felkerson
OFFICE: 573-308-3725
FAX: 573-308-3652
jfelkerson@usgs.gov
U.S. Geological Survey
1400 Independence Road
Rolla MO 65401
Don Ficklen
OFFICE: 210-671-4844
FAX: 210-671-2241
holmes.fwklen@lockland. af.mil
U.S. Air Force
Lockland Air Force Base
William S. Fischer
OFFICE: 513-564-1787
FAX: 513-564-1776
wfish@mailcity. com
Cincinnati Health Dept.
3845 William P. Doley By-Pass
Cincinnati OH 45223
Jeff Flege
OFFICE: 614-265-6686
FAX:
jeff.flege@dnr. state.oh. us
Ohio Department of Natural Resources
Fountain Square, Building C-2
Columbus OH 43224-1386
Terry Flum
FAX:
513-569-7715
513-569-7609
USEPA-NERL MS 642
26 W. Martin Luther King Drive
Cincinnati OH 45268
FINAL
12
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Charles Frank
OFFICE: 610-935-4703
FAX: 610-935-5583
cfrank@envstd. com
List of Attendees
Environmental Standards Inc.
1140 Valley Forge Road
Valley Forge PA 19482-0810
Cincinnati, Ohio
William T. Frederick
OFFICE: 716-942-2563
FAX: 716-942-2247
frederw@wv. doe.gov
Dames & Moore
10282 Rock Springs Road
West Valley NY 14171-9799
Dr. Robert C. Frey
OFFICE: 614-466-1069
FAX: 614-644-7440
rfrey@gw.odh.state.oh. us
Ohio Department of Health
246 North High Street
Columbus OH 43266-0588
Lawrence Friedl
OFFICE: 202-564-6933
FAX: 202-565-2431
friedl.lawrence@epa.gov
US EPA
401 MSt.,SW(8101R)
Washington DC 20460
Joseph Frizado
OFFICE: 419-372-7202
FAX: 419-372-7205
frizado@bgnet. bgsu.edu
Bowling Green State University
Bowling Green State University
Dept. of Geology
Bowling Green OH 43403
Kevin Frysinger
OFFICE: 610-935-5577
FAX: 610-935-5583
Environmental Standards
1140 Valley Forge Road
Valley Forge PA 19482
Florence Fulk
OFFICE:
FAX:
fulk.florence@Epa.gov
EPA/NERL
26 W. Martin Luther King Drive
Cincinnati OH 45268
Richard Futrell
OFFICE: 606-622-1581
FAX:
antfutre @acs. eku. edu.
Eastern Kentucky University Sociology Dept.
223 Keith
Richmond KY 40475
Robert Galbraith
OFFICE: 513-576-0009
FAX: 513-576-9756
galbraib@srw environmental, com
SRW Environmental Services
55 West TechneCenter, Suite C
Milford OH 45150
Dr. Achal Garg
OFFICE: 513-357-7209
FAX: 513-357-7262
achal. garg@chdburn. rcc. org
Cincinnati Health Department
3101 BurnetAve.
Cincinnati OH 45229
Donald Gasper
304-472-3704
FAX:
WV Department of Natural Resources
4 Ritchie Street
Buchannon WV 26201
FINAL
13
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Glee H. Gasper
OFFICE: 304-942-3704
FAX:
Upshur County Litter Control Bd.
4 Ritchie St.
Buckhannon WV 26201
David R. German
OFFICE: 605-688-5611
FAX: 605-688-4917
wrisdsu@mg.sdstate. edu
South Dakota Water Resources Institute
SDSUBox2120
Brookings SD 57007
Patricia Germany
OFFICE: 502-574-3645
FAX: 502-574-1389
pgermany@louky. org
City of Louisville / Health & Environment
1514 Hale Avenue
Louisville KY 40210
Tim Gessner
FAX:
513-326-1500
BHE Envir.
11733ChesterdaleRd.
Cincinnati OH 45204
Nick Giardino
OFFICE: 210-536-6128
FAX: 210-536-1130
nicholas.giardino@brooks.af.mil
IERA/RSRE
2513 Kennedy Circle
San Antonio TX 78235-5123
Michael J. Gilbrook
OFFICE: 407-872-7801
FAX: 407-872-0603
mgilbroo@hdrinc. com
HDR Engineering, Inc.
201 S. Orange Avenue, Suite 925
Orlando FL 32801-3413
PAPER
Rebecca Glos
OFFICE: 703-318-4797
FAX: 703-736-0826
glosr@saic.com
SAIC
11251 Roger Bacon Drive
Reston VA 20190
Haynes Goddard
OFFICE: 513-569-7685
FAX: 513-569-7111
goodard. haynes@epamail. epa.gov
USEPA
26 West Martin Luther King Drive
Cincinnati OH 45268
Maria A. Gomez-Balandra
OFFICE: 52-73-19-4000x407 or x410
FAX: 52-73-20-8638
magomez@tlaloc. imta. mx
Water Technology Mexican Institute
Paseo Cuauhnahuac 8532
Morelos, Mexico 62550
James A. Goodrich
OFFICE: 513-569-7605
FAX: 513-569-7185
goodrich.jam es@epa.gov
USEPA, NRMRL/WSWRD/WQMB
26 W. Martin Luther King Drive
Cincinnati OH 45268
PAPER
Walter M. Grayman
OFFICE: 513-281-6138
FAX: 513-281-6139
grayman@fuse.net
W.M. Grayman Consulting Engineer
730 Avon Fields Lane
Cincinnati OH 45229
FINAL
14
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Don Green
OFFICE: 615-837-5492
FAX: 615-837-5025
dgreen@mail.state.tn.us
List of Attendees
Cincinnati, Ohio
TN Nonpoint Source Program
Ellinton Ag. Center, Box 40627, Holeman Bldg.
Nashville TN 37204
Chris Griffith
OFFICE: 513-732-8036
FAX:
ccghd@fuse.net
Clermont Co. General Health District
2275 Bauer Road Suite 300
Batavia OH 45103
Corey Gullion
OFFICE: 765-288-4057
FAX: 765-288-4057
cgullion@home.com
Ball State Univ. Natural Resource Env. Mgr.
1616 W.Gilbert G-100
Muncie IN 47303
Beth Hailstock
OFFICE: 513-357-7206
FAX: 513-357-7262
beth. hailstock@chdburn. rcc. org
Cincinnati Health Dept.
3101 BurnetAve
Cincinnati OH 45229
Tonia Hampton
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
David T. Hansen
OFFICE: 916-978-5268
FAX: 916-978-5290
dhansen@mp. usbr.gov
U.S. Bureau of Reclamation-MPGIS
2800 Cottage Way
Sacramento CA 95825-1898
PAPER
Todd Hanson
OFFICE: 218-335-7415
FAX: 218-335-7430
lldrm@paulbunyan.net
Leech Lake Reservation Water Resources
6530 Hwy 2 NW
Cass Lake MN 56633
Jon Harbor
OFFICE: 7654949610
FAX:
jharbor@purdue.edu
Dept. of Earth & Atmospheric Sciences
Purdue University
West Lafayette IN 47906-1397
USA
PAPER
Deborah D. Harris
OFFICE: 513-791-8330
FAX: 513-791-7335
logictree@aol. com
Natl. Technology Assoc.-Cincinnati
P.O. Box 42356
Cincinnati OH 45242
Jasper L. Harris
OFFICE: 919-530-6394
FAX: 919-530-7966
jasperharris@y:
North Carolina Central University
1801 Fayetteville Street
Durham NC 27707
Roderick L. Harris, R.S.
OFFICE: 615-353-8363
FAX:
rlhl914@yahoo.com
Meharry Medical College
1408 Mountain Valley Bend
Nashville TN 37209
FINAL
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As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Joel Harrison
OFFICE: 304-637-0245
FAX: 304-637-0250
jharrison@dnr.state.wv. us
WV Division of Natural Resources
Ward Road
Elkins WV 26241
Robin Harrover
OFFICE: 425-649-7232
FAX: 425-649-7098
rhar461 @ecy.wa. gov
Washington State Department of Ecology
3190 160th Avenue, SE
Bellevue WA 98008-5452
Paul Marten
OFFICE: 513-569-7045
FAX: 513-569-7471
harten.paul@epamail. epa.gov
U.S. EPA
26 W. Martin Luther King Drive
Cincinnati OH 45268
Megan Hartgrove-Del Gaudio
OFFICE: 410-974-7276
FAX: 410-974-7200
mhart@menv.com
Maryland Environmental Service
2011 Commerce Park Drive
Annapolis MD 21401
Patrick L. Havens
OFFICE: 317-337-3465
FAX: 317-337-3235
phavens@dowagro. com
Dow AgroSciences LLC
9330 Zionsville Road 306/A2
Indianapolis IN 46468
Fred Hayden
OFFICE: 510-526-7140
FAX:
Haydens@Earthlink. net
Hydrosource
639 Madison Street
Albany CA 94706
Benjamin R. Hayes
OFFICE: 570-372-4215
FAX: 570-372-2726
bhayes@susqu.edu
Dept. of Geo. and Envir. Sc. Susquehanna U.
514 University Ave.
Selinsgrove PA 17870-1001
USA
PAPER
Linda Haynie
OFFICE: 512-239-6821
FAX: 512-239-5687
LHAYNIE@TNRCC.STATE.TX. US
TX Natural Resource Conservation Commission
P.O. Box 13087
Austin TX 78711
David J. Healy
OFFICE: 802-229-1879
FAX: 802-229-5417
dhealy@stone-env. com
Stone Environmental, Inc.
58 East State Street
Montpelier VT 05602
PAPER
Richard H. Heath
OFFICE: 207-287-7637
FAX: 207-287-7826
richard. h. heath@state.me. us
Maine Department of Environmental Protection
State House Station #17
Augusta ME 04333
Conrad Heatwole
OFFICE: 5402314858
FAX:
heatwole@yt.edu
Biological Systems Engineering
Virginia Tech
Blacksburg VA 24061-0303 USA
FINAL
16
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Pat Heider
OFFICE: 614-644-2752
FAX: 614-644-2909
List of Attendees
Cincinnati, Ohio
Ohio EPA
P.O. Box 1049
Columbus OH
43216-1049
Beverly Henderson
OFFICE: 614-644-8065
FAX: 614-644-7740
bhenders@gw. odh. state, oh. us
Ohio Dept. of Health Assessment Section
246 N. High Street, 8th Floor
Columbus OH 43215
Nereida Hernandez
OFFICE: 787-764-8824
FAX: 787-766-0150
jcaemer@prtc. net
Puerto Rico Environmental Quality Board
P.O. Box 11488
San Juan PR 00910
Bob Hilbert
OFFICE: 716-942-2417
FAX:
hilbert@wv. doe.gov
West Valley Nuclear Services
10282 Rock Springs Road
West Valley NY 19171
Sara Hines
OFFICE: 502-573-2886
FAX: 502-573-2355
sara. hines(a),mail.state.ky. us
Kentucky State Nature Preserves Commission
801 Schenkel Lane
Frankfort KY 40601-1403
Terri Hoagland
OFFICE: 513-569-7783
FAX: 513-569-7111
hoagland. theresa@epamail. epa.gov
U.S. EPA
26 W. Martin Luther King Drive MS-466
Cincinnati OH 45268
Molly Hodgson
OFFICE: 216-910-1941
FAX: 216-910-2010
molly-hodgson@aquaalliance.com
Metcalf& Eddy, Inc.
Suite 1215, 1300 E. Ninth Stree
Cleveland OH 44114
USA
PAPER
Matthew Hopton
OFFICE:
FAX:
hopton@toast. net
Dept. of Biological Sc., University of Cincinnati
Cincinnati OH 45221-0006
George E. Host, Ph.D.
OFFICE: 218-720-4264
FAX: 218-720-4328
ghost@sage. nrri. umn.edu
Natural Resources Research Institute; U of MN
5013 Miller Truck Highway
Duluth MN 55811
PAPER
Kevin House
OFFICE: 502-564-3080
FAX: 502-564-9195
kevin.house(fl),mail.state.ky. us
Kentucky Division of Conservation
663 Teton Trail
Frankfort KY 40601
David Howerter
OFFICE: 204-467-3292
FAX: 204-467-9426
d howerter(fl),dveb.com
Institute for Wetland and Waterfowl Research
P.O. Box1160
Oak Hammock Marsh
Stonewall MB ROC2ZO
PAPER
FINAL
17
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Hsuan-Tsung (Sean) Hsieh
OFFICE: 405-744-8974
FAX: 405-744-7008
hsieh@seic. he. okstate. edu
List of Attendees
Oklahoma State University-SEIC
Oklahoma State University
201 CITD
Stillwater OK 74074
Cincinnati, Ohio
Karla Hyde
OFFICE: 2078799496
FAX:
Rdingle@neamaine.com
Northern Ecological Associates, Inc.
386 Fore St. Ste. 40
Portland OR 04101
USA
Peter J. Idstein
OFFICE: 606-624-8722
FAX: 606-622-2876
idstein@gateway. net
Ewers Water Consultants
971 Villa Drive, Apartment #27
Richmond KY 40475
Timothy I. Ingram
OFFICE: 513-326-4503
FAX: 513-772-6405
Tim.Ingram@health.hamilton-co.org
Health Commissioner, Hamilton County General Health District
Chester Towers, Suite 600, 11499 Chester Road PAPER
Cincinnati OH 45246
USA
Brian Jacobson
OFFICE: 724-349-5733
FAX:
BAJ133@PSU.EDU
Penn State University
905 F W. Aaron Drive
State College PA 16803
Karen Jarocki
OFFICE: 505-768-7706
FAX: 505-768-7601
jarocki@mrcabq. com
Mission Research Company
5001 Indian School Road NE
Albuquerque NM 87110
Tony Jasek
OFFICE: 210-536-5448
FAX: 210-536-1130
tony.jasek@brooks. af.mil
US Air Force
2903 Oak Falls
San Antonio TX
78231
Bruce Jeffries
OFFICE: 517-335-0183
FAX: 517-335-6565
jeffries@mshda,cis.state.mi.us
MSHDA
401 S. Washington Square
Lansing Ml 48933
Becky Jenkins
OFFICE: 614-265-6631
FAX: 614-263-8144
becky.jenkins@dnr.state.oh.us
Ohio Division of Wildlife
1840 Belcher Dr., Bldg. G-2
Columbus OH 43224
Ralph Johanson
OFFICE: 502-584-4244
FAX: 502-584-4246
rjohanson@grwinc. com
GRW Engineers
433 South 5th Street
Louisville KY 40202
Brian C. Johnson
OFFICE: 518-474-5488
FAX: 518-473-2534
brian.johnson@oag.state. ny. us
NY State Attorney Gen. - Env. Protection Bureau
The Capitol
Albany NY 12224
FINAL
18
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Robert L. Johnson
OFFICE: 614-466-1060
FAX: 614-564-2410
bjohnson@gw. odh. state, oh. us
List of Attendees
Ohio Department of Health
246 North High Street
Columbus OH 43215
Cincinnati, Ohio
J. Kent Johnson, PhD
OFFICE: 319-626-6053
FAX: 319-626-6053
JKENT@inav. net
Iowa Institute of Hydraulic Research
University of Iowa
Iowa City IO 52242
Rick Jones
OFFICE: 317-247-3105
FAX: 317-247-3414
jonesrw@in-arng. ngb. army.mil
Military Dept. of Indiana
2002 S. Holt Road
Indianapolis IN 46241
Terri Justice
OFFICE: 719-567-4035
FAX: 719-567-4036
terri.justice@schriever.af.mil
US Air Force 50 Ceslcecr
300 O'Malley Ave., Suite 19
SchrieverAFB CO 80912-5091
Jeff Kaczka
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management;
Ball State University
Muncie IN 47306
K.E. (Kim) Kalen
OFFICE: 613-992-0757
FAX: 613-996-3925
Dept. of National Defense-North WarningSystem
219 Laurier Avenue West
Ottawa, Ontario K1AOK2
Kelly Kaletsky
OFFICE: 5132856075
FAX:
kelly.kaletsky@epa.state. oh. us
Office of Federal Facilities Oversight - Ohio EPA
401 East Fifth Street
Dayton OH 45402-2911
USA
PAPER
Sumana Keener
OFFICE: 513-556-2542
FAX: 513-556-2522
skeener@uceng. uc. edu
University of Cincinnati
P.O. Box 210071
Cincinnati OH 45227
Bud Keesee
OFFICE: 410-278-6755
FAX: 410-278-6779
bkeesee@dshe. apg. army.mil
Div. of Safety, Health, and Environment
STEAP-SH-ER - Bldg 5650
Aberdeen Proving Group Garrison
MD 21005
Larisa J. Keith
OFFICE:
FAX:
larisaj_k@yahoo. com
Northern Kentucky Area Planning Commission in Kenton County
146 Stafford Ridge Rd. PAPER
Sanders KY 41083
USA
Jay H. Kim
OFFICE: 304-285-6140
FAX: 304-285-6111
jhkO@cdc.gov
CDC I NIOSH
1095 Willowdale Road
Morgantown WV 26505
FINAL
19
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Carl Kinkade
OFFICE: 402-441-6246
FAX: 402-441-8323
ckinkade@ci. lincoln. ne. us
List of Attendees
Cincinnati, Ohio
Lincoln-Lancaster County Health Dept.
3140 N Street
Lincoln NE 68510
Lyn Kirschner
OFFICE: (765)494-9555
FAX:
kirschner@ctic.purdue. edu
Conservation Technology Information Center
1220 Potter Dr., Ste. 170
West Lafayette IN 47906
Karen KM ma
OFFICE: 202-260-7087
FAX: 202-260-7024
klima.karen@epa.gov
USEPA/Office of Water
401 M Street, SW Mail Code 4503F
Washington DC 20460
Randy Knight
OFFICE: 319-398-5893
FAX: 319-398-5894
rknight@kirkwood. cc. ia. us
Kirkwood Community College
6301 Kirkwood Blvd. SW
Cedar Rapids IA 52406
Jill Korach
OFFICE: 513-921-5124
FAX: 513-921-5136
imago@one. net
IMAGO, Inc.
553 Enright Avenue
Cincinnati OH 45205
Angel J. Kosfiszer
OFFICE: 214-665-2187
FAX: 214-665-6490
kosfiszer. angel@epamail. epa.gov
US EPA Region 6
1445 Ross Avenue
Dallas TX 75202-2733
Jeff Kreider
OFFICE: 608-266-0856
FAX: 608-267-2800
kreidj@dnr. state, wi.us
Wisconsin Dept. of Natural Resources
101 S. Webster Street
Madison Wl 53707
Jim Kreissl
OFFICE: (513)569-7611
FAX:
kreissl.james@epa.gov
U.S. EPA, CERI, TTB
26 W. Martin Luther King Dr.
Cincinnati OH 45268
Dennis Kreitzburg
OFFICE: 216-739-0555
FAX: 216-739-0560
DJK&HaleyAldrich. com
Haley & Aldrich, Inc.
5755 Granger Road, Suite 100
Independence OH 44131
Eric J. Kroger
OFFICE: 513-648-4473
FAX: 513-648-4473
eric. kroger@fernald. gov
Fluor Daniel Fernald, Inc.
P.O. Box 538704
Cincinnati OH 45253
Rosanne Kruzich
OFFICE: 502-895-4559
FAX: 502-895-4559
rkruzich@bellsouth. net
RKX Consulting, Inc.
750 Zorn Avenue #56
Louisville KY 40206
FINAL
20
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Dr. Sudhir R. Kshirsagar
OFFICE: 513-474-9780
FAX: 513-474-9781
president@gqc. com
List of Attendees
Global Quality Corporation
7828 Beechmont Avenue, Suite 202
Cincinnati OH 45255
Cincinnati, Ohio
Tony Lafferty
OFFICE: 314-949-6620
FAX: 314-949-6735
tlafferty@esri. com
ESRI St. Louis
207 South Main St.
St. Charles MO 63301
Mark S. Landry
OFFICE: 540-951-4899
FAX: 540-951-4896
m -landry@vt. e du
VA Polytechnic Institute and State University
Dept. of Agric. & Applied Economics
Blacksburg VA 24061-0401
PAPER
Robert Langstroth
OFFICE: 608-264-8801
FAX: 608-264-8795
rlangstroth@commerce.state.wi.us
Wisconsin Dept. of Commerce
201 W. Washington Ave.
Madison Wl 53701
Elizabeth L. Lanzer
OFFICE: 360-705-7476
FAX: 360-705-6833
lanzere@wsdot.wa.gov
WA Department of Transportation
P.O. Box 47331
Olympia WA 98504-7331
PAPER
Jill Leale
OFFICE: 616-696-1606
FAX:
lealej@river. it.gvsu. edu
Grand Valley State University
14990 Simmons Ave.
Cedar Springs Ml 49319
Lawrence A. Lee
OFFICE: 919-560-6344
FAX: 919-530-7973
larrylee@wpo. nccu. edu
North Carolina Central University
1801 Fayetteville Street
Durham NC 27707
Sang-Suk Lee
OFFICE: 614-292-0585
FAX: 614-292-7688
lee.1099@osu.edu
The Ohio State University
223 Mendenhall Lab., 125 South Oval Mall
Columbus OH 43210
Jay Lee, Ph.D.
OFFICE: 3306723222
FAX:
jlee@kent.edu
Dept. of Geography
Kent State University
Kent OH 44242-0001
PAPER
USA
David R. Legates, Ph.D.
OFFICE: 302-831-2294
FAX: 302-831-6654
legates@udel. edu
University of Delaware, Center for Climatic Research
216 Pearson Hall
Newark DE 19716-2541
PAPER
Geoff Leking
OFFICE: 419-373-3092
FAX: 419-373-3125
geoff.leking@epa.state. oh. us
OHIO EPA
347 N. Dunbridge Rd.
Bowling Green OH 42402
FINAL
21
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
April Lewis
OFFICE: 937-255-5654x3512
FAX: 937-255-4645
april. lewis@afit.af.mil
Air Force Institute of Technology
AFITICEL 2950 P Street Bldg. 643
Wright Patterson AFB OH 45433-7765
Norma Lewis
OFFICE: 513-569-7665
FAX: 513-569-7471
lewis, norma. @epamail. epa. gov
U.S. EPA
26 W. Martin Luther King Drive
Cincinnati OH 45268
Xaan Li
614-292-0585
FAX: 614-292-7688
li@geology. ohio-state, edu
Dept. of Geological Studies Ohio State U.
Columbus OH 63210
Jun Liang
OFFICE: 513-569-7619
FAX:
liangjun@hotmail. com
EPA NERL GIS Lab
435 Riddle Rd. Apt.9
Cincinnati OH 45220
Susan Licher
Ball State Graduate Student
FAX:
sslicher@hotmail. com
Jason Linn
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
Beth Linton
OFFICE: 770-677-0330
FAX: 770-399-0535
United Capitol Insurance Company
400 Perimeter Center Terrace, Suite 400
Atlanta GA 30346
Amy Liu
OFFICE: 5135697171
FAX:
liu. amy@epamail. epa.gov
PAI/SAIC, US EPA, NRMRL
MS 690, 26 W. Martin Luther King Dr
Cincinnati OH 45268
USA
PAPER
Lin Liu, Ph.D.
OFFICE: 513-556-3429
FAX: 513-556-3370
lin.liu@uc.edu
University of Cincinnati
Department of Geography
714 Swift Hall
Cincinnati OH 45221-0131
Bill Lohner
OFFICE: 937-285-6051
FAX: 937-285-6404
bill. lohner@epa.state.oh. us
Ohio EPA
401 E. 5th Street
Dayton OH 45402
PAPER
Cornell Long
OFFICE: 210-536-6121
FAX: 210-536-1130
Cornell, long@brooks.af.mil
USAF Institute for ESOH Risk Analysis
2513 Kennedy Circle
Brooks AFB TX 78235-5123
FINAL
22
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Colleen Lovett
OFFICE: 937-656-3637
FAX: 937-656-3663
colleen. lovett@wpafb. af.mil
List of Attendees
U.S. Air Force
4225 Logistics Ave., Suite 23
Wright - Patterson AFB OH 45433-5762
Cincinnati, Ohio
Damon Lowe
OFFICE: 317-598-8742
FAX:
dlowe@worldnet. att. net
Ball State University
6836 Challenge Lane
Indianapolis IN 46250
James A. Lynch, Ph.D.
OFFICE: 814-865-8830
FAX: 814-863-7193
jal@psu.edu
Penn State University
311 Forest Resource Lab
University Park PA 16802
Stan Lynn
OFFICE: 513-564-8349
FAX: 513-241-0354
lynns@ttemi. com
Tetra Tech EM Inc.
625 Eden Park Drive, Suite 100
Cincinnati OH 45202
Debbie E. Maes
OFFICE: 505-846-8568
FAX: 505-853-1793
debbie.maes@ao. dtra.mil
Defense Threat Reduction Agency
1680 Texas Street, SE
Kirtland AFB NM 87117-5669
Robert Magai
OFFICE: 573-522-3779
FAX: 573-525-5797
nrmagar@mail. dnr.state.mo. us
Missouri DNR
205 Jefferson Street
Jefferson City MO 65102
Laura Mahoney
OFFICE: 615-255-2370x406
FAX: 615-256-8332
lmahoney@brwncald. com
Brown & Caldwell
227 French Landing Drive
Nashville TN 37228
Sarada Majumder
OFFICE:
FAX:
majumder. sarada@epa.gov
SBI EnvAISEPA
26 W. Martin Luther King Drive
Cincinnati OH 45219
Miguel A. Maldonado
OFFICE: 787-764-8824
FAX: 787-766-0150
jcaemer@prtc. net
PREQB
P.O. Box 11488
San Juan PR 00910
Richard M. Males
OFFICE: 513-871-8566
FAX:
males@iac.net
RMM Technical Services, Inc.
3319 Eastside Avenue
Cincinnati OH 45208
Christopher Mantia
OFFICE: 937-776-7724
FAX: 937-586-3059
cmantia@corbus.com
Corbus, LLC
33 W. First Street, Suite 300
Dayton OH
FINAL
23
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Renante Marante
OFFICE: 312-742-0123
FAX: 312-744-6451
rmarante@ci.chi.il. us
List of Attendees
Dept. of Environment Chicago Brownfields
30 N. La Salle, Room 2500
Chicago IL 6060
Cincinnati, Ohio
Joanne Marker!
OFFICE: 3607057444
FAX:
markertj@wsdot.wa.gov
Washington Dept. of Transportation
310 Maple Park Ave., SE
Olympia WA 98504-7331
USA
Lawrence Martin
OFFICE: 202-564-6497
FAX: 202-565-2926
martin.lawrence@epa.gov
EPA/ORD
401 M Street. NW (8103R)
Washington DC 20460
Robert C. Martin, Jr.
OFFICE: 404-894-8446
FAX: 404-894-2184
bob.martin@gtri.gatech.edu
Georgia Tech Research Institute - Electro-Optics
Environment and Materials Laboratory
Atlanta GA 30332-0837
Margaret B. Martin, P.E.
OFFICE: 410-962-3500
FAX: 410-962-2318
margaret. b.martin@nab02. usace.army
USAGE / HTRW Design Center
P.O. Box 1715
ATTN: CENAB-EN_
Baltimore MD 21203
Dana Martin-Hayden
OFFICE: 419-373-3067
FAX: 419-352-8468
dana.martin-hayden@epa. state, oh. us
OHIO - EPA
347 North Dunbridge Road
Bowling Green OH 43402
Carmen Maso
OFFICE: 312-886-1070
FAX: 312-353-6519
maso. carmen@epa.gov
US EPA
77 West Jackson
Chicago IL 60604
Jason Masoner
OFFICE: 580-436-8508
FAX:
jmasoner@usgs.gov
USGS
P.O.1198
Ada OK 74820
Gregg Matthews
OFFICE: 613-541-6000x6099
FAX: 613-541-6596
matthews-g@rmc. ca
Environmental Sciences Group, RMC
P.O. Box 17000 Stn. Forces, Bldg. 62
Kingston - Canada ON K7K7B4
Robert Matzner
OFFICE: 703-305-5975
FAX: 703-305-6309
matzner. robert@epa.gov
US EPA/OPPTS/OPP/EFED
401 M St., SW
Mail Code 7507c
Washigton DC 20460
Dr. Eric F. Maurer
OFFICE: 606-257-7652
FAX: 606-257-1717
efmaur@pop. uky.edu
Univ. of Kentucky, Biological Sciences
101 Morgan Building
Lexington KY 40506
FINAL
24
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Chris Mazza
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
List of Attendees
Cincinnati, Ohio
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
R.B. (Rusty) McAllister
OFFICE: 501-682-0022
FAX: 501-682-0010
mcallister@adeq. state, ar. us
Arkansas Dept. of Environmental Quality
8001 National Drive
Little Rock AR 72209
Andy McCammack
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
Larry McCanoless
OFFICE: 513-648-3772
FAX: 513-648-4029
Flour Daniel Fernald
P.O. Box 538704
Cincinnati OH 45253
Jeff McCormack
OFFICE: 810-239-1154
FAX: 810-239-1180
jmccormack@ssoe. com
SSOE, Inc.
111 E. Court St.
Flint Ml 48502
Seth McCoy
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
George McKee
OFFICE: 513-684-3073
FAX: 513-684-3039
george. m. mckee@lrdor. usace. army.mil
Army Corps of Engineers
5717 Nickview Drive
Cincinnati OH 45247
Iver McLeod
OFFICE: 207-287-8010
FAX: 207-287-7826
iver.j.mcleod@state. me. us
Maine Department of Environmental Protection
State House Station #17
Augusta ME 04333
Christine McMahon
OFFICE: 6186921478
FAX:
mcmahon(a)icon-stl. net
209 N. Fillmore Street
Edwardville IL 62025
USA
Robert B. McMaster
OFFICE: 6126259883
FAX:
mcmaster(fl),atlas.socsci. umn. edu
Dept. of Geography
414 Social Sciences Bldg.
Minneapolis MN 55455
USA
Stephanie McSpirit
OFFICE: 606-622-3070
FAX:
antmcspi@acs. eku
Eastern Ky. University
223 Keith
Richmond KY 40475
FINAL
25
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Jeremy Mennis
OFFICE:
FAX:
jmennis@gis.psu.edu
List of Attendees
Cincinnati, Ohio
Dept. of Geography, Pennsylvania State Univ.
302 Walker Building
University Park PA 16802
USA
PAPER
Jim Metzger
OFFICE: 812-665-2207
FAX: 812-665-5041
jmetzger@osmre.gov
Indiana DNR/Div. of Reclamation
RR 2 Box 129
Jascksonville IN 47438
Jim Meyer
OFFICE: 573-882-9291
FAX: 573-884-2199
jcmeyer@showme.missouri. edu
CARES - University of Missouri
130MumfordHall
Columbia MO 65211-6200
Michael V. Miller
OFFICE: 5417524271
FAX:
mmiller@ch2m.com
CH2M Hill
2300 NW Walnut Blvd.
Corvallis OR 97330
PAPER
USA
Bruce Milligan
OFFICE: 317-290-3200x347
FAX: 317-290-3225
bruce.milligan@in. usda.gov
Natural Resources Conservation Service
6013 Lakeside Blvd.
Indianapolis IN 46278
Scott Minamyer
OFFICE: (513)569-7175
FAX: (513)569-7585
minamyer.scott@epa.gov
U.S. EPA, CERI, TTB
26 W. Martin Luther King Dr.
Cincinnati OH 45268
Ken Mix
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
Sara Moola
FAX:
smoola@esri. com
ESRI
2205 A West Balboa Blvd.
Newport Beach CA 92663
Mike Moore
OFFICE: 717-783-7258
FAX: 717-783-7267
mmoore@dcnr.state.pa. us
PA Geological Survey
1500 North 3rd St.
Harrisburg PA 17102-1910
Shawn Moore
OFFICE: 937-255-5654
FAX: 937-255-4645
shawn.moore@afit.af.mil
AFIT/CEM
2950 P Street Bldg. 643
Wright Patterson AFB OH
45433-7765
Maggie Moorhouse
OFFICE: 361-883-6016
FAX: 361-883-7417
maggie@moorhousecc.com
Moorhouse Associates, Inc.
5826 Bear Lane
Corpus Christ! TX 78405
FINAL
26
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Bruce Motsch
OFFICE: 614-265-6772
FAX:
bruce.motsch@,dnr.state. oh. us
Ohio Department of Natural Resources
1952 Belcher Drive C-2
Columbus OH 43224
Erin T. Mutch
OFFICE: 209-532-0361
FAX: 209-532-0773
emutch@condorearth.com
Condor Earth Technologies, Inc.
21663 Brian Lane
Sonora CA 95370
Wolf Naegeli
OFFICE: 423-584-4806
FAX:
wnn@utk. edu
Energy, Environment & Resource Center, U of TN
4425 Balraj Ln
Knoxville TN 37921-2938
Jill Neal
OFFICE: (513)569-7277
FAX:
niel.jill@epamail. epa.gov
U.S. EPA, ORD, NRMRL
26 W. Martin Luther King Dr.
Cincinnati OH 45268
PAPER
Dean Nelson
OFFICE: 423-483-3191
FAX: 423-483-8617
dnelson@usit. net
Peer Consultants, P.C.
78 Mitchell Road
Oak Ridge TN 37830
Tamas Nemeth
FAX:
lacus@rissac. hu
GIS Lab, Research Inst. For Soil Science & Agri. Chemistry
Hungarian Academy of Sciences
H-1022 Bud Herman Otto ut 15
Hungary
Bruce Nielsen
OFFICE: 317-290-3200
FAX: 317-290-3225
bruce.nielsen@in.usda.gov
USDA-Natural Resources Conservation Service
6013 Lakeside Blvd.
Indianapolis IN 46278
Douglas A. Nieman
OFFICE:
FAX:
Dnieman@normandeau. com
Normandeau Associates
3450 Schuylkill Rd.
Spring City PA 19475
PAPER
USA
Eugene O'Brien
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
Toby Obenauer
OFFICE: 305-242-7892
FAX: 305-242-7836
toby_obenauer@nps.gov
National Park Service - Everglades
40001 SR 9336
Homestead FL 33034-6733
Ramon A. Olivero
OFFICE: 919-572-2764
FAX: 919-572-2765
rolivero@lmepo. com
Lockheed Martin Environmental Services
100 Capitola Drive, Suite 111
Durham NC 27713
PAPER
FINAL
27
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Deborah Olszowka
OFFICE: 513-231-7719
FAX: 513-231-7761
dmo@orsanco.org
List of Attendees
Cincinnati, Ohio
ORSANCA
5735 Kellogg Avenue
Cincinnati OH 45228
Lindell Ormsbee
OFFICE: 606-257-1302
FAX:
lormsbee@engr. uky.edu
University of Kentucky
Dept. of Civil Engineering
Lexington KY 40506-0281
Steven D. Ortiz
OFFICE: 512-239-2008
FAX: 512-239-4007
sortiz@tnrcc. state, tx. us
TX Natural Resource Conservation Commission
12100 Park 35 Circle, MC -108
Austin TX 78753
Kurt Overmyer
OFFICE: 616-336-3086
FAX: 616-336-2436
overmyer@iserv. net
Kent County Health Department
700 Fuller, N.E.
Grand Rapids Ml 49503
David A. Padgett
OFFICE: 615-963-5471
FAX: 734-939-5813
padgettdavid@netscape. net
Tennessee State University
3500 John A. Merritt Blvd.
Nashville TN 37219
PAPER
David Padgett, Ph.D.
OFFICE: 4407758747
FAX:
david.padgett@oberlin.edu
Oberlin College
USA
Mercedes Padilla
OFFICE: 787-767-8181x2243
FAX: 787-766-0150
jcaemer@prtc. net
PR Environemtal Quality Board
P.O. Box 192785
San Juan PR 00919
Susan Pagano
OFFICE: 610-696-4150
FAX: 610-696-8608
spagano@thcahill. com
Cahill Associates
104 South High Street
West Chester PA 19382
Harry Parrott
OFFICE: 414-297-3342
FAX: 414-297-3808
hparrott/r9@fs.fed. us
USDA-Forest Service
310 W.Wisconsin Ave.
Milwaukee Wl 53203
Laszlo Pasztor, Ph.D.
OFFICE: 361-356-3694
FAX: 361-212-1891
lacus@rissac. hu
GIS Lab, Research Inst. for Soil Science & Agri. Chemistry
Hungarian Academy of Sciences
Herman Otto ut 15
H-1022 Bud Hungary
Clair (Pat) Patterson
OFFICE: 410-260-8512
FAX: 410-260-8595
ppatterson@dnr.state. md. us
Maryland DNR-Forest Service
Tawes State Office Building E1 580 Taylor Avenue
Annapolis MD 21401
FINAL
28
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Michael J. Pavilonis
OFFICE: 412-442-4066
FAX: 412-442-4194
pavilonis.michael@dep. state, pa.us
Pennsylvania DEP
400 Waterfront Drive
Pittsburgh PA 15222-4745
Alan Peacock
OFFICE: 317-337-3414
FAX: 317-337-3235
apeacock@dowagro. com
Dow AgroSciences
9330 Zionsville Road
Indianapolis IN 46268
Norman E. Peters
OFFICE: 770-903-9145
FAX: 770-903-9199
npeters@usgs.gov
U.S. Geological Survey
3039 Amwiler Rd., Suite 130
Atlanta GA 30360-2824
Carl Petersen
OFFICE: 502-564-8390
FAX: 502-564-2088
cpeteOO@pop. uky.edu
Unversity of Kentucky
1854 Bellefonte Drive
Lexington KY 40503
Herb Petitjean
OFFICE: 502-564-6717
FAX: 502-564-5096
herb.petitjean@mail.state.ky.us
Kentucky Dept. for Environmental Protection
14 Reilly Road
Frankfort KY 40601
Rebecca Petty
OFFICE: 614-466-4801
FAX: 614-466-4556
rpetty@gw.odh.state.oh.us
Ohio Department of Health
246 N. High Street, 5th Floor
Columbus OH 43266-0588
Kenneth Pew
OFFICE: 216-881-6600x814
FAX: 216-881-2738
pewk@neorsd. org
N.E. Ohio Regional Sewer District
4415 Euclid Avenue
Cleveland OH 44103
Robert R. Pierce
OFFICE: 770-903-9113
FAX: 770-903-9199
rrpierce@usgs.gov
USGS/WRD
3039 Amwiler Road, Suite 130
Atlanta GA 30360-2824
Michael Plastino
OFFICE: 202-260-0048
FAX: 202-260-7926
plastino.michael@epa.gov
EPA Office of Water
401 M Street, SW(MC 4102)
Washington DC 20460
Mark Porembka
OFFICE: 412-442-4327
FAX: 412-442-3428
porembka.mark@dep. state, pa.us
PA Dept. of Environmental Protection
400 Waterfront Drive
Pittsburgh PA 15222
Jacob Prater
OFFICE: 765-759-6898
FAX:
Ball State University
6613 W.Jackson St.
Muncie IN 47304
FINAL
29
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Garry Price
OFFICE: 717-772-5630
FAX: 717-787-5259
price. garry@dep. state, pa. us
List of Attendees
PA Dep.
400 Market Street
Harrisburg PA 17105
Cincinnati, Ohio
Eric R. Pruitt
OFFICE: 812-323-8871
FAX:
erpruitt@indiana.edu
Indiana University
714 E Cottage Grove West
Bloomington IN 47408
Tom Quinn
OFFICE: 513-489-6611
FAX: 513-489-6619
tomq@pes.cin.com
P.E.S.
7209 E KemperRd.
Cincinnati OH 45241
Nancy J. Radle
OFFICE: 651-779-5102
FAX: 651-779-5109
nancy, radle @dot. state, mn.us
Minnesota Department of Transportation
3485 Hadley Avenue, N - Mail Stop 620
Oakdale MN 55128
Charles Rairdan
OFFICE: 916-557-7833
FAX: 916-557-7848
crairdan@spk. usace. army.mil
USAGE - Sacramento District
1325 "J" Street
Sacramento CA 95814
PAPER
Alexandrine Randriamahefa
OFFICE: 256-726-7436
FAX: 256-726-7055
drrand@oakwood.edu
Oakwood College
7000 Adventist Blvd.
Huntsville AL 35896
Ravi Rao
OFFICE: 404-562-8349
FAX:
rao. ravi@epamail. epa.gov
USEPA Region 4, OPM, PAB
61 Forsyth Street
Atlanta GA 30303
Alan Rao, Ph.D.
OFFICE: 6179241770
FAX:
arao@vhb.com
Vanasse Hangen Brustlin, Inc.
101 Walnut Street
02471
USA
Andrew Rawnsley
FAX:
ronz@ravensfield. com
Ravensfield Geographic Resources, Ltd.
P.O. Box 410
Granville OH 43023
USA
PAPER
Alan Rea
OFFICE: 208-387-1323
FAX: 208-387-1372
ahrea@usgs.gov
US Geological Survey
230 Collins Road
Boise ID 83702-4520
Jeffrey Reese
OFFICE: 765-213-1269
FAX: 765-747-7744
masine@iquest. net
Delaware County, Indiana GIS
100W. Main St., Room 204
Muncie IN 47305
FINAL
30
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Jon Reid
OFFICE: 513-558-1723
FAX:
jon. reid@uc.edu.
List of Attendees
Cincinnati, Ohio
University of Cincinnati
3223 Eden Ave. ML 056
Cincinnati OH 45267-0056
Chad K. Rhodes
OFFICE: 313-237-3093
FAX: 313-224-1547
rhodesc@EnvAfrs. ci. detroit.mi. us
City of Detroit Dept. of Environmental Affairs
660 Woodward Avenue, Suite 1590
Detroit Ml 48226
Carl Rich
OFFICE: 303-202-4316
FAX: 303-202-4354
clrich@usgs. gov
U.S. Geological Survey
P.O. Box 25046, MS-516, Denver Federal Center
Denver CO 80225
Ian Richardson
OFFICE: 519-884-0510
FAX: 519-884-0525
irichardson@rover. com
CRA
651 Colby Dr.
Waterloo
Ontario
John Richardson
OFFICE: 402-562-8290
FAX: 404-562-8269
richardson.john@epa.gov
US EPA Region 4
61 Forsyth Street
Atlanta GA 30303
James M. Rine, Ph.D.
OFFICE: 803-777-7792
FAX: 803-777-6437
jrine@esri. esri. sc.edu
Earth Sciences & Resources Institute
University of South Carolina
901 Sumter Road, Room 401
Columbia SC 29208
PAPER
Jim Rocco
OFFICE: 330-562-9391
FAX: 330-562-9391
roccojl@worldnet. att. net
Sage Risk Solutions LLC
360 Heritage Road
Aurora OH 44202
Edward Rogers, Jr.
OFFICE: 9085982600
FAX:
erogers@bemsys. com
BEM Systems, Inc.
100 PassaicAve.
Chatham NJ 07928
PAPER
USA
Cynthia Root
OFFICE: 319-398-5678
FAX: 319-398-1250
croot@kirkwood. cc. ia. us
Kirkwood Community College
6301 Kirkwood Blvd. SW
Cedar Rapids IA 52406
Lloyd Ross
OFFICE: 216-739-0555
FAX: 216-739-0560
lsr@haleyaldrich. com
Haley & Aldrich, Inc.
5755 Granger Road, Suite 100
Independence OH 44131
Randall Ross, Ph.D.
OFFICE: (580)436-8611
FAX:
ross. randall@epamail. epa.gov
R.S. Kerr Environ. Research Center U.S. EPA/ORD/NRMRL PAPER
919 Kerr Research Dr., P.O. Box 119
Ada OK 74820
FINAL
31
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Holly Roten
OFFICE: 419-353-1531
FAX:
rotenh@bgnet. bgsu.edu
List of Attendees
Bowling Green State University
455 South Enterprise, Apartment H.
Bowling Green OH 43402
Cincinnati, Ohio
Chris Rudnick
OFFICE: 502-564-6716
FAX: 502-564-5096
chris.rudnick@mail.state.ky.us
Kentucky Environmental Protection-State Superfund
14 Reilly Road
Frankfort KY 40601
Nel Ruffin
OFFICE: 606-257-1299
FAX:
nruffin@engr. uky.edu
Kentucky Water Resources Research Institute
233 Mining & Minerals Resource Building
University of Kentucky
Lexington KY 40506
Steve Saines
OFFICE: 614-644-2752
FAX: 614-644-2909
Ohio EPA
P.O. Box1049
Columbus OH
43216
Scott Samson
OFFICE: 606-257-3767
FAX: 606-257-4354
ssamson@pop.uky.edu
University of Kentucky
Rural Sociology Program, Garrigus 500
Lexington KY 40546
Vicki Sandiford
OFFICE: 919-541-2629
FAX: 919-541-7690
sandiford. vicki@epa.gov
US EPA
MD-13
RTP NC 27711
George Sarapa
OFFICE: 412-262-5400
FAX: 412-262-3036
sarapg@lrkim ball, com
L. Robert Kimball & Associates, Inc.
415 Moon Clinton Road
Moon Township PA 15108
Gary Schaal
OFFICE: 614-265-6769
FAX: 614-267-2981
gary.schaal@dnr. state, oh. us
Ohio Department of Natural Resources
1952 Belcher Drive C-4
Columbus OH 43224
Devin Scheak
OFFICE: 513-921-5124
FAX: 513-921-5136
IMAGO@ONE.net
IMAGO, Inc.
553 Enright Avenue
Cincinnati OH 45205
Tom Schneider
OFFICE: 937-295-6466
FAX: 937-285-6404
tom.schneider@epa. state.oh. us
Ohio EPA
401 E. 5th Street
Dayton OH 45402
Theresa Schnorr
OFFICE: 513-558-5984
FAX:
thsl@cdc.gov
N.I.O.S.H
4676 Columbia Parkway
Cincinnati OH 45220
FINAL
32
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Susan Schock
OFFICE: 513-569-7551
FAX: 513-487-2513
schock.sue@epamail.epa.gov
List of Attendees
Cincinnati, Ohio
U.S. EPA/ORD/NRMRL
26 W. Martin Luther King Drive
Cincinnati OH 45268
Chris Schroeder
OFFICE: 402-441-6272
FAX: 402-441-8323
cschroed@ci. lincoln. ne.us
Lincoln-Lancaster County Health Dept.
3140 "N" Street
Lincoln NE 68510
Joe Schubauer-Berigan
OFFICE: 513-569-7734
FAX:
Schubauer-Berigan.joseph@epa.gov
U.S. EPA National Center for Envir. Assessment
26 W. Martin Luther King Drive
Cincinnati OH 45220
Susan Schultz
OFFICE: 513-861-7666
FAX: 513-559-3155
schultz23@fuse. net
Mill Creek Restoration Project
42 Calhoun Street
Cincinnati OH 45219
Robert Scott
OFFICE: 404-675-1753
FAX: 404-675-6246
bob_scott@mail.dnr.state.ga.us
Georgia EPD
4220 International Parkway, Suite 101
Atlanta GA 30354
Irena Scott, PhD
OFFICE: 614-644-8020
FAX: 614-644-7740
iscott@gw.odh.state. us
Ohio Department of Health
246 North High Street
Columbus OH 43266-0118
Tom Seibert
OFFICE: 502-564-6716
FAX: 502-564-7484
torn. seibert@mail. state, ky. us
Kentucky Division for Waste Management
14 Reilly Road
Frankfort KY 40601
Gabriel Senay, Ph.D.
OFFICE: 5135697096
FAX:
SENAY.GABRIEL@epa.gov
PAI/SAIC, US EPA
26 W. Martin Luther King Dr.
Cincinnati OH 45268
USA
PAPER
Barbara Seymour
OFFICE: 301-622-9696x108
FAX: 301-622-9693
bseymour@plexsc. com
Plexus Scientific
12501 Prosperity Drive, # 401
Silver Spring MD 20904
Jo Ann Shaw
OFFICE: 573-751-9370
FAX:
nrshawj@mail. dnr.state.mo. us
MO Dept. of Natural Resources
205 Jefferson St.
Jefferson City MO 65102
Jerry Shi
OFFICE: 614-292-6193
FAX: 614-292-7688
shi. 19@osu.edu
The Ohio State University
125 South Oval Mall
Columbus OH 43210
FINAL
33
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Andy Shirmeyer
List of Attendees
Cincinnati, Ohio
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
Massoud Shoa
OFFICE: 502-564-6717x219
FAX: 502-564-2705
massoudshoa@mail.state.ky.us
Kentucky Division of Waste Management
14 Reilly Road
Frankfort KY 40601
Leila Shultz
OFFICE: 617-327-4294
FAX: 617-327-4294
shultz@oeb. harvard, e du
Harvard University
22 Divinity Avenue
Cambridge MA 02138
Amardeep Singh
OFFICE: 916-653-2726
FAX: 916-653-6366
asingh@fnd. csus.edu
csus
905 23rd Street #3
Sacramento CA 95816
Nathan Sloan
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflirt l@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
Ryan Sloan
OFFICE: 765-289-7816
FAX:
resloan(a)hotmail. com
Dept. of Natural Resources , Ball State Univ.
323 1/2
Muncie IN 47304
Mike Sloop
OFFICE: 770-673-3624
FAX: 770-396-9495
msloop@brwncald. com
Brown and Caldwell
41 Perimeter Center East
Suite 400
Atlanta GA 30346
Kathleen Smaluk-Nix
OFFICE: 502-574-1358
FAX: 502-574-1389
ksmaluk@louky. org
Office of Health and Environment
600 W. Main St. , 4th Floor
Louisville KY 40202
Art Smith
OFFICE: 502-574-2511
FAX: 502-574-1389
asmith@louky. org
City of Louisville Office of Health and Environment
600 W. Maine Street, 4th Floor
Louisville KY 40202
Christopher Smith
OFFICE: 608-221-6330
FAX: 608-221-6353
Wisconsin DNR
1350 Femrite Drive
Monona Wl 53716
Gerry Snyder
OFFICE: 303-312-6623
FAX: 303-312-6063
snyder.gerry@epamail. epa.gov
US EPA
999 18th Street, Suite 500, 8IG
Denver CO 80202-2466
FINAL
34
As of: Thursday, September 30, 1999
-------
Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Marilyn Sommer
List of Attendees
Cincinnati, Ohio
OFFICE: 502-564-3256
FAX:
maras425@ntr. net
Kentucky Resource Cabinet
1933 Deerwood
Louisville KY 40210
Puneet Srivastava
OFFICE: 501-682-0018
FAX: 501-682-0010
ADEQ
800 National Drive
P.O. Box 8913
Little Rock AR 72205
Greg Starkebaum
OFFICE: 303-763-7188
FAX: 303-763-4896
gstarkebaum@techlawinc. com
TechLaw, Inc.
300 Union Blvd. #600
Lakewood CO 80228
Steven K. Starrett, Ph.D
OFFICE: 785-532-1583
FAX: 785-532-7717
steveks@ksu. edu
Kansas State University
160SeatonHall
Manhattan KS 66506-2905
Dr. Gerry L.
OFFICE: 301-415-5265
FAX: 301-415-5348
gls3@nrc.gov
U.S. Nuclear Regulatory Commission
TWFN-MS T7C6-11545 Rockville Pike
Rockville MD 20852
Joel Stocker
OFFICE: 860-345-4511
FAX: 860-345-3357
jstocker@canr. uconn.edu
UConn CES, NEMO Project
1066 Saybrook Rd., Box 70
Haddam CT 06438-0070
Brad Stone
OFFICE: 502-564-6716
FAX: 502-564-4049
brad, stone @mail. state, ky. us
KY Div. of Waste Management
14 Reilly Road
Frankfort KY 40601
William Story
OFFICE: 775-687-4670
FAX: 775-687-6396
bstory@ndep. carson-city. nv. us
Nevada Division of Environmental Protection
333 W. Nye Lane
Carson City NV 89706
Steven V. Strausbauch
OFFICE: 210-536-6134
FAX: 210-536-1130
steven.strausbauch@brooks.af.mil
IERRA/RSRE
2513 Kennedy CIR
Brooks AFB TX 78235
Ryan Stults
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
Prathiba Subramaniam
OFFICE: 513-474-9780
FAX:
prathiba@gqc. com
Global Quality Corporation
3304 Jefferson Avenue #104
Cincinnati OH 45220
FINAL
35
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Bhagya Subramanian
OFFICE: 5135697349
FAX:
subramanian. bhagya@epa.gov
US EPA / NERL
26 W. Martin Luther King Dr.
Cincinnati OH 45268
USA
PAPER
Parrish Swearingen
OFFICE: 912-926-1197x114
FAX: 912-926-9642
parrish. swearingen@robins. af.mil
WR-ALC/EMX
455 Byron Street
Robins AFB GA 31098
Jane C. Thapa
OFFICE: 518-402-7713
FAX: 518-402-7599
jct02@health. state, ny. us
NYDOH
547 River Street, Room 400
Troy NY 12180-2216
Geoff Thompson
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
Gordon Thompson
OFFICE: 513-782-4556
FAX:
gwthompson@theitgroup. com
The IT Group
312 Directors Drive
Knoxville TN 37923
Richard E. Thornburg
OFFICE: 513-564-1785
FAX: 513-564-1776
Rick. Thornburg@cinhlthe.rcc. org
Cincinnati Health Dept.
3845 Wm. P. Dooley By-Pass
Cincinnati OH 45223
Larry Tinney
OFFICE: 702-897-3270
FAX: 702-897-3285
ltinney@lmepo. com
Lockheed Martin Environmental Services
980 Kelly Johnson Dr.
Las Vegas NV 89119
Alejandro F. Tongco
OFFICE: 405-744-8974
FAX: 405-744-7008
al@seic. lse.okstate.edu
Oklahoma Spatial & Environmental Information Clearinghouse,
CITD
Stillwater OK 74076
Oury Traore
OFFICE: 606-986-2373
FAX: 606-986-2619
otraore@maced. org.
MACED
433 Chestnut St.
Berea KY 40403
Michael E. Troyer, Ph.D.
OFFICE: (513)569-7399
FAX:
troyer.michael@epamail.epa.gov
U.S. EPA, ORD, NRMRL
26 W. Martin Luther King Dr.
Cincinnati OH 45268
PAPER
Amy Tucker
OFFICE: 765-281-1026
FAX:
amyetucker@prodigy. net
Ball State University
100S. CalvertAve.
Muncie IN 47303
FINAL
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As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
List Of Attendees Cincinnati, Ohio
Hull and Associates, Inc.
4700 Duke Drive, Suite 172
Mason OH 45040
September 22-24,1999
W. Lance Turley
OFFICE: 513-459-9677
FAX: 513-459-9869
hurley @hullinc. com
PAPER
Rachel Tyler
OFFICE: 352-378-6517
FAX:
faca@aol.com
Florida Rural Community Assistance Project
6212 NW 43rd St. Suite A
Gainesville FL 32697
Tom Ungar
OFFICE: 216-623-2738
FAX: 216-621-4972
thomas. ungar@mw.com
Montgomery Watson
1300 East 9th Street, Suite 2000
Cleveland OH 44114
Andre van Delft
OFFICE: 31152695286
FAX: 31152695335
a.vandelft@bouw.tno.nl
TNO Building and Construction Research
P.O. Box 49
Delft - Netherlands 2600 AA
Laurens Van der Tak
OFFICE: 703-471-6405
FAX: 703-471-1508
Ivandert@ch2m.com
CH2M Hill
13921 Park Center Rd., Suite 60
Herndon VA 20171
USA
PAPER
Robert J. van Waasbergen
OFFICE: 408-737-7697
FAX: 408-737-7978
robertvw@aeds. com
Applied Environmental Data Services
201 W. California Ave. #206
Sunnyvale CA 94086
Dirk Vandervoort
OFFICE: 360-475-6915
FAX: 360-475-6901
vandervoort@ctc. com
Concurrent Technologies Corporation
510 Washington Avenue, Suite 120
Bremerton WA 93777-1844
James M. Vanek
OFFICE: 412-442-4031
FAX: 412-442-4328
vanek.james@dep. state.pa. us
PA Dept. of Environmental Protection
400 Waterfront Drive
Pittsburgh PA 15222
Paul Vermaaten
OFFICE: 517-788-4075
FAX: 517-788-4641
City of Jackson
161 W. Michigan Ave.
Jackson Ml 49201
Janet Vick
FAX:
770-822-7473
770-822-7479
Gwinnett County
75 Langley Drive
Lawrenceville GA 30045
Todd Vikan
OFFICE: 937-259-9850
FAX: 937-259-9869
tvikan@kpmg. com
KPMG LLP
3139 Research Blvd. Suite 200
Dayton OH 45420
FINAL
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As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999 List Of Attendees Cincinnati, Ohio
Jack Wachter
OFFICE: 513-352-6992
FAX: 513-352-4970
jack.wachter@cinems. rcc. org
City of Cincinnati
Office of Environmental Mgmt.
805 Central Ave., Suite 610
Cincinnati OH 45202-1947
Scott Wade
OFFICE: 734-332-1200
FAX: 734-332-1212
swade@limno. com
LIMNO-Tech, Inc.
501 Avis Drive
Ann Arbor Ml 48108
Tim Wade
OFFICE: 702-798-2117
FAX: 702-798-2208
•wade.timothy@epa.gov
U.S. EPA
944 E. Harmon Avenue
Las Vegas NV 89119
Jerry Wager
OFFICE: 614-265-6619
FAX: 614-262-2064
jerry.wager@dnr.state.oh. us
Ohio Department of Natural Resources
1939 Fountain Square Ct. E-2
Columbus OH 43224
James Wang
OFFICE: 404-562-8280
FAX:
wang.james@epamail. epa.gov
USEPA Region 4
61 Forsyth Street
Atlanta GA 30303
Lizhu Wang
OFFICE: 608-221-6335
FAX: 608-221-6353
wangl@mailo. dnr. state, wi. us
Wisconsin DNR
1350 Femrite Drive
Monona WI 53716
Xinhao Wang, Ph.D.
OFFICE: 513-556-0497
FAX: 513-556-1274
xinhao. wang@uc.edu
School of Planning, University of Cincinnati
621ODAAP Building
Cincinnati OH 45221-0016
USA
PAPER
Robin Wankum
OFFICE: 913-458-6538
FAX: 913-458-6633
wankumrd@bv. com
Black and Veatch Special Projects
6601 College Blvd.
Overland Park KS 66211
PAPER
Stephen Want
OFFICE: 301-951-4681
FAX: 301-652-1273
stephen.want@lexis-nexis.com
ENVIRONMENTAL ABSTRACTS
Congressional Information Service, Inc.
4520 East-West Highway
Bethesda MD 20814-3389
Mark Warrell
OFFICE: 502-564-6716
FAX: 502-564-2705
mark.warrell@mail.state.ky. us
Kentucky Division of Waste Management
14 Reilly Road
Frankfort KY 40601
Steve Webb
OFFICE: 405-702-8195
FAX: 405-702-8101
steve.-webb@deqmail.state.ok. us
Oklahoma Dept. of Environmental Quality
707 N. Robinson Ave.
Oklahoma City OK 73102
FINAL
38
As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Kevin Wehrly
List of Attendees
Cincinnati, Ohio
OFFICE: 513-569-7301
FAX: 513-569-7609
wehrly. kevin@epa.gov
USEPA-NERL MS-642
26 W. Martin Luther King Drive
Cincinnati OH 45268
Tom Weir
OFFICE: 215-685-9436
FAX: 215-685-7593
thomas. weir@phila. gov
Air Management Services
321 University Avenue / Spelman Building
Philadelphia PA
Joseph P. Wellner
OFFICE: 513-251-2730
FAX: 513-251-0200
•wellnerj@ttnus. com
Tetra Tech NUS, Inc.
1930 Radcliff Drive
Cincinnati OH 45204
Russell Weniger
OFFICE: 210-671-4844
FAX: 210-671-2241
russell.weniger@lackland. af.mil
US Air Force
2240 Walker Avenue
Lackland AFB TX 78236-5637
Bill Wheaton
OFFICE: 919-541-6158
FAX: 919-541-5929
Research Triangle Industries
3040 Cornwallis Road - P.O. Box 12194
Research Triangle Park NC 27709-2194
Brad White
OFFICE: 937-384-4215
FAX: 937-384-4201
•whiteb@mail.rfweston.com
Roy F. Weston, Inc.
2566 Kohnle Drive
Miamisburg OH 45342
Ron White
OFFICE: 513-648-5920
FAX: 513-648-4029
Ronaldjwhite@fernald.gov
Fluor Daniel Fernald
P.O. Box 538704
Cincinnati OH 45253-8704
Charlotte White-Hull
OFFICE: 513-627-1197
FAX: 513-627-1208
whitehull. ce@pg. com
Procter & Gamble Company
11810 East Miami River Road, Room 1A04T BTF
Ross OH 45061
D. Whitmire
FAX:
765-289-1099
Ball State University
6613 W.Jackson St.
Muncie IN 47304
Derik Whitmire
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental
Ball State University
Muncie IN 47306
Duane Wilding
OFFICE: 410-974-7276
FAX: 410-974-7200
dwild@menv. com
Maryland Environmental Service
2011 Commerce Park Drive
Annapolis MD 21401
FINAL
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As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Frances Wildman
OFFICE: 309-782-7907
FAX: 309-782-5038
wildmanf@ria. army.mil
List of Attendees
Cincinnati, Ohio
Rock Island Arsenal
5IORI-SEV, Bldg210
Rock Island IL 61299
Dave Williams
OFFICE: 270-827-3414
FAX: 270-827-1117
williams@kgs.mm. uky. edu
Kentucky Geological Survey
P.O. Box 653
Henderson KY 42419
Matt Williams
OFFICE: 765-285-2327
FAX: 765-285-2606
jeflinl@gw. bsu. edu
Department of Natural Resources and Environmental Management
Ball State University
Muncie IN 47306
Steve Williams
FAX:
614-644-2752
614-644-2909
Ohio EPA
P.O. Box1049
Columbus OH
43216-1049
Lesley Hay Wilson
OFFICE: 512-367-2952
FAX: 512-367-2953
hay_wilson@mail. utexas. edu
University of Texas at Austin
10100 Burnet Road, PRC Building 119
Austin TX 78758
PAPER
James Wolfe
OFFICE: 304-696-6042
FAX: 304-696-5454
jawolfe@marshall. edu
Marshall University
400 Hal Greer Blvd.
Huntington WV 25755-2585
Tim T. Wright
OFFICE: 513-732-7499
FAX: 513-732-7969
CCGHD@FUSE.NET
Clermont County GHD
2275 Bauer Road Suite 300
Batavia OH 45103
Patricia Yaden
OFFICE: 502-564-6716
FAX: 502-564-4049
patty.yaden@mail.state.ky. us
NREPC
14 Reilly Road
Frankfort KY 40601
Thomas K. Yeager
OFFICE: 717-772-4018
FAX: 717-772-3249
yeager. thomas@dep.state.pa. us
Pennsylvania DEP-Water Supply Management
P.O. Box 8467, 11th Floor, RCSOB
Harrisburg PA 17105-8467
Greg Young
OFFICE: 513-576-0009
FAX: 513-576-9756
youngg@srwenvironmental. com
SRW Environmental Services
55 West TechneCenter, Suite C
Milford OH 45150
Pao-Chiang Yuan
OFFICE: 601-968-2466
FAX: 601-968-2344
pcyuan@yahoo. com
Jackson State University
P.O. Box18489
Jackson MS 39217
FINAL
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As of: Thursday, September 30, 1999
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Environmental Problem Solving with Geographic Information Systems Conference
September 22-24,1999
Donald Zelazny
OFFICE: 703-916-7987
FAX: 703-916-7984
dzelazny@plaii. com
List of Attendees
Cincinnati, Ohio
Platinum International Inc.
5350 Shawnee Road, Suite 200
Alexandria VA 22312
David Zellmer
OFFICE: 540-552-8185
FAX: 540-552-8189
dzellmerCciiyt. edu
Virginia Tech
428 Winston Avenue
Blacksburg VA 24060
Guang Zhao, Ph.D.
OFFICE: 8037344833
FAX:
zhaog@columb30. dhec.state.se. us
Information Technology Section, SC Dept. of Health & Env. Control
2600 Bull Street PAPER
Columbia SC 29201
USA
Peter Max Zimmerman
OFFICE: 410-887-2188
FAX: 410-887-3182
People's Counsel for Baltimore County
Courthouse 47, 400 Washington Ave.
Towson MD 21204
FINAL
41
As of: Thursday, September 30, 1999
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Environmental Problem Solving
with Geographic Information Systems
September 21 - 23, 1999
This presentation was not made available.
Please contact the presenter with questions and comments.
Back to
1999 Agenda
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The Watershed Assessment Project: Tools for Regional
Problem Area Identification
Christine Adamus
St. Johns River Water Management District, Palatka, Florida
The St. Johns River Water Management District of Flor-
ida recently completed a major water resources plan-
ning effort. As part of this planning effort, the St. Johns
River Water Management District created a geographic
information systems (CIS) project called the Watershed
Assessment, which included a nonpoint source pollution
load model. This paper introduces the planning project
and the Watershed Assessment, and describes how the
results of the model are being used to guide water
management activities in northeast Florida.
Background
The St. Johns River Water Management District (Dis-
trict), one of five water management districts in Florida,
covers 12,600 square miles (see Figure 1). The St.
Johns River starts at the southern end of the District and
flows north; it enters the Atlantic Ocean east of the city
District Boundary
Major Drainage
Basin Boundaries
Figure 1. St. Johns River Water Management District, Florida.
of Jacksonville. The cities of Orlando, Daytona Beach,
and Jacksonville are partially or entirely within the Dis-
trict boundaries. Ad valorem taxes provide primary fund-
ing for the District.
The District boundaries are somewhat irregularly
shaped because Florida water management districts are
organized on hydrologic, not political, boundaries, which
greatly improves the District's ability to manage the
resources. On the north, the District shares the St.
Mary's River with the state of Georgia and on the south,
shares the Indian River Lagoon with another water man-
agement district. Most of the water bodies the District
manages, however, have drainage basins that are en-
tirely contained within the District's boundaries.
Water management districts in Florida have amassed
extensive CIS libraries, which they share with local and
statewide agencies. These libraries include basic data
layers such as detailed land use, soils, and drainage
basins. Districts also coordinate data collection and
management to ensure data compatibility.
District Water Management Plan
All activities and programs of the water management
districts are related to one or more of the following
responsibilities: water supply, flood protection, water
quality management, and natural systems management.
Each water management district recently completed a
district water management plan (Plan). The main pur-
pose of these Plans is to provide long-range guidance
for the resolution of water management issues. The
Florida Department of Environmental Protection will use
these five Plans as the basis for a state water manage-
ment plan. Each water management district used the
same format, which comprised the following components:
• Resource assessment: What are the problems and
issues related to each of the four responsibilities
listed above?
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• Options evaluation: What options are available for
addressing the problems?
• Water management policies: What existing District
policies influence the decisions that must be made?
• Implementation strategy: What is the best plan for
addressing the problems?
The Watershed Assessment Project
The District created the Watershed Assessment project
as part of is resource assessment. This CIS project
examines the entire District to identify problems related
to flood protection, ecosystems protection, and surface
water quality.
The flood protection component is the only part of the
Watershed Assessment that is not complete. It will in-
volve simple overlays of floodplain boundaries with ex-
isting and future land use. Floodplain boundaries are
defined as Federal Emergency Management Agency
(FEMA) flood insurance rate map 100-year flood hazard
areas. In many areas, these designations are not very
accurate, yet we decided to proceed with their use
because they are the best available information for
many parts of the District. In areas where little hydrologic
information is available and where the District has not
conducted any related studies, the FEMA data are a
helpful starting point. This echoes a theme of the Water-
shed Assessment project: the assessment is primarily
intended to fill in gaps where we have not performed
previous resource assessments, not to supplant existing
information.
The ecosystems protection component of the Water-
shed Assessment is based heavily on a project identify-
ing priority habitat in Florida, conducted by the Florida
Game and Fresh Water Fish Commission (1). It is similar
to gap analyses that the U.S. Fish and Wildlife Service
currently is conducting in many parts of the country. For
the Watershed Assessment, we modified the data some-
what and examined ways to protect the habitat in coop-
eration with local agencies.
The surface water quality component of the Watershed
Assessment has two main parts. The first uses water
quality data from stations that have been spatially refer-
enced so that we can map them and combine the infor-
mation with other information, such as the second part
of the water quality component. This second part is a
nonpoint source pollution load model, which is dis-
cussed in more detail below.
The Pollution Load Screening Model
The nonpoint source pollution load model is the Pollu-
tion Load Screening Model (PLSM), a commonly used
screening tool in Florida. It is an empirical model that
estimates annual loads to surface waters from storm-
water runoff. Our goal in designing this model was to
identify pollution load "problem areas" for examination in
the Plan.
In these types of models, annual pollutant loads are a
function of runoff volume and mean pollutant concentra-
tions commonly found in runoff. Runoff volume varies
with soil and land use, while pollutant concentrations
vary with land use. For the PLSM, pollutant concentra-
tions were derived from studies conducted solely in Flor-
ida. A report describing the model in detail is available (2).
Usually, this kind of model combines CIS with a spread-
sheet: the CIS supplies important spatial information
that is input into a spreadsheet where the actual calcu-
lations are made. The PLSM is different, however, be-
cause we programmed it entirely within CIS. The
District's CIS software is ARC/INFO, and the model
employs an ARC/INFO module called GRID, which uses
cell-based processing and has analytical capabilities
(3). All the model calculations are done in the CIS
software, resulting in a more flexible model with useful
display capabilities.
Model input consists of grids, or data layers, with a
relatively small cell size (less than 1/2 acre). We chose
this cell size based on the minimum mapping unit of the
most detailed input data layer (land use) and the need
to retain the major road features. The model has four
input grids: land use, soils, rainfall, and watershed
boundaries. For any given cell, the model first calculates
potential annual runoff based on the land use, soil, and
rainfall in that cell. It then calculates annual loads by
applying land-use-dependent pollutant concentrations
to the runoff.
For this model:
• Land use is from 1:24,000-scale aerial photography
flown in 1988 and 1989. The model incorporates 13
land use categories.
• Soils are the Soil Conservation Service (SCS)
SSURGO database, which corresponds to the county
soil surveys. The PLSM uses the hydrologic group
designation of each soil type.
• Rainfall was taken from a network of long-term rainfall
stations located throughout the District.
• Watersheds were delineated by the United States
Geological Survey (USGS) on 1:24,000-scale,
7.5-minute maps and digitized.
Model output consists of a runoff grid and six pollutant
load grids. We calculated loads for total phosphorus,
total nitrogen, suspended solids, biochemical oxygen
demand, lead, and zinc. We chose these pollutants
because reliable data were available and because they
characterize a broad range of nonpoint pollution-gener-
ating land uses, from urban to agricultural. The model
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calculates runoff and loads for any point in space, allow-
ing the user to see the spatial distribution of loads. An
example of a total phosphorus load grid for one sub-
basin in the Jacksonville, Florida vicinity is shown in
Figure 2.
The grids themselves provide a detailed view of model
output. Model results can also be summarized by water-
shed, using the watershed boundary grid, and the infor-
mation can be examined from a basinwide perspective.
We have applied PLSM results in other useful ways at
the District. For example, District staff felt that previous
sediment sampling sites were not appropriately located,
so the District water quality network manager used
model results to locate new sampling sites, focusing on
problem areas as well as areas where we expect to see
little or no nonpoint impact.
Figure 2. Distribution of total phosphorus loads, Ortega River
subbasin (darker areas represent higher loads).
Application of Model Results in the Plan
Because the goal of the model was to identify potential
stormwater runoff problem areas, we needed to simplify,
or categorize, the model results for use in the Plan. We
calculated the per acre watershed load for each pollut-
ant and defined "potential stormwater runoff problem
areas" as those individual watersheds with the highest
loads for all pollutants. Problem areas for one major
basin in the District, the lower St. Johns River basin, are
depicted in Figure 3.
We also ran the model with future land use data ob-
tained from county comprehensive plans. Because the
Watersheds
•^^ Subbasins
h 1
Potential Stormwater
Runoff Problem Areas
Scale in Miles
0 4 8 12 16
Figure 3. Potential stormwater runoff problem areas, lower St.
Johns River basin.
county maps are guides to future development, and not
predictions of actual development, we exercised caution
when using the results. Problem areas were defined as
those watersheds with projected loads greater than or
equal to existing problem areas. Also, District planners
combined model results with information about individ-
ual counties' regulations and policies to evaluate where
problems are most likely to occur.
Prior to compiling the Plan, the District conducted work-
shops in each county in the District, in which problem
areas identified by the PLSM were discussed with local
agency staff, officials, and the public. We provided large,
hard copy maps depicting stormwater runoff problem
areas combined with results of a separate water quality
analysis on county-based maps. These maps proved to
be powerful tools for initiating discussions and gathering
feedback.
-------
In the Plan, stormwater runoff problem areas were re-
ported for each of the 10 major drainage basins in the
District. The information was also repackaged in a
county-based format to create a quick reference for local
agencies. District planners recommended strategies for
addressing problems; these strategies vary as appropri-
ate for each county. Examples include the need to as-
sess compliance with existing stormwater permits,
encourage stormwater reuse during the stormwater and
consumptive use permitting processes, coordinate with
municipalities that are implementing stormwater man-
agement plans, encourage and assist significantly af-
fected municipalities to create stormwater utilities, and
improve monitoring in problem areas that do not have
sufficient water quality data.
In conclusion, the Watershed Assessment CIS project
has proved to be useful not only to the St. Johns River
Water Management District, but also to local govern-
ments. Large projects such as this could not be com-
pleted in a reasonable time without the use of CIS. Also,
for ARC/INFO users who have been restricted to vector
processing, the cell-based processing available in GRID
is a powerful modeling tool.
References
1. Cox, J., R. Kautz, M. MacLaughlin, and T. Gilbert. 1994. Closing
the gaps in Florida's wildlife habitat conservation system. Florida
Game and Fresh Water Fish Commission, Office of Environmental
Services, Tallahassee, FL.
2. Adamus, C.L., and M.J. Bergman. 1993. Development of a non-
point source pollution load screening model. Technical Memoran-
dum No. 1. Department of Surface Water Programs, St. Johns
River Water Management District, Palatka, FL.
3. ESRI. 1992. Cell-based modeling with GRID. Redlands, CA: En-
vironmental Systems Research Institute, Inc.
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Update of GIS land use attributes from land surface texture
information using SIR-C images
Francisco J. Artigas1
Abstract
The Meadowlands in northern New Jersey were used as dumping grounds for decades and
today dense canopies of common reed (Phragmites australis) cover most of the District's open
spaces. Our objective is to evaluate the utility of multi-frequency SAR in updating GIS land use
information by means of prospecting for anomalously high backscatter in open spaces that
could indicate the presence of building or metallic waste concentrations near the surface. We
combined a land use vector coverage with a co-registered SIR-C C-HV image acquired October
10, 1994 to isolate officially designated "open space" parcels. Groups of four or more pixels with
anomalously high backscatter values were converted to a vector coverage and draped over a
very fine spatial resolution color infrared digital orthophoto of the District. We discuss the
implications for the use of operational imaging radar for monitoring land use and management
of open spaces within a dynamic and complex urban environment.
Introduction
The Hackensack Meadowlands Development Commission (HMDC) overseas the orderly
development of the Meadowlands District (District) which is a 82 square kilometer degraded
urban estuary four kilometers west of New York City in northern New Jersey. The unregulated
use of District lands as disposal sites for solid and industrial waste for more than 150 years has
turned these meadows and wetlands into one of the most environmentally assaulted areas in
the U.S. (Grossman 1992). The HMDC (a New Jersey State Agency) oversees the preservation
and development of more than 4,375 parcels of land including 1,012 hectares of landfill. This
study presents the results of an effort to use Geographical Information Systems (GIS) in
combination with Synthetic Aperture Radar (SAR) images to update the District's current land
use database. This was accomplished by detecting and documenting areas in open spaces that
showed unusual surface texture roughness as a consequence of land use change or
disturbance by disposal of solid waste.
1 Rutgers University, Center for Information Management, Integration and Connectivity. 180 University
Ave., Newark NJ 07102. artigas@cimic.rutgers.edu
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Land use research based on SAR images is emerging as a promising new operational
technology for the applied earth sciences. Images of this area from the Shuttle Imaging Radar
(SIR) with a ground resolution of 12.5 m were first made available in 1994. In 1995, Canada
launched Radarsat-1 (ground resolution 8-100 meters) which some consider the starting point
for the true application and commercialization of radar images (Glackin 1997). By the year 2002,
NASA's LightSAR will be collecting radar images of the earth from a space platform on a
continuous basis. In just a few years it is expected that there will be an abundance of high-
resolution radar imagery which have some notable advantages over traditional spectral
sensors.
The main advantage of radar over spectral remote sensing sensors (e.g. Landsat, Spot, SPIN-
2) is that it can capture images of the earth's surface under any weather conditions, day and
night. Radar can also penetrate vegetation canopies and at certain wavelengths penetrate the
first centimeters of the soil (Xia et al 1997). The intensity of backscatter from radar microwave
pulses is highly correlated to surface texture. A useful rule-of-thumb in analyzing radar images
is that the brighter the backscatter on the image, the rougher the surface being imaged
(Freeman 1996).
Since SIR-C images became available there has been great interest by scientists to document
radar backscatter from diverse earth surfaces (Alpers and Holt 1995, Beaudoin et al 1994,
Cordey et al 1996, Freeman and van de Broek 1995). The areas of interest have predominantly
been large (10 to 5000 square kilometers), and focused on relatively homogeneous surfaces
(e.g. deserts, boreal forest, crops, ocean etc.). There have been a less number of studies
reported for urban areas (Taket et. al, 1991; Xia et al. 1997) and no reported studies that look at
spots of less than 0.1 hectares (0.25 acres) within a complex urban matrix.
Our research used an image from an October 1994 Space Shuttle flight (SIR-C) that captured
the New York City and North New Jersey Metropolitan areas. As far as radar scattering is
concerned, such an urban scene is a target of considerable complexity. It provides a variety of
surfaces with sharp edges that go from scales of several hundred feet (buildings) to just a few
centimeters (building surfaces and fields with rubble).
Our specific objective was to evaluate the use of radar images in locating structures and debris
fields in the District and lay out an effective methodology for updating GIS land use attributes
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based on landscape surface texture information. We discuss the implications of this technology
for monitoring land use and management of open spaces within a dynamic and complex urban
environment.
Methods
The HMDC maintains a GIS system to manage zoning, land use and block and lot information
of District properties (parcels) for the purpose of land use management and planning. A geo-
referenced parcel information coverage (Figure 1A.) was used to identify open space areas. The
areas selected included: landfill, parkland, riparian, vacant and open water. A binary vector
polygon coverage was created where open area parcel id's acquired the value of one while the
rest of District parcels acquired a value of zero. The binary coverage was converted to a raster
image (pixel size 12.5 meters on each side) where pixels representing open space areas (black)
have a value of one and all other pixels have a value of zero (Figure 1B.).
A set of images for the District were documented and manipulated with raster software. The
SIR-C sensor acquired images at three microwave wavelengths: L-band ~ 24 cm; C-band ~ 6
cm and X-band ~ 3 cm resolution (Freeman et al 1995). However, only L and C bands were
available for the study. Pixel size is 12.5 meters on each side. Incidence angle was 64 degrees
with illumination from the southwest. SAR transmits pulses of microwaves in either horizontal
(H) or vertical (V) polarization and receives in either H or V. The polarization available for each
band (L and C) were HH (horizontally transmitted and horizontally received) and HV. A third
image for each band labeled total power (TP) is the combination of all polarizations in one
image. Images used in this study were C-band HH, C-band HV, C-band TP, L-band HH, L-band
HV and L-band TP (figure 3). The radar backscatter value for each pixel is measured in decibels
(dB). In our case values ranged from -40dB, very smooth surfaces in light gray indicating open
water, to +5dB very rough surfaces in dark gray indicating building structures (Figure 3).
Determining the accuracy of each radar pixel on the ground is important since we used the x, y
coordinates of radar pixels to navigate to spots on the ground and verify anomalous returns.
The original radar images were geo-referenced to the New Jersey State Plane Coordinate
System by using 11 control points from a geo-referenced GIS vector coverage of District parcels
(Figure 1C). The average RMS error of the re-sampling procedure was 1.3 m. However, the
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N
B
D
IKm
Figure 1. A.- GIS parcel vector coverage of the District. B.- Binary raster image of
the District, black areas correspond to open spaces. C.- Geo-referenced radar
image with an overlay of District parcels. D.- Image containing radar pixel values for
open space areas.
parcel vector coverage also had digitizing and tolerance value errors. The original parcel map
was digitized from a 1": 200' scale map of the district. The resolution of the digitizing table was
0.0127 m (0.005 inches). The digitizing error on the ground was in the order of 30 cm. The
tolerance level for the coverage (minimum distance before vector lines snap) was originally set
to 4.2 meters. Therefore, the estimated error of parcel limits is 4.5 m. When the RMS error is
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included, then the total error on the ground of radar pixels for this study is at least 6 meters or
half a pixel.
Once the images were geo-registered, the open area binary layer (Figure 1B) multiplied them
each. As a result, all pixels not corresponding to open spaces were made zero. All other pixels
maintained their dB values (Figure 1D). To avoid working with negative numbers, pixel values
(dB) were re-scaled to fall within the range of 0-255. Pixel frequency distributions for all six radar
images were graphed (Figure 2.). Backscatter distributions of images were inspected for
speckle effects and contrast.
In order to select an image for field verification, two main criteria were used: 1- The image
should clearly discriminate between water and different vegetation types, and 2- The image
should have a minimum of speckle or background noise. The speckle or background noise is
usually associated with strong reflecting surfaces from structures in the vicinity of open
spaces.
Backscatter values from known power line towers in the middle of open spaces were used to
select a threshold pixel value for an anomalous return. An anomalous return in an open space
area with vegetation would be brighter than normal and most likely due to a corner reflector (i.e.
two surfaces in 90-degree angle). Known power line tower locations in the middle of open
spaces provided good examples of corner reflectors within a vegetation patch. High energy
double-bounce scatter mechanisms prevail from rectangular metallic surfaces compared to the
less energetic volumetric scatter mechanism that prevails from a canopy of vegetation. A similar
reflectance mechanism (double bounce) would be expected from rubble (e.g. concrete slabs or
metal artifacts) such as old cars and drums exposed or hidden under vegetation.
All pixels exceeding the threshold value were extracted and clusters of more than four pixels
plotted. Clusters of at least four were selected to avoid selecting single pixels with unusual
brightness values located exactly on parcel boundaries that in reality do not correspond to true
open spaces. These clusters were converted to vector polygons and draped over aim
resolution geo-referenced digital orthophoto (USGS 1995). The draped orthophoto images were
visually inspected for obvious structures on the ground that may have produced the bright
backscatter return (Figure 6 and 7). Parcels classified as open space that contained structures
were re-classified in the GIS database. Spots were selected for ground verification when visual
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inspection of the draped orthophoto images failed to reveal structures on the ground that could
explain the anomalous return. Moreover, ease of ground access and clusters size were
important considerations in selecting specific spots for field verification. Radar image
coordinates were used to navigate to the sites. Known reference points in the field were
selected and azimuth and distance from these points to the center of the anomalous spots were
used to navigate in the field. Sites were finally located in the field by using a 30-meter
measuring tape and a compass. Historical aerial photography from 1966 was used to confirm
and explain some of the disturbances detected by radar.
Results
Although several theoretical models have been developed to describe how ground objects
reflect radar energy (Taket 1991, Evans et al 1988), most knowledge comes from practical
observations. There is no strong set of rules indicating what bands or polarizations work best on
different surfaces and under specific sensor conditions. Given the time and resources available,
it was important to find one best image out of the six available to field verify.
An inspection of the pixel brightness frequency distribution values for all images (Figure 2)
shows that the distributions tend to be bimodal except for HH polarization. Cross-polarization
(HV) images function better to capture the difference between water and vegetation surface by
clearly separating water and vegetation into two distinct peaks. Of all images, C-band HV has
the best contrast (a greater valley between peaks) as well as a sharper break between the end
of the vegetation values (tall peak) and the tail of the extreme bright values.
The amount of speckle was another criteria for image selection. Speckle effects, or "noise", are
common in radar images capturing complex surfaces. Speckle can be caused by an object with
complex dielectric properties that behaves as a very strong reflector at a particular alignment
between itself and the spacecraft. Structures such as metal bridges, elevated highways, and
power line towers behave as strong reflectors in urban areas. These structures influence the
value of neighboring pixels making them appear much brighter than the surfaces they actually
represent. In our case speckle effect is clear in C-band HH, C-band TP and L-Band HH (Figure
2B, 2C and 2E.). In all these cases there is an unusual high frequency of bright returns at the
high end of the distribution. Unfortunately, speckle effects affect open areas near strong
reflector objects. In our case this is very clear for C-band HH (Figure 2B). Speckle effect is also
clear in L-band TP. However, in this situation background noise manifests itself by cross and
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C-baiidHV
C-bandHH
C-band IP
28.40 SI .80 77./D lijifu li! LIL> 15i* 17880 JMi'D J2*6if 255.00 • I1 - •*!• HI - ' Jl I! * U -:-. £*« -''--' I.OD 26.40 fl?D 77 iu IDiM liKn> 15j* '78.80 384.10 229.60 255,00
L-band IP
1.00 26.<0 51.80 77.20 102(0 12:0; 15242 ^I'SO 2^10 22V S5 255.00 1.00 26,-tO 51.80 77.20 10262 '2SOO '5':4Ii 173ai 2042: 22060 2
JB.-tt 51.30 77.20 1026i' -2ftLi[l '52: W 172 KO 204.20 22360 25500
Figure 2. - Pixel brightness frequency distributions for C and L bands HH and HV
polarizations. HV and TP polarizations for both bands, clearly separate open water
and vegetation surface texture (bi-modal distribution). C-band HV shows the
greatest contrast between surface textures (valley between peaks) and a sharp
break between the vegetation peak and the upper tail of the brightness distribution.
star like patterns that have their origin at points with rough features and high dielectric constants
(Figure 3).
The final criteria used in selecting an image were based on radar wavelength. Shorter
wavelength (C) have better resolution than longer wavelength (L), thus improving the overall
spatial resolution and the ability to delimit detail and boundaries (Xia and Henderson 1997).
Based on these observations and since our objective is to determine surface texture differences
within small areas, of the two best images available (C-band HV and L-Band HV) we choose the
smaller wavelength image C-band HV.
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C-band HV
L-band HV
2 Km
C-band TP
L-band TP
Figure 3. - Images of the Hackensack Medowlands (C and L bands, HH and HV
polarizations. Flat smooth surfaces in gray represent mainly open water bodies. The lower
right corner of the images shows the Hudson river and a part of Manhattan. West of the
Hudson is the Hackensack and Passaic rivers draining into Newark Bay. Other white-gray
surfaces represent wetlands. Dark gray and black areas represent developed urban areas.
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Figure 4. -Pixel clusters from open spaces that
were classified as anomalous returns
(brightness value greater than 176 out of 255).
"vegetation pixels" from brighter backscatter returns. For this study, a value of 176 was selected
as the threshold value for an anomalous return. Figure 4 shows all pixels from open spaces that
exceeded the threshold value and were classified as anomalous returns for the District.
There were a total of 128 clusters of anomalous returns. These clusters varied from four to 53
pixels per cluster. The greatest number of clusters (61) were made out of only four pixels
(Figure 5). The most common clusters (82%) were made out of 10 pixels or less. The most
extensive cluster was made out of 53 pixels representing an area of 0.8 hectares.
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o
i
I
61
n
44
Pixel Cluster Size
Figure 5. - Frequency distributions for clusters of pixels that exceeded the
brightness threshold value of 176. More than 80% of the clusters were 10 pixels
or less.
Figure 6 shows how a several clusters of radar anomalous returns overlay at least three distinct
parcels of land at the southwest corner of the District. These parcels were classified as vacant
in the GIS. Parcels range from 0.5 to 1.5 hectares. By zooming in the ortho-photo one can
actually see that these parcels contain structures (trailers and construction equipment) and are
not vacant.
Similarly, Figure 7, shows another anomalous return from a border of a vacant lot (arrow) that
turned out to be a 0.1 hectares (0.25 acre), seven meter high bulldozed mound of earth mixed
with metal rubble, tires, old batteries, cloth, etc. It is impossible to tell by zooming in the ortho-
photo that this structure actually exists since it blends with the colors of the surrounding area. A
list of similar sites and their coordinates were detected and documented.
10
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Figure 6. - Anomalous radar clusters are shown as white polygons draped
over a 1-meter resolution orthophoto. White lines represent parcel
boundaries. Red circles mark parcels classified as vacant by the GIS yet
they contain structures (metallic rigs and machinery) which offer corner
reflector surfaces detected by radar.
The most interesting anomalous returns originated from what appeared to be a field with a
dense homogeneous canopy of Phragmatis australis. (Figure 7 circled area). After navigating to
the anomalous spots in the field, the only consistent difference in surface texture was a much
lower and sparse canopy of the common reed. These "open" spots seemed to be correlated to
differences in soil compaction. The effect is a sharp boundary between the tall reed canopy and
the sparsely vegetated areas inside the selected spots. In some of these spots the soil was
littered with waste such as leather scraps, plastic wallpaper, roofing material, tiles and even
junked cars. Soil compaction, sharp canopy boundaries and scattered rubble provided the
surface features that favored double bounce reflections as opposed to volumetric scatter from
the immediate surrounding areas.
11
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Figure 7. - Radar anomalous returns (white polygons) for the South Hackensack
part of the District. The white arrow shows a 40X40-meter area where there is a
bulldozed mound of rubble. This mound, hidden under vegetation would be
difficult to separate from the colors of the surrounding vegetation by using
traditional spectral sensors. The anomalous returns included in the white circle
indicate an area of what seems to be a homogenous canopy of the common
reed. However, field inspection of these spots revealed differences in surface
texture due to past disturbances
Recently declassified intelligence satellite photography from 1966 (3-m resolution) of the same
area (Figure 8) revealed a construction staging area exactly where the anomalous returns from
radar originate. What is today a "dense" stand of Phragmites australis, was in the late 1960's a
staging area for the construction of the New Jersey Turnpike. Roads and trails can be identified
in this area from the 1966 satellite photograph. This explains the differences in soil compaction
and the presence of debris in what seems today to be the middle of a common reed field.
12
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Figure 7. - Declassified spy satellite photograph from 1970 showing the
same area of Figure 6. The brighter area inside the white circle shows a
surface disturbance created by heavy machinery and rubble disposal that
created differences in surface texture hidden under vegetation and
detected by radar 24 years later
Discussion and Conclusions
The use of SAR images to detect disturbed or incorrectly classified land uses based on surface
texture proved to be a reliable and effective approach. Detection and navigation to the
previously unrecognized disturbed areas was done with less than 10 meters of error on the
ground. This in itself demonstrates the practicality and effectiveness of the method. One critical
part of the study was selecting the appropriate band and polarization to identify sites on the
ground since resource constraints prohibited the evaluation from both bands and all
polarizations in the field. Our criteria for image selection was in agreement with most research
to date which seems to prefer cross-polarized (HV) images for urban and land use cover
mapping over co-polarization (Bryan 1974, Henderson 1985). In our case the C-band HV
13
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distribution better separates between "vegetation pixels" and a class of brighter pixel values
than L-band HV (Figure 2A and 2D). Contrary to findings reported in the literature (Xia 1997),
surface features appear smoother at shorter wavelengths (C-band HV) than at larger
wavelengths (L-band HV). Selecting C-band over L- band may have resulted in a loss of
information associated with surface texture within vegetation patches. However, overall, we
found that shorter wavelength best separated known targets from the surrounding vegetation.
Selecting a threshold value, in other words, determining which pixels were brighter than normal
for a given surface was a critical decision of the study. There are many models for scatter
mechanisms (Evans 1988, Jacob 1993,) and they all tend to agree that the double bounce
mechanism carries the greatest amount of energy back to the receiver creating brighter pixels.
In this case the microwave bounces off one surface, hits another surface and returns back to
the receiver (corner reflector effect). We considered this mechanism to prevail in the case of a
target such as an electrical power tower, or slabs of concrete and metal rubble hidden under
vegetation or ground that has been compacted in open spaces surrounded by hard stems. Our
data suggests that volumetric scatter would be the dominant scatter mechanism from a canopy
of Phragmatis australis. Incident radiation would bounce off stems, branches and leaves
scattering in all directions and less radiation would return to the receiver creating less bright
returns from these surfaces.
Incidence angle and "look" angle for each image vary according to the position of the sensor in
space in relation to its ground target. In this case, our image had a fixed 64 degrees incidence
angle illuminating from the southwest. Different fly-over passes of the sensor will create images
with different incidence angles and therefore different backscatter patterns for the same area.
Images will clearly have to be selected according to the most favorable incidence angles and
illumination direction. Topography will also influence the backscatter pattern, as shadows from
mountains will hide surfaces that can not be imaged by radar. These factors emerged as
important limitations to surface texture detection using radar. In our specific case, the relatively
flat topography of the District would favor the use of radar.
Radar was able to identify specific parcels that were incorrectly classified in the GIS database.
Some parcels contained reflector surfaces from structures such as trailers, metallic rigs and
machinery which were easily recognizable from the ortho-photos.
14
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In other cases (Figure 7 arrow), radar was able to detect rubble hidden under vegetation. The
40X40-meter mound could not have been separated from the surrounding vegetation by using
any other type of spectral sensor i.e. aerial photo or satellite image. In this case, the orientation
height (7 m) and the flat surfaces of metallic rubble associated with the mound made it possible
for radar to detect and separate this particular structure from the surrounding vegetation.
Finally, radar was able to detect past disturbances within certain areas of normal looking reed
fields. In these sites, soil compaction by heavy machinery and dumping of construction rubble
and waste from almost 40 years ago altered the natural hydrology and influenced community
plant development. Again, with radar it was possible to separate surface feature roughness and
detect these disturbances.
One mission of the HMDC is to balance development with preservation. This process involves
making informed decisions regarding what areas are to be developed and what areas are to be
set aside for preservation. To make these decisions, agency executives and technicians need to
be well aware of the location, quality and characteristics of each site. Currently, the land use
management team updates land use of District parcels by field recognizance every four to five
years. Under the current method, there may be enough time in between updates for an
undetected illegal land use practice to create considerable damage.
Our methodology proved that based on surface texture detection it is possible to monitor and
update parcel information from areas as small as 0.1 hectare. Once periodic radar images
become available, standard backscatter returns for District parcels would be documented and
become a template for the current surface texture pattern. New images (with same incidence
angle and illumination direction) would be checked against the template. Changes in surface
texture due to constructions of structures, disturbance of soil surface, change in vegetation
cover or waste dumping would create a different backscatter pattern from the template that
would translate into the identification of parcels where changes took place. The challenge ahead
is to integrate these systems and methodologies into an expert system with the ability to detect
change in surface texture, update the GIS database automatically, and continuously repeat this
process as new images become available. This approach should be able to keep up with the
dynamic and complex land use changes in the District and help managers make informed
decisions based on timely land use information.
15
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Acknowledgment
This work was preformed with funding from a grant from the Hackensack Meadowlands
Development Commission and sponsored by the Rutgers University NASA Regional Application
Center and the Center for Information Management Integration and Connectivity (CIMIC). I
would like to thank Dr. Geoffrey Henebry for providing the images and for his comments on
radar backscatter mechanism. Finally, I would like to thank Matthew Ceberio and Alon Frumer
for their help with field verification.
References
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(IGARSS'95), July 10-14, Florence Italy, pp. 1588-1590.
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Sarabandi 1995. SIR-C data quality and calibration results, IEEE Transactions on
Geoscience and Remote Sensing, Vol. 33, no. 4, pp. 848-857.
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Freeman, A., and B. van den Broek, 1995. Mapping vegetation types using SIR-C data,
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Astronomica Vol. 41, Nos. 4-10. pp. 413-420.
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Richards J.A. 1986. Remote Sensing Image Analysis. Berling, Springer.
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GIS in the Confirmation Process
Dr. Raymond E. Bailey and Mr. Madhukar Mohan
The ultimate goal of remediation of the Department of Energy (DOE) Weldon Spring mixed
waste site is to release the site for unrestricted use to the extent possible. This dictates that an
accurate assessment of post cleanup activities is performed to confirm that contaminated soils
have been successfully treated or removed. The assessment begins by developing an accurate
3-dimensional picture of the spatial distribution of contamination prior to the start of cleanup
activities. A geographic information system (GIS) was selected over traditional manual methods
to map the initial spatial distribution of contamination (pre-cleanup levels) and to manage the
confirmation database. The Weldon Spring site, consisting of approximately 217 acres, was
divided into eleven remedial units (RU). Each RU was divided into approximately 2,000 m2
areas known as confirmation units (CU). Upon completion of remedial activities within each RU,
Environmental Safety and Health technicians conducted a walkover survey with a 2-inch by 2-
inch sodium iodide (Nal) scintillation detector to establish removal of surface contamination to
levels at or below background levels of gamma-emitting radioactivity. Following the surface
scan, soil samples were collected and analyzed for radiological and chemical contaminants of
concern listed in the Record of Decision. Results of laboratory radiological and chemical
analyses were used to populate the attribute portion of the GIS database. Geographic locations
in the database were obtained from surveys of the sample locations. This established an
accurate location for each sample, provided confirmation that the as-built excavation had
achieved the designed depths, identified areas that had achieved cleanup levels, and, if above
target cleanup levels, identified locations of contamination requiring additional remediation.
The Department of Energy (DOE) Weldon Spring Site Remedial Action Project (WSSRAP) is a
mixed waste site located in St. Charles County, Missouri, approximately 48 km (30 mi.) west of
St. Louis (Figure 1).
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ILLINOIS
LOCATION OF THE
WELDON SPRING CHEMICAL PLANT
AND THE
WELDON SPRING QUARRY
FIGURE 1
GUI |"" 7i7f-"i~
Figure 1
The WSSRAP consists of a 217-acre chemical plant area initially used by the U.S. Department
of the Army during the 1940s to produce the explosives trinitrotoluene (TNT) and dinitrotoluene.
After World War II, the structures were razed, decontaminated, and the site was re-graded. The
U. S. Atomic Energy Commission (predecessor to the DOE) built a chemical plant upon the
former Army site to process uranium and thorium ore concentrates. Production operations
proceeded throughout the 1950s and 1960s, resulting in the disposal of radioactively and
chemically contaminated waste on site.
Contaminated areas at the Weldon Spring Site included material from 40 building foundations,
four raffinate pits, two ponds, and two former dump areas (Figure 2).
The contaminants of concern requiring treatment are radioactive contaminants (primarily
radionuclides of the natural uranium and thorium-232 decay series) and chemical contaminants
(including naturally occurring metals and inorganic anions, as well as organic compounds such
as polychlorinated biphenyls and nitro-aromatic compounds). The remediation alternative
selected consists of removing material from contaminated areas, treatment as appropriate by
chemical stabilization and/or solidification, and disposal in an engineered disposal facility
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Figure 2
constructed on site. The ultimate goal of site remediation activities is the release of the site for
unrestricted use to the extent possible. To achieve this goal, requires an accurate assessment
of post-cleanup activities (the confirmation process).
The site geographic information system, utilizing Arclnfo software operating on a Sun Unix
workstation, was selected to create an accurate 3-dimensional picture of the spatial distribution
of contamination prior to the initiation of remediation activities and to manage the confirmation
database. The geographic database was populated with Arclnfo coverages of the Weldon
Spring Site topography in 1954, aerial surveys of WSSRAP in 1993 and 1998, coordinates of
anomalies identified during geophysical surveys, and the location of characterization drilling and
sampling points. A complete picture of the spatial distribution of the pre-cleanup contamination
was provided by linking the analytical laboratory results of the characterization samples with
their geographic locations.
The confirmation database was created by dividing the 217-acres site into eleven remedial units
(RU). Each RU was further divided into approximately 2,000 m2 (0.5 acres) areas known as
confirmation units (CU) (Figure 3).
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763,000 E
754,000 E
755,000 E
756,000 E
200 100 0
P"H H r- -
FEET
Figure 3
The CU is the area for which a decision is made as to whether cleanup standards have been
attained. The size of the CU was selected to provide an area of approximately the same size as
that used in the risk assessment for a future residential lot. This size also provided manageable
areas capable of supporting the construction schedule when an excavated area needed to
remain open pending the confirmation that cleanup standards had been attained.
Upon completion of remedial activities within each RU, Environmental Safety and Health
technicians conducted a walkover survey with a 2-inch by 2-inch sodium iodide (Nal)
scintillation detector to establish removal of surface contamination to levels at or below
background levels of gamma-emitting radioactivity. Areas showing elevated readings greater
than 1.5 times background were designated as "hot spots", and additional material was
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removed. After obtaining a surface scan at or below background readings, a 10 meter by 10
meter grid was surveyed and soil samples were collected and analyzed for radiological and
chemical contaminants of concern listed in the Record of Decision. Results of laboratory
radiological and chemical analyses were used to populate the attribute portion of the GIS
confirmation database. Geographic locations in the database were obtained from surveys of the
sample locations and compared with design excavation limits. This established an accurate
location for each sample, provided confirmation that the as-built excavation had achieved the
designed depths, identified areas that had achieved cleanup levels, and, if above target cleanup
levels, identified locations of contamination requiring additional remediation.
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Vulnerability Assessment of Missouri Drinking Water to Chemical Contamination
Christopher J. Barnett, Steven J. Vance, and Christopher L. Fulcher
Center for Agricultural, Resource, and Environmental Systems, University of Missouri,
Columbus, Missouri
Introduction
In 1991, the Missouri Department of Natural Resources
(MDNR) implemented the Vulnerability Assessment of
Missouri Drinking Water to Chemical Contamination pro-
ject. MDNR's Public Drinking Water Program (PDWP)
contracted with the Center for Agricultural, Resource,
and Environmental Systems (CARES) to conduct this
assessment. They designed the project to determine
which, if any, public water supplies are threatened by
chemicals being tested under the Safe Drinking Water Act.
Under Phase II of the Safe Drinking Water Act, the
United States Environmental Protection Agency (EPA)
required that all public drinking water systems be rou-
tinely monitored for 79 contaminants beginning January
1,1993. If a selected chemical parameter is not detected
in an area that would affect a water supply (where
"detected" is defined as used, stored, manufactured,
disposed of, or transported regardless of amount), then
the water supply need not be tested for that chemical.
Instead, that system would be granted a use waiver,
meaning that the state would not test for that chemical.
EPA grants use waivers for 43 of the 79 contaminants.
Use waivers can result in considerable cost savings.
Because use waivers are granted based on the spatial
relationship between drinking water sources and con-
taminant sources, accurate positional data needed to be
collected for those items. A geographic information sys-
tem (CIS) was used to store and analyze this informa-
tion in a spatial context.
Water Sources
Water sources, as defined for this study, are the points
where water is drawn from a river, lake, or aquifer for
use in a public water supply. Our efforts focused primar-
ily on the development of the water source layers for the
CIS. These layers, containing wellheads, impoundment
intakes, and river intakes, were created in house or
obtained from state and federal agencies. MDNR
regional office personnel inspected these water source
layers in the spring of 1993. Since these personnel
routinely inspect Missouri public drinking water supplies,
their knowledge of these locations is exceptional.
The updated water source information was mapped
on 1:24,000-scale USGS topographic quadrangles at
the regional offices, then entered into the CIS. MDNR's
PDWP provided available attribute information, which
was associated with these layers. The layers offer the
most accurate and current information available. Only
the community (e.g., cities, subdivisions, mobile home
parks) and the nontransient, noncommunity (e.g.,
schools, large businesses) water supply systems were
considered for water source mapping. This study did not
consider private wells.
The information is stored in the CIS in the form of
geographic data sets or layers. The wellhead layer con-
tains 2,327 public wells and their attributes (e.g., well
depth, casing type). The majority of the wellheads are
located in the Ozarks and Southeast Lowlands. Natu-
rally poor ground-water quality prohibits a heavy reli-
ance on ground water for drinking water in other areas
of the state. The surface water impoundment layer con-
tains 105 points representing the intake locations for
systems that rely on lake water. Additionally, the drain-
age basin and lake area are mapped for these systems.
The majority of the systems that rely on lake water are
located in northern and western Missouri. The final layer
represents the systems that use river water. The major-
ity of the 50 intakes are located on the Mississippi and
Missouri Rivers and on the major streams in the Grand
and Osage River basins.
Contaminant Sources
Contaminant sources, as defined for this study, are the
points or areas where existing databases indicate the
presence of a chemical contaminant. Incorporation of
contaminant data into the CIS proved to be the most
difficult task. These data usually contained very precise
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information about what contaminants were found at a
site and who was responsible, but the quality of the
locational information was often poor.
Ninety-three state and federal databases were reviewed
for contaminant information before performing the final
use waiver analysis. The contaminant information was
broken into two separate types, contaminant sites and
pesticide dealerships. The contaminant sites were loca-
tions at which certain chemicals were known to exist.
The pesticide dealerships were dealerships licensed to
distribute restricted use pesticides. Information about
contaminant sites was extracted from the databases
and entered into Microsoft Excel, a spreadsheet pro-
gram. The small amount of data with coordinate (lati-
tude/longitude) or map information was readily
converted to the CIS. The majority of the contaminant
records, however, contained only address information,
often appearing as a rural route address or post office
box number.
While the water source locations were being verified,
personnel at the MDNR regional offices reviewed the
contaminant site records. The regional office personnel
were familiar with their respective territories and could
assist CARES personnel in locating the contaminant
sites. The Missouri Department of Agriculture pesticide
use investigators provided additional information about
the locations of contaminant sites. All contaminant
source information was also mapped on the 1:24,000-
scale USGS topographic quadrangles and transferred
to the CIS.
Of more than 2,800 contaminant sites found in these
databases, 88 percent were geographically located and
used in the study. At this time, the contaminant site layer
contains 2,493 points representing the information col-
lected on the 43 chemical contaminants required by
MDNR. Each point contains a seven-digit chemical code
indicating the chemical it represents and serving as a
link to the chemical contaminant files. The contaminant
sites tend to be concentrated more in urban areas than
rural areas. Even though this layer is being continually
updated, the basic distribution of contaminant sites re-
mains the same.
A second contaminant source layer represents Mis-
souri's licensed pesticide dealers. This information is
included to indicate potential contamination even though
specific chemicals at dealership locations are not
known. At this time, we have been able to locate 1,344
dealerships out of 1,650. Two types of dealerships are
included in the layer, active dealers and inactive dealers.
Of the active dealerships in 1991, 91 percent were found
and entered into the CIS. Of the inactive dealerships, 79
percent were located.
Spatial Analysis
The final parameters for the use waiver analysis were
developed from EPA and MDNR guidelines and account
for the capabilities of the CIS. These parameters were
designed to present a conservative list of the systems
that needed to be tested for the possible presence of
studied chemicals. Parameters forthe wellhead analysis
are as follows:
• A 1/4-, 1/2-, and 1-mile radius around each wellhead
was searched for contaminant sites and pesticide
dealerships (see Figure 1). Any contaminant sources
found within those radii were reported to PDWP.
(PDWP requested that the results of the three radius
analyses be reported, but the 1/2-mile radius was
used to determine the issue of the use waiver.)
• Any wellheads found within a contaminant area were
denied a use waiver for that contaminant.
• Each highway and railroad within 500 feet of a well-
head was recorded. This indicates the threat posed
by the transport of chemicals near wellheads.
• Additionally, the percentage of the county planted in
corn, soybeans, wheat, sorghum, tobacco, cotton,
and rice was listed for each well to indicate the threat
posed by agricultural chemical use within that county.
The parameters for the systems relying on lake water
are as follows:
• Any contaminant sources found within a surface
water impoundment drainage basin caused the asso-
ciated intake(s) to fail use waiver analysis for those
contaminants.
X = Contaminant
Figure 1. Use waiver search radius distances.
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• Any area of contamination overlapping a drainage
basin caused the associated intake to fail use waiver
analysis for that contaminant.
• Transportation corridors passing through a drainage
basin were noted to indicate the threat posed by
transport of chemicals within the basin.
• The percentage of the county planted in the seven
crops mentioned above was listed to indicate agricul-
tural chemical use within the drainage basin.
Many of the rivers that supply water to systems in Mis-
souri have their headwaters outside the state. To fully
evaluate the potential for contamination within those
drainage basins, we would have to collect data for large
areas outside of the state. For example, the Mississippi
and Missouri River drainage basins cover large portions
of the United States. Because collecting data for those
areas would be impractical, we have recommended to
MDNR that use waivers not be granted to river supplies.
The following provides details on how the analysis was
performed. The CIS searches around each wellhead for
each radius and notes which contaminant sites affect
which wellheads. If a contaminant falls within that ra-
dius, we recommend that the wellhead be monitored. In
this example, the well is affected by one contaminant
within the 1/4-mile radius, two within the 1/2-mile radius,
and four within the 1-mile radius.
Results
The results of the use waiver analysis indicate which
systems may be affected by the use of a chemical near
a water source. Several results show the substantial
savings realized from our analysis. For example, the
analysis showed that only five wells serving four public
drinking water systems were potentially affected by di-
oxin and should be monitored. By not testing the remain-
ing systems for dioxin, the state can realize a
considerable cost savings, as the test for dioxin is the
most expensive test to perform.
The final wellhead system analysis shows that the
1/2-mile buffer analysis affected a total of 447 well-
heads in 241 systems. That is, a chemical site or pesti-
cide dealership was found within 1/2 mile of 447 public
wellheads. A result form was generated for each of the
1,340 systems in the state listing each well or intake and
the potential threat posed by nearby contaminant
sources.
The cost of testing all wellhead systems for all 43 con-
taminants without issuing use waivers is more than $15
Table 1. Estimated Cost Savings for Public Drinking Water
Systems
Method
Estimated
Total Cost
Estimated
Mean Cost
per System
Estimated
Total Cost
Savings
No use waiver $15,533,100 $12,200 $0
With use waiver $1,813,900 $1,400 $13,719,200
million (see Table 1). According to our analysis, CARES
estimates that only $1.8 million need be spent to monitor
vulnerable wells. Therefore, the state can save more
than $13.5 million in monitoring costs.
Summary and Recommendations
To date, the investment the state made in the vulnerabil-
ity assessment project has provided many benefits. The
state saved several million dollars in testing costs and
developed several spatial and nonspatial databases that
will have many uses. In addition, the project established
a basic framework for future assessments, which EPA
requires on a regular basis.
The basic data required for use waiver analysis are the
locations of water sources and the locations of potential
contamination sources. CARES determined that the
available data did not contain the information necessary
to map these locations or that the data were of question-
able quality. Many layers required update and correc-
tion. Considerable effort was necessary to improve
existing locational information for both water source lay-
ers and chemical contaminant files. Local knowledge of
an area was heavily relied upon to determine accurate
locations, particularly contaminant sites. The vast ma-
jority of these sites contained only the address as the
geographic reference. An address is not a coordinate
system; it does not indicate a fixed location on a map.
Because the location of any chemical detection site is
of vital importance, state and federal agencies that col-
lect these data need to record more complete geo-
graphic information. Ideally, a global positioning system
could be employed to generate coordinates. Realisti-
cally, the recording of legal descriptions or directions
from an easily located point would substantially improve
the quality of the current databases.
In many cases, data resided in digital format; however,
due to regulations or lack of agency cooperation, they
could only be distributed in paper format. Reentering
data from paper format into digital format required con-
siderable time and expense. Interagency cooperation
should be emphasized to reduce unnecessary data entry.
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Using GIS to Evaluate the Effects of Flood Risk
On Residential Property Values
Alena Bartosova, David E. Clark, Vladimir Novotny, Kyra S. Taylor
Marquette University, Milwaukee, Wl 53201-1881
1. Introduction
Annually, flooding causes more property damage in the United States than any other type of
natural disaster. One of the consequences of continued urbanization is the tendency for
floodplains to expand, increasing flood risks in the areas around urban streams and rivers.
Hedonic modeling techniques can be used to estimate the relationship between residential
housing prices and flood risks. One weakness of hedonic modeling has been incomplete controls
for locational characteristics influencing a given property. In addition, relatively primitive
assumptions have been employed in modeling flood risk exposures.
We use GIS tools to provide more accurate measures of flood risks, and a more thorough
accounting of the locational features in the neighborhood. This has important policy implications.
Once a complete hedonic model is developed, the reduction in property value attributed to an
increase in flood risks can, under certain circumstances, be interpreted as the household's
willingness to pay for the reduction of flood risk. Willingness to pay estimates can in turn be
used to guide policymakers as they assess community-wide benefits from flood control projects.
2. Hedonic Theory and Literature
The hedonic price model used in this study has its roots in the works of Lancaster (1966) and
Rosen (1974). It is based on the premise that individuals can choose consumption levels of
local public goods such as environmental quality through their residential location choice. The
model views the price of individual houses as dependent on a bundle of housing characteristics.
These characteristics include those related to the structure (e.g., lot size, number of bathrooms,
etc.); the neighborhood (e.g., average commute time, median household income, etc.); the
environment (e.g., variables related to flood risk); and fiscal factors (e.g., property tax rates).
There are several underlying assumptions in this model. The model assumes that the study
area is a single market for housing services. It also assumes that all buyers and sellers have
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perfect information on the alternatives that exist and that the housing market is in equilibrium.
This last assumption means that all households have made their utility maximizing choice in
terms of residential location given the prices of alternatives, all of which just clear the market.
The relationship outlined here can be linear only when repackaging of the house is possible,
and in general, this is not the case. When an individual makes a residential location decision,
they are accepting the entire bundle of housing characteristics. It is not possible to trade a
house with two full baths upstairs for the exact same house with one full bath upstairs and one
downstairs. Thus, the function is nonlinear.
Given the previous assumptions, the market clearing price of the house is treated as parametric
and can be represented as p(Z), where Z = Zi,z2 zn is a vector of n structural, neighborhood,
and environmental characteristics. The housing market implicitly reveals the hedonic function,
p(Z), which relates prices and characteristics. This price function p(Z) is a reduced form
equation representing both supply and demand influences in the housing market. The implicit
price of attribute n is given by the partial derivative of p(z) with respect to attribute n, or pn(z) =
3p/3zn. That is to say, the partial derivative with respect to any of the aforementioned
characteristics in the function can be interpreted as a marginal implicit price of that
characteristic. This marginal implicit price is the additional amount that must be paid by any
household to move to a bundle of housing services with a higher level of that characteristic. For
example, the coefficient on the number of rooms in a home may be interpreted as the price that
must be paid by the household to move from a house with eight total rooms to the same house
with nine total rooms, all else constant. Since the function for housing is nonlinear, the marginal
implicit price depends on the quantity of the characteristic being purchased.
Several hedonic studies specifically address the issue of flooding including the effect of
floodplain regulations on residential property values (Schaefer 1990), the impact of subsidized
and non-subsidized flood insurance on property values (Shilling et al., 1987), and the influence
of flood risk on property values (Barnard 1978; Park and Miller 1982; Thompson & Stoevener
1983; Donnelly 1989; Speyrerand Ragas 1991; Shabman and Stephenson 1996). For the most
part, the results from these studies indicate that location in a floodplain, or proxies for flood risk,
negatively impacts residential property values. One study examined a major flood event
(Babcock and Mitchell 1980); however, this was done by a comparison of prices before and
after the event, and thus was vulnerable to bias due to omitted factors in the analysis. None of
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these studies measure flood risks directly, nor do they investigate the impact of a specific
flooding event in a hedonic framework.
3. Definition of Flood Risks
A flood is defined for the purpose of this paper as a stream discharge greater than the capacity
flow of the channel. This is obviously a very simplistic definition. For example, Williams (1978)
presented 11 definitions of the channel bankfull flow, from which the flow that reaches the valley
active floodplain is the one accepted by most river morphologists. A flood of certain magnitude
occurs or is exceeded with a certain frequency. The most common flow used for delineation of
floodplain is the flow with the recurrence interval Tr = 100 years, i.e. the risk of flooding is r = 1 / Tr
= 1/100 = 0.01.
The delineation of the floodplain for a flow of given frequency is a tedious task. Such tasks usually
involve the development of a complex hydrologic/hydraulic model. Once calibrated, the model can
be used to simulate a wide range of flows and the flow-elevation relationship can be obtained.
Hydraulic models can be combined with GIS systems to delineate a floodplain for any recurrence
interval (e.g., McLin, 1993, Correia et al., 1998). However, this requires a considerable amount of
data and substantial effort. Thus, a simplifying alternative has been proposed in this study.
The extent of 100-year floodplain, often used for engineering and flood insurance purposes, is
delineated by Federal Emergency Management Agency (FEMA). The flood risk varies within the
floodplain and decreases with increased distance from the channel. The properties located within
the 100 years floodplain are under different risks of flooding and hence there is a need to express
a flood risk relation in the urban floodplain.
A schematic representation of the following concept is shown in Figure 1. The channel can
contain a flow with a certain recurrence interval. This flow is called a capacity flow, or bankfull
flow. As one moves away from the river's edge, the probability of flooding decreases, and at some
point at a distance x from the river the recurrence interval of flooding becomes 100 years, i.e., the
risk of flooding is r(x) = 0.01. This is the extent of the 100-year floodplain that is useful for many
engineering and flood insurance purposes.
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Channels of natural streams are in equilibrium with the flow. Leopold, Wolman, and Miller (1995)
document that channels of rivers in eastern and Midwestern US have a channel capacity that can
contain a flow that has an approximate recurrence interval of about 1 1/4 years. For example, if the
smallest flow that leaves the channel is about a 2-year flow before urbanization, then the risk of
flooding at the edge of the river is r(0) = 1 / 2 = 0.5.
Figure 1: Concept of flood risk
Flood risk
r(x)
r(X100)
The scale of the risk function r(x) should be logarithmic, i.e., a zero risk of flooding is expected to
occur at an infinitely large distance x from the river edge. The logarithmic form of the risk function
is selected for convenience and simply expresses the fact that floods on rare occasions may
extend further than the 100-year floodplain limits. The logarithmic risk function can be expressed
as
rfc) = CIO*' Eq
The function parameters in Eq. 1 can be easily estimated from the knowledge of the risk of
exceeding the bankfull capacity flow and from the extent of the 100-year floodplain: C
corresponds to the risk of exceeding the bankfull flow, or, C = /tO). The risk function can be
integrated across the floodplain cross-section, as shown in the following equation, in which
subscripts L and R correspond to the left and right bank floodplains:
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(x) dx+l ^ (x) dx= r(0) J [10"K^ + 10"K^ ] dx Eq. 2
The magnitude of the floodplain shape coefficient, K, can be obtained from the extent of the
100-year floodplain at the point of interest on the river, denoted as X10o, and from the risk of
exceeding the bankfull discharge, /tO):
tog
r(X100)
C
= tog
0.01
r(0)
= -KX100 Eq. 3
and
K = ~3"'"/J ' " Eq. 4
X 100
Finally, substituting for K in Eq. 2 from Eq. 4 yields the following expression for the floodplain risk
parameter:
R =
/ ,
23(2+tog r(0)J
The dimension of the floodplain risk parameter R is length/time, and a possible unit is meter/day.
However, the unit does not have a physical meaning, as R is only a measure of the flood risk over
a floodplain. R increases with an increase in the size of the floodplain and with an increase in the
risk of overbank flow. This floodplain risk parameter changes along the stream. The integration of
the flood risk over the watershed represents an overall risk of flooding of the watershed, the flood
risk factor that can be used in comparing watershed management alternatives.
This characterization of flood risks will be used to assign unique values of flood risk to each
property within the floodplain. The flood risk measure, FRM, calculated in a GIS environment, is a
negative logarithm of the flood risk r(x). The anti-logarithm of the flood risk measure is basically a
recurrence interval, i.e., FRM = 2 for Tr= 100 years.
4. Empirical Model
a. Study Area
The study area for this analysis is located approximately 11.5 miles (18.5 km) along the middle
to lower sections of the Menomonee River through the cities of Wauwatosa and Milwaukee,
5
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Wisconsin. The Menomonee River is a 71.85 (15.5 km) mile river system and discharges into
the Milwaukee River about 0.9 mile upstream of where the Milwaukee River enters Lake
Michigan. This region was selected to encompass two significant areas, the city of Wauwatosa
and the Valley Park neighborhood in Milwaukee. Wauwatosa makes up a great portion of the
study area and lies within the Menomonee River watershed boundaries. Located west of
Milwaukee in northern Milwaukee County, Wauwatosa is just over 13 square miles (34 km2) with
a population of 49,300. Furthermore, it is a high density residential area, with more than 22.8
persons per net residential acre (55 persons/ha). Valley Park, the other area of concern, is the
smallest and most isolated neighborhood in Milwaukee. The study area is shown in Figure 2.
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Essaypro.shp
/\/Mr_rivr.shp
Floodpln.shp
Figure 2: Menomonee River watershed. Location of properties in 100-year floodplain.
These two areas are significant for this study as a result of their susceptibility to flooding.
Specifically, the study examines the short and intermediate run impacts of a 100-year flood that
occurred in June of 1997. The flood was the worst rain for the Milwaukee Metropolitan area
since August 6, 1986. After the first night of the rainfall, totals ranged as high as 9.78 inches (25
-------
cm), indicating a flood recurrence interval exceeding 100 years. Roads were shut down and
many residents lost power. Damage for Milwaukee County alone was estimated to be $37
million, including $24 million to residential property. About 70 homes in the County incurred
major damage including collapsed basements and roofs forcing residents to evacuate their
homes. Approximately 2100 homes sustained lesser damage. As a result of the flood,
Wauwatosa submitted a Hazard Mitigation Grant Program application for the acquisition of a
number of structures located in the floodway on the Menomonee River. They used Community
Development Block Grant funds to acquire flood prone structures as a means of creating open
space in the riverfront floodway. Of the 20,289 structures in Wauwatosa, about 738 are located
in the special flood hazard area, 669 of which are residential. Due to its susceptibility to flood
disaster, Wauwatosa was invited by FEMA in June of 1998 to participate in a nationwide effort
to become a "Project Impact" community. This program would develop efforts to minimize the
risk of damage from natural disasters. Valley Park also suffered from the flood in terms of water
levels. However, there is a great sense of community in the neighborhood, which became
evident in the recovery period following the disaster. Both Wauwatosa and the city of
Milwaukee, in which "Valley Park" resides, are participants in the National Flood Insurance
Program (NFIP); Wauwatosa entering in 1978 and Milwaukee in 1982. The NFIP implements
floodplain management regulations which ensure that development in flood-prone areas is
protected from flood damages. However flood insurance is mandatory only for those properties
residing within the 100-year floodplain. This increase in cost associated with location in the
floodplain may reduce property value for those houses.
b. CIS Analysis
ArcView, a Geographical Information System (GIS), was used in several aspects of this study.
First, it was used to spatially define flood risks. Second, properties were geocoded to the street
address, and finally location specific data were matched to each property. We describe each of
these activities below.
The properties were geocoded to the precise street address using the ArcView GIS package. A
key to the geocoding process is the accuracy of addresses, the geographic files, and matching
of the addresses to the geographic files. The addresses and geographic files received from
outside sources (MLS and Wisconsin Department of Transportation) are believed to be accurate
given the sources' own incentive for accuracy of the files. ArcView assigns a score to each
8
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match made for the properties. Of the 1475 observations, 1402 of them (or approximately 95%)
were given a score of 75 or above on a 100 point scale. The majority of these received a score
between 98-100.1 The resulting sample size is 1431, as 44 were unable to be geocoded and
eliminated from the sample. Once geocoding of properties was completed boundary files for
geographic areas were digitized if they were not already available as ArcView shape files. For
example, the 100-year floodplain was geocoded from FEMA maps and maps provided by the
Southeastern Wisconsin Regional Planning Commission (SEWRPC). Other spatial boundary
data (e.g., school district boundaries, historic preservation district data) were also manually
digitized.
Once the geocoding was completed, properties were matched to locational attributes of the
neighborhood using one of three techniques. When a neighborhood characteristic was defined
by a point in space (e.g., proximity to air quality monitors), straight line distance calculations
between the property and the attribute were used. If the attribute was defined by a polygon
(e.g., school districts, census block groups), then individual properties were mapped to the
underlying polygon, and attributes of the polygon were attached to the property. Finally, buffers
were defined for various types of line data (e.g., roads, railroads) and properties falling within
the buffer zone were identified.
Turning to the calculation of property specific flood risks, two basic approaches were considered.
The first is a vector-based approach that employed a custom developed ArcView Avenue scripts
program. This approach permits estimation of risks only at specific points rather than for complete
areas. The second more general approach works in a grid (raster) environment, and makes use of
the Spatial Analyst Extension for ArcView. It permits flood risk to be calculated for the entire
watershed, and specified points can be assigned the corresponding value from the underlying
polygon. The second approach was selected because of its future applicability in watershed
management applications.
1 A possible reason for a score at the lower end of the spectrum would be misspellings. For example, if an
address appears as "Menomone Pkwy" and the correct spelling would be "Menomonee Pkwy," the
addresses may still be matched and assigned a lower score as a result. For this reason, the matches
receiving a score of less than 80 were interactively re-matched by the author to ensure accuracy and
minimize error.
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When we refer to the floodplain in this paper, it should be understood as the 100-year floodplain.
The width of the floodplain is the key parameter in calculation of the flood risk, when r(0) is kept
constant. The floodplain width for any specified point, both inside and outside the floodplain, is the
distance of the flood fringe from the river bank for the river cross-section on which this point is
located. The calculation of the floodplain width corresponding to the selected locations had to be
done separately for inside and outside of the floodplain. The floodplain width is calculated as
X = X + X Eq. 6
100 W F '
or
y = y - y EQ 7
100 W F ""
where Xw is the distance from the river channel and XF is the distance from the floodplain (see
Figure 3)
river channel
floodplain
Figure 3: Calculation of floodplain width for locations inside and outside the floodplain
The floodplain was digitized as a polygon and used as such in calculations for the areas outside
the floodplain. For the areas inside the floodplain, it had to be converted into a polyline and
divided into several reaches. The calculation of the floodplain width for points inside the floodplain
was calculated separately also for left and right banks, although the calculation followed the same
procedure. The data essential for risk calculations include digitized maps of the river channel and
100-year floodplain, as well as the watershed boundaries. The risk associated with the capacity
flow has been estimated separately using the information from USGS on capacity flow and the
annual maximum series for the gage station in Wauwatosa. This station is located in the same
area as the majority of the properties. The recurrence interval associated with the capacity flow is
approximately 1 year, i.e., r(0) = 1.
10
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/\/river channel
I l 100-year floodplain
-log R
-
15-10
1 10-20
I 1 20 - 100
I—1100 -1000
0.001 -0.444
I 1 0.444 - 0.889
| 1 0.8B3 - 1 .333
1-333- 1.778
1.778-2
Figure 4: Flood risk measure
Figure 4 shows the flood risk measure, i.e., the negative logarithm of the flood risk, in the area
where the properties are located. Individual properties were assigned a value corresponding to
the underlying cell. The higher is this value, the lower is the likelihood of flooding for the specific
property. An increase in this variable of one implies that flood risks decrease by an order of
magnitude. For example, as you move from flood risk measure of 2 to 3 you move from a risk of
0.01 (i.e., once per 100 years) to 0.001 (i.e., once per 1000 years).
c. Description of the Data
Detailed house attribute data as well as the sales prices of the houses were obtained from the
Multiple Listing Service (MLS) for the Milwaukee Metropolitan Area. Information was collected
for each transaction, listed through the MLS, for the time period January of 1995- July of 1998.
This time frame provides an adequate period for property value fluctuation to occur as a result
11
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of the flooding event in June of 1997, if this is the case. A total of 1,965 properties were listed
through the MLS in the study area for the time period examined. From this total, properties were
eliminated as a result of missing data for: the lot size (290), age of the house (198) and taxes
(2). Furthermore, the MLS database only includes properties sold through realtors, and thus
leaves out of the sample properties sold directly by the owner. This may reduce the possibility of
including "non-market" transactions in the sample, assuming that properties sold to relatives or
close friends may be transacted by this means. Finally, as noted above, 44 properties were lost
as a result of geocoding difficulties, yielding a total sample of 1431 properties.
The variables in the model are organized into six categories: Structural, Neighborhood, Fiscal,
Disequilibrium, Time Related and Flood. Many influences are controlled within the neighborhood
category in order to avoid misspecification biases and to account for spatial influences. For
simplicity, the fiscal variable (tax rate) and the disequilibrium control (days on the market) are
included in the Neighborhood category for the specification. Following Cropper (et al.) a semi-
log specification is chosen, and the model is specified by Eq. 8.
LnRPRICE = f (Structural,Neighborhood, Time Related,Fiscal,Disequilibrium,Flood) Eq. 8
The variable definitions and data sources are reported in Table 1, and descriptive statistics are
in Table 2. The dependent variable is the log of real sale price of housing and is deflated by the
housing component of the CPI (1982-84) for the month in which the property sold.
i. Structural Variables
The structural characteristics include the number of bedrooms, bathrooms, other rooms,
presence of an attached garage, as well as square footage of the lot and the property. It is
expected that an increase in any one of the previous characteristics will increase the sale price,
assuming that these attributes increase the housing services a property provides. Measures of
area are included in linear and quadratic form to account for non-linearity in these variables.
Finally, the age of the house is included expecting a negative relationship between the age of
the house and the sale price. This is based on an assumption that older homes may have dated
technology lacking several beneficial features that would increase the housing service provided
by the property.
12
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ii. Locational Variables
Each property was matched to numerous locational variables, including those in the
Neighborhood category. To account for various demographic characteristics, census data was
attached accordingly to the appropriate property. The census block group data captures the
racial and ethnic mix of the neighborhood. The sign for these variables cannot be predicted
without knowledge of a home purchaser's cultural preferences. The characteristics also include
measures of income and poverty, home occupancy, age of the neighborhood. Also, the model
controls for the travel time to work and the population density of the neighborhood. The latter
variable is included to control for aspects of the neighborhood correlated with density which are
not measured (e.g., crime, cultural amenities).
The property tax is included to account for fiscal effects, expecting that increases in taxes would
decrease the sale price. Also capturing fiscal impacts is the teacher student ratio for the high
school district in which the property resides. A dummy variable is included to account for
residence within Wauwatosa or Milwaukee, which may capture a submarket influence and
perceptions associated with living in Wauwatosa (versus Milwaukee). The number of days a
property was on the market is used in the model as a disequilibrium control variable.
Past studies have found historical preservation districts to positively impact property values
(Clark and Herrin 1997; Coffin 1989). The coefficients may be positive in the case that creation
of the district provides people with additional information about the housing stock and revitalizes
the neighborhood, yet also may be negative if the structural restrictions reduce housing
demand. There are a total of six preservation districts in this study area, three in Milwaukee and
three in Wauwatosa. Dummy variables are included for each of the districts.
As indicated in the theoretical review of the hedonic price model, one of the influences on the
property sale price is environmental quality. Several variables controlling for environmental
quality factors are included within the neighborhood category including measures of air quality,
and proximity to Toxic Release Inventory sites. Accounting for the impact of local annoyance
factors is the proximity of a residence to both highways and rail lines, as well as being located
on a major road. One would expect these factors to negatively affect property sale price in most
cases. A variable is also included to capture scenic benefits of residing along the river, a
positive environmental attribute. This is measured by a dummy variable for those properties
13
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residing on the Menomonee River Parkway. While some of the properties along the
Menomonee River Parkway may also be susceptible to flooding, only 7 of the 13 properties
along the Parkway are also in the 100-year floodplain. Thus, the effect of this variable should
pick up the scenic benefits of the river, while holding constant the risk associated with flooding
(accounted for by variables in the Flood category).
iii. Time Related Variables
The model also includes dummy variables in the Time Related category for both the year and
season in which the property was sold. Business cycles may affect property values, and the
year variables are incorporated to capture the possibility of that influence. Furthermore, the year
variables may capture an interest rate effect. Similarly, the season dummies control for trends
that may be associated with time. There are no expected signs for the variables relating to time.
iv. Flood Variables
Finally, variables representing the focus of this study are included in the Flood category and
also capture environmental quality. Other studies (Speyer and Ragas 1991, Schaefer 1990,
Donnelly 1989, Park and Miller 1982, Thompson and Stoevener 1983) have used dummy
variables accounting for a property's location inside or outside of the 100-year floodplain. All,
with the exception of Schaefer, have found a significant negative relationship between location
in the floodplain and the sale price of a property. This study differs from the previous studies in
that a continuous measure of risk is derived. This permits floodplains of any periodicity to be
defined. We investigate floodplains in 100-year increments from 100-500 year floodplains. Over
the 3-year period, 15 properties sold in the 100-year floodplain, and 32 sold within the 500-year
floodplain. In addition, we examine the rate at which property values change within each
increment.
A second objective is to analyze the short run and intermediate run effects of a specific flood
event that occurred in June of 1997. To do so, two different measures are used. First, to
measure the short run impact, the floodplain dummy is interacted with a dummy variable for
whether the property was sold after the flood event. Of the 1431 properties in the sample, 512 of
them were sold after the flood event and 4 of these were within the 100-year floodplain whereas
12 were within the 500-year floodplain. Second, to measure intermediate run effects, the
floodplain dummy is interacted both with the dummy for whether the property was sold after the
14
-------
flooding event and the number of days between the flooding event and the sale of the house. If
present, one would expect short run effects to be stronger than intermediate impacts, assuming
that the consequences of the flood event will taper off in the minds of homeowners and buyers
as time passes.
5. EMPIRICAL FINDINGS
The coefficients on control variables in the structural, neighborhood, fiscal, disequilibrium and
time related categories differ minimally among the tables. To conserve space, these variables
are reported only once, with subsequent regressions reporting only the flood category variables.
Heteroskedasticity, a non-constant variance in the model's error term, is expected in this sample
of data since variance in selling price is likely to differ between the low-end and high-end of the
market. To test for the presence of heteroskedasticity, White's test is used and the null
hypothesis of no heteroskedasticity is rejected at the 95% level of confidence for each
regression (Gujarati, 1995). White's correction is employed to generate consistent estimates of
the standard errors. All models estimated explained approximately 91% of the variation in the
real housing price.
/'. Structural Variables
All structural variables are significant at the 99% level of confidence, except the dummy
accounting for whether the garage is attached. The number of garage spaces is significant, with
each additional space increasing the value of the home by 4.8%. The number of bedrooms,
other rooms, half baths, and full baths all positively impact property sale price. One additional
half bath, full bath, bedroom, and other room, increases the property value by 11.2%, 6.2%,
5.0%, and 5.8% respectively. The large magnitude of the coefficient on the half bath variable
suggests that it may be serving as a proxy for other structural features of the house. Both
square footage variables, interior and lot, increase property value at a decreasing rate reflected
by positive linear terms and negative quadratic terms. The partial derivative of sale price with
respect to the interior square footage (3Real Price/3Building area) is equal to [• AREA+ 2
*• AREAso*Building area]. Evaluated at the mean for interior square footage (705.7 sq.ft. or 0.65
m2), property value increases by 6.8% for an increment of 100 square feet (or 0.72%/m2).
Similarly, an increment of 1000 square feet for the lot size increases sale price by 1.7% (or
0.18%/m2 evaluated at the mean). Finally, other things equal, age has a negative effect on
15
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property value (i.e., 1.6% for each additional 10 years). Inclusion of a quadratic term for age
made both the linear and quadratic terms insignificant.
/'/'. Locational Variables
Evaluating the demographic variables taken from the block group data, many coefficients
appear to be significant at the 99% confidence level. Exceptions include population density and
the percent of occupied housing units, and percent owner occupied units. Population density
has a negative relationship with property value suggesting that on the net, urban scale related
disamenities have a stronger influence than that of amenities, yet the variable is insignificant.
The racial variables reveal that higher concentrations of Asian (as compared to nonwhite other
race) populations in a neighborhood positively affect property values. Specifically, a 1%
increase in the Asian population increases property value by 3%. The impact of Hispanic
populations, on the other hand, decrease real home sale prices by 2.5%. Percent White is
positive and significant, raising prices 1.3% per 1% increase, whereas percent Black is not
significant. Note, that most of the neighborhoods in the study areas have relatively few minority
households. As expected, higher poverty rates in a neighborhood decrease home sale price, yet
the effect is not great. Median household income, also reflecting socioeconomic dimensions of
the neighborhood, positively impacts property values. Measured by the median year of houses
built in the neighborhood, older neighborhoods have significantly higher priced housing in the
study area. This is somewhat contrary to the sign on the age variable, yet it may suggest that
people prefer historic surroundings in a neighborhood along with the benefits of a
technologically advanced home. Finally, in line with the existing theory, each additional 10
minutes of commute time decreases the home sale price by 9%.
The tax rate, incorporating fiscal effects into the model, negatively impacts property value.
Specifically, a 1% increase in the property tax rate (e.g. 4.3% to 5.3%) decreases the property
sale price by 2.0%. The teacher student ratio included to proxy the quality of education does
have a positive effect, yet is insignificant. Also insignificant is the number of days a house was
on the market. The dummy variable accounting for city jurisdiction is significant indicating higher
sales prices (by a magnitude of 19%) in Wauwatosa than in Milwaukee. However, Valley Park is
only one small area in Milwaukee and the dummy accounting for location in Valley Park was
insignificant.
16
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The effect of historic preservation districts was positive in all cases confirming that historic
preservation districts provide home buyers with additional information regarding the housing
stock and serve the purpose of revitalizing the neighborhood. The influence of five of the six
districts was significant. The most dramatic of all influences was that of The McKinley Boulevard
Historic District in Milwaukee, increasing property value by 49%. The Concordia Historic District,
also in Milwaukee, has a similar effect with 41% increase in property value as a result of
residing within the district. The one historic preservation district that did not have a significant
impact was The Wauwatosa Avenue Historic District. These districts were also interacted with
age, yet the resulting variables were insignificant and doing so overwhelmed the significance of
the individual dummies. Therefore, they were not included in the final regression.
Several other variables in the neighborhood category were indicative of the surrounding
environmental quality. The quality of the air measured by the sulfur dioxide reading negatively
impacts property sale price as we would expect, and this effect is significant at the 99% level of
confidence. Furthermore, location within one mile of a Toxic Release Inventory site has the
effect of reducing home sale prices by 2.8%, all else constant. Two of the variables representing
local annoyance factors significantly reduce the sale price of a home. Specifically, residence on
a major road and residence within a quarter of a mile of rail lines reduce home sale prices by
5.7% and 6.0% respectively. On the other hand, residence within a quarter of a mile of
Interstate 94 increased sales prices for homes by 8.5%. It is possible that this variable is
controlling for non-work related travel accessibility in addition to an annoyance factor. Finally,
residence along the scenic Menomonee River Parkway has the significant effect of increasing
property value by 7.1%, all else constant.
/'/'/'. Time Related Variables
The seasonal dummy variables are insignificant indicating that the season in which a house is
sold has no impact on the sales price. The year dummy variables indicate that real housing
prices have fallen over the time period 1995- July of 1998. The effect in 1996 is insignificant;
however, housing prices significantly decreased for both 1997 and 1998.
iv. Flood Variables
There are two objectives in terms of flood risk for this study. The first objective is to determine
the effect that flood hazard in general has on property value. In the first regression reported in
17
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Table 3, we proxy flood risk using the negative log (base 10) of the expected flood frequency ,
i.e., flood risk measure (see
Figure 4). The log of the value is included due to the rapid rate at which flood risks fall as
distance from the river increase, and elevation rise. The findings indicate a clear relationship
between reduced flooding risk, and increased property values. However, the value of the
coefficient is extremely low. This finding is not surprising, given that the vast majority of
properties are well beyond even the 1000-year floodplain. Hence a reduction of risk from say
10E-23 to 10E-24 is of negligible value to those residents.
To investigate the variation of flood risks within floodplains, we explore several different
specifications. First, we examine the 100-year floodplain. Although flood risk is continuously
defined, lenders only require that properties in the 100-year floodplain purchase flood insurance.
In Table 4, we report the findings on a regression that includes a dummy variable for whether
the property lies within the 100-year floodplain. In addition, we interact that variable with the
recurrence interval, i.e., anti-log of the flood risk measure. The recurrence interval takes on
values between 6.3 (i.e., a flood is expected with a probability of 1/6.3) for the property closest
to the river, and 100 for a property at the edge of the 100-year floodplain. Both the dummy
variable and the risk interaction term are statistically significant. The findings suggest that
properties at the edge of the river would sell for approximately 7.8% than those outside the
floodplain. However, as flood risk diminishes by 10 years (e.g., from a one-year flood frequency
to an 11-year frequency) property values would increase by 2.3%. This implies that the
detrimental effect of the flood risk is eliminated after the expected flood risk falls to once every
33.3 years.
In Table 5, we add a second interaction term to consider the effect of a flooding event. The
variable Days since is the number of days since the flood in June of 1997. Hence, it measures
the effect of the flooding event on the impact of the 100-year floodplain. The inclusion of this
variable renders the floodplain dummy variable insignificant, although it remains negative. This
is due to multicollinearity between the two variables. Treating the coefficient on the dummy
variable as point estimate, it suggests that properties (at the edge of the river) selling in the
floodplain prior to the flood sold for 5.1% less than comparable properties outside the floodplain
prior to the flood. Those selling a year after the flood would sell for 18.9% less than properties
outside the floodplain. The pattern did not appear to be nonlinear, although note that it was not
18
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possible to capture longer-term effects due to the fact that the sample did not extend further into
the future. Thus, it appears that at least over the short term, the flooding event did reduce
property values beyond what they were prior to the flood.
In the final model presented in Table 6, we explore whether wider floodplains generate
detrimental effects on properties within those areas. Thus, we define floodplains between 100
and 200 hundred years, 200 and 300 years, and so on. Given that the detrimental effects of
flood risk appear to dissipate within the 100-year floodplain, it is not surprising that none of the
other floodplain categories are negative and significant. Indeed, the region between the 300 and
400-year floodplain sells at a premium over those outside the floodplains. We also explored
whether the flooding event negatively influenced any of the property values within the 200 year
and beyond areas, and found no evidence of detrimental impacts.
6. Conclusions
This study employed GIS tools to more accurately characterize flood risks in an urban
watershed. An interpolation scheme to evaluate the level of flood risk in the watershed has been
developed and applied to the Menomonee River watershed. Together with a wide range of other
locational attributes, flood variables were matched to geocoded properties to investigate
impacts on housing prices. Our findings support the hypothesis that increases in flood risk
decrease values for residential properties within the 100-year floodplain. Unlike other studies
which conclude that there are uniform impacts within the floodplain, we find declining effects
with reduced risk. Furthermore, there is evidence suggesting that flooding events heighten
sensitivity to such risks and raise the property price premium associated with a given level of
flood risk. Negative impacts beyond the 100-year floodplain are not found.
The use of GIS tools to complement statistical analyses of urban spatial problems will continue
to grow as PC-based GIS software becomes more powerful, and geographic data sources more
abundant. In addition, GIS tools can serve as a conduit for interdisciplinary work as geographic
modeling in the physical sciences and engineering is integrated with spatial modeling by social
scientists.
19
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Acknowledgements
This research is sponsored by the EPA/NSF Watershed Management Program in a form of a
grant to Marquette University. Views and findings included in the presentation are those of the
authors and not of the funding agency.
20
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Table 1: Variable Definitions and Data Sources
Dependent Variable and Variables in the Structural Category
Variable Name
Real Price
Age house
Full bath
Half bath
Bedrooms
Other rooms
Building area
Garage spaces
Garage attached
Lot size
Definition
[mean, standard deviation]
Real sale price of the property
(1982-84 dollars)
Age of the house in years
Number of full baths in house
Number of half baths in house
Number of bedrooms in house
Total rooms minus number of bedrooms
Area of the master
bedroom+bedroom2+livingroom+kitchen
in square feet
Note: Due to data limitations, all of the
square footage is not captured
Number of garage spaces
1 = garage attached, 0 = otherwise
Lot area in square feet
Source
MLS
MLS
MLS
MLS
MLS
MLS
MLS
MLS
MLS
MLS
Predicted
Sign
LnRPRICE
is the
dependent
variable
-
+
+
+
+
+
+
+
+
Variables in the Neighborhood, Fiscal, and Disequilibrium Control Categories
Variable Name
Sulphur Dioxide
Major road
Menomonee
Parkway
% mile I94
Commute time
% railroad
Toxic Release Inv.
Definition
[mean, standard deviation]
Distance weighted value of the nearest air
monitor, computed as sulfur
dioxide/distance of monitor to property
1 = property resides on a primary road,
0 = otherwise
1= property resides on the Menomonee
River Parkway,
0 = otherwise
1= property within a quarter of a mile of
Interstate 94, 0 = otherwise
Average household travel time to work for
the block group in minutes
1= property within a quarter of a mile of
railroad tracks, 0 = otherwise
1= property within a quarter of a mile of a
manufacturing facility on the Toxic Release
Inventory, 0 = otherwise
Source
LandView III
ArcView
ArcView
ArcView
1990
Census of
Population
and
Housing
ArcView
BASINS
Predicted
Sign
-
+
~
~
21
-------
Variable Name
Historic Preservation
Districts
TS ratio
Pop density
Median year built
Median HH income
% Asian
% Black
Definition
[mean, standard deviation]
HPDTOSA 1= resides within The
Wauwatosa Avenue Historic District, 0=
otherwise
HPDCHURCH 1= resides within The
Church Street Historic District, 0=
otherwise
HPDWASH-HIGH 1= resides within The
Washington Highlands Historic District,
0= otherwise
HPDCONCORD 1= resides within The
Concordia Historic District, 0=otherwise
HPDMCKINLEY 1=resides within The
McKinley Boulevard Historic District,
0=otherwise
HPDHIMOUNT 1= resides within The
Washington-Hi Mount Boulevards Historic
District, 0=otherwise
Teacher student ratio for the school district
in which the property resides
Population density in the block group,
measured as people per square mile
Median year of houses built in the block
group
Median household income of the block
group
Percent of the block group population that
is Asian
Percent of the block group population that
is Black
Source
Maps
received
from:
Wauwatosa
City
Planning
(first three)
Milwaukee
City
Planning
(last three)
Respective
High
Schools
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
Predicted
Sign
+
?
?
+
?
?
22
-------
Variable Name
%Hispanic
%Other
%Occupied units
%Owner occupied
% Poverty
Tax rate
Wauwatosa
Valley Park
Days on market
Definition
[mean, standard deviation]
Percent of the block group population that
is Hispanic
Percent of block group population which
falls into the "other" category
Percent of the block group housing units
that are occupied
Percent of block group housing units that
are owner occupied
Percent of block group population that is
below the poverty line
Tax payment/ [sale price/1000]
1 = property resides in Wauwatosa,
0 = Milwaukee
1 = property resides in Valley Park,
0 = otherwise
Number of days the house was on the
market
Source
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
1990
Census of
Population
and
Housing
MLS
MLS
ArcView
MLS
Predicted
Sign
?
+
+
+
-
+
?
~
Time Related Variables
Variable Name
Seasonal Dummy
Variables
Year
Definition
[mean, standard deviation]
SPRING=1 (March-May), 0=otherwise
SUMMER=1 (June-Aug), 0=otherwise
FALL=1 (Sept-Nov), 0=otherwise
WINTER=1 (Dec-Feb), 0=otherwise
1= dwelling sold in ith year, 0=otherwise
i = 1995, 1996, 1997, 1998
Source
MLS
MLS
Predicted
Sign
?
Winter is
omitted
variable
?
1995 is
omitted
variable
23
-------
Variables in the Flood Category
Variable Name
Floodplain10o
Floodplain2oo
Floodplain30o
Floodplain40o
Floodplain50o
Flood Risk Measure
Recurrence Interval
After
Days since
Definition
[mean, standard deviation]
1= resides in the 100-year, 0=otherwise
1= resides in space beyond 100 year
flood and within 200 year flood,
0=otherwise
1= resides in space beyond 200-year and
within 300 year flood, 0=otherwise
1= resides in space beyond 300-year and
within 400 year flood, 0=otherwise
1=resides in space beyond 400-year and
within 500 year flood, 0=otherwise
Minus log of flood risk
The expected number of years between
flooding events
1= after the June 1997 flood,
0 = otherwise
The number of days since the June 1997
flood.
Source
ArcView
Arcview
ArcView
ArcView
ArcView
Predicted
Sign
+
+
?
?
24
-------
Table 2: Descriptive Statistics
Dependent Variable and Structural Characteristics:
Variable
Mean
SD
Maximum
Minimum
RPRICE
Agehouse
Full bath
Half bath
Bedrooms
Other rooms
Building area
Garage space
Garage attached
Lot size
Variables
Variable
Sulpher Dioxide
Major road
Menomonee
Parkway
% mile 194
Commute time
% mile railroad
Toxic Release Inv.
HPDTosa
HPD Church
HPDWash.
Highlands
HPD Concord
HPD McKinley
HPD Himount
TS ratio
Pop. Density
Median Year Built
Median HH income
%ASIAN
%BLACK
%HISPANIC
%OTHER
%OCCUPIED
%OWNEROCC
%POVERTY
Taxrate
79048.1
59.970
1.278
0.423
3.211
3.488
705.214
1.793
0.193
7081.323
in Neighborhood,
Mean
153080
0.062
0.009
0.042
16.991
0.093
0.468
0.006
0.006
0.003
0.003
0.004
0.008
0.118
7247.6333
1945.530
40259.25
1.137
2.970
1.369
0.460
0.977
72.808
5.021
0.028
34708.90
16.678
0.487
0.497
0.741
0.990
155.137
0.639
0.395
3768.827
360962.6
138
4
2
7
8
1917
4
1
58344
Fiscal, and Disequilibrium Control
SD
53632.03
0.241
0.094
0.200
2.239
0.291
0.499
0.078
0.078
0.058
052
0.064
0.087
0.082
3530.725
7.017
11716.96
1.754
11.040
1.537
0.906
0.024
18.028
9.20
2.181
Maximum
504252
1
1
1
32.633
1
1
1
1
1
1
1
1
0.21
27743.90
1975
66,649
18
90
13
9
1
99
81
0.077
7348.029
1
1
0
2
0
400
0
0
1381
Categories
Minimum
91485.71
0
0
0
12.435
0
0
0
0
0
0
0
0
0.03
752.500
1939
7557
0
0
0
0
0.765
5
0
0.009
25
-------
Valley Park
Wauwatosa
Days on Market
Variable
Spring
Summer
Fall
Winter
Year95
Year96
Year97
Year98
Variable
Flood Risk Measure
Recurrence Interval10o
Recurrence Interval50o
Floodplain10o
After
Days since
0.009
0.633
54.023
Time
Mean
0.282
0.336
0.234
0.401
0.157
0.302
0.321
0.220
Flood
Mean
24.562
36.9
174.102
0.0105
0.358
69.317
0.094
0.482
67.673
Related Variables
SD
0.450
0.472
0.424
0.490
0.364
0.459
0.467
0.414
Related Variables
SD
26.104
29.258
167.652
0.102
0.479
113.661
1
1
1095
Maximum
1
1
1
1
1
1
1
1
Maximum
179.42
100
489.778
1
1
397
0
0
0
Minimum
0
0
0
0
0
0
0
0
Minimum
0.8
6.8
6.8
0
0
0
26
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Table 3 - Hedonic Regression with Log Flood Risk
Variable
Intercept
Coefficient
10.81558
t-score
3.3085
Structural Characteristics
Agehouse
Bedrooms
Full bath
Half bath
Other rooms
Garage space
Garage attached
Building area
Building area *
Building area
Lotsize
Lotsize*Lotsize
-0.001594
0.049593
0.061932
0.112181
0.057908
0.047633
0.013503
0.001224
-3.85E-07
2.10E-05
-2.49E-10
-3.149
7.0307
6.0275
12.078
11.015
6.6189
1.1273
10.133
-5.542
6.7995
-4.832
Neighborhood and Fiscal Characteristics
Sulpher Dioxide
Major road
% mile 194
% mile railroad
Commute time
Toxic Release Inv.
Teacher Student ratio
Population Density
Median HH Income
%Asian
% Black
%Hispanic
%White
%Owner occupied
% Occupied units
% Poverty
Tax rate
Median year built
R-squared
Mean dep. variable
F-statistic
-1.16E-06
-0.057245
0.084733
-0.059753
-0.008686
-0.027812
0.028262
-2.91E-06
3.07E-06
0.030403
0.006825
-0.02546
0.013137
-0.000667
-0.001439
-0.004957
-0.020374
-0.003079
0.917731
6.574281
335.6265
-3.134
-3.99
3.1272
-3.279
-4.69
-2.633
0.3231
-1.355
3.9097
4.2097
1.1918
-3.941
2.3295
-1.26
-0.003
-3.852
-19.32
-1.894
Variable Coefficient t-score
Time Dummy Variables
Year 1996 -0.014904
Year 1997 -0.075591
Year 1998 -0.079498
Spring quarter -0.00728
Summer quarter -0.009696
Fall quarter -0.001184
Historic Preservation Districts and
variables
HPD Church 0.063261
HPDConcordia 0.412596
HPD High Mount 0.141946
HPD McKinley 0.486035
HPDWauwatosa 0.069102
HPD Wash. Highlands 0.213099
Wauwatosa 0.198344
Valley Park -0.023755
Menomonee Pkwy 0.071265
Flood Risk Variables
Flood Risk Measure 0.000253
Disequilibrium Control
Days on market -8.17E-06
Adjusted R-squared 0.914996
S. E. of regression 0. 1 3761 1
Log likelihood 831.532
-1.295
-6.212
-5.296
-0.595
-0.845
-0.093
locational
2.982
3.312
2.039
5.299
1.198
8.95
10.31
-0.264
1.795
2.003
-0.115
27
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Table 4: Model II—Flood Risk within thefloodplain
LnRPRICE = f (Structure, Neighborhood, Time Sold, Flood),
Variable Coefficient t-statistic
Floodplain10o
Floodplain10o*Recurrence Interval
-0.078337
0.002332
-1.931
3.4425
Table 5: Model III—Flood Risk and a Flooding Event
LnRPRICE = f (Structure, Neighborhood, Time Sold, Flood),
Variable
Coefficient t-statistic
Floodplain10o
Floodplain10o*Recurrence Interval
Floodplain10o*Days Since Flood
-0.050991
0.002091
-0.000378
-1.041
2.6966
-2.233
Table 6: Model III—Flood Risk in Expanded Flood Zones
LnRPRICE = f (Structure, Neighborhood, Time Sold, Flood),
Variable
Coefficient t-statistic
Floodplain10o
Floodplain10o*Recurrence Interval
Floodplain10o*Days Since Flood
Floodplain2oo
Floodplain30o
Floodplain40o
Floodplain50o
-0.05261
0.002184
-0.000366
-0.020201
-0.046497
0.143638
-0.007187
-1.064
2.5027
-2.177
-0.323
-1.366
4.87
-0.118
28
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REFERENCES
Babcock, M., and Mitchell, B. 1980. "Impact of Flood Hazard on Residential Property
Values in Gait (Cambridge), Ontario." Water Resources Bulletin, 16: 532-537.
Barnard, J.R. 1978. "Externalities from Urban Growth: The Case of Increased Storm
Runoff and Flooding." Land Economics, 54: 298-315.
Clark, D.E., and Herrin, W.E. 1997. "Historical Preservation Districts and Home Sales
Prices: Evidence from the Sacramento Housing Market." The Review of Regional
Studies, 27: 29-48.
Coffin, D.A. 1989. "The Impact of Historical Districts on Residential Property Values."
Eastern Economic Journal, 15: 221-28.
Correia, F.N., Rego, F.C., Da Graca Saraiva, M., and Ramos, I. 1998. "Coupling GIS
with hydrologic and hydraulic flood modeling." Water Resources Management,
12(3):229-249.
Cropper, M.L., L.B. Deck, and K.E. McConnell. 1988. "On the Choice of Functional Form
for Hedonic Price Functions." Review of Economics and Statistics, 70: 668-675.
Damianos, D., and Shabman. L.A. 1976. "Land Prices in Flood Hazard Areas: Applying
Methods of Land Value Analysis." Offices of Water Resources Research. Project
A-054-VA.
Donnelly, W.A. 1989. "Hedonic Price Analysis of the Effect of a Floodplain Location on
Property Values." Water Resources Bulletin, 25: 581-586.
Gujarati, DamodarN. 1995. Basic Econometrics McGraw-Hill, Inc., New York.
Kiel, K.A., and McClain, K.T. 1995. "House Prices during Siting Decision Stages: The
Case of an Incinerator from Rumor through Operation," Journal of Environmental
Economics and Management, 28: 241-55.
Lancaster, K.J. 1966. "A New Approach to Consumer Theory," Journal of Political
Economy, 74:132-157.
McLin, S.G. 1993. "Combined GIS-HEC procedure for flood hazard evaluation." Report
No. LAUR933008, Los Alamos National Lab., NM, USA
Park, W.M., and Miller, W.L. 1982. "Flood Risk Perceptions and Overdevelopment in the
Floodplain." Water Resources Bulletin, 18: 89-94.
Rosen, S. 1974. "Hedonic prices and implicit markets: product differentiation in pure
competition." Journal of Political Economy, 82: 132-57.
Shabman, L. and Stephenson, K. 1996. "Searching for the Correct Benefit Estimate:
Empirical Evidence for an Alternative Perspective." Land Economics, 72: 433-39.
29
-------
Shaefer, K.A. 1990. "The Effect of Floodplain Designation/Regulations on Residential
Property Values: A Case Study in North York, Ontario." Canadian Water
Resources Journal, 15: 319-333.
Shilling, J.D., Sirmans, C.F., Benjamin, J.D. 1987. "Flood Insurance, Wealth Distribution,
and Urban Property Values." Journal of Urban Economics, 26: 43-53.
Speyrer, J.F., and Ragas, W.R. 1991. "Housing Prices and Flood Risk: An Examination
Using Spline Regression." Journal of Real Estate Finance and Economics, 4:
395-407.
Thompson, M.E., and Stoevener, H.H. 1983. "Estimating Residential Flood Control
Benefits Using Implicit Price Equations." Water Resources Bulletin, 19: 889-95.
Tobin, G.A., and Newton, T.G. 1986. "A Theoretical Framework of Flood Induced
Changes in Urban Land Values." Water Resources Bulletin, 22: 67-71.
US EPA (1998): http://www.epa.gov/ ...BASINS
Williams, G.P. (1978): Bank-full Discharge of Rivers. Water Resources Research
Zimmerman, R. 1979. "The Effect of Flood Plain Location on Property Values: Three
Towns in Northeastern New Jersey." Water Resources Bulletin, 15: 1653-6
30
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A GIS Demonstration for Greenbelt Land Use Analysis
Joanna J. Becker
Environmental Planning Services, Santa Rosa, California
Purpose
The goal of this project was to demonstrate what analy-
ses could be undertaken with a GIS program without
substantial GIS training ortime input. The demonstration
attempted to show how planning staff and decision-makers
could easily and usefully employ GIS. It was not intended
as a complete study of all possible variables. Only avail-
able data were used. Diverse techniques were pre-
sented while keeping the content as simple and relevant
as possible. The project was designed as a demonstra-
tion using regional scale data and was combined with
another parcel-specific demonstration that showed
urban GIS applications.
The demonstration showed the following modeling
techniques:
• Buffer zones
• Combination of variables (overlays)
• Weighting of values
• Absolute value variables
• Reclassification of final values
Site Location
The San Luis Obispo watershed comprises an area of
approximately 84 square miles. The watershed drains
into the Pacific Ocean at Avila, California. The major
creek in the watershed, San Luis Obispo Creek, is a
perennial creek, but many of its tributaries have only
seasonal flow. Agriculture and grazing are the major
land uses in the watershed, although a significant num-
ber of areas have been developed. Growth of these
areas is moderate to limited but has a pronounced effect
on the watershed. The watershed also supports a large
amount of riparian and other natural vegetation. Figure 1
demonstrates the distribution of land cover/land use
within the watershed.
Watershed
Study Perimeter
City Greenbelt
Pacific Ocean
Figure 1. A generalized map of the San Luis Obispo area show-
ing the location of the two ARC/INFO GIS study areas:
(1) the parcel analysis in the Dalideo area and (2) the
regional analysis in the city greenbelt stippled area
surrounding the city. All boundaries are approximate
and are for schematic purposes only (1, 2).
Background
The City Council approved the open space element of
the San Luis Obispo General Plan in January 1994 and
identified a greenbelt area that extended from the Urban
Reserve Line approximately to the boundaries of the
San Luis Obispo Creek watershed. The intent of this
greenbelt area is to provide a buffer to the city and to
preserve the agricultural and natural resources of the
area.
-------
The data forthe watershed were already available through
the work of the Landscape Architecture Department of
California Polytechnic State University at San Luis
Obispo for a study of San Luis Obispo Creek (1), and
the city's greenbelt area lay approximately within those
boundaries (see Figure 1). Variables were selected that
could be extracted from the available data.
The data for the creek study were initially entered into
workstation ARC/INFO in a polygon format. They were
then transferred to MacGIS, a PC raster program, for
simplicity of use. The final product was then transferred
back to ARC/INFO as a grid format and viewed in a PC
version of ARC/VIEW using DOS files.
In interpreting the overlay of values, the assumption was
made that the occurrence of high values for the most
variables would result in the most suitable land for that
land use. This was presented as a range of values
derived from the total values divided into three approxi-
mately equal groups of high, medium, and low. In addi-
tion to providing a composite analysis, however, any one
of the data sets can be queried separately such that, for
example, slopes greater than 20 percent could be iden-
tified or two layers such as storie index and distance
from roads could be compared.
Criteria
Note that the ratings of high, medium, and low are based
on available data, and the rating of low implies no suit-
able use. In addition, these ratings do not imply that
categories rated low could not be used for a particular
land use but rather that other land uses might be more
appropriate. For example, open space use was rated
low for flatter slopes but only because this category
would likely be more suitable for agriculture.
The demonstration used an existing cell size of the data
on the MacGIS program of 75 meters per side, which is
assigned during the initial conversion process. There-
fore, buffer zones are in aggregates of 75 meters. This
size cell does not allow for minute analysis but reduces
the size of the files, which may become extensive in
raster format.
In presenting the final analysis, land contained within the
urban reserve limit line has been excluded.
Procedure
Initially, eight variables from the available data were
deemed suitable for this analysis:
• Slope
• Storie index (indicating soil fertility)
• Distance from major roads
• Distance from creeks
• Erosion hazard1
• Oak woodlands
• Land use compatibility
• Grasslands2
After selecting the six variables, the categories were
receded to conform to a rating of high, medium, or low.
Each land use was then evaluated separately for every
variable except for combining the variables of slope and
distance from creeks for rangeland analysis. In this case,
composite values were assigned to the two variables, then
receded to produce a high, medium, or low rating.
After obtaining maps for each of the variables according
to land use, the maps were compiled to indicate the
density of overlays for each land use category. In as-
sessing the suitability of land for the three land uses, the
values of all the variables, except land use, were aggre-
gated and a rating system developed. In addition, a
double weight was assigned to the storie index in evalu-
ating agriculture (because this is a primary index for
considering prime agricultural land). If less than 75 me-
ters, the distance from creeks also received added
weight in consideration of open space preservation (be-
cause this is likely to ensure the least erosion and
pollution to the waterways). The weighting then altered
the scores as follows:
Land Use
Agriculture
Rangeland
Open space
Attributes
5
4
5
Number of Values
6
4
6
After the values had been assigned for each ranking,
further receding established three categories of high,
medium, and low for each land use.
The land use buffer was added to this receded aggre-
gate map and resulted in an additional three values due
to the interaction of the buffer with each category. These
additional values were receded according to each land
use to produce a final map with three values.
The Urban Reserve Area was overlaid on the final map
to exclude urban areas.
Assumptions
In determining what properties would be most suitable for
each land use, the following assumptions were made.
1 After reviewing the material, erosion hazard was eliminated because
it was similar to the Soil Conservation Service (SCS) storie index
data while the identification of native grasslands fell only within the
area currently designated as open space land, so it was not included.
2 See above note.
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Open Space
This land is desirable to preserve as open space be-
cause of the existence of scenic or significant natural
resources. It could also be land that is inappropriate for
other uses due to the presence of such factors as steep
slopes or poor soils. A distinction is made in the final
map between land that is designated open space for
recreational uses, such as parks, and land preserved for
habitat or species protection. Open space adjacent to
an urban area would be rated high if public accessibility
was desirable but low if its purpose was resource pro-
tection or preservation. Separate maps based on two
types of proposed uses present the contrast in analysis.
The analysis of the variables thus was rated as follows:
A. Land with steep slopes and therefore less suited for
other purposes.
B. Land that has oak woodland vegetation resources.
C. Riparian land.
D. Low storie index indicating a lack of suitability for
agriculture or rangeland.
E. At least approximately one-eighth of a mile from a
major road to avoid negative impact on wildlife.
F. Either approximately one-quarter of a mile from ur-
ban areas if designated to protect resources or adja-
cent to urban areas if designated to serve as parks and
recreation.
Agriculture
This land use includes all forms of agricultural activity;
obviously, its suitability for specific crops and practices
would vary. The determination of suitability would need
to be made on a site-specific basis.
For the purpose of general agricultural suitability, the
highest land suitability for agriculture was a rating of the
variables as follows:
A. Land that does not have oak woodland.
B. Land that is not close to perennial creeks (to avoid
fertilizer/pesticide runoff contamination).
C. The flattest slopes.
D. The highest storie index.
E. Proximity to a major road (considered an advantage
for trucking and farm equipment access).
Rangeland
Some types of livestock can graze under most condi-
tions, but for purposes of this analysis, land more suited
for either open space or agricultural designation was
rated above that of rangeland. The major limitation to
suitability of land for rangeland was a combination of
steep slopes and proximity to creeks. A rating of medium
for the other variables was considered the most desir-
able for rangeland purposes.
Details of the Variables
Storie Index
In determining the most suitable uses according to soil
fertility, the SCS storie index rating was used, with a
modification of the categories to three to accommodate
the ratings of high, medium, and low that were used
throughout the analysis. Therefore, the first two SCS
categories of excellent and good were combined into
Category 1. Categories fair and poor were combined
into Category 2, and categories very poor, nonagricul-
tural, urban, and mines were combined to compose
Category 3.
Subsequently, receding was undertaken to prioritize
these categories according to land use:
Agriculture
Rangeland
Open space
Roads
High for Category 1
High for Category 2
High for Category 3
The five principal arterials of the watershed were used
in this analysis:
• Highway 1
• Highway 227
• Los Osos Valley Road
• U.S. 101
• Avila Valley/San Luis
A buffer on each side of the road was created, which
was then receded into the three categories of more than
75 meters, 75 meters to 150 meters, and greater than
150 meters. Each land use was then evaluated accord-
ing to these criteria, with agriculture deemed the most
suitable closest to the roads and open space the least
suitable.
Slope
The slope categories in the San Luis Obispo watershed
data set were divided into a number of classes, which were
receded into the most appropriate grouping of three
classes for the analysis of the three land uses. The existing
categories were not altered for the purpose of the demon-
stration, so they do not necessarily represent the most
ideal slopes for the particular uses. A separate category of
less than 10 percent slopes was provided for agriculture
because most agricultural practices require flat land.
-------
Slopes between 10 and 21.5 percent would be limited
to such activities as orchards or vineyards.
Agriculture:
Open space:
< 10 percent
10 to 21.5 percent
> 21.5 percent
< 10 percent
10 to 46 percent
> 46 percent
High (H)
Medium (M)
Low (L)
L
M
H
No slope analysis was undertaken for rangeland be-
cause this category was combined with that for streams
(see Streams section).
Urban Adjacency
Urban adjacency was treated as an overlay of the ag-
gregate map of the other variables because it is an
absolute value. That is, this variable has no ranking.
Land is either within or outside the buffer. The suitability
of each land use adjacent to urban areas was deter-
mined, then the aggregate map was adjusted according
to a comparison of the receded aggregate values with
the designated ranking of land use suitability.
The first step was to recede the existing data on land
uses (interpreted from 1989 aerial photography ob-
tained from the United States Department of Agriculture
[USDA], Agricultural Stabilization and Conservation
Service, Atascadero, California) into urban/commercial
areas and nonurban/noncommercial areas. A buffer
zone of approximately one-quarter of a mile was then
applied around the urban/commercial areas and an
analysis undertaken of suitability for the three land uses
to be located within this buffer.
In making the analysis, the following assumptions were
made:
Land Use
Ranking
Agriculture L
Rangeland M
Open space H
L
Conflict with dust, noise,
pesticides, and urban use
Fire hazard of open
grassland near buildings
Most suitable if used as
parks/recreation
Least suitable if
designated for
habitat/wildlife protection
Therefore, the analysis provided for two planning alter-
natives for open space, with the scenarios presented as
separate maps overlaid on the aggregate map for the
other rangeland variables.
In interpreting this map, the combined values were rated
according to the above criteria, with any values lower
than the desired ranking receiving a low value no matter
what the aggregate value had been. This produced the
following results:
• Agriculture: A rating of low for any land within the
buffer zone.
• Rangeland: A rating of medium for any land within
the buffer zone except that rated low for the aggre-
gate map.
• Open Space: A rating equivalent to the rating of the
aggregate map if the land was to be used for
parks/recreation, or a rating of low for any land within
the buffer zone if the land was to be used for habitat
protection.
Streams
The original data for streams (from 1:24,000-scale
USGS maps), which included both intermittent and per-
ennial creeks, were used for rangeland analysis. A buffer
of 150 meters was then applied to these streams, and
these data were combined with the slope analysis. This
combination was important in evaluating the erosion
hazard and resulting stream pollution caused by nitro-
gen waste and hoof disturbance. The complete stream
complex for the watershed area was therefore evaluated
using the following matrix:
Stream Buffer
< 75 meters
75 to 150 meters
> 300 meters
<21.5
percent
1/L
4/M
7/H
Slope
21.5 to 46
percent
2/M
5/H
8/H
>46
percent
3/L
6/M
9/M
The stream data were then modified for agricultural and
open space land use analysis to indicate only the per-
ennial creeks as defined by the California Department
of Fish and Game. Buffers were created for this as
follows:
Stream Buffer
< 150 meters
150 to 300 meters
> 300 meters
Oak Woodlands
Open Space Agriculture
H
M
L
L
M
H
The receding of oak woodland data for agriculture was
different than for the other two land uses because
the presence of oak woodlands is not conducive to
agriculture:
No oak woodlands
< 10 percent
> 10 percent
H
M
L
-------
The suitability of oak woodlands for open space and References
rangeland was ranked as follows: ^ Ha||ock B G L s Bowker WD Bremer and D N Long 19g4
Open Space Rangeland Nutrient objectives and best management practices for San Luis
—* * " Obispo Creek. San Luis Obispo, CA: Coastal Resources Institute,
California Polytechnic State University.
< 33 percent L M
2. Community Development Department of San Luis Obispo. 1994.
33 to 75 percent M H Open space element for the city of San Luis Obispo general plan.
> 75 percent - high H L san Luis Obispo, CA.
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Small Is Beautiful: GIS and Small Native American Reservations—
Approach, Problems, Pitfalls, and Advantages
Jeff Besougloff
Upper Sioux and Lower Sioux Indian Communities, Redwood Falls, Minnesota
Background
The Lower Sioux Indian Reservation
The Lower Sioux Indian Reservation covers 1,743 acres
in southwestern Minnesota bordering the Minnesota
River. The land base consists of several hundred acres
of prime, flat agricultural land, a large wetlands slough
complex, prairie pothole wetlands, bottom land wet-
lands, small lakes, and approximately 250 acres of tim-
ber and brush. The elevation ranges from Minnesota
River level to the adjacent bluffs several hundred feet
higher.
This rural reservation contains a moderate amount of
infrastructure, including paved and dirt roads, 12-acre
sewage lagoon serving a moderately sized casino, com-
munity water system composed of a tower and small
treatment plant for the 90 mostly single-family dwelling
homes, convenience store/gas station/gift shop, com-
munity center, small two-room schoolhouse, pottery works
with gift shop, warehouse, and church. The casino-
fueled economic boost to the community recently re-
sulted in improvements to infrastructure and plans for
additional projects.
Office of the Environment
The tribal government was formed under the Indian
Reorganization Act of 1934. The governing body is an
elected five-person tribal community council that admin-
isters several government departments and is responsi-
ble for all government activities.
Under a U.S. Environmental Protection Agency (EPA)
Region 5 multimedia grant, the Upper Sioux and Lower
Sioux Office of the Environment (OE) was formed in late
1992. This unique joint venture between two tribes and
EPA envisioned moving the tribal governments into com-
pliance with major federal environmental legislation. At
the present time, only the Lower Sioux are developing
a tribal geographic information system (GIS). Therefore,
this article is solely applicable to this community, al-
though adoption of an Upper Sioux Community GIS
would likely follow a similar lifeline.
Environmental Regulation in Indian Country
Reservations are subject to a bewildering array of envi-
ronmental regulations. Numerous meetings, publications,
projects, and court decisions are devoted to determining
what law does or does not apply on any particular
reservation. In very general terms, the following can be
stated: state environmental regulations do not apply,
federal regulations do apply, and tribal regulations may
apply. From a tribal environmental employee's point of
view, numerous environmental regulations (whether fed-
eral or tribal) do exist that apply to reservation activities
and land, and they require compliance.
The Problem and the Solution
The OE's responsibility is to bring the reservation into
compliance with the 14 major pieces of environmental
legislation administered through EPA and directly appli-
cable to tribes. The OE finds itself responsible for any
and all other applicable environmental regulations and
all other less-regulated environmental media. The OE
currently has a staff of one.
In addition to the responsibility of moving the tribes into
compliance with federal environmental regulations, the
OE also develops environmental infrastructure, insti-
tutes environmental programs, and performs grant writ-
ing. Lower Sioux programs currently include Clean
Water Act (CWA) and Safe Drinking Water Act (SDWA)
compliance, solid waste planning including develop-
ment and institution of a household recycling program,
wetlands regulations compliance, wetlands mapping
and restoration, National Environmental Policy Act
(NEPA) compliance and site assessments, basic hydro-
logical data gathering and mapping, radon testing and
mitigation, environmental education as necessary, SARA
-------
Title III compliance and planning, and a variety of related
tasks.
Contracts or grants are currently being administered
under several Bureau of Indian Affairs (BIA) programs,
U.S. Geological Survey (USGS) and U.S. Army Corps
of Engineers (COE) matching funds programs, two EPA
programs, one Federal Emergency Management
Agency (FEMA) program, one Administration of Native
Americans (ANA) program, one Great Lakes Intertribal
Council (GLITC) program, and a cooperative project
with the National Tribal Environmental Council (NTEC).
Needless to say, responsibilities of the OE are limited by
staff hours rather than need. As distressing as the res-
ervation's unaddressed environmental needs are,
equally distressing (prior to CIS development) was the
helter skelter manner in which the OE digested the data
and information flowing into the office. Because of its
broad responsibilities and the administrative problems
being encountered, the OE began to investigate devel-
oping a tribal CIS.
The Lower Sioux GIS
System Selection
The Lower Sioux GIS system is a networked PC station
through the Bureau of Indian Affairs Geographic Data
Service Center's (BIA GDSC's) two Sun MP690
SparcServers in Golden, Colorado. The GDSC is the
hub of BIA's GIS and remote sensing program, known
as the Indian Integrated Resources Information Pro-
gram (IIRIP). The purpose of the IIRIP is twofold: first,
make GIS and remote sensing technology available to
tribes and BIA personnel; second, transfer these tech-
nologies to tribal organizations.
Database development and management functions,
technical support, development of simplified user inter-
faces, remote sensing interpretation, and implementa-
tion of equipment directives are performed by
approximately 30 GDSC employees for approximately
230 GDSC users. User technical support is also avail-
able through BIA field offices, each of which has a
designated GIS coordinator. Simplified user interfaces
for specialized programs have been developed includ-
ing the Lightening Display System and the Land Title
Mapping System. Quality control is provided for non-
BIA-produced data that will be inputted.
The GDSC has standardized on the ARC/INFO family
of software produced by Environmental Systems Re-
search Institute (ESRI). GDSC has developed a number
of hardware/software configuration options depending
on tribal needs and financial resources and based upon
GDSC experience. The OE happily relies on this expe-
rience to avoid the familiar horror stories related to
equipment and software incompatibility.
Based upon GDSC configuration advice, the initial GIS
setup will be on the OE's existing Compaq PC using
Tektronix Terminal Emulation software (EM4105) and a
Multitech modem (MT932BA). The system can use the
OE's Hewlett Packard (HP) DeskJet 500, although a
significant upgrade, possibly to an HP Paint or HP Excel
Paint, is soon expected. Initial startup hardware and
software costs are minimal in this configuration. Costs
for the above equipment and introductory training are
less than $5,000.
GIS Users at Lower Sioux
Initial setup and data loading will be in the OE, and the
OE employee will receive introductory training on the
system. Because the OE is formed through a coopera-
tive agreement between two tribes, the Upper Sioux and
the Lower Sioux, the OE is centrally located between
the reservations. The system will probably be relocated
to the Lower Sioux Community Center within 1 year. A
tribal government employee will receive advanced GIS
training and be available for all tribal government depart-
ments and businesses.
Funding
In addition to tribal contributions, funding has come
through several sources and joint agreements with the
tribe and BIA, EPA, and ANA.
Training
The GDSC supplies no-cost training to tribes. The Geo-
graphic Data Service Center 1995 Training Catalog (no
federal document number available) offers eight formal
courses repeatedly throughout the year, a 5-week intern
program, and a cooperative student program. Courses
are held at the GDSC or by request at BIA field offices
and tribal locations.
The GDSC also produces the monthly The Service Cen-
ter Review (ISSN 1073-6190), a helpful compilation of
current issues, available resources, system bugs, and
other items of interest to GDSC users.
Data Collection and Input
Data collection can be divided into three categories:
aerial photography, portable global positioning system
(GPS) data, and ARC/INFO export files created under
contracted studies.
Aerial Photography
Surface features and topography will be obtained us-
ing aerial photography reduced to GIS format, then
downloaded to the GDSC. Coverages will consist of 62
categories of features on a scale of 1 inch = 100 feet
with 2-foot contour intervals.
-------
Global Positioning Systems
Use of a portable Trimble, Inc., GPS Pathfinder Pro XL
submeter GPS mapping system purchased with assis-
tance from an ANA grant will allow updating of surface
features and addition of nonsurface features as neces-
sary. It will also facilitate input of attribute data.
The GPS will also be used during field work by USGS
on the Lower Sioux hydrological mapping project to
obtain data that otherwise would not be put into
ARC/INFO export file for any reason (i.e., it might not be
directly related to the project at hand or outside the
agreed upon data to be converted to ARC/INFO export
file form but nevertheless is of importance to the OE).
The alternative is that this type of information never
makes it into the CIS and is lost.
ARC/INFO Export Files
Fortunately, most federal agencies that supply funding
to tribes for environmental work are well versed in CIS
applications and the need for CIS-ready data. The OE
now requires all information and mapping to be deliv-
ered as an ARC/INFO export file with data registered to
a real world coordinate system. Downloading of this
data to the GDSC mainframes allows for direct input of
the data. The OE has contemplated, but not acted on,
conversion of existing data for the CIS. This is an ex-
pensive and time consuming process that must be
weighed in comparison with recollecting the data. Ironi-
cally, the lack of reservation data therefore becomes a
benefit because time consuming and expensive data
conversion is unnecessary.
The Intertribal GIS Council
Information gathering, networking, and addressing
uniquely tribal problems were some of the accomplish-
ments at the first annual meeting of the Intertribal GIS
Council (IGC) held in June 1994. Vendors as well as BIA
regional office and GDSC representatives answered
questions and presented panels. This annual confer-
ence is likely to become a major benefit to the tribe as
it continually develops the GIS.
The Future
As the tribal government becomes more familiarwith the
GIS, its uses, and advantages, recognized governmen-
tal needs will likely drive the development of further
coverages. The OE also expects to access existing
governmental data of importance to the tribe in an effort
to expand the GIS database and is actively seeking
sources of such information.
Philosophical Caveat
Albert Einstein stated that, "The significant problems we
face cannot be solved at the same level of thinking we
were at when we created them." Some assume that GIS
is the next level of reasoning in the environmental pro-
fession because we can accomplish tasks more quickly,
more efficiently, with more variables accounted for, and
beyond what we could have hoped to accomplish prior
to GIS.
Essentially, what we have gained is speed and the
capacity to include additional data, which is not what
Einstein was referring to when he spoke of the next
level. Wisdom, in the sense of a higher level of
understanding, is the necessary ingredient to the
solution of current environmental problems; in other
words, movement beyond the paradigm that created
the problem. GIS may be the tool that pushes the envi-
ronmental professional to the next level of wisdom by
presenting the data and information in a manner that
allows the user to stand back and see more clearly on
a higher plane. But that level can be found only within
the environmental professional himself or herself and
not within GIS.
-------
Reach File 3 Hydrologic Network and the Development of GIS Water Quality Tools
Stephen Bevington
Water Quality Section, Division of Environmental Management, North Carolina Department of
Environment, Health, and Natural Resources, Raleigh, North Carolina
Introduction
The application of geographic information system (GIS)
tools to water quality management is limited by the lack
of geographically referenced data describing the surface
water environment. Ongoing efforts at the local, state,
and federal level are producing a multitude of GIS data
coverages describing land use/cover and relevant water
quality data files. As these data coverages become
available, water quality managers will need to develop
new analysis techniques to take advantage of the vast
amount of geographically referenced data. A key step in
the development of analytical tools for water quality
management will be the development and maintenance
of a coverage describing the structure and hydrology of
surface waters.
Reach File 3 (RF3) is one potential source of surface
water maps and topology for the development of a
CIS-based water quality analysis tool. This paper de-
scribes a pilot project designed to examine the suitability
of RF3 as a network system for the collection, integra-
tion, and analysis of water quality data.
To be considered an appropriate water quality analysis
tool, RF3 should provide the following functions:
• Present a working environment that allows users to
explore geographic relationships between surface water
features, landmark features, and data coverages.
• Allow users to select specific stream segments, in-
cluding all points upstream and downstream of a
given point.
• Provide tools to assist users in partitioning water quality
databases into hydrologically meaningful subsets.
Reach File 3
RF3 is a hydrographic database of the surface waters
of the United States. The database contains 3 million
river reaches mapped at 1:100,000 scale. The source
for RF3 arcs were digital line graphs (DLGs).
Attribute data for RF3 arcs include the major-minor DIG
pairs, stream name, water-body type, stream order, and
a unique identifying reach number. The unique reach
numbers are structured in such a way as to provide a
logical hydrologic framework. Reach numbers can be
used to sort the database for all reaches in any specified
watershed or locate all upstream or downstream
reaches.
The U.S. Environmental Protection Agency (EPA) origi-
nally designed RF3 as a tabular data set. It evolved into
a GIS data coverage, and EPA and the U.S. Geological
Survey (USGS) will likely maintain it as a surface water
mapping standard. At present, RF3 as a GIS data layer
is not widely used for water quality applications.
RF3 Pilot Study: Uppeffadkin River Basin
The Upper Yadkin River basin (USGS h03040101) was
selected to test RF3 water quality applications (see
Figures 1 and 2). The Upper Yadkin was chosen be-
cause of the availability of water quality and stream flow
data layers in that area. Also, the Upper Yadkin RF3 file
contained arcs depicting lakes and double-line rivers as
well as simple stream networks. These two-dimensional
water features present interesting complications to net-
work routing and path-finding.
Figuie 1. The Upper Yadkin River watershed, North Carolina
and Virginia.
-------
Figure 2 RF3 hydrography for the UppeYadkin River basin.
Two forms of point source data were used in the study:
National Pollutant Discharge Elimination System
(NPDES) wastewater discharge points and USGS
gages. The NPDES coverage includes data on the per-
mit limits such as daily flow, dissolved oxygen, bio-
chemical oxygen demand (BOD), and ammonia. The
USGS gage coverage includes data on several flow
statistics for each USGS gage in the basin. Both data
layers contain information about the location of the site
and stream with which it is associated.
Coverages of counties and cities were also made avail-
able for geographic orientation.
Preparing the Network
The original RF3 file received from the USGS had sev-
eral topological issues that needed to be addressed
before RF3 could function as a stream network. First,
not all arcs were connected to each other (see Figure 3).
The ARC/INFO command TRACE was used to select all
connected arcs. This revealed three major blocks of
connected arcs and many isolated arcs. The three major
blocks were easily connected in ARCEDIT by extending
the main tributary links between the blocks. Processing
of the isolated arcs was not pursued for this study.
Complete processing of arcs for this RF3 basin would
not be difficult or time consuming, with the possible
exception of the many arcs surrounding the lake. A
functional network encompassing a high percentage of
the arcs was not difficult to achieve, however.
The second network issue concerned the direction of the
arcs. RF3 has all arcs oriented toward the top of the
watershed, with the exception of one side of double-line
streams. Arcs that make up double-line streams are
oriented up one side of the double-line section and down
the other (see Figure 3). Clearly, this complicates rout-
ing. To allow for accurate downstream routing, arcs on
the downward-facing side of the stream were flipped
using ARCEDIT. With all arcs in the network facing
upstream, most hydrologic routes can be traced. Given
the network system alone, upstream routing from dou-
ble-line streams does not function properly, ignoring all
tributaries on one side of the double-line stream.
Double-Line Stream Routing
Many possible solutions exist for the problems caused
by double-line streams. Some involve improving the
network (e.g., by adding center-line arcs down the mid-
dle of double-line streams). This would involve not only
adding arcs but establishing conductivity with all tribu-
taries. This option will involve significant topological
changes to RF3. To maintain compatibility with other
-------
Figure 3 Original conductivity of RF3 hydrograph
RF3 and DIG sources, this option should be considered
only as part of a major RF3 upgrade.
At the other end of the technological spectrum, one
could simply instruct users to watch for double-line
streams and select arcs from both sides of the river.
Users may have trouble with this option, however, if they
are not working at an appropriate scale to easily differ-
entiate between double- and single-line streams.
A third option is to program an arc macro language
(AMI) to check for double-line streams and run up-
stream traces from both sides of the stream. The diffi-
culty in this method is to find the appropriate starting
place on both banks. The algorithm developed to do this
goes as follows:
• Select stream segment and trace upstream. (Results
in incomplete trace.)
• Find the minimum segment and mile of selected dou-
ble-line streams.
• Unselect all double-line streams below minimum seg-
ment and mile.
• Add to selection all non-double-line streams.
• Trace from original point both upstream and down-
stream. (Results in completed upstream trace.)
Results and Conclusions
AMLs and menus were written that can perform up-
stream and downstream traces on the RF3 stream net-
work and select data points within 500 feet of the
stream. Lists of attributes can be returned to the screen.
This system is easy to use and can be used to quickly
identify general watersheds and water quality data
points. An AMI can be used to trace upstream from a
double-line stream given only one point on the stream
(see Figure 4). The success of these methods suggests
that two-dimensional surface water features can be suc-
cessfully integrated into RF3 water quality analyses.
This system could be further developed to support poly-
gon analysis using the ARC command BUFFER. Other
developments could include the procedures to write se-
lected attributes to files and increased flexibility for the
screen environment and outputs.
This pilot project demonstrates only a few of the poten-
tial applications of RF3 to water quality. Success in this
pilot project suggests that RF3 is a potentially valuable
water quality analysis tool. It may also be a valuable tool
for demonstrating the results of water quality analyses
to managers or the public.
Because RF3 will require some processing before network
algorithms can be run, it is important to plan for the inte-
gration of RF3 into other CIS tools and data coverages.
-------
,, , Mj',< , ', V . ^
*> , V ^ • ' " \ j
i. -'- <-; v \ '"%
' Jj 'f "f *
Figure 4 Upstream and downstream traces of RF3 hydrogrgph
Ongoing efforts to update RF3 may address some of proceed in a way that is compatible with ongoing efforts
these problems. If RF3 is to be developed into a produc- to update RF3 and the development of new data
tive water quality management tool, it is important to sources.
-------
Assessing the Long-Term Hydrologic Impact of Land Use Change Using a GIS-NPS
Model and the World Wide Web*
BUDHENDRABHADURI1
JON HARBOR2
BERNIE ENGELS
KYOUNG.J. LiM3
DON JONES3
This paper covers the contents of two presentations in the Diffuse Source session: "Assessing Long-
Term Impact of Land Use Change on Runoff and Non-Point Source Pollution Using a GIS-NPS Model"
and "A Web-based CIS Model for Assessing the Long-Term Impact of Land Use Change (L-THIA CIS
WWW): Motivation and Development".
Oak Ridge National Laboratory, PO Box 2008, MS 6237, Oak Ridge, TN 37831-6237; Phone: (423) 241
9272; Email: bhaduribl@ornl.gov
: Department of Earth & Atmospheric Sciences, Purdue University, West Lafayette, IN 47906-1397;
Phone: (765) 494 9610; Email: jharbor@purdue.edu
1 Department of Agricultural & Biological Engineering, Purdue University, West Lafayette, IN 47907-1146;
Phone: (765) 494 1198; Email: engelb@ecn.purdue.edu. kjlim@ecn.purdue.edu,
jonesd@ecn.purdue.edu
-------
ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
ABSTRACT:
Assessment of the long-term hydrologic impacts of land use change is important for optimizing
management practices to control runoff and non-point source (NPS) pollution associated with
watershed development. Land use change, dominated by an increase in urban/impervious
areas, can have a significant impact on water resources. Non-point source (NPS) pollution is the
leading cause of degraded water quality in the US and urban areas are an important source of
NPS pollution. Despite widespread concern over the environmental impacts of land use
changes such as urban sprawl, most planners, government agencies and consultants lack
access to simple impact-assessment tools that can be used with readily available data. Before
investing in sophisticated analyses and customized data collection, it is desirable to be able to
run initial screening analyses using data that are already available. In response to this need, we
developed a long-term hydrologic impact assessment technique (L-THIA) using the popular
Curve Number (CN) method that makes use of basic land use, soils and long-term rainfall data.
Initially developed as a spreadsheet application, the technique allows a user to compare the
hydrologic impacts of past, present and any future land use change. Consequently, a NPS
pollution module was incorporated to develop the L-THIA/NPS model.
Long-term daily rainfall records are used in combination with soils and land use information to
calculate average annual runoff and NPS pollution at a watershed scale. Because of the
geospatial nature of land use and soils data, and the increasingly widespread use of GIS by
planners, government agencies and consultants, the model is linked to a Geographic
Information System (GIS) that allows convenient generation and management of model input
and output data, and provides advanced visualization of the model results. Manipulation of the
land use layer, or provision of multiple land use layers (for different scenarios), allows for rapid
and simple comparison of impacts. To increase access to L-THIA, we have begun development
of a WWW-accessible version of the method. Using databases housed on our computers, the
user can select any location in the US and perform L-THIA/NPS analyses.
In this paper we present applications of the WWW-based L-THIA/NPS and L-THIA/NPS GIS
model on the Little Eagle Creek (LEG) watershed near Indianapolis, Indiana. Three historical
land use scenarios for 1973, 1984, and 1991 were analyzed to track land use change in the
watershed and to assess the impacts of land use change on annual average runoff and NPS
pollution from the watershed and its five sub-basins. Comparison of the two methods highlights
the effectiveness of the L-THIA approach in assessing the long-term hydrologic impact of urban
-------
ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
sprawl. The L-THIA/NPS GIS model is a powerful tool for identifying environmentally sensitive
areas in terms of NPS pollution potential and for evaluating alternative land use scenarios to
enhance NPS pollution management. Access to the model via the WWW enhances the usability
and effectiveness of the technique significantly. Recommendations can be made to community
decision makers, based on this analysis, concerning how development can be controlled within
the watershed to minimize the long-term impacts of increased stormwater runoff and NPS
pollution for better management of water resources.
INTRODUCTION:
For decision makers, such as land use planners and watershed managers, it is important to
assess the effects of land use changes on watershed hydrology. At present numerous
hydrologic models are available that focus on event-specific assessment and management of
hydrologic impacts of land use change. Traditionally the focus of these urban surface water
management models has been on the control of peak discharges from individual, high
magnitude storm events that cause flooding. Models such as those developed by the US Army
Corps of engineers (HEC-1, 1974), the US Department of Agriculture (TR-20, 1983; TR-55,
1986), and the US Environmental Protection Agency (Huber and Dickinson, 1988) are routinely
used in assessing how proposed land use changes will affect runoff quantity. Although
hydrologic impact assessment based on individual, high magnitude storm events is an
appropriate approach for designing runoff control facilities for reducing local flooding and
improving water quality, it is of limited use for attempts to understand the long-term hydrologic
impacts of land use change. However, it has been increasingly realized that there is a long-term
hydrologic impact associated with land use change, and that this is dominated by runoff
generated from frequently occurring, smaller storm events rather than extreme, high magnitude
storms (Harbor, 1994; McClinktocket al., 1995).
Realizing the importance of NPS pollution, over the last 25 years, models including SWMM
(Huber and Dickinson, 1988), AGNPS (Young et al., 1989), and WEPP (Nearing et al., 1989,
Flanagan and Nearing, 1995) have been created with capabilities to assess the impacts of NPS
pollution on runoff quality in addition to standard assessments of peak discharges. Because
NPS pollution from agricultural areas was originally identified as the major cause of water
quality degradation, most NPS pollution models focus on typical agricultural pollutants such a
sediment, nutrients (nitrogen and phosphorus), and organic compounds (pesticides and
herbicides). However, heavy metal pollution from urban areas has recently been identified as a
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
leading cause of NPS pollution problems (Novotny and Olem, 1994) but estimation of heavy
metal pollution from urban areas with existing hydrologic/NPS pollution models is quite limited.
In assessing the long-term hydrologic impacts of land use change planners, developers, and
community decision makers usually avoid using the existing hydrologic models because these
models are too complex, data intensive, time consuming, expensive, and requires considerable
user-expertise (Harbor, 1994). To overcome the limitations of traditional hydrologic models, the
Long-Term Hydrologic Impact Assessment (L-THIA) model was developed as a user-friendly
tool for long-term runoff estimation (Harbor, 1994). L-THIA is built around the Natural Resources
Conservation Service's Curve Number (CN) technique that is the core component of many
sophisticated hydrologic models (Williams et al., 1984; Young et al., 1989). Curve numbers or
CN values represent surface characteristics of a soil-land use complex. In L-THIA a long-term
(typically 30 years) daily precipitation record is used along with soil and land use information to
compute daily runoff for estimating annual average runoff. The model was initially developed as
a simple spreadsheet application (Harbor, 1994; Bhaduri et al., 1997). Subsequently a C
program was developed for the model to facilitate input data handling and model application.
The L-THIA model was further expanded to L-THIA/NPS by adding a NPS pollution assessment
module. To enhance spatial data management, spatial analyses, and advanced visualization of
model results Geographic Information Systems (GIS) have been utilized. L-THIA GIS (Grove,
1997) and L-THIA/NPS GIS have been developed as customized applications of commercial
GIS software. Recently, a WWW-based version of the L-THIA/NPS model has been developed.
In the WWW-based implementation of the L-THIA/NPS model, the user provides land use and
hydrologic soil group information and L-THIA/NPS is run using long-term daily precipitation data
queried from an ORACLE database. By determining and comparing the average annual runoff
depths and NPS pollutant loads for land use scenarios from different time periods, it is possible
to assess the absolute and relative changes in runoff and NPS pollution due to land use
change.
METHODOLOGY
Structure of L-THIA and L-THIA/NPS Model
The L-THIA model was originally developed as a preliminary hydrologic impact assessment tool
that focused on predicting the percent increase in annual average runoff from a watershed due
to some land use change represented by a change in the CN value for the watershed. The
model utilizes a lumped parameter design to minimize model complexity and to reduce the
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
expense and time involved in data collection. For a watershed with multiple land use categories
and/or sub-watersheds, the model can be applied as a lumped (composite CN) as well as a
distributed (distributed CN) approach (Grove, 1997, Grove et al., 1998).
Runoff Calculation:
Daily runoff is calculated using the USDA NRCS Curve Number (CN) method for a daily
precipitation data set spanning many years (typically 30 years). The CN method is an empirical
set of relationships between rainfall, land use characteristics, and runoff depth. CN values,
ranging from 25 to 98, represent land-surface conditions and are a function of land use,
hydrologic soil group (or soil permeability), and antecedent moisture condition (USDA SCS,
1986). The basic equations used in the CN method for standard or average conditions are:
R=(P 0. R = OforP<0.2S (1)
(P + 0.8S)
(2)
(CN )
where:
R = runoff depth (inches)
P = precipitation depth (inches)
S = potential maximum retention (inches)
CN = Curve Number
Antecedent Moisture Condition (AMC) and CN Variation
The effect of antecedent rainfall and associated soil moisture conditions has long been
recognized as a primary source of variability in runoff determination. To account for this, the
Natural Resources Conservation Services (NRCS) introduced the concept of an antecedent
moisture condition (AMC), also referred to as antecedent runoff condition (ARC).
Three AMCs are defined as a step function of 5-day antecedent rainfall, and an AMC remains
constant for the specific range of antecedent rainfall values. Definitions of growing and dormant
seasons are not easily available and to keep calculations simple and consistent, growing and
dormant seasons were assumed to begin on April 15 and on October 15 of any year,
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
respectively. CN values for AMC 1 and 3 are determined by the following relationship as
described in NEH-4 (USDA SCS, 1985).
CN, = •
4.2CN,
10-0.058CAT,
and
CN, = -
23CN,
10 + 0.13CAT,
[where, CN!, CN2, and CN3 represent CN values for AMC 1, 2, and 3 respectively.]
Runoff analyses were performed with the CN values for AMC 1 , 2, and 3 where AMC is a step
function of 5-day antecedent rainfall (Table 1).
AMC
1
2
3
5-day Antecedent Rainfall (
Dormant Season
< 13
13-38
>28
mm)
Growing Season
<36
36-53
>53
Table 1: Criteria for determination of Antecedent Moisture Conditions (SCS, 1972).
NPS Pollution Calculation:
L-THIA/NPS GIS: Pollutant Build-up and Washoff
The most common urban NPS pollutant estimation technique in current deterministic water
quality models including STORM and SWMM is the pollutant "buildup-washoff" function (Huber,
1986; Barbe et al., 1996). "Buildup" refers to all dry-weather processes that lead to
accumulation of solids and associated pollutants on a watershed surface which are "washed off"
during subsequent storm events. In developing a NPS pollution sub-model for L-THIA, it was
assumed that pollutants accumulate on a land surface as a linear function of time.
j = (number of days) x
[U = accumulation rate for pollutant i (mass/area/day);
Mi = Ultimate pollutant accumulation (mass/area);]
For this study, daily accumulation rates of solid particles (dust and dirt) for urban land uses (low
and high density residential, industrial, and commercial) were adopted from the SWMM manual.
Daily dust and dirt accumulation values are reported as a function of curb (road) density, and
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
thus road densities for the urban land uses were required to produce dust and dirt accumulation
values as mass/area. Although road density values for different urban areas have been reported
in the literature, for this study values of road density for Tulsa, Oklahoma (Heany et al., 1977)
were chosen as representative of the Indianapolis, Indiana area where the model was applied.
Daily build up values of pollutants on non-urban land uses (agricultural, grass/pasture, and
forest) could not be found in literature and were not included in the daily simulations of NPS
pollution analyses. However, annual average loading rates for non-urban land uses were taken
from literature and used to calculate NPS pollution in the GIS analysis. The NPS pollutant
loading values are reported in Table 2.
Pollutant
Total N
Total P
Lead
Copper
Zinc
Annual average loading rate (kg/ha/year)
Agricultural
26.00
1.05
0.10
0.02
0.08
Grass/Pasture
6.20
0.50
0.10
0.02
0.08
Forest
6.20
0.50
0.10
0.02
0.08
Low-density
Residential
0.86
0.09
2.90
0.17
0.58
High-density
Residential
1.92
0.20
6.93
0.25
0.98
Commercial
2.30
0.33
12.80
0.52
3.78
Table 2. Annual average pollutant loading values used in L-THIA/NPS GIS simulations.
For the washoff function, a non-linear washoff equation was used. The washoff relationship is
an exponential function of the runoff depth. This approach has been successfully used in
numerous studies (Haith and Shoemaker, 1987; Dikshit and Loucks, 1996) and was utilized in
the NPS simulation for the L-THIA model because daily runoff depths are calculated in the
runoff sub-model which then can be used in the washoff function. The washoff function is
expressed as:
wk,,= 1 -exp(-1.81 a,,,)
where:
M = fraction of the pollutant mass removed from the land use k on day t;
Qkt = runoff from land use k on day t (cm);
WWW-based L-THIA/NPS: Event Mean Concentration (EMC)
In the Web-based version of L-THIA/NPS, Event Mean Concentration (EMC) data were
introduced to predict NPS pollutants for non-urban areas as well as urban areas (Baird and
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
Jennings, 1996). The EMC data used were compiled by the Texas Natural Resource
Conservation Commission (Baird and Jennings, 1996). Numerous literature and existing water
quality data were reviewed by Baird and Jennings (1996) with respect to eight categories of land
use and several parameters. Land use categories defined were (1) industrial; (2) transportation;
(3) commercial; (4) residential; (5) agricultural cropland (dry land and irrigated); (6) range land;
(7) undeveloped/open; and (8) marinas. The total pollutant load for a NPS pollutant divided by
runoff volume during a runoff event yielded the Event Mean Concentration for that pollutant.
EMCs should be reliable for determining average concentrations and calculating constituent
loads (Table 3).
NPS Pollutant
Total Nitrogen (mg/L)
Total Phosphorus
(mg/L)
Total Lead ( g/L)
Total Copper ( g/L)
Total Zinc ( g/L)
Land use classification
Residen
-tial
1.82
0.57
9
15
80
Comm
-ercial
1.34
0.32
13
14.5
180
Indus-
trial
1.26
0.28
15
15
245
Transt-
ion
1.86
0.22
11
11
60
Mixed
1.57
0.35
12
13.9
141
Agricu-
ltural
4.4
1.3
1.5
1.5
16
Range
0.7
0.01
5
10
6
Table 3. Event Mean Concentration for each land use classification
(Baird and Jennings, 1996)
L-THIA/NPS GIS Setup
The L-THIA model has been linked with Arc/INFO® GIS software as a GIS application (Grove,
1997). The ArcView® GIS software was chosen for L-THIA/NPS GIS application because
ArcView® is the dominant desktop GIS, and it has a friendlier graphical user interface than
Arc/INFO®. The GIS application is implemented through a linked-model approach that utilizes
both the graphical and spatial data handling capabilities of a GIS as well as the speed and
flexibility offered by a standard executable program. The required input data is initially selected
in the GIS before the L-THIA/NPS executable is called by the GIS. The executable calculates
the annual average runoff depths for all land uses and annual average dust and dirt amounts
(kg/km2) for all CN values for urban land uses (low density residential, high density residential,
industrial, and commercial). These calculations are based on daily rainfall data spanning many
years. The output file created by the L-THIA/NPS executable is then read back into the GIS and
used to produce final results.
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
WWW-Based L-THIA/NPS Setup
A user-friendly L-THIA WWW interface was developed using Java/Java Script, HTML, and CGI
scripts (http://pasture.ecn.purdue.edu/~sprawl/lthia2). This interface provides easy access to the
model and potentially improves understanding of the results through graphical representation.
Figure 1 shows the L-THIA/NPS WWW interface.
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Soil Group
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Indiana
Tippecanoe -• I
Indiana
LAND USE
Agricultural
HYD.
SOIL
GROUP
AREA
VEAR1 YEAR2 YEARS
Iteo : Ibo ! Eo
HD Residential -
Commercial
His
10
i20
i Mtp :/A^ww. epa.gov/region5/
•^ a
Figure 1. LTHIA/NPS WWW Interface.
Depending on the location the user selects, weather data for the nearest weather station are
queried from the database and reformatted for the L-THIA run. The user selects one of the eight
land use classifications, hydrologic soil group information and provides the area for this
combination for each time step the L-THIA/NPS WWW system is to be run. Tables, bar charts,
and pie charts for runoff and NPS pollution are generated on the fly for display in the user's
WWW browser. The tabular output provides all information the user provided in the input
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
interface, the Curve Number, runoff depth, and runoff volume for each time step. Bar graphs
provide runoff depth, runoff volume, total volume, average runoff depth, and NPS pollution
information. Pie charts provide land use and runoff volume for each time of interest. LTHIA/NPS
WWW has several advantages over the traditional model and decision support system
approach: 1) It is accessible through the Internet using only a WWW browser, 2) Database and
GIS data are maintained at a single location, 3) All model users access the same version of the
model, and 4) All data are verified by the model maintainer so errors due to input data can be
minimized.
STUDY AREA
The L-THIA/NPS model was applied to the Little Eagle Creek (LEG) watershed, a rapidly
urbanizing watershed in the northwest section of Indianapolis, Indiana and its suburbs. The LEG
watershed is 70.5 km2 in size and consists of five smaller sub-basins (Figure 2). This watershed
has experienced extensive urbanization over the past three decades. Land uses ranging from
non-urban natural grass and forested areas and agricultural areas to typical urban residential,
commercial, and industrial categories exist in the LEG watershed.
As part of a long-term hydrologic impact assessment study (Grove, 1997), digital land use data
were generated from LANDSAT satellite imagery (80m resolution Landsat Multi-Spectral
Scanner imagery) for 1973, 1984, and 1991 and these three images represented temporal
changes in land use in the watershed. In this study, these land use coverages along with the
Soil Survey Geographic (SSURGO) soils data (1:20,000) were used to analyze the long-term
impact of land use change on runoff and non-point source pollution. Only hydrologic soil groups
B and C are present in the watershed. The watershed and sub-watershed boundaries were
delineated from a Digital Elevation Model (DEM) using the Arc/INFO GRID module (Grove,
1997). Curve Numbers ranged from approximately 60 to 97 for all sub-basins in the watershed.
Six land use categories were delineated using ERDAS Imagine software and were used in L-
THIA/NPS GIS simulations. These areas of these land use categories and hydrologic soil B and
C groups were used in the WWW L-THIA/NPS simulations.
10
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL .
Figure 2. Location of the Little Eagle Creek watershed.
RESULTS AND DISCUSSION
Land Use Change
There is a significant increase in urban land uses between 1973 and 1991 with the majority of
the changes taking place between 1973 and 1984 (Table 4). Grouping agricultural, forest, and
grass/pasture as non-urban and low density residential, high density residential/industrial, and
commercial as urban land uses, 49.3%, 63.5%, and 68.1% area of LEG watershed was urban in
1973, 1984, and 1991 respectively. Thus, there was a 14.2% increase in urban land uses
between 1973 and 1984 and a 4.6% increase in urban areas between 1984 and 1991. The
increase in urban land uses is not uniformly reflected in all the urban land use categories.
11
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
Land Use
Agricultural
Commercial
Forest
Grass/Pasture
HD Residential /
Industrial
LD Residential
Water
Area (km2)
1973
10.82
5.82
13.74
10.90
8.12
20.83
0.27
1984
10.21
10.56
5.72
9.64
19.25
14.91
0.21
1991
9.23
11.31
5.14
7.76
21.44
15.30
0.32
% Change in Individual Category
1973-
1984
-5.66%
81.40%
-58.37%
-11.62%
137.21%
-28.42%
-21.84%
1984-
1991
-9.58%
7.12%
-10.19%
-19.47%
11.34%
2.63%
52.73%
1973-
1991
-14.70%
94.32%
-62.61%
-28.83%
164.10%
-26.53%
19.38%
Table 4. Land use distributions in Little Eagle Creek watershed for 1973, 1984, and 1991.
For individual land use categories, high density residential and commercial areas show
tremendous increase in the watershed while low density residential areas show a 28.4%
decrease between 1973 and 1984 and a 2.6% increase between 1984 and 1991. The initial
decrease in low density residential areas is possible conversion of low density residential to high
density residential areas. The increase in urban areas is followed by an equivalent decrease in
non-urban areas. However, all the non-urban land uses decrease at the same rate. Forested
areas show the greatest loss with a 62.6% decrease, followed by grass/pasture with a 28.8%
decrease between 1973 and 1991. Agricultural areas show minimum change (14.7% decrease)
during the same time period.
Impact of Urbanization on Annual Average Runoff and NPS Pollution
L-THIA/NPS analyses were performed to assess the impact of land use change on average
annual runoff and NPS pollution for the LEC watershed. There are significant changes in
average annual runoff volumes and NPS pollution loads from the LEC watershed as a result of
land use change. The results from L-THIA/NPS GIS and L-THIA/NPS web-versions are
presented in Table 5 and Table 6 respectively. However, changes in runoff volume or NPS
pollution do not have a simple or linear relationship with land use change.
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
Change in runoff and NPS pollution using L-THIA/NPS GIS simulation:
Pollutant
Runoff (m3)
Nitrogen (kg)
Phosphorus
(kg)
Lead (kg)
Copper (kg)
Zinc (kg)
Year
1973
2547736
47848.90
2887.40
18608.45
888.21
4313.06
1984
4255457
43183.45
2682.04
30374.48
1296.23
6756.24
1991
4581833
39722.04
2526.83
32855.31
1387.92
7238.03
% Change
1973 to
1984
67.03%
-9.75%
-7.11%
63.23%
45.94%
56.65%
1984 to
1991
7.67%
-8.02%
-5.79%
8.17%
7.07%
7.13%
1973 to
1991
79.84%
-16.98%
-12.49%
76.56%
56.26%
67.82%
Table 5. Average annual runoff volume and NPS pollution from LEG watershed using L-
THIA/NPS GIS that uses daily pollutant build-up and washoff functions for
pollution calculation.
Change in Runoff and NPS pollution using WWW L-THIA/NPS simulation:
Pollutant
Runoff (m3)
Nitrogen (kg)
Phosphorus
(kg)
Lead (kg)
Copper (kg)
Zinc (kg)
Year
1973
5012456
8599
2307
46
50
581
1984
7926728
13051
3530
81
89
1032
1991
8379366
13684
3713
87
95
1104
% Change
1973 to
1984
58.14%
51.77%
53.01%
76.09%
78.00%
77.62%
1984 to
1991
5.71%
4.85%
5.18%
7.41%
6.74%
6.98%
1973 to
1991
67.17%
59.13%
60.94%
89.13%
90.00%
90.02%
Table 6. Average annual runoff volume and NPS pollution from LEG watershed using
web-based L-THIA/NPS that uses Event Mean Concentrations (EMC) for pollution
calculation.
The annual average runoff volumes predicted by L-THIA/NPS GIS are approximately half of
those predicted by the web-version of the model. This is primarily because two different sets of
daily precipitation data were used for the two simulations and the one used for the web version
had several large storm events that produced significantly more runoff. However, considering
the relative change in runoff volume, very similar results were obtained from both simulations.
The amounts of urban or impervious areas dominantly control the volume of runoff produced
from a watershed. For example, in LEG watershed, 87% of the total runoff volume (81% with
web-based L-THIA/NPS) was produced from urban areas that occupied only 49% of the total
watershed area in 1973. In 1984 and 1991, urban areas occupied less than 70% of the total
watershed area but contributed over 93% of the annual average runoff volume (over 90% with
13
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
web-based L-THIA/NPS). Percent increase in average annual runoff volume is greater between
1973 and 1984 than between 1984 and 1991 because a greater percentage of non-urban land
use is changed to urban (more impervious) land use during the former time interval (Figure 3).
100.00%
30.00%
60.00% -
40.00% -
20.00% -
0.00%
-20.00%
\
\
\
1984-
1973-198-1
L-THIA/NPS GIS
1984-1991
1973-1984
WWW L-THIA/NPS
Figure 3. Changes in annual average runoff and NPS pollution from the Little Eagle Creek
watershed.
For the NPS pollutants, the relative change in annual average NPS pollution from LEG
watershed is not only controlled by the nature of land use change but also by the nature of the
pollutants. The time period between 1973 and 1984 experienced a much greater amount of
urbanization than the time period between 1984 and 1991. This pattern of land use change is
also reflected in changes in average annual runoff volume and metal pollution from the LEG
watershed. However, total pollutant loads predicted by web-based L-THIA/NPS are roughly
14
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
higher by an order of magnitude than those predicted by the L-THIA/NPS GIS model. This
difference can be attributed primarily to the different methods of pollution calculations by the two
simulations and also the different concentration values of the pollutants used. Using EMC
values in the web-version of the model, two different days with the same amount of runoff will
produce the same pollutant loads. In the GIS version, that uses pollutant build-up and washoff
functions, two similar runoff events can produce significantly different pollutant loads depending
upon masses of pollutants that accumulated before those two runoff events. Moreover, Bhaduri
(1998) showed that more than 90% of the days in the study area are dry (AMC 1), and thus
before any runoff event there will be a significant amount of pollutant accumulated on the
watershed.
One significant difference between the two approaches of pollution calculations can be
observed in the predicted changes in nutrient pollution. L-THIA/NPS GIS predicts decreasing
nutrient pollution with increasing urbanization in the watershed. Nitrogen and phosphorus are
dominantly produced from agricultural areas. Moreover, the other non-urban land uses (forest
and grass/pasture) show significant decrease between 1973 and 1984. Thus, this small change
is nutrient loading between 1973 and 1984 is most plausibly related to the small reduction in
agricultural area in the watershed. On the contrary, the web-version of L-THIA/NPS predicts
changes in nitrogen and phosphorus loads that conform to the increasing trend in urbanization.
Because nitrogen and phosphorus are typically identified as non-urban pollutants, it might be
assumed that conversion of non-urban land uses to urban areas would significantly reduce
nutrient pollution from a watershed. Our analyses on LEG watershed indicate that, between
1973 and 1991, a conversion of 19% areas from non-urban to urban land uses results in annual
average nitrogen and phosphorus loads being increased by about 60%. This is primarily
because there is only a small reduction of agricultural area and a large increase in urban areas
in the watershed. Although urban areas produce nutrients at a much lower rate than non-urban
areas, but increases in urban land uses produce runoff at a significantly higher rate and thus,
the web-version predicts very high nutrient loads.
Heavy metals, such as lead, copper, and zinc, are considered "urban" pollutants because urban
land uses contribute a major portion of the metal pollution from a watershed. For the LEG
watershed using L-THIA/NPS GIS, we found that only 49% of the area had urban land uses but
they contributed 98% of total lead load, 92% of total copper load, and 93% of the total zinc load
from the watershed in 1973. However, for individual metal pollutants, this 18% increase in urban
15
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
areas (or an equivalent decrease in non-urban areas) between 1973 and 1991 results in 76.5%,
56.2%, 67.8% increase in lead, copper, and zinc loads from the watershed respectively.
Predictions of changes in metal pollution from web-based L-THIA/NPS simulation were similar
to those from the GIS version (Figure 3).
CONCLUSIONS
Assessment of the long-term hydrologic impacts of land use change is important for optimizing
management practices to control runoff and non-point source (NPS) pollution from urban
sprawl. The L-THIA/NPS model uses the popular curve number technique and empirical
relationships between land uses and pollutant accumulation and wash off processes to estimate
the relative impacts of land use change on annual average runoff and NPS pollution. L-
THIA/NPS uses readily available data to overcome the difficulties of long-term modeling by
existing hydrologic models because of their complexity and extensive input data requirements.
Moreover, most traditional hydrologic/NPS pollution models do not emphasize the changes in
loads of typical urban pollutants such as heavy metals, which can be addressed by L-
THIA/NPS. The model is linked to a GIS to enhance input data generation, data management,
and advanced visualization of model results. The GIS version computes NPS pollution using
daily pollutant build-up and washoff functions. A World Wide Web based version of the model
has also been developed that provides easy access to the model through the Internet. This
web-based version of the model uses Event Mean Concentrations (EMC) of pollutants for
predicting NPS pollution.
L-THIA/NPS was applied to the Little Eagle Creek (LEG) watershed, an urbanizing watershed
near Indianapolis, Indiana, to provide estimates of changes in annual average runoff volumes
and NPS pollution loads for three time periods: 1973, 1984, and 1991. Increases in urban land
uses were much higher between 1973 and 1984 than between 1984 and 1991. Non-urban land
uses, particularly agricultural areas, are the dominant sources of nutrient (nitrogen and
phosphorus) pollution but the majority of the metal pollution is contributed from urban areas.
Overall, increasing urbanization resulted in increases in annual average runoff volume and
metal loads. The L-THIA/NPS GIS simulations predicted decreases in nitrogen and phosphorus
loads from the LEG watershed. However, the web-based version of the model indicated
increases in nutrient pollution with increasing urbanization. This difference can be explained by
the two different methods of pollution calculations by the GIS and web-based versions of the
model. This difference in pollution calculation is also reflected in the absolute values of pollution
16
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
predicted by the two versions. However, considering relative change in runoff and NPS pollution
from urbanization, both simulations indicate a very similar trend and direction of changes in NPS
pollutants for the Little Eagle Creek watershed.
L-THIA/NPS GIS is a simple and user-friendly model that makes it attractive for applications to
other watersheds. Although L-THIA/NPS GIS is designed to run with easily available data, such
data is often not readily available for most of the watersheds. Thus, compilation of model input
data sets through field experiments for a variety of watersheds characterized by different
geography, climate, and land uses will greatly enhance model applications and performance in
a wider range of watersheds. These field-measured data sets should be used to calibrate the L-
THIA/NPS model and validate the model predictions. Future work should also explore the
sensitivity of the L-THIA/NPS model to the spatial and temporal scales of input data. Work in
progress is aimed at allowing a user to access a modified web-based L-THIA/NPS model that
will take advantage of GIS functionality in the analysis. In this modified web-version, the users
will not only be able to access the model through a web-browser, but will also be able to select
or define a watershed using system-supplied maps, and then run L-THIA/NPS analyses run
using land use, soils and climate databases stored on our server. The user will then be able to
manipulate the GIS land use data in the browser environment or on a remote computer, and run
multiple L-THIA analyses to compare hydrologic impacts from different land use scenarios.
ACKNOWLEDGEMENTS
The authors would like to thank Dr. Darrell Leap and Dr. Darrell Norton of Purdue University for
their valuable suggestions, comments, and review of the manuscript. Daniel Pack of Oak Ridge
National Laboratory provided valuable help with some of the graphics. Funding for this work was
provided in part by the Purdue Research Foundation and the United States Environmental
Protection Agency.
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ASSESSING THE LONG-TERM HYDROLOGIC IMPACT OF LAND USE CHANGE USING A GIS-NPS MODEL ...
REFERENCES:
Baird, C., and M. Jennings. 1996. Characterization of Nonpoint Sources and Loadings to the
Corpus Christi Bay National Estuary Program Study Area, Texas Natural Resource
Conservation Commission.
Barbe, D. E., J. F. Cruise, and X. Mo. 1996. Modeling the buildup and washoff of pollutants on
urban watersheds. Water Resources Bulletin, Vol. 32, No. 3, p. 511-519.
Bhaduri, B. 1998. A geographic information system based model of the long-term impact of land
use change on non-point source pollution at a watershed scale. Unpublished Ph.D.
dissertation, Purdue University, West Lafayette, Indiana 47907-1397.
Bhaduri, B., M. Grove, C. Lowry, and J. Harbor. 1997. Assessing the long-term hydrologic
impact of land use change. Journal of the American Water Works Association. Vol. 89,
No. 11, pp. 94-106.
Flanagan, D. C. and Nearing, M. A., 1995. USDA-Water Erosion Prediction Project: Hillslope
profile and watershed model documentation. NSERL Report No. 10. West Lafayette,
Indiana: National Soil Erosion Research laboratory, USDA-ARS.
Grove, M. 1997. Development and application of a GIS-based model for assessing the long-
term hydrologic impacts of land-use change. Unpublished MS Thesis, Purdue University,
West Lafayette, IN 47907-1397.
Grove M. and J. Harbor, and B. Engel. 1998. Composite versus distributed curve numbers:
effects on estimates of storm runoff depths. Journal of the American Water Resources
Association. Vol. 34, No. 5, pp. 1015-1023.
Haith, D. A. and L. L. Shoemaker. 1987. Generalized watershed loading functions for stream
flow nutrients. Water Resources Bulletin, Vol. 23, No. 3, pp. 471-478.
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Harbor, J. 1994. A practical method for estimating the impact of land use change on surface
runoff, groundwater recharge and wetland hydrology. Journal of American Planning
Association, Vol. 60, pp.91-104.
Heany, J. P., W. C. Huber, M. A. Medina, Jr., M. P. Murphy, S. J. Nix, and S. M. Haasan. 1977.
Nationwide evaluation of combined sewer overflows and urban stormwater discharges -
Vol. II: Cost assessment and impacts. EPA-600/2-77-064b (NTIS PB-266005),
Environmental Protection Agency, Cincinnati, Ohio.
Huber, W. 1986. Deterministic modeling of urban runoff quality. In Torno, H. C., Marsalek, J,
and Desbordes, M. (Eds.) Urban Runoff Quality. Springer-Verlag. 893 p.
Huber, W. C., and R. E. Dickinson. 1988. Storm Water Management Model, Version 4: User's
Manual. US Environmental Protection Agency, Report No. EPA-600/3-88-001a. 569 pp.
McClintock, K., J. Harbor, and T. Wilson. 1995. Assessing the hydrologic impact of land use
change in wetland watersheds, a case study from northern Ohio, USA. In: McGregor, D.
and Thompson, (eds.) Gee/morphology and Land Management in a Changing
Environment, pp. 107-119.
Nearing, M. A., Foster, G. R., Lane, L. J., and Finkner, S. C., (1989). A process-based soil
erosion model for USDA-water erosion prediction project technology. Transactions of the
ASAE, Vol. 32, No. 6, pp. 1587-1593.
Novotny, V. and H. Olem. 1994. Water Quality. Van Nostrand Reinold. New York. 1054 pp.
U.S. Army Corps of Engineers, 1985. Flood hydrograph package HEC-1. The Hydrologic
Engineering Center, Davis, CA.
U.S. Department of Agriculture, Soil Conservation Service, 1983. Computer programs for
project formulation - hydrology. Technical Release 20.
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U.S. Department of Agriculture, Soil Conservation Service, 1986. Urban hydrology for small
watersheds. Technical Release 55.
USEPA, 1983. Results of the National Urban Runoff Program. Vol. 1. USEPA, Washington,
D.C.
Williams, J. R., Jones, C. A., and Dyke, P. T., 1984. The EPIC model and its application. Proc.
ICRISAT-IBSNAT-SYSS Symposium on Minimum Data Sets for Agrotechnology
Transfer, March 1983, Hydrabad, India, p. 111-121.
Young, R. A., C. A. Onstad, D. D. Bosch, and W. P. Anderson. 1989. AGNPS: A non-point
source pollution model for evaluating agricultural watersheds. Journal of Soil and Water
Conservation Society, Vol. 44, No. 2, p. 164-172.
20
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Ecological Land Units, GIS, and Remote Sensing:
Gap Analysis in the Central Appalachians
Ree Brannon, Charles B. Yuill, and Sue A. Perry
West Virginia University, Morgantown, West Virginia
Abstract
The gap analysis team in West Virginia is assessing the
state's natural communities as part of a nationwide,
comprehensive planning effort. Underrepresented or
unrepresented habitats represent gaps in the present
network of conservation lands and conservation activi-
ties. After identifying these gaps, we can assess
whether our current management direction will maintain
natural diversity and will prevent additional species from
being classified as threatened or endangered.
The relationship between vegetation and ecological
variables serves as the basis for classifying ecological
land units. To characterize the ecological land units,
many layers of physical data can be integrated in a
geographic information system (GIS). Satellite imagery
and videography map existing conditions over the state.
The existing vegetation is classified to reflect physiog-
nomic and floristic elements to correlate with vertebrate
and butterfly habitat requirements. This correlation of
vegetation and wildlife habitat creates mappable habitat
types. Analysis of these habitat types with land-
ownership data indicates where the species-rich areas
occur in the landscape and whether the most species-
rich areas are protected.
Introduction
To respond to the urgency of habitat loss and its effect
on species diversity, scientists must implement a meth-
odology for rapid assessment and documentation of
natural communities at a scale pertinent for regional
management activities.
Geographic information systems (GIS) and remote
sensing support the development of ecological land
classifications over large regions. CIS-based mapping
of ecological land classes allows users to combine and
display environmental variables for spatial modeling and
refinement of ecological land units (1).
The Gap Analysis Project is a comprehensive planning
effort for land conservation in the United States. The
objective of the Gap Analysis Project is to identify spe-
cies, species-rich areas, and vegetation types underrep-
resented or unrepresented in existing biodiversity
management areas. Unprotected communities are the
gaps in the conservation strategy. The Gap Analysis
Project is not merely identifying communities with the
largest number of species; its ultimate goal is to identify
clusters of habitats that link the greatest variety of
unique species.
Local areas with considerable diversity of habitat or
topography usually have richer faunas and floras (2).
Nature reserves, which incorporate a variety of habitats,
may be the best guarantee for long-term protection of
biodiversity. By protecting species-rich regions, we can
reduce the enormous financial and scientific resources
needed to recover species on the brink of extinction.
The West Virginia Gap Analysis Project began in 1991.
The objective is to map existing vegetation and to use
that as the foundation to model potential distribution of
vertebrate and butterfly species. High cost precludes
intensive field inventory and monitoring of wildlife.
Therefore, habitat modeling is critical to predict wildlife
species composition and potential ranges over the vari-
ous landscape types of West Virginia. Lastly, the vege-
tation map will provide a record of the existing habitat to
use in monitoring changes due to human activities and
natural disturbances.
Pilot Study Area
Distribution of wildlife and plant communities will be
modeled for the entire state. Initially, we will focus on a
smaller pilot study area. This region includes approxi-
mately 50,000 hectares in the central Appalachian
Mountains and spans several physiographic provinces
and vegetative communities. Generally, soils in the pilot
study area are of two kinds: acidic soils that develop a
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clay horizon from extensive leaching over time and
younger soils that are found on steep slopes and where
environmental conditions, such as cold climate, limit soil
development (3, 4).
The vegetation types include spruce-fir, oak-pine, high
elevation bogs, northern hardwoods, Appalachian oak,
mixed-mesophytic, open heath barrens, and grass
balds. The mixed-mesophytic, the most diverse in West
Virginia, lies primarily west of this area, but localized
stands do occur in the lower elevations. Cover types
within the Appalachian oak and mixed-mesophytic types
are not discrete and will be difficult to delineate.
The pilot study area includes a variety of land uses, such
as residential, commercial, industrial, mining, and agri-
culture. Portions of the Monongahela National Forest in
this area are the Fernow Experimental Forest, and the
Otter Creek and Laurel Forks Wilderness Areas.
Methods
The following discussion describes the methods formu-
lated and data compiled for the West Virginia Gap Analy-
sis Project.
Describe Ecological Land Units With Existing
Vegetation
Davis and Dozier (1) note that a landscape can be
partitioned by ecological variables, which contributes to
an ecological land classification. This process is applied
frequently to analysis and mapping of natural resources.
Davis and Dozier classified vegetation in California
based on the documented associations of vegetation
with terrain variables. They based this approach on the
assumptions that the arrangement of natural landscape
features is spatially ordered by an ecological interde-
pendence among terrain variables and that actual vege-
tation is a reliable indicator of these ecological
conditions. Similar documentation exists for the distribu-
tion of vegetation types in West Virginia, and the gap
analysis team is proceeding along a similar course.
West Virginia lies in two major provinces, the Eastern
Broadleaf Forest and the Central Appalachian Broadleaf
Forest-Coniferous Forest-Meadow Provinces (5). Within
these provinces are several broad vegetation types. The
gradient diagram in Figure 1 (6) illustrates the range of
these types. The vertical axis represents elevation in
feet. Three vegetation types emerge distinctly along the
elevation gradient. The horizontal axis is not quite as
explicit. This gradient spans moist, protected slopes to
dry, exposed ridgetops, and the vegetation types are
much less distinct. Drier oak and pine types occur al-
most exclusively on exposed ridgetops. The vegetation
types along the horizontal axis are the mixed mesophytic
forest association of the Allegheny and Cumberland
Mountains and can have 20 to 25 overstory and under-
story species per hectare in North America (7).
The distribution of the vegetation along gradients such
as elevation and soil moisture lends itself to a CIS
analysis. Physical data such as elevation and soil mois-
ture regime can be incorporated into a CIS. These lay-
ers of information can be manipulated graphically or
mathematically to model the spatial distribution of vege-
tation types or to provide useful ancillary data for clas-
sification of satellite imagery (see Figure 2). Much of this
data is digital and can be used to substantially reduce
the time required to develop a database. To standardize
output, members of the national gap team have
specified ARC/INFO as the software to generate final
products.
Classify Satellite Imagery To Create a Map of
Current Distribution
Remote sensing provides an effective means to classify
forests, and the Gap Analysis Project has successfully
used it in the western United States (1, 8, 9).
The Gap Analysis Project is using Landsat Thematic
Mapper imagery in all states to standardize the baseline
information. The hypothesis is that the spectral data
from the imagery is related to the distribution of the
ecological land units and land use across the landscape.
The data include all spectral bands, except thermal, for
the entire state. The West Virginia project is using two
seasons of data, spring and fall. Temporal changes,
which record phenologic variation in the deciduous spe-
cies, enhance classification accuracy. The spectral reso-
lution is 30-meter pixels. This is equivalent to
approximately 1/6 hectare (1/2 acre). Our final product
will be a series of 1:100,000 maps. The minimum map-
ping unit is 40 acres.
The mountainous terrain in the Appalachian Mountains
offers disadvantages and advantages for using remotely
sensed data. Irregular topography can cause inconsis-
tencies in the spectral data that diminish the classifica-
tion accuracy. Similar cover types may have different
spectral signatures; for instance, if one stand is in sun-
light and the other is shaded. Also, phenology can vary
due to microclimatic influences. Conversely, topo-
graphic features influence the distribution of vegetation
types, and ancillary data, such as digital elevation mod-
els (OEMs), enhance classification results of the im-
agery. The West Virginia gap analysis team selected the
following strategy for image classification.
1. Stratify the imagery using ecological units based on
a hierarchical scheme. Bauer et al. (10) found that
an initial stratification of physiographic regions was
necessary to reduce the effect of broadscale
environmental factors caused by changes over
latitude. Therefore, stratification enhanced the
-------
5,000
4,500
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
MESIC
Red Spruce
Forest ._. .,
Heath
Barren
Red Spruce -
Yellow Birch
Northern Hardwoods
Forest
Red
--------- , Oak
._--"' " ', ' Forest /
x' Hemlock I . ,'' /
and ,' ,-' /' '
Hemlock- ' -' / ,-'*
Cove / Hardwoods ,' / ,-'
Hardwoods ' ''
White /'
Oak /
1 / ' '
, / ; i
/ Red Oak - / chestnut/
/ / White Oak / oak ''
Forest / ,'
Scarlet/
,' \ ,' I Black Oak
Coves Flats Sheltered Open Slope
Canyons Draws/Ravines Slopes NE, E, S, W, NV
Grass
Bald
/
Pitch/
Shortleaf
Pine
Heath
Scrub
Pine
Post/
Blackjack
Oak
Ridges
V XER\C
Figure 1. Environmental gradients for vegetation.
efficiency of the training data. An interagency
committee, including ecologists from the Monongahela
National Forest, West Virginia Division of Forestry,
and geologists from the State Geologic Survey,
generated a draft map of physiographic regions.
They delineated sections based on geomorphology
and climate. Sections were divided into subsections:
those most typical of the section or those that are
transitional, or irregular, to the section. Figure 3 is a
draft map of these sections and subsections in West
Virginia.
2. Classify stratified imagery using the ancillary data.
High resolution imagery has not been used widely in
the eastern United States, where forests are not
homogeneous stands of relatively few tree species
as they are in the West. Researchers who classified
eastern forests from satellite imagery attempted to
find the most distinctive spectral band combinations
for discriminating cover types (10-13). One recent
technique (14) uses a nonparametric approach that
combines all spectral and informational categories to
classify imagery. We are testing a variety of methods
such as nonparametric processes, traditional
clustering techniques, and use of derived vegetation
indexes to find the most successful method.
3. Assess accuracy with random plots from
videography. Videography will be acquired in the
spring of 1995. Aerial transects, which extend the
length of the state, will be flown with approximately
30-kilometer spacing. By regulating flight altitudes,
the resolution per frame can be captured at 1
kilometer per frame. About 7,000 frames will be
collected, which make up a 3 percent sample of the
state. About 2 to 3 percent of the videography frames
will be field verified. With this strategy, we will test
the effectiveness of using videography, instead of
intensive field plots, to verify classification of satellite
imagery. Areas of special interest, which the
systematic transects may not capture, will require
separate flights. The bulk of the videography will
provide training data for supervised image
classification. The remaining frames will be used to
assess the accuracy of the classification.
;>, Streams
."I;;-- Ecological Subregions
";:>- Digital Elevation Models
i-* Soils
Agricultural, Tilled
High Elevation, Red
Spruce
Low Elevation, Coniferous
Forest (Hemlock)
Open Grassland, Deep
Soil (Grass Bald)
High-Density Urban
Landscape Unit
Classes
Figure 2. GIS and the development of ecological land units.
-------
Figure 3.
Legend
g] Pilot Study Area
Physiographic regions of West Virginia and pilot
study area.
In summary, 100 percent of the state will be classified
using the Thematic Mapper imagery. Aerial video-
graphy, covering approximately 3 percent of the
state, will help to verify the image classification, and
2 to 3 percent of the videography will be verified from
transects on the ground.
4. Determine sources of data for image classification.
Due to the increasing interest in CIS, digital data are
more readily available from a variety of sources,
such as the federal government, state agencies, and
private companies. Acquisition of available data sets
can substantially reduce the time and cost of
database development. Users must bear in mind,
however, that databases are developed with differing
objectives and techniques, so one must consider
scale and standards of production when deciding
which data sets are appropriate for project design.
The West Virginia gap team determined that the
following CIS coverages are important for image
classification.
The U.S. Geological Survey's (USGS's) graphic infor-
mation retrieval and analysis system (GIRAS) ear-
marked land use/land cover data. The classification was
done several years ago, and although these data are not
current, they provide excellent information concerning
urban and agricultural land use. Land-cover categories
represent Level II classifications from Anderson's (15)
system. The maps are produced at a 1:250,000 scale,
so they require few CIS operations to piece together a
regional coverage of land use.
The Southern Forest Experiment Station mapped U.S.
forestland using advanced very high resolution radiome-
ter (AVHRR) satellite imagery (16). This imagery is rela-
tively current (1991 to 1992), but the resolution is coarse
at 1 kilometer per pixel (100 hectares or 247 acres). The
classes are based on Forest Inventory and Analysis
plots established by the U.S. Department of Agriculture
(USDA) Forest Service and Kuchler's (17) potential
natural vegetation types. We are using the maps to
depict broad changes in forest type over a region, such
as the state of West Virginia. This coverage does not
show land use.
The eastern region of the Nature Conservancy has com-
pleted a draft of the classification of the terrestrial com-
munity alliances (18). The classification hierarchy is that
prescribed by the national gap team, and as such, re-
flects physiognomic and floristic characteristics neces-
sary for correlating vegetation structure and floristic
composition with vertebrate habitat requirements. The
descriptions include the range of alliances and charac-
teristic species of the overstory, understory, and herba-
ceous layer. This provides information on associated
species not detectable by image classification.
The National Wetlands Inventory data are available digi-
tally. Maps have been digitized at a scale of 1:24,000,
and the classification scheme is from Cowardin et al.
(19). Coverages come with attribute data for each poly-
gon, arc, or point as needed. A labor-intensive effort is
required to join the maps in a CIS for an area the size
of West Virginia, but the information will be invaluable
for masking water and forested wetlands on the satellite
imagery. The U.S. Fish and Wildlife Service includes
detailed instructions for converting the data to coverages.
Field data, much of it already digital, has been acquired
from many sources. Commercial timber companies pro-
vided data for timber stand composition and age groups.
The USDA Forest Service ecologist conducted transects
throughout the Monongahela National Forest to charac-
terize ecological land units. Forest inventory and analy-
sis plots are also available. We acquired these data to
verify videography classification.
The USGS has digital data of terrain elevations. The
West Virginia gap analysis team acquired 3 arc-second
OEMs as an additional band in the satellite imagery. We
will use these data to generate coverages of slope,
aspect, and elevation classes to further stratify the
physiographic regions of the state. This will increase the
accuracy of the classification.
The Soil Conservation Service created a statewide da-
tabase called STATSGO, produced at a scale of
1:250,000. For West Virginia, the map of the soil map-
ping units consists of approximately 450 polygons. Each
mapping unit is an aggregation of soil components that
occupy a certain percentage of the mapping unit area.
The database is extensive and includes information on
soil attributes such as soil taxonomy, soil chemistry, soil
structure, and interpretations for erodability and wildlife
habitat. For an attribute such as soil temperature, each
component has an individual value so that each
mapping unit may have several different values for soil
-------
temperature. Attribute information is difficult to query in
ARC/INFO, where there is a one-to-many relationship
between polygons and database entries (for instance,
each mapping unit, or polygon, has several soil compo-
nents). We found that exporting the attribute information
from ARC/INFO to another software package such as
Excel was easier. The values can be aggregated by
attribute and then imported into ARC/INFO to produce
individual coverages such as soil texture, soil depth, or
soil group. STATSGO data can provide useful informa-
tion for the physical variables that influence vegetation,
such as soil moisture and nutrient availability.
To review, the project researchers will first identify the
physical parameters that govern the distribution of
ecological land units and the existing vegetation in the
state. Then, the team will gather applicable ancillary
data of physical data in CIS to support the image clas-
sification. Once the imagery has been classified, the
wildlife models can be incorporated.
Integrate Terrestrial Vertebrate Models
Once classification is completed for the state, the gap
analysis team will integrate terrestrial vertebrate mod-
els. Concurrent with the image classification, the team
will develop a species profile for each vertebrate species
known to occur in the state. These profiles, when com-
pleted, will be condensed into rule-based models for
associated species that can be linked to the ecological
land units (see Figure 4). This step will create habitat
types. After integration, the team will generate maps that
display species richness of vertebrates for each habitat
type (see Figure 5). These maps link spatial data to the
species database. This enables users to identify areas
in the landscape that combine habitats with the greatest
number of unique species. When a coverage of land-
ownership is overlaid on this map, land managers or
conservation groups can take a proactive stance to seek
protection of critical habitats. Additional analyses that
Wildlife
Database
Landownership
Assess
Protection
Status
Figure 4. Informational flow chart for wildlife data.
users can perform are displays of the potential distribu-
tion of vertebrate groups, such as upland salamanders.
Another analysis would be to report the species that
occur in the fewest habitats and that would be most
vulnerable to landscape changes. Clearly, CIS provides
a powerful environment for quick and efficient retrieval
of spatial data for management decisions.
Summary
To summarize, the West Virginia gap analysis team is
assessing the natural communities in the state as a part
of the national comprehensive planning effort. We need
to conduct the assessment rapidly, compiling existing
information and integrating these data with CIS and
remotely sensed data. Ecological land units are classi-
fied according to the relationship between vegetation
and ecological variables. Satellite imagery is used to
map existing conditions over the state. The existing
vegetation is classified to reflect physiognomic and flor-
istic elements to correlate with vertebrate and butterfly
habitat requirements.
The gap analysis team is using many widely available
data sets such as OEMs, land use/land cover, wetlands
inventory, and soils data. While these can reduce the
time and cost of developing an ecological database,
they do present implications for project design and ac-
curacy. When the user combines maps of different
scales, accuracy is constrained by the map with the
smallest scale. Additionally, data sets may be con-
structed with objectives for an intended use that is not
compatible with project needs. The classification of
AVHRR data reflects forest types but not land use, so
another source may be required for these data.
The Gap Analysis Project is not a substitute for intensive
biological studies at a fine scale. It is merely a quick
assessment at a broad scale to provide information on
existing conditions. While accumulating data and mod-
eling potential wildlife distributions, we will identify
where inventory data may be lacking. Additional work
must be done to verify wildlife models and the classifi-
cation of vegetation, but this preliminary analysis will be
a valuable framework that will direct future studies of
biological diversity. Finally, this effort will provide a data
set that can be used to monitor changes to land cover
and land use.
Acknowledgments
This study is being funded by the Cooperative Research
Units Center, National Biological Survey, United States
Department of the Interior. This manuscript is published
as Scientific Article No. 2499 of the West Virginia Agri-
culture and Forestry Experiment Station.
-------
Legend
• Very High
n High
E3 Moderately High
m Moderate
H Moderately Low
n Low
B Very Low
H Comm. Timber Lands
H Public Lands
N
Figure 5. Relative species richness.
References
1. Davis, F.W., and J. Dozier. 1990. Information analysis of a spatial
database for ecological land classification. Photogramm. Eng.
and Remote Sensing 56:605-613.
2. Scott, J.M., B. Csuti, K. Smith, J.E. Estes, and S. Caicco. 1991.
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ancing on the brink of extinction: the Endangered Species Act
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and classification, 2nd ed. Ames, IA: Iowa State University Press.
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6. Fortney, R. 1994. Vegetation pattern of central Appalachian
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and R.G. Wright. 1993. Gap analysis: A geographic approach to
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Walsh, O.K. Walters, W Befort, and D.F. Heinzen. 1994. Satellite
inventory of Minnesota forest resources. Photogramm. Eng. and
Remote Sensing 60:287-298.
0 10
Kilometers
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sat-4 thematic mapper and multispectral scanner data for forest
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12. Hopkins, PR, A.L. Maclean, and T.M. Lillesand. 1988. Assess-
ment of thematic mapper imagery for forestry application under
lake states conditions. Photogramm. Eng. and Remote Sensing
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types in New Hampshire using multitemporal Landsat Thematic
Mapper data. Papers presented at ASCM/ASPRS Annual Con-
vention and Exposition, New Orleans, LA (February), pp. 333-342.
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sification, visualization, and enhancement using n-dimensional
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59:1,755-1,764.
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A land use and land cover classification system for use with
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and description of terrestrial community alliances in the Nature
Conservancy's eastern region: First approximation. Washington,
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Department of the Interior Fish and Wildlife Service.
-------
GIS Watershed Applications in the Analysis of Nonpoint Source Pollution
Thomas H. Cahill, Wesley R. Horner, and Joel S. McGuire
Cahill Associates, West Chester, Pennsylvania
Introduction
Geographic information systems (GIS) have been used
to evaluate the impact of nonpoint source (NPS) pollu-
tion in a variety of watersheds and drainage systems
over the past 20 years (1-6). During that period, our
understanding of the sources and hydrologic transport
mechanisms of NPS pollutants, both in particulate and
soluble forms, has greatly increased (7-9). Our ability to
create and manipulate land resource data, however, has
advanced at a far more dramatic rate. Whereas 20 years
ago, both computer system capabilities and peripheral
hardware limited the process of encoding, storing, and
displaying spatial data, today we can encode land re-
source data, analyze it, and produce stunning visual
displays at a relatively low cost.
The question is: what has this experience told us regard-
ing the yet unresolved problem of water quality degra-
dation from NPS pollution in our streams, lakes, and
coastal waters (10, 11)?
The purpose of this paper is to report on several recent
studies of this nature that created a GIS as a tool to
analyze NPS pollution. This paper will not cover all
aspects of these studies; detailed reports on each pro-
ject are available from the authors or respective clients.
The objects of these studies were:
• A medium-sized lake draining a fairly small watershed
• A riverine system with multiple use impoundments
• A 100-mile stretch of Atlantic coastal estuary
These water bodies all have one common ingredient:
NPS pollution significantly affects them. While the pri-
mary focus of these studies was to understand the
dynamics of surface water quality, and specifically the
NPS component, the further objective was to document
the causal link between identified water resource prob-
lems and the watershed-wide management actions
needed for their remediation. Thus, GIS serves not only
as a mechanism for analysis of NPS pollution sources
but also as the tool by which to evaluate alternative
methods that would reduce or prevent this pollution.
Study Concepts
These three studies illustrate different approaches to
both aspects of this problem. In the 93-square-mile Up-
per Perkiomen Creek watershed (UPW) study, the ob-
jective was to develop a management program that
would reduce nutrient load in a system of reservoirs at
the base of the watershed. An essential element in the
analysis underlying GIS design (ARC/CAD) was to be
able to differentiate and evaluate pollution sources in the
watershed, while providing the technical basis for an
innovative and far-reaching management program on all
levels of government; that is, GIS was used not only to
analyze the problem but to help formulate the solution.
In the more focused Neshaminy Creek study, Cahill
Associates (CA) designed a detailed pixel/raster format
for GIS to support detailed hydrologic modeling (12) and
NPS loading analysis. This study, carried out under
Pennsylvania's Act 167 stormwater management pro-
gram, was under a legal requirement to translate tech-
nical findings into subdivision regulations that all 30
watershed municipalities would adopt. This mandate
required much more geographically specific rigor in the
GIS approach and in the management recommenda-
tions the law stipulated.
These two projects (see Figure 1), when taken together,
illustrate the critical relationship between understanding
the appropriate level of detail in GIS system design, GIS
development with modeling and other analytical require-
ments, and ultimately, the proposed management ac-
tions for watershed-wide implementation.
In the New Jersey Atlantic Coastal Drainage (ACD)
study, the objective was to document more completely
the magnitude and sources of NPS pollutants, espe-
cially nutrients, entering New Jersey estuarine coastal
waters. The GIS design placed special attention on the
role of urban or developed land uses situated along the
coastal fringe, particularly the maintained or landscaped
portions of developed sites. Most previous studies have
largely ignored this factor. Instead, they have focused
water quality analysis typically on NPS loadings as a
-------
Upper Perkiomen
Watershed
Pennsylvania
N. HAMPTON
^x >Vi
LEHIGH
Perfiomen Creek Watershed^^y
Figure 1. Regional location of Upper Perkiomen and Neshaminy
basins.
function of impervious area coverage, with the assump-
tion that loadings increase as imperviousness increases.
On the contrary, the CA thesis states that certain pollut-
ant loadings, such as nutrients, maximize in areas with
relatively moderate densities (1/2- to 1-acre lots) and
percentage impervious cover but with large maintained
lawnscapes. Because sandy soils allow soluble NPS
pollutants to pass as interflow to points of surface dis-
charge with surprising ease, they exacerbate the prob-
lem of nutrient applications in typical coastal drainage
areas. CIS application in this case enabled estimation
of the nutrient loading to coastal waters. Existing fertil-
ized lawn areas were calculated to be a significant
source of nutrient pollution, with loadings from new land
development posited as an even more serious problem
for New Jersey's coastal waters. CIS was then applied
to evaluate the suitability of various best management
practices (BMPs), based on the physical and chemical
properties of the soil mantle and the existing and antici-
pated land use.
The Upper Perkiomen Creek
Watershed Study
Background
The UPWin southeastern Pennsylvania is a tributary of
the Schuylkill River in the Delaware River basin (see
Figure 2). Serious eutrophication problems occurring in
the system of reservoirs lying at the base of this rela-
tively rural watershed prompted the study. The study
New Jersey
HUNTERDON
BERKS
Upper Perkiomen Creek Water:
Scale in Miles
Figure 2. The Perkiomen Creek watershed in the Delaware River
basin.
effort evolved from concerns on the part of the Delaware
Riverkeeper, a private nonprofit environmental organi-
zation dedicated to promoting the environmental well-
being of the Delaware River watershed. The Upper
Perkiomen Creek has experienced various water quality
problems, especially the eutrophication of Green Lane
Reservoir, a large raw water supply storage reservoir
(see Figure 3). Green Lane's highly eutrophic condition
has been a constant since shortly after initial construc-
tion over 35 years ago, but the relative importance of
NPS inputs has dramatically increased. Whereas 10
years ago point source input was the major source of
phosphorus, elimination of some point sources and ad-
vanced waste treatment for others has greatly reduced
that component of pollutant loading, while NPS sources
have remained constant or increased. Current analysis
indicates that NPS pollution constitutes over 80 percent
of the annual load of phosphorus (see Figure 4) into the
Green Lane Reservoir and is well in excess of the
desired loading to restore water quality (see Figure 5).
Nonpoint Source Analysis
Calculating the NPS load was an essential ingredient in
the study and relied on developing accurate measure-
ment of NPS transport during stormwater runoff periods.
Certain pollutants, specifically those associated with
sediment and particulate transport such as phosphorus,
have produced a "chemograph" that parallels but does
not exactly follow the traditional form of the hydrograph
(see Figure 6). The pollutant mass transport associated
with this runoff flux frequently constitutes the major frac-
tion of NPS discharge in a given watershed (8, 13). In
-------
1SZ
O
CD
(D
Q.
OJ
CL
,
;
Figure 3. Green Lane Reservoir in the Upper Perkiomen watershed, 814 acres, 4.3 BG.
-------
Dustfall
2.50%
500 Pounds
per Year
Waterfowl
2.50%
500 Pounds
per Year
Nonpoint Sources
Dry Flow
5,604 Pounds
perYear^ 30%
Nonpoint Source
Total = 84%
Point Sources
Direct 2,486
Pounds per Year
Point Sources
to Tributaries 592
Pounds per Year
Direct Drainage
1,172 Pounds
Per Year
Nonpoint Sources
Storm Flow 8,036
Pounds per Year
Total Load = 18,889 Pounds per Year
Figure 4.
Sources of total phosphorus mass transport into the
Green Lane Reservoir from the Upper Perkiomen wa-
tershed (71 square miles) in an average flow year—in
pounds per year.
Average Year, Riverkeeper-
1993 = 18,889 Pounds per Year
Eutrophic = 8,416 Pounds per Year
Oligotrophic = 4,208 Pounds per Year
Riverkeeper -1993 Eutrophic
Oligotrophic
the UPW study, operating continuous sampling stations
at two key gage locations above the reservoir allowed
the measurement of stormwater chemistry of this type
and produced estimates of wet weather transport of phos-
phorus and sediment. Surprisingly, the NPS transport
during dry weather, calculated by subtracting the point
sources, was also significant and is attributed to live-
stock discharges and septage drainage.
But the wet weather proportion of NPS pollution still
dominates lake water quality. Many have said that water
quality in a given watershed is a function of land use,
but that statement is as unsatisfying as saying that
runoff is a function of rainfall. Experience has taught us
that neither process is quite that simplistic, nor does
either follow a direct linear relationship of cause and
effect. The causal mechanisms that generate a certain
mass load of pollutant in a drainage basin certainly
result from how much mass of that pollutant is applied
to the landscape within the drainage, which in turn is
scoured from the landscape during periods of surface
saturation, transported in, and diluted by runoff. The end
result is a concentration of pollutant in the stormwater
that might be several orders of magnitude greater than
during dry weather flow, the hydrologic period tradition-
ally used to measure and define water quality.
Developing NPS analysis or algorithms for stormwater
quality modeling requires replicating the specific hydro-
graph and its associated chemograph, as well as defin-
ing the mechanisms by which pollutants are scoured
from the land surface, transported in runoff, and pass
through the river system. Total phosphorus (TP), for
example, is transported with the colloidal soil particles
(see Figure 7), so sediment transport and deposition
constitute a key mechanism.
Figure 5. Reduction in annual phosphorus load required to
achieve improved trophic level.
3/4/930:00 3/4/9312:00 3/5/930:00 3/5/9312:00 3/6/930:00 3/6/9312:00
Figure 6. Storm hydrograph in the Upper Perkiomen watershed
illustrating the dramatic increase in total phosphorus
and suspended sediment during runoff.
Adding to these complications is the question of whether
to model single or multiple events. Is the chemodynamic
process one in which the transport takes place over a
series of storm events, so that each storm moves the
pollutant mass a given distance in the drainage and then
allows it to settle in the channel only to resuspend it with
the next peak of flow? Or does the total mass transport
occur in one single dynamic, from corn field or suburban
lawn to lake, estuary, or other sink, that is hours or days
downstream in the drainage? The issue of how storm-
water transport of pollutants takes place is of paramount
importance in current planning and regulatory imple-
mentation (11) because many of our current BMPs are
relatively ineffective in removing NPS pollutants. This
understanding is critical even as we attempt to intervene
in the pollutant generation process by changing the way
we cultivate the land, fertilize our landscapes, or for that
matter, how we alter the land surface during growth.
-------
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ouu •
n _
*
V'
.* V."**" *
^*r" **
^"'" •
.*•"
*>*
*
X
'. « "
„/'
.'".
, Main E
0.9
" Regre;
.-'•"
ranch rA2 =
3 65 Samples
ssion ~
500 1,000 1,500
Suspended Solids (PPM)
2,000
2,500
Figure 7. The relationship between total phosphorus and suspended sediment concentrations during runoff is strong but varies
with different watersheds.
GIS Evaluation
The GIS data files on land use/land cover that were
created for the UPW show that the bulk of the area is
still quite undeveloped and rural (see Figure 8), with the
steeply sloped and igneous rock areas in the headwa-
ters in forest cover (38 percent) and the valleys in mixed
agriculture (44 percent). The urban/suburban land com-
poses the remaining 18 percent and largely consists of
several older, historic boroughs linked together in a
lineal pattern with widely scattered, low-density residen-
tial areas. Much of the existing housing is turn-of-the-
century at quite high densities, mixed with a variety of
commercial and other uses. This pattern contrasts
sharply with typical large-lot suburban subdivisions. In
fact, these watershed boroughs resemble the "village"
concepts that innovative planning theorists advocate in
a variety of important ways.
The watershed (see Figure 9) is blessed, or cursed,
depending upon one's perspective, with a multiplicity of
local governments including four different counties and
18 different municipalities. This arrangement poses spe-
cial challenges for management program implementa-
tion. Population projections indicate that additional
development will occur at moderate rates throughout the
watershed, reflecting recent trends.
Farming, both crop cultivation and dairying, is a major
existing land use in the watershed, although agriculture
is not especially robust and appears to be declining. This
lack of agricultural vibrancy becomes a major factor in
determining how to impose additional management
measures on agricultural pollution sources. GIS tabula-
tion of agricultural land totals some 19,000 acres above
the reservoir, which can be compared with the estimated
TP and suspended solids (SS) mass transport reaching
the lake. Considering only the agricultural land to be
the source of this NPS input (not quite true) suggests
an average annual yield of 180#/acre/year-SS and
0.22#/acre/year-TP.
This sediment/phosphorus yield is more than sufficient
to maintain a eutrophic condition in the reservoir system.
The problem with this yield, however, is that it is two
orders of magnitude less than commonly accepted
methodologies of soil erosion, such as the universal soil
loss equation (14), would suggest might come from such
a watershed. Analysis of the cultivation practices taking
place on farmland in the watershed estimates soil ero-
sion to be approximately 5 to 10 tons per acre or more
per year, far more than is observed passing out of the
basin into the reservoir. The phosphorus applications on
both cultivated and maintained residential landscapes
also appear much greater than the mass transport actu-
ally measured in the flowing streams, which represent
perhaps 7 percent or less of the annual land application.
The implication for NPS analysis is that the standard
shopping list of either agricultural or urban BMPs might
only reduce the mass transport by a relatively small
fraction, even if successfully applied throughout the
drainage. As Figure 7 illustrates, most of the phosphorus
transport occurs on the colloidal fraction of sediment
particles, which tend to remain in suspension as storm-
waters pass through conventional detention structures,
terraces, or grassed swales.
To consider more radical measures, GIS was used to
determine possible alternatives, such as creating a
stream buffer system (see Figure 10) with various set-
back distances from the perennial stream network, and
to evaluate how great an impact this might have on
agricultural land use and urban development. Land
-------
-^
•
(
Topography
Figure 8. GIS data files showing land use/land cover characteristics for the Upper Perkomien watershed.
-------
Figure 9. Existing land use/land cover GIS file for the Upper Perkiomen watershed. The 95-square-mile basin includes portions of
four counties and 18 municipalities.
-------
Legend
x' X -1"-" s
/ S i
//<
//
HSG
(Hydrologic Subgroup)
/' '
Key Map
Stream
—-*
•] 11
Structures
Area
of
Detail
Figure 10. GIS analysis of stream corridors allows evaluation of riparian buffer systems, potential agricultural land loss, potential
septic system discharges, and related NFS reduction with selected best management practices.
-------
use at varying distances (100 feet, 200 feet, and 1,000
feet) from streams was tabulated, including all land area
in the "active" agriculture categories. This CIS documen-
tation allowed estimation of the significant NPS reduc-
tion in loadings that a riparian corridor management
program could achieve.
In the same way, CIS analysis helped estimate pollutant
loadings from malfunctioning onsite septic systems.
Counts of structures in nonpublicly sewered areas
within varying distances from the stream system were
developed using CIS data files. The nearly 300 potential
systems within a 200-foot radius of those streams drain-
ing into the Green Lane Reservoir identified in this man-
ner, with pollutant generation factors applied, became
the basis of a dry weather pollutant estimation. Although
this approach was dependent on a variety of assump-
tions, alternative approaches of evaluating the problem,
such as field visits to actual onsite systems throughout
the watershed, would not have been feasible.
For urban and suburban development, the management
focus was to estimate NPS loadings from future land
development. CIS was used to demonstrate NPS pollut-
ant load implications of future growth envisioned in the
watershed's keystone municipality, Upper Hanover
Township. Here, an increase of 15,000 residents would
convert 1,772 acres into residential, commercial, and
industrial uses. Nonpoint pollutant loadings generated
by this new land development constituted significant
increases in phosphorus, suspended solids, metals,
oil/grease, and other pollutants, and would reverse any
improvements in Green Lane Reservoir water quality
that recent wastewater treatment plant upgrades
achieved.
From a water quality perspective, future alternative land
use configurations that concentrate development and
minimize ultimate disturbance of the land surface
yielded would substantially reduce NPS pollutant load-
ings into the reservoirs. This entire process of testing
land use implications of different management ap-
proaches for their water quality impacts indicated that
pollutant loads could be minimized far more cost effec-
tively through management actions, both structural and
nonstructural, which varied from the areawide to the
site-specific.
Neshaminy Creek Watershed Stormwater
Management Study
Background
The Neshaminy Creek watershed, including 237 square
miles of mixed urban and rural land uses, lies primarily
in Bucks County, Pennsylvania, and flows directly into
the Delaware River (see Figure 1). The 1978 Pennsyl-
vania 167 Stormwater Management Act, which required
that counties prepare Stormwater management plans for
all 353 designated watersheds in the state, mandated
the Neshaminy study. This act further stipulated that
municipalities then needed to implement the watershed
plans through adopting the necessary municipal ordi-
nances and regulations. In fact, the Neshaminy study
had three water resource management objectives:
• Prevent worsened flooding downstream caused by
increased volumes of runoff from land development.
• Increase ground-water recharge.
• Reduce NPS pollutant loadings from new development.
In the initial study design, water quality and NPS issues
were secondary to flooding concerns. When Pennsylva-
nia's Stormwater management program was conceived,
the state focused on preventing watershed-wide flood-
ing. Clearly, detention basins have become the primary
mode of managing peak rates of Stormwater discharge
site-by-site in most communities. Because detention ba-
sins only control peak rates of runoff and allow signifi-
cantly increased total volumes of water discharged from
sites, however, the increased Stormwater volumes can
theoretically combine and create worsened flooding
downstream. Consequently, most Act 167 planning has
focused on elaborate hydrologic modeling designed to
assess the seriousness of potential cumulative flooding
in watersheds under study.
In the case of the Neshaminy, however, the record sug-
gested that although localized flooding could be an is-
sue, an existing network of eight multipurpose flood
control structures constructed during the 1960s served
to prevent significant flooding. Water quality certainly
was a serious Stormwater concern, however, especially
in the areas flowing into the reservoirs where recrea-
tional use had become intense. Several of the existing
impoundments were multipurpose, their permanent
pools providing critical recreational functions for a bur-
geoning Bucks County population. At the same time, the
proliferation of development in the watershed, with its
increased point and nonpoint sources, had degraded
streams and seriously affected the reservoirs. While the
total stream system in the watershed was of concern,
the future of the reservoirs came to be particularly im-
portant in developing the total Stormwater management
program for the Neshaminy watershed.
The Neshaminy lies at the heart of Bucks County, Penn-
sylvania's primary population and employment growth
county (see Figure 11). Although the Neshaminy water-
shed has already experienced heavy development, es-
pecially in the lower or southern portions, farmsteads
and large areas of undeveloped land still exist, espe-
cially in headwater areas. Agriculture has been a major
land use in the past, but farms rapidly are converting to
urban uses as the wave of urbanization moves outward
from Philadelphia and from the Princeton/Trenton met-
ropolitan areas. Growth projections indicate continu-
-------
Nl SM 1MIM rK'-.FK V, ATlTttSHLO
S'lORWAlflH MANAGEMENT PI AN
Figure 11. Land use/land cover in the Neshaminy basin of Bucks County, Pennsylvania. The watershed covers 237 square miles
in southeast Pennsylvania.
10
-------
ation of this rapid growth and a continuing change in
existing land use/land cover, together with projected
development with in the required 10-year planning horizon.
Physiographically, the watershed spans both the Pied-
mont and Atlantic coastal plain provinces, with rolling
topography and relatively steep slopes underlain by Tri-
assic formation rock, including the Lockatong, Bruns-
wick, and Stockton formations. This bedrock ranges
from being a poor aquifer (Lockatong) to an excellent
aquifer (Stockton) where the many rock fractures allow
for considerable ground-water yields. Soils are quite
variable, ranging from good loam (hydrologic soil group B)
to clays and other types with poor drainage charac-
teristics (e.g., high water table, shallow depth to bed-
rock). A large proportion of the soils in the watershed are
categorized as hydrologic soil group C, which is mar-
ginal for many stormwater management infiltration tech-
niques (see Figure 12) and produces a relatively large
proportion of direct runoff. With an annual rainfall of 45
inches, base flow accounts for about 12 inches and
direct runoff accounts for 10 inches.
The system of eight stormwater control structures,
which were built over the past three decades under the
federal PL 566 program, have altered the hydrology of
the watershed (15). In addition, in heavily developed
portions of the watershed, impervious surfaces com-
bined with numerous detention basins prevent the bulk
of the precipitation from being recharged, and the vol-
ume of total runoff proportionally increases. An elabo-
rate system of municipal and nonmunicipal wastewater
treatment plants also adds to this alteration of the hy-
drologic cycle. These plants discharge wastewater efflu-
ent that, in some cases, constitutes the bulk of the
stream flow during dry periods. While the impact of NPS
was evident throughout the drainage, it was of special
interest in the impoundment network, especially those
impoundments that were conceived as multipurpose in
function and constitute major recreational resources in
the watershed.
CIS Design
Act 167 requirements and the needs of the hydrologic
and other modeling used in planning both heavily influ-
enced the CIS developed for the Neshaminy. Spatial
data files, including existing land use, future land use,
and soil series aggregated by hydrologic soil groups,
were created by digitizing at a 1-hectare (2.5-acre) cell
resolution. The encoding process that helped design the
CIS used a stratified random point sampling technique
that similar studies had developed and applied (1, 3).
The encoding process used a metric grid of 5-kilometer
sections, subdivided into 2,500 1-hectare cells (100 me-
ters on a side), aligned with the Universal Transverse
Mercator (UTM) Grid System. This grid appears in blue
on U.S. Geological Survey (USGS) topographic maps.
These maps served as the framework of reference for
all data compilation. Within each 100-meter cell, a ran-
domly located point was chosen (see Figure 13) at
which the specific factor was encoded as representative
for the cell, using a digitizer tablet. This approach al-
lowed extraction of the data from the respective source
documents with some rectification necessary for many
types of source maps and photographs.
The combination of soil series and cover in each cell
helped to calculate the curve number and unit runoff per
cell. The 45,000-cell data file was then used to calculate
total runoff for a range of events in each of 100 subbas-
ins that averaged 1.95 square miles each. The resultant
hydrographs, used in combination with a separate linear
data file in CIS describing the hydrographic network of
stream geometry, routed and calibrated the hydrologic
model (TR-20). NPS mass transport loadings were es-
timated on an annual basis by cell, again using the land
use/land cover data file, and total loads summed by
groups of subbasins above critical locations. This issue
was particularly important with respect to the drainage
areas above the impoundments, where NPS pollutants
were of greatest concern.
The soil properties data file was especially useful in
evaluating certain management objectives, such as the
opportunity for recharging ground-water aquifers. The
spatial variation in relative effectiveness of infiltration
BMPs was considered for both quantity and quality miti-
gation because the best methods for NPS reduction
usually include recharge where possible. The soil series
corresponding with new growth areas were classified
regarding their suitability for these BMPs, which are
most efficient on well-drained or moderately well-drained
soil. Thus, the alternative impacts of future growth could
be considered in terms of potential generation (or man-
agement) of NPS loads. A BMP selection methodology
(see Figure 14), which was developed forthe 30 munici-
palities within the watershed, focused on new land de-
velopment applications and considered both water
quantity and quality management objectives. BMP se-
lection is a function of several factors, including:
• The need for further peak rate reduction.
• The recharge sensitivity of the project site (defined as
a function of headwaters stream location, areawide re-
liance on ground water for water supply, or presence
of effluent limited streams).
• The need for priority NPS pollution controls (location
within reservoir drainage).
Development of two "performance" levels of BMP selection
techniques gave municipalities some degree of flexibility
in developing their new stormwater management pro-
grams. This system required only the minimally acceptable
techniques but recommended the more fully effective
ones, hoping that municipalities would strive to incorporate
11
-------
VH:I
-------
Region With Labeled Zones
29
(4428°°°mN)
28
A
5
46 47 48 49
(446°°°mE)
Zone With Labeled Cells
288
286
284
282
280
4f
/
A
50 462 464 466 468 470
Figure 13. Raster/pixel design of GIS for Neshaminy modeling
study. Each pixel is 1 hectare (2.47 acres).
recommended management measures wherever possi-
ble. The BMP selection methodology also was sensitive
to type of land use or proposed development, assigning
typical single-family residential subdivisions different
BMPs than, for example, multifamily and other nonresi-
dential proposals (including commercial and industrial
proposals). The selection process also determined size
of site to be a factor, differentiating between sites of 5
acres or more because of the varying degrees of cost
and effectiveness of different BMP approaches. The
methodology, if properly and fully implemented, should
achieve the necessary stormwater-related objectives—
both quantity and quality—that the analysis had deemed
necessary (16).
GIS was especially important in its ability to test how
reasonable the BMP selection methodology was. Such
tests included the ability to evaluate, for each municipal-
ity, the following factors:
• The nature and extent of the projected development.
• The size of development/size of site assumptions.
• Other vital BMP feasibility factors such as soils and
their appropriateness for different BMP techniques.
GIS also enabled analysis of the water quantity and
quality impacts of projected growth on a baseline basis,
assuming continuation of existing stormwater manage-
ment practices. Water quality loadings to individual res-
ervoirs and to the stream system could be readily
demonstrated. Because overenrichment of the reser-
voirs was so crucial, researchers could estimate phos-
phorus and nitrogen loadings from projected
development assuming existing stormwater practices,
even on a municipality by municipality basis.
New Jersey Atlantic Coastal
Drainage Study
Background
The third study considered a much larger coastal water-
shed in New Jersey (see Figure 15). The New Jersey
1' Impoundment I
Drainage
INon Impoundment!.
Drainage F
Non Rechargel
Sensitive r
I Impoundment I
\ Drainage |
INon Impoundment!
'(Drainage \
J Impoundment
1 Drainage
INon Impoundment!.
Drainage I
I Impoundment
1 Drainage
INon Recharge|_
Sensitive
INon Impoundment!
"[Drainage T
Not Applicable
Required: Multi-Resi and Non-Resi Overs Acres, Porous Pave. With
Underground Recharge Beds for Paved Areas and Infiltration Devices for
Non-Paved Areas, Sized for Peak; Other Uses, Infiltration Devices for
Paved and Nonpaved Areas, Sized for Peak
Not Applicable
Required: Multi-Resi and Non-Resi Overs Acres, Dual Purpose Detention
Basins for Paved/Nonpaved Areas, Sized for Peak; Other Uses, Detention
Basins Sized for Peak
Recommended: All Uses/Sizes, Porous Pave. With Underground Recharge
Beds for Paved Areas; Minimum Disturbance or Wet Ponds/Artificial
Wetlands for Nonpaved Areas, All Sized for Peak
Required: All Uses and Sizes, Porous Pave. With Underground Recharge
for Paved Areas; Minimum Disturbance for Nonpaved Areas
Required: Multi-Resi and Non-Resi Overs Acres, Porous Pave. With
Underground Recharge Beds for Paved Areas and Infiltration Devices for
Nonpaved Areas
Recommended: All Uses/Sizes, Porous Pave. With Underground Recharge
for Paved Areas; Minimum Disturbance and/or Infiltration Devices After
Site Stabilization
Required: All Uses/Sizes, First-Flush Settling Basins for Paved Areas; for
Nonpaved Areas, Minimum Disturbance/Wet Ponds/Artificial Wetlands
Recommended: for All Uses/Sizes, Porous Pave. With Underground
Recharge Beds for Paved Areas; Minimum Disturbance for Nonpaved
Areas
Required: Multi-Resi and Non-Resi Overs Acres, First-Flush Settling
Basin; Other Uses, Detention Basins (No Change)
Recommended: Porous Pave. With Underground Recharge Beds for Paved
Areas; for Nonpaved areas, Minimum Disturbance/Wet Ponds/Artificial
Wetlands
Figure 14. BMP selection methodology used with the GIS database in the Neshaminy basin modeling study.
13
-------
Atlantic Coastal Drainage (ACD) includes an area of
2,086 square miles, with barrier islands (50 square
miles), wetlands/bays/estuaries (285 square miles), and
a unique scrubby pitch pine-cedar forest, known as the
Pine Barrens, largely covering the 1,750 square miles
of mainland interior (see Figure 16). This flat coastal
plain comprises a series of unconsolidated sedimentary
deposits of sand, marl, and clay, which increase in thick-
ness toward the coastline. Over the past 16,000 years,
as the ocean level has risen, the water's edge has
progressed inland to its present position. Ocean cur-
rents and upland erosion and deposition have created a
long, narrow series of barrier islands that absorb the
energy of ocean storms and buffer the estuary habitats
from the scour of waves and currents. Between the
mainland and barrier islands are embayments and es-
tuaries of different sizes and configurations. Inland erosion
and marine sediments have gradually filled many of
these areas, creating extensive wetlands (17).
In this ACD region, new land development and popula-
tion growth have caused significant degradation of water
quality from an increase in both point source and NPS
pollution. Although the array of pollutants is ominously
broad, increased nitrogen and phosphorus loadings
have resulted in enrichment of back bays, estuaries, and
nearshore waters, contributing to algal blooms, declining
finfish and shellfish populations, diminished recreational
Atlantic
Ocean
Legend
Atlantic Coastal Drainage
Cafra Management Area
— Pine Barren Region Including
Cedar-Pine Fringe
Figure 15. The ACD of New Jersey includes approximately 2,000 square miles of land area from the Manasquan River to Cape May.
14
-------
Figure 16. Aerial photograph of New Jersey illustrating the Barrier Islands and estuary system situated along the Atlantic coast.
15
-------
opportunities, and a variety of other problems (18). A
major source of these nutrients is point source sewage
treatment plants (STPs), but the effluent outfalls of almost
all these STPs discharge into nearshore ocean waters
beyond the barrier islands. Thus, NPS pollutants almost
totally dominate the water quality in the estuaries and
back bays (19, 20).
These NPS pollutants, which rain scours from the land
surface and flushes into coastal waters with each rain-
fall, comprise a largely unmeasured and unmanaged
flux of contaminants. Prior research on coastal water
quality has given considerable attention to NPS pollution
generated from paved or impervious surfaces, particu-
larly roadways and parking lots where hydrocarbons,
metals, suspended solids, biologic oxygen demand
(BOD), and other pollutants have been measured.
Although these NPS pollutants are certainly of concern
in New Jersey's coastal waters, the enrichment issue
has led to a focus on NPS pollution produced when
creating large areas of pervious and heavily maintained
landscape, such as lawns and other landscaped areas,
in the sandy soil context of the coastal area. Typically,
significant quantities of fertilizer and other chemicals,
which are applied on these new pervious surfaces, are
naturally low in nutrients. Although a modest portion of
the applied fertilizer runs off directly into surface waters,
larger quantities of soluble pollutants, such as nitrates
and herbicides, quickly percolate down through the
sandy soil, then move rapidly as interflow to the estuary
system.
In this coastal drainage of unconsolidated sediments,
the hydrologic cycle differs from inland watersheds. Of
the 45-inch average annual rainfall, only a small fraction
(2.5 inches per year) becomes direct runoff, with the
balance rapidly infiltrating into the sand strata (21).
Most of the infiltration that reaches the ground water
(20 inches per year) discharges to surface streams
(17 inches per year) within a few hours following rainfall,
producing a lagging and attenuated hydrograph. This
rapid infiltration, combined with the sand texture of the
soil, has a major bearing on the water quality implica-
tions of new land development. Thus, urbanization of
coastal regions has dramatically altered hydrologic
response, with every square foot of new impervious
surface converting what had been approximately 41.5
inches of infiltration into direct runoff to bays and estu-
aries, with a turbid soup of NPS pollutants.
Even in areas that have maintained infiltration, the
coastal soils do not remove NPS pollutants as efficiently
as other areas of New Jersey that overlie consolidated
formations with heavier clay soils. These soils provide a
much more thorough removal of NPS pollutants through
physical, chemical, and biologic processes, as rainfall
percolates through the soil mantle.
With development of coastal areas, increased impervi-
ous areas and changing flow pathways (inlets and storm
sewers) convey nonpoint pollutants introduced by devel-
opment (from both pervious and impervious surfaces)
directly to the coastal waters. In addition, freshwater
recharge to the underlying aquifer decreases with the
increase in impervious surfaces, with resulting in-
creases in saltwater intrusion into the sand aquifers and
contamination of ground-water supply wells along the
coast. Further compounding the loss of the stormwater
for ground-water recharge are increased ground-water
withdrawals necessary for new watersupply. In sum, urban
growth within the ACD, with its 1.13 million permanent
residents (and still growing) and an additional 1.5 million
summertourists, has dramatically altered the natural drain-
age system (and landscape) in a way that significantly
increases the discharge of NPS pollutants (22).
GIS Approach
New Jersey's Department of Environmental Protection
already had developed a computerized GIS system
(ARC/INFO) for environmental analysis and resource
planning, so this study aimed to use existing GIS work
and to refine this GIS system. Although data files for
municipal boundaries, watershed areas, and a variety of
other factors already existed, land use/land cover data
had not been developed and constituted a major work
task. The subsequent land use/land cover file included
the entire 2,000 square miles of the ACD, but this fo-
cused on the urbanized area (212 square miles) that
occupied about 11 percent of the coastal fringe. The end
product was a polygon file that described about 2,500
polygons of urban/suburban land, each averaging about
0.1 square miles (see Figure 17).
Using aerial photographs combined with USGS base
maps and extensive field reconnaissance, each polygon
was classified by:
• Land use type.
• Percentage of impervious cover and maintained areas.
• Degree of maintenance (fertilization) being provided
to these maintained areas.
Although classifying land use type and extent of imper-
vious cover/maintained areas was a relatively straight-
forward evaluation process (rated within one of 11
categories by percentage, 0 to 5 percent, and so forth),
the third variable, degree of maintenance, required spe-
cial treatment and data development procedures. De-
gree of maintenance was translated into high, medium,
and low categories, with high maintenance exemplified
by golf courses or other intensively maintained areas.
Medium maintenance assumed chemical application
rates comparable with those recommended by Rutgers
University state agronomists. Finally, low maintenance
was typified by a wooded or otherwise naturally vegetated
16
-------
Figure 17. Urban land use polygons digitized for the New Jersey coastal drainage. The 2,500 polygons shown cover approximately
212 square miles (11 percent) of the ACD area of 2,000 square miles.
17
-------
lot and assumed little or no regular chemical application.
Research staff executed considerable field reconnais-
sance to objectify this judgment-based rating technique
(see Figure 18).
Nonpoint Source Analysis
Because the drainage is almost entirely estuarine, the
hydrologic aspects in this study were almost irrelevant
except as a tool to describe the pollutant transport proc-
ess. Such coastal drainage systems do not allow the
measurement of hydrographs and chemographs (see
Figure 6), except on inland riverine segments or se-
lected infrastructure points of discharge (storm sewer
outfalls). Thus, the NPS analysis focused on the pollut-
ant production and transport process, especially the
nutrients applied to the maintained landscapes, which
are a major part of coastal urbanization.
This study required a great deal of effort to produce an
indextable relating urban cover characteristics (percent-
age impervious, amount of chemical application) to NPS
production potential. For each of the 2,500 urban land
polygons CIS described, estimates of the NPS loading
for a number of pollutants were generated. Potential
loadings were then aggregated by subwatershed. Total
NPS loadings could then be compared with point source
loadings for the entire coastal drainage. Interestingly,
the NPS loading dominated water quality in the estu-
arine drainage while the point sources, discharged by
ocean outfalls to nearshore waters beyond the barrier
islands, were the major source of nutrient pollution in
this portion of the coastal environment (see Figure 19).
Given the estimates of NPS pollution, the major ques-
tion involves how to control or reduce these loads. The
suitability of selected BMPs for the reduction/prevention
of pollutant generation was then evaluated and spatially
identified within the drainage (see Figure 20). This figure
evaluated the use of constructed wetland systems as a
structural measure. That is, CIS allowed state regulators
to identify not only what works best in terms of water
quality protection measures, but also where these meth-
ods could work successfully. This analysis was driven
by a detailed evaluation of the combinations of natural
conditions CIS identified within the study area. For ex-
ample, certain BMPs can be applied on soils that have
a certain set of characteristics (permeability, depth to
seasonal high water table) and that are presently in a
given land cover and planned for urbanization.
CIS also aided in evaluating alternative BMP tech-
niques, including reduction in nutrient applications and
land management BMPs such as elimination of artificial
landscapes, again using a combination of natural fea-
tures and land use patterns (see Figure 21). The result
of this analysis considered the relative proximity of ur-
ban land uses to the coastal waters as significantly
increasing the potential for NPS transport. State regula-
tory programs establish minimum setback criteria for
development in sensitive areas, and these criteria may
be modified to consider pollutant production potential
based on CIS delineation of pollutant production.
New Jersey has been striving to develop NPS manage-
ment programs for coastal areas to reduce existing
sources of pollution as well as prevent the creation of
Legend
R = Residential Use
C = Commercial Use
# = Percent Impervious
H = High Maintenance
M = Medium Maintenance
L = Low Maintenance
Figure 18. Classification of urban polygons by land use, percentage impervious cover, and degree of land fertilization.
18
-------
\
Maryland s
Wildwood
N = 224 Tons'
P = 52 Tons eve"Me Ocean City
N = 142 Tons N = 151Ton;
P = 33 Tons
P = 33 Tons
Atlantic County
N = 1,183 Tons
P = 276 Tons
Ocean County Monmouth, Bayshore
rvpanrn,,niv Ocean County North Monmouth, NE
Ocean County Centra| N = 859 Tons Long Branch, Deal
South N = 871 Tons P = 201 Tons ocean, Asbury Park
N = 267 Tons P = ,n, T_ South Monmouth
N = 1,966 Tons
P = 458 Tons
P = 62 Tons
Atlantic Ocean
P = 203 Tons
Figure 19. Point and NFS discharges to the ACD. Data shown are in tons of TP and NOs-N per year.
Legend
BMP Suitability
• - (1) Suitable
g- (2) Generally Suitable
M~ (3) Limited Suitability
Unsuitable Areas
D- (4) Unsuitable Soils
H- Open Water
E3~ Urbanized Areas
Figure 20. BMP analysis using GIS. Files consider soil suitability, current vegetative cover, and BMP criteria on vacant and
developed land parcels.
19
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pollution. As most regulatory agencies have discovered,
NPS management programs can be difficult to imple-
ment, especially when confronting issues of land use
management. To substantiate the need for new manage-
ment programs amidst these controversies, the ability to
document causal linkages (i.e., to generate data and
statistics that make the case for NPS pollutant gener-
ators and resultant water quality degradation) is very
important. The need for documentation of various types
is especially great given the less than perfect data re-
cord of water quality in coastal and other waters. All of
these factors come together to make the value of a CIS
system for water quality management very real.
Conclusion
This CIS-driven analysis indicates that NPS pollutants,
especially the nutrients phosphorus and nitrogen, gen-
erated from fertilized fields or maintained landscapes
surrounding new residential, commercial, and other
types of development in drainage systems, contribute
significantly to water quality degradation. In effect, the
particulate-associated phosphorus and the soluble ni-
trates serve as surrogates for the full spectrum of NPS
pollutants that each rainfall washes from the land. A
comprehensive water quality management program
must include structural measures to remove pollutants
this runoff conveys, as well as management of the con-
tributing landscape to reduce (and perhaps eliminate)
the application of these chemicals within the drainage.
In planning new development, management actions
should occur on a variety of levels or tiers. On an
areawide basis, growth should proceed (with guidance
and management) in a manner that would reduce total
pollutant discharges; therefore, the total amount of
maintained area being created should be as concen-
trated as possible. On the more site-specific level,
measures and construction techniques that reduce the
quantity of pollutants generated are essential. Required
development guidelines must include, but not be limited to:
• Prevention of excessive site disturbance and ongoing
site maintenance (described as a policy of minimum
disturbance and minimum maintenance).
• Use of special materials for reduction of storm-
water runoff (porous pavement and ground-water
recharge).
• Use of stormwater treatment systems (water quality
detention basins, artificial wetlands).
In sum, the regulatory framework must contain both
"how to build" guidelines, as well as "where not to build"
guidelines. CIS can be a powerful tool in both of these
processes.
While inland lakes serve as nutrient traps for these NPS
pollutants, perhaps the greatest potential impact is the
gradual process of excessively enriching our coastal
waters. As population continues to migrate to coastal
areas, the importance of protecting this fragile ecosystem
Figure 21. For certain regulatory criteria, the proximity of land uses to the water's edge was a consideration in BMP selection.
20
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increases. The pollution that new land development
generates, including the discharge of point source
wastes, should not be allowed to enter coastal waters;
it should not be allowed to destroy the natural balance
that exists between land and water. The concept of
stormwater management takes on an entirely different
meaning when viewed as one of the basic mechanisms
of this NPS pollution transport. For centuries, engineer-
ing of the shoreline has intensively focused on protect-
ing human developments from the ravages of ocean
storms. Now, however, the converse seems to be
emerging: ocean waters need protection from the im-
pacts of human development.
References
1. Bliss, N., T.H. Cahill, E.B. MacDougall, and C.A. Staub. 1975.
Land resource measurement for water quality analysis. Chadds
Ford, PA: Tri-County Conservancy of the Brandywine.
2. Cahill, T.H., R.W. Pierson, Jr., and B.R. Cohen. 1978. The evalu-
ation of best management practices for the reduction of diffuse
pollutants in an agricultural watershed. In: Lohr, R.C., ed. Best
management practices for agriculture and silviculture. Ann Arbor,
Ml: Ann Arbor Science.
3. Cahill, T.H., R.W. Pierson, Jr., and B.R. Cohen. 1979. Nonpoint
source model calibration in the Honey Creek watershed. #R-
805421-01. Athens, GA: U.S. EPA Environmental Research
Laboratory.
4. Cahill, T.H. 1980. The analysis of relationships between land use
and water quality in the Lake Erie basin. Burlington, Ontario:
International Association of Great Lakes Research.
5. Cahill, T.H., J. McGuire, and C. Smith. 1993. Hydrologic and
water quality modeling with geographic information systems. Pro-
ceedings of the Symposium on Geographic Information Systems
and Water Resources, AWRA, Mobile, AL (March).
6. U.S. EPA. 1991. Remote sensing and GIS applications to nonpoint
source planning. Proceedings of the U.S. EPA Workshop for Re-
gion 5 and Northeast Illinois Planning Commission, Chicago, IL
(October 1990).
7. Baker, D.B., and J.W Kramer. 1973. Phosphorus sources and
transport in an agricultural basin of Lake Erie. Proceedings of the
16th Conference, Great Lakes Research, Ann Arbor, Ml (Septem-
ber).
8. Cahill, T.H., and T.R. Hammer. 1976. Phosphate transport in river
basins. Proceedings of the International Joint Committee on Flu-
vial Transport Workshop, Kitchener, Ontario (October).
9. Cahill Associates. 1989. Stormwater management in the New
Jersey coastal zone. Trenton, NJ: State of New Jersey, Depart-
ment of Environmental Protection, Division of Coastal Resources.
10. Delaware Riverkeeper. 1993. Upper Perkiomen Creek watershed
water quality management plan. Lambertville, NJ: Delaware
Riverkeeper/Watershed Association of the Delaware River.
11. U.S. EPA. 1993. Guidance specifying management measures for
sources of nonpoint pollution in coastal waters. EPA/840/B-
92/002. Washington, DC.
12. Soil Conservation Commission. 1982. TR-20, project formula-
tion—Hydrology. Technical Release No. 20. PB83-223768. Land-
ham, MD: Soil Conservation Service.
13. Cahill, T.H., M.C. Adams, and W.R. Horner. 1990. The use of
porous paving for groundwater recharge in stormwater manage-
ment systems. Presented at the 1988 Floodplain/Stormwater
Management Symposium, State College, PA (October).
14. Soil Conservation Commission. 1974. Universal soil loss equa-
tion. Technical notes, Conservation Agronomy No. 32. Portland,
OR: West Technical Service Center.
15. Cahill, T.H., M. Adams, S. Remalie, and C. Smith. 1988. The
hydrology of flood flow in the Neshaminy Creek basin, Pennsyl-
vania. Jamison, PA: The Neshaminy Water Resources Authority
(May).
16. BCPC. 1992. Neshaminy Creek watershed stormwater manage-
ment plan, Vol. 1: Policy document, and Vol. II: Plan implemen-
tation. Doylestown, PA: Bucks County Planning Commission
(January).
17. Clark, J. 1977. Coastal ecosystems: Ecological considerations for
management of the coastal zone. Washington, DC: The Conser-
vation Foundation.
18. New Jersey Department of Environmental Protection. 1988. The
state of the ocean: A report by the blue ribbon panel in ocean
incidents. Trenton, NJ.
19. Cahill, T.H., M. Adams, C.L. Smith, and J.S. McGuire. 1991. GIS
analysis of nonpoint source pollution in the New Jersey coastal
zone. With Whitney, S., and S. Halsey, New Jersey Department
of Environmental Protection, Division of Coastal Resources. Pre-
sented at the National Conference on Integrated Water Informa-
tion Management, Atlantic City, NJ (August).
20. Cahill, T.H., M. Adams, C.L. Smith, and J.S. McGuire. 1991.
Living on the edge: Environmental quality in the coastal zone.
With Whitney, S., and S. Halsey, New Jersey Department of
Environmental Protection, Division of Coastal Resources. Pre-
sented at the International Conference on Integrated Stormwater
Management, Singapore (July).
21. Martin, M. 1989. Ground-water flow in the New Jersey coastal
plain. Open File Report 87-528. West Trenton, NJ: U.S. Geologi-
cal Survey.
22. National Oceanic and Atmospheric Administration. 1989. Se-
lected characteristics in coastal states, 1980-2000. Rockville, MD:
U.S. Department of Commerce, National Oceanic and Atmos-
pheric Administration, Strategic Assessment Branch, Ocean As-
sessment Division (October).
21
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GIS Standards for Environmental Restoration and Compliance
Bobby G. Carpenter, P.E.
The CADD/GIS Technology Center
Waterways Experiment Station
Army Engineer Research and Development Center
Vicksburg, Mississippi
The CADD/GIS Technology Center (formerly Tri-Service CADD/GIS Technology Center) was
established at the Information Technology Laboratory (ITL), U.S. Army Engineer Waterways
Experiment Station (WES), Vicksburg, Mississippi in October 1992. The CADD/GIS Center's
primary mission is to serve as a multi-service vehicle to set computer-aided design and drafting
(CADD) and geographic information system (GIS) standards; coordinate CADD/GIS facilities
systems within the Department of Defense (DoD); promote CADD/GIS system integration; support
centralized CADD/GIS hardware and software acquisition; and provide assistance for the
installation, training, operation, and maintenance of CADD and GIS systems.
The term geospatial data refers to data that can be referenced to a specific geographic location on
the Earth. The specific geographic location can be depicted by a graphic feature on a map or
drawing (e.g., a building, monitoring well, road, location where an environmental sample is
collected, etc.).
Environmental restoration activities involve the investigations and cleanup efforts associated with
the identification and removal of chemical or radioactive contaminants present in the soil,
groundwater, surface water, or sediment. The most common environmental restoration programs
within the DoD include the Installation Restoration Program (IRP) (involves all Air Force, Army, and
Navy installations) and the Defense Environmental Restoration Program - Formerly Used Defense
Sites (DERP-FUDS) (administered by USAGE).
Environmental compliance activities involve conducting everyday business practices in a manner
which complies with U.S. Environmental Protection Agency (EPA) and state/local environmental
regulatory agency laws and regulations. Environmental compliance activities include the
management or removal of "toxic substances" (asbestos containing materials, lead paint, PCBs),
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proper handling of hazardous materiels, proper handling and disposal of hazardous wastes,
monitoring and management of regulated storage tanks, monitoring and management of permitted
surface water discharges and permitted air emissions.
Why Do We Need Data Standards? The collection, storage, management, and analysis of
geospatial data are critical components of environmental restoration and compliance activities.
Geospatial data can be stored in a number of ways (i.e., paper, microfilm, and/or electronically)
which may not be readily accessible and usable, or easily shared with, or reported to others. CADD
and GIS technology can provide cost-effective and efficient tools to apply and manage such data.
However, careful planning and the use of consistent data storage and CADD/GIS system
implementation standards are necessary to achieve the full potential offered by CADD and GIS
technology.
Tri-Service Spatial Data Standards (TSSDS) and Tri-Service Facility Management Standards
(TSFMS) Development. One of the CADD/GIS Center's (http://tsc.wes.army.mil) major initiatives
has been development of the TSSDS and TSFMS. This project involves the development of
graphic and non-graphic standards for GIS and facility management implementations at Air Force,
Army, and Navy installations, and Army Corps of Engineers Civil Works activities. The
TSSDS/TSFMS are the only "nonproprietary" standard designed for use with the predominant
commercially available "off-the-shelf" GIS (e.g., ESRI ARC/INFO & ArcView; Intergraph MGE &
GeoMedia; AutoDesk AutoCAD, Map and World; and Bentley MicroStation & GeoGraphics) and
relational database software (e.g., Oracle & Microsoft Access). This design, in conjunction with it's
universal coverage, have propelled the TSSDS into the standard for GIS implementations
throughout the DoD, and well as the De Facto standard for GIS implementations in other Federal,
State, and Local Government organizations; public utilities; and private industry throughout the
United States and the World.
The TSSDS/TSFMS are distributed via CD-ROM and the Internet (http://tsc.wes.army.mil). A "User
Friendly" interactive Microsoft Windows based software application installs the TSSDS on desktop
computers and networks, provides viewing and printing capability, and generates SQL code for
construction of the GIS database. The CADD/GIS Center annually updates and expands the
TSSDS and TSFMS data coverages. Release 1.80 of the TSSDS/TSFMS was completed in
February 1999.
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Contributors and Coordination. The TSSDS and TSFMS have been developed based on input
from various technical experts; review and analysis of existing working DoD and state GIS; review
and analysis of various existing database management systems used throughout DoD and the
federal government; and content contributions from federal, state, local, and private sector sources.
The CADD/GIS Center has organized Field Working Groups (FWGs) whose membership is
composed of subject matter, CAD, and GIS technical experts to assist in the development of the
Tri-Service Standards and other CADD/GIS projects. The CADD/GIS Center's Environmental FWG
has been very active is assisting in the development of the TSSDS and TSFMS.
The CADD/GIS Center is coordinating development of the TSSDS/TSFMS with other DoD and
Federal standards initiatives such as the Defense Environmental Security Corporate Information
Management (DESCIM) program, the Federal Geographic Data Committee (FGDC), the Defense
Information Standards Agency (DISA), the National Imagery and Mapping Agency (NIMA), and the
Environmental Protection Agency (EPA).
Some of the specific DoD and Federal initiatives contributing to the environmental restoration and
compliance content of the TSSDS/TSFMS include: (1) Air Force "Environmental Restoration
Program Information Management System", (ERPIMS) (formerly called IRPIMS); (2) USAGE
Alaska District "Environmental Data Management System" (EDMS); (3) Army Environmental Center
(AEC) "Installation Restoration Data Management Information System" (IRDMIS); (4) Southwest
Division Naval Facilities Engineering Command, "Navy Environmental Data Transfer Standard"
(NEDTS); (5) USAGE "Formerly Used Defense Site (FUDS) Database"; (6) Air Force Aeronautical
Systems Center (ASC) and USAGE District, Louisville "Draft System Specification for the Technical
Data Management System"; (7) DESCIM Cleanup, Explosive Safety, and other data modeling work
groups; (8) Edwards AFB, Patuxent River Naval Air Station, and other DoD GIS; (9) CADD/GIS
Center's Environmental FWG, (10) Environmental Protection Agency; (11) FGDC Facilities
Standards Working Group, and (12) EPA's Environmental Data Registry.
TSSDS/TSFMS Data Model Structure. Both graphic (i.e., symbols, text fonts, line styles/types,
and level/layer schemas) and nongraphic (e.g., database attribute tables and domains) geospatial
data requirements are addressed in the TSSDS/TSFMS. The TSSDS/TSFMS data model consists
of five basic levels of hierarchy: Entity Sets, Entity Classes, Entity Types (includes Entities) (TSSDS
only), Attribute Tables, and Domain Tables.
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Entity Sets (or Themes) are broad groupings of features and related data. The TSSDS/TSFMS
structure currently includes the following twenty-five themes: (1) Auditory, (2) Boundary, (3)
Buildings, (4) Cadastre, (5) Climate, (6) Common, (7) Communications, (8) Cultural, (9)
Demographics, (10) Environmental Hazards, (11) Ecology, (12) Fauna, (13) Flora, (14) Geodesy,
(15) Geology, (16) Hydrography, (17) Improvements, (18) Landform, (19) Land Status, (20) Military
Operations, (21) Olfactory, (22) Soil, (23) Transportation, (24) Utilities, (25) and Visual.
Entity Classes are logical groupings of features and data within an Entity Set for data management
purposes.
The TSSDS Entity Classes contain logical groupings of "real-world", geographically referenced
(geospatial) features (entity types & entities) with related (graphic) database attribute tables. Each
Entity Class consists of a separate map or drawing file (i.e., category or design file in MGE;
workspace in ARC/INFO; design file in MicroStation; drawing file in AutoCAD). The current TSSDS
Entity Classes in the Environmental Hazards Entity Set include: (1) characterization, (2) surface
water pollution, (3) munitions remediation, (4) emergency preparedness (spills, etc.), (5) general,
(6) groundwater pollution, (7) hazardous materiels/hazardous waste, (8) munitions
materiel/munitions waste, (9) pollution remediation, (10) regulated tanks, (11) sediment pollution,
(12) sites, (13) building environmental concerns, (14) solid waste, (15) air pollution, and (16) soil
pollution.
The TSFMS Entity Classes contain logical groupings of (non-graphic) database attribute tables
which contain temporal or event data for specific "business" activities or functions. The TSFMS
Classes in the Environmental Hazards Entity Set include: (1) hazardous materiel management, (2)
munitions waste management, (3) asbestos containing materiel management, (4) surface water
discharge management, (5) hazardous waste management, (6) regulated tank management, (7)
PCB (polychlorinated biphenyl) management, (8) lead paint management, (9) indoor air
management, (10) field measurements management, (11) remediation management, (12)
environmental management, (13) munitions materiel management, and (14) air quality
management.
Entity Types are a grouping or collection of like, or similar, features (entities) which appear
graphically on a map or drawing. Each entity type has an associated attribute table. Entities can be
represented as one of the following three categories:
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• Boundary (Polygon) - A line string (or group of arcs) which forms the perimeter of an area.
An example would be the boundary of a lake.
• Point - A single point representing the geographical location of a feature; e.g., a well. Points
are normally represented on a map by a symbol. The TSSDS provide symbols in the native
formats of AutoCAD, MicroStation, and ARC/INFO.
• String/Chain - A line or group of arcs.
An Attribute Table is a relational database table containing non-graphic, or attribute, information
about an entity. Attribute Tables which are linked directly to a graphic entity and contain data
directly related to that entity can be classified as "graphic" (i.e., TSSDS) attribute tables. Attribute
Tables not directly linked to an entity but which contain data required for a "business process" or
function, along with data and relationships linked through specific data field ids which may be
queried for geospatial and relational analysis, can be classified as "nongraphic" (i.e., TSFMS)
attribute tables.
Domain Tables contain lists of codes (i.e., permissible or valid values) for populating specific fields
in the Attribute Tables; e.g., units of measure, material types, etc.
Join relationships are mechanisms by which relational databases link multiple records of a common
attribute or item and provide access to the records through the use of queries. Join relationships
are established in the TSSDS/TSFMS through the use of "Primary Key" attribute fields in a "parent"
attribute table and "Foreign Key" attribute fields in related "child" attribute tables.
Integration of Approved FGDC Geospatial Data Standards into the TSSDS. Executive Order
12906, "Coordinating Data Acquisition and Access: The National Spatial Data Infrastructure"
(NSDI), which was signed by the President on 11 April 1994, requires that all Federal agencies use
the FGDC Metadata Standard to document new geospatial data and make them electronically
accessible through the use of a National Geospatial Data Clearinghouse. Executive Order 12906
also assigned authority for the development of national geospatial data standards to the FGDC.
The FGDC standards development program ensures that standards are created under an open
consensus, with participation by non-federal and federal communities.
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The FGDC geospatial data standards provides a "Logical Data Model" consisting of descriptive
feature names (entity), attribute names, and domain names. However, this data model must be fully
developed into a "Physical Data Model" before it can be implemented in a GIS. That is, all
symbology (e.g., symbols, colors, fonts, line types); level/layer schemas; coverages; file table,
attribute, and domain names which are compatible with commercially available GIS and relational
database management systems must be developed. The TSSDS provides the "Physical Data
Model" for implementation of the approved FGDC geospatial data standards in a GIS. The TSSDS
has been designed to comply with the Spatial Data Transfer Standard (SDTS) data model, and has
been updated to permit compliance with the recently revised FGDC Metadata Standard. Provisions
of the FGDC Bathymetric Geospatial Standard (International Hydrographic Standard (IHO S-57))
were incorporated into the TSSDS Release 1.6. The FGDC Vegetation, Wetlands, and Soils
standards have been incorporated into the CADD/GIS Center's TSSDS/TSFMS Release 1.8. In
addition, two of the standards currently under development by the FGDC Facilities Working Group
(Environmental Hazards Geospatial Standard and Utilities Geospatial Standard) originated from the
TSSDS.
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Planning Strategies for Siting Animal Confinement Facilities:
The Integrated Use of Geographic Information Systems
and Landscape Simulation Technologies
T. L Cartlidge1, B. Chamberlain2, D. G. Pitt1, M. Olson3,
B. Halverson2 and T. Harikrishnan1
1 Department of Landscape Architecture, University of Minnesota, Minneapolis, MN
2Hoisington-Koegler Group, Minneapolis, MN
Department of Landscape Architecture, The Pennsylvania State University, University Park, PA
Abstract
Through presentation of a case study, a series of planning strategies for siting animal
confinement facilities in the rural landscape are compared and contrasted. The strategies use
geographic information systems (GIS) technology to develop an environmental protection
framework for a 31,000-hectare watershed in west central Minnesota. The framework is based
on desires to maintain landscape integrity as reflected in enhanced biological diversity,
conserved soil and improved water quality as well as to maintain neighborhood cohesion among
farm and non-farm neighbors in the rural landscape. Design of the production landscape is
compared and contrasted under three alternative scenarios: a) the use of Euclidean zoning; b)
the use of overlay zoning; and c) the use of technical assistance to small-scale operators to
enhance adoption of whole farm planning. Low elevation aerial oblique renderings are
presented as a means of communicating to stakeholder groups the spatial organization and
visual appearance of agriculture in the rural landscape following implementation of the
strategies.
Introduction
The location and operation of animal confinement facilities in rural landscapes is an issue of
large environmental concern in states having economic bases that include livestock production.
In the recent gubernatorial elections in Minnesota, for example, all candidates were expected to
voice their position on a legislatively proposed moratorium on animal confinement facility siting.
The State's Environmental Quality Board is preparing a Generic Environmental Impact
Statement (GEIS) on animal confinement facilities in Minnesota. Among other concerns, the
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GEIS is examining the effects of animal confinement facilities on water quality, air quality, the
structure of local economies, animal health, human health and the changing characteristics of
rural communities. In addition, the GEIS will also examine the effectiveness and capability of
local land use planning strategies in considering animal confinement facility siting.
The diversity of issues being considered in the GEIS is symptomatic of the complexity of the
issues surrounding animal agriculture in the contemporary rural landscape. No longer can the
focus rest solely on the impacts of production facilities on physical and biological characteristics
of the environment. Evaluation of animal agricultural issues in the twenty-first century also
requires assessment of social, economic and political concerns. The changing structure of
American agriculture has affected the manner in which farmers conduct their business as well
as the definition of who is a farmer. Scale of operation has increased significantly. Corporate
structures of farm enterprises often break the traditional land-based agrarian ties of operators as
land becomes little more than another factor of production. Rural community structure
deteriorates as seemingly strangers operate land once cared for by trusted neighbors. Small
township governments sometimes find themselves overpowered by the corporate structures
with which they must often interact in issues related to animal agriculture.
The physical, biological, social economic and political issues associated with animal agriculture
must be examined at the scale of the individual production facility, the immediate landscape
context of the facility as well as the region within which the facility is located. Examining the
spatial dimensions of these issues at the landscape and regional levels lends itself directly to
the use of geographic information systems (GIS) technology. Planners attempting to resolve the
complexities of animal agriculture issues can use GIS technology to integrate the diverse issues
across space and spatial scales, and they can use the technology to develop sophisticated map
representations of their findings and recommendations. The map representations can serve as
a basis for creating realistic image simulations to present recommendations to various
stakeholder groups.
The University of Minnesota Extension Service sponsored a Rural Landscape Project to
demonstrate how local units of government could use geographic information systems (GIS)
technology and image simulation technologies to enhance their abilities to plan for animal
confinement facilities. The demonstration project was located in the 31,000-hectare Sacred
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Heart drainage basin, a tributary of the Minnesota River in west central Minnesota. Figure 1
illustrates existing land use and cultural settlement patterns in the basin.
Figure 1. Land Use and Cultural Settlement
Sacred Heart Basin
Renville and Redwood Counties, MN
Land Use
d] Cropland
•• Pasture
'.':.;,.-'.I Farmstead
•nil Forest
m Other rural development
1.11 Urban
• Open water
• Wetland
Transportation
and Drainage
/y State highway
/^County state-aid highway
County road
V City street or township road
Drainage
Cultural
Settlement
* Farmer
" Non-farmer
• Retired farmer
Objectives of the demonstration project were:
• To evaluate at the local level - in rural communities- the land management
alternatives that can be used to sustain animal confinement agriculture in the rural
landscape. This objective sought the identification of strategies to conserve soil,
maintain water quality, enhance biological diversity, contribute to regional economic
health, maintain the viability of individual farm enterprises and enhance the well
being of people living and working in the landscape. The objective sought means of
building and sustaining healthy ecological systems, healthy economies and healthy
communities in the Sacred Heart basin.
• To broaden input and further discussion of critical issues related to animal agriculture
and the rural landscape between producers and their neighbors, policy-makers and
communities, and state and local governments.
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• To help foster consensus on principles for sustaining the rural landscape of west
central Minnesota.
The case study is proceeding in three phases. The first phase involved a series of in-depth
interviews and focus group discussions with selected animal agriculture operators in west
central Minnesota. Along with an examination of agricultural statistics for the region and a
review of relevant literature, the interviews and discussions enabled the demonstration team to
better understand the issues associated with animal agriculture operation in the region. The
second phase of the case study, and the subject of this paper, developed a series of planning
strategies to enable continuation of sustainable animal agriculture in one of the region's
watersheds. In developing the strategies, plan view mapped representations of the designs
were created. To better communicate the design strategies to different sets of stakeholders
during a subsequent round of workshops, the strategies were also represented as hand drawn,
low-elevation aerial oblique renderings. The renderings were prepared to offer stakeholders a
sense of how the design strategies would affect spatial organization of agriculture in the rural
landscape as well as engender a sense of the landscape's visual appearance following
implementation of each strategy. The third phase, to be conducted in the fall of 1999, will
engage a series of stakeholder groups in the region in conversation about the planning
strategies. The workshops will involve use of the GIS mapped information as well as the aerial
perspective renderings of the various landscape design alternatives.
Designing an Environmental Protection Framework
The first step in creating a planning strategy for siting animal confinement facilities involved
development of an environmental protection framework. The purpose of the framework was to
identify those components of the landscape in the Sacred Heart basin that warrant special
protection in meeting the objectives of conserving soil, protecting water quality, enhancing
biological diversity and promoting community values. The framework was defined in terms of
two dimensions. Landscape integrity, a measure of the ability of landscape structure to maintain
ecological function, defined a balance between environmental quality and agricultural production
values. Neighborhood cohesion defined relationships between farm and non-farm residents of
the rural landscape.
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Defining Landscape Integrity
Landscape dimensions critical to defining integrity included:
a) slopes exceeding 12% slope;
b) highly erodible soils;
c) existing surface hydrology;
d) soils that are occasionally or regularly flooded;
e) hydric soils as defined by USDA-NRCS criteria.
f) existing forest, wetland, open water and grassland communities;
g) National Wetland Inventory sites;
h) land enrolled in the Conservation Reserve Program;
i) that one-fourth of the watershed's total area whose soils contain the lowest potential
productivity for corn and soybean;
j) locations of existing animal confinement facilities; and
k) existing field pattens.
Data related to these dimensions were gathered from the Soil Survey Manual of Renville
County, Minnesota, digital aerial ortho-photographs of the watershed, digital raster graphic
images of the 7-1/2 minute Topographic Quadrangles for the watershed and various existing
data sources maintained by agencies of state government (e.g. National Wetland Inventory
sites, existing animal confinement facilities). The data were compiled into a GIS data base using
Arc-lnfo™technology. Figure 2 illustrates the data used in defining landscape integrity.
Determinants of Soil Conservation and Water Quality. Soils that were steeply sloping, highly
erodible, occasionally or regularly flooded or contained hydric conditions, especially where they
were located in close proximity to surface hydrologic features, were defined as being critical to
maintaining surface and ground water quality.
Determinants of Biological Diversity. The original pre-settlement vegetation of the watershed
contained upland conditions of xeric, mesic and hydric prairie and bottomland conditions
predominated by cottonwood, elm, silver maple and other floodplain species, oak forest and oak
openings existed on the south-facing bluffs along the Minnesota River. One hundred and twenty
years of agriculture in the watershed has essentially eliminated the prairie vegetation from the
landscape, and over 80% of the wetlands have been drained. The cultural landscape of farming
introduced upland forest vegetation to the late nineteenth century landscape in the form of
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Figure 2. Components of Landscape Integrity
Hydro log i
Features
Erodible
Landscape
•*V* Perennial stream
.•'-..• Intermittent stream
/V Drainage channel
^B Open water
I I Wetland
HI National Wetlands Inventory site
^^ Frequently or occassionally flooded land
Hydric soil
Productivity
of Soil
I I Open water
EBI Highly erodible land
HI3I Slopes exceeding 12 percent
Wildlife
Habitat
^| Open water
j-.'.."i Not prime agr land
ill Lowest soil productivity
I I Wetland
^H Open water
l;:;:;:-l Grassland
Forest windbreak
•1 Forest patch
• CRP I and holdings
shelterbelt patches and strips of hedgerow and fencerow vegetation. As economic scale of farm
operations increased in the last thirty years, many of the hedgerows and fencerows were
removed. Similarly, many shelterbelt patches associated with abandoned farmsteads were
removed. The result of these cultural influences was the creation of a mono-typical landscape
matrix characterized by increasingly larger fields of either soybean or corn. This pattern was
broken only where farmers enrolled less productive land in the Conservation Reserve Program
(CRP) during the 1980's. Identification of remnant wetland systems, forest patches and strips,
grassland communities and CRP land holdings became a priority step in defining patterns of
biological diversity in the watershed. Forest patches were differentiated from strips using a 200-
meter forest patch width criterion.
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Defining Neighborhood Cohesion
Assessing neighborhood cohesion involved mapping the locations of farmers, retired farmers
and rural non-farm residents in the landscape. The density of non-farm residents in each land
section was mapped. This strategy assumed that non-farm rural residents have greater aversion
to the externalities of animal confinement operation (e.g. odor, impacts of transportation, etc.).
Figure 3 illustrates the density of non-farm residents in each land section.
Figure 3. Density of Non-farm Rural Residents Per Section
Sacred Heart Basin
Renville and Redwood Counties, MN
/\/ State highway
County state-aid road
\/ County road
\ / Township road or city street
• Non-farm resident
|i:i:i|||iii|:| One non-farm resident per section
Two non-farm residents per section
Three non-farm residents per section
Design of Environmental Protection Framework
The dimensions of landscape integrity and neighborhood cohesion provided the raw material for
creation of the environmental protection framework (see Figure 4). Within the context of these
dimensions, design of the framework pursued five principles:
Define and protect riparian corridors within the watershed. Accomplishment of
this design principle ranged from protection of the extensive floodplain systems that
exist along the bottomlands of the Minnesota River and its tributaries to delineation
of ten meter vegetative buffer strips along the constructed upland drainage systems.
Definition of the riparian corridors included bluff and steeply sloped landscapes
adjacent to the floodplain and drainage systems. It also included integration of any of
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the dimensions of either landscape integrity or neighborhood cohesion that were
adjacent to the riparian system. For example, corridor definition incorporated areas
that were adjacent to the riparian corridor and contained soils with low productivity
values.
• Provide a network of upland connections among riparian corridors. This
objective established an upland network of connectivity among the riparian corridors.
Components of the network were defined initially on the basis of remnant wetland,
woodland and grassland communities as well as existing CRP holdings. The location
of such factors as the presence of low productivity or hydric soil conditions, steep
slopes, highly erodible soils, or National Wetland Inventory sites connected remnant
communities to themselves and to the riparian corridors.
• Provide buffering around non-farm residents living in the rural landscape.
Areas containing high concentrations of rural non-farm residents became
components of the environmental protection matrix.
• Use existing patterns of cultural settlement to establish the boundaries of the
framework. Within the context of maintaining the integrity of the spatial pattern of the
environmental protection framework, existing field patterns provided a basis of
defining framework edges.
• Define uses appropriate to landscape characteristics. Within the environmental
protection framework, land uses would be permitted based on their appropriateness
to the resource characteristics of the landscape. For example, low input perennial or
agro-forestry crops were considered appropriate in vegetative buffers of upland
drainage riparian systems.
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Figure 4. Environmental Protection Framework
Sacred Heart Basin
Renville and Redwood Counties, MN
Legend
/%/" State highway
/\/ County state-aid road
/ \ , County road
/ Township road or city street
Drainage
Open water
Environmental Protection
Framework
6 Miles
onmental
Framework
Defining Farm Production Units in the Landscape
The environmental protection framework established an armature within which commodity
production values in the landscape can be realized. Farm production units involved in pursuit of
these values were classified along three continua: crop diversity; farm scale; and operation
distribution. Crop diversity varied from highly specialized production of one or two cash crop
commodities (e.g. corn and soybean) to farm units that combine dairy and pork production with
forage and pick-your-own truck crop production. Farm scale referred to the capital intensity of
the farm enterprise, and it generally correlated with geographic size of the operation. Finally,
operation distribution referred to the geographic dispersion of the enterprise across the
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landscape. Distribution varied from farm units operating in one concentrated locus to units
operating across several different locations. As illustrated in Figure 5, these three criteria were
used to describe farm units operating in the Sacred Heart watershed.
Figure 5. Farm Production Units Can Be
Characterized Along Three Continua.
Crop Diversity
Mono
Multi
Small
Large
Operation Distribution
I
Centralized
Dispersed
Designing the Production Landscape
Landscape not included in the environmental protection framework was defined as production
landscape. Three principle functions occurred within the production landscape: animal
production; crop production; and residential uses associated with both farm operators as well as
non-farm inhabitants. The case study investigated three scenarios by which these uses might
be managed and spatially organized within the production landscape to establish a sustainable
rural environment. Each scenario described a prototypical set of conditions that might be found
in areas wherein the policies inherent in the scenario were adopted. A low-elevation, oblique
aerial perspective rendering accompanies the presentation of each scenario to depict its spatial
structure and appearance in the landscape.
10
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Scenario One: Large Scale Zoning Approach
The first design scenario (see Figure 6) involved establishment of four exclusive use zoning
districts:
a) an environmental protection zone;
b) an animal agriculture enterprise district;
c) an intensive cropping district; and
d) a rural residential district.
Characteristics of the environmental protection zone were previously defined, and the
characteristics of the remaining districts are described below.
Animal Agriculture Enterprise District
An animal agriculture enterprise district consisted of large-scale confinement facilities clustered
within the same geographic proximity. Animal agriculture was the permitted use within the
district. Location of the considered such criteria as: a) proximity to components of the
environmental protection zone; b) maintaining a 1.5 mile buffer between the enterprise district
and components of the rural residential district; and c) existing transportation infrastructure. A
set of performance criteria relating to air and water quality, resource recycling and the
enhancement of biological diversity regulated operation of the animal agriculture uses within the
zone. The enterprise districts were large enough to permit establishment of centralized
treatment facilities for manure management and treatment.
A manure management cooperative, structured similarly to a rural electric cooperative,
managed the waste products of several enterprise districts. The scale of the cooperative's
operation permitted it to become involved bye-product. The cooperative established and
maintained integrated relationships between generators of manure sludge within the enterprise
district and users of manure sludge in the adjacent cropping district. The cooperative also
managed integrated relationships between forage producers in the cropping district and forage
consumers in the enterprise district. These cooperative relationships allowed the creation of
closed production systems in which size of the enterprise district was determined by the forage
production and sludge handling capabilities of producers in the adjacent cropping district.
11
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Cropping District
Within a cropping district, large scale crop production, intended in part to supply forage for and
process manure sludge from adjacent animal enterprise districts, was the preferred use. The
location of the cropping districts was based on three criteria: a) soil productivity, b) constraints
and opportunities defined by the environmental protection framework; and c) provision of a 1.5
mile buffer between the enterprise districts and the rural residential districts. The transportation
infrastructure within a cropping district was maintained primarily to foster delivery of products to
market and interaction between individual producers in the cropping district and animal
producers in the enterprise district. Management of this infrastructure involved the conversion of
redundant township roads into field roads or the transformation of these rights-of-way into
components of the environmental protection framework. Performance standards and incentives
operating within the cropping district encouraged adoption of best management practices,
establishment of a routine fallow program for fields and creation of the environmental protection
framework. Since lands within the cropping district would be down-zoned to crop production
uses, a transfer of development rights program (TDR) or a purchase of development rights
program (PDR) was established to permit landowners to participate in the development
windfalls that accrued to landowners in the rural residential district. Either of these programs
allowed development rights transfers into the rural residential district were such non-farm uses
were permitted and encouraged.
Rural Residential District
The fourth zoning district was a rural residential district. Within this district, rural residential uses,
hobby farms and small-scale farm operations were primary uses. Pre-existing residential uses in
the cropping or enterprise districts remained in their current locations, although they would be
eligible for relocation into a rural residential district. All future rural residential development
would occur within a rural residential district. Future development was encouraged to follow
cluster strategies in implementation, and density bonuses became available to developers
pursuing cluster strategies. Location of the rural residential districts were based on: a)
constraints and opportunities defined by the environmental protection framework; b) provision of
a 1.5 mile buffer between the enterprise districts and the rural residential districts; and c) the
suitability of soils for development and domestic waste management (either as septic tank
drainfield effluent or alternative technologies). Within the rural residential districts, the existing
pattern of transportation infrastructure was maintained.
12
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Policy Environment of Large Scale Zoning Approach
The policy environment of the large scale zoning approach involved an intensive regulatory
framework. Having defined the environmental protection framework, a significant amount of
centralized planning established the boundaries of the other three districts. An administrative
framework managed the performance standards and incentive systems operating in the
districts, and the framework established the operating procedures of the development rights
Figure 6. Large Scale Zoning Approach
transfer program. A forage production and manure sludge-handling cooperative managed
integrated relationships between forage production and sludge handling.
Scenario Two: Mixed Scale Overlay District Approach
Rather than relying on the rigidity of an Euclidean approach to land use separation through
exclusive use districts, scenario two adopted the flexibility of overlay district zoning. In this
scenario, an application for development or expansion of an animal confinement facility
triggered a request to implement an overlay district. The district consisted of all lands within a
1.5 mile radius from the confinement facility (see Figure 7). The application was reviewed in a
site selection process that examined the overlay district's location relative to: a) constraints and
opportunities defined by the environmental protection framework; b) existing transportation
infrastructure; and c) proximity to rural non-farm residential uses.
13
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Applicants were required to negotiate and secure operating agreements with all landowners
within the confines of the overlay district. The requirement to negotiate operating agreements
with all landowners within a 1.5 mile radius of the led to the agglomeration of such facilities
within the rural landscape. However, rather than legislating these concentrations as was true in
the zoning approach, the agglomerations evolved through market activity. Performance criteria
relating to air and water quality, resource recycling and the enhancement of biological diversity
governed operation of the livestock facility within the overlay district. To the extent practical,
operators were encouraged to pursue bye-product recovery (e.g. capture of methane production
for small-scale energy generation). The existing pattern of transportation infrastructure within
the overlay district was maintained.
The overlay district concept required a less intensive regulatory framework than did the large
scale zoning approach. Administration of the site selection and overlay district creation process
was required along with administration of performance standards. Otherwise, administrative
functions occurred in the private sector through landowner to landowner transactions or through
adjudicatory processes initiated to resolve compensatory claims issues.
Figure 7. Mixed Scale Overlay District Approach
14
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Scenario Three: Small Scale Diversified Farming Approach
The third scenario (see Figure 8) assumed a livestock commodity price and economic structure
that favored a return to smaller scale more diversified farm operations. In this scenario,
individual farms pursued both crop and livestock production, and alternative and low-input
agricultural systems became more widely accepted. Individual operators became both forage
producer and forage consumer; both manure producer and manure consumer. Farming became
a closed system wherein operating scale was based on potential productivity and manure
assimilative capacity of soils. Farmers pursued whole farm planning with technical assistance
from USDA-NRCS personnel. Implementation of this planning activity, coupled with incentives
to encourage adoption of best management practices, enabled establishment and maintenance
of the environmental protection framework. Performance criteria relating to air and water quality,
resource recycling and the enhancement of biological diversity governed farming operations.
The policy environment of scenario three required regulatory activities associated with
administration of performance standards. The technical assistance involved in whole farm
planning coupled with educational efforts and incentives associated with adoption of best
management practice adoption also required administrative activity.
Figure 8. Small Scale Diversified
Farming Approach
15
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Conclusions
None of the three scenarios, by itself, represents the single planning approach to
accommodating animal confinement facilities in the rural landscape. Collectively, the scenarios
represent a catalog of strategies that can be applied in different physiographic and cultural
contexts. In many instances, the most appropriate solution may involve a combination of
strategies. Regardless of how the production landscape is designed and managed, it is
important to frame the designs around the establishment and maintenance of the environmental
protection framework. GIS technology is well suited for design of the framework. Execution of
the alternative production landscape designs is also well suited to the application of GIS
technology. Maps produced from GIS technology lend themselves readily to the production of
illustrative graphics, which can be used to further explain the spatial structure and appearance
of a sustainable rural landscape for animal production.
16
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A GIS-Based Approach to Characterizing Chemical Compounds in Soil and
Modeling of Remedial System Design
Leslie L. Chau, Charles R. Comstock, and R. Frank Keyser
ICF Kaiser Engineers, Inc., Oakland, California
Introduction: The Problem
The cost-effectiveness of implementing a computerized
geographic information system (CIS) for environmental
subsurface characterization should be based on long-
term remedial objectives. A CIS project was developed
to characterize soil contamination and to provide design
parameters for a soil vapor extraction remedial system,
as part of a $120-million remediation and "land sale"
project in California. The primary purposes of the CIS
were to efficiently combine and evaluate (model) dispa-
rate data sets, provide "new" and more useful informa-
tion to aid in short-term engineering decisions, and
support the development of long-term cleanup goals.
The project had a major change in scope early on, and
the schedule was expedited to allow for the develop-
ment of "land sale" options and for actual site redevel-
opment at the earliest opportunity. Characterization of
chemically affected soil would have been compromised
given the above circumstances without an ambitious
undertaking of concurrently developing and implement-
ing a CIS with three-dimensional (3-D) geostatistical
and predictive modeling capabilities.
The GIS Approach
Computer solutions included the use of a cross-platform
(DOS and UNIX) GIS to quickly and systematically in-
corporate spatial and chemical data sets and to provide
a distributed data processing and analysis environment
(see Figure 1). Networked, DOS-based relational data-
bases were used to compile and disseminate data for
the numerous investigatory and engineering tasks.
UNIX-based computer aided design (CAD) and model-
ing applications received data from databases, per-
formed quantitative analyses, and provided 3-D
computer graphics. Given the aggressive project sched-
ule, exclusive use of one platform would not be realistic
due mainly to the limited data modeling capacity and 3-D
graphics in DOS systems. On the other hand, the high
startup and operating costs of several UNIX worksta-
tions would render their exclusive use much less cost-
effective.
The hardware and software configurations were inte-
grated in a client/server Intergraph InterPro 6400 with
48 megabytes of memory. It is largely a 3-D CAD system
with add-on modules of geologic mapping and 3-D vis-
ual models capable of consolidating both environmental
and engineering parameters for analysis (see Figure 1).
Textual environmental and geologic data were extracted
by SQL queries from relational databases and were
transferred to mapping and modeling modules via
PC/TCP cross-platform linkage.
The GIS assisted in making short- and long-term deci-
sions regarding health-risk-based regulatory strategy
and engineering feasibility. Use of spatial statistical and
predictive models was part of a CIS-based decision-
making loop (see Figure 2). The process supported
concurrent activities in:
• Data collection: field program.
• Numerical models of remedial system configurations.
• Development of cleanup goals from health risk
assessments.
• Remedial design with CAD capability.
Site Background
In early 1993, the remedial investigation of the operable
unit for soil at a former aircraft manufacturing facility in
southern California was thought to be ready for remedial
alternatives feasibility study. ICF Kaiser Engineers, Inc.,
was awarded the contract to perform feasibility studies
on applicable soil cleanup technologies and to sub-
sequently design and manage the installation and early
operation of the selected technologies. After $700,000
was spent evaluating data collected by previous consult-
ants, it was decided that an additional $5 million worth
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DOS-Based
Token Ring Network
Site
Survey
Activities
Sampling
h*
*
RDBMS
(Paradox)
UNIX-Based
Data Visualization and Computer-Aided Engineering
PC/TCP
Informix
RDBMS
Laboratory
Analytical
Results
Visualization
CADD
Health Risk
Assessment
Modeling Module
Regulatory
Reporting/
Interaction
3-D Kriging Spatial
Analysis
3-D Vapor Flow
Analysis
3-D Chemical Mass
Transport
Analysis
High Impact 3-D
Solid Models
Volumetric
Analysis of
Static and
Dynamic Mass
Flow
Subsurface
Engineering
3-D Piping
Electrical
Treatment
System Housing
Accelerated Implementation To Establish Cle
Require Low Overhead Upfront Cost and Off-the
PC-Based
Data Visualization
Report Generation
anup Goals
Shelf Software
Regulatory and
Public
Interaction
Client
Deliverables
Extended Engineering Phase
Specialized CAD Software and Analytical Staff
Figure 1. Multiplatform GIS project.
Ongoing Site Investigation
Soil/Soil Vapor Sampling
Data Processing
1. Storage (RDBMS)
2. Dissemination
Modeling
1. Spatial Correlation
2. Fate and Transport
Conceptual
Treatment Alternatives
Health Risk
Assessment
Permitting
Environmental Planning
Site Redevelopment
Establishment of
Cleanup Goals
Final Remedial
Design
Figure 2. GIS-assisted decision tree.
of field activities were required to more definitively esti-
mate the volume of chemically affected soil and the
nature and extent of contamination at the facility. Be-
cause of the data gaps, the selection and design of
alternatives could not be addressed with a high degree
of certainty. Hence, computer assisted data processing
was crucial to speed up the feasibility study, accelerate
downstream work, and reduce the overall project sched-
ule to the minimum.
The site is environmentally complex, covering an area
of approximately 120 acres. As a result of nearly half a
century of aircraft production and development, soil be-
neath the facility is affected by fuel and heavy oil hydro-
carbons (TPH) commingled with volatile compounds,
mainly perchloroethylene (PCE) and trichloroethylene
(TCE) (see Figure 3). Ground water at 170 feet below
ground surface is affected by TCE and PCE, but it is not
part of the drinking water aquifer. The facility has been
demolished, and shallow contaminated soil has been
excavated and back-filled to an interim grade.
Methodology
Health-Risk-Based Cleanup Goals
Central to determining the volume and kinds of data to
be collected was the question of whether chemicals in
soil represented potentially unacceptable risks to human
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PCE+TCE TOTAL CONCENTRATION
•••
• •1.0.
VAPOR DISTRIBUTION
Figure 3. Aircraft manufacturing facility in California. Outline of demolished buildings located at the 120-acre site are shown as
surface features for reference. A geostatistical model of a 3-D kriged VOC soil vapor cloud in the subsurface was simulated
with Intergraph's MGVA. Views displayed are: 3-D isometric, vertical section of chemical isoplaths, and a nearly plan view.
Digital simulation also illustrates VOCs affecting ground water in a dispersive nature at a depth of nearly 170 feet bgs
(shown at bottom of isometric view).
health and to the environment, with the former being of
particular concern to construction workers onsite during
redevelopment.
Because site redevelopment was scheduled to begin in
the near term, data collection and CIS analysis concen-
trated on shallow depths (top 20 feet), with decreasing
sample density at greater depths. A health-risk-based
cleanup goal (HBCG) approach to collecting more data
was to establish cleanup goals for near-term remedia-
tion of the shallow soils as well as for long-term remedial
measures of contaminated soils at greater depths. Fur-
ther, various regulatory agencies had to approve the
estimated cleanup goals in a short time. Ongoing site
demolition and excavation schedules encouraged the
aggressive regulatory negotiations. The shallow cleanup
goals for volatile organic compounds (VOCs) and TPH
determined the volume of contaminated soil to be re-
moved. At greater depths, data gaps were minimized to
more definitively characterize the nature of TPH and
VOC contaminations and to facilitate the implementation
of long-term remedial objectives (i.e., in situ soil vapor
extraction).
In situ soil vapor and soil sampling composed the field
program, which provided data to map the subsurface
distribution of volatile organic compounds, including
TCE and PCE. Only in situ soil sampling was used for
characterizing TPH. The ratio of soil vapor to soil sam-
ples was 4:1. No previous soil vapor information was
available. ICF Kaiser has been refining the technique of
comparing results from paired soil vapor and soil sam-
ples in past and similar projects. Hydraulic probes were
used instead of drilling to acquire soil vapor samples at
shallow depths. This minimized waste and cost in the
field program significantly.
Risk Assessment and Spatial Analysis
Human health risk analyses were conducted for the
entire site, and risk factors were contoured and overlaid
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on maps of past usage and known soil contamination
areas. Before the risk modeling could proceed, chemical
and lithological data gathered in the past 7 years and
those acquired by ICF Kaiser populated the environ-
mental relational databases. Approximately 522 soil va-
por probes were located in 100-square-foot spacings
with additional probes in areas requiring better plume
definitions. The database contains approximately
15,000 xyz-records of soil and soil gas laboratory ana-
lytical results. This information in text and graphics form,
combined with site infrastructures and building outlines
with attributes of "past usage," were stored as map
layers, making up the CIS nucleus. Accuracy of site
maps was verified with aerial photographs when avail-
able. Data types combined for computerized evaluation
included known locations of contaminated soil, contami-
nated ground water, soil types, and site features. Com-
posite risk maps of the above data were analyzed for
data gaps at discrete depth intervals. This analysis was
performed while the field program was in progress
and hence gave guidance to optimize the locations of
additional data points and to minimize the number
of samples taken.
The MGLA/MGLM mapping module and the MSM ter-
rain modeling module tracked the earth excavation and
removal of contaminated soil. Excavation was largely
part of site demolition. It also expedited the removal of
TPH contaminated soils, however, because no other
short-term means of remediation are available for these
substances. Tracking of removed soils was essential
because concurrent field activities were occurring in site
demolition, data gathering, and risk modeling.
The CIS coordinated all three. Geologists and surveyors
provided terrain data from daily excavation activities,
which were transcribed into database formats. Maps
illustrated the locations of excavated soil and removed
chemicals in soil at various depths. Although TCE and
PCE were of foremost concern as health risks, all com-
pounds and some metals identified in soil were
screened for unacceptable risk. Terrain modeling (map-
ping) as part of health risk assessment may seem un-
usual, but results of estimated cleanup levels and
accurate locations of left-in-place contamination, mostly
soils at greater depths, were critical to the cost-effective-
ness and proper design of long-term remedial systems.
Characterization of Subsurface VOCs
In situ soil vapor extraction (SVE) of total volatile com-
pounds in dense nonaqueous, liquid, gaseous, and ad-
sorbed solid forms in the subsurface produced favorable
results that have been well documented in recent years.
ICF Kaiser proposed a very large-scale SVE system
(see Figure 4), perhaps the largest yet, as long-term
remedial technology for this former aircraft manufactur-
ing site. The primary design problem was speculating on
air flow capacity and operating time of the complex
system components. The SVE system comprises three
fundamental elements:
• Front-end, in situ subsurface vents (totaling 193
corings).
• Applied vacuum and air transport manifolds linking
the subsurface vents to the treatment compound (dis-
tance of one-quarter mile with over 100 manifolds).
• A multivessel activated carbon treatment system.
To size the pipes, carbon vessels, and vacuum required
to achieve a certain rate of VOC removal, the total mass
and nature of sorption had to be known. Due to the
schedule-driven nature of this project, the SVE design
accounted for the time needed to accomplish the
cleanup goals.
To estimate the extent and total mass of VOCs in the
subsurface, soil vapor data were input to a 3-D kriging
algorithm (1) to produce a concentration continuum
model (see Figure 3). This solid model of predicted total
VOC concentrations took the form of a uniformly spaced
3-D grid-block that completely encased the site. Cell
sizes ranged from 10 to 20 cubic feet, depending on the
model run, number of data clusters, density of data
points in areas of clustered data, and the standard
deviation of variances for estimated values in all cells.
The Fortran program estimated a concentration value
for each cell based on the nearest field sample(s).
The validity of such "block kriging" models can be judged
by the size of the variances, smoothness, and agree-
ment with nearby field data. Because volume is a known
quantity in kriging, the total mass can be calculated by
incorporating soil bulk density or porosity, both of which
were less than abundant for this investigation. Render-
ings of kriged results in 2-D plan view contour maps,
cross-sectional maps, and 3-D "vapor cloud" (see Figure
3) were included in client reports and used in regulatory
presentations and public forums.
Remedial Design Layout
Final Extension of a Fully Integrated CIS
With the total mass and extent of VOCs derived from
3-D kriged results, the applied vacuum at individual vent
heads and the cumulative pressure (negative) neces-
sary to extract and transport VOC vapors from the sub-
surface to the treatment system can be estimated. We
performed 3-D air flow analysis by use of finite differ-
ence fluid flow models and chemical transport models.
The Fortran codes used to approximate compressible
flow and chemical transport were AIR3D (2) and VT3D
(3), respectively. Air flow simulations focused on maxi-
mizing vacuums at the shallow depths down to 20 feet
to expedite remediation of contaminated soils that were
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Figure 4. A rendering by the Intergraph 3-D Plant Design System of an in situ soil vapor extraction and treatment system. The
cutaway section located near the upper left portion of the figure exposes some of the 193 subsurface extraction vents
bottoming at 120 feet (bgs). These vents are located in a cluster for long-term extraction of the VOC vapor cloud presented
in Figure 3. Vents are connected to a system of parallel airflow manifolds (right side of figure), which runs one-quarter of
a mile to the treatment compound (foreground of figure).
not removed during site demolition and excavation. The
lower depths were also included in each simulation.
Transient mass transport models incorporate flow fields,
given by flow models, and predicted cleanup times
based on established HBCG cleanup goals. As VOC
concentrations in an operating SVE system fall below
cleanup levels in the top 20 feet, thus minimizing human
risk, available vacuums thereafter will be diverted to
vents at lower depths to be part of long-term extraction
scenarios. Models suggested that cleanup for the top 20
feet can be accomplished within 1 year.
Numerical models prescribed vacuum levels at each
vent head, which is the aboveground segment of a
subsurface SVE vent. The 193 vents are connected to
a system of parallel manifolds (see Figure 4) that trans-
port vapor to the treatment system. With the vacuums
known at vent heads, the size of manifolds and capacity
of vacuum blowers can be determined and integrated
into the overall system design. With 3-D Plant Design
module as part of the Intergraph CAD/GIS, manifold
layouts and treatment compound can be modeled in 3-D
and easily checked for pipe routing interferences. The
final layout of the SVE system was overlaid onto contour
maps of total VOC concentrations to check on accuracy
and completeness of vent locations and manifold layouts.
Conclusion
Maximized Visual and Analytical Responses
One goal of this project was to expedite regulatory
negotiations and gain early acceptance of cleanup
goals. The computerized data processing and visualiza-
tion contributed generously to the rapid understanding
of modeling results by expert regulators and the lay
public. Likewise, the CIS facilitated the response to
regulatory comments. Positive comments first came
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from the client's in-house review of model results and
the high impact 3-D color rendering of kriged VOC dis-
tributions in the subsurface (see Figure 3).
Analytically, benefits were derived from the efficiency of
electronic data access and the ability to "predict" the
presence of contaminant in areas with sparse field data.
The process of kriging involves the linear interpolation
and extrapolation of existing data. The resultant con-
taminant distribution is a "conservative" model that pro-
vided the best fit with field data and validated
conceptualized subsurface conditions. Further, models
provided conservative estimates of mass and extent of
PCE and TCE contaminations. Kriging also provided
information on the uncertainty of the predicted chemical
distribution, which is extremely useful for regulatory dis-
cussion and system design. The efficiency of computer
models allowed investigators to perform numerous
model runs with varied boundary parameters, such as
cell size and search radii, in the kriging process.
Accurate mapping of excavated soil and the removal of
most TPH source areas provided the incentive to criti-
cally assess the feasibility of a no-action remedial sce-
nario for these substances at greater depths. With
removal of many TPH source areas, 1 -D finite difference
models (4) were used to assess the mobility of TPH in
NAPL and adsorbed residual phase. Specifically, mod-
els assessed the likelihood of largely residual-phase
TPH affecting ground water and migrating upward to
affect indoor air volumes via gaseous diffusion. Results
were extremely favorable; models predicted negligible
likelihood of TPH affecting ground water or indoor air
volumes. Combined with CIS graphic evidence of spe-
cific areas of excavated soil and the absence of TPH
sources, regulatory agencies accepted the model re-
sults, and the no-action remedial alternative for TPH
was approved.
References
1. Deutsch, C.V., and A.G. Journal. 1992. Geostatistical software
library and user's guide. New York, NY: Oxford University Press.
2. U.S. Department of the Interior Geological Survey. 1993. AIR3D:
An adaptation of the ground-water flow code MODFLOWto simu-
late three-dimensional air flow in the unsaturated zone. Books and
open file reports. Denver, CO.
3. Zheng, C. 1994. VT3D: Numerical model for VOC removal from
unsaturated soil (draft). Bethesda, MD: S.S. Papadopulos and
Associates, Inc.
4. Rosenbloom, J., P. Mock, P. Lawson, J. Brown, and H.J. Turin.
1993. Application of VLEACH to vadose zone transport of VOCs
at an Arizona Superfund site. Groundwater monitoring and reme-
diation (summer), pp. 159-169.
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Using GIS/GPS in the Design and Operation of Minnesota's Ground Water
Monitoring and Assessment Program
Tom Clark, Yuan-Ming Hsu, Jennifer Schlotthauer, and Don Jakes
Minnesota Pollution Control Agency, St. Paul, Minnesota
Georgianna Myers
Water Management Consultants, Denver, Colorado
Abstract
Minnesota's Ground Water Monitoring and Assessment
Program (GWMAP) is administered by the Minnesota
Pollution Control Agency (MPCA) to evaluate baseline
ground-water quality conditions regionally and state-
wide. The program uses a systematic sampling design
to maintain uniform geographic distribution of randomly
selected monitoring stations (wells) for ground-water
sampling and data analysis. In 1993, geographic infor-
mation system (CIS) and global positioning system
(GPS) technologies were integrated into GWMAP, auto-
mating the selection of wells and the field determination
of well locations.
GWMAP consists of three components: the statewide
baseline network, regional monitoring cooperatives,
and a trends analysis component. In the statewide
baseline network, Minnesota is divided into over 700
121-square-mile grid cells, each with a centralized,
9-square-mile sampling region. Wthin each target area,
single-aquifer, cased and grouted wells are sampled for
about 125 metals, organic compounds, and major cat-
ions and anions. We are currently finishing the second
year of a 5-year program to establish the statewide grid.
When complete, the statewide baseline component will
consist of about 1,600 wells representing Minnesota's
14 major aquifers.
In 1993, approximately 4,000 well construction records
were selected for geologic and hydrologic review, using
a CIS overlay, from a database of 200,000 water well
records maintained in the state's County Well Index
(CWI). Using GPS, 364 wells were sampled and field
located. The semiautomatic well selection process uses
existing electronic coverage of public land survey (PLS)
data maintained in CWI in conjunction with the digitized
systematic sampling grid. CIS has greatly reduced the
time needed for selecting sampling stations. With the
combination of CIS and GPS, program costs have de-
creased, allowing more resources to be applied toward sam-
pling, while efficiency and quality of data have improved.
Introduction
Quantitative assessment of ground-water quality condi-
tions requires a highly organized data collection pro-
gram that includes statistical evaluation of monitoring
results (1, 2). States have difficulty providing the staff
and financial resources necessary to generate state-
wide quantitative ground-water information. Wth the
use of geographic information system (CIS) and global
positioning system (GPS) technologies, however, states
have the potential to improve the quality of environ-
mental monitoring programs and to reduce the amount
of staff time needed to collect and evaluate data, thus
decreasing costs. The degree to which states realize
these potential benefits depends largely on how effec-
tively the technology can be incorporated into the design
of the monitoring program. This paper describes how
CIS and GPS technologies are being integrated into the
design and operation of Minnesota's Ground Water
Monitoring and Assessment Program (GWMAP) to im-
prove overall effectiveness.
The Minnesota Pollution Control Agency (MPCA) has
sampled and analyzed ambient ground-water quality in
the state's 14 principal aquifers since 1978. In 1990, the
MPCA began a redesign of its ground-water monitoring
program to better assess water quality conditions state-
wide (3). Three program components resulted from the
redesign: a statewide baseline network for complete
geographic coverage, a trends analysis component for
intensive studies of how ground-water quality in specific
areas changes with time, and a regional monitoring
cooperative link to governmental units such as counties
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to meet specific local ground-water assessment needs.
This paper describes the design and operation of the
statewide baseline network.
The design of the statewide network is geographically
and statistically based to automate well selection and
data interpretation. In 1993, the MPCA began integrat-
ing CIS and GPS technologies into this part of the
program. The implementation of CIS and GPS sur-
passed our expectations by reducing staff time re-
quired to select wells and evaluate analytical results
(see Table 1). In addition, through the elimination of
previously uncontrollable variables, the use of CIS and
GPS has increased the accuracy of GWMAP data.
Monitoring Program Description
Since 1992, GWMAP has selected 150 to 250 existing
water supplies yearly for ground-water sampling and
analysis of about 125 parameters, including major cat-
ions and anions, metals, and volatile organic com-
pounds. Well selection is a fundamental element of
GWMAP that, if efficiently performed, supports the pro-
gram objectives by upholding the quality of the monitor-
ing data and minimizing the operating costs.
A key to the interpretation of monitoring data is the
technique used to select wells for sampling (2, 4, 5).
Minnesota has over 200,000 active water wells with
approximately 10,000 new installations annually. For
each well selected for GWMAP monitoring, a hydrologist
must individually review many well construction records.
An automated prescreening mechanism to facilitate well
selection can result in considerable time (and therefore
cost) savings. GWMAP chose CIS as the best tool for
this task. CIS enables the program to combine a sys-
tematic sampling technique with hydrogeologic criteria
to ensure an efficient and consistent selection process.
As Table 1 shows, CIS allowed us to more than triple
our geographic coverage and wells initially selected,
while dramatically reducing the records that must be
individually reviewed. We realized a time savings of 2
months compared with the time required before CIS
implementation.
In general, systematic sampling techniques use a ran-
domly generated uniform grid to determine sampling
locations in space and/or time (5). Systematic sampling
was initially implemented in GWMAP in 1991 using a
manually generated spatial grid defined by the public
land survey (PLS) (3). Although the PLS is not 100
Table 1. Well Selection in 1992 and 1993
percent geographically uniform, it was selected for the
grid to expedite well selection from existing digital data-
bases in which wells are organized by PLS location.
Systematic Sample Site Selection
Using GIS
Systematic sample site selection is a three-step process.
First, a database search of Minnesota's County Well Index
(CWI) (6), containing nearly 200,000 driller's records, is
conducted to include all available water wells in the
region of interest. Second, the candidate pool is reduced
to those wells located within regularly spaced grid cells.
Third, further wells are eliminated from the candidate
pool by applying geologic and well construction criteria
mandated in the GWMAP design (7).
Generating the Sampling Grid
The statewide sampling grid was generated from a randomly
selected origin (8). This grid consists of approximately 700
square cells, 11 miles on a side (see Figure 1). The
centroid of each cell is consecutively numbered and was
extracted to produce the origin of the sampling zone.
Figure 1. Statewide baseline network sampling grid.
Year
1992
1993
Area
Covered
9 counties
26 counties
PLS Sections
Selected
500
1,659
Well Logs
Selected
3,000
11,000
Well Logs
Reviewed
3,000
834
Wells
Sampled
158
206
Time
Spent
6 months
4 months
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Establishing the Sampling Zone
Each sampling zone consists of a 3- by 3-mile box from
which potential sampling sites are selected. It is gener-
ated by computing the coordinates of the four corners of
the box using the grid cell's centroid as the origin. To link
the sampling zone and grid cell, both are identified with
the same numerical code.
These sampling "target" zones, a series of regularly
spaced, 9-square-mile boxes, are then made into a CIS
coverage and overlaid on top of the PLS coverage to
extract those sections that are associated with each of
the sampling zones. Ideally, each sampling zone should
cover exactly nine PLS sections (3). Due to irregularities
in the PLS system, however, portions of 16 to 20 sec-
tions usually fall within the sampling zone of each cell
(see Figure 2).
Watonwan
Legend
/ \ / PLS Boundary
Sample Zone
Sample Grid
County Border
Figure 2. PLS and the sampling grid, Watonwan County.
Selection of PLS Sections
The PLS coverage was derived from the Minnesota
Land Management Information Center (LMIC) "GISMO"
file. It was originally created in 1979 by digitizing every
section corner in Minnesota from the U.S. Geological
Survey (USGS) 7.5-minute quadrangle map series.
The PLS section information is necessary in the well
selection process because the original well construction
logs, maintained by the Minnesota Geological Survey
(MGS), are organized by PLS. Although most of the well
selection process can be automated, manual file
searches for well records are still necessary and require
the PLS information.
Well Selection
After identifying the PLS sections within the sampling
grid, the statewide well database is imported as a point
coverage and overlaid with the selected PLS section
coverage. Thus, all wells that fall within the 16 to 20
sections are selected as potential candidates. The ac-
curacy of the well locations in CWI varies; most of the
point locations are approximated to four quarters
(2.5 acres). The CWI does not contain all well construc-
tion information, however, requiring that copies of
driller's logs be made for GWMAP files.
The final well selection is done after applying the
9-square-mile sampling zone over the potential pool of
candidates. For wells that fall within the zone, the well
construction records are pulled from MGS files, copied,
and submitted for hydrologist review. Depending on the
target cell location, the number of candidate wells requir-
ing review may range from a few to more than 100. For
newly installed water wells whose records have not yet
been digitized by LMIC, the PLS locations of the wells
are manually plotted onto a map to confirm whether they
fall into a sampling grid cell. Typically, from 5 percent to
as many as 20 percent of selected wells that meet the
location criteria are sampled. This accounts for the hydro-
geologic and well construction criteria and the coopera-
tion of well owners participating in the program.
Currently, interest in ground-water protection programs
runs high in rural Minnesota, with an acceptance rate of
up to 80 percent.
The implementation of CIS in well selection helped
GWMAP excel in two major areas. First, the develop-
ment of the statewide CIS grid eliminated previously
uncontrolled variables by removing the PLS spatial in-
consistencies from the systematic grid. Second, the CIS
reduced the manual workload with the automation of two
important steps in the well selection process: the gen-
eration of PLS section information to facilitate the data-
base search, and the identification of wells that meet the
geographic location criteria. The success of GWMAP
relies largely on the ability to use existing CIS cover-
ages. In using coverages created by other entities, this
program identified the need for a uniform standard for
data conversion and transfer.
Application of Global Positioning
Systems in Ground-Water Sampling
In 1991, the U.S. Environmental Protection Agency
(EPA) established a policy that all new data collected
after 1992 should meet an accuracy goal of 25 meters
or better (9). The purpose of EPA's Locational Data
Policy (LDP) is to establish principles for collecting and
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documenting consistently formatted locational data to
facilitate cross-programmatic, multimedia analyses. Ac-
curate geographic information is important to the spatial
analysis of well sampling results. Any uncertainty in
sample location can compromise hydrogeologic analy-
sis (10). GPS is an easy, cost-effective solution.
Global Positioning System Field Application
Beginning in October 1992, GWMAP employed GPS in
the field to assist in locating sample sites. Applying GPS
in the field has proven to be quite easy. The program
uses a multichannel C/Acode receiver with internal data
logging capability. Typically, the receiver is placed di-
rectly on top of the wellhead and logs 100 to 150 GPS
readings into the receiver's internal memory in approxi-
mately 5 minutes.
The GPS is also used for navigation in the field to locate
sampling sites. Because sampling sites are predeter-
mined, their locations can be extracted from a topo-
graphic map. The approximate coordinates can then be
loaded into a GPS receiver. In most cases, the receiver
successfully led the field team within visual range of the
sampling site.
Because of the inherent selective availability (SA) of the
GPS, raw field data must go through a differential cor-
rection process to achieve the goal of 25-meter accu-
racy (9, 11).
Data Management and Processing
Once the GPS receiver is brought back from the field,
data are downloaded to a personal computer (I486 proc-
essor at a speed of 50 MHz) and differentially corrected
(11). The average or mean of the 100 or more readings
collected onsite is calculated and reported as the site
location.
The MPCAdoes not operate a GPS base station for the
purpose of differential correction. The base station data
are obtained through a computer network (Internet) from
the Minnesota Department of Health (MDH) base station
located in Minneapolis.
To facilitate future data integration and document data
accuracy for secondary application, GWMAP proposed
quality assurance codes for GPS data collected by the
MPCA. The value of the accuracy proposed is a nominal
value rather than an absolute number (see Table 2).
Each of the seven processing methods is assigned a
separate code.
In the field experience of GWMAP, a nominal accuracy
of 2 to 5 meters has been consistently achieved after
the postdifferential correction and averaging have been
applied to the data. This technology is suitable for any
program that is designed to conduct either large-area or
intensive monitoring activities. It helps to cut costs by
Table 2. Proposed Nominal Accuracy Reference Table
Type of GPS
Receiver Used
Processing Method
Used To Correct Data
Nominal
Accuracy
(meters)
Navigational
quality C/A code
receiver
Navigational
quality with carrier
aid receiver
Survey quality
receiver (dual or
single frequency)
Postdifferential corrected 2-5
Real-time differential
corrected (RTCM) 2-5
Autonomous mode (no
correction) 15-100
Postdifferential corrected < 1
Real-time differential
corrected (RTCM) < 1
Autonomous mode (no 15-100
correction)
Postdifferential corrected <0.1
increasing efficiency and accuracy of the data. The data
collected by GWMAP can be used not only in a regional
study but could be used directly in a site-specific inves-
tigation as well.
GWMAP also found that GPS can be used most effi-
ciently by separating the two roles of field operator and
data manager. The field operators receive only the brief
instructions necessary to operate a GPS receiver before
going into the field. The data manager handles the data
processing details. The field operators can then concen-
trate their efforts on obtaining ground-water samples
and conducting the hydrogeologic investigation.
Conclusions
CIS and GPS technologies made it possible for the
MPCA to implement the statewide GWMAP project by
optimizing the available funding and staff time. CIS mini-
mized staff time spent on identifying sampling areas,
manipulating the sampling grid, and selecting monitor-
ing sites. In addition, CIS enabled GWMAP to integrate
a variety of databases and maps of different scales.
Using GPS to locate sampling sites enabled GWMAP to
efficiently obtain accurate geographic locational data
with relative ease. This eliminated the degree of uncer-
tainty that previously might have compromised the sta-
tistical evaluation of the hydrogeologic data.
GWMAP's success in integrating existing digital data to
automate the well selection process clearly demon-
strated the importance of the ability to share information
with others and the great need for a broadly applied
standard for data conversion and transfer.
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Acknowledgments
The authors wish to thank Renee Johnson of the Min-
nesota Department of Natural Resources for her work
to convert the PLS data layerto CIS coverage. Susanne
Maeder of LMIC supplied the statewide CWI coverage,
and Susan Schreifels of MPCA conducted research on
the LDP and made valuable suggestions on implement-
ing GPS.
References
1. Nelson, J.D., and R.C. Ward. 1981. Statistical considerations and
sampling techniques for ground-water quality monitoring. Ground
Water 19(6):617-625.
2. Ward, R.C. 1989. Water quality monitoring—A systems approach
to design. Presented at the International Symposium on the De-
sign of Water Quality Information Systems, Colorado State Uni-
versity, Ft. Collins, CO.
3. Myers, G., S. Magdalene, D. Jakes, and E. Porcher. 1992. The
redesign of the ambient ground water monitoring program. St.
Paul, MN: Minnesota Pollution Control Agency.
4. Olea, R.A. 1984. Systematic sampling of spatial functions. Series
on Spatial Analysis, No. 7. Kansas Geological Survey, University
of Kansas, Lawrence, KS.
5. Gilbert, R.0.1987. Statistical methods for environmental pollution
monitoring. New York, NY: Van Nostrand Reinhold.
6. Wahl, I.E., and R.G. Tipping. 1991. Ground-water data manage-
ment—The county well index. Report to the Legislative Commis-
sion on Minnesota Resources. Minnesota Geological Survey,
University of Minnesota, St. Paul, MN.
7. Clark, T, Y Hsu, J. Schlotthauer, and D. Jakes. 1994. Ground-
water monitoring and assessment program—Annual report. St.
Paul, MN: Minnesota Pollution Control Agency.
8. ESRI. 1993. ARC/INFO version 6.1, ARCPLOT command refer-
ences. Redlands, CA: Environmental Systems Research Insti-
tute, Inc.
9. U.S. EPA. 1992. Global positioning systems technology and its
application in environmental programs. CIS Technical Memoran-
dum 3. EPA/600/R-92/036. Washington, DC.
10. Mitchell, J.E. 1993. A characterization of the influence of (x,y)
uncertainty on predicting the form of three-dimensional surfaces.
Proceedings of the AWRA Spring Symposium on Geographic
Information Systems and Water Resources, Mobile, AL. pp. 559-
567.
11. Trimble Navigation, Ltd. 1993. GPS Pathfinder System, general
reference. Sunnyvale, CA: Trimble Navigation, Ltd.
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EPA's Reach Indexing Project—Using GIS To Improve Water Quality Assessment
Jack Clifford
Office of Wetlands, Oceans, and Watersheds, U.S. Environmental Protection Agency,
Washington, DC
William D. Wheaton and Ross J. Curry
Research Triangle Institute, Research Triangle Park, North Carolina
Abstract
The Waterbody System (WBS), which the U.S. Environ-
mental Protection Agency (EPA) originally developed to
support preparation of the report to Congress that Sec-
tion 305(b) of the Clean Water Act requires, is a poten-
tially significant source of information on the use support
status and the causes and sources of impairment of U.S.
waters. Demand is growing for geographically refer-
enced water quality assessment data for use in inter-
agency data integration, joint analysis of environmental
problems, establishing program priorities, and planning
and management of water quality on an ecosystem or
watershed basis.
Because location of the waterbody assessment units is
key to analyzing their spatial relationships, EPA has
particularly emphasized anchoring water bodies to the
River Reach File (RF3). The reach file provides a nation-
wide database of hydrologically linked stream reaches and
unique reach identifiers, based on the 1:100,000 U.S.
Geological Survey (USGS) hydrography layer.
EPA began the reach indexing project to give states an
incentive to link their water bodies to RF3 and to ensure
increased consistency in the approaches to reach index-
ing. After a successful 1992 pilot effort in South Carolina,
an expanded program began this year. Working with
Virginia, a route system data model was developed and
proved successful in conjunction with state use of PC
Reach File (PCRF), a PC program that relates water
bodies to the reach file. ARC/INFO provides an extensive
set of commands and tools for developing and analyzing
route systems and for using dynamic segmentation.
One important advantage of the route system is that it
avoids the necessity of breaking arcs; this is an impor-
tant consideration in using RF3 as the base coverage in
a geographic information system (GIS). Using dynamic
segmentation to organize, display, and analyze water
quality assessment information also simplifies use of the
existing waterbody system data. Because of the variabil-
ity in delineation of water bodies, however, other states
used a number of different approaches. Working with
these states has defined a range of issues that must be
addressed in developing a consistent set of locational
features for geospatial analysis.
Wider use of these data also depends upon increased
consistency in waterbody assessments within and be-
tween states. Several factors complicate the goal of
attaining this consistency in assessment data:
• The choice of beneficial use as the base for assess-
ment of water quality condition.
• The historical emphasis on providing flexible tools
to states.
• The lack of robust standards for assessment of water
quality condition.
This paper explores possible resolutions to the problem
of building a national database from data collected by
independent entities.
Section 305(b) of the Clean Water Act and
the Waterbody System
Background of Section 305(b)
Since 1975, Section 305(b) of the Federal Water Pollution
Act, commonly known as the Clean Water Act (CWA),
has required states to submit a report on the quality of
their waters to the U.S. Environmental Protection Agency
(EPA) administrator every 2 years. The administrator
must transmit these reports, along with an analysis of
them, to Congress.
State assessments are based on the extent to which the
waters meet state water quality standards as measured
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against the state's designated beneficial uses. For each
use, the state establishes a set of water quality criteria
or requirements that must be met if the use is to be
realized. The CWA provides the primary authority to
states to set their own standards but requires that all
state beneficial uses and their criteria comply with the
'fishable and swimmable' goals of the CWA.
Assessments and the Role of Guidelines
EPA issues guidelines to coordinate state assessments,
standardize assessment methods and terminology, and
encourage states to assess support of specific benefi-
cial uses (e.g., aquatic life support, drinking water sup-
ply, primary contact recreation, fish consumption). For
each use, EPA asks that the state categorize its assess-
ment of use support into five classes:
• Fully supporting: meets designated use criteria.
• Threatened: may not support uses in the future unless
action is taken.
• Partially supporting: fails to meet designated use cri-
teria at times.
• Not supporting: frequently fails to meet designated
use criteria.
• Not attainable: use support not achievable.
In the preferred assessment method, the state com-
pares monitoring data with numeric criteria for each
designated use. If monitoring data are not available,
however, the state may use qualitative information to
determine use support levels.
In cases of impaired use support (partially or not sup-
porting), the state lists the sources (e.g., municipal point
source, agriculture, combined sewer overflows) and
causes (e.g., nutrients, pesticides, metals) of the use
support problems. Not all impaired waters are charac-
terized. Determining specific sources and causes re-
quires data that frequently are not available.
States generally do not assess all of their waters each
biennium. Most states assess a subset of their total
waters every 2 years. A state's perception of its greatest
water quality problems frequently determines this sub-
set. To this extent, assessments are skewed toward
waters with the most pollution and may, if viewed as
representative of overall water quality, overstate pollu-
tion problems.
Assessment Data Characteristics
Each state determines use support for its own set of
beneficial uses. Despite EPA's encouragement to use
standardized use categories, the wide variation in state-
designated beneficial uses makes comparing state uses
an inherent problem. This affects the validity of aggre-
gation and use of data across state boundaries. Com-
parably categorizing waters into use support categories
also poses a problem; different states apply the qualita-
tive criteria for use support levels in very different ways.
Further limiting the utility of Section 305(b) data is that
data are aggregated at the state level and questions
about the use support status of individual streams can-
not be resolved without additional information. While
some states report on individual waters in their Section
305(b) reports, EPA's Waterbody System (WBS) is
the primary database for assessment information on
specific waters.
State monitoring and assessment activities are also
highly variable. States base assessments on monitoring
data or more subjective evaluation. The evaluation cate-
gory particularly differs among states.
Waterbody System
The WBS is a database and a set of analytical tools for
collecting, querying, and reporting on state 305(b) infor-
mation. It includes information on use support and the
causes and sources of impairment for water bodies,
identification and locational information, and a variety of
other program status information.
As pointed out earlier, although some states discuss the
status of specific waters in their 305(b) reports, many do
not. The WBS is generally much more specific than the
305(b) reports. It provides the basic assessment infor-
mation to track the status of individual waters in time
and, if georeferenced, to locate assessment information
in space. By allowing the integration of water quality
data with other related data, the WBS provides a frame-
work for improving assessments.
WBS has significant potential for management planning
and priority setting and can serve as the foundation for
watershed- and ecosystem-based analysis, planning,
and management. In this respect, it can play a vital role
in setting up watershed-based permitting of point
sources. The primary function of WBS is to define where
our water quality problems do and do not exist. WBS is
increasingly used to meet the identification requirement
for waters requiring a total maximum daily load (TMDL)
allocation. It can serve as the initial step in the detailed
allocation analysis included in the TMDL process. In
addition, WBS is an important component of EPA par-
ticipation in joint studies and analyses. For instance,
EPA is currently participating with the Soil Conservation
Service (SCS) in a joint project to identify waters that
are impaired due to agricultural nonpoint source (NPS)
pollution. WBS can also anchor efforts to provide im-
proved public access at the state and national levels to
information on the status of their waters.
It is important to recognize that use of WBS is voluntary.
Of the 54 states, territories, river basin commissions, and
Indian tribes that submitted 305(b) reports, approximately
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30 used the WBS in the 1992 cycle. While submissions
for the 1994 cycle are not complete, we anticipate about
the same level of participation. This represents about a
60-percent rate of participation in WBS, which may be
the limit for a voluntary system. This severely limits use
of WBS assessment data for regional and national level
analysis. If data at the national level are required, man-
datory data elements, formats, and standards may be
necessary.
EPA is currently attempting to achieve consistency
through agreement with other state and federal agen-
cies. The recent work of the Interagency Task Force on
Monitoring offers hope for eventual consensus on the
need for nationally consistent assessment data and mu-
tually agreed upon standards for collection, storage, and
transfer. The Spatial Data Transfer Standards already
govern spatial data, allowing movement of data between
dissimilar platforms. The Federal Geographic Data
Committee provides leadership in coalescing data inte-
gration at the federal level; it provides a model for gov-
ernment and private sector efforts. This level of
cooperation, however, has not always been present in
water assessment data management. Assuming that
national and regional assessment data are needed, if
consistency is elusive through cooperative efforts, regu-
lations may be necessary. Developing a national data-
base may not be feasible without a mutual commitment
by EPA and the states to using common assessment
standards.
WBS was originally developed as a dBASE program in
1987. It has undergone several revisions since then, and
the current Version 3.1 is written in Foxpro 2.0. The
WBS software provides standard data entry, edit, query,
and report generation functions. WBS has grown sub-
stantially in the years since its inception, primarily in
response to the expressed needs of WBS users and
EPA program offices. The program's memory require-
ments and the size of the program and data files, how-
ever, are of growing concern to state WBS users and
the WBS program manager. Because of the wide range
of WBS user capabilities and equipment, users must be
equipped to support an array of hardware from high
capacity Pentium computers to rudimentary 286 ma-
chines with 640 Kb of memory and small hard disks. This
range makes memory problems inevitable for some users.
While WBS contains over 208 fields, exclusive of those
in lookup tables, approximately 30 fields in four files
provide the core data needed to comply with 305(b)
requirements. These fields contain:
• Identification information for the water body.
• The date the assessment was completed.
• The status of use support for beneficial uses.
• The causes and sources of any use impairment in
the water body.
The uses WBS considers are both state-designated
uses and a set of nationally consistent uses (e.g., overall
use, aquatic life support, recreation) specified in the
305(b) guidelines. The other essential piece of informa-
tion is the geographic location of the water body, which
the remainder of this paper discusses in detail.
Significant differences exist in the analytical base as well
as in assessments. EPA provided little initial guidance
on defining water bodies; therefore, states vary widely
in their configurations of water bodies. Water bodies are
supposed to represent waters of relatively homogene-
ous water quality conditions, but state interpretation of
this guidance has resulted in major differences in water-
body definition.
Initially, many states developed linear water bodies, and
these were often very small. The large number of water
bodies delineated, however, created significant difficul-
ties in managing the assessment workload and were not
ideal in the context of the growing need for watershed
information. Some states, such as Ohio, developed their
own river mile systems.
As discussed below, some states indexed their water
bodies to earlier versions of the reach file, and therefore,
the density of the streams these water bodies include is
fairly sparse. Recently, many states have redefined
their water bodies on the basis of small watersheds
(SCS basins, either 11-digit or 14-digit hydrologic unit
codes [HUCs]).
Locating water bodies geographically is a necessary
prerequisite to assessing water quality on a watershed
or ecosystem basis. The WBS has always included
several locational fields, including county name and
FIPS, river basin, and ecoregion. These fields have not
been uniformly populated, however. One of the WBS
files includes fields for the River Reach File (RF3)
reaches included in the water body. While a few states
had indexed their water bodies to older versions of the
reach file (RF1 and RF2), however, no state had indexed
to RF3 until 1992.
In 1992, EPA initiated a demonstration of geographic
information system (CIS) technology in conjunction with
the South Carolina Department of Health and Environ-
mental Control. This project involved:
• Indexing South Carolina's water bodies to RF3.
• Developing a set of arc macro languages (AMLs) for
query and analysis.
• Producing coverages of water quality monitoring sta-
tions and discharge points.
• Using CIS tools in exploring ways to improve water
quality assessments.
-------
South Carolina has defined its water bodies as SCS
basins.
The results have been very encouraging. First, South
Carolina took the initial coverages and decided they
needed much more specificity in their use support de-
terminations and their mapping of the causes and
sources of impairment. As a result, they mapped these
features down to the reach level. Next, they decided that
they needed better locational information, so they used
global positioning satellite receivers to identify accurate
locations for discharges and monitoring stations. They
then used CIS query and analysis techniques to relate
their monitoring and discharge data to their water quality
criteria. South Carolina is using CIS to actively identify
water quality problems and improve their assessments.
In 1993, EPA worked cooperatively with several states
to index their water bodies to the reach file. Virginia, the
next state to be indexed, demonstrated the successful
use of PC Reach File (PCRF) software (described later
in this paper) for indexing water bodies to the reach file.
Ohio and Kansas also are essentially complete. Each of
these states required a somewhat different approach
than Virginia. The need for flexibility in dealing with
states on reach indexing issues is essential. Existing
waterbody delineations often represent considerable in-
vestment; therefore, EPA must provide the capability to
link the state's existing assessment data to the reach file
in order to encourage state buy-in.
Figure 1 shows the results of Ohio's indexing of a typical
cataloging unit (CD). Figure 2 reflects part of the output
of the Kansas work. We can link use support, cause, and
source data to each of these water bodies now. In the
future, we hope to map these attributes at a higher level
of resolution, down to the reach segment level. CIS has
proven to be a useful assessment tool. With higher reso-
lution, it should prove to be even more helpful in identify-
ing water quality problems, picking up data anomalies,
and assessing management actions, strategies, and poli-
cies. This entire process has taught us much and has
strengthened enthusiasm for place-based management.
The Reach Indexing Project—
Georeferencing the Waterbody System
Purpose and Overview
The reach indexing project is designed to locate water
bodies using RF3 as an electronic base map of hydrog-
raphy and to code RF3 reaches with the specific water-
body identifier (WBID). After linking water bodies to their
spatial representation, they can be queried and dis-
played with assessment data located in WBS files.
Reach indexing includes several steps. First, the state
must supply waterbody locations and WBIDs. The next
step entails developing a set of procedures for indexing.
Finally, the coded RF3 data must be produced.
Input data to the indexing process includes:
• A list of valid WBIDs. In most cases, the state has
already input these identification numbers to the WBS.
• Information about the location of each water body. Lo-
cational information may be found in marked-up
paper maps showing waterbody locations or electronic
files from WBS containing waterbody indexing
66022
66023
66024
66025
67001
66001
66002
66003
66004
66005
66006
66007
fifinnp
DDUUO
66009
66010
= 66011
— 66012
<*"*•»« 66013
oc*xx> 66014
=, 66015
•=a=r 66016
fifim 7
DDU 1 /
66018
= 66019
__-. 66020
Figure 1. State of Ohio water bodies in CU 04100008.
-------
Figure 2. State of Kansas water bodies in CU 11070202.
expressions (discussed later), orit may be embedded
in the WBID itself.
• A complete set of RF3 data for the state being indexed.
Depending on the type of information the state supplies,
procedures used to index water bodies can be almost
fully automated, semiautomated, or completely manual.
The final result of the indexing processes is a set of RF3
coverages that contain a WBID attribute. This product
allows querying and displaying of assessment data,
which is collected and stored by water body, in a CIS
environment.
The Reach File Database
The reach file is a hydrographic database of the surface
waters of the continental United States. Elements within
the database represent stream segments. The elements
were created for several purposes:
• To perform hydrologic routing for modeling programs.
• To identify upstream and downstream connectivity.
• To provide a method to uniquely identify any particu-
lar point associated with surface waters.
The unique reach identifier has succeeded in associat-
ing other EPA national databases, such as STORET, to
surface waters. Any point within these databases can be
associated with and identified by a specific location on
any surface water element, such as a reservoir, lake,
stream, wide river, or coastline.
There are three versions of the reach file. The first was
created in 1982 and contained 68,000 reaches. The
second version, released in 1988, doubled the size of
Version 1. The third version (RF3) includes over
3,000,000 individual reach components.
The base geography of RF3 is derived from U.S. Geo-
logical Survey (USGS) hydrographic data (1:100,000
scale) stored in digital line graph (DIG) format. Unlike
DIG data, which are partitioned by quad sheet bounda-
ries, RF3 data are partitioned by CU. A CU is a geo-
graphic area that represents part or all of a surface
drainage basin, a combination of drainage basins, or
a distinct hydrologic feature. The USGS uses CUs
for cataloging and indexing water-data acquisition
activities.
The continental United States comprises over 2,100
CUs. CUs are fairly small; for example, 45 units fall
partially or completely within the state of Virginia (see
Figure 3).
RF3 is a powerful data source used in hydrologic appli-
cations for many reasons, including the following:
• RF3 has spatial network connectivity that topological
upstream/downstream modeling tools use.
-------
Figure 3. Cataloging units in Virginia.
• RF3 has attributes that describe connectivity, which
offers the ability to accomplish upstream/downstream
navigation analytically (without topological networking).
• RF3 has a simple and consistent unique numbering
system for every stream reach in the United States.
• RF3 has built-in river mileage attributes that describe
upstream/downstream distances along river reaches.
Use ofRF3 in the Indexing Process
When importing Reach File data from EPA's mainframe
computer, an arc attribute table (AAT) is automatically
built for each RF3 coverage. The AAT contains the
standard AAT fields, plus the items found in Table 1.
The CD item stores the USGS CD number of this piece
of RF3. Every arc in the coverage has the same value
for CU.
The SEG item stores the number of the stream seg-
ment to which the particular arc is assigned. SEG num-
bers start at 1 and increase incrementally by 1 to 'N' for
each CU. A SEG could represent all the arcs of a
mainstream, the arcs of a tributary, or piece of a main-
stream or tributary. SEG numbers were defined in the
production of RF3.
Ml stores the marker index for each particular arc. The
Ml resembles a mile posting along a stream. In reality,
the Ml field does not truly measure mileage along the
RF3 stream network. It does, however, represent a
method of producing a unique identifier (in combination
Table 1. Fields Found in Arc Attribute Table
12070104-ID CU SEG Ml
UP
DOWN
1
2
3
4
5
6
7
1 20701 04
1 20701 04
1 20701 04
1 20701 04
1 20701 04
1 20701 04
1 20701 04
1
1
1
2
3
3
4
0.00
1.30
2.10
0.00
0.00
1.15
0.00
-1
-1
-1
-1
-1
-1
-1
0
0
0
0
0
0
0
with the CU number and the SEG number) for every
reach in the United States (see Figure 4).
Together CU, SEG, and Ml uniquely identify every arc
in RF3 nationwide. These three items are combined in
the redefined item called RF3RCHID. This provides a
powerful scheme for consistently identifying locations
along streams everywhere in the country.
Along with the AAT file, a second attribute file is auto-
matically created for RF3 coverages. This file is always
named COVER.DS3. The DS3 file stores a wealth of
information about arcs in the coverage. Some of the
important fields in the DS3 file contain:
• Upstream and downstream connectivity for navigat-
ing along reaches.
• Codes to describe the type of reach (e.g., stream,
lake boundary, wide river).
• DIG major and minor attributes.
-------
4.05
2.4
o
1.0
1.30
WBID
KS- KR-04-R001
KS-KR-04-W020
KS-KR-04-W030
o
WBID WBNAME
KS-KR-04-R0001 Mainstem
KS-KR-04-W020 Tributaries
KS-KR-04-W030 Lakes
Figure 4. RF3, SEG, and Ml data elements.
Waterbody Locations
Because states define water bodies, they provide the
only information on waterbody location. South Carolina
was indexed to RF3 in 1992, followed by Virginia. Vir-
ginia indexed its water bodies using the PCRF program
instead of indexing in a CIS environment with ARC/INFO.
PCRF is a PC-based system that indexes water bodies
and locates other assessment data from WBS. PCRF
stores the definitions of water bodies (including their
location) in a file that is linked to other WBS database
files that contain information about the assessment
status and quality of the waters.
A water body is a set of one or more hydrologic features,
such as streams, lakes, or shorelines, that have similar
hydrologic characteristics. Water bodies are the basic
units that states use to report water quality for CWA
305(b) requirements. Depending on the state's assess-
ment goals and resources, water bodies can be defined
in several ways, including (see Figure 5):
• All streams within a watershed
• All lakes and ponds within a watershed
• Sets of streams with similar water quality conditions
PCRF stores locational data for a water body with a
unique WBID. WBS uses this WBID as a common field
to relate the water body's definition and location to de-
scriptive data about the water body's assessment status
and quality. The two most important files used in PCRF
are the SCRF1 and SCRF2 files.
The SCRF1 file simply lists the unique water bodies by
state. Table 2 offers an example. The most relevant data
for reach indexing in this file are the WBID, WBNAME,
and WBTYPE, as defined by the state. The WBID, as
stated, is a unique identifier for each water body the
state has defined. The WBNAME stores a verbal de-
Figure 5. Potential definitions of water bodies.
scription of the water body. Finally, the WBTYPE de-
fines the type of water body; for example, R is for river,
L is for lake.
The SCRF2 file contains an explicit definition of each
water body. Because of the complexity involved in de-
fining water bodies, this file may include more than one
record for each water body. The SCRF2 file can be
considered a waterbody definition language because it
contains specific codes, attributes, and keys that can be
converted into specific reaches on the RF3 data, if read
properly (see Table 3). The WBBEGIN and WBEND
fields contain explicit CD, SEG, and Ml attributes to
define the location of the starting point and ending point
for the water body. The WBDIR field contains an attrib-
ute that describes whether to go upstream or down-
stream from the WBBEGIN to the WBEND. In addition,
a blank WBEND field denotes that the water body
should include all upstream or downstream reaches
(depending on the WBDIR) of the WBBEGIN reach.
Virginia used PCRF to create an SCRF2 file that con-
tains reach indexing expressions for all of their defined
water bodies. ARC/INFO macros were then written to
process this file and expand the expressions into the set
of specific arcs that compose each water body. The
macros will be described in more detail later.
Table 2. Example of SCRF1 File Data
WBID WBNAME WBTYPE
WBSIZE
KS-KR-04-R001
KS-KR-04-W020
KS-KR-04-W030
KS-KR-04-W040
KANSAS
RIVER
LOWER
WAKARUSA
RIVER
MUD CR
CAPTAIL CR
R
R
R
R
15.20
61.60
39.43
15.63
-------
Table 3. Example of SCRF2 File Data
WBID WBDIR WBBEGIN
WBEND
RFORGFLAG
KS-KR-04-R001
KS-KR-OR-W020
KS-KR-04-W030
KS-KR-04-W040
U
D
U
U
10270104001 0.00
10270104005 10.80
10270104059 12.05
10270104038 0.00
1027010400115.20
102701040050.00
102701040078.10
2
2
3
3
States that have not already generated indexing expres-
sions in PCRF must provide locations in some other
way. The most basic method is for the state to supply a
set of 1:100,000 USGS quad sheets that they have
marked up with locations of each water body. The
maps can be used in conjunction with a digitizer to
manually select the appropriate RF3 reaches and
code them with the WBID.
The state of Ohio created a CIS database of its river
reaches several years ago. The CIS coverage is rep-
resentational in nature. The stream reaches are 'stick-
figures' only. Generally, they fall along the paths of the
actual streams, but they are schematic in nature and do
not show the true shape of streams. The CIS layer,
however, contains the attributes of Ohio's stream reach
numbering system, which is used to identify water bod-
ies as well. Ohio's river reach coverage contains infor-
mation on the locations of water bodies and is being
manually conflated to transfer the WBIDs to RF3. The
conflation process will be covered later in this paper.
The state of Kansas had previously defined its water
bodies on RF2, the precursor to RF3. Kansas defined
some indexes by a set of RF3 SEG numbers in a CD
and some by the RF3 reaches in a small watershed
polygon within a CD. The locations were, in effect, de-
fined within the WBID itself.
Indexing Procedures
Procedures developed for performing waterbody index-
ing include automated, semiautomated, and manual
systems.
Automated Indexing Procedures
As stated, Virginia used PCRF to perform the indexing
operation. The state delivered an SCRF2 file containing
indexing expressions for all of its water bodies. AMI
programs were created to read the SCRF2 file and
select the reaches specified by each indexing expres-
sion. The selected sets of reaches were then coded with
the appropriate WBID. The macros were designed to run
on one RF3 CD at a time, so the operator specified runs
of up to 10 CDs at a time. The macros had to accom-
modate indexing expressions that included:
• Select reaches upstream of a specified location.
• Select reaches on a reach-by-reach basis.
• Select reaches within a given polygon area.
• Select shorelines of lakes or ponds given latitude and
longitude coordinates.
• Select reach downstream from a specified location.
Kansas water bodies were also indexed through an
automated process. Kansas supplied an ARC/INFO
coverage of small watershed polygons (sub-CD poly-
gons) containing a watershed identifier. The state's
WBID contained all other information necessary to de-
termine the RF3 CD and the set of reaches making up
each water body. An example of a Kansas WBID is
KS-KR-02-W030. This is explained by the following:
• KS refers to the state. All WBIDs in Kansas begin
with KS.
• The second component (in this case KR) is an ab-
breviation of the basin in which the water body falls.
KR indicates that this water body is in the Kansas-
Lower Republican River basin.
• The third component contains the last two digits of
the eight-digit CD number. Although basins comprise
several CDs, the last two digits of each CD in a basin
are unique; therefore, between the basin (e.g., KR)
designation and the last two digits of the CD (e.g.,
02), the complete eight-digit CD number in which the
water body falls is defined.
• The next letter (in this case W) denotes whether the
water body is defined by a watershed polygon (W),
an RF2 SEG (R), or a lake or pond shoreline (L).
• Finally, the WBID ends with the number of the poly-
gon (in this case 030) that contains the reaches for
the water body in the watershed coverage.
The completed macros could index the entire state in a
single run provided that all the WBIDs were contained
in single file.
In all cases, Kansas has indexed to RF2 reaches. Only
RF3 reaches originally created in RF2 production, there-
fore, are coded with a WBID.
-------
Manual Indexing Procedures
Because Ohio already has a coverage of river reach
codes, WBIDs from this coverage had to be transferred
to the RF3 reaches they represent. This entailed using a
manual conflation process. The operator displayed a CD
of RF3 along with the Ohio river reach system for the
same area. In a simple process of 'pointing and clicking,'
the operator first selected an Ohio river reach arc, then
the RF3 arcs that seemed to coincide. As each RF3 arc
was selected, it was coded with the WBID of the pre-
viously selected Ohio river reach arc.
Other states that have no means of describing water
bodies in electronic files may have to mark up paper maps
to show waterbody locations. These maps can then be
used in a manual process of selecting RF3 reach and
coding them with WBIDs either in ARC/INFO or in PCRF
Using the Route System Data Model To
Store Water Bodies
Because water bodies can be defined as noncontiguous
sets of arcs and portions of arcs, a robust linear data-
base model is necessary to model these entities.
ARC/INFO's route system data model seems well suited
for this application. The route system data model allows
one to group any set of arcs or portions of arcs into
routes. Each route is managed as a feature in itself.
Attributes of water bodies are stored in a route attribute
table (RAT) and relate to all the arcs defined as the water
body. Figure 6 helps illustrate the route system model.
Each route comprises one or more arcs or sections of
arcs. ARC/INFO manages the relationship between arcs
and routes in the section table (SEC). The structure of
the SEC, which is an INFO table, is defined in Table 4.
Tables reflects how the sections that make up the above
routes would appear.
cover.ratwbs
WBS#WBS-ID
1 1
WBID
KS-KR-04-R0001
KS-KR-04-W020
KS-KR-04-W030
Table 4. Definition of Structure of SEC INFO Table
ROUTELINK* The route upon which the section falls
ARCLINK* The arc upon which the section falls
F-MEAS The measurement value at the beginning of the
section
T-MEAS The measurement value at the end of the section
F-POS The percentage of the distance along the arc at
which the section begins
T-POS The percentage of the distance along the arc at
which the section ends
SEC# The internal identifier of the section
SEC-ID The user identifier of the section
Table 5. How Sections Appear in SEC INFO Table
ROUTE-
LINKS ARCLINK8 F-MEAS T-MEAS F-POS T-POS SEC# SEC-ID
1
1
1
2
3
3
3
1
2
3
4
5
6
7
0
1.30
2.10
0
0
1.15
0
1.30
2.10
4.05
1.20
1.15
2.4
2.5
0
0
0
0
0
0
0
100
100
100
100
100
100
100
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Figure 6. The route attribute table containing waterbody data.
Representing Water Bodies as Routes
ARC/INFO offers several ways of grouping sets of arcs
into discrete routes. One can use ARCEDIT to select a
set of arcs to group them into a route, or ARCSECTION
or MEASUREROUTE in ARC to group arcs into routes.
The method described here uses the MEASUREROUTE
command. This method requires that the AAT or a re-
lated table has an attribute containing the identifier of
the route to which an arc should be assigned. In the
application the authors employed, they converted the
SCRF2 file into an INFO table containing, for each arc
in the coverage, the RF3RCHID of the arc and the WBID
to which the arc should be assigned. The WBID item is
used to group arcs into routes. One route exists for each
unique WBID. Table 6 illustrates the table used in the
MEASUREROUTE command method. This table is re-
lated to the AAT of the RF3 coverage by the RF3RCHID.
An RAT is automatically created for the coverage, which
now can be related to other WBS assessment files for
display and query. Figure 4 illustrates the RAT. The most
important characteristic of the file is that it has only one
record for each water body. This simplifies the display
and query of water bodies based on water quality data.
Using EVENTS for Subwaterbody Attributes
Water bodies, as states define them, often constitute a
gross aggregation of the water in an area. States often
have more specific data about particular stretches of
streams within a water body. A system is needed to
-------
Table 6. Table Used in MEASUREROUTE Command Method
To Group Arcs Into Routes
$RECNO
RF3RCHID
WBID
1
2
3
4
5
6
7
8
9
1 02701 04
1 02701 04
1 02701 04
10270104
10270104
10270104
10270104
10270104
10270104
1 0.00
1 1.30
1 2.10
2 0.00
3 0.00
3 1.15
40.00
5 0.00
6 0.00
KS-KR-04-R0001
KS-KR-04-R0001
KS-KR-04-R0001
KS-KR-04-W020
KS-KR-04-W020
KS-KR-04-W020
KS-KR-04-W020
KS-KR-04-W030
KS-KR-04-W030
4.05
2.4
o
1.0
1.30
WBID
KS- KR-04-R001
KS-KR-04-W020
KS-KR-04-W030
query and display data at the subwaterbody level.
ARC/INFO's dynamic segmentation tools and event ta-
bles are useful for this application. Once water bodies
have been defined and reporting methods have been set
up based on those water bodies, the task of redefining
them is cumbersome.
Event tables can help to keep these waterbody defini-
tions yet still offer the ability to store, manage, and track
data at the subwatershed level. Event tables are simple
INFO files that relate to route systems on coverages.
This concept and data structure can work in conjunction
with the predefined waterbody system. We have already
seen how a route system called WBS is created in RF3
to group arcs into waterbody routes. This works quite
well when displaying water bodies and querying their
attributes. A route system based on the WBID cannot,
however, act as an underlying base for subwaterbody
events because the measures used to create the WBS
route system are not unique for a particular route. For
example, in the route depicted in Figure 7, three loca-
tions are defined as being on WBID KS-KR-04-W020 and
having measure 1.0.
The mileage measurements along SEG, however, are
always unique (see Figure 8). To use EVENTS, therefore,
a second route system must be created based on the RF3
SEG attribute, which provides a unique code for each CD.
The ARCSECTION command, instead of the MEASURE-
ROUTE command, is used to create the SEG route
system. This is because the measurement items (Ml on
the AAT and UPMI on the DS3) already store the
summed measures along particular SEGs. Table 7 lists
the contents of the resulting RAT table.
Because the name of the route system is SEG, the
SEG# and SEG-ID are the names of the internal and
user IDs. The SEG item contains the actual SEG num-
ber in the RF3 coverage. Because the SEG numbers for
each RF3 CU coverage start at 1 and increase incre-
Figure 7. Measurements along SEGs.
O
Figure 8. Events located on RF3 data.
Table 7. Route Attribute Table
SEG-ID SEG#
SEG
mentally by 1, the SEG item looks much like the SEG-ID
and SEG#.
Event tables contain a key item, the WBID or SEG, to
relate them to the appropriate route system (see Figure 8).
They also contain locational information on where to lo-
cate the events on the route (either WBID to indicate the
water body on the WBS route or SEG to identify the
route in the SEG route system). Separate event tables
can then relate use support, causes, and sources as
linear events. FROM and TO store the starting and
10
-------
ending measures for each event. Using event tables
allows us to apply many useful cartographic effects
(e.g., hatching, offsets, text, strip maps). Events can be
queried both in INFO and graphically. Event data can
help in producing overlays of two or more event tables.
An event table can display use support information (see
Table 8). WBS users can update their event tables using
RF3 maps supplied by EPA without having proficiency
in ARC/INFO. ARC/VIEW2 is expected to support
events and route systems. This will give users powerful
tools for spatial query of assessment data. Developing
event tables would also display and query data on the
causes and sources of use impairment. These events
can be offset and displayed to show the areas of inter-
action. More permanently, preparing line-on-line over-
lays can show intersections and unions.
An alternative approach is to use an EVENT-ID as a
unique identifier for each event. The SEG field stores the
number of the route (SEG) upon which the event occurs.
FROM and TO store the beginning and end measures
along the route upon which the event occurs. WBID
contains the identifier of the water body upon which the
event occurs (see Table 9). An event can occur within
a single SEG, across two or more SEGs, within a single
water body, or across two or more water bodies.
Additional attribute tables can be created to store de-
scriptive attributes for each event. These tables would
resemble the SCRF5 and SCRF6 files except that in-
stead of using the WBID to relate to a water body, a field
called 'EVENT-ID' would link the use, cause, and source
data to a particular event (see Table 10).
Both approaches offer some advantages. In either case,
they allow us to map our water quality assessment data
and communicate it in a meaninaful and useful wav.
Table 8. Event Table That Reflects Use Support Information
USE
SEG FROM TO WBID USE SUPPORT
1 0.80 1.30 KS-KR-04-R0001
1 1.30 2.10 KS-KR-04-R0001
1 2.10 2.31 KS-KR-04-R0001
1 0.50 1.30 KS-KR-04-R0001
3 0.00 1.15 KS-KR-04-W030
4 0.00 2.5 KS-KR-04-W040
21 Fully
21 Partial
21 Not supported
40 Threatened
21 Fully
40 Not supported
Table 9. Using EVENT-ID as a Unique Event Identifier
EVENT-ID SEG FROM TO WBID
1 1 0.80 1.30
1 1 1.30 2.10
2 4 0.00 2.5
Table 10. Using EVENT-ID To Link Use,
Data to an Event
EVENT-ID ASCAUSE
1 900
1 -9
1 0500
2 1200
2 0900
KS-KR-04-R0001
KS-KR-04-R0001
KS-KR-04-W040
Cause, and Source
ASSOURC
1200
1100
1100
9000
8100
11
-------
Nonpoint Source Water Quality Impacts in an Urbanizing Watershed
Peter Coffin, Andrea Dorlester, and Julius Fabos
University of Massachusetts at Amherst, Amherst, Massachusetts
Abstract
As part of the larger Narragansett Bay Estuary Project,
the University of Massachusetts Cooperative Extension
Service contracted with the university's METLAND re-
search team to develop a geographic information sys-
tem (CIS) database, generate watershed-wide maps,
perform analyses, and develop a modeling procedure.
The objective was to educate local officials about the
impacts of development on water quality and to help
local boards minimize the effect of nonpoint sources of
pollution.
Because the receiving waters of the Narragansett Bay
are located far downstream in Rhode Island, the up-
stream communities in Massachusetts are reluctant to
enact measures to improve water resources outside of
their jurisdictions. A CIS was used to create awareness
of existing downstream problems and to show the up-
stream communities how development will ultimately
affect water resources in their own backyards.
To nurture this awareness, a "buildout" analysis was
conducted for an entire upstream subwatershed, the
Mumford River watershed, containing parts of four
towns, and roughly 50 square miles. This buildout was
coupled with a loading model using Schueler's Simple
Method to illustrate the potential impacts of future devel-
opment, and encourage local boards to minimize future
nonpoint sources of pollution.
CIS proved its usefulness by developing customized
maps for each town, by generating several "what if
scenarios showing the impacts of different zoning
changes, by facilitating long-range planning for small
towns without professional staff, and by encouraging a
regional perspective on development issues. The entire
planning process was most successful in creating a
series of partnerships that will continue after the grant
expires. The university shared coverages with the state
CIS agency, creating new coverages not previously
available, specifically soils, ownership, and zoning.
Small towns learned about the potential of the new
technology. Students gained from hands-on experience
with real-world problems. State agencies saw their ef-
forts understood at the local level, especially as they
reorganize on a basin approach and begin to implement
a total mass daily loading (TMDL) procedure to coordi-
nate permitted discharges and withdrawals.
As greater emphasis is placed on controlling nonpoint
sources of pollution, more attention needs to be focused
on local boards, who control land use decisions in New
England.
Introduction
Project Description
Narragansett Bay is a vital resource for southern New
England. The health of its waters is critical to the re-
gional economy, supporting fisheries, tourism, and qual-
ity of life. Increased development along the bay's
shorelines and throughout its drainage basin threatens
the quality of these waters, however. The U.S. Environ-
mental Protection Agency (EPA) recognized the threats
to this important water body and designated the Narra-
gansett Bay under its National Estuary Program in 1985.
Completing a Comprehensive Conservation and Man-
agement Plan (CCMP) for Narragansett Bay took 7
years. The CCMP identified seven priority areas for
source reduction or control, including the reduction of
agricultural and other nonpoint sources of pollution. The
nonpoint source strategy identified United States De-
partment of Agriculture (USDA) agencies, conservation
districts, and other public and private organizations as
having principal roles in nonpoint source management.
Whereas the vast majority of Narragansett Bay lies
within the boundaries of Rhode Island, a significant
portion of its pollution load originates in Massachusetts.
Recognizing that the watershed extends beyond state
boundaries, the USDA provided 3 years of funding to
Cooperative Extension and the Soil Conservation Serv-
ice (SCS) in both Massachusetts and Rhode Island to
-------
coordinate their efforts in an innovative attempt to re-
duce the impact of nonpoint sources of pollution on
Narragansett Bay. While water quality is a relatively new
focus for Cooperative Extension, it fits well with the
historic mission of extending the knowledge base of the
land-grant colleges out into the community, and provid-
ing training and capacity building for local officials and
community organizations.
With such a large area of concern, the management
team decided to focus on a smaller subwatershed area
in each state for the first 2 years. The strategy was first
to develop a program for the mitigation of nonpoint
source pollution on the smaller scale of a watershed of
roughly 50 square miles, then take the lessons learned
and apply the most appropriate efforts throughout the
larger watershed. By using similar strategies in Rhode
Island and Massachusetts, but choosing subwatersheds
that differ in terms of location relative to the receiving
water, size, staffing, and sophistication, the two states
gained from each other's experience, sharing the suc-
cessful techniques and avoiding each other's mistakes.
For its pilot study, Rhode Island chose Aquidneck Island,
home of Newport, Portsmouth, and Middletown, with a
special focus on protecting surface water supply reser-
voirs. Massachusetts chose an upstream watershed in
the Blackstone Valley, somewhat rural in character, but
rapidly undergoing a transformation to suburbia.
Watershed Description
The Blackstone River drops 451 feet in its 48-mile jour-
ney from Worcester, Massachusetts, to Pawtucket,
Rhode Island. In the 19th century, this drop of roughly
10 feet per mile was ideally suited to providing power to
mills during the early years of the industrial revolution.
By the Civil War, every available mill site was developed,
earning the Blackstone River the name "The Hardest
Working River."
The Blackstone has a long history of pollution. First, the
textile industry, then steel, wire, and metal finishing in-
dustries used the river for power, in their manufacturing
process, and for waste disposal.
In Massachusetts, the Blackstone River is the major
source of many pollutants to Narragansett Bay. Based
on total precipitation event loading calculations, the
Blackstone River is the principal source of solids, cad-
mium, copper, lead, nitrate, orthophosphate, and PCBs
to the bay (1). The Blackstone River has an average flow
of 577 million gallons per day or 23.2 percent of the
freshwater input to the bay.
The watershed area in Massachusetts equals 335
square miles; with a population of 255,682, this results
in a density of 763 people per square mile. The Black-
stone Valley has 9,000 acres in agricultural use, with
more land in hay (4,500 acres) than crops (3,700 acres)
to support its 4,400 animals.
Based on aerial mapping flown in 1987, the Blackstone
Valley has lost 5 percent of its cropland, 9 percent of its
pasture, and 21 percent of its orchards since 1971. The
valley remains more than 60 percent forested, but that
represents a decrease of 5 percent. The forest and
farmlands were lost to development as low density
housing grew by 45 percent, commercial use grew by
15 percent, and transportation grew by 54 percent.
Waste disposal grew 52 percent to 582 acres, and min-
ing, which in this region represents gravel pits, grew 22
percent to 1,100 acres.
Watershed soils consist mainly of compact glacial till on
rolling topography, with 3 to 15 percent slopes. The river
and stream valleys are underlain by glacial-derived sand
and gravel outwash, which provide drinking water to all
towns in the area except Worcester and support the
large gravel pits. The high clay content in the till soils of
the uplands makes for a high water table, which is
beneficial for growing corn but causes problems for
septic systems.
Following a preliminary study of the subwatersheds, the
Mumford River in the Blackstone Valley was selected as
the focus watershed based on its size, location, land
use, and existing water quality (see Figure 1). The Mum-
ford River watershed has an area of 57 square miles,
with a length of 13 miles, and lies within the towns of
Douglas, Northbridge, Button, and Uxbridge. These
towns share the attributes of small, rural communities
undergoing rapid development, with no professional
planning staff (see Figure 2). According to the 1990
Census, Douglas grew 46 percent in 10 years to 5,438;
Uxbridge experienced 24 percent growth to 10,415; Sut-
ton increased 17 percent to 6,824; and Northbridge grew
9 percent to 13,371.
Project Strategy
Because the generation of nonpoint sources of pollution
is so closely tied to land use, and because local boards
composed of citizen volunteers have principal control
over land use in New England, the key focus of this
program is to train local boards to recognize and begin
managing the threat that nonpoint sources of pollution
pose to water quality. Local planning boards, conserva-
tion commissions, and boards of health address land
use issues and can regulate and shape existing and
proposed development. By developing a program to
train local officials, Cooperative Extension can focus its
outreach where it will have the greatest impact in both
the short and long term. Local boards have the strongest
opportunity to comment on how land is to be used as it
undergoes development. Therefore, this project focused
on preventing future deterioration as opposed to fixing
-------
Figure 1. Map of Mumford River watershed study area.
existing problems. This is especially appropriate in a
rapidly urbanizing setting.
Both Massachusetts and Rhode Island chose to utilize
CIS technology because of its ability to store, analyze,
transform, and display geographic, or spatial informa-
tion. Its database management and analytical capabili-
ties make it a useful tool for pollution load modeling and
buildout scenario development, while its mapping capa-
bilities make it an excellent tool for sharing information
with local officials. This paper documents a case study
on how CIS technology was used to apply a watershed-
wide pollution loading model and to develop buildout
scenarios for demonstrating to local officials the poten-
tial impacts of future development on water quality.
This project used CIS in four different applications:
• Printing customized, large-scale maps: This most ba-
sic application of a CIS proved the most useful for
local officials. It was a revelation for some officials to
see how their current zoning related to actual land
use. In one town, these maps inspired a change in
zoning to protect the area of a future water supply
reservoir. These maps helped officials see how their
towns fit into the regional picture and how their zoning
and land use affected the adjoining towns, and vice-
versa.
• Performing "buildout" analysis: A "buildout" analysis
demonstrates the consequences of existing zoning.
It assumes that all land that can be developed will be
developed at some future date. In essence, it is a
spreadsheet that divides the land available for develop-
ment in each zone by the required lot size, subtracting
Northbridge
Legend
I I Agriculture/Open Land
HU Developed Land
V/\ Forest
ggj Unforested Wetlands
PH Lakes and Ponds
Scale = 1:90,000
Douglas
Figure 2. Land use/land cover map of Mumford River watershed.
-------
a certain percentage for the road network and steep
slopes. It is best used to evaluate different develop-
ment scenarios, substituting different zoning require-
ments.
• Applying a watershed-wide pollutant loading model:
CIS provided the input needed to apply the "Simple
Method" for estimating existing and potential pollutant
loads. Future pollution loading was estimated using
a buildout with existing zoning and again assuming
the implementation of cluster zoning. The Simple
Method was compared in one subwatershed with the
Galveston Bay Method, which accounts for the hy-
drologic class of the soils.
• Promoting planning for a greenway: Land use maps
were overlaid with parcel ownership to show the
existing network of preserved open space and to
identify those parcels of land having significant wild-
life habitat and recreational value. In one town, these
maps were used to gain funding for planning a river
walk.
Database Development
The most daunting aspect of using a CIS is the prospect
of spending a great deal of time and money creating a
useful database. Fortunately for Massachusetts, many
of the basic coverages needed for regional planning are
housed in a state agency, MASS CIS, and are available
for a small processing fee. These coverages include
most of what appears on the standard United States
Geological Survey (USGS) map: roads, streams, town
boundaries, as well as watershed boundaries and land
use data generated from the interpretation of aerial pho-
tographs. The university entered into an agreement
whereby we gained access to this data at no charge, in
return for sharing the new coverages that the project
would generate.
New coverages needed for the study included: zoning,
soils, sewer and water lines, and land ownership, or
parcels taken from the assessor's maps. The soils maps
were obtained from the SCS, digitized by hand, then the
scale was converted with a computer program, "rubber-
sheeting," to achieve a uniform scale of 1:25,000. All
other new coverages were transferred onto a USGS
topographical map at a scale of 1:25,000, then digitized
directly into the computer. We obtained elevation data,
but the triangulation process used to convert elevation
data to slopes would require so much time and memory
that, for our purpose, deriving a slope map from the four
classes identified on the soils map was sufficient.
While CIS computer programs are powerful enough to
perform most overlay and analysis functions necessary
in nonpoint source pollution load modeling, database
development and accuracy issues can limit the effec-
tiveness of such modeling. The choice of which model
to use is a function of which data are available for input.
Physics-based distributed models are more precise but
require detailed input parameters, beyond the scope of
this project. The extent of our database limited us to
lumped-parameter empirical models. We chose two
such models, the Simple Method and the Galveston Bay
Method.
GIS Applications
The Simple Method
Schueler (2) developed the Simple Method, one of the
simplest lumped-parameter empirical models. The input
data necessary to compute pollutant loading with the
Simple Method are land use, land area, and mean an-
nual rainfall. Land use determines which event mean
concentration (EMC) values and percentage of impervi-
ousness to use in the computation. The amount of rain-
fall runoff is assumed to be a function of the
imperviousness of various land uses. More densely de-
veloped areas have more impervious surfaces, such as
rooftops and paving, which cause stormwaterto run off
the land rather than be absorbed into the soil. The
Simple Method can generate rough figures for annual
pollutant loading within a watershed and can effectively
show relative increases in pollutant levels as land is
developed.
The formula used in the Simple Method is as follows:
(C) * (A) * (2.72)
(load) = (runoff) * (EMC) * (area)
where:
L = pounds of pollutant load per year
P = rainfall depth (inches) over the desired time
interval (1 year)
Pj = percentage of storms that are large enough to
produce runoff (90 percent)
Rv = fraction of rainfall that is converted into runoff
(Rv = 0.05 + 0.009 (I), where I represents the
percentage of site imperviousness)
C = flow-weighted mean concentration (EMC) of
the targeted pollutant in runoff (milligrams per
liter)
A = area (in acres) of the study region
The Simple Method can be applied using a hand-held
calculator or a computer spreadsheet program. For this
project, the calculations were performed entirely within
the ARC/INFO GIS environment, where the input data
were stored. Results were exported to the Excel spread-
sheet program for presentation purposes.
The application of the Simple Method consists of three
major steps.
-------
Step 1: Aggregate Land Uses and Obtain Area Fig-
ures for Land Use Categories Within Each Subbasin
The land use coverage in our database has 21 catego-
ries. For the purpose of applying the Simple Method,
these were aggregated into the following six major cate-
gories, based on development density: undeveloped
forest and other open land, large-lot single-family resi-
dential, medium-density residential, high-density resi-
dential, commercial, and industrial. The aggregated land
use categories were matched with study basins from the
Nationwide Urban Runoff Program (NURP) for the pur-
pose of assigning EMC values.
Step 2: Enter Percentage Imperviousness and Event
Mean Concentrations for Each Land Use Type
The TABLES module of ARC/INFO was used to assign
percentage of imperviousness and EMC values to indi-
vidual land use polygons within the watershed's subbas-
ins. The estimated percentage of imperviousness was
obtained from Schueler's guide to using the Simple
Method (2). EMC values for three pollutants—phospho-
rous, nitrogen, and lead—were taken from selected
NURP study basins and were assigned to the aggre-
gated land uses within the watershed.
Step 3: Input the Simple Method's Mathematical
Loading Formula, Calculate Loading Results for
Each Distinct Land Use Area, and Sum Results by
Watershed Subbasin
Finally, the pollutant load was calculated for each dis-
tinct land use area within the Mumford River watershed
by inputting the loading formula through the TABLES
module of ARC/INFO. The mean annual rainfall figure
was assumed to be that of Worcester, Massachusetts,
or 47.6 inches. After calculating loading figures for phos-
phorous, nitrogen, and lead for each distinct land use
area, these numbers were summed for each watershed
subbasin, using the ARC/INFO frequency table report-
ing capability.
The Galveston Bay Method
As an experiment, we applied the Galveston Bay
Method to one of the subbasins to compare results with
the Simple Method. The slightly more sophisticated
Galveston Bay model considers soil drainage charac-
teristics in addition to land use/imperviousness to deter-
mine rainfall runoff. This method is similar to the Simple
Method, in that amount of rainfall runoff and EMCs for
particular land uses are multiplied by land area to deter-
mine total pollutant load (3). Runoff in this method,
however, is calculated using the USDA SCS's TR 55
runoff curve model. The SCS model calculates runoff as
a function of both land use and soil type. Runoff equals
total rainfall minus interception by vegetation, depres-
sion storage, infiltration before runoff begins, and con-
tinued infiltration after runoff begins (4).
The formula used with the Galveston Bay Method has a
structure similar to that of the Simple Method and is as
follows:
P-0.2[(1000/CN)-10]2
P + 0.8[(100(yCN)-10]
(load) =
where:
(runoff
(EMC)
(area)
L = milligrams of pollutant load per year
P = mean annual rainfall amount
CN = runoff curve number, which is a function of
soil type and land use
EMC = event mean concentration
A = area (in acres) of the study region
The application of the Galveston Bay Method consists
of four major steps.
Step 1: Aggregate Land Uses and Obtain Area Fig-
ures for Land Uses Categories. Aggregate Soils Ac-
cording to Drainage Classes
Land use types were aggregated into the same six major
categories as the Simple Method in orderto match EMC
values and to allow for later comparison of the two
pollution loading methods. Soils were aggregated ac-
cording to drainage classes for use with the USDA SCS
TR 55 runoff formula. The SCS identifies four classes of
soils according to their drainage capacity:
Class A = excessively to well-drained sands or
gravelly sands.
Class B = well to moderately drained, moderately
coarse soils.
Class C = moderately to poorly drained fine soils.
Class D = very poorly drained clays or soils with a
high water table.
Step 2: Overlay Soils Data With Land Use Data and
Clip This New Coverage Within the Subbasin
The ARC/INFO CIS overlay capability was used to over-
lay land use and soils maps for the Mumford River
watershed on top of each other. This created new, dis-
tinct areas of different land use and soils combinations.
Because we were only applying this model in one sub-
basin, the subbasin boundary was used in conjunction
with the ARC/INFO "clip" command to cut out (like a
cookie cutter) that portion of the watershed within the
subbasin.
Step 3: Assign Runoff Curve Numbers and EMC
Values to Each New Land Use/Soils Polygon Within
the Subbasin
EMC values were assigned to each distinct land
use/soils area in the same manner as they were as-
signed to land use areas using the Simple Method.
-------
Runoff curve numbers were assigned to each distinct
land use/soils area within the subbasin according to
values established by the USDA SCS.
Step 4: Calculate Loading Results for Each Distinct
Land Use/Soils Area and Sum Results for the Entire
Subbasin
Finally, the pollutant load was calculated for each dis-
tinct land use/soils area within the subbasin by inputting
the loading formula through the TABLES module of
ARC/INFO. After calculating loading figures for phos-
phorous, nitrogen, and lead for each distinct land
use/soils area, these figures were then summed for the
subbasin using the ARC/INFO frequency table reporting
capability. Results of this modeling were converted from
milligrams per acre peryearto pounds per acre peryear
to facilitate later comparisons.
Buildout Scenarios
For planning purposes, CIS is most useful in its ability
to quickly generate alternative scenarios. When these
development scenarios are coupled with a pollutant load
model as described above, alternative scenarios can be
evaluated according to their impact on water quality.
This project generated two different scenarios for each
of the four towns in the watershed: a maximum buildout
with existing zoning and a maximum buildout with clus-
tered development.
Maximum Buildout
A maximum buildout scenario was used to show the
worst case for development according to current zoning
regulations (see Figure 3). The result of this buildout is
expressed both in the number of new residential units to
be built and in the area of land to be converted from
undeveloped to residential and other urban uses.
Step 1: Eliminate From Consideration All Land That
Is Already Developed
Step 2: Eliminate From Consideration All Land That
Is Under Water
Step 3: Eliminate From Consideration All Land That
Is Protected From Development
These protected lands included cemeteries, parks, and
all land permanently restricted from development.
Step 4: Reduce the Remaining Amount of Land by
20 Percent To Account for New Roadways and Ex-
tremely Steep Slopes
The remaining land was considered to have "developa-
ble" status. Wetlands were included in this category
because while a house probably would not be built on a
wetland, wetlands can and often do constitute portions
of the required lot size of large residential lots.
Step 5: Overlay the Land Use Coverage With Zoning
and Minimum Lot Size Information
This created new land use areas as a function of zoning.
All forests and fields were converted to a developed
status.
Step 6: Divide Net Developable Land Area Within
Each Zone by Minimum Lot Size Allowed To Obtain
the Number of New Units
Results from the buildout are expressed in the number
of new units. Results can also be shown spatially by
shading in areas on the map according to future density
Sutton
Legend
Already Developed
<1/4-Acre Lot Size
1/4-to 1/2-Acre Lot Size
1/2-to 1-Acre Lot Size
1-to2-AcreLotSize
!Z3 >2-Acre Lot Size
! I Protected/Public Land
Scale = 1:90,000
Figure 3. Maximum buildout scenario within the Mumford River watershed.
-------
of development (darker shades for higher density, lighter
shades for lower density).
opment can reduce future levels of water pollution, es-
pecially from nutrients (see Figures 4 and 5).
Clustered Buildout
Another alternative development scenario was gener-
ated assuming the implementation of clustered develop-
ment. All areas zoned for lots larger than 1 acre were
changed to cluster zones, where three-fourths of the
land area remains undeveloped, and the remaining one-
fourth of the land area is developed at a density of
1/2-acre lot size. With clustering, an area zoned for
2-acre house lots still supports the same number of new
units, but three-quarters of the land area remains open
space for passive recreation, protected wildlife habitat,
and as a buffer zone to filter runoff.
Step 1: Select All Land Available for Development
Zoned for 1-Acre Lots or Larger
Step 2: Multiply Selected Land by 0.75 and Add to
the Category of Protected Land
Step 3: Multiply Selected Land by 0.25 and Change
the Minimum Lot Size to One-Half an Acre
Step 4: Divide Step 3 by 20 Percent To Allow for New
Roads and Steep Slopes
Step 5: Divide Step 4 by 21,780 (One-Half an Acre)
To Determine Number of New Housing Units
Results
Lumped-parameter empirical models were chosen for
this project and were applied to watershed subbasins
ranging in size from 1 to 20 square miles and having an
average of 4 square miles. The application of the Simple
Method to existing land use conditions allowed for a
comparison of the Mumford River watershed's subbas-
ins for the purpose of identifying the subbasins that
contribute the highest levels of pollutants per acre per
year. The development of a maximum buildout scenario
identified those areas within the watershed that will
sustain the greatest amount of new growth. The appli-
cation of the Simple Method to this maximum buildout
scenario revealed that pollutant levels in surface water
runoff would increase substantially for all subbasins in
the watershed. This finding supports the theory of a
positive relationship between development and in-
creased pollutant levels from surface water runoff.
The development of a customized buildout scenario for
future development identified those areas that are cur-
rently zoned for large-lot residential "sprawl" and that
can support higher development density under cluster
zoning, while protecting a significant amount of open
space that can support a variety of beneficial uses. The
application of the Simple Method to the customized
buildout scenario revealed that the use of cluster devel-
Results determined by applying the Galveston Bay
Method to one subbasin were compared with those
obtained using the Simple Method. The predicted pollut-
ant loading from current conditions differed significantly
Nitrogen Loading Estimates
Total
Whitin Reservoir
West Sutton
Wallis Pond
Tuckers Pond
Stevens Pond
Rivulet Pond
Mouth
Morse Pond
Manchaug Pond
Lackey Pond
East Douglas Jl
Cross Street
Badluck Pond
50 100 150
Percent Change
200
n 1985-Cluster
Buildout
1985 - Maximum
Buildout
Figure 4. Chart showing difference in simple method results
for nitrogen loading between maximum and custom-
ized buildout scenarios.
Phosphorus Loading Estimates
Total
Whitin ._-
Reservoir
West Sutton
Wallis Pond
Tuckers Pond
Stevens Pond
Rivulet Pond
Mouth
Morse Pond
Manchaug Pond
Lackey Pond
East Douglas
Cross Street
Badluck Pond
I
-10 0
10
20 30 40
Percent Change
50
60
70
1985-Cluster
Buildout
1985 - Maximum
Buildout
Figure 5. Chart showing difference in simple method results
for phosphorus loading between maximum and cus-
tomized buildout scenarios.
-------
between the two methods; the Simple Method consis-
tently predicted five times the amounts generated by the
Galveston Bay Method.
When the two methods were applied to both the maxi-
mum and customized buildout scenarios, however, the
percentage growth of predicted pollutant loadings was
remarkably similar for both methods; the Simple Method
consistently predicted loadings 10 to 15 percent greater
than the Galveston Bay Method. This indicates that
while the Galveston Bay Method may provide more
accurate results in predicting actual pollutant loading,
the Simple Method is adequate enough for evaluating
and comparing different development scenarios (see
Figures 6 and 7).
Discussion
As states begin to implement a TMDL approach to regu-
lating water quality, they face the quandary of how to
determine the extent of nonpoint source pollution in the
rivers. The crudest method is to subtract from the total
load those quantities generated by point sources and
call all the rest nonpoint source. While this is appropriate
in some settings, it is unacceptable in a watershed with
a long history of pollution because a significant source
of pollution is the resuspension of historical sediments
stirred up by storms. The situation demands the devel-
opment of a model to predict the loading from nonpoint
sources. Only a computer can handle the multiple fac-
tors that interact to generate nonpoint sources of pollution.
As greater emphasis is placed on watershed planning,
the abilities of a CIS to input, store, manipulate, analyze,
and display geographic information become indispensa-
ble. As the scientific community improves its knowledge
base for determining the critical factors influencing non-
point source pollution, CIS technology is improving in
its ability to store and handle large amounts of data.
While a detailed, physics-based distributed model would
be more accurate than the lumped-parameter models
used for this project, they are difficult to apply at the
watershed scale. The real limiting factor is the provision
of all the data coverages needed to apply complex mod-
els. Lumped-parameter models, such as the Simple
Method and the Galveston Bay Method, are ineffective
for accurately predicting pollutant loads, but they are
suitable for comparing and evaluating alternative devel-
opment scenarios.
Time, and the development community, will not wait until
all the answers are known. Local officials continue to
approve development with no thought to the impacts on
water quality. These officials need to be informed about
the implications of haphazard growth. A CIS, with its
ability to generate customized maps and quickly evalu-
ate alternative development scenarios, is a powerful tool
to help local officials visualize how the decisions they
Nitrogen Loading
Total
Uxbridge
Sutton
Northbridge
50 100 150
Percent Change
200
D Galveston Method Percent
Change 1985 to Cluster
ED Simple Method Percent
Change 1985 to Cluster
Galveston Method Percent
Change 1985 to Maximum Buildout
Simple Method Percent
Change 1985 to Maximum Buildout
Figure 6. Chart showing difference between Simple Method re-
sults and Galveston Bay Method results for nitrogen
loading.
Lead Loading
Total
Uxbridge
Sutton
Northbridge
50 100 150
Percent Change
200
D Galveston Method Percent
Change 1985 to Cluster
E3 Simple Method Percent
Change 1985 to Cluster
Galveston Method Percent
Change 1985 to Maximum Buildout
Simple Method Percent
Change 1985 to Maximum Buildout
Figure 7. Chart showing difference between Simple Method re-
sults and Galveston Bay Method results for lead
loading.
make on paper today will have an impact on the land
tomorrow.
References
1. Metcalf & Eddy, Inc. 1991. Assessment of pollutions in Narragan-
sett Bay. Draft report to the Narragansett Bay Project.
2. Schueler, T. 1987. Controlling urban runoff: A practical manual for
planning and designing urban BMPs. Washington, DC: Metropoli-
tan Washington Council of Governments.
-------
3. Newell, C., et al. 1992. Characterization of nonpoint sources and
loadings to Galveston Bay, Vol. I, Technical Report: Galveston Bay
National Estuary Program (March).
4. U.S. Department of Agriculture (USDA) Soil Conservation Service.
1975. Urban hydrology for small watersheds. Technical Release
55. Springfield, VA: U.S. Department of Agriculture.
Additional Reading
Arnold, C.L., et al. 1993. The use of Geographic Information System
images as a tool to educate local officials about the land use/ water
quality connection. Proceedings of Watershed Conference, Alexan-
dria, VA.
Chesebrough, E. 1993. Massachusetts nonpoint source management
plan. Massachusetts Department of Environmental Protection, Office
of Watershed Management (October).
Joubert, L, et al. 1993. Municipal training for water quality protection.
Contribution #2845. College of Resource Development, University of
Rhode Island/Rhode Island Agricultural Experiment Station.
Massachusetts Geographic Information System. 1993. MassGIS
Datalayer descriptions and guide to user services. Boston, MA: Ex-
ecutive Office of Environmental Affairs.
McCann, A., et al. 1994. Training municipal decision-makers in the
use of Geographic Information Systems for water resource protection.
Contribution #2927. College of Resource Development, University of
Rhode Island/Rhode Island Agricultural Experiment Station.
Narragansett Bay Project. 1992. Comprehensive conservation and
management plan: A summary (January).
U.S. EPA. 1983. Results of the Nationwide Urban Runoff Program
(NURP), Vols. I and II. Final report prepared by Water Planning Divi-
sion. NTIS PB84185537. Springfield, VA: National Technical Informa-
tion Service.
U.S. EPA. 1992. Compendium of watershed-scale models for TMDL
development. E PA/841 /R-92/002. Washington, DC (June).
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The GIS Connection to Residential Yard Soil Remediation
Jennifer L. Deis and Robin D. Wankum, P.E.
Black & Veatch Special Projects Corp.
Overland Park, Kansas 66211
Abstract
By using innovative approaches to geographic information system (GIS) applications, the U.S.
Environmental Protection Agency (EPA) and Black & Veatch Special Projects Corp. (BVSPC), as
a contractor to EPA, were able to implement a site investigation concurrently with the site's
cleanup. This effort ultimately saved the EPA approximately $1.2 million dollars. The purpose of
the investigation was to locate and prioritize the residential yards that were adversely affected by
mining activities. BVSPC used an environmental data management system (EDMS) to
consolidate x-ray fluorescence (XRF), global positioning system (GPS), and laboratory analytical
data into a unique and flexible electronic, GIS-compatible database. The application of the EDMS
with a desktop GIS allowed effective completion of the investigation of the Oronogo-Duenweg
Mining Belt Site. This paper will present the GIS approach used to expedite the investigation and
cleanup activities at the site and will identify the benefits of this process.
Site History
The Site is part of the Tri-State Mining District, which covers hundreds of miles in southwestern
Missouri, southeastern Kansas, and northeastern Oklahoma. Mining, milling, and smelting of lead
and zinc ore began in the 1850s and continued in the District until the 1970s. The activities
generated approximately 9 million tons of mine and mill wastes and smelter related materials.
These wastes, which contain high levels of lead, cadmium, and zinc, were deposited throughout
Jasper County. Additionally, air emissions during smelting operations resulted in the
contamination of soil surrounding the smelters. Approximately 6,500 residences are now located
within the 60 square-mile Superfund Site where lead and zinc were mined.
The Missouri Department of Health (MDOH) conducted an exposure study at the Site to evaluate
health effects on residents living in the area (1). The study concluded that the most significant
source of contamination resulting in elevated blood-lead levels was residential yard soils.
Preliminary characterization of the surface soils within a 3/4-mile radius of the largest smelter
indicated that 86 percent of those yards exceeded the 800 parts per million (ppm) target cleanup
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level for lead. In addition, previous investigation activities conducted by others concluded that 50
percent of the homes within 200 feet of mine and mill waste pile locations also exceeded the
target cleanup level for lead (2). The site location and its designated areas, areas where mining
activities and/or wastes were known to occur, are shown on Figure 1. The above findings led the
EPA to develop an overall strategy to prioritize and expedite Site cleanup. EPA contracted the
U.S. Army Corps of Engineers (USAGE) to manage the cleanup activities at the site concurrently
with the BVSPC investigation of the site.
Investigation Objectives
The ultimate goal of the investigation was to identify and locate those properties exceeding the
established 800 ppm surface soil cleanup level for lead within the designated areas. These areas
included the radius of the main "smelter zone," other small smelter areas, and specific mine and
mill waste areas. The area to be investigated around the smelter zone was estimated by
comparing the data from previous characterization activities at the Site. The lead smelter
location and the historical smelter stack height were compared with the prevailing southeast
wind direction to determine the impacted zone.
A secondary objective of the investigation was to prioritize the cleanup of residential yards.
Considering census information obtained from each property access agreement and the XRF
data, special attention was directed to the following two scenarios:
• Residences with toddlers and children under 7 years of age; and
• Residences with soil lead levels greater than 2,500 ppm.
The first scenario was selected based on the findings of the MDOH study. Fourteen percent of
children under the age of 7 years in the study area had elevated blood-lead concentrations
resulting primarily from residential yard soils (1). The second scenario was chosen because those
residential yard soils exceeding 2,500 ppm were considered to pose an excessive risk to residents
of all ages coming into contact with them.
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Figure 1. Site and Designated Area Location Map
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To prevent any further exposure to the elevated lead levels, EPA desired the cleanup activities to
be conducted at the same time as the investigation. The flexibility of the EDMS and the desktop
GIS allowed this to occur by providing the USAGE with timely information to focus their cleanup
activities on the areas of concern.
The GIS Approach
The use and ease of applying GIS were supported by the following activities:
• Availability of the background database and mapping resources;
• Using ArcView GIS™, a desktop GIS, to maintain the sample location information; and
• Field reconnaissance during the investigation to confirm and catalog sample locations not
previously listed in the background information.
BVSPC, with assistance from EPA, requested existing database coordinate files from Jasper
County officials. These files were acquired in two databases. The first database was the City of
Joplin 911 database, and the second database was a developing rural Jasper County 911
database. The databases received by BVSPC were a modified subset of the original database,
containing address and coordinate information only. This subset served to maintain the privacy of
each individual resident.
In addition, electronic US Geological Survey Maps of Jasper County were obtained from the
USAGE during their time-critical cleanup activities at the site. Images of the areal extent of the
main smelter zone and the designated mine waste areas were imported from AutoCAD into
ArcView. These AutoCAD files were created during previous project activities at the site. The
USGS maps and the AutoCAD images provided the base map for the sampling areas of concern.
With the use of ArcView, the combination of the 911 databases and the base map enabled
BVSPC to identify the residential properties within the areas of concern. Those properties falling
within the areas of concern shown on the base map were selected to separate them from the
main databases that contained properties for the entire County. ArcView allowed the selected
subset of properties to be exported to Microsoft Excel™, where the new database of geographical
data was sorted by various fields for ease of reference. This subset initially contained 8,000
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# Property Requring Sampling
H Denied Access
StreetsyRoads
and Mining Areas
Figure 2. Residences "To Be Sampled" Map
properties requiring sampling within the Site for determination of surface soil lead levels. Figure 2
shows a portion of the properties to be sampled.
Field reconnaissance of these initial 8,000 properties was conducted as part of the access
agreement process. Access agreements were required from the owner of each property to be
sampled and included obtaining the owner's permission and taking a census of the residents living
at the property. The EDMS significantly reduced the effort for field reconnaissance by
incorporating the database information into a GIS presentable format, allowing BVSPC to easily
identify the boundaries of the investigation. During reconnaissance, approximately 2,900
properties were recorded as being commercial properties, previously remediated properties, or
properties where the owner denied access for sampling. These properties were omitted from the
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initial subset of 8,000. In addition to the remaining 5,100 properties in the database that required
sampling, approximately 1,400 other residences were identified. These additional residences were
mainly identified outside the city of Joplin, in smaller rural cities and towns. The county 911
database did not include the properties of homes within "city limits" since it was established as a
rural property identification system. The rural 911 database was in the process of being developed
and was incomplete in certain areas. The additional 1,400 identified residences would also require
sampling and surveying to add them into the GIS database.
Portable GPS units, provided by EPA, were used to survey the 1,400 individual properties. EPA
provided BVSPC with the coordinate location of the BVSPC Project Office in Joplin to ensure that
the data points obtained at each property could be within + 3 feet. Some interference was
encountered during the GPS survey from various "line of sight" obstructions, including trees and
electrical lines in the neighborhoods. GPS verification was obtained after several survey attempts
for all additional locations requiring data. The GPS data was added to the existing geographical
database for the properties that were sampled.
GIS Data Manipulation
The EDMS for this project, as demonstrated in Figure 3, was a comprehensive data management
package that allowed BVSPC to manage large quantities of data. The data types included
analytical, hydrogeological, and geographical information related to specific sampling locations. It
served as the focal point of a "hands off" approach for input and output of environmental and GPS
data. This "hands off" approach was an essential element during the project due to the extensive
volume of data collected during the investigation. On average, three to five samples were
collected per residential yard during the investigation. The data for each sample location, including
XRF, laboratory confirmation, and GPS, were associated with the corresponding residential
property. In essence, the EDMS eliminated the potential of transcription errors and data entry
errors for 35,000 environmental data records and 6,500 coordinate and GPS records, while
diminishing the duplication efforts of BVSPC personnel to manually review data and prioritize
contaminated residential properties.
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Smelter
Zone Verification
X-Ray Fluorescence (XRF)
- 25,000 analytical data points
Electronic Data
Deliverables
(EDDs)
-10,000
confirmation
data pts
EDMS
(EnviroEDGE/
Access)
Laboratory
Confirmation
Data
Global Positioning System (GPS)
- 6,500 positioning records
Microsoft Office
Package
Lead Result
Notification Letters
Remedial Action
Priority Query
Intermediary
File Conversions
Figure 3. Electronic Data Management Flow Diagram
During this investigation, the EDMS allowed the analytical data to be sorted through a querying
process. The following list includes examples of the types of queries run during the project:
• Lead concentrations above and below the action level;
• Lead concentrations indicating necessary remediation in ascending or descending order; and
• Areal sorts according to street name, city, or zip code, to further pinpoint the areas of
greatest remediation need.
7
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Each of these queries was performed using Access and exported to Excel as a spreadsheet
file. Throughout the project, various specific preliminary queries were conducted at the request
of the EPA. Representative queries included the following:
• Specific residence queries;
• Continuous updates of those residences having soil lead concentrations exceeding action
levels;
• Updates of residences with children under age 7 and high lead levels; and
• Updates on homes with soil lead concentrations above 2,500 ppm.
XRF Data. Field environmental data was downloaded from XRF equipment into an electronic file
that was transferred from the field office via e-mail to the main office. In the main office, the XRF
file was formatted by an Excel macro to allow uploading into the EDMS. The data upload was
simplified with the help of another macro written in Access that requested information from the
database manager before searching for a specific data file to incorporate it into the EDMS. This
brief process of transferring and uploading approximately 25,000 XRF data points allowed access
to the data within three days of sampling. All data was immediately referenced to the
corresponding geographical data to evaluate the existence of the "hot zones".
Laboratory Data. Laboratory environmental confirmation data was uploaded directly into the
EDMS using a pre-designed electronic data deliverable (EDO) package provided by the
laboratory. Prior to receipt of the EDO, the laboratory data was validated and qualified by an
outside contractor. During the upload process into the EDMS, an analytical verification process
was accomplished that noted missing data. Approximately 10,000 confirmation data points were
entered into the EDMS through this process.
GPS Data. GPS data was downloaded directly from the field GPS units into an electronic file that
was transferred from the field office via e-mail to the main office. The GPS data underwent a post-
processing procedure to correlate latitude and longitude with each residential property. BVSPC
converted the latitude and longitude data to the State Plane-83 projection format used in ArcView.
Once these procedures were completed, the 6,500 coordinate and GPS records were
incorporated into the EDMS and the final presentation of data could begin.
GIS Presentation of Data
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Presentation of the data was achieved through the availability of the created geographical
database
ArcView.
database and several additional software packages, including Surfer, Microsoft Office™, and
Surfer. Surfer, a 2-dimensional gridding and contouring package, was utilized to verify the main
"smelter zone". After approximately 15,000 analytical samples were collected, data was queried
from the EDMS and converted to a format able to be gridded and contoured in Surfer. With the
additional sampling data obtained during the investigation, a contour map of soil lead
concentrations was produced. The contour of the additional coverage area was exported from
Surfer as an AutoCAD file that could be imported into ArcView for comparison with the original
smelter zone. Upon review of the new contours, changes in the coverage of the original estimated
smelter zone were identified (Figure 4). The same identification process, as described in previous
paragraphs, was used to define the additional properties requiring sampling. The smelter zone
was expanded in several areas and reduced in others, optimizing investigation efforts and
maximizing the protection of public health.
Microsoft Office Package. The Microsoft Office Package gave BVSPC the flexibility of converting
the various types of data into acceptable formats. These formats were used to prepare notification
letters to property owners, to present requested queried information to EPA, and to query
information for use in Surfer and ArcView for final geographical information.
Property owner notification letters were prepared with the help of Excel files and Word mail-merge
capabilities. The letters informed the owners of the XRF lead sampling results for the high and low
concentrations in their soils and gave a tentative timetable in which the remediation efforts would
be conducted, if deemed necessary. These notices were sent to property owners within one
month or less of the sampling date for their property. Without these electronic capabilities, each of
the thousands of letters would have been completed individually. The effort for individual letters
would have required much more personnel time and would not have been an expedient response
to concerned residents.
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342000-
340000-
338000-
M 336000-
334000-
332000-
330000-
328000-
3500
3000
2500
2000
1500
1000
500
Lead
(PPM)
2774000 2776000 2778000 2780000 2782000 2784000 2786000 2788000 2790000 2792000
Easting
Figure 4. Smelter Zone Verification demonstrated in Surfer
The preliminary queries described earlier and the quick turnaround of the property owner
notification letters allowed reassurances to concerned property owners and allowed the
remediation prioritization for homes with children and those posing an excessive danger to
residents.
ArcView GIS. ArcView was the final step in the presentation of all information to be used by EPA
and the remediation contractor. It was used to create over 40 full-size maps of the remediation
area (Figure 5) for the final design. ArcView easily illustrated the individual residences that
exceeded the established lead soil cleanup level for this Site as well as those residences with lead
concentrations below the action level. In addition, each residence identified on the map was linked
to a table containing pertinent information, including property owner information, ages of any
children living at the residence, and values of each XRF lead sample analysis (Figure 6). This
procedure allowed a visualization of the extent and location of residential yards requiring soil
remediation. Commercial locations, previously remediated properties, and those properties where
10
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sampling access was denied were also illustrated on the investigation summary maps. The maps
allowed BVSPC personnel to confirm, track, and suggest the future progress of the soil
investigation. These maps were a great asset to USAGE, who used them to track the yards
requiring remediation and those yards that had already been remediated. EPA also used the
maps as illustrations at public meetings held in the area to keep residents informed of the
progress.
Conclusions
The investigation was completed effectively and efficiently due to the flexibility and adaptability of
the desktop GIS and our EDMS. As demonstrated above, the benefits of using ArcView in
conjunction with the EDMS included the following:
=> Prioritized cleanup activities based on investigation results.
=> Maintained concurrent investigation and remediation efforts.
=> Provided a visual geographical tool for the investigation personnel.
=> Provided a visual geographical tool for the USAGE cleanup crews.
Cost requirements were minimized due to the flexibility of ArcView GIS and the EDMS resources.
The efforts for obtaining the geographical distribution of the contaminated properties would have
required a much greater cost consideration during the implementation of the investigation and
continuation of remedial efforts had a GIS system not been implemented during the project.
11
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Jasper County
Yard Soil Remedial Design
Area 7
Sampled Property
o No Remediation Required (< BOO ppm Pb)
• Remediation Required (> 800 ppm Pb)
A Properties already remediated
* Commercial Properties
a Denied Access
| | Joplin City Streets
Figure 5. ArcView Presentation of Data
12
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file Edil View Iheme Eraphics Window Help
HO
Scale 1:| 14,207 2.JS6-Z
/\/ County Streets/Roads
VV/Smelter and Mining Areas
^Identify Result
jjflstart| Task Tracker 6 03 | jj|lnbox - Microsoft Outlook | ^Microsoft Ward 11 '.\ArcView CIS Version 3....
Figure 6. Linked Attributes Table to ArcView Property Location
13
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BIBLIOGRAPHY
1. Missouri Department of Health (MDOH), 1994, Jasper County, Missouri Superfund Site Lead
and Cadmium Exposure Study, May 1994.
2. Dames & Moore, 1994, Residential Yard Assessment Report, Seven Designated Areas,
Jasper County Site, September 1994.
3. U.S. Environmental Protection Agency, 1996, Record of Decision Declaration for the Oronogo-
Duenweg Mining Belt Site, Operable Units 2 and 3, Jasper County, Missouri, USEPA Region VII,
August 1996.
4. Smith, Robert A., W. Todd Dudley, and Thomas L. Rutherford, 1995, GIS Applications in
Hazardous Waste Remediation, Black & Veatch, 1995.
5. Black & Veatch Special Projects Corp., 1997, Oronogo-Duenweg Mining Belt Site, Remedial
Design, Statement of Work, Residential Yard Soils, February 1997.
14
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Decision Support System for Multiobjective Riparian/Wetland Corridor Planning
Margaret A. Fast and Tina K. Rajala
Kansas Water Office, Topeka, Kansas
Kansas has numerous programs that affect riparian cor-
ridors and associated wetlands. These programs in-
clude planning, monitoring, assistance, research, and
regulatory activities. Although administration of these
programs often overlaps, integration of program objec-
tives into a holistic, multiobjective approach to resource
planning and management has been lacking. A large
amount of resource data was routinely collected and
compiled, but no effective way had been developed to
integrate these data into the decision-making process.
The Kansas Water Office (KWO) was awarded a grant
in September 1992 from the U.S. Environmental Protec-
tion Agency (EPA) to develop a geographic information
system (CIS) decision support system (DSS) that would
enable the state to augment its ability to manage ripar-
ian/wetland corridors. The project used CIS to differen-
tiate between reaches of a stream corridor to evaluate
their environmental sensitivity. The Neosho River basin,
one of 12 major hydrologic basins in Kansas, was used
as a pilot to demonstrate the feasibility of the concept.
The KWO will use the DSS to help target sensitive areas
in the Neosho basin for further planning activities. The
project will also benefit other state agencies in their
riparian/wetland corridor efforts. The implementation of
planning objectives may involve local units of govern-
ment and, ultimately, private landowners.
Major phases of the project included:
• A needs assessment study
• A feasibility analysis
• A system design
• Construction of the DSS for the Neosho River basin
• A final evaluation of the DSS capabilities
An interagency project advisory group (IPAG), consist-
ing of representatives from eight agencies directly or
indirectly involved in riparian and wetland protection
activities, was formed to assist in project design and
evaluation.
Major steps involved in designing the DSS included:
• Selection and CIS development of databases used
for riparian corridor evaluation.
• Creation of riparian corridor segments.
• Development of an analysis methodology to apply to
corridor segments.
• Evaluation of the DSS.
Databases Selected for Decision Support
System Development
Many types of data were reviewed for the DSS. Several
were not used due to the costs associated with geo-
graphically referencing the data, given the current data
format.
The databases listed in Table 1 are available in the DSS.
During the system design phase of the project, the IPAG
identified the need to develop a pilot study area for the
DSS. The IPAG had difficulty understanding how a DSS
would use geographically referenced data sets (cover-
ages). Before committing to a design for the develop-
ment of a basinwide system, the IPAG decided first to
develop a pilot study area, with a specific focus (appli-
cation), that could be on-line and demonstrated early.
This would allow time for further refinement of the scope
of work and identification of coverages to be developed
prior to basinwide development of the DSS. For the pilot
study application, the IPAG chose to assess the value
and vulnerability of the riparian areas in two 11-digit
hydrologic unit code (HUC11) watersheds to allow the
user to evaluate a corridor segment and compare be-
tween segments and to prioritize or target segments for
further planning activities.
As development of data layers progressed for the pi-
lot, the IPAG quickly determined that the DSS project
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Table 1. DSS Database List
DSS Name Data Description
Source
Boundary
Buffer
Channels
Con_ease
Contam
Corridor
County
Dams
Dwrapp
Gages
Geology
Huc11
HydMOOk
Kats
Landc
Lc_stats
MDS
NPS
Perenial
Pop
PPL
Publand
Roads
Sections
Streamev
Neosho River basin boundary
Riparian corridor
Stream channelization
Conservation easements
Water contamination
Riparian corridor
County boundaries
Dam structures
Water appropriations
United States Geological
Survey (USGS) stream gaging
stations
Surface geology
11 -digit hydrologic unit
boundaries
Hydrology
Kansas water quality action
targeting system
Land cover
Land cover statistics
Minimum desirable stream
flow monitoring gages
Nonpoint source pollution
Perennial hydrology
Population
Populated places
Public lands
Roads
Section corners
1981 stream evaluation
Soil Conservation Service (SCS) HUC11 drainage basins; 1 :100,000-scalea
Original buffer on mainstem Neosho and Cottonwood; 147 corridor segments split
on tributary confluences
Division of Water Resources (DWR) legal description of locations
Locations of important natural resources that could be purchased by the state from
willing landowners for conservation protection
Kansas Department of Health and Environment (KDHE) contamination locations3
Final riparian corridor; 63 corridor segments developed from HUC11 boundaries
Kansas Geological Survey (KGS) cartographic database; 1 :24,000 scale3
DWR legal descriptions of locations
DWR legal descriptions of locations3
USGS latitude-longitude descriptions; CIS cover developed by USGS
KGS 1 :500,000-scale3
SCS HUC11 drainage basins; 1 :100,000-scale3
USGS 1:100,000-scale digital hydrology3
KDHE target valuable and vulnerable scores by HUC11 drainage basin
1 :100,000-scale developed from satellite imagery by the Kansas Applied Remote
Sensing Program, University of Kansas3
Summary statistics on land cover by corridor segment
Subset of USGS gaging stations
Target watersheds identified in the Kansas Water Plan
Reselected perennial streams from 1:100,000 USGS digital hydrology
Urban land cover (from landc) with 1980 and 1990 Census population data
Geographic names information system (GNIS) entries for Kansas; GIS cover
developed by USGS
State and federally owned land digitized from 1:100,000-scale USGS quad maps
USGS 1:100,000-scale digital roads3
KGS cartographic database; 1 :24,000-scale3
U.S. Fish and Wildlife stream evaluation study; Kansas Department of Wildlife and
Parks (KDWP) provided data on paper maps
T_and_e Threatened and endangered
species
Temussel Threatened and endangered
species
Tigrcity City boundaries
Twp Townships
Watrfowl Water fowl locations
Wq_eff Water quality: effluent
Wq_grnd Water quality: ground water
Wqjake Water quality: lake
Wq_strm Water quality: stream
Stream locations of state and federal identified threatened and endangered
species; KDWP provided data on paper maps
Locations of state endangered floater mussels; KDWP provided data on paper maps
U.S. Census 1:100,000-scale TIGER line data; boundaries only, areas not named
(use with PPL)
KGS cartographic database; 1:24,000-scale3
KDWP locations and counts of annual waterfowl migration; data developed from
paper maps (Restrict public distribution of data per KDWP request.)
KDHE sampling sites; GIS cover developed by KDHE
KDHE sampling sites; GIS cover developed by KDHE
KDHE sampling sites; GIS cover developed by KDHE
KDHE sampling sites; GIS cover developed by KDHE
Data available at the Kansas Data Access and Support Center (DASC).
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parameters would have to be limited to the riparian
corridor along the mainstem of the Neosho and Cotton-
wood Rivers. The costs associated with developing ri-
parian corridor segments for all perennial waters in the
Neosho basin was fargreaterthan the available funding.
Creation of Riparian Corridor Segments
A buffer width of one-half mile (one-quarter mile from
each stream bank) for the mainstem of the Neosho and
Cottonwood Rivers was used to produce the riparian
corridor. If more time and funding had been available,
riparian corridors for all perennial streams in the Neosho
basin could have been developed. The development of
this second view of data, organized by the HUC11 wa-
tershed, would then have been useful for individual
watershed analysis because all perennial streams in the
watershed could be analyzed.
The intersection of the HUC11 basin boundaries seg-
mented the corridor. In several instances, small sliver
polygons were produced where the HUC11 boundary
paralleled the river within the 1/4-mile corridor. The sliver
polygons were dissolved into the majority HUC11. In
other words, this project assumed that the 1/4-mile cor-
ridor buffer was more accurate and useful than the
1:100,000-scale HUC11 boundary.
Many of the HUC11 boundaries that the Soil Conserva-
tion Service (SCS) developed actually follow the course
of the Kansas streams, ratherthan intersect them. When
this occurred along the Neosho and Cottonwood Rivers,
we found that the resulting opposing corridor segments
did not always balance with an equivalent length. Also,
some HUC11 boundaries would first follow the river,
then cross the river. This resulted in corridor segments
that encompass both sides of the river for a portion of
the segment and follow only one side of the river for
another portion of the segment. To address these situ-
ations, the KWO arbitrarily added intersections to create
equivalent left and right bank corridor segments and to
create corridor segments that encompassed either one
side of the river or both sides of the river.
Once the corridor segments were finalized and numbered,
the corridor segment identification number (corrseg-id)
was attached to the other CIS covers. This allows the
reselection of data for a given corridor segment, using
Boolean expressions in the DSS.
Development of an Analysis
Methodology: Land Use
The IPAG determined that one of the most significant
factors associated with the quality of the riparian corridor
is land cover. Land cover was analyzed for the riparian
corridor segments; the CIS cover lc_stats contains sum-
mary statistics for each corridor segment. The calcula-
tions discussed in the following paragraphs identify the
data found in the lc_stats cover. Due to the size of the
land cover data set in the Neosho River basin, the DSS
includes only the land cover within the riparian corridor.
One way of identifying corridor segments in need of
protection or remedial action is to determine the ratio of
the number of acres in the corridor segment that contain
the preferred riparian land cover types (grasses, woods,
and water) to the number of acres that contain the least
preferred types of land cover (crops and urban areas).
The corridor segments can then be ranked according to
that ratio.
Other calculations are useful:
• bad_pct: percentage of the corridor segment that con-
tains crop and urban land cover types.
• bad_tbad: percentage of all crop and urban land
cover for the entire riparian corridor that resides in
the corridor segment.
• 'type'_pct: percentage of the corridor segment that is
crop, grass, wood, water, and urban. Type' refers to
each of the five land cover types; lc_statsuses: a
separate value for each (e.g., crop_pct).
• 'type'_t'type': percentage of each type of land cover
for the entire riparian corridor that resides in the cor-
ridor segment (e.g., crop_tcrop).
• 'type'_acres: total acreage of each type of land cover
in the corridor segment (e.g., crop_acres).
• good_acres: total acreage of grass, wood, and water
in the corridor segment.
• bad_acres: total acreage of crop and urban in the
corridor segment.
Another significant benefit of the DSS is the ability to see
where the land cover types are in relation to the river.
As an example, the ability to identify corridor segments
that have crop land extending to the river on both banks
is useful because they are the segments most vulner-
able to bank erosion. Those segments can then be
targeted for further remedial activities planning.
Decision Support System Requirements
The DSS data sets were developed and analyzed using
ARC/INFO on a UNIX-based workstation. The final cov-
ers were then exported and transferred to a microcom-
puter for use in ARC/VIEW. Hardcopy prints are printed
to a Tektronix Phaser III color wax printer with 18 Mb of
RAM, running in Postscript mode.
The DSS data sets total 26 Mb. ARC/VIEW version 1
requires 8 Mb of RAM to load the program. To run the DSS
efficiently, a 486DX-66 with 16 Mb of RAM is preferred.
The DSS is slower on a 486DX-33 with 8 Mb of RAM. It
was not tested on any other PC configuration, so a con-
figuration in between the two may be satisfactory.
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Processing GIS Data
Reselecting the perennial streams in the Neosho basin
and further identifying the mainstem of the Neosho and
Cottonwood Rivers using United States Geological Sur-
vey (USGS) 1:100,000-scale hydrography can be time
consuming. Perhaps the River Reach III covers should
replace that data in the future.
Attaching census data to the urban land cover polygons,
as was done for the pop cover, is not recommended.
Use of the TIGER line files and cover would give a more
accurate distribution of the population. Because less
than 5 percent of the riparian corridor had urban land
cover, the KWO did not use the pop cover in its evalu-
ation. Several summary covers of TIGER and census
data will soon be available from DASC.
Clipping the other ARC/INFO covers to the Neosho
basin and attaching the corrseg-id, using the identity
command, was unremarkable.
Processing Non-GIS Digital Data
Channels and dams were in digital format but were not
in ARC/INFO format. The files were processed using the
LeoBase conversion software from the KGS, then gen-
erated into ARC/INFO covers. Some records were lost
in the conversion. The LeoBase program fails to convert,
or incorrectly converts, legal descriptions for sections
that do not have four section corners (e.g., northeastern
Kansas). The Division of Water Resources is in the
process of attaching latitude-longitude to the point loca-
tions. Processing these data should take only a few
hours at most.
Processing Nondigital Data
Several covers were developed on contract from paper
maps or legal descriptions. They were: conservation
easements (con_ease), public lands (publand), stream
evaluation (streamev), threatened and endangered spe-
cies (t_and_e and temussel), and waterfowl (watrfowl).
Most of the data for these covers were drafted on a
1:100,000-scale USGS quad map and digitized. The
stream evaluation data were developed using a
scanned paper map of the coded streams as a backdrop
for the 1:100,000-scale hydrography; the digital streams
were reselected and coded.
In summary, KWO's GIS personnel needed approxi-
mately 275 hours to develop the riparian corridor seg-
ments, process the land cover data and summary
statistics, export the covers, transfer and import the
covers for ARC/VIEW, and assist in the development
and presentation of the DSS demo. Contract personnel
spent approximately 183 hours developing GIS covers
for the DSS. This does not include the time spent iden-
tifying the perennial and mainstem hydrology in the
USGS 1:100,000-scale hydrology.
Final Evaluation of the Decision
Support System
In its final evaluation of the system, the IPAG determined
the system to be useful and an excellent start at consoli-
dating a variety of data that have application for riparian
corridor/wetland issues. Many IPAG members found
ways to use the DSS in their own programs. Additional
comments on the system evaluation are as follows:
• Concern about the lack of complete wetland data.
The land cover data available could not identify wet-
land areas.
• Need for more detailed woodland data. Again, the
resolution of the land cover data precluded detailed
identification of woodland areas. The Kansas Biologi-
cal Survey (KBS), the KWO, and EPA are now pur-
suing options to develop more detailed land cover
data, including wetlands and woodlands.
• The lack of information on the tributaries did not allow
full basin analysis, which would be desirable. This
issue is addressed in the "construction" discussion
above.
• Desirability of expanding the project with elevation
and temporal data.
• Lack of definition of the floodplain. Federal Emer-
gency Management Agency (FEMA) floodplain data
are not easily incorporated into a GIS. Other options,
including satellite imagery of the flood of 1993, will
be evaluated.
• Project development requires extensive communica-
tion between program people and GIS technicians.
This can be a daunting task due to the technical
vocabularies involved and the many other ongoing
activities of the participants.
• Consideration of the requirements for transferring the
project to other potential users. GIS applications gen-
erally use large databases. User microcomputers
may not have the CPU, RAM, and storage capacity
necessary for the DSS application and often have a
limited number of options for data transfer.
• Concern about costs and time associated with the
expansion of the DSS to other basins in the state.
This project was focused on one of the 12 major
hydrologic regions in Kansas. Funding options, pro-
ject scope, and system refinements based on the
physical characteristics of the other basins need to
be pursued.
The KWO learned that clearly defining a single DSS
application at the outset of the project is critical. The
KWO originally believed that the DSS could be devel-
oped with general descriptions of the broad range of
program applications, utilized by multiple agencies, that
could benefit from the DSS. Each participating agency
-------
could bring its programs and needs to the IPAG for areas for further planning activities, the IPAG became
discussion; the resulting DSS would then serve those more confident in its advisory role. Upon completion of
multiple programs and needs. Instead, the ambiguity of the project, the IPAG members could readily identify
the objective confused the IPAG. Once the IPAG chose how the DSS could be enhanced, modified, or directly
to focus on a single application, the assessment of used in their own programs.
riparian corridor value and vulnerability to target priority
-------
Integration of GIS and Hydrologic Models for Nutrient Management Planning
Clyde W. Fraisse, Kenneth L. Campbell, James W. Jones,
William G. Boggess, and Babak Negahban
Agricultural Engineering Department, University of Florida, Gainesville, Florida
Introduction
Recent evidence that agriculture in general, and animal
waste in particular, may be an important factor in surface
and ground-water quality degradation has increased the
interest in nutrient management research. The presence
of nitrogen and phosphorus in surface water bodies and
ground water is a significant water quality problem in
many parts of the world. Some forms of nitrogen and
phosphorus, such as nitrate N and soluble P, are readily
available to plants. If these forms are released into
surface waters, eutrophic conditions that severely impair
water quality may result. Advanced eutrophication (pH
variations, oxygen fluctuations or lack of it in lower
zones, organic substance accumulation) can cause
physical and chemical changes that may interfere with
recreational use and aesthetic appreciation of water. In
addition, possible taste and odor problems caused by
algae can make water less suitable or desirable for
water supply and human consumption (1).
Increases in nutrient loadings to water resources have
recently been observed in the southeastern United
States, where well-drained sandy soils with low nutrient
retention capacity and high water table conditions are
found in most coastal areas. Those increases were
associated statistically with nutrient sources such as
agricultural fertilizers and dense animal populations (2,
3). Repetitive occurrences of extensive blooms of blue-
green algae that threatened the overall health of Lake
Okeechobee, located in southern Florida, were attrib-
uted to an increase in nutrient loadings to the lake. The
South Florida Water Management District (SFWMD) re-
ported an increase of phosphorus concentrations in the
lake water from an annual average of 0.049 milligrams
per liter in 1973 and 1974 to a peak of 0.122 milligrams
per liter in 1988 (4).
Most water quality problems concerning phosphorus
result from transport with sediment in surface runoff into
receiving waters. Continuous high loadings from animal
waste on sandy soils with low retention capacity, however,
may contribute significant quantities of labile phospho-
rus to subsurface drainage. Ground-water aquifers may
also become polluted due to recharge of high loadings
of nitrogen. Drinking water with nitrate N concentrations
higherthan 10 milligrams per liter may lead to methemo-
globinemia in infants. Ground-water monitoring of the
Middle Suwannee River area in Florida has shown high
concentrations of nitrate nitrogen near intensive agricul-
tural operations. The U.S. Geological Survey has inten-
sively monitored dairy and poultry farms and has found
high nitrate levels below these operations compared with
nearby control wells (5).
Animal waste management has always been a part of
farming, but historically has been relatively easy due to
the buffering capacity of the land. In fact, land applica-
tion of animal waste at acceptable rates can provide
crops with an adequate level of nutrients, help reduce
soil erosion, and improve water holding capacity. As the
animal industry attempts to meet the food requirements
of a growing population, however, it applies new tech-
nologies that reduce the number of producers, but cre-
ate larger, more concentrated operations. That, in
addition to the decreasing amount of land available for
waste application, has increased the potential for water
quality degradation.
Successful planning of an animal waste management
system requires the ability to simulate the impact of
waste production, storage, treatment, and use on water
resources. It must address the overall nutrient manage-
ment for the operation, including other nutrient sources
such as supplemental fertilizer applications. Livestock
operations are highly variable in their physical facilities,
management systems, and the soil, drainage, and cli-
matic conditions that affect the risk of water pollution
from animal wastes (6). Linkage between geographic
information systems (GIS) and hydrologic models offers
an excellent way to represent spatial features of the-
fields being simulated and to improve results. In addition,
a GIS containing a relational database is an excellent way
-------
to store, retrieve, and format the spatial and tabular data
required to run a simulation model.
This paper examines some of the issues related to the
integration of hydrologic/water quality models and CIS
programs. In addition, the paper discusses the ap-
proaches used in the Lake Okeechobee Agricultural
Decision Support System (LOADSS), which was re-
cently developed to evaluate the effectiveness of differ-
ent phosphorus control practices (PCPs) in the Lake
Okeechobee basin. The paper also details a dairy
model, designed to simulate and evaluate the impacts
of alternative waste management policies for dairy op-
erations, that is currently under development.
Hydrologic Models and GIS
By using models, we can better understand or explain
natural phenomena and, under some conditions, make
predictions in a deterministic or probabilistic sense (7).
A hydrologic model is a mathematical representation of
the transport of water and its constituents on some part
of the land surface or subsurface environment. Hydro-
logic models can be used as planning tools for determin-
ing management practices that minimize nutrient
loadings from an agricultural activity to water resources.
The results obtained depend on an accurate repre-
sentation of the environment through which water flows
and of the spatial distribution of rainfall characteristics.
These models have successfully dealt with time, but
they are often spatially aggregated or lumped-parame-
ter models.
Recently, hydrologists have turned their attention to GIS
for assistance in studying the movement of water and
its constituents in the hydrologic cycle. GIS programs
are computer-based tools to capture, manipulate, proc-
ess, and display spatial or georeferenced data. They
contain geometry data (coordinates and topological in-
formation) and attribute data (i.e., information describing
the properties of geometrical objects such as points,
lines, and areas) (8). A GIS can represent the spatial
variation of a given field property by using a cell grid
structure in which the area is partitioned into regular grid
cells (raster GIS) or by using a set of points, lines, and
polygons (vector GIS).
A close connection obviously exists between GIS and
hydrologic models, and integrating them produces tre-
mendous benefits. Parameter determination is currently
one of the most active hydrology-related areas in GIS.
Parameters such as land surface slope, channel length,
land use, and soil properties of a watershed are being
extracted from both raster and vector GIS programs,
with a focus on raster-based systems. The spatial nature
of GIS also provides an ideal structure for modeling. A
GIS can be a substantial time saver that allows differ-
ent modeling approaches to be tried, sparing manual
encoding of parameters. Further, it can provide a tool for
examining the spatial information from various user-
defined perspectives (9). It enables the user to selec-
tively analyze the data pertinent to the situation and try
alternative approaches to analysis. GIS has been par-
ticularly successful in addressing environmental prob-
lems.
Approaches for Integrating GIS and Models
A significant amount of work has been done to integrate
raster and vector GIS with hydrologic/water quality mod-
els. Several strategies and approaches for the integra-
tion have been tried. Initial work tended to use simpler
models such as DRASTIC (10) and the Agricultural
Pollution Potential Index (11). In these cases, the mod-
els were implemented within the GIS themselves. These
studies attempted to develop CIS-based screening
methods to rank nonpoint pollution potential. The use of
more complex models requires that the GIS be used to
retrieve, and possibly format, the model data. The model
itself is implemented separately and communicates with
GIS via data files. Goodchild (12) refers to this mode as
"loose coupling," implying that the GIS and modeling
software are coupled sufficiently to allow the transfer of
data and perhaps also of results, in the reverse direc-
tion. Fedra (8) refers to this level of integration as "shal-
low coupling" (see Figure 1). Only the file formats and
the corresponding input and output routines, usually of
the model, must be adapted. Liao and Tim (13) describe
an application of this type, in which an interface was
developed to generate topographic data automatically
and simplify the data input process for the Agricultural
Nonpoint Source (AGNPS) Pollution Model (14), a water
quality model.
Shared Databases and Files
j
1
GIS
User Interface
i
MODEL
User Interface
Figure 1. Loose or shallow coupling through common files (8).
-------
Higher forms of connection use a common interface and
transparent file or information sharing and transfer be-
tween the respective components (see Figure 2). The
dairy model, currently under development, is an appli-
cation of this kind. It will link the Ground-Water Loading
Effects of Agricultural Management Systems (GLEAMS)
(15) model and CIS to evaluate potential leaching and
runoff of both nitrogen and phosphorus.
LOADSS is an extension of this type of application
because it includes an optimization module that enables
the system to select the best PCPs at the regional scale,
based on goals and constraints defined by the user.
Both applications use ARC/INFO's arc macro language
(AML), a high-level application language built into the
CIS. A subset of functions of a full-featured CIS, such
as creation of maps (including model output) and tabular
reports, as well as model-related analysis, are embed-
ded in the applications, giving the system great flexibility
and performance. Fedra (8) describes a deeper level of
integration that would merge the two previous ap-
proaches, such that the model becomes one of the
analytical functions of a CIS, or the CIS becomes yet
another option to generate and manipulate parameters,
input and state variables, and model output, and to
provide additional display options. In this case, software
components would share memory rather than files.
The choice between integrating a water quality model
with a raster or vector CIS depends on the importance
of spatial interactions in the process being studied and
the nature of the model itself. Some water quality mod-
els, such as GLEAMS, are field-scale models that pro-
vide edge-of-the-field values for surface runoff and
erosion as well as deep percolation of water and its
constituents. In this case, spatial interactions between
adjacent fields are ignored and a vector CIS can be
used to describe the system. Moreover, important fac-
tors in the simulation process, such as land use and
management practices, are normally field attributes and
thus, are better represented in a vector structure.
Other factors playing an important role in the hydrologic
process, such as field slope, aspect, and specific catch-
ment area, are hard to estimate in vector systems,
however. A raster-based CIS is better suited for handling
watershed models in which the routing process is impor-
tant and spatial interactions are considered. For those,
several algorithms for estimating important terrain attrib-
utes are often incorporated in commercially available
raster-based CIS programs.
LOADSS
LOADSS was developed to help address problems cre-
ated by phosphorus runoff into Lake Okeechobee. It was
designed to allow regional planners to alter land uses
and management practices in the Lake Okeechobee
Shared Databases and Files
GIS
MODEL
Common User Interface
Figure 2. Deep coupling in a common framework (18).
basin, then view the environmental and economic ef-
fects resulting from the changes. The Lake Okeechobee
basin coverage incorporates information about land
uses, soil associations, weather regions, management
practices, hydrologic features, and political boundaries
for approximately 1.5 million acres of land and consists
of close to 8,000 polygons.
The SFWMD, responsible for managing Lake Okeechobee,
has initiated numerous projects to develop effective con-
trol practices for reducing the level of phosphorus in
agricultural runoff as part of the Lake Okeechobee Sur-
face Water Improvement and Management (SWIM)
Plan. These projects, numbering more than 30, were
designed to develop information on the control and man-
agement of phosphorus within the lake basin and to
determine the costs and effectiveness of selected man-
agement options. Three types of control options are
being studied:
• Nonpoint source controls, such as pasture management.
• Point source controls, such as sewage treatment.
• Basin-scale controls, such as aquifer storage and
retrieval.
After completing most of these research efforts, the
need arose for a comprehensive management tool that
could integrate the results for all three classes of PCPs.
In response to these needs, design and implementation
of a decision support system was initiated with the fol-
lowing objectives (16):
• Organize spatial and nonspatial knowledge about
soils, weather, land use, hydrography of the lake ba-
sin, and PCPs under a GIS environment.
-------
• Develop and implement algorithms for modeling non-
point source, point source, and basin-scale PCPs.
• Develop and implement mechanisms for evaluating
the performance of the entire Lake Okeechobee basin
under different combinations of PCPs applied to the
basin.
• Design and develop a user interface that would fa-
cilitate use of the system by noncomputer experts.
The goal in developing LOADSS was to create an infor-
mation system that would integrate available information
to help regional planners make decisions. LOADSS can
generate reports and maps concerning regional land
attributes, call external hydrologic simulation models,
and display actual water quality and quantity sampling
station data. LOADSS is a collection of different
components:
• The regional scale CIS-based model used to develop
and manipulate regional plans for reducing phospho-
rus loading to Lake Okeechobee.
• The Interactive Dairy Model (IDM) used to develop
field-level management plans for dairies and run the
Field Hydrologic and Nutrient Transport Model
(FHANTM) simulation model for nutrient transport
modeling.
• An optimization module that enables the system to
select the best PCPs at the regional scale (currently
under development).
Although these components can run independently, they
are fully integrated in the LOADSS package and can
exchange information where necessary. A design sche-
matic of LOADSS is given in Figure 3.
Regional-Scale CIS-Based Model
LOADSS serves both as a decision support system for
regional planning and as a graphic user interface for
controlling the different components. One consideration
in the design of LOADSS was the size of the database
that was being manipulated. Because the land use da-
tabase consisted of nearly 8,000 polygons, running the
simulation models interactively would not be a feasible
option. Thus, the CREAMS-WT (17) runoff model was
prerun for different levels of inputs and management for
each land use, soil association, and weather region (18).
Depending on the land use and its relative importance
as a contributor of phosphorus to the lake, anywhere
from one (background levels of inputs to land uses like
barren land) to 25 (dairies, beef pastures) levels of
inputs were selected. Each set of inputs to a particular
land use was given a separate PCP identification code.
A CREAMS-WT simulation was performed for each
PCP, on each soil association and weather region. This
resulted in approximately 2,600 simulation runs. Annual
average results were computed for use in LOADSS.
CREAMS-WT provides an average annual estimate of
phosphorus runoff from each polygon. Phosphorus
assimilation along flow paths to Lake Okeechobee are
estimated as an exponential decay function of distance
traveled through canals and wetlands (4).
The imports, exports, and economics of each PCP are
based on a per production unit basis. Depending on the
type of polygon, the production unit can be acres (e.g.,
pastures, forests), number of cows (dairies), or millions
of gallons of effluent (waste treatment plants and sugar
mills). Developing a regional plan in LOADSS involves
assigning a PCP identification code to each one of the
polygons in the Lake Okeechobee basin. Accessing the
results of a regional plan involves multiplying the pro-
duction unit of each polygon by its appropriate database
import, export, or economic attribute and summing the
resulting values overall polygons in the Lake Okeechobee
basin. LOADSS runs in the ARC/INFO Version 6.1.1 CIS
software on SUN SPARC stations.
Interactive Dairy Model
Although the LOADSS level of detail is adequate for
regional planning, a more detailed model was necessary
to analyze individual dairies in the Lake Okeechobee
basin, as dairies were one of the large, concentrated
sources of phosphorus runoff into the lake. Thus, the
IDM was developed and incorporated into LOADSS.
IDM utilizes FHANTM to simulate phosphorus move-
ment in dairy fields. FHANTM is a modification of
DRAINMOD (19) with added functions to handle over-
land flow routing, dynamic seepage boundary, and sol-
uble phosphorus algorithms for P input, mass balance,
and transport (20).
Unlike in LOADSS, FHANTM is run interactively, as IDM
requires. Furthermore, in LOADSS, the user can only
select from a number of predefined PCPs, while in IDM,
the user has access to more than 100 input and man-
agement variables, all of which can take a range of
values. This allows for the development and evaluation
of detailed dairy management plans that otherwise
would be impossible at a regional scale. While LOADSS
only provides average annual results, IDM displays daily
time series simulation results. IDM uses the same as-
similation algorithm and can produce the same phos-
phorus budget maps and reports as LOADSS.
Optimization Module
A variety of factors must be considered in planning
nutrient management programs. Production and envi-
ronmental goals need to be balanced, and these goals
are often incompatible. Performing this exercise on a
regional scale, comprising many fields for which a variety
of land uses and management options can be assigned,
is a tremendously time-consuming, if not impossible,
-------
CIS Databases
LOADSS User Interface
POLITICAL BOUNDARIES
Spatial
Databases
Input Attribute
Databases
PHOSPHORUS,
MATERIAL &
ECONOMIC BUDGETS
NONPOINT SOURCE
PCP'S CREAMS-WT
POINT SOURCE PCP'S
PROCESS ANALYSIS
BASIN TREATMENTS
PROCESS ANALYSIS
ASSIMILATION
INDIVIDUAL FIELD
MANAGEMENT
FHANTM
SIMULATION MODEL
ASSIMILATION
A
Optimization Module
LOADSS
Models and
Analysis Tools
IDM and
Analysis Tools
Figure 3. LOADSS design schematic (16).
task. The optimization component of LOADSS, currently
under development, will determine the best combination
of agricultural, environmental, and regulatory practices
that protects and maintains the health of Lake
Okeechobee and also maintains the economic viability
of the region. The optimization process will provide an-
other method for modifying the PCPs assigned to indi-
vidual fields. Different optimization solution methods,
such as linear programming and integer linear program-
ming, will be available for solving the optimization prob-
lem that the user defines.
Dairy Simulation Model
The dairy model was expected to be fully functional by
the end of 1994. It is designed to be an additional tool
for answering questions related to the environmental
costs and impacts of dairy operations. A design sche-
matic of the dairy model is given in Figure 4. It differs
from the LOADSS/IDM model in the following aspects:
• It is designed to be generic so that any dairy repre-
sented by a coverage for which relevant data, such
as topography, soil characteristics, weather, and field
boundaries, are available can be simulated.
• The GLEAMS water quality model will be used for
simulating nutrient transport of nitrogen and phosphorus.
• The user will be able to assign a larger variety of
crops and crop management practices to the individual
fields, including crop rotation.
GLEAMS (15) is a field-scale water quality model that
includes hydrology, erosion/sediment yield, pesticide,
and nutrient transport submodels. GLEAMS was devel-
oped to use the management oriented CREAMS (21)
model and incorporate more descriptive pesticide
subroutines and more extensive treatment of the flow of
water and chemicals in the root zone layers. The water
is routed through computational soil layers to simulate
the percolation through the root zone, but the volume of
percolation in each layer is saved for later routing in the
pesticide component. A minimum of three and a maxi-
mum of 12 layers with variable thickness may be used.
Soil parameter values are provided by soil horizon, and
-------
GIS Databases
DAIRY MODEL User Interface
Dairy Model
Analysis Tools
GLEAMS Simulation
Shell Controller
Figure 4. Dairy model design schematic.
the crop root zone may have up to five horizons. The
values for parameters, such as porosity, water retention
properties, and organic content, are automatically fitted
into the proper computational layers. Two options are
provided in the model to estimate potential evapotran-
spiration, the Priestly-Taylor method (22) and the Pen-
man-Monteith method (23). The nutrient component of
the model simulates land application of animal wastes
by creating appropriate nitrogen and phosphorus pools
for mineralization. It considers ammonia volatilization
from surface-applied animal waste by using a relation-
ship developed by Reddy et al. (24).
The graphic interface is designed to help the user plan
a balanced nutrient management program for the dairy
being simulated. First, total nutrient production and ac-
counting are estimated, based on information related to
the dairy management such as herd size, confinement
system, waste characterization, and handling. Figure 5
shows the general structure of the graphic interface and
a first version of the menu used to estimate the total
amount of nitrogen and phosphorus available for assign-
ment to the various fields. Nutrient losses during waste
storage and treatment vary widely depending on the
method of collection, storage, and treatment. Climate
can also have a great effect on the losses. Covering all
possible methods of storage and treatment is practically
impossible, especially in an application that is designed
to be generic and applied in any part of the country. A
simplification was made to overcome this problem: the
user must provide the percentage of original nitrogen
and phosphorus that is retained after waste storage and
treatment. The menus designed to enter information
related to the management of fields and crops are given
in Figure 6.
For each field, a sequence of crops can be defined in
the Field Management Table, and for each crop, the
sequence of practices or field operations is defined in
the Crop Management Table. Every time a waste appli-
cation operation is defined or a field is used as pasture
for a certain period, the corresponding amount of nutri-
-------
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HIITKTEHTS ncr.nuHrTHfi {pEDCEiirnnE)
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Figure 5. The nutrient production table of the dairy model interface.
ents will decrease from the amount available for assign-
ment and the total available for future assignment will
be updated. Once the total amount of nutrients is as-
signed, the model can be run for the several fields in the
dairy and the results evaluated in terms of nutrient load-
ings to the edge of fields and ground water. Alternative
plans can be designed and saved for comparison and
selection of best management options. The best solu-
tions in terms of reducing nutrient loadings to surface
and ground water must also consider economic aspects.
A producer's decision about competing waste manage-
ment practices is ultimately economically motivated.
Thus, the system will eventually include a tool for eco-
nomic analysis of alternative management options.
Summary and Conclusions
The search for solutions to the many problems concern-
ing nutrient management that affect water resources
implies a continued demand for the development of
modeling systems that can be used to analyze, in a
holistic approach, the impact of alternative management
policies.
The development of LOADSS exemplifies how the inte-
gration of hydrologic models and a CIS can be used for
analyzing nutrient control practices at different scales.
The addition of optimization algorithms further enhances
the ability of policy- and decision-makers to analyze the
impact of alternative management practices and land
uses at the regional level.
The first part of LOADSS (Version 2.2) that includes the
CREAMS-WT regional-scale model and the IDM com-
ponents is fully functional and currently available at the
SFWMD. Preliminary results show that LOADSS be-
haves consistently with measured data at the lake basin
scale. Some of this, however, is due to offsetting errors
in model behavior at the subbasin scale, particularly in
subbasins that are adjacent to or very far from the lake.
Currently, projects are underway to further verify and
calibrate the model at the subbasin level to improve its
-------
Figure 6. Field and crop management tables of the dairy model interface.
performance at smaller scales (16). Initial results of the
optimization component are currently being evaluated.
The dairy model represents a different approach in inte-
grating water quality models and CIS in that it is de-
signed to be generic and focused mainly on the farm
level. It is primarily designed to help policy- and decision-
makers analyze the effects of alternative dairy waste
management practices on the farm level. The framework
can easily be adapted to handle different types of animal
wastes (such as beef cattle and poultry) and to simulate
the impact of other crop management practices such as
pesticide applications.
References
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Using GIS To Rank Environmentally Sensitive Land
In Orange County, Florida
Michael J. Gilbrook, HDR Engineering, Inc.1
ABSTRACT
In 1994, the Orange County (Florida) Planning Department selected HDR Engineering to conduct a
GIS-based environmental constraints and development suitability study to support a new, proactive
planning initiative. The goal of the project was to conduct a McHargian overlay analysis which
would identify environmental constraints to development, and rank all non-urban lands within
Orange County according to their environmental value. The project required development of a
database consisting of eleven environmental constraint factors including vegetative cover,
wetlands, wildlife habitat preferences, habitat corridors, floodplains, aquifer recharge potential and
septic tank use suitability. A raster-based spatial model prepared in ARC/INFO GRID was used to
identify and rank environmental constraints to future urban development based on the distribution
and coincidence of the various environmental constraint factors. The HDR environmental science
staff worked closely with the County Planning Department and the County's Environmental
Mapping Committee (comprised of scientists, regulatory personnel, environmental activists, land
owners, and planners) to prepare the GIS model and weighting scenarios. The final products
included two baseline maps (one for ecological constraints, and one for physicochemical factors),
and several maps generated by alternative weighting strategies.
INTRODUCTION
In December 1993, Orange County (Florida) hosted a Growth Management Exposition as a
forum for unveiling the County's proposed Development Framework. The County's Planning &
Development Division Newsletter released concurrently with the Exposition noted that:
"The Orange County Comprehensive Policy Plan commits the County to the
preparation of a Development Framework Element as a vision statement to guide
the pattern of future development. ...The Comprehensive Policy Plan should
contribute to, and be an embodiment of, the common vision that represents the
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shared values and beliefs of the community for guiding future growth and
development."
A "environmental suitability analysis" was among the tools cited by Orange County as
necessary to the preparation and implementation of the Development Framework:
"...[t]he addition of the concept of environmental suitability analysis into the
Development Framework will strengthen the local planning process, facilitate
efforts to obtain public ownership of environmentally sensitive lands, and
increase the effectiveness of existing regulatory processes and procedures. It will
also help separate and differentiate between planning for the future and
regulating development."
That last point is crucial to understanding the importance of the County's environmental
constraints analysis project. A typical regulatory approach to environmental planning addresses
potential impacts piecemeal, as development is proposed, and attempts to minimize or mitigate
ecological impacts. In contrast, Orange County wanted to develop a pro-active planning
approach driven by an appreciation of the "carrying capacity" of the environment through
composite mapping of environmental constraints to development. The environmental constraints
mapping would then provide a long term guide for shaping future development patterns. Maps
of environmental constraints can also be construed, inversely, as maps of development
suitability (Twiss, 1975). Of course, a complete evaluation of development suitability would
require combining the findings of the environmental constraints analysis with information on
transportation systems, district plans, urban services, and proposed capital improvements.
In summary, the purpose of this study was to advance the County's movement towards a more
holistic, ecosystem oriented approach to environmental planning. Specifically, HDR was tasked
to "[d]evelop an environmental suitability index and map for Orange County that will compare
and rank areas for environmental compatibility and development suitability according to the
ecological characteristics of the region."
1 HDR Engineering, Inc., 201 S. Orange Avenue, Suite 925, Orlando, FL 32801-3413; Voice (407) 872-
7801, ext. 230; FAX (407) 872-0603; Email mgilbroo@hdrinc.com
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OVERVIEW OF OVERLAY ANALYSIS
In his book, Design With Nature, planner Ian McHarg popularized the notion of map overlay
analysis to evaluate land development suitability (McHarg, 1971). His method involved mapping
landscape features which imposed some kind of limitation on urban development (such as
wetlands, floodplains, or erosive soils) onto transparencies. For each map, the relative
development suitability (or, conversely, environmental sensitivity) of landscape features would
be ranked in order from high to low. Each map would be drawn at the same scale and
registered to the same geographic area. The unsuitable areas on each map would be shaded in
such a way that when the maps were overlaid one on another and viewed simultaneously on a
light-table, areas which were mapped as unsuitable on all the contributing maps would appear
very dark. Land areas which were unsuitable for development on fewer maps would appear
proportionally lighter, providing a graduated scale from completely suitable to entirely
unsuitable.
McHargian overlay analysis, as it came to be known, is a powerful planning technique.
However, it was also inefficient and often impractical to perform by manual means. In the 25
years since the publication of McHarg's book, his technique has been refined though the use of
computerized Geographic Information Systems (GIS). Examples of McHargian-type analyses
using GIS are now quite common; the author had personally conducted two (Gilbrook, 1989a;
Gilbrook, 1989b) prior to this study. The first step in conducting a McHargian analysis for
Orange County was determining what environmental values would be incorporated into the
evaluation process. The County Planning Department identified a tentative list of environmental
criteria for the study. Working from that list as a starting point, HDR identified the following
environmental issues which formed the basis for the environmental constraints analysis in
Orange County:
• Floodplain Protection Development in floodplains places people and property at risk.
Cumulative flood storage losses can exacerbate flooding elsewhere in the watershed.
Alteration of floodplains adversely affects the hydrological, biological and
physicochemical relationship between surface waters, wetlands, and uplands.
• Protection of Wetlands Wetlands provide critical habitat to a number of native central
Florida species. Wetlands provide flood storage, hydrologic attenuation and erosion
control functions.
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Maintaining Viable Wildlife Populations Wildlife populations must maintain minimum
numbers in order to resist extinction from genetic inbreeding depression, disease, or
climatic catastrophes. Viable populations require a minimum amount of suitable habitat
to support them. Without protection (and maintenance) of adequate habitat, wildlife
populations will likely become extinct over time.
Preserving Biodiversity The natural environment is comprised of a complex assemblage
of interdependent and co-evolved species. The effects of urbanization (e.g., forest
fragmentation, enhancement of edge effects, fire suppression, invasive exotic species)
serve to simplify and eliminate this diversity. Simplified ecosystems support fewer
species and are less resilient to environmental challenges (e.g., storms, disease).
Conservation Lands and Insular Ecology Conservation lands may maintain intact flora
and faunal assemblages representative of the ecosystems present within those
preserves as long as they enjoy contiguity with similar (or at least compatible) adjacent
landscapes. Once isolated from similar habitat, conservation lands become essentially
terrestrial islands subject to the species-area effects described by the science of insular
ecology; the preserves lose species in geometric proportion to their final, isolated size.
Groundwater Protection Drinking water supplies in central Florida are drawn from the
Floridan aquifer, a deep, water-bearing geological stratum. The aquifer is recharged only
by rainfall on relatively rare areas which have the necessary geologic conditions
conducive to movement of water from the surface to the deep aquifer below. However,
these areas are also conduits for potential contamination of the water supply for the
same reason that they are good recharge sites. Similarly, the potential for contamination
at the locations of drinking water wellheads and drainage wells (i.e., stormwater disposal
wells to the aquifer) were also concerns for groundwater protection.
Surface Water Protection Surface waters may be contaminated by pollutants borne by
stormwater runoff from artificial impervious surfaces, made turbid by eroding sediment,
or affected biologically by inappropriate management of shoreline wetlands or littoral
vegetation. Septic tanks sited in areas of unsuitable soils may cause contamination of
surface waters, presenting both an environmental hazard and a human health threat.
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To conduct the study, each of these themes were translated into specific layers of digital data
for analysis with GIS.
CIS DATA SOURCES
The environmental constraints analysis tapped a number of existing GIS data sources. Orange
County and the two Water Management Districts which have jurisdiction over the County (St.
Johns River WMD and South Florida WMD) were the source for most of the GIS data sets.
Other important sources included the East Central Florida Regional Planning Council (Existing
Land Use), the Florida Game and Fresh Water Fish Commission (Strategic Habitat
Conservation Areas and Biodiversity "Hot Spots"), and the Florida Natural Areas Inventory
(element occurrence records for rare plants and animals).
One of the most important data themes required was a current digital map of land cover to
supply the information about wetlands, rare vegetative communities, and vegetative biodiversity
needed to perform the environmental constraints analysis. A detailed digital Existing Land Use
(ELU) map of Orange County, last updated in 1989, was available. Before these data could be
applied to the environmental constraints analysis they had to be updated to reflect urban
development which had taken place over the previous five years. To conduct the 1994 update,
HDR scanned and georeferenced recent aerial photography for use in performing "heads up"
digitizing of new urban land uses not reflected in the ELU 1989 data. The final Existing Land
Use map appears in Figure 1; an inset of the map showing some of the features in the
Econlockhatchee River basin of east Orange County appears in Figure 2.
Many of the GIS data sets were prepared from map sources compiled at a scale of 1:24,000
(i.e., 1" = 2,000') or smaller. This placed constraints on how finely the data could be interpreted.
For example, the Florida Game and Fresh Water Fish Commission's (GFC's) Strategic Habitat
Conservation Areas were developed from Landsat satellite imagery which had a minimum pixel
resolution of 30 meters (about 98 feet, or 0.22 acres per pixel). In contrast, the Existing Land
Use 1994 (ELU 94) data developed by HDR were captured at 1:24,000 scale, but had a
minimum mapping unit of 2 acres; polygons of homogenous ground cover smaller than 2 acres
were not necessarily mapped. Although these relatively low resolution input data sets limited
their applicability for examining environmental constraints on small, individual parcels of land,
their scale was appropriate for the regional analysis required by Orange County for the purpose
of guiding comprehensive land planning.
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Existing Land Use, 1994 Update
Figure 1. Existing Land Use 1994 Map. Dark gray areas are urbanized, other color
represent undeveloped land cover. Inset area of Econlockhatchee River
headwaters area appears in Figure 2 and subsequent map figures.
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Figure 2. Inset of Existing Land Use 1994 in the Econlockhatchee River Headwaters Area,
Southeastern Orange County.
CIS MODELING METHODOLOGY
This study employed ARC/INFO 6.1.1. GIS software manufactured by Environmental Systems
Research Institute (ESRI). The software ran on a Sun Microsystems SPARC 10 Model 40
workstation under the Solaris 2.2 operating system (a variant of Unix System V).
ARC/INFO is principally a vector GIS system in which linear features (lines or polygons) appear
as smooth lines connecting the many vertices whose coordinates define the shape of those
features. In this respect an ARC/INFO visual display looks little different from a map prepared
using Computer Aided Design (CAD) software. Nearly all of the preliminary GIS manipulation
needed to produce the input data themes used in the study was conducted within the
ARC/INFO vector environment. However, one drawback to the vector GIS environment is that it
is a computationally intensive process. For ARC/INFO to combine two sets of countywide
polygon data to create a third using a vector overlay process (e.g., UNION or INTERSECT)
would have required considerable time. Overlays involving multiple input themes would have
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had to be processed in a series of pairs, until the final desired product was achieved. Even then,
the GIS analyst would have had to use tabular database functions to resolve the meaning of the
multiple layers of polygon data which had been combined.
Although the environmental constraints analysis could have been performed this way, a better
approach was available through use of ARC/INFO's GRID module. Combining and manipulating
GIS data in GRID was computationally much easier (and faster) than the topologically complex
process of resolving the output from overlays involving many arbitrarily shaped vector polygons.
Furthermore, the GRID process allowed for direct mathematical modeling of the input data sets
to generate the desired composite output map; for example, differential weighting of the input
data was as easy as multiplying all cells in an individual data layer by a numerical constant.
Given its clear advantages, HDR used GRID for modeling in this study. Each of the vector
polygon GIS input data themes was converted to a grid with a cell size of 98 feet. The 98 feet
corresponded to the 30 meter resolution of the Landsat data which formed the basis for the
GFC's Strategic Habitat Conservation Areas, Biodiversity "Hot Spot" areas, and the SJRWMD's
Regionally Significant Habitat areas. A cell resolution of 98 ft resulted in a minimum mapping of
0.22 acres, which we deemed small enough to adequately represent details in the various input
coverages collected at 1:24,000 scale.
Following examination of the available GIS data sets, consideration of the study's objectives,
discussions with the Orange County Planning Department staff, and input from the
Environmental Mapping Advisory Committee, HDR determined that the most appropriate end
product would not be a single map of environmental constraints, but two composite maps: an
"Ecological Constraints Map" and a "Physical Constraints Map." The Ecological Constraints
Map was derived from those factors whose attributes (mostly biological) denoted resources that
were sensitive to any land alterations. For example, a forested area that was important for the
maintenance of wildlife populations was likely to be adversely affected by substantial clearing.
The type of urban development (e.g., residential, commercial or industrial) makes no difference;
it is the loss of forest habitat, the reduction in forest interior, or the increase in fragmentation and
edge effects which are important. Protection of such areas would require attention to
development intensities.
The Physical Constraints Map was generated from input data themes which denoted constraints
based on physical factors. Protection of these resources would require attention to the type of
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land use or the development standards imposed on that development. Aquifer recharge areas
are the best example. The presence of a high recharge area does not necessarily preclude
urban development. Instead, a recharge area may influence the type of land use considered
suitable (e.g., no chemical industries), the enactment of special development standards (e.g.,
higher on-site runoff retention requirements), or both. This paper will only address the
methodology used to prepare the Environmental Constraints Map; the process for generating
the Physical Constraints Map was identical, differing only in the types of inputs involved.
Several GIS layers contributed to each of the two composite maps of environmental constraints.
In both models, we used a five-point scale or index to rank environmental sensitivity for a given
GIS data layer. In all cases, a "1" indicated presence of "Very Low" environmental constraints
for urban development, a "3" a "Moderate" level of environmental sensitivity, and a "5" indicated
the presence of "Very High" environmental constraints. In the GIS analysis, the various
contributing themes were combined or overlaid in such a way that each spot on the map
represented an average of all the environmental constraint scores contributing to that map.
Areas which had many high environmental sensitivity scores from contributing input data
themes received a high total score, while areas with few or no constraints received a low
composite score.
Initially, all factors were given equal weight in the overlay procedure to produce two "baseline"
maps. After the "baseline" maps were produced, weighting factors were assigned to each of the
GIS input layers to reflect the relative importance of their contribution to the ranking of
environmentally sensitive lands in the County. The value of the weighting analysis was two-fold.
First, application of weighting to a particularly important input data layer would permit it to "shine
through" the muddle which might otherwise result from the combination of so many disparate
input data sets. Second, the use of weighting tested the robustness of the findings of the GIS
analysis. That is, the environmental sensitivity of areas which ranked similarly on several maps
despite alternative weighting schemes could be interpreted with confidence. For instance, one
would conclude that an area which showed up as having "Very High" environmental constraints
in every map, regardless of weightings, was clearly dominated by environmentally sensitive
factors. The weighting factors were developed in a workshop meeting of the Environmental
Mapping Advisory Committee in concert with HDR and the Orange County Planning Department
staff. The particulars of the weighting process are described below, following the discussion of
the input data themes.
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INPUT DATA FOR THE ECOLOGICAL CONSTRAINTS MAP
We identified nine different GIS input data layers, or themes, for use in generating the
Ecological Constraints Map (Table 1). With two exceptions, all the input data layers were
"derived" by combining two or more of the original GIS data sources in various ways, or by re-
casting the original data set in a more useful form. Each input data layer is described below.
TABLE 1
Input Data Layers For Ecological Constraints Map GIS Model
Environmental Constraint Factor
Low Moderate High Very High
GIS Input Data Layer Very Low (1) (2) (3) (4) (5)
Floodplain Areas
Wetland Areas
Ecological Integrity
Vegetative Community
Rareness
Habitat Corridors/Biological
Connectivity
Wetland Dominance
Floodplain Dominance
Vegetative Biodiversity
Non-Floodplain
Uplands
All Other Land
All Other Land
Very Low
Proximity Or
Urban
< 20 percentile,
Urban or Water
< 20 percentile,
Urban or Water
< 20 percentile,
Urban or Water
N/A
N/A
SJRMWD
RSH Areas
Area
> 2%,
< 1 0%
Low
Proximity,
Agricultural
lands
20-40
percentile
20-40
percentile
20-40
percentile
N/A
Wet Prairie
5 - 7+ Focal
Species
FNAI S3 or
Area
>1%,
<2%
Medium
Proximity,
Grassland
41 -60
percentile
41 -60
percentile
41 -60
percentile
100 Year
Floodplain
Non-
Forested
Wetlands
GFC
SHCAs
FNAI S2 or
Area
> 0.5%,
<1%
High
Proximity,
Forested
61 -80
percentile
61 -80
percentile
61 -80
percentile
Regulatory
Floodways
Forested
Wetlands
FNAI & GFC
E&T
Occurrence
Point
Locations
FNAI S1 or
Area < 0.5%
Very High
Proximity,
Wetland or
Water
>80
percentile
>80
percentile
>80
percentile
Notes: (1) Class 5 reserved for Regulatory Floodways in Floodplain Dominance theme, but not used in
this study; (2) SJRWMD = St. Johns River Water Management District; (3) GFC = Florida Game
& Freshwater Fish Commission; (4) FNAI = Florida Natural Areas Inventory; (5) SHCA =
Strategic Habitat Conservation Area; (6) RSH = Regionally Significant Habitat; (7) The
Environmental Sensitivity score for Habitat Corridors/Biological Connectivity depended on both
habitat type and proximity to the most direct path between two preserves (see Table 2).
10
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Floodplain Areas. This was one of the two "non-derived" data themes. Floodplains were a
"dichotomous" factor: areas above the 100-year floodplain boundary were ranked "1," whereas
all lands identified as being within the 100-year floodplain (i.e., those designated "Zone A" on
FEMA Flood Insurance Maps) were assigned a environmental constraint factor of "4." There
were no intermediate classes. Class "5" was reserved for regulatory floodways, which would
have received the greatest protection from alteration. Regulatory floodway boundaries were not
available for this study, but their spot was reserved so that they could be inserted into the
analysis and the model run again at a later time.
Wetland Areas. This is the other non-derived theme. Wetlands were ranked in order of their
relative difficulty to maintain or re-create. Forested wetlands as a rule take a considerable time
to grow to maturity, and are not easily re-created by humans; consequently, they were ranked
"5." Non-forested wetlands (i.e., marshes) are extremely productive wetland environments, but
are somewhat more readily replaced than forested wetlands; hence, they were assigned a value
of "4." Since vegetative communities similar to natural wet prairie areas are commonly created
on pastures, such areas were assigned a "3." All other (upland) areas were designated as "1."
Scientists, planners and developers often disagree on whether wetland size is a valid criterion
for evaluating wetland value. While it is true that small, isolated wetlands in an urban setting
may have little or no wildlife habitat value, this is emphatically not true of such wetlands
immersed in a matrix of natural upland vegetation. Small, ephemeral wetlands are essential to
the life cycles of many amphibians which cannot survive predation by fish in larger ponds.
Furthermore, small wetlands are essential to the feeding (and nesting) success of wading birds,
particularly the endangered wood stork. And although large wetlands might seem to dominate
surface water hydrology, the depressional storage capacity of many small wetlands may be
considerable. Since there was no good way to rank the ecological importance of wetlands
based on size, size did not contribute to the ranking of the Wetland Areas theme for this study.
Ecological Integrity. This is a term which has gained in popularity to describe the process of
protecting natural diversity, at scales ranging from populations to entire ecosystems (Minasian,
1994). Consistent with that concept, this input data layer was comprised of several data sources
which had been combined to represent areas of hierarchically greater or lesser importance to
the maintenance of natural floral and faunal populations in the County. Most sensitive on this
scale (i.e., a ranking of "5") were the reported locations of species listed as endangered,
11
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threatened or species of special concern by the U.S. Fish & Wildlife Service, Florida Game and
Fresh Water Fish Commission, or by the Florida Natural Areas Inventory. These point
occurrences were obtained from the FNAI Element Occurrence database, and were
represented by a 1,000 ft. radius circular area generated as a buffer around each point location.
These circles, which are comparable to those used by Cox, et al. (1994) to highlight point
occurrences of listed species or other significant wildlife, encompassed an area of about 72
acres each.
The map data used to identify lands ranked "4" for this data theme were obtained from digital
maps of Strategic Habitat Conservation Areas (SHCAs) prepared by the Florida Game and
Fresh Water Fish Commission (Cox, et al., 1994). Using a statewide map of vegetative cover
derived by interpretation of Landsat satellite imagery collected during the mid- to late 1980's, the
GFC identified polygons of vegetative cover which represented the habitats of 30 "focal
species," most of which were listed as endangered or threatened. (Seventeen of the GFC's
focal species occurred in Orange County, including the red-cockaded woodpecker, Florida
scrub jay and gopher tortoise.) By mathematically modeling the minimum viable population
sizes needed to ensure survival of the selected focal species for 200 years, the GFC then
estimated the minimum necessary habitat required to maintain such populations in perpetuity.
Following a GIS analysis of the Landsat-derived vegetative cover maps, the GFC located
SHCAs which, if preserved, would secure the long term survival of the 30 focal species
evaluated.
Using the same Landsat-derived vegetative cover maps, the GFC prepared a map of
biodiversity "hot spots." To identify areas which might jointly serve a number of important wildlife
species, the GFC overlaid the individual habitat maps of their focal species and identified areas
whose habitat could support seven or more species, five to six species, or less than five
species. Those areas identified as supporting either 5-6 or 7+ species were combined and
ranked "3" in the Ecological Integrity data theme.
The St. Johns River Water Management District used the SHCA data as the basis for its
identification of Regionally Significant Habitat (RSH). Since the method the GFC employed to
identify SHCAs was tied to the habitat requirements of individual species, their process
sometimes identified polygons of vegetative cover which were surrounded by other native
vegetation not included in the SHCA. While preparing their RSH maps, the SJRWMD
12
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recognized the need to identify areas which could be either effectively regulated, or publicly
acquired and managed; disjunct SHCAs embedded in other natural land cover wouldn't qualify.
Consequently, the SJRWMD used the Cox (1994) GIS data in combination with vegetative
cover from Orange County's 1989 Existing Land Use to "extend" the SHCA boundaries to the
limits of immediately adjacent natural vegetative communities. The SJRWMD RSH areas were
assigned a ranked of "2," and all other lands not included in one of the above classes were
ranked as "1." A small part of the Ecological Integrity theme appears in Figure 3.
FNAI were assigned to ranks 5, 4, and 3, respectively. All other community types were ranked
as "1." To identify rare community types in Orange County, HDR used the 1994 Existing Land
Use (ELU '94) data to calculate the total acreage for each non-urban, non-agricultural cover
type. Based on these data, we assigned a rank of 5 to those communities comprising 0.5% or
less of the total natural vegetative cover of approximately 291,000 acres. Rank 4 was assigned
to communities representing between 0.5% and 1.0% of the total natural land cover, while those
communities with 1% to 2% were assigned a rank of "3," and a rank of "2" attributed to
communities falling between 2% and 10%. Natural land cover types with greater acreage were
all assigned ranks of "1." Where the FNAI and Orange County ELU '94 derived ranks disagree,
the vegetative community was assigned the higher (i.e., more sensitive) rank. Figure 4
illustrates a portion of the Vegetative Community Rareness theme.
Habitat Corridors/Biological Connectivity. This GIS data layer identified areas which may be
important to the maintenance of biological connectivity between managed conservation areas.
Put another way, this layer identified areas which should be protected to prevent the adverse
effects of forest fragmentation and biological isolation in natural preserves. The idea of
considering conservation lands as the anchor points for habitat corridors has a strong following
in Florida (Noss, 1991; Harris and Atkins, 1991). We constructed this data theme from a
SJRWMD coverage of existing and proposed conservation lands (hereafter called "preserves"),
and habitat value as derived from the 1994 Existing Land Use data. Using the ARC/INFO GRID
functions COSTDISTANCE and CORRIDOR, we identified areas representing the most direct
connections between pairwise sets of proposed or existing preserves. We eliminated from
consideration any preserve pairs for which there were either interposing urban or preserve
areas, or which were more than 12 miles apart, and rated areas within the corridor in
descending order according to their proximity to the shortest distance path: 1,000 feet, 0.5
miles, 0.75 miles and 1 mile. The corridors were overlaid with the habitat rank grid derived from
13
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J_ .
Figure 3. Ecological Integrity Theme. Dark colors represent areas of highest environmental
constraints, light areas lowest. Dark circles are the 1000-foot radius circles around
the observed locations of endangered and threatened species.
14
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Figure 4. Vegetative Community Rareness Theme. Common natural communities appear in
light colors. Darker shades of brown indicate communities of increasing rarity.
TABLE 2
Environmental Constraint Values Assigned To The Habitat Corridor
Input Data Theme Based On Habitat Value And Corridor Centerline Proximity Scores
Proximity
Rank
1(>1.0mi.)
2 (< 1.0 mi.)
3 (< 0.75 mi.)
4 (< 0.5 mi.)
5 (< 1000 ft.)
Habitat Value Rank
1
(Urban)
1
1
1
1
1
2
(Agricultural)
1
2
2
2
2
3
(Non-
Forested)
1
2
3
3
3
4
(Forested
Uplands)
1
2
3
4
4
5
(Wetlands)
1
2
3
4
5
15
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the ELU '94 land cover data to differentiate among corridor alternatives based on preferred
vegetative cover. The proximity/land cover matrix used to assign final environmental constraint
rank values to grid cells for this theme appears in Table 2, and a part of the Habitat Corridors
map appears in Figure 5.
Wetland Dominance. The Wetland Areas GIS data layer addressed those parts of the
landscape actually occupied by a wetland community type. However, in planning for the
protection of natural resources in Orange County, it will not be possible to delineate every
individual wetland for protection. Nonetheless, large upland areas characterized by many small
wetlands are themselves ecologically valuable. Faunal and floral biodiversity of many small
wetlands interspersed throughout an upland matrix will likely be greater than that of a single
large wetland of equivalent acreage. Furthermore, the life cycles of many species are
dependent upon smaller wetlands. Amphibians prefer small, ephemeral wetlands for breeding
because such wetlands do not support a large population of predatory fish. Wading birds
(notably the wood stork) benefit from the concentration of fish and amphibian prey within small,
ephemeral wetlands. To address these issues, we used ARC/INFO to calculate the ratio of
wetlands to non-urban uplands within each of the 1,000 one-square mile sections of the Public
Land Survey (PLS). We assigned each PLS section a rank of 1 to 5 based on its placement in a
quintile distribution.
Floodplain Dominance. As with wetlands, isolated floodplains may collectively have great
hydrological or ecological value if they are sufficiently numerous or collectively large enough. To
identify those areas which have a large portion of their land area within the 100-year floodplain,
we generated this GIS data theme using exactly the same procedure as that for Wetlands
Dominance, only using the Floodprone Areas GIS layer as the starting point.
Vegetative Biodiversity. All other factors being equal, an area with many different vegetative
cover types (i.e., a high biodiversity) will typically be more ecologically valuable than an equal
sized area with fewer different vegetative communities. To evaluate biodiversity, we could have
the GIS simply count the number of different kinds of vegetative cover types in a PLS section,
then use the quintile procedure outlined above. However, the mere number of different kinds of
community types does not tell the whole story. For example, two sections might have the same
number of different vegetative community types, but one might have six equally sized polygons,
while the other section has one very large polygon and five very small ones. Clearly, the section
16
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Figure 5. Habitat Corridors/Biological Connectivity Map. The darker colors represent areas of
high habitat value near to the most direct path linking two existing or proposed
preserves. Dark green solid lines border existing preserves, light green dashed lines
surround proposed preserves.
with more equally sized polygons is more diverse, since the other section is almost entirely
dominated by one cover type. In order to quantify landscape diversity, we applied the Simpson
C' Index, which is generally accepted as a measure of population diversity (Krebs, 1989). Using
the acreage information for vegetative communities within the ELU '94 GIS data, we calculated
Simpson C' indices for all PLS sections, then subjected those values to a quintile analysis like
that used for Wetlands and Floodplains Dominance, above. PLS sections were assigned the
appropriate 1 through 5 ranks, with "5" representing the most diversity (highest Simpson's C'
indices), and "1" the least diverse assemblages of community types. Barren or agricultural areas
that did not contribute to the biodiversity measurement were assigned a value of "1," thereby
ensuring that an entire PLS section did not receive a high biodiversity score based on a small
natural remnant on an otherwise barren landscape. A sample of the Vegetative Biodiversity Map
appears in Figure 6.
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Figure 6. Vegetative Biodiversity Theme. Dashed lines represent section lines from the Public
Land Survey. Each one-square mile section is color coded, from light to dark, based
on its quintile rank among the distribution of all Simpson's C' indices calculated for
the 1000 square miles in Orange County.
Areal Analysis Of Environmentally Sensitive Input Data. Figure 7 shows the relative amounts of
undeveloped Orange County which were assigned to each of the five classes of environmental
sensitivity for each of the input data layers contributing to the Ecological Constraints Maps. For
most of the input data themes, the majority of County land was rated "Very Low," with a much
lower percentage assigned to each of the more environmentally sensitive classes. The Wetland
Dominance, Floodplain Dominance and Vegetative Biodiversity themes illustrated a nearly
equitable distribution of land in each of the five constraints classes, owing to the quintile
assignment methodology employed for those layers.
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Figure 7
Comparison of Acreage Assigned to Environmental Sensitivity Classes
for Each of the Ecological Constraints Input Data Themes
Input Data Themes
Constraint Value
ALTERNATIVE WEIGHTING ANALYSES
Table 3 depicts the three alternative weighting schemes used for the Ecological Constraints
composite maps. The first was dubbed the "Ecological Integrity" weighting option, since it
weighted those factors most closely associated with that concept (i.e., the Ecological Integrity
layer and the Habitat Corridor layer). The "Habitat Diversity" model weighted the Vegetative
Biodiversity and Vegetative Community Rareness layers. Finally, the "Wetlands" model
weighted the wetland boundary and wetland dominance layers.
In all cases, a maximum weight of "2" was used. This was the minimum whole-integer weight that
can be applied, yet have a demonstrably visible effect on the outcome of the composite maps. To
evaluate the appropriateness of the "2" factor, we conducted a sensitivity analysis on the
Ecological Constraints map by running the Ecological Integrity model with weights of 2, 3, 4 and 5.
We found that increases in the weight beyond 2 merely had the effect of making the output
composite map look more like the weighted layers, and diminished the value of all other inputs.
In contrast, the weight of 2 left most composite map effects intact, while emphasizing certain
features from the weighted layers.
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TABLE 3
Ecological Constraints Map Alternative Weighting Factors
Alternative Weighting Schemes
GIS Input Data Layer
Ecological
Integrity
Habitat
Diversity
Wetland
Protection
Floodplain Areas
Wetland Areas
Ecological Integrity
Vegetative Community
Rareness
Habitat Corridors/Biological
Connectivity
Wetland Dominance
Floodplain Dominance
Vegetative Biodiversity
1
1
2
1
2
1
1
1
1
1
1
2
1
1
1
2
1
2
1
1
1
2
1
1
RESULTS & DISCUSSION
The areas ranked as most environmentally sensitive by the Baseline Ecological Constraints
model (Figure 8) encompassed many locations already recognized by conservation planners as
having high ecological value, including: Wekiwa Springs State Park, Kelly Park, Moss Park, Split
Oak Mitigation Park, the various public lands associated with the St. Johns River, and the
various areas proposed for acquisition within the headwaters of Reedy Creek, Shingle Creek
and the Econlockhatchee River. The fact that the Baseline Ecological Constraints model
identified existing and proposed conservation areas as among the most environmentally
important lands in Orange County validated the model's credibility.
The three composite maps produced using weighting schemes were mostly very similar to the
Baseline map, while exhibiting some minor differences attributable to the weighting scenario.
The similarity of the weighted composite maps further reassured us that the basic premise of
the environmental constraints map was sound, since the general pattern of environmentally
sensitive land was relatively insensitive to changes in the input weights. However, the variations
in the weighted maps did afford an opportunity for County planners to further evaluate those
areas which appeared different and determine if they required more (or, perhaps, less)
20
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consideration in the development of land use plans or environmental protection ordinances.
Some brief comments about the three weighted maps follow:
Ecological Integrity Weighted Map. The pattern on this map looked very much like that on the
Baseline Model map. Areas along the potential habitat corridor between the Econlockhatchee
River and the St. Johns River conservation lands ranked slightly higher than those same areas
in the Baseline model, illustrating the effect contributed by the Habitat Corridors/Biological
Connectivity theme to this map.
Habitat Diversity Weighted Map. As with the Ecological Integrity Weighted Map, the pattern of
this map appeared very similar to the Baseline. It showed much less area designated as having
"Very High" environmental constraints, but the loss occurred mostly within the limits of existing
and proposed public lands. This effect reflects the fact that many public lands were dominated
by large wetlands, whereas the Habitat Diversity Weighted model was more sensitive to rare
upland communities, or diverse mixes of uplands and wetlands.
Wetland Importance Weighted Map. This map had much greater areas of "Very Low" and "Low"
environmental constraints, especially in eastern Orange County. In other words, by emphasizing
the importance of wetlands, the ecological value of upland areas was diminished. Most of the
areas identified as "High" or "Very High" in the Baseline Map appeared similarly ranked here,
but a greater amount of these areas followed wetland boundary lines. Furthermore, this map
showed an increased tendency for "Moderate" ranked areas to conform to PLS section lines, an
effect no doubt produced by the section-based wetland dominance layer. The headwaters areas
of Reedy Creek, Shingle Creek and Lake Sheen still ranked very high.
Relative Acreages For Ecological Constraints Composite Maps. Figure 9 compares the percent
Orange County acreage which fell to each of the class ranks for each model type. The overall
pattern for all maps appears almost identical: Maximum acreage was associated with the "Low"
category, with gradually diminishing acreage values for the higher ranks. The most visible
difference in the amount of acreage in each constraint class appeared in the "Very High" level,
in which the Wetland Importance model ranked nearly 8% of the County's non-urban, non-water
area, compared to just under 5% for the Baseline and Ecological Integrity models, and 2.4% for
the Habitat Diversity model.
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Ecological Constraints
Baseline Map
Figure 8. Baseline Ecological Constraints Composite Map. Gray areas represent existing
urbanization. Environmental constraints in undeveloped areas are represented by
shades of brown, from lightest ("Very Low Constraints") to darkest ("Very High
Constraints").
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Figure 9
Comparison of Percent of Undeveloped Acreage Assigned to
Each Environmental Sensitivity Rank for the
Ecological Constraints Models
25.0
I 20.0
Constraint Level
D)
±
Wetland Importance
Habitat Diversity
Ecological Integrity
Baseline Ecological
Composite Models
CONCLUSIONS
The ARC/INFO GRID based environmental constraints model proved to be an efficient and
effective way to prepare a composite map of environmental suitability as envisioned in Orange
County's Development Framework. The modular nature of the GIS model provides for relatively
easy updating of the input data layers and production of updated constraints maps as new data
become available.
The maps produced by the alternative weighting models for the Ecological Constraints Map
produced results which were very similar to that of the Baseline Map. The constancy of certain
core areas on all weighting models bolsters the conclusion that these areas are truly
environmentally sensitive. The areas which were affected by changes in weighting parameters
provide an opportunity to evaluate which areas, outside of the obvious core areas, should be
included in future public lands acquisition plans, special future land use planning, or other
appropriate protection mechanisms.
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ACKNOWLEDGEMENTS
The author wishes to recognize the members of the Environmental Mapping Advisory
Committee for donating their valuable time and input to the successful completion of this study:
Jack Amon, Lester Austin, III, Jim Bradner, Vera Carter (Chair), Nancy Christman, Michael
Dennis, Barbara Durkin, Jeff Jones, Nancy Prine, John Richardson, Jim Sellen, P.K, Sharma,
Bill Stimmell, Jack Stout, Renee Thomas, Jim Thomas (Vice Chair), Rick Walker, and John
Winfree. The insightful comments and suggestions of this group frequently found their way into
the final product.
The staff of the St. Johns River Water Management District provided invaluable assistance in
supplying digital data, particularly Jim Cameron, Stuart Dary, Mike Kleinman and Linda McGrail.
John Stys of the Florida Game and Fresh Water Fish Commission quickly supplied us with the
digital raster files of the Commission's gap analysis study. Thanks also to Scott Taylor of the
Florida Natural Areas Inventory for his assistance in preparing copies of FNAI's element
occurrence data.
Finally, I'd like to recognize the assistance and cooperation afforded by the Orange County
Planning Department staff, especially that of Andre Anderson and Danielle Justice, who many
times facilitated the progress of the project. This project was funded by Orange County Contract
#Y4-635.
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LITERATURE CITED
Cox, J., R. Kautz, M. MacLaughlin, and T. Gilbert. 1994. Closing the Gaps In Florida's Wildlife
Habitat Conservation System. Office of Environmental Services, Florida Game and
Fresh Water Fish Commission. Tallahassee, Florida.
Gilbrook, M.J. 1989a. Marina Siting Suitability in the Coastal Estuaries of East Central Florida.
Florida Department of Environmental Regulation, Coastal Zone Management Program,
Contract CZM-200. East Central Florida Regional Planning Council. Winter Park,
Florida.
Gilbrook, M.J. 1989b. Wekiva River Basin Acquisition Study. Final report to the St. Johns River
Water Management District. East Central Florida Regional Planning Council. Winter
Park, Florida.
Harris, L.D. and K. Atkins. 1991. Faunal movement corridors in Florida. In: Hudson, W.E. (ed.).
Landscape Linkages and Biodiversity. Island Press. Washington, D.C.
Krebs, C.J. 1989. Ecological Methodology. Harper & Row, Publishers, Inc. New York.
McHarg, I. 1971. Design With Nature. Doubleday/Natural History Press. Doubleday & Company,
Inc. Garden City, New York.
Minasian, L. 1994. Interpreting and applying ecosystem management principles. Environmental
Exchange Point. Florida Department of Environmental Protection, 4(3): 17-23.
Noss, R.F. 1991. Landscape connectivity: Different functions at different scales. In: Hudson,
W.E. (ed.). Landscape Linkages and Biodiversity. Island Press. Washington, D.C.
Twiss, R.H. 1975. Commentary- Nine approaches to environmental planning. In: Burchell, R.W.
and D. Listokin (eds.). Future Land Use: Energy, Environmental and Legal Constraints.
Center For Urban Policy Research, Rutgers University. New Brunswick, New Jersey
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GIS Watershed Delineation Tools
James A. Goodrich1, Lucille Garner1, Jill Neal1, Lee Bice2,
Rick Van Remortel2, Ramon Olivero3
1USEPA, NRMRL, WSWRD, Cincinnati,
2Lockheed Martin, Las Vegas, NV
\ockheed Martin, RTP, NC
BACKGROUND
The 1996 amendments to Section 1453 of the Safe Drinking Water Act require the states to
establish and implement a Source Water Assessment Program (SWAP). Source water is the
water taken from rivers, reservoirs, or wells for use as public drinking water. Source water
assessment is intended to provide a strong basis for developing, implementing, and improving a
state=s source water protection plan. This program requires individual states to delineate
protection areas for drinking water intakes, identify and inventory significant contaminants in the
protection areas, and determine the susceptibility of public water supply systems to the
contaminants released within the protection areas. SWAP can be used to focus environmental
public health programs developed by federal, state, and local governments, as well as efforts of
public water utilities and citizens, into a hydrologically defined geographic area.
INTRODUCTION
The Environmental Protection Agency is assisting the states in conducting source water
assessment by identifying potential sources of data and pointing to methods for assessing
source waters. This presentation provides guidance to states, municipalities, and public water
utilities for assessing source waters using geographic information system (GIS) technology. The
GIS platforms used to organize, analyze, and manipulate available data and generate new data
for source water protection areas, as well as provide capabilities for presenting the data to the
public in various forms, including maps and tables are also discussed. In addition, the National
Risk Management Research Laboratory is developing a coordinated research approach for
watersheds that include contaminated sediments, urban watersheds, ecological restoration, and
source water protection. Included in the full report, as appendices, are three case studies
demonstrating the use of selected GIS-based software and hydrologic models to conduct
hypothetical source water evaluations. Contamination of water supplies may be responsible for
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more human sickness than any other anthropogenic activity (Anderman and Martin, 1986). Since
limited water resources are increasingly shared by competing consumers, there is a growing
concern about the quality of source waters. This concern has led to the establishment of laws
and programs designed to help protect drinking water sources. Frequent evaluation and
identification of sources of contamination are required by federal and state rules. A successful
SWAP reduces the cost of water treatments and disinfections required. Following enactment of
the SDWA, a number of programs were developed for public water supply protection and
supervision, including watershed protection and control, sanitary surveys, and WHPPs. An EPA
document titled AStates Source Water Assessment and Protection Programs Final Guidance®
(1997a) discusses how a SWAP can use information provided by the current water programs.
Some of the programs include:
- a watershed control program (WCP) under the Surface Water Treatment Rule (EPA,
1990),
- sanitary surveys (EPA, 1995), and
- wellhead protection programs (EPA, 1995).
METHOD FOR ASSESSING SOURCE WATERS
Using a GIS for any application involves following some basic steps including:
- designing the GIS database,
- building the GIS database,
- using the GIS to analyze the data and show results.
For assessing source waters, elements of the design of a GIS database include:
- establishing the study area,
- delineating the watershed,
- determining data needs,
- inventorying data sources,
- determining coordinate system and scale, and
- deciding on the GIS infrastructure.
Building the database requires collecting data to characterize the study area and inventorying
sources of contamination. Analyzing the data entails assessing potential sources of
contamination, delineating source water protection areas, and producing display products.
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CHARACTERIZE THE STUDY AREA
After deciding on the data requirements of the GIS database, the data should be obtained and
converted to the chosen projection and units (feet, meters). The data types include descriptions
of physical watersheds and contamination sources and types. To understand how contamination
from a source reaches a drinking water intake, the factors that affect its flow should be
described. These factors include, but are not limited to terrain, soils, hydrography, land use and
land cover, and contaminant characteristics. For example, after a precipitation event, the type(s)
of contamination resulting from surface runoff into a stream depends on the land use and land
cover interactions (e.g., pesticide and fertilizer from agriculture, salts and grease from parking
lots). The directional flow of surface runoff depends on the topography, and soil infiltration
properties affect how much surface water reaches the groundwater. The following sections
provide information about some of the data sets needed for assessing source waters.
Watershed Boundaries
Watershed or HUC boundaries are available from the USGS. The HUC boundaries are available
at 1:2,000,000 scale and 1:250,000 scale. The USGS also provides information describing the
hydrologic unit coding scheme. A watershed boundary data set can be created by delineating
the boundary on large-scale maps that have elevation contour lines; the boundary can then be
digitized.
Terrain
Terrain data can be derived from Digital Elevation Models (OEMs). OEMs are digital records of
terrain elevations for ground positions that are horizontally spaced at regular intervals. The
SPOT Image Corporation provides OEMs at 10-meter spacing created by digital autocorrelation
of SPOT satellite image stereopairs which are stored in a format known as Terrain Access Made
Easy (TAME) (ESRI, 1992). The USGS also provides 30-meter spaced OEMs at four scales:
7.5-minute, 15-minute, 2-arc-second, and 1-degree. The 7.5-minute (large-scale) data are
produced in 7.5- by 7.5-minute blocks from digitized cartographic map contour overlays or from
scanned National Aerial Photography Program (NAPP) photographs. The DEM data are stored
as profiles in which the elevations are spaced 30 meters apart. The number of elevations
between each profile will differ because of the variable angle between the quadrangle's true
north and the grid north of the Universal Transverse Mercator (UTM) projection coordinate
system. The DEM data for 7.5-minute units correspond to the USGS 7.5-minute topographic
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quadrangle map series for all of the United States and its territories, except Alaska. The
15-minute DEM (large-scale) data correspond to the USGS 15-minute topographic quadrangle
map series of Alaska. The unit size changes with the latitude. The 15-minute DEM data are
referenced horizontally to NAD27. The elevations along profiles are spaced 2 arc-seconds of
latitude by 3 arc-seconds of longitude. The first and last data points along a profile are at the
integer degrees of latitude.
Soils
The U.S. Department of Agriculture (USDA) Natural Resource Conservation Service (NRCS),
formerly the Soil Conservation Service (SCS), has three soil geographic databases of varying
scales. The data include physical and chemical soil properties for approximately 18,000 soil
types. Each database has three categories: soil properties (particle size, bulk density, available
water capacity, organic matter, salinity, and soil recreation), locational properties (flooding, water
table depth, bedrock depth, and soil subsidence), and use and management properties (sanitary
facilities, building site development, recreational development, rangeland potential, construction
material, crops, woodland suitability, and wildlife habitat suitability). The most detailed level of
information is provided by the Soil Survey Geographic data (SSURGO), which is available in 7.5-
minute topographic quadrangle units (1:24,000) and is distributed as coverages for soil survey
areas, usually containing over ten quadrangle units. State Soil Geographic data (STATSGO) is a
coarser database designed for regional, multi state, river basin, state, and multi county resource
planning, monitoring, and management. The STATSGO database is at 1:250,000 scale (1- by 2-
degree quadrangle) and is distributed as statewide coverages. National Soil Geographic data
(NATSGO) is a database which is suitable for national or regional resources assessment and
planning. With a scale of 1:5,000,000, the NATSGO database has information about the major
land resource areas.
Hydrography
Hydrography is available from several federal sources at a 1:24,000 scale and may be available
in greater detail from state and local government agencies. The USGS digital line graphs (DLGs)
are readily available and provide information on 5 main types of data categories: boundaries,
public land survey, transportation (including pipelines and power lines), hydrography (streams
and water bodies) and hypsography (elevation contours). The DLG data can be converted into
other formats compatible with GIS software. The EPA Reach File system has a series of
hydrologic databases that uniquely identify and interconnect stream segments (reaches) for the
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nation. RF3-Alpha is the latest and most detailed version of the reach file system, containing
more reaches than the previous versions, RF1 and RF2. Stream segments have unique reach
codes for determining the upstream and downstream reaches and identifying the stream name
for each reach. River Reach data can be obtained from the STORE! User Assistance Group in
the EPA Office of Water.
Land Use and Land Cover
Land use and land cover data are available from several federal sources. In many cases, the
federal data will be either out-of-date or not detailed enough. More detailed (large-scale) land
use data may also be obtained from county assessor maps, which are available at various
scales (e.g., 1:200, 1:2,400, 1:4,800). County assessor maps may provide better detail for
inventorying contamination sources in urban areas. The various departments of highways and
transportation can provide maps for city streets and other local and regional road maps.
Inventory Potential Sources of Contamination
Potential sources of contamination, also known as sanitary defects, are conditions that may
result in contamination of a water supply. These may be point and nonpoint source pollutants,
connections to unsafe water supplies, raw water bypasses in treatment plants, improperly
designed or installed plumbing fixtures, or water and sewer pipes leaking into the same ditch. All
known and potential sources of contamination should be included in the GIS database.
Pollutants may be classified into categories depending on the likelihood of their introduction into
the water supply and the level and significance of contamination that can result from them. A
contaminant inventory can include records of operation, discharge, disposal, construction, and
other permitted activities, as well as zoning and health records obtained from local government
agencies. All relevant information should be gathered while focusing the search for
contamination sources at sites of particular concern. These include, but are not limited to (EPA,
1991a):
- discharge sites: septic tanks, irrigation pipes
- storage, treatment, or disposal sites: landfills, underground tanks, mine tailings
- substance transporting sites: pipelines
- activities that result in discharges: highway construction, fertilizer application
- natural sources impacted by anthropogenic activities
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Further information on contaminant inventory activities is provided in the EPA Guide for
Conducting Contamination Source Inventories for Public Drinking Water Supply Protection
Programs (EPA, 1991 a). Some of these data, such as Toxic Chemical Release Inventory (TRI)
data can be obtained from the EPA. Other data may need to be obtained through field surveys.
Table 1 lists federal data sets that can be accessed for much of the above mentioned data.
Table 1. Federal Spatial Data Set Sources
U. S. Environmental Protection Agency (EPA)
Web Page: http://www.epa.gov/enviro/html/nsdi/spatial_extent.html
Description: The EPA Envirofacts Warehouse - Geospatial Data Clearinghouse
Web Page: http://www.epa.gov/OWOW/watershed/landcover/lulcmap.html
Description: This EPA Office of Water site contains land cover digital data
Web Page: http://earth1.epa.gov/oppe/spatial.html
Description: This EPA Office of Policy, Planning and Evaluation site contains access to GIS
spatial data sites at the federal, state, and local levels.
Web Page: http://www.epa.gov/OWOW/NPS/rf/rfindex.html
Description: This EPA Office of Water site contains information on the EPA river reach files.
U.S. Geological Survey
Web Page: http://nhd.fgdc.gov/
Description: U.S. Geological Survey site containing information on the Digital Line Graphs
(DLG) hydrography files and the EPA Reach File Version 3.0 (RF3).
Web Page: http://edcwww.cr.usgs.gov/doc/edchome/ndcdb/ndcdb.html
Description: U.S. Geological Survey site containing FTP file access to Digital Elevation Models
(DEM), Digital Line Graphs (DLGs), and Land Use and Land Cover (LULC).
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Web Page: http://mcmcweb.er.usgs.gov/
Description: U.S. Geological Survey site for the Mid-Continent Mapping Center in Rolla,
Missouri, containing information on Digital Raster Graphics (DRG) as well as other products.
Web Page: http://water.usgs.gov/public/GIS/background.html
Description: U.S. Geological Survey Water Resources site containing metadata and FTP file
access to numerous national coverages commonly used in water resources studies.
Web Page: http://edcwww.cr.usgs.gov/webglis/
Description: The U.S. Geological Survey Global Land Information System (GLIS) site provides
descriptions and prices for geospatial data available from the USGS.
U.S. Fish and Wildlife Service
Web Page: http://www.nwi.fws.gov/nwi.htmDescription:
Description: The U.S. Fish and Wildlife Service National Wetland Inventory (NWI) site provides
access to NWI data.
U.S. Department of Agriculture
Web Page: http://www.ftw.nrcs.usda.gov/nsdi_node.html
Description: U.S. Department of Agriculture (USDA) National Resources Conservation Service
site containing FTP access to soils and other USDA data.
U.S. Bureau of the Census
Web Page: http://www.census.gov/ftp/pub/mp/www/rom/msrom12i.html
Description: The U.S. Census Bureau site provides brief descriptions of the TIGER/Line files,
1997 version. The data is available for the entire U.S. on 6 CD-ROMs for $1,500 or S250/CD-
ROM for different sections of the country. Data is in TIGER/Line format.
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Contamination Source Risk Analysis
After the GIS database has been built, the data can be analyzed to assess the risk associated
with potential sources of contamination, delineate protection areas, and develop display
products.
Classify the contaminant data into risk groups depending on the threat of contamination they
pose to the source water. A method for prioritizing and weighing the level of risk from various
forms of contamination is described in an EPA document (EPA, 1991b). Similar approaches may
be adopted for surface water sources. The tasks in this phase may reveal the need for a new
inquiry or a more thorough data gathering effort with respect to particular sites or contaminants.
For more information, see Managing Groundwater Contamination Sources in Wellhead
Protection Areas: A Priority Setting Approach (EPA, 1991b). Susceptibility analysis identifies the
location, frequency, and significance of potential contaminants in the source water protection
area and determines the likelihood the PWS will be contaminated by these sources. Water
quality models may be used for estimating contamination levels and determining the significance
of selected contaminants in the protection area or in the watershed.
Proximity Analysis and Delineation of Protection Areas
After potential sources of contamination are identified, their proximity to the water supply intakes
can be mapped. A set of maps at various scales can be produced from the GIS database
illustrating the proximity of potential pollutants to the water supply system. With data
documenting geographic locations of actual and potential contaminants, a source water
protection area can be delineated. Surface water sources used for drinking water supplies may
be protected by delineating a protection area around or upstream from the source intake. Three
approaches for delineating a protection area for surface water systems are topographic area,
buffer distance, and stream-flow time of travel (TOT) (EPA,1997b). For systems using
groundwater sources, approaches for delineating a WHP area are based on fixed-radius,
hydrogeologic/geomorphic characteristics, and modeling, which includes analytical, semi-
analytical, numerical flow and solute transport models (EPA, 1993). The appropriate method for
a particular system is chosen as a balance between ease of use, level of detail needed, and
available resources. The PWS systems using a combination of groundwater and surface water
sources may consider conjunctive delineation of source water protection areas. Conjunctive
delineation is the integrated delineation of the zone of groundwater contribution and the area of
surface water contribution to a PWS. Further information on this subject can be found in
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Delineation of Source Water Protection Areas, a Discussion for Managers; Part 1: A Conjunctive
Approach for Groundwater and Surface Water (EPA, 1997c).
Topographic Area
Topographic area is defined as the watershed for the surface water feature. Watersheds are
delineated by drawing a line along the highest elevation around the surface water feature. In this
case, the study area itself is the source water protection area.
Buffer Zone
A buffer zone may be delineated for the purpose of protecting drinking water intake and is
typically dependent on the hydrogeology, topography, and stream hydrology. A protection buffer
for a source surface water intake is an upstream strip of vegetated land along the shore of the
stream or lake. Buffer widths vary from 15 to 60 meters (approximately 50 to 200 ft) depending
on topographic, land use, political, and legal factors (EPA, 1997b). Buffer zones reduce water
quality impacts from runoff, increase wildlife habitat and improve stream-bank integrity. Systems
with groundwater sources may use a fixed-radius protection area (buffer) around source wells
depending on aquifer properties. The radius could be fixed arbitrarily or based on TOT (EPA,
1993).
Time of Travel
Water supply systems tapping rivers that are designated for commercial transportation or for
industrial and municipal wastewater discharge may use TOT for source water intake protection.
The time it takes a pollutant introduced into an upstream section of a river to travel to a source
water intake is estimated using the stream-flow TOT. The contamination level of the pollutant at
the intake can be evaluated using various water quality models. The TOT method provides tools
for predicting impacts from spills or discharges at sections upstream of a drinking water intake,
thereby enhancing protection strategies for emergency spills. A TOT is also used for delineating
protection areas for groundwater-based systems by estimating contaminant transport into
drinking water wells. Groundwater flow is significantly slower than that of surface water (e.g.,
years versus hours or days, respectively), allowing more response time for controlling or
remediating spills and other plumes. The EPA (1993) provides comparisons of TOT-based
methods used for delineating WHP areas.
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Modeling
Surface runoff and groundwater models can be used for delineating a source water protection
area. Analytical, semi-analytical, and numerical flow and solute transport models can estimate
the potential water quality impacts from one or more pollution sources upstream of a drinking
water intake. With knowledge of land uses (e.g., agricultural, industrial, residential), soil
properties, and precipitation rates in an area, potential contaminant loadings from runoff or
infiltration can be estimated. Modeling provides analytical tools for assessing water quality
impacts resulting from land use changes, and may be used to identify effective water quality
protection strategies. Some models need site-specific data which may, in turn, require field
surveys.
Stream Network Analysis: Water Quality Study
Stream network analysis provides tools for studying how contaminants are transported in
streams. Distributions of contaminate concentrations along a stream can be studied using the
physical and chemical properties of the contaminant as well as the hydraulics of the stream.
Most GIS software packages, such as ARC/INFO=s network analysis, have capability for
modeling linear processes. More complex analyses can be performed by linking appropriate
water quality models in ARC/INFO (e.g., Grayman et al.,1993).
Generate Display Products
Maps are graphic representations of geographic information, and, as such, provide powerful
visual communication of ideas. The Surface Water Assessment Program requires strong public
participation in all processes involving development of methods for, and implementation of,
source water assessment. State agencies proposing or conducting a SWAP may use sets of
maps for displaying the geographic extent of the SWAP program. For example, maps for public
presentation can show stream segments with highlighted buffer areas and marked with potential
pollution sites. A GIS provides the capability for generating such maps at various scales with
selected sets of themes.
HARDWARE
The GIS hardware includes the computer on which the GIS operates and the peripherals used
for data entry, transfer, and output. A wide range of hardware types are used, from centralized
computer servers to desktop computers used as stand-alone stations or in networked
configurations. The type and number of components in a system is dependent on the needs of
the organization. Software vendors can help in recommending appropriate system
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configurations. The input and output devices (e.g., digitizers and plotters) are usually shared
within an organization with more than one GIS user. Centralized computer servers and
networking software can be used to enable multiple users to share GIS hardware and software.
Hardware costs are not provided because costs are constantly changing, usually in favor of the
buyer. Examples of GIS hardware components are listed below.
GIS Workstation
A GIS workstation should at a minimum include a high-speed central processing unit, keyboard,
mouse, disk space, high-resolution color monitor for graphics display, and a compact disk read-
only memory (CD-ROM). An external disk drive may be used for additional disk space. The GIS
workstation can be either an IBM-compatible personal computer (PC) using a Windows
operating system or a high-end graphics workstation using a Unix operating system. The Unix
systems provide a more powerful environment for GIS than PCs. Unix workstations are usually
faster than PCs in the analysis and display of complex digital data. However, they also cost more
($ 15,000+ vs. < $ 10,000 for a PC). A set of workstations loaded with GIS software may use a
common server with a large amount of disk storage space. Also, data input and output devices
may be attached to the server so all users can share them.
Data Transfer and Backup Devices
A GIS should include one or more data transfer and backup devices such as a compact disk
writer, tape drive, or disk drive. These devices allow the user to transfer GIS data to a compact
medium that can be easily stored or physically transferred. These devices are useful for
performing data backups or transferring data between workstations or organizations that are not
networked.
Data Output Devices
Output devices allow the user to print data and displays from the GIS. Printouts of GIS data are
useful for data quality assurance and quality control (QA/QC) checks and for displaying results.
The common GIS output devices are printers and plotters. These devices are available in a
variety of sizes, produce output in color or black and white, and can vary widely in price. Most
organizations will want at least a standard laser jet printer as well as a large-format color output
device for plotting color maps for display and presentations.
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Data Input Devices
GIS data input devices include digitizing tables, scanners, and GPS receivers. These devices
enable a user to capture geographic information in digital form. A digitizing table is used for
generating vector-based coordinate information directly from hard copy maps or photographs. A
scanner is used to generate raster-based data from hard copy maps or photographs. A GPS
receiver enables the user to capture coordinate data for features in the field. Once captured,
GPS data must be post-processed on a workstation with specialized software to generate real-
world coordinates.
SOFTWARE
Three categories of information processing software are used to assess source waters when
using GIS technology: GIS, image processing, and relational database management. Examples
of software for each of these categories are listed below. A software package listed in one
category may also be capable of performing functions in another category. For example, a GIS
package such as GRASS can be used for image processing. Similarly, some of the image
processing software packages can be used as GIS tools. The names of the software are listed
for informational purposes only and do not indicate endorsement. The PC-based software
packages such as GRASS and ArcView can range in cost from free or low-cost ($200-$300) to
several thousand dollars. High-end software packages such as Arclnfo, ERDAS Imagine, or
Intergraph GIS will cost $10,000-$20,000. Prices for all software packages depend on current
market value, whether the purchaser is eligible for discounts, and what additional modules are
purchased in addition to the baseline package.
GIS Software
The GIS software is used for storing, analyzing, and displaying geographic data. The main
components of a GIS software are the tools for data input and manipulation, database
management, geographic query and analysis, and visualization and output. Several GIS
packages are presented below for information.
Arc/Info
Arc/Info is a commercial software package developed by the Environmental Systems Research
Institute (ESRI) and Henco Software, Inc. (Henco). Arc/Info provides tools for automation,
management, display, and output of geographic and associated data. Arc/Info is a vector-based
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GIS software that runs on Unix and Windows NT workstations. Arc/Info costs between $10,000 -
$20,000. For more information contact ESRI at http://www.esri.com.
A rcView
ArcView is also produced by ESRI and is a menu-driven GIS with a subset of the functionality
provided by Arc/Info. What ArcView lacks in functionality, it makes up for in a less steep learning
curve and an easy-to-use graphical user interface (GUI). ArcView is a vector-based GIS
software that runs on Unix or PC workstations. ArcView costs approximately $1,000. For more
information contact ESRI at http://www.esri.com.
GRASS
The Geographic Resources Analysis Support System (GRASS) is a public-domain, raster-based
GIS software used for geographic data management, image processing, graphics production,
spatial modeling, and data visualization. GRASS was written by the U.S. Army Construction
Engineering Research Laboratories (USA-CERL) branch of the U.S. Army Corps of Engineers
and is currently maintained at the Department of Geology at Baylor University. GRASS runs on
Unix and PC workstations. More information on GRASS can be found at
http://www.baylor.edu/~grass. Additional information on some of the hydrology models that have
been integrated into the GRASS GIS is available on
http://soils.ecn.purdue.edu/~aggrass/models/ hydrology.html.
IDRISI
IDRISI is a raster-based GIS software that provides GIS, image processing, and spatial statistics
analytical capabilities on DOS and Windows-based PCs. IDRISI provides analytical functionality
of GIS, remote sensing, and databases for resources management. IDRISI was developed and
is maintained by Clark Labs, a non-profit research organization within the Graduate School of
Geography at Clark University. A commercial/private single-user license for IDRISI costs $990.
Licenses for non-profit, government, and academic institutions cost less. For more details see
http://www.clarklabs.org.
Intergraph GIS
Intergraph provides Windows-based software and a range of computing services for
engineering, design, modeling, analysis, mapping, information technology, and creative
graphics. The GIS MGE package provides data collection and editing, data import, image
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display and analysis, advanced spatial query and analysis, and cartographic quality maps. MGE
costs approximately $10,000-$20,000. More information on Integraph GIS is available at
http://www.intergraph.com.
Image Processing Software
Image processing software is used to process raster data, particularly remote sensing imagery
data such as satellite imagery.
ENVI
The Environment for Visualizing Images (ENVI) is an image processing system which provides
analysis and visualization of single-band, multispectral, hyperspectral, and radar remote sensing
data. ENVI can process large spatial and spectral images, and runs on Unix; LINUX; Windows
3.1, NT, 95; the Macintosh; and the Power Mac. For more details contact ENVI at
http://www.envi-sw.com/index.htm.
ERDAS Imagine
The ERDAS Imagine software is an image processing and raster GIS package that has a variety
of applications ranging from simple image mapping to advanced remote sensing. Imagine
provides tools for geometric correction, image analysis, visualization, map output,
orthorectification, radar analysis, advanced classification tools, and graphical spatial data
modeling. Imagine runs on Unix workstations and Windows platforms. ERDAS Image costs
approximately $10,000-$20,000. More information on Imagine is available at
http://www.erdas.com.
ER Mapper
ER Mapper provides integrated mapping software featuring image processing, map production,
3-D presentations, and GIS integration for Windows 95/NT and Unix. The ER Mapper software
uses a concept that separates data from the image processing steps allowing the user to apply
and view results from a single enhancement procedure in real time. The PC version of ER
Mapper costs $4,300; the Unix version of ER Mapper costs $18,300. See
http://www.ermapper.com for more information.
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PCI
EASI/PACE image processing provides a variety of applications including image processing,
geometric correction, vector utilities, and multilayer modeling. PCI implements the Generic
Database (GDB) concept, which allows PCI programs to access image and other external data
files without import and export. Contact PCI for more details at http://www.pci.on.ca.
TNTmips
TNTmips is a map and image processing system that contains fully featured GIS, CAD, and
spatial database management systems. TNTmips has tools that interactively integrate elements
of on-screen image processing and photo interpretation, and provides a diverse set of tools for
registering, rectifying and stitching imagery, which are particularly useful for low-altitude aerial
photography and videography. More information on TNTmips is available at
http://www.sgi.com/Products/appsdirectory.dir/
Applications/GIS_Defense_lmaging/ApplicationNumber7857.html.
Relational Database Management Software
Relational database management system (RDBMS) software enables large amounts of data to
be entered, updated, related, viewed, queried and, otherwise, managed in an efficient manner.
The data in an RDBMS is stored in a series of related tables which are designed to optimize the
effort required for data entry, maintenance, and retrieval. RDBMS software is available for use
on PCs, Unix workstations, networked systems, and mainframe computers. Most GIS software
packages use an RDBMS to manage data such as maintaining topology and providing ways to
efficiently enter, update, and query attribute data. Major RDBMS software includes Info, dBASE,
MS Access, Ingres, Informix, Oracle, and Sybase.
SOFTWARE SUPPORT TOOLS
There are numerous software support tools available for use in assessing source waters. These
tools operate within specific operating and software system environments. The information
presented here is not an endorsement of any of these products. New products and
improvements to existing products are continuously being introduced; therefore, users should
conduct their own investigation of software tools to ensure they are getting the latest information.
The selections are considered some of the more promising and potentially useful that were
encountered during this GIS evaluation. It should be noted that there are hundreds of available
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hydrologic models described in the scientific literature, but many of these will probably not be
suitable for use in a source water assessment. Principal purveyors of other downloadable
software and hydrologic models not listed here include the EPA Center for Exposure
Assessment Modeling (http://ftp.epa.gov/epa-ceam/wwwhtml/softwdos.htm), the USGS Water
Resources Division (http://water.usgs.gov/software/), and the U.S. Army Corps of Engineers=
Hydrologic Engineering Center (http://www.hec.usace.army.mil/). A selection of the software
support systems to consider include:
- Better Assessment Science Integrating Point and Nonpoint Sources (BASINS)
from the EPA Office of Science and Technology (OST). Full documentation of BASINS
Version 2.0 is available in detail at http://www.epa.gov/ostwater/BASINS/.
- Riverine Emergency Management Model (REMM) was originally developed for
hydrologic modeling in the upper Mississippi River in Minnesota. REMM is public-
domain software and is freely available by the U.S. Army Corps of Engineers office in St.
Paul, Minnesota (e-mail: webmaster@mvp-wc.usace.army.mil).
- Watershed Modeling System (WMS) Model was developed by the Environmental
Modeling Research Laboratory of Brigham Young University in cooperation with the U.S.
Army Corps of Engineers Waterways Experiment Station. The WMS is proprietary
software and is available via the Engineering Computer Graphics Laboratory at Brigham
Young University in Provo, Utah (http://www.ecgl.byu.edu). The software cost ranges
from $500 to $2,600 depending on the desired modules.
- Underground Storage Tank (UST)-Access Software was developed using the
Microsoft Access 2.0 relational data base management system. All UST-Access
installation files are stored as self-executable archive files on the Cleanup Information
(CLU-IN) Bulletin Board System of the EPA Office of Solid Waste and Emergency
Response.
- Spatially Referenced Regressions on Watersheds (SPARROW) Model is an
extension from the Hydrologic Simulation Program - Fortran (HMPF) modeling
framework. The HMPF and SPARROW models are public-domain software freely
available through USGS (http://www.usgs.gov) for the cost of pressing a CD (about $35).
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- Hydrology Extension for the ArcView Spatial Analyst Software is a new Hydrology
Extension for ArcView=s Spatial Analyst 1.1. ArcView Spatial Analyst 1.1 is proprietary
software distributed by ESRI (http://www.esri.com). Price varies widely depending on the
user=s affiliation, such as with government or industry.
- MassGIS Watershed Tools for the ArcView Spatial Analyst Software was
developed by the State of Massachusetts, Division of Watershed Management, GIS
Division (MassGIS) for use with the ArcView Spatial Analyst. ArcView Spatial Analyst is
proprietary software distributed by ESRI (internet: www.esri.com). Price varies widely
depending on the user=s affiliation with government or industry. The MassGIS watershed
tools are public-domain software (john.rader@state.ma.us).
PERSONNEL REQUIREMENTS
To use a GIS effectively in any project, it is important to have personnel with a variety of specific
skills. All of the software mentioned above (GIS, image processing, and RDBMS) require lengthy
learning curves to be used effectively.
Data Entry Technician
Data entry includes automation or digitizing of maps, creating attribute tables, and importing
databases. The data entry technician should have some knowledge of spatial concepts and
experience in basic GIS use for creating thematic layers, and attribute data entry. Depending on
the amount of data entry required, one or more technicians may be needed.
Spatial Data Analyst
The spatial data analyst is skilled in manipulating geographic data to retrieve pertinent, project-
specific information such as mapping sources of contamination and their proximity to source
waters, and delineating protection areas. This person must have a thorough understanding of
the concepts presented in this Chapter and be experienced in using GIS and image processing
technology. The spatial data analyst should also have some experience in working with utilities,
hydrogeology, soils, environmental engineering, or sanitary engineering.
Field Surveyor
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A field surveyor may be required if geographic or attribute data is not available and must be
gathered in the field. The surveyor should be skilled in field survey management, GPS
technology, and database development and have knowledge of sanitary or environmental
engineering, soil science, or hydrogeology. Depending on the amount of field surveying required
and the size of the area being surveyed, the field surveyor may require a support staff to assist
with gathering information.
Soil Scientist
A soil scientist may be needed to evaluate the condition and physical properties of soils in the
survey area. The Natural Resources Conservation Service (NRCS) formerly called the Soil
Conservation Service may be contacted for technical assistance in this area.
System Administrator
A system administrator may be needed to administer the GIS and its peripherals such as
digitizers, printers, and plotters. This is especially true for systems that require a network and
have multiple users. A system administrator can help with hardware and software maintenance
and replacement, network maintenance, system backups, and other administrative duties.
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REFERENCES
Anderman, W.H. and G.Martin. 1986. Effect of public sewers on watershed contamination,
Journal of Environmental Health 4(2):81-84.
EPA. 1997a. States source water assessment and protection programs final guidance. EPA
816-R-97-009. U.S. Environmental Protection Agency, Office of Water, Washington, DC.
EPA. 1997b. State methods for delineating source water protection areas for surface water
supplied sources of drinking water. EPA 816-R-97-008. U.S. Environmental Protection Agency,
Office of Water, Washington, DC.
EPA. 1997c. Delineation of Source Water Protection Areas, a Discussion for Managers; Part 1:
A Conjunctive Approach for Ground Water and Surface Water, U.S. Environmental Protection
Agency, Office of Water, Washington, DC. (Expected August 1997)
EPA. 1995. EPA/State Joint Guidance on Sanitary Surveys. U.S. Environmental Protection
Agency, Office of Water, Washington, DC.
EPA. 1994. The quality of our nation=s waters. EPA 841-S-95-004. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
EPA. 1993. Guidelines for delineation of wellhead protection areas. EPA 4405-93-001. U.S.
Environmental Protection Agency, Office of Water, Office of Groundwater Protection,
Washington, DC.
EPA. 1991 a. Guide for Conducting Contamination Source Inventories for Public Drinking Water
Supply Protection Programs. EPA 570/9-91-033. U.S. Environmental Protection Agency, Office
of Water, Washington, D.C.
EPA. 1991b. Managing Groundwater Contamination Sources in Wellhead Protection Areas: A
Priority Setting Approach. EPA 570/9-91-023. U.S. Environmental Protection Agency, Office of
Groundwater and Drinking Water.
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EPA. 1990. Guidance manual for compliance with the filtration and disinfection requirements for
public water systems using surface water sources, U.S. Environmental Protection Agency, Office
of Drinking Water, Washington, D.C.
ESRI, Inc. 1992. Understanding GIS: The ARC/INFO Way. Environmental Systems Research
Institute, Redlands, CA.
Grayman, W.M., S.R. Kshirsagar, and R.M. Males. 1993. A Geographic Information System for
the Ohio River Basin, Risk Reduction Engineering Laboratory, Office of Research and
Development, US Environmental Protection Agency, Cincinnati, Ohio.
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A GIS for the Ohio River Basin
Walter M. Grayman
W.M. Grayman Consulting Engineers, Cincinnati, Ohio
Sudhir R. Kshirsagar
Global Quality Corporation, Cincinnati, Ohio
Richard M. Males
RMM Technical Services, Inc., Cincinnati, Ohio
James A. Goodrich
Risk Reduction Engineering Laboratory, Office of Research and Development,
U.S. Environmental Protection Agency, Cincinnati, Ohio
Jason P. Heath
Ohio River Valley Water Sanitation Commission, Cincinnati, Ohio
Abstract
Much of the information used in the management of
water quality in a river basin has a geographic or spatial
component associated with it. As a result, spatially
based computer models and database systems can be
part of an effective water quality management and
evaluation process. The Ohio River Valley Water Sani-
tation Commission (ORSANCO) is an interstate water
pollution control agency serving the Ohio River and its
eight member states. The U.S. Environmental Protec-
tion Agency (EPA) entered into a cooperative agreement
with ORSANCO to develop and apply spatially based
computer models and database systems in the Ohio
River basin.
Three computer-based technologies have been ap-
plied and integrated: geographic information systems
(GIS), water quality/hydraulic modeling, and database
management.
GIS serves as a mechanism for storing, using, and
displaying spatial data. The ARC/INFO GIS, EPAs
agencywide standard, was used in the study, which
assembled databases of land and stream information for
the Ohio River basin. GIS represented streams in hydro-
logic catalog units along the Ohio River mainstem using
EPAs new, detailed RF3-level Reach File System. The
full Ohio River basin was represented using the less
detailed RF1-level reach file. Modeling provides a way
to examine the impacts of human-induced and natural
events within the basin and to explore alternative strate-
gies for mitigating these events.
Hydraulic information from the U.S. Army Corps of En-
gineers' FLOWSED model enabled EPAs WASP4 water
quality model to be embedded in a menu-driven spill
management system to facilitate modeling of the Ohio
River mainstem under emergency spill conditions. A
steady-state water quality modeling component was
also developed under the ARC/INFO GIS to trace the
movement and degradation of pollutants through any
reaches in the RF1 representation of the full Ohio River
basin.
Database management technology relates to the stor-
age, analysis, and display of data. A detailed database
of information on dischargers to the Ohio River mainstem
was assembled under the PARADOX database manage-
ment system using EPAs permit compliance system as
the primary data source. Though these three technolo-
gies have been widely used in the field of water quality
management, integration of these tools into a holistic
mechanism provided the primary challenge of this study.
EPAs Risk Reduction Engineering Laboratory in Cincin-
nati, Ohio, developed this project summary to announce
key findings of the research project, which is fully docu-
mented in a separate report of the same title.
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Introduction
During the past 25 years, computers have been actively
used in water quality management, demonstrating their
potential to assist in a wide range of analysis and display
tasks. Technologies such as geographic information
systems (CIS), database management systems (DBMS),
and mathematical modeling have been applied in the
water quality management field and have proven to be
effective tools. For computers to achieve their full poten-
tial, however, they must become integrated into the
normal programmatic efforts of agencies and organiza-
tions in the planning, regulation, and operational areas
of water quality management.
Recognizing this need for routine use of computer-
based tools, the Ohio River Valley Water Sanitation
Commission (ORSANCO) and the Risk Reduction En-
gineering Laboratory (RREL) of the U.S. Environmental
Protection Agency (EPA) commenced a study in 1990.
The goals of the study included the adaptation, devel-
opment, and application of modeling and spatial data-
base management (DBM) tools that could assist
ORSANCO in its prescribed water quality management
objectives. These goals were consistent with EPAs on-
going programs involving the use of CIS and modeling
technology. The study's goals also coincided with EPAs
Drinking Water Research Division's work over the past
decade, which applied similar technology to study the
vulnerability of water supplies on the Ohio and Missis-
sippi Rivers to upstream discharges.
Methodology Overview
To address the goals of this project, three basic tech-
nologies have been applied and integrated: CIS, water
quality/hydraulic modeling, and DBM. CIS serves as a
mechanism for storing, using, and displaying spatial
data. Modeling provides a way to examine the impacts
of human-induced and natural events within the basin
and to explore alternative strategies for mitigating these
events. DBM technology relates to the storage, analysis,
and display of data. Though these three technologies
have been widely used in the field of water quality
management, integration of these tools into a holistic
mechanism provided the primary challenge of this study.
CIS Technology
The guiding principle in developing the CIS capability
was to maximize the use of existing CIS technology and
spatial databases. The study used ARC/INFO CIS,
EPAs agencywide standard. Remote access of
ARC/INFO on a VAX minicomputer facilitated the initial
work. Subsequently, both PC ARC/INFO and a worksta-
tion-based system were obtained.
EPA has developed an extensive spatial database re-
lated to water quality and demographic parameters. This
served as the primary source of spatial data for the
study. Following is a summary of spatial data used in
this study:
• State and county boundaries.
• City locations and characteristics.
• Water supply locations and characteristics.
• Locations and characteristics of dischargers to
water bodies.
• Toxic loadings to air, water, and land.
• Dam locations and characteristics.
• Stream reaches and characteristics.
The primary organizing concept for the water-related
information was EPAs Reach File System (1). This sys-
tem provides a common mechanism within EPA and
other agencies for identifying surface water segments,
relating water resources data, and traversing the nation's
surface water in hydrologic order within a computer envi-
ronment. A hierarchical hydrologic code uniquely identi-
fies each reach. Information available on each reach
includes topological identification of adjacent reaches,
characteristic information such as length and stream
name, and stream flow and velocity estimates. The origi-
nal reach file (designated as RF1) was developed in the
early 1980s and included approximately 70,000 reaches
nationwide. The most recent version (RF3) includes
over 3,000,000 reaches nationwide.
As part of this project, an RF1-level database was es-
tablished for the entire Ohio River basin. The RF3 reach
file was implemented for the Ohio River mainstem and
lower portions of tributaries. River miles along the Ohio
River were digitized and established as an ARC/INFO
coverage to provide a linkage between the reach file and
river mile indexing used by ORSANCO and other agen-
cies along the river. Figure 1 shows the RF1 reach file
representation of the Ohio River basin along with state
boundaries.
The study incorporated several EPA sources of informa-
tion on dischargers to water bodies. The industrial facil-
ity discharger (IFD) file contains locational and
characteristic data for National Pollutant Discharge
Elimination System (NPDES) permitted discharges. De-
tailed permit limits and monitoring information was ac-
cessed from the permit compliance system (PCS). The
toxic release inventory (TRI) system includes annual
loading of selected chemicals to water, land, air, and
sewer for selected industries based on quantity dis-
charged. All water data are referenced to the NPDES
permit number, which is spatially located by reach and
river mile, and by latitude and longitude.
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0 meter,
147884
Figure 1. RF1 reaches in the Ohio River basin.
Spill Modeling
An important role that ORSANCO fills on the Ohio River
relates to the monitoring and prediction of the fate of
pollutant spills. Typically, ORSANCO serves as the over-
all communications link between states during such
emergency conditions. ORSANCO coordinates and par-
ticipates in monitoring and serves as the information
center in gathering data and issuing predictions about
the movement of spills in the river. In the past, a series
of time-of-travel nomographs, based on National
Weather Service flow forecasts, Corps of Engineers
flow-velocity relationships, and previous experience,
were used to predict the movement of spills. This project
combined a hydraulic model with a water quality model
to serve as a more robust method for making such
predictions.
The U.S. Army Corps of Engineers' FLOWSED model
was selected as the means of predicting daily flow quan-
tities and water levels along the mainstem and portions
of major tributaries near their confluence with the Ohio
River (2). The Ohio River Division of the Corps of Engi-
neers applies FLOWSED daily as part of its reservoir
operations program. The Corps can generate 5-day
forecasts of stage and flow for 400 mainstem and tribu-
tary segments, and ORSANCO can access the results
via phone lines.
EPAs WASP4 water quality model was selected for use
in the project (3). WASP4 is a dynamic compartment
model that can be used to analyze a variety of water
quality problems in a diverse set of water bodies. Because
the primary use of the model in this project is quick
response under emergency situations, only the toxic
chemical portion of the model with first order decay is
being used. The FLOWSED and WASP4 models have
been combined into a user-friendly spatial decision sup-
port system framework described later in this project
summary.
Discharger Database Management System
EPA's PCS and historical records maintained by
ORSANCO furnish a rich source of data on dis-
charge information for the Ohio River. To organize
these data and make them available for analysis, a
database was developed using the PARADOX DBM
system.
The database was established using a relational struc-
ture with a series of related tables (two-dimensional flat
files). Individual tables contain information on facilities,
outfalls, permit limits, monitoring data, and codes used
in the other tables. The NPDES permit number is used
as the primary key in each data table. A mechanism for
downloading and reformatting data from the national
PCS database has been developed along with custom
forms for viewing and editing data, and custom reports
for preparing hard copy summaries. Latitude and longi-
tude values for each facility can provide the locational
mechanism for use of this data in conjunction with CIS.
Integration of GIS/Modeling/Database
Technologies
A major objective of this study was the integration of
CIS, modeling, and DBMS technologies into a holistic
tool for use by ORSANCO. Several integration mecha-
nisms were implemented as summarized below.
Steady-State Spill Tracing
The NETWORK component of the ARC/INFO CIS pro-
vides a steady-state, transportation-oriented routing ca-
pability. This capability was used in an arc macro
language (AML) program to construct a routing proce-
dure for determining downstream concentrations and
travel times. The pollutant may be treated as a conser-
vative element or represented by a first order expo-
nential decay function. This capability has been
implemented for use with the RF1 reach file repre-
sentation of the full Ohio River basin. The user may
select from six flow regimens: average flow, low flow,
and four multiples of average flow ranging from one-
tenth to 10 times average flow. This system gives
ORSANCO the ability to estimate the arrival time of a
spill from any RF1 tributary to the Ohio River mainstem.
-------
Sp/7/ Management System
A PC-based spatial decision support system (SDSS)
was built as a spill management system to be a quick
response tool for analyzing and displaying the results of
pollutant spills into the Ohio River. The schematic in
Figure 2 illustrates the components in this computerized
spill management system. The system is implemented
in the C language using a commercial menuing system
and a series of graphic display routines developed at
EPA. Custom, written routines have been used to read
the output from the U.S. Army Corps of Engineers'
FLOWSED model, to generate input files for EPAs
WASP4 model, to create output reports and output plots,
and to provide an animated representation of the con-
centration profiles moving down the river. Figure 3 pre-
sents an example of a graphic output the system
generated. Additionally, the system generates a file in
5-DAY RIVER FLOW
AND STAGE FORECAST
PREPARED
DAILY BY CORPS OF
ENGINEERS USING
FLOWSED MODEL
KDOO-H
via phone line
ARC/INFO CIS
DATABASE
SPATIAL DECISION SUPPORT SYSTEM
FOR TOXIC SPILL MODELING
Menu driven user interface linked to
EPA WASP 4 water quality model.
Tabular /graphical screen and hard
copy outputs, spill animation and
GIS DBF format output file
GRAPHS
Arc View Spatial data
base display system
CONCENTRATION
REPORT
by
Segment & Time
REPORTS
Figure 2. Schematic representation of spill modeling system process.
141 ;47 1991
Figure 3. Graphic output from the basinwide network spill model.
-------
DBF format that may be read by ARC/VIEW (the com-
panion software to ARC/INFO for user-friendly viewing
of spatial data).
Hardware Platform
Within the study, the initial hardware platform was a
combination of local PCs (in Cincinnati) and a remote
access terminal to a VAX computer located at EPA's
National Computer Center in Research Triangle Park,
North Carolina. The final platform, and the one on which
the completed system was installed, comprised a UNIX-
based Data General workstation and a PC workstation.
The full hardware configuration is shown schematically
in Figure 4.
Conclusions
The application of computer-based display, analysis,
and modeling tools in conjunction with CIS technology
proved to be an effective strategy for water quality man-
agement. This study used an existing CIS package and
DBMS in conjunction with existing water quality and
hydraulic models. The study focused primarily on as-
sembling available spatial and relational databases and
integrating the systems to provide a usable, effective tool.
References
1. Horn, R.C., and W.M. Grayman. 1993. Water-quality modeling with
EPA reach file system. J. Water Res. Planning and Mgmt.
119(2):262-274.
2. Johnson, B.H. 1982. Development of a numerical modeling capa-
bility for the computation of unsteady flow on the Ohio River and
its major tributaries. Vicksburg, MS: U.S. Army Engineer WES.
3. Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W Schanz. 1988.
WASP4, a hydrodynamic and water quality model: Model theory,
users' manual, and programmers' guide. Athens, GA: EPA Envi-
ronmental Research Laboratory. NTIS PB88185095.
UNIX WORKSTATION
1.44MB
FLOPPY
DRIVE
i
/
I \
150MB
TAPE
DRIVE
8 MM
DAT
2GB
HARD
DRIVE
1/4" TAPE
DRIVE
Figure 4. Hardware configuration.
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Data Quality Issues Affecting GIS Use for Environmental Problem-Solving
Carol B. Griffin
Henrys Fork Foundation, Island Park, Idaho
"Abandon hope, all ye who enter here." Dante's quote
might well be the advice that experienced geographic
information system (GIS) users give to nonusers about
to confront data quality issues associated with GIS
use. Indeed, after reading this paper, some decision-
makers might abandon attempts to use a GIS because
of the error associated with it. Others may want to spend
an inordinate amount of time and money trying to elimi-
nate all error associated with GIS use. Neither option is
prudent.
Data quality is important because it affects how reliable
CIS-generated information is in the decision-making
process. Too often, the availability of inexpensive digital
data overshadows data quality concerns; people fre-
quently use digital data because they are available, not
because they have the necessary accuracy.
A GIS can help decision-makers use spatial informa-
tion more fully than manual methods allow, but some-
times data quality issues cause concern about using
CIS-generated outputs. Making environmental deci-
sions without adequate consideration to data quality
may lead to an erroneous decision, erode public confi-
dence, or cause an agency to incur liability. This paper
attempts to encourage decision-makers to become
more aware of data quality issues, including the sources
and magnitude of error.
GIS error research has necessarily progressed in a
linear fashion, beginning with identifying and classifying
sources of error. This paper discusses both inherent
(source) error and the error that GIS operations intro-
duce (operational error) during data input, storage,
analysis/manipulation, and output (1). Strategies for
coping with error and research into error reduction tech-
niques have only recently received attention. Unfortu-
nately, the answers to error management questions
such as, "How will the error affect decision-making?" are
not clear. The end of this paper covers several error
management suggestions and anticipated software im-
provements designed to reduce errors, however.
Data Quality Concepts and Their
Importance
Data quality is a major issue for CIS-generated maps,
much more so than it is for paper maps. In part, this is
because a GIS can perform operations on spatial data
that would be nearly impossible without a GIS because
of scale, complexity, and generalization issues (2). Car-
tographers adjust for these problems when they manu-
ally manipulate and instantly combine paper maps by
adhering to long-standing cartographic principles, but
GIS personnel may not be fully trained in these princi-
ples. A GIS enables an analyst, whether trained in car-
tographic principles or not, to combine or manipulate
data in appropriate or in inappropriate, illogical, and
erroneous ways. Lack of training coupled with the speed
of spatial data manipulation can have serious conse-
quences for an agency whose personnel produce and
use CIS-generated maps.
Limited scientific understanding, limited ability to meas-
ure data, sampling error, inherent variability, and inade-
quacy of mathematical representations all contribute to
uncertainties associated with spatial data. Uncertainty
about spatial data consists of two parts: ignorance and
variability. Ignorance means that variables have a "true"
value, but it is unknown to us, whereas variability means
one value cannot represent the variables.
Data quality defies a simple definition. For this paper,
data quality can roughly mean how "good" the data are
for a given purpose. People usually think of data quality
in terms of error, but the term is broader and encom-
passes the six components outlined in the next section.
Error can mean the difference between the observed
values and the "true" value. The "true" value of a variable
is usually unknown and unknowable, but for this paper's
purposes, "true" could be the known value or the value
one would obtain from field measurements (the discus-
sion of data collection tries to dispel the notion that there
is one "true" value for many variables, such as soil type
in a given area orwatertemperature in a lake). Imperfect
-------
equipment or observers and environmental effects
cause spatial error. According to Thapa and Bossier (3),
errors fall into three categories:
• Gross errors and blunders (people or equipment).
• Systematic errors (which introduce bias).
• Random errors (due to imperfect instruments and
observers).
In addition, another view divides spatial error into two
different components: accuracy and precision. Accuracy
means how close a value is to the "true" value or a
known standard (absence of bias). Precision can have
two definitions: it can be a measure of dispersion (stand-
ard deviation) of observations about a mean, or it can
refer to the number of decimal digits used to represent
a value (4). In the first definition of precision, a meas-
urement of 6 feet plus or minus 1 foot is more precise
than one of 6 feet plus or minus 3 feet. In the second
definition, a value of 6.1794 feet is more precise than
one of 6.1 feet. Figure 1 provides a graphic explanation
of the difference between error, accuracy, and precision.
Error
Accuracy
Precision
True Value
4 1 Mean 2 3
Observations
Figure 1. Relationship between error, accuracy, and precision.
Data are not accurate or inaccurate. Instead, data accu-
racy exists on a continuum, ranging from low to high
accuracy. Although people strive for accurate (error-
free) data, obtaining 100-percent accurate data is im-
practical. The list below provides some of the reasons
why total accuracy is not obtainable (5):
• Objects to be measured are often vaguely defined.
• Some phenomena are variable in nature.
• Classification schemes are imprecise.
• Measurements are inherently imprecise.
• Gross errors of a nonstatistical nature can occur during
measurement.
• Attributes encoded on an ordinal scale (high, me-
dium, low) are approximate.
• Data represent a past state of reality.
Users of geographic data should strive for data that are
only as accurate as they need. A variety of factors, of
course, can determine need:
• Intended use of the data
• Budget constraints
• Time constraints
• Data storage considerations
• Potential liability
The main barrier to highly accurate data is lack of funds.
Male (6) suggests that rather than abandoning a CIS
project because funds are not sufficient to achieve the
desired accuracy, an agency should collect data at the
desired accuracy from smaller areas, such as areas
being developed or redeveloped. Overtime, data collec-
tion at the desired accuracy can expand to include areas
that lacked data due to budgetary constraints. Smith and
Honeycutt (7) outline the use of a value of information
approach in determining the need for more data (or
more accurate data) based on the expected costs and
benefits associated with data collection. If the benefits
of increased data accuracy are greater than the ex-
pected costs, additional funds should be allocated to
obtain more accurate data.
The intended use of data affects the type of data, as well
as the data quality needed. Beard (8) divides CIS appli-
cations into six types (see Table 1). The specific type of
data quality one needs (e.g., positional accuracy, attrib-
ute accuracy) also varies with the intended application.
Analysts with inventory applications such as agricultural
production are less concerned about positional accu-
racy than with an accurate assessment of anticipated
crop yields (attribute accuracy). Decision-makers must
Tabl e 1. Types of GIS Applications (8)
Application Example
Siting Finding optimal location (fire station, waste site)
Logistic Movement or distribution through space
(emergency response, military movement)
Routing Optimal movement through a known network (mail,
school bus)
Navigation Way finding; may or may not involve a known
network (ground, sea, air)
Inventory Count and location of objects for a given time
(census, tax rolls)
Monitoring/ Examining processes over space and time
Analysis (ecological, zoological, geological, epidemiological
studies)
-------
decide which data quality component is the most impor-
tant for their use because optimizing all six components
can be very expensive (9). An obvious conflict arises
when local and state governments must meet multiple
application needs simultaneously and thus feel forced to
try to optimize several data quality components.
The nature of the decision may also help decision-makers
determine the data quality they need. Beard (8) lists sev-
eral of these factors (see Table 2). A political, high-risk
decision requires higher quality data than a nonpolitical,
low-risk decision because more public attention focuses
on the former decision.
Table 2. Factors That May Affec t the Data Quality Needed for
Decision-Making (8)
Lower Data Quality
Possibly Needed
Higher Data Quality
Possibly Needed
Routine
Nonpolitical
Minimal risk
Noncontroversial
Indefinite
Local implication
Nonroutine
Political
High risk
Controversial
Immediate
Global implication
Components of Data Quality
The National Committee for Digital Cartographic Data
Standards (9) identifies six components of digital carto-
graphic data quality. This section discusses each of
these components:
• Lineage
• Positional accuracy
• Attribute accuracy
• Logical consistency
• Completeness
• Temporal accuracy
Most components of data quality apply to both source
and operational error.
Lineage
Because uses and users of data change, those at the
national level have noted a recent push to include docu-
mentation when disseminating spatial data. Data line-
age, also known as metadata or a data dictionary, is data
about data. Metadata consists of information about the
source data such as:
• Date of collection
• Short definition
• Data type, field length, and format
• Control points used
• Collection method, field notes, and maps
• Data processing steps
• Assessment of the reliability of source data
• Data quality reports
Access to this information can help CIS personnel de-
termine if the data are appropriate for their use, thereby
minimizing risks associated with using the wrong data
or using data inappropriately. According to Chrisman
(10), the only ethical and probably best legal strategy for
those who produce spatial data is to reveal more infor-
mation about the data (metadata) so that users can
make informed decisions. Eagan and Ventura's article
(11) contains a sample of a generic environmental data
lineage report. The U.S. Environmental Protection
Agency's (EPA's) new locational data policy requires
contractors to estimate data accuracy and provide infor-
mation about the lineage of the data (12).
Positional A ecu racy
Anyone who has used a map has probably come across
features that are not located where the map says they
should be located and has experienced low positional
accuracy. (Undoubtedly, they have also detected fea-
tures that were not on the map, but that is a different
issue.) Positional accuracy, frequently referred to as
horizontal error, is how close a location on a map is to
its "true" ground position. Features may be located inac-
curately on maps for many reasons, including (13):
• Poor field work.
• Distortion of the original paper map (temperature,
humidity).
• Poor conversion from raster to vector or vector to
raster data.
• Data layers are collected at different times.
• Natural variability in data (tides, vegetation, soil).
• Human-induced changes (altering reservoir water
levels).
• Movement of features (due to scale of the map and
printing constraints) so they can be easily discerned
by the map reader.
• Combining maps with different scales.
• Combining maps with different projection and coordi-
nate systems.
• Different national horizontal datum in source materials.
• Different minimum mapping units.
Positional accuracy has two components: bias and pre-
cision. Bias reflects the average positional error of the
-------
sample points and indicates a systematic discrepancy
(e.g., all locations are 7 feet east of where they should
be). Estimating precision entails calculating the stand-
ard deviation of the dispersion of the positional errors.
Usually, root mean square error (RMSE) is reported as
the measure of positional accuracy, but it does not dis-
tinguish bias from precision (14). RMSE is frequently
monitored during digitizing to minimize the introduction
of additional positional error into the CIS.
To determine positional accuracy, one must compare the
location of spatial data with an independent source of
higher accuracy. Federal agencies that collect data and
produce maps adhere to National Map Accuracy Stand-
ards (NMAS) for positional accuracy. Maps such as
United States Geological Survey (USGS) topographic
maps that conform to NMAS carry an explicit statement
on them. Other groups also have developed standards
for large-scale mapping (15).
NMAS for positional accuracy require that not more than
10 percent of well-defined points can be in error by more
than one-thirtieth of an inch for maps at a scale of
1:20,000 or larger. For smaller scale maps, not more
than 10 percent of well-defined points can be in error by
more than one-fiftieth of an inch (16). Thus, less than 10
percent of the well-defined locations on a USGS
1:24,000 map can stand more than 40 feet from their
"true" location; the other 90 percent of the well-defined
points must stand less than 40 feet from their "true"
location. Table 3 shows the acceptable positional accu-
racy for commonly used maps. Note that as scale de-
creases from 1:1,200 to 1:100,000, positional accuracy
decreases.
Several important issues relate to NMAS. First, not all
maps adhere to NMAS, which means their positional
accuracy may be lowerthan NMAS or may be unknown.
Second, NMAS do not indicate the location of points in
error. Third, 10 percent of the well-defined points can
have a positional error greater than the standards allow,
but neither the location nor the magnitude of these
errors are known. Fourth, NMAS apply to well-defined
points; therefore, areas that are not well defined may
Tabl e3. NMAS Horizontal (Positional) Accuracy
Scale
1:1,200
1 :2,400
1 :4,800
1:12,000
1 :24,000
1:63,360
1:100,000
1 Inch = x Feet
100
200
400
1,000
2,000
5,280
8,333
Horizontal
Accuracy +/- Feet
3.33
6.67
13.33
33.33
40.00
105.60
166.67
have even lower positional accuracy. The implication of
these errors in location is that users should use caution
in making decisions that require high positional accu-
racy. Positional accuracy issues are particularly trouble-
some for CIS operations on small-scale maps or when
combining large-scale maps (1:1,200) with small-scale
maps (1:100,000).
Recently, global positioning systems (GPS), which the
U.S. military developed, have helped to obtain more
accurate feature locations. GPS is not without error,
however. The list below notes some of the possible
sources of error associated with GPS use, some of
which can be controlled while others cannot (17):
• Errors in orbital information.
• Errors in the satellite clocks.
• Errors in the receiver clocks.
• Ionospheric or tropospheric refraction.
• Deliberate degrading of the satellite signal.
• Obstructions that block the signal.
• Reflection of the GPS signal off buildings, water, or
metal.
• Human error.
The importance of positional accuracy depends on the
intended use of the data. In an urban area, a posi-
tional error of 1 foot on a tax map may be unaccept-
able because 1 foot may be worth millions of dollars.
In a rural area, however, tax boundaries mapped
within 10 feet of their surveyed location may be accu-
rate enough (6). Somers (18) reports that positional
accuracy of 10 to 20 feet may be sufficient for envi-
ronmental analysis. She says the cost of increasing
accuracy to 5 feet could increase the cost of data col-
lection by a factor of 10. The decision-maker must de-
termine the needed positional accuracy.
A ttribute A ccuracy
Attribute accuracy refers to how well the description of
a characteristic of spatial data matches what actually
exists on the ground. For some spatial data, the location
does not change overtime, but the value of the attribute
does (e.g., the location of a census tract does not
change, but the population within a census tract
changes). Attribute accuracy is reported differently for
continuous data (i.e., elevation, which has an infinite
number of values) or discrete data (i.e., gender, which
has a finite number of values).
NMAS exist for elevation contour lines on topographic
maps. NMAS for vertical accuracy state that not more
than 10 percent of the points tested shall be in error by
more than one-half of the contour interval (16). A well-
defined point on a USGS topographic map with a 10-foot
-------
contour interval could vary by 10 feet because the actual
elevation could be 5 feet higher or lower than the map
indicates. The implications of these errors are similar to
the ones for positional accuracy. In addition, errors in
elevation are important because small changes in ele-
vation may significantly affect some CIS analysis opera-
tions such as the determination of aspect, slope,
viewshed, and watershed boundaries.
NMAS do not exist for discrete variables such as land
use derived from satellite imagery. Instead, a classifica-
tion matrix reports attribute accuracy. Field checking or
checking a portion of the classified image against a map
of higher accuracy determines the accuracy of the land
use classification. The result of the comparison is a table
from which to calculate overall, producer's, and user's
accuracy. Table 4 is an example of a classification accu-
racy matrix.
Table 4. Example of a Classification Accuracy Matrix (19)
Reference Data ("GrouriH-uth")
Number of Cells
Classified Data
(Satell ite Imacp)
Number of Cells
Forest
Water
Urban
Total
Forest
28
1
1
30
Water
14
15
1
30
Urban
15
5
20
40
Total
57
21
22
100
Overall Accuracy (sum of the main diagonal)
63
100
= 63%
Producers Accuracy
(column total)
OQ
Forest = ^ = 93%
oU
Users Accuracy
(row total)
28
Forest = -
= 49%
Water = ^ = 50%
oU
Urban = ^ = 50%
Water = ^
20
Urban = —
71%
91%
Overall accuracy is the percentage of correctly classified
cells calculated as the sum of the main diagonal (19).
Producer's accuracy is the total number of correct pixels
in a category divided by the total number of pixels of that
category as derived from the reference data (column
total). It corresponds to how well the person classifying
the image (the "producer") can correctly classify or map
an area on the earth. In this example, the producer most
accurately classified forested land (93 percent). User's
accuracy describes the probability that a sample from
the classified area actually represents that category on
the ground. The map "user" is concerned about the
map's reliability. In this example, the most accurately
classified land use from the user's perspective is urban
(91 percent).
The significance of overall, producer's, and user's accu-
racy depends on the intended use of the data. As an
example, Chrisman (20) says that the error in distin-
guishing wetland from pasture may not matter to some-
one estimating open space, but the difference is critical
if the person is estimating the amount of wildlife habitat
available. Story and Congalton (19) provide an example
of how to interpret a classification matrix. A forester
looks at the classification matrix and sees that forest
classification is 93 percent accurate (producer's ac-
curacy); therefore, the analyst did not identify only 7
percent of the forest on the ground. Once the forester
field checks the supposed forested area, she finds that
only 49 percent (28 cells) of the sites mapped as forest
are actually forest; the rest are water (14 cells) or urban
(15 cells) areas.
A report of overall, producer's, and user's accuracy can
help decision-makers determine the appropriateness of
the classified image for their use by identifying potential
errors in classification. This can help direct field work,
which can improve the classification of the image and
perhaps subsequent images. Because CIS analysis fre-
quently uses land use, decision-makers need to know
that significant variability can result when several ana-
lysts classify the same image. Bell and Pucherelli (21)
found that consistency in classification can improve by
having one person classify the entire image. McGwire (22)
even found significant differences between analysts in
unsupervised classification of Landsat imagery. Com-
puters primarily perform unsupervised classification,
which implies that different analysts would classify the
same image in the same way.
Logical Consistency
Logical consistency focuses on flaws in the logical rela-
tionships among data elements. For example, a vector
CIS should label all polygons with only one label per
polygon, and all polygons should be closed. Logical
inconsistency can also occur by collecting data layers at
different times or from different scale maps with different
positional accuracies. For example, the edge of a lake
on the hydrology data layer should coincide with the
edge of land in the land use data layer. If data on the
lake were collected during a wet year rather than a dry
year, the lake's volume would be higher than normal,
affecting its location on the map. If land use data for the
same area were collected during a dry year, the bound-
ary of the lake on the two layers would not be the same.
Logical inconsistencies usually do not appear until the
two maps are overlaid and the boundaries do not coin-
cide (see Figure 2). The user must determine the "cor-
rect" location of the feature that appears misaligned on
one or more data layers. The inconsistency between the
-------
Land Use
Hydrology
Overlay
Figure 2. Logical inconsistency in lake and forest location.
location of the two layers resolves through a process
called conflation. All maps are adjusted so that the
feature on each data layer lines up with the same feature
on the base map.
Completeness
Completeness focuses on the adequacy of data collec-
tion procedures. Robinson and Frank (5) discuss two
kinds of uncertainty associated with collecting spatial
data that can lead to error. One type of uncertainty is the
inability to measure or predict an inherently exact char-
acteristic or event with certainty. Examples of this are
blunders in data collection or measurement error, nei-
ther of which can be accurately predicted. The other kind
of uncertainty is associated with concepts that are inher-
ently ambiguous. Crisp data sets, such as property
boundaries, have little ambiguity; the only issue related
to error is the positional accuracy in measuring the
boundary. Because land use data are not crisp data
sets, the challenge is to accurately represent an inher-
ently inexact concept.
Although we know spatial data are variable, our classi-
fication systems generally ignore the second type of
uncertainty. Analysts map data as though all variables
had exact boundaries and all polygons consisted of
homogeneous data. Burrough (4) reports that spatial
variation of natural phenomena is "not just a local noise
function or inaccuracy that can be removed by collecting
more data or by increasing the precision of measure-
ment, but is often a fundamental aspect of nature that
occurs at all scales. . . ."
Mapping spatial data is a function of how humans ag-
gregate and disaggregate data either in space, catego-
ries, quantities, or time; spatial data seldom exist in
nature the way maps depict them (23). Data and rela-
tionships between data are sensitive to the scale and
the zoning system in which the data are reported (24,
25). The modifiable area unit problem occurs because
an analyst can recombine a given set of units or zones
into the same total number of units producing very dif-
ferent results (see Figure 3).
The scale problem occurs because an analyst can com-
bine a set of small units into a smaller number of larger
units, which can change the inferences that can be
Figures. Modifiable area unit. (Number of units is constant;
location of units changes.)
made from the data. In Figure 4, the area containing the
highest values changes from the southwest corner in the
first picture to the northern half in the second picture.
For example, water quality data are scale-dependent
because they vary based on the size and location of the
collection area (e.g., adjacent to a point source dis-
charge, a stream segment, the entire river, or the lake
the river discharges into).
Kennedy (25) reports on a similar problem known as the
small number problem. This problem occurs when cal-
culations use a percentage, ratio, or rate for a geo-
graphic area for which the population of interest
(denominator) is sparse or the numerator is a rare event
(1 case of cancer per 1 million people). CIS-generated
maps may highlight a statistically insignificant change in
rare events. Small, random fluctuations in the numerator
may cause large fluctuations in the resulting percent-
age, ratio, or rate. If policy-makers use these maps,
priorities for public health policy may change because
of the erroneous belief that an area is experiencing more
unwanted rare events.
Data can be collected using a tag- or count-based sys-
tem, which affects their usefulness. The tag approach
categorizes items based on the dominant or average
attribute and is ideal for planners who want only one
value for each area. For example, each polygon in a
county soil survey is tagged with one soil type. Accord-
ing to soil taxonomy rules, however, only about 35 per-
cent of a delimited area on a soil survey must match its
classification, and up to 10 percent may be a radically
different soil (26). Although the text in the soil survey
sets limits on data accuracy by listing major impurities
found with each soil type, the CIS seldom carries that
information because analysts only digitize soil bounda-
ries and label data with the dominant attribute. This
Figure 4. Scale problem (number of units changes).
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leads to the depiction of apparently homogeneous soil
units although the text specifies that the data are not
homogeneous (27).
Some soil or land cover phenomena, even though pre-
sent in small quantities and thus not mapped, may have
great significance for hydrologic models, which makes
the tag approach to data collection troublesome. Data
collected using the count system allow the analyst to
tabulate the frequency of occurrence or areal extent of
a particular phenomenon. Environmental modelers pre-
fer count data but are usually forced to use tag data,
which can introduce error into their models (26). The
new digital soils databases, STATSGO and SSURGO,
are collected and depicted using a count format, which
will help experienced analysts use the data more fully.
Figure 5 shows the difference between tag and count
methods of data collection.
Data are seldom complete because analysts use clas-
sification rules to indicate how homogeneous an area
must be before it is classified a particular way (e.g.,
more than 50 percent, more than 75 percent). Another
decision an analyst must make is where to draw the
boundary between two different areas; it is seldom clear
where a forest leaves off and a rural development be-
gins. Analysts must also decide how or if to show inclu-
sions (e.g., a forested area in the middle of agricultural
land uses).
Temporal A ecu racy
Collecting data at different times introduces error be-
cause the variable may have changed since data collec-
tion. The effect of time, reported as the date of the
source material, depends on the intended use of the
data. Some natural resource data have daily, weekly,
seasonal, or annual cycles that are important to con-
sider. For example, obtaining land use data from re-
motely sensed imagery in November for North Dakota
produces a very different land use map than data ana-
lysts obtain during the July growing season.
In addition, demographic and land use information
changes quickly in a rapidly urbanizing area. Data col-
lected at several times can produce logical inconsis-
tency between data layers, forcing the analyst to adjust
the location of features to coincide with the base map.
Another problem with collecting data at different times
Soil A
Soil B
Soil B 55%'
Soil C 45%
Soil A 30%
Soil B 25%
Soil C 20%
Soil D 25%
30%
Tag Count
Figure 5. Tag and count methods of data collection.
is that data may be collected using different standards,
which may not be apparent to the user (4).
Source Errors in a GIS
Source (or inherent) error derives from errors in data
collection. The amount of error present in collected data
is a function of the assumptions, methods, and proce-
dures used to create the source map (28). Primary data
refers to data collected from field sampling or remote
sensing. Causes of the errors associated with this data
are (3, 4, 8, 14,29):
• Environmental conditions (e.g., temperature, humidity).
• Sampling system (e.g., incomplete or biased data
collection).
• Time constraints.
• Map projection.
• Map construction techniques.
• Map design specifications.
• Symbolization of data.
• Natural variability.
• Imprecision due to vagueness (e.g., classifying a forest).
• Measurement error from unreliable, inaccurate, or
biased observers.
• Measurement error from unreliable, inaccurate, or
biased equipment.
• Lab errors (e.g., reproducibility between lab proce-
dures and between labs).
The process of converting primary data to secondary
data (usually a map) introduces additional error. Many
of the data layers that a GIS analyst acquires are sec-
ondary data. Some of the errors associated with map-
making are (3):
• Error in plotting control points.
• Compilation error.
• Error introduced in drawing.
• Error due to map generalization.
• Error in map reproduction.
• Error in color registration.
• Deformation of the material (temperature, humidity).
• Error introduced due to using a uniform scale.
• Uncertainty in the definition of a feature (boundary
between two land uses).
• Error due to feature exaggeration.
• Error in digitization or scanning.
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Converting paper maps to digital data for entry into a
CIS (tertiary data) introduces still more error (the errors
generated from converting paper maps into a digital
format are discussed in the section on input error), in
part because the purpose for which the data was col-
lected differs from the intended use of the data.
Many types of error are associated with data collection:
• Data for the entire area may be incomplete.
• Data may be collected and mapped at inappropriate
scales.
• Data may not be relevant for the intended application.
• Data may not be accessible because use is restricted.
• Resolution of the data may not be sufficient.
• Density of observations may not be sufficient.
The following discussion explains these types of errors.
Data for the Entire Area May Be Incomplete
An incomplete data record may be due to mechanical
problems that interrupt recording devices, cloud cover
or other types of interference, or financial constraints.
Possible solutions to this problem include collecting ad-
ditional data for the incomplete area, using information
from a similar area, generalizing existing large-scale
maps to match the less detailed data needed, or con-
verting existing small-scale maps to large-scale maps to
obtain data at the desired scale. Collecting additional
data may not be a feasible solution because of time or
money constraints. Extrapolating data from the surro-
gate area to the desired area can cause problems be-
cause the areas are not identical and the scale,
accuracy, or resolution of the surrogate area data
may be inappropriate for the intended use. The sec-
tion on analysis/manipulation of data within a CIS
covers the effect of generalization on data quality
as well as the effect of converting small-scale maps
to large-scale maps.
Data May Be Collected and Mapped at a Scale
That Is Inappropriate for the Application
A variety of guidelines suggest the appropriate map
scale to use for various applications (see Table 5). Also,
Table 5. Relationship Between Map Scale and Map Use (6)
Map Scale Map Use
1:600 or larger
1:720to 1:1,200
1:2,400 to 1:4,800
1:6,000 and smaller
Engineering design
Engineering planning
General planning
Regional planning
some maps and digital databases suggest the type of
application for which they are appropriate (e.g., the
STATSGO digital soil database is suitable for state and
regional planning, whereas SSURGO is suitable for lo-
cal level planning). Tosta (30) cites an example of com-
bining wetland data with parcel boundaries to determine
ownership of the land containing a wetland. If wetland
mapping was done to plus or minus 100 feet positional
accuracy and parcels are 40 feet wide, then the scale of
the wetland map is inappropriate for determining if a
wetland is located on a specific parcel.
Identifying the optimal scale of the necessary data is
crucial because at some point, the cost of collection and
storage exceeds the benefits of increasing the map
scale. Lewis Carroll (1893) summed up the quest for
data mapped at an ever larger scale and the problems
associated with large-scale maps:
"What do you consider the largest map that would
be really useful?"
"About six inches to the mile."
"Only six inches!" exclaimed Mein Herr. "We very
soon got to six yards to the mile. Then we tried a
hundred yards to the mile. And then came the
grandest idea of all! We actually made a map of the
country, on the scale of a mile to the mile!"
"Have you used it much?" I enquired.
"It has never been spread out, yet," said Mein Herr.
"The farmers objected: they said it would cover the
whole country, and shut out the sunlight! So now we
use the country itself, as its own map, and I assure
you it does nearly as well."
Data Collected May Not Be Relevant for the
Intended Application
Frequently, using surrogate data is quicker or cheaper
than collecting needed data (e.g., Landsat imagery
rather than data field collection used to determine land
use) (4). The accuracy and classification scheme used
in collecting the data depends on the intended use of
the data, which may not coincide with the analyst's
purpose. For instance, soil maps were developed to aid
farmers in determining what crops they should plant and
for estimating crop yield. Soil maps, however, see wide
use for very different purposes (e.g., hydrologic and
other environmental models). In addition, STORETdata,
collected at points, are typically extrapolated to repre-
sent water quality in an entire stream stretch.
Data May Not Be Accessible Because
Use Is Restricted
An example of restricted data is Census data on individ-
ual households. An agency may not want to release data
that reveal the location of endangered species. Another
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example is that people may not even want the informa-
tion mapped. For example, some cavers do not want to
reveal the location of caves to the U.S. Forest Service,
which is charged under the federal Cave Resources
Protection Act with protecting caves, because they think
the best way to protect the caves is to not map them
(31). The National Park Service is putting the location of
petroglyphs in the Petroglyph National Monument into a
CIS. Making their location known to the public, however,
is troublesome because this may, in fact, encourage
their vandalism (32). Other problems in obtaining data
include difficulty in acquisition even if access is not
restricted, expensive collection or input, or unsuitable
format (4, 14).
Resolution of the A vailable Data
May Not Be Sufficient
Spatial resolution is the minimum distance needed be-
tween two objects for the equipment to record the ob-
jects as two entities; that is, resolution is the smallest
unit a map represents. To obtain an approximation of a
map's resolution, divide the denominator of the map
scale by 2,000 to get resolution in meters; for instance,
a 1:24,000-scale map has a resolution of approximately
12 meters (33).
Resolution relates to accuracy in that different map
scales conform to different accuracy standards. Two air
photos shot from the same camera at the same distance
above the ground have the same scale. If one photo has
finer grain film, however, smaller details are evident on
it, and this photo produces a map with higher resolution
(34). According to Csillag (33), analysts cannot simulta-
neously optimize attribute accuracy and spatial resolu-
tion. As spatial resolution increases, attribute complexity
increases (35). Also, the finer the spatial resolution, the
greater the probability that random error significantly
affects a data value.
Resolution of the data is not necessarily the same as
the size of a raster cell in a database. Statistical sam-
pling theory suggests using a raster cell size that is half
the length (one-fourth of the area) of the smallest feature
an analyst wishes to record. Raster data have a fixed
spatial resolution that depends on the size of the cell
employed, but a CIS analyst can divide or aggregate
cells to achieve a different cell size. Frequently, an
analyst transforms data collected at one level of resolu-
tion to a higher level of resolution than existed in the
original source material. According to Everett and Si-
monett (23), "Geographic analysis, however, can be no
better than that of the smallest bit of data which the
system is capable of detecting." Vector data are limited
by the resolution of input/output devices, limits on data
storage, and the accuracy of the digitized location for
individual points (36). The spatial resolution of the data-
base and the processes that operate on it should be
reduced to a level consistent with the data's accuracy.
The spatial resolution needed depends on the intended
use of the data, cost, and data storage considerations.
As resolution increases, so does the cost of collection
and storage. Resolution sufficient to detect an object
means that an analyst can reveal the presence of some-
thing. Identification, the ability to identify the object or
feature, requires three times the spatial resolution of
detection. Analysis, a finer level of identification, re-
quires 10 to 100 times the resolution that identification
needs (23). Increasing resolution increases the amount
of data for storage, with storage requirements increas-
ing by the square of the resolution of the data. For
example, if the resolution of the data needs to change
from 10-meter to 1-meter pixels, file size increases by 102
or 100 times (14).
Density of Observations May Be Insufficient
The density of observations serves as a general indica-
tor of data reliability (4). Users need to know if sampling
was done at the optimum density to resolve the pattern.
Burrough determined that boulder clay in The Nether-
lands could be resolved by sampling at 20-meter inter-
vals or less, whereas coversand showed little variation
in sampling from 20- to 200-meter intervals.
Some strategies for reducing data collection errors are to:
• Adhere to professional standards
• Allocate enough time and money
• Use a rigorous sampling design
• Standardize data collection procedures
• Document data collection procedures
• Calibrate data collection instruments
• Use more accurate instruments
• Perform blunder checks to detect gross errors
Documenting data collection procedures and distribut-
ing them along with data allows potential users to deter-
mine if the data are suitable for their purposes. By not
documenting procedures, errors in the source material
are essentially "lost" by inputting the data to a CIS, and
the errors become largely undetectable in subsequent
CIS procedures. The result is that agencies that make
decisions based on the CIS-generated map assume the
source data are accurate, only to discover later that the
map contains substantial errors in part due to errors in
the source material.
Operational Errors in a GIS
Data input, storage, analysis/manipulation, and output
can introduce operational errors. Digital maps, unlike
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paper maps, can accumulate new operational errors
through CIS operations (8). Even if the input data were
totally error-free, which the last section demonstrated is
not the case, CIS operations can produce positional and
attribute errors. The CIS operation itself determines to
a large extent the types of errors that result.
Input Errors
The process of inputting spatial and attribute data can
introduce error. The major sources of input error are
manual entry of attribute features and scanning or dig-
itizing spatial features. Manual entry errors include in-
complete entry of attribute data, entering the wrong
attribute data, or entering the right attribute data at the
wrong location. Digitizing errors originate from equip-
ment, personnel, or the source material (see Table 6).
Digitizing errors, such as under- and overshoot of lines
and polygons that are not closed, can introduce error
(see Figure 6). CIS software can "snap" lines together
that really do not connect. Depending on the tolerance
Table 6. Types of Digitizing Errors (4, 14, 37)
Personnel Errors
Changes in the origin
Incorrect registration of the map on the digitizing table
Creation of over- and undershoots
Creation of polygons that are not closed
Incomplete spatial data when data are not entered
Duplication of spatial data when lines are digitized twice
Line-following error (inability to trace map lines perfectly with the
cursor)
Line-sampling error (selection of points used to represent the map)
Physiological error (involuntary muscle spasms)
Equipment Errors
Digitizing table (center has higher positional accuracy than the
edges)
Resolution of the digitizer
Differential accuracy depending on cursor orientation
Errors in Source Material
Distortion because source maps have not been scale-corrected
Distortion due to changes in temperature and humidity
Necessity of digitizing sharp boundary lines when they are gradual
transitions
Width of map boundaries (0.4 mm) digitized with a 0.02-mm accu-
racy digitizer
Undershoot Overshoot Polygon Not Closed
Figure 6. Common digitizing errors.
selected, this can result in the movement of both lines,
which can decrease the accuracy of the resultant map.
Despite the long list of personnel errors associated with
digitizing, a good operator probably contributes the least
error in the entire digitizing process (38). Giovachino
discusses methods that can help determine equipment
accuracy, including checking the repeatability, stability,
and effect of cursor rotation. Digitizing accuracy var-
ies based on the width, complexity, and density of the
feature being digitized but typically varies from 0.01
to 0.003 (3).
One problem with digitized data is that the data can
imply a false sense of precision. Boundaries on paper
maps are frequently 0.4 mm wide but are digitized with
0.02-mm accuracy. The result is that the lines are stored
with 0.02-mm accuracy, implying a level of precision that
far exceeds the original data.
Minimizing digitizing errors is important because the
errors can affect subsequent CIS analysis. Campbell
and Mortenson (39) provide a list of procedures they
used to reduce errors associated with digitizing and
labeling:
• Use log sheets to ensure consistency and account-
ability, and to provide documentation.
• Check for completeness in digitizing all lines and
polygons.
• Check for complete and accurate polygon labeling.
• Set an acceptable RMSE term for digitizing (usually
0.003).
• Always overshoot rather than undershoot when
digitizing.
• Overlay a plot of the digitized data with the source
map to check lines and polygons. If light passes be-
tween the digitized line segment and source map,
redigitize it.
• Check digitized work immediately to provide feed-
back to the digitizer operator and to help identify and
correct systematic errors.
• Limit digitizing to less than 4 hours a day.
• Involve people in doing CIS-related jobs other than
digitizing to decrease turnover and increase the level
of experience.
Storage Errors
Data storage in a CIS usually involves two main types
of errors. First, many CIS systems have insufficient
numerical precision, which can introduce error due to
rounding. Integers are stored as 16 or 32 bits, which
have four significant figures. Real numbers are stored
as floating point numbers either in single precision (32 bit,
10
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7 significant figures) or double precision (64 bit, 15 or 16
significant figures). If the data in a CIS range from
fractions of a meter to full UTM coordinates, typical
32-bit CIS systems cannot store all the numbers. Using
double precision (64 bits) reduces this problem but in-
creases storage requirements.
Second, CIS processing and storage usually ignore
significant digits (data precision). As a result, the preci-
sion of CIS processing frequently exceeds the accuracy
of the data (40). When a CIS converts a temperature
recorded and entered as 70 degrees Fahrenheit (near-
est degree) to centigrade, the CIS stores the tempera-
ture as 21.111 degrees rather than 21 degrees, which
the significant figures in the original temperature meas-
urement would dictate. Using the accuracy of the data,
not the precision of floating point arithmetic, partially
resolves this but requires the user to make a special
effort because the CIS does not automatically track
significant figures.
Analysis/Manipulation Errors
CIS analysis/manipulation functions, designed to trans-
form or combine data sets, also can introduce errors.
These errors originate from the measurement scale
used or during data conversion (vector to raster and
rasterto vector), map overlay, generalization, converting
small-scale to large-scale maps, slope, viewshed, and
other analysis functions. One of the biggest problems
associated with CIS use is that data in digital form are
subject to different uses than data in paper form be-
cause the user has access to multiple data layers.
Measurement Scale
Four measurement scales can depict spatial data: nomi-
nal, ordinal, interval, or ratio scales. A name or letter
describes nominal data (e.g., land use type, hydrologic
soil group C). Performing mathematical operations such
as addition and subtraction on nominal data is meaning-
less. Ordinal or ranked data have an order to them such
as low, medium, and high. Interval data have a known
distance between the intervals such as 0, 1 to 5, 6 to 9,
more than 9. Ratio data are similar to interval data
except ratio data have a meaningful zero (e.g., tempera-
ture on the Kelvin scale).
Often during CIS operations, analysts convert interval
or ratio data into nominal data (e.g., low slope is 0 to
3 percent, medium slope is 4 to 10 percent), resulting in
a loss of information. Analysts should preserve the origi-
nal slope values in the CIS in case the user later wants
to modify the classification scheme. Robinson and
Frank (5) describe the tradeoff between information con-
tent and the meaning that can be derived from it, which
partly helps explain why interval data are frequently
converted to nominal data. The authors identify a con-
tinuum progressing from nominal data at one end that is
highly subjective, has low information content, and high
meaning (low slope means something to the average
user) to ratio data that has low subjectivity, high infor-
mation content, and low meaning (a slope of 7 percent
may not mean much to the average user).
Data Conversion
Errors can occur in converting a vector map to a raster
map or a raster map to a vector map. For instance,
remotely sensed data are collected using a raster-based
system. Using a vector CIS, however, requires conver-
sion from raster to vector data. The size of the error
depends on the conversion algorithm, complexity of fea-
tures, and grid cell size and orientation (13).
A line on a vector map converted to a raster map has
lower accuracy in the raster representation because
vector data structures store data more accurately than
raster ones. When polygons in a vector CIS are con-
verted to a raster CIS, the coding rule usually used
assigns the value that covers the largest area within the
cell of a categorical map to the entire cell (see Figure 7).
For example, when placing a grid over a vector map with
an urban land polygon adjacent to an agricultural poly-
gon, the cell placement can include part of both poly-
gons. If the resultant cell comprises 51 percent urban
and 49 percent agricultural land, the cell is assigned 100
percent urban. Converting a numerical map between
raster and vector systems requires spatial interpolation
procedures. CIS software packages use different inter-
polation methods that can produce a different output
even when using the same input data.
Vector Raster
Figure 7. Polygon conversion from vector to raster data.
Map Overlay
Map overlay, used extensively in planning and natural
resource management, is the combining of two or more
data layers to create new information. In a vector CIS,
slivers or spurious polygons can result from overlaying
two data layers to produce a new map (slivers cannot
be formed in a raster-based CIS). When combining the
data layers, lines do not coincide, resulting in the crea-
tion of a new polygon or sliver that did not exist on either
layer (see Figure 8). Unfortunately, as accuracy in digit-
izing increases, so does the number of slivers (41).
Positional error in the boundaries can occur because of
11
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o
o
Sliver
Figure 8. Sliver example.
mistakes in measuring or converting the data to digital
form, incremental expansion or recession of a real world
boundary over time, or the fact that certain boundaries
are difficult to determine and thus are generalized
differently (42).
The number of map layers, accuracy of each map layer,
and the coincidence of errors at the same position from
several map layers all determine the accuracy of the
map overlay procedures (43). Using probability theory,
Newcomer and Szajgin determined that the highest ac-
curacy to expect from a map overlay is equal to the
accuracy of the least accurate map layer. The lowest
accuracy in map overlay occurs when errors in each
map occur at unique points.
In the quest for more accurate results, CIS modelers
have increased the complexity of their models and
therefore have increased the number of data layers
needed. Guptill (44) states, "Conventional wisdom
would say that as you add more data to the solution of
a problem, the likelihood of getting an accurate solution
increases. However, if each additional data layer de-
grades the quality of the combined data set, and hence
the accuracy of the solution, then additional data sets
may be counterproductive."
Generalization
Monmonier (45) provides an extensive discussion of
geometric and content generalization procedures used
in map-making. Table 7 lists common types of generali-
zation. Generalizing data on a map helps to focus the
user's attention on one or two types of information and
to filter out irrelevant details. Generalizing is performed
by reducing the scale of the data; a 1:24,000-scale map
can be generalized to a 1:100,000-scale map so that all
data layers have the same scale. With generalizing,
areas on a large-scale map become point or line
features on a small-scale map (35). Obtaining some
measurements from small-scale maps, however, re-
quires caution. For example, a map may depict a
40-foot wide road as a single line one-fiftieth of an inch
wide. On a 1:100,000 map, one-fiftieth of an inch
translates into a 160-foot wide road—four times the
actual width of the road.
Several studies have pointed to errors that can result
from generalization. Wehde (46) compared soil maps
generated from 0.017-acre grid cells and 11 progres-
sively increasing grid cell sizes. He found that as grid
Table?. Generalization Operations (45)
Geometric Generalization
Generalizing a Line
Simplification
Displacement
Smoothing
Enhancement
Selection
Generalizing a Point
Selection
Displacement
Graphic association
Abbreviation
Aggregation
Area conversion
Generalizing an Area
Selection
Simplification
Displacement
Smoothing
Enhancement
Aggregation
Dissolution
Segmentation
Point conversion
Line conversion
Content Generalization
Selection
Classification
cell size increased, map accuracy decreased. More re-
cently, Stoms (47) found that generalizing a habitat map
from 1 to 25, 100, and 500 hectares decreased the
number of habitat types and the number of species
predicted.
Transforming Small-Scale Maps to
Large-Scale Maps
Converting small-scale maps (1:250,000) to large-scale
maps (1:24,000) is advisable only if the analyst fully
appreciates the effect of this procedure on map quality.
Data mapped at a small scale are subject to different
accuracy standards than data mapped at a large scale.
Connin (48) reports, "Problems with accuracy arise
when positions are reported to decimal parts of a foot or
meter, but the method of data capture may cause the
positional error to be as much as hundreds of feet or
meters." Yet when converting the data from small- to
large-scale, the data appear to have the accuracy of the
large-scale map. Theoretically, data should not be trans-
formed and used at a scale larger than the scale of the
document from which the data are derived (3).
12
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Slope andMewshed
CIS software packages use a variety of algorithms to
calculate slope and viewsheds and can produce very
different results. Algorithms are an unambiguous set of
rules or a finite sequence of operations used to carry out
a procedure. Smith, Prisley, and Weih (49) used six
different CIS algorithms to determine slope on 5,905
acres of land in order to calculate the amount of land
deemed unsuitable for timber harvesting. They found
that unsuitable land varied from 175 to 1,735 acres,
indicating that different algorithms produce very different
results. Felleman and Griffin (50) found that CIS pack-
ages with different algorithms generate alternate view-
sheds (the area that can be seen from a point).
Output Errors
A variety of errors are associated with data output:
• Output devices create error.
• Paper shrinks and swells.
• Line implies certainty that may not exist because
boundaries are gradual.
• A cell or polygon implies homogeneity.
• Scale can be modified to imply higher accuracy than
exists in the source data.
• Precision can be modified to imply higher precision
than exists in the source data.
• Depiction of symbols and colors may not follow
convention.
An important problem associated with CIS-generated
maps is that users make informal assessments about
data quality, partially based on how they perceive the
quality of the output. A hand-drawn map connotes a
lower level of accuracy than a five-color, CIS-produced
map complete with scale and agency logo. Another
problem with output is that distinguishing highly accu-
rate data from less accurate data is impossible on a
CIS-generated map. Users want the output from a CIS
to look like maps they usually see, perpetuating the
notion that lines mark exact boundaries and that poly-
gons or cells are homogeneous. Maps that federal map-
ping agencies produce frequently follow NMAS, but
CIS-generated maps seldom adhere to published map
accuracy standards. An agency could require that CIS
map products meet NMAS, which would establish and
maintain data standards from data collection to output.
A pen stroke of one-fiftieth of an inch on an output device
translates to an error of 40 feet on the ground for a
1:24,000-scale map (6). Small changes in paper maps
due to changes in temperature and humidity can repre-
sent several feet on the ground. As previously noted,
analysts can modify the scale of CIS maps to whatever
they desire. The basic rule of informational integrity is
that the implied precision of data output should not
exceed the precision (spatial, temporal, or mathemati-
cal) of the least precise input variable (26).
CIS-generated maps probably do not differ significantly
from paper maps in their implication that lines and poly-
gons on the map represent certainty and homogeneity.
CIS-generated maps, however, may not depict standard
symbols, sizes, shapes, colors, and orientation. For ex-
ample, paper geological maps use dashed lines to show
inferred, rather than actual, field collected data, but geo-
logical maps in a CIS may not follow the same conven-
tion (27). Cartographers conventionally use blue lines to
indicate water, but a CIS map-maker can show water as
red rather than blue.
Even more troublesome are the color schemes that
some analysts use in depicting model output. Analysts
often give little thought to assigning the colors to model
results depicted as ordinal rankings. For example, areas
of high erosion might be blue, medium erosion might be
red, and low erosion might be green. This selection of
colors ignores the intuitive meaning that people assign
to colors. It has been suggested that the color ordering
used in stop lights might provide a better option. In that
case, areas of high erosion would be red, medium ero-
sion would be yellow, and low erosion would be green.
Error Reduction Techniques
Although CIS users and researchers develop error re-
duction strategies, ultimately users must rely on CIS
software developers to implement new error reduction
techniques in CIS packages. Error reduction techniques
range from simple software warnings to prohibiting a
user from performing selected CIS procedures. Dutton
(51) predicts that future CIS programs will automate
data manipulation (i.e., size, format, and placement
of feature labels on maps) in keeping with standard
cartographic principles. Dutton (51) and Beard (8) also
predict that future CIS packages will enforce metadata-
based constraints such as operations that are illegal or
illogical (e.g., determining the average value of nominal
data such as land use), or are inadvisable (e.g., over-
laying maps with widely different scales).
Another change Dutton anticipates is that software ven-
dors will include information in manuals that explains
how executing a specific command may affect the data-
base. Graphic techniques to depict error are being de-
veloped for nonexpert users while experts tend to use
spatial statistics. Felleman (52) and Berry (53) present
an interesting graphic portrayal of an error map that may
indicate the future of error maps. Additional research
must determine what effect errors will have on decision-
making.
13
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Error Management
Ultimately, the decision-maker must determine what to
do with the information in this paper. A decision-maker
has a variety of possible courses of action, ranging from
prudent steps that attempt to minimize error and the
effect it has on decisions, to other less useful options.
Possible actions are to:
• Abandon use of a CIS.
• Ignore the error associated with CIS use.
• Attempt to collect "error-free" data.
• Determine if the data are accurate enough for the
intended purpose.
• Develop and use data quality procedures.
• Obtain and use an error report with CIS-generated
output.
• Ask that CIS-generated maps show potential errors.
• Continually educate users about the appropriate use
of spatial data.
First, the decision-maker could abandon any attempt to
use a CIS because of the errors associated with its use.
At times, this may be the appropriate strategy, but this
approach ignores the potential benefits associated with
CIS use.
Second, the decision-maker could ignore the error associ-
ated with CIS use and continue to use the CIS for deci-
sion-making. This type of "head in the sand" approach is
not advisable because of the potential liability associated
with making decisions based on inaccurate data.
Third, the decision-maker could engage in an expensive
and time-consuming effort to collect highly accurate er-
ror in hopes that error becomes a nonissue. Depending
on the intended use of the data, the cost of collecting
more accurate data may exceed the benefit.
Fourth, the decision-maker could assess whether the
information available is accurate enough for the in-
tended purpose. If data quality is too low, the decision-
maker may opt to collect new data at the desired quality.
If collecting additional data is not possible, the decision-
maker can explore what types of decisions are possible
given the attainable data quality. For instance, Hunter
and Goodchild (54) found that the data they were using
were suitable only for initial screening rather than for
regulatory and land-purchasing decisions.
Fifth, procedures to ensure high quality data could be
developed and used in the data collection, input, and
manipulation stages of building a CIS database.
Sixth, the decision-maker could require a quantitative or
at least a qualitative report on the sources, magnitude,
and effects of errors. The absence of an error report
does not mean the map is error-free (36). Dutton (51)
predicts that in the near future users of geographic data
will demand error reports, confidence limits, and sensi-
tivity analyses with CIS-generated output.
Seventh, the decision-maker could ask for CIS-generated
maps that adequately portray the error in the final map. For
example, areas where the uncertainty is high could appear
in red on maps. Another option is to place a buffer around
lines to indicate the relative positional accuracy of a line or
to show transition zones. Finally, an analyst can present
the output in ways other than a dichotomous yes or no;
instead, the analyst may use yes, maybe, or no depictions
or even more gradations.
Finally, Beard (8) introduced the concept of directing
efforts toward educating users about use error. She
defines use error as the misinterpretation of maps or
misapplication of maps to tasks for which they are not
appropriate. "We can't assume that CIS will automat-
ically be less susceptible to misuse than traditional
maps, and it may, in fact, exacerbate the problem by
expanding access to mapped information." Beard ar-
gues that money directed to reducing source and opera-
tional error, while important, may not matter if use error
is large.
Conclusions
CIS is a powerful tool for analyzing spatial data. Every-
one who uses CIS-generated output, however, must be
aware of source errors and operational errors intro-
duced during data input, storage, analysis/manipulation,
and output. Increased awareness of the sources and
magnitude of error can help decision-makers determine
if data are appropriate for their use. Decision-makers
cannot leave data quality concerns to CIS analysts be-
cause efforts to improve data quality are not without
cost, and the decision-makers typically control funding.
Decision-makers must not get caught up in the glamour
of the spatial analyses and outputs that a CIS can
produce. These attributes may lead decision-makers to
ignore issues associated with uncertainty, error, accu-
racy, and precision. Inexpensive digital data can make
analysts and decision-makers ignore data quality. If sub-
sequent management decisions are made based on
poor quality data, the resultant decisions may turn out
wrong. This would give decision-makers a jaded view of
the usefulness of CIS. An adequate understanding of
data quality issues can help decision-makers ask the
right questions of analysts and avoid making decisions
that are inappropriate given the data quality.
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16
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Expedition of Water-Surface-Profile Computations Using GIS
Ralph J. Haefner, K. Scott Jackson, and James M. Sherwood
Water Resources Division, U.S. Geological Survey, Columbus, Ohio
Abstract
Water-surface profiles computed by use of a step-back-
water model such as Water Surface PROfile (WSPRO)
are frequently used in insurance studies, highway de-
sign, and development planning to delineate flood
boundaries. The WSPRO model requires input of hori-
zontal and vertical coordinate data that define cross-
sectional river-channel geometry. Cross-sectional and
other hydraulic data are manually coded into the WSPRO
model, a labor-intensive procedure. For each cross sec-
tion, output from the model assists in approximating the
flood boundaries and high-water elevations of floods with
specific recurrence intervals (for example, 100-year or
500-year). The flood-boundary locations along a series of
cross sections are connected to delineate the flood-prone
areas for the selected recurrence intervals.
To expedite the data collection and coding tasks re-
quired for modeling, the geographic information system
(GIS), ARC/INFO, was used to manipulate and process
digital data supplied in AutoCAD drawing interchange
file (DXF) format. The DXF files, which were derived
from aerial photographs, included 2-foot elevation data
along topographic contours with +0.5-foot resolution
and the outlines of stream channels. Cross-section
lines, located according to standard step-backwater cri-
teria, were digitized across the valleys. A three-dimen-
sional surface was generated from the 2-foot contours
by use of the GIS software, and the digitized section
lines were overlain on this surface. GIS calculated the
intersections of contour lines and cross-section lines,
which provided most of the required cross-sectional ge-
ometry data for input to the WSPRO model.
Most of the data collection and coding processes were
automated, significantly reducing labor costs and hu-
man error. In addition, maps at various scales can be
easily produced as needed after digitizing the flood-
prone areas from the WSPRO model into GIS.
Introduction and Problem Statement
Losses due to flood damage generally cost the Ameri-
can public hundreds of millions of dollars annually. In
1968, the National Flood Insurance Act established the
National Flood Insurance Program (NFIP) to help re-
duce the cost to the public and provide a framework to
help reduce future losses. The Federal Emergency Man-
agement Agency (FEMA) administers the NFIP. As listed
in Mrazik and Kinberg (1), the major objectives of the
NFIP are to:
• Make nationwide flood insurance available to all com-
munities subject to periodic flooding.
• Guide future development, where practical, away
from flood-prone areas.
• Encourage state and local governments to make ap-
propriate land use adjustments to restrict develop-
ment of land that is subject to flood damage.
• Establish a cooperative program involving the federal
government and the private insurance industry.
• Encourage lending institutions, as a matter of national
policy, to assist in furthering program objectives.
• Authorize the continuing studies of flood hazards.
Studies of flood-prone areas typically involve using
step-backwater computer algorithms (digital models) to
estimate river water-surface profile elevations and flood-
inundation patterns along the topography of the river
and its overbanks. FEMA recognizes the U.S. Geologi-
cal Survey's (USGS's) step-backwater model, Water
Surface PROfile (WSPRO), as a suitable computer
model for use in flood insurance studies (2, 3). Basic
data input for step-backwater models includes:
• Estimates of flood discharge and initial water-surface
elevations.
• Stream cross-sectional geometry.
• Roughness coefficients for cross sections.
-------
• Contracted opening geometry if bridges or culverts
are located along the study reach.
Obtaining meaningful model results typically requires
numerous stream cross sections referenced to a com-
mon elevation datum along a stream reach. The data-
collection efforts to obtain these cross-sectional data
require costly, labor-intensive fieldwork. Study efforts
along lengthy stream reaches may, however, involve the
generation of a contour map using aerial photogram-
metric mapping techniques. Processing the spatial data
may still require extensive labor to extract the cross-
sectional data needed for the WSPRO model.
The development of geographic information systems
(CIS) technology has greatly enhanced analyses of spa-
tial data such as topography. In an effort to improve the
quality of mapping and delineation of flood-prone areas
in Summit County, Ohio, the USGS developed a method
of using a CIS as a pre- and postprocessor of the input
and output data for the WSPRO model. This paper
describes the steps the USGS used to develop this
interface and discusses some difficulties encountered
during the process.
Approach
Several steps were taken that resulted in the delineation
of a flood-prone area in Summit County, Ohio. These
steps are shown in a flow chart (see Figure 1) and
described below.
Data were obtained for this study via aerial photography
during April 1990. These data include mappable fea-
tures at the given scale including topography at 2-foot
contour intervals, stream boundaries, roads, and build-
ings. The data are estimated to be vertically accurate to
+0.5 feet. The data were put into AutoCAD and were
prepared for delivery to the USGS on 3.5-inch floppy
disks in AutoCAD drawing interchange file (DXF) ASCII
format. ARC/INFO was used to convert the DXF file into
two separate data layers containing only the topography
and traces of stream banks within the study area.
A three-dimensional surface was generated from the
topographic data using the ARC/INFO software package
Triangulated Irregular Network (TIN). Cross-section
lines were digitized over the topography data layer. The
cross sections were placed according to standard step-
backwater criteria (4) and were generally:
• Perpendicular to stream flow
• At major breaks in streambed profiles
• At minimum and maximum cross-sectional areas
• At major changes in stream conveyance
• Spaced about one cross-section width apart
Obtain
Aerial
Photography
Put Data in DXF Format
Process DXF Data With CIS (ARC/INFO)
Establish Relation of Attribute Data to Spatial Data
Generate Three-Dimensional Surface
Overlay Cross Sections and
Calculate Intersections
Verify Elevations With Topographic Controls
Generate Input Files for Model With
Cross-Section Data
Use WSPRO Model To Compute Water-Surface
Elevations for Each Cross Section
Plot Model Output on Topography Data Layer and
Connect Endpoints of Cross Sections
Figure 1. Flowchart of data conversion and processing for use
in the Water Surface PROfile.
The cross-section lines were then overlaid on the three-
dimensional surface of topography, and CIS calculated
the intersections of the contour lines and cross sections.
The locations and elevations of these intersections were
output as an ASCII file and slightly modified for input into
the WSPRO model.
These CIS data were used along with the aforemen-
tioned required data as input to the WSPRO model.
Input for the model included estimates of the 100-year
flood discharge (5), stream cross-sectional geometry
(supplied by this work), and estimates of roughness
coefficients for cross sections. The WSPRO model was then
run, providing output in the form of water-surface eleva-
tions at specific distances along section lines correspond-
ing to the simulated elevation of a 100-year flood.
Points corresponding to the flood elevations along the
cross-section lines were plotted on the topography data
-------
layer and were connected manually (to delineate flood
boundaries) by interpolating the elevations with respect
to adjacent contours. A polygon of the flood "surface"
was generated and drawn on a map (see Figure 2).
Results
The supplied topographic data were of sufficient quality
and resolution to substitute for field-surveyed eleva-
tions; however, field surveys to verify the elevations
along the cross sections would augment this quality
control process (see Figure 1). Typically, a crew of two
individuals may take up to 4 days to survey and reduce
the field data for the study area chosen for this study.
Because aerial photography is commonly substituted for
land surveying, the most significant effort and source of
error may come from manually extracting elevations and
distances along cross sections for input into the WSPRO
500
I
1,000
11.1.
I|MM|MM|IIM|ITIT[
100 200 300 400 500 Meters
1,500 2,000 2,500 Feet
Legend
100-Year Flood-Prone
Area
Topographic Contours
(Contour Labels Omitted
for Clarity; Contour
Interval 2 Feet)
Cross-Section Lines
Stream-Bank Trace
Intersection of 100-Year
Flood-Prone Area and
Cross-Section Line
Figure 2. Watershed showing delineation of 100-year flood-prone area.
-------
model. Initial development of the method to use CIS for
this analysis took approximately 1 week to refine; how-
ever, future analyses would probably only take one per-
son 1 day to perform. This represents a significant cost
savings. Additionally, reducing the amount of human-in-
duced error can substantially improve the reliability and
accuracy of the computer-generated flood-prone area
data.
Because topography, stream traces, and other features
are supplied in the DXF file, these data can easily be
brought into CIS. Maps can be made that show these
features in relation to the predicted flood-prone area.
Maps showing a variety of features can be produced at
any scale, with accuracy limited only by the accuracy of
the source scale. Additionally, CIS can calculate the
intersection of map features that may lie within the
flood-prone area, such as buildings that may contain
hazardous materials. CIS can also overlay land use
data layers within the flood-prone area to define areas
that should not be developed or that have already been
overdeveloped in accordance with the aforementioned
NFIP objectives.
FEMA now requests that future flood-study mapping be
completed using CIS format, a common goal that both
the USGS and FEMA are working toward. These data
are important to land planners, flood-plain regulators,
and insurance companies that rely on accurate esti-
mates of flood-prone areas. By increasing the accessi-
bility of the data by using CIS, we can substantially
improve our ability to analyze spatial data efficiently.
Problems Encountered
Problems using data supplied in DXF format in conjunc-
tion with CIS resulted primarily from the fact that the
DXF data were prepared for the purpose of making a
topographic map, not a CIS data layer. The contour lines
were segmented; that is, where ends of segments met,
they were not physically connected to form a topologi-
cally viable data layer. The data layer needed to be
edited because CIS requires topology for spatial-data
processing. Additionally, in areas where the topographic
gradient was particularly steep, contour lines were omit-
ted. In both cases, an attempt was made to allow CIS
to establish a physical connection of contour lines, but
subsequent manual interpolation was also required.
This may have introduced error into the data set. If future
work requires the use of DXF data, the request for data
should specifically state that all topographic contours be
continuous.
AutoCAD stores data differently from CIS, so a relation-
ship needed to be established between the data file
containing elevations and the data file associated with
the lines that make up the topography data layer. Sev-
eral lines from the DXF file did not have any data asso-
ciated with them, thus necessitating the addition of
contour elevation data by context with the adjacent con-
tours that did have data. This step may also have intro-
duced errors, but quality-control measures to verify the
topographic contours and contour elevations could help
to minimize these errors.
Output from the WSPRO model is in the form of a series
of points along cross sections that were connected by
manual interpolation. This step also may introduce some
error, but the same process must be performed when
not using CIS.
Conclusions
This report documents an example of how CIS can be
used to facilitate step-backwater modeling of flood-
prone areas. The results of the study show that signifi-
cant savings may be expected in the form of reduced
labor requirements. Furthermore, FEMA now requires
the use of CIS to conduct flood-study mapping, thus
providing a means to conduct additional spatial analy-
ses more efficiently. As aerial photography and CIS
technology improve, although additional sources of error
may arise, the overall accuracy, reliability, and repro-
ducibility of the model input and results should also
improve.
References
1. Mrazik, B.R., and H.A. Kinberg. 1991. National flood insurance
program: Twenty years of progress toward decreasing nationwide
flood losses. Water Supply Paper 2375. U.S. Geological Survey.
2. Shearman, J.O., W.H. Kirby, V.R. Schneider, and H.N. Flippo.
1986. Bridge waterways analysis model. Research Report
FHWA/RD-86/108. Federal Highway Administration.
3. Shearman, J.O. 1990. Users manual for WSPRO, a computer
model for water-surface profile computations. FHWA-IP-89-027.
Federal Highway Administration.
4. Davidian, J. 1984. Computation of water-surface profiles in open
channels: Techniques of water-resources investigations of the
United States Geological Survey. In: Applications of Hydraulics,
Vol. 3.
5. Koltun, G.F., and J.W Roberts. 1990. Techniques for estimating
flood-peak discharges of rural unregulated streams in Ohio. In-
vestigations Report 89-4126. U.S. Geological Survey, Water Re-
sources.
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Reporting on the Development of an Environmental GIS Application
Wetlands Restoration in the Central Valley of California
David T. Hansen1
Introduction
Appropriate documentation of information used in addressing environmental problems is a
common issue. This paper focuses on information supporting geographic information system
(GIS) applications on wetlands in the Central Valley of California. It identifies the role GIS
played in the preparation of a report to Congress on wetlands and water supply in the Central
Valley. It also describes the role that GIS is playing in the dissemination of information on
wetlands and potential wetland habitat development to communities and groups in the Central
Valley. Information or data documentation are major elements supporting this data and these
GIS applications. For GIS data, this information is commonly referred to as metadata in
conformance with the "Content Standards for Digital Geospatial Metadata (FGDC, 1994 and
1998).
GIS is an integrative technology. It typically has involved specialists from a variety of disciplines
who associate and integrate different data sets into a spatially referenced system. The
development of GIS data typically follows a series of steps:
1. Based on a conceptual model of the environmental issue, geographic features or data of
interest are identified.
2. These features are mapped or the data is spatially referenced in a map coordinate
system.
3. The mapped features or data are digitally captured and processed in a software system
to create a GIS data theme containing a graphical representation of the feature and
associated information as attributes.
4. This digital data are reviewed and assessed as to fitness for the application.
5. The digital data is then ready for display, query, and analysis with other digital data for
that geographic area.
1 U.S. Bureau of Reclamation, Mid Pacific Region, Dept. of the Interior, 2800 Cottage Way, Sacramento,
CA 95825-1898; (916) 978-5268; (916) 978-5290 FAX; dhansen@mp.usbr.gov
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6. The resulting data output from GIS analysis then is again reviewed and assessed
against the requirements for the application.
7.
Increased speed and capacity of computer systems and the development of graphical user
interfaces (GUI) have brought GIS and geospatial analysis to the computer desktop.
Technologies such as global positioning systems (GPS), remote sensing, and scanning
technologies have combined some of these steps and shortened the time required for digital
data development. Many of these steps which were essentially GIS back office operations can
now be performed by managers and increasingly the public. This has enabled managers and
the public to access and use data in a map or geographic format to address environmental
problems.
In the case of the Central Valley Joint Venture, desktop GIS has permitted the direct
involvement of managers and partners in data development and GIS analysis. Desktop GIS
applications developed as part of this program permit analysis of the data at the local level with
community groups. Many GIS applications are robust enough to permit the loading and use of
locally developed data in modeling environmental systems. Providing information supporting this
data and the GIS application are critical in the dissemination of the data and application down to
the local level. This case study will follow these steps identifying the information needed by the
Central Valley Joint Venture partners for evaluating the data for application and use. This
information is metadata and terms used will follow the elements of the "Content Standards for
Digital Geospatial Metadata" (FGDC, 1994 revised 1998). However, this represents only a
subset of the elements in these standards. The relationship between FGDC metadata elements
and GIS data development are further described in a draft guides for documenting the
development of a GIS data theme (Hansen, 1998) and reporting metadata for data
management, data catalogs, and data transfer (Hansen, 1998).
Conceptual Model - Identification of Data Requirements
The Central Valley Joint Venture was established as part of the North American Waterfowl
Management Plan signed by Canada and the United States in 1986. In 1990, the Central Valley
Joint Venture issued an implementation plan (CDFG, 1990) for wetland habitat restoration and
enhancement. Wetland and adjacent upland habitat are important wintering areas for waterfowl
in the Pacific Flyway. By the mid 1980's, waterfowl populations were approaching 30 percent of
long term averages. Much of this decline is associated with the loss of wetland habitat since the
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turn of the century. The Central Valley is a semi arid area. Successful wetland habitat
restoration in the valley requires a dependable and adequate water supply. Water supply for
wetlands must be balanced against environmental requirements as well as water requirements
use for agriculture and municipalities. The Central Valley is a major agricultural producing area
for California and the nation. Surface water flowing primarily from snow melt in the Sierra
Nevada mountains provides approximately 70 percent of the water used for agriculture and
municipalities in the State. The plan attempts to balance water requirements for wetlands,
agriculture and municipal use without impairing the supply for other aquatic and terrestrial
species.
In 1995, the Central Valley Joint Venture partners prepared a statement of work for a report to
Congress based on the implementation plan (USFWS, 1995). Main topics for this report are the
identification of methods for improving the reliability of water supply for existing private wetlands
and identification of water requirements for an additional 120,000 acres to be restored to
wetland habitat in the Central Valley. As part of the statement of work, the cooperator in the
report was to incorporate any appropriate data into a desktop GIS.
The statement of work identified a variety of information needed for the report. Much of this was
geographic in nature such as the location of existing wetlands, lands under wetland or
conservation easements, and lands suitable for wetland habitat development. Information was
also identified that was not strictly geographic in nature such as the identification of constraints
affecting the protection or restoration of wetlands or the reliability of water supply for wetlands.
The statement of work recognized that not all information required to address issues in the
report were suitable or could be developed in time for analysis as GIS data themes. It
recognized that other tools would be needed to address some issues for the report to Congress.
As part of the statement of work, metadata on the collected data was to be provided by the
contractor. The statement of work, itself, provided some of the initial information called for in the
"Content Standards for Digital Geospatial Metadata" (FGDC, 1994 and 1998). The statement of
work and the implementation plan prepared in 1990 form the basis of the conceptual model for
identifying data and information requirements. Data themes were identified and the purpose for
collecting this information. The time period of data content, target source scale (1:24,000), and
GIS data format and system were identified. Information not explicitly identified in the statement
of work included actual data sources, map coordinate system, and method of coordinate control.
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The statement of work was a working document which was revisited as information was
developed to adjust for changes in the availability of data for the report.
Mapping, Digital Capture, and Database Development
Mapping, digital capture, and database development are distinct process steps. Increasing with
new technologies, these steps occur concurrently. Such a theme for the partners in the Joint
Venture was the identification of existing wetland habitat on the valley floor. The program
participated in a joint project with other organizations which identified wetlands, riparian habitat,
and other land use (DU, 1997). This GIS theme achieved a minimum resolution for wetlands at
about 0.8 hectares (2 acres) using remote sensing techniques. Many of the other GIS data
themes were already mapped independently of the program requirements. The cooperator
following the statement of work digitally captured the features represented on these maps,
merged separate sources for a particular theme together, and constructed databases of
attributes for the digital features.
Focus areas or areas for analysis is another GIS theme that required definition, mapping, and
digital capture for the Joint Venture partners. The floor of the Central Valley covers
approximately 4 million hectares (10 million acres). This was too large an area for data
development within the time constraints of the program. One of the initial tasks of Joint Venture
was to narrow the focus of data collection efforts to smaller areas within the Valley floor. The
wetland habitat GIS theme as it was being developed and the personal knowledge of the Joint
Venture Partners assisted in identifying focus areas for intensive data collection efforts. The
resulting focus areas represent approximately 0.8 million hectares (2 million acres) of the valley
floor. These areas include virtually all the areas of existing Public and private wetlands.
For the partners in the Joint Venture Program, digital capture and database development
represented the black box phase of GIS data development. Key information from this stage for
the Joint Venture partners included the sources of data for the GIS themes, definition of criteria
used in digitally capturing the features, database definitions, and criteria or rules for classifying
the attributes of those features. Since the area of interest or focus areas for Joint Venture cover
such a broad area, multiple sources of data were required for each GIS theme. Often, these
sources represented different time periods for mapping. Different sources also raised issues of
consistency in mapping criteria and in attributes identified for the mapped features.
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Evaluation of Digital Data for Application and Use
Using the desktop GIS, information collected and digitally captured as a GIS data theme could
be reviewed directly by the Joint Venture partners. This evaluation occurred repeatedly during
data development. This was helpful to the Joint Venture partners as well as the GIS data
developer. Managers as well as staff could directly evaluate the data against the issues
identified in the statement of work and their own knowledge of the area. GIS analysis could be
interactively performed to evaluate the various data themes for addressing issues required in
the report. This review identified GIS themes that were not useful in addressing the issues. The
Joint Venture could then focus attention on other methods for developing information to address
those issues. Surrogates to represent information that could not be directly represented in GIS
could be addressed and evaluated. The Joint Venture partners could visually review:
• Extent of coverage of a particular GIS theme for the areas of interest,
• Data gaps between GIS themes for the same area,
• Attributes carried by the GIS themes and the definitions for those attributes, and
• Attributes relationship to the issues identified for the report.
Information that could not be directly displayed in GIS were:
• Sources used to construct each GIS theme,
• Time periods represented by the GIS theme,
• Consistency of a GIS theme for all areas of the Valley, and
• Criteria used in classifying the attributes of the GIS themes.
This metadata was not available at this stage to the Joint Venture partners.
This information represents a subset of information identified in the "Content Standards".
Although available to the data developer, this information was not in a form easily provided to
the data users. While the data provider had some experience in spatial data capture, the
provider had little experience in working directly with data users and in recognizing information
that they might need. The data developer was deferring metadata compilation until the end of
the data development. The "Content Standards" had been recently adopted by FGDC and the
data provider had little experience in addressing and applying the standards. This hindered the
evaluation of the GIS data by the Joint Venture partners.
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GIS Display, Query, and Analysis and Evaluation of Analysis
For the Joint Venture program, GIS analysis and evaluation occurred concurrently. With the GIS
desktop application, the partners in the Joint Venture program were involved directly in applying
GIS to address some of the issues for the report. This included display, query and reporting of
the following information for the report:
• Location and extent of public managed wetlands,
• Private lands under easements for wetland habitat and conservation,
• Private lands managed for waterfowl or duck clubs, and
• Major water supply agencies for those lands.
The Joint Venture partners ran a variety of different scenarios using the desktop GIS to identify
lands suitable for wetland habitat restoration. These scenarios were based on criteria defined
and run by the Joint Venture partners at their meetings. These scenarios were primarily based
on the following GIS data themes:
• Soil characteristics suitable for wetland habitat development,
• Land use,
• Existing Publicly managed wetlands,
• Lands with easements for wetland habitat or wildlife conservation, and
• Private wetlands.
A variety of other GIS data themes were available for display with these themes for review with
the results of the scenarios.
The Joint Venture partners could evaluate the results of the scenarios for issues required in the
report. Criteria for the scenarios could be evaluated and adjusted to meet specific needs for
different areas of the Central Valley. At the time of these meetings, the desktop GIS was not
exploited to the full in documenting the various scenarios that the participants posed.
Documentation of the final scenarios do form the basis for the some of information reported to
Congress.
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Application Development - Public Outreach
Several of these GIS data themes and other data developed in concert with the Joint Venture
program led to the development of a stand alone application. This application contains several
GIS data themes and software for assessing the suitability of areas for wetland habitat
restoration (Ducks Unlimited, 1998). The application is basically a modeling tool for ranking and
weighting various GIS themes as to their suitability for developing and maintaining wetland
habitat. The user selects the themes that they want to use in the analysis, weights or assigns
values contained in the attribute table, and ranks the theme on basis of its importance for
wetland habitat. The application converts the themes into a raster data structure and combines
the raster data sets into new data set or surface whose cell values represent suitability of that
cell for wetland habitat. The user selects the cell size for processing. This application has been
issued on a CD-ROM containing GIS data sets, metadata, and some sample scenarios. The
data on the CD requires desktop GIS software, but the application is open in the sense that
other spatial data can be loaded and used in the analysis.
This tool was developed not only to assist offices of the individual partners in the Joint Venture
program but also as an outreach tool to local community groups and for education. This
application provides the opportunity to address issues at the local level. Locally developed as
well as other data can be used in the application. Metadata describes the GIS data themes and
a user guide has been prepared for using the application. While metadata is included with the
GIS data themes, it is expected that this information will not address all questions posed by the
users of the data. Describing the GIS application following the "Content Standards of Digital
Geospatial Metadata" is beyond the scope of these standards. As is true for most guides, the
guide for the application focuses on the mechanics of operating the application. The guide
probably does not address all questions on how the various themes could be ranked or
weighted.
The local user is responsible for evaluating the results of running the analysis. It is up to the
user to decide on the cell size for appropriate for the selected GIS themes. The user evaluates
the resulting output surface against the cell size used, themes selected, and the values
assigned to the themes. The guide can not answer all questions on what spatial analysis is, how
the model process actually runs, or what uncertainty can be assigned to the results of analysis.
To some users, it will appear to be another black box. Access to additional information can be
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provided by citations to other documents and providing contact information for individuals and
organizations involved with the data or the application.
Summary
This has been a review of the information needed at different stages in the development of GIS
data and applications for the Central Valley Joint Venture Program of California. Partners in
Joint Venture were able to evaluate the data, to run spatial analysis, and to review results from
multiple scenarios. Issues were identified that could not be adequately addressed as GIS data
themes. Resources could then be focused on other methods to develop this information. Some
of these GIS themes along with other GIS themes supported the development of an
independent GIS application. With this application, community groups and the public can
develop their own scenarios for identifying wetlands at the desk top level.
The development of relatively easy to use desktop GIS software provides the opportunity for
final data users to be directly involved in data evaluation and spatial data analysis. New and
improved technologies have compressed the steps and time required for spatial data
development and the incorporation of that data into GIS applications. Environmental GIS
applications are increasingly available for use by local communities and the public. Many are
robust enough to permit the loading of locally developed data. This represents current trends as
described in "The Future of Spatial Data and Society" (NRC, 1997). Under this trend, spatial
data proliferate rapidly and the tools to use this data are widely available. Under this scenario,
key issues are education or training in using spatial data and access to information to evaluate
and apply the data. Lack of adequate information can to lead to increased uncertainty in the use
of GIS data and increased litigation.
Joint Venture partners were hindered in their evaluation and application of the GIS data themes
because some information was not available at their meetings. This information was not
available because the data developer lacked experience in working concurrently with users in
developing and applying data. As these applications are reaching down to community groups
and the local level, the GIS users are further removed from data producers and application
developers. To effectively run these applications, GIS data users can be expected to need more
information than can be easily contained in our existing metadata descriptions or in a user
guide.
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Documentation during this development is critical for the effective application and use of the
data or the application. The "Content Standards for Digital Geospatial Metadata" (FGDC, 1994,
revised 1998) provides a lexicon of commonly accepted terms for describing GIS data. It
identifies what metadata should accompany the digital data when it has been completed and
transferred. It does not address how metadata is to be developed or presented during data
development. These standards are a comprehensive list of elements of metadata for geospatial
data but they are not exhaustive. These standards do not address the development of GIS
applications for use at the local level.
We as GIS data or application developers need to be directly involved in the application and
development of these standards. The involvement of data developers and application
developers is needed for guides on the development and use of GIS data and applications. The
Environmental Protection Agency has been an active participant in the development of many
standards related to environmental quality (Johnson, 1996). For environmental data, there are a
variety of standards and guides often specific to a particular discipline supporting the data
collection and modeling efforts. The "Content Standards for National Biological Information
Infrastructure Metadata" (USGS-BRD, 1995) has been issued for describing biological data. The
process of developing of GIS environmental applications is similar to the development of a
ground-water model to a site specific application (ASTM D5979) and the steps followed in
environmental site characterization (ASTM D5730). For water quality and monitoring, there are
a host of commonly adopted standards. This paper is offered to continue discussion and to
encourage involvement in the development of guides for describing environmental data and
applications using GIS.
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Bibliography
ASTM, 1996, "D 5979 - Guide for Conceptualization and Characterization of Ground-Water
Flow Systems" ASTM, 100 Barr Harbor Drive, West Conshohocken PA 19428-2959.
ASTM, 1996, "D 5730 - Guide for Site Characterization for Environmental Purposes with
Emphasis on Soil, Rock, the Vadose Zone and Ground Water" ASTM, 100 Barr Harbor Drive,
West Conshohocken PA 19428-2959.
ASTM, 1996, "D 5714 - Specification for Content of Digital Geospatial Metadata" ASTM, 100
Barr Harbor Drive, West Conshohocken PA 19428-2959.
California Department of Fish and Game, California Waterfowl Association, Ducks Unlimited,
February, 1990, "Central Valley Habitat Joint Venture Implementation Plan", Sacramento
California (CDFG, 1990).
Ducks Unlimited, January, 1997, "California Wetland and Riparian Geographic Information
System Project', Prepared for California Department of Fish and Game, California Wildlife
Conservation Board, U.S. Bureau of Reclamation; Sacramento, California (DU, 1997).
Ducks Unlimited, November 1998, "Central Valley Project Improvement Act GIS Model Version
1.0, User's Guide", Sacramento, California (DU, 1998).
Federal Geographic Data Committee, June 1994 (revised 1998,), "Content Standards for Digital
Geospatial Metadata", Washington, D.C. (FGDC 1994 and 1998).
Hansen, David T., 1998, "Guide for Documenting the Development of Geospatial Data for Site
Investigations", Draft for review by ASTM Technical Sub Committees of D18, 100 Barr Harbor
Drive, West Conshohocken PA 19428-2959 (Hansen, 1998)
Hansen, David T., 1998, "Guide on Reporting Geospatial Metadata for On-line Data
Management, Data Catalogs, and for Data Transfers", Draft for review by ASTM Technical Sub
Committees of D18, 100 Barr Harbor Drive, West Conshohocken PA 19428-2959 (Hansen,
1998)
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Johnson, A. I., 1996, "The Accelerated Development of Standards for Environmental Data
Collection", Sampling and Environmental Media, ASTM STP 1282, James Howard Morgan Ed.,
ASTM, 100 Barr Harbor Drive, West Conshohocken, PA 19428-2959 (Johnson, 1998).
Mapping Sciences Committee, National Research Council, 1997, "The Future of Spatial Data
and Society: Summary of a Workshop", National Academy Press, Washington, D.C. (NRC,
1997).
U.S. Fish and Wildlife Service, 1995, "Scope of Investigations and Plan of Work under
Cooperative Agreement for Wetland Water Supply Investigations", Sacramento California.
(USFWS, 1995).
U.S. Geological Survey, Biological Resources Division, 1995, "Content Standard for National
Biological Information Infrastructure Metadata", formerly National Biological Service,
Department of Interior, Washington, D.C. (USBS-BRD, 1995).
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Using a Geographic Information Systems Application to Implement Risk
Based Decisions in Corrective Action
Lesley Hay Wilson, P.E., Andrew P. Romanek, and David R. Maidment, Ph.D., P.E.,
The University of Texas at Austin
James R. Rocco, Sage Risk Solutions LLC
Abstract
The implementation of site-wide corrective action using risk-based decision making at large and
complex industrial, energy or defense facilities presents a number of challenges. To address these
challenges, a spatial environmental risk assessment methodology has been developed by
connecting Geographic Information Systems (GIS), relational databases and spreadsheets. The
methodology is based on a description of the facility and a site conceptual model. A case study site
with multiple potential sources, transport mechanisms and receptors has been used to evaluate the
methodology. The case study facility is an approximately 300-acre crude oil refinery and petroleum
products terminal that has operated since the early 1900's. This paper will discuss the development
of two key elements of the spatial environmental risk assessment, the Digital Facility Description and
the Spatial Site Conceptual Model using the GIS application.
Introduction
Risk-based decision making provides a mechanism to determine the necessary and cost-efficient
strategies for protection of human health and the environment. It is an iterative process that begins
with a planning phase that incorporates risk management decisions with a site conceptual model.
The process then proceeds to an evaluation phase where data collection, and fate and transport
analysis, provide the basis for evaluation of the exposure pathways identified in the site conceptual
model. Finally, the process ends with a decision phase in which the plan is compared to the results of
the analysis to determine whether further evaluation or remedial action is warranted or if "no further
action" is appropriate. Risk-based decision making requires the interrelationship of three basic
processes: risk assessment, risk management and risk communication.
Risk Assessment
Risk assessment is a process that quantifies the potential for adverse effects to human health and
the environment caused by an exposure to a chemical of concern released to the environment.
Where there are no current or potential exposures to a chemical of concern, or where the
concentration of a chemical of concern is not harmful to human health or the environment then, the
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risk assessment will conclude that there is no unacceptable risk. Risk assessment is accomplished
by collecting information to construct exposure hypotheses for a chemical of concern, or a group of
chemicals of concern, and evaluating those hypotheses to determine the potential for adverse effects
from human or ecological exposures to chemicals of concern in the environment. This process is
based on the National Academy of Sciences Risk Assessment paradigm (MAS, 1983).
Effective risk assessment is based on several important activities. First, a comprehensive site
conceptual model is needed to provide the working hypothesis for a site. The site conceptual model
is the understanding of the potential exposure pathways based on the chemical characteristics and
the physical setting of the site. In order for the site conceptual model to be comprehensive, it needs to
identify all of the potential exposure pathways and be updated, as new information becomes
available. It provides the mechanism for determining the necessity and scope of data collection, and
a template for evaluating exposure pathway completeness. Second, effective data collection is
needed to evaluate the exposure pathways identified in the site conceptual model. Data collection
can be both qualitative (e.g., location of source areas, historical release information) or quantitative
(e.g., concentration of chemicals of concern in environmental media, hydrogeological characteristics).
It requires spatially defined and relationally organized property characteristics such as physical
features, and information related to chemical and media characteristics such as analytical results and
hydrogeological information. Data collection needs to be focused on developing and updating the site
conceptual model, evaluating exposure pathways, determining appropriate initial response actions
and comparing site conditions to the corrective action goals. Third, the value of the information that is
being collected must be considered. Only the quantity and quality of data necessary to provide a
sound basis for the decisions to be made should be collected. Collecting data for the sake of more
data is not necessary or cost-effective. Data collection must consider the use of the data to be
collected and the potential for that additional data to change the decisions that will be made. Fourth,
the fate and transport of chemicals of concern in the environment needs to be considered when
evaluating exposure pathways. However, the results of fate and transport analysis must be confirmed
through the collection of empirical data. Finally, "no further action" is not always the appropriate result
of a risk assessment. Interim remedial action, remedial action as well as further evaluation are
alternatives to be considered.
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Risk Management
Risk management decisions are necessary to support site-specific determinations that are protective
of human health and the environment and to provide a means for similar decisions to be made for
similar circumstances. Many risk management decisions rely on the application of scientific
methodologies such as the determination of the chemicals of concern to be considered in the risk
assessment; the appropriate toxicity factors for the chemicals of concern; and the appropriate data
quality and quantity. Other risk management decisions rely on non-scientific factors for definition such
as the determination of a process for stakeholder involvement; an approach for ground water
resource and use; and a consistent set of criteria for the objective comparison of alternatives. Clearly,
a risk-based decision cannot be made without having defined the appropriate risk management
decisions.
Risk Communication
Risk-based decision making requires the identification and involvement of the individuals,
organizations and other entities that are directly affected by the corrective action process, commonly
referred to as the stakeholders. Since risk management decisions are necessary to support
determinations that are protective of human health and the environment and require the
consideration of a combination of scientific, social, political, personal and economic factors, it is
critical that the risk management decisions are acceptable to most if not all of the stakeholders. In
addition, the application of the risk management decisions within the risk assessment process must
be clearly understood and accepted by all of the stakeholders. Therefore, early, effective and regular
risk communication is critical to the successful implementation of risk-based decision making.
Risk communication, however, has been the most overlooked component of risk-based decision
making and all too often undertaken after the evaluation has been completed and the decisions have
been made rather than as part of the process. When applying risk-based decision making there will
typically be a number of alternatives for solving or addressing the environmental condition of a
property. Each alternative will have characteristics that define its benefits and its risks to the
stakeholders. There will also be stakeholders with differing interests, objectives, and levels of
knowledge. However, the perception of these benefits and risks may vary among the stakeholders
and it cannot be assumed that all stakeholders will have the knowledge or background to effectively
participate in the process. Therefore, effective risk communication must make information concerning
a corrective action easily accessible to the stakeholders, provide a mechanism for the stakeholders to
visualize the results and be interactive to allow for participation in the process by the stakeholders.
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Spatial Environmental Risk Assessment
In recent years, the use of risk assessment and risk-based decision making in environmental
management has gained increasing attention (Rocco and Hay Wilson, 1998, Washburn and
Edelmann, 1998). However, there are a number of significant challenges to the practical application
of risk-based decision making at large, complex industrial, energy or defense facilities. For these
types of facilities, the implementation of a risk-based approach has been difficult and a practical
methodology has not been demonstrated (Hay Wilson etal., 1998).
The most significant challenges arise from the multiple potential sources, multiple chemicals of
concern and multiple potential receptors. In these large facilities, there can literally be hundreds of
potential exposure pathways to analyze. In practice, at complex facilities, sources are evaluated
individually or in small groups. As an example, the corrective action program under the Resource
Conservation and Recovery Act (RCRA) encourages this piecemeal evaluation through its focus on
individual solid waste management units (SWMU). In general, the implementation of program-
specific (e.g., air, water, waste) regulations by the Environmental Protection Agency (EPA) has also
perpetuated this non-holistic approach. The same is true for the regulation of Department of Defense
and Department of Energy facilities. Individual areas of a facility are studied using a straightforward
process to analyze exposure pathways for each source-receptor pair. However, there is typically no
attempt to understand the interaction of all of the sources and pathways on facility-wide risks or the
affects of these multiple sources and pathways on the environmental management decisions. It has
also been the case in the past that the goals for corrective action projects were based on very low to
non-detect concentrations of chemicals of concern, so the need to understand all of the potential
exposures was not as great. In addition, often many individuals are involved, over a number of years,
at a significant cost, in the calculations of the risks for each of the different areas for a facility. Many of
these facilities are also regulated under different regulatory programs, with different regulatory
agencies and no one investigator examines all of the results (Hay Wilson, 1998).
The availability of cost-effective computing power and information systems applications can provide
the foundation for the development of computer-based systems to manage information and perform
calculations for large numbers of pathways. In particular, the information processing capabilities of
Geographic Information Systems (GIS), relational databases, spreadsheets and other computer
code-based models can provide a mechanism to construct a methodology to implement risk
assessment at large, complex facilities (Hay Wilson, 1998). In the spatial environmental risk
assessment methodology the ESRI software ArcView® is being used for the GIS functions (ESRI,
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1998). The Microsoft Office products Excel® and Access® are being used for the spreadsheet and
relational database components, respectively (Microsoft, 1997).
Building the Digital Facility Description
In order to conduct the analyses required for making risk-based decisions and to provide a common
point of reference for all of the stakeholders, an understanding of the features of the facility and
surrounding area must be developed. This understanding can best be represented in digital files so
that analyses may be conducted, data managed and calculations performed efficiently. These digital
files make up the digital facility description, that is, the description of natural and man-made
features and information about the physical, geological, hydrological and chemical characteristics of
the facility and the region. The digital facility description is the foundation upon which the site
conceptual model is developed and all of the exposure analyses conducted.
The digital facility description consists of two major components; a spatial database and a tabular
database. The spatial database contains the GIS shape files and coverages of geographic features
related to regional information (e.g., surface water flow, geology) and facility information (e.g.,
locations of current and former process areas and storage tanks). It must include the coverages
necessary to conduct the exposure pathway analyses and should include coverages that define the
physical setting of the facility and provide contextual information (Romanek, 1999). The regional
information can be obtained from various websites, including the United States Geological Survey
(USGS) the Environmental Protection Agency (EPA), and includes topography, land use, and digital
elevation data coverages. The facility information can be obtained from digital aerial
orthophotographs or can be developed based on CAD files that have been converted to GIS files.
The tabular database contains the facility feature descriptions and chemical and physical data (e.g.,
soil property measurements, chemical of concern concentration data) linked to each other through
common fields, thus forming a relational database. The relational database provides an easily
manageable format for storing, adding, and retrieving different types of information (Romanek, 1999).
The information included in the digital facility description for the case study is a comprehensive
compilation of available information for the facility from environmental, geo-technical and other
investigations or activities that have been conducted at the facility over the past ten to fifteen years
and the available regional spatial data. The regional information for the case study digital facility
description includes data from regional data sources, primarily state agency web sites and EPA web
sites. The facility information for the case study digital facility description includes facility data
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sources, an environmental measurements database developed by the facility and the facility
geographic coverages developed from a 1997 aerial survey. In addition, facility information has been
collected from site investigation reports, historical maps, engineering drawings and older aerial
photographs.
The case study facility is an approximately 300-acre crude oil refinery and petroleum products
terminal that has operated since the early 1900's. For this case study site, an aerial survey was
conducted and the facility coverages of operating units, tanks, waste areas, surface waters, etc.,
were digitized from the resulting orthophotographs. Figure 1 includes six facility scale coverages of
the physical site features. When displayed together these depict a typical site plan. The types of
analyses that are expected to be conducted will dictate the resolution needed for the mapping. As an
example, for the case study site it was important to understand the potential for surface water runoff
to the adjacent creeks and the river, so a digital terrain model (DTM) was developed from the
orthophotographs to provide a detailed description of the land surface.
Developing the Spatial Site Conceptual Model
The site conceptual model is a critical component of the risk assessment process and the focal
point of risk-based decision making. It provides the working hypothesis of all of the potential exposure
pathways associated with the chemicals of concern identified at the many potential sources, their
movement in the environment and their relationship to potential receptors. The site conceptual model
is a synthesis of spatial and observational data. The challenge in assessing environmental risk at a
large, complex facility lies in capturing the complexity of multiple sources and receptors. Typically,
simplified site conceptual models are used to represent the relationships between the sources and
receptors at facilities. However, using a simplified site conceptual model can potentially lead to an
inaccurate understanding about the effects on receptors and expected results from implementing a
remedial action alternative (Koerner etal., 1998). This is particularly true when assessing the
environmental risk to the many potential receptors existing on and off of a facility resulting from
multiple sources and receptors, as is the case at most large industrial facilities. It is also often the
case that the site conceptual model is developed at the beginning of the risk assessment project as a
static display of the pathways thought to be of importance to the investigation at that time. The typical
representation is a series of flow charts (ASTM, 1995, ASTM 1998, BP, 1997). For a facility with
multiple sources of many chemicals of concern and various potential receptors, the presentation of
flow charts is not very informative.
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/V/ Boundary' Line
' Fences and Walls
/ Railroads
Storage Tanks
Structures
Surface Hydrology
Figure 1. Compilation of Six Facility Coverages to Display a Site Plan
The public has received the process of risk assessment with skepticism, largely because it is not
generally perceived to be an open and transparent calculation process. It has been viewed by many
as a "black-box" approach. However, through the use of spatial and tabular databases, a spatial site
conceptual model can be developed to describe the working hypothesis of the potential exposure
pathways for a facility and provide a mechanism to communicate what is known and what is not
known about the site and the exposure scenarios among the engineers, decision-makers and other
stakeholders. The spatial site conceptual model can, therefore, be a means for different stakeholders
to identify the exposure scenarios for which they have the greatest concerns and quickly identify the
scenarios that have already been analyzed. To accomplish this the spatial site conceptual model is
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linked to the environmental modeling process and to the calculations of risk or corrective action
goals. The Risk Assessment Data Model shown in Figure 2 depicts an exposure pathway evaluation
that is tailored to the digital and spatial processes (Hay Wilson, 1998).
Cross-media
pathways
Geographic
pathways
Corrective Action Goal Calculation
Figure 2. Risk Assessment Data Model
The spatial site conceptual model is developed by identifying each complete or potentially complete
exposure pathway for a site. The exposure pathways are segmented into the functional elements of
sources, cross-media transfer elements, geographic transport and receptors. Segmenting the
pathways in this way allows for the connection of the elements to their computational counter parts.
The descriptions of the sources are linked to the tabular database that describes the release history
for each of the sources, or the sources can be linked to the spreadsheet that is used to calculate the
representative concentration of the chemical of concern at a particular source area given the
analytical results from environmental media sampling. Transfer and transport segments are linked to
the spreadsheet calculations describing the environmental processes.
The spatial site conceptual model is an evergreen description of the current understanding of the site.
Developing it using PC-based software makes the application accessible to all of the members of the
project team (e.g., state regulators, responsible party project managers, consultants) and provides a
mechanism to update the entire exposure pathway evaluation as new data is added. This saves time
during modeling runs and reduces errors in data transfers. The fact that the exposure pathway
evaluation results are stored in a tabular database means that the outcomes can be catalogued and
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tracked. This provides the stakeholders with the documentation they seek to identify the pathways
analyzed and the outcomes. This process makes the risk assessment more reliable and repeatable.
Itemizing the exposure pathways in a database and segmenting the pathways facilitates the tracking
of many multiple sources using conservative analytical models (Koerner, 1998). The sources that
affect an individual receptor area can be identified and remedial action scenarios can be evaluated
through an iterative calculation procedure. Cataloguing all of the potential impacts of the many
multiple potential sources at a facility will provide the data and understanding of the site to support a
site-wide analysis of exposures and ultimately of risks.
The spatial site conceptual model includes a spatial representation of the elements for multiple
sources, pathways and receptors. The components of the spatial site conceptual model (e.g.,
sources and receptors) are represented as individual themes or data layers in vector representation.
The user identifies the potential sources, transport mechanisms and potential receptors within the
GIS application. Themes for potential sources are constructed based on the digital facility description
data, the historical information and the environmental measurements. The sources are defined as
point coverages. These coverages may include individual points for releases (e.g., the location of an
emissions stack) or they may be the center points for source areas (e.g., the center of a defined area
of soils containing chemicals of concern). Receptor locations are identified based on the spatial data
and the current and potential future activities on the facility and surrounding properties. The receptor
locations are defined by points, represented by points of exposure (e.g., the location of a drinking
water well) or areas, represented by potentially affected areas (e.g., the residential neighborhood
near a facility). The areas for which receptor identification is needed can be identified in the GIS
application based on land use, census data or digital ecological habitat data. The transport
mechanisms that link the sources to the receptors are grouped in themes based on environmental
media. Lines define the lateral transport mechanisms such as groundwater flow. All of the elements
are drawn in the GIS application. Scripts are used to assign distances, coordinates and areas to the
spatial objects. Figure 3 includes one source area, six potentially affected areas, six points of
exposure, and four geographic transport mechanisms.
The modeling of the environmental process is implemented using a tiered approach. For simple
evaluations the single point to point relationships are analyzed using vector operations and one-
dimensional algorithms. Simple fate and transport algorithms have been assembled in a group of
spreadsheets. In higher level evaluations the modeling is accomplished as raster or grid functions.
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Figure 3. Example Spatial Representation of the Site Conceptual Model Components
Grid-based groundwater and surface water models have been developed using the methodologies
presented by Maidment, 1996 and Romanek, 1999.
Conclusions
Risk-based decision making is an iterative process that provides a mechanism to determine the
necessary and cost-efficient strategy for protection of human health and the environment. It requires
the interrelationship and interoperation of risk assessment, risk management and risk
communication. Risk Assessment is accomplished by collecting information to construct exposure
hypotheses for a chemical of concern or group of chemicals of concern and evaluating those
hypotheses to determine the current and future potential for adverse effects to human or ecological
exposures from chemicals of concern in the environment. The incorporation of risk assessment as an
10
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integral component of an overall decision making process provides a mechanism to determine the
necessary and cost-efficient strategy for protection of human health and the environment. In addition,
improved methods for engineers and scientists conducting risk assessments and greater acceptance
of risk assessments by the stakeholders will support the general environmental regulatory agency
and community goals of environmental protection and sustainable economic development.
The contribution of this research is a new spatial risk assessment methodology using GIS to
practically implement a holistic environmental risk assessment that accounts for the multiple
exposure pathways at large, complex facilities and support risk-based environmental management
decisions. This methodology provides an information processing system that more clearly ties the
data and information for a study area to the risk-based decisions that are made for an environmental
management project. In addition, the application of a spatial site conceptual model methodology
helps automate the selection of exposure pathways to be considered, provides a mechanism to
document the pathway completeness evaluation and provides connections to the transport
calculation components and to the site conceptual model tabular database. In this manner, the
process of evaluation and calculation can be more clearly understood and presented.
Acknowledgements
BP Amoco provided funding to support this research. Dr. Robert Gilbert, Dr. Randall Charbeneau
and Ms. Susan Sharp provided insights and support.
11
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References
American Society for Testing and Materials (ASTM). 1995. Standard Guide for Developing
Conceptual Site Models for Contaminated Sites. E 1689-95.
American Society for Testing and Materials (ASTM). 1998. Standard Provisional Guide for Risk-
Based Corrective Action. PS104-98.
BP Exploration & Oil Inc. (BP). 1997. "Risk-Based Decision Process Guidance Manual." BP
Exploration & Oil Inc. Cleveland Ohio.
Environmental Systems Research Institute (ESRI). 1998. ArcView GIS, Version 3.1, Redlands, CA.
Hay Wilson, L. 1998. "A Spatial Risk Assessment Methodology for Environmental Risk-Based
Decision-Making at Large, Complex Facilities," Dissertation Proposal, The University of Texas at
Austin, Department of Civil Engineering, Austin, Texas.
Hay Wilson, L. L. N. Koerner, A. P. Romanek, J. R. Rocco, R. B. Gilbert. 1998. "Critical Success
Factors for Implementing Risk-Based Decision Making at a Large Refinery Site," Contaminated Sites
Remediation Conference: Challenges Posed by Urban & Industrial Sites Proceedings, March 23-25,
1999, Fremantle Western Australia.
Koerner, L. N., L. Hay Wilson, A. P. Romanek, J. R. Rocco, S. L. Sharp, R. B. Gilbert. 1998.
"Maximizing the Value of Information in Risk-Based Decision-Making: Challenges and Solutions,"
American Nuclear Society Conference, April 3-5,1998, Pasco WA.
Koerner, L. N. 1998. "Development of a Site Conceptual Model Using a Relational Database,"
Masters Thesis. University of Texas at Austin, Department of Civil Engineering, Austin, Texas.
Maidment, D. R. 1996. "Environmental Modeling within GIS," in GIS and Environmental Modeling:
Progress and Research Issues. Goodchild, M. F. etal., eds., GIS World, Inc. Fort Collins, Colorado, p
315(9).
Microsoft Corporation 1997. Microsoft Office Professional Edition 97, Seattle, WA.
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National Academy of Sciences (MAS). 1983. Risk Assessment in the Federal Government.
Managing the Process. National Academy Press, Washington, D.C.
Rocco, J. R. and L. Hay Wilson. 1998. "The Evolution of Risk-Based Corrective Action," Published in
proceedings: Geo-Congress 98, Risk-Based Corrective Action and Brownfields Restoration, ASCE,
Boston, Massachusetts, p44(11).
Romanek, A. P. 1999. "Building the Foundation for Environmental Risk Assessment at the Marcus
Hook Refinery using Geographic Information Systems," Masters Thesis. University of Texas at
Austin, Department of Civil Engineering, Austin, Texas.
Washburn, S. T. and K.G. Edelmann. 1998. "Development of Risk-Based Remediation Strategies,"
Published in proceedings: Geo-Congress 98, Risk-Based Corrective Action and Brownfields
Restoration, ASCE, Boston, Massachusetts, p30 (14).
13
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Characterizing the Hydrogeology of Acid Mine Discharges from the Kempton Mine
Complex, West Virginia and Maryland
Benjamin R. Hayes
Department of Geological and Environmental Sciences
Susquehanna University
Edgar W. Meiser, Meiserand Earl, Inc.
Constance Lyons, Maryland Department of the Environment, Bureau of Mines
INTRODUCTION
The Kempton Mine Complex consists of an area of interconnected, abandoned underground
coal mine workings in the Upper Freeport Coal seam in southwestern Maryland and
northeastern West Virginia (Figure 1). The mine complex was operated by the Davis Coal and
Coke Company from 1885 to 1950. The workings encompass an area of 31.3 km2. Groundwater
discharges from the abandoned mine contribute on the order of six million gallons per day of
acid mine drainage (AMD) to the North Branch of the Potomac River and the North Fork of the
Blackwater River.
OBJECTIVES
The Maryland Bureau of Mines is undertaking a comprehensive investigation of the Kempton
Mine Complex to develop remedial measures to reduce AMD by (1) decreasing the quantity of
recharge to the deep mine and/or (2) improving the quality of the discharge from the mine pool.
Before specific remedial measures are identified and evaluated, a GIS was developed to refine
our understanding of the mine structure and hydrology. The visualization and computational
capabilities of GIS enhance our ability to conceptualize the geometry and hydrology of the deep
mine complex.
GIS DEVELOPMENT STRATEGY
Sources and Types of Data. Data incorporated into this study are diverse and of varying quality.
The watershed characteristics and mine information included published geologic maps and
boring data from the West Virginia Geologic Survey, Maryland Geologic Survey, U. S.
Geological Survey, and U.S. Bureau of Mines, as well as records from the coal company
including mine inspection reports, coal production reports, and water management reports.
These data were synthesized, converted into digital form, and archived into Microsoft Access™
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databases. Selected figures and important pages from old reports were scanned and archived
as raster images in the database. Geologic maps, structural contours, hypsography, hydrology,
and transportation features were digitized from the Blackwater, Davis, Lead Mine, and Mozark
Mountain USGS 7.5 minute quandrangles. Detailed maps of underground mine workings at a
scale of 1 inch equals 100 feet that were originally surveyed by mine engineers with the Davis
Coal and Coke Company were digitized in AutoCAD and incorporated into ArcView GIS
coverages of the mine region. Boring locations and coal depths were incorporated into the
coverages. Details of each coverage were documented in metadata files in HTML format that
can be read and written by any web browser.
Historical aerial photographs from the 1950s through 1980s were scanned and combined in the
GIS with recent (March, 1999) low-altitude orthophotographs of the mine region. The recent
orthophotos were georeferenced and rectified to control points surveyed on the ground using
precision GPS survey techniques.
Hydrologic and water quality data collected at field monitoring stations were organized in
Microsoft Excel™ spreadsheets (flat files). These spreadsheets were used to analyze time
series of mine discharges, stream flow hydrographs, and water quality samples.
Data Analysis. The GIS proved valuable at integrating the historical boring data and mine maps
with the ongoing mine discharge and water quality data. Three-dimensional stratigraphic cross-
sections and isopach maps help identify hydrogeologic features such as stratigraphic pinch
outs, changes in dip, and mine pool barriers. Large sets of spatial data can be integrated and
analyzed to generate digital terrain models, correlate stratigraphic units, and compute the
locations of outcrops of selected strata at the ground surface. Algorithms are currently being
developed to generate new maps of flooded mine areas, mine pool shorelines, and directions of
groundwater flow.
MINE STRUCTURE
In order to characterize the hydrogeology of the mine complex, one must first understand the
details of the structure of the mine. The structural features include the location, orientations, and
dimensions of the Upper Freeport coal and the underground workings within it.
Specific questions about the mine structure include:
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1. regional geologic structure of the coal seam;
2. precise locations of underground mine workings;
3. degree of coal extraction;
4. locations of coal outcrops; and
5. proximity to overlying coal seams that have been mined.
Regional geologic structure of the coal seam. The Upper Freeport coal seam is found in a
broad, north-east plunging syncline in Upper Potomac coal basin. The fold axis trends about 5
degrees and plunges 2 to 3 degrees to the northeast. The dip of the syncline limbs varies from 2
to over 16 degrees, with minor variations. Over most of the Kempton Mine, the Upper Freeport
coal seam is 4 to 4.5 feet thick and pinches out on the eastern limb of the syncline (Figure 2).
No major faults are mapped on the regional geologic maps, but numerous faults and offsets are
described in the mine reports.
Figure 2. Kempton Mine and elevation contours of the base of the Upper Freeport coal seam.
A detail structure contour map of the base of the Upper Freeport coal was prepared by the West
Virginia Geological Survey from over 230 borings compiled from multiple sources. The boring
logs provide top and bottom Upper Freeport coal elevations which were incorporated into the
GIS database. We used the GIS to generate trend surface and three-dimensional boring
diagrams from the boring database.
Precise locations of underground mine workings. The ability to generate maps showing the
precise locations of underground mine workings in relation to present-day features is one of the
most valuable contributions of the GIS in this study. The original (1885-1950) mine maps at a
scale of 1 inch to 100 feet were carefully digitized by the current property owner, Western
Pocahontas Properties in AutoCAD format. The coverages were georeferenced in West Virginia
State Planer Coordinates and verified using a network of 12 monuments surveyed using
precision GPS technology. The CAD coverages were imported into ArcView GIS and draped on
top of digital orthophotographs and (DRG) maps of the region (Figure 3).
Degree of coal extraction. Headings and pillars are clearly visible on the old mine maps. The
digital maps of the old mine workings were extensively cleaned and edited to generate closed
polygon coverages of the subsurface mines (Figure 4).
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Secondary mining, i.e. removal of coal pillars, occurred over most of the mine area. The
remaining sections of the mine apparently remain intact as a labyrinth of tunnels and pillars. A
noteworthy exception to this is the unmined coal barrier that trends east-west, located 3,000 feet
north of Coketon (Figure 4). This barrier has a width of approximately 400 feet and begins near
Thomas and extends 5700 feet to the west. The western tip of this barrier lies at elevation 2870
feet MSL according to the coal structure elevation contours.
Locations of coal outcrops. The outcrop of the Upper Freeport coal has been surface-mined
along its entire length. Because of the surface disturbance of the coal at its outcrop, the actual
location of the coal crop line is difficult to define and locate in the field. By projecting the
structural contour surface of the coal seam onto the ground surface topography, the GIS
enables us to generate maps showing actual position of coal crop lines. The crop lines,
corresponding to the edge of the coal underclay, or pavement, are needed to calculate
watershed areas contributing to the deep mine.
Proximity to overlying coal seams that have been mined. The Bakerstown coal, which lies
approximately 180 to 200 feet above the Freeport coal, was extensively deep-mined and
surface-mined. Because the Bakerstown coal is relatively close to the surface, and because it
has been surface-mined in many areas above the Kempton Complex, it is very effective in
capturing recharge from precipitation. Once the water has "sumped" into the Bakerstown coal
seam, it inevitably drains into the underlying deep mines. We suspect however, that in some
areas the opposite situation is true, where flow from the Kempton complex may leak upward into
and discharge from the Bakerstown coal mines.
MINE HYDROLOGY
Once the deep mine maps were refined accurately using GIS, our ability to visualize the
relationships between the Kempton Mine and overlying aquifers and areas of deep mining in the
Bakerstown coal was improved greatly. The visualization capability of GIS enables us to
develop the fundamental concepts of the deep mine's role in the regional groundwater
hydrology.
Questions about the mine hydrology include:
1. the extent of mine flooding and elevations of mine pools;
2. interconnection between deep mine to surface water runoff;
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3. leakage from streams and wetlands into the deep mines;
4. quantities of recharge and discharge; and
5. hydraulic gradients and groundwater flow directions in strata overlying the mine.
Extent of mine flooding and elevations of mine pools. Flooding in the abandoned mine workings
is divided into two distinct and separate mine pools. The northern mine pool has a surveyed
elevation of 2656 feet MSL and discharges from an 8-foot diameter air shaft and 12-inch
borehole into Laurel Run about one mile north of Kempton, Maryland. These discharges are
located approximately 4.5 miles upstream of the North Branch Potomac River. The Kempton
mine pool elevation is constant and controlled by the elevation of the air shaft.
The southern mine pool has an approximated elevation of 2870 feet MSL and discharges from
several deep mine entries immediately south of the village of Coketon, West Virginia into the
North Fork of the Blackwater River. The elevation of this pool must be controlled by the intact
coal barrier west of Thomas, West Virginia described earlier. This barrier is relatively
impermeable, considering that in the vicinity of Rose Hill Cemetery northwest of Thomas, it
narrows to 200 feet in width and impounds hydraulic heads exceeding 200 feet.
Interconnection between deep mine to surface water runoff. Mine discharge records show
significant increases in response to large rainfall events, suggesting direct inflow of surface
water into the mine. We used the GIS to delineate areas of shallow cover above the mines
(Figure 5). These areas will be examined in future field efforts to identify any sources of direct
inflow.
Leakage from streams and wetlands into the deep mines. Leakage from wetlands along the
Potomac River has been observed in abandoned mine shafts at the town of Kempton,
Maryland. Streamflow losses are predictable in streams draining the east slope of Backbone
Mountain, as they cross the outcrop of Upper Freeport coal where the Kempton deep mines are
closest to the surface. We used the GIS to overlay stream coverages with coal outcrop maps in
order to delineate contributing watersheds and to identify locations of potential leakage. At these
locations, predicted streamflows can be compared with measured streamflows to determine
whether significant leakage is occurring.
Quantities of recharge and discharge. In October and November, 1998, eleven weirs were
installed to gage discharges from the Kempton Mine Complex. Two weirs were installed on
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discharges from the Kempton pool to Laurel Run; one on the air shaft and the other on the
borehole. Nine weirs were installed on discharges from the Coketon pool to North Fork
Blackwater River.
We are using the GIS to analyze time series of discharge records from the weirs and
precipitation records from nearby gaging stations. We are developing ArcView AVENUE™
scripts to plot hydrographs for each weir and to compute the total volume of discharge for any
specified period of time. The total discharge volumes are then compared with the respective
watershed areas contributing to the deep mine, delineated by the GIS, to compute the mine
recharge rates (gpm/acre). The computed mine recharge rates can be compared to measured
precipitation, as well to as average regional recharge, to determine where recharge to the
Kempton Mine is anomalously high.
Based on preliminary weir measurements, recharge rates in the Coketon mine pool watershed
appear to be greater than recharge to the Kempton mine pool watershed. The Kempton mine
pool discharges up to 6 millions gallons per day while the Coketon discharges a maximum of
approximately 4.4 million gallons per day - 75 percent of the northern mine pool. However, the
Coketon pool has a recharge area of only 2,900 acres - 63 percent of the northern mine area of
4,600 acres.
Hydraulic gradients and groundwater flow directions. In late summer/early fall 1999,
piezometers will be installed in aquifers overlying the Upper Freeport coal. Comparison of
hydraulic heads in these aquifers to mine pool levels will dictate vertical directions of
groundwater flow and hydraulic gradients. The head data will be incorporated into the GIS and
gradients computed and cross-sections generated showing groundwater flow directions.
FUTURE WORK
As field investigations continue, the GIS is being used to locate new test borings and monitoring
wells, delineate areas for subsidence monitoring, integrate historical and current mine discharge
water quality data, and characterize groundwater recharge rates and flow directions. By
analyzing the spatial variability of the hydrogeologic data, we plan to use the GIS to discover
relationships among various parameters. Temporal variations in certain parameters can also be
analyzed to discern patterns of change not apparent with static, two-dimensional maps. For
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example, areas of mine pool fluctuations can be combined with coverages of coal structure to
delineate areas of AMD generation.
ACKNOWLEDGEMENTS
This project is being accomplished and funded through the cooperation and team effort of
numerous state, federal, and private entities: Maryland Department of the Environment,
Maryland Department of Natural Resources, University of Maryland, West Virginia Department
of Environmental Protection, Susquehanna University, U.S. Environmental Protection Agency,
Region 3, U.S. Department of Energy, U.S. Office of Surface Mining, Anker Coal Company,
Buffalo Coal Company, Mettiki Coal Corporation, Western Pocahontas Properties, Meiser and
Earl, Inc.
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Mine Discharges
*
Wine Pttimetei
V Mine Pillars
Ay
V Undennad Mine W Drirlngs
Davis, WVQuad
Leadmine.Wv Quad
Figure 1. Study location map. Inset shows GIS coverage of the mine pillars and haulways draped over USGS quadrangle maps.
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Figure 2. Kempton Mine and elevation contours of the base of the Upper Freeport coal seam.
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N
Scale 1:14,000
500 1000 1500 2000 2500 3000 3500 Feet
Figure 3. GIS coverages of northern section of Kempton Mine complex showing detailed mine working maps draped on top of color
NAPP aerial photograph.
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• ; SSSSSSSSSS SSSSSSSSS
Mine Discharges
0
^f\ Mine Perimeter
Mine Pillar;
A/
Undefined Mine W ork ings
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Leadmine. W V Quad
Figure 4. Southern portion of the Kempton Mine complex showing location of intact mine barrier.
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Figure 5. Overburden thickness map of Kempton Mine Complex.
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Use of GIS Tools for Conducting Community On-site Septic
Management Planning
David Healy, Chief, GIS Services & Bruce F. Douglas, Senior Geoscientist
Stone Environmental, Inc.
58 East State Street
Montpelier, Vermont, USA
802229-1879
dhealy@stone-env.com
ABSTRACT
This paper presents a summary of the authors' experiences in using GIS tools in support of
Community Septic Management. This experience examines a number of Massachusetts and
Vermont case studies in which the use of GIS was a primary tool in the planning and
implementation of community septic management. These are the towns of Duxbury and Tisbury,
Massachusetts, and the towns of Jericho, Vermont. Based on these case studies lessons can
be drawn on how to further evolve the use of the technology in aiding community environmental
management. This effort is based on the State of Massachusetts' nation-leading efforts in
addressing the issue of management of on-site septic systems. The state's efforts are designed
to address current failures, the protection of environmentally sensitive areas, and to assist
communities develop tools and capabilities for insuring that problems are addressed and
systems remain functional at all times. The Vermont community experiences are driven by a
desire at the local level to avoid environmental pollution and costly centralized sewer systems.
The paper will describe all aspects of acquiring and using state and local GIS data to carry out
the plans in four communities. Issues surrounding availability, quality, and data formats will be
described. Each of the case study communities approached the problem slightly differently
based on the available data. Each community used GIS to different levels for decision making
and management. From each of these experiences, a summary of the successes, pitfalls and
challenges will be described.
Overview
Decentralized wastewater management is a growing trend in the United States. Faced with
soaring costs for large centralized treatment systems, communities are increasingly turning to
the smarter management philosophy associated with the decentralized approach. Traditionally,
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one expensive solution has been available to communities that have outgrown or outlived their
on-site septic systems — a sewer system and an expensive treatment facility. With federal
funds becoming increasingly scarce, most small communities can not afford this type of
conventional centralized approach.
By definition, decentralized wastewater management employs all available treatment and
disposal technologies. The appropriate technologies, in a measure that meets current needs
and takes into consideration future growth, are matched with the treatment and disposal
requirements that have been identified. The end result is a unique municipal wastewater
management solution that includes a program of preventive maintenance designed to identify
weaknesses or potential failures before they become a problem.
This approach also simplifies future maintenance and planning. Community-wide decentralized
wastewater management offers an opportunity to track the condition of individual systems, the
relationship of those systems to other community infrastructure, like drinking water sources, and
to the environment.
The Role of Geographic Information Systems
Geographic Information Systems (GIS) are a powerful tool for identifying and examining
problem areas to display information for public understanding. GIS played a significant role in
supporting a number of elements in each of the following case histories. The overlay capability
of GIS was used to show environmentally sensitive areas, soil suitability for on-site systems,
and areas of the community using subsurface disposal for prioritizing replacement systems. GIS
was used for mapping soil types to determine suitability for siting systems, mapping community
infrastructure and existing systems, tracking plans for future development, mapping
environmentally sensitive areas, and tracking maintenance, repair and upgrade information for
an entire community. Maps can be readily generated from the GIS to make decision making
more timely and public education more effective. The maps effectively communicate complex
information at-a-glance in a graphical format that is easily understood.
Database Applications
Databases are considered essential to enable the efficient management of on-site systems at
the local government level. Databases can be used to track permits, inspections, pump-outs
and failures of on-site systems, maintain on-site system records, and prepare mailings for
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necessary system maintenance. The database should be capable of storing, retrieving and
reporting all data pertinent to all of the on-site systems. The foundation of the database should
consist of the following key data areas:
1. System related information including components, septic tank size, application
and permit numbers, maintenance (septic tank pump-out dates, inspection dates
and inspection results), plans, and images.
2. Parcel map and lot designation, related information including soil test data and
structures, number of bedrooms and design flow.
3. People related information. Names and addresses of people used for lookup in
various sections of the database.
4. Easy linkage to GIS. Any database characteristic can be displayed graphically
through GIS.
Jericho, Vermont
The Town of Jericho is a suburban and rural community located in the Lake Champlain Valley
area of Northwestern Vermont. The town includes 92 square kilometers (35 square miles) of
land. The 5,000 residents rely on individual and cluster type on-site wastewater treatment and
disposal systems (septic systems).
Town officials recognized the potential for treatment and disposal problems to develop as the
town grows and existing septic systems age. The Jericho Board of Selectmen hired consultants
to determine the most appropriate means of addressing short-term and long-term wastewater
disposal needs for the town. The process involves assessing existing systems, characterizing
environmental conditions, and analyzing the community's future options.
The study focused on 3 areas:
Jericho Center — a traditional 19th century New England village.
Jericho Village — a traditional village with some commercial properties.
Route 15 district — a recently developed commercial zone.
Data Availability: Data for Jericho came from two primary sources the town planning office and
the Vermont Center for Geographic Information. The primary databases used for the analysis
include:
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SSURGO Digital Soils
Parcels
Roads
Surface Waters
Well Locations
Source Protection Areas
Swimming Areas
Wetlands
Floodways
Quality: This data was primarily digitized from 1:5000 Orthophotos and 1:24000 USGS Quad
Sheets. FGDC type metadata did not exist for town developed data. The later issue poses long
term problems for understanding the source parameters used. For this community mixing the
two scales of data did not pose a major problem. For certain the FEMA flood data is highly
inaccurate, but was not essential for this study.
Data Formats: Local data was in Maplnfo format in Vermont State Plane Feet. State provided
data was provided in Arclnfo format. Local data was converted to Arclnfo format and put into
Vermont State Plane Meters, NAD 83.
The study team conducted a thorough assessment of existing systems and conditions. They
identified environmentally sensitive areas and parcels with special conditions or limitations and
conducted analyses and developed maps.
The GIS analysis revealed that soils in the study areas are generally suitable for on-site
systems on terraces in the major stream valleys where the densest residential and commercial
development is typically located. In individual neighborhoods, a 2 to 30 percent rate of on-site
system failure in a 10-year period was evaluated. The failures were predominantly due to age,
design and construction of systems. Most of these failures have been effectively resolved by on-
site replacement of the septic tank and/or soil absorption system with conventional
technologies. Due to the low density of development, centralized collection systems do not
appear to be cost-effective. Due to existing use of the river nearest the most densely developed
areas in town for swimming, direct discharge of treated wastewater was not an option either.
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CIS Integration: The town has received the consolidated set of reprojected data and various
ArcView projects developed for this project on CD-ROM. It has acquired ArcView and is using is
for various planning and zoning/regulatory functions.
The consultants identified specific options for community wastewater management based on the
town's current needs and plans for future growth. They concluded that existing septic systems
and a locally developed septic system management program would be the most cost-effective
option. To implement this as a sustainable option, Jericho has established a local Wastewater
Planning Committee with three objectives:
1. Develop a decentralized wastewater management plan based on a town wide
assessment of need.
2. Develop a community homeowner and student education program to increase
the local understanding of on-site systems.
3. Demonstrate the effectiveness of septic tank risers and septic tank effluent filters
in facilitating the inspection and maintenance of on-site systems.
Jericho's forward-thinking approach to decentralized wastewater management is saving the
town money by using existing wastewater infrastructure, protecting homeowners' investments in
their current systems, and avoiding the high cost of developing a centralized sewer and
wastewater treatment facility.
Duxbury, Massachusetts
The coastal community of Duxbury, is located 35 miles south of Boston. Duxbury has a
population of approximately 14,000 and a land area of approximately 61 square kilometers (24
square miles). Greater than 95 percent of the residents rely on individual on-site sewage
systems and the community is committed to using on-site systems as a long-term solution to
wastewater management. The town relies on an extensive sand and gravel aquifer for
community drinking water supplies. In addition to aesthetic and recreational value of the
freshwater and saltwater resources of the community, there are numerous cranberry bogs in
commercial cultivation and a significant potential for shellfish harvesting in the coastal
embayments.
Over the past three decades, Duxbury has been making substantial efforts to protect their
groundwater and surface water quality with a permitting program for on-site wastewater disposal
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systems. In 1996, the town dedicated two shared soil absorption systems designed to address
severe problem areas along Duxbury Bay. These shared systems (each with less than 37,800
litres per day design flows) handled wastewater from three parcels along the Bluefish River and
18 parcels in the Snug Harbor area by conveying the wastewater to sites located inland that are
suitable for soil absorption systems. The establishment of these systems has enabled the
opening of shellfish harvesting areas due to a decrease in bacteria in the coastal embayments.
The town has recently voted to design and build another 37,800 litres per day cluster on-site
system to serve approximately 30 households in a residential area along Kingston Bay to
improve water quality in a historic shellfish harvesting area. To continue their on-going efforts in
this area, the town has recently completed a Community Septic Management Plan (CSMP) to
provide a clear process for decentralized wastewater management.
Duxbury's CSMP consists of the following components:
1. Comprehensive inventory of existing systems.
2. Parcel information permit records and septic tank pump-out records stored in a
database to track permitting and maintenance.
3. Development of a local GIS system and training of Town personnel to identify:
a. Drinking water source protection areas
b. Freshwater wetlands, ponds, vernal pools, rivers and streams
c. Saltwater wetlands, coastal resource zones
d. Buffer zones around these areas
e. Parcel maps to determine environmental sensitivity of particular parcels.
4. Public Education and Information development including a brochure explaining the
existing situation and the community septic management plan.
5. Betterment Loan/Upgrade Program.
6. Ranking environmental sensitivity by category to determine priority of parcels for loan
program to assist homeowners in upgrading failed systems.
7. Voluntary Maintenance program that recommends setting routine system
maintenance in conjunction with the following requirements in local ordinance:
detailed reporting of septic tank condition, liquid and solid levels at septic tank
pumping.
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Data Availability: Data for Duxbury came from two primary sources the University of
Massachusetts, Boston which had completed an Open Space Plan for the town and from
MassGIS office. The primary databases used for the analysis include:
Digital Soils
Parcels
Land Cover
Roads
Surface Waters
Well Locations
Source Protection Areas
Swimming Areas
Wetlands
Floodways
Title 5 Setbacks
Quality: This data came from highly mixed sources. MassGIS is primarily 1:24000 scale data
digitized from USGS Quad Sheets and 1:20000. FGDC type metadata did not exist for UMass
data.
Data Formats: MassGIS data was in Massachusetts State Plane Meters Feet North American
Datum (NAD) 1983. On the other hand, Umass' GIS data was in an unknown and
undocumented format. It turned out after much trial and error that it was in Massachusetts State
Plane Feet, NAD 1927.
Specific GIS Processing: The ranking system devise for the betterment loan program was
based on a assignment of weighted values to six factors. In this community the intersecting of
coverages with the weighted values was competed to derive a composite weighting. A "grid-
based" math algebra operation could have just as easily been used.
GIS Integration: Duxbury has received a complete copy of all the data and ArcView projects
developed for the project. Two days of training were provided to staff members in the Planning
and Health Departments. They are currently using GIS in many diverse planning and
management functions.
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Figure 1: Priority Ranking Results
Tisbury, Massachusetts
The Town of Tisbury is a coastal resort community located on the island of Martha's Vineyard
located 8 km (5 miles) off the southwestern coast of Massachusetts, USA. The Town covers an
area of 54 square kilometers (21 square miles). The population of Tisbury is approximately
3,000 year round residents and 10,000 seasonal residents. Approximately three-quarters of
Tisbury's residents rely on groundwater from municipal wells that tap a glacial sand and gravel
deposit underlying the western half of the island. The remaining residents utilize individual wells,
generally tapping the same aquifer. Currently all properties in town are served by on-site
sewage disposal systems. The Tisbury Board of Health estimates that there are currently
approximately 2,500 individual on-site systems.
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The intent of Tisbury's wastewater management program is to provide an institutional and
regulatory framework enabling the long-term viability of the on-site wastewater treatment and
disposal facilities in the Town. The program includes a Community Wastewater Management
Plan (CWMP), Watershed Management Strategy, Public Outreach and Education, Institutional
and Regulatory Requirements, and a Program Implementation Strategy. The CWMP is in the
final planning stage and has not been adopted by the community.
The town is taking a very pro-active approach to on-site system management. The local Board
of Health and Wastewater Planning Committee is developing a management program with
required inspections for every system and requiring pump-outs of septic tanks based on
expected rates of solids accumulation for the specific tank size and system usage. GIS has
been used to delineate environmentally sensitive areas and parcels; a ground water flow model
has been used to determine water table contribution to surface waters; and a database is being
developed to track the management program and notify residents of compliance requirements.
This approach enables the residents to easily see the relationship between the areas that
utilized on-site systems and environmentally sensitive areas such as aquifers, streams, coastal
ponds and wetlands using a risk-based management strategy.
Environmentally sensitive areas in Tisbury will be used to identify high priority areas are for the
management of on-site systems. Initial wastewater management districts have been defined to
address the downtown Vineyard Haven area, and the low elevation (less than 3 meters (10 feet)
above mean sea level) areas with the potential for systems to have the least vertical separation
to groundwater.
Periodic maintenance is critical to ensuring the long-term success of even the most basic septic
system. An inspection and maintenance program has been designed to assess current
infrastructure conditions, ensure proper use and maintenance of on-site systems, and reduce
future failures. A relational computer database, the Septic Information Management System
(SIMS), will be established to maintain an up-to-date inventory of all onsite systems in town and
to track the permitting of new systems, upgrades of existing systems, and inspection and
maintenance program. The database will also be useful to ensure that all owners are
adequately addressing the unique needs of their system. One of the primary goals of the
management plan is to provide for better septage (solids and liquid pumped out of septic tanks)
management by creating a predictable and manageable production of septage. This plan
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pertains to those parcels outside of the area serviced by the centralized wastewater collection
and treatment system. While the central service area will have management requirements, they
will likely differ from the rest of town and will be addressed in a separate management program.
On-site systems are designed to treat domestic wastewater before reaching the groundwater
and down-gradient surface waters. The residual components of this treatment process, such as
nitrates, have an impact on the environment. However the degree of impact is relative to the
sensitivity of the groundwater beneath the on-site systems and the sensitivity of the surface
waters where the groundwater discharges. The watershed management strategy is designed to
protect the environmental resources of Tisbury at an appropriate level to the sensitivity of the
different areas in town.
A key element of the watershed management strategy is to use a risk assessment/risk
management approach. During the risk assessment process, the town will develop rankings
regarding the value and vulnerability of local receiving environments to impacts from on-site
systems, and to define the areas in town which contribute flow to the receiving environments. A
steering committee of stakeholders will be established to address the needs of the community in
this process. The rankings will be used to determine appropriate levels of treatment and develop
a risk management program, in order to protect public health and the environment. For
example, specific areas may be delineated where nitrogen removal is required for upgrades and
new on-site systems to reduce nitrate loading to a particularly sensitive and valuable receiving
environment.
The second program in the watershed management strategy is the development of a long-term
groundwater monitoring program. A network of surface and groundwater sampling stations will
be established up to monitor trends in water quality.
Environmental professionals, municipal departments, and community environmental groups will
conduct the risk assessment/risk management process. The water quality monitoring program
will be run in conjunction with the Martha's Vineyard Commission, the University of
Massachusetts Extension program, the Town of Tisbury, and local professionals. Funding for
the watershed management program is will be provided through a combination of local and
federal sources.
10
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Data Availability: Data for Tisbury came from two primary sources the MassGIS office and a
previous consultant who had worked for the town. The primary databases used for the analysis
include:
Parcels
Building Footprints
Title 5 Buffered Footprints
FEMA Flood Data
Land Cover
Surface Waters
Roads
Well Locations
Source Protection Areas
Wetlands
Possible Discharge Areas
Color Infrared Orthophoto
Quality: This data came from two sources. MassGIS is primarily 1:24000 scale data digitized
from USGS Quad Sheets and 1:20000. The consultant's data came primarily from the town's
CAD-based tax maps for which were assembled and rubber sheeted to fit with the MassGIS
data. FGDC type metadata did not exist for the consultant data. The FEMA data was the least
accurate of the data used. The focus of the study was for the village of Vineyard Haven. Given
the coastal nature of the data, the town boundaries varied widely depending on the original
source.
Data Formats: Most Data was acquired in State Plane Meters, Mainland Zone, NAD 1983.The
one exception was the digital CIR Orthophoto which came from the Massachusetts Coastal
Zone Office in State Plane Meters, Island Zone, NAD 1983. Since we derived contours for the
study area from this data, it had to be reprojected to State Plane Meters, Mainland Zone. These
kinds of issues working with available data always presents problems in all studies to date.
CIS Integration: The town has recently embarked on an effort to develop a town-wide GIS
program. This has begun with an evaluation of existing data and assessment of the many
department needs.
11
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12
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Geographic Information and Tools for Informed Decisions:
The Lake Superior Decision Support Project
George E. Host, Lucinda B. Johnson, Carl Richards
Natural Resources Research Institute
Pat Collins
MN Department of Natural Resources
Introduction
Recent trends in land use, such as increased population movement from urban to rural areas,
conversion of forest land, and increased development have emerged as key issues affecting
natural resource management in the Lake States. As units of government ranging from local
townships to the federal governments of the US and Canada plan for the future, the need for
data and tools for sound decision-making has become critical. Nonetheless, at the scale of the
Lake Superior Basin, we lack synoptic information on even the most fundamental data layers
required for sound planning. Among these are comprehensive coverages of land use/land
cover, transportation infrastructure, hydrography, demography, and even the bathymetry and
shorelines of Lake Superior itself.
In addition to the lack of spatial data, smaller units of government often embark on land use
planning exercises with few tools at their disposal. While computer simulation models, draft
ordinances, and decision support tools are receiving wider use in planning, these tools are often
out of reach of local governments who lack equipment and expertise required for their use.
Finally, in natural resource management, the general public is often faced with information that
has been slanted in favor of the perspectives of industrial or environmental advocacy groups.
There is a critical need for a source of neutral data to allow the public to develop informed
opinions on current issues.
To this end, we have begun a project to help resolve some the issues of data accessibility and
interpretation with respect to land use planning in the Lake Superior Basin. We have three key
objectives:
1) To develop synoptic databases across the Lake Superior Basin, and to make these
data available through the Internet and other data distribution formats.
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2) To concurrently develop decision support tools to assist local units of government in
land use planning activities. These tools will consist of CD-based resources,
including spatial data, prototype planning documents, and flowcharts to guide users
through the planning process.
3) To develop and deploy information kiosks in Visitor's Centers and other publicly
accessible locations around the Lake Superior Basin.
The following paper charts our experiences and progress to date, and provides some insights
into issues related to integrating information and decision support tools into the planning
process.
Motivation
This project was motivated by several key factors:
1. Forestry and forest products industries dominate the basin's economy.
2. Demand for tourism and leisure activities is replacing mining and other previous
contributors to the region's economy, with an associated increase in development to
leverage this demand. The Lake Superior Basin's wealth of natural features is a major
factor driving these changes.
3. The Basin is a unique harbor of biodiversity, on a world-wide basis (TNC). A
considerable portion of the Basin's biodiversity resources are located along coastal
shores and wetlands.
4. The Basin's natural and biodiversity resources are increasingly in conflict with both
forestry and development. And increasingly, the three are in conflict at a given place
simultaneously. People want to visit and live, by and large, where natural resources are
rich and biodiversity is highest.
It stands to reason that the Basin's future is directly dependent on the resolution of these land
and resource use conflicts. But the resolution of these conflicts will not occur as a result of a
small number of large government agencies or private land holders developing and
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implementing policies that address land use issues. The real impacts of development result
from the cumulative effects of a large number of small land use decisions, spread across time
and space. When integrated across time and space, these land use decisions have major
impacts on the Basin's resources.
The unique role of the Lake Superior Decision Support project is to develop and foster the use
of an infrastructure, based on GIS and computer models, upon which the Basin's incremental
land use decisions can be made more effectively. Our target users are local governments,
resource management agencies, commercial interests, citizen volunteers, advocacy groups,
aboriginal groups, and educational organizations. There are two fundamental missions of the
project, both intended to sustain the Basin's resource in a truly ecosystem-based approach (i.e.,
sustaining humans as a part of the natural world). The first mission is to provide practical,
useable tools that can be used by people involved in day-to-day land use decisions. The second
mission is to provide a context and a demand for these tools by providing educational and
interpretive information for their use.
Beneficiaries
The direct beneficiaries of this project are those organizations and individuals that will directly
use the GIS applications and databases developed by this project. But there will also be many
indirect benefits that are difficult, if not impossible, to quantify. For example:
1. Local governments will gain tools to more effectively create and implement zoning
ordinances that mold day-to-day land-use decisions.
2. Educational and interpretive organizations will gain resources that will help them inform
the public about these resources and energize their target audiences to encourage their
use in the planning process.
3. Regional agencies (including state/provincial and federal agencies) will, over time, gain
access to a consistent GIS database for assessing regional and basin scale issues. In
this process, data gaps are identified.
4. All parties to local and regional land use decisions and policies will gain from a collective
knowledge base and shared and unbiased information base.
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5. Ultimately the Basin and its people benefit from more effective land use decisions that
help to sustain and steward the Basin's resources.
Data development
Data development began by prioritizing a list of key data layers critical for land use planning and
capable of being mapped synoptically across the Lake Superior Basin. The geographic scope of
the data was the Lake Superior
watershed boundary plus a 50 km buffer
(Figure 1). The buffer allows us to
access impacts to the basin that may be
due to activities outside the watershed
boundary. The watershed boundary was
constructed by merging a number of
independently-derived boundaries
developed at state or provincial scales -
it was informative that neither the
watershed boundary nor the shoreline of
Lake Superior previously existed in a
continuous, fine-resolution GIS coverage.
Figure 1. Lake Superior basin boundary with 50
km buffer.
The base scale of the data aggregated across the whole basin was set at 1:250,000; a
compromise between the desired accuracy of the data and the ability to easily store, manipulate
and visualize the data. We then conducted a broad survey of agencies involved in development
of spatial data for natural resource management to identify the source, scope, and resolution of
key data layers.
A critical step in the identification and acquisition of data was the development of Memoranda of
Understanding and Cooperative Agreements with collaborative groups, such as management
agencies and industry. A key issue is that many agencies operate on a cost-recovery basis, in
which GIS products are sold to recover costs of development. In many cases, licensing
agreements require that data be made available only in image format (e.g. GIF or TIF), rather
than as spatially-referenced GIS databases. In addition, providing appropriate
acknowledgments, respecting publication rights, and developing well-defined dissemination
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criteria was essential for this effort. To date, we have assembled approximately 30 databases
across all or part of the Basin, as shown in Table 1.
Table 1.
Major spatial data themes, geographic extent and key attributes for data compiled for this project.
Data theme
Geographic
extent
T
Attributes
Scale
Resolution
Bathymetric Model
Census data
Civil divisions, minor
Climate
DEM
Ecological classification
system
Ecological classification
system
Ecological classification
system
Forest inventory (FIA)
Forest inventory
(presettlement)
Geology
Habitat megasites
Habitat projects
Habitat sites
Hydrography
Land Cover: Forest
vegetation
Land Cover: General
Land Cover: Original
vegetation
Land ownership
Pollutants-point source
Pollutants-point source
Public Land Survey
Satellite imagery
Lake Superior
MN, Wl, Ml,
Ontario
VIN, Wl, Ml
VIN, Wl, Ml, ONT
.ake Superior
Basin
VIN, Wl, Ml
N MN, N Wl
DNT
VIN, Wl, Ml
VIN
VII, MN, Wl, ONT
NEMN
Lake Superior
oasin
Lake Superior
oasin
VIN, Wl, Ml, ONT
VIN, Wl, Ml, S.
AN
Great Lakes
Basin
Vll,MN,WI
Vll,MN,WI
Lake Superior
oasin
Lake Superior
oasin
VIN, Wl, Ml, ONT
Lake Superior
Interpolated depth values
Population, demography, housing
Townships
Interpolated data, monthly temp,
rainfall
Digital Elevation Model
ECS, Province to Subsection
Land Type Associations
Hierarchy: ecozone, region and
district
Forest inventory data by sample
point, 1990s
GLO witness trees, section corner
description
Surficial geology, landforms
Wilderness areas, ecoregions,
parks etc.
Habitat projects, location,
[description
Habitat sites, location, description
Lakes, streams, rivers
24 classes of forest vegetation
Anderson II, Land cover/use
40 classes derived from GLO
survey notes
Public/private ownership
ndustrial point source locations
Municipal point source locations
Township, range and section
boundaries
Mosaic of Landsat MSS, mid
1:547,000
1:100,000
1:100,000
1:1,000,000
1:100,000
1 km
5 km
1 km
1 km
200m
40 acre
200m
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Table 1.
Major spatial data themes, geographic extent and key attributes for data compiled for this project.
iBasin
Lake Superior
Satellite imagery Basin
Lake Superior
Satellite imagery Basin
Soils ONT
Soils STATSGO
Transportation
Watersheds, Major
Watersheds, Major
Watersheds, Minor
MN, Wl
MN, Wl, Ml, ONT
.ake Superior
basin
MN, Wl
MN
1980's
AVHRR NDVI Composite
Night image of city lights
Drainage, surface material,
deposition, etc
Many attributes: texture, drainage,
depth
Classification of roads, railways,
others
Major Watershed boundaries
Major Watershed boundaries
Minor watershed boundaries
1:100,000
1 km
1 km
Metadata
Documenting the source, accuracy, resolution, and other key attributes of spatial data is critical
for its effective use. We are using the Minnesota Geographic Metadata Guidelines, developed
by the Minnesota Land Management Information System, a state agency that serves as a
clearinghouse for spatial information. The Geographic Metadata Guidelines are a simplified set
of the Federal Geographic Data Committee's Content Standards for Digital Spatial Metadata.
They consist of 99 elements divided into seven categories describing the following data
characteristics:
1. What kind of a database is this?
2. What is its quality?
3. How is it organized?
4. Where is the database located?
5. What features does this database describe, and how are they characterized?
6. Is this database distributed? If so, how?
7. Who documented this data?
We are implementing the metadata with a software tool called DataLogr, developed by the State
of Michigan. The metadata for each data layer will be available on-line on the LSGIS web site.
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Delivering Information: Web sites, Kiosks, and Exhibits
The LSGIS web site can be found at www.nrri.umn.edu/lsgis. It was developed and maintained
with Microsoft Frontpage® and allows for the interactive guerying of maps using ESRI's Internet
Map Server® (IMS). IMS provides a number of tools for retrieving information from maps, and
includes the ability to provide progressively more information as a user zooms in to finer scales
of resolution.
In parallel with the Internet-based information, we are developing a touchscreen computer
display that provides a similar suite of information. These touchscreen computers will be
deployed in kiosks at several visitors' centers around the Lake States. We are also currently
working with the Great Lakes Aquarium at the Lake Superior Center in Duluth, MN to integrate
information collected in this project into a public "town meeting" forum that allows participants to
assess data and make decisions on a series of problems facing the Lake Superior watershed.
The touch screens use Site Explorer®, a map-oriented information utility developed by
cooperator Mike Koutnik.
Decision Support Projects
A key part of this project is the implementation of the data and tools to address land use
planning issues. We have two ongoing projects in this area, as described below:
Hydrologic Modeling of the Miller Creek Watershed
Miller Creek is a relatively high quality trout stream that runs through the cities of Duluth and
Hermantown, MN. The stream originates in wetlands and undeveloped shrublands and
woodlands northwest of Duluth, flows through a heavily commercialized area around Miller Hill
Mall, through residential areas as it drops rapidly towards Lake Superior, and ends in an
industrial area near its mouth. The upper part of the watershed is fairly flat, and the stream has
a moderate gradient. Near the mall it is channelized for several hundred meters and starts to
receive stormwater discharge from storm drains. Further downstream, in the more heavily
residential areas, multiple stormwater channels enter Miller Creek. The stream develops a high
gradient near its mouth. The stream is routed underneath the city in stormwater conduits and
ditches until it enters the St. Louis River estuary of Lake Superior
The Miller Creek watershed contains several new strip malls, and further commercial
development is under construction. This has generated a wide public concern over issues over
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environmental quality, particularly with respect to degradation of trout habitat resulting from
increased stream temperatures, sediment inputs, and degraded water quality. To inform the
decision-making process, we have assembled detailed land use maps of the Miller Creek
watershed, and employed the EPA's PC-SWMM, a well-documented hydrologic model, to
predict flow and pollution loads to the creek.
Our first goal was to model, as accurately as possible, the volume and temporal distribution of
flow in Miller Creek, based on overland, ground water, storm sewer conduit, and natural channel
flow using PC-SWMM. The second objective was to estimate pollution loads and concentrations
in runoff based on land use, road densities, local street cleaning programs, and locally
calculated and published land use-specific pollution loading rates. The modeling of these
parameters will allow planners and others to determine the likely effects of proposed changes in
the basin and evaluate options for reducing pollution loads to Miller Creek.
The model predicts the hydrograph associated with rain events fairly well (Figure 2). Since a
large part of the pollution concentration model is based on flow rates, this accurate flow model
was needed before proceeding to the pollutant modeling stage. Hydrologic modeling was
complicated by the large amount of wetlands in the headwaters of the Miller Creek basin, but
results to date show significant differences in water yield related to land use. For example, the
effects of the Miller Hill Mall commercialized area on flow in Miller Creek is shown in Figure 3:
the impervious surfaces around the mall result in a large increase in flow volume from a
relatively small area, in spite of flow mitigation from detention ponds. If the flow volume needed
to be reduced, the model could be used to determine the size of pond required to reduce the
volume to the desired level. The pollution model is nearly complete as well, and should provide
similar comparative data and scenario analysis capabilities.
£
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in
70 -,
60 -
50 -
40 -
30 -
20 -
10 -
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Miller Creek - SWMM Prediction
Miller Creek - Gauge Data
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Figure 2. Predicted and observed discharge from middle reach of Miller Creek.
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o
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
8A.M.
. Miller Hill Mall: Outlet to Miller Creek (29.2 acres)
Subcatchment # 21; Wooded (44.6 acres)
9:00
2:00
3:00
10:00 11:00 12P.M. 1:00
Time (Aug. 16, 1998)
Figure 3. Comparison of hydrographs above and below mall development area on Miller Creek.
Land use planning resources for northern Wisconsin
Our second prototype effort involves compiling spatial data and other resources to support local
units of government in citizen-based land use planning exercises. The objective of this exercise
is to compile data and tools relevant to local-scale land use planning onto a CD-ROM for use by
local units of government. Within the watershed, these are primarily towns and townships. The
CD will contain the following elements:
basic information describing the processes involved in planning
spatial data - land use, transportation, rivers and lakes, natural features, political
boundaries, and other data layers relevant to local-scale planning.
planning tools - example surveys and ordinances language, zoning policies,
development/preservation strategies, and other instruments that a local government
could tailor to its specific needs
landscape graphics - air photo or line drawing examples of different scenarios
illustrating housing density and patterns (clustered or dispersed), riparian buffers,
and other land use strategies. This would provide information on what a future
landscape might look like under particular management strategies
Example landscape plans developed by cooperating governments
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The CD will be written in standard HTML code to allow access with available web browsers.
Publicly available map viewing tools such as Arc Explorer® will also be provide to provide basic
querying of map products. The CD will be designed to stand alone, but will also contain links to
web sites to access a broader range of information.
Education and citizen involvement are central to understanding the data and tools available on
the CD (Figure 4). To assist in issue identification,
example citizen surveys will be available from the Land-
use Issues menu. Data will be available at both
township and county levels, the latter allowing for
contextual analysis of issues. Tools for assessing the
suitability of planning elements will also be available.
Example landscape plans will be available, along with
information on implementing and administrating
landscape plans once they are developed.
This CD-based resource will be developed and tested
in cooperation with a number of partners from local and
state governments. The anticipated completion and
delivery date of the CD is June 2000. The information
on the CD will also be available through the LSGIS
web site.
Figure 4. Schematic of LSDSS
planning support CD architecture.
Summary
The Lake Superior Decision Support project is an integrated effort to provide data and planning
tools to citizens and local units of government to assist in land use planning efforts. This work
should provide a means of reducing the cumulative impacts to the Lake Superior Basin through
informed decision-making at the local level.
Acknowledgements
This project was funded by the US Environmental Protection Agency through the Minnesota
Department of Natural Resources. We acknowledge the encouragement and support of the
Lake Superior Ecosystem Cooperative, where the key ideas of the project originated. Mark
White, Gerry Sjerven, Jesse Schomberg and Amy Trauger have been integral parts of this
10
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effort. A special acknowledgement goes to cooperator Mike Koutnik of Environmental Systems
Research Institute for his support and contributions to data and kiosk development. Sandy
Shultz of Ashland-Bayfield-Douglas-lron Land Conservation Department and David Lonsdale of
the Great Lakes Aquarium have provided valuable support and advice to the project.
11
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A GIS-based Approach to Predicting Wetland Drainage and Wildlife
Habitat Loss in the Prairie Pothole Region of South-central Canada
David Howerter
Institute for Wetland and Waterfowl Research
Ducks Unlimited Canada
P.O. Box1160
Stonewall, MB, Canada ROC 2ZO and
Lee Moats
Ducks Unlimited Canada
16064th Avenue Regina
Saskatchewan, Canada S4P 3W7
Background
The prairie pothole region covers approximately 870,000 km2 of the north-central United States
and south-central Canada (Batt 1996). The area extends from the tall grass prairies of
northwestern Iowa west to the short grass prairie of southern Alberta and north to the boreal
transition aspen (Populus tremuloides) parklands of central Saskatchewan (Figure 1).
Approximately 13,000 years ago, retreating glacial ice revealed a rolling terrain comprised of
end, ground and lateral moraines; glacial drift and till; and eskers (Winter 1989, Batt 1996).
Remnant ice remained within the till after the main body of the glaciers retreated. As this ice
melted, kettles were created. Subsequently, these kettles filled with water and became the
potholes of modern vernacular. (Pielou 1991, Batt 1996). The majority of potholes are not
connected by a natur; Figure 1. Prairie Pothole Ecoregion
Prairie wetlands serve a variety of hydrological/ecological functions including storage and
control of surface water (Winter 1989, Miller and Nudds 1996, Murkin 1998), recharge of
groundwater supplies (Winter 1989, LaBaugh et al. 1998, Murkin 1998), filters for sediments
and agricultural chemicals (Grue et al. 1989, Neely and Baker 1989, Gleason and Euliss 1998,
Goldsborough and Crumpton 1998, Murkin 1998), sinks for excess nutrients (Crumpton and
Goldsborough 1998, Murkin 1998), and habitat for a wide array of invertebrate, fish and wildlife
species (e.g., Batt et al. 1989, Fritzell 1989, Peterka 1989). This area is particularly important to
waterfowl where > 50% of the continental duck populations are annually produced (Batt et al.
1989).
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Grassland
Parkland
Figure 1. Prairie Pothole Ecoregion
European settlement of the PPR began around 1878. Farming was the primary motivation for
settling the area and agriculture remains a dominant economic force in the region (Leitch 1989,
Leitch and Fridgen 1998). Accompanying this expansion of agriculture was a dramatic
transformation of the landscape. Samson and Knopf (1994) estimate that as little as 23% of the
pre-settlement prairie remains in Canada. Conversion in the parklands (Figure 1) has been even
more extensive (Turner et al. 1987). Similarly, > 70% of the wetlands in the PPR have been
drained or severely degraded (Turner et al. 1987, Dahl 1990, Batt 1996).
These changes to the landscape have had consequences for a variety of ecosystem functions
throughout the region. For example, Miller and Nudds (1996) attributed increased flooding within
the Mississippi River valley over the past decades to reduced natural upland vegetation and
wetland drainage Other hydrologic functions such as groundwater recharge and the ability to
filter agrochemicals are similarly reduced by drainage. The focus of this paper, however, is the
effect these changes have had on the PPR's ability to support wildlife populations-specifically
upland-nesting duck species.
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In 1986, in response to declining duck populations attributed to land use changes throughout
North America and exacerbated by prolonged drought on the prairie nesting grounds, the
governments of the United States and Canada signed into law the North American Waterfowl
Management Plan (NAWMP; joined in 1994 by Mexico). The goal of the Plan was to return
waterfowl populations to the levels of the mid-1970's—a period with abundant waterfowl
populations. The Plan was structured as a number of joint ventures targeting either species of
concern or regions of special importance to waterfowl populations. Two of these joint ventures
(Prairie Habitat - Canada; Prairie Pothole - U.S.) focus on restoring nesting habitats within the
PPR.
During the planning stages for the Prairie Habitat Joint Venture (PHJV), one of the underlying
tenets was that policy changes would ensure that existing habitats remained intact. Ultimately,
this has not occurred. Draining wetlands and conversion of lands for
agriculture continues unabated—particularly in Canada. For instance, between 1971 and 1996
natural land has declined by 2.4 million hectares within the prairie pothole region of Canada
(Figure 2) while there has been a corresponding increase in cultivation (Figure 3). Likewise,
wetlands continue to be lost. For example, in a single region near Wadena, Saskatchewan, the
number of wetland hectares declined from 3,019 to 563 between 1949 and 1998. Between 1980
and 1998, 41% of the remaining ha were drained (Ducks Unlimited, unpublished data). In the
U.S. portion of the PPR, nearly 15,000 wetland ha continue to be degraded annually, despite
provisions that have linked wetland protection to agricultural subsidies (Tiner 1984). If, as
currently proposed, conservation provisions are de-coupled from agricultural subsidies, the
incentive to retain wetlands will be further weakened (J. K. Ringelman, Ducks Unlimited, Inc.,
Bismarck, ND).
Exacerbating the problem of continued loss of existing upland and wetland habitats, recent
assessments of PHJV habitat programs indicate that restored grasslands may be less
productive than existing native/naturalized cover for nesting ducks (Institute for Wetland and
Waterfowl Research, unpublished data). Therefore, a paradigm shift was in order for habitat
managers faced with a constantly eroding habitat base coupled with restoration efforts that may
be less effective than envisioned. Instead of primarily converting cropland back to grassland
vegetation, it has become increasingly clear that preserving existing habitats may be a more
effective strategy. However, limited conservation resources preclude the possibility of securing
all existing cover, and conversion rates vary spatially. Therefore, to prioritize which habitat
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Figure 2. Decline of natural land in the Canadian prairie
pothole region 1971-1996. (Source: Statistics Canada 1996)
Figure 3. Change in cropped areas 1971-1996 (Source:
Statistics Canada Agriculture Census 1996)
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parcels to secure, habitat managers need to identify the areas with the highest potential for
waterfowl production that are also at high risk of conversion to agriculture. Our objective, then,
is to develop a spatially-explicit model to project where land conversion is most likely. This
model ideally will be will be hierarchical allowing identification of "high risk" areas at both the
regional and local scales. For this paper, we discuss our plan for the regional model in some
detail and touch only briefly on additional considerations for the local model.
Hypothesized relationships
For conservation dollar investment decisions to be made wisely, resource managers require a
spatially-explicit tool to project which areas are most likely to be converted to cropland in the
near future. To allow quantification of the relationships that will allow optimization of resource
expenditures, risk of conversion to agriculture can be thought of as an expected rate of habitat
loss. In essence, the decision about where to expend resources on securement of existing
habitats within the foreseeable planning horizon takes the form:
8P = f(P, Cs, R), where:
8P = change in duck production/habitat expenditure
P = current duck production (1 [Amount of suitable upland cover, wetland
abundance])
Cs = site-specific cost of conservation, and
R = risk = (E[loss of habitat]).
Our hypothesized relationships are depicted in Figure 4. In Figure 4a, we've speculated that Cs
for a piece of land is likely correlated with its inherent fertility. Therefore, the least expensive
land most likely has low productivity for agriculture and ducks (e.g., Manitoba's Interlake).
Alternatively, the most costly (productive) land likely has been already largely converted to
agricultural and/or residential purposes. As a result, the areas that are currently most productive
for duck production likely occur on land with marginal suitability for farming and at intermediate
land costs.
Following similar reasoning, we have hypothesized that R, too, is highest for lands with
intermediate costs (Figure 4b); poor quality land (low cost) likely isn't worth converting to
cropland for economic reasons, and high cost land likely already has been converted.
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c
o
'•5
T3
O
Cost
Cost
(c)
c
o
'o
T3
O
O
Q
Risk
(d)
Figure 4. Hypothesized relationships between: (a) cost of securing a given
piece of habitat and potential duck production, (b) the risk of habitat being
converted to agriculture and cost of securing the habitat, (c) potential duck
production and risk of conversion, and (d) all three components.
Because we have hypothesized that both P and R are unimodal in relation to Cs, P and R are
positively associated when plotted against each other (Figure 4c). Combining these
relationships into a single model results in a relationship with one realization that may resemble
Figure 4d. Figure 4d depicts several interesting points. First, the best land for duck production is
also the land at highest risk of conversion. Second, happily, this land likely is of only
intermediate cost.
Model Development
Several techniques have been proposed for predicting land use change. The simplest of which
is to use historical trends to project forward in time making the assumption that past trends will
continue into the future. Empirical models based on posited driving forces may, however,
provide better predictions (Robinson et al. 1994) and allow for exploration of causative factors.
-------
To develop our model we plan to use relatively recent (e.g., since 1971) conversion data
(source: Statistics Canada - Census of Agriculture) and a number of candidate explanatory
variables (Table 1). An information theoretic approach (e.g. Akaike's Information Criterion;
Burnham and Anderson [1998]) will be used with regression analysis to select the most
parsimonious set of explanatory variables.
Table 1. List of candidate variables useful for predicting land conversion to agriculture in
the prairie pothole region.
Candidate variable
Topography
Landowner demographics
Soil type
Crop profitability
Proximity to an existing
water conveyance
Hypothesized relationship
Areas with relatively steep topography
are difficult to cultivate and/or drain
Conversion most likely when land
changes ownership
Related to cropping practices
Input costs and crop prices influence an
individual's decisions on how best to
use their land
Proximity to water conveyance makes
drainage less costly
Data Source
Canada Surveys
and Mapping
Statistics Canada
Canada Soil
Information System
Provincial
Agriculture
Departments
Provincial water
boards
Topography and proximity to an existing water conveyance both relate to the cost of conversion,
while soil type and crop profitability both relate to the benefit received by the manager for
converting. Crop profitability will require a separate "sub-model" with spatially-explicit crop
prices and input and transportation costs. Alternatively, land prices may serve as a suitable and
less data-intensive proxy for crop profitability for model development.
Evaluation/monitoring
The model we have described will be developed using (recent) historical data. We realize,
however, that extrapolating forward based on past relationships is inherently dangerous.
Therefore, a series of monitoring stations will be established throughout the areas targeted as
important for nesting ducks. Through an adaptive process uncertainty will be reduced in time.
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Using empirical Bayesian techniques, these stations will allow us to iteratively update model
parameters. This process will yield immediate results with increasing confidence overtime.
Local
Because much of the data used to build the regional risk model will be at a fairly coarse scale
(50-200 km2), additional information likely will be needed at the finest geographic scale to
accurately predict the risk of an existing patch of habitat being converted to cropland. Factors
such as landowner demographics and topography will likely still be important predictors, but
added factors such as size of the habitat patch and surrounding land use will also likely be
significant. Also, while true crop profitability might be predictable through entirely empirical
information, it almost certainly will be much more difficult to predict the vagaries of individual
landowner decisions. To attempt to explain how decisions are made, new data will need to be
gathered to determine how producers determine profitability when clearing, breaking or draining
land.
Integrating the Model: A Decision Support System
The model that we have described in this paper represents one component of a decision
support system designed to optimize expenditures of conservation dollars. Simultaneously,
spatially-explicit models to predict duck production and the cost of securing important habitat
areas also will be developed (Figure 5). The combined output from these 3 components should
allow habitat managers to make informed decisions about how best to prioritize expenditures.
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Decision Support System
T V.-".'-^.
Figure 5. Conceptual model of Decision Support System
Acknowledgements
We thank K. Guyn for helpful comments on a previous draft of this paper. J. Holland and B.
Kazmerick provided technical assistance and assisted with preparation of the figures. K.
LePoudre provided unpublished data.
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References
Batt, B. D. J. 1996. Prairie ecology—prairie wetlands. Pages 77-88 in F. B. Samson and F. L.
Knopf, editors. Prairie conservation: preserving North America's most endangered
ecosystem. Island Press, Washington, D.C. 339pp.
_, M. G. Anderson, C. D. Anderson, and F. D. Caswell. 1989. The use of prairie potholes
by North American ducks. Pages 204-227 in A. van der Valk, editor. Northern prairie
wetlands. Iowa State University Press, Ames. 400pp.
Burnham, K. P. and . R. Anderson. 1998. Model selection and inference: a practical information-
theoretic approach. Springer-Verlag, New York. 353pp.
Crumpton, W. G. and L. G. Goldsborough. 1998. Nitrogen transformation and fate in prairie
wetlands. Great Plains Research 8:57-72.
Dahl, Thomas E. 1990. Wetlands losses in the United States 1780's to 1980's.U.S. Department
of the Interior, Fish and Wildlife Service, Washington,D.C. Jamestown, ND: Northern
Prairie Wildlife Research Center Home Page.
http://www.npwrc.usgs.gov/resource/othrdata/wetloss/wetloss.htm
(Version 16JUL97).
Fritzell, E. K. 1989. Mammals in prairie wetlands. Pages 268-301 in A. van der Valk, editor.
Northern prairie wetlands. Iowa State University Press, Ames. 400pp.
Gleason, R. A., and N. H. Euliss, Jr. 1998. Sedimentation and prairie wetlands. Great Plains
Research 8:97-112.
Grue, C. E., M. W. Tome, T. A. Messmer, D. B. Henry, G. A. Swanson, and L. R. DeWeese.
1989. Agricultural chemicals and prairie pothole wetlands: meeting the needs of the
resource and the farmer—U.S. perspective. Transactions of the North American Wildlife
and Natural resources Conference 54:43-58
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Goldsborough, L. G., and W. G. Crumpton. 1998. Distribution and environmental fate of
pesticides in prairie wetlands. Great Plains Research 8:73-95.
LaBaugh, J. W., T. C. Winter, and D. O. Rosenbery. 1998. Hydrologic functions of prairie
wetlands. Great Plains Research 8:17-37.
Leitch, J. A. 1989. Politicoeconomic overview of the prairie potholes. Pages 3-14 in A. van der
Valk, editor. Northern prairie wetlands. Iowa State University Press, Ames. 400pp.
_, and P. Fridgen. 1998. Functions and values of prairie wetlands: economic realities.
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Miller, M. W., and T. D. Nudds. 1996. Prairie landscape change and flooding in the Mississippi
River valley. Conservation Biology 10:847-853.
Murkin, H. R. 1998. Freshwater functions and values of prairie wetlands. Great Plains Research
8:3-15.
Neely, R. K. and J. L. Baker. 1989. Nitrogen and phosphorus dynamics and the fate of
agricultural runoff. Pages 92-131 /nA. van der Valk, editor. Northern prairie wetlands.
Iowa State University Press, Ames. 400pp.
Peterka, J. J. 1989. Fishes in northern prairie wetlands. Pages 302-315 in A. van der Valk,
editor. Northern prairie wetlands. Iowa State University Press, Ames. 400pp.
Pielou, E. C. 1991. After the ice age: the return of life to glaciated North America. University of
Chicago Press, Chicago. 366p.
Robinson, J., S. Brush,, I. Douglas, I.E. Graedel, D. Graetz, W. Hodge, D. Liverman, J. Melillo,
R. Moss, A. Naumov, G. Njiru, J. Penner, P. Rogers, V. Ruttan, and J. Sturdevant. 1994.
Land-use and land-cover projections: report from working group C. Pages 73-92 in W. B.
Meyer and B. L. Turner, II, editors. Changes in land use and land cover: a global
perspective. Cambridge University Press, Cambridge, U.K. 537pp.
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Samson, F. and F. Knopf. 1994. Prairie conservation in North America. Bioscience 44:418-421.
Tiner, R. W., Jr. 1984. Wetlands of the United States: current status and recent trends. U.S.
Dept. of the Interior, Fish and Wildlife service, Washington, D.C. 59pp
Turner, B. C., G. S. Hochbaum, F. D. Caswell, and D. J. Nieman. 1987. Agricultural impacts on
wetland habitats on the Canadian Prairies, 1981-1985. Transactions of the North
American Wildlife and Natural Resources Conference 52:206-215.
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der Valk, editor. Northern prairie wetlands. Iowa State University Press, Ames. 400pp.
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Onsite Wastewater Management Program in Hamilton County, Ohio
- An Integrated Approach
to Improving Water Quality and Preventing Disease
Tim Ingram, Terry Hull, Travis Goodman, and Staff
Division of Water Quality, Hamilton County, Ohio
Background
Postwar economic and population growth of the 1950s launched Hamilton County, Ohio,
families into suburbia. Ohio's most southwestern county and home of Cincinnati expanded
typically. Construction of suburban residential housing outstripped the development of traditional
urban infrastructure. But extended aeration technology made it possible to put a miniature
wastewater treatment plant in the yard of every new homeowner. Home aeration units were
marketed as virtually maintenance-free devices capable of producing effluent with the quality of
drinking water. These fallacies, in concert with the natural limitations of the sites being
developed and a community willing to believe that which was too good to be true, brought public
health consequences.
Since most of Hamilton County escaped the advance of the last (Wisconsinan) glacier, its steep
shale ridges remain covered with thin top soils and slowly-permeable silty-clay subsoils. Only
small areas in the valleys of the three principal streams are underlain by the more-permeable
glacial deposits. Seasonal rain and snow melt runoff cut the ridges with innumerable erosion
channels that join to form intermittent streams. Conditions that limited the use of leaching
devices appeared to be optimal for use of aeration units with surface discharges. Supported by
exaggerated claims of operational efficiency, public officials were quick to accept the technology
as a way to support new tax-generating development without the cost of sewer construction.
Developers enthusiastically supported a public policy that reduced their capital costs and raised
profits. Homeowners believed that they had an effective, low cost, state-of-the-art sewage
treatment device. With discharges running to any downhill drainage way, out of sight/out of mind
became the prevailing attitude, and it took a long time to change.
By the mid-1980's, sewage contamination of the West Fork of the Mill Creek elicited strong
public reaction. When a hepatitis A outbreak occurred in 1989, the public clamor over lack of
public sewers, ineffective sewer system operation, and poorly functioning home aeration units
intensified. Public alarm further increased when a child playing in a stream polluted by aeration
-------
systems caught a rare protozoan infection and was hospitalized for days.
By the early 1990's, the total number of units was unknown, but estimates ranged from 20,000
to 40,000 units. Public perception regarding the public health impact was markedly changed. An
irate homeowner persisted until the Hamilton County General Health District's Board of Health
officially declared her neighborhood a public health nuisance and requested the State to order
construction of a public sewer. Public concern peaked when the Board of Health authorized use
of aeration units in a proposed subdivision where the effluent would drain directly into a
downstream homeowner's recreation lake. The resulting legal action brought Ohio
Environmental Protection Agency and Ohio Department of Health sanctions against the Board
of Health. A political response occurred in 1993 as Board members were replaced and a new
Health Commissioner (pubic health officer) was employed. The new Board determined that
water quality improvements and waterborne disease prevention were dependent on the
effective use of household sewage disposal systems. Thus, the Board made a commitment to:
• Update its household sewage regulation, which had been in force since 1959;
• Reconsider its previous approval actions regarding the usage of aeration sewage
systems in residential subdivisions;
• Inventory and evaluate existing home sewage systems;
• Build community partnerships and educate homeowners about system operation and
maintenance requirements;
• Expedite complaint investigations; and
• Strengthen enforcement when sewage system repairs are necessary.
This paper discusses the establishment of an operation permit program which was developed to
achieve several of these commitments. The purpose of this paper is to describe the components
of the operation permit program and the steps that have been taken to improve water quality in
Hamilton County streams.
Building the Operation Permit Program
Policy Development
In Ohio, local health districts have sewage system permitting authority for one-, two-, and three-
family dwellings. These districts enforce either the minimum state code or more stringent
regulations adopted by the local board of health. Under the minimum code, a newly constructed
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system is automatically permitted for operation upon final construction approval. This operation
permit remains in force until it is revoked by board action. Such permits are rarely revoked.
Some health districts issue operation permits for existing sewage systems. In 1994, the
Hamilton County General Health District (HCGHD) initiated a comprehensive operation permit
program. Under this program operating permits are issued for both new and existing household
sewage disposal systems.
On December 13, 1993, the Board of Health adopted a new household sewage disposal code
and created a new division to enforce it. Previously, the sewage permitting functions were a part
of the Health District's plumbing division. To implement the Board's commitments, the Division
of Water Quality and Waste Management was established. The main function of this division
was to implement Regulation 529, the Household Sewage Disposal Code.
To guide the new program, the Hamilton County Board of Health established the following
policies:
• No permit fee will be billed until an inspection of the household sewage disposal
system(HSDS) is completed and the owner is provided with a written inspection
report.
• All household sewage disposal systems (HSDS) with electrical components (aeration
units, etc.) are subject to an annual inspection and, if the system is found to be
operating properly, a one-year operation permit will be issued.
• All non-mechanical or non-electrical household sewage disposal systems (HSDS)
will be inspected once every three years and, if found to be operating, a three-year
operation permit will be issued (effective on Sept. 11, 1995).
• All newly-constructed dwellings with newly-installed household sewage disposal
systems (HSDS), permitted and approved by the HCGHD, will be exempted from the
operation permit program for a period of two years following construction approval.
Critical to the success of the operation permit program was the establishment of inspection
criteria for determining proper operation. Considering the varying ages of the systems in use
and the different standards under which they were manufactured and installed, a balanced
approach was needed that would achieve water quality objectives while maintaining public and
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political support. The Health Commissioner and staff were charged with the development of the
inspection criteria.
It was decided that system performance would be evaluated by observation rather than effluent
sampling. This decision took into account the fact that many HSDS had no separate access for
sampling, as well as the unlikelihood that most older systems could meet effluent quality
standards established by the Board of Health. Also, considered was the cost to sample as many
as 20,000 sewage systems and that many individual systems were connected to common
collector lines. Collector lines function as private sewers that transport effluent from several
homes. The ten observational criteria selected to establish improper performance for
mechanical HSDS, thus resulting in the designation "disapproved" are:
1) Motor missing,
2) Motor inoperable (cold),
3) Motor not drawing air or insufficiently drawing air,
4) Broken lid(s), i.e., piece missing or broken to the extent that it allows entrance of
surface water, or lid cannot be lifted without collapse; decayed metal grating,
5) Flooded filter,
6) Visual evidence of septic sewage, i.e., black, odorous,
7) Visual evidence of electric service problem,
8) Components are not functioning in accordance with design standard,
9) Discharge creates a public health nuisance, and
10) An access riser has not been brought to grade over each compartment requiring
maintenance.
Fee Structure
Policies concerning program fees have changed as program activities evolved and knowledge
was gained regarding operating costs and the willingness of HSDS owners to pay. At the outset,
the Board of Health established a single operation permit fee of $40.00. As program
implementation progressed, two problems became apparent. First, there was no way to recover
the cost of the additional inspections required to achieve and confirm system repairs when the
initial inspection resulted in disapproval. Second, no permit was issued to HSDS owners who
failed to pay their permit fee. Consequently, those who were delinquent soon forgot about the
need for an operation permit as well as their indebtedness. The Health District then entered a
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new era-marked by the need for dunning letters and small claims court appearances-in which it
acted somewhat like a small utility company.
In July of 1995, the fee structure was changed based on a report issued to the Board of Health
entitled, Aeration Sewage Disposal Systems in Hamilton County. This report evaluated the
progress made to date. Based on community input, the Board implemented a variable fee
structure. The annual fee covering the initial inspection and permit was reduced to $30.00. A
reinspection fee of $30.00 was established for second and subsequent re-inspections. As an
inducement for HSDS owners to provide regular maintenance, the second reinspection fee was
set at $15.00 for owners holding a maintenance contract with a registered and bonded service
company.
Delinquent permit fees rose as high as 62% but annually averaged around 13% for the 1994-
1995 time period. This amounted to $32,320 of uncollected permit fees at the end of 1995. In
1996 a collection agency was retained by the Health District at a cost of $4.95 per outstanding
account. Also, a $10 late fee was assessed against each delinquent account. Delinquent fees
dropped to an average of 9% in subsequent years. Many homeowners were more threatened by
a bad credit rating than criminal prosecution. Nonetheless, the percentage of unpaid accounts
was still too high. In 1998, the HCGHD worked with local Ohio legislators, and legislation was
passed allowing for unpaid operation permit fees to be certified by the Health Commissioner to
the County Auditor for placement as a lien on the property. With the passage of this legislation,
the "playing field" has been leveled so that all HSDS owners have a financial stake in the
operation permit program.
Staff Development
The development and implementation of policies regarding Health District staff and staff activity
has been crucial to successfully carrying out the operation permit program. These policies have
been established largely by management and include issues such as, personnel qualifications,
inspection protocol, and training.
Field work was done initially by registered sanitarians and sanitarians-in-training. During the first
year staff turnover was unacceptably high. Newly-graduated environmentalist quickly gained
valuable field experience, but were just as quickly tired by the large volume of inspections and
the repetitive, sometimes confrontational nature of the work. Experienced sanitarians soon
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found the work not challenging. Management found the solution rested in the employment of
technicians as inspectors. With specialized training and sanitarian oversight, these staff have
maintained their enthusiasm and perform their inspections in a highly competent and efficient
manner. The water quality technicians have worked well with HSDS owners and the repair
contractors, too.
The effectiveness of the inspection staff can be attributed to the development of operating rules
which emphasize thorough training, clear identity, and consistency in performance of duties.
Each inspector receives detailed training regarding the operation of each brand of aeration unit
that he/she will inspect in the field. Contact with repair contractors is encouraged, and where
possible, attendance at manufacturer's training school is supported. Inspector training in
communication and public relations is also provided, while membership in professional
environmental associations is encouraged.
Inspectors adhere to a dress code wearing distinctively colored shirts and jackets. Clearly
visible photo identification badges are worn. Each inspection begins with a stop at the front door
to explain the purpose of the visit to the citizen, followed by a brand-specific standardized
inspection of the sewage system, and, if the owner is present informing him of the inspection
findings.
The Inventory - What Was Found
Aeration sewage systems are a high maintenance type of household sewage disposal system.
These systems contain electrical components and filters which require maintenance by the
homeowners. Homeowners were unaware, for the most part, of these maintenance needs.
The media reported that 40,000 aeration sewage systems discharged untreated waste water
into homeowners' backyards. However, no one knew for sure the exact number nor their
operating condition. Installation permit records were not complete. While citizens worried about
disease and pollution, an Ohio EPA connection ban and class action lawsuit put the Hamilton
County Board of Health in a reactive mode. There was a rush to inventory and evaluate the
operating status of all existing aeration sewage systems first. The Board of Health directed the
Health Commissioner to address the following questions:
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1) How many aeration sewage systems exist in Hamilton County?
2) What are the manufactured types?
3) Where are the aeration sewage systems located?
4) What is the operational status of these systems?
5) How much water pollution and how many health nuisances have been created
from these aeration sewage systems?
From January 1994 through July 1995, sanitarians and water quality technicians blanketed the
County inspecting home aeration systems. The staff visited over 10,000 properties - many on
several occasions -to find aeration systems, locate collector lines, consult with homeowners, or
reinspect systems for compliance. Inspection sheets were filled out and information entered into
a custom-designed database for ease of tracking.
Within eighteen months 9,145 home aeration sewage systems were located and inspected. Six
manufactured-types of home aeration sewage systems were found. (See Table 1). The high
percentage of the Cavitette brand in use was a significant concern because there was no active
manufacturer or replacement parts available. The Cavitette brand was manufactured locally in
the late 1950's through the 1960's. The design of this system was based on less stringent
standards than the National Sanitation Foundation Standard 40.
Table 1
Percent of Total Home Aeration Units by Manufacturer
Manufacturer Percent
Cavitette 22.4
Coate 10.6
Jet 36.6
Multiflo 2.4
Norweco 0.1
Oldham 27.9
Of the 9,145 aeration systems inspected, 34 percent or 3,077 were disapproved during this time
period. Table 2 summarizes the number of that had been repaired by July 1995 and the average
number of reinspections to obtain compliance.
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Table 2
Disapproved Systems with Completed Repairs
Number of Number of Mean reinspects
systems reinspects to compliance
Totals
2,741
3,861
1.41
Many of the home aeration sewage systems connected to a common yet private sewer line
known as a collector line. These collector lines served as few as two homes or as many as 50
homes. The effluent from the collector lines discharged into storm water sewers, ephemeral
streams, and onto the ground surface. None of the collector lines had National Pollutant
Discharge Elimination Systems (NPDES) permits. It had been observed and reported that the
discharges from these collector lines had deteriorated the water quality in the county's streams
and waterways. Prior to 1994, there was no documented wastewater monitoring of these
collector lines. However, in the summer of 1994 as a part of the operation permit program,
Project CLEAN (Collector Line Evaluation and Assessment of Needs), was implemented.
Wastewater samples were taken from 197 collector lines. The samples were analyzed for
biochemical oxygen demand (BOD), suspended solids (SS), and fecal coliform bacteria. The
data results from the initial round of sampling are shown in Table 3.
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Table 3
Project CLEAN Effluent Sample Data
Township/ Number of Fecal
Municipality samples under 5,000
Anderson 7 2
Colerain 47 13
Crosby 1 0
Delhi 6 2
Green 104 34
Harrison 4 2
Madeira 13 1
Miami 7 0
Springfield 4 0
Sycamore 1 1
Symmes 2 0
Whitewater 1 0
Total 197 55
Samples
Water Quality Standards
Fecal Coliform (colonies/100 ml)
Suspended Solids (mg/l)
Biochemical Oxygen Demand (mg/l)
Ammonia Nitrogen (NH3) (mg/l)
Dissolved Oxygen (mg/l)
Fecal
5,000 to 50,000
2
13
0
2
29
0
6
0
1
0
0
0
53
HCGHD
5,000
40
20
none
none
Fecal
Fecal
over 50,000 TNTC
0
7
1
0
29
0
3
5
1
0
2
0
48
ODH
none
40
20
none
none
3
14
0
2
12
2
3
2
2
0
0
1
41
Ohio EPA
1,000
12
10
1 (summer),
6
Mean
SS
273
100
230
44
83
208
67
30
60
10
26
148
197
3 (winter)
Mean
BOD
11
37
74
10
32
30
50
29
32
8
604
40
197
No. meeting
standards
0
12
0
0
17
0
1
0
0
1
0
0
31
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When the Board of Health adopted a new sewage code in late 1993, they also codified
discharge standards for suspended solids, biochemical oxygen demand, and fecal coliform
bacteria, which are:
• BOD: 20 mg / L
• Suspended Solids: 40 mg / L
• Fecal Coliform Bacteria: < 5,000 colony forming units /100 ml
Table 3 reveals that 31 or 16% of the collector lines met the Board of Health discharge
standards. Nearly three-fourths (72%) failed to meet the standard for fecal coliform bacteria.
This was not surprising since disinfection devices had not been installed on many of the
individual aeration sewage systems. The initial inventory and evaluation of aeration sewage
systems was completed.
Non-mechanical Sewage Systems
A contention by County citizens early in the program was that the Health District ignored the
pollution created by non-mechanical household sewage disposal systems, like leach lines and
dry wells. Once the initial inventory and evaluation of aeration (or mechanical) sewage systems
was completed, the Health District staff began the inventory and evaluation of non-mechanical
sewage systems in 1996. Table 4 shows the number and the type of non-mechanical systems
inspected in 1996 and 1997, and the number of total units that failed the initial inspection.
Table 4
Non-mechanical Sewage Systems
1996
1997
Type
Dry we 1 1
Leach Lines
Subsurface Sandfilter
Privy
Other
Total
Number
783
370
181
1
84
1419
Type
Dry we 1 1
Leach Lines
Subsurface Sandfilter
Privy
Other
Total
Number
162
476
1
212
27
878
Total initial failure 59 (4.2%)
Total initial failure 117 (13.3%)
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The inventory and evaluation of non-mechanical sewage systems is a work-in-progress. By May
17, 1998, a total of 2605 non-mechanical sewage systems had been assessed.
The following inspection criteria are used to approve or disapprove a non-mechanical household
sewage disposal system.
Approve if:
a) System components can be located and
b) The system uses in-soil dissipation and there is no surface seepage of
gray, malodorous effluent (minor seasonal wetness is acceptable).
c) The system uses surface discharge, the discharging effluent is clear, and
not malodorous, does not pond on the inspected property or on adjacent
property, and the discharge pipe is accessible with sufficient freeboard to
allow collection of an effluent sample.
Disapprove if:
a) Gray malodorous sewage is seeping to the ground surface creating a
nuisance.
b) Gray, malodorous sewage is discharging from the sewage system to an
adjacent property or roadside drainage way.
c) Any similar condition is occurring which is creating public health nuisance.
Upon the initial inspection, the percentage of non-mechanical systems disapproved overall
averaged 9%.
The inventory and evaluation of these sewage systems continues. The District did not meet its
goal of inspecting and inventorying all non-mechanical sewage systems by December 31, 1998.
On March 8, 1999, the Board of Health changed the non-mechanical permit from a three-year
permit to a five-year permit.
Building Community Partnerships
All the media attention over the lack of governmental oversight and widespread pollution did not
convince the vast majority of citizens with home sewage systems that the operation permit
program was necessary. Media and citizenry criticism for the Health District's historic lack of
oversight was replaced by concern about Health District persistence in requiring sewage system
repairs and payment of permit fees. During the first eighteen months of the program, the Health
11
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District staff handled 20 to 40 telephone calls per week. About 265 citizens felt strongly enough
to write letters protesting the program. The theme of the letters ranged from why inspect septic
systems to a governmental invasion of their property rights and privacy.
The Health District initiated a community education program in order to gain public acceptance
of the operation permit program, to improve political support, and to teach homeowners about
HSDS operation and maintenance. The education program utilized a variety of strategies.
Brochures were developed and mailed to homeowners and handed out at neighborhood
festivals and community halls. Numerous press releases and editorials in the local newspapers
about the benefits of the operation permit program to the community were published. Health
District staff conducted presentations at neighborhood gatherings or backyard barbecues about
the importance of HSDS.
The Metropolitan Sewer District (MSD) and the Health District forged a partnership and
collaborated at several public meetings. The two agencies worked in concert to extend public
sewers into those watersheds where sewage nuisances were prevalent and HSDS upgrades
were not feasible.
A public sewer assessment credit was established by the Board of Hamilton County
Commissioners. This credit was the brainstorm of MSD and Health District officials as well as a
group of western Hamilton County business leaders and elected officials. The credit program
stated that all single family dwellings with HSDS in existence on or prior to September 20, 1995,
were eligible for $5,000.00 credit towards their public sewer assessment. The public sewer
assessment credit helped convince many homeowners to sewer their neighborhoods.
Using GIS
In 1996, the Health District began using a geographical information system (GIS) known as
CAGIS (Cincinnati Area Geographic Information System). CAGIS technology allows the Health
District to place all home sewage systems, stream quality data, and communicable disease data
on computer generated maps. CAGIS technology allows layers of information to be overlaid on
top of each other in order to carry out analyses. For instance, the public sewer system layer can
be overlaid with the County home sewage system layer. This allows the user to quickly observe
the proximity of public sewers to home sewage systems. Other layers of data, such as stream
data and communicable disease information, are overlaid to look for patterns or clusters of
disease or pollution associated with home sewage systems. The CAGIS technology is a
powerful public health surveillance tool for targeting resources.
12
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Results - Has the Operation Permit Program Made a Difference?
Data comparison between two time periods, 1994 -1995 and 1996-1997, will be used to reveal
program successes and failures. Table 5 compares pass / fail inspections of home aeration
systems for the two time periods.
Table 5
Pass / Fail Inspections of Home Aeration Sewage Systems
1994-1
Number inspected
14,992
995
Percent failed
first inspection
33.1% (4,962 no.)
1996-1997
Number inspected
17,685
Percent failed
first inspection
6% (1,061 no.)
As of January 1, 1998, the total number of aeration systems located by Health District staff had
been 9,515. The staff continues to find additional aeration systems in remote areas of the
County.
When the data between the two time periods is compared, clearly there is a large reduction in
number of systems failing the first inspection. Homeowners are assuring their aeration systems
are maintained. The number of homeowners with private maintenance contracts increased from
1,623 in 1995 to 2,274 in 1996. However, in 1997 the number of homeowners with private
maintenance contracts decreased slightly to 2220.
Additional sampling of collector lines to determine program effectiveness was carried out. A
12% randomly selected subset (23 no.) of the original Project CLEAN sampling locations were
sampled and analyzed for BOD, SS, and fecal coliform bacteria. The collector line discharges
were sampled once each year over the course of a three-year period. (See Graphs, pages 14-
15).
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Graph 1. Biochemical Oxygen Demand
1995
1996
Sampling Period
1997
Graph 2. Suspended Solids
1995
1996
Sampling Period
1907
14
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140
120
Graph 3. Fecal Coliform Levels
1995
1996
Sampling Period
1997
Graphs 1, 2, and 3 reveal the reduction of BOD, suspended solids, and fecal coliform bacteria
loading over the three-year period. The average BOD levels for all samples taken in 1997 show
a fourfold decrease in BOD when compared to the samples taken the first two years of the
program. Similar decreases in suspended solids and fecal coliform bacteria occurred. These
graphs demonstrate that the operation permit program for home aeration systems connected to
collector lines has reduced the potential for disease conditions and reduced pollution in those
neighborhoods.
The stream sampling program was initiated in 1997 as an outgrowth of Project CLEAN. Forty
sampling locations were selected. In 1997, quarterly stream samples were taken from these
locations in an effort to observe differences in water quality over time. During sample collection,
stream velocity, air temperature, and overall weather condition are noted. The goal of the
stream monitoring program is to assess the water quality impact within the watershed. Table 6
shows the stream sampling data. But, there are not enough data points to establish any trends
other than random variation.
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Table 6
Stream Sampling Mean Values
Minimum n = 40
Sampling data
3/12/97
6/19/97
9/25/97
12/17/97
4/2/98
5/6/98
BOD
1.63
6.26
4.45
5.22
5.18
5.91
Fecal
435.98
11541.46
1637.5
5007.73
4546.17
31992.93
NH3
0.07
0.13
0.27
0.6
0.5
0.54
ss
7.34
26.29
6.9
6.91
14.59
113.69
Citizen Opinion Survey
During the spring of 1998, the Health District mailed out 1,000 customer surveys to
homeowners with aeration sewage systems. The sample selection was produced using the
number on the cash register receipt in the home aeration system database. Questions asked on
the survey form included the categories of program effectiveness, program personnel, and
program components. The response has been good. As of June 5, 1998, 314 survey
questionnaires were completed and returned. Table 7 shows the results.
16
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Table 7
Citizen Survey Results
Program Effectiveness:
Odors have improved
Steam clarity improved
Visible sewage decreased
Service good value
Fees affordable
Program Personnel:
Inspectors identifiable
Inspectors courteous
Inspectors helpful
Office courteous
Office helpful
Program Components:
Information adequate
Information helpful
Information understandable
Process understood
Information received
Mean of
Responses
2.92
2.96
2.95
2.83
2.91
2.59
2.17
2.41
2.56
2.61
2.35
2.35
2.26
207 yes, 37 no
166 yes, 65 no
Standard
Deviation
1.10
0.95
0.90
1.18
1.19
0.92
0.76
0.85
0.8I
0.80
Program Effectiveness and Program Personnel: Respondents were given six choices
that were rated on a scale of one to six: (1) strongly agree, (2) agree, (3) neutral, (4)
disagree, (5) strongly disagree, (6) N/A.
Program Components - information adequate, information helpful, information
understandable: Respondents given same six choices listed above.
Program Components - process understood, information received: respondents given
two choices, yes / no.
Under "program effectiveness," slightly more people agree than disagree that the operation
permit program has reduced odors, improved stream clarity, and provides a valuable service.
Written comments from respondents included nine people wanting sewers, seven providing
compliments, nine noting fewer odors, six citizens wanted lower fees, five questioned fairness of
the program relative to pollution from semi-public sewage plants, three people resented
governmental intrusion and eleven citizens considered the operation permit program redundant
because of private maintenance contracts with service companies.
17
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Under the categories, "program personnel" and "program components," a larger number of
people agreed than disagreed that the staff were helpful and the information received was
helpful. Written comments received from citizen survey respondents were similar to the
comments mentioned above.
Enteric Diseases and Sewage
With the advent of GIS technology at the Health District, the staff are also monitoring for any
association between communicable disease reports and their proximity to home sewage
systems. In 1997 an elevated number of cases of Giardia were reported in Hamilton County. A
grouping of giardia cases appeared to be located in and around collector line discharges.
However, upon further investigation by public health nurses and sanitarians, the exposure
source was determined to be outside of Hamilton County. The GIS technology will continue to
be used at the Health District as a public health surveillance tool.
What's Next?
The operation permit program has reduced pollution and disease conditions. Odors and stream
clarity have improved in many neighborhoods. The Health District plans to continue advancing
water quality improvements, improving existing programs and adding new ones. For example,
work has begun on a program that will index stream conditions so that children may play and
explore their backyard creeks and not worry about the threat of disease.
Some program initiatives will be changed. Instead of a assessing home sewage systems and
water quality by political jurisdiction, a new approach currently underway is to use a watershed-
based approach for improving water quality. A big help to accomplishing this will be the new
automated land and development permitting system. The major building and development
permitting agencies of Hamilton County and the City of Cincinnati are installing an automated
land and infrastructure management system known as Permits Plus. This new permitting
process will coordinate all permitting agency activity by placing all data regarding a parcel of
land into a computer file. This will then allow a specific project to be monitored from concept to
completion and thereafter for maintenance support. The integrated approach will provide up-to-
date land use and infrastructure changes in a watershed and allow for the Health District to
assess these impacts on water quality.
Another proposed project includes a review by the Health Commissioner of the contract
between the Ohio EPA and HCGHD for the Health District to regularly inspect semi-public
sewage disposal systems. It is estimated that between 250 to 300 semi-public sewage disposal
18
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systems exist in Hamilton County. Semi-public sewage systems serve small businesses.
Neither the Ohio EPA nor the HCGHD are inspecting these systems.
The Health District will use comments received from the citizen surveys to form new local
coalitions. These coalitions will assist the Health District in its endeavor to educate home
owners about the necessity of maintenance as an important part of owning and operating a
sewage system. If these local coalitions could be assigned to a sub-watershed in which they
reside, the coalition could become an environmental neighborhood watch group. They could
report not only stream impacts from malfunctioning sewage systems but also illegal dumping.
The Health Dstrict should begin educating zoning commissions and the elected officials about
the importance of considering home sewage systems as a part of the local utility infrastructure.
Citizens in these decision making roles tend to default land use decisions in unsewered areas to
local Boards of Health. Future zoning decisions need to take into consideration the watershed
impact created by changes in land use and the choice of sewage utility to serve that use.
Last Words, for Now
No one imagined forty-five years ago the impact that the indiscriminate usage of household
sewage disposal systems, especially aeration systems, would have on the neighborhoods and
backyard streams of Hamilton County. Improper siting and the disregard of maintenance needs
has created a costly cleanup for many citizens today. The passage of a new sewage code and
the implementation of a multifaceted operation permit program has slowed further water quality
degradation and facilitated needed remediation efforts. However, it has taken a lot of effort and
community support to reach this point. The Health District will strive to meet citizen
expectations.
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GIS and GPS in Environmental Remediation Oversight
at Federal Facilities in Ohio
James L. Coon, Kelly Kaletsky, William G. Lohner, and Thomas A. Schneider
Ohio EPA, Office of Federal Facilities Oversight
Dayton, Ohio
Abstract
This paper presents some of the experiences and plans of the Ohio EPA Office of Federal
Facilities Oversight's (OFFO) use of GIS and GPS for environmental remediation oversight at
the U.S. Department of Energy's (DOE) Fernald, Mound, and associated sites. The Fernald site
is a former uranium metal production facility within DOE's nuclear weapons complex. Mound's
mission included production, development, and research in support of DOE's weapons and
energy related programs. Contamination of soil and groundwater at both sites is being
remediated under binding agreements with the State of Ohio and USEPA. The primary
contaminants of concern at the sites include several radionuclides and chlorinated solvents.
OFFO uses GIS/GPS to enhance environmental monitoring and remediation oversight. These
technologies are utilized within OFFO's environmental monitoring program for sample location
and parameter selection, data interpretation, and presentation. GPS is used to integrate sample
data into OFFO's GIS and for permanently linking precise and accurate geographic data to
samples and waste units. It is important to spatially identify contamination because as
remediation progresses visual references (e.g., buildings, infrastructure) are being removed at
both sites.
Availability of the GIS allows OFFO to perform independent analysis and review of DOE
contractor generated data, models, maps, and designs. This ability helps alleviate concerns
associated with "black box" models and data interpretation. OFFO's independent analysis has
increased regulatory confidence and the efficiency of design reviews. GIS/GPS technology
allows OFFO to record and present complex data in a visual format aiding in stakeholder
education and awareness.
GIS and GPS are used in planning and evaluating natural resource restoration projects at
Fernald. These systems can be used to map wetland mitigation projects and evaluate functional
changes including floristic and hydrological parameters. For restoration planning, post
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excavation topography models are used to develop appropriate landscape plans. OFFO
oversight, including GIS support, is an important element ensuring DOE's fulfillment of
commitments to wetland mitigation and Natural Resource Damage restoration.
This presentation is intended to highlight some of OFFO's activities covered above and some
lessons learned in implementing the GIS/GPS program. OFFO's five years of GIS/GPS
development have resulted in numerous lessons learned and ideas for increasing effectiveness
through the use of GIS/GPS.
Background
One of the responsibilities of the Ohio Environmental Protection Agency's Office of Federal
Facilities Oversight is to conduct regulatory oversight of the environmental restoration activities
at the United States Department of Energy's (DOE) Fernald and Mound sites as well as several
Department of Defense (DOD) sites. The Fernald site, formerly known as the Feed Materials
Production Center, is a 1050-acre facility located in a rural residential area, 8 miles northwest of
Cincinnati, Ohio. Production of uranium metal, including slightly enriched, depleted, and natural
uranium began in 1953.Small amounts of thorium metal were also produced. Production
stopped in July 1989 to focus resources on environmental restoration. In December 1989, the
site was added to the United States Environmental Protection Agency's (USEPA) National
Priorities List.
The Fernald site is located over the Great Miami Aquifer (GMA), which is a valued natural
resource and has been designated a sole source aquifer by the USEPA. Ground water has
been contaminated with uranium, various other radionuclides, inorganic, and organic
contaminants. A plume of uranium contaminated ground water extends approximately 1 mile
down gradient of the facility. Six waste pits, used during production, contain approximately
430,910 metric tons of waste, including uranium, thorium, and other radioactive and chemical
contaminants. Four concrete silos were constructed at Fernald to store radioactive materials.
Two of them, the K-65 silos, contain high radium-bearing residues, one contains lower-level
dried uranium-bearing residue, and one has not been used. In addition to the ground water
contamination, surface water, sediment and soil have been contaminated by past activities at
the facility.
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The Mound Plant is located in Montgomery County within the city of Miamisburg, Ohio,
approximately 10 miles southwest of Dayton, Ohio. The Miamisburg downtown area, five
schools, six parks, and many city and township residences are located in the immediate vicinity
of the plant. The Mound Plant is also located within 2000 feet of the Great Miami River (GMR),
which flows for 170 miles through southwest Ohio to its confluence with the Ohio River. A
portion of the Mound Plant overlies the GMR Buried Valley Aquifer (BVA), which is a sole
source aquifer providing potable water for a major portion of Miami Valley residents and
industries.
The Mound Plant was established in 1946 as a facility to support atomic weapons research. Its
primary mission was the process development, production engineering, manufacturing,
surveillance, and evaluation of explosive components for the United States nuclear defense
stockpile. Secondary missions over the next 40 years included nuclear material safeguards,
radioactive waste management and recovery, building and testing of nuclear generators for
NASA spacecraft, and purification of non-radioactive isotopes.
OFFO was created in the spring of 1994 by the Ohio Environmental Protection Agency. It was
created to coordinate and conduct regulatory oversight of the investigation and remediation
activities at federal facility cleanup sites within Ohio. OFFO facilitates environmental monitoring,
emergency response, regulatory oversight, and public outreach at DOE and Department of
Defense sites in Ohio. Funding for Ohio EPA's oversight of the DOE Fernald and Mound sites is
provided in Cost Recovery Grants between the State of Ohio and DOE. Additional information
on OFFO is available on the Internet (http://offo2.epa.state.oh.us).
The Fernald group within OFFO has been employing Geographic Information Systems (GIS)
and a Global Positioning System (GPS) since early 1995 for regulatory oversight and
environmental monitoring at the site. As our experience and confidence with the GIS has
increased, our reliance on it for informed decision making has increased. The GIS provides
timely and accurate information in various formats for problem solving and decision making. The
GPS has been employed to geographically reference new data as it is collected as well as to
acquire positional information on existing structures and waste units.
The Mound group, also within OFFO, has also been using GIS/GPS for regulatory oversight and
environmental monitoring at the Mound plant. The GIS has allowed for verification of sampling
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results within Potential Release Sites (PRS) as well as providing independent modeling of
potential contaminant plumes within a PRS. The GPS at Mound and related sites has been
used for data collection similar to the Fernald site.
GIS/GPS Program
OFFO uses Intergraph's Modular GIS Environment (MGE) and Environmental Resource
Management Applications (ERMA) software. The ERMA software suite includes modules for
groundwater modeling, data management, geologic and subsurface analysis. MicroStation is
used for graphical manipulation of resulting images. The attribute data for this project is stored
in the Oracle relational database management software. Oracle provides the database tools
necessary to store, organize, and manipulate the large amount of environmental data collected
at the Fernald and Mound sites over the past decade. Oracle also works in conjunction with the
MGE and ERMA software from Intergraph. The Intergraph system was selected in order to
remain consistent with the system used by DOE's operating contractors at both sites.
An Ashtech Reliance Husky GPS field unit, Reliance 12-channel GPS base station and
associated Ashtech software are used to complete field mapping projects. Specifically, GPS is
used to support OFFO's GIS, monitoring, and oversight programs. Air, water, soil, and biota
samples are collected by members of the environmental monitoring team. GPS coordinators
accompany monitoring team members into the field and gather GPS data for specific sampling
locations. Following data collection, post-processing occurs at Ohio EPA using Ashtech
Reliance software. Dependent upon the quality of the data and location environment, the
resulting sampling points may achieve an accuracy within 0.1 meters.
After post processing, the geographic information which was recorded in the field is loaded into
the GIS where it is used for sample location mapping, natural resource projects, data
interpretation and presentations. The information is also used to determine future sampling
locations based on existing contaminant data, select analytical parameters including matrix and
contaminants for future sampling, and the creation of contours, maps, and models.
Geographically referenced data is easily integrated within the existing Oracle database for
timely evaluations of temporal data changes. These maps and data analyses are readily
converted into presentations and demonstrations for public education concerning ongoing
remediation and monitoring. The ability to have a graphical representation of environmental
sampling locations has proven beneficial in educating the public regarding OFFO's
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environmental monitoring program.
Currently, remediation activities are taking place at the Fernald site. During remediation, all
buildings will be demolished and the site will become a series of excavations, thus eliminating
most visual references. Waste units will be remediated to risk based cleanup levels leaving
above-background concentrations of contaminants in place. Having the ability to navigate back
to the former waste units will allow OFFO to determine if cleanup levels have been attained and
if any remaining contaminants are migrating at a rate which was not predicted by site modeling.
Before OFFO had GIS/GPS capabilities, environmental models and maps presented in DOE
submittals were reviewed on paper. Document reviews required significant staff time reviewing
binders of data, or an assumption that results and interpretations provided by DOE were correct.
The review process has been significantly enhanced with the acquisition and implementation of
OFFO's GIS/GPS program. These new tools have provided OFFO the capability to interactively
manipulate and review environmental models and maps produced by DOE contractors and to
independently verify conclusions presented in the models.
A series of environmental contaminant visual images have been initiated by OFFO utilizing
bubble or graduated circle mapping. The use of bubble maps allows for quick and relatively
easy visual representation of contamination of a specified area. These projects involved
querying and posting data from OFFO's soil database in order to create bubble maps containing
contaminant ranges that break down according to appropriate regulatory levels. The total
uranium contaminant concentrations of importance are those greater than or equal to the Final
Remediation Level (FRL), which is 80 mg/kg in soil for much of the site, and those exceeding
the Waste Acceptance Criteria (WAC) for Fernald's onsite disposal facility, 1030 mg/kg. Larger
symbols on the map indicate higher corresponding total uranium contaminant values for soil
samples. OFFO utilized bubble maps at Fernald to determine the extent of total uranium in the
soil for a railroad expansion just north of the former production area. This technique was also
used in the Operable Unit 4 silos area to help visualize radium contamination in soils that had
eroded into Paddys Run and separately to assess the presence of RCRA metals in the Paddys
Run corridor. These visualizations were used in evaluating an appropriate response actions and
waste disposition options.
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Much of the work at the Mound site is proceeding based on administrative release blocks or
parcels. Within each release block or parcel there are more potential release sites (PRS) which
represent discrete areas of potential contamination. Contamination within each PRS is being
evaluated and remediated, as necessary, prior to release of the PRS to the Miamisburg Mound
Community Improvement Corporation (MMCIC) for industrial redevelopment. MMCIC is a
nonprofit corporation funded in part by DOE to foster the prompt industrial reuse of the Mound
facility as DOE's work at the site concludes.
To evaluate each PRS, OFFO uses the GIS to generate graphics that display all sampling
locations in and near each PRS. This enables staff to evaluate not only what contaminants are
known to exist within the PRS but also contaminants nearby that could impact the PRS. An
example of this is approach is illustrated in work surrounding PRS 420. PRS 420 is a relatively
small area where initial document submittals to the agency showed only isolated contamination.
The GIS was then used to plot contamination in and around the PRS revealing significant
contamination had been present nearby. Upon more detailed review it became apparent that a
minor area of contamination in PRS 420 was actually part of a larger area of contamination that
was mostly outside of the PRS 420 boundaries. The GIS enabled a rapid areal review
surrounding PRS 420 that otherwise could have only been accomplished after days of pouring
through stacks of documents and drawings.
Identification of this larger area lead to improvements in the process used by DOE and its
contractor to screen data relevant to each PRS being evaluated. Another outcome of this work
was the determination that prior cleanup occurred in the area and that no means existed to
determine what data represented previously remediated areas. Subsequent work has gone into
establishing an appropriate data tracking system that ties data to the location and flags data
from remedial projects so that proper historical and current status can more easily be
determined. This data tracking improvement will save money and reduce the work put into
resampling areas where cleanups have been completed in the past. Now OFFO staff and
citizens reviewing PRS submittals have increased confidence that they are looking at the full
extent of information available about the PRS.
OFFO has also used Voxel Analyst to create three-dimensional models depicting potential
contamination at Mound.PRS266 is a hillside where thorium contaminated soils were dumped
many years ago. Initially, as part of the typical regulatory regime, site generated models were
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reviewed. To increase our understanding of the waste disposal areas nature and extent, OFFO
independently generated a three-dimensional model based upon sampling results obtained in
and around the hillside. This modeling resulted in similar results to the site generated models
and improved regulatory confidence in project decisions.
Our most recent project involving GIS and GPS is the monitoring of construction and success of
six acres of mitigation wetland at the Fernald site. Ohio EPA is using GPS to survey monitoring
points for ongoing restoration research. The research project evaluates the impacts of using
donor site soils in the constructed wetland basins. In addition to surveying monitoring points the
GPS has been used in a roving mode to delineate the boundaries of the constructed wetland.
As data are collected from the treatment and control plots they are entered into the GIS which
allows for geographic referencing and visual analysis of the data. Data such as percent canopy
cover, soil moisture, microbial activity, soil source and treatment type are all collected in the
GIS. These data can then be used to evaluate possible correlations and differences in the
treatment regime and success monitoring. GPS and GIS can also be used to evaluate overall
success of the restoration project including plant survival rates, percent cover in seeded areas,
water levels, etc. The wetland mitigation project is an early phase of what will be a much larger
natural resource restoration effort at Fernald. As a part of a proposed settlement of a Natural
Resource Damages claim, DOE will be completing natural resource restoration on more than
800 acres of the Fernald site. OFFO continues to evaluate methods for using GIS and GPS in
our oversight and monitoring of DOE's natural resource restoration activities.
Remediation of the Fernald site will require large excavations of varying depths across large
portions of the site. Remediation of soil contaminated with uranium will require excavations in
excess of 25 feet deep in some areas. OFFO has used estimated excavation depths and areas
generated by Fluor Daniel Fernald along with existing topographic maps to develop
visualizations of the post remediation topography of the site. Developing this post excavation
visualizations have been useful in evaluating future land uses for the site. When viewing the
extreme variations in post excavation topography and considering the volume of soils that would
be required to reestablish the original site topography, decisions regarding final land use are
much easier. OFFO has used these visualizations of current and future topography in meetings
with DOE and their contractor as well as with the public. Using the information from these
visualizations and input from stakeholders DOE has developed a conceptual site model that
involves the creation of large open water bodies, interconnecting wetlands, expanded riparian
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zones and areas of upland forest.
Conclusion
The Ohio EPA has successfully developed and implemented a GIS/GPS program which is
being integrated into the environmental remediation oversight process. GIS tools have allowed
the Ohio EPA to perform advanced data analysis and gain spatial insight on the problems
encountered at the Fernald site. We are improving the data analysis process by creating maps
and models with environmental data that has been generated at the Fernald and Mound sites.
The GIS allows us to view the massive amounts of data that have been collected at the Fernald
site in a spatial context. The ability to view, manipulate and analyze these data sets allows us to
draw conclusions about spatial correlations and distribution across the three-dimensional
landscape. This information, which is generated using GIS and environmental modeling tools, is
then incorporated into the decision making process in the remediation of the Fernald and Mound
sites. It is the Ohio EPA's belief that the system has helped the agency provide better oversight
for the State of Ohio and assisted DOE in cleaning up the sites in a efficient and cost-effective
manner.
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Environmental Justice in Kentucky: Examining the Relationships
Between Low-Income and Minority Communities and the Location of
Landfills, and TSD Facilities
Larisa J. Keith
Northern Kentucky Area Planning Commission
1.1 Problem
Environmental justice has been a topic of discussion among environmentalists since around the
early part of the century, although it was not termed such at that time. This topic reaches from
lead based paint to nuclear waste dumps. These toxins and all of the hazards in between that
may cause human health problems have been the cause of controversy based on their location
and their remediation. Studies have been conducted to try to determine the severity of inequality
in the siting of such facilities in low income or minority communities. This project attempts to
identify relationships between selected socio-economic variables and treatment, storage and
disposal facilities, within the Commonwealth of Kentucky in the United States, and to define
some of the problems associated with the possible inequality of siting such facilities. These
cause and effect relationships are outlined in problems in Figure 1.
Kentucky has several landfills, hazardous waste management facilities, and incinerators located
throughout the state. In fact, a report on environmental justice cites Kentucky, along with
Tennessee and Mississippi as states where minorities face some of the most disproportionate
risks when its comes to hazardous waste siting. It was found that the percentage of minority
residents who live near waste sites in these two states was more than twice that in other
statewide populations (Charlier, 1994). A community activist in Harlan County, Kentucky, stated
that the reason for this might be that poor communities often don't have the financial or
technical resources to deal with the problem (Melnykovych, 1993). Often the complaints of low
income or minorities are not heard. However, in one case in Kentucky, a $20,000 grant was
awarded to Louisville's Justice Resource Center, to aid in empowering citizens to fight against
the locating of polluters in the West End (First Comes Awareness, 1996). In response to this
another $312,000 federal grant was awarded to the city to reduce emissions from the
Rubbertown chemical plant complex and other industry (Melnykovych, 1996). These examples
illustrate that environmental justice is perceived as a problem in cities and counties in Kentucky.
1
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Figure 1
Problem Tree
Causes
Inequalities of the
distribution of
health risks.
Industries and gov't
feel that they know
economic versus
environmental
benefits best.
Federal level
requirements and
solutions not
addressed at the
local level.
i
r
Lack of adequate
standards.
Core
— bi
Industries and
government do not
provide enough
information.
Lack of community
involvement.
Inequality in the
distribution of TSD
facilities.
Increased
environmental
problem because
clustering of toxic
facilities
Environmental
movement focused
on biological and
health risks without
much thought of
social equality.
EIA and site
selection focuses
only on
environment and
not social issues.
Lack of awareness
of the inequalities in
a given area.
Too many toxic
facilities in low
income and
minority
communities.
Effects
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1.2 Objective
The purpose of this study is to examine the relationships between the location of treatment,
storage and disposal facilities and low income or minority communities in Kentucky. This project
will locate the facilities and attempt to determine if there are any significant relationships
between their concentration and the racial make up, economic status and type of householder.
Also, this project seeks to determine if distance from these facilities has any affect on the
characteristics of the surrounding population. For example, are more minority communities
close to the facilities than there are at a further distance? The project does not attempt to
determine any historical relationship, such as whether or not the facility located itself in the
communities before or after the demographic characteristics were in place. This study attempts
to find any basic statistical relationships that may be present. This study will begin to develop
some initial evidence of any cause and effect relationship, but will not address these cause and
effects in detail. In the future, this study may be used as the basis of further studies of historic
themes and why the hypothesized relationships may exist. Also, if these relationships are found
to suggest causality then policies may need to be implemented to address inequalities in TSD
facility siting. The ultimate objective is to learn whether or not these relationships exist so that
further studies may be suggested, successful siting methods highlighted, and possible policy
solutions created.
1.3 Methods
This study comes in two parts, the first of which is devoted to the relationships between the
number of treatment, storage, and disposal facilities (TSDFs) and population characteristics.
This section seeks to answer if there is a relationship between all of the variables combined,
including the percentage African American, Hispanic, and other minority groups, the percentage
of persons below the state poverty level, the percentage of female-headed households, and the
per capita income, and the number of treatment, storage and disposal facilities at the county
and zip code level. Also, are the relationships between each variable and the number of
treatment, storage and disposal facilities positive or negative? The use of these questions will
help to determine if there is a direct spatial relationship between these variables and the TSDFs.
The second section of the project will be dedicated to the effect of distance from treatment,
disposal, and waste facilities on population characteristics. Do the percentages of minorities
increase or decrease with distance from a facility? Do percentages below the state poverty level
increase or decrease with distance from a facility? Do the percentages of female-headed
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households increase or decrease with distance from a facility? Do the per capita incomes
increase or decrease with distance from a facility? This section will only utilize zip code data.
Again, these questions will aid in determining if there are any spatial relationships between
variables and waste management sites, and whether distance has a relationship with the
distribution of socio-economic characteristics of the communities surrounding facilities.
1.4 Hypotheses
The following expected correlations are under the assumption that the facilities were in place
after the population characteristics were created. The hypotheses for the first section of the
project, assuming that there are relationships between the dependent socioeconomic variables
and the independent facility variable (number of sites) at both the county and zip code level, are
as follows:
1) The greater the number of facilities, the greater the percentage of African Americans
(positive relationship);
2) The greater the number of facilities, the greater the percentage of Hispanics (positive
relationship);
3) The greater the number of facilities, the greater the percentage of other minority groups
(positive relationship);
4) The greater the number of facilities, the greater the percentage of all persons below the
state poverty level (positive relationship);
5) The greater the number of facilities, the greater the percentage of female headed
households (positive relationship); and
6) The greater the number of facilities, the lower the per capita income (negative
relationship).
The hypotheses for the second section of the project, assuming that there are relationships
between the dependent population variables and the independent variable (distance from the
sites) at the zip code level, are as follows:
1) As the distance from each facility gets greater, the percentage of the population that is
African American gets lower;
2) As the distance from each facility gets greater, the percentage of the population that is
Hispanic gets lower;
3) As the distance from each facility gets greater, the percentage of the population labeled
as other minority groups gets lower;
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4) As the distance from each facility gets greater, the percentage of all persons that is
below the state poverty level gets lower;
5) As the distance from each facility gets greater, the percentage of the female headed
households; and
6) As the distance from each facility gets greater, the per capita income gets higher.
Literature indicates that these hypotheses could be accepted. These studies are discussed
further in the literature review.
This project does not attempt to make a causal connection between socio-economic variables
and TSDF sites. The objective is to find any relationship that may indicate that further studies
are needed to connect causes to effects. This is not a historical review determining who came
first, the minority and poverty communities or the facilities; it is a review of the conditions at one
moment in time.
The significance of this study is that it will increase knowledge on the level of environmental
injustices in Kentucky, related to the location of TSD facilities. This will aid in creating a basis for
both residents of the state and decision-makers within industry and government to address the
issue of environmental justice with a background of the current situation through policy making.
Also this study will create awareness of some possible solutions to any recognized problems
with the methods of siting in regard to social characteristics.
This research problem can be addressed by beginning with a background analysis of the
subject of environmental justice, its causes and effects, and some of the actions taken to avoid
it. The following chapter applies to these topics and others to give a framework of the situation
of environmental justice at the current time and how it evolved to be such.
2.1 Background of Environmental Justice
To better understand this project it is necessary to provide a background and the definitions of
used terms. Understanding the background of environmental justice and some of the key
proponents will aid in clearly defining the scope of this project. As with any issue, environmental
justice has taken several levels within its lifecycle (Figure 2). The arrival of the issue, the
popularization of the issue in government, and the implementation of the solutions to the issue
given by government have all been steps leading up to the current situation of environmental
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justice. This life cycle of the environmental justice movement is discussed first in this chapter
with the issue of toxic waste and health risks, which are related to the cause for the initial
uprising of the issue. Next, the grassroots organizations that first brought the issue to light within
their own communities and throughout the nation are discussed. The issue being placed on the
national agenda for political approval is also discussed through a review of the acts and orders
created by the Office of the President. Implementation is also discussed here through EPA
regulations toward siting and treatment, storage and disposal facility requirements, which is
more specifically related to this study. The categories outlined in the following sections give a
background to the lifecycle of environmental justice to date. The EPA is currently doing
evaluation of the effectiveness of these policies and reformulation may or may not occur after
this is released.
2.1.1 Environmental Justice and the Environmental Movement
In the United States there have been essentially three major themes in the environmental
movement according to Ortolano (1996). These themes include the anthropocentric point of
view, which is the dominant theme, the environmental justice issue, and the biocentrism point of
view. The biocentric view is simply the realization that all species have an intrinsic value. This
view was popular in the early 1900's and reemerged in the 1970's with environmental ethics.
The anthropocentric theme includes several motives for protecting the environment. The first of
which is public health. Early in the nineteenth century environmental NGOs were stressing the
importance of public health related to the environment. This has also been the case inside the
environmental justice movement itself. These health risks are later discussed in this chapter.
Other motives included: 1) technological and scientific management of resources and the
acceptance of waste as an unavoidable use of the environment; 2) the effects of human
interference on biological systems, especially after WWII and the problem with DDT in the
1960's; 3) preservation of wilderness in the late nineteenth century; and 4) the resurgence of
transcendentalism in the 1970's (Ortolano, 1996). However, each of these problems was usually
raised by those individuals with sufficient income to allow them to delve into such issues.
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Figure 2
Life Cycle of Environmental Justice
1.Issue
creation by
inequality of
the distribution
8.Future-
reformulation
or termination
2.Issue
Magnified by
Grassroots Org
/.Evaluation -
currently being
done.
3.Issue taken
on by
Environmental
Organizations.
6.EPA
implements new
requirements for
federal agencies
anrl nprmittinn
4.Publications
and media
involvement.
5.Presidential
Acts and
Orders
The environmental justice theme was first raised in raised in the 1980's when it was seen that
environmental programs often imposed unequal costs on people and groups that they effect,
which raised further questions about the fairness of distribution (Weale, 1992). The
environmental justice events that are further discussed in this chapter fall into the time period
extending from the 1960's and 1970's when health issues were highly recognized throughout
the anthropocentric environmental realm, through the uprising of grassroots organizations and
environmental groups. Hazardous waste became popular with the public in the 1970's, just as
other issues dealing with the environment. The upwelling of interest in these issues coincided
with the passage of several acts in legislation. The environmental justice issue was developed
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at the same time as pollution regulation and policymaking was expanded to toxic chemicals.
The Toxic Substances Control Act of 1976 was the first legislation to raise the issue of toxic
materials. After this came the Resource Conservation and Recovery Act of 1976, which was
amended in 1984 to give specific regulations to TSDs. In 1980, The Comprehensive
Environmental Response, Compensation, and Liability Act (CERCLA) was passed, which
helped to deal with toxins after they had been deposited in the environment.
The recognition of the dangers of toxic waste seems to have occurred in both the political and
public communities. However, in the public's view, these legislations and the programs were not
being applied in an evenly distributed manner. This led to the conflicts in several areas across
the nation, where communities got involved in the siting of such facilities. The increase in
knowledge about the dangers of toxic materials and the recognition of this by legislation was
enough to spur residents to take an active part in an issue that could have negative impacts on
their lives. Following these reactions from the public, further legislation (discussed further in this
chapter) was enacted to try and address these problems of inequality. This did not occur until
the mid 1990's and the effects of these programs and legislations are still being evaluated in
1999.
2.1.2 Toxic Waste and Health Risks
There are many health risks associated with toxic materials. Although it is often difficult to
account for the precise amount of risk involved, it is important to realize the possible
consequences to human life. In the United States the definition of risk includes several aspects.
First, it includes the probability of the occurrence of an event, such as a discharge or spill of
toxic materials. Second, it includes the probability that toxins will be released by an event. Third,
it addresses the probable quantity, concentrations, transport and fate of toxins released into the
environment, as determined by conditions at the time of the event. Risk also means the
probability of exposure of individuals, populations, or ecosystems to toxic substances released
into the environment. Finally, risk involves the probability of adverse human health or
environmental effects from exposure to toxins released into the environment (Cohrssen and
Covello, 1989).
It is understandable that no numerical level of risk will be acceptable to everyone, and also that
eliminating all risk to all people is impossible. Therefore it is important to balance the potential
risks to individuals from toxins against the broader benefits to the community. This is an element
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of the concept of environmental justice. No one should be unnecessarily subjected to potential
risks more than others should.
The risks of exposure to toxins can occur through the manufacturing process, transportation to
market, use in consumer goods, and disposal as waste (Heath, 1989). Each of these can result
in toxic releases into the groundwater, atmosphere, the food chain, surface water and soil,
which can lead to increased, and often unrealized, human exposure. These exposures can
result in several health problems. Acute biological abnormalities may include skin rashes, acute
neurological effects, acute hepatic or renal disease, and acute gastrointestinal or cardiovascular
illness. Other sub-clinical acute effects include abnormalities in liver enzyme function, nerve
condition sloping and chromosomal damage (Heath, 1989). These effects, of course, depend on
the type and amount of the chemical. Creating these causal relationships is often difficult and
depends on the accuracy of information regarding exposure and abnormalities. However, any
possibility of negative health impacts that may or may not create a decrease in the quality of life
of human beings is a significant issue that needs to be addressed with the utmost caution.
2.1.3 Grass Roots Organizations
Historically, the location of locally unwanted land uses has followed the path of least resistance;
those associated with health and environmental risks, such as toxic waste sites, included.
However, throughout the years, although low income and minorities have been subjected to a
disproportionate amount of pollution in their neighborhoods and workplaces, they have only
been slightly involved in the environmental movement. Low income and minority communities
have had few advocates at the national level and within the environmental movement (Bullard,
1990). It was not consistently large toxic corporations that created opposition at the start of the
environmental justice movement but also the smaller, home based dangers. Lead-based paint
illnesses were, in the early part of the century, one of the first signs that minorities were getting
more than their fair share of health risks due to toxins. Lead was a middle class issue in the
broad population in the inner city from the 1940's through the 1970's. The first major effort to
deal with it was by a black community in Chicago in 1965. While lead was a big issue to society
for a while, failure came within policy to address relationships between housing community
health and the inner city environmental where many people of color lived. This reinforced the
notion that environmental issues were white and middle class. Lead was a continuing problem
in the 1980's. In substandard housing, particularly, contamination was high (Gottlieb, 1993).
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Another distinctive difference in the distribution of health risks was in the realm of low income
and low skilled workers. Low-income industrial workers, who, in the early 1900's, were the
majority black, were also subject to the dangers of non-controlled environmental risks
associated with industry. This was a time when recruiting on the basis of race was not an
unusual practice for particularly hazardous jobs. There were asbestos cover-ups from the
1930's through the 1950's, and coal mining cover-ups in the 1960's. In 1967 the Association of
Disabled Miners and Widows was formed as the first black lung protection group. In West
Virginia the Black Lung Association in 1969 spurred legislation such as the Coal Mining Health
and Safety and the Occupation Safety and Health Act (Gottlieb, 1993).
These sorts of communities in which environmental injustices have occurred sometimes did so
because the cities had to make a choice between environmental conservation and economic
development. Cities in need of employment opportunities, although they may have tried to
attract clean industry, often had to settle for dirty industry. The view of the communities was
often not to oppose anything that would bring in jobs (Bullard, 1990).
Also in the 1960's and 1970's, came the emergence of concern for pesticides due to the
publication of Silent Spring, not only in conjunction with wildlife hazards, but also farm worker
health and safety. This was also spurred by minority groups, specifically Chicano and Mexican
born workers. However, as of 1990, farm workers were still excluded from any pesticide policy
debates.
During the period of the Cold War, uranium activities in the southwest were booming, mostly in
Native American areas. Soon after, concerns emerged about health and environmental impacts
produced an "environmental legacy" of radiation that was linked to illnesses and deaths. Native
Americans were now unrecognized victims as well (Gottlieb, 1993).
The environmental movement itself though the sixties and seventies, which was concerned with
booming population growth, was said to have racial implications. This was related to the
usurpation of resources and increased pollution (Gottlieb, 1993). In response, since the 1970s,
grass roots organizations have been involved by challenging local governments on the control
of such things as factories and landfills (Brown, 1994). The Natural Resource Defense Council,
the Lawyer's Committee for Civil Rights Under Law, the Sierra Club and the Committee for
Racial Justice of the United Church of Christ all became involved by endorsing legislation and
10
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by cooperating to gain Senate approval of the environmental justice legislation (Coyle and
Carmody, 1993). In 1979, a suburban African American neighborhood in Houston filed the first
lawsuit to charge environmental discrimination. This lawsuit charged Browning Ferris Industries
with locating a municipal solid waste landfill in their community. All five of the city's other
sanitary landfills and six of the eight municipal solid waste incinerators were in mostly African
American neighborhoods. However only 28% of the city's population was African American itself
(Bullard, 1994).
In the 1970's an organization called the Urban Environmental Conference (UEC) began as a
legislative and lobbying environmental counterpart to the Leadership Conference of Civil Rights.
In 1983 this organization funded a conference on toxics and minorities in New Orleans which
was the first major attempt to identify toxic issues as issues of discrimination and social justice.
Unfortunately, the UEC lost its funding and failed to adequately create a connection between
civil rights and the environment (Gottlieb, 1993).
In the 1980's, new groups were formed raising issues of risk discrimination in environmental
terms now known as environmental justice. "Primary actors in these new campaigns were
alternative environmental groups for which the ethnicity factor remained prominent" (Gottlieb,
1993). Environmental justice gained national attention in 1982 when a protest broke out in a
predominantly African American community in North Carolina against the siting of a burial site
for soil contaminated with PCBs (Knollenberg, 1998). This site was a minority community, and
was found to be scientifically unsuitable, but was still developed (Bullard, 1990). Prompted by
these demonstrations, in 1983 the General Accounting Offices (GAO) studied hazardous waste
landfill siting in the District of Columbia region and found a relationship with black percentages
(Bullard, 1990). Due to the upwelling of public protest, the interest of many other organizations
consequently turned to the topic of environmental justice. For example, the Commission for
Racial Justice of the United Church of Christ did one of the most well known studies on the topic
in 1989. This study concluded that race was a likely determinant of the location of commercial
waste facilities and uncontrolled toxic waste sites (Brown, 1994). Around the same time Bullard
released his study entitled Dumping in Dixie (1990). Both proved to be crucial documents in
situating environmental racism.
After these studies on environmental justice, public attention continued to grow. Since the
Church of Christ study, several other grassroots organizations have been involved in the fight
11
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for environmental justice. For example, the Los Angeles City Energy Recovery Project, The
South West Organization Committee in Albuquerque who held the "International Hearing of
Toxics in Minority Communities" in 1989, the Louisiana Toxics Project, and the United Farm
Workers in California. These organizations, along with the study by the Church of Christ, helped
in recognizing the "disparate distribution of environmental contaminants in their communities."
The Church of Christ study also pushed the topic to academia. It suggested that universities
give assistance to minorities seeking training in the environmental fields and that the universities
should develop curricula in environmental sociology (Lee, 1992). In response, the EPA
cosponsored the conference on the Environment, Minorities and Women, designed internship
program for educators at black colleges and universities, and created an Indian Task Force to
be sure they were aware of, and took advantage of, EPA programs (Lee, 1992).
During the 1990's new groups and coalitions were being formed to combat injustice in the
environmental movement. Louisiana based non-Anglo activists and the New Mexico based
South West Organizing Project began stressing the problem with the CEO's of several major
companies. This involvement created significant concern among mainstream groups such as
The Sierra Club, The Audubon Society, and Friends of the Earth (Gottlieb, 1993). In 1991 the
"People of Color Environmental Summit" was held in California. This summit resulted in a public
policy agenda to address environmental injustices. It recommended that Congress and the
President address it through legislation, executive orders, and other governmental policy (Bass
1998).
2.1.4 Acts/Government Orders/ EPA Regulations
When any issue gets large enough to draw attention from the public then it is probable that the
government will get involved. This has been the case with environmental justice. In response to
the fore mentioned public outcry, the government became more concerned. The Environmental
Justice Act of 1993 was a bill directing the Environmental Protection Agency (EPA) and other
federal agencies to cooperate in the clean up of the 100 most toxic counties in the nation. This
bill would trigger additional governmental assistance to these counties in most need (Coyle and
Carmody, 1993).
Finally, in February of 1994, President William Clinton gave Executive Order 12898 that would
lend Federal Actions to Address Environmental Justice in Minority Populations and Low-income
Populations (Bass, 1998). This policy established several things. It gave federal agencies the
12
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mission to achieve environmental justice by identifying and addressing any disproportionately
high and adverse human health or environmental effects of its programs, policies and activities
on minority populations and low income populations of the United States and its territories and
possessions (U.S. Congress, 1998). The Executive Order created the Interagency Working
Group of Environmental Justice that included the heads of several executive agencies and
offices such as the Department of Defense (U.S. Congress, 1998). The Executive Order
required that each federal agency develop an agency wide environmental justice strategy listing
all programs, policies, planning and public participation practices, enforcement, and rule
makings related to human health and the environment (Bass, 1998). The Working Group had to
submit to the President a report describing the implementation of the order and the strategies
(U.S. Congress, 1998). The Executive Order created the development of agencies and the
Working Group to implement the policy for environmental justice. However, the Executive Order
did not create new opportunities for legal challenges against federal agencies for violations of
the order (Bass, 1998).
The next step of the President was to put out a presidential memorandum. This directed federal
agencies to do five things: 1) to analyze the environmental effects of federal actions including
effects on minority and low income communities when required by NEPA; 2) to address adverse
effects of any mitigation measures outlined or analyzed in an Environmental Assessment, and
Environmental Impact Statement or a record of decision on minority and low income
committees; 3) to provide opportunities for community input in the NEPA process, to include and
identify potential effects and mitigation measures in consultation with the affected communities,
improving the accessibility of meeting and providing access to crucial documents and notices; 4)
that EPA, when reviewing NEPA documents, had to ensure that the involved agency had fully
analyzed the environmental effects on minority and low income communities; and 5) to ensure
that the public, including minorities and low income communities, had access to public
information relating to human health or environmental planning, regulations, and enforcement
when required under the Freedom of Information Act, The Sunshine Act, or the Emergency
Planning and Community Right to Know Act (Bass, 1998).
More recently, the EPA proposed a new policy that will guide the investigation of any complaint
that state or local permitting decisions has violated federal civil rights laws (Johnson, 1998). It is
an interim guidance policy for environmental justice cases (Information Access Company,
1998). It will provide a framework for EPA's Office of Civil Rights to handle complaints under
13
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Title VI of the Civil Rights Act of 1964 that prohibits discrimination (Johnson 1998). If the EPA
finds discrimination and the permitting agency can not eliminate it, then EPA will deny, annul,
suspend or terminate its funding. It may also refer the matter to the Department of Justice for
litigation (Williams et al, 1998). This process is applicable to all that receive federal funds
(Williams et al, 1998). Some say that the Title VI guidance from the EPA fails to define many
important terms, including how they will determine whether a particular community has suffered
"disproportionately high adverse human health or environmental effects" (Johnson, 1998). The
EPA's Guidance has initiated a lot of controversy because of its vagueness and is being
evaluated and may be changed based on suggestions that have been made. A report that will
evaluate the interim guidance policy is scheduled to come out in 1999 (Information Access
Company, 1998).
While those given legislations may seem vague when considering where to locate a toxic waste
facility, there are more rigid standards that focus on treatment, storage and disposal facilities
specifically. These standards are based on The Resource Conservation and Recovery Act
(RCRA) of 1976. For example, if the generator is not notified by TSDFs that waste has been
received within a certain period of time specified by the EPA, the generator files a report to
indicate that a departure from the required procedure has occurred. In addition, if the waste
received by a TSD facility is different than the waste detailed on the manifest given by the
generator, then a report must be again filed describing the discrepancy (Ortolano, 1997).
All TSD facilities that receive solid hazardous waste must also obtain a permit. There are
several requirements that must be satisfied to acquire this. These requirements include records
and reports that notify authorities of releases, provisions that will ensure that unauthorized
persons do not enter the facility, technical specifications such as liners in landfills that prevent
hazardous material from entering groundwater, monitoring to detect releases of hazardous
materials, corrective action for any release that may have human health and environmental
risks, detailed plans for closing the facility and maintenance of monitors, and financial
responsibility in the form of bonds or insurance that can sufficiently pay for any damages
incurred. The EPA also issues requirements on treatment of hazardous materials so that they
must use the best available technology to clean out contaminants (Ortolano, 1997).
More relevant to this study, there are specific standards for siting of facilities. Most of which
involve topography and natural environment. For example, location standards specific to
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Kentucky include consideration of seismic conditions and flood plains. However, there is no
mention of any standards based on population characteristics (KAR, 1997).
It has been stated that typically the process of site selection is based upon technical
engineering, environmental and economic suitability, and public and political acceptability. Site
suitability is dependent on the characteristics of the facility, characteristics of the site, and
economic and legal feasibility. It involves regulatory approval by a federal agency or a federally
approved state agency, and site approval by state or local government, as well as the previously
mentioned permits under RCRA. These local government regulatory influences include land use
controls, nuisance and construction controls, and taxes and fees implied on facilities. Site
acceptability to the host community is also a concern. This concern stems from the possibility of
negative consequences, due to the siting, and not usually the probability of such. Therefore, in
order to obtain acceptability, information on impacts must be made readily available and open
communication and negotiation must be continued throughout site selection. Environmental
impacts are often assessed through environmental impact statements and socioeconomic
impacts are addressed through proof of economic need for the facility, compensation,
preventive measures and mitigation (Andrews and Lynn, 1989).
The regulations and legislation presented throughout this section have begun to create some
support for the topic environmental justice at the national and federal level. Local powers may
have a hand in siting decisions, however no equality issues seem to be addressed at that level
either. It has yet to be seen how the federal regulations will specifically address the issue of
siting without inclusion of explicit regulations within state or local statutes.
2.1.5 Specific Literature Related to the Study
The books and articles presented in this section provide a background to this project that has
leant several methodologies, limitations, and information on the subject of environmental justice.
There have been a number of studies done that support the objectives of this project. This type
of study has been done on several areas, including Texas, Florida, Michigan, and Los Angeles
County, as well as on a national scale (Yandle and Burton, 1996, Pollock and Vittas, 1995,
Hockman and Morris, 1998, Boer, Pastor, Sadd and Snyder, 1997, and Oakes, Anderton, and
Anderson, 1996). These studies were key in determining the methodology and hypotheses for
this project. Several of the cited studies utilize similar variables, and methodologies, and have
similar sets of conclusion.
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There seems to be some disagreement on how to deal with measuring the impacts of waste
facilities on surrounding communities. Should a study use census tracts, census block groups,
or zip codes? Most of the studies reviewed for this project utilize census tracts for the study
area. However, in Michigan, zip codes were used (Hockman and Morris, 1998) and in Florida,
census block groups were used (Pollock and Vittas, 1995). Although research indicates that
census tracts will give the best indicators of the concentration of waste sites in low income or
minority communities, this study will utilize zip codes due to the limited data availability on such
a small scale. While the zip codes may not be as appropriate to the study as census tracts, as
discussed in section 3.1 and 5.2, there are acceptable because they are relatively small areas,
in this study, ranging from 0.45 to 454.65 square miles. The population ranges within these zip
codes are from 27 to 46,612 persons. This yields a density range of 1.4 to 7985.8 persons per
square mile within the zip codes. These numbers give a relatively good variance that will
provide for a adequate analysis.
Variables that are best for use as indicators of disproportionate siting are also not identical in
each of these studies. What are common are the percentage of the population that is minority,
and some form of income indicator. The type of waste facility used varies among each as well.
In both the studies on Los Angeles County and at the national level, treatment, storage and
disposal facilities were used (Boer, Pastor, Sadd, and Snyder, 1997, and Oakes, Anderton, and
Anderson, 1996) and in Texas hazardous waste landfills were used.
Several different methods of statistical analysis have been used in studying this topic. One of
the most widely used statistical methods that were used in the previous studies is regression.
Others that were cited include t-test and Wilcoxon nonparametric Z-test (Oakes, Anderton, and
Anderson, 1996, and Boer, Pastor, Sadd and Snyder, 1997) as well as chi-square and Cramer's
V (Yandle and Burton, 1996).
The national tract level study indicates that commercial TSDFs are on an average sited in
communities that are neither disproportionately poor nor minority. The study also notes that this
finding does not rule out specific local bias in TSDF siting or the impacts of siting on one specific
community (Oakes, Anderton, and Anderson, 1996). At the conclusion of the analyses in the
Florida, Texas, Michigan, and Los Angeles County some sort of relationship was found with at
lease one of the variable used in each study. More specifically, the study by Pollock and Vittas
(1995) found that African Americans and Hispanics reside closer to potentially hazardous
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sources in Florida. The study by Yandle and Burton (1996) found that there was a statistically
significant relationship between relative poverty and hazardous waste landfill siting in
metropolitan Texas. It was found that both race and income combined were strong predictors of
hazardous waste facilities siting in Michigan, and that separately, race was a more potent
predictor (Hockman and Morris, 1998). In Los Angeles County, Boer, Pastor, Sadd, and Snyder
(1997) found that most communities affected by TSDFs are working class communities of color
located near industrial areas. The Commercial Appeal studied census tracts in which landfills,
sewage-treatment plants, hazardous-waste processors, medical waste incinerators, leading
toxic polluters and abandoned hazardous waste sites were located in Shelby County, Kentucky.
This study found that of the 53 facilities that were studied, 26 were in census tracts where
Blacks were the majority, and 25 were in tracts where whites were the majority (1994). These
smaller scale examples give the foundation for the need for further projects focused on more
specific areas such as counties and census tracts in the Commonwealth of Kentucky.
The studies just cited are the most helpful in determining what things to look for in the first
section of this project. Methodologies, sources of information, breadth of study area, and time
periods, as well as the pros and cons of each are seen. This is useful information in determining
the course of action used for this project.
The second section of this project, which concentrates on how socio-economic variables are
influenced by distance from the facilities, is modeled after a study done by Wang and Auffrey
(1998) at the University of Cincinnati, in Ohio. The variables used in this study included mortality
rates and related socio-economic factors and information from the Master Sites List and the
Toxic Release Inventory from the Ohio Environmental Protection Agency. The study area was
the City of Cincinnati and the units of study used were census block groups. For this study of
Kentucky the variables and study area will be different but the methods of analysis will be
basically the same. These methods will be discussed further in the methodology section of this
proposal.
Other authors have made comments on the Yandle and Burton article that are also of use to this
project. One such article is by Robert D. Bullard (1996). Bullard offers a list of assumptions that
have been unjustly made by Yandle and Burton in their article. Assumptions given in this article
provide for some points of thought that may be applied to this project. These assumptions
include the following: 1) environmental justice is limited to waste facility siting, 2) leaving out
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some facilities and communities will not affect study results, 3) all census tracts are created
equal with respect to population density and proximity of the population to facilities, 4) census
tracts represent homogeneous neighborhoods, 5) all hazardous waste landfills are created
equal and, 6) environmental justice and environmental racism are the same.
This study realized that environmental justice includes several factors, not only the location of
TSDFs. It is not an assumption of this project that generalities may be made to all facilities
based on the findings. Leaving out facilities and communities may limit the study, however, the
information available often dictates such things. Although census tracts are not used in this
project, the same limitation may be stated for zip codes. These limitations are recognized and
are discussed further in the analysis. Environmental justice has been previously defined in this
paper, so not to confuse the reader with different terminology. This project does not attempt to
create new or to redefine definitions of environmental racism or environmental justice.
2.1.6 Definitions
Environmental justice is defined by the EPA as "equitable treatment of all people, regardless of
race, income, culture or social class with respect to the development, implementation and
enforcement of environmental laws, regulations, and policies" (Heaton, 1999). Equitable
treatment, in this case, means that no group should bare a disproportionate share of negative
environmental impacts because of government actions (Bass 1998).
Minority and low income communities in this project are defined by following census variables:
1) the percentage of Blacks, Hispanics, and other nonwhites; 2) the percentage of all persons
that are below the state poverty level; 3) the percentage of female-headed households; and 4)
per capita income. The definitions of each of these separate variables came from the US
Bureau of the Census (1990). Black is defined as persons who indicated their race as "Black or
Negro" or reported entries such as African American, Afro-American, Black Puerto Rican,
Jamaican, Nigerian, West Indian or Haitian. Hispanic is defined by the 1990 Census by Hispanic
Origin. This group includes those who classified themselves as Mexican, Puerto Rican, Cuban,
or Other Spanish/Hispanic origin such as those from Spain, the Spanish-speaking countries of
Central or South America, or the Dominican Republic or those who generalized themselves in
categories such as Spanish, Spanish-American, Hispanic, Hispano, and Latino. Origin is
defined by the census as the ancestry, nationality group, lineage, or country of birth of the
person or the person's parents or ancestors before their arrival in the United States. Persons of
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Hispanic origin may be of any race. In this study "other" is a combination of the remaining
nonwhite races. Within this group are included American Indian, Eskimo, Aleut, Asian and
Pacific Islander. American Indian is defined as persons who indicated their race as "American
Indian," entered the name of an Indian tribe, or reported such entries as Canadian Indian,
French American Indian, or Spanish American Indian. Eskimo is also in this category, this label
includes persons who indicated their race as "Eskimo," or reported entries such as Arctic Slope,
Inupiat, and Yupik. Aleut is included in this category and is composed of persons who indicated
their race as "Aleut," or reported entries such as Alutiiq, Egegik, and Pribilovian. Asian and
Pacific Islander categories are comprised of two different groups. Asian is composed of persons
who indicated their race as Chinese or who identified themselves as Cantonese, Tibetan, or
Chinese American, Taiwanese or Formosan, Filipino, Philipino, Philipine, or Philipino American,
Japanese, Nipponese or Japanese American, Asian Indian, Bengalese, Bharat, Dravidian, East
Indian, or Goanese, Korean and Korean American, Vietnamese and Vietnamese American,
Cambodian, Hmong, Laohmong, or Mong, Laotian, Laos or Lao, Thai, Thailand or Siamese,
Bangladeshi, Burmese, Indonesian, Pakistani, Sri Lankan, Ameriasian, or Eurasian. Pacific
Islander within this category includes persons who indicated their race as Hawaiian, part
Hawaiian or Native Hawaiian, Samoan, American Samoan, or Western Samoan, Guamanian,
Chamorro or Guam, and others such as Tahitian, Northern Mariana Islander, Palauan, Fijan,
Polynesian, Micronesian or Melanesian (U.S. Bureau of the Census, 1990).
Female-headed households are defined as families with a female householder and no spouse
of householder present (U.S. Bureau of the Census, 1990).
Persons categorized as being below the poverty level are all determined by the Social Security
Administration definition modified by the Federal interagency committees and prescribed by the
Office of Management and Budget. The core of the definition is the 1961 economy food plan,
the least costly of four nutritionally adequate food plans designed by the Department of
Agriculture. This is based on the determination that families of three or more persons spend
approximately one-third of their income on food, hence, the poverty level for these families was
set at three times the cost of the economy food plan. For smaller families and persons living
alone, the cost of the economy food plan was multiplied by factors that were slightly higher to
compensate for the relatively larger fixed expenses for these smaller households. Thresholds
were set by the Census and if the total income of each family or unrelated individual in the
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sample was less than the corresponding threshold, it was classified as "below the poverty level".
The poverty thresholds were applied on a national basis (U.S. Bureau of the Census, 1990).
Per capita income is defined by the 1990 Census as the mean income, which is calculated by
dividing the total income of a particular statistical universe, in this case counties and zip codes,
by the number of units in that universe. It needs to be understood that this number is usually
recorded by memory and are only estimates and often therefore underreported or
overestimated. Procedures were also used by the Census to estimate appropriate values for
unreported numbers (U.S. Bureau of the Census, 1990).
The hazardous facility that is to be used in this study is labeled as a treatment, storage, and
disposal facility (TSDF). TSD facilities include treatment units such as incinerators, dewatering
facilities, and waste solidification facilities, landfills, surface impoundments, and underground
injection wells. Treatment, storage and disposal facilities are the last stop for hazardous waste
in the system of tracking hazardous waste under The Resource Conservation and Recovery Act
(RCRA) of 1976 (Ortolano, 1997). Title 401 of the Kentucky Administrative Regulations (KAR),
which lays out the standards for TSDF's, gives specific definitions for each type of facility. A
treatment facility is termed as using any method, technique or process including neutralization,
designed to change the physical chemical or biological character or composition of any
hazardous waste so as to neutralize such waste, to recover energy or material resources from
the waste, or to render such waste either nonhazardous or less hazardous. This type of facility
makes the waste safer to transport, store, or dispose of; or amenable for recovery, amenable for
storage, or reduced in volume. A storage facility is simply a place in which hazardous waste is
held for a temporary period, at the end of which the hazardous waste is treated, disposed of, or
stored elsewhere. A facility at which hazardous waste is intentionally placed into or on any land
or water, and at which waste will remain after closure is termed a disposal facility. These are the
definitions accepted by the Natural Resources and Environmental Protection Cabinet and the
Department for Environmental Protection Division of Waste Management, and so they are
accepted for this study. The specific TSD facilities that are used in this project were obtained
from the Kentucky Department of Waste Management's Notifier's list. This list is compiled of all
hazardous waste generators (large, small, and conditionally exempt generators), treatment,
storage, and disposal facilities, recyclers, burner/blenders and transporters of hazardous waste
in the Commonwealth of Kentucky. All of the above facilities (except conditionally exempt
generators) are required by law to notify the State of their hazardous waste generation or
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transportation. Conditionally exempt generators (CEG'S) generate less than 100 pounds of
hazardous waste a month and therefore are not required to register, but Kentucky encourages
notification of the CEG's.
This background provides the needed information to understand the problem of environmental
justice and to see the need for studies such as this one. These definitions also lead to the next
chapter, which applies particular methodologies to the variables mentioned in this chapter, and
which will allow for the previous hypotheses to be tested.
3.1 Methodology and Data
The purpose of this project is to analyze the relationships between the following variables and
the number of treatment, storage and disposal facilities in the State of Kentucky. Data on the
percentage of African Americans, Hispanics and other nonwhite groups, the percentage of all
persons that are below the state poverty level, the percentage of female headed households,
and the per capita income were obtained from the U.S. Bureau of the Census. The 1990 data
will be used at both the county and the zip code level. There are 120 counties in the State of
Kentucky within its 39,732.3 square miles (Figure 3). This gives rise to a rational comparison
with zip codes. This rationality will be important in evaluating the change of effect on areas of
different sizes. The particular population variables that have been chosen typically reflect the
characteristics of the population as being minority or low income. These variables have often
been sited as key in determining if disproportionately high and adverse health or environmental
effects are imposed on minority and low-income populations.
The location of treatment, storage and disposal facilities was obtained from the Kentucky
Division of Waste Management. The data retrieved on these facilities include the name of the
facilities, both the facility and the mailing addresses, the type of facility, and a contact person for
each facility. This material was manipulated to determine the number of sites in each county
and in each zip code. Some facilities were not used in the zip code analyses that were used in
the county analysis because of lack of data from the census. Also, for GIS purposes, the
address information created a means to locate the facilities on a map for graphic representation
of the relationships to the population variables and for distance analysis.
The waste management facility sites and the population, poverty and income variables are used
to determine any relationships that may imply disproportionate siting of facilities. These
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variables are used in statistical analyses to show the strength of the relationship between all of
the variables combined and the number of TSDFs. The method of analysis to be used for the
first part of the project is regression and bivariate correlation coefficient.
For the second part of the project, concentrating on distance, as before mentioned, the methods
of analysis somewhat follows those in the study by Wang and Auffrey (1998). Here, GIS was
used for geocoding and buffering analysis. Statistical analysis, including the independent t-test
for equality of means and bivariate correlation coefficients, are calculated to determine the
relationships and their direction.
Close attention is paid to any limitations created by the statistical analysis used, the study area
used, the types of data collected, and other specifics of the project, and what influences these
decisions have on the outcomes.
The data and statistical methodology, along with GIS, give the information on the relationship of
population characteristics and the siting of treatment, storage and disposal facilities in Kentucky.
This will begin to determine if there are disproportionately distributed facilities in Kentucky that
may in turn create a higher amount of adverse health or environmental effects on low income or
minority populations.
3.1.1 Comparison Analysis
For the first section of this analysis, a simple creation and comparison of maps is done. This
entails making use of Geographic Information Systems, specifically in the form of ArcView. The
information that was given on counties and zip codes in the form of shape files from ArcView is
utilized for creation of the images. Attribute files listing the characteristics of both counties and
zip codes were then attached to these images. The information within these attribute files was,
again, obtained originally from the U.S. Bureau of the Census (1990), and was manipulated to fit
the form and content required in ArcView. The addresses for the treatment, storage and
disposal facilities that were obtained from the Kentucky Division of Waste Treatment were also
inputted into ArcView for the geocoding and comparison.
3.1.1.1 Geocoding
The addresses obtained from the Division of Waste Treatment were geocoded in ArcView by zip
code. This function entailed comparison between the zip code given for each facility and the
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data within the zip code file in ArcView. This function gives each match a score from zero to one
hundred. Each facility used for the creation of this map received a score of 100, although not all
facilities listed were matched. This non-matching occurred because of the missing data with the
ArcView zip code file. This resulted in locating the precise location of most of the facilities on the
zip code level. The locations were then translated into an attribute form that was more easily
comparable with the other variables. The maps that were created from this information show the
number of facilities in each county (Figure 4) and in each zip code (Figure 5). The number of
facilities for the county map increased from the number in the zip code map because each
facility was matched to a county that was present in the ArcView shape file of counties and not
all zip codes containing facilities were present in that file.
3.1.1.2 Boundary and Attribute Files
The data obtained from the U.S. Bureau of the Census (1990) was also translated into map
form that made comparison possible. County municipal lines and zip code lines within the
Commonwealth of Kentucky were used as the boundaries for this project. As with the facility
information, the variables used were in an attribute file that was joined to the county and zip
code shapes. These files, along with the additional facility information, were used to create
graduated color schemes for each variable. The patterns of the color schemes for each variable
are compared to determine similarities.
3.1.1.3 Multiple Linear Regression and Bivariate Correlation
A regression analysis was done at both the county and zip code level. In order to determine the
relationship between multiple variables and the number of facilities, the socioeconomic variables
were plotted as the independent variables and the number of facilities as the dependent
variable. This analysis gives the nature of the relationship between a group of variables. It is a
mechanism of forecasting (Levin and Fox, 1988). The statistic given is r2' which is the coefficient
of determination. It yields the proportion of variance in the dependent variables that is explained
by the independent variable. It gives this proportion as a percentage, which is r2 multiplied by
one hundred. The significance level used for this study was 95 percent.
Bivariate correlation was also conducted with Microsoft Excel to test the significance of the
relationship between the facilities and the socioeconomic variables. This statistic gives the
degree and type relationship between two variables. This analysis results in a number from
positive one to negative one, positive one being a perfect positive relationship and negative one
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being a perfect negative relationship. This number is defined as "the ratio of the covariance
between X and Y to the product of their respective standard deviations" (Levin and Fox, 1988).
Numbers other than one will show different strengths in the relationships. Positive or negative
0.6 is a strong relationship, positive or negative 0.3 is a moderate relationship, positive or
negative 0.1 is a weak relationship and 0.0 is no relationship (Levin and Fox, 1988). For this
analysis, weak relationships will range specifically from 0.1 to 0.30, moderate relationships will
range from 0.31 to 0.6 and strong relationships from 0.6 and up. This statistic will create the
basis of acceptance or rejection of the hypothesis.
3.1.2 Distance Analysis
In order to account for the variable of space it was necessary in this project to deal with distance
from each of the facilities and the spatial effect on the populations. Since zip codes are the
smallest unit used, these are the focus of the distance analysis. The use of distance is an
important factor of understanding the possible relationships between the location of facilities and
socioeconomic characteristics. If some pattern is found to exist for one or more of the variables
with distance from the facilities, then it may suggest a spatial connection.
3.1.2.1 Buffering Analysis of Zip Code Data
The buffering was again made possible by ArcView GIS. As the use of zip code data made it
difficult to assign precise rings around each of the facilities, a buffer of zip codes immediately
adjacent to zip codes with facilities was used. The act of buffering entailed selecting from the zip
code theme all of the zip codes that contained one or more facilities. A separate shape file was
created from these. Next, the zip codes designated to be immediately adjacent to these zip
codes with facilities were selected from the original zip code theme. This was done by the select
by theme function. Then, zip codes that were found to be adjacent to the first ring were selected
in the same manner. Deletion of the overlapping zip codes from the previous selection was
done. The same was done again for adjacent zip codes two more times.
3.1.2.2 Reclassification
The buffering method used to reclassify zip codes into five categories was basic adjacency to
zip codes with facilities present. The zip codes with facilities were grouped into the first category
and labeled with the number five to denote the closest characteristics to the facilities. The first
ring around the zip codes with facilities was created from all of the zip codes that were
immediately adjacent to those zip codes. These were labeled with the number to four to denote
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that they were at a different distance from the facilities. This continued on to create three more
categories labeled from three to one; with one representing the zip codes that were furthest
away from those zip codes with facilities.
3.1.2.3 Bivariate Correlation and T-test
As done in the previous analyses, the bivariate correlation is used in this section of the study.
The analysis will be a bit different, however, in that it utilizes different data indicating zip codes
with and without facilities. By this it will address the distance factor by assigning numbers to the
zip codes based on their distances from those zip codes with facilities. This created a sort of
ranking of the zip codes.
Also in this section of the analysis, a t-test for the difference in means is conducted. This
analysis utilizes the adjustment for unequal variances since one sample variance is more than
twice the other is. This statistic gives a way to determine the differences between two means of
the same variable, those with facilities, and those without. This is done by calculating the mean
for each group, finding the standard error of the difference between means and the determining
the critical value for t. Then the critical value from the t table is compared to the actual t value. If
the actual t value is greater than or less than the positive and negative t table critical value then
one can say there is a difference in the means (Levin and Fox, 1988). This test will find any of
the following relationships: 1) meanwith > meanwithout; 2) meanwith < meanwithout; 3) meanwith =
meanwithout. This will be done using the zip code data only. The test done will be two tailed and
the level of significance is 0.05. This analysis will be done using Microsoft Excel.
These methodologies and data lead to the following chapter, in which the results will be shown
of the tests and mapping laid out in this section. Some things mentioned in this chapter will be
further explained and analyzed in the following chapters.
4.1 Comparison Analysis
This analysis begins by comparing maps created in ArcView for each socioeconomic variable
with the maps of the number of facilities. Each variable was spit into four categories. This was
done by the quantile method. This method involves splitting the number of data points by four so
that each category will contain approximately the same number of entries. This was used for
both county and zip code maps Using this type of methodology makes it easy to compare each
map. The map analysis done here is a basic description of the patterns of each characteristic
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within the counties and the zip codes. These patterns may or may not show evident likenesses
between each variable and the number of TSDs. Patterns are compared and more detailed
information is given on significant numbers for each variable. This analysis is followed by the
statistical results from the regression analyses and the bivariate correlation analyses for both
county and zip code information.
4.1.1 County Map Analysis
When comparing the maps showing the number of treatment, storage and disposal facilities
(Figure 4) and the percentage of the population that is black (Figure 6) in each county it is
noticeable that both are somewhat concentrated more in the western and central section of the
state than in eastern Kentucky. Of the counties that contain three to twenty-five facilities the
percentage of the population that is black ranges from 0.0 to 17.0. Counties with the three
highest percentages of Blacks include Jefferson (17.0), Christian (18.7), and Fulton (24.5), each
of which contains facilities. Christian and Fulton County contain one facility, and Jefferson has
twenty-five. Those counties containing facilities and that have the three lowest percentages of
Blacks include Marshall (0.0), Martin (0.1), and Grayson (0.4).
Counties that are in the highest category for both variables include Jefferson, Fayette, Shelby
and Hardin. Those in the second highest category of percent Blacks and the second highest
category of facilities include Anderson, Mercer, and Muhlenburg. Harrison is also included in
this category with a percentage between 2.3 and 5.5 and has three facilities, which places it in
the top category for number of facilities. Those counties in the third highest category of Blacks
that contain facilities include Boone, Campbell, Carroll, and Hancock each with one facility,
Bullitt, Ohio, and Pulaski with two facilities, and Boyd, with ten facilities. Of the forty counties
with facilities, three are in the lowest category of black percentages.
In comparing the number of facilities and the percentage of the population that has Hispanic
origin, it can still be seen, though not to such an extent that both are more concentrated in the
western and central areas of Kentucky (Figure 7). Of the counties that contain three to twenty-
five facilities the percentage of the population that has Hispanic origin ranges from 0.1 to 2.7.
Counties with the highest percentages of Hispanic include Meade (2.0), Hardin (2.7), and
Christian (3.4). These counties all include one TSD facility, Hardin with 3. Henry County has the
lowest percentage of Hispanics, at zero. There are several other counties containing facilities
that have 0.1 and 0.2 percent Hispanic. Counties that are in the highest category for both
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variables include Jefferson, Boyd, Fayette, and Marshall. Those in the second highest category
of percent Hispanic and the second highest category of facilities include Mercer, Hopkins, and
Pulaski. Those counties in the third highest category of Hispanic that are also in the third
category of facilities include Campbell, Bell and Galloway, each with one facility. Of the forty
counties with facilities, eight are in the lowest category of Hispanic percentages.
In comparing the number of facilities and the percentage of the population that has Hispanic
origin, it can still be seen, though not to such an extent that both are more concentrated in the
western and central areas of Kentucky (Figure 7). Of the counties that contain three to twenty-
five facilities the percentage of the population that has Hispanic origin ranges from 0.1 to 2.7.
Counties with the highest percentages of Hispanic include Meade (2.0), Hardin (2.7), and
Christian (3.4). These counties all include one TSD facility, Hardin with 3. Henry County has the
lowest percentage of Hispanics, at zero. There are several other counties containing facilities
that have 0.1 and 0.2 percent Hispanic. Counties that are in the highest category for both
variables include Jefferson, Boyd, Fayette, and Marshall. Those in the second highest category
of percent Hispanic and the second highest category of facilities include Mercer, Hopkins, and
Pulaski. Those counties in the third highest category of Hispanic that are also in the third
category of facilities include Campbell, Bell and Galloway, each with one facility. Of the forty
counties with facilities, eight are in the lowest category of Hispanic percentages.
The map of the percentage of the population that is other nonwhite is more evenly dispersed
than the two previous variables, however the highest percentages do appear to still be slightly
concentrated in western and central areas (Figure 8). There are counties throughout each
region that contain some small percentages of other races. Of the counties that contain three to
twenty-five facilities the percentage of the population that is other nonwhite ranges from 0.1 to
3.9 Counties with the three highest percentages of other nonwhite include Hardin (3.9),
Christian (3.7), and Meade (2.4). Both Christian and Meade contain one facility, Hardin with
three. The lowest percentage of the population of the counties that is other nonwhite is zero.
Counties that are in the highest category for both variables include Jefferson, Fayette, Boyd,
and Hardin. Those in the second highest category of percent other nonwhite and the second
highest category of facilities include Anderson, Bullitt, Mercer, Boyle, and Pulaski. Those
counties in the third highest category of other nonwhite that contain facilities include Bath,
Hancock, Daviess, Bell, and Todd, each with one facility, Muhlenburg and McCracken with two
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facilities, Harrison with three and Marshall with five. Of the forty counties with facilities, four are
in the lowest category of other nonwhite percentages.
The map showing the percentage of female-headed households has a random pattern
dispersed throughout the state (Figure 9). Of the counties that contain three to twenty-five
facilities the percentage of the population that is black ranges from 11.6 to 20.8 Counties with
the three highest percentages of female-headed households include Fulton (22.9), Fayette
(20.8), and Galloway (20.1) Fulton and Galloway each have one facility, and Fayette has six.
Those counties containing facilities and that have the three lowest percentages of female-
headed households include Bullitt (7.4), Oldham (8.2) and Meade (8.4).
Counties that are in the highest category for both variables include Jefferson, Fayette, and
Boyd. Those in the second highest category of percent female-headed households and the
second highest category of facilities include Mercer, Hopkins, and Muhlenburg. Also included in
the second highest category of female-headed households is Harrison with three facilities and
Marshall with five. Those counties in the third highest category of female-headed households
and facilities include Grayson, and Christian, each with one facility. Of the forty counties with
facilities, six are in the lowest category of female-headed household percentages.
The map showing the percentage of the population below poverty level has a pattern increasing
from northwest to southeast (Figure 10). The highest percentages are found almost solely in the
eastern part of the state. Of the counties that contain three to twenty-five facilities the
percentage of the population that is below poverty ranges from 12.3 to 16.6 percent. Counties
with the three highest percentages of Blacks include Owsley (51.0), McCreary (45.3), and Wolfe
(43.6). None of these counties contain facilities. Those counties with the three lowest
percentages of persons below poverty include Oldham (5.9), Boone (7.3), and Woodford (7.7).
Oldham and Boone both contain one facility.
There are no counties that are in the highest category for both variables. Counties that are in
the highest category for the percentage of persons below poverty level include Martin, Bell and
Cumberland, each of which have one facility. Those in the second highest category of percent
below poverty include Carroll, Bath, Union, Grayson and Fulton, each with one facility, and
Ohio, Muhlenburg, and Pulaski, each with two. There is a wide range of the number of facilities
in the counties included in the third highest category of persons below poverty. Those counties
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that are also in the third category in the number of facilities include Mason, Henry, Hancock,
Ballard, Warren and Todd. Of the forty counties with facilities, eighteen are in the lowest
category of below poverty percentages.
The map of the per capita incomes for each county shows the highest amounts in the northern
Kentucky area and the Louisville to Lexington corridor (Figure 11). The lowest seems to
coincide, of course, to the higher percentages of those below poverty level in the southeastern
part of the state. Since the relationship hypothesis is negative, this part of the map analysis will
discuss it in this manner as well. Of the counties that contain three to twenty-five facilities the
per capita income ranges from ranges from $10,271 to $14,962. Counties with the three highest
per capita incomes include Woodford ($14,151), Fayette ($14,692) and Oldham (15,510). Those
counties with the three lowest per capita incomes are McCreary ($5,153), Owsley ($5,791), and
Wolfe ($5,998).
Counties with the lowest per capita incomes with facilities present include Bell ($6,858) and
Cumberland ($7,037). Both of these counties have only one TSD facility. There are no counties
that are in the lowest category for both variables. Those in the second lowest categories for per
capita income with facilities present include Bath, Meade, Martin, Ohio, Grayson, Pulaski and
Todd. Each of these counties contains either one or two facilities. The counties contained in the
third lowest category of per capita incomes that contain facilities include Carroll, Mason, Henry,
Madison, Ballard, Christian, Galloway and Fulton, with one facility, Muhlenburg with two, and
Harrison, with three. Of the forty counties with facilities, twenty-one are in the highest category
of per capita incomes.
It is interesting to note that those counties listed as the highest percentages with facilities
present were often sited more than once. For example, Christian County was in the top three
percentages with facilities for three variables. Hardin, Meade, Fulton, Bell and Cumberland were
listed in the top for two. Also noticeable for some variables is that the highest overall
percentages were the same as the highest percentages for counties with facilities. For example,
female-headed households, Blacks, other nonwhites and Hispanics all listed the same
respective counties for both the top three overall percentages and the top three for those with
facilities present. Finally, it is interesting to note that Jefferson County, which had the highest
number of facilities (25), was listed in the highest percentage categories for all of the variables
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except the percentage of persons below poverty level. These kinds of occurrences could point
toward more specific siting issues with respect to a combination of socioeconomic variables.
4.1.2 Zip Code Map Analysis
Given that zip codes are simply smaller sections of the counties, when combined they should
create approximately the same numbers given for each variable in the county data. Due to this
many of the same patterns are repeated in the zip code maps concerning the location of
concentrations of each category.
It can be seen by looking at the map of the percentage of the population that is Black that the
concentrations are again in the western and central section of the state (Figure 12). Of the
highest category, which ranges from 5.1 to 16.0, there are only three zip codes that are also in
the highest category for the number of facilities. These zip codes are 40213, 40214, and 40216,
which are all located in Louisville and all contain four facilities. There are twenty-eight other zip
codes included in this category of percent Blacks that contain facilities, as well. In the second
highest category of percentage Black, there are three zip codes that are also in the second
highest category of number of facilities. The zip codes are located in Louisville (40272),
Lawrenceburg (40342), and Harrodsburg (40330). Also in this category are two zip codes with
three facilities, these include Cynthiana and Ashland. There are seven other zip codes that
contain facilities included in this category. There are twelve zip codes with facilities in the third
category of percentages and five in the lowest category.
The zip codes that contain the most facilities range in percentages of Blacks from 0.0 to 13.3.
The highest three percentages of those zip codes with facilities are 40211with 96.0 percent,
40203 with 54.3 percent, and 40212 with 49.6 percent, all of which are within the city of
Louisville. The lowest percentage found in zip codes with facilities is 0.0. The highest
percentages of Blacks found in all of the zip codes include 40211 with 96 percent.
Concentrations of the highest percentages of Hispanic are also in the western and central part
of the state (Figure 13). Of the highest category, which ranges from 0.6 to 21.6, there are only
two zip codes that are also in the highest category for the number of facilities. These zip codes
are 40214 (Louisville) with four facilities, and 41101 (Lexington) with three facilities. There are
fifteen other zip codes included in this category of percent Hispanic that contain facilities, as
well. In the second highest category of percentage Hispanic, there are two zip codes that are
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also in the second highest category of number of facilities. The zip codes are located in
Louisville (40213), and Harrodsburg (40330). Also in this category are two zip codes with more
than two facilities, these include Louisville (40212) with four and Calvert City (42029) with five.
There are ten other zip codes that contain facilities included in this category. There are twenty-
two zip codes in the third category of Hispanics that contain facilities. Seven zip codes with
facilities are within the lowest percentages.
The zip codes that contain the most facilities range in percentages of Hispanics from 0.0 to 2.3.
The highest three percentages of those zip codes with facilities are 40121 (Fort Knox) with 7.8
percent, 40511 (Lexington) with 4.4 percent, and 41101 (Ashland) with 2.3 percent. The lowest
percentage found in zip codes with facilities is 0.0. The highest percentages of Hispanics found
in all of the zip codes include 42523 (Belcher) with 21.8 percent.
Once more the highest concentration of the other nonwhite percentages are located in the
western and central part of the state, although this variable is a bit more evenly dispersed than
the previous two (Figure 14). Of the highest category, which ranges from 0.7 to 25.9, there are
only three zip codes that are also in the highest category for the number of facilities. These zip
codes are 41101 in Ashland containing three facilities, and 40214 and 40216 in Louisville each
containing four facilities. There are twenty-seven other zip codes included in this category of
percent other nonwhites that contain facilities, as well. In the second highest category of
percentage other nonwhite, there are two zip codes that are also in the second highest category
of number of facilities. These zip codes are located in Lawrenceburg (40342), and Harrodsburg
(40330). Also in this category are two zip codes with more than two facilities, these include
Louisville (40213) with four facilities, and Calvert City with five. There are twelve other zip codes
that contain facilities included in this category. There are fourteen zip codes in the third highest
category of percent other nonwhite and no zip codes with facilities in the lowest category.
The zip codes that contain the most facilities range in percentages of other nonwhites from 0.1
to 2.0 percent. The highest three percentages of those zip codes with facilities are 40121 (Fort
Knox) with 7.4 percent, 40509 (Lexington) with 3.3 percent, and 40510 (Lexington) and 40208
(Louisville) both with 2.5 percent. The lowest percentage found in zip codes with facilities is 0.1.
The highest percentages of other nonwhites found in all of the zip codes include 40061 (Saint
Catharine) with 25.9 percent.
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The map of the percentage of female-headed households is evenly dispersed throughout the
state (Figure 15). Of the highest category, which ranges from 16.0 to 78.0. There are only three
zip codes that are also in the highest category for the number of facilities. These zip codes are
40213, 40214 (both in Louisville) with four facilities, and 41031 (Cynthiana) with three. There
are twenty-eight other zip codes included in this category of percent female-headed households
that contain facilities, as well. In the second highest category of percentage female headed
households, there is only one zip code that is also in the second highest category of number of
facilities. This zip code is located in Lawrenceburg (40342). Also in this category are three zip
codes with more than two facilities, these include 40216 (Louisville) with four facilities, 42029
(Calvert City) with five, and 41129 (Catlettsburg) with six. There are thirteen other zip codes that
contain facilities included in this category. There are eight zip codes in the third highest category
of percent female-headed households and four zip codes with facilities in the lowest category.
The zip codes that contain the most facilities range in percentages of female-headed
households from 10.4 to 19.5. The highest three percentages of those zip codes with facilities
are 40203 with 34.1 percent, 40206 with 28.3 percent and 40208 with 26.0 percent, all of which
are within the city of Louisville. The lowest percentages of female-headed households that
contain facilities are 42058 (Ledbetter) with 12.6 percent, 40109 (Brooks) with 12.4 percent and
40121 (Fort Knox) with 0.1 percent. The lowest percentage found in all zip codes is 0.0. The
highest percentages of female-headed households found in all of the zip codes include 41640
(Hueysville) with 58.0 percent.
The map of the percentage of persons below poverty shows that the highest percentages are
mostly concentrated in the eastern portion of the state (Figure 16). Of the highest category,
which ranges from 33.8 to 100.0 percent, there are only three zip codes that contain facilities.
These zip codes are 40203 and 40211, both in Louisville, and 41267 in Warfield. In the second
highest category of persons below poverty, there is only one zip code that is also in the second
highest category of number of facilities. This zip code is located in Louisville (40212). There are
ten other zip codes that contain facilities included in this category. There are twenty-one zip
codes in the third highest category of persons below the poverty level and twenty-five zip codes
with facilities in the lowest category.
The zip codes that contain the most facilities range in percentages of persons below poverty
from 10.9 to 16.4. The highest three percentages of those zip codes with facilities are 40203
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(Louisville) with 46.4 percent, 41267 (Warfield) with 42.2 percent and 40211 (Louisville) with
34.5 percent. The lowest percentages of female-headed households that contain facilities are
40059 (Prospect) and 41017 ((Fort Mitchell) with 2.9 percent, 41042 (Florence) with 7.1 and
40121 (Fort Knox) with 7.2. The lowest percentage found in all zip codes is 0.0. The highest
percentages of persons below poverty found in all of the zip codes include 41351 (Mistletoe),
42250 (Huff), and 40825 (Dizney), each with 100 percent.
The map of the per capita incomes for zip codes, just as it was for the counties, shows the
highest amounts in the northern Kentucky area and the Louisville to Lexington corridor (Figure
17). Again, since the relationship hypothesis is negative, this part of the map analysis will
discuss it in this manner as well. Of the category with the lowest per capita incomes, ranging
from $0 to $7,030, there are only two zip codes that contain facilities. These include 40203
(Louisville) and 42717 (Burkesville). In the category with the second lowest per capita incomes
there are seven counties containing facilities, two of them with two. These include 40211 and
40212 in Louisville. There are sixteen zip codes in the category with the third lowest per capita
incomes and fifteen zip codes with facilities in the highest per capita income category.
The zip codes that contain the most facilities range in per capita incomes from $10,630 to
$12,246. The highest three incomes of those zip codes with facilities are 40059 (Prospect) with
$31,530, 40510 (Lexington) with $23,349, and 41017 (Fort Mitchell) with $18,535. The lowest
per capita incomes that contain facilities are 42343 (Fordsville) with $7,393, 42717 (Burkesville)
with $6,992, and 40203 (Louisville) with $6,660. The lowest income found in all zip codes $930
in Dizney (40825). The highest per capita income is found in Prospect.
It is interesting to note that those counties listed as the highest percentages with facilities
present were often sited more than once. For example, 40203 (Louisville) was in the top three
percentages with facilities for four variables. 40211 (Louisville), 40208 (Louisville), and 40121
(Fort Knox) were listed in the top for two. Also noticeable for some zip codes is that they occur
in the top categories for several variables. For example, 40214 (Louisville) is listed in four top
categories. It has four facilities. 41101, 40213, 40216 and 41031 are all listed in two variables'
highest category. These kinds of occurrences could point toward more specific siting issues with
respect to a combination of socioeconomic variables.
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4.1.3 Statistical Analysis
The regression statistics for the county analysis gives a r2 value of 0.25 (Table 1). This number
means that approximately 25 percent of the variance in the number of facilities is explained by
all of the combined socioeconomic variables combined. This means that 75 percent of the
variation in the number of facilities in a county is explained by variables other than those used in
this study. The regression statistics for the zip code analysis give a r2 value of 0.08. This
number means that approximately 8 percent of the variance in the number of facilities is
explained by the socioeconomic variables combined. This means that 92 percent of the
variation is explained by other external variables. This shows that there is not a very strong
cause and effect relationship between the variables that can be seen from this type of statistic.
The coefficients noted in Table 2 show that the parameters are not significantly different from
zero, which means that separately, there are no significant contributions from the independent
variable towards the variation of the dependent variable.
Table 1
Regression Statistics
R Squared
County
.25
Zip Code
.08
The bivariate correlation analysis for the county information shows that most of the variables
used had a weak relationship with the number of facilities. The strongest relationship, still only
moderate, is seen with per capita income, at 0.39, although it is not the hypothesized
relationship. The correlations, as can be seen in Table 3 are all positive, with the exception of
the percentage of persons below poverty at -0.24. The weakest correlation can be seen with
the percentage of Hispanic origin at 0.21. Each of the correlations did occur, although
moderately, in the hypothesized direction except the per capita income and the percentage
persons below poverty. The positive relationships that did occur in the hypothesized direction
included all of the racial information and the female-headed household data. This type of
correlation means that as the number of TSD facilities increases, the percentage of these
variables increases as well. The two variables that did not have the hypothesized direction of
relation indicate that TSD facilities may be located in higher income or more prominent counties.
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Table 2
Regression Coefficients
Coefficients
Variable
% Black
% Hispanic Origin
% Other Nonwhite
% Female Headed
Households
% Persons Below
Poverty
Per Capita Income
County
0.046
0.17
0.60
0.11
0.15
0.000934
Zip Codes
0.014
0.0042
0.0056
0.0035
-0.0017
0.000013
Significance level - .05
Table 3
Bivariate Correlation Coefficient
Variable
% Black
% Hispanic
% Other Nonwhite
% Female Headed
Households
% Below Poverty
Per Capita Income
County
Correlation Coefficient With #
of Facilities
0.34
0.21
0.26
0.24
-0.24
0.39
Zip Code
Correlation Coefficient With #
of Facilities
0.25
0.06
0.08
0.12
-0.14
0.17
Significance level = .05
Weak = +.1 to+.3
Moderate = +.3 to+.6
Strong = +.6 to +1
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The bivariate correlation coefficients for the zip code data show relationships that are not as
strong as at the county level (3). The variable with the strongest relationship is the percentage
Black with 0.25. The other variables only show weaker relationships ranging from 0.06 to 0.17.
Again, the racial variables and the female headed household variable do have the hypothesized
direction of correlation, and the per capita income and the percentage of persons below poverty
have the opposite. The weakest correlation is again with the Hispanic origin percentages.
4.2 Distance Analysis
This analysis begins by creating buffers around those zip codes with facilities. As previously
mentioned, each zip code was assigned a number to coincide with the distance from the zip
codes containing facilities. The rings that were created by these assignments were separated
and displayed on an ArcView map (Figure 18). These rings were created by the select by theme
tool within ArcView. The zip codes immediately adjacent to the zip codes with facilities were
selected for the first ring. The zip codes immediately adjacent to those zip codes in the first ring,
not including those with facilities were selected for the second ring, and so on. The map
analysis done here is a basic description of the patterns and zip codes within each ring, and
some of the characteristics from the socioeconomic patterns within each. This analysis is
followed by the statistical results from the bivariate correlation analysis, which was created in
Excel from the transferring of the coinciding reclassification numbers for each ring, from
ArcView. Also included in the statistical analysis is a t-test for equality of means. The same
numbers were used here as in the bivariate coefficient, but manipulated into two groups, one
with facilities and one without. The means of each variable are calculated and the t-value is
analyzed in this chapter.
4.2.1 Zip Code Map Analysis
There are 731 zip codes shown on the map exhibiting the assigned buffers. Of those, 60 zip
codes have facilities, 225 zip codes are immediately adjacent to the zip codes with facilities and
categorized in the first ring, 181 zip codes are included in the second ring, 92 zip codes in the
third and 173 zip codes in the fourth. The majority of the zip codes (30.8 percent) are within the
first ring. The lowest percentage of zip codes is found in the third ring at 12.6 percent. The
averages for each variable within each ring are included in Table 4. The highest averages for
each variable is found in the zip codes with TSD facilities. These averages decrease, except for
the percentage of other nonwhite persons, with distance from the zip codes with facilities.
Although some decreases are not extremely large, they can still be seen in the numbers. In
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general, it would seem that the mapped patterns might be somewhat the same for the variables,
as are seen for the map with buffers around the facilities.
4.2.2 Statistical Analysis
The bivariate correlation analysis for the distance information shows that most of the variables
used had a moderate to weak relationship with the distance from facilities. Moderate
relationships included the percentage Black, the percentage below poverty and the per capita
income. The strongest relationship is seen with the percent of persons below poverty, at -0.44,
although it is not the hypothesized direction of the relationship. The correlations, as can be seen
in Table 5 are all positive, with the exception of the percentage of persons below the poverty
level. The weakest correlation can be seen with the percentage of Hispanic origin at 0.06. Each
of the correlations did occur in the hypothesized direction except the per capita income and the
percentage persons below poverty. The positive relationships that did occur in the hypothesized
direction included all of the racial information and the female-headed household data. This type
of correlation means that as the assigned number denoting distance from the zip codes with the
TSD facilities increases, the percentage of these variables increases as well. In other words, the
distance from the zip codes is associated with the percentages of the variables; as the distance
from the zip codes with facilities increases, the percentage of these variables decreases. The
two variables that did not have the hypothesized direction of relation indicate that higher income
or more prominent neighborhoods are closer to the facilities than low income.
Table 4
Averages for Zip Codes with Facilities and Each Surrounding Buffer
Variable
% Black
% Hispanic
% Other Nonwhite
% Female headed
Households
% Below Poverty
Per Capita Income
Zip Codes
With
Facilities
10.3
0.7
1.0
16.2
17.8
$11,309
Ring 1
3.7
0.4
0.5
13.0
18.6
$10,533
Ring 2
2.5
0.4
0.3
12.6
23.7
$9,195
Ring 3
0.9
0.3
0.5
11.1
31.6
$7,505
Ring 4
0.4
0.3
0.3
10.9
33.6
$7,245
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Table 5
Bivariate Correlation Coefficient for Distance
Variable
% Black
% Hispanic
% Other Nonwhite
% Female Headed Households
% Persons Below Poverty
Per Capita Income
Zip Code
Correlation Coefficient with
Distance Classification
0.29
0.06
0.11
0.20
-0.44
0.43
Significance level = .05
Weak = +.1 to+.3
Moderate = +.3 to+.6
Strong = +.6 to +1
The t-test statistical analysis for the difference in means showed that there were significant
differences in the means for those zip codes with facilities and those without (Table 6).
Variables that were found to have significantly lower means for zip codes with out facilities than
for those with include the percentage Black, percentage Hispanic, percentage other nonwhite,
and per capita income. For these variables the critical value was found to be approximately 2.00
and the t statistic was found to be around -4.00. This indicates that there was indeed a
significant difference in the means for these percentages between those zip codes with facilities
and those without. However, it indicates that the percentages in non-facility areas are lower than
in areas with facilities. This means that higher amounts of these variables are in areas with no
facilities. Therefore, in these cases the null hypothesis could not be accepted at the significance
level 0.05, and indications are that environmental justice is taking place in siting of TSDFs.
Concerning the percentage of persons below poverty, again there was a significant difference in
the means, this time the mean is higher in zip codes without facilities than in those with facilities
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present. In this case the critical value is again 2.00 and the t-statistic is 6.48. Again the null
hypothesis could not be accepted, as the t-value exceeded the critical value.
Table 6
t-Test for Difference in Means
Variable
% Black
% Hispanic
% Other Nonwhite
% Female Headed
Households
% Below Poverty
Per Capita Income
Critical Value
+2.00, -2.00
+2.00, -2.00
+ 1.99, -1.99
+ 1.99, -1.99
+ 1.99, -1.99
+2.00, -1.99
t-Statistic
-3.82
-4.70
-4.03
-4.03
6.48
-4.76
These results have shown the relationships in the case of Kentucky between the socioeconomic
variables and the number of TSD facilities. The following chapter addresses these analysis in
discussion and conclusions and gives some recommendations based on these findings as well
as for future references. The limitation of this analysis and its results is also discussed.
5.1 Discussion
In discussing this study the first section to look at is the map analysis. This analysis found some
interesting likenesses in some of the specific zip codes and counties with facilities in that
several of the counties listed in the highest percentage of one variable was often listed in others.
This occurred in both the county and zip code comparisons. Most notably, Jefferson County and
zip codes within Jefferson County were at the top of the list on several occasions. This could
point to the need for a more detailed analysis to determine if there are disproportionate risks in
this specific area.
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The results of found in this study indicate that while there may be some slight relationships with
the amount of minority populations and the number of facilities in the hypothesized positive
direction (as the number of facilities goes up, so do the variable percentages), the hypothesized
relationships for the number of persons below poverty and the per capita income relationships
were exactly opposite. In fact the relationships with these two variables and the number of TSDs
was among the strongest, especially within the distance analysis. This relationship may be
explained by the reasoning that these facilities may pay high wages and that their workers live
near the facility. It also may be caused by the fact that these facilities may be located in an
industrial area that also has high wages and employ surrounding residents. This may be seen
as compensation for the risks. Income in these areas could also be skewed by a few extremely
high incomes that would drive the average up and not show precisely the characteristics of the
neighborhoods immediately adjacent to the facilities. The difference in incomes could also be a
function of the urban and rural separation.
Since the highest correlation for both zip codes and counties, excluding the distance analysis,
was found to be with the percentage of Blacks, this would seem to be the strongest relationship
and should be the one concentrated on in future studies. This relationship may be moderately
attributed to environmental injustices, or could be a factor of inner city versus rural population
characteristics. The fact that the other variables had a higher correlation with the county data
than the zip code data may be a factor of distance and population dispersal.
The percentage of female-headed households is an interesting variable. Although it looks on the
map as if it has no specific pattern, in the statistical analysis it has a weak relationship at the
county level and is among the strongest (although still weak) at the zip code level.
For the distance analysis, as before mentioned, the strongest correlations were found with the
percentage below poverty and the per capita income. The relationships with the minority
variables indicate weak correlations with each variable. The female-headed household
correlation was again between weak and moderate. The distance analysis did show some
moderate relationships with three of the variables. This indicates that the distance from a TSD
facility is a factor that needs to be included in determining any relationship of this sort. Proximity
is key in determining spatial relationships, i.e. the areas in close contact with high health risks
and their population characteristics.
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Finally, the hypotheses findings for the county data are as follows:
1) Accepted at a moderate level - The greater the number of facilities, the greater the
percentage of African Americans (positive relationship);
2) Accepted at a weak level - The greater the number of facilities, the greater the
percentage of Hispanics (positive relationship);
3) Accepted at a weak level - The greater the number of facilities, the greater the
percentage of other minority groups (positive relationship);
4) Accepted at a weak level - The greater the number of facilities, the greater the
percentage of female-headed households (positive relationship);
5) Rejected (the direction of the relationship was opposite of the hypothesized) - The
greater the number of facilities, the lower the percentage of persons below the state
poverty level (negative relationship); and
6) Rejected (the direction of the relationship was opposite of the hypothesized) - The
greater the number of facilities, the higher the per capita income (positive relationship).
The hypotheses findings for the zip code data are as follows:
1) Accepted at a weak level - The greater the number of facilities, the greater the
percentage of African Americans (positive relationship);
2) Accepted at a weak level - The greater the number of facilities, the greater the
percentage of Hispanics (positive relationship);
3) Accepted at a weak level - The greater the number of facilities, the greater the
percentage of other minority groups (positive relationship);
4) Accepted at a weak level - The greater the number of facilities, the greater the
percentage of female headed households (positive relationship);
5) Rejected (the direction of the relationship was opposite of the hypothesized) - The
greater the number of facilities, the lower the percentage of persons below the state
poverty level (negative relationship); and
6) Rejected (the direction of the relationship was opposite of the hypothesized) - The
greater the number of facilities, the higher the per capita income (positive relationship).
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The hypotheses findings for the second section of the project which included distance are the
following:
1) Accepted at a moderate level - As the distance from each facility gets greater, the
percentage of the population that is African American gets lower;
2) Accepted at a weak level - As the distance from each facility gets greater, the
percentage of the population that is Hispanic gets lower;
3) Accepted at a weak level - As the distance from each facility gets greater, the
percentage of the population labeled as other minority groups gets lower;
4) Accepted at a weak level - As the distance from each facility gets greater, the
percentage of female-headed households gets lower;
5) Rejected (the direction of the relationship was opposite of the hypothesized) - As the
distance from each facility gets greater, the percentage of the persons below poverty
increases; and
6) Rejected (the direction of the relationship was opposite of the hypothesized) - As the
distance from each facility gets greater, the per capita income gets higher.
The t-test statistical analysis resulted in the rejecting of the null hypothesis for all of the
variables. This indicates that the relationship between the averages of each variable, were
significantly different in zip codes with and without TSD facilities. However, these differences
are opposite of the expected. The statistic shows that the zip codes lacking facilities have the
highest per capita income and the least percentages of minorities, female-headed households
and persons below poverty. Based on this statistic alone it would seem that there are no
prejudices being produced from the siting of the facilities used in this study. However, the other
tests done within this study should also be considered. Taking into account that the bivariate
correlation coefficient found some weak relationships between the variables, it can be seen that
the variables used in this study are not a likely determinant of TSD facility siting in Kentucky.
5.2 Conclusion
In conclusion, this project found minimal relationships between treatment, storage and disposal
facilities and the percentages of low income and minorities. The strongest relationships were
found with the percentage of Blacks, the percentage of persons below poverty and the per
capita income. With both the percent Black and the per capita income, it was found that to some
extent, the percentage and dollar amount increased with the number of facilities. The
42
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percentage of persons below poverty was found to decrease with the number of facilities. These
findings implicate that there is only a slight bit of inequality in the siting of TSD facilities in
Kentucky. Most likely, other factors are a higher determinant to siting that race or poverty.
This study shows that while not on a case by case basis, overall, there are very little
environmental injustices occurring with resect to TSDF siting. This implies that the current
policies and methods for site selection have been somewhat sufficient. However, since the
tendencies could be there, policies could be strengthened and siting methodology and
evaluation could be done more comprehensively, utilizing social issues and community
participation.
5.3 Limitations
As with all projects this study has several limitations, from the data to the analysis. First the data
for the socioeconomic variables was collected in 1990 and the data for the treatment, storage
and disposal facilities was collected in 1998. This is a problem if several of the facilities were
opened after 1990. In this case, the socioeconomic variables may have greatly changed since
those facilities were created. This study assumes that the socioeconomic variables have not
changed significantly since the 1990 Census was taken. This limits the analysis in such a way
that more information will need to be collected and updated as it becomes available. For
example, when the 2000 Census is taken, these numbers should be substituted in the statistical
and map analysis in order to update the information and make for a more accurate analysis.
Also, some data was not given in the census for some zip codes. This created a problem again
for the analysis in that some facilities were not included in the statistical and map analyses
because data was not available. Also in the ArcView shape files for Kentucky, not all of the zip
codes were present as well. This affects the data in such a way as to not give a complete
picture of where the facilities are located and what the surrounding characteristics are. These
facilities and socioeconomic data may have either weakened or strengthened the analyses.
As for the socioeconomic characteristics themselves, specifically the poverty and income data
could be questionable in this study. The definition of poverty given by the census may not
always be reflective in the neighborhood opinions. What may be a low quality of life to some
may not be to others. This may affect the actual perception of what a poverty stricken or low-
income area may be. As well, it is worth noting that in some areas the incomes of a few wealthy
43
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persons may have skewed the average so that it does not accurately depict the characteristics
of the area.
Since zip codes and counties were utilized for this analysis, it is necessary to discuss the
limitations of using data in such a manner. Although zip codes are relatively small units, they
often vary in size and population characteristics. It would have been better to utilize census tract
information since theoretically they are supposed to be somewhat homogeneous in
characteristics. However, due to data availability, zip codes were chosen. When looking at the
results, one needs to understand how the units that were used affect the outcome. As for the
county data, it also is varied in size and composition. Several counties differ in basic
characteristics such as number of persons. Again, this needs to be considered when consulting
the results.
Also, as before mentioned, this is not a historical review of TSD facility siting and the
characteristics of the surrounding communities before and after the siting. This fact creates an
unknown circumstance of which came first, and thus time is the limitation in this case.
This study focuses on only one type of toxic waste facility. Therefore, it needs to be understood
that the results in this study can not be generalized to other types of facilities. This means that
for any other type of facility, while the methodology may be the same, the data collection and
use of the information may need to different. As well, generalities may not be made, based on
the results in this study, to specific sites. The outcomes of this project are for an overall view of
TSD sites in Kentucky.
Finally, as with any other statistical analysis, the results of the methods used in this project do
not represent fact.
5.4 Further Studies/Recommendations
The findings in this study should not be taken as a final step in the analysis of the location of
treatment, storage and disposal facilities in Kentucky. Further studies need to be conducted in
order to come to an overall conclusion on the situation of this issue. These studies include a
historical analysis detailing the socioeconomic conditions of the surroundings before and after
siting of the facilities. This analysis would help to determine if possibly a large composition of
minorities or low income persons preceded the location of the facility, or if the composition was
44
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created after the facility was sited in that specific area. As well, a case study on one specific
facility or one specific zip code or county with several facilities could be done. This analysis
could entail an in depth look at the actual siting of the facility or facilities, such as specifics on
why the site was chosen and what the alternatives were. Here the economic and the
environmental issues that played a part in the siting decision could be discussed, along with any
social issues. Also within this type of analysis, it would be important to understand what the
opinion of the community was, at the time of siting, and is presently, as well as the demographic
characteristics before and after siting. Yet another interesting twist to this topic would be to
delve into a policy analysis of exactly how the national environmental justice policies and
regulations trickled down to the local level to effect the specific siting of a facility in Kentucky.
Although obviously no community will welcome health risks into their back yard, if incentives
and compensation are given in addition to possibly much needed jobs for the low skilled
residents of the community, such facilities may not always be seen in a negative light. For
example, if the general public is invited to be involved in the early stages of planning for a toxic
facility then they will be less likely to be suspicious of the company. If the community residents
are not involved until halfway through the process when someone complains, it often seems like
the company is trying to hide something. One good way to actively involve the community is to
initially present them with a list of criteria for siting the facility and allow them to add to and make
comments on such. Finally, when an evaluation of sites has been done, the community
participants should be provided with reasons why possible sites are or are not acceptable based
on the requirements set forth previously, as well as any external pros or cons to allowing such a
facility to site in the neighborhood. Of course, it is not always easy to get residents to participate
in such events. This is the role of the environmental organizations. Their addition to the
community awareness of the issues could be the driving force of enhanced participation in the
siting decision making.
There can be several pros and cons of inviting such a toxic waste facility as TSDs to the
community. As with any new industries there is potential for an increased tax base, more jobs,
greater infrastructure and services, more diversity in economic activity and investment into the
community, and more dollars being generated within the community via the multiplier effect.
However, although less calculable, the health risks of such facilities are ever present. These
risks come from the potential spillage of toxins into all aspects of the environment, from which it
has been hypothesized, will increase mortality rates over time. These toxic facilities also create
45
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environmental degradation and can limit the future use of the land. All of these things are
important factors in choosing a site for such a facility and should be presented to the public so
they can be well informed to make the decision to support the facility or not. Other incentives
may be included that may serve to sway doubting communities. These incentives from the
industry may include the following: 1) higher tax payment, contributions to the community for
schools or other worthy causes; 2) employment guarantees, such as guaranteeing that a certain
percentage of the new jobs will go to the local workers; 3) high wages and benefits to workers;
4) payment for and creation of new infrastructure such as roadways; and 5) maintenance of
special dedicated green space to make up for any environmental degradation that may occur
due to the incoming industry.
There may also be a need for increased policy creation on the subject of siting toxic facilities
that may prove hazardous to the health of surrounding persons. Again, compensation for risk
could be written into the local legislation for siting, drawing from any development or economic
needs that the community may have. Also policies passed down from the EPA could be
enhanced. For example, the environmental impact statement could be utilized as a tool for
social issues as well as environmental. While it is the role of all federal agencies to incorporate
environmental justice measures in their work, it may be that this is not adequate enough to
address the problem. EIA could be a tool not only to evaluate the soil, air, water and other
environmental issues, but also to take into account the social characteristics of the surrounding
community, what other toxic sites are located in the area, if any alternative sites are suitable that
will offer more equality in distribution, and the community's awareness and approval or
disapproval of the facility.
At the local level, comprehensive planning and zoning could be an excellent tool to guide
development of such toxic facilities. In this case, each municipality should address the issue of
siting from a proactive point of view. Having standards and areas zoned for industrial,
specifically toxic industries, will make it easier for those industries to choose where to locate and
easier for localities to have a say where they may or may not do so. If zoning is implemented in
such a manner as to take into account the suitability of the land to the industry as well as the
socioeconomic characteristics and community opinion, then facility siting will not be considered
an issue of injustice.
46
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This process would include several of the important factors mentioned in this study and would
be easy to create and to comply with. From this standpoint, it should be the initiative of the
government to choose satisfactory sites for such facilities through municipal planning and
zoning ordinances, created with community participation and overall approval. Making the
community feel as if they have a hand in choices concerning toxic waste, and their own health,
will reduce the fear of such industries. However, this recommendation does depend on the area
having a comprehensive plan and zoning ordinances in place.
Each of these recommendations has its strengths and weaknesses. What is common
throughout is that, no matter through what medium, community participation is an important
factor for creating consensus and reducing suspicion of these types of facilities. Although this
study did not find outstanding significances in the relationships between socioeconomic factors
and the number of TSDs in Kentucky, creating an easier and more agreeable site selection
process for toxic waste facilities, including TSDs, through community participation, will be an
asset to all parties involved.
47
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oo
Figure 3
Kentucky Counties,
1999
A
50
100
150 Miles
-------
CD
Figure 4
Number of Treatment, and Facilities,
by County, Kentucky,
/*•
Number of Facilities
^•3-25
0 50 100 150
-------
en
o
Figure 5
Number of Treatment, Facilities by Zip
Kentucky,
| | No Data
Number of Facilities
^m 3-6
0
50 100 150
-------
Figure 6
Population by County,
Kentucky,
Percent Black
0.0-0.4
5.7-24.5
A
N
0
50 100 150
-------
en
IV)
Figure 7
Hispanic Population by County,
Kentucky,
Percent Hispanic
| 10.0-0.3
0.3 -0.6
^m 1.2-3.4
A
o
N
50 100
150
-------
en
Figure 8
Other Nonwhite Population by County,
Kentucky,
Percent Other Nonwhite
| [0.0-0.3
IBl 0.3-0.7
2.0-3.9
A
N
0 50 100 150
-------
Figure 9
Households by County,
Kentucky
en
Percent Female Headed Households
| | 1.7- 10.5
10.5- 14.0
^m 16.7-22.9
A
N
0
50 100 150
-------
Figure 10
of the Population by County,
Kentucky,
en
en
Percent Poverty
| 10.0-15.9
16.0-20.0
28.7-51.0
A
N
0
50 100 150
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GIS in Statewide Ground-Water Vulnerability Evaluation to Pollution Potential
Navulur Kumar and Bernard A. Engel
Department of Agricultural Engineering, Purdue University, West Lafayette, Indiana
Abstract
The ground-water vulnerability of Indiana to pollution
potential was evaluated using a geographic information
systems (GIS) environment. The Geographic Re-
sources Analysis Support System (GRASS) and the
GRID submodule of ARC/INFO were used to conduct
the analysis and to identify and display the areas sensi-
tive to ground-water pollution potential. The state soils
geographic (STATSGO) database was employed to re-
trieve statewide soils information required for the analy-
sis. The information from the STATSGO database was
used in two models, DRASTIC (acronym representing
the following hydrogeologic settings: Depth to water
table, aquifer Recharge, Aquifer media, Soil media,
Topography, Impact of vadose zone, and hydraulic
Conductivity of the aquifer) and SEEPAGE (System for
Early Evaluation of Pollution Potential of Agriculture
Ground-Water Environments). These models employ a
numerical ranking system and consider various hydro-
geologic settings that affect the ground-water quality of
a region. Ground-water vulnerability maps were pre-
pared for the state of Indiana based on DRASTIC and
SEEPAGE results. Continuing work is planned to deter-
mine the accuracy of the results by comparing the ex-
isting well-water quality data. The DRASTIC Index and
SEEPAGE Index number (SIN) maps show great poten-
tial as screening tools for policy decision-making in
ground-water management.
Introduction
Ground-water contamination due to fertilizer and pesti-
cide use in agricultural management systems is of wide
concern. In 1989, reports of ground-water contamination
in New York wells led the U.S. Environmental Protection
Agency (EPA) to conduct a nationwide survey on well
contamination in the United States. These wells were
tested for presence of nitrate, pesticides, and pesticide
breakdown products (1). Statistically, the wells selected
represent more than 94,600 wells in approximately
38,300 community water systems. Over 52 percent of
the community water systems and 57 percent of the
rural domestic wells tested contained nitrates (2).
Indiana has abundant ground-water systems providing
drinking water for 60 percent of its population. A study
on well-water quality detected pesticides in 4 percent of
wells tested in Indiana. Also, 10 percent of private wells
and 2 percent of noncommunity wells contained exces-
sive nitrate levels (3).
Statewide maps showing the areas vulnerable to
ground-water contamination have many potential uses
such as implementation of ground-water management
strategies to prevent degradation of ground-water qual-
ity and monitoring of ground-water systems. These
maps will be helpful in evaluating the existing and po-
tential policies for ground-water protection. Ground-
water models such as SEEPAGE (System for Early
Evaluation of Pollution Potential of Agriculture Ground-
Water Environments) and DRASTIC (acronym repre-
senting the following hydrogeologic settings: Depth to
water table, aquifer Recharge, Aquifer media, Soil
media, Topography, Impact of vadose zone, and hy-
draulic Conductivity of the aquifer) can be applied on a
regional scale to develop such maps.
The data layers required forthese models are commonly
available data such as pH and organic matter content.
For most states, the statewide ground-water vulnerabil-
ity maps generated using DRASTIC were produced
from 1:2,000,000-scale data (4). EPA (2) found that
these maps did not correlate well with the water quality
analysis performed for the national survey of pesticides
in drinking water wells. States need more detailed and
accurate maps to implement ground-water management
programs. The state soils geographic (STATSGO)
database at the 1:250,000-scale might be useful for
studies at a larger scale.
The geographic information systems (GIS) environment
is widely applied for diverse applications in resources
management and other areas. It offers the facilities to
store, manipulate, and analyze data in different formats
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and at different scales. The DRASTIC and SEEPAGE
models can be integrated within the CIS environment to
produce the final ground-water vulnerability maps.
Objectives
The purpose of the study was to prepare maps showing
areas in Indiana vulnerable to ground-water pollution.
This goal was accomplished by considering hydro-
geologic factors in each region that affect the mobility
and leaching of the contaminant reaching the aquifer.
The prime objectives of this research were to:
• Evaluate Indiana's ground-water vulnerability to
pollution potential using the DRASTIC and SEEP-
AGE models:
- Integrate and evaluate the models in a CIS envi-
ronment (Geographic Resources Analysis Support
System [GRASS] ARC/INFO).
- Develop a graphic user interface (GUI) in ARC/INFO
to conduct the analyses.
• Compare the pollution potential map from the
DRASTIC model with the map developed using the
SEEPAGE Index number (SIN).
• Validate the accuracy of the present approach by
comparing the vulnerability maps with the existing
well-water quality data sampled across the state.
DRASTIC
DRASTIC is a ground-water quality model for evaluating
the pollution potential of large areas using the hydro-
geologic settings of the region (4-6). EPA developed this
model in the 1980s. DRASTIC includes different hydro-
geologic settings that influence a region's pollution po-
tential. A hydrogeologic setting is a mappable unit with
common hydrogeologic characteristics. This model em-
ploys a numerical ranking system that assigns relative
weights to parameters that help evaluate relative
ground-water vulnerability to contamination.
The hydrogeologic settings that make up the acronym
DRASTIC are:
• [D] Depth to water table: Compared with deep water
tables, shallow water tables pose a greater chance
for the contaminant to reach the ground-water surface.
• [R] Recharge (net): Net recharge is the amount of
water per unit area of soil that percolates to the aqui-
fer. This is the principal vehicle that transports the
contaminant to the ground water. Higher recharges
increase the chances of the contaminant being trans-
ported to the ground-water table.
• [A] Aquifer media: The material of the aquifer deter-
mines the mobility of the contaminant traveling
through it. An increase in travel time of the pollutant
through the aquifer increases contaminant attenuation.
• [S] Soil media: Soil media is the uppermost portion
of the unsaturated zone/vadose zone characterized
by significant biologic activity. This, in addition to the
aquifer media, determines the amount of water per-
colating to the ground-water surface. Soils with clays
and silts have larger water holding capacity and thus
increase the travel time of the contaminant through
the root zone.
• [T] Topography (slope): The higher the slope, the
lower the pollution potential due to higher runoff and
erosion rates, which include pollutants that infiltrate
the soil.
• [I] Impact of vadose zone: The unsaturated zone
above the water table is referred to as the vadose
zone. The texture of the vadose zone determines the
travel time of the contaminant. Authors of this model
suggest using the layer that most restricts water flow.
• [C] Conductivity (hydraulic): Hydraulic conductivity of
the soil media determines the amount of water per-
colating through the aquifer to the ground water. For
highly permeable soils, the travel time of the pollutant
is decreased within the aquifer.
The major assumptions outlined in DRASTIC are:
• The contaminant is introduced at the surface.
• The contaminant reaches ground water by precipitation.
• The contaminant has the mobility of water.
• The area of the study site is more than 100 acres.
DRASTIC evaluates pollution potential based on the
seven hydrogeologic settings listed above. Each factor
is assigned a weight based on its relative significance in
affecting the pollution potential. Each factor is also as-
signed a rating for different ranges of the values. Typical
ratings range from 1 to 10, and weights range from 1 to
5. The DRASTIC Index, a measure of pollution potential,
is computed by summation of the products of rating and
weights of each factor as follows:
DRASTIC Index =
DrDw + RrRw + ArAw + SrSw + TrTw + Irlw + CrCw
where:
Dr = Ratings for the depth to water table
Dw = Weights for the depth to water table
Rr = Ratings for different ranges of aquifer recharge
Rw = Weights for the aquifer recharge
Ar = Ratings for the aquifer media
Aw = Weights for the aquifer media
Sr = Ratings for soil media
Sw = Weights for soil media
Tr = Ratings for topography (slope)
Tw = Weights for topography
Ir = Ratings for the vadose zone
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Iw = Weights for the vadose zone
Cr = Ratings for different rates of hydraulic conductivity
Cw = Weights for hydraulic conductivity
DRASTIC assigns two different weights depending upon
the type of contaminant. Pesticides are given different
weights than general contaminants. In assigning the
weights, DRASTIC considers the different properties of
pesticides as they travel through the vadose zone and
root zone of the soil media.
The higher the DRASTIC Index, the greater the relative
pollution potential. The DRASTIC Index is divided into
four categories: low, moderate, high, and very high. The
sites with high and very high categories are more vul-
nerable to contaminations and hence should be re-
viewed by the site specialist. These weights are relative,
however. Low pollution potential does not necessarily
indicate that a site is free from ground-water contami-
nation. It indicates only that the site is less susceptible
to contamination than sites with high or very high
DRASTIC ratings.
SEEPAGE
The SEEPAGE model is a combination of three models
adapted to meet the Soil Conservation Service's
(SCS's) need to assist field personnel (7, 8). SEEPAGE
considers hydrogeologic settings and physical proper-
ties of the soil that affect ground-water vulnerability to
pollution potential. SEEPAGE is also a numerical rank-
ing model that considers contamination from both con-
centrated and dispersed sources.
The SEEPAGE model considers the following parameters:
• Soil slope
• Depth to water table
• Vadose zone material
• Aquifer material
• Soil depth
• Attenuation potential
The attenuation potential further considers the following
factors:
• Texture of surface soil
• Texture of subsoil
• Surface layer pH
• Organic matter content of the surface
• Soil drainage class
• Soil permeability (least permeable layer)
Each factor is assigned a numerical weight ranging from
1 to 50 based on its relative significance, with the pa-
rameter that has the most significant effect on water
quality assigned a weight of 50 and the least significant
assigned a weight of 1. The weights are different for
concentrated or site-specific sources, and dispersed or
nonspecific sources.
Similar to DRASTIC, each factor can be divided into
ranges and ratings, varying from 1 to 50. The ratings of
the aquifer media and vadose zone are subjective and
can be changed for a particular region. Once the scores
of the six factors are obtained, they are summed to
obtain the SIN. These values represent pollution poten-
tial, where a high SIN implies relatively more vulnerabil-
ity of the ground-water system to contamination. The
SIN values are arranged into four categories of pollution
potential: low, moderate, high, and very high. A high or
very high SIN category indicates that the site has signifi-
cant constraints for ground-water quality management (7).
GIS
CIS has been widely used for natural resources man-
agement and planning, primarily during the past decade.
A GIS can be combined with a ground-water quality
model to identify and rank the areas vulnerable to pol-
lution potential for different scenarios and land use prac-
tices. Many GIS software packages are available.
GRASS is a raster-based public domain software devel-
oped by the U.S. Army Construction Engineers Re-
search Laboratory (9). This software can assign different
weights to, or reclass, the data layers and combine map
layers, and is suitable for implementing the DRASTIC
and SEEPAGE models. ARC/INFO is a GIS software
developed by Environmental Systems Research Insti-
tute (ESRI) in Redlands, California. The GRID sub-
module of ARC/INFO facilitates the handling of raster
data. Also, the capability to develop a menu-based GUI
helps users easily implement the models. The GRID
submodule also can reclass and manipulate the map
layers suitable for conducting the analyses.
Methodology
Developing the Data Layers in GRASS and
ARC/INFO
The STATSGO database from SCS comes at a scale of
1:250,000 and is distributed in different data formats.
This study used the STATSGO database in the
ARC/INFO format. The database is organized into map
units that have up to 21 components. These map com-
ponents have information assigned to layers of soil ho-
rizons. Each layer is attributed various soil properties
such as pH or organic matter content (10). Each prop-
erty is assigned a high and a low value for a map unit.
The STATSGO map for Indiana is available in the vector
format. This map was exported to GRASS as a vector
coverage (11) and was converted into a raster coverage
within the GRASS GIS environment. This was used as
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the base map for the DRASTIC and SEEPAGE analy-
ses. The hydrogeologic parameters required for the
models were identified from the corresponding INFO
data tables and were exported into an ASCII file. Code
was developed to generate a GRASS reclass file as-
signing the weighted values of the parameters to the
corresponding map units in the base map. The STATSGO
base map imported into GRASS was reclassed for each
hydrogeologic setting (e.g., topography, pH) to create the
data layers required for DRASTIC and SEEPAGE analyses.
The map layers of the hydrogeologic parameters in
GRASS were then exported to ARC/INFO as raster
coverages. A GRASS command was developed that
allows the output ASCII file from GRASS to be imported
into ARC/INFO directly without further modifications to
the header in the ASCII file.
Developing a Graphic User Interface in
ARC/INFO
The dynamic form-menu option (12) was used to develop
a GUI for both DRASTIC and SEEPAGE analyses (see
Figures 1 and 2). Because ratings for some parameters
are subjective, the GUI provided an option to change the
weights assigned to hydrogeologic settings. The cover-
ages must already be assigned ratings before using the
interface, however. The interface also allows users to
reclassify the final vulnerability maps qualitatively (13) into
four categories (low, moderate, high, and very high) after
viewing the range of DRASTIC Index or SIN values.
Conducting the Analyses
The data layers were developed separately for the high
and low values of the hydrogeologic settings. Once all
the data layers were compiled, the corresponding rat-
ings and weights were assigned and the analyses were
conducted using the GUI. The data layers aquifer re-
charge, aquifer media, and vadose zone media were not
available, so the analyses were conducted without these
base maps. The SEEPAGE analysis was performed for
concentrated/point sources of pollution. The final vulner-
ability indexes from the analyses were classified into
four categories (low, medium, high, and very high) (see
Table 1) to generate the final statewide vulnerability maps.
Table 1. Pollution Potential Categories Using SEEPAGE and
DRASTIC Indexes
Range of DRASTIC/SEEPAGE Index
Analysis
SEEPAGE
DRASTIC
Low
1-24
30-70
Moderate
25-48
71-100
High
49-70
101-110
Very High
>70
> 110
Validating the Accuracy of the
Vulnerability Maps
The ground-water vulnerability maps produced by
DRASTIC analysis were compared with those generated
using the SEEPAGE model. The final statewide ground-
water vulnerability maps from either approach were
compared with the well-water quality data sampled from
over 2,500 wells (see Figure 3), and the number of wells
falling into each vulnerability category was tabulated.
Results and Discussion
Statewide analysis of ground-water vulnerability to pol-
lution potential was conducted using the DRASTIC and
SEEPAGE analyses at a scale of 1:250,000 in the raster
format. The analyses were conducted for both the high
and low values of the hydrogeologic settings, and the
final vulnerability maps were prepared for the state of
Indiana (see Figures 4 and 5). The vulnerability maps
from both approaches were compared in the GRASS
environment.
In both analyses, the low values of hydrogeologic set-
tings resulted in more areas being classified as high and
very high categories, compared with the high values of
hydrogeologic settings in a map unit. The DRASTIC
analysis placed more areas in the very high vulnerability
category, compared with the SEEPAGE analysis, which
categorized the same areas as high vulnerability. The
nitrate-nitrogen concentrations observed in the wells
were compared with the final vulnerability maps, and the
number of wells falling into each of the four vulnerability
categories (low, moderate, high, and very high) were sum-
marized (see Tables 2 and 3).
Approximately 80 percent of the wells with concentra-
tions less than 5 parts per million are classified under
the moderate vulnerability category in SEEPAGE analy-
sis. Overall, the results from the analyses did not corre-
late satisfactorily with the observed well-water quality
data. Unavailability of the data layers aquifer media,
aquifer recharge, and vadose zone media may have
caused these results. The well-water quality data of
nitrate-nitrogen contaminations was considered only for
testing map accuracy, whereas the analyses do not
account for the type of contaminant, its severity, and its
volume in the generation of vulnerability maps. Other
limitations of these approaches, including that the fac-
tors influencing aquifer contamination (e.g., direction of
water flow, land use, population at risk, point sources of
pollution) are not considered in the ground-water vulner-
ability evaluation, might also have led to the observed
results.
The DRASTIC and SEEPAGE analyses can be improved
by incorporating data layers such as land use and nitrate
loadings in computing the DRASTIC and SEEPAGE
Indexes. The STATSGO database can be used for
-------
Figure 1. GUI for DRASTIC analysis.
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Figure 2. GUI for SEEPAGE analysis.
-------
Figure 3. Sampling sites (wells) for water quality data.
f Low
, Moderate
High
Very High
^ Low
H Moderate
High
Very High
Figure 5. Ground-water vulnerability map using SEEPAGE
analysis.
Table 2. Comparison of SEEPAGE Results With Observed
Nitrate-Nitrogen Concentrations in Wells
Nitrate-Nitrogen Levels
Figure 4. Ground-water vulnerability map using DRASTIC yerv hjqh
analysis.
Category
Low
Moderate
High
Very high
Table 3.
Category
Low
Moderate
High
Very high
0-5 5-10
7 2
1 ,322 76
249 11
194 2
>10
8
384
1
64
Comparison of DRASTIC Results With Observed
Nitrate-Nitrogen Concentrations in Wells
Nitrate-Nitrogen Levels
0-5 5-10
11 2
541 41
720 39
500 9
> 10
9
290
213
65
-------
developing most of the data layers required for the
analyses. CIS is a useful tool for integrating ground-
water quality models and facilitates testing the models for
different scenarios. The GUI helps users easily conduct
analyses and facilitates changing the weights for subjec-
tive hydrogeologic settings. DRASTIC and SEEPAGE ap-
proaches show great potential as screening tools for policy
decision-making in ground-water management.
Summary
Ground-water pollution from agricultural management
systems is of wide concern. Few models address
ground-water vulnerability on a regional scale. The
DRASTIC and SEEPAGE models are numerical ranking
models that consider various hydrogeologic settings af-
fecting the contamination of a region. The data required
for these models are commonly available data, and the
STATSGO database at 1:250,000-scale was used in this
study. These models were integrated in the CIS environ-
ment of GRASS and ARC/INFO in the raster format. A
menu-based GUI was developed in ARC/INFO for con-
ducting the analyses. The vulnerability maps generated
from DRASTIC and SEEPAGE analyses were com-
pared. The statewide vulnerability maps also were com-
pared with the well-water quality data to validate the
accuracy of the models.
References
1. U.S. EPA. 1990. National pesticide survey: Phase I report. Wash-
ington, DC.
2. U.S. EPA. 1992. Another look: National survey of pesticides in
drinking water wells, Phase II report. EPA/579/09-91/020. Wash-
ington, DC.
3. Department of Environmental Management, Groundwater Sec-
tion. 1989. Indiana ground-water protection: A guide book (June).
4. U.S. EPA. 1987. DRASTIC: A standardized system for evaluating
ground-water pollution potential using hydrogeologic settings.
EPA/600/2-87/035. Washington, DC.
5. U.S. EPA. 1985. DRASTIC: A standardized system for evaluating
ground-water pollution potential using hydrogeologic settings.
EPA/600/2-85/0108. U.S. EPA, Robert S. Kerr Environmental Re-
search Laboratory, Ada, OK.
6. Deichert, L.A., and J.M. Hamlet. 1992. Nonpoint ground-water
pollution potential in Pennsylvania. ASAE Paper No. 922531.
American Society of Agricultural Engineers International Winter
Meeting, Nashville, TN.
7. Richert, S.E., S.E. Young, and C. Johnson. 1992. SEEPAGE: A
GIS model for ground-water pollution potential. ASAE Paper No.
922592. American Society of Agricultural Engineers International
Winter Meeting, Nashville, TN.
8. Engel, B.A., and D.D. Jones. 1992. Technique for developing
ground-water vulnerability to nitrate maps for large areas: Re-
search proposal 12-1992.
9. U.S. CERL. 1990. GRASS: Geographical Resources Analysis
Supporting System user's manual. Champaign, IL: U.S. Army
Construction Engineers Research Laboratory.
10. SCS. 1992. State Soils Geographic Database (STATSGO) user's
guide. Soil Conservation Service Publication No. 1492.
11. ESRI. 1992. ARC/INFO command references: Arc command ref-
erences. Redlands, CA: Environmental Systems Research Institute.
12. ESRI. 1992. ARC/INFO user's guide: AML user's guide. Red-
lands, CA: Environmental Systems Research Institute.
13. ESRI. 1992. ARC/INFO user's guide: Cell-based modeling with
GRID. Redlands, CA: Environmental Systems Research Institute.
-------
Using a Geographic Information System for Cost-Effective Reductions
in Nonpoint Source Pollution: The Case of Conservation Buffers
Mark S. Landry, Darrell J. Bosch, and Tone M. Nordberg1
Abstract
Policy and management decisions to control agricultural nonpoint source (NPS) pollution can be
based on different levels of information about the site-specific pollutant reductions and costs of
control practices. While targeting practices based on site-specific criteria can reduce NPS
pollution control costs, the best procedure for implementing targeting must be determined
empirically. A geographic information system (GIS) can be used to evaluate and implement
targeting procedures. The objective of this study is to determine the potential to reduce costs of
controlling phosphorus (P) runoff by using a GIS to target the location of riparian buffer systems
(RBS) based on full and partial information about site characteristics. Full information assumes
knowledge of the potential P runoff reduction per dollar of RBS expenditure at each site. Partial
information assumes knowledge of the animal numbers and animal density on each farm
adjacent to a stream.
The study area is the Muddy Creek watershed in Rockingham County, Virginia, which is
dominated by dairy and poultry operations. The watershed has been identified as a high priority
watershed on the 1998 Virginia 303(d) Total Maximum Daily Loads list. A GIS was used to
estimate the cost and potential P runoff reduction of each RBS and the animal density and
animal numbers of each farm containing a potential site for locating a RBS.
Public and private costs of RBS installation and maintenance were included. Public costs
include transaction costs and government cost share for RBS installation and maintenance.
Private costs include the farmer's share of installation and maintenance costs and opportunity
costs of land removed from production. Phosphorus runoff interception by the RBS was based
on the Universal Soil Loss Equation (USLE), which was adapted to account for P runoff. The
GIS was used to construct dairy, poultry, and beef farms which approximate the actual number
of farms and livestock in the watershed.
1 Graduate Research Assistant and Professor, Department of Agricultural and Applied Economics, and
Graduate Research Assistant, Department of Biological Systems Engineering, Virginia Polytechnic
Institute and State University, Blacksburg. The authors express appreciation to Conrad Heatwole for
research assistance.
-------
Uniform installation of RBS resulted in a 60% reduction of P runoff at an annualized cost of
$45,332 or $26 per Ib. Targeting with full information resulted in a 47% reduction in P runoff at
an annualized cost of $24,535 or $18 per Ib. Although P runoff was 377 Ibs. more per year
compared to uniform buffer installation, costs per Ib. of P control were reduced by 30%. Total P
runoff was reduced by 37% when RBS were targeted based on animal density. Costs of
reductions were $21 per Ib. Targeting based on animal numbers reduced P runoff by 30% at a
cost of $25 per Ib.
Targeting with the aid of a GIS can increase the cost effectiveness of RBS installation for
controlling P runoff when full or partial information is available on RBS pollution reduction and
costs at specific sites. An additional benefit of the GIS is the ability to visualize the locations of
farms where RBS are located under alternative targeting criteria.
A GIS can be a useful tool for evaluating NPS pollution control options in watersheds. Further
research is needed on ways to enhance the effectiveness of GIS in supporting NPS pollution
control decisions.
Introduction
Over the past several decades, there has been increasing emphasis on the assessment and
management of nonpoint source (NPS) pollution from agricultural lands. Agriculture accounts for
more than 60% of the total NPS pollution of surface water bodies in the United States (U.S.
EPA). The inherent variability of agricultural NPS pollution is a function of the heterogeneous
landscape in a watershed, the complex interactions between ecological components, the total
amount of pollutants, and the spatial distribution of land uses (National Research Council). The
effectiveness of watershed policy and management decisions to control agricultural NPS
pollution depend on these site-specific factors.
Potentially costs of NPS pollution control can be reduced by targeting sites with low-cost control
opportunities. Targeting was examined in the early 1980's by the USDA for the purpose of
allocating limited Soil Conservation Service funding and technical assistance to aid in
conserving soil resources on highly erodible land in the U.S. (Nielson). A number of studies
have identified the potential for reducing costs or increasing environmental benefits by targeting
policies. Ribaudo (1986; 1989) showed the effects of considering off-site impacts of sediment
erosion on the priority ranking of watersheds in the U.S. for erosion control efforts. Carpentier,
-------
Bosch and Batie found that targeting nitrogen (N) runoff reductions to farms with lowest costs
reduced farm and taxpayer costs by 75 percent compared to the uniform policy. Studies by Fox,
Umali and Dickinson; Dickinson, Rudra and Wall; and Setia and Magleby found that targeting
resulted in large reductions in costs of controlling sediment delivery to streams.
The most effective method for targeting sites depends on watershed socioeconomic and
physical characteristics and type of pollutant to be controlled. A systematic integration of site-
specific, farm-level physical and economic models is necessary to capture the dynamic
environmental processes and economic behavior of farmers in association with NPS pollution
control policy (Antle and Just; Segerson and Wu). Empirical evaluation of targeting strategies
can be done using a spatial decision support system (SDSS). Spatial decision support systems
have been described as "flexible, integrated software for accessing, retrieving, and generating
reports on database information plus simulation and decision models for conducting alternative
testing, sensitivity analysis, and automated goal seeking." (Covington, et al., pp. 25-26) Spatial
decision support systems can include programming models, simulation models, data
manipulation systems as well as a user interface (Armstrong and Densham). The SDSS are
distinguished by their ease of use and flexibility (Armstrong and Densham; Geoffrion). Examples
of SDSS developed for evaluating NPS pollution control in agriculture include LOADSS
(Negahban, et al.), MIKE SHE (Danish Hydraulic Institute), and WAMDSS (CARES).
The most critical component of an effective watershed SDSS is the use of a GIS (Fulcher,
Prato, and Zhou). A GIS can be defined as the process of acquiring, processing, storing,
managing, manipulating, and displaying spatial data that are associated with user-specified
attributes (Aronoff). Interfacing a GIS in a decision support system is a logical approach to
maximizing the capacity of a watershed assessment tool for targeting BMPs and/or other policy
constraints (Bosch, Batie, and Carpentier).
In designing the GIS to evaluate alternative targeting strategies, a key question is what
variables should be included. Detailed information about costs and effectiveness of control
practices at each site might provide the most efficient targeting scheme. However some
information may be very expensive or unavailable. Perhaps targeting based on partial
information about costs and effectiveness of control practices can reduce pollution control costs.
-------
The objective of this study is to determine the effectiveness of using information from a GIS for
targeting riparian buffer strips (RBS) for the purpose of reducing total P in runoff from an
agriculture-intensive watershed. The following three policies for installation of RBS's are
compared in terms of private costs, public costs, and reductions in P runoff:
1. Uniform design standard Requiring all farms in the watershed to install a 60-foot wide
RBS along streams and channels;
2. Targeted design standard with full information Targeting 60-foot wide RBS on specific
farms throughout the watershed based on full information about buffer costs and
pollution interception; and
3. Targeted design standard with partial information Targeting 60-foot wide RBS on specific
farms based on partial information about buffer costs and pollution interception.
Case Application: Targeting Riparian Buffer Strips in Muddy Creek Watershed, Virginia
StudyArea:The Muddy Creek watershed is located in the Shenandoah Valley, approximately
10 miles to the west-northwest of Harrisonburg, Virginia in Rockingham County (see Figure 1).
Muddy Creek flows south to its confluence with Dry River, which discharges into the North River
then into the Shenandoah River and eventually into the Chesapeake Bay. The Muddy Creek
watershed is 20,025 acres in size with approximately 100-110 farms. The watershed is listed on
the 1998 Clean Water Act 303(d) Total Maximum Daily Load (TMDL) high priority list as an
impaired water body in terms of fecal bacteria concentration (VA-DEQ, 1998).
About 65% of the farms are predominantly dairy, 30% are beef, and 5% are poultry. Dairy and
beef operations also have poultry. Beef and dairy manure and poultry litter are typically spread
on cropland and pastures. Most farms use a seven-year crop rotation, five consecutive years of
corn/corn silage followed by two years of alfalfa. Corn is often rotated with rye, which is used
either as a cover crop or harvested as silage. Pasture may be improved or native rangeland.
Cattle are usually not restricted from surface water sources and most farms do not currently use
field or riparian buffers to improve runoff water quality. While a nutrient management plan is
required on farms with poultry (VA-DEQ, 1999), most farmers without poultry have not
developed a nutrient management plan although a majority of this group have manure storage
facilities.
-------
Figure 1. Muddy Creek Watershed, Rockingham County, Virginia.
Muddy Creek Watershed
Area = 20,025 acres
/\/ Stream Network
Farm Enterprise and Land Uses
Dairy
Dairy with Poultry
Beef Cattle
Beef Cattle with Poultry
Poultry
Forest or Residential
0 1 2 Miles
Rockingham County,
Virginia
Riparian Buffer Strips: Riparian buffer strips (RBS) are areas that are managed to reduce the
amount of pollution suspended in runoff. A RBS is installed downslope of a point source (e.g.,
feedlot) or nonpoint source (e.g., cropland) area on which pollution is generated. A RBS
reduces pollution exiting the site via runoff by promoting infiltration of water and water soluble
constituents, increasing the adsorption of pollutants onto the vegetation and soil, and increasing
the absorption of nutrients into plants (Dillaha et al.; Landry and Thurow). The appeal of the
RBS is that it can be easier and more economical to install and maintain than physical
-------
structures (Pritchard et al.), can be aesthetically pleasing and can provide a source of income
as pasture, hay, timber, or wildlife refuge (Purvis et al.; Shabman and Smith).
The USDA launched the National Buffer Initiative in 1997 emphasizing the importance and
effectiveness of riparian buffers for achieving water quality protection objectives (USDA-NRCS).
The national goal is to install two million miles (up to seven million acres) of conservation buffers
by the year 2002. The Buffer Initiative is sponsored by numerous government agencies and
agricultural producers working together to promote the use of RBS and their eligibility in
incentives programs such as cost-sharing through the Conservation Reserve Program (CRP).
Policy Scenarios
Uniform design standard: This policy assumes a 60-foot wide RBS is required along both sides
of all streams and channels. Farms required to install RBS's are assumed to take advantage of
all available government incentives. Two government assistance programs are considered 1)
the Conservation Reserve Program (CRP) (10 year contract, 50% cost-share towards RBS
establishment, and a $70/acre annual rental payment) and 2) the Virginia Water Quality
Improvement Act (VWQIA) (25% cost-share towards BMP installation up to $17,500).
Targeted Design Standard - Full Information: Full information is defined as knowing the P runoff
reduction per dollar of buffer expenditure. Phosphorus runoff reduction was calculated using the
GIS and was based on soil type, infiltration rate, plant or crop nutrient uptake, manure and/or
litter application rate, slope, weather characteristics, and RBS pollutant interception coefficients.
Targeted design standard - partial information: This scenario is used to determine whether
limited, readily available information can be used to designate RBS installation sites in a manner
that approaches the total costs and water quality improvement given full information. We
targeted 60-foot wide RBS's under two partial information policy scenarios based on farms
having the greatest number of cattle (dairy or beef) and by farms with the highest manure
application rates per acre based on animal density (animal units per acre of land).
Empirical Procedures
Riparian Buffer Cost Specification: Private and public costs associated with RBS installation and
maintenance are illustrated in Table 1. Private costs equal the annualized sum of opportunity
costs plus establishment and maintenance costs minus government cost-share (the RBS is
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Table 1. Costs and returns of riparian buffer strip.3
Item
Information or Cost Description
Life of project (years)
Vegetation composition
10
Fescue grass and Ladino Clover
Farmer Costs (present value)
Returns
Land opportunity costs0
Seed
Lime
Fertilizer
Labor and Equipmentd
(a) Total
Cost-share
CRP 50% cost-share
CRP rental payment ($70/ac)
VWQIA cost-share
(b) Total
Net farmer costs (a-b)
Annualized farmer cost
Public Costs (present value)
CRP 50% cost-share
CRP rental payment ($70/acre)
VWQIA cost-share
Net public costs
Annualized public cost
Transaction Costse
Information
Contracting
Enforcement
Annualized transaction cost
Corn Silage
($/acre)
0
1,105
61
54
26
166
1,412
154
541
77
772
640
83
Hay Land
($/acre)
0
772
61
54
26
166
1,079
154
541
77
772
307
40
f$/acre)
154
541
77
772
100
($/farm)
28
1
77
106
Pasture0
($/acre)
0
116
15
14
7
166
318
101
541
51
693
-375
-49
Internal rate of return equals 5%.
b Fescue grass - Ladino Clover pasture with 25% additional seed, lime, and fertilizer added in buffer.
c We assumed a relatively high yield for corn silage and hay/pasture based on the likelihood of moderately sloping,
high productivity soil in the bottomland adjacent to the stream. The land opportunity cost for corn silage was equal to
the expected yield (16.5 tons/ac) x $25/ton - total variable costs ($254), for hay land was equal to the expected yield
(3.3 tons/ac) x $70/ton - total variable costs ($131), and for pasture was equal to the expected yield (1.7 tons/ac) x
$70/ton -total variable cost ($101) (Virginia Cooperative Extension).
d Taken from Leeds, Forster, and Brown.
e Values taken from Carpentier and Bosch estimates for strip cropping. Costs are annualized based on a 5% real
interest rate.
unharvested and produces no revenue). Land opportunity costs represent the income given up
when land is taken out of production and used for buffers. These costs varied based on soil
productivity and land use. Cropland was used in corn silage production; hay land was used for
orchard grass or timothy grass; and pasture was used for Fescue grass-Ladino clover pasture.
For example, the income given up from corn silage minus variable costs of production is $254
per year (see footnote to Table 1). The present value of corn silage income given up over a 10-
-------
year period is $1,105. After adding other buffer installation costs, the present value of total
buffer costs is $1,412. Federal and state cost-share and rental payments are subtracted leaving
a net present value of $640, or $83 on an annualized basis.
Public costs include federal and state cost share and rental payments as well as transaction
costs. The present value of public cost share plus rental payments for a 10-year period is $772
or $100 on an annualized basis. Transaction costs include the costs of informing farmers about
buffer requirements, contracting with farmers to install buffers, and enforcement to insure that
buffers are installed and maintained. Annualized transaction costs are $106 per farm.
NFS/Water Quality Modef. The Universal Soil Loss Equation (USLE) was used to determine the
average annual soil loss. Average annual manure applied to the field was used to find the
available N and P in the soil. Enrichment ratios were used to calculate the N and P
concentrations in the runoff that reached surface waters.
ArcView was used to do the GIS modeling, with a USGS DEM, soils map, landuse and streams
as input themes. The sinkholes of the DEM were filled and a slope grid and flow direction grid
for the watershed were derived from the filled DEM. The slope of each field polygon from the
landuse theme was found by summarizing the landuse by each field polygon with respect to the
slope grid, hence an average slope for each field was created. The length factor was found
based on the assumption that each polygon was a square, the perimeter was calculated and
then divided by four equal sides. This simplification resulted in some fields exceeding the max
length of 100 m proposed for the use of the USLE (Novotny and Olem). Then the slope-length
factor of the USLE equation was calculated from equation (1) below (Novotny and Olem), where
L equals length in meters and S equals slope as a decimal fraction.
(1)
The cropping management factor, C, and the erosion-control practice factor, P, were selected
for each landuse from Novotny and Olem, Gupta, and Schwab et al. For the rainfall energy
factor, R, a value of 175 was taken from Novotny and Olem. The soil erodibility factor, K, was
found for each soil in the Rockingham County Soil Survey (USDA-SCS), an average value for K
was used if different values were listed for different soil depths.
-------
The amount of the eroded soil calculated with the USLE that would actually leave each field and
reach surface waters was based on a sediment delivery ratio of 30% (Novotny and Olem). It
was assumed that the only N and P available in the soil came from the manure applied to the
fields. The concentration of sediment-bound P in the soil (fraction of a gram P/ gram soil) was
based on the assumption of a 1.3 kg/m3 bulk density for the soil and a 5 cm depth of interaction
assumed for the surface applied manure. The sediment bound P loss was then calculated with
an enrichment ratio of 1.6 (Edwards et al.). The available N in manure was assumed to be in
organic form, with 83.3% TKN (Edwards et al.). The concentration of N in sediment was
calculated in the same manner as was done for P. The enrichment ratio of N was assumed to
be equal to the organic enrichment ratio, equation (2) (Novotny and Olem). The organic matter
of the soils was assumed to be an average of 2.5 % for the watershed.
ERor = - — r +1.08 ,~
(% organic matter) \ >
The runoff from each field was not routed through the watershed and attenuation of nutrients in
stream was not considered. The reductions of N and P loadings by the buffers were assumed to
be constant and not to change by field location with respect to the streams. This is a
simplification, since the buffers would have a greater impact for fields located closer to the
streams. Possible reductions in the long term effectiveness of buffers were not considered in the
study.
The pollutant delivery calculations assumed all manure and litter were land applied on the
producing farm and commercial fertilizer was applied at a rate that was equal to 70% of crop
removal. Commercial fertilizer application rates were set at levels needed to make total nutrient
applications (manure plus commercial fertilizer) result in excess nutrient availability at levels
approaching mean values observed by Bosch et al. These commercial fertilizer rates may
overstate excess nutrients given recent nutrient management planning efforts in the area.
Data in Table 2 were used to calculate N and P application rates assuming 70% of the N and
100% of the P are available to plants based on annual land application of all manure and litter
produced. For example, an acre of corn silage on high productivity soil removes 217 Ibs. of N
and 82 Ibs. of P annually. The N and P application rates are based on the amount of manure
and litter produced as a function of animal density and the nutrient content. Manure and litter
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are applied to meet 30% of the vegetation requirements and excess N and P are available for
export via runoff and leaching.
Table 2. Dry weight manure and litter nutrient content.3
Unit Percent Dry Weight Organic P
Moisture N^
— /o — — IDS. — IDS./Unll
Dairy/Beef Cattle Manure 1,000 gal 94.5 457 12.3 5.3
Poultry Litter ton 66.2 1,323 49.0 27.8
a Source: Pease, Parsons, and Kenyon.
b Inorganic N content of manure was rv
75%) when manure is unincorporated as was assumed in this study.
b Inorganic N content of manure was not considered. Inorganic N is subject to heavy volatilization losses (25 to
Table 3 indicates the average expected NPS pollution reductions for the 60-foot wide RBS
based on typical soil characteristics (silty loam), vegetation composition (Fescue grass and
Ladino clover), and management activities (no harvesting) for the buffers in the watershed.
Table 3. Pollutant trapping reductions (%) used for a 60-foot riparian buffer strip in
Muddy Creek Watershed, Rockingham County, Virginia.
Constituent
Total Suspended Solidsb (TSS)
Total Phosphorus (TP)
Total Kjeldahl Nitrogen (TKN)
Percent Pollutant Reductions
1-5% 5-10%
80 65
60 50
55 40
(% slope)3
10-20%
50
40
25
Average percent slope of the original field or installed riparian buffer adjacent to the stream.
b Constituent used to measure sediment delivery.
CIS: A GIS was built for the study area with ArcView (ESRI) using data from federal and state
agencies (see Table 4). Soil productivity and potential corn, hay, and pasture yields were based
on the Virginia Agronomic Land Use Evaluation System (VALUES) (Simpson et al.).
10
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Table 4. Data included in the GIS for the Muddy Creek watershed.
Data Type
Data Source
Digital elevation map (DEM)
Road network
Soil data
Stream network
Land use, land use proportions
Streamflow data, nutrient monitoring data
Weather data
Watershed size
U.S. Geological Survey (USGS)
U.S. Census Bureau - Tiger Files
U.S. Department of Agriculture - Natural Resources
Conservation Service (NRCS)
NRCS, stream network delineator extension
developed for ArcView GIS
Virginia Department of Conservation and Recreation
(OCR)
United States Geological Service (USGS)
Virginia State Climatology Office
Virginia Hydrologic Unit Atlas (NRCS)
Farm-scale information was necessary to determine animal numbers, manure application rates,
and numbers of acres eligible for buffers on each farm. Farm boundaries were unavailable,
therefore, a field clustering routine developed in the GIS was used to allocate 71 diaries and 36
beef cattle farms based on criteria representative of the area (Schroeder; VA-DEQ, 1999;
Parsons; US-Dept. of Commerce). Fields were assigned to farms based on the Thiessen
method according to proximity and greatest area within polygons formed around the nearest
point representing central farm nodes.
Farm enterprises consisting of three sizes of dairies (60, 100, and 150 cows) and three sizes of
beef cattle operations (40, 70, and 150 cows) were distributed across the watershed according
to farm size. For example, the smallest dairies were assigned 60 cows, medium sized dairies
were assigned 100 cows, and the largest dairies were assigned 150 cows. The locations of
broiler and turkey houses were included in the land use GIS data coverage, thus farms which
included these structures were assigned 25,000 broilers per house or 16,000 turkeys per house,
accordingly. The aggregate number of animals on the farms generated using the GIS
11
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approached the total number of animals in the watershed: 6,533 dairy cows; 3,134 beef cows;
500,000 broilers; and 350,000 turkeys (Schroeder; VA-DEQ, 1999).
Targeting RBS based on full information assumes that NPS pollution reduction is maximized for
a given sum of costs (public and private). The full information targeted buffers scenario limited
the amount of stream miles that could be buffered to one-half the distance considered in the
uniform design policy. The farms adjacent to streams and channels were ranked according to
those having fields which resulted in the highest P reductions per dollar of cost. Fields ranked in
the top half of all fields in pounds of P reduction per dollar of cost were targeted for buffer
installation. Costs include cash and non-cash costs, where the cash costs equal the government
outlays for transaction costs and cost-share and farmers' outlays for buffer establishment. The
non-cash costs are the farmers' opportunity costs for the land idled by the buffer.
Under the partial information scenario based on animal number, the farms were ranked
according to the number of cows per farm. Farms with equal numbers of cows were ranked
based on numbers of broilers or turkeys. The second partial information policy scenario required
farm size to determine animal density in acres. With this added information, we calculated N
and P application rates/acre based on the nutrient content of the manure and/or litter produced
and ranked the farms accordingly. For both partial information scenarios all other data were
considered unknown, including potential yields on lands used for buffers, RBS costs, public
costs, and the NPS pollutant interception rates of the buffers. The RBS's were allocated across
the watershed until 50% of the potential stream miles were buffered.
Results
Analysis using the GIS indicated that 69 farms were subject to the RBS policies based on
stream proximity. Baseline annual pollutant loadings from fields adjacent to streams and
channels for sediment; phosphorus; and nitrogen were 23,059 tons; 2,900 Ibs.; and 1,653 Ibs.;
respectively. Approximately 279 acres of buffers were installed under the uniform design
standard. Nineteen of the 32 stream-miles were considered eligible for RBS installation. The
remainder were ineligible based on adjacent land uses such as forestland, farmsteads, and
residences which do not support animal production. Table 5 illustrates variation in policy
effectiveness across the four policy scenarios under analysis.
12
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Table 5. Costs and effectiveness of riparian buffer strips in alternative policy scenarios.
Policy Scenario
Uniform
Targeted:
Full Information
Targeted:
Animal Number
Targeted:
Animal Density
Phosphorus
Reduction3
(Ibs.)
1,740(60%)
1,363(47%)
870 (30%)
1,073(37%)
Private
Costs
($)
10,098
4,724
4,419
5,014
Public
Costs
($)
35,234
19,811
17,279
17,799
Average
Cost
($/lb.)
26
18
25
21
Number
of
Farms
69
55
36
30
a Number in parentheses represent the percent reduction compared to the baseline. The baseline had 2,900 Ibs. of
phosphorus runoff.
Figure 2 compares the various NPS pollution control policies based on the targeting criteria
used. The fields affected by each policy vary. The spatial impacts, which are displayed using the
GIS, illustrate the distribution and relative effectiveness of each policy scenario.
The uniform design standard achieved a 60% reduction in P runoff (1,740 Ibs.) at a total (public
and private) annualized cost of $45,332 or $26 per Ib. The levels of NPS pollutant reductions
under the uniform design are consistent with the expected reductions for the 60-foot wide RBS.
Requiring all eligible stream miles to be buffered implies that all 69 farms along the streams are
affected by the policy.
Full information RBS targeting on 50% of the eligible stream miles affected 55 farms, which had
at least one field bordering the stream requiring a buffer. Total (public and private) annualized
costs were $24,535. Total P runoff reduction was 47% of the baseline (1,363 Ibs.) or $18 per Ib.
The average cost per pound of P interception was reduced by 30% although total P interception
was 377 Ibs. less under this scenario.
13
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Figure 2. Fields Affected by Targeting Criteria Associated with Each Policy Scenario.
Uniform Design Standard
Targeted Design Standard:
Full Information
Targeted Design Standard:
Partial Information (Animal Number)
Targeted Design Standard:
Partial Information (Animal Density)
Number of Cows
100
150
Stream NetwDrk
0
2 Miles
14
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Targeting based on animal numbers and animal density removed less P than full information.
Total P interception was 37% (1,073 Ibs.) for animal density targeting and 30% (870 Ibs.) for
animal number targeting. Total costs were $22,813 for animal density targeting ($21 per Ib.) and
$21,698 for animal number targeting ($25 per Ib.). The number of farms affected was 30 for
animal numbers targeting and 36 for animal density targeting. Partial information resulted in less
efficient targeting than full information in terms of average cost per pound of P reduction, but
was more efficient than a uniform standard. The partial information targeting scenarios suggest
that average pollution control costs can be reduced by targeting control measures using basic
information about farms' pollution potential.
Summary and Conclusions
The public is increasingly concerned to reduce NPS pollution. Given public ambivalence about
increasing taxes or regulations, it is important that pollution control costs be lowered. Cost
reduction will require that effective policies be designed both in terms of the type of policy
instrument used and how the instrument is targeted. Policy selection and targeting can be
enhanced by considering fixed spatial attributes of farms that affect their pollution potential and
their costs of controlling pollution. A GIS can be used to assist in identifying and targeting farms
with high potential for reducing pollution at low cost.
A case analysis of Muddy Creek watershed in Virginia demonstrates the potential use of GIS to
assist in determining the costs and pollution reduction from installing RBS's within the
watershed. The case study results suggest that using a GIS to target RBS's based on full or
partial information about pollution control costs can be an effective tool for reducing pollution
control costs. Targeting pollution control measures might be politically difficult due to farmers'
concerns with fairness. One way to overcome this concern would be for governments to
compensate farmers for all buffer installation and opportunity costs.
Because of their user accessibility, GIS and SDSS are likely to increase in importance for
evaluating and implementing water quality protection policies. For example, they can be used to
assess plans for achieving TMDLs in watersheds with impaired waters (U.S. EPA). Continued
development of SDSS capabilities will be needed to expand their usefulness. The appropriate
spatial resolution of the watershed which balances design and implementation costs with
accuracy may change depending on watershed physical and economic conditions and the
15
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objectives of the users. How can SDSS's be designed to operate at multiple scales of resolution
from very large watersheds (e.g. Chesapeake Bay drainage) to small local watersheds?
The physical and economic variables that are most influential in determining NPS pollution
responses to changing policy and economic conditions should be identified. While these
variables are likely to change depending on the type of problem being addressed (e.g. pesticide
pollution vs. nutrient pollution) and the type of agricultural watershed, further research could
identify some general relationships. For example, what is the relative importance of farmers'
knowledge, attitudes, and goals compared to the fixed physical characteristics of their land in
determining potential pollution? While farmers' attitudes and knowledge are often unknown to
the researcher and subject to rapid change, a better understanding of their importance would
help analysts better characterize the uncertainty from the SDSS.
Output from complex decision support models are subject to errors due to inherent randomness
in the processes being simulated, errors and gaps in input data, and uncertainty about the
relationships being modeled (Suter and Barnthouse). How can uncertainty be incorporated into
the SDSS and communicated to users? Research is needed to extend the capability of SDSS to
handle changes over time. This capability would facilitate tracing firms in space and time as they
respond to economic trends, policy changes, or other exogenous shocks. For example, what
are the watershed implications of continued expansion of intensive livestock industries?
Similarly, SDSS need better capability to model physical changes (e.g. pollution) overtime.
Further development of dynamic capabilities within SDSS would allow them to be used for inter-
temporal optimization decisions.
More study is needed of the most likely users of SDSS for NPS pollution control. What are the
decisions they make and what types of information do they need to help make those decisions?
Better knowledge of user needs can guide data collection strategies, enhance SDSS design,
and show where further knowledge of underlying socioeconomic behavior and physical
processes is needed.
16
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Merging Transportation and Environmental Planning Using
Geographic Information Systems (GIS)
Elizabeth Lanzer, Washington State Department of Transportation
Abstract
The Washington State Department of Transportation annually plans over 400 highway
construction projects ranging from repaying efforts to developing entirely new roads. The
challenge brought to the Environmental Affairs Office at WSDOT is how to effectively and
consistently make use of existing Geographic Information System (GIS) information in the
transportation planning process. This paper describes the development of two GIS efforts that
examine environmental data in relationship to highway construction projects.
The two examples in this paper describe how WSDOT is actively incorporating GIS as a tool for
environmental assessments. The first example, Environmental GIS Workbench (Workbench), is
an application that was developed to provide new GIS users with easy access to existing
environmental data. The application shows environmental and transportation data on screen for
visual examination by the users. The second example, Environmental Screening (Screening), is
a GIS product that offers additional analysis. Highway projects are evaluated for proximity to
environmental information (e.g. wetlands, rivers, etc.) and then ranked high, medium, low or
none according to potential environmental impacts.
One of the challenges of both efforts is the complexity and volume of environmental data. Data
sources range from private, local, state and federal sources. Many of which differed in format,
scale, projection, data quality, documentation and maintenance requirements. The GIS
products both utilized the same data, however the level of analysis done by the GIS products
were different.
Introduction
The Washington State Department of Transportation (WSDOT) is responsible for efficiently
building, maintaining, operating and promoting safe and coordinated transportation systems to
serve the public1. These transportation systems include highways, ferries, railroads, airports
and selected river systems that link people, freight and locations across the state. The highway
system is the largest of these with over 7,000 miles of state roads. WSDOT annually plans over
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400 highway construction projects. For the purposes of this paper, references to "projects"
should be interpreted as highway construction projects ranging from re-paving to developing
entirely new roadways. Unfortunately, environmental requirements are not always optimally
considered. However, recent mandates, such as the listing of many salmonid species under the
Endangered Species Act, have created a paradigm shift within WSDOT that emphasizes
consideration of environmental factors and created the opportunity for these GIS efforts.
The challenge brought to the Environmental Affairs Office at WSDOT is how to effectively and
consistently make use of existing Geographic Information System (GIS) information in the
transportation planning process. Traditional environmental reports are generated through the
labor intensive process of obtaining maps, data or reports from government entities and then
reviewing that information on a project by project basis. This process is time consuming and
inefficient when trying to meet deadlines. Limitations of comparing paper maps against each
other, repetition of data collection and information inaccessibility hinder the environmental
review of highway projects. GIS, on the other hand, integrates software, hardware, data and
people allowing more efficient environmental assessments.
The two examples in this paper describe how WSDOT is actively incorporating GIS as a tool for
environmental assessments. The first example, Environmental GIS Workbench (Workbench), is
an application that was developed to provide users with easy access to environmental data, but
provides relatively little analysis. The application shows environmental and transportation data
on screen for visual examination by the users. The second example, Environmental Screening
(Screening), offers additional analysis. Highway projects are evaluated for proximity to
environmental information (e.g. wetlands, rivers, etc.) and then ranked high, medium, low or
none according to potential environmental impact.
Background
The Washington State Department of Transportation's (WSDOT) agency standard GIS
software is ArcView 3.1 (NT) and ARC/INFO 7.2.1 (NT and UNIX) from ESRI (Redlands, CA).
The Department has approximately ten ARC/INFO licenses and over 150 ArcView licenses
statewide. Training and support is provided for GIS users through the WSDOT GIS Support
Team which includes GIS professionals from throughout the department.
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Agency available GIS data (vector and imagery) is stored on a central GIS server. Vector data
is stored in geographic projection, decimal degrees, spheroid GRS80 and datum NAD83.
Imagery data is generally stored as delivered by the source agency (e.g. stateplane south,
NAD27, feet). All GIS data that is stored on the central server has Federal Geographic Data
Committee (FGDC) standard metadata reports in both plain text and HTML (web page) format.
GIS data is administered centrally, but managed by a distributed group of business specialists
called data stewards. A typical data steward is a GIS or technology specialist housed within a
specific functional office within the agency. Working with their colleagues, they determine data
requirements and standards to meet the needs of their function within the agency. As data
meeting these needs are gathered or created, the data steward posts the data with the agency
GIS data administrator and defines rules for accessing and updating the data they are
responsible for.
GIS data that is available on the server is either generated internally in WSDOT or obtained
from external data sources. WSDOT creates and maintains a dynamically segmented state
highway model and a vast amount of associated tabular data. These tabular records are
mapped within the GIS linear highway model as events. Event data are placed on the GIS
system using milepost information. For example, construction projects can be located on the
GIS coverage by knowing the state route number, beginning and ending mileposts. Milepost
information is collected for environmental data such as fish passage barriers and deer kill
locations. Partnerships have also been formed with the Washington Department of Health to
create GIS coverages on drinking water wells.
A significant amount of WSDOT's environmental data is obtained from public and private
sources. The information is formatted for use on WSDOT's systems and placed on the central
GIS server when appropriate (See Attachment A). In some cases, data sharing agreements
with other agencies are necessary to obtain the information, particularly when the data is
considered sensitive (e.g. certain wildlife habitats). Data collected from outside sources are
updated on an established basis (in some cases, every six months and in other cases every two
to three years) so that reasonably current data is available to the WSDOT GIS user community
at WSDOT. This data forms the foundation upon which WSDOT GIS products and analysis are
based.
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Environmental GIS Workbench Application (Workbench)
The Environmental GIS Workbench Application (Workbench) is a desktop GIS application that
provides easy access to existing environmental data and environmental data processing tools
needed in the development of environmental reviews for highway projects planned for
construction within two years. This application includes a convenient method for accessing data
by subject and location in order to analyze the data, and a means of capturing the results for
documentation. The application is programmed using Avenue programming language and
incorporated into an ArcView extension. The intention is to reduce the amount of training and
the learning curve presently needed for new users to access the existing data thereby
improving the efficiency and the quality of the review process.2 The Environmental Review
Summary form provides environmental documentation regarding air quality, fish habitat,
wetlands, water quality and other environmental factors (See Attachment B). Data for the
application are imported from various Washington State Department of Transportation
application systems, or obtained from other agencies where appropriate.
Prior to developing the Workbench application, the environmental review forms took
approximately two to eight hours to complete. Most of that time was spent gathering the
necessary maps and documents. Much of that information was already available on the
WSDOT GIS server, but was not easy to access or query. The Workbench utilizes GIS
technology to create layers of maps that display available environmental data (See Attachment
A).
A steering committee was convened to guide the application development process. This group
defined the budget and general expectations of the product as well as provided the conduit for
management and policy support. A smaller workgroup was also formed to define the specific
data and tool requirements. The Environmental Review Summary form that is completed for all
projects provided the foundation for determining the questions the Workbench would be
expected to answer. The workgroup identified which existing data was appropriate to
incorporate and created a list describing new data to be obtained. There were several datasets
that were identified as critical but not currently available at WSDOT. The top seven were
prioritized, obtained and added to the application.
Most of the environmental data included in the Workbench have statewide coverage, but there
are occasional county specific coverages. County specific coverages are included in the
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Workbench, but are clearly labeled as such to prevent confusion. Whenever possible, the
coverages for the application are saved as shapefiles to improve application speed. The scale
of the included data varies from 1:24,000 to 1:500,000. All data that is accessible through the
Workbench has accompanying Federal Geographic Data Committee (FGDC) compliant
metadata created by WSDOT in HTML format. Usually, the data source supplies metadata files.
These files are hotlinked to the WSDOT HTML metadata reports. The WSDOT HTML metadata
reports are accessible through a tool in the Workbench. By providing the users with metadata,
the environmental specialists utilizing the Workbench are able to make decisions about whether
a particular data set is appropriate to answer their questions.
Training users is an essential part of success for the Workbench. Training is structured as a
half day session on ArcView and the Workbench extension. The second half of the day is
devoted to discussions on data quality, what assumptions can or cannot be made and how to
understand and use the metadata. GIS data usually looks different than the maps and reports
the specialists are accustomed to seeing, so this also provides an opportunity to introduce them
to how to use GIS data and how to query it.
Maintenance of the Workbench is important so that users trust the application to have
reasonably current data. Data that is already programmed into the Workbench are updated
seamlessly by using the same coverage/shapefile naming conventions. Additional programming
is required when adding entirely new data to the application. The Workbench is scheduled for
deployment in July of 1999. Beta testing is complete and so far the feedback is positive. A
feedback database is being maintained to consolidate comments from users and use these
comments to make future improvements in the Workbench. Future versions are anticipated with
the first comprehensive review of the application tentatively scheduled for Spring 2000.
Environmental Screening (Screening)
Environmental Screening was developed to evaluate highway construction projects for proximity
to environmental information (e.g. wetlands, rivers, etc.) and then ranked high, medium, low or
none according to potential environmental impact. This environmental flagging will be included
in the long term planning and budgeting for the project.
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Figure 1. Example Interface of the Washington Department of Transportation's
Environmental GIS Workbench Application.
Yjew Iherne Eraphics Window Help
ADD ENVIRONMENTAL DATA
Air Quality
C Drive Setup
(~ Add Base Map
Groundwater (Aquifers, Wells)
Habitat
<~ Wetlands (NWI)
Flood Plains (FEMA)
Major Public Lands
C Locate Proiect
Buffer
Print Map
r Metadata
SRVie™
C Archive Project
;Fro[Sctionj
Delete Themes
Cultural, Tribal Lands
Parks, Forest/Timber
Hazardous Waste
<~ River/Stream
C Visual Quality
Water Quality/Storm Water
Environmental and GIS specialists analyzed highway projects and environmental data using
ArcView software. Eleven environmental categories were originally chosen for investigation: air,
cultural resources, flooding and wetlands, geologic hazards, habitat, hazardous materials,
noise, recreation, environmental justice, visual impacts, and water quality. Upon further
investigation, insufficient data or time existed to complete the visual impacts, geologic hazards,
recreation, environmental justice and noise sections.
The air and cultural resources categories were visually analyzed. All relevant data was mapped.
Subject area specialists visually examined the paper map for intersections between the
proposed highway projects and the relevant data. Broad conclusions were drawn as to the
extent of potential impacts.
-------
The remaining categories: flooding and wetlands, habitat, hazardous materials and water
quality were analyzed using a query based method. All available relevant data was mapped.
Subject area specialists determined an appropriate buffer distance from the highway, within
which, an intersection with an environmental data feature would constitute a "hit". The
environmental data was weighted (See Attachment C) and if the highway project received a
"hit" on the environmental data, then that segment was assigned a weight, according to the
severity of potential environmental impact. After all queries for a particular subject area were
completed, all weights were totaled for each highway project, and a final rating of high, medium,
low, or none, was assigned. Paper maps showing the ratings (See Figure 2) and the proportion
of highway projects with environmental impacts were produced (See Figure 3). Again, broad
conclusions were drawn as to the extent of potential impacts.
Figure 2. Example of Results for the Washington Department of Transportation's
Environmental Screening Project.
1997 Environmental Overview of 20 Year Projects
Summary Map: Flooding and Wetlands
Environmental Affairs Office (OSC)
Washington State Dept of Transportation
Olympia, WA
20 Year Projects
/\/High Probable Impact
/\/Medium Probable Impact
/\/ Low Probable Impact
/\/ State Routes
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Figure 3. Proportion of Highway Projects with High Environmental Impacts by
Washington Department of Transportation Regions.
Water Quality
Wetlands/Floods
Proportion of Highway Projects With High Environmental Impact
20 Year System Plan Improvements
Washington State Dept. of Transportation
Relative Proportion of Impacts Acfoss
Four Environmental Categories
Environmental Affairs Office (OSC)
Washington Stale Depl. of Transportation
Olympia, WA
September25, 1997
Lambert Conforma! Conic Projection,
Washington State Plane North
North American Datum 1933
97ea_sum spr (em tykdwn) mem
Relatvie size of pie charts show
proportion of high impact
deficiencies by region.
lasetf on highway system plan data
from WSDOTPlanning Office, 4/96.
Next Steps for Environmental Screening
This effort was originally conducted in 1996 -1997 as a demonstration of how GIS could
incorporate environmental information earlier in the transportation planning process. Limitations
of these analyses included a lack of appropriate environmental data, that the results did not
reflect multiple "hits" per project and that weighting and rating systems varied among the
subject areas making cumulative analyses difficult. Since 1997 there have been improvements
to the amount and quality of appropriate environmental data and enhancements to the software
(ArcView) used for conducting the queries.
In May 1999, this project was re-evaluated by the Environmental Affairs Office and the Planning
Office at WSDOT. It was decided to continue developing the Screening and improve the
previous effort to support the year 2000 Washington Transportation Plan.
-------
Several process changes are being made in the current Environmental Screening effort. More
people are involved in the product/output definition phase with the potential for it to be
programmed as a GIS application. The data weighting and ranking systems are also being re-
evaluated. The current effort is expected to be applied to all modes of transportation in
determining effects, not just highway projects. The long term vision of this activity is to use
environmental data to help identify transportation alternatives (e.g. using transit or rail as a
solution in urban areas with air quality problems instead of highway improvements which may
increase air pollution problems). There are some limitations in the GIS coverages for other
modes besides highways, but that is one of the issues to be addressed as work progresses.
One of the goals for the current effort is to set up a clearly documented and repeatable process
that allows new transportation projects to be promptly evaluated for potential environmental
effects.
Discussion
The Workbench and Screening tools are fundamentally different in terms of how the data is
applied. In the Workbench, the data is simply retrieved from the server and displayed. Most of
the queries applied to the data are to aid in the visual display. In the Screening, the data is
retrieved, criteria is applied to the data then an evaluation is made on the highway project. The
Workbench relies on the user to complete the environmental analysis while the Screening
depends on environmental criteria programmed into the application.
The Workbench requires a greater degree of confidence in the data even though there is less
pre-programmed analysis than in the Screening. The purpose of the Workbench is to analyze
specific sections of the highways. This may require zooming into one to two mile sections of a
roadway and visually examining the available data. The better the information going into the
Workbench, the greater the confidence in the results coming back out. The Screening, on the
other hand, is used to raise warnings about potential future impacts. Since the projects are still
20 years from construction, data with less resolution is acceptable for the task. As a particular
highway project progresses towards construction, better quality data will be needed to evaluate
potential environmental impacts.
The famous "Garbage in-Garbage out" theory applies here. Using the GIS data steward
concept at WSDOT is one data quality control point. Information is not stored on WSDOT's
central GIS server unless it meets certain minimum standards and has accompanying
-------
metadata. Business specific GIS applications expand the audience using GIS data. Complete
and accurate metadata is essential for these products to be successful. Discerning users are
skeptical of the data being shown on-screen so that easy to use documentation is important.
Achieving user buy-in so that the system or application will be used is also a critical success
factor.
Another advantage to the Washington Department of Transportation's central GIS server
concept, is that both efforts draw upon the same information sets. Some efficiencies come from
the ability to develop data that will be useful in both analyses. At this point, data that is more
detailed than 1:24,000 is difficult to obtain consistently on a statewide basis. Local governments
generally have more detailed environmental data but how to incorporate that information into
WSDOT and associated GIS products is still a work in progress. As improvements are made,
they can be integrated with associated GIS applications and products.
One of the challenges of both projects is the complexity and volume of available environmental
information. Data sources range from private, local, state and federal entities. Even when data
is available in GIS format, many differ in format, scale, projection, data quality, documentation
and maintenance requirements. Some critical environmental data is simply not available in GIS
format and will be expensive to convert. Additional data problems occur when several state
agencies maintain similar but different GIS layers. For example, at least four different state
agencies currently maintain a 1:100,000 stream layer each with different attributes important for
environmental analysis. Coordinating and determining appropriate uses of each stream layer
takes considerable time. The Washington Geographic Information Council (WAGIC) is
addressing these data coordination issues at the state level. WSDOT, state, local, federal and
tribal entities are all represented in this forum. During the 1999 State Legislative session,
several bills and money appropriations were passed that will help state agencies tackle data
coordination problems. Improvements made at the state level can eventually be integrated into
GIS products at WSDOT.
Conclusions/ Summary
These two GIS products are anticipated to provide a solid foundation for WSDOT to develop
GIS as a standard tool in the agency for conducting environmental analyses. Developing GIS
solutions for environmental problems are inherently multi-disciplinary efforts that require
teamwork and partnerships. WSDOT will be looking at partnering with other public and private
10
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entities to combine money, technology and ideas for incorporating environmental analysis into
transportation planning in more efficient and effective ways.
Acknowledgments
The author wishes to acknowledge several organizations and individuals whose technical
support helped make these GIS projects and this paper possible.
Marci Carte and Patty Lynch, Washington State Department of Transportation
Washington State Department of Transportation: Environmental Affairs Office, GeoServices,
Program Management, Regional Environmental Offices
Key Data Providers:
Washington State Department of Health
Washington State Department of Ecology
Washington State Department of Natural Resources
Washington State Department of Fish and Wildlife
11
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Attachment A : List of Data used in the Workbench or Screening GIS Products.
(The complete WSDOT Geospatial Catalog is at
http://www.wsdot.wa.gov/gis/GeoDataCatalog).
Data Set Title
General Reference Data:
Transportation
State Routes (mainlines) LRS
Roadside Landscape
Classifications
Political and Admin. Boundaries
County Boundaries, statewide
City Limits of Washington State
Major Cities (points)
DOT Regions
Major Public Lands by WA Dept. of
Natural Resources:
City Parks
County Parks
DNR Managed Lands
Experimental Forests
Federal/State Fish Hatcheries
Federal/State Medical Facilities
Military/Tribal Reservations
(see also Tribal Lands, Military
Lands)
Monuments
Municipal Watersheds
National Forests
National Historic Parks
National Parks
Public School Lands
Recreation
State Parks
Wilderness Areas
Wildlife Refuges
Geographic Reference
Townships
TIGER-Census Bureau base maps
ENVIRONMENTAL DATA:
Air Quality:
Carbon Monoxide Non-Attainment
Areas
Ozone Non-Attainment Areas
Particulates Non-Attainment Areas
Source
Scale
500K
500K
500K
24K
500K
500K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
100K
500K
100K
500K
500K
500K
Originator Included in
Workbench?
WSDOT
WSDOT
WSDOT
WSDOT
WSDOT
WSDOT
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
WADNR
USCB
WADOE
WADOE
WADOE
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Included in
Screening?
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
12
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Data Set Title
Source Originator Included in Included in
Scale Workbench? Screening?
Fish and Wildlife:
Chinook Evolutionary Significant
Units
Chum Evolutionary Significant
Units
Coastal Cutthroat Trout
Evolutionary Significant Units
Coho Evolutionary Significant
Units
Endangered Species Act Listing
Status for Salmon and Trout
Fish (Salmonid) Passage Barriers
Marbled Murrelet Point Sitings
Marbled Murrelet Critical Habitat
Seabird Colonies
Sockeye Evolutionary Significant
Units
Spotted Owl Critical Habitat
Spotted Owl Special Emphasis
Areas
Steelhead Evolutionary Significant
Units
Sensitive Environmental Data
Priority Habitat and Species
Spotted Owl Nests
Wildlife Heritage Data
Groundwater and Wells:
Critical Aquifer Recharge Areas,
Clallam County
Critical Aquifer Recharge Areas,
Clark County
Critical Aquifer Recharge Areas,
Franklin County
Critical Aquifer Recharge Areas,
Island County
Critical Aquifer Recharge Areas,
King County
Critical Aquifer Recharge Areas,
Kitsap County
Critical Aquifer Recharge Areas,
Lincoln County
Critical Aquifer Recharge Areas,
Pend Oreille County
Critical Aquifer Recharge Areas,
Pierce County
250K
250K
250K
250K
none
500K
24K
100K
none
250K
100K
none
250K
24K
24K
24K
24K
24K
250K
none
100K
24K
500K
24K
none
NMFS
NMFS
NMFS
NMFS
WADOE
WDFW
WDFW
USFW
WDFW
NMFS
USFW
WADNR
NMFS
WDFW
WDFW
WDFW
WDFW
WSDOT/
Clallam Co.
WSDOT/
Clark Co.
WSDOT/
Franklin Co.
WSDOT/
Island Co.
WSDOT/
King Co.
WSDOT/
Kitsap Co.
WSDOT/
Lincoln Co.
WSDOT/
Pend Oreille
Co.
WSDOT/
Pierce Co.
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
13
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Data Set Title
Source Originator Included in Included in
Scale Workbench? Screening?
Critical Aquifer Recharge Areas,
Thurston County
Critical Aquifer Recharge Areas,
Whatcom County
Sole Source Aquifers:
Wellhead Protection Zones-
statewide
Wells, Group A, WA State
Wells, Group B, WA State
24K
500K
100K
24K
24K
24K
WSDOT/
Thurston Co.
WSDOT/
Whatcom
Co.
EPA
WSDOT
WSDOT
WSDOT
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Hazardous Materials:
CERCLIS-Comprehensive none WADOE
Environment Response
Compensation and Liability
Information System (Superfund
sites)
RCRA Facilities-generators, none EPA
transporters, treaters, storers, and
disposers of hazardous waste
Toxic Cleanup Program sites- none WADOE
confirmed and suspected
hazardous materials sites
Hydrography:
Coastlines, Puget Sound and 500K WSDOT
Columbia River (Major Shorelines)
Floodzones (by county)-statewide
Major Lakes of Washington
Major Rivers of Washington
National Wetlands Inventory
(1,500+ individual quadrangle files)
Salmonid Habitat Locations
WA County Series, Hydrography
Washington Resource Inventory
System (Watersheds)
Plants:
Rare and Native Plants-WA state 24K WADNR
Water Quality
1994 303d listed water bodies- 10OK WADOE
does not meet state water quality
standards
National Pollutant Discharge
Elimination System Permit Areas:
National Pollutant Discharge none WADOE
Elimination System Sites: sites
holding permit to discharge
wastewater to surface water
Y
Y
Y
Y
24K
500K
100K
24K
100K
24K
100K
FEMA
WSDOT
WADOE
USFW
WDFW
WSDOT
WADOE
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
14
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Data Set Title
Source Originator Included in Included in
Scale Workbench? Screening?
Clark County 500K WSDOT
Island/Snohomish Co. 500K WSDOT
South Puget Sound 500K WSDOT
Spokane County 500K WSDOT
Stormwater Outfalls along State 24K WSDOT
Routes:
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
DEFINITIONS-
ABBREVIATIONS—
ORIGINATORS—
Data Set Title or commonly used name of the data
Title: set.
File The path by which the data set is located
Location: on WSDOT's GIS servers.
Originator: The creator or source of the data set.
Source The scale denominator of the data set's
scale: source material.
For example, 24K indicates data derived
from sources at 1:24,000 scale.
24K 1:24,000 scale—1 map inch represents
2,000 feet
100K 1:100,000 scale—1 map inch represents
1.58 miles
250K 1:250,000 scale—1 map inch represents
3.95 miles
500K 1:500,000 scale—1 map inch represents
7.89 miles
LRS Linear Reference System
no scale data is of mixed scales or scale not
applicable
DCTED Washington Department of Community,
Trade and Economic Development
ESD Washington Employment Security
Department
FEMA Federal Emergency Management
Agency
NMFS National Marine Fisheries Service
PSRC Puget Sound Regional Council
USEPA United States Environmental Protection
Agency
USFW United States Fish and Wildlife Service
USGS United States Geological Survey
USCB United States Census Bureau
15
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IACOR Interagency Committee on Outdoor
Recreation
WADNR Washington Department of Natural
Resources
WADOH Washington State Department of Health
WADOE Washington Department of Ecology
WDFW Washington Department of Fish and
Wildlife
WSDOT Washington State Department of
Transportation
16
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Attachment B: Environmental Classification Summary
(Environmental Review Summary).
File EEdit Mode Select Format Script Window Help
I I I I I I I K I I I I I I I5I I I I I I I 1EM
I 1 I l_LLiJ9l I.I.I I I.J J1Pl J J I I I I1!1 I .LLLLlJiflLLLLLi
Records:
1
Pint I Pi eject Descnption
D Preliminary D Fi
D2D.204 G .20 2D5 G ^ iC'fi 020.209 DO
P.nt 2 Permits ,mcl A[>|>i ovals Requiit
Ho Permit ..-r .:;.,.i. .,ji
rirr
Corps of engineers D See.
D Nationwide Type _
D Individual Peimil Ho.
Guard
D D ritical Aua Ordinance (CAO) P(
c Practice /tot Permrt
Local Building or Srte Deveiopme
Local Clearing and Grading
Hatl. Historic Preservation Act - Section 106
(NPDESJrAjnicipal Storm water Discharge
National Pollutant Discharge Simination System
D Storm water Site Plan
D Temp. &osion Sediment Control Plan (TESC)
.t Permits
Permit cr .approval
D Shoreline Permit
D State Waste Discharge Permit
D Temp. rutoditication of VUater Quality Standards
D Section 4Cfy6(f): UUildlife Refuges, Recreation
Cl Water Rights Permit
d Vi/ater Quality Certification - Sec. 451
D Tribal Permrti± i. i.lt ,
D Other PenriFts. including 6M«.rbit).
Part 3 E
ic,itJoii
NEPA
LI Class I - Environvental Impact Statement (EIS)
D Class II - Categorically Excluded (CE)
D Projects Not Requiring Documentation for FHW;
Approval (LAG 24.22(sJ)
D Project? Reqijmng Dc'Cijmentation Without
Further FHWA Approval (LAG 24.22(b))
D Projects Requiring Documentation and FHWA
Browsa ~r\ * |
SEPA
D Categorically exempt per WAC 197-11-600
D Determination of Non-Significance (DNS)
D Environmental Impact Statement (EIS)
nsiair:1^r.1:^Ta-^f.»3ic:iiaiiii = g.Ttf.i^T(r:in^^.L.Jt....T.T:T,i..1L«r.T:T:iiir^
3| File E.dit Mode Select Format Script Window Help
ECS Form -
Records:
1
I L I I I I
|J_LJ
I K I I I I I I I5I I I I I I I I6! I I I I I I I7I I I I I I I I6I I I I L I.L L3I. l.L.l.1.1 I 1^1 I I I I I I1!1! I I I I I l1^! I I I I I
Is the project included in Hfetropolrtan Transportation Plan? D
Is the project located in an flir Quality N on-Alain merit A~ea (for c
Is the project exempt from Ar Quality conformity requirements?
2. CriticaVSensiiive Areas Identify any known Critical or Sensitive
Growth Management Art ordinances.
b. Geologically Hazard
tlandj &timate impacted categories and
(D A-e metlands present? D Ves D H
i"2) Estimated area impacted:
17
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Attachment C: Example of Weighting and Ranking System
for Environmental Screening
FLOODING & WETLANDS SUBJECT AREA
Subject Area Specialist: Gloria Skinner, Wetland Biologist, Washington Department of
Transportation
Analysis: Query-Based
Buffer Distance: 1/4 mile either side of roadway
Data: Weight:
(3 = most serious]
Coastline 3
Floodways 1
(not available for all counties)
HSP 0
Major Lakes 2
Major Rivers 2
Native Plant Wetland Subset 2
Streams 2
Wildlife Wetland Subset 3
(includes threatened/endangered)
Wildlife Wetland Subset 2
(no threatened/endangered)
Final Rating: Cumulative Score
Low(1) 0-1
Medium (2) 2-6
High (3) 7-15
and any impact to a data
category weighted 3
1 1998-2003 Strategic Plan. Washington Department of Transportation. Olympia, WA. August 1998.
2 Environments
January 1999.
2 Environmental CIS Workbench Charter. Washington Department of Transportation. Olympia, WA.
18
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Assessing and Managing the Impacts of Urban Sprawl on
Environmentally Critical Areas: A Case Study of Portage County, Ohio
Jay Lee1 and Lynne J. Erickson2
Introduction
This project attempts to evaluate how the processes of uncontrolled growths of urbanized areas
impact on environmentally critical areas and agricultural lands. Funded by Environmental
Projection Agency, we have carried out this project using Portage County, Ohio as an example
to test how GIS technology can be used to evaluate the effectiveness of various growth
management strategies. GIS is also used to generate alternative build-out scenarios for policy
makers to interact with the public. The results of this project provide valuable experience that
can be applied in other regions for conserving environmentally critical areas and farmlands.
The uncontrolled urban growth is widely referred to as urban sprawl. It often implies negative
impacts. Urban sprawl brings with it leapfrogging and low density developments of residential,
commercial, and industrial land uses that are said to be undesirable and wasteful of lands.
While the processes of urbanization and sub-urbanization are still active in northeast Ohio, the
inevitable growth has greatly impacted on the adjacent areas. Even though planners have
proposed and used various growth management strategies, the process as happened in reality
take long time to see the out come and is unfortunately irreversible.
To assist local governments and the general public in understanding what uncontrolled urban
growth may mean to their regions, what alternatives they may have to control the growth, or
how the impacts may be if various strategies are used, GIS technology is used to develop
simulation programs that incorporate various growth management strategies and tools for
assessing the impacts on farmlands and environmentally critical areas (EGA) by urban growth.
In this project, a total of three scenarios have been implemented: (1) continued growth, (2)
compact growth and (3) environmentally conservative growth.
1 Applied Geography Laboratory, Department of Geography, Kent State University, Kent, OH 44242-0001.
Email: ilee@kent.edu
2 Portage County Regional Planning Commission, Ravenna, OH 44266. Email: lerickson@pcrpc.com
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Portage County in Northeast Ohio
Affected by the expansion of the greater Cleveland-Akron-Canton metropolitan region, Portage
County, Ohio has experienced a significant growth in recent years. Farmlands and EGA are
being rapidly converted to residential, industrial and commercial developments. With the
concern for the loss of EGA and farmlands and for a more efficient way of controlling the urban
sprawl, the Portage County Regional Planning Commission has worked with Kent State
University Applied Geography Laboratory to develop GIS tools for modeling and managing
urban growth in the county.
Portage County is a semi-rural county that is 45 miles from Cleveland and 12 miles from Akron-
Canton areas. While the growth of residential, commercial and industrial lands have been
steady in the past decades, the most recent 10 years have seen especially fast growth.
GIS Technology
GIS technology is an effective tool to simulate the form and process of urban development on
the landscape and to provide decision makers with the opportunity to evaluate alternative
development policies on farmlands and EGA. The GIS tools developed for this study include: (1)
the first stage of evaluating past trends of urban sprawl, (2) the second stage of simulating
future growth with various growth management strategies, and (3) the third stage of assessing
the impact by the simulated growth. Our study incorporated data sets from public and private
agencies as well as recent Thematic Mapper remote sensing data. The GIS tools developed in
this study prove to be efficient in simulating, mapping and measuring the impacts by urban
sprawl. Using similar data sets, these GIS procedures can be easily adapted to carry out similar
work in other regions.
At the core of the GIS tools developed for this project are a series of computer programs coded
in Visual C++. These programs first process and combine GIS data layers to bitmaps to reduce
the size of data storage needed. The simulations of future land use are implemented so that
they are run according to specifications defined by the analysts. The simulated build-out
scenarios are then imported to Arclnfo™ and ArcView™ for assessing the impacts by the
simulated growths.
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Assessment
To support the assessment of impacts by urban sprawl, a total of 13 layers of information were
integrated into a GIS database. These layers include data for past and present land use
patterns, soils, slopes, ground water availability, ground water pollution potential, wetland
inventory, farmlands, and natural heritage data describing locations where special
plants/animals/geologic structures are recorded. For EGA, areas of steep slopes, high ground
water pollution potential, wetlands and those areas adjacent to special plants/animals were
extracted from GIS layers and combined to form a new layer.
To simulate future urban growth, past and present land use patterns have been used to
evaluate the spatial patterns and magnitude of urban growth in the county. A total of three
management strategies were applied: (a) continued growth model - continuing the trend found
from analyzing the past/present land use patterns, (b) compact growth model - applying
moderate growth control such as open space development, growth centers, etc., and (c)
environmentally conservative growth model - growing first in non-ECA and only when
absolutely necessary in EGA.
In each growth model, projections of growth in residential units, commercial establishments and
industrial establishments are estimated based on past trends and local master plans. The
simulations can be performed for any given years as specified by the analysts. Furthermore,
residential growths have been separated into residential units along frontage of roads and units
in residential subdivisions with or without open space designs. For commercial and industrial
establishments, areal sizes were estimated using average size of existing facilities in respective
neighborhoods.
For continued growth model, trends were calculated from the land use patterns of 1975, 1985,
1995 and 1997. We calculated the proportion of frontage/subdivision residential developments,
individual/conglomerated/striped commercial and industrial facilities as the basis for simulating
future growth. In compact growth model, the open space design of residential units is used in
neighborhoods where local zoning codes are appropriate. In addition, centers for future growth
have been designated by local planners to guide the simulations so to control the growth to be
within a half mile of radius from the centers. Finally, the environmentally conservative growth
model requires future growth to occur first in areas that are not classified as EGA. The
-------
simulations place future development in EGA only when non-ECA are exhausted in the
neighborhoods.
In the simulations, we did not have much problems of finding sites for new development of
residential units. However, projected new development for commercial and industrial lands are
limited by the amount of lands zoned for such uses in each township, city, or village. In several
occasions we found some townships do not have enough land zoned for commercial and/or
industrial to accommodate all the projected growth for these uses. This, of course, indicates that
these townships need to re-consider their zoning districts and revise their master plan.
To allow more realistic simulations, we have structured the simulation programs to have the
function of handling spill-over developments. If a particular township, city, or village does not
have enough zoned commercial areas, for example, the commercial areas in the adjacent
township is used to accommodate the growth. The simulation program has this function as a
choice, not a mandatory step, so that analysts can generate a wide variety of scenarios to test
their management strategies.
To assess the impact by urban sprawl, layers of simulated land use patterns are overlaid onto
layers of information describing farmlands and other EGA. Tabulated results of loss of EGA and
farmlands were correlated with changes of criteria used in simulations to observe their impacts.
Table 1: Acreage of projected new development, farmland loss, and loss of
environmentally conservative areas (EGA).
(In acres)
Model 1 cities
villages
townships
Model 2 cities
villages
townships
Model 3 cities
villages
townships
Residential
Lands
9,458
201
12,617
3,112
89
7,249
5,007
102
8,834
Commercial
Lands
446
18
237
157
12
138
240
15
172
Industrial
Lands
1,420
26
500
760
12
292
870
16
376
Farmlands
Loss
970
7
5,321
332
3
3,023
420
6
3,546
EGA
Loss
645
18
627
148
6
362
27
2
57
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Given the printing scale and the extent of the entire Portage County, it is not possible to include
maps showing all the details of simulated land uses here. Alternatively, the following three maps
show only the most northwest corner of Portage County as it is the are that is the closest to
Cleveland. As the three maps show, the continued growth model consumes developable areas
in a least efficient way. The compact model, on the other hand, provides the least waste of
lands between simulated development. Finally, the environmentally conservative growth model
shows developments in areas that are not environmentally sensitive.
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Figure 1: Continued Growth Model. The simulated land use is scattered around the
entire region, showing the least control and the most wasted land.
Given the printing scale and the extent of the entire Portage County, it is not possible to include
maps showing all the details of simulated land uses here. Alternatively, the following three maps
show only the most northwest corner of Portage County as it is the are that is the closest to
Cleveland. As the three maps show, the continued growth model consumes developable areas
in a least efficient way. The compact model, on the other hand, provides the least waste of
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lands between simulated development. Finally, the environmentally conservative growth model
shows developments in areas that are not environmentally sensitive.
Figure 2: Compact Growth Model. The simulated land use shows a greater degree of
conglomeration, avoiding wasted land between developments.
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I
fWrf^
Figure 3: Environmentally Conservative Growth Model. The simulated land use is
mostly located in areas that are not environmentally sensitive as defined by
EGA.
Concluding Remarks
We have found that our approach of using a combination of simulation programs and GIS
technology is able to provide realistic evaluation of how urban sprawl impacts EGA. The
simulations can be guided by different projections for future growth under various growth
management strategies. This provides great flexibility for planners and other decision makers to
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experiment what they think as appropriate growth scenarios. GIS technology is used in this
project for data management, mapping, and overlaying analyses. This project has demonstrated
the feasibility of such approach to efficient modeling of simulating future urban growth. Finally,
the GIS procedures developed in this study can be easily adopted for other regions with similar
data sets.
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A High-Resolution Weather Data System (HRWxDS)
for Environmental Modeling and Monitoring
David R. Legates
University of Delaware
Newark, Delaware 19716 USA
Kenneth R. Nixon, Thomas Stockdale, and Geoffrey E. Quelch
Computational Geosciences Inc.
Norman, Oklahoma 73069 USA
Abstract
The High-Resolution Weather Data System (HRWxDS) is a real-time, site-specific, operational
system that couples this new weather information with surface observations, hydrological
modeling, and an interface to facilitate more informed decision-making tasks. Spatially- and
temporally-distributed meteorological and hydrological fields produced by the HRWxDS include
precipitation, wind velocity, air temperature, and atmospheric humidity and pressure. Derived
fields include crop stress factors, soil moisture content, and runoff potential. Both digital and
graphical products are produced that can be used for monitoring and analyzing meteorological
and hydrological conditions for a particular location or region. This system is designed to
improve site-specific water resource management for a variety of purposes including river
management for optimal hydroelectric power generation, soil moisture monitoring for optimal
irrigation scheduling or for wildfire prediction, wind estimation for pesticide drift applications, and
rainfall/flood monitoring for enhanced emergency management.
1. Introduction
Surface observations are usually the only source of meteorological data for applications
involving environmental modeling and monitoring. Advances in Geographic Information Systems
(GIS) methodology and sophisticated spatial interpolation techniques, as well as the
development and installation of the national network of Doppler weather radars, now has
allowed for improved, high-resolution weather data to be used in the newer generation of
distributed (i.e., grid-based) environmental models. Our High-Resolution Weather Data System
(HRWxDS) is designed to facilitate more informed decision-making tasks by coupling this new
weather information with surface observations, hydrological modeling, and a graphical user
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interface in a GIS-based structure. The system operates in real-time to provide site-specific
hydrometeorological information of spatially and temporally distributed meteorological and
hydrological fields including precipitation (from raw radar estimates, surface observations, and
gage-calibrated radar estimates), surface wind velocity (speed and direction), air temperature,
atmospheric humidity (dewpoint and relative humidity), and atmospheric pressure. Modeling of
the surface water hydrology using these observational inputs and land surface information (soils
and vegetation) allows for estimation of crop stress factors (both temperature and moisture
stress), soil moisture content and deficit, and runoff potential. The HRWxDS produces both
digital and graphical products to allow for monitoring and further analysis of hydrometeorological
conditions for a particular location or region.
The purpose of the HRWxDS is to provide improved site-specific hydrometeorological
information to better manage our nations' water resources. Applications include river stage
management for optimal hydroelectric power generation, soil moisture monitoring for wildfire
potential or optimal irrigation scheduling, surface wind estimation for pesticide drift applications,
air temperature monitoring for identification of electric power demands or freeze potential, and
rainfall/flood monitoring for enhanced emergency management. Presently, the HRWxDS and its
components are being used in a wide variety of applications (Nixon and Legates, 1994; Legates
et a/., 1996; 1998) including operation of the front-end of a river management system to model
the real-time water flow for the Catawba River Basin in North Carolina (by Duke Energy
Corporation) and an assessment of the spatial and temporal distribution of rainfall for several
flooding events in Texas. Irrigation scheduling and pesticide drift applications in southwestern
Oklahoma also are under development.
Sophisticated software engineering techniques incorporating the latest in software development
methods were used in the development of the HRWxDS. With its design for spatial analysis, the
HRWxDS incorporates advanced GIS tools and its products easily can be input to commercial
GIS packages for further analysis and presentation. Moreover, the design of the HRWxDS
incorporates a modular, extendible architecture to facilitate easily the development of new
products and the incorporation of new algorithms. Research on the HRWxDS extends directly
from a successful technology transfer project first began in the Center for Computational
Geosciences at the University of Oklahoma.
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2. Overview of the High-Resolution Weather Data System
Five major subsystems comprise the HRWxDS (Figure 1). The Setup and Processing Control is
a graphical user interface that allows the user to optimize the data acquisition and
processing/analysis to the specific region/task at hand. It performs system initialization, process
control, event timing, and system shutdown. The Data Acquisition Module is responsible for
acquiring the necessary surface observation station data and radar products. As the HRWxDS
is a real-time system, this module is responsible for data exception handling when, for example,
internet connections fail or a radar site is down. The Product Processing Component (PPC)
generates the gridded hydrometeorological products for each of the observational fields (i.e.,
precipitation, air temperature, atmospheric humidity, wind velocity, and atmospheric pressure).
The Event Logging Facility (ELF) provides the capability to log all significant processing events
and notify someone (via e-mail, pager, etc.) when exceptional conditions - extreme or unusual
hydrometerological events or system failure ~ warrant. Finally, the Decision Support/Display
Subsystem provides display of gridded products generated by the PPC. This subsystem can
analyze the hydrometeorological products or it can be tailored to form an application-specific
user decision support system.
Gin
Setup & Processing Control
Data
Acquisition
PPC
Decision
Support/
Display
Subsystem
t
v *
ELF
Figure 1: A Schematic Overview of The HRWxDS Subsystems.
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3. Hydrometeorological Data Acquisition Requirements
One of the main advantages to the HRWxDS is that it uses generally available meteorological
data from the National Weather Service (NWS) in addition to surface observation
measurements assessable from local or private mesonetworks. The two primary inputs are
surface station observations and the WSR-88D Digital Precipitation Array (DPA) - both
obtained generally on an hourly time-step (although the HRWxDS can be operated using
virtually any time step).
Surface observation station data used by the HRWxDS may include data from the NWS First-
Order Station Network, from local or private mesonetworks, or both. Applications of the
HRWxDS have used the Oklahoma Mesonet data for analyses within the State of Oklahoma,
I FLOWS data from the US Geological Survey in the Appalachian Mountains, and local rain gage
networks in and around Houston, Texas - in addition to the national NWS network.
Hydrometeorological measurements utilized by the HRWxDS include air temperature, wind
speed and direction, atmospheric pressure, relative humidity or dew point temperature, and
precipitation. Although the NWS First-Order Station Network does not include solar radiation,
the HRWxDS also can use this variable if a local network provides such information (e.g., the
Oklahoma Mesonet).
Biases - most often underestimates ~ in precipitation measurement with can-type rain gages
can occur from several sources. These include the deleterious effect of the wind, wetting losses
on the interior walls of the gage, evaporation from the gage between the end of precipitation and
its measurement, splashing into and out of the gage, mechanical errors (e.g., friction of pen
plotters and the inability of tipping-bucket gages to accommodate heavy precipitation rates), and
observational effects (Groisman and Legates, 1994; 1995). Using the surface wind speed
measurements, the HRWxDS is able to estimate the biases that result from the most significant
forms of the gage bias ~ wind effects and wetting losses. These biases can amount to
approximately six percent in summer for much of the United States to more than thirty percent in
the northern tier of states in the winter (Legates and Deliberty, 1993a,b). Thus, the HRWxDS
can reduce gage undercatch biases caused by the gage measurement process.
In addition to the surface observations, the HRWxDS also includes precipitation estimates from
the NWS national network of weather radars. In the 1990s, the NWS began installation of the
next generation of weather radars, now known as the WSR-88D (Weather Surveillance Radar ~
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1988 Doppler). These radars provide a variety of meteorological information, including several
precipitation products. Among these include graphical images - used primarily for visual
analysis and television weather displays -- and a single digital precipitation product known as
the Digital Precipitation Array (DPA). The DPA is a running hourly precipitation estimate
developed from the radar reflectivities (microwave energy bouncing from hydrometeors)
measured over the previous hour. It is updated every five or six minutes when the radar has
detected precipitation somewhere in the radar umbrella. For more information on the DPA, see
Fulton etal. (1998) or Legates (2000).
For some applications, a single WSR-88D radar may not cover the entire region of interest or a
single site may be covered by more than one WSR-88D radar. The advantage of the HRWxDS
is that it ingests data from multiple radars and can mosaic their DPAs to provide spatial
coverage of virtually any geographic area of interest. Applications for the Catawba River Basin
in North Carolina and over the State of Oklahoma require four and eight radars, respectively, for
complete coverage. Indeed, in an assessment of precipitation for the Southern Great Plains,
twenty-three radars were used. This ability to ingest, utilize, and mosaic multiple radars makes
the HRWxDS an invaluable tool for spatial analysis of hydrometeorological conditions for large
geographic regions.
4. Gridded Hydrometeorological Data Products
A suite of gridded hydrometeorological data products is produced by the HRWxDS. All products
have a spatial resolution of approximately 4 km x 4 km (i.e., at the nodes of the Hydrological
Rainfall Analysis Project or HRAP grid) with individual estimates for each hydrometeorological
variable for each HRAP cell. The suite of gridded products includes five precipitation products
and seven hydrometeorological companion products. All products are provided in both a
common GIF and netCDF format for maximum flexibility and portability.
With respect to precipitation, the HRWxDS provides five fields -- Gage-Based Precipitation,
Radar-Based Precipitation, Gage-Calibrated Radar Precipitation, and 12- and 24-Hour
Precipitation Accumulation. The Gage-Based product provides gridded precipitation estimates
based solely on rain gage observations adjusted for gage measurement biases that include the
effect of rain gage design (e.g., shielded or unshielded gage, size of orifice opening, and height
of gage orifice above the ground) as well as the meteorological conditions around the rain gage.
The Radar-Based product presents precipitation estimates from the radar DPAs using an NWS-
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specified fixed reflectivity-to-rainfall conversion equation and is not calibrated using rain gage
observations. Moreover, it is a mosaic of multiple radars using either an averaging or
maximization (default) of radar overlap areas, depending upon the user's preference.
The most important component of the PPC is the Gage-calibrated Radar Precipitation product.
Gage observations are paired with the hourly composite radar reflectivities to remove errors in
the radar precipitation estimates. Such errors can arise from (1) uncertainties in obtaining
accurate estimates of reflectivity, (2) inconsistencies in the reflectivity-to-rainfall conversion
algorithms, and (3) effects occurring below the radar beam. The calibration procedure utilizes a
variety of spatial statistical techniques using the bias-measurement-adjusted gage observations
to estimate and remove the radar bias. Each radar is calibrated separately and the calibrated
estimates then are combined to form a composite mosaic from multiple radars. In addition,
adding these calibrated mosaics to the desired temporal resolution produces the 12-hour and
24-hour Precipitation Accumulation products. For more information on radar biases, please
consult Wilson and Brandes (1979), Doviak and Zrnic (1984), and Fulton et al. (1998). Legates
et al. (1999b) and Legates (2000) also provide a more detailed discussion of the HRWxDS
calibration algorithm.
Air temperature, dew point temperature, relative humidity, wind speed and direction, solar
radiation, and atmospheric pressure, in addition to precipitation, also are interpolated to the
HRAP grid intersections. Spatial interpolation is accomplished through a modified version of
Shepard's inverse distance weighting method (Willmott et al., 1984) that accounts for both the
distance and distribution of the observations around the interpolate point. This method
represents an exact pass through the data (the interpolation returns the observed value when
an observation is coincident with the grid intersection). Air temperature is interpolated by
reducing the observations to sea-level equivalent (potential temperature), interpolating potential
temperature, and then returning the observations to the elevations of the HRAP grid cells using
the environmental lapse rate and a digital elevation model (DEM) of the geographic area to be
interpolated. Since dew point temperature is upward bounded by air temperature, the return of
air temperature to the HRAP elevation follows the moist adiabatic lapse rate when saturation is
reached. Dew point temperature then is set to air temperature in that case (i.e., a relative
humidity of 100%). Relative humidity is calculated directly for each grid cell from the dew point
and air temperature estimates for each grid cell with no interpolation. For more information on
the interpolation procedure, please consult Legates et al. (1999a).
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5. The Graphical User Interface and User/Setup Options
A graphical user interface (GUI) is included in the HRWxDS to assist in customizing and
tailoring the system to meet specific operational requirements and set processing control
options. Several user-adaptable features provided through the GUI. For example, the HRWxDS
can be easily setup to analyze and monitor one or more specific geographic regions from a
small river basin to large regions of the country. Once a geographic region has been defined,
the HRWxDS automatically generates the HRAP grid that covers this area. Then the user can
select the specific region to be processed and the HRWxDS can switch from one region to
another.
The HRWxDS can be run in either "Attended" or "Unattended" modes of operation. In
"Unattended" mode, the system selects from predetermined responses to handle anticipated
operational exceptions. This mode is most frequently used for real-time, operational monitoring
of environmental conditions. In "Attended" mode, a user has the capability to dynamically
interact with the processing and handle exception conditions as they occur. This mode is
frequently used for historical/forensic analyses for regions or times when the HRWxDS was not
running in real-time or to update missing data during power outages or other interruptions.
5.1 Data Initialization Specifications
To incorporate radar data for a specific geographic region, the radars that cover the area of
interest can be dynamically added or deleted, depending upon interest and availability. In
addition, the HRWxDS has the capability to incorporate surface observation measurements of
national (e.g., NWS), local/private mesonetworks, or both. As with radars, stations can be
dynamically added or deleted. Each rain gage location is described in a rain gage metadata file
which includes information about the rain gage design (shielded or unshielded gage), size of
orifice opening, height above ground, and sheltering conditions. These data are used to provide
estimates of the gage measurement bias adjustments.
5.2 Processing Control Setup and Knobs
The GUI also provides specifications for the processing start time and processing interval. To
ensure that all pertinent data have been received, a delay in starting time can be included. For
example, processing for the current hour (at the top of the hour) may be delayed until fifteen
minutes past the hour to ensure that all station observations and radar DPAs have been
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transmitted. In addition, time limits also are set to specify which input data are to be considered
valid for the current processing cycle.
A set of "processing control knobs" also is provided to adjust and fine-tune the processing.
Although the PPC uses a large array of control knobs, only a few are described here. "Mosaic
Knobs" control the numerical technique (averaging or maximization) used to develop the
mosaics for both the Radar-Based and the Gage-Calibrated Radar products. "Hail Detection
and Suppression Knobs" provide absolute (derived from reflectivity) and relative (grid cell-to-grid
cell comparisons) rainfall rates for the detection and removal of hail contamination in the radar
reflectivities. Additionally, the disposition of cells can be specified for which hail contamination
has been identified (i.e., cells can be set to missing, truncated to the maximum allowable
precipitation rate, or interpolated from surrounding cell values). A "Tropical Rain Event Knob"
identifies a precipitation event as tropical (e.g., tropical storm, hurricane, tropical depression)
which adjusts (upwards) the acceptable upper limit of possible rainfall rates and suppresses the
identification of heavy rainfall as hail contamination. The "Missing Data Exception Handling
Knob" is a processing control feature that automatically substitutes interpolated rain gage data
in the Gage-Calibrated Radar product for areas where radar data are missing (a radar may be
down or data retrieval has been delayed).
A number of other processing knobs controls the spatial interpolation procedure. For example,
the "Radius of Influence Knobs" are used to specify the distance over which an observation will
influence the interpolated values. A separate knob controls each weather variable (e.g., rain
gage measurements, air temperature, dewpoint temperature, and atmospheric pressure). The
"Optimization/Calibration Knob" provides for the operational calibration and optimization of the
spatial interpolant. It allows distance decay effects and elevational influences to be estimated
from the observed data rather than assuming constant values. "Elevation Effect Knobs" allow for
elevation effects to be either enabled or disabled in the interpolation of each weather variable.
Some variables (e.g., air temperature) are affected by elevation whereas others (e.g., sea-level
atmospheric pressure) are not.
The HRWxDS system also includes a feature that allows a user in "Attended" mode the
opportunity to add temporary observations. These can be one-time field measurement or report
(e.g., a single hourly observation from a station that does not report regularly) or data to
manually adjust the Gage-Calibrated Radar Precipitation product at the user's discretion. This
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"Virtual Rain gage Facility" allows for inclusion of observations from either rainfall or snowfall
data (conversion from snow to liquid water equivalent is adjusted by the "Snow Density Knob").
Rainfall also can be suppressed by the Virtual Rain gage Facility to provide adjustments for
virga or ground clutter conditions.
5.3 General Operational Features
Other controls available through the GUI allow for the selection of measurement units, either
English or Metric, a fixed or variable color bar, and display scale thresholds. The overlay feature
provides the capability to display radar or rain gage locations, basin delineation, HRAP grids,
political boundaries, rivers and streams, roads, or other overlay combinations on the GIF
images that are produced. An animation capability is included to display a time-series of
products to provide a visual representation of changing meteorological conditions. Individual cell
values also can be interrogated by simply moving the cursor to the cell of interest. Latitude and
longitude of the cell are displayed with the cell's value for the currently displayed product. In
addition, the HRWxDS has the capability to display the contribution of each radar to the Radar-
Based Precipitation and the Gage-Calibrated Precipitation products through the DPA Viewer.
6. The Event Logging Facility (ELF)
The HRWxDS contains a robust Event Logging Facility (ELF) that records system configuration
information (e.g., data paths, processing parameters, and region definitions), data retrieval
status messages, statistics for input and output data, and processing status information. It is a
central facility that tracks all events of which the user may need to be made aware. Destination
for event logs may be specified to a disk file and/or a screen dialog box. Each message is
assigned to an appropriate category ~ status (memory and disk usage), processing information,
statistics, operating system errors, minor error, major error, or fatal error. A "Log Viewer"
provides the capability to view the ELF file/records by filtering the category of records to be
displayed (e.g., show only major/fatal error messages or show processing information and
statistics).
7. An Example of Weather Information Produced by the HRWxDS
At present, the HRWxDS is continuously running in operational mode at Duke Energy
Corporation in Charlotte, NC where it is being used to provide precipitation inputs to a River
Management System for the Catawba River Basin in central North Carolina and northern South
Carolina. In addition, it also provides real-time estimates for the State of Oklahoma and for a
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region covering much of the Southern Great Plains (see Figure 2). More information about the
HRWxDS can be found on the Computational Geosciences Inc. Web Page at
http://www.telepath.com/compgeo.
To illustrate the products generated by the HRWxDS, an example from 5 GMT (Midnight CDT)
on June 5, 1999 for the Southern Great Plains Region (Oklahoma, northern and central Texas,
and surrounding regions) will be shown. Station observations (small squares) and radar
locations (large squares) are presented in Figure 2 along with the HRAP rectangle that was
used for the analysis.
Figure 2: Locations of the radars (large squares) and surface observing stations (small
squares) used over an area of the southern Great Plains (approximately 1000 km x
1000 km). Higher station densities over Oklahoma are attributable to use of the
Oklahoma Mesonet. The white, four-sided border shows the boundary of the
HRAP grid used for this region.
10
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During the early morning hours, a line of convective showers with precipitation rates exceeding
25mm per hour moved into western Oklahoma and south-central Kansas. The HRWxDS was in
operational, "unattended" mode during this event and provided real-time estimates of hourly
surface weather conditions for this storm. Images of the precipitation for this hour are shown
(Figure 3) for the Gage-Based, Radar-Based, Gage-Calibrated Radar, and 24-hour Precipitation
products. Note that although these fields are illustrated here in graphical form, the main purpose
of the HRWxDS is to produce high-resolution digital representations of the various
meteorological variables. Thus, these fields from the HRWxDS can be readily input to a
distributed hydrological model or any raster-based GIS. Although the HRWxDS does not utilize
any commercial GIS package, it contains sophisticated spatial analysis and interpolation
algorithms operating in a spatial (GIS) framework. Output from the HRWxDS is easily
incorporated as ARC/INFO™ overlays.
A comparison of the Gage-Based (Figure 3, top) and Radar-Based Precipitation products
(Figure 3, bottom) clearly illustrates the advantages of the radar precipitation estimates. Outside
Oklahoma, the NWS first-order weather station network is used. Note that the sparse density of
the NWS/Oklahoma Mesonet combination only is able to resolve two areas of relatively heavy
precipitation in northwestern and west-central Oklahoma and a smaller area of lighter
precipitation in southwestern Oklahoma. By contrast, the radar mosaic for this area illustrates a
much larger region of precipitation with two embedded cells of heavy rainfall in central- and
northwestern Oklahoma. Precipitation in southwestern Oklahoma can be seen to extend farther
back into Texas where the gage network is much more sparse and is not supplemented by the
Oklahoma Mesonet, a relatively dense network of 111 stations (see Figure 2 for a station
distribution). The spatial fidelity resolved by the radars is much higher than even the Oklahoma
Mesonet can provide and a better picture of the convection area over this region. Note that the
area of light rain across much of the region is under-represented by the gage network and,
since no gages were located beneath the smaller, embedded storm cells, the magnitude of rain
falling from these cells also is considerably underestimated.
The NWS radar product (i.e., the Level III DPA) tends to underestimate rainfall rates for a
variety of reasons (see Wilson and Brandes, 1979; Fulton et a/., 1998; Legates, 2000). This was
particularly true for this event as well. Consequently, calibration of the radar reflectivities using
the real-time gage measurements has inflated the radar estimates to be commensurate with the
gage observations. Note that for the precipitation cell in west-central Oklahoma, the
11
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Figure 3: Estimates of precipitation from the Gage-Based Precipitation product (top) and
the Radar-Based Precipitation product (bottom) for 5:00 GMT on June 3,1999.
12
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calibration resulted in a slight decrease in precipitation intensities. This shows the difference
between the radars and the precipitation estimates obtained from the different radars that
viewed different portions of the precipitation. The mean fit error for this hour was less than 10
mm indicating a good agreement between the gage observations and the calibrated radar
product (Figure 4, top) was obtained. The 24-hour Precipitation product also is shown (Figure 4,
bottom - Note the different color bars used for the 24-hour product).
Throughout much of the United States, rain gage densities are seldom as dense as the
Oklahoma Mesonet and are usually as sparse as the NWS network is here (Figure 2).
Precipitation, and especially convective precipitation, events are under-represented by existing
gage networks. Weather radars help to provide a more complete picture of the true distribution
of precipitation, although some exceptions exist. Most notably, mountains obscure the radar
beam so that some remote and relatively inaccessible areas of the western United States are
not covered adequately by the radar network. Given the sparse gage networks, use of the
WSR-88D radar precipitation estimates, when calibrated properly by the HRWxDS, clearly
provide a more accurate representation of the true precipitation over virtually all of the
conterminous forty-eight states.
In addition to precipitation, shown here are the air temperature (Figure 5, top) and wind vector
(Figure 5, bottom) fields for the same hour. Air behind the advancing convective system has
been cooled by the precipitation by as much as 20°F (11°C) in some places and a strong
horizontal temperature gradient lies just behind the area of convection. Airflow is predominantly
from the south and southeast ~ as is common for late spring ~ with the largest area of winds
exceeding 20 mph (10 ms"1) feeding the convection region on the southern flank. Lighter and
more easterly winds are found over the northeastern part of the region.
13
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Figure 4: Estimates of precipitation from the Gage-Calibrated Radar product (top) and the
24-hour Precipitation product (bottom) for 5:00 GMT on June 3, 1999.
14
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15
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Figure 5: Estimates of air temperature (top) and wind vector (bottom) for 5:00 GMT on
June 3, 1999.
8. The HRWxDS and Its Role within the Water Resource Decision Support System
The HRWxDs is but one component of a larger project, the Water Resource Decision Support
System (WRDSS) which is designed to be a real-time tool for water resource decision makers
(Legates et a/., 1996; 1998). Using the real-time data ingest and weather product generation
capabilities of the HRWxDS, Computational Geosciences Inc. has begun development of the
WRDSS which couples soil moisture and runoff simulation models with the HRWxDS in a
framework that allows development of application-specific decision support systems (Figure 6).
Through the WRDSS, water resource managers can be supplied with readily available and
easily understood information about current meteorological and hydrological conditions over a
wide geographic area. In addition to the graphical representations, all weather and surface
hydrological products are available in a digital format so the estimates can be imported to other
hydrological models and applications.
Water Resource Decision Support System
Radnr Data
(DPA)
High-Resolution Weather Data System
(HRWxDS)
Gridded
Weather
Products
Water Budget
Accounting
Model
Surface Water
Runoff Model
Decision Support
* Hydroelectric Power
* Irrigation Scheduling
* Kangeland Management
* IYsticiik- Application
* Wildfire Prediction
* Flood Monitoring
Figure 6: Schematic of the Water Resource Decision Support System (WRDSS).
16
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9. Conclusion
Obtaining accurate, high-resolution, real-time estimates of surface meteorological variables has
been a problem that has long faced hydrologists and hydrometeorologists. Development and
implementation of the national network of WSR-88D weather radars and their ability to
represent each storm's "precipitation footprint" has led to a revolution in hydrological modeling
and monitoring. When properly calibrated with rain gage observations, as is accomplished by
the High-Resolution Weather Data System (HRWxDS), these estimates have the potential to
provide water resource decision managers with more reliable real-time precipitation
assessments and, consequently, allow for more accurate spatial information to be included in
the decision making process. The spatial interpolation procedures developed and contained
within the HRWxDS provides the "state-of-the-art" in estimation of surface meteorological fields
from irregularly-spaced station observation networks reporting in real time.
Development of the HRWxDS coupled with the Water Resource Decision Support System
(WRDSS) provides decision-makers with an integrated tool that allows for real-time
assessments of water resource and hydrological conditions. This tool, which converts weather
and hydrological data into usable information, will prove invaluable by eliminating much of the
assumptions, guesswork, and tedious monitoring and maintenance of inadequate gage
networks that presently characterize water resource management. Both the HRWxDS and the
WRDSS system will continue to evolve to meet the needs of decision-makers and to exploit
advances in estimating precipitation by weather radars and obtaining high-resolution estimates
of surface meteorological fields.
18
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References
Doviak, R.J., and Zrnic, D.S., 1984: Doppler Radar and Weather Observations. Orlando,
Florida: Academic Press, Inc.
Fulton, R.A., Breidenbach, J.P., Seo, D.-J., Miller, D.A., and O'Bannon, T., 1998: The WSR-88D
Rainfall Algorithm. Weather and Forecasting, 13:377-395.
Groisman, P.Ya., and Legates, D.R., 1994: Accuracy of Historical United States Precipitation
Data. Bulletin of the American Meteorological Society, 75:215-227.
Groisman, P.Ya., and Legates, D.R., 1995: Documenting and Detecting Long-Term
Precipitation Trends: Where We Are and What Should Be Done. Climatic Change,
31:601-622.
Legates, D.R., 2000: Real-Time Calibration of Precipitation Estimates. The Professional
Geographer, forthcoming.
Legates, D.R., and DeLiberty, T.L., 1993a: Measurement Biases in the United States Rain gage
Network. Symposium on Geographic Information Systems and Water Resources,
American Water Resources Association, 547-557.
Legates, D.R., and DeLiberty, T.L., 1993b: Precipitation Measurement Biases in the United
States. Water Resources Bulletin, 29(5), 855-861.
Legates, D.R., Nixon, K.R., Stockdale, T.D., and Quelch, G.E., 1996: Soil Water Management
Using a Water Resource Decision Support System and Calibrated WSR-88D
Precipitation Estimates. Proceedings, AWRA Symposium on GIS and Water Resources,
American Water Resources Association, 427-435.
Legates, D.R., Nixon, K.R., Stockdale, T.D., and Quelch, G.E., 1998: Use of the WSR-88D
Weather Radars in Rangeland Management. Specialty Conference on Range/and
Management and Water Resources, American Water Resources Association, 55-64.
Legates, D.R., Nixon, K.R., Quelch, G.E., and Stockdale, T.D., 1999a: Environmental Modeling
and Monitoring using a High-Resolution Weather Data System (HRWxDS). Proceedings
of the Fourth International Conference on GeoComputation, Fredericksburg, VA,
forthcoming.
Legates, D.R., Nixon, K.R., Stockdale, T.D., and Quelch, G.E., 1999b. Real-time and Historical
Calibration of WSR-88D Precipitation Estimates. Proceedings, Eleventh Conference on
Applied Climatology, American Meteorological Society, Dallas, TX, 76-77.
Nixon, K.R., and D.R. Legates, 1994: Water Resource Decision Support System SBIR Phase I
Report for the US Department of Agriculture.
Wilson, J.W., and Brandes, E.A., 1979: Radar Measurement of Rainfall ~ A Summary. Bulletin
of the American Meteorological Society, 60:1048-1058.
19
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Fractal Dimension as an Indicator of Human Disturbance in
Galveston Bay, Texas
Amy J. Liu1'3, Guy N. Cameron2*
1. Department of Biology, University of Houston, Houston, TX 77204
2. Department of Biological Sciences, University of Cincinnati, Cincinnati, OH 45221-0006
3. Present address: National Risk Management Research Laboratory, MS 690, US EPA,
Cincinnati, OH 45268
ABSTRACT
High productivity and accessibility to humans have made coastal wetlands attractive sites for
human settlements. This study analyzed the wetland landscape patterns in Galveston Bay,
Texas. The study described the relationships between the fractal dimension and factors which
affect the wetland landscapes: land use, vegetation type, size, location, and level of human
disturbance. The perimeter-area method was used to calculate the fractal dimension. There was
a significant difference in the fractal dimension of wetlands when classified according to land
use, vegetation type, size, and level of human disturbance. Furthermore, increasing the size of
the road buffers did not have a significant effect on the fractal dimension of wetlands. These
results will be important in determining how wetlands can be managed as natural resources and
nature reserves.
INTRODUCTION
Wetlands are amongst the most important of the world's ecosystems. They play a significant
role in supporting higher levels of biological diversity, as well as primary and secondary
productivity, modulate flows of water, nutrients, and materials across the landscape, and
provide wildlife habitat (Holland et al., 1991). If wetlands are to be properly used and protected,
it is important that their spatial properties be well-understood and monitored.
Shaw and Fredine (1956) estimated that 35% of the wetlands in the United States had been lost
by the 1950s, and Frayer et al. (1983) estimated a net loss of more than 3.7 million hectares, or
8.5%, between the 1950s and 1970s. Dahl and Johnson (1991) measured a net loss of 1.5%, or
28,400 hectares, between the 1970s and 1980s. These estimates of wetland losses provide an
incomplete picture of the dynamics of change because they only describe the area of wetlands
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lost to agriculture, industry, and urban development and do not show the conversion of wetlands
from one land use class to another by human activities. From 1970s to the mid-1980s, there
was a net gain in freshwater marshes, despite the destruction of approximately 200,000
hectares, because 288,800 hectares of swamps were converted to freshwater marshes (Dahl
and Johnson, 1991).
Human activities are the principal causes of loss of wetland (Craig et al., 1979). Wetland
hydrology, such as drainage conditions and circulation patterns, makes wetlands more sensitive
than woodlands to highway construction activities (McLeese and Whiteside, 1977). Land is lost
in coastal areas through flood control measures, agricultural practices, and canal construction
(Gagliano, 1973; Craig et al., 1979). Construction of canals and roads which establish
permanent barriers to the growth of a particular land patch also will change the nature of the
interaction with adjacent patches of land and will prevent the patch from becoming the dominant
patch in the landscape (Forman et al., 1986). Therefore, because of the significant effect of land
losses to wetland spatial dynamics, it is important to examine the space occupation properties
(fractal properties) found in wetland ecosystems.
Fractals are based on the premise that a measure assigned to an object depends on the
object's appropriate dimension. The fractal dimension describes the relationship between a
quantity Q, and the length scale, L, over which Q is measured, where Q(L)=LDq, and Dq is the
fractal dimension. Dq describes how the quantity Q varies with scale. Hence, for larger values of
Dq, the length changes faster because the curve is more complex.
Having a useful knowledge of fractal dimensions has several implications for ecologists. First,
the probability of an organism encountering a boundary increases as the fractal dimension of
the patch increases (Weins, 1992). Second, the functionality of an ecotone is dependent on the
surface area over which the ecotone extends. Since the fractal dimension is a more accurate
measure of length (and thus, surface area), the functionality of a wetland patch is directly tied to
its fractal dimension (Kent and Wong, 1982). Third, organisms requiring a particular amount of
edge habitat may be restricted if the fractal dimension of a wetland patch falls below a critical
threshold (Henderson et al., 1985). Fourth, an important feature of a fractal curve or surface is
that its length or area becomes disproportionately large as the unit of measurement is
decreased (Mandelbrot, 1977).
* To whom correspondence should be addressed.
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Krummel et al. (1987) evaluated the fractal dimension (D) of patterns of deciduous forests in
Mississippi using the perimeter/area method on aerial photographs. Their analysis revealed that
small areas of forest tended to be smoother (D = 1.20), while larger areas had more complex
boundaries (D = 1.52). This was interpreted to indicate that human disturbances predominated
at smaller scales, allowing for smoother geometry and a lower fractal dimension, while natural
processes tended to predominate at larger scales. Bradbury et al. (1984) investigated the
possibility of hierarchical scaling in an Australian coral reef. They detected three ranges of
scales, which corresponded with the scales of three major reef structures: individual coral
colonies (D = 1.1), whole adult living colonies (D = 1.05), and groves and buttresses (D = 1.15).
Since the major effect of low fractal dimension is to reduce the intimacy of contact of the living
surface of the reef with the surrounding water, the authors speculated that corals attempted, at
the adult colony level, to reduce the level of their contact with the medium.
As with any mathematical representation of nature, fractal geometry is an attempt to search for
order in the complex patterns characterizing living systems. Life is composed of interactions and
fluxes of matter, energy and information through interfaces, which suggest an "interpenetration
volume" between two adjacent interacting elements. These interfaces, Frontier (1987)
suggested, were neither surfaces or volumes, but fractals.
Fractals, then, seem to be an appropriate tool for investigating wetlands, since wetlands are
contact zones between terrestrial and aquatic systems -- they are characterized by fluxes of
energy and matter (nutrients and hydrology). Only recently has there been an appreciation for
the ways in which ecotones frequently intensify or concentrate these activities. Johnston (1991)
determined the net effect of wetlands on water quality, reporting that retention of nitrogen in
natural freshwater wetlands ranged from 14 to 100%, and retention of phosphorus ranged from
4 to 80%. Peterjohn et al. (1984) found that riparian forests in the eastern United States exerted
major influences on the flow of nitrogen and phosphorus as these chemicals moved from
agricultural fields across forests and into streams. Much of the nitrogen and phosphorus was
captured in the vegetation and soils of the riparian ecotone, with 60-75% of all nutrients
captured occurring in the first 20 m of the riparian forest ecotone. The surface area of the
contact zone, then, is important in the economy of the surrounding ecosystems and watersheds.
The surface area of the contact zone depends on the length of the boundary, or, more precisely,
on its fractal dimension (where length is not uniquely defined and depends on the scale at which
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it is being measured).
Changes in landscape patterns may relate to the flows of material or energy across landscapes.
For example, erosional processes, or sediment movement across landscapes and the
abundance and distribution of wildlife might be predictable using indices of landscape pattern
(Turner, 1987). Species that favor or require particular types of edges may decline if the amount
of edge (which is directly related to the fractal dimension) declines, whereas species requiring
extensive areas of land may benefit from the increasing size of patches (Henderson et al.,
1985).
However, few studies have examined the importance of fractal dimensions in wetlands. The
amount of wetland edge was closely related to the size of the harvest of offshore shrimp
(Browder et al., 1989), and the rate of water loss from small sloughs varied directly with the
length of shoreline per unit area (Millar, 1971). In addition, fractal dimension was significantly
related to the level of human impact for riparian forests in Iowa (Rex and Malanson, 1990).
Wickham et al.'s (1994) findings that the fractal dimension of wetlands increased with the
number of agriculture and residential land cover components contrasted with the expected
decrease in fractal dimension, with an increase in surrounding agricultural and human land use
increased (Forman et al., 1986; Kummel et al., 1987).
While previous studies have linked size, presence of disturbance, and organizational structure,
to shape complexity in ecological systems, the present study broadens the scope of fractal
analysis by examining the relationships between ecological and anthropogenic processes and
spatial patterns in coastal wetlands. Specific objectives included:
• Describe the relationship between the fractal dimension and specific factors which affect
wetland landscapes, such as land use (wetland vs. non-wetland), vegetation type, size
of wetland, location of wetland, and level of human disturbance or impact.
• Present a methodology to evaluate the effects of anthropogenic scaling which will be
helpful in formulating hypotheses concerning the spatial scales of process-pattern
interactions.
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METHODS
Digitized maps of wetlands in the Galveston Bay were obtained from the United States National
Wetlands Inventory (NHI) database. A wetland was defined as "land where saturation with water
is the dominant factor determining the nature of soil development and the types of plant and
animal communities living in the soil and on its surface" (Coward in et al., 1979). Descriptions of
the NHI classifications (Coward in et al., 1979) were used to group wetlands down to class into
different vegetation types, which constituted a wetland patch. Thirty-one 7.5 minute topographic
quadrangles comprised the area surrounding Galveston Bay: Harris County, Chambers County,
Galveston County, and Brazoria County. Quadrangles were edge matched and merged using
ARC/INFO and ARCEDIT, and wetland sites were selected for analysis in the following
categories: land use (wetland vs. non-wetland), vegetation type, size (large vs. small), location
(palustrine vs. estuarine), and level of disturbance (Rex and Malanson, 1990). For the size
comparison, all wetland patches in the study set of wetlands in the Galveston Bay system were
used. For the level of disturbance studies, digitized road maps which detailed the transportation
uses of land down to residential streets around the Galveston Bay system were obtained from
the Texas Department of Transportation (TxDOT).
Perimeter and area information of each wetland patch or polygon were obtained from the
digitized maps using ARC/INFO. The degree of complexity of a polygon was characterized by
the fractal dimension D, such that the perimeter P of a patch was related to the area A of the
same patch by P = A(D/2). The perimeter-area method for calculating fractal dimensions
regressed log(P) on log(A) to evaluate D. The fractal dimension of each data set of wetland
polygons (described below) was estimated by regressing the logarithm of polygon perimeter on
the logarithm of polygon area (Mandelbrot 1977; Kummel et al. 1987). The fractal dimension
was computed as twice the regression slope. Regression slopes were compared by testing for
homogeneity of slope and homogeneity of elevation using an analysis of covariance (Snedecor
et al., 1980). Each data set (for vegetation type) included at least 30 data points (wetlands or
polygons) for inclusion in the data analysis (for statistical robustness). Tukey's HSD test was
used to compare which pairs of regression slopes were significantly different in the comparison
of vegetation type.
Vegetation type comparisons: Wetlands were grouped according to vegetation types, which are
described in the attribute table of each digitized map coverage: estuarine intertidal emergent,
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estuarine intertidal scrub-shrub, palustrine emergent, palustrine scrub-shrub, and palustrine
forested wetlands (Coward in et al., 1979). Estuarine intertidal emergent wetlands are salt
marshes (inundated with water of salinity > 0.5 %o) consisting of herbaceous plants, such as
Spartina alterniflora (smooth cordgrass), Distichlis spicata (spike grass), and Juncus
roemerianus (needlegrass rush). Estuarine intertidal scrub-shrub wetlands are salt marshes with
woody vegetation that is < 6 m (20 feet) in height, such as Iva frutescens (big-leaf sumpweed)
and Baccharis halimifolia (sea-myrtle). Palustrine emergent wetlands are freshwater marshes
(inundated with water of salinity < 0.5 %o) consisting of herbaceous plants, such as Spartina
patens (saltmeadow cordgrass), Scirpus californicus (California bulrush), and Phragmites
australis (common reed). Palustrine scrub-shrub wetlands are freshwater marshes with woody
vegetation that is < 6 m (20 feet) in height, such as Salix nigra (black willow) and Sapium
sebiferum (Chinese tallow). Palustrine forested wetlands are freshwater marshes with woody
vegetation that is > 6 m (20 feet) in height, such as Taxodium distichum (bald cypress),
FraxinusPennsylvania (green ash), and Acer rubrum (red maple).
Land use comparsions: Wetland polygons consisted of polygons from the vegetation types
described above. Non-wetland polygons consisted of all upland areas in the data set.
Location comparisons: Estuarine wetlands consist of salt and brackish marshes, including
coastal wetlands, and palustrine wetlands are inland or freshwater wetlands (White et al., 1993;
Mitsch and Gosselink, 1993; Moulton et al., 1997).
Size comparisons: All wetland patches (n=3101) were ranked according to area. The largest
third (1000) was designated as large wetlands, and the smallest third (1000) was designated as
small wetlands.
Comparison of level of disturbance: Human impact can be evidenced by the amount of
transportation corridors (roads and railroads). The most recent NHI wetland coverage (1989)
was overlaid with digital TxDOT road coverages. The roads coverage from TxDOT was buffered
to explore the extent of road effects on wetland boundaries, using: a 10-m buffer, a 25-m buffer,
a 50-m buffer, and a 100-m buffer. The wetland coverage was then overlaid with a buffered road
coverage, resulting in a coverage containing segments of wetland boundary which fell into the
road buffer. The level of disturbance surrounding a patch of wetland was defined as the percent
of the wetland perimeter that had clear evidence of impact (a wetland boundary which fell into
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the road/railroad buffer region). The percentage of impact was quantified as a ratio: the length of
the impact divided by the total perimeter of the wetland polygon. This method of measuring the
level of disturbance was similar to that described in Rex and Malanson (1990). Wetlands were
then ranked according to the percentage of impact, divided into thirds, and classified into a low,
medium, or high category, based on the ranking.
RESULTS
Fractal dimension of wetlands. There was a significant difference in the fractal dimensions of
wetlands with different vegetation types (F=3.97, df=4, P=0.0032; Table 1). The palustrine
forested wetlands had the highest fractal dimension (0=1.358), and the estuarine scrub-shrub
wetlands had the lowest fractal dimension (0=1.224). Comparisons of the fractal dimensions of
the wetland types divided the wetlands into three groups: estuarine intertidal emergent and
palustrine emergent; palustrine scrub-shrub and palustrine forested; and estuarine scrub-shrub.
The intercepts of the regressions (used in calculating the fractal dimensions of wetlands of
different vegetation types) were significantly different (F=12.90, df=4, P=0.0001). There was no
significant difference in the fractal dimensions of estuarine and palustrine wetlands (F=0.11,
df=1, P=0.7431, Table 2). However, there was a significant difference in the intercepts of these
two regressions (F=37.06, df=1, P=0.0001). The fractal dimension of the 1000 smallest wetland
polygons (area=196 m2 to 3782 m2; 0=1.136) was significantly smaller than the fractal
dimension of the 1000 largest wetland polygons (area=17,016 m2 to 37,609,893 m2; 0=1.344;
F=46.32, df=1, P=0.0001; Table 3). There was no significant difference in the intercepts of the
regressions (F=0.04, df=1, P=0.8381). Wetlands had a significantly higher fractal dimension
(1.324) than non-wetlands (1.228; F=211.74, df=1, P=0.0001; Table 4). There was also a
significant difference in the intercepts of these regressions (F=13.94, df=1, P=0.0002).
Wetlands with a high degree of impact had a significantly lower fractal dimension than wetlands
with a low degree of impact; this trend was also present in wetlands when I used a 10, 25, 50, or
100-meter buffer (Tables 5-8; cf. 1.366 vs. 1.254 in Table 6). In addition, wetlands that were
classified as having a low level of impact did not have a significantly different fractal dimension
when the size of the road buffer changed (F=0.08, df=3, P=0.9728; Table 9). This was also true
for wetlands classified as having a medium level of impact and a high level of impact (Tables
10-11).
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DISCUSSION
Wetlands had a significantly higher fractal dimension than non-wetlands (Table 7). This
difference may be related to the fact that wetlands are formed by natural processes which form
complexly folded boundaries. Certain topological and hydrological patterns, such as tides and
storm surges, may be generated by diffusion processes which result in a convoluted boundary.
On the other hand, non-wetlands are more often bounded by residential areas, agricultural
fields, road constructions, and drainages which shape the boundaries in a more linear fashion.
The fractal dimensions of different wetland types also differed (Table 1). One conclusion might
relate this difference to the location (or salinity level) of the wetlands. However, the fractal
dimension of coastal wetlands was not significantly different than the fractal dimension of inland
wetlands (Table 2). Data on the vegetation types indicate that wetlands with woody vegetation
had a significantly different fractal dimension than wetlands with herbaceous vegetation.
Palustrine forested wetlands usually occur along rivers and streams, whose edges were formed
by natural factors. Palustrine forested and palustrine scrub-shrub wetlands may require more
nutrients to support their higher biomass than the herbaceous plants do; thus, a longer and
more complex edge may facilitate the flow and intake of nutrients into their systems.
Fractal dimension differed for small patches compared with large patches of wetlands (Table 3),
supporting the results of previous studies (Kummel et al., 1987; Bradbury et al., 1984) and
suggesting that two separate processes ~ natural and anthropogenic ~ may have operated to
create these patch shapes. These differences may imply changes in spatial scale in the
underlying processes that control the shape complexity of wetland patches in the Galveston Bay
(Kummel etal., 1987).
There was a significant difference between the intercepts of the regressions slopes used to
calculate the fractal dimension for all comparisons except for those comparing size and road
buffers (Tables 3, and 9-11, respectively). In creating fractal landscapes, Milne (1992) asserted
that it was important to interpret the intercepts of the regression slopes, which "relates to the
sheer preponderance of the pattern relative to the extent of the map" within which the pattern
occurs. A small value for the intercept with a high fractal dimension indicates a relatively
compact pattern occupying a small portion of the study region. A high value for the intercept
with a low fractal dimension indicated a highly dispersed pattern spanning a majority of the
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study area. More research into the interplay between the intercept and the fractal dimension is
needed to examine the topic of scale extrapolation.
Wetlands that were heavily impacted by human disturbance had a significantly lower fractal
dimension than wetlands that were not as heavily impacted (Tables 5-8). This was explained by
the fact that heavily impacted wetlands usually were bounded by more roads and canals which
had a rectilinear edge, exhibiting a lower fractal dimension. Previous studies have shown that
the presence of human disturbance can affect the fractal geometry of a patch (Forman et al.,
1986; Kummel et al., 1987). The present study examined the effects of different levels of impact
(roads and railroads) on wetland boundaries. Roads have a significant impact on wetland
ecosystems because they may reduce regional biodiversity by impeding migration of small
mammals between local populations (Merriam et al., 1989), modifying wetland hydrology and
siltation patterns (Andrews, 1990), increasing the amount of edge in habitat patches (Soule,
1992), increasing mortality through roadkills (Fahrig et al., 1995), facilitating the invasion of
exotic species (Lonsdale and Lane, 1994), and/or increasing human access to wildlife habitats
(Young, 1994). Findlay et al. (1997) found that species richness of plants, birds, and herptiles
decreased as the density of paved roads surrounding southern Ontario wetlands increased.
Wetlands affected by the same level of impact did not have significantly different fractal
dimensions as the size of the road buffer increased (Tables 9-11). It seems that the effect of
roads on wetland boundaries does not change as area between them increases to 100 meters.
Further research should be conducted to examine how different levels of impaction affect
wetland cycles, community composition, and productivity.
Since different levels of human disturbance (Tables 5-8), different vegetation types (Table 1),
and different wetland sizes (Table 3) cause landscapes with different fractal dimensions, it
would be interesting to examine whether different generative processes truly form landscape
features with different fractal dimensions, and how these processes contribute to patch shape. A
mathematical model describing the relative contribution of each factor to overall wetland patch
shape may be helpful in identifying the principal components of wetland change. This
information would be useful for developing studies to determine interrelationships between
ecological, hydrological, and anthropogenic processes operating at different spatial scales. For
instance, how could fractal dimension serve as an index of the overall health of wetlands? Or,
should fractal dimension be an important parameter in the design of coastal wetlands? Minello
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et al. (1994) stated that "both marsh surface elevation and edge should be considered when
designing salt marsh habitats," since edge habitat is used by high densities of nekton in coastal
salt marshes. Data show that vegetation along the marsh edge is used to a greater extent than
inner marsh habitat (Peterson and Turner, 1994). Since the fractal dimension is a more accurate
description of edge length, perhaps fractal wetlands offer a practical solution to the design of
new wetlands. Further research should define and resolve to these processes and form the
basis of recommendations about how wetlands can be better managed as natural resources
and nature reserves.
Table 1. Comparison of Fractal Dimension D of Wetlands by Vegetation Type
(Different superscripts indicate significant difference)
Wetland Type
D
R-Squared Residual MS n
(a)
(b)
(c)
(d)
(e)
Estuarine
Estuarine
Palustrine
Palustrine
Palustrine
Intertidal Emergent
Scrub-Shrub
Emergent
Forested
Scrub-Shrub
1.322
1.224
1.314
1.358
1.340
1
2
1
3
3
0
0
0
0
0
.2141
.5707
.1934
.0049
.0790
0.97
0.95
0.96
0.95
0.94
0.05474
0.04177
0.05511
0.06328
0.04634
1164
108
1267
243
314
Comparison of slopes: F=3.97, P=0.0032 (significant difference)
Comparison of intercept (a): F=12.90, P=0.0001 (significant difference)
Table 2. Comparison of Fractal Dimension D of Wetlands by Location
Location D a R-Squared Residual MS n
Coastal
Inland
1 .3204
1 .3234
0.2184
0.1520
0.97
0.95
0.05452
0.05480
1273
1826
Comparison of slopes: F=0.11, P=0.7431 (no significant difference)
Comparison of intercept (a): F=37.06, P=0.0001 (significant difference)
10
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Size
Table 3. Comparison of Fractal Dimension D of Wetlands by Size
_D a R-squared Residual MS n
Small
Large
1.136
1.344
0.9000
0.0741
0.90
0.89
0.01096
0.11244
1000
1000
Comparison of slope: F=46.32, P=0.0001 (significant difference)
Comparison of intercept (a): F=0.04, P=0.8381 (no significant difference)
Table 4. Comparison of Fractal Dimension D of Wetlands vs. Non-Wetlands
Category D a R-sguared Residual MS n
Non-Wetlands
Wetlands
1.228
1.324
0.5617
0.1700
0.97
0.96
0.2860
0.5531
2881
3100
Comparison of slopes: F=211.74, P=0.0001 (significant difference)
Comparison of intercept (a): F=13.94, P=0.0002 (significant difference)
Table 5. Comparison of Fractal Dimension D of Wetlands Impacted
by a 10-meter Road Buffer
Cateaorv
Low impact
Medium impact
High impact
D
1.366
1.310
1.254
a
0.0244
0.2500
0.3740
R-sauared
0.97
0.95
0.95
Residual MS
0.08032
0.04334
0.02538
n
210
210
209
Comparison of slopes: F=7.38, P=0.0007 (significant difference)
Comparison of intercept (a): F=23.08, P=0.0001 (significant difference)
11
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Table 6. Comparison of Fractal Dimension D of Wetlands Impacted
by a 25-meter Road Buffer
Cateaorv
Low impact
Medium impact
High impact
D a R-sauared
1.376 -0.0330 0.97
1.316 0.2319 0.95
1.254 0.3769 0.95
Residual MS
0.07891
0.04320
0.02475
n
220
220
220
Comparison of slopes: F=9.46, P=0.0001 (significant difference)
Comparison of intercept (a): F=21.73, P=0.0001 (significant difference)
Table 7.
Cateaorv
Low impact
Medium impact
High impact
Comparison of Fractal Dimension D of
by a 50-meter Road Buffer
D a R-sauared
1.376 -0.023 0.97
1.318 0.234 0.96
1.236 0.456 0.96
Wetlands Impacted
Residual MS n
0.0795 235
0.0406 235
0.02313 234
Comparison of slopes: F=13.42, P=0.0001 (significant difference)
Comparison of intercept (a): F=28.05, P=0.0001 (significant difference)
Table 8.
Cateaorv
Low impact
Medium impact
High impact
Comparison of Fractal Dimension D of
by a 1 00-meter Road Buffer
D a R-sauared
1.374 -0.0159 0.96
1.300 0.3161 0.95
1.238 0.4477 0.95
Wetlands Impacted
Residual MS
0.09199
0.03713
0.02449
n
265
265
265
Comparison of slopes: F=13.54, P=0.0001 (significant difference)
Comparison of intercept (a): F=29.86, P=0.0001 (significant difference)
12
-------
Table 9. Comparison of Fractal Dimension D of Wetlands That Are
Low Impacted with Various Road Buffers
Cateaorv
1 0-meter buffer
25-meter buffer
50-meter buffer
1 00-meter buffer
Comparison of slopes: F=0
Comparison of intercept (a]
Table 10.
Cateaorv
1 0-meter buffer
25-meter buffer
50-meter buffer
1 00-meter buffer
D
1.366
1.376
1.376
1.374
.08, P=0.
i: F=0.08
a R-squared
0.0244 0.97
-0.0330 0.97
-0.023 0.97
-0.0159 0.96
9728 (no significant difference)
, P=0.9729 (no significant difference)
Comparison of Fractal Dimension D of
Medium Impacted with Various Road
D
1.310
1.316
1.318
1.300
Comparison of slopes: F=0.41, P=0.
Comparison of intercept (a): F=1.18
Table 11.
Category
1 0-meter buffer
25-meter buffer
50-meter buffer
1 00-meter buffer
a R-sauared
0.2500 0.95
0.2319 0.95
0.2340 0.96
0.3161 0.95
7441 (no significant difference)
, P=0.3180 (no significant difference)
Residual MS n
0.08032 210
0.07891 220
0.0795 235
0.09199 265
Wetlands That Are
Buffers
Residual MS
0.04334
0.04320
0.04060
0.02449
n
210
220
235
265
Comparison of Fractal Dimension D of Wetlands That Are
High Impacted with Various Road Buffers
D
1.250
1.204
1.236
1.238
a R-squared
0.4451 0.96
0.3769 0.96
0.4566 0.96
0.4487 0.95
Residual MS
0.02837
0.02238
0.02324
0.02442
n
209
220
234
265
Comparison of slopes: F=1.17, P=0.3207 (no significant difference)
Comparison of intercept (a): F=3.30, P=0.0199 (no significant difference)
13
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Environmental Risk Assessment Using GIS
Issues of Scale, Resolution, Methodology, and Place
Robert B. McMaster, Eric Sheppard, Helga Leitner, Hongguo Tian, and Jeffrey Matson
Department of Geography
University of Minnesota
Abstract
In studying the relationship between geographic information science/systems (GIS) and society,
many, or even most, of the key issues-access, democratization, privacy-play themselves out in
the area of environmental risk assessment. In particular, the application of GIS-and spatial
methodologies in general-in order to better understand a given population's exposure to
technological hazards has increased rapidly over the past decade. Furthermore, the empirical
geographical relationship between exposure to toxic chemicals, race and poverty in the US has
been shown to vary dramatically depending on the definitions of risk and proximity used, the
spatial scope or scale study area examined (neighborhood, inner city, metropolitan region, state
or province, or nation state), and the geographical resolution of the data used in the analysis
(whether the information analyzed is recorded for counties, municipalities, census tracts, block
groups or individual blocks). This environmental risk assessment project focuses specifically on
our findings related to issues of scale, resolution, methodology, and place, obtained from an
analysis of the Twin Cities Metropolitan region.
Introduction
In looking at the relationship between geographic information science/systems (GIS) and
society, many, or even most, of the key issues-access, democratization, privacy-play
themselves out in the area of environmental risk assessment. In particular, the application of
GIS-and spatial methodologies in general-in order to better understand a given population's
exposure to technological hazards has increased rapidly over the past decade. One can point to
a plethora of studies that have applied GIS to these human-produced hazards at a variety of
scales and resolutions, and have applied significantly different spatial methodologies.
Unfortunately, the effects of scale, resolution, and methodology are poorly understood.
Additionally, the place itself, as constructed through a complicated set of economic, political,
social, and economic forces and relationships, has rarely been used to develop a deeper and
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historical sense of the intermingling of the hazards and communities/neighborhoods in which
they exist. Our multiyear project is attempting to unravel some of these vexing problems.
First we provide the context and definitions needed for our research. These definitions evolve
from the growing literature on, and series of national-level events related to, environmental
justice, including the 1979 environmental justice challenge to the siting of a waste facility by the
City of Houston, Texas, the 1986 passage of the SARA Title III legislation, the 1987
environmental justice study by the United Church of Christ, the 1992 establishment of the Office
of Environmental Equity, and the 1994 Presidential Executive Order 12898 that requires federal
agencies to adopt the principle of environmental justice in programmatic decisions. A thorough
review of the environmental justice movement may be found in Toxics Watch 1995 (Inform,
1995). Since human environment hazards effect the air, water, and soil, this project, based in
the Twin Cities metropolitan region, utilizes a series of hazardous materials sites, including TRI
(Toxic Release Inventory), Superfund, Petrofund, and Land Recycling. However, most of the
methodological work involves the airborne toxic releases produced by the TRI sites. In order to
assess potential exposure to such sites (remembering that while exposure can be roughly
calculated, risk itself is based on a complicated set of physiological, historical, and toxicological
variables) a set of both geodemographic (based on census data) and institutional populations
were used. Whereas environmental risk assessment is the more comprehensive term that
applies to assessing the relationships between all types of environmental hazards and the
humans impacted, environmental justice or injustice is the special case of assessing the
unequal spatial distribution of hazardous materials sites, where the assumption is that
disadvantaged populations (the poor, minorities, the elderly) are disproportionately affected. In
our own work, these hazardous materials and population data have been analyzed at a variety
of scales and resolutions, ranging from the regional seven-county level to the individual
neighborhood level, using a variety of spatial methodologies. This paper focuses specifically on
our findings related to scale, resolution, methodology, and place. The empirical geographical
relationship between exposure to toxic chemicals, race and poverty in the US has been shown
to vary dramatically depending on the definitions of risk and proximity used, the spatial scope or
scale study area examined (neighborhood, inner city, metropolitan region, state or province, or
nation state), and the geographical resolution of the data used in the analysis (whether the
information analyzed is recorded for counties, municipalities, census tracts, block groups or
individual blocks). For a fuller analysis of these differences see McMaster, Leitner and
Sheppard (1997) and Mohai (1995).
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Scale
Geographic scale is the extent of areal coverage used in an inquiry, and is applied in the
opposite sense of cartographic scale, which is based on a strict mathematical relationship
between map space and earth space using the representative fraction. Geographically, large
scale represents larger areas; cartographically, large scale represents smaller areas (but with
more detail). The misuse of these two terms causes significant confusion among the many
interdisciplinary researchers that work in this area. Our work has looked at environmental justice
issues at regional scales-the seven-county metropolitan area--to the individual neighborhood
scale. Interpretations of environmental justice change as one looks at the problem from these
differing scales.
Our work in Minneapolis suggests that spatial analysis of environmental equity at different
scales can help reveal different patterns. In this section, we examine the effect of scale on
patterns, and its implications for processes, holding both data resolution and definitions of
proximity and vulnerable populations constant.
Figures 1-4 show, respectively, the relationships between race and proximity to Toxic Release
Inventory sites for Phillips neighborhood, a racially diverse low income inner city neighborhood
of Minneapolis; for the City of Minneapolis itself; for Hennepin County, the metropolitan-wide
county within which Minneapolis is located but which also extends to the northern and western
suburbs; and the entire seven-county metropolitan region. Although the use of the category
"non-white" largely excludes the Hispanic population from our analysis, they represent only a
very small presence in three districts of the Twin Cities metropolitan area. For Phillips
neighborhood and Minneapolis (Maps 1 and 2), there is no clear relation of proximity between
TRI sites and non-white residential concentrations. Indeed for Minneapolis, TRI sites are
disproportionately concentrated in northeastern Minneapolis, whereas communities of color
are concentrated in the near north side and in south Minneapolis, suggesting that TRI sites in
fact are closer to white communities. At the scale of Hennepin County (Figure 3), however, the
concentration of communities of color (within Minneapolis) overlaps with the largest cluster of
TRI sites. More generally, this map shows that the concentration of both TRI sites and the
non-white population decreases westward and northwestward from the City of Minneapolis.
One could make the argument, therefore, that at this scale there is evidence of environmental
injustice, as both TRI sites (nearly one-half) and populations of color are concentrated in the
central city. The regional map provides yet another picture (Figure 4). Here one can detect a
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toxic "corridor" that tends to follow the Mississippi River as it flows form the northwest, through
the City of Minneapolis, and southeastward through southern Ramsey County and St. Paul.
This pattern tends to disperse itself as the distance from the river increases. The pattern
undoubtedly illustrates the importance of the river in the early industrial history of the region.
Table 1. Percentage of persons living in poverty in
Block Groups with and without a TRI site.
With
TRI Site
Without
TRI Site
All Metro
counties
10.54
7.73
Anoka
6.95
5.08
Carver
4.43
4.83
Dakota
6.96
4.09
Hennepin
12.62
8.75
Ramsey
12.90
10.91
Scott
3.67
4.16
Washington
7.96
4.05
In light of this, it would be erroneous to conclude from the analysis of Phillips neighborhood, or
Minneapolis, that there is environmental justice. This would commit a form of ecological fallacy
by presuming that processes affecting the spatial distribution of populations and industrial sites
only operate at the neighborhood scale. Even with no environmental injustice within Phillips
neighborhood, neighborhood residents as a whole may be disproportionately exposed to toxic
chemicals compared to others in the central cities; and almost certainly are more exposed
than most suburban communities. Tables 1 and 2, which utilize the block-group level census
data for the entire seven-county metropolitan region, show comparisons between those block-
groups that contain, and do not contain, a TRI site for two census variables: percentage of
persons living in poverty and percentage of persons over age 16 unemployed. As would be
expected, the most significant differences occur with the poverty comparison where, for
instance, in Hennepin County 12.6% of the population that resides in a block-group with a TRI
site lives in poverty compared with 8.7% in those block-groups without a TRI site.
Table 2. Percentage of persons over age 16 and unemployed
in Block Groups with and without a TRI site.
With
TRI Site
Without
TRI Site
All Metro
counties
2.65
2.61
Anoka
2.67
2.79
Carver
1.39
2.00
Dakota
2.72
2.14
Hennepin
2.91
2.72
Ramsey
2.40
2.71
Scott
2.72
2.14
Washington
2.55
2.12
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These contrasting patterns can be explained by paying attention to the geographical
processes of urban development underlying them, and in particular, to which kinds of
processes dominate intra-urban patterns at different scales. The positive association between
TRI sites and communities of color at the metropolitan scale (i.e., Hennepin County) reflects
the history of suburbanization of selected population groups. The central city emerged early as
a center of manufacturing, with areas zoned for this purpose. In part in reaction to the
environmental consequences of living close to industries and the pollution and congestion
associated with them, and taking advantage of new transportation technologies and of Federal
subsidization of highway construction and house purchases, better-off white residents began
to move out of the central city rapidly after the Second World War. It is well documented that
suburbanization was much easier and more attractive for white and middle-income people
than for racial minorities and those in poverty. Indeed, suburbanization too often was a means
to escape inner city neighborhoods with new African American and other minority inmigrants.
Once suburbs were established, a variety of exclusionary and discriminatory practices often
prevented minority and low income groups from moving to the suburbs. Inner city communities
became, in turn, places of protection and self-reinforcement for ethnic groups excluded from
mainstream society. Although such discriminatory practices have been outlawed (if not
eliminated), these processes, reinforced by the limited opportunities for low income
households in the market for new housing, have been inscribed in the urban landscape; the
presence of communities of color outside the central city remains minimal (Figure 3). The
contrasting association between TRI sites and communities of color at different scales can
thus be accounted for by the way in which, at a metropolitan scale, suburbanization enabled
more wealthy and white households to escape the many 'problems' of the inner city, including
those of industrial pollution. Industry has also been suburbanizing, as can be seen in the
locations of TRI sites in Bloomington, Eden Prairie, Plymouth, St. Louis Park and Brooklyn
Park (Figure 3 and 4). Nevertheless, it is harder for dirty or hazardous industries to gain
acceptance in suburban communities, and the greatest concentration remains in the central
city.
A focus on TRI sites only addresses one way in which the quality of the urban environment
varies socially and spatially (Figure 5). It is important to add to this other environmental 'bads'
such as other fixed sources of potentially toxic chemicals (e.g., Superfund or Petrofund sites),
hazardous emissions from ambient sources (especially transportation), soil pollution, radon and
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lead in houses, and even social risks in certain environments (e.g., undesirable social
institutions, and crime). At the same time, inequities in access to urban environmental 'goods'
should also be accounted for, such as lakes, green space and bike paths. We have done no
systematic analysis of these, but casual observation suggests a similar gradient from low
income inner city neighborhoods, to suburban and higher income inner city communities,
suggesting that the relationships described above, and the processes generating them are
generalizable to a broader group of environmental goods and bads.
The resolution, or granularity, of the data also affects measures of environmental injustice. Our
data sets have applied census data at the census tract, block-group, and block levels. We
currently are working with parcel-level data, which allows for a household-level analysis.
Interpretations can change significantly when address-specific institutional data are included,
such as that on schools and day care centers. Further improvements will involve the addition of
the 2000 census and approximations of day-time populations.
Geographical Methodology
Whereas most studies use relatively simple measures for establishing the relationship between
hazardous materials and at-risk populations, it is clear that more robust methodologies are
needed. For instance, relationships are often based on simplistic measures of proximity-
hazardous materials sites falling within a block group with a high percentage of minorities-
which can not deal with differential toxicity, actual distance from site, and exact meteorological
conditions. While GIS-based buffer analysis provides a better measure of proximity, it is
nonetheless problematic in that all buffers are normally generated at the same size. The
generation of plumes ameliorates this problem, but assumes that exact meteorological
conditions are known ahead of time. Additionally, one needs to think carefully about the
statistical significance of the existing distribution. To address this concern we have ventured into
the realm of Monte Carlo simulation, where, through a randomization process, we can compute
the sampling distribution with respect to which the importance of observed statistics can be
assessed.
Plume modeling
Let us first provide the results of some preliminary plume modeling. The model we worked with,
ALOHA, is an example of a Gaussian Plume Disperson Model. Further details may be found in
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the ALOHA User's Manual (NOAA, 1996). As with any type of model, certain assumptions must
be realized. For the ALOHA model, these include the following:
-All heavy gas releases originate at ground level.
-95% of the time the wind will not blow the pollutant outside
of the dashed exposure line generated by the program.
-The ground below a leaking tank or a puddle is flat.
-The average concentrations will be highest near the release
point and along the centerline of any pollutant cloud and will
drop off smoothly and gradually in the downwind and cross wind directions.
-A dispersing chemical cloud does not react with the gases that make up the
atmosphere.
-The ground below a dispersing cloud is flat and free of obstacles.
Figure 6 and 7 illustrate a series of plumes generated for the City of Minneapolis using two
different chemicals—Toluene and Nitric Acid. Figure 6 depicts four plumes representing four
different months—January, April, June, and September. For each of the four months,
atmospheric conditions representing average temperature, humidity, wind speed, and prevailing
wind conditions were applied and the assumption was made that 20% of the chemical was
released over a one-hour period. The thirty-eight TRI sites for Minneapolis are identified with the
blue point symbols. Comparing the two plume maps shows a striking difference in the area
impacted by a potential chemical release. Whereas the composite toxic footprint (based on the
four different months) is somewhat localized for toluene (Figure 6), the nitric acid plume covers
close to 40% of the City of Minneapolis (Figure 7). Thus one can see that plume modeling
represents a significant improvement in measuring potential exposure as compared to a
simplistic GIS-based buffer analysis. Our research in exploring the potential for plume modeling
is ongoing, with the calculation of plumes for all chemicals within the City, with measuring the
impact on various populations, and with computing plumes for the entire Hennepin County
Region.
Monte Carlo Simulation
Increasingly, geographers are applying Monte Carlo simulation methods to enable a comparison
between an existing spatial pattern, and a theoretical distribution derived from a repeated
randomization—called bootstrapping~of the existing data. This allows researchers to apply what
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is equivalent to a test of significance in determining where an existing statistic falls in relation to
a large number (n normally is well above 1000) of "theoretical" statistics. First, we present some
basic information on what we define as proximity ratios for the City of Minneapolis.
Table 3. Poverty rates, and proximate, and non-proximate block groups for different
races and young children.
Proximity
Measure
Within
TRIBG
Outside
TRIBG
Proximity
Ratio
Whites
22
11
1.98
African
Americans
43
40
1.07
American
Indians
64
52
1.22
Asians
52
45
1.15
Hispanics
47
26
1.80
Children
<5
47
32
1.46
Total
30
18
1.72
Table 3 is based on a comparison of those census block groups that contain a TRI site (Within
TRIBG) and those block groups that do not contain a TRI site (Outside TRIBG). The proximity
ratio is the ratio of the within TRIBG poverty rate and the outside TRIBG poverty rate. This
represents, of course, a simple measure of proximity—the assumption that a given population
within the same enumeration unit as a hazardous site is at higher risk. This same measure—the
proximity ratio—was also applied to comparing populations inside and outside of GIS-calculated
buffers. We apply the buffering technique since many existing environmental risk studies also
use a circular buffer as a measure of proximity. Table 4 compares the block group proximity
ratios with those produced through 100-, 500, and 1000-yard buffers.
Table 4. Differences in proximity ratios for four different measures of proximity—block
group comparison, and 100-, 500-, and 1000-yard buffers.
Proximity
Measure
Block
Groups
100 yard
buffer
500 yard
buffer
1000 yard
buffer
Whites
1.98
1.83
2.00
2.22
African
Americans
1.07
1.27
1.28
1.39
American
Indians
1.22
1.13
1.23
1.37
Asians
1.15
1.26
1.33
1.71
Hispanics
1.80
1.66
1.48
1.70
Children
<5
1.46
1.48
1.65
2.21
Total
1.72
1.78
1.94
2.21
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The critical question, of course, is whether these proximity ratios represent a "significant"
difference? To determine this, the Monte Carlo technique was implemented using two methods.
For method 1, given that 38 block groups contain a TRI site, these 38 were randomized 1,500
times, with the census statistics—percent white in poverty, percent African American in
poverty...) recomputed for each randomization. 1,500 statistics (means, variances) are thus
generated to create the theoretical sampling distribution.
Method 2 involved 1,500 randomizations of the actual thirty-eight TRI sites using x-y coordinate
pairs. For each randomization, 100-, 500-, and 1000-yard buffers were generated and census
statistics inside and outside the buffers were calculated. Table 5 provides the observed poverty
levels as percentiles of the simulated distribution for populations near TRI sites. In this table, a
value of 99.9% means the observed value was greater than 1,500 of the simulations. One can
see that, as the measure of proximity is increased from the block-group level to the 1000 yard
buffer level, the percentiles increase. Whereas the "true" mean of Asians in poverty was only
greater than 73.1% of the simulations, this same value was greater than 99.9% of the
simulations when a 1000-yard buffer was applied. Further details may be found in Sheppard, et.
al., 1999).
Table 5. Observed poverty levels as percentiles of the simulated distribution for
populations near TRI sites, 1995.
Proximity
Measure
Block
Groups
100 yard
buffer
500 yard
buffer
1000 yard
buffer
Whites
99.9%
99.9
99.9
99.9
African
Americans
86.8%
95.5
98.8
99.9
American
Indians
87.1%
78.8
96.6
99.9
Asians
73.1%
86.4
88.8
99.9
Hispanics
80.1%
94.1
94.5
99.9
Children
<5
97.9%
97.8
99.9
99.9
Total
99.9%
99.9
99.9
99.9
Proximal Spaces.
A final method of assessing the relationship between TRI sites and geodemographics involves
the calculation of "Proximal" spaces based on Thiessen Polygons. The characteristic of the
Thiessen Polygons, of course, is that any block group within a polygon is closer to that
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polygon's TRI site than any other TRI site. The structure of the Thiessen Polygons also
effectively provides a visualization of the density and distribution of the city's TRI sites.
Place
The neighborhoods in Minneapolis are intricate places with complex social and industrial
histories. Questions of environmental risk assessment, and justice, have to carefully account for
the historical sequencing of industrial toxic sites and migrations of populations in to and out of
the neighborhood. This type of work involves a careful historical reconstruction using company
records, census data, and historical land records, maps and atlases (Pulido, 1996). In the end,
claims of environmental injustice must include a meticulous accounting of the place, not just a
modern analysis of easy-to-obtain institutional data. Additionally, we have discovered that the
standard institutional databases such as TRI are often insufficient for characterizing a
neighborhood's environmental concerns, where many toxic sites must be determined through
the acquisition of local neighborhood knowledge.
Local Knowledge and Neighborhood Environmental Inventories
When place-based analyses of environmental justice are carried out at local, neighborhood
scales, the possibility exists of a form of geographical analysis that changes the relationship
between the researcher and the community researched, with far reaching implications. A
common characteristic of spatial analytic approaches, but also of many intensive analyses of
places, is that the researcher defines the nature of the problem to be studied and the methods
to be used. This restriction is clear in the case of the kinds of spatial analyses described above.
The data used for such analyses, typically TRI sites, represent just one source of toxic risk.
Local communities have other perspectives on risk that suggest the need for other data. Indeed,
one of the most publicized cases of exposure to toxics, Love Canal, was based on
neighborhood data collection in the absence of public data about the site in question.
Neighborhood analysis revealed the existence of toxic waste that simply had been forgotten
about and eventually had dropped out of public discourse over the years since the canal was
filled with chemicals and covered over.
Figure 8 provides an example of the impact of including local knowledge. It shows the additional
sites of risk identified in an environmental inventory completed by the Marcy Holmes
neighborhood organization in Minneapolis in the summer of 1996. The map shows how the
addition of locations identified by the community, as places where toxic chemicals may be
10
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stored and emitted, creates a denser pattern of potential sources of exposure than that derived
from public databases. It also shows the proximity of schools, community centers and daycare
centers to these locations, which the community wished to highlight in order to assess the
proximity of vulnerable groups to hazardous facilities.
Research in four Minneapolis neighborhoods has revealed that different communities have very
different perceptions of the environmental risks that they are exposed to (Table 6); perceptions
which may have little to do with proximity to TRI sites (Leitner and Elwood, 1998). Furthermore,
many of the sources of toxic risk identified within communities, such as lead pollution in the soil,
dry cleaners, old gas stations, or ambient air pollution, are not recorded in standard data bases
and thus fall outside current TRI-oriented spatial analyses. Finally, there can exist extreme
differences even within a neighborhood about what the greatest problems are, reflecting the
differently situated experiences of different types of neighborhood residents (cf. Haraway,
1991). Jeff Osleeb (personal communication) reports that in one Bronx neighborhood the Irish
residents, living in a largely paved area of the neighborhood, focus on greening public space,
whereas Jewish women are concerned about environmental exposure to the risk of breast
cancer.
This is a specific example of the concern that standardized geographical techniques tend to
reinforce particular ways of thinking about and representing issues that may be very different
from those held by others, and that the widespread adoption of such techniques tends to
marginalize other ways of thinking about the issue that may be equally legitimate. This
possibility has received much attention in recent debates about 'GIS and society' which have
focused on the types of representations prioritized by current GIS technologies and practices
(cf. Rundstrom, 1993; Sheppard, 1995; Harris and Weiner, 1996). In contradistinction to an
approach where the researcher defines the problem at hand, Heiman (1997) advocates 'science
by the people.' In this view, researchers work in a participatory manner with community
residents, giving them voice to express their different views about environmental risks, allowing
those different views to be altered in debate amongst them, and providing communities with
advice about how to collect the information necessary to determine the geography of potential
exposure to these risks in the neighborhood.
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Table 6. Localized environmental knowledge gathered from
four neighborhoods in Minneapolis.
Neighborhood
Hawthorne
M a rcy- Holmes
Phillips
Prospect Park
Socioeconomic
Status
Low Income
Racially diverse
Mid-income
Large student pop
Mostly white
Low Income
Racially diverse
(Af Am&AM Indian)
Mid-income
Mostly white
Data Needs
Parcel-level data
Housing data
Crime data
Little interest census
Plume analysis
Parcel-level data
Traffic volume/
pollution
Toxic sites
Parcel-level data
Housing data
Crime data
Toxic sites
Traffic volume/
pollution
Noise levels
Crime data
Toxic sites
Environmental
Problems
Toxic corridor along
river
Traffic-related noise
& pollution
Concern w/
kondirator
Traffic-related noise
& pollution
Toxic industrial
activity
Brownfields
UofM coal plant
Large industrial sites
Traffic-related noise
& pollution
High lead levels
Brownfields in SE
Three superfund
sites
Traffic-related noise
& pollution
Noxious and
unpleasant odors
Degradation of
watershed
This approach is similar in spirit to the 'geographical expeditions' organized by Bill Bunge in the
1970s into inner city neighborhoods in Detroit and Toronto, to collect information about the
exposure of residents to risks missing from standard databases (Horvath, 1971; Bunge and
Bordessa, 1975). It provides residents with the power to influence the questions that should be
asked. Such research also can contribute to an activist research agenda by raising awareness
in communities, and helping them collect information, and learn to use tools, which can make
them more effective in struggles to reduce potential exposure by negotiating with local firms and
local government (cf. Bryant, 1995).
12
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Such approaches entail certain difficulties. Perceptions of risk may be very different from actual
exposure, and concerns have been expressed about the ability of communities to rigorously
collect information, and about the politics associated with activist research. Yet it is easy to
overlook that fact that similar problems exist with approaches that are labeled as scientific. TRI
data are self-reported, and thus at risk of being inaccurate and subjective. The accuracy of
census data used for geo-demographic analysis in many inner city areas has been widely
questioned, as has the appropriateness of the very categories used for data collection by the
census. There is thus a politics to all databases. By contrast, cases exist where activist data
collection by communities has been done with care, and has revealed important and unknown
environmental risks. A carefully planned participatory research agenda that gives communities a
voice over data collection, challenges local perceptions by engaging them with alternative
viewpoints, and provides communities with the help necessary to gain the expertise for rigorous
data collection and analysis, has the potential to make such community-based studies no more
subject to bias than more standardized approaches.
It should be emphasized that this kind of research is far more effective when carried out within
local place-based communities. It is at this scale that varieties of local knowledge about toxics
and potential exposure can be recorded and confronted with one another, and that it is possible
to undertake systematic data collection of missing information. It is at the local scale that
differences in soil types, wind flow paths, and groundwater flow can be most accurately
determined. Local knowledge and local archives also can be tapped to develop an
understanding of the history of the community necessary to interpret both the types of
knowledge people hold, why these conflict, and the reasons for the geographical inequities
identified. The availability of portable GPS and GIS technologies and environmental recorders
and testing kits also make it possible for neighborhood residents to collect and analyze their
own data. Important questions remain unanswered about whether technologies developed for
public and private can be adapted adequately to meet the purposes of communities; but at least
these are now being asked, for example, by those examining the potential for public
participatory GIS.
Summary
This study attempts to tackle multiple problems/issues related to environmental risk
assessment, in particular as applied to the assessment of environmental justice. Using the Twin
Cities region, we have systematically begun to better understand how the scales and
13
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resolutions used, the methodologies applied, and a clearer interpretation of place, can provide
an improved understanding of this intrinsically geographical problem. Given the examples
provided for the Twin Cities region, it is clear that researchers must acquire a deeper knowledge
of the underlying spatial and social processes to fully understand and analyze issues of
environmental justice. In addition to the obvious effect of scale (and data resolution),
researchers must realize the spatial methodology selected-and even the specific GIS applied--
will significantly effect the conclusions. In our own work, we feel the application of Monte Carlo
simulation has tremendous potential for demonstrating the significance of the relationship
between a given toxic site and the specific targeted distribution. Lastly, the actual expertise,
knowledge, and concerns of the neighborhood itself must be considered in environmental
justice research. As demonstrated in our neighborhood-based research, neighborhoods
themselves often maintain an unofficial yet very detailed knowledge of the sources and histories
of toxicity within their spaces.
14
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References
Bryant, B., 1995. "Pollution prevention and participatory research as a methodology for
environmental justice." Virginia Environmental Law Journal, 14(4), 589-613.
Bunge, W. W. and R. Bordessa, 1975. The Canadian Alternative: Survival, expeditions and
urban change. Toronto: York University, Atkinson College.
Haraway, D., 1991. Simians, cyborgs and women: The reinvention of nature. New York:
Routledge.
Harris, T. and D. Weiner, 1996. GIS and Society: The Social Implications of how People, Space
and Environment are Represented in GIS. Report for the Initiative 19 Specialist Meeting,
March 2-5, 1996, Koinonia Retreat Center, South Haven MN. Technical Report 96-7,
City: National Center for Geographic Information and Analysis.
Heiman, M., 1990. "From 'not in my backyard' to 'not in anyone's backyard!' Grassroots
challenge to hazardous waste facility siting." American Planning Association Journal, 56,
359-362.
Inform, Inc, 1995. Toxics Watch 1995. New York, Inform, Inc.
Leitner, H. and S. Elwood, 1998. "GIS and Community-Based Planning: Exploring the Diversity
of Neighborhood Perspectives and Needs." Cartography and Geographic Information
Systems, 25(2), 77-89.
Sheppard, Eric, H. Leitner, R.B. McMaster, and H. Tian, 1999. "GIS-based
measures of environmental equity: Exploring their sensitivity and significance,"
Journal of Exposure Analysis and Environmental Epidemiology, 9(1), 18-28.
McMaster, R. J., H. Leitner and E. Sheppard, 1997. "GIS-based environmental equity and risk
assessment: Methodological problems and prospects." Cartography and Geographic
Information Systems, 24(3), 172-189.
Mohai, P., 1995. "The determination of dumping revisited: Examining the impact of alternative
methodologies in environmental justice research." Virginia Environmental Law Journal,
14(4), 615-653.
National Oceanic and Atmospheric Administration, and the U.S. Environmental Protection
Agency, 1996. ALOHA User's Manual. Washington, D.C.: National Safety Council.
Pulido, L., S. Sidawi and R. O. Vos, 1996. "An archeology of environmental racism in Los
Angeles." Urban Geography, 18, 419-439.
Rundstrom, R. A., 1993. "The role of ethics, mapping, and the meaning of place in relations
between Indians and whites in the United States." Cartographica, 30(1), 21-28.
Sheppard, E., 1995. "GIS and Society: Towards a Research Agenda." Cartography and
Geographical Information Systems, 22, 5-16.
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Methodological Issues in
CIS-Based Environmental Justice Research
Jeremy L. Mennis
Department of Geography, The Pennsylvania State University
302 Walker Building, University Park, PA 16802
Abstract
Research in environmental justice investigates whether certain unempowered segments of the
population, typically minorities and/or the poor, bear a disproportionate burden of environmental
risk. Geographic information systems (GIS) have been used to carry out 'conventional' statistical
approaches to environmental justice research whereby the socioeconomic character of
communities that host environmentally hazardous facilities are compared to non-host
communities. However, methodological issues associated with the conventional approach, such
as scale of analysis, continue to make GIS-based statistical assessments of evidence of
environmental injustice problematic. GIS has the potential to mitigate many of these
methodological problems through mapping/visualization, improved modeling of environmental
risk, multi-scale analysis, and raster surface-based representations of population. The case
study presented in this paper, concerning environmental injustice in the Philadelphia,
Pennsylvania region, demonstrates how raster GIS can facilitate the investigation of the
relationship between the distribution of demographic character and the location of hazardous
facilities across a variety of scales of analysis. This study finds that in the Philadelphia region
there is a clear and predictable relationship between socioeconomic status and proximity to
environmentally hazardous facilities that can be interpreted as evidence of environmental
injustice. By using GIS to create improved representations of population character and
environmental risk, environmental justice research can move beyond the simple statistical
comparison of groups of areal units to the exploration of demographic patterns and their spatial
relationship with the distribution of environmental risk.
Introduction
Research in environmental justice research investigates whether certain unempowered
segments of the population, typically minorities and/or the poor, bear a disproportionate burden
of environmental risk. The recent attention on environmental justice can be traced to the release
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of studies by the U.S. General Accounting Office (GAO, 1983) and the United Church of Christ's
Commission for Racial Justice (CRJ, 1987) which reported evidence of racially-based
discrimination in the locational distribution of environmentally hazardous sites, such as waste
treatment, storage, and disposal facilities. Subsequently, the U.S. Environmental Protection
Agency (EPA) has recognized the need to mitigate racial and economic discrimination in the
siting of environmentally hazardous facilities (EPA, 1992).
Since these two 'early' studies, much statistically-based research has investigated the issue of
environmental justice at local, regional, and national scales. Some of these studies have
challenged the claims of the landmark CRJ (1987) study as inaccurate and misleading due to
the choice of data and methodology, the Anderton et al. (1994) article being perhaps the most
prominent. However, all spatial statistical analyses of environmental justice necessarily make
some assumptions concerning the data and methodology used in the study. These data and
methodology issues broadly concern two representational themes: 1) the definition and
measurement of environmental risk and 2) the definition and spatial delineation of 'community.'
Many studies, including those on both 'sides' of the environmental justice debate, have used
geographic information systems (GIS) to manage and structure environmental justice analyses.
The benefits of using GIS for environmental justice research are relatively straightforward:
Environmental justice is an inherently spatial (and temporal) issue (i.e. what is the spatial
relationship between the distribution of people and environmental risk) and GIS provides an
efficient environment for the management, analysis, and display of spatial environmental justice
data. However, GIS software and GIS data also adhere to particular models of the real world
that impose representational and methodological constraints and assumptions on the way
environmental justice is understood and therefore analyzed. Many of these methodological
issues lie at the foundation of the dispute over the interpretation of statistical evidence of
environmental injustice.
Unfortunately, the methodological choices made by environmental justice researchers often go
unacknowledged in the interpretation of evidence of environmental injustice. The purpose of this
paper is to describe the methodological issues associated with using GIS in environmental
justice research so that these issues may be brought to the forefront of the environmental
justice debate. While there are no methodological 'solutions' that would create a completely
accurate and objective assessment of environmental justice, there is certainly value in
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incorporating the impacts of the methodological assumptions and constraints into the
interpretation of study results. The remainder of this paper reviews GIS-based environmental
justice research, highlights primary methodological issues, and proposes a novel environmental
justice GIS analysis method that is applied to the Philadelphia, Pennsylvania region as a case
study.
GIS and the 'conventional' approach to environmental justice research
In nearly all statistically oriented environmental justice studies, justice is defined according to
whether the environmentally hazardous facilities in a particular region are spatially distributed in
a socioeconomically equitable versus inequitable manner. This environmental equity approach
to measuring environmental justice generally entails identifying those communities that host
environmentally hazardous facilities (however 'community' may be defined), tallying the racial
and economic character of those host communities, and comparing that socioeconomic
character to those communities in the region that do not host environmentally hazardous
facilities (or to the character of the region at large). Evidence of injustice is then defined as when
communities that host environmentally hazardous facilities have significantly higher rates of
minority and/or poor persons than non-host communities.
This type of analysis is easily implemented in a GIS using U.S. Bureau of the Census
demographic and boundary data, hazardous facility data derived from publicly available U.S.
Environmental Protection Agency (EPA) databases, and basic statistical functions found in most
commercial GIS packages. For example, Glickman et al. (1995) use GIS to examine evidence
of environmental injustice in Allegheny county, Pennsylvania, which includes the city of
Pittsburgh. These authors investigate the spatial relationship between statistically derived
socioeconomic status and proximity to Toxic Release Inventory (TRI) facilities listed in the EPA
TRI database as the indicator of environmental injustice. The TRI database is composed of
manufacturers that are required by law to report to the EPA certain toxic chemicals that they
release to the environment. While the TRI database is certainly not a comprehensive source of
information for a region's environmental risk, as Glickman et al. (1995) note, it is easily
obtainable and is often used in environmental justice investigations.
Glickman et al. (1995) define community using five different spatial delineations: census block
group, census tract, municipality, and half-mile and one-mile distance 'buffers' around each TRI
facility. Averages of census socioeconomic variables for each of these community zone
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schemes were calculated, including percent minority, percent living below the poverty line,
percent unemployed, percent over the age of 65, percent under the age of 5, and other census
variables that indicate socioeconomic status or at risk populations. Glickman et al. (1995) report
mixed, sometimes contrary results concerning evidence of injustice. For instance, when
communities are defined by census block groups or tracts, the percentage of minorities in TRI
host communities is not significantly different than that in non-host communities. However, when
municipalities, a generally larger areal unit than block groups or census tracts, form the basis for
defining community, TRI-host communities have significantly higher proportions of minorities
than non-TRI-host communities.
These results mirror those found in other studies and indicate one of the primary methodological
issues in environmental justice research, the spatial delineation of community and scale of
analysis. The CRJ (1987) study was criticized by Anderton et al. (1994) for using zip codes as
the areal unit of analysis because these authors felt that zip codes are too large to capture the
spatial relationship between socioeconomic status and proximity to hazardous facilities. Instead,
Anderton et al. (1994) use census tracts and find that minorities and the poor are not more likely
than non-minorities and the non-poor to live in a census tract that hosts a hazardous facility.
They therefore conclude that their study finds no evidence of environmental injustice.
Significantly, however, these authors did find a positive relationship between disadvantaged
socioeconomic status and proximity to hazardous facilities within a 2.5 mile radius of hazardous
facilities.
Goldman and Fitton (1994), in a follow-up to the original CRJ (1987) study, note that although
the CRJ (1987) and Anderton et al. (1994) studies reach opposite conclusions about the
evidence of environmental injustice, their statistical results suggest a similar, and somewhat
startling, demographic pattern: 'bands' of socioeconomically disadvantaged persons
surrounding a 'core' of non-socioeconomically disadvantaged persons concentrated around
environmentally hazardous facilities. It is open to debate whether this pattern represents a
typical demographic scenario, or even if it does, whether it is, in fact, evidence of environmental
injustice. However, it is worth noting that the political motivation to 'objectively' demonstrate the
existence or non-existence of environmental equity often subsumes and sabotages the analysis
itself by biasing the interpretation of analytical results (Pulido, 1996) (Anderton et al. (1994)
were funded by Waste Management Incorporated, a waste industry organization).
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GIS innovations in environmental justice research
Other GIS-based environmental justice studies attempt to expand the conventional approach to
the statistical analysis of environmental justice by using the analytical and display capabilities of
GIS. Burke (1993) investigates environmental equity in Los Angeles by using various mapping
and visualization schemes to expose the subtle relationships between race, class, population
density, and the location of TRI facilities. This author finds evidence that "at a given income
level, Hispanics and African-Americans are more likely to be living in close proximity to TRI
facilities than whites or Asians" (Burke, 1993: 50).
Typically, environmental justice studies do not attempt to explicitly define the spatial distribution
of environmental risk as it is an extremely complex task which differs according to type of
facility, type of toxic release, and a host of environmental variables that control the dispersion of
the toxic material through the environment. Instead, most studies simply consider the people in
the 'community' (whether defined by census-based areal unit or distance buffer) that hosts the
hazardous facility to be at risk. Chakraborty and Armstrong (1997) use GIS to improve on the
definition of at risk population by delineating the areas surrounding each toxic facility that are
most likely affected by toxic releases based on a numerical model of toxic dispersion. This
model generates a 'plume' footprint that defines an at risk area within which socioeconomic
variables may be tallied.
Chakraborty and Armstrong (1997) also explore the impact of using different representations of
population data in environmental justice analyses. Usually, population data are represented by
assignment to polygonal areal units. For distance buffer approaches to defining community or at
risk population, polygonal population data in certain GIS packages are considered within the
distance buffer if any portion of the areal unit overlaps with the buffer. Chakraborty and
Armstrong (1997) refer to this method as the polygon containment method. This method may
lead to misleading calculation of with in-buffer population character since the people living within
the overlapping areal unit may in actuality be concentrated in a particular portion of the areal
unit that is not actually within the distance buffer.
Zimmerman (1994) notes that GIS methods can be developed to partition the population data
assigned to an areal unit that is only partially within a distance buffer into inside-the-buffer and
outside-the-buffer portions based on the percentage of the of the areal unit that lies within and
without the distance buffer, respectively. Chakraborty and Armstrong (1997) refer to this method
-------
as the buffer containment method. However, this approach assumes an homogeneous
distribution of population throughout the areal unit. An alternative is to represent population data
as assigned to an areal unit centroid point, called the centroid containment method
(Chakraborty and Armstrong, 1997). If the centroid falls within the distance buffer, the
population data for the entire areal unit represented by that centroid is considered within the
buffer. Again, however, error may occur if the centroid falls within the buffer but the actual
population is concentrated in a portion of the areal unit outside the buffer.
I performed a brief comparative test of each of these population representation methods in an
analysis of the relationship between percent minority and distance to TRI facility in Delaware
county, Pennsylvania.. TRI sites in Delaware county are concentrated in industrial and urban
waterfront areas, many of which have high concentrations of minority populations. Significantly,
I found that the polygon containment and centroid containment methods tended to under-
represent the percentage of minorities living in very close proximity to TRI sites as compared to
the buffer containment method. These two former methods were less sensitive to variation in
demographic character at close proximities to TRI facilities because they tended to incorporate
more distantly located, non-minority populations than the buffer containment method using the
same distance buffer. In other words, percent minority calculations at close proximities were
diluted by the inclusion of a larger area with lower concentration of minority population.
Chakraborty and Armstrong (1997) reported similar findings in their comparison of population
representation methods.
Another very prominent issue in environmental justice research, related to the issue of defining
community, is that of scale of analysis. Scale of analysis concerns both the scope of analysis,
the region that the study covers, and the resolution of analysis, which generally refers to the
choice of areal unit at which demographic data is represented and tallied. For instance, the CRJ
(1987) study was done at the zip code resolution. However, this definition of resolution is
problematic because zip codes (and nearly all census-, or other organization-, based zonation
schemes) vary widely in their areal extent; they are typically much smaller in urban areas than in
rural areas.
This issue of choice of resolution in spatial analysis is associated with what is called the
modifiable areal unit problem (MAUP) in the geographic literature (Openshaw, 1983). The
MAUP refers to the fact that different aggregation and/or zonation schemes for spatial data may
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result in vastly different spatial analysis results. The detrimental impact of the MAUP on the
analysis of census data is well established (Fotheringham and Wong, 1991; Openshaw, 1984).
The difference in results between the CRJ (1987) and Anderton et al. (1994) studies may be
attributed in part to the MAUP.
A number of authors (Anderton et al., 1994; Glickman et al., 1995) argue that there exists an
'appropriate' areal unit of analysis, or that evidence of environmental equity must not vary with
the scale of analysis in order to be regarded as valid. However, simply assuming that there is
such a thing as an 'appropriate' unit of analysis for environmental justice research immediately
violates the principles of the MAUP. Sui (1999) notes that an environmental justice study done
at any one scale or based on one particular areal unit cannot, by definition, produce a reliable
indication of environmental justice or injustice; there is no such thing as the single 'best' or most
'appropriate' scale of analysis in environmental justice research.
A number of authors have suggested that GIS be used to support multi-scale environmental
justice analysis (McMaster et al., 1997; Sui, 1999). I argue that the purpose of multiscale
analysis is not to find the 'best' scale of analysis but to investigate how demographic character
and its spatial relationship with environmentally hazardous facilities varies across scales. This
information may indicate the subtle and complex demographic patterns that lie at the root of the
environmental justice debate. As Been (1995) notes, environmental justice is infinitely more
complex than disproportionate numbers of hazardous facilities being sited in census tracts (or
block groups, municipalities, etc.) with a high percentage of minorities. Rather, environmental
injustice should be viewed as a complex intertwining of various socioeconomic characteristics
distributed in certain spatial patterns. It should be the goal of environmental justice studies to
'uncover' these often 'hidden' patterns that are embedded in the social and environmental data
that is available.
A case study: environmental injustice in the Philadelphia, Pennsylvania region
Nearly all GIS approaches to environmental justice research have been vector- (as opposed to
raster-) based because most commercial GIS are vector-based (although there are a growing
number of GIS packages offering raster data handling). In addition, most population and
hazardous facility data are also vector-based. However, raster modeling of population offers
many advantages. Principally, the raster-based approach to representing population allows for
data aggregation to nearly any areal unit, facilitates the exploration of how demographic
-------
character varies across scales, and provides the means to create more informative
visualizations of the distribution of demographic character (Bracken, 1993; Martin and Bracken,
1991).
Here, I describe a combined vector-raster analysis of environmental justice in southeast
Pennsylvania which encompasses the city of Philadelphia (which is identical to Philadelphia
county) and its four closest counties in Pennsylvania: Bucks, Delaware, Chester, and
Montgomery. The goal of this study is to understand the distribution of socioeconomic character
and its spatial relationship to environmentally hazardous facilities. I hypothesize that
socioeconomic character has a strong relationship with proximity to hazardous facilities; in other
words, the socioeconomic character of a location can be predicted as a function of distance to a
hazardous facility. I test this hypothesis by modeling population as a raster surface. This allows
for demographic variables that indicate population character to be tallied within a series of
distance buffers generated from the hazardous facility locations. Regression is then used to test
the strength of the relationship between socioeconomic character and distance to hazardous
facility.
Three demographic variables that are often used in environmental justice analyses are used to
indicate socioeconomic status in this study: number of minorities, number of people living below
the poverty line, and number of people over the age of 25 with a bachelors or graduate degree.
These population data were acquired from the U.S. Bureau of the Census at the block group
level. Data on facilities that store or release toxic materials in the Philadelphia region were
acquired from EPA databases including sites listed in the TRI database as well as treatment,
storage, and disposal (TSD) facility sites listed in the Biennial Reporting System (BRS)
database. Procedures for improving the locational accuracy of these hazardous facility sites and
eliminating redundant database listings were followed according to Scott et al. (1997).
A variety of procedures for generating population surfaces from areal unit demographic data
have been proposed including areal weighting (Flowerdew et al., 1991), interpolation from areal
unit centroids (Bracken and Martin, 1989), and the use of remote sensing imagery and
dasymetric mapping (Langford and Unwin, 1994). Dasymetric mapping is a technique that uses
ancillary data to redistribute mapped thematic data in a more accurate and logical way. It is
used here to improve upon the methods of population data representation that are typically used
in environmental justice research. The dasymetric mapping/raster surface generation method
-------
described here is a variation on the method described by Langford and Unwin (1994) and uses
urban density classification data derived from satellite remote sensing to redistribute population
within the original block group data boundaries. This procedure was carried out using ArcView
GIS by Environmental Systems Research Institute (ESRI), Inc.
Urban density data for Pennsylvania were acquired from the Environmental Resources
Research Institute (ERRI) at the Pennsylvania State University. These data were
photointerpreted from Landsat Thematic Mapper (TM) imagery overlaid with a road network to
produce a polygon coverage that partitions the state into areas of high density urban, low
density urban, and non-urban. Note that 'density' in this case refers to the degree of
urbanization (i.e. development), not population density. While degree of urbanization is by no
means a perfect proxy for population distribution (Forster, 1985), its utility in modeling
population has been demonstrated in a variety of contexts (Langford et al., 1991; Mesev, 1999).
The urban density classification data were converted from vector to raster format with a grid cell
size of 100 meters. This resolution was chosen because it meets the analytical requirements
and yet is not so fine that it interferes unduly with processing time. Each grid cell was assigned
a population value according to three factors: the population of its host block group, the
population density of its urban density classification (derived from empirical measurement), and
the percentage of the area of the host block group occupied by its urban density classification.
This procedure preserves what Tobler (1979) referred to as the pycnophylactic property:
summing the population for all the grid cells within any block group produces the same
population figure as that originally assigned to that block group. The raster surface generation
calculations were carried out primarily in the ArcView GIS Tables module and can be described
mathematically as:
PGCu.c.b = (PCTu.c.b * PBGb) I GCu.b
Where:
PGCu.c.b = Population assigned to one grid cell with urban density
classification u, in county c, and in block group b
PCTu.c.b = Percent of population assigned to urban density
classification u, in county c, and in block group b
-------
GC
u.b
PBG,
= Number of grid cells (area in 10,000 sq. meter units) of
urban density classification u in block group b
= Population of block group b
Each demographic variable was distributed homogeneously according to the distribution of the
total population for each block group. Surfaces of percent minority, percent living below the
poverty line, and percent over the age of 25 with a bachelors or graduate degree were created
by dividing the 'count' grids for each of these variables by the grid of total population. For a
more thorough description of this areal interpolation technique see Mennis (forthcoming).
Distance buffers around each hazardous facility were created that described the area within 500
meters of a hazardous facility, within 1000 meters, and so on up to 10,000 meters, which
encompasses 99.9% of the total population. Percent minority, percent living below the poverty
line, and percent over the age of 25 with a bachelors or graduate degree were then tallied within
each of these distance buffers. Cumulative tallies determine these variables within 500 meters
of a hazardous facility, within 100 meters, within 1500 meters, etc. while zone tallies determine
these variables within 500 to 1000 meters of a hazardous facility, within 1000 to 1500 meters,
and so on.
*
_c
1 °-29
o
Q_
0 25 -
Percent Minority by Cumulative Distance to Toxic Site
*•— *
\
V
*^*"*^»-,
""-*-«-»--— »-]
0 2 4 6 8 10
Cum ulative Distance to Toxic Site (km )
o
•E
£
Percent Minority by Distance to Toxic Site Zone
* *
«»«•*
*
»»**»«*«»*!
024 6 8 10
Zone Distance to Toxic Site (km )
Figure 1. The relationship between percent
minority and cumulative distance to
hazardous site (top) and zone distance to
hazardous site.
The relationship between presence of minorities
and distance to hazardous facilities is presented in
figure 1. As distance to hazardous facilities
increases, percent minority decreases, percent
living below the poverty line decreases (not shown),
and percent over age 25 with a bachelors or
graduate degree increases (not shown). The break
in slope at approximately 5000 meters, evident in
the graphs of all the variables, is related to the fact
that 92.0% of the total population and 98.1% of all
minorities live within 5000 meters of a hazardous
facility.
10
-------
Regression tests that predicted percent minority, percent living below the poverty line, and
percent over the age of 25 with a bachelors or graduate degree based on cumulative distance to
hazardous site up to 5000 meters yielded R2 values of 0.886, 0.907, and 0.926, respectively. R2
values for these same variables, but predicted by zone distance to hazardous site up to 5000
meters, yielded values of 0.688, 0.886, and 0.979, respectively. All results were significant at
the 0.001 level. Multiple stepwise regression with cumulative distance to hazardous facility up to
5000 meters as the dependent variable and the three demographic variables as independent
variables excluded percent minority and percent living below the poverty line and included
percent over the age of 25 with a bachelors or graduate degree to yield an R2 of 0.926
(significant at the 0.001 level). A similar test that predicted zone distance to hazardous site up to
5000 meters excluded percent living below the poverty line and included percent minority and
percent over the age of 25 with a bachelors or graduate degree to yield an R2 of 0.987
(significant at the 0.001 level). Clearly, the poor, minorities, and the lesser educated tend to live
in closer proximity to hazardous facilities than the non-poor, non-minorities, and the educated.
These results conjure an image in which each hazardous facility is surrounded by poor,
uneducated minorities and that gradually this pattern gives way to wealthier, educated non-
minorities as distance to the hazardous facility increases. However, maps that depict the
distribution of these variables overlaid with the locations of hazardous facilities demonstrate that
this is not at all the case (e.g. figure 2 which shows areas with percent minority greater than the
regional mean of 26%). There are, rather, various 'clusters' of hazardous facilities that appear to
correspond to a variety of interrelated historic, cultural, and infrastructure factors. For instance,
many hazardous facilities stretch along the Delaware and Schuylkill Rivers while others are
clustered around population centers.
TRI and TSD Sites and Percent
Minority in the Philadelphia Region
TRIand
TSD
Figure 2. The location of hazardous
facilities relative to percent minority.
This apparent, but in fact false, discrepancy
between statistical and visual summation can be
attributed to the difference between the
measurement of percent and density of
demographic character. For example, while there
are areas outside Philadelphia with high percent
minority, nearly all minorities in the Philadelphia
region are clustered within certain neighborhoods
of Philadelphia (figure 3). While non-minorities are
11
-------
Density of Minorities in
the Philadelphia Region
Density of Non-minorities
in the Philadelphia Region
Figure 3. Density of minorities and non-
minorities in the Philadelphia region.
also clustered around Philadelphia, they are much
less concentrated in specific areas. The same is
true with percent and density of people living below
the poverty line. Concerning education, it appears
that while higher education attainment is
concentrated in suburban areas, hazardous
facilities are concentrated primarily in urban areas
and secondarily in rural areas.
So while hazardous facilities are not necessarily
concentrated in poor, uneducated, and minority
portions of the greater Philadelphia region, these
portions of the population are concentrated in one
particular area, the city of Philadelphia. Because the
city of Philadelphia is home to one of many clusters
of hazardous facilities, nearly all those of
unempowered socioeconomic status are in
relatively close proximity to hazardous facilities
compared to other persons of the Philadelphia
region. However, there are many non-poor, non-
minorities, and educated persons who are also in
relatively close proximity to hazardous facilities.
Further statistical analysis and mapping/visualization may reveal other demographic/hazardous
facility patterns. For example, spatial autocorrelation measures may indicate the degree of
socioeconomic regionalization at a variety of scales. Cluster analysis and point pattern analysis
of the hazardous facility data may show a statistical relationship between demographic
character and spatial clusters of facilities. Choropleth and bivariate mapping schemes, as well
as cartograms, can be used to further visually investigate the demographic patterns embedded
in the data.
12
-------
Conclusion
This paper is intended as both a caution and an encouragement for the use of GIS in
environmental justice research. On the caution side, the data representations that are
embedded within GIS present potential pitfalls to researchers who do not explicitly acknowledge
how GIS data and methods of analysis can control analytical results. While the issue of making
explicit an investigation's analytical assumptions exists for nearly any analysis, whether using
GIS or not, the ease of use of many GIS often serves to make this issue transparent to the
casual user. On the encouragement side, however, GIS provides an environment for creating
new and innovative ways of investigating environmental justice. The use of raster
representations of population and environmental risk and the use of advanced spatial statistical
and visualization techniques hold particular promise in moving environmental justice research
forward towards a more exploratory, pattern recognition approach.
13
-------
References
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15
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Pollution Exposure Index Model
Measures Airborne Pollutants in National Forests
Mike Miller, CH2M HILL
Corvallis, OR
CH2M HILL developed the Pollution Exposure Index (PEI) model for the U.S. Forest Service to
measure the exposure of sites located in National Forests to a variety of airborne pollutants.
The exposure is calculated for user-specified receptor sites based on pollutant emission rates of
sources, seasonal wind frequencies, terrain landforms, and distances between the receptors
and pollutant sites.
The source data, which encompasses most of the eastern United States, was pre-processed
into a standardized GIS structure for PEI modeling. The data includes; Digital Elevation Model
(DEM) and USFS boundary data supplied by the Forest Service, meteorological wind
frequencies acquired from the EPA Support Center for Regulatory Air Models (SCRAM) bulletin
board system, and pollutant information derived from the EPA Aerometric Information Retrieval
System (AIRS) and National Acid Precipitation Assessment Program (NAPAP) inventories.
The PEI model was developed with ESRI's ArcView 3.0 desktop GIS mapping software using its
object oriented Avenue programming language. It also accesses the grid-based Spatial Analyst
extension package. The model was developed on an NT computer with a Pentium 586/133
processor and 32 mbs of RAM. A large color monitor, at least 14 inches, is recommended.
The PEI model was conceived by and developed under the direction of Bill Jackson of the
USFS, Region 8. The model was originally developed as a predictive tool to assess seasonal
impacts of controlled forest burns. Recently, it was used for an Ozark Mountain Regional
assessment of sulfur, nitrogen and particulate matter. Kent Norville from CH2M HILL's Portland,
OR office helped design the model and Mike Miller from CH2M HILL Corvallis, OR programmed
the model.
This paper describes the pre-processing procedures and the basic components of the PEI
model.
I. Pre-Process Source Data
Source data was acquired for the study area from various sources and pre-processed as GIS
data for PEI modeling. The source data are listed as follows:
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1. U.S. National Forests in Southern Appalachian Mountains
2. Elevation OEMs
3. Meteorological Stations
4. Regional AIRS Pollutants
5. County Pollutants
6. State Boundaries
The source data were pre-processed as Arc/Info data coverages. The pre-processing tasks
include data conversion, modification, and standardization. All the graphic data is standardized
to Albers equal area projection, Clark 1866 Spheroid, NAD27 datum measured in single
precision meters. The Albers projection parameters are:
1st latitude = 29 degrees 30 minutes
2nd latitude = 45 degrees 30 minutes
Central meridian = -96 degrees
Latitude origin = 23 degrees
Finally, the coverages were translated as shapefiles because ArcView processes them much
faster than coverages.
The remainder of this section contains detailed descriptions of how the source data was pre-
processed.
/. 1 National Forests and Southern Appalachia Assessment Area
The USFS provided Arc/Info coverages of the Southern Appalachia Assessment Area and
seven National Forests contained in the Assessment Area. The National Forests include
Cherokee, Jefferson, George Washington, Pisgah, Nantahala, Chattahoochee, and Sumter.
Both coverages were translated from latitude/longitude decimal degrees to the Albers
projection, then spatially indexed and translated as ArcView shapefiles.
In addition, 250 and 500 kilometer buffer coverages were generated from the Assessment Area
coverage and used to define the maximum extent of the pollutant source data and the study
area, respectively.
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1.2 Elevation OEMs
An elevation DEM file for the study area was purchased from the National Geophysical Data
Center. The file contains sample points every 30 seconds or 1 kilometer (1,030 meters)
referenced to latitude/longitude decimal degrees and elevations measured in feet. Initially, the
DEM file was converted to the Arc/Info grid format (ASCIIGRID), then projected to Albers feet.
The 1 km grid was used to interpolate elevations for all the meteorological station and pollutant
source points. In addition, it was used to generate a 100 foot contour coverage and shapefile
that is accessed by the PEI model to interpolate receptor elevations and derive the highest
elevations between the pollutant and the receptor sites.
1.3 Meteorological Stations
Wind frequency data is a critical PEI model input. It is required to identify prevailing winds
between pollutant sources and receptors. Wind frequencies are routinely measured and
recorded at various National Weather Service (NWS) meteorological stations, generally located
at airports. The latitude/longitude coordinates of the stations are also known.
The wind frequency data were retrieved from the Support Center for Regulatory Air Models
(SCRAM) section of the US EPA Technology Transfer Network (TTN) bulletin board system and
pre-processed. Two ASCII files were developed from the SCRAM data. One file contains
information related to the location of the stations and their names. The second lists the seasonal
wind frequency data from 16 directions (every 22.5 degrees) for each station. Initially, the first
file was converted as an Arc/Info point coverage and projected from latitude/longitude to Albers
coordinates. The wind frequencies data in the second file was copied in an Info database table.
The station point features and wind frequency table are linked for modeling by station numbers.
Appendix I - Meteorological Stations lists the fields in the station locations and wind frequency
tables.
Eventually, the elevations of the meteorological stations were interpolated from the 1 kilometer
DEM grid.
Meteorological station areas-of-influence (AOIs) were delineated representing homogeneous
landforms throughout the PEI study area. The process involved several steps. First, a Thiessen
program was run to apportion the station points into regions. The result was a polygon coverage
of regions, each containing a single station. Each region has the unique property that any
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location within a region is closer to the region's station than any other station. Next a plot of the
stations, station regions, and topographic relief was produced. The Thiessen generated region
boundaries were compared to the relief and the region boundary modifications were drafted on
the plot to reflect areas of homogeneous landforms. The boundaries of the region coverage
were revised as drafted, resulting in the station AOI coverage which is used for PEI modeling.
Finally, the coverages were translated as ArcView shapefiles and spatially indexed for
modeling. See Appendix II - Meteorological Station Areas of Influence for description of
database.
1.4 Pollutant Data
The PEI model has been developed to measure the emissions of six pollutant types, which
consist of carbon monoxides (CO), nitrous oxides (NOx), sulfur dioxides (SO2), volatile organic
compounds (VOCs), particulate matter (PM10), and lead (PB).
The pollutant information was derived from the U.S. EPA Aerometic Information Retrieval
System (AIRS) database and reformatted by CH2M HILL for model input. Note that although the
EPA maintains the database, each individual state agency can change, update, or delete
individual source entries. An ASCII file was prepared for each pollutant type from the AIRS data.
It contains information related to the locations, names, and emission rates of the pollutants. The
latitude/longitude coordinate locations were used to generate Arc/Info point coverages, which
were subsequently projected to Albers coordinates. Eventually, the elevations of the pollutant
sites were interpolated using the 1 km DEM grid. See Appendix III - EPA AIRS Pollutant Source
Points for a description of the AIRS data.
In addition, County Area and Point CO, NOx, SO2, VOC, and PM10 pollutant data were
developed from EPA's National Acid Precipitation Assessment Program (NAPAP) inventories.
The Area sources refer to roads, landfills, etc, while the Points are usually industrial facilities. A
County shapefile, comprised of points located near the centers of their respective Counties, was
coded with a unique state/county identifier which is related to the County Area and Point
databases containing the pollutant type, category sources, emission rates, and other attributes.
The elevations of the County points were also interpolated using the 1 km DEM grid. Appendix
IV - National Acid Precipitation Assessment Program describes the County source data and
Appendix V - NAPAP County Area and Point Pollutant Tables lists the database structures
required for PEI modeling.
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Points representing each County were generated, transformed to Albers, spatially indexed, and
translated as ArcView shapefiles. The County Area and Point attribute data was also translated
as dBASE files. The County points are related to their respective Area and Point attributes by
State name and County FIP identifiers.
1.5 State and County Boundaries
The State boundaries, which serve as a display reference, cover most of the eastern United
States. The coverage delineates a total of 27 States and the District of Columbia. It was derived
from ESRI's ArcUSA data source.
The States coverages were transformed to Albers, then translated as ArcView shapefiles for
modeling.
II. PEI Model
The PEI Model is comprised of ArcView display and analysis scripts written in the Avenue object
oriented programming language. It is run using the PEI Model pulldown menu, illustrated below.
To execute the model, simply select the menu components, from top to bottom, and respond to
queries.
PEI Model Pulldown Menu:
Create Receptors
Add Sites
Delete Sites
Interpolate Elev
Identify Source Data
PEI Model
Change Threshold
Print Variables
About PEI Model
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The following describes the basic functional components of the model from startup to shutdown.
Except for model startup, the descriptions directly relate to the pulldown menu components and
include general use guidelines, functionality overview, view, theme and variable lists, and scripts
names. There are one or more scripts associated with each menu component.
11.1 PEI Model Startup
The PEI Model is launched when the ArcView PEI3.APR project is activated. Initially, the U.S.
Forest Service symbol and a brief overview of the model are displayed. When the user is ready
to start modeling, the domain view is opened displaying the State and Southern Appalachian
National Forest boundaries for the PEI study area, which includes most of the eastern half of the
U.S. The meteorological stations areas-of-influence theme is also added to the domain view.
Next, the default values are set for variables required for modeling. Global variables identifying
pathnames to the GIS data, scripts, projects, and other file subdirectories in the pei3 directory
are defined. Then, default data variables including; the season, mixing heights, pollutant type
emission rate thresholds, receptor/pollutant search radius, terrain evaluation mode, and the PEI
threshold percentage, are set. Mixing heights, directly related to the seasons, are automatically
set for the specified season.
The global variable names and their default values are listed as follows:
Variable Description
Directory Pathnames:
PEI home directory
Shape file directory
ArcView projects directory
Receptors directory
ArcView scripts directory
Text directory
Database tables directory
Temporary work directory
Elevation grid file
Data Variables:
Season
Annual mixing height
Winter mixing height
Spring mixing height
Summer mixing height
Fall mixing height
Variable Name
_peiDir
_peiShps
_peiProjs
_peiRcps
_peiScrp
_peiText
_peiTab
_peiTemp
_peiElevGd
_Season
_mhAnn
_mhWin
_mhSpr
_mhSum
_mhAut
Default Value
d:\data\pei3
_peidir\shapes
_peidir\projs
_peidir\receptor
_peidir\scripts
_peidir\text
_peidir\tables
_peidir\temp
_peidir\grid\ele1km
Annual
2689 feet
2497 feet
3300 feet
281 8 feet
2 136 feet
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Carbon monoxide emission threshold _meCO 100 tons/year
Nitrous oxides emission threshold _meNOx 40 tons/year
Sulfur dioxide emission threshold _meSO2 40 tons/year
Volatile organic compound emission _meVOC 40 tons/year
threshold
Particulate matter emission threshold _mePM10 15 tons/year
Lead emission threshold _mePB 0.6 tons/year
Receptor/Pollutant search radius _srchRad (domBufDis) 50 kilometers
Terrain evaluation mode _ternMod complex
PEI threshold percentage _peiThrsh 100
Windrose Directions _winDirs 16
Windrose degrees/direction winDegs 22.5
Script:
- pei.1 Startup
The PEI Model pulldown menu is then accessed to create the receptors, identify pollutant data
and execute the PEI model. Descriptions of functions and scripts follow.
//.2 Add and Delete Receptors
The receptors that represent the sites for which PEIs will be calculated must first be sited in the
PEI study area. Initially, Create Receptors is selected from the pulldown menu which prompts
the user to specify a new or existing receptor file name.
If a new receptor file is entered, the user may add receptors for an area or enter discrete point
by selecting the Add Sites option from the PEI pulldown menu. To enter receptors for an area,
the cursor is used to 'digitize' points outlining the area in the domain view (eastern U.S.). The
area is subsequently filled with receptors at a user-specified increment. Multiple areas may be
populated with receptors for a single receptor file. Discrete points may also be added by double-
clicking on the site with the cursor. Receptor sites may be deleted using similar techniques after
selecting the Delete Sites pulldown menu option. After the receptors are generated or deleted
the model refreshes the domain view.
If an existing file name is specified, the receptors will be saved, but all related tables and PEI
calculations will be deleted. The user may then access the PEI pulldown menu to identify if new
receptors are to be added or existing receptors are to be deleted from the file using the Add
Sites or Delete Sites options.
Scripts:
-------
- pei.SlnitReceptor
- pei.4AddPolyPts
- pei.4AddPolyPtsMenu
- pei.4DelPolyPts
- pei.4DelPolyPtsMenu
11.3 Interpolate Receptor Elevations
After the receptors are generated, the Code Elevations entry in the PEI pulldown menu must be
selected to interpolate elevations for each receptor from the one kilometer grid file. The
interpolation is actually calculated by the Spatial Analyst ArcView extension. After the receptors
have been assigned elevations, the theme will be enabled for display in the PEI study area
view.
Scripts include:
- pei.SReceptorsElev
11.4 Identify Pollutant Types and Sources
Identifying the pollutant types and sources and other related operations for PEI evaluation are
initiated by selecting the Identify Source Data from the PEI pulldown menu. This first prompts
the user to specify a distance that will be used as a search radius. Note that 50 kilometers is the
default. The radius is measured from the receptors to select the pollutants that will be used for
modeling.
The user then specifies the pollutant types for modeling. The types include carbon monoxides
(CO), nitrous oxides (NOx), sulfur dioxides (SO2), volatile organic compounds (VOC),
particulate matter (PM10), and lead (PB). The pollutant types selected depend on the sources
that are subsequently identified. The sources include the EPA AIRS and County Area and Point
data. EPA AIRS is contains all six pollutant types, however there is no PB data in the County
Area and Point sources. After the pollutant types are identified, the minimum pollutant emission
thresholds may be increased, if desired. Next, pollutant source(s) are selected. These include
AIRS, County Area and Point, or combinations of these sources. Any combination may be
specified except AIRS and County Point data, which are mutually exclusive. At this point, a
series of operations are executed to create shapefiles and themes for the specified pollutant
types and sources.
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AIRS data is relatively simple to develop since the site locations and related attribute codes
reside in preprocessed shape files for each pollutant type. AIRS sites that are within the search
radius of the receptors and of the specified pollutant type are generated as shapefile(s) for
subsequent processing.
The County source data is more complex to process for two reasons. First, the preprocessed
County sites are in a shape file and the descriptive attributes are in separate Area and Point
dBASE files. Second, not only are the County data identified by pollutant type, but it is further
subdivided into pollutant source categories. The following briefly describes how the County data
is handled.
Initially, the County sites within the receptor search radius are identified. Next, the Area and
Point attribute records within the search radius and of the specified pollutant type(s) are
selected. The data may be further refined by selecting pollutant type categories. The Area
and/or Point attribute data is appended by pollutant type and summarized by State/County
emission rates. That is, if there are several records describing the same County site for a
particular pollutant type, then their emission rates are summed in one record for modeling. For
example, if the Metals Processing and Other Industrial Processes categories were selected for
CO evaluation and information for both were referenced for some Counties, then the emission
rates of these will be summed for each County. Finally the summarized attributes are joined to
the pollutant type receptor site shapefiles and theme(s) are generated.
The AIRS and County pollutant type files are also referenced with meteorological station
identifiers. Pollutants contained in the station areas of influence are assigned that areas station
identifier. This is eventually used to develop wind frequencies for each receptor/pollutant pair.
Finally, the common pollutant type theme(s) derived from various sources as individual pollutant
type theme(s) are merged. For example, SO2 extracted as themes from the AIRS and County
Area sources are merged as one SO2 theme for PEI evaluation.
An additional attribute field called 'emissdup' is added to the pollutant database tables. The user
may copy the original emission rates into the new field using ArcView's Table document Field
Calculator. The user may then modify the emission rates, using ArcView tools, and maintain a
copy of the original source values in the 'emissdup' field.
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The County Area and Point dbf database templates are attached.
Scripts:
- pei.2SourceData
- pei.2CountySum
- pei.2Category
- pei.2MetAoi
- pei.2MergePolThm
11.5 PEI Model
The PEI model scripts performs several complex functions. First, it creates database tables for
each pollutant type containing the receptors and pollutant sites that are located within the
specified search radius. The component data comprised of; emission rates, terrain landform
factors, wind frequencies, and distances between the receptors are then calculated for each
receptor/pollutant pair. The PEIs are then calculated for each receptor/pollutant pair by pollutant
type from the component data. Finally, the PEIs of the pairs are summed for each receptor and
entered in the receptor database table, by pollutant type.
Before the PEIs are actual modeled global variable defaults representing the season, mixing-
height, terrain factor and PEI threshold percentage may be changed.
A detailed description of the PEI module is provided below. The basic components and
associated variables used to calculate the PEI are:
Emission rate = Q
Terrain landform factor = T
Wind frequency = F
Receptor/Pollutant distance = D
Pollution Exposure Index = PEI
Initially, database tables are developed, by pollutant type, containing receptor/pollutant pairs.
The pairings are based on the user-specified search radius. Those pollutants that fall within the
search radius of the receptor are paired with the receptor in the receptor/pollutant database
table. After the receptor/pollutant pairs are established, the component data are calculated.
First, the pollutant emission rates (Q) are copied from the pollutant type database tables to the
receptor/pollutant tables. Emission rates less than the rate threshold will not be evaluated. Next,
10
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the distances between the pollutants and receptors (D), measured in kilometers, are calculated
and also entered in the receptor/pollutant tables. If the distance is less than 100 meters, then it
is set to 100.
The seasonal wind frequencies (F) are also derived and entered into the tables for each
receptor/pollutant pair. The process involves calculating the azimuthal angles from the receptors
to the pollutants, then interpreting the seasonal wind frequencies for the azimuthal angles from
the meteorological station wind frequency database tables. Recall that station identifiers are
assigned to the pollutants and are used to derive the appropriate wind frequency data. Finally,
the wind frequencies are copied to the receptor/pollutant tables.
Terrain landform factors (T) are also interpreted for each receptor/pollutant pair. Two alternative
methods are used to derive the terrain factors depending on the evaluation mode. If the
evaluation mode is simple, then the terrain factors of all the receptor/pollutant pairs are set to
one. If the evaluation mode is complex, the default, then they are calculated from mixing height,
pollutant and receptor elevations. The equations used to interpret the complex terrain landforms
are described as follows:
1. Ee = max(Er,Em), where E = elevation
r = receptor site
m = highest point between receptor and pollutant sites
- Spatial Analyst to interpolate receptor elevations (Er) from the 5 km elevation grid . Note
that pollutant site elevations (Es) are pre-processed.
- Derive the highest elevation (Em) between each pollutant and receptor (Er) sites. The
model draws virtual lines between the locations of the receptor/pollutant pairs that cross
100 foot elevation contours. The highest contour elevation that is intersected is recorded
as Em.
- The receptor elevation (Er) and highest elevation between receptor/pollutant (Em) are
compared to identify the higher elevation (Ee).
2. T = Mh/(Mh + (Ee-Es)), where T = landform factor
Mh = mixing height
11
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E = elevation
e = highest elevation (above)
s = pollutant site
- Subtract elevations of pollutant site (Es) from highest point between receptor/pollutant
(Ee) and add to mixing height. Divide the result into the mixing height to calculate the
terrain landform factors (T).
After the basic terrain factor (T), wind frequency (F), emission rate (Q), and distances between
receptors and pollutants (D) modeling components are derived for the receptor/pollutants, the
PEIs are calculated and recorded for each pair in the receptor/pollutant database table. The
equation used to calculate the PEI by pollutant type for each receptor/pollutant pair is:
3. PEIrp = Trs* Frs * Qp/ Drs, where PEI = pollutant exposure index measure
R = receptor site
s = pollutant site
p = pollutant type
In addition, the percentage each pollutant contributes to the total PEI for each receptor is
calculated and entered into the table. See Appendix VI - Receptor/Pollutant Tables and
Appendix VII - Receptor Tables for descriptions of receptor/pollutant and receptor databases.
Finally, the PEIs are calculated for each receptor by pollutant type from the data in the
receptor/pollutant pair tables. The PEI of each receptor (r) for pollutant (p) is calculated by
summing the emission rates Q, distance between receptor/pollutants D, wind frequencies F, and
terrain factor T for each receptor/pollutant pair using the equation:
PEIrp = [SUM (Trs * Frs * Qsp / Drs)]
The receptor PEIs may be re-calculated by changing the PEI threshold percentage. Only the
PEIs of the pollutants contributing less than the PEI threshold percentage are used to sum the
PEI for each receptor. Those above the threshold are ignored. Therefore, the default of 100
percent will result in the summation of all receptor/pollutant pair PEIs to determine the receptor
PEIs. This option will change only the receptor PEI summations, not the individual
receptor/pollutant table entries. Ultimately, the receptor identifiers, their elevations, and adjusted
PEI for the modeled pollutant types are recorded in the receptor database table.
12
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Scripts:
- pei.6Model
- pei.6ModThresh
11.6 PEI Contours
The Spatial Analyst ArcView extension is used to interpret isoline contours from the PEI values
calculated for each receptor for a particular pollutant. For best results, it is recommended that
the inverse distance weight (IDW) method of interpolation be applied using a grid cell size of
approximately 75 meters.
11.7 Shutdown
The shutdown module saves the receptor/pollutant pairs database table, the receptor file, and
the contour files. The variable settings and pollutant sources used to produce the receptor file
are recorded in the pei/text/variable.txt text file. The script also deletes extraneous polcoX,
modX, and CatAre dBASE files, where X is the receptor file number. Finally, the module re-
initializes the PEI project model.
Scripts:
- pei.12Shutdown
11.8 Print Variables
A text file may be generated by the user in the pei/text/Variable.txt file that describes the values
of the global variables when the user selects the Print Variable item from the PEI Model
pulldown menu and at the time the model is shutdown.
11.9 About PEI Model
This opens a file with a descriptive overview of the PEI model and each of its scripts.
13
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Appendix I
Meteorological Stations DBF Database
There are 108 meteorological stations derived from the EPA SCRAM bulletin board, which are
used to interpret wind frequencies for the PEI model. The attributes are described in two dbf
database files. The first, MET.DBF, contains information related to the location of the stations
and their names. The second, MET.LUT, lists the seasonal wind frequency data from 16
directions (every 22.5 degrees) for each station. The LUT contains 1728 records.
The elevations for each site were interpolated during pre-processing from the 1 kilometer
elevation grid file.
File
Name
Field
Name
Field
Description
MET.DBF
MET.LUT
STATION_
LONGITUDE
LATITUDE
REGION
STATE
NAME
ELEVATION
STATION_
DIRECTION
WINWNDHRS
SPRWNDHRS
SUMWNDHRS
FALWNDHRS
ANNWNDHRS
Station number
Decimal degree longitude
Decimal degree latitude
Region identifier
State abbreviation
Station name
Station elevation
Station number
Wind direction degrees
Winter hours of wind
Spring hours of wind
Summer hours of wind
Fall hours of wind
Annual hours of wind
14
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Appendix II
Meteorological Station Areas of Influence DBF Database
There are 133 meteorological station areas of influence.
File
Name
Field
Name
Field
Description
METAOI.SHP
STATION_
ELEVATION
NAME
Station number
Station elevation
Station Name
15
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Appendix III
EPA AIRS Pollutant Source Points DBF Database
Two sets of database files will be generated for each of the six pollutant type shape files,
comprised of CO, NOx, SO2, VOC, PM10, and PB. The first file, polltype.SHP, identifies the
source plant identifier, name, and emission rates, while the second, polltype.lut, contains the site
locations and miscellaneous information. There are a total of 8938 pollutant sources, including
931 carbon monoxide (CO), 2180 nitrous oxides (NOx), 2164 sulfuric oxide (SO2), 1459 volatile
organic compound (VOC), 2180 particulate matter (PM10), and 24 lead (PB) sites.
Note that there are no emission years identified in database for NO2. The station number field is
populated during the execution of the model.
The database file structure for the AIRS pollutant types follows:
File
Name
Field
Name
Field
Description
polltype.SHP
polltype.LUT
SOURCEJD
PLANT
EMISSION_RATE
STATION_
ELEVATION
SOURCE-ID
LONGITUDE
LATITUDE
REGION_
STATE
COUNTY_
ZIP
SIC
YEAR
PROCESS FLAG
Pollutant source identifier
Plant name
Emission rate (tons/yr)
Met station number
Source elevation
Pollutant Source identifier
Decimal degree longitude
Decimal degree latitude
Region number
State abbreviation
County code
Zip code
SIC code (plant type)
Emission year
Data reliability
16
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Appendix IV
National Acid Precipitation Assessment Program Source Data Description
The Countywide area and point source data came from EPA's 1985 National Acid Precipitation
Assessment Program (NAPAP) inventory. These data include Countywide PM, NOx, SO2,
VOC, and CO emission estimates for point and area sources. For each county, there are 14
main categories, which are further broken down into 84 different sub categories. For use in the
PEI model, these sub-categories were grouped into 17 new categories. The 17 new categories
are shown in Table 1.
Table 1. Generalized County-wide Point and Area Source Categories Used in PEI
New Cat Number and Name
i FUEL COMB. ELEC. UTIL.
2 FUEL COMB. INDUSTRIAL
3 FUEL COMB. OTHER
4 CHEMICAL & ALLIED PRODUCT MFC
5 METALS PROCESSING
6 PETROLEUM & RELATED INDUSTRIES
7 OTHER INDUSTRIAL PROCESSES
8 SOLVENT UTILIZATION
9 RESIDENTIAL WOOD & OTHER
10 WASTE DISPOSAL & RECYCLING
11 HIGHWAY VEHICLES
12 OFF-HIGHWAY VEHICLES
13 NATURAL SOURCES
14 MISCELLANEOUS
15 OFF-HIGHWAY OTHER
16 AGRICULTURE & FORESTRY
17 FUGITIVE DUST
The distribution of NAPAP original 84 sub categories to the new categories is shown in Table 2.
These data were provide by EPA in either an ASCII text or Foxpro database format on a state-
by-state level. The data were put into a standard format in an Access database, merged, and
formatted for use in PEI.
17
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Table 2. EPA Point and Area Source Category Distribution
1
2
3
4
5
6
7
Main Category Number and Name
Sub Category Number and Name
FUEL COMB. ELEC. UTIL
1 Coal
2 Oil
3 Gas
4 Other
5 Internal Combustion
FUEL COMB. INDUSTRIAL
1 Coal
2 Oil
3 Gas
4 Other
5 Internal Combustion
FUEL COMB. OTHER
1 Commercial/Institutional Coal
2 Commercial/Institutional Oil
3 Commercial/Institutional Gas
4 Misc. Fuel Comb.(ExceptResid
5 Residential Wood
6 Residential Other
CHEMICAL & ALLIED PRODUCT MFG
1 Organic Chemical Mfg
2 Inorganic Chemical Mfg
3 Polymer & Resin Mfg
4 Agricultural Chemical Mfg
5 Paint, Varnish, Lacquer, Ename
6 Pharmaceutical Mfg
7 Other Chemical Mfg
METALS PROCESSING
1 Non-Ferrous Metals Processing
2 Ferrous Metals Processing
3 Metals Processing NEC
PETROLEUM & RELATED INDUSTRIES
1 Oil & Gas Production
2 Petroleum Refineries &Related
3 Asphalt Manufacturing
OTHER INDUSTRIAL PROCESSES
1 Agriculture, Food, & Kindred
2 Textiles, Leather, & Apparel
3 Wood, Pulp & Paper, &Publish
4 Rubber & Miscellaneous Plastic
5 Mineral Products
6 Machinery Products
7 Electronic Equipment
8 Transportation Equipment
9 Construction
10 Miscellaneous Industrial Proce
New
Category
Number
1
1
1
1
1
2
2
2
2
2
3
3
3
3
9
9
4
4
4
4
4
4
4
5
5
5
6
6
6
7
7
7
7
7
7
7
7
7
7
Main Category Number and Name
Sub Category Number and Name
8 SOLVENT UTILIZATION
1 Degreasing
2 Graphic Arts
3 Dry Cleaning
4 Surface Coating
5 Other Industrial
6 Nonindustrial
7 Solvent Utilization NEC
9 STORAGE & TRANSPORT
1 Bulk Terminals & Plants
2 Petroleum & Petroleum Product
3 Petroleum & Petroleum Product
4 Service Stations: Stage I
5 Service Stations: Stage II
6 Service Stations: Breathing...
7 Organic Chemical Storage
8 Organic Chemical Transport
9 Inorganic Chemical Storage
10 Inorganic Chemical Transport
1 1 Bulk Materials Storage
12 Bulk Materials Transport
10 WASTE DISPOSALS RECYCLING
1 Incineration
2 Open Burning
3 POTW
4 Industrial Waste Water
5 TSDF
6 Landfills
7 Other
11 HIGHWAY VEHICLES
1 Light-Duty Gas Vehicles
2 Light-Duty Gas Trucks
3 Heavy-Duty Gas Vehicles
4 Diesels
12 OFF-HIGHWAY
1 Non-Road Gasoline
2 Non-Road Diesel
3 Aircraft
4 Marine Vessels
5 Railroads
13 NATURAL SOURCES
1 Biogenic
2 Geogenic
3 Miscellaneous
14 MISCELLANEOUS
1 Agriculture & Forestry
2 Other Combustion
3 Catastrophic/Accidental Release
4 Repair Shops
5 Health Services
6 Cooling Towers
7 Fugitive Dust
New
Category
Number
8
8
8
8
8
8
8
6
6
6
6
6
6
4
4
4
4
4
4
10
10
10
10
10
10
10
11
11
11
11
12
12
15
15
15
13
13
13
16
14
14
14
14
14
17
18
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Appendix V
NAPAP County Area and Point Pollutant Tables DBF Database
The County Area and Point pollutant information for all pollutant types are stored in individual
tables that are related to the County point shapefiles. The ACATEGORY and PCATEGORY fields
contain about 37 categories. The values in the Total' field are used to derive the cumulative Area
or Point emission rates for the Counties by pollutant type.
The attribute fields for the point shapefiles and the COUNTY.ARE and .PNT tables are listed
below. The tables are sorted by the stco redefined item. Neither contains PB pollutant source type
records. Note that there are 2626 County points referenced with 89,672 Area records and 18,186
Point records. There are 7986 Area records that identify the total emission rates by pollutant types
for the Counties and 4466 describing the cumulative rates for the Points.
File
Name
Field
Name
Field
Description
COPOLL.SHP
COPOLL.ARE
COPOLL.PNT
STATE
COUNTY_NUM
LATITUDE
LONGITUDE
REGION_
STATION_
ELEVATION
STCO
SOURCEJD
POLL_TYPE
STATE
COUNTY_NUM
CATEGORY
EMISSION_R
POLSTCO
STCO
SOURCEJD
POLL_TYPE
STATE
COUNTY_NUM
CATEGORY
EMISSION_R
POLSTCO
STCO
State abbreviation
County fips code
Decimal degree latitude
Decimal degree longitude
Region number
Met station number
Source elevation
State and County
Pollutant source identifier
Pollutant type identifier
State abbreviation
County fips code
Category type
Area emission rate
Pollutant, state and county
State and County
Pollutant source identifier
Pollutant type identifier
State abbreviation
County fips code
Category type
Point emission rate
Pollutant, state and county
State and Count
19
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Appendix VI
Receptor/Pollutant Tables DBF Database
One pollutant source/receptor database file is generate for each pollutant source type containing
the following information.
File Field Maximum Type
Name Name Char Width I,C,N
RC#type.DBF RCPID 5 1
SRCID 5 C
RCPELEV
SRCELEV
DISTANCE
AZIMUTH
EMISSION
WINDFRQ
EM
T
PEI
PEI PCEN
T
Decimals
(inc.)
0
0
2
2
3
2
Field
Description
Receptor identifier
Pollutant point identifier *
Receptor elevation
Pollutant elevation
Receptor/Pollutant
Distance
Angle - receptor to
pollutant
Emission rate
Wind Frequency
Highest elev between
recp/poll
Terrain landform factor
recp/poll pair PEI
Percent PEI contribution of
recp
* For the AIRs data the srcid is
the srcid is the appended state
srcid equal to AL102).
a unique source-id, however for the County Area and Point data
abbreviation and the County number (e.g. AL and 102 makes the
20
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Appendix VII
Receptor Tables Info Database
File Field Field
Name Name Description
RECnum.SHPRcpid 5 Receptor identifier
ELEVATION Receptor elevation
PEI_TYPE PEI by pollutant type
21
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GIS Uncertainty and Policy: Where Do We Draw the 25-Inch Line?
James E. Mitchell
Institute for Environmental Studies, Louisiana State University, Baton Rouge, Louisiana
Abstract
The growing availability of improved hardware and soft-
ware for geographic information systems (GIS) has out-
stripped most users' ability to identify and represent
uncertainty in the available data. In practice, the prolif-
eration and compounding of errors and uncertainty in-
crease as information becomes more easily handled
and combined from different sources.
Various stages of GIS database development and analy-
sis generate different forms and amounts of error and
uncertainty. In most cases, inherent uncertainty within
source data is simply ignored and its nature eventually
lost through subsequent processing. Both the location
of features and their attributes can include error and
uncertainty. By the time decision-makers receive mapped
information, it is typically represented as correctly lo-
cated and attributed.
The use of weather and climate information provided by
the National Climatic Data Center (NCDC) is a common
example of this scenario. Weatherstation locations pro-
vided by NCDC are reported to the nearest truncated
degree-minute. A minute is one-sixtieth of a degree of
arc. In the center of the continental United States, 1
minute of latitude averages approximately 6,000 feet
and 1 minute of longitude averages approximately 4,800
feet. Thus, the station location is only known to lie within
a box of approximately 1 square mile. Map repre-
sentations of these data should reflect this uncertainty.
Under the Municipal Solid Waste Landfill (MSWLF) Cri-
teria, the U.S. Environmental Protection Agency has
dictated that the 25-inch precipitation contour line be
used as a regulatory boundary for the level of protection
required at municipal landfill sites. The way in which
these lines are created and interpreted has important
policy implications. Indeed, the cost and practicality of a
given location must take this into account. If the 25-inch
precipitation figure is critical, characterizing its uncer-
tainty is also important.
In this work, uncertainty is considered a property of the
data (1). A Monte Carlo procedure is used to represent
the stochastic character of contour lines generated from
point data with known locational uncertainty. The 30-
year normal precipitation data for Kansas are used as
an example. The results of this study are compared with
the 25-inch contour used for regulatory purposes in
Kansas. This study demonstrates that the method of
interpolation greatly influences the resulting contours. In
addition, locational uncertainty changes the results un-
predictably using four different contouring methods. Fi-
nally, the differences have potentially significant policy
implications. The nature and origin of these factors are
discussed.
Problem Statement
The increasing power of geographic information sys-
tems (GIS) and the availability of digital data have en-
abled users and decision-makers to perform complex
spatial analyses for a great variety of environmental
applications (2). The rapid expansion of GIS has re-
sulted in a parallel growing concern about the quality of
data (3).
An understanding of error and uncertainty is critical for
proper use of spatial information. For the purposes of
this discussion, error is defined as a deviation between
the GIS representation of a feature and its true value (4).
For a location, this might arise from rounding or truncat-
ing digits. Attribute error can involve misclassification of
a feature or some other form of incorrectly accounting
for its nature. Error is a measurable value quantifying
these differences.
Furthermore, uncertainty shall refer to a characteristic
for which the exact location and/or quantity cannot be
calculated (5) or an attribute whose value represents a
distribution or some other ensemble (composite) meas-
ure. Locational uncertainty often arises when inappro-
priate measurement systems are used. An example of
this is the use of a Public Land Survey designation (often
-------
referred to as a legal location) to specify a point location.
This system is designed to represent a tract of land (an
area). It is not accurate for locating points (1).
The uncertainty associated with an attribute is an impor-
tant characteristic of that feature. It quantifies the preci-
sion of a stochastic quantity; that is, one that is not
accurately represented by a single value. Annual pre-
cipitation is represented by a single number, typically the
30-year mean annual precipitation (30-year normal).
This number varies each year, however, and that uncer-
tainty can be quantified by the variance or other statis-
tical measures. In this sense, uncertainty is a known or
calculable value that can be used in spatial analyses.
Unreliable CIS data and products may lead to adverse
environmental and legal consequences. The National
Center for Geographic Information Systems and Analy-
sis (NCGISA) chose data quality as the first initiative on
its CIS research agenda (6). Many efforts have been
made priorto this, and since, to understand and manage
error and uncertainty in CIS applications.
CIS analyses are inherently subject to propagation of
error and uncertainty (4, 7). No data set can represent
every spatial reality of a geographically dispersed phe-
nomenon. Monmonier (8) points out that as long as the
three-dimensional earth's surface is transformed to a
two-dimensional plane, error and uncertainty of various
forms will be produced. Goodchild and Min-Hua (9) point
out two issues that are important when dealing with error
and uncertainty:
• Minimization of error in the creation of CIS products.
• Measurement and presentation of error and uncer-
tainty in a useful fashion.
CIS technology introduces error and uncertainty through
two major sources: (1) inherent error and (2) operational
error. Inherent error is the error present in source data.
It is generated when the data are collected. Operational
error is generated during data entry and manipulation
(7, 10-13). Examples include locational shifts due to
projection or combining information from different
source scales.
Most error and uncertainty contained in CIS data cannot
be eliminated. Instead, they are actually created, accen-
tuated, and propagated through CIS manipulation pro-
cedures (14-16). Most operational errors are difficult to
estimate.
The selection by the U.S. Environmental Protection
Agency (EPA) of a 25-inch per year local precipitation
limit as one of the criteria to determine whether small
municipal solid waste landfills (MSWLF) are subject to
the provisions of Subtitle D provides an excellent exam-
ple of how uncertainty and errors enter into a CIS analy-
sis and its subsequent products. It demonstrates all of
the major forms and purveyors of error and uncertainty:
• Spatial (locational) error
• Statistical (sampling) uncertainty
• Temporal (time domain) error
• Error proliferation (processing error)
• Analytical (choice of methodology) error
• Cartographic representation error
Many of these are avoidable; some are known and
understood, yet they remain largely ignored by users of
CIS technology. This work presents each of these fac-
tors, discusses their origins, and shows how CIS could
have been used to better serve the policy and regulatory
processes. The Kansas example demonstrates that ig-
noring the factors influencing error and uncertainty can
result in incorrect conclusions and inappropriate policy
decisions.
Data Requirements and Sources
To perform an analysis of precipitation, data are typically
obtained from the National Climatic Data Center
(NCDC), located in Asheville, North Carolina. This is the
national repository for such data. These data are also
available through state or regional climate centers. The
Kansas Weather Library at Kansas State University pro-
vided data for this study. The 1990 "normal precipitation"
data (17) and locations were obtained and generated
into an ARC/INFO point coverage. Figure 1 displays the
locations of the precipitation stations used in this study.
Normal precipitation is defined as the average annual
precipitation for a three-decade (30 years) period at
each station for which reliable data are available. To
avoid "edge effects" (processing anomalies due to a lack
of data along edges of an area), all stations in Kansas
and some from neighboring states were used. A total of
380 stations compose this data set. In addition, precipi-
tation contours from the "Availability of Ground Water in
Kansas Map" (18) were digitized from a [paper] source
map. The Geohydrology Section of the Kansas Geologi-
cal Survey provided base map coverages of carto-
graphic features.
All data represent the best available information from the
source institutions noted above. Those organizations
use the data in their analytical and cartographic re-
search and production operations.
Methodology
To examine the influence of locational uncertainty on the
representation of three-dimensional, natural phenom-
ena, a Monte Carlo approach was adopted (1). Using
this technique, random realizations of point locations are
generated for each rain gauge, in each of 50 separate
simulations. From this, 50 possible representations of
the unknown locations of each gauge are used to create
-------
• •' • • •
•
• . •
*; •* • . •
• .
* *•
Figure 1. Location of rain gauges used in this study.
50 different sets of contours. All Monte Carlo calcula-
tions and data generation were performed using Statis-
tical Analysis System (SAS) (19, 20).
These 50 simulations were sequentially processed us-
ing the four different contouring methods available within
ARC/INFO. This provided a means to examine analyti-
cal error propagation. The first of the four methods is
kriging (21, 22). This is referred to in the paper as the
UK method, for its use of linear universal kriging (23).
The other three are manipulations of the triangular-
irregular network (TIN) contouring algorithm available in
ARC/INFO. These differ by the number of interpolation
points used along the edges of the elements in the TIN
data structure (24). The first used the default 1, the
second used 5, and the third used 10 (the largest value
available). These are labeled D1, D5, and D10, respec-
tively.
CIS operations used in this work include overlay analy-
sis, areal calculation, and arc intersection. ARC/INFO
was used for all CIS and cartographic production in
this work.
Identifying the Sources of Uncertainty
Spatial Error
Data obtained from NCDC is provided with the knowl-
edge that weather station locations are reported using
truncated degrees and minutes of longitude and latitude.
NCDC cannot provide any better locational accuracy at
this time. Because each location is reported with error,
this clearly has the potential to affect any contours or
other three-dimensional features interpolated from the
data. The magnitude and nature of this influence are
unknown and unpredictable (1).
In addition to the poorly defined station locations, exami-
nation of the data revealed other anomalies. The loca-
tions in the publication reporting normal precipitation
(17) were not identical to those identified by the Kansas
State climatologist and NCDC. Some of the discrepan-
cies were quite large. These anomalies were brought to
the attention of all parties involved. No resolution was
provided to this investigator's satisfaction, however.
The contours digitized from the "Availability of Ground
Water in Kansas Map" are stated to originate from the
1960 normals (18). No documentation exists, however,
concerning the way the lines were derived or the number
of rain gauges used. Presumably, they were contoured
by hand.
Statistical Uncertainty
This is a sampling consideration based on the size of
the data, the nature of the process being sampled, and
its variability. Unfortunately, precipitation is a particularly
"patchy" phenomenon. That is, rain falls in a discon-
tinuous fashion, and adjacent gauges can depict very
different patterns. This is confounded by the fact that
most contouring algorithms and other approaches to
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represent three-dimensional surfaces assume a rela-
tively smooth (locally) and continuous process.
Areal processes are almost always sampled as point
information. Most contouring algorithms require a regu-
lar grid from which to interpolate surface features. In
practice, rain gauges, as well as other environmental
sampling programs, are irregularly distributed. Place-
ment often depends on factors other than grid sampling
(e.g., convenience, access to communications, fi-
nances, Congressional districts). This creates a "nonex-
perimental sampling" design (25). Nonexperimental
sampling can contribute to uncertainty (26, 27).
Temporal Error
The normals are recalculated each decade and can
change drastically in local areas. These changes arise
for various reasons. First, some stations enter and drop
from the database. Stations are deleted due to changes
in location or extended periods of data collection prob-
lems. On occasion, new stations are added. Thus, the
size and areal coverage of the data set changes with time.
In addition, weather patterns change with time. Ex-
tended periods of drought or excess rain or snow alter
measured precipitation. In turn, three-dimensional rep-
resentations change unevenly.
Error Proliferation
Once an error enters into the database and is included
in CIS operations, spatial analysis, or spatial interpola-
tion, its effect passes into the next stage of processing.
In the 1990 normal precipitation data for Kansas, two
stations are reported in Garden City. Despite the fact
that the two are only a few miles apart, their annual total
precipitation differs by 2 inches! In consultation with the
state climatologist (Mary Knapp, Kansas State Univer-
sity), one was eliminated from the analysis. This process
was repeated for an additional six stations where re-
ported values appeared to be anomalous compared with
nearby stations or the previous normal precipitation
(1951 to 1980).
Errors can also proliferate through the normal handling
of data. With geographic data, this often occurs while
converting data from raster to vector and vector to raster
forms (28). Some CIS operations are best accomplished
in one form or another. As a result, transformations are
often "hidden" from the user. Commonly, features
"move" slightly after each step in an analysis.
Analytical Error
Different techniques have been developed for perform-
ing spatial interpolation, and an abundance of software
is available for this purpose. All these methods have
strengths and weaknesses. Each is based on a specific
set of assumptions about the form and nature of the
data. Some are more robust (less sensitive to data
anomalies) than others. Most importantly, some provide
additional information useful in data analysis.
Unfortunately, users often "take the defaults" when using
sophisticated techniques and ignore the assumptions
behind the method. Parameters can be varied and their
effect evaluated, as in a sensitivity analysis (29). Often,
the best approach is to try several methods and evaluate
their joint performance (30, 31).
Another difficulty is the need to assign values to areas.
By definition, polygons in a CIS are considered to be
homogeneous. In reality, they bound areas that are a
gradation from one characteristic to another. On the
other hand, contours are commonly used to depict sur-
face gradients but are useless (within a CIS) for analyti-
cal or modeling purposes. Ultimately, data sampling is
accomplished as a point process (except, perhaps, in
remote sensing), while many forms of data analysis and
processing require areal information.
Cartographic Representation Error
Communicating the uncertainty of map features is not a
trivial endeavor. Maps can be produced in two basic
forms: as a raster (e.g., orthophotoquads or satellite
images) or a composition of vectors (e.g., contour
maps). The printing process, however, often reduces all
of this to a raster representation at a very fine pixel size.
Each method poses its own problems in depicting un-
certainty.
Rasters can be used effectively in conjunction with color
information theory to produce a continuum of shading
within a thematic map layer (32). The choice of colors,
however, can influence the interpretation of the data,
and no universal scheme exists for depicting thematic
variability. For example, blue shades often represent
water or cold, while yellow and/or red often represent
temperature or heat.
Vectors present a different suite of problems. Contouring
is the primary technique for using vectors to depict areal
variation. By definition, however, contour lines represent
an exact isoline or single value along its length. Uncer-
tainty cannot be represented in a line. Rather, a com-
posite of lines can be displayed that represents a set of
possible interpretations of the data. This is not a practi-
cal solution for mapping, however, as it can create a
jumble of intersecting lines that makes interpretation
difficult and is not an aesthetic means of presentation.
Challenges Encountered in This Study
This work attempts to discover and account for sources
of error and uncertainty in CIS analysis. Given this
information, the challenges are to find the best way to
incorporate it into the analysis and to represent it in a
useful manner. Another challenge is finding ways to use
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CIS uncertainty to support policy and management de-
cisions. Addressing these manifold problems starts with
identifying the sources of error and uncertainty, the way
they enter the analysis, and the manner in which they
are propagated through the use of CIS.
This study includes a number of known sources of un-
certainty. In practice, this is not always the case. Users
of CIS data and technology should always assume that
the sources of uncertainty discussed in this paper are
present and attempt to determine their nature. Uncer-
tainty should be considered a property of the data and
appropriately represented (1). This is the approach
taken in this work.
After examining these factors, a Monte Carlo simulation
was deemed the most appropriate approach to capture
the nature of the locational uncertainty. Four different
methods of contouring were used to examine analytical
uncertainty (uncertainty due to the choice of a contour-
ing algorithm). In addition, the contribution of statistical
(sampling) uncertainty could have been addressed
through incorporating information about the standard
error of the point precipitation measurements (normals)
used as the base data. Time limitations precluded ex-
amining this dimension of the question. Comparing the
contours resulting from the 1960 and 1990 precipitation
normals demonstrates the effect of temporal variation.
The greatest challenge is communicating the uncer-
tainty in a manner useful to decision-makers. This paper
presents a series of maps, figures, and tables aimed at
addressing this problem. Some of the maps (see Fig-
ures 2 through 5) show the uncertainty resulting from
each of the contouring methods. Figure 6 depicts the
union (overlay) of the four approaches and displays their
correspondence. The pie chart in Figure 7 is a nonspa-
tial representation of this correspondence and the rela-
tive area represented within the different combinations
of overlapping regions of uncertainty. Tables 1 through
3 further compare these quantities. Figure 8 is the map
that the Kansas Department of Health and Environment
(KDHE) chose to define the regulatory boundary (the
25-inch contour). The contours resulting from this study
can be seen in Figure 9. Finally, the map in Figure 10 is
a cartographic comparison of the differences between
the contours used by KDHE (based on the 1960 nor-
mals) and those generated by the currently available
data (the 1990 normals).
There is no single best approach for meeting these
challenges, and there may never be one. The real chal-
lenge to address is how to educate technical CIS pro-
fessionals and the users of their work to look for
uncertainty and consider its influence on their decision-
making process.
Results
The zones of uncertainty defined by the results from the
four contouring methods used in this study are displayed
in Figures 2 through 5. For each method, these zones
represent the areal extent of the overlain contour lines
produced in the 50 simulations. Each region is bounded
by the furthest west or east contour generated along any
length of the region. Table 1 shows the relative area
falling within each of these zones as they traverse the
state of Kansas. Clear differences exist between the
total areas of uncertainty. It is their placement and rela-
tive location, however, that have policy and manage-
ment implications. CIS is required to examine these
questions.
Table 1. Comparison of Absolute and Relative Area of
Uncertainty Arising From Four Methods of
Determining the 25-Inch Precipitation Contour
Method3
UK
D1
D5
D10
Total Area
(square
miles)
289.33
494.38
602.13
631.11
Difference
Between
This and
UK Method
(square
miles)
—
205.05
312.80
341 .78
Percentage
of UK
Method
—
170.87
208.11
218.13
Percentage
of Combined
Areab of
Uncertainty
19.65
33.58
40.90
42.87
UK = universal kriging with linear drift, D1 = TIN interpolation with 1
subdivision, D5 = TIN interpolation with 5 subdivisions, D10 = TIN
interpolation with 10 subdivisions (23, 24).
bA union (overlay) of all four sets of regions of uncertainty creates a
combined area of 1,472.07 square miles. This includes the zones of
uncertainty for each method of contouring and areas not included
within any of the four regions of uncertainty (gaps between them).
Figures 2 through 5 clearly show differences in both the
extent of uncertainty in the 25-inch contour line and its
positional interpretation. Each method has a slightly
different bend or twist. Islands (isolated regions where
the 25-inch line appears as a closed loop) are mani-
fested differently depending on the interpolation
scheme. It is interesting to note the relative correspon-
dence between the general shape of the D1 and UK
methods. In the south-central border region, D1 and UK
represent the local uncertainty as a bulge, while D5 and
D10 depict it as an island of lower precipitation.
Areal correspondence and difference are depicted in
Figure 6 and Table 2. Figure 7 is a pie chart visualizing
the information in Table 2. These results are somewhat
surprising in that areas where none of the four methods
located the 25-inch line ("None Present") represent the
second largest composite area. Because of the large
number of "sliver polygons," the graphic representation
of the overlay is somewhat difficult to interpret. Table 2
clarifies these interrelationships by breaking down the
various categories. The average area per polygon value
-------
Figure 2. Regions of uncertainty produced by the UK method of contouring.
Figure 3. Regions of uncertainty produced by the D1 method of contouring.
6
-------
Figure 4. Regions of uncertainty produced by the D5 method of contouring.
Figure 5. Regions of uncertainty produced by the D10 method of contouring.
-------
UK
D1
D5
D10
Not Present
Figure 6. Union of the regions of uncertainty from all four methods of contouring. The numerous "sliver" polygons make this a
difficult presentation to interpret at this scale. The black areas appear prominently, however. These represent areas where
no method placed contour lines.
adds to the interpretation of the relative areas by incor-
porating the number of polygons in each category. An
inspection of this column makes those categories with
a multitude of very small polygons stand out. It also
displays a number of large jumps in magnitude. As this
number increases, the significance of the correspondence
increases.
Table 2 highlights the correspondence between the D5
and D10 approaches and the D1 and UK methods of
interpolation. This relationship is interesting because the
algorithms used by the UK and D1 methods both are
forms of linear interpolation. The D5 and D10 algorithms
are designed to provide more "smoothing" and appear
to create increasingly more "bull's eyes." Only D5 and
D10 generate these features. Of the 50 simulations, a
particular bull's eye appears west of the 25-inch contour
(see Figures 3 and 4) four times using D5 and 39 times
using D10. The size and location of these anomalies
also vary with the input data. Polygons containing con-
tours from all four methods rank ninth in total area and
seventh in average area (out of 17 categories). This
supports the conclusion that the four chosen methods
have a relatively low spatial correspondence.
Table 3 breaks down the area of uncertainty by county.
Although the zones of uncertainty appearto be relatively
small when displayed on a statewide basis, they have
important impacts in local areas. In particular, combining
this information with soils, topography, ground water,
and other information can clearly indicate whether a
specific location is suitable for a landfill. Often, informa-
tion developed at one scale is used in another. In this
case, statewide information is being used fora site-specific
application.
Figure 8 is a copy of the map the KDHE used to deline-
ate the 25-inch precipitation contour. The results from
LRGflty
12.13%
D1,03. acd DIG
11.11%
Ml Oiling;
Norn Praianl
22.13%
Figure 7. Breakdown of the total area in each category result-
ing from the union (overlay) of the regions of uncer-
tainty from all four contouring methods. The "None
Present" category represents a surprisingly large
proportion among the 17 possible combinations.
-------
Table 2. Comparison of Absolute and
Relative Area
of
Table 3.
Uncertainty Arising From Four Methods of
Determining the
Methods of
Contouring
Found Within
Area
D5 and D10
None present
D1 only
UK only
D1, D5, and D10
D10 only
UK and D1
D5 only
Common to all
UK, D5, and D10
D1 and D5
D1 and D10
UK and D5
UK, D1, and D5
UK, D1, and D10
UK and D10
Combined total
N
28
28
37
27
24
69
19
73
10
14
38
38
19
12
12
13
461
25-Inch Precipitation Contour
Total Area
(square
miles)
350.13
326.06
221 .02
178.60
163.61
70.07
65.82
34.68
23.45
15.47
10.68
6.50
2.21
1.89
1.41
0.47
1472.07
Percentage
of Total
Combined
Area
23.78
22.15
15.01
12.13
11.11
4.76
4.47
2.36
1.59
1.05
0.73
0.44
0.15
0.13
0.10
0.03
100.00
Average
Area per
Polygon3
(square
miles)
12.50
11.64
5.97
6.61
6.82
1.02
3.46
0.48
2.35
1.11
0.28
0.17
0.12
0.15
0.12
0.04
County
Barber
Barton
Clark
Area of Uncertainty for Each County Arising From
Four Methods of
Determining the 25-Inch
Precipitation Contour
UK Area
(square
miles)
36.38
28.47
12.64
Comanche 44.30
Edwards
Ellis
Jewell
Kiowa
Osborne
Pawnee
Pratt
Rush
Russell
Smith
Stafford
Total
aA dash
method
— a
—
—
15.15
43.87
—
18.84
0.04
27.17
27.59
34.88
289.3
D1 Area
(square
miles)
24.29
96.84
11.49
52.94
—
6.14
6.65
14.32
99.82
—
11.30
8.51
22.71
68.47
70.90
494.4
indicates that no contours appeared
specified.
D5 Area
(square
miles)
28.05
56.16
12.97
51.43
—
9.27
23.05
17.81
168.2
0.11
11.56
31.32
28.30
67.38
96.49
602.1
D10
Area
(square
miles)
28.31
59.56
13.02
52.23
9.44
8.90
24.26
31.12
172.6
0.30
11.43
29.51
28.00
66.34
96.05
631.1
in that county for the
Average Area per Polygon =
to compare the relative size
(Total Area) / N. This a useful measure
of each polygon in each classification.
the UK method were selected as the best available
representation of normal precipitation across Kansas
(see Figure 9). The figure displays the undipped contour
lines generated from the data. This is done to point out
the importance of "edge effect." Note the incoherent
behavior of the contour lines at their termini. If a smaller
window of data points were used, interpolation problems
would have lain across the region of interest. When
present, these features require more handling and time
for analysis. They often introduce additional error and
uncertainty.
The policy implications of this example are demon-
strated in Figure 10. Here, the map shows the combina-
tion of the "official" KDHE map and the data interpreted
in this study. The pattern of noncorrespondence is note-
worthy. The lightest areas are regions that currently
experience higher annual precipitation than forecast by
the 1960 normals (from the KDHE map). Black areas
are expected to have lower precipitation under current
climatic conditions. Therefore, large areas of Kansas
that should be under regulation according to the MSWLF
regulations are not.
In summary, the figures and tables clearly show that
locational uncertainty of data measured as points is
propagated into contour lines. The nature and magni-
tude of that uncertainty varies with location and method
of interpolation and shows no regular (predictable) pat-
tern. Perhaps most importantly, uncertainty that appears
small at one scale can be relatively more significant at
another. In addition, seemingly small geographic feature
and uncertainty can be an important factor in decision-
making.
Discussion and Conclusions
CIS is an established and accepted technology, espe-
cially in applications related to natural resource and
environmental management. Despite the widespread
proliferation of CIS into these areas, the available data
are not always appropriate for the intended application.
Furthermore, adequate documentation is not always
available to determine whether the data are adequate
for a given use. The development of metadata standards
will play an important role in addressing this problem.
Errors and uncertainty will always be present in CIS
data. Recognizing their presence, incorporating them
into the analysis, and representing them in CIS products
will remain a constant challenge.
This study demonstrates the influence that various
sources of CIS uncertainty can leverage on the results
of an analysis. The example of the 25-inch precipitation
-------
Yield of Greater Than
500 Gallons per Minute
* Precipitation Contours in Inches Per Year
Yield of 100 to 500 Gallons
of Water per Minute
Yield of Less Than 100
Gallons of Water per Minute
100 Miles
Figure 8. The map that the KDHE selected as the definitive source for the location of the 25-inch precipitation contour (18).
^s
^
Figure 9. Map of the precipitation contours resulting from applying the UK method with linear drift to the 1990 normals (17). Here,
the contour interval is 5 inches. Note the incoherent behavior of the contours around the margins of the map. This is
referred to as "edge effect."
10
-------
±i
Higher Precipitation in
1990 Data
Lower Precipitation in
1990 Data
Figure 10. This map represents the union (overlay) of the information in Figures 8 and 9. The lighter regions represent areas
exhibiting higher precipitation in the 1990 normals (1961 to 1990) than was apparent in the 1960 normals (1931 to 1960).
The darker areas show the opposite relationship.
line in Kansas is a clear example of how the use of
inappropriate data can have far-reaching effects on pol-
icy and management. The regulatory agency, KDHE,
chose the wrong map upon which to base its regulatory
authority. As a result, numerous potential sites for small
municipal solid waste landfills will be considered that are
in violation of the letter and intent of the law.
Ultimately, the responsibility for proper use of CIS tech-
nology lies in the hands of practitioners. Technical staff
performing CIS analysis must be knowledgeable about
sources of error and uncertainty and ensure that users
of their work are aware of their influence on CIS output.
The problems demonstrated in the Kansas example
could have been avoided simply by investigating the
appropriateness of the data. Instead, a convenient
source was chosen without seeking any other sources
of "better" information. Indeed, familiarity with the nature
of the data (30-year normals) should have led the policy
analyst to select the most current data and not data that
are 30 years out of date! An understanding that contour
lines represent a generalization of the point precipitation
measurements should also have led to the conclusion
that locations near the boundary line ought to be moni-
tored for compliance. Both the temporal and spatial
characteristics of climate can change, as exemplified by
the difference in the 1960 and 1990 normals. The "Dust
Bowl" periods of the 1930s and 1950s significantly
influenced the 1960 normals (33). As a result, they are
not appropriate for this application.
Although a powerful tool, CIS does not hold all the
answers. The technical community and policy-makers
must work together to ensure its proper use. In reality,
no 25-inch precipitation line floats over Kansas. It is
merely the interpretation of scientists and policy-makers
who select its location. The only way to arrive at a
reasonable answer is to gather the best available infor-
mation and allow all parties to scrutinize it. CIS can be
a wonderful tool to do this.
Acknowledgments
The author wishes to express his gratitude to the Kan-
sas Geological Survey for its computing support before,
during, and after his move to Louisiana. Also, the author
wishes to thank Mr. M. Schouest, who made the move
both possible and tolerable. Mr. C. Johnson of Johnson
Controls in Baton Rouge, Louisiana, produced the ex-
cellent presentation graphics. Finally, this work could not
have been performed without the Internet.
References
1. Mitchell, J.E. 1993. The influence of (x,y) uncertainty on predic-
tion error and contour lines from a three-dimensional surface.
Water Resources Bull. 29(5):863-870.
11
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12
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Design of GIS Analysis To Compare Wetland Impacts on Runoff in Upstream
Basins of the Mississippi and Volga Rivers
Tatiana B. Nawrocki
Natural Resources Research Institute, University of Minnesota, Duluth, Minnesota
Introduction
The attention given in hydrologic studies to wetlands
differs significantly between the United States and Rus-
sia at the present time. Fundamental theories and
mathematical models are developed in both countries to
describe hydrologic processes and impacts of water-
shed conditions on surface runoff. In the United States,
however, theoretical investigations are directly pointed
at wetlands and are supported by large-scale field stud-
ies and advanced technological capabilities to manage
spatially distributed information. Unlike in Russia, in the
United States, special scientific symposia are devoted
to wetland hydrology, where major tasks for hydraulic
and hydrologic research needs are formulated. Among
these tasks are the understanding and assessment of
relationships between various hydrologic modifications
and wetland functions, especially wetland flood convey-
ance and water quality protection functions (1). Water-
shed-scale comprehensive field studies of wetland
functions are underway, for example, at constructed
experimental wetlands in the Des Plaines River basin in
Illinois (2). A new long-term goal—strategic restoration
of wetlands and associated natural systems—has been
formulated (3).
The intensive efforts of many U.S. scientists yielded
numerous results and attracted more attention to the
complicated nature of wetlands processes. Wetlands
were evaluated as runoff retention basins, and it was
found that, in northwestern states, up to 12 inches of
water could be accumulated per wetland acre (4, 5).
Overtime, piecemeal loss and degradation of wetlands
in many areas of the United States have seriously de-
pleted wetland resources. Researchers also discovered
that adverse impacts from wetland degradation could
appear indirectly with little obvious spatial or temporal
connections to sources. As described by Johnston (6):
Cumulative impacts, the incremental effect of an
impact added to other past, present, and reasonably
foreseeable future impacts, has been an area of
increasing concern. . . . Impacts can accumulate
over time or over space and be direct or indirect. An
indirect impact occurs at a location remote from the
wetland it affects, such as the discharge of pollut-
ants into a river at a point upstream of a wetland
system.
The process of solving environmental problems related
to wetlands is increasingly complex. Analyzing diversi-
fied data over increasingly broad areas becomes essen-
tial for making competent decisions.
Comparing wetland hydrologic functions in headwaters
of the Mississippi River (United States) and the Volga
River (Russia) could provide additional information
about how alternative management strategies affect
runoff, peak flow, and water quality under changing
climates. A macro-scale "field experiment" in both of
these naturally similar areas is already underway. Wet-
land conservation as opposed to drainage is now the
prevailing policy in the upper Mississippi basin. In Rus-
sia, however, economic problems have prevented this
type of policy from becoming a priority. Instead, peat
mining, reservoir construction on lowlands, and drain-
age for farming and private gardening are common.
This project, which is being implemented at the Natural
Resources Research Institute (NRRI), University of Min-
nesota, Duluth, has the following goals:
• Developing a multilayered hierarchical base of geo-
graphic information system (GIS) data for headwater
watersheds of the Mississippi and Volga Rivers.
• Developing a comparative analysis of wetland im-
pacts on the hydrology of the rivers.
• Studying the relationships between natural and hu-
man-induced factors on wetland functions under cli-
mate change and variable strategies of wetland
conservation.
-------
• Defining criteria and thresholds for wetland system
stability with regard to flood risk and water quality.
• Outlining recommendations for wetland management
in the headwaters.
The methodology for comparative assessments in-
volves statistical analysis, hydrologic models, CIS, and
remote sensing. Representative watersheds will be
studied in more detail, and procedures for scaling infor-
mation from the local to the regional level will be
developed.
Input Data
In recent years, U.S. governmental and state agencies,
as well as a number of private companies, have ex-
pended considerable efforts to compile the available
data in CIS format for multidisciplinary analysis of wa-
tershed problems. Among the major sources of informa-
tion essential for studies of wetland hydrologic functions
are:
• The National Wetlands Inventory, conducted by the
U.S. Fish and Wildlife Service.
• Digital elevation maps (OEMs) developed by the U.S.
Geological Survey (USGS).
• Major and minor watershed boundaries, outlined for
Minnesota by the Minnesota Pollution Control Agency
(MPCA).
• The water quality sampling network from the U.S.
Environmental Protection Agency (EPA).
• The digital chart of the world (DCW), issued by the
Environmental Science Research Institute (ESRI) in
scale 1:1,000,000.
These and other sources, listed in Table 1, were used to
compile the map illustrations for this paper.
Almost no similar data in CIS form could be found for
the territory of the former USSR, however. Any specific
data (e.g., detailed maps, hydrology records, water
quality sampling data) are generally in paper files dis-
persed among many agencies and are hard to obtain.
The forms of information storage and means of its analy-
sis are out of date, and most maps exist in single or few
copies in paper files. In Russia, the time lag grows
between the dynamic changes in the environment and
the traditional pattern and inertia of management
structures.
The CIS situation in Russia developed some interesting
paradoxes. During the first few decades of space pro-
grams, certain state agencies accumulated an outstand-
ing bank of world image data. When economic
hardships hurt the previously privileged space industry,
numerous joint ventures with foreign companies were
created to distribute images on the world market for hard
currency. These data are hardly available for domestic
uses, however. The current domestic price for image
data is 20,000 to 25,000 rubles for a black-and-white
picture of an area 60 by 60 kilometers, or 60,000 to
70,000 rubles for the same image on a computer disk.
With the present level of funding for scientific research,
the price is too high. Security regulations still restrict
access to later data, showing land use changes.
Another paradox is scientists' attitude toward their data.
Abandoned by the state, agencies and institutes are
reluctant to share their specific data in multidisciplinary
projects. Data files are now a commodity. Accomplishing
an overlay and integration of special data coverages,
which is essential to any watershed CIS study, is almost
impossible.
The third paradox is the attitudes of local, regional, and
central authorities toward CIS. Many authorities are still
ignorant about the potential of this technology. Those
who are knowledgeable prefer not to promote CIS for
watershed-related tasks because it involves land use
analysis. With the onset of land privatization, the best
pieces of property (e.g., the waterfront lots adjacent to
drinking water reservoirs in the Moscow region) are
rapidly allocated to the most powerful landowners. Thus,
limiting access to this kind of information is deemed
safer.
Experts in Russia have not yet applied CIS to wetland
hydrology studies because CIS is still a very new and
mostly unfamiliar technology. This makes the current
study unique both for its results and for its application of
CIS methodology.
Closer review of data sources indicates that most of the
input data for the project is available, though dispersed
among many agencies. Table 1 is a preliminary list of
data and data sources.
Project Design
The project addresses the following questions:
• How do the extent and positioning of wetlands in the
headwaters of large rivers affect runoff and peak
flow?
• What are the spatial relationships between wetland
and other land uses regarding flood risk and water
quality under variable climate conditions?
• What is the role of wetlands for diffuse pollution pre-
vention and sediment deposition control under alter-
native management?
• What determines major criteria for wetland conserva-
tion in headwaters, ensuring environmentally sustain-
able development under multiobjective land and
water resource uses?
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Table 1. Data Sources for a GIS Study of Wetlands in the Basins of the Mississippi (United States) and the Volga (Russia)
Data
Level 1
Base maps
Stream network
Urban and rural areas
Wetlands, unclassified
Forests
Agricultural lands
Level 2
Watershed boundaries
Digital elevation maps
Digital orthophotos
Soils
Hydrologic records
Water quality records
Land uses
Wetlands, classified
United States
DCW3
DCW3
DCW3
NWIa
DCW3
LMDb'c
MPCAa
USGSa
USGS, LMICa
SCSb'c
USGSb
EPA/MPCAa'b
LMIC, LSAT3
NWIa
Russia
DCW3
DCW3
DCW3
DCW3
MGUb
MGUb
RWRCC
NA
CD, RPIb'c
RPIC
CHMb'c
CHM, RCP, RPIC
LSAT, CD, RPI3'C
RPIC
Data available in GIS ARC/INFO format.
bDatabases; needed conversion to ARC/INFO.
cData available in paper files; needed digitizing.
Key:
CD = Commercial distributors
CHM = Russian Committee on Hydrometeorology
DCW = ESRI digital chart of the world
EPA = U.S. Environmental Protection Agency
LSAT = Satellite image data
LMD = Published literature and map data
LMIC = Minnesota Land Management Information Center
MGU = Moscow State University
MPCA = Minnesota Pollution Control Agency
NA = Data not available
NWI = U.S. National Wetland Inventory
RCP = Russian State Committee on Natural Resources and Conservation
RPI = Miscellaneous planning agencies and research institutes
RWRC = Russian Water Resources Committee
SCS = U.S. Soil Conservation Service
USGS = U.S. Geological Survey
Tasks established to address these questions include:
• Developing a multilayered hierarchical base of GIS
data for headwater watersheds of the Mississippi and
Volga basins.
• Performing a comparative analysis and simulation of
wetland impacts on hydrology and water quality at
representative watersheds.
• Deriving the relationships between natural and hu-
man-induced factors and wetland functions under cli-
mate change with regard to variable strategies of
wetland conservation.
• Defining the criteria and thresholds for wetland sys-
tem stability with regard to flood risk and water quality
parameters.
• Outlining recommendations for land and water re-
sources management and wetland positioning in the
headwaters.
GIS is the essential tool for manipulating and integrating
the many types of spatial data on water resources, soils,
vegetation, land use, economics, and the environment.
GIS compiles many sources (e.g., maps, field notes,
remote sensing, statistical data) into a consistent, inter-
pretable database used for specific scientific goals and
development decisions. The user can run GIS ARC/INFO
software on workstation and PC platforms and apply the
hierarchical approach to GIS data management, devel-
oped earlier (7). At the task level, data resolution and
corresponding modeling tools vary.
Level 1 contains the basic reference information for
large regions (e.g., the Upper Volga and the Minnesota
portion of the Upper Mississippi River basins). It covers
an area of several hundred thousand square kilometers,
with a map scale approaching 1:1,000,000. Landsat
thematic map data and the DCW (8) are used as
sources of data at this level. Vogelmann et al. (9) dem-
onstrated the methodology for detection of freshwater
wetlands using remote sensing data based on maximum
-------
likelihood supervised classification. A graphic data file
on CIS focuses on basic physical characteristics such
as stream network, geology, soils, wetland classifica-
tion, and other major land uses. A complementary tabu-
lar database or attribute file contains information on
stream flow, water quality, pollution sources, and wet-
land impacts on material fluxes. At this level, the general
physiographic and statistical information is accumulated
and analyzed, territories are classified, and major prob-
lems and typical case study watersheds are defined.
This information is compiled from literature, cartographic
data in paper and digitized form, statistics, and space
image data.
In Level 2, the more detailed CIS analysis and scenario-
based modeling is implemented at the watershed scale
with a map scale of approximately 1:25,000. The water-
shed demonstration focuses on alternative approaches
to priority-setting in wetland management, climate im-
pact analysis, and resulting interactions with landform,
soils, biosphere, and runoff. The sources of data are
special, topographic maps and air photo interpretation.
Simulation studies of water balance and fluxes among
the various reservoirs are implemented at Level 2. De-
veloping procedures for scaling information from the
local to regional level is the important task at this level.
CIS assists in handling the input parameter library and
analyzing the output. CIS studies, involving area meas-
urements and distribution analysis, evaluate cumulative
impacts on runoff and its quality from the loss of wetland
area, caused by drainage or filling, under stationary and
changing climate.
Wetland functions are considered under two sets of
scenarios. Management scenarios compare different
wetland and farming allocations, conservation practices,
and agricultural chemical use. Climate scenarios as-
sume rainfall and temperature changes under global
warming. Scenario-based simulation is applied in the
analysis of watershed runoff, wetland moisture regime,
soil erosion, and water quality processes.
Methodology
CIS database structure is related to the selected meth-
odology. CIS serves as a linking tool for input-output
data analysis and transfer between models, used at
different levels and stages of studies.
Scientists in both the United States and Russia devel-
oped statistical methods to obtain quantitative relation-
ships between stream flow and wetland area in the river
basins. Johnston (6) summarized the U.S. findings:
Empirical equations for predicting streamflow, de-
veloped by U.S. Geological Survey in Wisconsin
and Minnesota, indicate that flood flow is propor-
tional to the negative exponent of wetlands and
lakes ratio on a watershed (10,11). This means that
relative flood flow is decreased greatly by having
some wetlands in a watershed, but a watershed with
a large proportion of wetlands does not reduce flood
flow much more than a watershed with an interme-
diate proportion of wetlands. For example, predicted
flood flow was 50 percent lower in Wisconsin water-
sheds with 5 percent lakes or wetlands than it was
in watersheds with no lakes or wetlands, but in-
creasing the proportion of lakes and wetlands to 40
percent decreased relative flood flow by only an
additional 30 percent (12).
Other estimates agree that wetland encroachment on a
watershed of less than 25 percent generally has a mini-
mum influence on peak flow (5, 13, 14).
Johnston and colleagues (15) applied these equations
to watersheds in central Minnesota. They found that a
watershed with 1.6 percent lakes and wetlands had a
flow per unit watershed area that was 10 times the flow
predicted for a watershed with 10 percent lakes and
wetlands, while watersheds with 10 to 50 percent lakes
and wetlands had about the same flood flow per unit
area.
Statistical analysis indicates that peak discharge in-
creases with decreasing wetland area within the drain-
age basin. The regression equation defines the
approximation for northwestern Minnesota (16):
QE.R A A 0.677 n N-0.506
AM=58.4AW (Ls)
LS=100(AL + AM)/AW+1
where:
QAM = arithmetic mean of the annual series, cubic
feet per second.
Aw = watershed area, square miles.
AL = lake area within the watershed, square miles.
AM = marsh area within the watershed, square miles.
A similar statistical approach was developed for peak
flow determination in Russia. Maximum flow discharge
from snow melt is calculated for the central European
zone as (17):
Qm=K0*hp*S1 *S2*S3/(A + 1)n
where:
Qm = flow discharge, cubic meters per second.
KQ = coefficient of flood concurrence, KO = 0.006
for plain river basins.
hp = calculated flood runoff for given probability,
millimeters.
A = drainage area, square kilometers.
n = coefficient, n = 0.17.
-------
B! = lake storage coefficient, if lake area is less
than 1 percent of A, then S1 = 1.
S2 = pond and reservoir storage coefficient, 82 =
0.9 with ponds and S2 = 1 without ponds.
S3 = combined wetland and forest storage
coefficient.
S3 = 1 - 0.8 Ig (0.05 Sf + 0.1 Sw + 1), where Sf
and Sw are forest and wetland area,
percentage to total drainage area.
Calculated flood runoff for a given probability, hp, is
determined based on average flood runoff h, millimeters,
coefficient of variation Cv, and tabulated parameter F, as
follows:
hp = (1 + F * Cv) h
h = K, hk
hk = 100 millimeters for Moscow region.
K, = land surface coefficient, Kt = 0.9 for plains
and sandy soils, K, = 1.1 for hills and clay
soils.
The studies mentioned above generally agree with an
assumption that the incremental loss of wetland area
would have a small effect on flood flow from watersheds
with 10 percent up to 40 to 50 percent wetlands, but a
large effect on flood flow from watersheds with less than
10 percent wetlands.
The existence of similar thresholds was found in relation
to wetlands abilities to intercept pollutants. As Johnston
stated (6),
The same 10 percent threshold was identified by
Oberts (18) for suspended solids, a measure of
water quality function. Stream-water draining water-
sheds having 10 to 20 percent wetlands had about
the same loading of suspended solids, so the con-
tribution of suspended solids was relatively constant
per unit area of watershed. However, the water-
sheds with less than 10 percent wetlands had load-
ing rates per unit area that were as much as 100
times greater than the loading rates from the water-
sheds with more than 10 percent wetlands.
CIS could be especially helpful in determining the im-
pacts on downstream water quality of the spatial posi-
tioning of wetlands within watersheds. Studies prove
that the location of wetlands can affect their cumulative
function with regard to water quality (6). In an earlier
work (15), Johnston developed an index of wetland
location and applied it to a landscape-level CIS study of
urban and rural stream watersheds in central Minne-
sota. The index is formulated as:
where:
PWP = relative wetland position.
j = stream order (19) of water quality sampling
station.
i = stream order of wetland.
Ai = area of Ah order wetlands.
Calculated values for the index ranged from 0 (i.e., all
wetlands were on streams of the same order as that of
the sampling station) to 2.6 (i.e., average wetland posi-
tion was 2.6 stream orders upstream of the sampling
station). Watersheds with wetlands located close to
sampling stations had significantly better water quality
(i.e., lower concentrations of inorganic suspended sol-
ids, fecal coliform, and nitrate; lower flow weighted con-
centrations of ammonium and total phosphorus) than
watersheds with wetlands located far from sampling
stations (6).
The review of methodological approaches, as shown
above, indicates that parameters describing wetland ex-
tent, positioning, and land surface characteristics are of
universal significance for any comprehensive water-
shed-scale wetland study.
In the current study, CIS is used at Level 1 to evaluate
wetland area per watershed and to develop input pa-
rameters for relative wetland position assessment. The
comparison and selection procedures for case study
watersheds in the Volga and Mississippi basins are
based on these values. The parameters, derived from
CIS, are as follows:
1. Total watershed area.
2. Lake, pond, and reservoir area.
3. Forest area.
4. Wetland area.
5. Ratio of wetland area to total watershed area.
6. Wetland area by subwatersheds of different order.
7. Relative wetland extent by subwatersheds of different
order.
8. Land surface coefficients.
Parameters listed in groups 1 through 4 are obtained
directly from CIS attribute tables as values of "area"
items for the respective land cover polygons. Parame-
ters 5 through 7 require calculations relating values of
area items for different polygon coverages. Land surface
coefficients (group 8) could be determined indirectly
based on basic soil, land cover, and topography data.
Most U.S. methodologies use hydrologic soil groups,
-------
based on soil permeability, rates of infiltration, and Soil
Conservation Service (SCS) runoff curve numbers (20).
Some Russian methodologies have adopted similar em-
pirical land surface coefficients. For example, for central
European Russia, this value varies from Kt = 0.9 for
plains and sandy soils, to Kt = 1.1 for hills and clay soils
(17).
Level 2 of analysis applies two hydrologic simulation
models:
• The Agricultural Watershed Runoff and Water Quality
Model (Agricultural Nonpoint Source Pollution Model
[AGNPS]), developed by the Agricultural Research
Service of the U.S. Department of Agriculture, con-
tains explicit procedures to evaluate the impacts of
management practices and landscape feature posi-
tioning on watershed runoff. AGNPS is a cell-based
runoff model that estimates water volume, peak flow,
eroded and delivered sediment, chemical oxygen de-
mand, and nutrient export from watersheds (20-22).
• The Forest Runoff Watershed Model (FRWM) com-
bines analytical and numerical methods for solving
hydro- and thermodynamics equations (23). This
model considers snow melt constituent in runoff in
more detail than does AGNPS. Hydrologic simulation
is based on physical process descriptions for snow
cover dynamics, freezing and thawing of soil, soil
moisture dynamics in frozen and thawed soils, inter-
ception of liquid and solid precipitations by vegeta-
tion, surface runoff, ground-water aquifers, and
channeled streams.
Both models use a similar set of watershed input data,
derived from CIS (e.g., elevations, slopes, channel
slopes, stream network configuration, soil texture, land
cover). The methodology, linking CIS with hydrologic
models, was already tested in the wetland study project
at the Voyageurs National Park in Minnesota. The
ARC/INFO GRID module was used to derive watershed
variables for input to AGNPS. CIS then presented and
interpreted the scenario-based results of the simulation
(24). The typical stages of such an analysis and inter-
pretation for a watershed-scale area are presented in
Figures 1 through 5.
Case Study Watersheds
The areas where wetland impacts on runoff are evalu-
ated are located in Minnesota (United States) and Mos-
cow and adjacent regions (Russia) (see Figures 6
through 15). They have mixed urban, rural, recreational,
and forest land uses. Both regions have a variety of
development pressures. The relative effects of different
alterations in watershed management are distinguished
and quantified. CIS provides metrics for comparative
assessments and analysis of related variables for both
areas.
Table 2 and Figures 7 through 14 present a general
overview of wetland extent in both areas. The case
study subwatersheds used for more detailed analysis
will include tributaries of the second and third order. At
this stage, several watersheds are considered for more
detailed analysis. The limitations imposed by data avail-
ability as well as by project resources could affect the
final selection. Table 2 serves, therefore, as a prelimi-
nary overview of several areas that could potentially be
adopted for more detailed studies.
CIS analysis shows that in the Upper Volga, wetlands
extent very much depends on allocation of populated
areas. The heavily urbanized Moscow metropolitan area
affects a large territory of many thousands of square
kilometers. The ratio of wetlands as a percentage of total
land area is one-tenth of that in the neighboring Tver
area, which has the same size but a smaller population
(see Figure 11). In areas of intensive agriculture (e.g.,
the Pronya basin located southeast of Moscow), almost
all wetlands were drained and have not existed for
several decades.
In Minnesota, despite the growing urbanization (e.g., the
Twin Cities area [7,330 square kilometers]), about half
of the presettlement wetlands still remain (25); wetlands
occupy 442 square kilometers, or over 6 percent of the
land area; and shallow lakes constitute an additional 114
square kilometers (1.56 percent). Some watersheds
within the Twin Cities metropolitan area have a high
wetland percentage, such as 18.9 percent in the Lam-
berts Creek watershed. Intensive studies with CIS ap-
plication of landscape feature functioning and wetland
impacts on stream flow and water quality demonstrated
an innovative approach and made detailed databases
available for this area (15).
Preliminary comparative analysis indicates that two
pairs of case study watersheds could be initially se-
lected for further studies in the Mississippi and Volga
basins:
• Upstream watersheds with wetlands area of 15 to 20
percent (Tver region in Russia and Cass and adja-
cent counties in Minnesota).
• Tributary watersheds downstream with wetlands area
of 1 to 2 percent (the Istra basin in Russia and sub-
watersheds of the Minnesota River basin, located in
Sibley, Scott, and adjacent counties in Minnesota).
Case study watersheds in the Mississippi and Volga
basins are situated on gently rolling plains in mixed
forest zones with southern portions extending into the
forest/steppe and prairies. The Quaternary sediments
are of glacial, glaciofluvial, lacustrine, and alluvial origin.
Wetlands have hydric soils with various degrees of gley
process development and/or peat accumulation, varied
by wetland type and soil moisture regimen (28). The
annual precipitation is 500 to 600 millimeters with similar
-------
Variable
Semiconstant
Constant
Climate
Resource Use
and
Control Practices
Soil and
Physiographic
Landscape Feature
Positioning Alternatives
and Recommendations
Improved Methodology for
Spatial Process Analysis
Factors
Input
Parameters
Processes
Analysis Based on
Models and CIS
Output Results
Demonstrated
on CIS
Figure 1. Conceptual framework of linking GIS and models for environmental management.
Hypsometric curve
With Ponds
•E 40 -
D
o
°20,
V
340 360 380 400
Value Outlet
Elevations (meters)
I—I <350
EZ3 350 to 360
m 360 to 370
m 370 to 380
•B >380
^B Beaver Ponds
Beaver Wetlands
rzn Wooded Wetlands
." l Coniferous Forest
I l Deciduous Forest
0 500 1,000 1,500 2,000
Figure 2. Stream network configuration derived from GIS ele-
vation map. Figure 3. Scenarios of land use.
-------
With Ponds
With Ponds
500 1,000 1,500 2,000
500 1,000 1,500 2,000
Figure 4. Scenario-related hydrologic curve numbers.
Table 2. Comparative Data on Wetland Extent in Minnesota and in the Upper Volga Basin (8, 25-27)
Figure 5. Patterns of sediment transfer between cells (%),
+ deposition, - erosion.
Total Area
Wetland Area
Region
United States
Minnesota
Beltrami Co.
Cass Co.
Hubbard Co.
Le Sueur Co.
Hennepin Co.
Sibley Co.
Wright Co.
Scott Co.
Lambert Creek
Russia
Tver region
Moscow region
Istra basin
Pronya basin
(Square Kilometers)
205,940.30
7,923.04
6,256.38
2,624.81
1 ,204.82
1 ,588.07
1 ,555.38
1 ,852.97
982.56
9.51
10,000.00
10,000.00
1 ,827.38
10,200.00
(Square Kilometers)
30,500.00
3,909.23
1,505.29
283.22
28.31
36.37
24.26
24.27
8.06
3.69
1,169.08
165.39
24.07
a
Percentage
14.80
49.34
24.06
10.79
2.35
2.29
1.56
1.31
0.82
18.90
16.90
1.70
1.32
a
aWetland area is insignificant and not identified by available maps.
-------
1 - Doibiza, 2 - Istra, 3 - Pronya
Figure 6. Location of study areas in the Volga basin.
Volga Basin, Russia
Land Features
• Urban Lands
H Peat Bogs
El Wetlands
aaa
H Reservoirs and Lakes
ii
/] Rivers
71 Roads
Statistics %
Total Area 100.00
Urban 12.38
Water 1.58
Wetlands 1.66
Kilometers
10 20 30 40
Figure 7. Wetlands and urban lands in the Moscow region.
-------
Figure 8. Wetlands and urban lands in the Tver region.
Land Features
B Urban Lands
I Peat Bogs
j Wetlands
11 Inundated
w
H Reservoirs and Lakes
7\ Rivers
7\ Roads
Statistics %
Total Area 100.00
Urban 1.22
Water 4.64
Wetlands 16.93
Kilometers
i • "1 f i
10 20 30 40
Wetlands, 1954
Wetlands, 1980s
Urban Lands
Reservoirs and Lakes
Kilometers
10
20
30
40
Figure 9. Wetland decline since 1954, Moscow region.
10
-------
Volga Basin, Russia
£59
£68
Land Features
Urban Lands
(Population, Thousands)
Peat Bogs
Wetlands
Reservoirs and Lakes
Rivers
Roads
l\ Watershed
Elevation (meters)
Square
Kilometers %
Total Area 1,827.38 100.00
Urban 61.31 3.36
Water 40.43 2.21
Wetlands 24.07 1.32
Kilometers
10
20
30
40
Figure 10. Istra watershed in the Moscow region.
Wetlands Moscow Region, Russia
Populated Areas
I "1 <10 (US 20-29 m 40-49 ^ffl 75-99
10-19 F~^l 30-39 50-74 100
Kilometers
50 100 150 200
Figure 11. Land cover, percentage of total in Moscow and Tver regions.
11
-------
Presettlement
Current
Wetland Drop, Percentage
and Location of Study Areas
0-10 ["""] 21-30 [~~] 41-50 KH 61-70
11-20 I I 31-40 ["-' •-. I 51-60 BiH >70
Kilometers
0 100 200 300 400
Figure 12. Wetlands in Minnesota, percentage of total area.
Mississippi Basin, Minnesota
Land Features
H Urban Lands
~~] Forests
j-.i
H Wetlands
HI Reservoirs and Lakes
2 Rivers |/\/| Roads
/] Watershed I A/1 Counties
Statistics
Total Area
Urban
Water
Wetlands*
100.00
0.04
14.5
18-24
'Indicated by County, %
Kilometers
10
20
30
40
Figure 13. Wetlands and urban lands in Cass County area.
12
-------
Land Features
| Urban Lands
] Forests
Wetlands
Reservoirs and Lakes
Rivers |/S/| Roads
Watershed |/'V'' | Counties
Statistics %
Total Area 100.00
Urban 9.0
Water 4.5
Wetlands* 1-2
Kilometers
I •••-'! I I
10 20 30 40
Figure 14. Wetlands and urban lands in Twin Cities area.
Mississippi Basin, Minnesota
Major Watershed l/v I Clean Streams
Minor Watersheds L-'Vl Impaired Quality
Urban
Lakes
Sampling Sites
Kilometers
10
20
30
40
Figure 15. Minnesota River watershed in Twin Cities area.
13
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seasonal distribution in both areas. Average runoff
ranges from 150 to 250 millimeters (7, 29).
Conclusion
The current status of the project indicates that most of
the input data is available, though dispersed among
many agencies. In both the United States and Russia,
research methodologies have been developed and ap-
plied to study landscape feature impacts on runoff quan-
tity and quality based on simulation and statistical
analysis. The comparative analysis of hydrologic and
diffuse pollution processes on watersheds in the Upper
Mississippi and Upper Volga basins will allow derivation
of metrics of wetland loss relative to impacts on runoff
and water quality.
The applications of CIS to watershed hydrology are
currently much more advanced in the United States than
in Russia. Initiatives emerging in the United States,
however, could considerably promote CIS use in Rus-
sia. Such promotion is beneficial for several reasons.
First, this kind of cooperative political activity is in full
compliance with the 1992 Freedom Support Act, ap-
proved by the U.S. Congress. Second, support of CIS
as a new information technology will create a favorable
infrastructure in many bilateral economic fields and busi-
nesses. Third, a better meshing of the CIS systems in
the two countries will lead to further international coop-
eration in responding to global changes.
Project implementation also helps meet the goal of pro-
viding a basis for sound environmental, technical, and
economic decision-making on the use of natural re-
sources. This knowledge is essential in developing prac-
tical guidelines for sustainable economic development
through applied research and technologies.
Acknowledgments
The Water Quality Division of MPCA provided valuable
assistance with CIS data for Minnesota. The National
Science Foundation (NSF/EAR-9404701) contributed
research support. Any opinions, findings, and conclu-
sions or recommendations expressed in this material
are those of the author and do not necessarily reflect the
views of the National Science Foundation.
References
1. Wetland management: Hydraulic and hydrologic research needs.
1987. In: Wetland hydrology: Proceedings of the National Wet-
land Symposium, Chicago, IL (September 16-18). Association of
State Wetland Managers, Inc. pp. 331-333.
2. Sather, J.H. 1992. Intensive studies of wetland functions: 1990-
1991 research summary of the Des Plaines River Wetland dem-
onstration project. Technical Paper No. 2. Wetland Research, Inc.
3. Hey, D.L. 1985. Wetlands: A strategic national resource. National
Wetlands Newsletter 7(1): 1-2.
4. Kloet, L. 1971. Effects of drainage on runoff and flooding within
the Pembina River basins, North Dakota and Manitoba. Unpub-
lished report by the Bureau of Sport, Fisheries, and Wildlife (De-
cember).
5. Simon, B.D., L.J. Stoerzer, and R.W. Watson. 1987. Evaluating
wetlands for flood storage. In: Wetland hydrology: Proceedings
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16-18). Association of State Wetland Managers, Inc. pp. 104-112.
6. Johnston, C.A. 1994. Cumulative impacts to wetlands. Wetlands
14(1):49-55.
7. Higgins J.M., T.B. Nawrocki, and N.A. Nazarov. 1993. Hierarchi-
cal approach to integrated watershed management: Joint
TVA/Russian demonstration project. Proceedings of AWWA
CONSERV93 Conference, Sessions 4B-1 through 7C-3, Las
Vegas, NV. pp. 1,177-1,197.
8. Environmental Science Research Institute. 1993. Digital chart of
the world.
9. Vogelmann, J.E., F.R. Rubin, and D.G. Justice. 1991. Use of
Landsat thematic mapper data for fresh water wetlands detection
in the Merrimack River watershed, New Hampshire (unpub-
lished).
10. Conger, D.H. 1971. Estimating magnitude and frequency of
floods in Wisconsin. Open File Report. Madison, Wl: U.S. Geo-
logical Survey.
11. Jacques, J.E., and D.L. Lorenz. 1988. Techniques for estimating
the magnitude and frequency of floods in Minnesota. Water Re-
sources Investigation Report 87-4170. St. Paul, MN: U.S. Geo-
logical Survey.
12. Novitzki, R.P. 1979. Hydrologic characteristics of Wisconsin's
wetlands and their influence on floods, stream flow, and sedi-
ment. In: Greeson, P.E., J.R. Clark, and J.E. Clark, eds. Wetland
functions and values: The state of our understanding. Minneapo-
lis, MN: American Water Resources Association, pp. 377-388.
13. Larson, L.A. 1985. Wetlands and flooding: Assessing hydrologic
functions. Proceedings of the National Wetland Assessment Sym-
posium, Portland, ME (June 17-20). pp. 43-45.
14. Ogawa, H., and J.W Male. 1986. Simulating the flood mitigation
role of wetlands. J. Water Res. Planning and Mgmt. 112(1).
15. Johnston, C.A., N.E. Detenbeck, and G.J. Niemi. 1990. The cu-
mulative effect of wetlands on stream water quality and quantity:
A landscape approach. Biogeochemistry 10:105-141.
16. Moore, I.D., and C.L. Larson. 1979. Effects of drainage projects
on surface runoff from small depressional watersheds in the
North Central Region. WRRC Bulletin 99. Water Resources Re-
search Center, University of Minnesota.
17. Maslov, B.S., I.V. Minaev, and K.V. Guber. 1989. Reclamation
manual. Rosagropromizdat (in Russian).
18. Oberts, G.L. 1981. Impacts of wetlands on watershed water qual-
ity. In: Richardson, B., ed. Selected proceedings of the midwest
conference on wetland values and management. Navarre, MN:
Freshwater Society, pp. 213-226.
19. Horton, R.E. 1945. Erosion development of streams. Geol. Soc.
Amer. Bull. 56:281-283.
20. Chow, V. 1964. Handbook of applied hydrology. New York, NY:
McGraw-Hill.
21. Wishmeier W.H., and D.D. Smith. 1978. Predicting rainfall erosion
losses: A guide to conservation planning. Agriculture Handbook
No. 537. U.S. Department of Agriculture.
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22. Young, R.A., C.A. Onstad, D.D. Bosch, and W.P. Anderson. 1987.
AGNPS: Agricultural nonpoint-source pollution model. U.S. De-
partment of Agriculture Conservation Research Report 35.
23. Nazarov, N.A. 1988. Model formation of the flood hydrograph of
Northern Plain Rivers. Water Resour. 15(4):305-315.
24. Nawrocki, T., C. Johnston, and J. Sales. 1994. CIS and modeling
in ecological studies: Analysis of Beaver Pond impacts on runoff
and its quality. Voyageurs National Park, Minnesota, case study.
NRRI Technical Report 94/01 (February).
25. Anderson, J.P., and W.J. Craig. 1984. Growing energy crops on
Minnesota wetlands: The land use perspective. Minneapolis, MN:
Center for Urban and Regional Affairs, University of Minnesota.
pp. 1-88.
26. Mitsch, W.J., and J.G. Gosselink. 1986. Wetlands. New York, NY:
Van Nostrand Reinhold.
27. Brown, R.G. 1987. Effects of wetland channelization on storm
water runoff in Lamberts Creek, Ramsey County, Minnesota. In:
Proceedings of the National Wetland Symposium on Wetland
Hydrology, Chicago, IL (September 16-18). pp. 130-136.
28. SCS. 1975. Soil taxonomy: A basic system of soil classification
for making and interpreting soil surveys. Agriculture Handbook
No. 436. Soil Conservation Service, U.S. Department of Agriculture.
29. USGS. 1988. Water resources data: Minnesota, water year 1988.
Vol. 2, Upper Mississippi and Missouri River basins. U.S. Geo-
logical Survey Data MN-88-1.
15
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Mapping Vulnerability of Soils to Nitrate Leaching at Different
Scales, Using Different Models
Tamas Nemeth, Laszlo Pasztor, Jozsef Szabo, Zsofia Bakacsi
Research Institute for Soil Science and Agricultural Chemistry of the
Hungarian Academy of Sciences
ABSTRACT
Various scale environmental information systems together with statistically supported GIS
techniques were used for mapping the vulnerability of soils to a specific degradation
process. In our work we present approaches for the evaluation of the vulnerability of soils for
nitrate leaching. In two pilot areas -with different physiographical conditions - a
methodological approach was initiated. A deterministic model family was introduced for the
evaluation of the land vulnerability for nitrate hazard at a scale of about 1: 25,000.
Compilation of vulnerability maps is a result of an iteration where data characteristics
(availability, scale, informativity) and model parameters are in interaction. A stochastic model
is also presented for the evaluation of the land vulnerability for nitrate leaching. This latter
method was applied to mapping N-leaching hazard in Hungary at a scale of 1: 1M
INTRODUCTION
The increase of soil degradation at an alarming rate all over the world requires modeling and
quantification of the regional extent and severity of these processes (FAO 1983, Varallyay
1991). The various scale soil information systems and the inherent techniques of GIS
provide unique basis for studies of environmental degradation in modeling of changes in soil
characteristics e.g. mapping the vulnerability of soils to degradation or pollution (Batjes and
Bridges 1997, Pasztor et al. 1998). The produced maps may increase awareness on the
potential nature, severity and extent of soil degradation at regional scales, and also permit
identification of environmental hot spots that is sensitive, vulnerable or conflict areas
(Pasztor et al. 1999).
In the agricultural practice application of nitrogen to enhance crop yields is necessary in
most of the countries. The unproper use of the fertilizer-N (added to the soil N-pool) might
play a significant role in nitrate contamination of subsurface waters, which are the main
drinking water supplies (Addiscott et al. 1991). The agricultural practice in a certain area
exists together with other activities, which also take part in the contamination processes, i.e.
animal husbandry, leakage in urban and industrial canalization (Boumans et al. 1999). There
-------
are also some natural contamination sources like wet and dry deposit, nitrogen
transformation within the soil, nitrogen originating from the subsoil (and rocks), etc.
The proportion of nitrogen present in mineral forms is greatly affected by climatic and soil
conditions, by soil microbial population, and by land use. The ratio between the organic and
inorganic forms of nitrogen in soil can be modified only slightly over the years. However,
under certain environmental conditions (dry climate, negative water balance,
overfertilization) a great part of the surplus nitrogen can accumulate in the soil profile,
leaving the rooting zone of various crops, in the form of nitrate after the growing season
(Nemeth, 1993a,b; Kovacs et al. 1995), even when land is cropped annually. Integration of
knowledge related to environmental conditions of a certain area with the soil, water, and crop
management practices helps to prevent the simultaneity of the unfavorable processes
leading to nitrate leaching, thus water resources may be protected from nitrate pollution of
agricultural origin. It is of increasing importance that such an approach be applied in crop
production.
It has been estimated that 80 percent of data have some geographic component using
economic, environmental, political, social etc. information. A GIS is a tool that uses the
power of the computer to pose and answer geographic questions by arranging and
displaying data about places in a variety of ways, such as maps, charts, and tables (http:
//www.esri.com, 1999). People have used maps for thousands of years to clearly present
information about places, and GIS is a modern extension of that ancient tradition (Longley et
al., 1999). Geographical Information Systems are designed for storing, querying, analyzing
as well as for displaying data and/or information with spatial characteristics. GIS represents
both the techniques to realize spatially manageable storage of spatial data and extended
opportunities of spatial analysis on the stored data (Maguire 1991, Scholten 1995). Recently,
this latter is getting more and more attention.
GIS often lacks any means to deal with stochastic processes (Burrough 1999), ignoring the
parallel development in spatial and/or multivariate statistics. In our work we are using
stochastic models (Linhart and Zucchini, 1986) and apply various methods of multivariate
statistical analysis (Lebart et al. 1984, Murtagh and Heck 1987). There are evidences in
recent works (e.g. Pasztor and Csillag 1995, Pasztor et al. 1998, Toth et al. 1998) that
automatizable multivariate descriptive data processing methods can efficiently be applied to
analyze and model various environmental processes, occasionally supplied with the tools of
information theory.
MATERIALS AND METHODS
-------
Hierarchical structure of modeling
VNL MODEL
FAMILY
1st level
2nd level
3rd level
4+h level
5+h level
INFLUENCING
FACTORS
DATA
DERIVED
FACTORS
MATHEMATICAL
MODEL
PARAMETERS
FINAL MEMBER
Figure 1. The sketch of hierarchical structure of modeling
GIS based mapping of soil vulnerability is composed of a hierarchical structure of models
and the final map is a result of subsequent model selections (Fig. 1). On the first level the
influencing factors are identified: 'Which environmental parameters determine the given
vulnerability feature?' On the second level the available data on the formerly selected
parameters are identified: 'What kind of datum is measured and/or available at all on the
given influencing parameter?' On the third level some derived factors may be calculated:
'May the input data be converted/transformed into more appropriate format?' On the forth
level the mathematical model is set up: 'What is the functional relation between the
vulnerability feature and the determining factors?' On the further levels the refinement of the
-------
mathematical model takes place: 'Which value should be assigned to parameters to get
good/better/the best result?'
The above-described procedure induces a model family. In this context compilation of the
vulnerability map is the result of iteration where data characteristics and model parameters
are in interaction.
Vulnerability of soils to N-leaching
Beside soil characteristics the effect of precipitation and the accessibility of groundwater
determine the possibility of nitrate contamination of groundwater (Boumans et al., 1999,
Nemeth et al., 1998). Physico-chemical characteristics of soils represent their buffer
capacity/resistance feature according to the transfer of pollution. Precipitation surplus
induces nitrate pollution to move downwards. Finally, location of groundwater table
determines the distance to be done by nitrate to reach the water body.
Large-scale approach
The objective of the large-scale nitrate vulnerability mapping project was to establish an
approach which is able to provide nitrate vulnerability maps at a scale of 1: 25,000 for
different geographical conditions. Two pilot areas were selected for the implementation. The
two regions differ in their physiographical conditions, land use, exposure to pollution and
even the two sets of data available for them are distinct. The two pilot areas are well-defined
physiographical units each. Csepel Isle is enclosed by River Danube, Watershed of Tetves
Creek is a subcatchment of Lake Balaton. Area of the former is 248 km2, that of the latter is
120 km2. A simple comparison of the two pilot areas is given in Table 1.
Table 1. Some characteristics of the pilot areas
Csepel Isle Tetves Creek
physiography: plain, island hilly, catchment
dominant land use: arable land forest, pasture
exposure to pollution: base of drinking water Lake Balaton
soil information: PemeTIR database Kreybig GIS
groundwater information: detailed, available incomplete, derived
precipitation information: poor poor
-------
The following data representation of the influencing parameters was available. For the
description of precipitation annual average precipitation measurements were used. In first
approximation it characterizes properly the degree of induction for leaching. For better
results seasonal variability in precipitation as well as effects of non-natural water input
(irrigation) should be considered. For the description of groundwater table measures on its
average depth were used. To achieve more precise results, seasonal changes also in this
parameter should be accounted for. For the representation of resistance of soils against the
transfer of nitrate pollution their physico-chemical characteristics were used, namely texture
and organic matter content together with depth of rootable depth of soils. For better results
vertical variability in these parameters should taken into account by description of soils
horizon by horizon (e.g. layer by layer).
Two factors were derived from the raw soil data. Organic matter content of soil is a density
type parameter, consequently its product with rootable depth provides a quantity featuring
column density of organic which is a much more proper parameter in our context. On the
other hand, texture information on soil is generally (and as it was in the present case) given
in categories. Thus according to their transferability, soils were described by numerical
values on ordinal scale based on the knowledge of their texture properties.
The next step was the set up of the mathematical model. As a first approximation, general
linear model was used as it is commonly used in multivariate methods.
4
V=Z w*Fj , where (1)
V is the measure of vulnerability, F, is the /th factor and w/ is its corresponding weight.
Small-scale approach
In our small-scale approach a stochastic model is put forward for the evaluation of the land
vulnerability for N-leaching. For the determination of nitrate leaching hazard in national scale
three influencing factors proved to be relevant and available.
> Hungarian soils were classified into nine main soil water management categories
according to their hydrophysical properties (Varallyay et al., 1980). The categories
characterize infiltration rate, permeability, and hydraulic conductivity, field capacity
and water retention features of Hungarian soils.
-------
> Input map of 'Annual average precipitation' is based on data collected by the National
Meteorological Institute and registered by meteorological stations for the period of
1951-1980.
> Map of 'Groundwater depth' is based on data collected in the frame of groundwater
observation well network of Scientific Research Center for Water Resources and
registered for the period of 1961-1980. The scale of this map is also a 1: 1,000,000
and covers non-mountainous region of the country.
Information on precipitation and soil has been available within AGROTOPO digital database
compiled in Research Institute for Soil Science and Agricultural Chemistry of the Hungarian
Academy of Sciences (RISSAC HAS) (Varallyay et al., 1985; Szabo and Pasztor, 1994).
AGROTOPO is composed of territorial information on soils at a scale of 1: 100,000 and
meteorological conditions at a scale of 1: 1,000,000 for the whole country.
The input data had to be harmonized because of the following reasons.
> Groundwater and precipitation information was originally collected in points, then
interpolated, which resulted in isolines, however our procedure requires polygon
features as input. Thus transforming contour information into polygon information
restructured these maps.
> Scale of these two maps and that of water management categories map was highly
dissimilar. Due to this fact information content of the latter must have been reduced,
that is the map was generalized also to a scale of 1: 1,000,000.
> Finally, since the map of groundwater depth is constrained to non-mountainous
regions, the other two maps must have been restricted to this extent, too.
Once having the accurate database as a set of topologically constructed, associated
coverages, geographic analysis could be performed. To complete our objectives, the maps
of various factors, as different layers, were overlaid (intersected). Feature attributes from all
coverages were joined, that is a polygon of the resulted map is characterized by three
attributes as opposed to the single attributes of the original maps.
The inherent errors of both analogue and digitized maps result in shifts of arcs. Thus even
the correlated (e.g. common geographical) boundaries deviate from each other in the
-------
different maps. As a consequence, in the course of overlay of maps displaying correlated
information sliver (that is small and/or elongated) polygons emerge. The number of this kind
of polygons can be significant. Since they apriori distort the results, as well as cause
overcomputation, they must have been eliminated. The criterion of elimination was set up
based on the distribution of area of resulted polygons. Defining a threshold value under
which polygons were eliminated cut the sharp peak of the distribution.
From mathematical/statistical point of view units of the resulted map are elements of a
multidimensional factor space. Statistical behavior of the almost 500 units in this three-
dimensional feature space was then studied. Since the number of units does not necessarily
reflect their extent, in the computations their areas were used as weights.
Applying pure clustering techniques where there is no a priori rule to define the optimum
number of groups, one needs some measure of the reality of the groups found by the
partitioning algorithm. Finding the extreme value of an information theoretic criterion can
provide the best fitting model and the best partition of the sample. Many model selection
procedures may be found in the literature. Most of them take the form of a penalized
likelihood, where a penalty term is added to the log-likelihood in order to compromise
between the goodness-of-fit and the number of parameters. The ancestor of these models
was developed by Akaike (1972) and we also turned to it, since it provides a versatile
procedure for statistical model identification. The definition of Akaike's Information Criterion
(AIC) is:
AIC=-2ln(maximum likelihood)+2 (# of parameters). (2)
One of the most desirable properties of AIC is that (as it penalizes for large degrees of
freedom) it tends to adopt simpler models and achieves a principle of parsimony. As AIC is
basically an estimator of the risk of a model selection, it should be minimized to select
among the alternative possibilities, that is the smaller is AIC the better is the classification of
the points into groups. Its estimate can be computed by,
AIC(estimated)=nln(R)+2p, (3)
where n is the number of objects to be grouped, p is the number of estimated parameters
and R\s the residual sum of squares of deviation from the fitted model (Akaike, 1974).
-------
For the determination of optimal classification a sequence of non-hierarchical clustering was
carried out and AlC(estimated) was calculated for each partition.
RESULTS
To get final result, w, should be determined/defined in formula (1). In the case of vulnerability
term of definition is more appropriate. In the simplest case role of the four (original or
derived) factors can be considered uniform. In this case weights merely help to standardize
factors that is to make their scale comparable. The resulted vulnerability maps of the two
pilot areas are displayed in Fig 2 and Fig3.
Spatial categorization (zoning) of land according to its sensitivity to a given pollution is
generally required by decision-makers. This involves regionalization of the resulted mapping
units, which are characterized by vulnerability values on nominal scale as a result of our
large-scale approach. Our small-scale approach, solved this problem together with the 'pure'
mapping procedure.
-------
EOIR 224.000
Mapping vulnerability
toN-
in
Isk
Derived vulnerability
features
MxlelNo. 1
least vulnerable
most vulnerable
settlements
na
2.5 0 2.5 5 7.5 10
Hungarian Unified Map Projection System
Conpiled inRISSAC GS Lab
Figure 2. Result of large-scale approach on the Isle of Csepel
-------
Maf
to TV
inH
Vdtte
'lity
lap Projection System
BOTR 152000
Figure 3. Result of large-scale approach in the watershed of Tetves Creek
10
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Estimated AIC function for our dataset showed two local minima at 5 and 12 categories
respectively. The 12-class solution in our scale provides too detailed thematic resolution,
however a possible spatial zooming-in in the future may require also this thematic
detailedness. Consequently, merely the 5-class solution was further studied. The
identification of vulnerability categories was facilitated by displaying of the resulted
categories on the units of the intersected map. The different categories showed well
recognizable patterns. Analyzing their geographical distribution and extent, it was also
possible to rank the resulted categories into a one-parameter sequence from severe hazard
to the case of no hazard (Fig. 4).
Vulr
Figure 4. Result of small-scale approach
Severely susceptible soils are located on calcareous alluvial deposits where annual
precipitation is relatively and groundwater level is actually high. They are characterized by
shallow humus layer but relatively deep and coarse textured pedon, pH is mostly neutral,
they have carbonate from the top, their texture class is mostly sand and loam. Highly
susceptible soils are located on alluvial plains, where annual precipitation is relatively low
(under 600 mm/year) and groundwater level is high. The pH, carbonate content and particle
size distribution of these soils depend on the flooding material. Moderately susceptible soils
are located on the coarse texture covered plains and chernozem areas. In spite of good
drainage conditions these pour and humus sandy soils are moderately susceptibility
11
-------
because of low annual precipitation and deep groundwater level. Slightly susceptible soils
are located on poor drainaged hydromorphic soils. Either flat areas are occupied by this
category or they are located in depressions, where level of ground water is relatively high,
soil texture tends to be heavier, amount of annual precipitation is the lowest in the country.
Relatively unsusceptible soils are located on salt-affected landscapes; on heavy textured
meadows of floodplains and peats; in poorly drained boggy and swampy depressions. In
spite of different properties of these soils, they are commonly featured by poor drain
conditions. 12-class solution of the procedure, by all means, might require more detailed,
e.g. multi-level or multi-furcated explanation.
CONLUSIONS
The presented procedures provide application-sensitive approaches to the general problem
of evaluating the vulnerability of various environmental elements for different degradation
processes. The methods rely upon the up-to-date tools of GIS, multivariate methods and
information theory.
Result of our large-scale approach represent a unique member of a model family where
further family members may provide similarly good or even better results. However this
member was reached in the course of subsequent model selections and each model
selection means a compromise between optimum and executibility.
In our small-scale case study, the resulted map was easily interpretable providing
straightforward characterization of vulnerability of Hungarian soils for leaching of nitrate
accumulated after the growing season.
Our procedures are proposed for application in other fields with similar conditions.
ACKNOWLEDGEMENTS
The present work was partly founded by the Hungarian National Research Foundation
(OTKA, Grant No-s T021275 and F026089).
12
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Pasztor L, F. Csillag, 1995, 'Reduction of high resolution spectra; Application to
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14
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Habitat Filters, GIS, and Riverine Fish Assemblages:
Sifting Through the Relationships
Douglas A. Nieman1, Gregg S. Sermarini, and William S. Ettinger
Respectively: Senior Scientist, Information Systems Manager, and Principal Ecologist
Normandeau Associates, Inc., 3450 Schuylkill Road, Spring City, Pennsylvania 19520
Thomas Proch
Regional Biologist, Pennsylvania Department of Environmental Protection
400 Waterfront Drive, Pittsburgh, Pennsylvania 15222-4745
John Arway
Chief, Environmental Services Division, Pennsylvania Fish and Boat Commission
450 Robinson Lane, Bellefonte, Pennsylvania 16823-9616
Jerry Schulte
Senior Biologist, Ohio River Valley Water Sanitation Commission
5735 Kellogg Avenue, Cincinnati, Ohio 45228-1112
ABSTRACT
Hierarchy theory suggests that natural systems are organized as an endogenous series of
levels that reflect differences in process rates acting over a range of spatiotemporal scales.
Several hierarchical classifications of spatial scales have been proposed for lotic systems (e.g.,
basin, network, reach, channel unit, patch). We used a classification similar to one by Lubinski
for navigation rivers (basin, network, reach, navigation pool, aquatic area, patch) to develop a
hierarchical filter model of habitat quality and assemblage composition in mainstem navigation
pools of the upper Ohio River basin. A macroscale filter first considers the geographic
distribution of species in the regional assemblage across 10 pools in the study area. This filter
reflects the influence of historical events and response of individual species to long-range
environmental gradients at the basin, network, and reach scales. Aquatic areas defined by
channel geometry comprise a mesoscale filter. Aquatic areas divide a navigation pool into
lateral and longitudinal components defining major within-pool gradients of depth, velocity,
substrate, and cover. Literature-based information on habitat requirements was used, via
canonical correspondence analysis, to group species and life-stages into ten "species-habitat
associations" that segregate species by anticipated habitat use along major lateral (margin to
midchannel, or primary to secondary/tertiary channel) and longitudinal (tailwater-riverine to
lower pool-lacustrine) habitat gradients within navigation pools. After accounting for macroscale
and mesoscale influences, filter models can be further refined by considering microscale habitat
preferences in relation to the distribution of cover and substrate patches mapped at finer spatial
1 Corresponding author, Dnieman@normandeau.com
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scales. Theoretical underpinnings of the filter concept are discussed with respect to habitat
features that appear to shape fish assemblage patterns across multiple spatial scales.
INTRODUCTION
Successful natural resources management requires knowledge of the past, present, and
potential future status of ecosystem components that sustain or comprise management units.
The elements of required knowledge can be divided broadly into realms of status (inventory)
and process (mechanisms). Status recognition involves the questions "what" and "how much",
which are addressed with techniques of classification, measurement, and statistical estimation.
Process estimation asks "how" and involves mechanistic understanding, which confers
predictive ability. The questions "where" and "when" bridge these realms by challenging us to
account for the mechanisms responsible for spatiotemporal variation. The advent of geographic
information systems (GIS) has provided tools to effectively manage large datasets, enabling
construction of spatially explicit ecological inventories. With GIS, spatiotemporal variation can
be measured and analyzed within a multi-scale context, facilitating construction of ecological
models that incorporate the kinds of spatial complexity observed in nature. However, effective
use of such tools can be hindered by lack of a conceptual framework for guiding applications.
Hierarchy theory (Allen and Starr 1982, O'Niell 1989, Levin 1995) provides a unifying
conceptual approach for understanding patterns of natural variation. A main tenet of this theory
is that complex systems have an endogenous organization that is structured in part by
differences in process rates acting over a range of spatiotemporal scales. For a given scale,
activities or patterns at finer, briefer scales appear as unpredictable "noise", while those at the
coarser, slower scales of higher levels appear as near-constant features of the environment that
constrain activities or the expression of variability at the chosen level of perception.
There are consequences arising from this conceptual framework that can provide clues to
mechanisms that generate ecological pattern. For example, higher-level features may constrain
expression of lower-level properties, causing variation in the lower level to appear non-random
and spatially organized (Legendre and Fortin 1989, Borcard etal. 1992). Repetition of
disturbance at lower levels through space and time eventually alters higher-level properties
(Lubinski 1993), leading to synergism, positive feedback, emergence, and other forms of
surprise (Casti 1994). Ecologists have learned that large-scale processes drive many patterns
at fine scales (Rabeni and Sowa 1996, Richards et al. 1996), and that the problem is not to
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select a correct scale for description, but to recognize that change occurs at many scales all at
once; interactions among scales should therefore be afforded great scrutiny (Levin 1995).
A common goal in applied ecology is to predict status of biological populations from knowledge
of physical habitat. Numerous methods, based on various conceptual approaches, exist for
making such predictions. A model well-suited for GIS application is the "hierarchical filter", which
contends that in order to "belong" to a local assemblage, a species must possess traits that
allow it to "pass" through a nested series of ecological filters operating at successively finer
spatiotemporal scales. Furthermore, if a given patch of habitat is to be suitable, the filter model
dictates that higher-level features of the environment, within which the patch is embedded, must
be suitable as well. Scales may range through several orders of spatial and temporal
magnitude, from broad-evolutionary (global, continental) through intermediate-ecological
(ecoregion, basin), to fine-stochastic (mosaic, patch). This model has appeared several times in
the ecological literature, especially in aquatic contexts (Smith and Powell 1971, Poff and Ward
1990, Tonn 1991, Imhoff etal.1996, Poff 1997).
Our objectives in this paper are first, to briefly review the literature on hierarchical filters,
second, to identify potential habitat filters that may shape fish assemblage patterns in the Ohio
River and other large navigation rivers; and third, to summarize GIS-based models of habitat
quality and fish assemblage composition we developed for navigation pools of the upper Ohio
River basin in Pennsylvania. We conclude with some thoughts on potential uses of this model
for fisheries research and management. Our goal is to foster a more holistic understanding of
fish-habitat relationships in navigation rivers than is provided by approaches that focus on only a
single level of ecological description, e.g., on local habitat features.
REVIEW OF HIERARCHICAL FILTER MODELS
Smith and Powell (1971) offer an early description of hierarchical filters in their discussion of
"screens" that helped shape fish assemblages in Brier Creek, Oklahoma, a small southwestern
warmwater stream. In his recent book on freshwater fish ecology, Matthews (1998) draws
heavily on hierarchical perspectives as a framework for organizing factors that shape patterns of
fish distribution and abundance. Tonn (1991) filtered the world's fish fauna through four levels to
show how global warming might influence local assemblages via different pathways. Imhoff et
al. (1996) adopted a hierarchical system linked to specific mechanisms regulating salmonid
abundance. Poff (1997) explored links between macroinvertebrate traits and habitat filters within
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levels of a nested landscape hierarchy. Other researchers concerned with description of
physical habitat have proposed hierarchical systems that reflect "endogenous organization"
(Frissell et al. 1986, Lubinski 1993, Wilcox 1993). These examples share the common attribute
of hierarchical structure, and are similar in terms of the number and characteristics of levels
(Table 1).
An important feature of natural hierarchies is a positive association between spatial extent and
temporal constancy. Larger spatial scales are generally associated with processes that occur
slowly over long periods of time, while local processes linked to finer spatial scales operate at
higher frequency (Poff and Ward 1990). Often, the major processes that drive ecosystem form
and function are grouped as distinct levels in a nested hierarchy (Kolasa 1989).
Although hierarchical classifications can organize patterns of variation in physical habitat
characteristics, understanding their role as "filters" depends on identification of the
environmental characteristics that selectively cull potential assemblage members at
successively finer scales. Thus, it is necessary to understand how species' traits confer ability to
resist selective environmental constraints that would act at some level to remove a species from
a local assemblage (Poff and Ward 1990, Poff 1997). Such traits encompass morphological,
physiological, behavioral, and other life-history characteristics of organisms (Imhoff etal. 1996,
Poff 1997, Schlosser 1990, 1991, Winemiller and Rose 1992). In Hutchinsonian terms, the filter
model contends that a species' niche requirements will only be met where "windows of
opportunity" overlap across the range of spatiotemporal scales that ultimately influence local
assemblages.
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Table 1. Examples of hierarchical filter models3 and habitat classifications'3 developed for aquatic systems.
Type of system
(Authors)
Levels in Description of filtering effects or levels
hierarchy (simplified; see original papers for details)
Fishes living in
freshwater
lakes and streams3
(Smith and
Powell 1971)
Fishes living
in north temperate
lakes3
(Tonn 1990)
Benthic macro-
invertebrates
in streams across
a landscape3
(Poff 1997)
Large navigation
riversb
(Lubinski 1993)
Controls on
stream habitatb
(Frissel et al. 1986)
Scales for
watershed
systems
analysis"
(Imhoffetal. 1996)
Gross physiology
Gross geography
Fine geography
Climate
Fine physiology
Continental filter
Regional filter
Lake-type filter
Local filter
-Filters freshwater fishes from world fauna
-Determines continental taxonomic composition
-Determines regional species pool
-Affects local distribution patterns
-Local suitability affects ultimate composition of assemblages
-Determines continental fauna
-Determines regional species pool
-Separates regional fauna into lake-type species pools
-Local fish assemblages selected from lake-type pools
Watershed/basin filter -Effects of history, climate, geology, and disturbance
Valley/reach filter -Effects of stream confinement, slope, lithology, channel morphology
Channel unit filter -Effects of stream morphology, hydraulics, substrate distribution
Microhabitat filter -Influence of depth, velocity, particle size, food availability,
biotic interactions, etc., on habitat selection
Basin
Stream network
Floodplain reach
Navigation pool
Habitat
Stream system
Segment system
Reach system
Pool/riffle system
Microhabitat system
Watershed
Subwatershed
Reach
Site
Habitat element
-Fundamental landscape units defined by watershed boundaries
-Flowing channels within basins; linkage between aquatic
and terrestrial ecosystem components
-Length of river defined by degree of interaction between river
and valley/floodplain in conjunction with disturbance regimes
-Length of river between adjacent dams; artificial element of the
landscape that fundamentally alters finer scale habitat features
-Fine-grained structural components most intimately associated
with biological assemblages
-Uplift and subsidence, volcanism, and climate change variables that
influence planation, denudation, and drainage development
-Minor glaciation, volcanism, earthquakes, landslides, and
valley development that control evolution of drainage pattern
-Debris torrents, landslides, meandering, human alterations that
influence stream bed elevations through modification of sediment
storage, bank erosion, and riparian vegetation.
-Delivery/transport dynamics of wood and sediment affected by local
bank failure, flood scour and deposition, and human activities
-Sediment load, organic matter transport, localized scour, and
seasonal plant growth affecting depth, velocity, accumulation
of fines, community metabolism, structural habitat heterogeneity
-Drainage divides between tertiary basins
-Boundaries of stream basins nested within watershed
-Minimally, two meander wavelengths within a stream segment
of a specific type (i.e., Rosgen 1994), bounded laterally by 1:20-year
(active profile) and 1:100-year (passive profile) flood elevations
-Channel segment bounding a single riffle or pool lengthwise,
bounded laterally by bankfull elevation
-Area of relative homogeneity in depth, velocity, and
substrate within a site
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HABITAT FILTERS FOR NAVIGATION RIVERS
We define navigation rivers generally as those that support commercial barge traffic. We restrict
our discussion (but not our definition) to systems where navigability is maintained by "low-head"
locks and dams, excluding systems where navigability is maintained by channel training
structures (e.g., lower Mississippi River, Baker et al. 1991) or by large dams with massive
reservoirs (e.g., many Tennessee River impoundments). We invoke a conceptual model of the
upper Mississippi River system (UMRS) by Lubinski (1993) as a framework for developing a
hierarchical approach to organizing patterns of habitat and assemblage variation in the Ohio,
Allegheny, and Monongahela River mainstems, where navigability is maintained by a chain of
38 locks and dams. These rivers share many similarities but exhibit important differences
compared to the UMRS, which we will note during the following discussion.
The model identifies five important spatial scales: basin, stream network, floodplain-reach,
navigation pool, and habitat. Table 2 lists important features of this model which are useful in
developing a "filtering framework" for estimating habitat suitability and fish assemblage
composition from knowledge of environmental conditions. In this section, we discuss each scale
within the context of the Ohio River, and identify possible filtering mechanisms, hypothesized
here or by others, and how they might influence fish assemblages across the range of identified
spatiotemporal scales. In examining potential habitat filters that shape fish assemblages, it is
important to remember that assemblages are composed of individual organisms that respond to
environmental selective forces operating within the context of local habitats (Poff 1997). Higher
level filters operate by constraining local conditions, making certain combinations of local factors
possible while ruling out others. Coarse filters may also preclude a species from otherwise
acceptable localities because they reflect the influence of history, or because they account for
long-range effects of local disturbance, changes in patterns of spatial connectedness, and other
phenomena that are determined by the way in which local habitats are distributed across a
landscape.
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Table 2. Spatial scales of the Ohio River, with major structural, functional, and scale characteristics at each level. Adapted from Lubinski (199
Spatial Scale:
Components:
Major features:
Basin
Stream network
Abiotic factors:
Biotic factors
Natural
disturbance
Human
disturbance
-Basins and sub-basins -Headwaters, tributaries,
mainstems
-Fundamental units of
landscape division;
-Provide structural and
functional constraints
on lower levels of
organization
-Size and location
-Climate and geology
-Vegetation
-Dispersal
-Glaciation
-Flood and drought
-Catastrophe
(volcanism, wildfire)
-Agriculture
-Urbanization
-Industrialization
-Resource extraction
Approximate range in scale
-space
-time 101-104yr
103-107km2
-Interactions between
aquatic and terrestrial
ecosystem components;
-Linear processes
emphasized, modified
by network shape and
position
-Drainage pattern
-Hydrology
-Channel morphology
-Water quality
-Vegetation
-Dispersal and migration
-POM dynamics
> Bankfull flow
-Intermittency
-Stream capture
-Channelization
-Dams and diversions
-Contaminants
-Exotic species
10"1-103 km2
10°-103yr
Floodplain-reach
-Reaches with
valley, floodplain,
channels, cutoffs
-Interactions between
channel and valley-
flood plain
-Annual floods
-Valley gradient and
floodplain morphology
-Thermal regime
-Sediments and nutrients
-Vegetation production
and diversity
Navigation pool
-Locks and dams and
associated navigation
pools
-Artificial component of
the landscape
-Modifies structural
components and
functional processes
at habitat scale
-Size and location
-Channel geometry
-Lateral, longitudinal, and
vertical gradients
-Vegetative cover
-Flood and drought
Habitat
-Aquatic areas and
habitat conditions
-Scale most intimately
with the abundance
and distribution of biota
-Absence of flood pulse
-Meandering (local
distrubance providing
long-range equilibrium)
-Levees and revettments -Water level management
-Harvests
101-103km2
1(J1-103yr
101-103 km2
10~1 -101 yr
-Velocity and turbulence
-Substrate and cover
-Depth and clarity
-Water chemistry
-Diel variation
-Vegetation (riparian,
submergent, emergent)
-Plankton
-"Engineer" species
-Bioturbators
-Windthrow, debris
-Ice
-Wind-generated waves
-Snag removal
-Dredging and disposal
-River traffic, fleeting
-Berming
10'3-104m2
1Q-2-10°yr
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Basin Scale
Basins are fundamental landscape units delimited by watershed boundaries (Petts 1989,
Lubinski 1993). Basins can be decomposed hierarchically as nested subbasins down to the
level of individual watersheds (Frissell etal. 1986). Characteristics of geology, landform,
climate, and vegetation are basin attributes that shape and constrain features at finer levels.
Lubinski (1993) defines such characteristics as "major factors" that control the range of
ecological heterogeneity at finer scales. These attributes are relatively static when viewed from
lower levels in a habitat hierarchy, but do change over long periods of time. Changes in such
factors are usually associated with high-magnitude, low-frequency events such as glacial
advance/retreat, earthquakes, and volcanism that qualify as "disturbance". Disturbance at
coarse scales fundamentally alters finer scale habitat, doing so over relatively brief periods of
evolutionary time. Frissell et al. (1986) note that such disturbance is "extrinsic" in that it changes
the system's potential by creating a "new" system with a different range of possible future states
from the destruction of the "old" system that had a different range of possible future states.
Between disturbances that either create or destroy them, systems are modified by "intrinsic"
developmental processes.
Application of the habitat filter concept at the basin scale should incorporate historical legacies
that determined biogeographic patterns of species' distributions and thus the pool of species
potentially available to local assemblages at finer spatial scales (i.e., within and between sub-
basins). Much of the modern broad-scale distribution patterns in the Ohio River basin were
shaped by events associated with glaciation and glacial retreat (Trautman 1981, Strange 1999).
Efforts to relate fish assemblages to local habitat variation should therefore be placed in a
context that recognizes regional history. Trautman (1981, pages 1-12) provides such a regional
analysis by summarizing how fish assemblages of present-day Ohio were shaped in part by
broad-scale, regional variation in climate, topography, and the effects of glaciation.
The importance of broad-scale phenomena can also be seen in the swift alterations to native
fish fauna in the Ohio River that followed European settlement, largely resulting from the
conversion of natural landscapes into agricultural, urban, and industrial systems (Trautman
1981, Pearson and Krumholz 1984). Such changes reflect the cumulative nature of human
disturbance, which, when repeated often at many places, begins to impact larger scales
(Lubinski 1993). Even small, seemingly isolated disturbances, can "scale-up" by inducing
positive feedback. Construction of a dam, or destabilization of a local stream bank, can alter
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stream channel geometry, hydraulics, and sediment transport over distances that far exceed the
boundaries of the local disturbance (Leopold 1994). Such magnification of individual
disturbances serves to decrease the number of such events required to modify basin-level
controls on aquatic ecosystems.
Stream Network Scale
As described by Lubinski (1993), water-carrying channels above a defined location within a
basin comprise the stream network scale, which is similar to the stream system scale of Frissell
et al. (1986). Stream networks resolve linkages between the aquatic and terrestrial components
of a watershed ecosystem. This scale focuses on interactions between streams and riparian
zones (Gregory et al. 1991, Schlosser 1991), longitudinal processes (Vannote et al. 1980), and
the influence of tributary spatial position on biotic assemblages (Gorman 1986, Osborne and
Wiley 1992, Fairchild et al. 1998). Natural disturbances at this scale include large floods,
drought, and events that alter drainage patterns (Lubinski 1993, Strange 1999). Human
disturbances include dams, altered hydrologic regimes, and fragmentation and isolation of the
network by numerous mechanisms (Sheldon 1987, Lubinski 1993, Pringle 1997).
Since our focus is on the larger mainstems of the drainage network, it is important to identify
how certain filters might operate at the stream network scale to influence "main river" fish
assemblages. As accumulators of flow from large areas, large rivers reflect their position in a
drainage network in terms of channel morphology, energy, sediment, water quality, flow regime,
and instream habitat (Vannote et al. 1980, Ryder and Pesendorfer 1989, Sedell et al. 1989,
Ward and Stanford 1989, Junk et al. 1989). Thus, the filtering influences on a regional species
pool at the stream network scale should reflect the segregation of species according to
preferred ranges of stream size, coupled with other habitat differences related to position within
the drainage network (Osborne and Wiley 1992). However, such influences are not simply those
that reflect differences in preferred habitat use related to stream size, which include inherently
local factors, i.e., local filters will remove species incapable of maintaining populations in a local
setting. Rather, network filters reflect geomorphologic and fluvial controls on local habitat and
patterns of connectivity that helped determine how different fish species came to be where they
are today.
Understanding the filtering role of habitat variation at the stream network scale on mainstem
assemblages should start by considering pristine mainstems. Accounts of habitat conditions in
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the pre-settlement Ohio River (Trautman 1981, Pearson and Krumholz 1984) paint a picture of
the Ohio River that is much different than the one we see today. The clearer water, lower
sediment load, shallower and faster riffles and rapids, and extensive floodplain marshes
supported a fish fauna that was markedly different than the present one. The pristine Ohio River
and major tributaries likely served as habitat for a greater array of obligate riverine species,
some of which specialized in "big river" habitat. Larger species such as paddlefish (Polyodon
spathula), shovelnose sturgeon (Scaphirhynchusplatorynchus), gars (Lepisosteusspp.), blue
sucker (Cycleptus elongatus), flathead catfish (Pylodictis olivaris), blue catfish (Ictalurus
furcatus), and some smaller species (e.g., mimic shiner, Notropis volucellus; emerald shiner, /V.
atherinoides) probably achieved maximum population densities in the main river and lower
reaches of major tributaries. Some species (e.g., many catostomids) maintained large adult
populations in mainstems, but relied more or less on tributaries for nursery habitat. Many other
species ranged throughout much of the drainage network, with populations found in both
mainstems and tributaries. Still others generally occurred in mainstems as strays from
populations centered in tributaries. For such species (e.g., many Etheostoma and Percina),
mainstems still served as important migration corridors that maintained connections between
separate local populations. Mainstems probably served as refugia for small stream species
during episodic disturbance (e.g., drought), conversely, tributaries could shelter fish from poor
main river conditions (e.g., floods or low dissolved oxygen, Gammon and Reidy 1981).
Mainstems were altered progressively after European settlement, beginning with large-scale
changes in physicochemical conditions linked to deforestation and conversion of the landscape
to agriculture (Trautman 1981). Somewhat later, industrial development marked the beginning
of an era of severe pollution that abated only recently. Such disturbances directly impacted
fishes using mainstems, leading to extirpations throughout many kilometers of river (Trautman
1981, Cooper 1983). Alterations to both tributaries and mainstems undoubtedly changed
metapopulation structures by interfering with the normal patterns of migration that maintained
gene flow and allowed local populations to recover from natural disturbance. Before the period
of severe pollution ended, construction of the navigation system fundamentally altered
mainstem habitat, likely influencing patterns of connectivity even further. As a repetitive feature,
the chain of navigation dams represents a "scaling up" of the local effects associated with any
one dam, possibly influencing a spatial scale higher up in the ecosystem hierarchy by altering
patterns offish movement within the drainage network.
10
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Filtering influences at the stream network scale may interact with heterogeneity at lower levels
in the Lubinski (1993) hierarchy. For example, Emery et al. (1999) document changes in the
species composition of catostomids within and across navigation pools in the Ohio River. They
cited possible causes for shifts in abundance between round and deep-bodied suckers that can
be related to heterogeneity across four scales. Round-bodied suckers depend more on lotic
conditions (e.g., fast currents, coarse substrates), while deep bodied suckers thrive in lentic
conditions. Thus, local assemblages at fine scales differ in sucker representation depending on
the relative mix of these habitat types. In navigation pools, lotic and lentic habitats are divided
between the upper and lower ends of each pool, respectively (Wilcox 1993). Chaining dams
together results in an alternating sequence consisting of a lotic tailwater below an upstream
dam that grades into a lentic environment above the next dam downstream. This pattern is then
modified over a series of dams by heterogeneity at the reach scale. The lowering of slope
downstream along the river results in longer pools, decreased sediment particle size, changes
in trophic dynamics, and increased distance between mainstem habitat favorable to round
bodied suckers. Off-channel habitat favorable to reproduction of deep bodied suckers is more
developed in lower gradient reaches where channel cutoffs, embayments, and flooded tributary
mouths are more extensive. An influence at the stream network scale is apparent because of a
decrease in the number of tributaries along the lower Ohio River compared to upstream (Emery
et al. 1999). As favorable areas for reproduction in the mainstem dwindle, maintenance of round
bodied sucker populations would depend on availability of tributary spawning habitat. Fewer
tributaries along a reach would therefore reduce round bodied sucker populations. In summary,
the shift in sucker composition appears to involve multiscale interactions among several factors
(sensu Levin 1995, Poff 1997).
Floodplain-reach Scale
This scale resolves floodplain reaches as structurally distinct river segments in which finer-
scaled ecological characteristics are controlled in part by the degree of interaction between the
river and its valley and floodplain (Lubinski 1993, Sedell et al. 1989, Ward and Stanford 1989).
Reaches can be distinguished based on geology, physiography, and large increases in drainage
area at major tributary junctions (Ward and Stanford 1989). Morphological aspects that help
distinguish reaches include valley width, slope, and floodplain development. Unlike mainstems
of the UMRS, those of the upper Ohio River basin are valley constrained and lack broad,
dynamic floodplains. As a result, upper basin mainstems have little "off-channel" habitat (sensu
Wilcox 1993), and flooded tributaries are rarely backwatered by more than a kilometer. Valley
11
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slope decreases and width increases in the middle and lower Ohio River, where there is a
general increase in off-channel habitat. None of the Ohio River approaches the complexity of
off-channel habitat associated with floodplains of the UMRS, however.
To our knowledge, formal reaches for the Ohio River system have not been defined, although
the "Three Rivers" union defines a clear reach boundary because of the difference in character
between the confluent rivers that create the Ohio River at Pittsburgh, PA. Various researchers
have referred to upper, middle, and lower thirds of the Ohio River as distinctive (Pearson and
Krumholz 1984, Emery et al. 1999), with the "Falls of the Ohio" at Louisville, KY appearing to be
a logical site for another reach boundary.
Filtering influences at the floodplain-reach scale on potential fish assemblages likely reflect
differences in the amount and character of off-channel habitat, possibly by filtering species that
require off-channel nursery sites from areas where those sites are lacking, or have been
disconnected by floodplain modification (Lubinski 1993, Gutreuter 1992, 1993). As the river
flows along major climatologic gradients, species may come and go at reach scales in relation
to thermal tolerance (Gutreuter 1992). Within and between reaches, assemblages fluctuate in
composition as a result of annual variation in flow regime, often associated with the presence or
absence of seasonal flood pulses (Junk et al. 1989, Lubinski 1993).
Navigation Pool Scale
The navigation pool scale is unique among spatial scales in representing an artificial element
and major modification of the riverine ecosystem (Lubinski 1993). Serial repetition of these
structures scales up to influence ecological processes at the broader reach and stream network
scales. Dams also constrain habitat variation at finer scales through both passive and active
mechanisms. Passively, dams set up longitudinal gradients in fine-scaled habitat features by
imposing a step-pool structure along the river continuum (Wilcox 1993). Tailwaters below dams
retain some of their original riverine character as sites of greater velocity, turbulence, and
substrate particle size, which then grade into slower, deeper impounded areas above dams
where finer sediments accumulate. Actively, short-term variation in fine-scale habitat is tied to
water-level management, which in the Ohio basin attempts to maintain a constant pool elevation
at each dam. As a result, depth varies more over time in upstream areas of the pool, thus
influencing inundation frequency and duration of aquatic-terrestrial transition zones along the
river margin (Junk et al. 1989). Less variation in depth above the downstream dam limits
12
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change in cross-sectional area, resulting in higher average velocity when passing elevated
discharge. These factors interact along the spatial axes of a pool to influence temporal
variability in fine-scale habitat, which fundamentally affects selective forces acting on biota (Poff
and Ward 1990). Similar interactions occur in the UMRS, although spatiotemporal dynamics
differ there because water levels are manipulated to approach stability at a mid-pool "hinge
point" (Lubinski 1993).
Habitat Scale
Within navigation pools, fine-grained features of the environment are functionally linked to the
success of individual fish at the habitat scale. Because each species perceives and responds to
environmental heterogeneity uniquely, no single description of fine-grained habitat will suit all
cases. Just how species perceive and respond to local heterogeneity, and how such responses
help shape fish assemblage pattern, is an area of active inquiry (Poff and Ward 1990, Taylor et
al. 1993). A hierarchical perspective on how "patches" of fine-scaled habitat are spatially
arranged leads to the general prediction that habitat "specialists" should outnumber "generalist"
species, but generalists will on average be more abundant and more broadly distributed in
ecological range and habitat use (Kolasa 1989).
Descriptions of habitat use by fish typically refer to hydraulic variables (e.g., depth and velocity),
substratum (e.g., particle size, embeddedness, organic matter content), cover (type and
amount), and physicochemical limits of tolerance. A common theme in lotic ecology is that
habitat conditions are arranged along a continuum at broad scales, while heterogeneity and
habitat use are patchy along fine-scaled gradients controlled in part by stream channel
geometry at local scales (Vannote et al. 1980, Bain and Boltz 1989, Lobb and Orth 1991).
Predictable environmental gradients occur at the habitat scale along the spatial axes of a
navigation pool, which are reflected in habitat classifications developed for navigation rivers
(Pearson and Krumholz 1984, Wilcox 1993, Arway et al. 1995). As Table 3 indicates, a
longitudinal riverine-to-lacustrine gradient interacts with cross-sectional geometry and meander
pattern to influence near-shore to mid-channel gradients in depth, velocity, substrate, and cover.
Where islands are present, the primary channel used for navigation usually differs distinctly from
smaller secondary or tertiary channels opposite the island. Meander pattern influences near-
shore erosion and deposition, resulting in steeply sloping banks with deep water near rocky
shores alternating with shallow flats where gravel, sand, and silt predominate.
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Natural patterns of regular habitat variation are interrupted by human modifications (e.g.,
bermed, rip-rapped, and bulkheaded shores and dredged channels) or by other natural, but
irregular features (e.g., delta formations at tributary junctions), creating a complex mosaic with
both predictable and unpredictable components. Temporally, a mosaic will change in a spatially
dependent manner, because some areas will be inherently more dynamic than others.
Terrestrial mosaics subjected to unpredictable patchy disturbance may still achieve an
equilibrium distribution in the relative proportion of patches within different successional states
(O'Niell 1989). Similarly, fluvial processes that continually alter the state of an alluvial channel at
a fine scale are also responsible for maintaining an equilibrium pattern in channel dimension at
a broader scale (Leopold 1994). Human disturbances at the broader reach and navigation pool
scales exert control over local assemblages in part by the way they influence spatiotemporal
variation at the habitat scale.
The filtering influence of fine-scale habitat heterogeneity ultimately determines which species in
the regional pool will succeed in a given environmental setting. Conditions suitable for growth
and reproduction must be maintained over long periods of time for a species to persist in a
given environmental setting. The degree to which a species is able to withstand unfavorable
conditions on occasion reflects traits that confer resistance to local habitat filters.
Generally, highly variable environments are occupied by assemblages composed of eurytopic
species in which abiotic controls on temporal variation predominate, while stenotopic habitat
specialists and biotic interactions become more important in stable environments (Matthews
1987, Poff and Ward 1989, 1990).
Temporal variation in lotic habitat is driven strongly by flow regime, which in large rivers has a
strong contingent component reflecting predictable seasonal flow patterns (Poff and Ward
1989). Assemblages facing regular cycles of variation typically include species with adaptations
for taking advantage of (or avoiding) favorable (or unfavorable) periods within a cycle of
variability. Many large river fishes, for example, are strongly dependent on seasonally inundated
floodplains and backwaters for reproduction. Such species can be reduced or eliminated from
local assemblages by regulated flows that deviate too far from natural pattern (Poff et al. 1997),
or by activities that disconnect spawning and nursery areas from the river.
14
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Table 3. Gradients of habitat conditions found within navigation pools of large navigation rivers.
Entries in "low" and "high" columns are habitat classification elements devised to
compartmentalize variation at the habitat scale.
Habitat
condition
Depth
Velocity and
turbulence
Substrate
coarseness
Cover density
(e.g., vegetation,
large woody debris)
Relative value
Low
near shore zone
tertiary channel
tail water
lower pool
near shore zone
tertiary channel
lower pool
tertiary channel
very wide border
island shore
midchannelzone
primary channel
valley shore
very narrow border
High
midchannel zone
primary channel
lower pool
tail water
midchannelzone
primary channel
tail water
primary channel
very narrow border
valley shore
near shore zone
tertiary channel
island shore
very wide border
Gradient
type
lateral
lateral
longitudinal
longitudinal
lateral
lateral
longitudinal
lateral
lateral
lateral
lateral
lateral
lateral
lateral
Gradient
strength
strong
intermediate
weak
intermediate
intermediate
intermediate
strong
intermediate
intermediate
weak
strong
intermediate
weak
weak
Trautman (1981) frequently comments on the negative or positive influence that the locks and
dams had on many fishes of the Ohio River. By and large, the physical changes resulting from
impoundment have negatively impacted most obligate riverine species that require moderate to
strong current and coarse, or at least silt-free substrates. Impoundment favored "generalist"
species, including many that are found naturally in both rivers and lakes.
HABITAT FILTERS AND CIS
An Aquatic Resource Characterization Study (ARCS) for the Allegheny, Monongahela, and Ohio
River in Pennsylvania (Arway et al. 1995) provides an example of a GIS application using the
habitat filter concept. The objective of ARCS was to develop a GIS for natural resources
15
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management of the "Three Rivers" area (ten navigation pools and 176 km of mainstem) near
Pittsburgh, PA. Base maps generated from 1:24,000-scale topography included the drainage
network out to the limit of counties containing a defined study area, which incompletely captured
the landscape at basin, network, and reach scales. Since our focus was on mainstems, main
channels at 1:24,000 scale were revised from aerial photography at 1:8,400 scale to provide
details for habitat mapping. Much of ARCS was devoted to habitat inventory and classification
(Arway et al. 1995), including videography of near-shore cover (e.g., submerged and emergent
vegetation, woody debris) and substrate (Nieman et al. 1996), and side-scan sonar of riverbed
substrate and morphology (Nieman et al. 1999). Channel border width was obtained from
navigation charts. Inventory data were ascribed to polygons and overlaid with maps of aquatic
areas defined by channel geometry. For our purposes, the habitat components of the GIS can
be envisioned as a mosaic resulting from the overlay of component coverages. Associated with
each polygon are attributes that describe the mix of habitat conditions and aquatic area type
found within it.
In a parallel effort, inventory data were linked to literature-based analyses of habitat
requirements so that GIS applications could be devised for estimating relative habitat suitability
for a selected fish species and life stage, or likely assemblage composition, for any user-defined
subset of the study area. Because this process was complex, a conceptual framework was
needed to organize the research effort. A habitat filter framework, broadly summarized in Figure
1, eventually arose as the conceptual model around which GIS applications were built. This
framework grew out of an effort to devise an ecologically based habitat classification, which led
to the realization that habitat influences on riverine biota must be organized hierarchically. The
result was a model that focuses on how channel geometry constrains microhabitat variation
along the spatial axes of a navigation pool, and that acknowledges further constraints on fish
assemblages stemming from broader reach, network, and basin scales.
The left column of text in Figure 1 broadly summarizes model components associated with three
levels of a simple hierarchy that correspond to one or more of the five spatial scales discussed
previously. To the right are listed the chief ecological consequences of each component. At the
"macro scale" level, some species in the regional species pool were removed a priori as small-
stream specialists, while others were assigned membership in only a few of the ten pools of the
ARCS study area, reflecting the influence of broad-scale constraints associated with existing
and historical influences at basin, network, and reach scales.
16
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At a "mesoscale", the influence of channel geometry on microhabitat distribution among aquatic
areas was compared to literature on fish habitat requirements. Scores were assigned to reflect
suitability for each species and life stage based on comparison of literature to "typical"
microhabitat within aquatic areas. Since aquatic areas define environmental gradients, scores
were analyzed along with descriptions of "typical" microhabitat (=depth, velocity, substrate,
cover) variation among aquatic areas using canonical correspondence analysis (ter Braak
1986). The resultant ordination placed life stages of each species into "species-habitat
associations" that reflect orientation along a riverine-to-lacustrine gradient and lateral gradients
17
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Figure 1. Summary of a CIS-based filter model for estimating habitat suitability and fish assemblage composition in navigation pools of the
upper Ohio River basin in Pennsylvania.
REGIONAL SPECIES POOL
Model begins with 100 species and 4 life
stages that are likely full or part-time
residents of upper Ohio River basin
mainstem navigation pools.
tCZ>
Geographic distribution across
navigation pools reflects historical
ecology and influence of long-range
environmental gradients at broad
spatial scales.
Aquatic areas based on channel
geometry define within-pool
gradients of depth, velocity,
substrate, and cover along which
patterns of habitat use are arrayed,
as determined by literature review of
habitat requirements.
Habitat inventory data and fine-scale
channel morphology mapped in CIS
includes: near-shore cover and
substrate from videography, channel
border width and tributary junctions
from navigation charts, submerged
substrate, cover, and riverbed
morphology from side-scan sonar.
tC^>
I
MACROS CALE FILTER
I
ME SOS CALE FILTER
I
MICROS CALE FILTER
LOCAL ASSEMBLAGE
Small stream specialists are
removed to reflect segregation
at the stream network scale.
Certain coolwater forms (mainly
percids) restricted to upper
Allegheny River; upper Ohio River is
at eastern edge-of-range for some
midwestern species. Most of
regionally available fauna are wide
spread across study area.
Obligate riverine species confined to
tailwaters; lacustrine forms dominant in
| lower pools; generalists distributed
widely; species/life stages segregated
along near-shore to midchannel benthi-
pelagic gradient, mainly by depth and
cover preferences; segregation also
occurs between primary and secondary
channels and off-channel areas.
1 Maps of features are overlapped with
I aquatic areas to create a patch
mosaic; each patch has a list of
attributes; each attribute can have a
positive, neutral, or negative effect on a
given species and life stage. From this
information, habitat quality for a target
organism, or likely assemblage
composition, can be estimated in a
spatially explicit manner.
-------
in the use of near-shore versus transitional off-shore and benthi-pelagic areas in mid-channel,
along with differential use of primary, secondary-tertiary channels, and off-channel areas.
A "micro scale" filter operating at the finest level of resolution in the environmental data further
modifies model output by accounting for local features that have positive, neutral, or negative
influence on habitat quality for a target organism. Customized analyses in GIS and spreadsheet
programs calculate and summarize weighted area statistics for individual cases, display
choropleth maps, and rank all species and life stages by a value that reflects relative likelihood
of occurrence in any user-defined subset of the study area.
There are many details of model development that are beyond the scope of this paper. Our
main intent was to show how the concept of habitat filters could be used to organize thinking
about relationships between fishes and riverine habitat. In summary, fish distribution and
abundance in navigation rivers like the Ohio River can be related to historical legacies and
environmental constraints at broad spatial scales, to the influence that channel geometry has on
the distribution of microhabitat in navigation pools, and to local factors that proximally influence
habitat use and the ability of individual species to persist in a given environment.
To date, the ARCS GIS has not been rigorously evaluated against field data. It does portray
patterns of habitat use variation found in several empirical studies (Gutreuter 1992, PFBC 1992,
Emery et al. 1999). Consideration of how hierarchical systems operate suggests that the ARCS
GIS filter model should be better able to predict statistical patterns in large data sets, rather than
details of individual sampling events, because of the influence of random noise at fine
spatiotemporal scales. For example, comparisons of model outputs from Allegheny River pools
5 and 6 with results of a PFBC (1992) field study compare favorably when aggregated for each
pool, despite unpredictable variation within individual field samples. Users of the ARCS GIS and
similar models should be aware of scale dependencies in ecological patterns, so that analyses
are applied and results interpreted properly. Field studies designed with these concepts in mind
are likely to yield new insights into fish-habitat relationships in large rivers.
19
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Sciences 49: 2196-2218.
25
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GIS as a Tool for Predicting Urban Growth Patterns and Risks
From Accidental Release of Industrial Toxins
Samuel V. Noe
University of Cincinnati, Cincinnati, Ohio
Introduction At the other end of the equation, we can construct
models for projecting patterns of urban growth. Those
The catastrophic Bhopal incident demonstrated to the responsible for planning, howeve r, have not made the
world what could happen when industry and population connections between these techniques. Hazardous fa-
are geographically incompatible. Many believe that the cilities sites are thus still permitted in areas that place
large urban population "should not have been there." A existing urban residents and their drinking water sup-
recent publication, "New York Under a Cloud," presents pHes at risk and new urban development grows in areas
a frightening map of New York State that indicates po- polluted by existing hazardous substance sites.
tential areas of serious population exposure to acciden-
tal releases of chemicals stored by area industries and Clearl y, this situation displays a need forthe coordinated
municipalities. application of scientific risk assessment techniques and
new approaches to regulating urban development.
Conventional urban planning and administrative prac- Equally critical, howeve r, is the need to give greater
tices at the local level do no t adequate! y provide for the attention to formulating appropriate public policy meas-
minimization of these risks. Local jurisdictions on the ures at the local and state levels for dealing with the
fringe s of metropolitan areas may be particularly ill- complex disputes that surround these issues.
equipped t o respond and plan e ffectivel y. Their elected
officials, supported by minimal professional sta ffs an(troiect Backaround
unaware of specific potential risks, may be more inter-
ested in soliciting new industrial development along with The project this paper describes addressed these
the tax base it brings. They therefore create industrial needs. It was undertaken by a team of faculty from the
zones without restricting facilities that ma y generateyniversit y of Cincinnati 's School of Planning, Depart-
hazardous substances and without recognizing th e pos-ment of Environmental Health, and the College of La w.
sibility that underground aquifers, which are current or The study team focused on the accidental release of
potential sources of drinking wate r, may underli e thesehazardous materials both into the air and into the
zones. Jurisdictions often permit facilities that could rou- ground-water suppl y. The team's purpose was to de-
tinely or accidentally release toxic substances into the Velop an integrated approach to scientific risk assess-
air without due regard for prevailin g wind patterns or ment, environmental analysis, urban planning, and
existing or projected urbanized areas that may be p0|jCy analysis to address conflicts between:
affected.
• Expected patterns of suburban residential growth.
Although the available data and methodology have
some gaps, much of the knowledge required to provide • Tne need to safeguard existing and new residential
adequate protection from these risks exists. We know areas, and their water supplies, from toxic chemical
how to identify the hazardous substances that these pollution.
sites may produce or store and how to calculate the . The promotion of industria| development on the pe-
types and levels of risks associated with them. We can rj herjes Qf urbgn jons whjch often |egds to the
accurately map the locations of streams, underground pro|iferation of hazardous substances sites.
aquifers, and their catchment areas. Although with less
precision, we also can indicate the areas more likely to • The need for effective regulation of these sites in
receive the outfall of airborne and waterborne pollutants. complicated multijurisdictional environments.
-------
This project examined these issues within a 100-square- the stud y area, was available as a result of reporting
mil e area on the northern edge of metropolitan Cincin- requirements mandated by several federal statutes. The
nati. The study area is not yet completely urbanized but study team assumed that a similar ratio reflecting sites
lies in the path of urbanization. It contains a significant containing hazardou s material s to the area of industri-
number of industrial or storage facilities tha t house supally developed land would continue into the future.
plies of hazardous materials . A major aquifer serving as Based on this assumption, the study team could ran-
a public water supply source passes under the area.
Approximately 17 local jurisdictions fall within the study
area: two counties, six townships, and nine municipali-
ties. The area encompasses an intricate mix of agricul-
tural, residential, and commercial land uses. In addition,
several major industrial concentrations, as well as a
number of jurisdictions, are aggressively soliciting new
industrial employment. Because of its proximity to most
major employment sites in southwest Ohio and to a
variety of large retail complexes, the area i s experiencing
rapid residential development.
Projecting Areas of Future Development
The study team used PC ARC/INFO geographic infor-
matio n system s (CIS) to project the locations of future
residential and industrial growth in the study area, to
show the locations of areas at various degrees of risk
from either airborne or waterborne industrial toxins, and
to reveal the potential areas of population exposure
resulting from the overlap of these areas.
Although this paper does not describe the models used
to project residential and industrial growth in the study
area, it does include the criteria used to make projections.
The criteria we used to project residential growth were:
• Travel times to major employment concentrations in
the region.
• Proximity to interstate highways, interchanges, and
main trunk sewers.
• Avoidance of areas composed of steep slopes and
flood plains.
• Land currently zoned for agriculture or housing.
The criteria for projecting industrial areas were:
• Relatively flat, not in a flood plain, and zoned industrial.
• Proximity to existing industrial development, main
trunk sewers, and interstate highways.
• Relative aggressiveness of local jurisdictions in at-
tracting industrial development.
Identifying Areas at Risk From
Airborne Releases
The study team determined the model project areas at
risk from airborne releases by using information avail-
able from the Ohio Environmental Protection Agenc
This information, which included the location, identit
and quantity of hazardous materials recently stored in
domly project new potential release sources.
The algorithm and associated software used for calcu-
lating the plume size of aerial dispersion of hazardous
chemicals was Aerial Locations of Hazardous Atmos-
pheres (ALOHA). The National Oceanic and Atmos-
pheric Administration (NOAA) developed this system,
which is in wide use by government and industry for the
preparation of emergency contingency plans. This soft-
ware is available for use on any Macintosh compute r, or
any IBM-compatible with an Intel 80286 (or better) CPU.
This software employs three classes of variables in
calculating the plume dispersion for a specifi c chemical:
• Chemical variables
• Meteorological options
• Source strength options
Chemical variables include both physical properties of
the chemical and parameters that define the human
health e ffects of the chemical. In the latter case, the two
variables are the threshold limit value (T LV) and the
immediately dangerous to life and health (IDLH) value.
The TLV is a measure of chronic toxicity of the chemical
in humans. It represents the maximum concentration of
the chemical in air to which a human can safely be
exposed for 8 hours per day o n a daily basis. The IDLH
is a measure of acute toxicity of the chemical in humans.
The IDLH represents the maximum concentration to
which a human ca n be exposed fo r a short tim e and not
experience death or some other severe endpoint.
Meteorological options describe the ambient atmos-
pheric conditions into which the chemical disperses.
ALOHA has the capability of downloading real-time data
from NOAA satellites. This case, howeve r, employed
average meteorological conditions for the study area
over the course of a yea r. The variables this stud y used
were atmospheri c inversion height (or no inversion),
wind velocit y, air temperature, ground roughness (rural
or urban), and stability class (a combined variabl e de-
scribing cloud cover and incoming solar radiation).
The source options quantify the amount of chemical
being released and describe how the chemica I is re-
leased (instantaneous or continuous).
The ALOH A model provides a procedure for showing the
IDLH and TLV risk zones from a single accidenta I re-
lease of a single chemical, given specified conditions of
yatmospheric stabilit y, wind direction, and air tempera-
y.ure. Obviousl y, climatic conditions change dail y, so
areas surrounding a single industrial site experience
-------
different degrees of risk depending on the variability of 3. When a single industrial location employed more
thes e conditions. Moreove r, a single site that can poten- than one chemical, the IDLH and T LV ris k patterns
tially release more than one chemical poses a higher for each were overlaid on on e other. Where the
risk to surrounding areas. Finall y, when release plumes overlap occurred, we added the risk factors togethe r.
from two or more sites that are located relatively close
to each other overlap, risks als o increase . To account 4- Final1 Y> wnen tne risk patterns from two or more sites
for these factors of climatic variation and overlapping overlapped, we added together the ris k factors
chemical release plumes, the study team constructed assigned to overlapping areas. The overlap capabilities
the model described belo w. of GIS allowed us to easily draw an d combine the risk
patterns, superimposed on a map of the industrial
In discussing these procedures, bear in mind that the sites under consideration.
risk factors are relative. Su fficient data are not available we exist combjned
to estimate the absolute probability of an accidental gpd JLV rjsk ^ { rjsk ^
release of industrial toxins into the a, r. Consequent! y, nowhen six £ pew sQurces ^ re|egses ™
absolute risk levels can be estimated. jected ^ Qf residentig| grQwth gre gdded ^
change s are rathe r dramatic. Of course, we must note
1 . We acquired NOA A climatic statistics for a full year thgt the jected SQurces Qf tentjg| re|egses gre h
for the weather station nearest the study. NOAA pothetica| and their ,ocations se|ected at random ,n
tabulates eight wind directions (N, NE, E, SE, S, S W, reg|it jse |ocgtions Qf gregs gt gter rjsk cgnnot
W, and NW). We sorted the average daily wind be djcted wjth d Qf certgjnt We cgn reg.
speeds and average daily temperatures according to songb| deduce from the mg howeve thgt sub.
the daily prevailing wind direction. Thus, for each of stgntjg| jncregse jn industries storing or using toxic
the eight wind directions, it was possible to determine chemica|S that might be accidentally released into the
the number of days in the year that the prevailing gjr cgn compound risk |eve|S much more than migh t be
wind comes from that direction, as well as the expected The maps we produced indicated five risk
average daily wind speed and temperature. levels
2. We then input the temperature and wind speed data, The study results also showed that an industry capable
derived as explained above, into the ALOH A model of generating accidental releases of airborne toxins will
to prepare plots of the IDLH and T LV zones, or plumes, very likely place at risk residents not only of the same
for each of the eight wind directions. Individual! y, theoommunity but those in neighboring jurisdictions as well.
plumes emanated downwin d from the source of theThis is particularly true on the fringes of metropolitan
release. In our stud y, IDLH plumes varied from 0.17 areas where highly fragmented political boundaries ex-
miles to greater than 10 miles. T LV plumes varied ist. This fact complicates tremendously the ability of
from 0.52 miles to greater than 10 miles. each jurisdiction to protect its citizens and suggests a
need for a more comprehensive approach to regulation
When the plumes for the eight di fferent wind directions than conventional land use zoning measures that each
were combined, the results were translated into a locality administers.
pattern of wedges representing various plume lengths
in each of the eight di fferent wind directions. These |dentif^ing Areas at Risk From Accidental
1-10 Ground-Water Supplies
of the release. Plumes blowin g in different directions
vary in length according to average temperature and As the introduction of this paper indicates, local govern-
wind velocities for the days the wind blew in each ment usual|V mana9es conventional land use planning,
direction. The numbers in the wedges represent risk while air quality is largely a state or federal responsibilit y.
factors assigned as indicated in Table 1. Thus< decisions re9ardin9 the regulation of industrial
location may not account for the types of industrial
Table 1. Assigned Risk Factors operations proposed, th e possible use of hazardous
materials , and possible risks to local residents from
Frequency of wind Risk Factor accidental release of toxins into the ai r. The same prob-
lem applies to the location of industries that have the
0-25 days/year 1 potential for accidental release of hazardous materials
26-so days/year 2 into ground-water supplies. In our stud y, we outlined a
51 -75 days/year 3 technique for predicting where local resident s may be
placed at risk by drinking water from sources vulnerable
_ 76-100days/year _ f _ to contamination by industrial toxins.
-------
As stated earlie r, the study area includes a major aqui- areas served by water companies whose supplies may
fer. We noted industrial and related facilities that use and be at risk. In this case, the projected risk s of polluted
store hazardous materials, and that are located over or water supplies were identical to existing risk level s be-
immediately adjacent to the area aquifers. We distin- cause no public water wells happened to be located in
guished between sites where a previous spill had been
reported, those where a waste well is located, and all
other locations where a hazardous material is used or
stored. The results clearly indicated a significant poten-
tial for contamination.
areas where additional potential sources of pollution
were projected.
Of course, wide areas in the study area had no public
water supply. Residents in these areas may be at risk
depending on where they dig private wells. The maps
we generated showing areas at risk nonetheless provide
useful guides to potentially hazardous areas.
To predict the number and locations of additional indus-
trial toxin sources that might appear over a 10-year
period following 1990 (the base year of the study), we
employed a procedure similar to that use d in th e airAs in the construction of all such models, we needed to
pollution section of the stud y. We assumed that during make a number of assumptions, simplifications, and
this period, the number of such sites would increase by value Judgments. In this case, these included the pro-
35 percent. The 35-percent increase represented an Jected number of new toxic sites and P°int scores as'
arbitrarily selected figure approximately midway be- S|9ned to hazardous materials sites and the various
tween estimates of 25- and 50-percent industry growth areas in th e DRASTIC maps. Also, for the sake of
in the study area. We used this increment t o project simplicit y, we projected no new well sites in preparing
additional waste well sites and sites that would experi-
ence a spill sometime during the decade, as well as the
total number of new sites.
We projected the locations of the additional sites by
overlaying a 5,000-foot by 5,000-foot grid on the study
area and assigning the new sites to grid cells using a
random number generate r. We considered only cells
lying completely or largely over the aquifers and also
falling within the area of projected industrial land use.
Each cell was assigned a relative contamination risk
factor based on the number of projected sites it con-
tained, with a multiplier of 3 applied to sites with a waste
well and a multiplier of 4 applied to sites assumed to
have had spills. We also assigned existing sites to grid
cells and scored them in the same manne r.
To obtain more information, we used a simplified version
of DRASTIC maps prepared by the Ohio Department of
Natural Resources. DRASTI C is an acronym foDepth
to wate r, netRecharge, Aquifer media ,Soil media ,To-
pograph y, Impact of the vadose zone, and hydraulic
Conductivit y of the aquife r. These factors contribute to
an index of the relative vulnerability of the aquifer to
pollution. Di fferent shades on the map represented the
relative vulnerability of sections of the aquifers to
ground-water pollution. We assigned two points to aqui-
fers with a DRASTIC pollution potential index less than
180 and four points to aquifers with a higher index. This
allowed us to use the CIS to combine the DRASTIC
vulnerability map with the maps that showed risks of
one of the maps. This additional element should prob-
ably be included, assuming local water companies could
provide projected well locations. Use of a CIS, howeve r,
makes it possible to explore the implications of adjust-
ment of any of these factors.
We must note two more significant omissions from the
model that the CIS cannot factor in. One was our inabil-
ity to identify from the data specific chemicals that each
sight might release and the relative e ffects of each. We
would have required more complicated techniques for
dealing with these variables and for projecting the travel
and dilution of plumes of contaminants in an aquife r. In
the interest of providing a simple, if relatively crude,
model capable of replication by a local planning agency
with a simple CIS, we elected not to propose use of
more sophisticated techniques.
Another obvious omission was the consideration of
water pumping and treatment measures that might miti-
gate risks of water contaminated by accidental release
of industrial toxins. Perhaps, with knowledge of the spe-
cific contaminants found in the water at any given time,
such mitigation might be possible. We elected not to
consider this factor for reasons of simplification but also
because this study aimed to provide planning agencies
the means to identify potential risks to local residents
and to prevent or minimize them through better land use
planning and regulatory measures.
Some Final Notes
pollution from the hazardous materials sites . The resul-
tant maps showed the existing and projected potential As stated earlie r, the purpose of the study was to pro-
risk of pollution in di fferent areas of the aquifers. pose a technique that planning agencies could use to
identify:
The next step in the study related the above information
to public water companies that extract water from di ffer* The locations of existing and projected pattern s
ent parts of the aquifers . Thus, we were able to associ- residential and industrial development in a multijuris-
ate risk levels with well locations as well as with the dictional suburban area.
of
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• The locations of existing and projected industrial,
storage, and disposal sites of hazardous materials.
• Residential areas that might be placed at risk by the
accidental release of these hazardous materials into
the air or into ground-water supplies.
• The relative levels of risks resulting from potential
exposure to more than one hazardous material at a
single site, or from multiple sites in the vicinit y.
The technique we used in the study permitted projection
of relative risk levels. Projecting absolute risk levels is
impossible without data on the actual incidence of acci-
dental releases of toxins overtime in this or in similar
areas. A related question is whether it is possible to
meaningfully indicate the combined risk from airborne
releases of industrial toxins and drinking water contami-
nation to a particular residential area. The issue of
weighting relative risk factors is central here. Could we,
for example, weight the risk levels of exposure to air-
borne releases three—or possibly four—times higher
than water contamination risks? Or can we say that the
risks may be the same, but the danger from airborne
releases is three or four times greater?
Obviousl y, these would be futile exercises, especially
becaus e the point scores in the separate mapping stud-
ies were arbitrarily assigned. The public should kno
howeve r, which present or projected residential areas
carry some level of risk from both types of exposure.
Thus, after calculating point scores to derive relative risk
levels from waterborne pollutants, we multiplied the
scores by 4 to bring their maximum ranges into the same
order of magnitude. Otherwise, the e ffect of waterborne
pollutants would not be apparent. Consequentl y, we
created two maps to show the combined existing and
projected risks; therefore, we highlighted the combined
risks that residents face from both airborne and water-
borne hazardous materials.
The maps of airborne releases used in these combina-
tions showed T LV. Continuing exposure within the T LV
areas over an extended perio d can also have adverse
health effects. This study focused only on accidental
releases, howeve r, and a single release is unlikely to
sustain continuous exposure. Of course, residential ar-
eas at risk from several sites might approach conditions
of sustained exposure. This situation is more analogous
to prolonged exposure to contaminated drinking water
supplies. We did not combine the maps of IDLH airborne
releases with th e maps of areas at risk from ground-
water contamination because they are not analogous
conditions. Nonetheless, the IDLH risk maps in them-
selves reveal the conditions that local planning o fficials
should most seriously conside r.
Replicating the procedures outlined in this study should
be technically and financially feasible for local planning
agencies. Armed with the results of such an investiga-
wtion, their next step should be to establish the planning
and regulatory measures that would minimize both ex-
isting and projected levels of risk to area residents. The
attorney on our team has outlined a range of possible
measures, but detailing them would be the subject of
another pape r.
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Use ofGIS in Modeling Ground-Water Flow in the Memphis, Tennessee, Area
James Outlaw and Michael Clay Brown
University of Memphis, Memphis, Tennessee
Abstract
Memphis, Tennessee relies solely on ground water for
its municipal and industrial water supply. Memphis Light,
Gas, and Water (MLGW) Division owns and operates
over 160 water wells in 10 production fields throughout
Shelby County. MLGW produces an average of approxi-
mately 200 million gallons per day, excluding much of
the industrial demand. The city obtains its water from a
thick, prolific aquifer known as the Memphis Sand,
which was thought to be separated from a surficial
aquifer by a thick confining layer. In recent years, evi-
dence of leakage from the surficial aquifer to the Mem-
phis Sand has been found.
The University of Memphis Ground Water Institute
(GWI) is developing a hydrogeologic database of the
Memphis area to study the aquifer. The database serves
as the basis for several ground-water flow models that
have been created as well as part of the wellhead
protection programs currently being developed for Mem-
phis and other municipalities in Shelby County. A geo-
logic database was developed and is constantly being
updated from borehole geophysical logs made in the
area. Well locations are being field verified using a
global positioning system (GPS).
Use of the database has allowed the development of a
three-dimensional model of the Memphis area subsur-
face. The database also contains locations of and infor-
mation on both private and public production and
monitoring wells, Superfund sites, underground storage
tanks, city and county zoning, land use, and other per-
tinent information. Procedures for linking the database
to ground-water flow and solute transport models have
been developed. The data visualization capabilities and
the ability to link information to geographic features
make geographic information systems (CIS) an ideal
medium for solving ground-water problems.
An example of CIS use in ground-water flow modeling
is the study of the Justin J. Davis Wellfield. The water
quality parameters of alkalinity, hardness, sulfate, and
barium have significantly increased over the past 10
years at this facility. To understand why these changes
are occurring, MLGW, the GWI, and the U.S. Geological
Survey (USGS) participated in a joint investigation of the
wellfield.
In the spring of 1992, a series of 12 monitoring wells was
drilled into the surficial aquifer nearthe production wells.
Geophysical logging and split-spoon sampling revealed
an absence of the confining layer, referred to as a
window, at one of the monitoring wells. All other wells
penetrated various thicknesses of clay. This window in
the confining layer suggests that the water quality
changes could be due to leakage from the surficial
aquifer to the Memphis Sand.
The CIS database was used to construct a flow model
of the Davis area. Also, using the surface modeling
capabilities of CIS, the extent of the confining layer
window was estimated and used to calculate leakage
between the two aquifers. The results of these analyses
also indicate that further subsurface exploration is
needed to more accurately define the extent of the con-
fining layer window.
Introduction
Memphis, Tennessee, relies solely on ground water for
its municipal and industrial water supply. The Memphis
Light, Gas, and Water (MLGW) Division owns and op-
erates over 160 water wells in 10 production fields
throughout Shelby County, as shown in Figure 1. MLGW
produces an average of approximately 200 million gal-
lons per day, excluding much of the industrial demand.
The city obtains its water from a thick, prolific aquifer
known as the Memphis Sand, which was thought to be
separated from the surficial aquifer by a thick confining
layer. In recent years, evidence of leakage from the
surficial aquifer to the Memphis Sand has been found.
The University of Memphis Ground Water Institute
(GWI) is developing a hydrogeologic database of the
Memphis area to study the aquifer. Several ground-water
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Mississippi Alluvia] Plain
N
A
0 25 50
Scale in Thousands of Feet
Figure 1. Physiographic description and MLGW Wellfields in
Shelby County.
flow models have been developed using the database.
Also, the database is an integral part of wellhead pro-
tection programs being developed for Memphis and
other municipalities in Shelby County. A geologic data-
base was developed and is constantly being updated
from borehole geophysical logs made in the area. Well
locations are being field verified using a global position-
ing system (GPS).
Use of the database has allowed the development of a
three-dimensional model of the subsurface of the Mem-
phis area. The database also contains locations of and
information on both private and public production and
monitoring wells; Superfund sites; underground storage
tanks; city and county zoning; land use; and other per-
tinent information. Water quality measurements for
every MLGW production well have been obtained, and
a history of water quality for the Memphis Sand is being
developed. Procedures have been developed for linking
the database to ground-water flow and solute transport
models (1). The data visualization capabilities and the
ability to link information to geographic features make
geographic information systems (CIS) an ideal medium
for solving ground-water problems.
GIS Database
The GWI has developed and is continuing to update a
hydrogeologic database for the Memphis area.
ARC/INFO (marketed by Environmental Systems Re-
search Institute, Redlands, California) is the GIS pro-
gram that the GWI is using. The program runs on a
network of 10 SUN SPARC stations. The capabilities of
ARC/INFO and the computational speed of the SPARC
stations allow very sophisticated ground-water analyses
to be performed and have allowed the development of
an extensive electronic database.
The basic unit of data storage in ARC/INFO is a cover-
age. A coverage is a digital representation of a single
type of geographic feature (e.g., points may represent
wells, lines may represent streets or equipotential lines,
and polygons may represent political boundaries or zon-
ing classifications). Information may be associated with
an individual geographic feature in a feature attribute
table. This information may then be queried and used in
analyses. ARC/INFO also has its own macro language
that allows the customization and automation of many
ARC/INFO procedures.
A relatively new feature of ARC/INFO is address match-
ing. This procedure compares a file containing the street
address of a particular feature with an address cover-
age. This coverage is basically a library of addresses
that are linked to a geographic coordinate. As the ad-
dresses from the input file are compared with the ad-
dress coverage, the matching points are written to a
second coverage. Any addresses in the input file that do
not match an address in the address coverage are
written to a "rejects" file. These can be matched by hand
on a one-by-one basis.
Address matching serves as an alternative to digitizing,
as long as a good address coverage for a specific area
exists. The GWI has used this capability extensively and
has developed a coverage of underground storage tank
(UST) locations inside Shelby County. A database of
private and monitoring wells is also being developed
and updated using address matching. The raw informa-
tion was obtained in an ASCII format from the appropri-
ate regulating agencies (i.e., the Tennessee Department
of Environment and Conservation Division of Under-
ground Storage Tanks and the Memphis/Shelby County
Health Department). The ASCII information was im-
ported into ARC/INFO and address matched. The crea-
tion of a suitable address coverage and completion of
the address matching of the UST file has taken almost
2 years. The private well coverage is currently being
updated from historical information provided by regulat-
ing agencies and local well drilling companies.
An important part of the database is the geologic infor-
mation obtained from geophysical logs in the area.
Gamma logs, resistivity logs, and spontaneous potential
(SP) logs are three major types of electric geophysical
logs. Gamma logs measure naturally occurring radiation
emitted from soil in the borehole. Clays and shales emit
gamma rays. A high gamma count indicates the pres-
ence of clay or shale, and a low gamma count implies
that little or no clay is present. Sand layers that contain
fresh water are located using resistivity logs. Maximum
values of resistivity indicate the possibility of a sand
-------
layer. Clays and sands that contain salt water may
exhibit similar resistivities. SP logs are used to differen-
tiate between the two (2).
Corroborating data such as formation logs, geologic
studies, and available material samples should be con-
sulted when reading and interpreting geophysical logs.
The accuracy and reliability of an application based on
well logs is completely dependent on a realistic interpre-
tation of the geophysical data. A sample interpretation
of a set of geophysical logs is shown in Figure 2. The
results of interpretations like this are entered into the
point attribute file of a well coverage.
The Triangulated Irregular Network (TIN) module of
ARC/INFO is used to create a three-dimensional sur-
face from information stored in a coverage. TIN creates
a surface from a set of nonoverlapping triangles defined
by a set of irregularly or regularly spaced points. In this
study, the points defining the triangular TIN surfaces are
the locations of the wells in the model area. TIN uses
various interpolation routines to estimate surface val-
ues. Once the surfaces have been developed, two-di-
mensional profiles can be made that show the relative
thicknesses of the various soil strata, as shown in Figure
3. These profiles aid in selecting boundary conditions
and defining layers in ground-water flow models (3, 4).
In addition to the creation of profiles, a process that
extracts surface values for use in a ground-water flow
model has been developed. The GWI uses the United
States Geological Survey (USGS) flow model, MOD-
FLOW (5). Being a cell-based model, MODFLOW re-
Gamma
SP Resistivity
Figure 2. Example interpretation of geophysical logs.
quires a value for each hydrogeologic parameter for
each cell in the model grid. A series of FORTRAN pro-
grams and arc macro language (AMI) programs were
coupled to extract the required hydrogeologic data from
surface models. For example, piezometric surface val-
ues are required to set initial conditions for the model. A
coverage of the piezometric surface of the Memphis
Sand was created, converted to a TIN surface, and the
required values for each cell in the model were extracted
using the procedure described above.
The results and hydrogeologic data from the calibrated
model can be read back into the database and con-
verted into coverages. This allows piezometric contours
to be developed and displayed with other information in
the database to aid in decision-making. Also, capture
zones forthe wells can be brought into the database and
compared with surface features like industries, landfills,
Superfund sites, LIST locations, or other sites that may
have an impact. This has proved especially helpful in
developing wellhead protection programs where a com-
plete contaminant source inventory must be performed
for the capture zone of each well and within a fixed
radius around the well. The procedure used to develop
model data from the CIS database is summarized in
Figure 4.
Me Cord Wellfield Wellhead
Protection Program
MLGWand the GWI performed a demonstration project
funded by the U.S. Environmental Protection Agency
(EPA) for the C.M. McCord Wellfield Wellhead Protec-
tion Program (6). This wellfield was selected because of
multijurisdictional problems that will be encountered dur-
ing plan implementation. The City of Memphis owns all
the wells, but many of them, and all future well lots, are
located within the city limits of Bartlett. A wellhead pro-
tection plan will have to involve the cooperation of both
municipalities. The existing wellfield is shown in Figure 5.
Tennessee wellhead protection regulations require the
delineation of two zones of protection for a city the size
of Memphis: a 750-foot radius around the wellhead and
a 10-year capture zone for the well. The 10-year capture
zone (called the Zone 2 area) was delineated using two
flow models and information obtained from the GWI
database. Results were imported into the CIS database
and compared with existing information. The Zone 1
area was delineated by buffering each well point in the
coverage by the appropriate radius. Each well location
was verified using a Trimble GPS unit and is accurate
to within 2 meters. A contaminant source inventory was
performed using the coverages developed by address
matching. The primary potential sources of contamina-
tion in this area are USTs. A windshield survey located
other potential sources, such as dry cleaners. These
locations were entered into the database also by using
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Surficial Aquifer
Transitional Layer
Confining Layer/Clay Lens
Memphis Sand
Figure 3. Two-dimensional profile constructed from surface models.
Study Area Defined and
Model Grid Developed
Within ARC/INFO
Model Input Files
Developed
V
Model Calibration
and Verification
the address-matching capabilities of ARC/INFO. The
Zone 2 areas for present wells and future wells, along
with the potential sources of contamination, are shown
in Figure 6.
Davis Wellfield Study
An example of CIS use in ground-water flow modeling
is the study of the Justin J. Davis Wellfield. The Davis
Wellfield is one of 10 producing fields operated by
MLGW It is located in the southwestern corner of
Shelby County and consists of 14 wells, as shown in
Figure 1. Production at the Davis Wellfield began in
1971, and an estimated 13 million gallons per day are
currently withdrawn from the Memphis Sand aquifer.
Since 1972, MLGW has collected water quality data
from the wells at the Davis Wellfield, including values for
alkalinity, hardness, chloride, sulfate, iron, and barium.
Water quality parameters of alkalinity, hardness, sulfate,
and barium have significantly increased in the past 10
years.1 A possible explanation for the change in water
quality is water leakage from the upper aquifer through
the confining unit to the Memphis Sand aquifer.1 The
water chemistry from the two aquifers is noticeably dif-
ferent. The surficial aquifers generally have a higher
total dissolved solids concentration, hardness, and alka-
linity than water from the Memphis Sand.1
Figure 4. Procedure for integrating GIS and flow model.
1 Webb, J. 1992. Memphis Light, Gas, and Water Division. Personal
interview.
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Shelby |
County
Well Location
Pumping
Station
Municipal Boundary
Figure 5. Existing McCord Wellfield.
MLGW, the GWI, and the USGS participated in a joint
investigation of the wellfield to determine why the water
quality changes are occurring. In the spring of 1992, 12
monitoring wells were drilled into the surficial aquifer
near the production wells, as shown in Figure 7.
Geophysical logging and split-spoon sampling revealed
an absence of the confining layer at one monitoring well,
GWI-3. All other wells penetrated various thicknesses of
clay. This "window" in the confining layer suggests that
the water quality changes could be due to leakage from
the surficial aquifer to the Memphis Sand. The logs for
these monitoring wells were combined with an existing
geophysical log coverage. The extent of the confining
layer window was estimated using CIS surface model-
ing capabilities, as shown in Figure 8.
Two-dimensional profiles were created to further show
the extent of the confining layer window. The locations
of the profiles in relation to various surface features are
shown in Figure 9. Profiles 1 and 2 were taken across
the river bluff, and Profiles 2 and 3 were taken across
the window. The profiles are shown in Figure 10.
Many important features of this area's geology can be
inferred by looking at the profiles. A connection of the
alluvial and fluvial aquifers is shown in Profile 2. Else-
where along the bluff, the connection of the two aquifers
is less prominent, as shown in Profile 1. The connection
of the two aquifers in Profile 2 may be the cause of a
peculiar mounding effect in the water table of the alluvial
aquifer in that area. The thinning of the top soil in Profiles
1 and 3 may indicate a local recharge area for the
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N
0 5 10
Scale in Thousands of Feet
Germantown
\ Road
10 -Year Capture Zones
Developed Wellfield
. . » a Fully Developed Wellfield
* Underground Storage Tank
& Aboveground Storage
Figure 6. Underground storage tanks and aboveground storage locations.
alluvial aquifer. Profiles 2 and 3 show the confining layer
window that suggests a connection between the surficial
aquifer and the Memphis Sand.
Following the convention of the USGS, a "window" is
defined as any area where the aggregate clay thickness
is less than 10 feet (8). Asurface of the thickness of the
confining layer was generated from the geophysical log
coverage. The surface model was converted to a con-
tour line coverage on a 5-foot interval. Using the ARC-
EDIT module of ARC/INFO, the contour line coverage
was converted to a polygon coverage. The area
bounded by the 10-foot contour of the surface model
was calculated to be 840,000 square feet (about 19
acres). The area was calculated by adding the areas
between the 10- and 5-foot contours and the area within
the 5-foot contour.
A flow model of the area was developed based on the
hydrogeologic data contained in the database. A steady
state model was calibrated to hydraulic conditions re-
corded during fall 1992 by the USGS and the GWI; the
root mean square (RMS) error for this model was 1.76
feet. A second steady state model was developed to
simulate conditions recorded during spring 1993 (a pe-
riod of high water levels in area lakes). The RMS error
for this simulation was 5.19 feet. This higher error may
indicate that the high water levels in surface water bod-
ies are not realistic for steady state boundary conditions.
Realistically, monitoring wells that are relatively far from
a surface water body are affected more by average
water levels over time ratherthan relatively short periods
of highs and lows.
Using average values of hi, h2 (head in upper and lower
aquifers), / (vertical flow distance), and VCONT (a pa-
rameter used in MODFLOWto allow for vertical conduc-
tance), the estimated flow rate through the window for
fall 1992 may be computed as:
k = VCONT 1 = 1.76e-3 x 199.2 = 0.351
ft
day
h1-h2 186.5-156.1
I ~ 199.2
= 0.153
Q = kAi = 0.351 x 840,000 x 0.153 = 45,111
ft3
day
45,111 -- x 7.48
day
= 0.34 MGD
The flow rate calculated from average spring 1993 (a
period of high water levels in the surficial aquifers)
model results was computed as:
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Horn Lake Cutoff
Sewanee Road
Horn Lake Cutoff
Raines Road
Shelby Drive
Legend
A GWI Well
mi Water Body
NUU^UU^ BlUff
Roads
01234
Scale in Thousands of Feet
Figure 7. Location of GWI monitor wells.
k = VCONT I = 176e-3 x 199.2 = 0.351 -^-
day
. h!-h2 196.4-156.8
I
199.2
Q = kAi = 0.351 x 840,000 x 0.199 = 58,673
58,673 -ft- x 7.48 gall°ns= 438,874 - _,
day ff day
day
Using the GIS-delineated window, an estimated 0.34 to
0.44 million gallons per day flow from the alluvial aquifer
to the Memphis Sand aquifer. This variation in the flow
rate was due to seasonal variations of water level in the
alluvial aquifer.
Since the wellfield pumps approximately 13 million gal-
lons per day, the effect of the window on the entire Davis
Wellfield may not be significant. The window lies within
the 30-year capture zone of two wells in the field, however.
Raines Road
Shelby Drive
Legend
Water Body
Bluff
GWI Well
Roads
Scale in Thousands of Feet
Thickness of Confining Unit
< 5 Feet
5 to 10 Feet
10 to 20 Feet
20 to 30 Feet
30 to 40 Feet
> 40 Feet
Figure 8. Location and extent of window in confining unit.
The flow rate through the window is approximately 20
percent of the total production of these two wells. Addi-
tionally, since the wells would probably not operate si-
multaneously, the flow rate through the window may
account for approximately 40 percent of the flow at
either well.
Particle tracking in the Memphis Sand was developed
using MODPATH (9) from MODFLOW results. Particles
were placed at model well screens and tracked back-
ward for 30 years. The output from MODPATH was read
into the CIS database for comparison with other data,
as shown in Figure 11. The hole in the confining unit lies
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Legend
Bluff
— Roads '""-
o s 10
„,,.,. Profile
15 20
Water Body
Scale in Thousands of Feet
Figure 9. Location of selected profiles.
within the 30-year capture zone of MLGW wells 418 and
419. Historically, 419 was the first well that experienced
water quality changes. The water quality is becoming
similar to water found in the alluvial aquifer.
A change in the water quality in well 418 is not as
immediately noticeable as in well 419. This inconsis-
tency in data may indicate that the window does not
extend northward from GWI-3, as the TIN model pre-
dicted. To determine which capture zone (418 or 419)
encompasses GWI-3, particles were tracked backward
for 40 years, as shown in Figure 12. GWI-3 lies on the
edge of the capture zone for 419. The flow lines from
418 and 419 move toward the northwest and southwest
in the Memphis Sand, pass up through the window, and
emerge in the upper aquifer.
Conclusions
The explanation of the Davis Wellfield investigation ad-
dressed some limitations of the database. The utility that
this hydrogeologic database provides greatly outweighs
the disadvantages, however. Without the ability to map
and define hydrogeologic features, this project may not
have been completed in the allotted time frame or may
not have been completed in the same level of detail. CIS
greatly enhances the development and evaluation of
ground-water flow models.
Specific conclusions that can be drawn from the analy-
sis performed in this project are:
• The delineation of a window in the confining layer
using a CIS database is possible.
• Based on the CIS-generated window, an estimated
0.34 to 0.44 million gallons per day flow from the
upper aquifer to the Memphis Sand, which may
account for as much as 40 percent of the flow at
either well 418 or 419.
• The drilling of more monitoring wells north, south,
east, and west of GWI-3 may provide for a more
accurate delineation of the window.
-------
Profile 1
East
West
Profile 2
North
Profile 3
Top Soil
Horizontal Scale in Thousands of Feet p**— i gg^ an(j Qrave|
a i 2 3 a [pH Confining Clay
Vertical Scale in Hundreds of Feet
Memphis Sand Aquifer
Figure 10. Selected subsurface cross sections in the Davis
Wellfield.
Sewanee Road
Shelby Drive
Legend
* MLGW Production Well
[U Window in Confining Unit
01234
Scale in Thousands of Feet
- — - Flow Path
Water Body
Bluff
General conclusions that may be drawn from this dis-
cussion are:
• CIS provides a convenient method of viewing flow
model input and output.
• A flow model may be developed and evaluated in a
relatively short time using CIS.
• CIS provides a convenient means of compiling and
managing the information required to develop a well-
head protection program.
Some CIS disadvantages that have been noted are:
• The time required to develop a database and learn
to apply the CIS program in a particular situation may
be prohibitive.
• CIS-generated results from a limited database may
be misleading and should be corroborated with other
analysis methods.
Acknowledgments
The authors would like to acknowledge the efforts of the
professors and students of the GWI, both present and
past, for their contributions to the database and this
Sewanae Road
Particles From Well
418 Emerging in
the Upper Aquifer >
GWI-3
A
Legend
•
*•
01234
Scale in Thousands of Feet __
_ Bluff
— Water Body
Figure 11. Backward tracking for 30 years.
Figure 12. Backward tracking for 40 years.
-------
project, especially: Dr. John W. Smith, Director, and Dr.
Charles V. Camp for their patience and guidance in
interpreting the model results and hydrogeologic condi-
tions of the area; and David W. Kenley, Brian A.
Waldron, and Robert B. Braun for their help in creating
the well log database.
References
1. Camp, C.V., J.E. Outlaw, and M.C. Brown. 1994. CIS-based
ground-water modeling. Microcomputers in Civil Eng. 9:281-293.
2. Driscoll, F.G. 1986. Groundwater and wells. St. Paul, MN: Johnson
Division.
3. Camp, C.V., and M.C. Brown. 1993. A CIS procedure for develop-
ing a three-dimensional subsurface profile. J. Computing in Civil
Eng. (July).
4. Camp, C.V., and J.E. Outlaw. 1993. Constructing subsurface pro-
files using CIS. Adv. in Eng. Software. 18:211-218.
5. McDonald, M., and A. Harbaugh. 1988. A modular three-dimen-
sional finite-difference ground-water flow model. Open File Report
83-875. U.S. Geological Survey.
6. Palazolo, P.J., J.W. Smith, J.L. Anderson, and C.V. Camp. 1994.
C.M. McCord wellhead protection demonstration grant. Final
report. Herff College of Engineering Ground Water Institute,
University of Memphis, Memphis, TN.
7. Richardson, G. 1989. A study of potential sources of leakage into
the Memphis sand aquifer beneath the Davis well field in Memphis,
Tennessee. M.S. thesis. Memphis State University, Memphis, TN.
8. Parks, W.S. 1990. Hydrology and preliminary assessment of the
potential for contamination of the Memphis aquifer in the Memphis
area, Tennessee. Water Resources Investigation Report 90-4092.
U.S. Geological Survey.
9. Pollock, D. 1989. Documentation of computer programs to com-
pute and display pathlines using results from the U.S. Geological
Survey modular three-dimensional finite difference ground-water
flow model. Open File Report 89-381. U.S. Geological Survey.
10
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Public Participation Geographic Information Systems Applications for
Environmental Justice Research and Community Sustainability
(Working Paper)
David A. Padgett
Tennessee State University
Nashville, TN
Lynn Pinder, Youth Warriors, Inc.
Baltimore, MD
INTRODUCTION
The primary objective of this study is the development and assessment of a community-based
geographic information systems (GIS) method for researching childhood lead-soil exposure and
subsequent poisoning. Means to fund and conduct research on childhood lead exposure
preventative measures have been addressed under the Residential Lead-Based Paint Hazard
Reduction Act (U.S. Congress, 1992) and the Lead Exposure Reduction Act (U.S. Congress,
1993). Among the major action elements listed in the U.S. EPA's 1991 "Strategy for Reducing
Lead Exposures" are recommendations for the development of GIS methods to identify and
delineate "geographical hot spots" (i.e. highly susceptible areas) (U.S. Environmental Protection
Agency, 1991). The U.S. EPA has recommended that GIS be applied in efforts to learn more
about the nature and distribution of environmental lead:
"Continued reanalysis of the data by independent investigators will reveal even
more information about the movement of lead in urban environments. These
analyses should include...GIS analysis." (U.S. Environmental Protection Agency,
1993)
Several studies utilizing GIS to link lead poisoning sources with specific sensitive population
characteristics have been conducted within the past decade (Mielke and Adams, 1989
Huxhold,1991; Guthe etal, 1992; Wartenburg, 1992; North Carolina Department of
Environment, Health, and Natural Resources, 1993; Bailey etal, 1994; Brinkmann, 1994; Dakin's
1994; Bocco and Sanchez, 1997; Margai etal, 1997; Bailey, Sargent, and Blake, 1998; Griffith et
al, 1998; Lutz et al, 1998). The issue of residential lead contamination has recently become the
subject of a major class-action lawsuit (Koff, 1999). While prior research has provided
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substantial information regarding the spatial nature of environmental lead, there is little to no
indication that members of potentially impacted communities have been actively involved in
implementing the projects. The application of the public participation GIS method developed in
this study brings forth several issues associated with community participation in scientific
research. Interest in community-based research is being more frequently raised and discussed
at environmental justice-oriented conferences. The general trend among "at-risk" neighborhoods
is that they are "fed up" with having their human environment studied by outsiders from
academia or the government without being able to fully or even partially participate themselves.
The term "guinea pig" has been commonly tossed around during the discourse.
ARGUMENTS FOR A COMMUNITY-BASED PUBLIC PARTICIPATION GEOGRAPHIC
INFORMATION SYSTEMS APPROACH
Four key areas have been identified supporting the need for full community involvement in
neighborhood level lead-soil GIS mapping research. First, the non-spatial organization of blood-
lead screening data may result in inaccurate mapping. Second, mapping the dynamics of long
and short-term human migration within the study may prove problematic with the static nature of
GIS. Third, the issue of "neighborhood" definition almost certainly requires input by study area
residents. Fourth, those same residents may have privacy issues surrounding the release of
children's health data, and residential lead contamination levels. Upon learning that they are
being used as subjects in research conducted by non-resident investigators, people within study
site communities may refuse to cooperate. In a worst case scenario, a community may act to
block the release or use of any data collected. Each key issue is discussed in greater detail
below.
Non-Spatial Nature of Public Health Data
Norman et al's (1994) research on the differences between rural and urban blood-lead
concentrations in North Carolina, focusing primarily upon ethnic, gender and age differences
among 20,000 participants, is a recent example of public health data being analyzed spatially.
However, the level of analysis is at the county scale without including street addresses, typical
public health data format. Earlier research conducted at Leeds, Alabama (U.S. Department of
Health and Human Services, 1991) is a classic example of the common omission of critical
spatial data from public health investigations. At Leeds, while lead-soil concentrations exceeded
16,000 ppm in some sections of the town, no children participating had blood-lead levels above
25 micrograms per deciliter (ug/dl) (U.S. Department of Health and Human Services, 1991).
-------
However, because the participant's addresses were unavailable, there was no way of knowing
whether any of the participants resided in the most contaminated areas. Children's blood-lead
levels were derived using parental reporting forms. Missing from the forms were requests for the
children's residential addresses. Without street addresses it is virtually impossible to determine
the existence of any spatial correlation between lead poisoned children and lead releases.
Further, compiling children's residential locations for GIS databases may be problematic as
records kept by health departments may list incorrect, inaccurate, or defunct addresses, or no
addresses at all. In some cases, the address given by a child's parent or guardian may not be
where the child actually resides. Further, even if a correct address is provided, that site may not
necessarily be the point of exposure. The spatial inconsistencies associated with blood-lead and
lead exposure data may render GIS address geocoding efforts very difficult, if not impossible to
do by outside investigators. The primary challenge is capturing the daily movements among
residences and public facilities by children under the age of six.
Dynamics of Human Movement Versus Static Data
The static nature of GIS may be limited with respect to the daily migratory patterns of human
beings, especially small children. Pin-pointing an exact lead exposure site for a poisoned child
may prove difficult considering that a child may move from his/her home to school, back home,
and then to a relative or friend's home within a given 24 hour period. Temporal GIS problems
and issues are discussed by Langram (1992), Monmonier (1991), and Peuquet (1994). Adams
(1995) discusses the difficulties inherent in dealing with people as "point entities." Daily, short
distance human migration cannot be captured on a non-real-time GIS image. In a static GIS
application, lead-poisoned children would have to be treated essentially as a "dynamic point file."
Low-income children, who are most susceptible to lead poisoning, tend to move from one home
to another more frequently than those from middle and high income homes, which adds to
address matching accuracy problems. With humans being non-stationary objects, tracking them
as they move through space during a particular time period may complicate any efforts to draw
sound conclusions about conditions within that space which may be impacting the humans
present. While recent research indicates that methods for accurate parental estimation of
children's soil ingestion rates have yet to be perfected (Calabrese, Stanek, and Barnes, 1997),
the community knowledge base remains the best source of information for potential childhood
lead-soil exposure incidences.
Neighborhood Delineation
Selecting the spatial limits of the target population in lead-soil exposure studies comes with a
3
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variety of challenges. Martin (1991) and Hunter (1979) discuss the difficulty of classifying
residential neighborhoods. Coombes et al (1993) discuss the pertinent ingredients which make
up a "community," and how the importance of those ingredients may be overlooked during GIS
database construction. In most cases, census tracts, school districts, and other readily-available
data polygons are not sufficient for targeting specific groups. Neighborhood and/or community
lines do not necessarily coincide with municipal boundaries. Tosta (1993) writes "If the bits and
bytes on our screens have become our knowledge about a place, and if we are using that
knowledge to make decisions . .. and we've never been in that space, then something is terribly
wrong." On-the-ground analyses of study areas are necessary for the creation of culturally,
socioeconomically, and racially homogenous polygons. In order to delineate the spatial extents
of a target population, it is necessary to peruse the study area. During inter-census periods,
many communities may undergo significant changes due to gentrification, filtering, and other
migratory phenomena. Groundtruthing by study area residents is most effective in establishing
the daily movement patterns of children. This step is best conducted during the summer months
when children have maximum opportunity for daily contact with playground soil. The observers,
practicing "street geography," are able to identify specific
exposure points and then spatially match those exposure points with locations of lead poisoned
children.
Privacy Issues
Because public health data contains information specific to private individuals, obtaining such
data in useful form may prove difficult. Parents of children with elevated blood lead levels will
most likely wish to remain anonymous. Privacy issues may interfere with sampling efforts as
parents of children and landlords may refuse access to private property for the collection of soil
samples. The omission of children's addresses in the aforementioned Leeds, Alabama study
may have been an effort to protect the children's privacy. The potential for lead poisoning to
result in learning disabilities and lowered I.Q. scores may be reasons for parents wishing for
anonymity. Childhood lead poisoning has also been linked to delinquent behavior (Needleman et
al, 1996). Such revelations will most likely further influence families' insistence to keep such
medical information private. Children with blood-lead levels above 10 ug/dl are now considered
at risk (National Research Council, 1993). With indications that the consequences and
prevalence of childhood lead poisoning may be more serious than earlier perceived, many
municipalities are now making efforts to compile more concise databases. New databases
include addresses of children having elevated blood-lead levels; however, due to their sensitive
-------
nature, they are relatively difficult to obtain by outside investigators. Potentially impacted
stakeholders have greater rights, if not access, to community children's health data.
Therefore, it is in their best interest to have some form of proprietorship over any data being
used to develop GIS-supported lead lead-soil mapping.
CHILDHOOD LEAD POISONING HEALTH RISKS ASSOCIATED
WITH LEAD-SOIL EXPOSURE
Research conducted by the U.S. Agency for Toxic Substances and Disease Registry (ATSDR)
and others have indicated that childhood lead poisoning, thought to have been eradicated
through the legislated ban on leaded gasoline and lead-based paint during the late 1970s,
remains to be a pervasive public health problem, especially for poor urban children (Chadzynski,
1980; Lin-Fu, 1980; Mushak and Crocetti, 1988; Breen and Stroup, 1992; Brewer etal, 1992;
Fernandezetal, 1992; National Research Council, 1993; Norman etal, 1994; U.S. General
Accounting Office, 1998; Mielke, 1999). The potential neurological, behavioral, and renal
damage associated with lead poisoning is well documented in public health related research
(Needleman etal, 1990; U.S. Department of Health & Human Services, 1991a; Needleman,
1992; Florini and Silbergeld, 1993; Mushak, 1993; Bernard etal, 1995; Needleman etal, 1996).
Youths under the age of seven are identified as being most susceptible to the harmful effects of
lead ingestion including nerve damage, brain swelling, and lowered IQ scores (Wriggins, 1997).
Lead contaminated soil is becoming increasingly recognized as a significant exposure pathway
for children. Among the most infamous childhood lead-soil poisoning cases are those of West
Dallas, Texas (Bullard, 1994) and Leadville, Colorado (Colorado Department of Health, 1990). At
Leadville, a study of residential surface soils found lead-soil levels higher than 1000 ppm with 80
percent of homes with concentrations over 500 ppm. Consequently, it was discovered that 41
percent of the children in Leadville had blood-lead concentrations higher than 10 micrograms
lead/decilter (ug/dl) blood. A multi-city study conducted by Mielke (1991) concluded that because
soil is a major reservoir for lead, there is a strong association between lead-soil levels and
childhood blood-lead elevation. White (1992), in an analysis of high lead-soil levels associated
with an abandoned landfill in Orleans Parish, Louisiana, found that nearly 70 percent of the
children in the area had elevated blood lead concentrations. Other research conducted in
Australia (Young, Bryant, and Winchester, 1992), Michigan (Francek, 1992), Minnesota (Mielke
et al, 1984), and Louisiana (Mielke, 1993) support the above findings linking lead-soil
contamination with childhood lead exposure hazard. Research conducted in Venezuela
5
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indicates that proximity of residential backyards to roadways may establish a link between lead-
soil levels and traffic volumes, adding yet another parameter to lead-soil pathway analysis
(Newsome, Aranguren, and Brinkmann, 1997).
In 1994, the U.S. EPA released guidelines for lead-soil hazard assessment with lead-soil
concentrations exceeding 400 ppm being considered potentially hazardous to human health
(U.S. Environmental Protection Agency, 1994a). However, as Page and Chang (1993) point out,
"There is no universally accepted safe level for lead in soil." The Centers for Disease Control
(CDC) recommends that cleanup should be considered when residential lead-soil is between
500 and 1000 ppm (Clickner, Albright, and Weitz, 1995). The CDC recommendation may be
complicated by the fact that lead may exist naturally in the upper horizons of all soils at
concentrations as high as 500 ppm (Zimdahl and Hassett, 1977). A study conducted during the
1980s at Cincinnati, Ohio estimated that childhood blood-lead levels increase at a rate of 6.2
ug/dl blood for each 1000 ppm increase in lead-soil (Burgoon, Rust, and Hogan, 1995). A U.S.
EPA (1993a) study indicated that for each 100 ppm lead in soil above 500 ppm, there is a one to
two ug/dl increase in children's blood-lead levels. The above indicates that a standard
quantitative relationship between lead-soil and blood-lead levels has yet to be established.
Other research indicates that outdoor lead-soil contamination may contribute significantly to
indoor lead-dust contamination (Sayre, 1981; Charney, 1982;Calabrese and Stanek, 1992;
Clickner, Albright and Weitz, 1995; Lanphear et al, 1995; Rust and Hogan, 1995). Duggan and
Inskip (1985) compiled an extensive list of childhood lead-dust exposure studies and estimated
the mean lead-dust to blood-lead relationship at about five ug/dl blood for every 1000 ppm lead
in indoor dust.
In order to measure the effectiveness of lead-soil abatement efforts, the U.S. EPA (1993a)
completed a major three-city study focused primarily upon the relationship between lead
contaminated soil and childhood blood-lead levels. Contaminated soil was removed from urban
communities having above average childhood blood-lead levels. Following abatement local
children's blood-lead concentration was analyzed in order to determine whether removal of the
soil would result in any reduction. The study concluded that a decrease of 1,000 ppm lead in soil
results in a reduction of approximately one ug/dl blood. With soil being the apparent starting
point of the lead exposure pathway, it is necessary that mitigation and/or abatement of
contaminated soil be of primary concern in efforts to lessen childhood lead poisoning hazards
both inside and outside of the home. The U.S. EPA study includes recommendations for
6
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mitigating hazards from contaminated indoor dust among its 1994 guidelines (1994a).
ENVIRONMENTAL RISK ASSESSMENT AND ENVIRONMENTAL JUSTICE USING
GEOGRAPHIC INFORMATION SYSTEMS
Geographic information systems is applied in this project primarily as a tool for
environmental risk assessment, which is generally considered to be a pro-active
approach to solving environmental problems. Environmental risk assessment involves
the analysis of physical and population data in order to estimate the potential magnitude
of hazard associated with various environmental problems. The U.S. Environmental
Protection Agency (EPA) has established that for its risk assessment data needs
"Geographic Information System technology could be used to identify high-risk
populations...the most exposed and highly susceptible populations in each region would
be targeted for enforcement actions" (U.S. Environmental Protection Agency, 1990).
Currently, GIS-based models, graphics, and statistical packages are being used and
developed in environmental risk assessments by environmental scientists at the U.S.
EPA (U.S. Environmental Protection Agency, 1990 and 1992; Stockwell et al, 1993).
In 1993 the "Environmental Justice Act" was introduced to Congress by Representative
John Lewis (U.S. Congress, 1993). Among the provisions of the Act are requirements for
the U.S. EPA and U.S. Department of Health and Human Services (HHS) to establish the
geographical units used for determining Environmental High Impact Areas (EHIAs) which
are the 100 geographical areas found to have the highest volumes of toxic chemical
releases. Padgett and Robinson (1999) have developed a GIS method for delineating
EHIAs. The Agency for Toxic Substances and Disease Registry (ATSDR) has
determined GIS to be the "best methodology for identifying potentially impacted minority
populations" (Harris and Williams, 1992). The spatial, socioeconomic, and demographic
nature of neighborhood level lead-soil contamination analysis offers significant
opportunities for academic researchers and people of color and/or low-income
communities to cooperatively employ GIS.
A SIX-STEP COMMUNITY-BASED PUBLIC PARTICIPATION GEOGRAPHIC INFORMATION
SYSTEMS APPROACH
For this study, a six-step public participation GIS method has been developed specifically for
lead-soil research. The steps are to be considered a guide for future research and need not be
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followed to the letter. Issues and concerns central to community action agendas should take
precedence in shaping the exact procedures followed.
Step One: Preliminary Groundtruthing
The assessment should begin by determining the extents of the community site, which must be
conducted on-the-ground. The goal is to delineate, to the best extent possible, contiguous
demographically, socioeconomically, and culturally homogenous communities. At this point it is
prudent to turn to community residents for greater insight into community characteristics.
Another critical task to be conducted here is the observation of children at play. Children's daily
movement patterns are most likely best assessed via first-hand community sources. An
"outsider's" simple observations may not be sufficient; for projects covering large areas with
diverse socioeconomic dynamics, an "insider's" viewpoint may be required to ensure an
acceptable level of accuracy. Knowledge indigenous to the community population most likely
provides the best indication of where children six months to six years old may frequently be
exposed to lead in play area soils.
Step Two: Community Soil Sampling and Analysis
Following the delineation of the community site(s) and the play areas therein, samples must be
collected from play area soils. The sampling and analysis methods applied will vary from case to
case. The amount of resources available to the researcher will determine the numbers of
samples he/she will be able to analyze. Costs for having soil samples analyzed may range from
$45.00 to over $80.00 per sample. Field assessments requiring several hundred samples could
obviously and easily become very expensive. For this study, recently available field test kits for
lead in soil, sensitive to 400 ppm, will be used (Hybrivet Systems, 1996). Soil samples will be
collected by neighborhood residents on a voluntary basis. Perusal of the aforementioned
research on GIS applications in lead-soil research generally indicates that, for community-level
studies, one to two samples per census tract is suitable for accurately establishing the nature of
neighborhood lead-soil concentration levels.
For this project, 400 ppm lead in soil will be the target concentration in agreement with U.S. EPA
sampling protocol (U.S. EPA, 1994); however, it is not a permanent value and should not be
treated as a constant. Whatever lead-soil level is most appropriate for an area under
investigation is the one which should be utilized. For example, according to the most recent EPA
assertions, residential lead-soil concentrations exceeding 400 ppm are considered potentially
-------
hazardous to human health (1994); however, in some cases, 1,000 ppm may be considered an
appropriate benchmark. It is in the primary interest of potentially impacted communities to locate
areas where concentrations exceed this standard. Therefore, in this study with qualitative
testing, it is not necessary to know exact lead concentrations above 400 ppm.
Step Three: Location of Sample Sites Exceeding Target Levels
The locations of play areas exceeding target levels should be noted either by street address or
through some alternative grid reference system. In this study mapping contaminated sites
identified by street addresses will be done voluntarily. Names of participants will not be included
on survey forms in order to protect the privacy of community residents. A sufficient number of
sampling points will be noted to produce a point attribute file of potential problem sites within the
selected community.
Step Four: Analysis of Community Childhood Blood-Lead Data
Blood-lead concentrations for children residing within the community site(s) should be obtained
and analyzed. Due to privacy concerns, this step may require a lengthy negotiation period. If this
procedure is followed by a public agency or otherwise with free access to blood-lead data,
attaining the information should be able to be done in relatively little time. The currently accepted
benchmark for blood-lead potentially being a health hazard in children is 10 ug/dl. The numbers
of children exhibiting elevated blood-lead levels residing in close proximity to potential hot spots
should be noted, as should their respective blood-lead concentrations in ug/dl. Methods used to
analyze blood-lead data may vary; however, the primary goal should be to determine whether
above-average blood-lead levels are exhibited by relatively high percentages of children within
the community site(s). Populations with more than five to ten percent of children having elevated
blood-lead should be considered above the norm (Society for Environmental Geochemistry and
Health, 1993). For this study, the lead community organization will be charged with determining
local childhood blood-lead levels from existing public health data. No blood samples will be
collected; however, parents may be advised to have their children tested at residences where
lead-soil levels above 400ppm are found.
Step Five: Development of Geographic Information Systems Point Attribute Files
In order to establish a spatial relationship between children and sites of potential lead-soil
exposure, home addresses where samples are collected will be submitted confidentially and
voluntarily. Residents who have received training in GIS point file geocoding processes and will
9
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collect the data from the volunteers and then build a point layer of sampling locations. Samples
found to have at least 400ppm lead in soil will be noted as potential hot spots. The final "hot
spot" maps will be maintained within the community, most likely by the lead community
environmental justice organization. The maps will not be released to the public outside of the
community without some form of consent from the voluntary participants.
Step Six: Community Education and Dissemination of Results
If the project results warrant it, potentially impacted volunteers and their children will be notified
of the existence and location of lead contaminated soils. Ideally, community residents with
proper access will be able to view the maps on-line via the internet at local public libraries or
other institutions. Hardcopy maps showing hot spot zones will also be made available to
qualified persons. If outside consultation is deemed necessary, and there is a general
agreement among the project participants and volunteers, data and graphics will be made
available to disciplinary experts, government decision-makers and others who may be better
able to analyze and mitigate significant incidences of lead-soil contamination.
CASE STUDY ASSESSMENT METHOD FOR THE SIX-STEP COMMUNITY-BASED
RESEARCH APPROACH
An instrument for measuring the effectiveness of the public participation GIS project has been
derived from Bullard's environmental conflict resolution assessment framework (Bullard, 1994),
and Barndt's method for evaluating public participation GIS programs (Barndt, 1998) (Table 1).
The qualitative criteria included at Table 1 are intended for the purpose of determining whether
the goals of the project have been attained. However, it should be understood that these criteria
are neither those of the community participants nor the lead environmental justice group. The
standards included in the instrument are parameters set forth for academic evaluation only. The
involved parties themselves are to independently determine their gains and losses from the
experience.
IMPLEMENTATION OF THE COMMUNITY-BASED APPROACH:
BALTIMORE, MARYLAND: JULY 1998-JUNE 1999
The lead community organization at the Baltimore, Maryland site is Youth Warriors, Inc., an
environmental justice organization founded in 1996, the primary purpose of which is to work with
local communities in Baltimore's inner-city to educate and mobilize young African Americans
around urban environmental education, training, leadership development, and organizing skills.
10
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The group works to "encourage our young people to be stakeholders in their communities - loving,
respecting, and valuing the worth of themselves and their neighborhoods." Youth Warriors has two
main campaigns: 1) the Urban Gardens Initiative, a collaborative with local neighborhood
associations to identify vacant lots and convert them into flower gardens, and 2) PROJECT LEAD,
Table 1. Case Study Assessment Criteria for Community-Based Public Participation
Geographic Information Systems
Assessment Dimensions
Issue Crystallization
Environment
Public Health
Equity and Social Justice
Economic Trade-Off
Type of Leadership Group
Mainstream Environmental
Grassroot Environmental
Social Action
Emergent Coalition
GIS Development and Management Process
Sustainable
Replicable
Efficient
Timely
Immediate
Sophisticated
Results/Outcomes
Appropriate
Actionable
Fits Schedule of Activities and Priorities
Accurate
Insightful
Offers Perspective
Synergistic
Combines Qualitative and Quantitative
Results in Changed Outcomes
Contribution to Broader Community Development Agenda
Integrates the Components of a Working Community Information System
Encourages a More Open Dialog Using the Information Organized
Increases Access to Information and Ensures the Right of Access
Keeps Priority Setting in the Hands of Community
Addresses Process Objectives
Recognizes the Value of Co-Production
Increases the Capacity of Local Community Systems to Use the Technology
Integrates into a Broader Community Development Process
11
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a lead poisoning prevention education program. In April 1998, Youth Warriors was awarded a
Conservation Technology Support Program (CTSP) Grant (www.ctsp.org) for GIS support which
includes the components listed at Table 2.
Table 2. Youth Warriors' Conservation Technology Support Program Grant
Award Components
Hardware: (Received - July 1998)
Processor (HP VL 266 64 MB RAM 4.3. GB HDD 24xCD Win95)
Monitor (HP Ultra VGA 17" Display)
Printer (HP DeskJet 1120Cse Printer)
CD-Writer (HP SureStore 7200I Internal IDE with software)
Software:
ArcView 3.0 Windows 95
ArcPress Extension
Training:
Five days ArcView GIS training on a space-available basis. (Training completed by On-Site
Coordinator, November 1998)
Two days ArcView 3.1 GIS Basic Conservation Training. (Training completed by GIS Team
Leader, July 1998)
The GIS hardware and software is being used in support of PROJECT LEAD, a lead poisoning
prevention education campaign through which teenagers from the local community are trained in
U.S. Environmental Protection Agency (EPA) approved sampling methods for indoor lead dust
content at residents' homes. Selected sampling areas are considered to have high risk for lead
exposure due to their socioeconomic and demographic characteristics. There are currently
approximately 60 participating households. If samples show high levels of lead dust, residents
are (1) informed immediately and told the implications of test results (2) sent a Help Packet listing
available resources in Baltimore City, and (3) referred by staff to appropriate Baltimore City
agencies for further assistance.
12
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With outdoor soils being the primary source of indoor dust, the scope of PROJECT LEAD is being
expanded to include sampling of residential outdoor soils. Spatial analysis of sample test results
are being conducted in order to determine the extent and nature of lead soil/dust contamination
throughout the community site. ArcView 3.1 GIS is being used to map inner-city residential indoor
dust/outdoor soil lead concentrations. Using GIS to support the on-going PROJECT LEAD project
is providing an inner-city community with an assessment of local lead exposure risks in an easily
understandable graphic format. The community-driven method developed here is applicable at
other urban areas. Student participants and organizers in Youth Warriors have gained
invaluable skills and training in GIS technology, environmental assessment, database
management, and scientific field methods. Student trainees will use Microsoft Excel to build a
database of the participants in PROJECT LEAD. Pertinent attribute information will include:
residential street addresses, census tracts, parts per million (ppm) lead in soil, household lead dust
concentration (ppm), and ages of children present (voluntary). Data is being obtained through
surveys and field sampling.
Geographic information systems training adds a community sustainability component to the
campaign. Training in ArcView 3.1 GIS and its extensions is enabling Youth Warriors participants
to develop skills very attractive to employers, or for entrepreneurial pursuits. The students and
young people (ages 13-30) are increasing their awareness about their urban environment, and also
gaining practical skills which will enable them access to resources that they may use for long-term
community restoration. In May 1999, several participants completed an eight-hour Lead Poisoning
Prevention Training course held at the National Safety Council Environmental Health Center.
PROJECT LEAD'S implementation plan is built around three components: public education,
community service, and grassroots organizing. Interwoven throughout each of these components
is the main purpose of providing opportunities for predominantly African Americans from low-
income urban communities to participate in high quality environmental training projects that foster
environmental education, cultural enrichment, leadership development, and civic responsibility.
It is building community capacity to identify local environmental justice problems and enhance
stakeholder understanding of environmental and public health information systems. Pertinent goals
include the education of 150 young people in lead poisoning's dangers and lead sampling
techniques, as well as training community-based organizations in GIS technology.
13
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SIX-STEP APPROACH AS APPLIED TO PROJECT LEAD: JULY 1998-JUNE 1999
1. July-September 1998: Training on GIS applications and lead sampling forGIS Team Leader,
On-Site Coordinator Assistant, and Youth Warriors participants
2. August-September 1998: Delineation of study area with assemblage of PROJECT LEAD
residential sampling sites ~ approximately 60 ~ and demographic/socioeconomic attribute
database construction.
3. October 1998-January 1999: Lead-soil and indoor dust sampling at PROJECT LEAD
residential sites and input of results into point file and attribute database format.
4. January-April 1999: Import of lead sampling results point file into ArcView 3.1 GIS for sample
site address geocoding for GIS point file map development.
5. February-April 1999: Development of initial community lead exposure hazard maps
using ArcView 3.1 GIS.
6. May-June 1999: Development of initial community lead hazard statistical graphics using
Arcview3.1 GIS.
7. May-June 1999: Development of community lead hazard maps and other graphics
using ArcPress for local distribution and target group education.
It should be noted that in keeping with Youth Warrior's agenda, the "six-step approach" has
been adjusted to seven steps. At the time of this working paper, the project had reached step
three above, approximately seven months behind schedule due to a variety of unforeseen and
unavoidable circumstances. Work continues at the study area shown at Figure 1. The
neighborhood generally encompasses Baltimore City census block groupl 605-5. The population
of 1,068 in the one-tenth mile area is 99.4 percent African American with approximately 10
percent being under six years of age (U.S. Department of Commerce, 1997). With a median
household income of $23,750 and only 7.7 percent of the population in poverty, the community
is not economically typical of those with high risk for lead exposure. However, the median year
built for homes is 1941, and 221 of the 471 housing units were built prior to 1940. Soil sampling,
GIS mapping, and assessment should be completed by October, 1999.
14
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Calverton Junior High School
James Mosher Elementary School*-
Figure 1. PROJECT LEAD study area: Baltimore, Maryland (U.S. Dept. of Commerce, 1997)
15
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23
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Polygon Development Improvement Techniques for Hazardous Waste
Environmental Impact Analysis
David A. Padgett
Austin Peay State University, Clarksville, Tennessee
Introduction
Recently, concern has arisen regarding the effect Super-
fund sites have on surrounding communities and, spe-
cifically, the distribution of those impacts on target
populations. In designing geographic information sys-
tems (CIS) applications for analyzing potential impacts
of hazardous wastes or waste sites on adjacent neigh-
borhoods, many challenges may be encountered. CIS
database design requires addressing questions of time,
space, and scale.
The U.S. Environmental Protection Agency (EPA) and
other federal agencies have conducted studies that in-
dicate that certain sectors of the population may be
more vulnerable to exposure to toxics than others. To
date, federal departments have enlisted in several CIS-
based research projects that attempt to delineate "geo-
graphic hot spots" of toxic contamination. Such CIS
applications at hazardous waste sites have typically
used polygons to represent data from census tracts
and/or municipal boundaries. In most cases, however,
census tract and other boundaries do not necessarily
jibe with community and neighborhood boundaries; there-
fore, the polygons representing characteristic data for
target populations may not be consistent with the actual
status of those populations.
The objective of this paper is to demonstrate CIS meth-
ods for producing, to the greatest degree possible, so-
cioeconomically and culturally homogenous polygons
for impact analysis of specific sensitive populations
and/or communities. The paper presents case studies
of community/neighborhood characterization problems
encountered in developing polygons during previous
field investigations involving lead (Pb) contamination,
toxic release inventory (TRI) sites, and solid/hazardous
waste sites. The paper attempts to demonstrate effec-
tive solutions and suggestions for improving polygon
development, including CIS data manipulations and
software applications. In addition, the paper provides
geographic and groundtruthing field methods to sup-
port and enhance the accuracy of remotely obtained
information. Finally, the discussion includes commu-
nity and geographic hot spot analyses for potential
public health impacts.
Background
In 1992, EPA established the Environmental Equity
Workgroup. Its members included personnel from the
Offices of Toxic Substances and Civil Rights, as well as
Policy, Planning and Evaluation. The workgroup con-
ducted an extensive study on environmental equity is-
sues. Their report offered several recommendations for
improving federal agency efforts in protecting minority
and low-income populations and recognized a need for
more spatial and demographic data. The final report,
titled Environmental Equity: Reducing Risk for All Com-
munities (1), was released in February 1992 and con-
cluded that "there is (sic) limited data on environmental
health effects by race; there are differences by race and
income in potential and actual exposures to some pol-
lutants." In response to the above findings, the work-
group offered the following recommendations (1):
EPA should establish and maintain information
which provides an objective basis for assessment of
risks by income and race, commencing with devel-
oping a research and data collection plan.
It (EPA) should revise its risk assessment proce-
dures to ensure . . . better characterization of risk
across population, communities or geographic ar-
eas. In some cases it may be important to know
whether there are any population groups at dispro-
portionately high risk.
The Agency for Toxic Substances and Disease Registry
(ATSDR) formed a community health branch to specifi-
cally examine the potential health impact of hazardous
waste sites upon people living in surrounding communi-
ties. The new branch's personnel direct ATSDR's minor-
-------
ity health initiative, which focuses upon health threats to
minority populations, including those from environ-
mental contaminants.
In addition, EPA established the Office of Environmental
Equity. The office's mission includes analyzing environ-
mental impacts upon minority populations, providing
technical assistance to disadvantaged communities,
and establishing environmental initiatives at minority
academic institutions (MAIs). The office serves as a
clearinghouse of environmental data and information for
groups and individuals involved in environmental equity
activities.
In 1993, Representative John Lewis (Democrat-Georgia)
introduced the Environmental Justice Act to Congress. The
act requires EPA and the Department of Health and Hu-
man Services (DHHS) to establish the geographic units for
determining environmental high-impact areas (EHIAs),
which are the 100 geographic areas found to have the
highest volumes of toxic chemical releases.
GIS Applications
CIS could potentially help address the above data and
information needs of EPA. The Agency specifically ac-
knowledges this in other recommendations (1):
EPA could further develop its enforcement prioritiza-
tion policy to target high-risk populations. Under this
scheme, the most exposed and highly susceptible
populations in each region would be targeted for
enforcement actions. Geographic Information Sys-
tem technology could be used to identify high-risk
populations.
Several recent environmental studies have employed
computer applications and spatial data. Goldman (2) used
GIS in a major study that graphically displayed counties
having high percentages of African-Americans, hazardous
wastes, and diseases. Mohai and Bryant (3) applied a
linear regression model to show a positive correlation
between increasing proportions of minority populations
and decreasing distances from hazardous waste sites in
Detroit. Lavalle and Coyle (4) conducted an extensive
analysis of computer databases that hold hazardous
waste law enforcement information for the past 10 years.
They found inequity in enforcement and remedial actions
in white communities versus nonwhite communities.
EPA has enlisted GIS for community environmental im-
pact projects at Regions II and III. EPAs Office of Health
Research (OHR) is investigating methods for linking
demographic data with TRI information to evaluate the
relationship between levels of hazardous waste re-
leases and exposure risks in minority communities. EPA
has also developed the TRI "risk screening" process,
which employs TRI, U.S. Census data, and GIS to iden-
tify TRI releases that may pose significant risk to human
health or the environment (5).
Both EPA (6) and the North Carolina Department of Envi-
ronment, Health, and Natural Resources (7) have recently
completed CIS-based environmental investigations. The
EPA study involved GIS analyses of TRI chemical releases
in the southeastern United States. The report included
numerous CIS-produced maps that show locations where
TRI releases may be affecting densely populated areas
and sensitive ecosystems. The North Carolina study
applied GIS in searching for sources of lead-poisoning
in children. Findings indicated a positive spatial correla-
tion between high lead-contamination risk communities
and those having certain socioeconomic characteristics,
such as low income, above-average African-American
population percentages, and above-average percent-
ages of residents receiving public assistance.
The ATSDR recently implemented a study using GIS to
evaluate and analyze the demographic characteristics
of populations near National Priorities List (NPL) sites.
According to the ATSDR, "As a result of our pilot tests,
we have determined GIS to be the best methodology for
identifying potentially impacted minority populations" (8).
Limitations of GIS
While GIS may be a viable tool for investigating environ-
mental inequity, it is not an absolute solution. Issues
involving hazardous waste impact assessments tend to
be very complex without the added dimension of racism
or discrimination. Efforts to determine a causal relation-
ship between the presence of minority communities and
environmental hazards must consider the questions of
time, scale, and place. Unfortunately, in many instances,
GIS applications may be unable to adequately illustrate
these three pertinent issues resulting in skewed or alto-
gether incorrect conclusions.
With respect to scale, among the immediate concerns
when applying GIS is selecting appropriate sizes for
polygons. As indicated above, EPA is in the process of
determining the scale for EHIAs. A polygon may be a
county or a census tract. Figure 1 illustrates problems
m
m m B *• s
o °
90 Percent Nonminority
Figure 1. Problems of scale in GIS polygon design.
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of scale associated with polygon size selection. At the
county-size scale, a case of environmental inequity ap-
parently exists with the presence of a Superfund site in
the sample county because 80 percent of the county's
population belongs to a minority ethnic group. A closer
look, however, reveals that the population residing in the
immediate vicinity of the waste site is predominantly
nonminority.
Useful as CIS may be, its output in some cases may
display static conditions without considering human
movements over time. Figure 2 displays a common
situation associated with the "filtering" phenomenon, in
which a nonminority population moves out of an area
while increasing numbers of an ethnic minority group
moves into it. The figure shows that in 1950, a nonmi-
nority community surrounded a TRI site (i.e., an active
industrial site releasing toxic substances). By 1990,
the demographics of the neighborhood had changed
along with the status of the TRI site, which is now an
abandoned Superfund site. A CIS database probably
would contain only information on the community from
1990. Such an instance could suggest that some form
of environmental injustice exists given the presence of
the Superfund site within the minority community. Ac-
counting for the dynamics of time and human move-
ment, however, would show that the waste site preceded
the minority population and that, in actuality, the minority
community moved toward the site. This conflicts with the
prior notion that unsavory forces placed the site in the
minority community.
Figure 3, a schematic of polygons used in an investiga-
tion into sources of lead-poisoning in children, also dis-
plays the limitations of CIS with respect to time and
human dynamics, but at a lesser time interval. The study
area is divided into low- and high-risk areas for lead
contamination. The locations at which children with lead
poisoning were found, however, do not correspond with
the areal risk factors. In this case, the CIS is limited in
its ability to follow human movements on a daily basis.
For instance, a parent with a child who exhibits un-
healthy blood-lead concentrations may report the child's
home address as someplace within the low-risk poly-
gon. The child may attend school in the high-risk area,
however. The children's points of contact with lead may
not necessarily correspond with their home addresses,
resulting in an inaccurate graphic display.
With respect to place, CIS may be limited in its ability to
determine the specific borders of a socioeconomically
and demographically homogenous human population.
Tosta (9) and Coombes et al. (10) discuss the dilemmas
associated with neighborhood boundary delineation in
CIS applications. Figure 4 displays a schematic of a
census tract. The CIS database may list the tract's per
Figure 2. Example of changing community demographics with
time near a hazardous waste facility.
• = Homes of Lead-Poisoned Children
Figure 3. Lack of correspondence between locations of lead-
poisoned children and high-contamination risk areas
due to daily dynamics of human movements.
Middle
Income
Community
Census Tract Schematic
Figure 4. Example of significant neighborhood-type variation
within a single polygon, possibly resulting in skewed
socioeconomic data.
-------
capita income as relatively low and may list the tract as
a low-income neighborhood. Further investigation may
find, however, that two very different socioeconomic
communities exist within the tract, one middle-class
and the other a public housing facility. Frequently,
middle-income, African-American communities have
low-income housing projects built adjacent to them. With
respect to the polygon in Figure 4, if an investigator
wanted to research health impacts of toxic wastes for
low-income households, using this polygon and others
like it would inaccurately depict communities within
them.
An additional problem in community definition is deter-
mining exactly what defines a minority community. The
most common indicator for discerning a minority com-
munity would be the existence of a clear majority of
some minority group as in Polygon A of Figure 5, or
where the minority group makes up more than 50 per-
cent of the population as in Polygon B. In some in-
stances, however, communities have received minority
status without the presence of the conditions in Poly-
gons A and B.
Previous investigations show a number of measures
used to identify minority communities and census tracts.
Greenpeace conducted a 1990 environmental justice
study that determined a community's status as minority
based upon the relationship between a community's
percentage of minority population and the selected mi-
nority group's national percentage (11).
Polygon C in Figure 5 depicts Greenpeace's minority
community definition. Taking the target ethnic group in
Polygon C as African-Americans and the hypothetical
extent of Polygon C as the United States (African-Ameri-
cans make up approximately 12 percent of the total U.S.
population), one may determine that Subpolygon C is a
minority polygon or community because its minority per-
centage is over twice that of the national percentage or
extent of the large population in Polygon C. The condi-
tion that Polygon C illustrates is also evident in a study
Subpolygon C
30 Percent Minority
Polygon A
80 Percent Minority
Polygon B
51 Percent Minority
Polygon C
12 Percent Minority
Figure 5. Problems in defining minority polygons.
by Mohai and Bryant (3). The authors claim that envi-
ronmental inequity exists in Detroit where they found
that on average, within a 1-mile radius of the city's
hazardous waste facilities, 48 percent of the population
is nonwhite.
Solutions With GIS and Supporting
Technological Methods
From the above discussion, GIS may appear very lim-
ited for use in environmental community impact investi-
gations, but GIS can actually be an extremely effective
tool if employed with appropriate supporting technology.
Preliminary Groundtruthing
To design GIS databases that reflect the true nature of
target groups and human dynamics, preliminary
groundtruthing may be necessary. In some cases, inves-
tigators make gross interpretations of suspected envi-
ronmental inequity without actually visiting the study
area. Without groundtruthing prior to final database de-
velopment, questions of time, scale, and human dynam-
ics may be left unanswered. The integrity of databases
produced this way and the antecedent conclusions
based upon them may fall into question. Thus, because
the nature of environmental and human health impact stud-
ies is complex, some on-the-ground work should precede
or at least accompany database construction efforts.
Cause-Effect Analyses
Epidemiological studies are increasingly employing
GIS. This use is important with respect to environmental
investigations because in many cases, proof of a corre-
lation between a waste site and community health prob-
lems may be necessary. Croner et al. (12) describe
statistics-supported GIS applications for linking "cancer
hot spots" with pollution sources. Without conclusive
evidence that waste sites and other environmental
hazards negatively affect health in socioeconomically
disadvantaged populations, claims of environmental in-
justice may be difficult if not impossible to prove.
In historical analyses of waste facility sitings, GIS may
be useful, along with the support of gravity models, in
investigating whether the sitings followed the prescribed
logic for such siting decisions. Noble (13) wrote that the
costliest aspect of waste facilities management is trans-
portation; therefore, siting decisions should favor loca-
tions in closest proximity to a selected community's
centroid of waste production. Using a GIS gravity model
with data for household wastes produced within a given
locality, the center of gravity of the volumes of wastes
produced could be located. Historical analyses of past
siting decisions may find that past siting decisions defied
logic. Instead of finding waste sites placed in environ-
mentally safe locations as close as possible to areas of
greatest refuse generation, analyses may find instead
that sites have been placed farther away in disadvantaged
-------
communities. Both the taxpaying and potentially af-
fected residents would pay for such unsavory siting
practices.
Conclusion
The potential for technological applications, including
CIS, in this arena is great. Increased involvement by
technologically trained environmental professionals is
imminent. Their future involvement, however, must fo-
cus on the scientific soundness of investigative meth-
ods, data integrity, and the equitable participation of
potentially affected citizens in any subsequent decision-
making processes.
References
1. U.S. EPA. 1992. Environmental equity: Reducing the risk for all
communities. EPA/230/DR-92/002. Washington, DC.
2. Goldman, B. 1991. The truth about where you live. New York,
NY: Times Books.
3. Mohai, P., and B. Bryant. 1992. Environmental racism: Reviewing
the evidence. In: Bryant, B., and P. Mohai, eds. Race and the
incidence of environmental hazards: A time for discourse. Boul-
der, CO: Westview Press.
4. Lavalle, M., and M. Coyle. 1992. Unequal protection: The racial
divide in environmental law. The Environmental Professional
153:S1-S12.
5. U.S. EPA. 1990. Toxics in the community: National and local
perspectives. EPA/560/4-90/017. Washington, DC.
6. Stockwell, J.R., J.W Sorensen, J.W. Eckert, Jr., and E.M. Car-
reras. 1993. The U.S. EPA geographic information system for
mapping environmental releases of toxic chemical release inven-
tory TRI chemicals. Risk Analysis 132:155-164.
7. North Carolina Department of Environment, Health, and Natural
Resources. 1993. Lead database evaluation and CIS modeling
project. PR-242592. Raleigh, NC: National Institute for Environ-
mental Health Sciences.
8. Harris, C.H., and R.C. Williams. 1992. Research directions: The
Public Health Service looks at hazards to minorities. EPA J.
181:40-41.
9. Tosta, N. 1993. Sensing space. Geo. Info. Sys. 39:26-32.
10. Coombes, M., S. Openshaw, C. Wong, and S. Raybould. 1993.
Community boundary definition: A GIS design specification. Re-
gional Studies 3:280-86.
11. Bullard, R.D. 1993. Environmental equity: Examining the evidence
of environmental racism. Land Use Forum (Winter), pp. 6-11.
12. Croner, C.M., L.W Pickle, D.R. Wolf, and A.A. White. 1992. A
GIS approach to hypothesis generation in epidemiology. In:
American Society for Photogrammetry and Remote Sens-
ing/American Congress on Surveying and Mapping/RT 92, Wash-
ington, DC (August). Technical papers, Vol. 3: GIS and
cartography, pp. 275-283.
13. Noble, G. 1992. Siting landfills and LULUs. Lancaster, PA: Tech-
nomic Publishing Company, Inc.
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Applicability of GIS Tools in Environmental Conflict Mapping:
A Case Study in Hungary
Laszlo Pasztor1*, Jozsef Szabo1, Zsofia Bakacsi1,
Simon T.D. Turner2, TiborTullner3
^Research Institute for Soil Science and Agricultural Chemistry
of the Hungarian Academy of Sciences, GIS Lab
2ADAS International, UK
3 Geological Institute of Hungary
ABSTRACT
Present paper demonstrates technical problems and their solution emerged during the
compilation of first draft Environmental Conflict Maps (ECM) in Hungary. The development of
a methodology elaborated for a pilot area, and the produced ECMs themselves are
presented. In developing this ECMs, local knowledge, that captures and identifies existing
environmental problems, was integrated with agri-environmental spatial databases. Various
input information sources, their spatial and/or thematic compatibility/incompatibility, data
availability, spatial analysis are presented and discussed. Definition of and examples for
direct and indirect conflicts are demonstrated.
INTRODUCTION
The Integrated GIS of the Ministry of Environment and Regional Policy of Hungary (MERP)
laid the foundation of a standard environment and user interface for environmental data
management first of all in Thematic Information Centres (TICs) and other regional authorities
of the Ministry. The completion of this project brought up the necessity to provide this kernel
system with extensions furnishing ministerial authorities decision-making tools for
environmental management and state of the environmental assessment. The ECM
methodology is regarded as one of the cornerstones of these extensions facilitating the
understanding, assessment, as well as the mitigation and/or elimination of environmental
conflicts occurring as a result of the interaction of socio-economic and natural factors.
The purpose of compiling ECMs upon standard methodology within the framework of the
related subproject can be summarised as follows:
H-1022 Budapest, Herman Otto ut 15. , Hungary, Fax: (36-1) 212-1891, E-mail: lacus@rissac.hu
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• identifying the users of the related data;
• identifying conflicting environmental factors;
• raising public awareness on the gravity of the issue;
• assessing the need, feasibility and format of communication links between TICs and
municipalities;
• setting up an evaluation scheme supporting complex assessment of different factors;
• full compatibility of the MERP with the Integrated GIS; and
• harmonising with EU standards and priorities.
Definition of environmental conflicts and their management is a sophisticated issue
considering both the complexity of interacting factors and local and regional organisations
involved in their evaluation. The possibility of working up a standard method is further
impeded by the multitude of local factors specific for the regarded geographic area. It was
thus necessary to involve a broad range of regional and local experts possessing
professional knowledge in handling these problems.
DEFINITIONS
The issue of ECM is extremely complex, the successful outcome of which will largely depend
on the issues involved, the stakeholders and scope for negotiation or mediation. However,
the application of maps, and notably environmental conflict maps can play a useful role in
the process of ECM. The use of a map to communicate complex environmental information
is well recognised. An environmental conflict will almost certainly be unique to a particular
set of circumstances and therefore to prescribe a rigid approach to producing an ECM would
probably give rise to a product that will not be useful to the stakeholders involved.
Despite the broad range of factors acting in environmental conflicts it is necessary to provide
its explicit definition as well as to distinguish between direct conflicts occurring invariably as
a result of a well-defined pollution load on a target surface and indirect conflicts always
related to actual or foreseen modification of the land use pattern. Environmental problems
can be defined as damage or the threat of causing damage to the quality of the environment.
Environmental Conflict: within and/or between spatial and/or temporal coincidence of
environmental pressure (pollution, /threshold of land and susceptibility of target surface)
and/or general environmental elements which is recently and/or might prove to be in the
future disturbing or harmful for the receptor (actual- and future state of landuse).
Direct conflict: between pollution load and susceptibility of target surface.
-------
Indirect conflict: between suitability for a human induced environmental management and
actual/future state land use/management.
PILOT PROJECT
In order to present a practical case study of ECM methodology, a pilot area was selected
along the NE shore of Lake Balaton. It represents an approximately 5 km. wide belt
amounting to approximately 100 km^. Selection of this area was based on the following
aspects:
• it is considered as the most important recreation area and tourist target in Hungary;
• it is affected by a number of factors providing sources to environmental conflicts; and
the
• availability of a number of thematic data necessary for complex evaluation.
ELEMENTS OF ECM CIS
Huge work was invested in the collection of all relevant data for the pilot area. Compromising
between data requirements and availability proved to be the greatest challenge. Finally the
following dataset was set up.
> Topography is represented by the following:
• DTM with a cell size of 50 meters,
• Road, railway, water networks,
• Lake Balaton is represented by a further theme, and
• Watersheds of Balaton are also represented by a polygon theme.
> In addition to the topography of the pilot area, the
• Country border of Hungary and the
• Main rivers of the country are added as a background.
> A satellite image theme covers the majority of the pilot area. Involvement of further
remotely sensed data was hindered due to their high cost. Actually, land cover
information of (CORINE; see later) is also based on satellite imagery.
> Information on soil is represented by the polygon theme AGROTOPO. AGROTOPO is a
soil information system on Hungarian soils in a scale of 1:100.000. Its mapping units
(agroecological units) are characterized with nine different attributes.
-------
M A) .
Figure 1
Some quick views on ECM ArcView project file
> Information on geology is also represented by one theme. An expert system based value
characterising the vulnerability of the geological environment is involved in the ECM
methodology.
-------
> Information on land cover is also represented by one theme (CORINE). Land cover
categories are given with a code of three digits where the first two digits refer to two
further, higher level categorization of actual land cover.
> A raster was filled up by local municipal experts. Environmental impact categories were
determined upon the experience of EU and Hungarian studies but considering the Sofia
priorities and the 5th Environmental Action Program as well. Moreover, they indicated
conflicting land use schemes occurring in their area and made a summary of the major
problems occupying them. The specified criteria (namely 15 different factors) were to be
classified and indicated by the municipalities for each cell of the raster on a scale ranging
between 0-4. The spatial resolution of the raster is 500x500 meters inside settlements
and 1000x1000 meters around settlements. This local knowledge based information is
shared in four individual themes. Data were provided for settlements and outer regions
for low and high (touristic) season.
> TIC data involves two kind of information:
• sub-catchments of Lake Balaton as a polygon theme without any important attribute
furthermore only three of them are touched by the pilot area.
• potential and/or actual polluter sites within pilot area as a point theme with a huge
number of descriptive data, which includes detailed information on industrial and
communal waste depositories, 75 are lying inside the pilot area.
ECM ArcView project collects all relevant and available data for Balaton pilot area in one
session. In one hand this project is highly suitable for computer demonstrations of the data
collection activities, on the other hand it also represents the starting point of any spatial
analysis carried out based on the ECM database in order to achieve ECMs.
SAMPLE ECMs
Sample ECMs are stored in various ArcView project files. These files originate from ECM
project file. They are supported with ArcView extension: Spatial Analyst for the execution of
spatial analysis. Unrelevant data layers are deleted, new themes created either as auxiliary
themes or ECM results. A final softcopy map as a layout is always compiled.
1. Groundwater vulnerability under TIC censused sites
The vulnerability of the geological environment is of outstanding importance in accelerating
or preventing the progress of pollution in groundwater and deep subsurface water horizons.
Various geological factors determine the capacity of preventing the migration of pollution into
-------
subsurface water tables. TICs are responsible for the management and operation of regional
environmental data as well as to collect information on pollutants/potential pollutants:
location, ownership, cadaster emission data, amount of processed and produced hazardous
waste, site and state of communal waste depositories. Potential and/or actual polluters
situated over locations with higher hazard of pollution vulnerability should be
treated/checked more seriously. Consequently, a co-evaluation, spatial analysis of the
conflicting environmental factors should be carried out to get a complex picture on
groundwater vulnerability endangered by TIC censused sites.
Sroundwater vulnerability to
migration of pollution
based on geological
factors
low
» no
hazard for subsurface pollution
Location of potential
point pollution sources
Compiled in W5SAC 615 lab m 199$ *i the frame at EnrnreniwitfiJ bfaiMiiwi System*.
HU9402-QI-QI-L2, Elaboration o-t methodology far EnvvwuMAfal Confhcf Mopping
Figure 2
Sample ECM: Groundwater vulnerability under TIC censused sites
An expert system based methodology of pollution vulnerability assessment subdivides the
considered area into 4 classes according to the combination of three geological factors
(namely permeability of the superficial geological formations, thickness of the eventual
impermeable overburden and the highest groundwater level). High, medium and low
vulnerability classes are defined considering the capacity of preventing the migration of
pollution. TIC censused sites were categorized according to their profiles into two groups:
-------
sites producing or not producing hazardous waste with risk to subsurface water tables. Sites
belonging to the former class then were further classified according to their geographical
location within the various geology based vulnerability categories. Sites are ranked as ones
with high, low or no hazard of subsurface pollution. The output ECM entitled 'Groundwater
vulnerability underlie censused sites' is displayed in Fig. 2.
2. Impact of traffic on Natural Conservation
Territory of Balaton National Park covers major part of northern Balaton region,
consequently overlaps the present ECM pilot area. Balaton region is also characterized as
tourist attraction which requires infrustructural background: road and railway network. Noise
and pollution of the traffic network conflicts with the functions of National Park, parts of
National Park close to elements of traffic network are less valuable and strongly exposed to
damages than farther areas. The co-evaluation, spatial analysis of the conflicting
environmental factors gives a complex picture on status of Balaton National Park
endangered by traffic caused impacts.
Impact of environmental elements represented as line features was modeled using buffering
techniques. Buffering generally means creating a polygon around spatial objects with a given
radius. Spatial Analyst module of ArcView however provided a more sophisticated
opportunity. According to the distance of the elements of a grid from a spatial unit grid cells
were classified; thus the whole territory was characterized with one operation. Co-evaluation
of buffer zones resulting from railway and road system and further merging with spatial
extension of National Park then provided vulnerability classification of Balaton National Park.
The output ECM entitled 'Impact of traffic on Natural Conservation' is displayed in Fig. 3.
3. Overall perception on state of local environment
Simultaneous environmental impacts sum up in overall perception on state of environment in
human mind. Too much load results in bad perception, which is a conflict in itself. This
situation can be treated as a 'polarized' conflict where is no other component, but any, even
the slightest, potential negative change in state of environment results in real conflicts.
Highly impacted sites are 'preconflict' areas.
Values for specific environmental elements provided by municipalities for pilot area raster
were summed up. Since high value for a given factor means bad feeling on state of
environment, higher the summed up value the more the environmental impact within a given
raster cell. Actually, summarizing category values representing as different factors involved
-------
Roads
Impact of traffic on Natural Conservation
2300 9QOO M«wi
Slight impact
Medium impact
High impact
Balaton National Park
Comp-ltd m 6I5SAC CIS Lab m 1998 •* the f romc of Environmental Information Systems:
HU9402-OI -OJ-U flobort)t«n of ineltotlolo^ for EFmronmefiTol Cwrflict A
Figure 3
Sample ECM: Impact of traffic on Natural Conservation
in data collection can be judged at the first sight. Nevertheless overthinking the procedure it
can be concluded that the result does give an indication about the state of local environment.
The output ECM entitled 'Overall perception on state of local environment' is displayed in
Fig. 4.
8
-------
Overall perception on state
of local environment
Rather good
mftlSSACoIS Latt * I99S in the frame of EnvirowvenTcl MafMftBl
HU94Q2-QI-QI-L2. ElotoratKw of mcfhodok^y f&«- FwirowwMal Conflict
Figure 4
Sample ECM: Overall perception on state of local environment
4. Water quality evaluation
The greatest attraction of Lake Balaton, as a tourist target, is its water. Consequently, water
quality is of prime significance. Quality of water is affected by multiple factors and shows
temporal/seasonal changes. About forty water quality parameters area measured in the
course of the year at sample sites along the shoreline. Cartographic treatment of this
problem also produces ECMs. The visualisation of the point, but multitemporal and/or
multivariate information was a challenging task for us. Our proposed solutions area
displayed in Fig 5 and Fig 6.
9
-------
Water quality along shoreline
June 1989
' <
quality according to
oxygen management
(biological oxigen requirement)
organic pollution
(mineral oil)
microbiological state
(coli bacteria)
in RISSAC MS Lab m L99B in the franc of environmental Iivf cn*ji*«
HW9402-OI-01-U. Etocroi-sn ei mct
Figure 5
Sample ECM: Water quality along shoreline: multivariate evaluation
10
-------
Water quality along shoreline
iT
-s*
nitrate
phosphate
Water quality
excellent
good
*
moderate
.
polluted
?
highly polluted
rri RI5MC fiI5 tab in 1996 in The frame of BMMHMfH Infonmation Syslems.
HUWO?-01-01-U. Etobofou&naf meThodolo^ fof EnwrtHVMnlid CnnMit
Figure 6
Sample ECM: Water quality along shoreline: multivariate/multitemporal evaluation
ACKNOWLEDGEMENT
Present work was carried out in the frame of PHARE project No. HU 9402-01-01-L2. The
ECM Project was very effectively aided by Chief Information Office, Departement for
Environmental Information Systems, Directorate for Environmental Strategy, Ministry of
Environment and Regional Policy, under chairmanship of Pal Bozo. The Ministry of
Environment and Regional Policy has made significant progress in the establishment of an
Environmental Information System. This has included, through close co-operation with the
UNEP-GRID programme, the establishment of GRID Budapest as a node on the GRID
network; the establishment of two Thematic Information Centres (TIC's) at Hortobagy and
Szekesfehervar covering the themes Nature Conservation and Environmental Protection
respectively. Our special thanks go to the following persons who were very active in the
project: Kalman Rajkai, Balazs Zagoni, (Research Institute for Soil Science and Agricultural
Chemistry of the Hungarian Academy of Sciences), Entire Farkas and Imre Szepesi,
(Environmental Protection Thematic Information Centres at Szekesfehervar), TiborCserny,
Gabor Turczy, (Geological Institute of Hungary, Division of Information Management) and
Zsuzsanna Flachner.
11
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Watershed Stressors and Environmental Monitoring and Assessment Program
Estuarine Indicators for South Shore Rhode Island
John F. Paul and George E. Morrison
Environmental Research Laboratory, U.S. Environmental Protection Agency,
Narragansett, Rhode Island
Abstract
The U.S. Environmental Protection Agency has initiated
the Environmental Monitoring and Assessment Program
(EMAP), a nationwide ecological research, monitoring,
and assessment program whose goal is to report on the
condition of the nation's ecological resources. During the
summers of 1990 through 1993, data were collected from
approximately 450 sampling locations in estuarine waters
of the Virginian Biogeographic Province (mouth of the
Chesapeake Bay to Cape Cod). During this period, sam-
pling stations were located in the coastal ponds and
coastal area of south shore Rhode Island.
One objective of EMAP is to explore associations be-
tween indicators of estuarine condition and stressors in
the watersheds of the sampled systems. Extensive wa-
tershed information for south shore Rhode Island is
available in geographic information system (CIS) for-
mat. Watershed stressors along south shore Rhode
Island were compared with EMAP indicators of estu-
arine conditions using CIS analysis tools. The indicator
values for coastal EMAP stations (those offshore from
coastal ponds) were associated with all of the aggre-
gated south shore watershed stressors. The coastal
pond indicator values were associated with stressors in
the individual coastal pond watersheds. For the total south
shore watershed, the major land use categories are resi-
dential and forest/brush land, followed by agriculture.
Closer to the coast, residential land use is more preva-
lent, while further from the coast, forests/brush lands
dominate. All coastal EMAP stations, with one exception,
exhibited unimpacted benthic conditions, indicating no
widespread problems. For the individual watersheds, the
major land use categories are residential and forest/brush
land. The population density (persons per square mile)
shows an increasing trend from west to east. Impacted
benthic conditions were observed at EMAP sampling sites
in two coastal ponds. These two impacted benthic sites
appear to be organically enriched.
Introduction
Since its inception in 1970, the U.S. Environmental Pro-
tection Agency (EPA) has had the responsibility for regu-
lating, on a national scale, the use of individual and
complex mixtures of pollutants entering our air, land, and
water. The Agency's focus during this period centered
primarily on environmental problems attributable to the
use of individual toxic chemicals. Regulatory policy,
while continuing to control new and historical sources of
individual chemicals (i.e., "end of the pipe") and remedi-
ate existing pollution problems, will have to address the
cumulative impacts from multiple stresses over large
spatial and temporal scales.
In this decade, the focus of environmental problems, or
"scale of concern," has shifted from point-source and
local scales to regional and global scales. Concurrently,
the focus has shifted from chemical to nonchemical
stressors. The threat posed by nonchemical stresses
(e.g., land use, habitat alteration and fragmentation,
species loss and introduction) presents a substantial
risk to the integrity of both specific populations and
ecosystems, and entire watersheds and landscapes.
The shift in the scale of concern for environmental prob-
lems presents a unique challenge for environmental
decision-making. Traditionally, environmental informa-
tion has been collected over local spatial and short
temporal scales, focused on addressing specific prob-
lems, limited in the number of parameters measured,
and collected with a variety of sampling designs that
were neither systematic nor probabilistic. It is not sur-
prising, then, that several scientific reviews concluded
that the information needed to assess, protect, and man-
age marine and estuarine resources was either insuffi-
cient or unavailable and recommended a national
-------
network of regional monitoring programs (1, 2). Two key
recommendations resulted from these reviews: (1) the
need for a national monitoring program designed to
determine the status and trends of ecological resources,
and (2) the need for an assessment framework for syn-
thesizing and interpreting the information being pro-
duced in a timely manner and in a form that the public
can understand and decision-makers can use. EPA's
response to these recommendations was to institute a
long-term monitoring program, the Environmental Moni-
toring and Assessment Program (EMAP), and to adopt
a risk-based strategy for decision-making.
EMAP is a nationwide ecological research, monitoring,
and assessment program whose goal is to report on the
condition of the nation's ecological resources. During
the summers of 1990 through 1993, data were collected
from approximately 450 sampling locations in estuarine
waters of the Virginian Biogeographic Province (mouth
of the Chesapeake Bay to Cape Cod) (3-5). During this
period, some of the sampling stations were located in
the coastal ponds and coastal area of south shore
Rhode Island. One objective of EMAP is to explore
associations between indicators of estuarine condition
and stressors in the watersheds of the sampled sys-
tems. Extensive watershed information for south shore
Rhode Island is available in geographic information sys-
tems (CIS) format.
The intent of this paper is to compare watershed stres-
sors with EMAP indicators of estuarine condition along
south shore Rhode Island using CIS analysis tools. The
indicator values for coastal EMAP stations (those off-
shore from coastal ponds) are associated with all of the
aggregated south shore watershed stressors. The
coastal ponds indicator values are associated with
stressors in the individual coastal pond watersheds.
The project reported on in this paper served as a pilot
for integrating watershed information with wide-scale
ecological data collected to assess condition of estu-
arine waters.
Ecological Risk Assessment Context
Robert Huggett, EPA's Assistant Administrator for Re-
search and Development, is using the risk assessment-
risk management paradigm as a framework to
reorganize the EPA research laboratories (6). Huggett is
also reorienting the research that EPA conducts to be
risk based (both human and ecological). The major
thrust of the research to be conducted in the EPA labo-
ratories will be directed toward reducing the uncertain-
ties in the risk assessment process. In this way, the risk
assessment context provides the "why" for the research
conducted.
Ecological risk assessment is defined as a process for
evaluating the likelihood that adverse ecological effects
have occurred, are occurring, or will occur as a result of
exposure to one or more stressors (7). The value of the
risk assessment framework lies in its utility as a proc-
ess for ordering and analyzing exposure and effects
information, and in its flexibility for describing past, pre-
sent, and future risks.
One way of depicting the ecological risk assessment
process is shown in Figure 1 (8). The key points are that
the process is continuous; the process can be oriented
in either direction, dependent upon the form of the ques-
tion or issue being addressed; and monitoring is at the
hub, providing information to all activities. The end result
of the effort is to provide better information for making
environmental management decisions.
Figure 1. Ecological risk assessment framework (8).
Overview of EMAP and Estuarine Results
EMAP has been described as an approach to ecological
research, monitoring, and assessment (9). It is not the
only approach but is an approach that is driven by its
goal to monitor and assess the condition of the nation's
ecological resources. The objectives of the program to
address this goal are to:
• Estimate the current status, trends, and changes in
selected indicators of the condition of the nation's
ecological resources on a regional basis with known
confidence.
• Estimate the geographic coverage and extent of the
nation's ecological resources with known confidence.
• Seek associations among selected indicators of natu-
ral and anthropogenic stress and indicators of eco-
logical condition.
• Provide annual statistical summaries and periodic as-
sessments of the nation's ecological resources.
-------
The approach used by the program to meet its objec-
tives and address its goal includes:
• Use of a large, regional scope that encompasses the
entire county but provides information on the scale that
federal and regional environmental managers require.
• Emphasis on ecological indicators to provide the in-
formation to assess condition (i.e., collect information
on the ecological systems themselves to determine
their condition or "health").
• A probability-based sampling design to produce sta-
tistically unbiased estimates on condition and to pro-
vide uncertainty bounds for these estimates.
• A vision of the program as long-term, continuing into
the next century, which is consistent with the large,
regional spatial scale being addressed.
• Development through partnerships with other agencies
that have natural resource stewardship responsibility.
The estuarine component of EMAP was initiated in
1990, with monitoring in the estuarine waters of the
Virginian Biogeographic Province (mouth of
Chesapeake Bay northward to Cape Cod) (10). Figure 2
depicts the biogeographic provinces of estuarine re-
sources of the country. These provinces have been deline-
ated based upon major climatic zones and the prevailing
offshore currents (11). This is comparable with the
ecoregion approach used to describe the distribution of
terrestrial ecosystems (12). The biogeographic province
is the comparable approach for coastal ecosystems.
Monitoring in the Virginian Province continued through
Columbian
1993; monitoring was conducted in the Louisianian
Province from 1991 to 1994; monitoring was initiated in
the Carolinian Province in 1994; and monitoring will be
initiated in the \Afest Indian Province in 1995.
A suite of measurements was collected at each of the
EMAP-Estuaries sampling sites that were selected with
a probability-based sampling design (13, 14). As indi-
cated above, the measurements emphasized ecological
conditions indicators, which included biotic indicators
such as benthicand fish abundance, biomass, diversity,
and composition, and also included abiotic indicators
such as dissolved oxygen, sediment contaminant con-
centration, and sediment toxicity (15).
In the Virginian Province, approximately 450 probability-
based sampling sites were visited during the summer
periods in 1990 through 1993 using consistent indica-
tors and collection and analysis procedures. An example
of the results is shown in Figure 3, which presents the
condition of benthic resources (16). The benthic condi-
tion is reported using a benthic index, which is an ag-
gregate of individual benthic measurements that were
combined using discriminant analysis to differentiate
impacted from unimpacted sites (3, 17). The figure pre-
sents results for values of the benthic index that were
determined to be impacted. The bar chart is the stand-
ard EMAP format for results: province-scale results with
95-percent confidence intervals about estimates. The
large, small, and tidal categories refer to the strata used
in the probability-based sampling design: large systems
are the broad expanses of water such as in Chesapeake
Bay, Delaware Bay, and Long Island Sound; small
Acadian
Californian
Lousianian
West Indian
Figure 2. Biogeographic provinces used by EMAP-Estuaries (13).
-------
Impacted EMAP Site
Baltimore
Washington
Figure 3. Condition of benthic communities in Virginian Province.
systems include the bays and harbors along the edges
of the major systems and embayments along the coast;
and large tidal rivers include the Potomac, James, Rap-
pahannock, Delaware, and Hudson Rivers.
The results indicate that 24 percent + 4 percent of the
estuarine waters of the Virginian Province have im-
pacted benthic communities. The small and tidal river
systems have proportionately more impacted area than
the large systems.
All the EMAP data are geographically referenced; there-
fore, the data can be spatially displayed to explore
patterns. The spatial display of the impacted benthic
community information is a simple spatial analysis of the
EMAP data. This analysis shows that the impacted ben-
thic resources are distributed across the entire province,
with more impacted sites in the vicinity of the major
metropolitan areas.
In addition to analyzing the EMAP results at the regional
scale, analyses have been conducted at the watershed
scale (see Figure 4). The probability-based sampling
design permits the data to be aggregated (poststratified)
in ways other than the way the original design was
stratified. The only restriction to the aggregation is the
number of available sample sites for the aggregation; a
small number of sites leads to large uncertainties in the
results. Figure 4 shows the aggregation for four major
watersheds: Chesapeake Bay, Delaware Bay, Hudson-
Raritan system, and Long Island Sound. This watershed
scale is close to the practical scale at which environ-
mental management decisions are implemented. The
data need to be analyzed at smaller scales, however, to
focus on environmental management of the smaller wa-
tersheds (e.g., contaminated sediments). This leads into
the need to conduct the pilot project addressing water-
shed information.
South Shore Rhode Island Pilot Project
EMAP's third objective relates to exploring associations
between indicators of estuarine condition and water-
shed stressors. Note that the word "watershed" was
added. One way to address environmental management
remediation strategies is to look at the watershed activi-
ties that could possibly be modified or changed to im-
prove estuarine conditions.
Watershed stressors for the estuarine environment in-
clude land-based sources of pollution, such as point
sources of pollution, and land use activities (i.e., how the
land is actually used, including landscape patterns).
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Figure 4.
LIS
Condition of benthic communities in major watersheds in Virginian Province (Chesapeake Bay, Delaware Bay, Hudson-
Raritan system, and Long Island Sound).
Which of these stressors is more important for a particu-
lar situation depends on the types of estuarine impact
(localized or systemwide) and the management ques-
tion that is being addressed.
The specific objective of the south shore Rhode Island
pilot project was to compare watershed stressors with
EMAP indicators of estuarine condition using CIS analy-
sis tools. This project was not intended to be a definitive
study by itself of south shore Rhode Island butto explore
the process necessary to undertake the comparisons, to
investigate the feasibility of pulling the necessary infor-
mation together, and to identify potential problems before
undertaking this comparison on a much larger scale.
The south shore Rhode Island study area is depicted in
Figure 5. This coastal area drains into the coastal waters
of Block Island Sound. The project was intentionally
restricted to a limited geographic area to avoid being
overwhelmed with the tremendous volumes of data that
could have been encountered.
All data sources used in this project were available
electronically. Digitized U.S. Geological Survey quad
maps were available from the Rhode Island Geographic
Information System (RIGIS) at the University of Rhode
Island. The National Pollutant Discharge Elimination
System (NPDES) was available for major dischargers
from the National Oceanic and Atmospheric Administra-
tion's National Coastal Pollution Discharge Inventory
(18). The 1990 census was also available from RIGIS.
The EMAP 1990 through 1993 estuarine data were
available from the EMAP-Estuaries Information System
at the EPA Environmental Research Laboratory in Nar-
ragansett, Rhode Island. The RIGIS data were already
available as ARC/INFO coverages. The NPDES and
EMAP data had to be converted to ARC/INFO point
coverages.
Two approaches were used to conduct spatial analyses.
Buffer zones at 1, 3, 5, 10, and 20 kilometers from the
south coast of Rhode Island were created and used to
clip the south shore area coverages (e.g., land use,
population, point sources). The watershed boundaries
of three south shore coastal ponds were manually de-
lineated and used to clip the south shore area cover-
ages. The ponds were Quonochontaug, Ninigret, and
Point Judith (west to east).
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Quonochontaug Pond
Block Island Sound
Figure 5. South shore Rhode Island study area.
South Shore Rhode Island Pilot
Project Results
The results for land use by distance from the coast are
presented in Figure 6. These results give the broad-scale
coastal perspective. For the total south coast watershed,
the major land use categories are residential and for-
est/brush land, followed by agriculture. Closerto the coast,
residential land use is more prevalent, while farther from
the coast, forests/brush lands dominate. Population (see
Figure 7) increases with distance from the coast, but popu-
lation density does not appear to be a function of distance
from the coast. Only one out of five coastal EMAP stations
exhibited impacted benthic conditions, indicating no wide-
spread benthic problems in the coastal waters. The one
station that was classified as impacted was dominated by
an extremely high number of individuals of one species.
A smaller scale view can be gained by looking at the
results for the individual watersheds. This view also
provides an east-west perspective compared with the
south-north perspective with the distance from the
coast. Again, for the individual watersheds, the major
land use categories are residential and forest/brush land
(see Figure 8). The population increases from west to
east, and population density shows an increasing trend
from west to east (see Figure 9). Impacted benthic
conditions were observed at the EMAP stations in
Quonochontaug and Point Judith Ponds. These stations
exhibited organic enrichment (total organic carbon in the
sediments exceeding 2 percent), possibly from histori-
cally improperly treated sewage. No benthic data were
available for the Ninigret Pond station; however, dis-
solved oxygen was observed to be low at this station.
No major NPDES point sources are located in the coastal
pond watersheds, although two are located on the eastern
edge of the Point Judith Pond watershed boundary.
Discussion
A pilot project was conducted for south shore Rhode
Island to compare watershed stressors with EMAP indi-
cators of estuarine condition. The results indicate that
such a comparison can be accomplished, with the wa-
tershed information providing a qualitative link to the
estuarine conditions observed. One potential problem is
the need to delineate the watershed boundaries for all
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4,500
4,000
0-1 Kilometer
0-3 Kilometers
0-5 Kilometers
0-10 Kilometers
0-18 Kilometers
Residential Commercial Transportation Recreational Agricultural
Land Use Categories
Figure 6. South shore Rhode Island land use by distance from south coast.
Forest
Barren
watersheds for which EMAP data are available.
ARC/INFO provides tools for doing this, but practical
application indicated that difficulties are encountered
when the topographic relief is relatively flat, and on-
screen corrections needed to be applied (19).
A restriction that needs to be understood before applying
the procedures used in this project to a much wider
geographic area is that the data sets for the watershed
stressors need to be available over the wider geographic
area. Further, these data sets need to be temporally
consistent and constructed with consistent methods and
land use classification schemes.
This project was conducted with only a small number of
actual EMAP sampling sites, particularly for the individual
watersheds. Because of this restriction, no statistical
analyses were conducted with the EMAP data for com-
parison with the watershed information. Only qualitative
comparisons were attempted. The next step is to in-
crease the number of individual watersheds so that a
rigorous statistical analysis can be conducted. This is
being conducted by Comeleo et al. for comparing wa-
tershed stressors for subestuary watersheds in the
Chesapeake Bay with estuarine sediment contamina-
tion (19). The steps after this will be to (1) apply the
techniques to the entire EMAP Virginian Province data
100,000 ,
80,000
c
g
I 60,000
40,000 •
20,000
0 •-
Q.
O
CL
0-1 Kilometer
0-3 Kilometers
0-5 Kilometers
0-10 Kilometers
0-18 Kilometers
500
0)
ra
cr
W
-------
2,500 -
2,000
1,500
in
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4. U.S. EPA. 1994. Statistical summary: EMAP-Estuaries Virginian
Province (1991). EPA/620/R-94/005. Narragansett, Rl.
5. U.S. EPA. 1994. Statistical summary: EMAP-Estuaries Virginian
Province (1992). EPA/620/R-94/019. Narragansett, Rl.
6. Newman, A. 1995. A new direction for EPA's Office of Research
and Development. Environ. Sci. and Technol. 29(3):126A-129A.
7. U.S. EPA. 1992. Framework for ecological risk assessment.
EPA/630/R-92/001. Washington, DC.
8. Gentile, J.H., J.F. Paul, K.J. Scott, and R.A. Linthurst. 1995. The
role of regional monitoring in ecological risk assessment: An
EMAP example for watersheds. Manuscript in review.
9. Messer, J.J., R. Linthurst, and W. Overton. 1991. An EPA program
for monitoring ecological status and trends. Environ. Monitor, and
Assessment 17(1):67-78.
10. Paul, J.F., K.J. Scott, A.F. Holland, S.B. Weisberg, J.K. Summers,
and A. Robertson. 1992. The estuarine component of the U.S.
EPA's Environmental Monitoring and Assessment Program.
Chemistry and Ecology 7:93-116.
11. Bailey, R.G. 1983. Delineation of ecosystem regions. Environ.
Mgmt. 7:365-373.
12. Omernik, J.M. 1987. Ecoregions of the conterminous United
States. Annals of the Assoc. of Amer. Geographers 77:118-125.
13. U.S. EPA. 1990. Near coastal program plan for 1990: Estuaries.
EPA/600/4-90/033. Narragansett, Rl.
14. U.S. EPA. 1991. Design report of the Environmental Monitoring
and Assessment Program. EPA/600/3-91/053. Chervils, OR.
15. U.S. EPA. 1990. Environmental Monitoring and Assessment Pro-
gram: Ecological indicators. EPA/600/3-90/060. Washington, DC.
16. U.S. EPA. EMAP-Virginian Province four-year assessment (1990-
1993). EPA report in review.
17. Engle, V.D., J.K. Summers, and G.R. Gaston. 1994. A benthic
index of environmental condition of Gulf of Mexico estuaries.
Estuaries 17(2):372-384.
18. NOAA. 1993. Point-source methods document. NOAA technical
report. Silver Spring, MD: Strategic Environmental Assessments
Division, National Ocean Service, National Oceanic and Atmos-
pheric Administration.
19. Comeleo, R.L., J.F. Paul, P.V August, J. Copeland, C. Baker, S.
Hale, and R.W Latimer. 1995. Relationships between landscape
stressors and sediment contamination in Chesapeake Bay estu-
aries. Manuscript in review.
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Nonpoint Source Pesticide Pollution of the Pequa Creek Watershed,
Lancaster County, Pennsylvania: An Approach Linking
Probabilistic Transport Modeling and GIS
Robert T. Paulsen
The Paulsen Group, Bowie, Maryland
Allan Moose
Southampton College, Southampton, New York
Abstract
The U.S. Environmental Protection Agency (EPA) has
mandated that each state prepare a state management
plan (SMP) to manage pesticide residues in the state's
environment. One aspect of an SMP involves identifying
specific soils and sites that may be vulnerable to the
transport of pesticides into water resources. A recently
developed system identifies vulnerable areas by cou-
pling probabilistic modeling that uses the Pesticide Root
Zone Model (PRZM) with a desktop geographic informa-
tion system (GIS-MAPINFO). A limited test of this sys-
tem succeeded in identifying and mapping individual soil
series in a watershed that were shown to have trans-
ported atrazine to surface and ground water.
During this project, various digital data sources were
evaluated for availability and ease of use, including:
• STATSGO.
• U.S. Geological Survey (USGS) digital line graphs
(DLGs).
• National Oceanic and Atmospheric Administration
(NOAA) climate data.
This study documents hands-on hints and tricks for
importing and using these data.
From 1977 to 1979, the USGS measured the movement
of atrazine off fields of application into water resources
in the Pequa Creek basin in Lancaster County, Pennsyl-
vania (1). Atrazine in surface water appeared at levels
exceeding 20 parts per billion in storm flow and above
the 3 parts per billion maximum contaminant level (MCL)
during base flow from Big Beaver Creek, a tributary to
Pequa Creek. Each soil series in the subbasin was
digitized into a GIS. PRZM allowed simulation of runoff,
erosion, and leaching of atrazine (applied at 2.24 kilo-
grams per hectare in conventionally tilled corn) for each
soil. This process included simulating each soil under
different slopes for an 11 -year period from 1970 to 1980.
Interpreting the results for each soil series determined
the probability distribution of atrazine in kilograms per
hectare for each mode of transport. GIS used these data
to thematically map each soil series for atrazine loss.
The results of this demonstration project suggest that
the Manor silt loam, with slopes varying from 6 percent
to 20 percent, had a high potential to transport atrazine
residues to surface water. This type of analysis could
suggest that this soil series be:
• Farmed using conservation tillage.
• Managed to install grass waterways or buffer strips
to stop runoff.
• Set aside from production to protect water resources.
Digital databases were available for the study area, but
many technical problems were encountered in using the
data. Researchers embarking on these types of model-
ing and GIS projects should prepare themselves for
significant expenditures of time and finances.
Introduction
A significant volume of published literature documents
pesticide residues in ground water, and the volume of
investigations of residues in surface water is expanding.
The growing acceptance of immunoassay techniques
for the determination of pesticide residues in water has
given the field of pesticide monitoring an accurate and
economical analytic methodology. This will result in an
increase in monitoring capability at the federal, state,
-------
local, and university levels. These increases in monitor-
ing capability have documented and will continue to
document the occurrence of pesticides in water re-
sources as the result of past transport through the soil
profile. The U.S. Environmental Protection Agency
(EPA) has mandated that each state prepare a state
management plan (SMP) to manage pesticide residues
in the state's environment. Lacking, however, is a reli-
able pesticide screening technique to indicate which
soils, on a countywide scale, may be sensitive to the
transport of a specific pesticide to deep within the soil
profile or to the surface water resources. These assess-
ments would greatly supplement the usability and valid-
ity of SMPs.
Electronic databases such as State Soils Geographic
(STATSGO), Data Base Analyzer and Parameter Esti-
mator (DBAPE), or the SOILS subsets found in Nitrate
Leaching and Economic Analysis Package (NLEAP)
provide easy access to detailed soil data and model
input estimator subroutines, thereby simplifying data en-
try to numerical models. Two groupings define soils: soil
series and soil associations. Soil series are the individ-
ual soil taxa found in a field. Soil associations represent
groups of soil series, usually three or four soil series
occurring together in an area, and are mapped as a
single unit on a county scale. Mapping of most soil
associations across the United States is complete, with
open access to the county scale maps. A digital soils
mapping data set called SSURGO contains many of the
soil series maps for the United States. Climatologic
databases also provide easy access to long-term data
from the National Oceanic and Atmospheric Administra-
tion (NOAA) weather stations, allowing a user the op-
portunity to input realistic climate data to pesticide
transport models.
Many numerical pesticide transport models, such as the
Pesticide Root Zone Model (PRZM), Ground-Water
Leaching Effects of Agricultural Management Systems
(GLEAMS), and Leaching Estimation and Chemistry
Model (LEACHM), can produce transport estimates for
specific pesticides in specific soils. Each model has its
own strengths and weaknesses, and detailing these
characteristics is beyond the scope of this paper. Sev-
eral authors, however, have described comparisons be-
tween models (e.g., Smith et al. [2], Mueller et al. [3],
and Pennell et al. [4]). These numerical models all gen-
erally require extensive site-specific soil, agronomic,
and climatologic databases. The results from these
models are extremely detailed. Their pesticide transport
estimates, however, are only valid for those locations for
which site-specific data are sufficient to allow calibration
of the model. Applying such site-calibrated model results
to larger scales (county scales) is inappropriate.
In one procedure, a user could identify soils and use
transport models that may have a limited ability to retard
pesticides from reaching water resources. This type of
modeling has recently been called probabilistic model-
ing (5). The concept behind this procedure is to use an
existing transport model, such as PRZM, and vary cer-
tain input parameters (e.g., slope, organic content, pes-
ticide Koc) to produce a probability of a given output
being equaled or exceeded. For example, a PRZM
model could be created for a soil series with an average
organic content of 1 percent and a slope of 8 percent in
the eastern corn belt. The model would use the 30 years
of historical climate data for a nearby station. The model
would vary the organic content and surface slope within
given ranges for the soil series and run 1,000 simula-
tions. The analysis could then entail plotting the results
(i.e., monthly runoff loads, erosion loads, and leaching
through the root zone) in a frequency diagram and gen-
erating probability curves. This analysis would allow the
user to estimate the anticipated pesticide losses, runoff,
erosion, and leaching for any given soil in the county.
The soils with greater probabilities for pesticide loss
could be identified and mapped using CIS.
Recent advances in computing speed and efficiency
have reduced the amount of time and expense needed
to run numerical pesticide transport models. This makes
it possible, in a relatively short amount of time, to quan-
titatively model not just one soil series, but the hundreds
of major soil series that occur in an entire state (e.g.,
357 major soil series combined into 464 different soil
associations in Wisconsin). This type of model can be
very useful to the development of SMPs as well as to a
variety of users, including pesticide registrants, bulk
pesticide handlers, custom appliers, county agricultural
extension agents, and individual growers.
Objective
The objective of this study was to use probabilistic mod-
eling analyses and a geographic information system
(CIS) to determine which soil series in a watershed may
contribute to nonpoint source pollution through runoff of
agricultural chemicals. Specifically, the study aimed to
locate a watershed with historical atrazine runoff, map
the soils, and perform transport modeling using histori-
cal precipitation. The results of this procedure would
determine which soil series had a high potential to con-
tribute to the nonpoint source pollution of the watershed.
Once the transport modeling was completed, a CIS
would help map the distribution of the sensitive soil
series. The mapping would act as a base for implement-
ing best management practices (BMPs) to reduce non-
point source pollution.
Background
Ward (1) described the water quality in the Pequa Creek
basin in Lancaster County, Pennsylvania, for the years
1977 through 1979. Flow from Pequa Creek (154-square-
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Figure 1. Location of Pequa Creek basin, Lancaster County,
Pennsylvania.
mile drainage area) eventually discharges into the
Chesapeake Bay (see Figure 1). The data collection
efforts (6) documented the occurrence of atrazine
(2-chloro-4-ethylamino-6-isopropylamino-s-triazine),
a commonly used herbicide for weed control in corn-
growing regions, and other agrichemicals in both base-
flow and storm-flow conditions of the Pequa Creek. A
subbasin of Pequa Creek, Big Beaver Creek, had the
greatest reported atrazine concentrations during the
sampling period. The maximum reported atrazine con-
centrations at the Big Beaver Creek sampling station,
near Refton, Pennsylvania, were 0.30 parts per billion
during base-flow conditions and 24.0 parts per billion
during storm-flow conditions. The Big Beaver Creek
basin is 20.4 square miles in area, and agriculture con-
stituted about 66 percent of the land use in 1979. Corn
was grown on 26.6 percent of the agricultural lands in
this subbasin. The average rainfall forthis basin is about
37 inches annually (1).
As noted, agriculture represented the major land use in
the area. The primary agricultural soils in Lancaster
County are silt loams (Typic Hapludults and Hapludalfs)
in texture with slopes that range from 0 percent to 8
percent (7). Upon inspection of the air-photo soil se-
ries maps found in the county soil survey, however,
agricultural crops grew on lands with slopes of up to
and exceeding 15 percent, with soils such as the
Manorsilt loam, Pequa silt loam, and Chestersilt loam
(7).
Natural soil organic contents in the agricultural soils
range from 0.1 percent to 2.0 percent. Water contents
of the agricultural soils range from 10 percent to nearly
30 percent. The soil Erosion Factor (K) for the surface
layer ranges from 0.17 for the relatively stable Lingers
Series to 0.43 for the Pequa Series. The greater the
value, the greater the susceptibility to sheet and rill
erosion. The soil Erosion Factor (T in tons/acre/year) for
the entire soil profile ranges from 2 for the relatively
stable Clarksburg Series to 5 for the Elk Series.
Methods
Determining which soil series in the Big Beaver water-
shed may contribute to nonpoint source pollution
through runoff of agricultural chemicals entailed per-
forming a combination of probabilistic modeling analy-
ses and a CIS data manipulation.
Physiographic and Soil Series Boundaries
The orientation of Pequa Creek, Big Beaver Creek, and
other surface water bodies was digitized directly from
the 1:50,000-scale county topographic map for Lancas-
ter County (8). This map also provided the basis for
digitizing the Pequa Creek drainage divide, location of
urban areas, and roadways. The MAPINFO CIS allows
for the creation of boundary files by tracing the boundary
off the topographic map with a digitizing tablet config-
ured to the latitude and longitude coordinates of three
points on the map. The latitude and longitude are dis-
played while the boundary is being traced, allowing the
user to verify the accuracy of the boundary against
known coordinates on the map. CIS contains a self-
checking boundary closure program to ensure that the
polygons are closed and that the boundary contains no
extraneous line segments. These boundary data are
already available from the USGS in a digitized format,
digital line graphs (DLGs). Because digitizing is an easy
task, however, and to minimize costs, the project used
manual digitizing rather than purchase the data.
The roadways and urban areas were digitized to allow
use of standard control points, such as road intersec-
tions and benchmarks, to configure the U.S. Depart-
ment of Agriculture (USDA) Soil Conservation Service
air-photo-based, 1:15,840-scale soil series maps for the
Big Beaver Creek watershed. Known land grid coordi-
nates were noted on the air-photo maps (7). We con-
cluded, however, that using known reference points,
such as roadways and towns, allowed for a better con-
figuration of the digitizing tablet to the air photos and
-------
eliminated concerns over scale distortions sometimes
common in air photos.
After configuring the air photos to the digitizing tablet, a
2-square-mile area around the surface water sampling
points was digitized. The next step entailed digitizing all
the mapped soil series units within this area. The loca-
tions of crop areas, as plowed fields, were noted. In
noting forested areas, it became apparent that only
minor acreages were not in agricultural production.
Those mapped units were generally the Manor and
Pequa Series soils with slopes exceeding 25 percent.
Pesticide Transport Modeling
The PRZM pesticide transport model helped to quantify
the ability of several soil series to retard the transport of
atrazine through the root zone as leachate, dissolved in
surface runoff and adsorbed on sediment that moved
during erosion. The PRZM model performed in an un-
calibrated or screening model mode. The input values
for soil properties came from both the EPA DBAPE
database and the Lancaster County Soil Survey (7). The
modeled soil profile was 150 centimeters thick and di-
vided into 5-centimeter compartments. The soil half-life
of atrazine was set at 57 days in accordance with values
that the PRZM manual listed (9). The primary soil prop-
erty that varied in this demonstration project was surface
slope. All other parameters, such as soil organic content,
moisture content, and bulk density, appeared as mid-
point values for the ranges listed in DBAPE.
The agronomic scenario that the model simulated was
for corn grown continuously for 10 years using conven-
tional tillage practices and planted on May 7 of each
year. Atrazine was surface applied at a rate of 2 pounds
per acre (2.24 kilograms per hectare) on May 1 of each
year. For climatic input, the model used the historical
precipitation regimen from 1970 through 1980, as meas-
ured at the Harrisburg, Pennsylvania, station.
PRZM simulations were made for each of the following
soil series:
• Chester
• Conestoga
• Elk
• Glenelg
• Glenville
• Hollinger
• Letort
• Manor
• Pequa
Monthly values were calculated for leachate, runoff, and
erosion per hectare. Unfortunately, no data for the
Pequa Series were available in the DBAPE database;
therefore, this portion of the analyses omitted it. In
addition, analyses of the Manor Soil Series included
more detailed probabilistic modeling where the surface
slope held constant (6-percent slope) and the surface
soil organic content varied to include the high, average,
and low organic contents as listed in DBAPE. PRZM
also calculated the volume of water as evapotranspira-
tion, runoff, and recharge through the root zone.
Results
The results of this study should demonstrate the appli-
cation of transport modeling to the possible protection
of water resources. Regulatory decision-makers should
not consider these results in their current form because
such decisions would require a much more rigorous
simulation strategy to increase the level of confidence in
the data. As a demonstration study, however, the results
do show the usefulness of this approach. Table 1 con-
tains the cumulative frequency data for the simulated
atrazine residues in runoff, erosion, and leaching that
occurred under 30 years of historical precipitation. The
data cover the 12 soils mentioned, with the surface
slope held constant at 6 percent.
Atrazine in Runoff and Erosion
The results of this analysis suggest that the Hagger-
stown Series had the greatest potential for yielding
atrazine in runoff; approximately 50 percent of the simu-
lated monthly atrazine in runoff values equaled or ex-
ceeded 0.0001 kilogram per hectare. Conversely, the
Elk Series yielded the least atrazine to runoff; 50 percent
of the runoff data were at residue levels of 1 x 10~6
kilograms per hectare. Within the Big Beaver Creek
subbasin, the Manor Series had the greatest potential
to yield atrazine in runoff.
As with the runoff data, the Haggerstown Series had the
greatest potential to yield atrazine in eroded sediments,
and the Elk Series yielded the least atrazine in erosion.
Within the Big Beaver Creek subbasin, the Manor Series
had the greatest erosion potential regarding atrazine.
GIS Analyses
After entering the results from the transport modeling
into a database, GIS could produce maps showing the
location of soils with high runoff potentials. Figure 2a
shows the orientation of soil series around the surface
water sampling points in Big Beaver Creek. Figure 2b
represents the same scene but fills in the soils with high
runoff potential. Using this type of analysis can help
areas that may be sources of nonpoint source runoff
contamination. Once identified, these soils can be tar-
geted for alternative management practices that may
reduce the amount of runoff and the degree of nonpoint
source contamination.
-------
Table 1. Cumulative Frequency of Simulated Atrazine Residues in Runoff for 12 Major Soils in Lancaster County, Pennsylvania
(values are percentage of data)
Load
(kilograms
per
hectare)
IE-10
IE-9
IE-8
IE-7
IE-6
IE-5
IE-4
IE-3
0.00
0.01
0.10
1.00
10.00
25.00
Bucks
83.33
78.79
74.24
66.67
56.06
47.78
36.36
24.24
15.91
6.82
0.00
0.00
0.00
0.00
Chester
84.85
80.30
76.52
68.94
46.97
35.61
21.97
15.15
6.82
0.00
0.00
0.00
0.00
0.00
Clymer
84.09
80.30
75.00
66.67
56.06
46.21
32.58
21.21
14.39
6.06
0.00
0.00
0.00
0.00
Soil
Connestoga
84.08
81.82
78.03
72.73
61.36
50.00
35.61
21.21
15.12
6.06
0.00
0.00
0.00
0.00
Series
Elk
78.79
75.76
69.70
59.09
50.00
43.18
31.82
21.21
15.15
6.82
0.00
0.00
0.00
0.00
in Lancaster County, Pennsylvania
Glenelg
84.85
80.30
76.52
68.18
56.06
47.73
34.85
21.21
13.64
3.79
0.00
0.00
0.00
0.00
Haggerstown
93.94
93.94
93.94
91.67
84.85
74.24
53.79
37.12
19.70
7.58
0.00
0.00
0.00
0.00
Hollinger
83.33
81.06
77.27
68.94
59.09
47.73
34.85
21.21
15.15
6.06
0.00
0.00
0.00
0.00
Lansdale
88.64
85.61
80.30
75.00
65.15
51.52
40.15
24.24
15.91
6.82
0.00
0.00
0.00
0.00
Letort
85.61
84.85
84.09
83.33
76.52
67.42
46.97
31.82
17.24
4.55
0.00
0.00
0.00
0.00
Manor
86.36
84.61
84.85
75.52
66.67
46.97
31.82
16.67
4.55
0.00
0.00
0.00
0.00
0.00
Lingers
84.09
84.09
81.82
75.76
67.42
53.03
39.39
24.24
15.15
4.55
0.00
0.00
0.00
0.00
This table reads as follows: Given the Elk Series, the first value
IE-10 kilograms per hectare. Similarly, within the same column, 6
than 0.1 kilograms per hectare.
reads that 78.79 percent of the simulated data were greater than or equal to
.82 percent of the data were greater than 0.01 kilograms per hectare but less
Pequa Creek
Pequa Creek
0 0.5 1
Figure 2a. Soil series in the Big Beaver Creek basin.
Detailed Modeling of the Manor Series
Performing an introductory probabilistic modeling exer-
cise allowed further investigation of the potential of the
Manor Series to release atrazine into runoff. The exist-
ing 30-year climate data and the stated agronomic data
were retained from the previous modeling. The organic
carbon content of the surface soil layer, however, was
allowed to vary between the published low, average, and
maximum values found in the DBAPE database. This
exercise followed the principles set forth by Laskowski
et al. (5) and others who describe probabilistic modeling
0.5
Figure 2b. Soils sensitive to atrazine runoff in the Big Beaver
Creek basin.
approaches. In essence, by varying input parameters
within known endpoints, the probabilistic approach can
generate a distribution of pesticide residue values that
statistically reflects the anticipated residues. Parame-
ters to vary may include:
• Organic carbon content
• Surface slope
• Kd (distribution coefficient)
-------
• Moisture content
By allowing input variables to vary according to a normal
distribution, this approach thereby eliminates some of
the uncertainty associated with pesticide transport mod-
eling. The probabilistic modeling approach requires the
creation of a significant database by performing many
runs (e.g., 1,000 model runs that generate 12,000
monthly values for each soil).
This study included a limited probabilistic modeling ex-
ercise. Table 2 lists the results for the mean atrazine
residues in runoff, erosion, and leaching for the Manor
Soil Series during the:
• Entire year
• Growing season
• Winter months
The surface slope was held constant at 6 percent, but
the soil organic carbon content varied within the publish-
ed range. The means for all months show limited vari-
ation in mean residues. Runoff was by far the major
Table 2. Summary Statistics for Detailed Modeling of the
Manor Soil Series in the Pequa Creek Watershed
(statistics based on 1,080 values)
Mean Atrazine Residue
(kilograms per hectare)
Percent
Organic
Carbon3
All Months
Low
Average
High
Growing Season
Low
Average
High
Winter Months
Low
Average
High
Runoff
0.01381
0.01229
0.01105
0.03290
0.02881
0.02540
0.00014
0.00045
0.00075
Erosion
0.00024
0.00042
0.00057
0.00058
0.00100
0.00130
< 0.000002
< 0.000001
< 0.000003
Leaching
0.00028
0.00012
< 0.000006
0.00017
< 0.000008
< 0.000004
0.00036
0.00014
< 0.000007
Data taken from DBAPE soils database as low, midpoint, and maxi-
mum reported organic contents.
source of atrazine. Erosion and leaching values were on
similar scales (trace amounts).
The greatest runoff and erosion values occurred during
the growing season. The greatest leaching, however,
occurred during the winter months. These results sup-
port the general observations that surface residues run
off during spring and summer but that as the crops grow
and evapotranspiration increases, recharge to ground
water decreases, subsequently limiting pesticide trans-
port to ground water. Conversely, during the winter
months, the surface soil pesticide residues generally
decrease because of exposure to months of photolysis,
hydrolysis, and biodegradation. Subsurface residues
have been protected from degradation, however, and
increased ground-water recharge, due to great reduc-
tions in evapotranspiration, transports the residues
through the soil column.
This limited exercise provided a valuable learning expe-
rience regarding probabilistic modeling. As computing
techniques and hardware advance, the cost in time and
money for each simulation should decrease dramati-
cally. Although researchers tend not to have great faith
in pesticide transport modeling, the advances in this
field will reduce uncertainty and instill greater confi-
dence in the modeling process.
GIS Pitfalls
CIS is a powerful tool and has great promise for use
in environmental problem-solving. Several points or
pitfalls, however, hinder broad acceptance of GIS. As
with most new technologies, cost is the overriding con-
cern in using GIS. Although technical staff and project
scientists understand the power of GIS and the effort
that data preparation requires, management and corpo-
rate staff often do not see the benefits for the costs.
Many managers assume that current GIS systems re-
semble those seen on "Star Trek," and when reality
becomes apparent, managers tend to discard GIS as
too costly and complex. Several points need considera-
tion when contemplating the use of GIS. Although vari-
ous products exist, this discussion focuses on
ARC/INFO and MAPINFO products.
Hardware
Computer hardware is plentiful if the available budget
can support a purchase. Many high- powered GIS pack-
ages (e.g., GRASS, ARC/INFO, INTERGRAPH, IDRISI)
run best on mainframes or minicomputers. Most techni-
cal staff, however, only have access to PC machines.
Corporate purchasing departments more readily expend
funds for PC technology because they will eventually
find use forthese machines even if they are not used for
GIS. A recent ARC/INFO advertisement (August 1994)
lists costs for SUN SPARC minicomputer systems with
ARC/INFO software at $12,000 to $15,000 depending
on configuration.
Minicomputers and mainframes require specialized staff
to configure and maintain the hardware. Today, many
staff level personnel can open and augment their PC
machines with a minimum of external support. GIS per-
formance reflects the tradeoff in hardware, particularly
-------
when a considerable amount of data manipulation is
required. For example, if linking discrete depth soil se-
ries data to STATSGO soil associations is necessary,
then a minicomputer system may be best. The postproc-
essed data could, however, be exported to a format that
will run on PC-based systems. If the user wants to
import and manipulate remote sensing imaging (e.g.,
SPOT or Landsat data), then minicomputers are recom-
mended. If the user wants to display already edited
images and preprocessed CIS data, then PC-based
computing may be sufficient. The ultimate use of CIS
drives the hardware selection.
Software
A great number of CIS software packages are available
to meet almost any level of use and expertise. Software
runs under both UNIX and DOS/Windows (denoted as
DOS forthe remainder of this paper) operating systems.
The UNIX-based software tends to be more powerful
and flexible than the DOS-based software. UNIX-based
packages require more specialized staff to optimize
CIS, however.
UNIX-based software packages include GRASS,
ARC/INFO, INTERGRAPH, and IDRISI. Costs vary
from public domain charges for GRASS and IDRISI to
vendor supplied ARC/INFO and INTERGRAPH, which
can cost several thousand dollars each.
DOS versions of ARC/INFO (e.g., PC ARC/INFO,
ARCAD, ARC/VIEW) are also available and provide the
userwith various levels of data editing and manipulation
abilities. Generally, PC ARC/INFO is the same as the
UNIX version, varying in speed of processing. ARCAD
is a CIS engine that uses AutoCAD for drawing and
displaying, giving the user most of the abilities of the
UNIX-based version. ARC/VIEW I was primarily a dis-
play and simple analysis tool. It allowed the user to view,
display, and manipulate existing arc data but did not
support image editing. Currently, ARC/VIEW II provides
more support for image editing and data manipulation.
Costs range from about $3,000 for PC ARC/INFO and
ARCAD (AutoCAD also costs about $2,000) to around
$500 for the ARC/VIEW products.
MAPINFO is a DOS-based CIS that was designed for
marketing and demographic applications. Several re-
searchers, however, have used MAPINFO for environ-
mental applications. The most outstanding feature of
MAPINFO is that it easily imports data layers as it reads
dBASE type files directly. MAPINFO V3 also reads da-
tabase files and recreates them as *.TAB files. In con-
trast to the "coverage and entity" concepts of the
ARC/INFO line of programs, MAPINFO reads latitude
and longitude coordinates and displays the results. This
simplifies data management because many researchers
who have already created custom databases can easily
import those data as long as latitude and longitudes
coordinates are present.
As with the ARC/INFO line of programs, many common
data layers can be purchased for use in MAPINFO.
These layers can be expensively priced, costing ap-
proximately $1,000 per county for roadway, census, and
demographic data. One major lapse is the poor library
of environmental layers, USGS topography, hydrogra-
phy, soil boundaries, or climate stations. MAPINFO does
sell a module that allows users to convert to and from
ARC/INFO coverages so that common data layers can
be established. Experience shows, however, that con-
version programs do not always work as advertised. For
example, large boundary files (STATSGO data for Indi-
ana) do not readily convert from ARC/INFO to MAP-
INFO. Third-party vendors may be needed to convert
data for use in MAPINFO.
One very important factor supporting the use of MAP-
INFO is that it has a business application slant; there-
fore, it is slightly easier to convince corporate
management to invest in CIS because marketing and
sales data (territories) can be relatively easily overlain
onto environmental data.
Finally, some packages that are add-ons to spreadsheet
programs tend not to be powerful or versatile enough for
use in environmental CIS work. These software pack-
ages may be valuable as an introduction to CIS tech-
niques, however.
Data A vailability and Format
After compiling the hardware and software into CIS, the
next step entails accessing data layers such as:
• State and county boundaries
• Land use covers
• Water boundaries
Currently, USGS DLGs for hydrography, land use, trans-
portation, and cultural features are available for minimal
costs. Shareware programs can convert the USGS
DLGS formats into DXF (data transfer files) for import to
CIS packages. These data require conversion to DXF
or ARC coverage type formats for use in either
ARC/INFO or MAPINFO.
The USDA Soil Conservation Service produces digital
data for soil types (STATSGO and SSURGO) that users
can import to ARC/INFO relatively easily. The STATSGO
data cost approximately $1,000 per state and are avail-
able for most states. The detailed soil series maps,
SSURGO, cost approximately $500 per county and are
not available for every county in the United States. Many
data layers are available for direct use by GRASS. As
of yet, however, no convenient conversion utilities exist
to move GRASS data to ARC/INFO or MAPINFO. The
-------
U.S. Fish and Wildlife Service now distributes data
layers from the National Wetlands Inventory on the
Internet (enterprise.nwi.fws.gov).
Other data sources available through private vendors
are listed in the MAPINFO and ARC/INFO user guides
and in any issue of CIS World. The user should be
prepared to absorb significant costs if purchasing all the
required data layers.
Conclusion
This study shows that the technology and software exist
for a water resource manager to couple pesticide trans-
port modeling with CIS to identify areas or individual
soils that may contribute to nonpoint source pollution of
water resources. The study used PC-based computing
system and software. Soils maps and hydrographic
maps can be easily digitized for limited cost. A skilled
scientist or technician, without being a CIS expert, can
run CIS to answer specific questions. The technologies
this study demonstrated may be extremely valuable to
managers responsible for producing SMPs.
The pesticide transport modeling performed during
this study was intended for illustrative purposes. More
detailed analyses, and additional simulations, would be
necessary to use these data for regulatory actions or
land use management. The study did, however, succeed
in identifying the Manor Soil Series, with slopes exceed-
ing 6 percent, within the Big Beaver Creek subbasin of
the Pequa Creek basin, as a potential source for
atrazine in runoff.
References
1. Ward, J.R. 1987. Surface-water quality in Pequa Creek Basin,
Pennsylvania, 1977-1979. U.S. Geological Survey. Water Re-
sources Investigation Report 85-4250.
2. Smith, M.C., A.B. Bottcher, K.L. Campbell, and D.L. Thomas. 1991.
Field testing and comparison of the PRZM and GLEAMS models.
Trans. Amer. Soc. of Agric. Eng. 34(3):838-847.
3. Mueller, T.C., R.E. Jones, P.B. Bush, and PA. Banks. 1992. Com-
parison of PRZM and GLEAMS computer model predictions with
field data for alachlor, metribuzin, and norflurazon leaching. Envi-
ron. Toxicol. and Chem. 11:427-436.
4. Pennell, K.D., A.G. Hornsby, R.E. Jessup, and P.S.C. Rao. 1990.
Evaluation of five simulation models for predicting aldicarb and
bromide behavior under field conditions. Water Resour. Res.
26(11):2,679-2,693.
5. Laskowski, D.A., P.M. Tillotson, D.D. Fontaine, and E.J. Martin.
1990. Probability modeling. Phil. Trans. Royal Soc. of London
329:383-389.
6. Ward, J.R., and D.A. Eckhardt. 1979. Nonpoint-source discharges
in Pequa Creek Basin, Pennsylvania, 1977. U.S. Geological Sur-
vey. Water Resources Investigation Report 79-88.
7. Custer, B.H. 1985. Soil survey of Lancaster County, Pennsylvania.
Washington, DC: U.S. Department of Agriculture, Soil Conserva-
tion Service.
8. U.S. Geological Survey. 1977. Lancaster County, Pennsylvania:
County map series (topographic) 1:50,000 scale.
9. Carsel, R.F., C.N. Smith, L.A. Mulkey, D.J. Dean, and P. Jowise.
1984. Users manual for Pesticide Root Zone Model (PRZM), Re-
lease I. EPA/600/3-84/109.
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Integration of GIS With the Agricultural Nonpoint Source Pollution Model:
The Effect of Resolution and Soils Data Sources on Model Input and Output
Suzanne R. Perlitsh
State University of New York, Syracuse, New York
Abstract
The assessment of agricultural nonpoint source pollu-
tion has been facilitated by linking data contained in a
geographic information system (GIS) with hydrologic
models. One such model is the Agricultural Nonpoint
Source (AGNPS) Pollution Model, which simulates run-
off, nutrients, and sediment from agricultural water-
sheds. Vector-based (ARC/INFO) and raster-based
(IDRISI) GIS systems were used to generate AGNPS
input parameters.
The objectives of this project were to generate AGNPS
input parameters in GIS format from GIS data at differ-
ent resolutions and different levels of detail (soil survey
soils data versus soils data currently available in digital
format from the United States Department of Agricul-
ture). Differences in the AGNPS model sediment out-
put based on the variations in CIS-generated AGNPS
model input were evaluated.
The study also evaluated the influence of cell size reso-
lution and soils data on sediment generated within each
cell in the watershed (SGW), sediment yield from each
cell in the watershed (SY), sediment yield at the water-
shed outlet, and peak flow. Model output was validated
by comparison with measured values at the watershed
outlet for a monitored storm event. Results of this study
indicate that the use of different resolution GIS data and
different soils data sources to assemble AGNPS input
parameters affects AGNPS model output. Higher reso-
lution data do not necessarily provide better results.
Such comparisons could affect decision-making regard-
ing the level and type of data analysis necessary to
generate sufficient information.
Introduction
Agricultural runoff is a major contributor to nonpoint
source pollution. Fifty-seven percent of the pollution in
impaired lakes and 64 percent of the pollution in im-
paired rivers of the United States can be attributed to
agricultural nonpoint source pollution (1). Sediment is
one of the most common agricultural nonpoint source
pollutants and is the largest pollutant by volume in the
United States (2). More than 3 billion tons of sediment
enter surface waters of the United States each year as
a result of agricultural practices (1).
Accurate assessment of the effects of agricultural activi-
ties on water quality within a watershed is vital for
responsible watershed management and depends on
our ability to quantify the spatial variability of the water-
shed and the complex interactions of hydrologic proc-
esses (3). Computer models have been developed to
simulate these hydrologic processes to provide esti-
mates of nonpoint source pollutant loads. Adequate
simulation of a watershed's spatial variability helps pro-
vide the best representation of hydrologic processes
within the watershed.
Preservation of spatial variability within hydrologic mod-
els can be accomplished using a distributed parameter
model. The distributed parameter model is more advan-
tageous than lumped parameter models, which gener-
alize watershed characteristics, because distributed
parameter models provide more accurate simulations of
the systems they model (4). One of these models is the
Agricultural Nonpoint Source (AGNPS) Pollution Model.
AGNPS is a distributed process model because it pro-
duces information regarding hydrologic processes at
grid cells within the watershed, thus enabling preserva-
tion of the spatial variation within the watershed. Distrib-
uted parameter models integrate well with GIS because
GIS can replicate the grid used in a distributed parame-
ter model. Manual compilation of AGNPS input parame-
ters required to evaluate small areas at low resolution
(large grid cells) is relatively easy. Manually assembling
data to evaluate larger areas at finer resolutions be-
comes tedious, however. The integration of GIS data
with the AGNPS model facilitates data assembly and
manipulation (5).
-------
Several researchers have integrated AGNPS with CIS
(4-11). Smaller cell sizes within distributed parameter
models are thought to best represent spatial variability
within a watershed (5, 10). Certain AGNPS input
parameters show sensitivity to changes in grid cell size,
affecting sediment yield output (11). The use of CIS to
generate input parameters for the AGNPS model en-
ables analysis of watersheds at higher resolutions than
would be practical using manual methods (5).
Research Hypotheses
The project investigated the following research hypotheses:
• AGNPS output at the highest resolution will better
approximate sediment yield at the watershed outlet.
• AGNPS output for sediment generated within each
cell in the watershed at highest resolution will best
reflect the watershed processes.
• AGNPS output generated from the more detailed soil
survey data will better estimate watershed processes.
The project also investigated other questions: will cer-
tain AGNPS input parameters (cell land slope, soil erodi-
bility [K], the cropping factor [C], and the U.S.
Department of Agriculture [USDA] Soil Conservation
Service [SCS] curve number [CN] show sensitivity to
changes in grid cell size? How does slope affect model
output? Does a qualitatively significant difference exist
between model input parameters and output calculated
from data sets generated at different resolutions with
different levels of detail in soils output?
Objectives/Tasks
The research in this project included analyses of:
• Certain AGNPS input parameters generated at differ-
ent resolutions (10- x 10-, 30- x 30-, 60- x 60-, and
90- x 90-meter resolutions).
• AGNPS output for sediment yield (SY) and sediment
generated within each cell (SGW) at different resolu-
tions (center cells of 10- x 10-, 30- x 30-, 60- x 60-,
and 90- x 90-meter resolutions).
• AGNPS output generated from different levels of de-
tail in the soils input data (soil survey versus STAT-
SGO data sources).
Significance
Version 4.03 of AGNPS was released in June 1994. Ver-
sion 4.03 allows for evaluation of 32,767 cells. This version
allows for cell sizes from 0.01 to 1,000 acres (approxi-
mately 6.36-x 6.36-meter resolution to 2,012-x 2,012-me-
ter resolution). Previous versions of AGNPS limited the
number of cells to 3,200 and the cell size resolution to 0.4
hectares (or 63.25 x 63.25 meters) (12). Reviewed litera-
ture provides no evidence that AGNPS has been used
to evaluate a watershed at 10- x 10-meter resolution.
The soils data in this study were compiled at two differ-
ent levels of detail. Soils data at the 1:20,000 soil survey
level were generated in digital format. This level of detail
was compared with soils data at the State Soil Geo-
graphic (STATSGO) database level with a scale of
1:250,000. Reviewed literature mentions no previous
studies comparing AGNPS output with input generated
from these two different levels of detail in soils input.
Technology for collecting and processing geographic
data is continuously improving. Currently, the United
States Geological Survey (USGS) 1:24,000 digital ele-
vation models (OEMs) are available at 30- x 30-meter
resolution. New satellite technology will enable DEM
data to be available at 10- x 10-meter resolution, or
higher. Certain satellites currently provide land cover
data at 10- to 30-meter resolution (13).
An important objective of this project was to determine
whether higher resolution data provide different results
when routed through AGNPS. Does spatial data at
higher resolutions provide better information? This pa-
per describes the results of an analysis of AGNPS out-
put based on different levels of both resolution and soils
detail in CIS data input sources.
Materials
The Study Area
An ongoing effort is underway to clean up Onondaga
Lake, Onondaga County, New York. To accomplish this
effort, areas contributing agricultural nonpoint source
pollution to the lake are being evaluated.
The Onondaga Lake watershed is approximately 287.5
square miles, with 40 subwatersheds. The subwater-
sheds in the agricultural portion of the Onondaga Lake
watershed (south of Syracuse, New York) have been
isolated for study of their potential nonpoint source con-
tributions to Onondaga Lake (see Figure 1). The study
area watershed (1.84 square miles, 1,177.5 acres) is
one of these agricultural subwatersheds. CIS data were
collected within the Otisco Valley quadrangle (USGS
1:2,400), which includes the southern portion of the
Onondaga Lake watershed. Elevations in the study wa-
tershed range from 1,820 feet to 1,203 feet, with an
average elevation of 1,510 feet. The watershed perime-
ter is approximately 6.5 miles (34,505 feet). The streams
in the watershed flow from south to north to Rattlesnake
Gulf, with a stream length of approximately 3.08 miles
(16,265.4 feet). The stem fall of the main stream stem
is quite steep at 283 feet per mile. The drainage density
of the watershed is 1.67 miles of stream persquare mile.
Land use in the watershed is predominantly agricultural
(82.8 percent).
-------
Study Area Watershed
Canty Hill Rd. \ Bardwell Rd
-I" ~~~
Figure 1. Onondaga Lake watershed and study area (not to scale).
The AGNPS Model
AGNPS was developed to analyze and provide esti-
mates of runoff water quality, specifically to evaluate
sediments and nutrients in runoff from agricultural
watersheds for a specific storm event (11). To use
AGNPS, a watershed is divided into cells of equal area.
Calculations for each of the model output values are
made within each cell based on the watershed data
contained in each cell. Approximately 1,000 people in 46
different countries use the AGNPS model. Users include
students, university professors, government agencies,
lake associations, and environmental engineers.1
AGNPS was developed in 1987 by the Agricultural Re-
search Service (ARS) in cooperation with the Minnesota
Pollution Control Agency (MPC) and the SCS. The
model runs on an IBM-compatible personal computer.
Data Sources
The CIS packages of ARC/INFO Version 3.4D (14) and
IDRISI Version 4.1 (15) were used to prepare input
parameters for the AGNPS model. AGNPS input pa-
rameters were derived from three base maps—land
use, a DEM, and soils. Table 1 shows the 22 input
parameters that AGNPS required, and the base source
for the data.
The land use map was obtained from a classified
ERDAS image (resolution of 28 x28 meters). The image
was converted to IDRISI, brought into ARC/INFO, and
regridded based on the resolution requirements of each
data set. USGS could not provide a DEM for the study
Personal communication from AGNPS Technical Support, September
1994.
area, so the OEMs were interpolated from points digit-
ized in ARC/INFO based on the Clarke method (16). The
OEMs were interpolated in IDRISI on 10- x 10-, 30- x30-,
60- x 60-, and 90- x 90-meter resolution surfaces.
Soil survey data were obtained from Onondaga County
Soil Survey air photographs. An orthophoto of the
7.5 minute quadrangle was obtained from the USGS
and was used with a zoom transfer scope to ortho-cor-
rect the soil survey data. The corrected soil polygons
were then digitized in ARC/INFO. The Otisco Valley
quadrangle comprises 79 soils mapping units. Thirty-
eight different mapping units occur in the study area
watershed.
The USDA SCS (now the Natural Resource Conser-
vation Service [NARCS]) provides digital soils data
from its STATSGO database. The mapping scale of
STATSGO data is 1:250,000, thus it is best suited for
broad planning and management uses. The number of
soil polygons per quadrangle is between 100 and 400,
and the minimum area mapped is 1,544 acres. The
STATSGO soil data used in this project were obtained
from the Onondaga County Soil Conservation Serv-
ice. Approximately seven STATSGO soil groups were
identified for the Otisco Valley quadrangle. Only one
STATSGO soil type occurs in the study area watershed
(Honeyoe silt loam).
Methods
AGNPS input parameters that showed sensitivity to
changes in grid cell size in previous studies were com-
pared between the resolutions. The AGNPS model was
run eight times using precipitation values from the actual
-------
Table 1. AGNPS Input Parameters
# AGNPS Parameter
Root Data Source
General Derivation of Input
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
Cell number
Cell division
Receiving cell number
Receiving cell division
Flow direction
SCS curve number
Land slope percentage
Slope shape factor
Average slope length
Manning's n coefficient
USLE K factor
USLE C factor
USLE P factor
Surface condition constant
Chemical oxygen demand
Soil texture
Fertilizer indicator
Pesticide indicator
Point source indicator
Additional erosion
Impoundment indicator
Channel indicator
Watershed map
Not applicable
Aspect map from DEM
Not applicable
Aspect map from DEM
Land use and soils coverage
Slope map from DEM
Algorithm
Table of values
Literature values
SCS and soil survey
Literature values
Literature values
Land use coverage
Land use coverage
Soil survey
Land use coverage
Land use coverage
USGS 1:24,000 map
Field survey, known gullies
1:24,000 map, field survey
Streams coverage
Program written to determine #
No cell division, assumed 0
Program written to determine #
No cell division, assumed 0
Reclassed 1-8 from azimuth map
SML written to determine CN
Provided in slope percentage from IDRISI
Assume uniform slope
Obtained from SCS
Attached to land use database
Attached to soils database
Attached to land use database
Attached to land use database
Attached to land use database
Attached to land use database
Attached to soils database
Assumed for agricultural land class
Assumed for agricultural land class
Points in ARC/INFO and IDRISI
Assume no additional erosion
Assume no impoundments
Assume no significant channel
storm that was monitored. Each time, the model was run
using an input file created with the different input data
sources as follows:
1. 30- x 30-meter resolution-soil survey data.
2. 30- x 30-meter resolution-STATSGO data.
3. 60- x 60-meter resolution-soil survey data.
4. 60- x 60-meter resolution-STATSGO data.
5. 90- x 90-meter resolution-soil survey data.
6. 90- x 90-meter resolution-STATSGO data.
7. Center cells of the 10- x 10-meter resolution—soil survey
data.
8. Center cells of the 10- x 10-meter resolution—
STASGO data.
As grid cell size increases, the time required to assemble
data as well as the space required to store the data files
increase. If a cell size resolution is cut in half, the
number of cells in that coverage quadruples. In the study
area watershed, increasing grid cell size from 90- x 90-
meter resolution to 60- x 60-meter resolution created
778 more cells within the watershed. Moving from
60- x 60-meter resolution to 30- x 30-meter resolution
added 3,646 cells to the watershed, and moving from
30- x 30-meter resolution to 10- x 10-meter resolution
added 40,115 cells to the watershed data set (see
Figure 2). Due to the 32,767-cell limitation of AGNPS
Version 4.03, AGNPS output for SY and SGW at the
10- x 10-meter resolution (which contains 45,104 cells)
could not be obtained. Input parameters at 10- x 10-meter
resolution, however, could be compared with input
parameters at 30- x 30-meter resolution.
Methodology of Data Analysis
The input parameter "maps" were converted to IDRISI
files and combined in a format that could be routed
through the AGNPS model. AGNPS model output for
soil generated within each cell and for sediment yield
was assembled. The 30- x 30-meter resolution maps
were compared with the 60- x 60-meter resolution maps;
the 60- x 60-meter resolution maps were compared with
the 90- x 90-meter resolution maps; and the 30- x 30-
meter resolution maps were compared with the 10- x 10-
meter resolution maps.
A method for comparing maps with different grid sizes
was developed so that maps of different resolutions
-------
45,104
"oi
O
-------
feet per second per square mile. The runoff volume per
day was 0.09 inches.
Total sediment yield was derived from the analysis of total
solids measured in field samples throughout the 24-hour
storm period. The samples were processed to evaluate
total suspended solids (TSS) using the vacuum filtration
procedure (17). A total of 1.204 tons of suspended sedi-
ment was predicted at the watershed outlet from field
data samples. A LaMotte field nutrient test kit was used
to measure nitrate and phosphate concentrations in the
stream. Nutrient values in this watershed for this storm
event were so small (phosphorous below 0.1 parts per
million and nitrogen 0.3 parts per million), they were not
selected as parameters to be used in evaluating and
validating the AGNPS model. The AGNPS predicted
nutrient output for the storm was not measurable (0.00
parts per million). The low levels of nitrogen and phos-
phorous in the stream channel during the storm event
can be attributed to the time of year in which the stream
was monitored. At the time of field validation, agricultural
activities were not operating.
Results at the Watershed Outlet: Peak Flow
The peak flow values that AGNPS calculated are largest
at the highest resolution and decrease as cell size in-
creases. The peak discharge from the watershed during
the monitored storm event was 11.15 cubic feet per
second. Comparisons of the AGNPS predicted peak
flow and the actual field-validated peak flow showed that
the 30- x 30-meter resolution cells best approximate the
peak flow of the watershed for the sampled storm event.
As grid cells increase from 30 x 30 to 60 x 60 and from
60 x 60 to 90 x 90, the peak flow is underestimated. As
grid cells decrease from 30 x 30 to 10x10, the peak
flow is grossly overestimated (see Table 2).
Results at the Watershed Outlet:
Sediment Yield
Sediment yield at the watershed outlet was determined
to be 1.204 tons. In all of the resolutions, the amount of
sediment deposited at the watershed outlet cell in-
creased as cell resolution increased (see Table 2). For
this particular watershed in this particular storm, the
AGNPS model overestimated the sediment yield pre-
dicted at the watershed outlet at the 10- x 10-, 30- x 30-,
and 60- x 60-meter resolutions and underestimated
sediment yield at the 90- x 90-meter resolution. Table 2
includes the information that AGNPS predicted for the
cell designated as the watershed outlet within each
resolution. (The results reported include output from the
center cells of the 10- x 10-meter resolution data set,
routed through the AGNPS model. Although these val-
ues are reported, the results from this data set cannot
be assumed to approximate the sediment output that
would result had the entire 10- x 10-meter resolution
data set been simulated.)
So/7 Survey Versus STATSGO Data
The Kappa statistic (14,18) was used as an indicator of
similarity to describe the differences between the
AGNPS output for SY and SGW generated from STATSGO
and soil survey data. Results (see Table 3) indicate that no
significant difference exists between the output derived
from the STATSGO and soil survey data inputs within
Relationship Between Streamflow Discharge
and Suspended Sediment
c
o
"c
CD
o
c
o
o
"c
CD
E
T3
CD
600 T
500 --
400 --
300
200 -• :
100 -•
i
CD
O)
Time (8/29/94 to 8/29/94)
Figure 4. Storm event hydrograph and pollutograph.
-------
Table 2. AGNPS Results at the Watershed Outlet Versus
Actual Field Values
Predicted
Soil Difference Predicted
Resolution Survey From STATSGO
(meters) Data Actual Data
10 x 10
(center cells)
Peak runoff 29.88 +18.73 29.88
rate (cfs)a
Total sediment 4.48 +3.49 4.69
yield (tons)
30x30
Peak runoff 11.27 +0.12 11.27
rate (cfs)
Total sediment 2.84 +1.64 3.11
yield (tons)
60 x 60
Peak runoff 9.96 -1.19 9.96
rate (cfs)
Total sediment 2.11 +0.91 2.12
yield (tons)
90x90
Peak runoff 8.87 -2.28 8.87
rate (cfs)
Total sediment 0.86 -0.34 0.87
yield (tons)
acfs: cubic feet per second.
Table 3. Kappa Coefficient of Similarity
Resolution (meters)
10 x 10 (center)
Soil survey versus STATSGO SY
Soil survey versus STATSGO SGW
30 x 30
Soil survey versus STATSGO SY
Soil survey versus STATSGO SGW
60 x 60
Soil survey versus STATSGO SY
Soil survey versus STATSGO SGW
90x90
Soil survey versus STATSGO SY
Soil survey versus STATSGO SGW
Difference
From
Actual
+18.73
+3.28
+0.12
+ 1.91
-1 .19
+0.92
-2.28
-0.33
Kappa
0.9866
0.9703
0.9859
0.9785
0.8960
0.7542
0.8743
0.6574
Table 4. RMS for AGNPS Sediment Loss Output
Description SGW Pounds SY Pounds
Soil Survey Data Constant
10 centers to 30-meter resolution 168.38 344.22
30- to 60-meter resolution 125.43 739.75
60- to 90-meter resolution 93.50 498.01
30- to 90-meter resolution 29.91 711 .49
STATSGO Data Constant
10 centers to 30-meter resolution 164.45 312.48
30- to 60-meter resolution 125.37 782.07
60- to 90-meter resolution 97.07 501 .81
30- to 90-meter resolution 122.04 747.51
Moving to higher cell resolutions increasingly affects
sediment generated within each cell; the largest differ-
ence in sediment generated within each cell occurs as
cell resolution increases from 30 x 30 to 1 0 x 1 0 meters.
Sediment generated within each cell is least affected by
moving from 60 x 60 to 90 x 90 meters. Sediment yield
per cell is most affected when cell resolution increases
from 60 x 60 to 30 x 30 meters and least affected by
increasing resolution from 30 x 30 to 10x10 meters.
These results prompted an assessment of the methods
used to compare resolutions, to determine whether the
effect on sediment yield between the 30- x 30- and
60- x 60-meter resolutions could result from the method
used in comparing the resolutions (expansion of the
60- x 60-meter resolution). The procedure of comparison
between the 30- x 30- and the 60- x 60-meter resolu-
tions was repeated; however, rather than expanding the
60 x 60 data file, the 60 x 60 data file was "resampled"
onto the 30- x 30-meter resolution grid, then the files
were compared. The RMS results (see Table 5) show
that both methods for comparing data between the reso-
lutions provide essentially the same results. The effect
of resolution on sediment yield per cell is, in fact, greatest
as resolution increases from 60 x 60 to 30 x 30 meters.
Results: AGNPS Input Parameters of Concern
Previous AGNPS analyses have shown sediment yield
(and sediment-associated nutrient yields) to be most
the same resolutions. This may be due to the homoge-
neity of the soil textures in both soils data sets (both
dominated by silty soils).
Effects of Resolution on SGW and SY
AGNPS output for SGW and SYwas evaluated for every
cell within the watershed. The RMS difference was ap-
plied to determine the relative effect of input data reso-
lution on SY and SGW output (see Table 4 and Figure 5).
affected by AGNPS inputs for cell land slope, the soil
erodibility factor (K), the Universal Soil Loss Equation's
Table 5. Comparison of RMS for 30 x 30 to 60 x 60
Method RMS SGW Pounds RMS SY Pounds
Expansion of 60 x 60
to 30 x 30
Resampling 60 x 60
to 30 x 30
Difference
125.43
125.37
0.06
739.75
739.30
0.45
-------
RMS Difference
Sediment Generated in Each Cell—Soil Survey Data
10 to 30
0 20 40 60 80 100 120140160180
RMS Difference
Sediment Yield per Cell—Soil Survey Data
60 to 90
30 to 60
10 to 30
0 100 200 300 400 500 600700 800
RMS Difference
Sediment Generated in Each Cell—STATSGO Data
60 to 90
30 to 60
10 to 30
RMS Difference
Sediment Yield per Cell—STATSGO Data
60 to 90
0 20 40 60 80 100 120140160180
0 100 200 300400 500 600700 800
Figure 5. RMS for AGNPS sediment output.
(USLE) cropping management factor (C) and the SCS
curve number (CN). To address the concerns regarding
the influence of these parameters on sediment yield, the
RMS differences (see Table 6 and Figure 6) and general
statistics (see Table 7) for these parameters were computed.
Discussion
When evaluating the RMS as an indicator of the effect
of resolution on input parameters and output sediment
values, looking at the overall trend between resolutions,
rather than focusing on specific values, is important. The
RMS statistics for the soil erodibility factor (K), the crop-
ping management factor (C), and the SCS curve number
(CN) are least affected by a decrease in cell size reso-
lution from 10x10 meters to 30 x 30 meters. These
parameters are most affected when cell size resolution
decreases from 30- x 30-meter to 60- x 60-meter reso-
lution. As resolution decreases further from 60 x 60
meters to 90 x 90 meters, the effect on RMS decreases.
The small-large-smaller trend in the RMS for these pa-
rameters is the same trend seen in the RMS for sedi-
ment yield throughout the watershed. The sediment
Table 6. RMS Difference: AGNPS Input Parameters of
Concern
Parameters of Concern
K value
Cropping factor
SCS curve number
Slope
10 to 30
0.0058
0.03
2.11
8.17
30 to 60
0.054
0.173
11.40
5.59
60 to 90
0.051
0.015
8.40
3.68
yield within each cell therefore seems to be most af-
fected by these input parameters. The general statistics
for each of these parameters of concern show that very
little difference exists in the values within each resolu-
tion, with the exception of slope. Slope values are higher
at the higher resolutions and decrease as resolution
increases. This is related to the method in which the CIS
calculates slope.
The RMS statistics comparing resolutions for sediment
generated within each cell follow the same trend as the
RMS statistics for slope percentage. As resolution in-
creases, so do the discrepancies between the compared
-------
Soil Erodibility (K)
60 to 90
30 to 60
10 to 30
0.01 0.02 0.03 0.04 0.05 0.06
USLE Cropping Management (C)
60 to 90
30 to 60
10 to 30
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18
SCS Curve Number (CN)
Slope '
60 to 90
30 to 60
10 to 30
60 to 90
30 to 60
10 to 30
10 12
01 234567
Figure 6. RMS difference for AGNPS input parameter of concern.
data sets. This trend between resolutions indicates that
slope values influence sediment generated within each
cell in the watershed.
The results from the 10- x 10-meter resolution data set
were obtained by selecting the center cells of the
10- x 10-meter resolution data set and routing the data
from this set through AGNPS using the flow pathways
developed for the 30- x 30-meter resolution. The results
do not provide the same information as would be pro-
vided had the entire 10- x 10-meter data set been routed
through AGNPS. The RMS values obtained from com-
parisons of the 10- x 10-meter resolution input parame-
ters with the 30- x 30-meter resolution input data reveal
that little difference exists between the data in these
resolutions. Comparison of the center cell 10- x 10-meter
data set output with field monitored data shows that the
10x10 center cell data overestimates both peak flow
and sediment yielded at the watershed outlet. This can
be attributed to the larger slope values in this resolution.
Conclusion
This study used CIS to generate data files for application
to the AGNPS model. The objectives of this project were
to evaluate the effect of different levels of detail used in
generating the input files on selected input and output
parameters. The results show that, for a watershed with
characteristics equivalent to those of the study area
watershed, differences exist in model output based on
the cell size resolution of the watershed.
The selected cell size resolution directly affects slope
values. The influence of the slope parameter dominates
AGNPS predictions for sediment generated within each
cell and sediment yield at the watershed outlet in the
study area watershed. The indicated parameters of con-
cern have the most influence on sediment yield for each
cell in the watershed. The greatest variation in the indi-
cated parameters of concern and thus the sediment
yield output occurs between the 30- x 30-meter and
60- x 60-meter resolutions. AGNPS estimates for sedi-
ment yield in files generated from STATSGO data were
larger than sediment yields from files generated with soil
survey soils data in the 30- x 30-, 60- x 60-, and
90- x 90-meter resolutions. For this watershed, how-
ever, no significant difference existed between data gen-
erated from soil survey and STATSGO data sources as
indicated by the kappa coefficient of similarity.
Results predicted by the AGNPS model at the water-
shed outlet were compared with results from an actual
storm monitored at the watershed outlet. The 30- x 30-
meter resolution data set provided the most accurate
-------
Table 7. Statistics for AGNPS
Value 10 x 10
Input Parameters of Concern
30 x 30 60 x 60 90 x 90
K Value (units of K)
Average
Maximum
Minimum
Standard deviation
C Factor (units of C)
Average
Maximum
Minimum
Standard deviation
CN (units of CN)
Average
Maximum
Minimum
Standard deviation
Slope (%)
Average
Maximum
Minimum
Standard deviation
0.2989
0.49
0.17
0.0453
0.0306
0.076
0
0.0212
71 .004
100
55
9.13
34.13
567
0
33.75
0.2989
0.49
0.17
0.0453
0.0306
0.076
0
0.0211
71.003
100
55
9.08
30.153
224
0
23.45
0.2882
0.37
0.17
0.0513
0.0295
0.076
0
0.0213
70.75
100
55
8.97
27.67
152
0
18.97
0.2889
0.49
0.17
0.0456
0.0295
0.076
0
0.0208
70.68
100
55
8.84
26.38
99
0
16.44
prediction for peak flow at the watershed outlet. AGNPS
output in the 10- x 10-meter center, 30- x 30-meter, and
60- x 60-meter resolutions overestimated the actual
sediment yield recorded at the watershed outlet for the
validated storm event.
For the study area watershed, cell size resolution of
30 x 30 meters seems appropriate based on the accurate
AGNPS model prediction for peak flow when validated
with the field-monitored storm. The steep slopes created
in the 10- x 10-meter resolution data set may lead to an
overestimation of sediment output, rendering data at this
resolution unreliable. At this time, the 10- x 10-meter
resolution is both impractical and infeasible for use with
the AGNPS model.
AGNPS output at the highest resolution does not pro-
vide a better approximation of sediment yield at the
watershed outlet. AGNPS output for sediment gener-
ated within each cell in the watershed at the highest
resolution does not accurately simulate the watershed
processes. AGNPS output generated from the more
detailed soil survey data is not significantly different from
data generated by the STATSGO digital soils database.
This study raises the following questions:
• What level of detail is both practical and acceptable
for policy-making and decision-making?
• What constitutes a cost-effective analysis?
Ultimately, these questions are best answered on a
case-by-case basis and should be determined based on
the size of the study area and on how the results of the
analysis will be used (i.e., to make a direct land use
decision or for broader planning). For broad planning
analyses on large watersheds, the benefit of digitizing
the soil survey data is outweighed by the cost in time
and effort to generate this detailed database. STATSGO
data may be sufficient. If a direct land management
decision is being made for a small area such as a farm
within a watershed, however, the analysis should use
the most detailed soils data.
Recommendations for Future Work
The original intent of this study was to use the capabili-
ties of AGNPS Version 4.03 to evaluate a watershed
using data generated at a high cell size resolution—
10x10 meters. AGNPS h
ad never been used to evaluate data at such a high
resolution. As discovered during this project, the newest
version of AGNPS is not, at this time, capable of han-
dling a data set that has more than 32,767 cells (19).
Once this limitation with the AGNPS model is remedied,
the entire 10- x 10-meter resolution data set should be
routed through the model so that definite conclusions
regarding the applicability of such a detailed data set
can be made.
References
1. U.S. Department of Agriculture (USDA). 1991. Riparian forest
buffers: Function and design for protection and enhancement of
water resources. N1-PR-07-91.
2. Ashraf, M.S., and O.K. Borah. 1992. Modeling pollutant transport
in runoff and sediment. Trans. Amer. Soc. Agric. Eng. 35:1,789-
1,797.
3. Barten, P., and K. Stave. 1993. Characterization of streamflow
and sediment source areas for the Little Beaver Kill Watershed,
Ulster County, NY (July).
4. Vieux, B., and S. Needham. 1993. Nonpoint-pollution model sen-
sitivity to grid cell size. J. Water Resour. Planning and Mgmt.
119(2).
5. Kumar, V. 1993. Geographic information system application for
nonpoint source pollution management. Logan, UT: Utah State
University.
6. Yoon, J., L. Padmanabhan, and L. Woodbury. 1993. Linking Ag-
ricultural Nonpoint Source Pollution Model (AGNPS) to a geo-
graphic information system. Proceedings of the AWRA
Conference on Geographic Information Systems and Water Re-
sources (March).
7. Haddock, G., and P. Jankowski. 1993. Integrating nonpoint
source pollution modeling with a geographic information system.
Department of Geography, University of Idaho.
10
-------
8. Mitchell, J., B. Engel, R. Srinivasan, and S. Wang. 1993. Valida-
tion of AGNPS for small watersheds using an integrated
AGNPS/GIS system. Proceedings of the AWRA Conference on
Geographic Information Systems and Water Resources (March).
9. He, C., J. Riggs, and Y. Kang. 1993. Integration of geographic
information systems and a computer model to evaluate impacts
of agricultural runoff on water quality. Proceedings of the AWRA
Conference on Geographic Information Systems and Water Re-
sources (March).
10. Freezor, D., M. Hirschi, and B. Lesikar. 1989. Effect of cell size
on AGNPS prediction. Paper No. #89-2184. ASAE/CSAE Meet-
ing, St. Joseph, Ml.
11. Young, R., C. Onstad, D. Bosch, and W Anderson. 1989.
AGNPS: A nonpoint-source pollution model for evaluating agri-
cultural watersheds. J. Soil and Water Conserv. 44(2).
12. U.S. Department of Agriculture (USDA). 1987. Agricultural Re-
search Service, AGNPS: Agricultural nonpoint-source pollution
model, a watershed analysis tool. Conservation Research Report
No. 35.
13. Star, J., and J. Estes. 1990. Geographic information systems: An
introduction. Englewood Cliffs, NJ: Prentice Hall.
14. ESRI. 1987. PC ARC/INFO user's guides. Redlands, CA: Envi-
ronmental Systems Research Institute, Inc.
15. Eastman, R. 1993. IDRISI technical reference. Worcester, MA:
Clark University.
16. Clarke, K.C. 1990. Analytical and computer cartography. Engle-
wood Cliffs, NJ: Prentice Hall.
17. Clesceri, L, A. Greenberg, and R. Trussell. 1989. Standard meth-
ods for examination of water and wastewater, 17th ed. pp. 2-75
to 2-76.
18. Rosenfield, G.H., and K. Fitzpatrick-Lins. 1986. A coefficient of
agreement as a measure of thematic classification accuracy.
Photogrammetric Engin. and Remote Sensing 52(2).
19. U.S. Department of Agriculture, Soil Conservation Service. 1995.
AGNPS Newsletter.
11
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Comparing Experiences in the British and U.S Virgin Islands in Implementing GIS
for Environmental Problem-Solving
Louis Potter
Office of the Chief Minister of the Government, British Virgin Islands
Bruce Potter
Island Resources Foundation, St. Thomas, U.S. Virgin Islands
The British and U.S. Virgin Islands:
Comparisons and Contrasts
British Virgin Islands
Three miles to the north and east of the U.S. Virgin
Islands (USVI) lie the British Virgin Islands (BVI), a
group of 36 islands and cays with a total area of 60
square miles. The four largest are Tortola (24 square
miles), Anegada (14 square miles), Virgin Gorda (8.5
square miles), and Jost Van Dyke (4.5 square miles).
Geologically, the BVI belong to the Greater Antilles, and
like the USVI and Puerto Rico, rise from the Virgin
Banks (or Puerto Rican Shelf). Rocks of the BVI, except
Anegada, consist of thick, steeply inclined, metamor-
phosed volcanic and sedimentary stratified series of
Cretaceous age, with dioritic and pegamitic intrusions.
Anegada is a 30-foot-high emergent coral limestone
platform, apparently from the Pleistocene age.
The BVI are a crown colony of the United Kingdom (UK)
with a total population in 1991 of 16,108. Most of the
population resides on Tortola (13,225 inhabitants). Be-
tween 1980 and 1991, population increased 46.6 per-
cent. The BVI have internal self-government with an
elected council headed by a chief minister. The UK
appoints a governor to represent the queen and to man-
age defense, internal security, external affairs, civil serv-
ice, and court administration.
The BVI economy is based mainly on tourism and serv-
icing international business. Sailing and diving are piv-
otal features in BVI tourism. The average number of
tourists per capita in the BVI is 221, compared with 119
for the USVI (1). The BVI 1990 per capita income was
recorded at $10,125. Prior to the 1970s, the BVI economy
was based on subsistence agriculture and remittances
from British Virgin Islanders who worked in the USVI.
U.S. Virgin Islands
The USVI are an unincorporated territory of the United
States, purchased from Denmark in 1917. The total popu-
lation in 1990 was about 101,000, divided among St.
Thomas (48,000), St. Croix (50,000), and St. John (3,500).
The tourism-dominated economy of the Virgin Islands gen-
erated a per capita Gross Territorial Product in 1990 of
approximately $11,000—the highest in the Caribbean (2).
Geographically, geologically, and topographically, St.
Thomas (28 square miles) and St. John (20 square
miles) are similar; they are both largely volcanic, have
deeply indented coastlines, and lie on the Puerto Rican
Shelf. St. Thomas and St. John are close to the BVI. St.
Croix is a relatively large (84 square miles) and mostly
limestone island that lies on its own submarine ridge,
which rises more than 4,000 feet from the bottom of the
Caribbean Sea. St. Thomas and St. John are about
5 miles apart, and St. Croix is 48 miles south of them.
During the height of tourism development (from the
late 1950s through the mid-1970s), the USVI experi-
enced average annual compound population growth
rates of over 6 percent, as well as a doubling in real
incomes. This unprecedented paroxysm of growth is
still being assimilated by a population that differs
greatly from the 30,000 people who lived in a predomi-
nantly agricultural USVI in 1950. In 1990, the USVI
received a daily average of 37 visitors per square
kilometer. This compares with a visitor load of 23
visitors per day per square kilometer in the BVI, which
is also a high-density tourist destination (1).
Background to GIS Implementation
Activities
British Virgin Islands
The idea of geographic information systems (GIS) ap-
plications in the BVI first arose with a presentation about
-------
a proposed project for St. Lucia, made by Dr. Jan Ver-
meiren of the Organization of American States at the
Caribbean Conference of Planners in Kingston, Ja-
maica, in 1984 (3). The Town and Country Planning
Department recognized that it could use GISs analytical
and display properties to make presentations to the chief
minister and the BVI Executive Council. This proposal
fell on fertile ground, given a relatively long-held tradition
of support for the Town and Country Planning Depart-
ment by the United Nations Development Programme
(UNDP) and the British Development Division, dating
back to the early 1970s.
Subsequent to this inspiration, the Town and Country
Planning Department requested budget authority to de-
velop a land use database. This database would include
buildings, property boundaries, and constructed and
natural features of importance. The Finance Department
hoped this project would help combat growing competi-
tion to the postal services by independent package de-
livery services. They renamed the project the National
Addressing System, and the legislature provided
$200,000 to provide a physical address for each prop-
erty in the territory.
The Town and Country Planning Department conducted
a pilot project, focusing on Road Town, the capital. The
pilot project demonstrated that the hard copy land own-
ership or cadastral maps that the Survey Department
was then using were inadequate for accurately account-
ing for properties, even in the BVI's most developed
urban areas. Therefore, the Town and Country Planning
Department expanded the scope of the National Ad-
dressing System project to identify options for increas-
ing the accuracy of property ownership records,
including maps.
Because the Town and Country Planning Department
had little experience with computerized land information
systems, departmental managers sought support from
the UNDP office in Barbados. The UNDP had previously
assisted with the department's development control ap-
plications database. This mode of operation, in which
the BVI purchase services provided through the UNDP,
which acts as a "vetting agent" for consultants and other
technical assistance, continues to this day. An expert
from the United Nations Community and Housing Serv-
ices (UNCHS) Nairobi office provided the first such con-
sultation by exploring how the BVI might implement a
CIS. Over the next few years, three other experts
provided input, which the Town and Country Planning
Department gradually integrated into a picture of how to
use a CIS within the technical and financial limitations
of a small island government.
In the meantime, external conditions were improving the
chances for the success of the BVI program. British
Virgin Islanders were receiving formal and informal train-
ing in computer applications in general, and specifically
in AutoCAD drafting systems, which increasing numbers
of local architects and engineers are using. In addition,
the power of rugged microcomputer systems that could
withstand the harsh operating conditions of the Virgin
Islands was also improving, and local dealers were
increasing their skills in support of such systems.
U.S. Virgin Islands
In 1988, a proposal to develop a locally supported CIS
was being discussed in detail in the USVI (4). This
project resulted in a formal application from the govern-
ment of the USVI for financial assistance from technical
assistance funds provided by the Office of Territorial
and Insular Affairs of the U.S. Department of the Inte-
rior. The grant was awarded in March 1991. The pro-
posed project combined existing information from
several USVI government agencies to produce CIS
overlays, as shown in Table 1 (5).
In addition, according to the grant application, (which
was written by the Virgin Islands government and may
not have represented U.S. Geological Survey's
[USGS's] intentions) the National Mapping Division
(NMD) of the USGS agreed to digitize the eight USGS
1:24,000-scale quad sheets ("quadrangles") that cover
the USVI, including the following categories:
• Roads and trails
- Power transmission lines
• Hydrography
- Stream networks
- Shorelines
- Wetlands
- Mangroves
- Reefs
Table 1. GIS Overlays
Agency
GIS Overlays
DPNR
WAPA
VITEMA and emergency services
DPW
Zoning
Flood plain
Subdivision
Water distribution
Aquifer profiles
Electrical distribution
Critical routes
Critical facilities
Sewer line network
Transportation
Flood plain
DPNR = Department of Planning and Natural Resources
WAPA = [Virgin Islands] Water and Power Authority
VITEMA = Virgin Islands Emergency Management Agency
DPW = Department of Public Works
-------
• Topographic contours
• Political boundaries
(These categories were subsequently adjusted based
on discussion between the USGS, WAPA, and govern-
ment agencies. Documentation for these new cover-
ages is not available, but the general idea holds,
although no VITEMAdata were used, and the USGS did
not digitize the WAPA power system.)
The underlying notion was that the whole would be
bigger than the parts—each organization would bring its
own corporate data and maps so all could share in the
digitized product.
Five objectives were identified for the project:
• Provide the USGS in St. Thomas with a complete
microcomputer (CIS) workstation.
• Develop a digitized
graphic maps.
database from USGS topo-
• Contract for the digitizing.
• Acquire digital data to load into CIS.
• Enter data not in digital format.
The proposal required a 50-percent local cost contribu-
tion to the project, including a matching suite of hard-
ware for data maintenance, backup, and analysis. The
application stated that itemized costs of $305,000 "will
be provided by the Virgin Islands Water and Power
Authority (WAPA) and the Office of Territorial and Inter-
national Affairs."
The project application is ambiguous about the nature
of the hardware and operating systems that the CIS
requires. A list of hardware for USGS use refers to a
"microcomputer workstation." Later references to "ESRI
ARC/INFO workstation software," however, indicate to
sophisticated users that these applications require UNIX
workstations and UNIX language operating systems. In
general, UNIX is not used or supported in the Virgin
Islands. Some Virgin Island officials associated with the
CIS project feel they were not fully informed about the
CIS operating environment and the long-term support
costs to which they were committing.
A frustration for USGS in the project stems from the
somewhat limited role the agency has had in providing
high-quality data conversion services (digitizing). One
USGS staff member explained that unfortunately the
agency's mandate only extends to providing data; the
agency "can't get involved in applications."
The initial project proposal also was unclear about owner-
ship of the digitized data. The proposal spoke in general
terms: "All available digital data and attributes will be com-
plete, accurate, and up-to-date at the end of the project,
and will be available for use and transfer to the Depart-
ment of Planning and Natural Resources (DPNR) (5)."
Given the conditions and environmental constraints dis-
cussed, a number of specific differences developed be-
tween the CIS implementation processes of the BVI and
the USVI.
Initial System Planning Activities
British Virgin Islands
Although the BVI had no formal systems plan, consult-
ants worked with the Town and Country Planning De-
partment on four different occasions to provide insight
into some aspect of CIS applications. Sometimes, the
benefits from the consultations were neither the type
nor the quality the department originally expected. In
general, however, each provided some additional per-
spective on the possible benefits and perils of imple-
menting a CIS.
U.S. Virgin Islands
The USVI apparently made few initial system planning
efforts, although the U.S. Department of the Interior and
USGS have wide experience in the territory. (An in-
formed source claims a grant was made, possible by
EPA, for a preceding $50,000 CIS project, but no men-
tion of this has been found in the materials available for
this article, either as a proposal, or in terms of specific
products.) Possibly, this very familiarity led to a series of
unexamined assumptions and diminished communica-
tions about the exact terms of the assistance and serv-
ices that the USGS exchanged with several agencies of
the Virgin Island government.
One indicator of the lack of system planning activities in
the Virgin Islands is a proposal that the Virgin Islands
Emergency Management Agency (VITEMA) circulated
fora "Geographical Information Systems: Technical Op-
erators Meeting." This proposal, from an agency that
has always been one of the most important participants
in CIS activities, called for a "technical working group"
to examine existing database management systems in
the territory to develop a planning strategy for imple-
menting CIS.1 This proposal was dated September
28, 1992, 10 days before the USGS announced a
demonstration of the completed products of Phase I of
the "comprehensive geographic information system be-
ing developed for the USVI (6)."
1 Ward, R.G. 1992. Geographical information systems: Technical op-
erators meeting. Memorandum to Cyrille Singleton. VITEMA, St.
Thomas, U.S. Virgin Islands (September).
-------
Software Selected and Rationale
British Virgin Islands
In part because of the extended timeframe for the BVI
planning and initiation of the CIS system, the Town and
Country Planning Department never committed to a
specific system configuration until the last stages of the
planning process.
This process of "creative procrastination" had three syn-
ergistic results:
• PC power increased (and prices decreased) to the
point where reasonably priced systems could perform
many of the compute- and data-intensive operations
demanded for graphics software mapping.
• ARC/INFO released the ARC/CAD version of its CIS
software, which worked on PCs within the well-known
AutoCAD drafting software. Architect and engineering
offices in the BVI already used computer-aided design
(CAD) software, so upgrading to include CIS func-
tionality was relatively easy.
• The fourth CIS consultant to work in the BVI was
experienced in implementing systems in the Carib-
bean and had special knowledge and access to early
versions of both ARC/CAD and Version 1.0 of
ARC/VIEW. These two tools, based on a last minute
proposal by International Development Advisory
Services (IDAS) of Miami, Florida, a private CIS sup-
port contractor, became the basis for the BVI CIS.
U.S. Virgin Islands
Workstation ARC/INFO 6.1 was selected as the basic
software for the USVI CIS project because it is the
USGS standard software. In the environment surround-
ing the USVI project, this seemed to be a sufficient
explanation, although there may have been other rea-
sons. Because of this decision, however, WAPAand the
DPNR lack any means of updating map or attribute data
files. Outside providers, such as the USGS's NMD, must
perform that service. The USGS has noted that the only
reason WAPA and DPNR lack these capabilities is be-
cause they (WAPA and DPNR) failed to provide the
matching suite of hardware and software specified in the
grant application.
The digitized water supply system offers an example of
the extra costs that such a condition creates. The USGS
built an ARC/INFO coverage by converting mapped data
from AutoCAD source files, which they then linked to
detailed attribute information about each element (e.g.,
pipe, valve, elbow) in the system. The USGS then used
ARC/INFO network software, purchased with project
funds, to build a network model that analyzes and dis-
plays the operation of the entire water distribution sys-
tem—but no software in the Virgin Islands can run the
network model.
Hardware Platforms
British Virgin Islands
The BVI CIS was originally installed on a Compaq 486-
50, with a 21-inch screen. The Town and Country Plan-
ning Department soon learned, however, that data input
would be more efficient if two or three smaller machines
split the work, with the Compaq available for analysis
and data quality checking. The department upgraded its
existing office computers to handle the data entry. Users
already feel the need for networked applications to
share data more quickly. Plans for CIS expansion to
other offices, such as the Electricity Corporation, in-
crease the pressure for an extended local area network.
U.S. Virgin Islands
The system that the USGS used to build the USVI CIS
database was a Data General UNIX workstation with
one large digitizing tablet and one pen plotter. No match-
ing or comparable hardware are installed anywhere in
the USVI, as the original project proposal had foreseen.
Observers tend to agree, however, that the failure to
provide a specific hardware configuration is less signifi-
cant than the lack of committed, senior, full-time techni-
cal staff. This staff is required to operate the level of CIS
facility that the USGS envisioned.
Base Map Priorities and Layers
Constructed
British Virgin Islands
Building a map database is proving to be a long process
for the Town and Country Planning Department. This is
complicated by the failure of a key digitizing contractor
in Texas to provide property lines in a format conducive
to constructing accurate property polygons. Operators
in the Town and Country Planning Department have
increased their data entry efficiencies, however, and
most properties on the most densely inhabited islands
have now been digitized.
Producing demonstration data displays accounts for a
significant part of the cost of developing databases for
the early phases of the BVI CIS implementation. These
demonstrations aim to illustrate possible new applica-
tion areas for other agencies and departments of the BVI
that are interested in cooperating and sharing costs of
additional system development. For example, the Elec-
tricity Corporation and the National Disaster Prepared-
ness Agency need to map emergency services.
Converting the data (i.e., digitizing) in house in the BVI
has produced costs and benefits. The costs revolve
-------
around the steep learning curve for data entry proce-
dures and the constant distractions of responding
quickly to "outsiders" who may be important long-term
supporters of the CIS. The benefits include increasing
staff skills and the ability to build constituencies for the
program by promptly responding to real needs.
Coverage priorities for the BVI CIS include a national
addressing system, completion of the territorial land
use mapping, and accurate cadastral mapping (which
has major environmental planning and management
implications).
U.S. Virgin Islands
The USGS produced 44 coverages for the Virgin Islands
from a variety of sources. Table 2 shows the major
coverages and scales, by island.
Table 2. Major Coverages and Scales for the USVI
St. Croix St. John St. Thomas
STC water
distribution
1 :2,400
STC roads
1 :2,400
STC building
footprints
1 :2,400
STC shorelines
1 :2,400
STC DIG
boundaries
1 :24,000
STC DIG
roads
1 :24,000
STC DIG
hydrography
1 :24,000
STC DIG
hypsography
1 :24,000
STJ water
distribution
1 :2,400
STJ roads
1 :2,400
STJ building
footprints
1 :2,400
STJ shorelines
1 :2,400
STJ DIG boundaries
1 :24,000
STC DIG roads
1 :24,000
STJ DIG
hydrography
1 :24,000
STJ DIG
hypsography
1 :24,000
STT water distribution
1:2,400
STT roads
1:2,400
STT building
footprints
1:2,400
STT shorelines
1:2,400
STT DIG boundaries
1 :24,000
STT DIG roads
1 :24,000
STT DIG
hydrography
1 :24,000
STT DIG
hypsography
1 :24,000
In addition, the following National Park Service data
were converted and added to the data set but were not
produced by the USGS:
• STJ NPS boundaries, 1:24,000
• STJ NPS roads, 1:24,000
• STJ NPS hydrography, 1:24,000
• STJ NPS hypsography, 1:24,000
• STJ NPS benthic communities, 1:24,000
• STJ NPS historical sites, 1:24,000
• STJ NPS vegetative cover, 1:24,000
Mapping for St. Thomas, St. John, and St. Croix identi-
fied a total of 10 coverages for each island. They are
based on information from the USGS ("quad sheets"
specifically demarking political boundaries, shorelines
and streams, topography, and roads) and higher preci-
sion WAPA mapping, which derives from 1986 aerial
photogrammetry, including left and right road bounda-
ries, building footprints, shorelines, and water supply
system data. The WAPA data are at 1:2,400 scale, an
order of magnitude more precise than the USGS base
map. St. John mapping consists of 10 added layers
based on data that the Virgin Islands National Park
(VINP) provided.
The original USVI project proposal referred to a two-phase
process of database development, shown below (5):
Phase
I. Base system
development
Agency extensions
(i.e., by USVI
agencies)
Tasks
Water distribution network
Power distribution network
Flood plain maps
Land use maps
Transportation networks
Emergency facility networks
Tax parcel/land value
The USGS announced that Phase I was completed in
October 1992 (6). Supposedly, the contents of these two
phases were subsequently adjusted to reflect a different
range of coverages, but the notion of a "Phase II" in
which local agencies would assume more operating
responsibilities was retained.
Ownership of, access to, and terms that govern the use
of this digital data are confused. The USGS says it is
unable to provide an authoritative catalog of the cover-
ages because "one has not been produced." WAPAsays
it has several diskettes of data in the safe but no equip-
ment to manipulate them. The VINP has learned that it
can use its own data as well as WAPA data converted
and attributed by the USGS, but the park does not
possess or use USGS digital line graph (DIG) data. To
personnel in USVI agencies, USGS statements have
clouded the question of access to the CIS information.
For example, one such statement announced that the
USGS Water Management Division cannot make the
digitized data available to Virgin Island government
agencies.
The DPNR apparently has no means of making direct
use of the digital data. First, DPNR has no hardware or
software that can use the data. Secondly, it has no
operators who can build the GIS systems to actually
apply the data to decision-making needs. The depart-
ment is said to be preparing a new GIS proposal for
training, hardware, and software for a new GIS system.
According to unconfirmed rumors, this system will be
based on MapGrafix, a Macintosh mapping system.
-------
In operational terms, the data seem to belong to WAPA,
which contributed major financial support and map re-
sources to the project. WAPA has been helpful in pro-
viding copies of the digital data to other groups and
agencies.
Environmental Problem-Solving in Local
Decision-Making
British Virgin Islands
In the BVI, the first priority of the CIS facilities is to
extend the National Addressing System and to improve
the property ownership system. This will both improve
postal services, as originally proposed, and provide bet-
ter information for important revenue and financial
analyses. Land use mapping and environmental impact
assessments are important second priorities for CIS
applications. Other features already developed for in-
terim studies and analyses include mapping of signifi-
cant coastal and wildlife features and environmentally
sensitive areas from the Anegada Development Plan
and mapping of important submarine habitats adjacent
to Virgin Gorda.
The BVI have concentrated on developing CIS applica-
tions to address strategically important issues in the
territory. Marine and coastal resources are vitally impor-
tant to the BVI economy. They embody historical and
cultural values, as well as maintain a high-quality envi-
ronment to support charter yacht-based tourism, which
is integral to the BVI economy. The Conservation and
Fisheries Department is working with the Town and
Country Planning Department to convert the country's
coastal atlas to digital form (see section on Principal
Users), as has been done on a demonstration basis for
the Anegada and Virgin Gorda mapping.
U.S. Virgin Islands
In the USVI, environmental decision-making generally
follows an adversarial, rather than a problem-solving
format. A combination of historical and cultural factors
have created the general assumption that the develop-
ment process creates winners and losers. In this envi-
ronment, information becomes an important tactical
weapon, making it difficult to gather support for activities
or programs that aim to make information more widely
accessible. Technology is more acceptable, and more
likely to receive leadership support, if it is justified on
technical, less "political" terms.
The USGS team made a presentation on the Virgin
Islands CIS in October 1992, after just completing the
digital coverages for Phase I of the CIS project. The
presentation emphasized that the CIS is intended to
provide decision-makers with easily accessible spatial
information (6).
Originally, the CIS was expected to benefit primarily the
territory's three coastal zone commissions in their as-
sessment of environmental effects of major develop-
ment proposals. The ground-water protection program
of the Division of Environmental Protection of the DPNR
is using CIS analyses produced by the Water Resources
Division of the USGS, employing the USVI CIS cover-
ages with added data (e.g., wells) that the Water Re-
sources Division is digitizing.
GIS Support Factors
According to the GIS support contractor for the BVI, a
successful GIS requires three key support elements:
• GIS policy leadership
• GIS technical leadership
• Competent outside expert assistance
The following summarizes the comparative experience
of the two programs for these three key implementation
support factors:
Support
Factors
GIS
policy
BVI
Town and
Country Planning
Department
director led
project from chief
minister's office
GIS BVI technician
technician trained in the
United States
USVI
No GIS manager
in government
Outside
support
UNDP and IDAS
No GIS specialist
in government
(draftsman at
WAPA)
USGS technical
support and U.S.
Department of
the Interior
financial support
Principal Users: Planned and Actual
British Virgin Islands
The BVI Department of Finance is the first user of data
products from the GIS, based on the initial funding for
the addressing system. This system is based on detailed
parcel maps of the BVI so that the effective base map
resolution of the BVI system is 1:2,500. This is consid-
erably finer than the 1:24,000 scale of the USVI maps.
-------
The Town and Country Planning Department, however,
is working to recruit other users to the system, including:
• Public Works.
• Water and Sewerage Department for a systemwide
map (which may eventually spin off as a separate
system, given this department's long-term interest in
engineering-quality facilities management information).
• Conservation and Fisheries.
• British Overseas Development Administration, which
funded a coastal atlas for the BVI (7). At the sugges-
tion of the Town and Country Planning Department,
this mapping was developed in ARC/INFO. A pro-
posal has been made to convert the coastal atlas to
digital form for natural resource management appli-
cations, with a demonstration already developed
showing the distribution of sensitive marine commu-
nities around Virgin Gorda.
In addition to these uses, the CIS group is starting to
experiment with the use of remote sensing products in
CIS production, which would encourage the use of CIS
for natural resource change detection.
The Town and Country Planning Department's CIS spe-
cialist, Mikey Farara, is being reassigned to provide
networking support (including CIS distribution over the
network) for several government agencies. Meanwhile,
the CIS operation is adding a cartographer to assist in
tailoring CIS products to users' needs.
As enthusiasm for the CIS has blossomed in the BVI,
managing for realistic expectations and stressing the
investment costs that participating agencies can expect
have become problems for the Town and Country Plan-
ning Department.
U.S. Virgin Islands
Complex evaluation issues face the USVI's three
coastal zone commissions (one on each island).
Therefore, land use planning in general and coastal
zone permitting specifically were assumed to be im-
portant first users of the CIS. The DNPR, however,
had no process to prepare the Division of Comprehen-
sive and Coastal Zone Planning to implement this
system. In addition, the scale of permitting decisions
may be too fine forthe CIS base map. (See discussion
of scale below.)
WAPA is not using the CIS data. One senior manager
characterized their experience with the CIS project as
"paying a lot of money for a diskette of data that we keep
locked in the safe."
The VINP (part of the U.S. National Park Service) and
Biosphere Reserve have purchased a PC-based CIS
system and employed an analyst to implement it for the
park and adjacent areas on St. John and the surround-
ing seas. With this system, they plan to enter the USGS-
developed data into the database. In addition, the Virgin
Islands Resource Management Cooperative (a collabo-
ration of research and resource management organiza-
tions) makes the VINP CIS data and analytical capabilities
available to members, including government members.
At this time, the only major Virgin Islands government
user of the CIS is the ground-water protection group of
the DPNR's Division of Environmental Protection. Be-
cause they lack equipment or software to manipulate the
CIS data already available, they use the Water Re-
sources Division of USGS as a CIS contractor. This
arrangement has two problems:
• High costs: Although the USGS "owns" the existing
digital data, and processing private contracts would
be complex, DPNR believes it could get similar serv-
ices at cheaper prices from other vendors.
• Inappropriate scale: Environmental management
processes in the Virgin Islands (and in most other
small island states) require knowledge of property
ownership, implying maximum map scales of
1:5,000 to 1:10,000. The USGS quad sheet scale
of 1:24,000 is too coarse for many management
purposes. Costs of remapping areas of concern at
the higher resolution are high, and the problems of
maintaining multiple map resolutions and sources
are not trivial.
What GIS Can Do
Joseph Berry has proposed seven basic categories of
"What GIS Can Do for You" (8). These applications can
be related to the GIS products and proposals forthe BVI
and USVI, with special attention given to natural re-
source and environmental issues. Table 3 shows what
coverages that have been or are being developed for
the two systems can do.
Table 3 illustrates two contrasting issues separating the
two jurisdictions. The USVI have the data available to
perform a number of relatively complex analytical proc-
esses, especially in St. John. They have no capability to
actually execute any such studies, however. The BVI,
on the other hand, have proposed and often developed
pilot or demonstration applications for several GIS uses
but still need to develop the data resources to support
these on a territorywide basis.
Lessons Learned
The comparative experience of these two very distinct
GIS programs reinforces three basic lessons of informa-
tion system design and implementation:
• Plan, don't assume: The prolonged, sometimes repe-
titious, planning process that evolved in the BVI
-------
Table 3. Coverage Capabilities
USVI
BVI
Questions:
Can you map it?
Where is what?
Where has it changed?
What relationships exist?
Where is it best?
What affects what?
What if...?
Analytical
Function
Mapping
Natural resource
management
Temporal
Spatial
Suitability
System
Simulation
Application
USGS and WAPA-based
coverages
DIG hydrography
DIG hypsography
Well inventory
STJ national park
coverages
DIG and WAPA
Boundaries and roads
1982 to 1989
STJ national park
coverages (limited
application)
STJ national park
coverages (limited
application)
None discussed
Status Application
Done Land use cadastral
Done Coastal atlas
Sensitive areas
Done Significant features
Population data
Done Land use
Done Sensitive areas
Done Land use updates population
Land use
Population data
Coastal atlas
Done Land use
Coastal atlas
Sensitive areas
Significant features
Done Land use
Coastal atlas
Sensitive areas
Significant features
Population data
Speculation, but no plans to
implement yet
Status
Done and
proposed
Proposed
and partial
Major
islands done
Proposed
Proposed
Partial
Proposed
Partial
Partial
Partial and
proposed
involved multiple consultants providing often conflict-
ing advice. This process served to educate policy-mak-
ers and managers in a much broader range of
possibilities and avoidable problems than were avail-
able to the USVI. A corollary to the need for careful
planning is the need to avoid making decisions or
commitments to specific systems before such deci-
sions are absolutely necessary. Especially in systems
involving high technology, premature decisions often
mean early obsolescence.
Implement in phases with early demonstration
products: Some issues, such as cadastral mapping
and scale, are so subtle to inexperienced users that
they need practice in real-life situations. If the USGS
had spotted the scale problems at an early stage in
the data conversion process, the USGS may have
been able to provide a better solution. Some USVI
critics claim the "1:24,000—one size fits all" attitude
characterizes the federal approach.
Identify critical success factors for each situation: In
some environments (e.g., USVI), CIS is most attrac-
tive for its ability to provide enhanced powers of
analysis. In others, such as the BVI, it is seen as a
data integration tool and as a way to better inform
political leadership and the public. To ensure suc-
cess, major CIS implementations also need to meet
the three major support requirements:
- A political/senior management "chief
- A technical "chief
- Competent outside technical assistance
Finally, implementers should recognize that they have a
stake in open information sharing. They should seek
ways to redefine the decision-making process as a
nonzero sum game: more information should benefit all
parties. Of course, such changed attitudes require fun-
damental value shifts that take a long time to achieve
and may have high short-term costs.
References
1. McElroy, J.L. 1991. The stages of tourist development in small
Caribbean and Pacific islands. In: Proceedings of the International
Symposium on the Island Economies: Policy Models for Planning
Development, Lesbos, Greece (November).
2. Bureau of Economic Research, Virgin Island Department of
Commerce. 1994. Economic indicators. St. Thomas, U.S. Virgin
Islands (July).
-------
3. Potter, L. 1984. Program notes from the Caribbean Conference of
Planners, Kingston, Jamaica.
4. Potter, B., K. Green, and M. Goodwin. 1988. Management of natu-
ral resource information for the Virgin Islands National Park and
Biosphere Reserve: Special biosphere reserve report. St. Thomas,
U.S. Virgin Islands: Island Resources Foundation.
5. Government of the U.S. Virgin Islands. 1991. Application for tech-
nical assistance funds: Virgin Islands GIS system. Office of Terri-
torial and International Affairs, U.S. Department of the Interior
(April).
6. Parks, J.E. 1992. Invitation to a demonstration of the Virgin Islands
GIS. U.S. Geological Survey, St. Thomas, U.S. Virgin Islands (Oc-
tober).
7. University of Manchester, British Overseas Development Admini-
stration. 1993. British Virgin Islands coastal atlas. Project was
collaboration between graduate students at Manchester University,
the British government, and the government of the British Virgin
Islands.
8. Berry, J.K. 1994. What GIS can do for you. GIS World. Boulder,
CO (May).
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The Application of Geographic Information Systems in the
Development of Regional Restoration Goals for Wetland Resources
in the Greater Los Angeles Drainage Area
Charles Rairdan and Richard F. Ambrose
U.S. Army Corps of Engineers - Los Angeles District
ABSTRACT
The 2,320 square-mile Greater Los Angeles Drainage Area historically supported a broad
diversity and extent of wetland resources that have been almost entirely lost or transformed by a
system of upstream impoundments, channelization of lowland watercourses, depletion of
ground water supplies, and widespread development.
Restoration planning efforts are now mostly site-specific, concentrated in the coastal zone, and
do not adequately consider the functional linkages with adjoining ecosystems or the effects of
urbanization in the contributing watersheds. Because it is unlikely that a significant likeness of
the region's wetland heritage can be recovered, it will be important to instead optimize the
functions performed by the remaining and restored systems.
Geographic Information Systems (GIS) was used in conjunction with a modified
Hydrogeomorphic (HGM) classification method to reconstruct recent historic (ca. 1870) wetland
resource conditions, and to perform a landscape-level comparison with current conditions to
determine the nature and extent of the associated functional losses.
Results indicated the widespread loss of individual wetland resource functions within the
categories of: hydrologic, water quality, nutrient cycling/food chain support, and habitat; and
severe disruption of the broadscale functions of landscape maintenance and biodiversity, which
relate the successful performance and continuity of the individual functions and the biological
integrity of supporting ecosystems.
In response to these findings, a set of fifteen, function-based regional restoration goals was
developed, the objectives of which are to rehabilitate key elements of the current landscape
(hydrology, sediment and nutrient budgets, water quality, and habitat) with restoration measures
that will enhance the long-term success and regional sustainability of remaining and restored
-------
wetlands, and that will lead to the creation of new restoration opportunities in a highly
constrained urban environment.
This study effectively demonstrates the capabilities of GIS in conducting landscape-scale
analyses of multiattribute systems in support of regional and watershed planning activities.
INTRODUCTION
Background
Wetland losses in southern California, among the highest in the U.S., are on the order of 90
percent (Dahl 1990), and are frequently higher in coastal areas where development is
concentrated. As momentum builds to restore the region's wetland resources, the range and
intensity of stakeholder interests, the extent to which the landscape has been altered, and
ongoing development pressures all act to hinder the coordinated progress of these efforts
(Sorensen and Gates 1984).
Restoration planning and research in the region have typically focused on individual tracts of
wetlands. Moreover, the functional linkages of coastal and inland systems have not been
sufficiently recognized and incorporated into the planning process (Rairdan 1998).In order to
promote a more cohesive approach to wetlands restoration planning in the metropolitan Los
Angeles area, a landscape-level comparison of recent historic (ca. 1870) and current wetland
resource conditions was performed with Geographic Information Systems (GIS) to serve as a
basis for the development of a comprehensive set of function-based restoration goals.
This paper outlines the role of GIS in that research and provides examples of the methodologies
and results generated. A full description of the study and its data are available in Rairdan
(1998).
Study Area
The Greater Los Angeles Drainage Area (GLADA) comprises a 2,320 square-mile area and five
constituent watersheds (Figure 1). The majority of the study area is covered by Los Angeles
County, but also contains portions of Ventura, San Bernardino, Riverside, and Orange Counties.
The current population of the Los Angeles metropolitan area is about 14 million people.
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Figure I.The greater Los Angeles drainage area and constituent watersheds.
The major landforms in the region include the high-ranging (3,000 to 10,000 feet elevation)
interior San Gabriel mountains, the interior (San Fernando and San Gabriel) valleys, and the
ring of coastal mountains (1,500 to 2,000 feet elevation) that encloses the coastal plain and
geologically separates it from the interior valleys. At the transition between these two landforms
are the mountain gaps (Glendale and Whittier Narrows) through which the Los Angeles and San
Gabriel Rivers pass, and where ground water flows from the interior valleys historically re-
emerged as surface flows during the dry season. In a similar manner, the Lower Santa Ana
River emerges from a gap in the coastal range in the southeastern portion of the study area and
formerly debauched its alluvial contents onto the coastal plain.
The orographic effect of the interior mountains and steep elevational gradient (approximately
5,000 feet over a 45-mile distance) of this coastal drainage result in periodic, high-volume, high-
velocity flood flows (flashy hydrology) that accompany the large winter storms of the region.
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The overall climate in GLADA is Mediterranean, marked by warm, sunny winters having widely
variable precipitation, and hot, rainless summers moderated by coastal breezes. The diversity
and arrangement of major landforms, however, coupled with the basin's proximity to the ocean,
result in a set of distinct microclimates-ranging from marine conditions in the coastal plain to
alpine weather in the interior mountains. The complex interactions of climate and major
landforms, in turn, once resulted in a broad diversity of wetland ecosystems across the semi-
arid landscape (Rairdan 1998).
Research Questions
Given the apparent extensive loss of wetland resources in the study area due to widespread
development over the last 120 years, GIS was utilized to facilitate the resolution of the following
research questions:
1. What were the recent historic wetland resource conditions in GLADA?
2. What are the current wetland resource conditions?
3. What has been the nature (in terms of ecosystem structure and function) and extent
of wetland resource losses in the region?
4. How might some of these losses be reversed or ameliorated at the regional scale of
planning?
-------
METHODS
GIS Database
Applying the principles of landscape ecology and computer modeling techniques (Forman and
Godron 1986, Haines-Young et al. 1993, Stow 1993), Arclnfo® GIS was employed to reconstruct
recent historic and current wetland resource conditions. However, only through the availability of
complete sets of early U.S. Coast and Geodetic Surveys (1859-1893; 1:10,000) and USDA Soil
Surveys (ca. 1910), augmented by a variety of other manuscript maps, historic photos, and
narrative accounts in university and regional archives, was the proposed research considered
feasible. The period of circa 1870 for recent historic conditions was decided by the timing of en
masse American settlement of the region, and the fact that most of the early landscape-level
impacts to wetland resources occurred in the coastal zone, for which the original wetland
configurations were known.
The standardization of the modeling scale (approximately 1:100,000) and wetland resource
feature resolution (about a 5-acre minimum patch size) were determined by the soil survey map
specifications, which included a scale appropriate for the level of detail needed for the study and
which could be consistently reconciled among the various historic data sources and between
recent historic and current conditions.
To reduce the amount of digitizing needed to construct the GIS model, USGS 1:100,000
hydrography data was imported from the Internet (http://nhd.usgs.gov/) and adapted as a
template layer for both recent historic and current conditions. Because the interior mountain
areas of GLADA had not been extensively altered by development, manual digitization of recent
historic conditions was limited primarily to the lowland areas and, for current conditions, the
revision of existing wetland resource features.
In addition to these principal data layers, supplemental layers of topography (DEM), land use,
former artesian areas, and microclimatic zones were adapted from existing data sources to
facilitate the landscape-scale analysis of the region's wetland resources.
To ensure topologic consistency between recent historic and current conditions, a system of
cross-referencing the available source maps, and field verification of the mapped features and
vestigial landforms (for recent historic conditions) was employed. Thomas Brothers® street maps
were indispensable to accomplishing this task in the large, ubanized study area. Access to a full
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set of high-resolution aerial photographs, however, would have greatly facilitated the data and
model validation process.
Wetland resource features were categorized in the GIS database as either point, linear, or areal
feature types depending on how much was known about them geographically and how they
were depicted on source maps. For instance, because little is known about the former extent of
the region's vernal pool complexes other than their approximate location, these systems were
identified as point features and, along with the other feature types, associated with a
characteristic set of structural and functional attributes. Riverine wetland resources were mostly
described as linear features, whereas marsh-like wetlands or systems having a discernable
area were delimited as polygon features.
Once the wetland resource features were established within the GIS database and delineated
with the below-described classification system, their relative quantities and distributions within
GLADA, the fate (converted, extirpated, or remaining) and extent of recent historic wetland
resources, and the extent of newly created wetland resources could all be determined by query
and overlay analyses.
Wetland Resource Classification System
Wetland resource types were classified with a system adapted from Brinson's (1993)
Hydrogeomorphic (HGM) method that grouped structurally-similar wetland resources into the
regional classes of riverine, depressional, slope, and estuarine fringe. The wetland resource
types within each class (Table 1) were further distinguished according to their functional
differences along the landscape gradients of geomorphic setting, substrate and vegetation,
water supply, and hydrodynamics. Table 2 presents an example of this classification scheme,
which served, in effect, as the primary attribute table for the digital wetland resource features.
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Table 1. Recent historic and current (in italics)
wetland resource types by regional class occurring
within the study area.
RIVERINE:
Upper Riverine
Dry Wash
Lower Riverine
Riverine Marsh
Braided Lower Riverine
Soft-Bottomed Channel
Concrete-Lined Channel
SLOPE:
Slope Marsh
Peat Marsh
DEPRESSIONAL:
Vernal Pool
Ephemeral Lake/Pond
Depressional Marsh
Non-Tidal Salt Marsh
Flood Control Basin
Reservoir/Recreational
Lake
Spreading Ground
Constructed Lake/Pond
ESTUARINE FRINGE:
Tidal Marsh
Marina/Harbor
Table 2. Example of the wetland resource classification system.
Wetland | Geomorphic Setting | Substrate and
Resource || || Vegetation
Water
Supply
Hydrodynamics
RIVERINE:
Braided
Lower
Riverine
Lower reaches of mainstem
channels characterized by a
wide flood plain and
meandering streams;
wetlands occur in linear
arrangements immediately
adjacent to streams and in
patchy distributions within
the broader flood plain.
Medium- to fine-
grained
sediments;
vegetation
charac-terized
by riparian shrub
and tree
communities on
point bars and
river banks.
Fresh;
intermit-tent
to seasonal;
meandering
and
overbank
flows,
shallow
ground water
table.
High volume,
low velocity
flows result in
wider flood plain
and longer
residence times;
some ponding
may occur.
Assessment of Wetland Resource Functions and Values
From the characterization of the physical attributes of the region's wetland resources, and in
conjunction with an extensive literature review, functions within the categories of hydrologic,
water quality, nutrient cycling/food chain support, and habitat (Table 3) were related to the
individual wetland resource types and evaluated with a qualitative rating scale as being
either:"not supported", "weakly supported", "moderately supported", or "strongly supported".
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Table 3. Wetland resource functions described in the study.
HYDROLOGIC: WATER QUALITY:
Flood Attenuation Sediment and Toxic Substance
Ground Water Recharge Removal
Sediment and Bank Stabilization Nutrient Removal
NUTRIENT CYCLING HABITAT:
/FOOD CHAIN SUPPORT: Macroinvertebrates
Primary Production Herpetofauna
Decomposition Fish and Shellfish
Nutrient Export Mammals
Perching Birds
Waterfowl and Shorebirds
The broadscale functions of landscape maintenance (relating to the successful performance
and continuity of the individual wetland resource functions along the landscape gradients of
hydrology, sediment and nutrient budgets, water quality, and habitat) and biodiversity
(landscape-level support of the richness and diversity of wetland-associated species) were also
considered, along with the ecosystem values of aesthetics and recreation, education and
research, and wetlands heritage with regard to their potential to further the realization of the
regional restoration goals ultimately defined by the study.
In combination, these evaluations were performed to: (1) characterize the functional attributes
(and ecosystem values) of GLADA's wetland resources; (2) assess the functional linkages of
the different wetland resource types within the landscape; and, (3) to estimate the functional
losses from recent historic times to the present. A complete description of these assessments is
contained in Rairdan (1998).
RESULTS
GIS Maps and Data
The graphic results of the completed GIS database were displayed on 44" x 36" color maps to
adequately capture the scale, resolution, and connectivity of the wetland resource features
within the context of the broader landscape, while quantitative data were summarized in tabular
form.
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By comparing recent historic and current conditions, the former diversity and magnitude of the
region's wetland resources were clearly revealed. Figure 2 provides an example of the dramatic
differences in wetland environments that have occurred between the two resource periods, and
illustrates the extent to which the landscape has been fundamentally altered from recent historic
times.
An example of the fate and current status (converted, extirpated, remaining, and created)
accounting of the region's wetland resources, generated by an overlay analysis of recent
historic and current conditions, is provided in Table 4. In addition to these quantities, the types
of conversion (from one wetland resource type to another) were also determined to ascertain
the nature of individual wetland resource transformations (Rairdan 1998).
Table 4. Example of the fate and current status accounting of
wetland resources in GLADA.
Lower
Riverine
Extent (in miles)
Recent
Historic (Total)
705
Converted
368
Extirpated
318
Remaining
21
Created
13
Current
(Total)
34
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• Ve-nal Pool
Upper Riverine
/\/ Lower Riverine
y\y Braided Lower Riverine
Dry Wuh
Ephemeral Lake/Pond
^H Depressiona! Marsh
Slope Marsh
H Peat Marsh
H Tidal Marsh
Shrinkage of Artesian Amas
- ' '• Original
I 1904
Hfl25
Pacilfc
Ocean
• Vernal Pool
Upper Riverine
/\/ LOWBC Riverine
A,/ Soft-Bonomed Crmnnel
y\y Concrete-Lined Channel
Constructed Lake.'Pond
Flood Control Basin
Dapresslonal Marsh
Tidal Marsh
Marinsi/Harbor
Urbanlzsd Araa
Pacific
Ocean
Milei
Figure 2. Gray-scale images of recent historic and current wetland resource
conditions in the coastal reaches of San Diego Creek watershed.
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From the comprehensive analysis of the GIS-based model, the estimated functional losses of
wetland resources in GLADA can best be described as widespread and extensive for the
individual functions, and as resulting in the severe disruption of the broadscale functions of
landscape maintenance and biodiversity. This examination of the data also validated a main
study premise that, at a regional scale of planning, the ecological integrity of the broadscale
functions is paramount to the successful maintenance and performance of the individual
wetland resource functions. This finding provided perhaps the greatest impetus for the
development of the regional restoration goals, and for reinforcing the ideas that a landscape
approach to restoration planning and implementation is necessary to achieve the optimum
restoration benefits, and will more likely result in the restoration or creation of regionally
sustainable wetland ecosystems.
Regional Restoration Goals
From the foregoing analyses and results, a set of 15 function-based regional restoration goals
was derived. Table 5 presents an abbreviated description of these goals, which were developed
in conjunction with a literature review of case studies in which similar landscape-scale goals and
restoration measures had been either identified or implemented.
The order in which the goals are presented approximates the manner in which the individual
wetland resource functions propagate within the natural environment and also considers the
practical constraints imposed by the existing urban environment. In Rairdan (1998), the
identification of these goals was followed by a discussion and examples of some of the ways in
which the goals might be applied throughout the region. Finally, it should be recognized that, in
order to be successful, these goals would have to be implemented in a coordinated fashion, and
that their time-frame for implementation (20 - 50 years) is commensurate with the spatial scale
at which they were developed.
DISCUSSION
The application of GIS in this study allowed for the development of an integrated, landscape
perspective of the region's wetland resources and associated functional capacities. And
although it was apparent at the outset of the study that the historic losses were substantial, the
revelation of the former diversity and extent of wetland resources by the GIS database was
nonetheless striking. The degree to which the current landscape has been altered had in effect
obscured the perception of a past landscape that was capable of supporting a wide range of
11
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highly-productive wetland ecosystems.
Table 5. Abbreviated description of the regional restoration goals.
Goal
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Function
Type(s)1
Hyd
Hyd, LM
Hyd,
NC/FCS, LM
Hyd, LM, B
WQ
WQ
WQ, B
All
All
Hab, B
Hab, B
Hab, B
B
B
B
Restoration Goal
Increase wetlands with high flood attenuation capacity at strategic
locations within the drainage.
Restore historic flow regimes to wetland ecosystems in conjunction with
regional flood control measures.
Restore the continuity of fluvial processes in each watershed by, for
example, modifying upstream reservoir operations, restoring natural
flood plain systems, and by active management of sediment and nutrient
budgets along the watershed continuum.
Restore or locate wetland resources with high ground water recharge
capacity or injection wells in areas that, in addition to replenishing
ground water supplies, would benefit downgradient wetland habitats.
Restore wetland resources that are conducive to the improvement of
water quality near storm water outfalls.
Implement effective storm water management practices in upland areas
to reduce the levels of non-point source pollutants entering wetland
environments.
Restore historic temperature regimes to the region's wetland resources
(e.g., by modified dam operations, revegetation of riparian corridors,
etc.).
Reduce the extent of concrete lined channel.
Establish riparian corridors either within or parallel to existing flood
control channels.
Eradicate or control the propagation of invasive non-native species in
existing and restored habitats.
Enhance the habitat values of flood control basins and water
conservation facilities.
Restore/enhance lost and degraded tracts of tidal and freshwater marsh;
increase the size and connectivity of viable habitats.
Create a network of freshwater inland habitats that function as stopover
sites and species pools to larger, more sustainable wetland sites.
For species having poor dispersal capabilities, initiate restocking
programs in conjunction with the creation/restoration of wetland habitats.
Increase the connectivity of existing and restored wetlands with adjacent
and upstream ecosystems.
1 B = Biodiversity; Hab = Habitat; Hyd = Hydrologic; LM = Landscape Maintenance; NC/FCS =
Nutrient Cycling/Food Chain Support; WQ = Water Quality.
By compiling the available wetland resource data in the GIS database according to a consistent
set of topologic and classification criteria, and by combining the primary data layers with other
supporting themes, a landscape-scale model of the region's wetland resources was successfully
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generated. The completed model was then able to provide reasonable approximations of both
recent historic and current wetland resource distributions, the fate and current status of these
resources, and a tangible depiction of the complex interactions of climate, major landforms,
hydrology, and land use in the creation and maintenance of the associated functional capacities.
Fundamental data gaps (e.g., only knowing the approximate location of historic vernal pool
complexes or not knowing the lateral extent of most riverine wetlands) were successfully
overcome by attributing the basic physical characteristics of each wetland resource type (i.e., its
geomorphic setting, the typical distribution of wetlands within that setting, substrate/vegetative
qualities, water supply, and hydrodynamics) to the corresponding point, line, or polygon feature
type. The physical attributes were then coupled with the functional qualitites (Rairdan 1998) of
each wetland resource type to evaluate their relative support levels and to assess the functional
linkages of the region's wetland resources along the landscape gradients of hydrology,
sediment and nutrient budgets, water quality, and habitat.
However, even when the areal extents of certain wetland resource types (e.g., estuarine and
inland freshwater marsh) were sufficiently known, the qualitative nature and quantitative
uncertainty of the model analysis required only an approximation of the magnitude at which the
individual and broadscale functions were performed. In other words, for the purposes of defining
a comprehensive and functionally-integrated set of regional restoration goals, we were more
interested in assessing the processes by which the wetland resource functions perform within
the landscape than in just producing a numerical accounting of estimated wetland resource
extents.
Other key advantages of the GIS database were the ease with which it could be modified and
refined during its development and subsequently navigated for analysis. The numerous
methodological questions that arose during the concurrent tasks of data entry, development of
the wetland resource classification system, wetland resource feature delineation, and attribute
coding all required iterative and sometimes substantial changes to the GIS model as it was
being built. Without the automated GIS capabilities, the heuristic qualities of the model and the
manual implementation of its many changes would have likely proved too time-consuming and
costly to warrant the construction of a non-digital version. Once the database was complete, the
overlays and quantitative analyses and the production of large color maps for visual
assessments were generally straightforward and justified the initial database efforts.
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Another time-saving aspect of the GIS resulted from the application of the principles of
landscape ecology to the digital technology. For example, by definition, a landscape consists of
a heterogenous mosaic of interacting ecosystem, or wetland resource, units that are
characterized by spatially distinct landforms and a number of other interrelated physical and
biological factors (Rairdan 1998).This definition was instrumental in determining where the
boundaries between the different wetland resource types occurred (e.g., the transition between
upper and lower riverine systems at the outlets of mountain canyons) within their characteristic
landscape or geomorphic setting. By verifying in the field what the typical landscape
configuration was at the boundary between adjoining wetland resource types and adjacent
upland ecosystems, these regionally consistent relationships were employed to delineate the
wetland resource boundaries without having to physically inspect every resource juncture
across the 2,300 square-mile study area. This technique also ensured greater data consistency
between recent historic and current conditions than could otherwise be achieved by cross-
referencing the available manuscript maps, narrative accounts, and historic photographs.
In conclusion, the GIS model facilitated an in-depth understanding of both past and present
wetland resource conditions in the greater Los Angeles drainage area and ultimately led to the
development of 15 function-based restoration goals that, if properly implemented over time,
would result in the increased functional capacities of remaining wetland habitats, the creation of
substantial new restoration opportunities in a highly constrained urban environment, and the
long-term sustainability of the region's wetland resources.
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REFERENCES
Brinson, M. M.1993.A hydrogeomorphic classification for wetlands. U.S. Army Corps of
Engineers, Washington DC. Technical Report WRP-DE-4.
Dahl, T. E. 1990.Wetland losses in the United States, 1780's to 1980's. United States
Department of the Interior, Fish and Wildlife Service, Washington, DC.
Forman, R. T. T., and M. Godron.1986. Landscape ecology. John Wiley & Sons, Inc., New York,
NY.
Haines-Young, R., D. R. Green, and S. H. Cousins. 1993.Landscape ecology and spatial
information systems, pp. 3-8.In R. Haines-Young, D. R. Green, S. H. Cousins
(eds.).Landscape ecology and geographic information systems. Taylor & Francis, London,
England.
Rairdan, C. 1998.Regional restoration goals for wetland resources in the greater Los Angeles
drainage area: a landscape-level comparison of recent historic and current conditions using
geographic information systems. Dissertation, Environmental Science and Engineering
Program, University of California, Los Angeles.
Sorensen, J., and S. Gates. 1984.New directions in restoration of coastal wetlands, pp. 1427-
1443.In O. T. Magoon, H. Converse, and L. T. Tobin (eds.).Proceedings from the third
symposium on coastal and ocean management .American Shore & Beach Preservation
Association/California State Lands Commission, Sacramento, CA.
Stow, D. A. 1993.The role of geographic information systems for landscape ecological studies.
pp. 9-21.In R. Haines-Young, D. R. Green, S. H. Cousins (eds.).Landscape ecology and
geographic information systems. Taylor & Francis, London, England.
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Use of GIS for the Investigation and Classification of Land
Redeveloped Under the Ohio Voluntary Action Program (VAP)
Andrew Rawnsley, Ravensfield Geographic Resources, Ltd.
Craig A. Kasper, P.E., and W. Lance Turley, P.G., Hull & Associates, Inc.
Ohio Voluntary Action Program, Background
In an effort to entice developers, business owners, and investors to redevelop brownfields, the
federal government and a number of state governments instituted various brownfield
redevelopment programs. They allow communities, property owners, and developers of
brownfield properties to voluntarily assess and remediate environmentally contaminated sites.
Many states also provide that sites which successfully participate in these programs are
released form future state environmental liability, eliminating one of the chief barriers to private
investment and site redevelopment.
Ohio's Voluntary Action Program (VAP) (Ohio Revised Code 3746) establishes a new approach
to traditional environmental cleanup programs. The new program offers a number of incentives
and new standards to prompt investigation and cleanup of contaminated properties or properties
suspected of contamination. The establishment of such a law should move Ohio several steps
forward by effectively joining environmental management with business development. The VAP
offers a mechanism to address contaminated or potentially contaminated sites in Ohio, sites that
might not otherwise be addressed. The program provides practical standards that consider the
future use of the property and cost/benefit in developing the standards, and provides a
mechanism that ends the owner's responsibility for further cleanup of the property.
A provision of the VAP is the Urban Setting Designation (USD), which designates areas in cities
or urban townships where groundwater is not used for drinking water purposes, and is not
required to meet drinking water quality standards. This reduces or eliminates the need to
remediate groundwater at a brownfield site. Groundwater within the USD may need to meet
other water quality standards.
Given the breadth of the program, applications for GIS in the redevelopment of properties under
this sort of regulatory umbrella are quite extensive, although, as is the case with many
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environmental projects, cost considerations can limit its use short of its full potential. This
discussion centers on two specific case studies, a complex investigation, construction, and
development effort involving a major industrial facility, and an equally complex scenario
involving the designation of a large portion of the City of Cleveland as a USD area. Both
projects were very public and politically charged, adding several very pervasive concerns to
already technically challenging projects.
An important issue with both of these case studies is that GIS and other mainstream data
management techniques were integrated from the beginning. Effective data management of any
kind is extremely difficult to shoe-horn or retrofit into an existing project. This is not unique to
regulatory projects; it is the classic "collection/conversion of the data is 75 percent of the cost"
scenario endemic to all GIS projects. With a long-term management system such as one
developed by utility or municipal government, this is an accepted portion of the cost of a project.
Many environmental projects, aside from ones that involve massive cleanup, are often on the
fringe of affordability for GIS application. Advanced GIS will simply not hold up in a cost/benefit
if it has to overcome the initial burden of extensive data conversion. The projects are simply
more finite in scope, and the use of GIS must be made be as efficient as possible. To do so, it is
of paramount importance that the data management issues be resolved and planned for as part
of the original scope of work, in the same way that sampling and analysis or QA/QC plan is part
of the original scope. In addition, while remediation activities are typically long-term,
investigations, particularly ones involving redevelopment, are typically accelerated, even more
so if a construction or resale schedule must be met. Under these conditions, data management
issues must be planned for in advance, or their full benefit will not be realized.
The specific goal of either construction or resale is one of the primary things that differentiates
many VAP projects from a standard site investigation. While redevelopment of a site may not be
the specific goal of all VAP projects, most do have that goal, and economic pressures are added
to regulatory ones. While under investigation or remediation, property is essentially idle, which if
not costing money is at least not making any. As will be seen in the Jeep case study,
construction schedules are often immutable. In these situations GIS is a valuable tool, if planned
for in advance. The entire purpose of effective data management is about standardization of
collection, form, and retrieval, allowing for increased efficiency and consistency. In situations
with compressed schedules, it is extremely useful.
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Another of the unique aspects of GIS use in this sort of redevelopment is that it combines
several classic GIS applications, from more economic, infrastructure, and property oriented
analysis to the modeling and data management required in extensive environmental site
investigations. In addition, the GIS professional must prepare for any number of fringe roles or
applications, such as construction management and high-profile, public projects where
geopolitical relationships must be understood.
Roles of GIS in Environmental Redevelopment
VAP projects vary considerably in scale and complexity. While some may involve fairly standard
Phase I style site assessments, where GIS is limited to searching and presenting information
from standard databases, some are full-blown Phase II investigations involving several hundred
borings, wells, or other sampling points and tens to hundreds of thousands of sampling results.
In projects of this scale, effective data management is critical, and use of GIS as a keystone of a
data management strategy solves many problems. The reasons for this are fairly obvious -
everything that happens in a site investigation occurs at some point in space.
One cannot in all honesty draw neat lines around the complex roles GIS can play in an any
situation, particularly in an environmental investigation, although for sake of organization
everyone tries. This discussion is no different, and centers on the management, analytical, and
presentational roles of GIS in environmental investigations. The discussion does not, however,
spend too much time on the role of specific technology, for the basic reason that it is relatively
unimportant. There are few tasks that are specific to a specific software package, and no
software package functions with complete satisfaction over a wide range of applications. In
situations such as environmental redevelopment projects, with compressed budgets and time
schedules, one uses the tools one has in the most expedient manner possible.
Management Roles
The management aspect of GIS in these situations cannot be underestimated. Much of the GIS
literature slights management in favor of analysis; however, one cannot analyze prior to proper
collection and arrangement, and certainly one cannot analyze repeatedly without proper
collection and arrangement. In addition, GIS tends to be too costly when applied as a surgical
tool, particularly in environmental projects where any expenditure is likely to displease
someone. While unique analysis and visualization may pique someone's interest, long-term
efficient management often convinces clients to use GIS.
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Repeatability is stressed in the above paragraph for a reason: redevelopment projects under a
regulatory framework such as the Ohio VAP are often either governed by sets of standards, or
are risk-based. The choice of what standard or risk level to target for a specific property is
mostly, if not wholly, a cost/benefit analysis. Different combinations of remediation and
construction scenarios are weighed against various standards or risk levels to produce the
optimal redevelopment strategy. One must be able to adjust and repeat analyses in an efficient
manner. Consistency and predictability of error are two of the main by-products of proper data
management.
Again, this centers on cost. As mentioned, many environmental projects are on the fringe of
affordability for GIS application. Redevelopment projects are unique in that there is some
component of added value to the investigation, and not viewed only as a cost. They are still not,
however, value-added to the degree that a government or utility GIS is, and is considerably
more finite in scope. In short, to the environmental professional the utility or benefit of GIS is not
always as immediately apparent, or at least not as obvious when costs are added. This
becomes a considerable part of the GIS professional's challenge when working in this area.
Virtually everything involved in an environmental investigation involves space and location. This
makes GIS the ideal tool for managing at least the spatial component of any site data. These
site data include:
• Aerial photographs.
• Infrastructure (roads, rail, utilities, etc.).
• Topography.
• Geologic/hydrogeologic features.
• Sampling points.
In addition, particularly true with regards to construction projects, GIS analysts may be required
to be the de facto coordinate transformation authority. In general, GIS analysts and operators
abhor local coordinate systems, and many state agencies are requiring coordinates in accepted
standard coordinate systems (State Plane, UTM, etc). Contractors and surveyors, however, are
strongly attached to them. It is not a duty to be taken lightly; in fact it is one to be avoided, as it
may expose the GIS operator to unneeded liability.
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While some ancillary attribute data is stored internal to the specific GIS, a complex data such as
analytical results are typically stored in an external database system of some type. The ability of
most GIS systems to link to external data sources, therefore becoming an effective
clearinghouse for any type of information, is one of its strong suits. The performance and utility
of individual GIS packages in this regard vary.
Analytical Roles
The analytical roles for GIS in redevelopment typically fall into two general categories, one
'introverted' (analysis of the site itself) and the other 'extroverted' (analysis of what surrounds
the site). Each of the case studies exemplify one or the other of these concepts. The Jeep
project is an extensive site investigation for purposes of meeting regulatory risk standards, while
the Cleveland USD is a optimization of an areas meeting regulatory criteria.
Of the two general categories, the first is often more complex, as it often involves longer-term
projects and greater amounts of information. Typical analytical processes in these situations
may be considered a subset of more general procedures done at any environmental site:
modeling contaminant distribution and travel, ground-water conditions, site geology, impacted
wetlands, etc. Again the goals of a VAP project will color what one does and how one does it.
Sites slated for redevelopment or resale have a different potential than one which is merely
seeking alternate remediation programs. The analysis often centers on analyzing risk levels to
meet certain regulatory goals in a fairly direct cost/benefit sort of fashion.
The second, 'extroverted' type of analysis, typified in the VAP by USD projects, resembles a
classic facility placement exercise more than an environmental management system. One is
attempting to either maximize a placement of a 'facility' (in this case a USD area), or prove that
a current 'facility' meets specific guidelines, based upon regional conditions. Data management
is not as critical, as it is more of a one-time, surgical procedure than a long-term need.
Presentational Roles
The presentation role that GIS may play in these sort of projects is critical, and one not always
completely acknowledged. In short, the data must be presented. Site layouts, analytical results,
standards comparisons, impacted areas: all of it must be shown at some time to someone who
will need to understand it, whether it be a property owner, a professional, a regulatory
representative, or the public.
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Lost in all the hype in the "digital revolution" and typical GIS marketing literature is the fact that a
paper map is still the most functional product of a GIS, and will continue to be so. Both case
studies discussed in this paper produced large numbers of unique large-format maps, into the
hundreds for the Jeep project. When discussing design issues, one quickly shifts from the
mechanical to the perceptual, and as many, many pages of print have been consumed with the
topic, little more will be said here. Effective design is not a mechanical process, and that
'unscientific' fact does not diminish the importance of effective design and presentation.
Case Studies
Jeep
The Chrysler Corporation operates
two plants in the City of Toledo: the
Jeep Parkway Plant and the Stickney
Avenue Plant in North Toledo. Toledo
has been home for the production of
the Jeep automobile since 1940 when
Willys Overland developed the first
prototype and began to mass produce
the Jeep military vehicle in Toledo for
use in World War II. Chrysler is the
city's largest employer, providing over
35,000 high-paying direct and
peripheral jobs, and generates
millions in tax revenues. Clearly, the
loss of Chrysler would not only
Chrysler Corp Stickney Ave. Jeep Plant
devastate the Toledo regional economy, but would also have a direct impact on the state's
economic well-being.
In late 1996, Chrysler announced its plans to construct a new facility to expand production and
replace the out-of-date Jeep Parkway Plant. After a year of intensive studies and assessments
of the Stickney Avenue property and infrastructure, and a relentless campaign by the City of
Toledo, Chrysler announced it would stay in Toledo and invest $1.2 billion into the expansion
and production of its Toledo operations. The city is contributing $27.5 million for roadway,
environmental, and other infrastructure improvements. The Stickney Avenue property, which
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possesses much of the necessary infrastructure such as adequate rail lines and access to major
highways, contains 400 acres of land and 13 businesses. Land use of the 400-acre property
includes railroad operations, aluminum smelting, a brass foundry, auto repair, and various other
commercial activities.
Phase I and Phase II Environmental Assessments are being conducted at all the properties,
whether acquired or not. Remedial measures will be integrated with plant construction to take
advantage of "dual-purpose" activities and engineering/institutional controls. The city is also in
the process of acquiring and relocating 83 residential properties, relocating a railroad spur, and
addressing a variety of environmental issues, including 43 acres of jurisdictional wetlands, of
which 25 acres will be impacted by the plant expansion. Although environmental issues often
represent only a small portion of a large-scale industrial redevelopment project such as this one,
they are critical in the overall plans to determine the most economical, yet protective solution for
a variety of property conditions.
Due primarily to availability of a comprehensive GIS, RGR was able to assist HAI in rapidly
assessing how changes in remediation goals would influence the project schedule and cleanup
costs. HAI was in turn able to alert the City to new investigative requirements and cleanup
strategies that would fit within development time frames. The primary challenge for this project
centered on time. Once Chrysler made the decision to build, the construction schedule was
quite aggressive, and the environmental investigation, while minor in comparison to the massive
construction plans, had to happen prior to any construction. Any delays due to the
environmental investigation would not have been received well.
To reiterate, it is difficult to draw neat lines around the roles GIS can play in a project of this
nature. In fact, it is a measure of the success of a GIS implementation if one cannot. If the use
of GIS is essentially transparent through all phases of the project, and one cannot easily tell
where it starts and ends, then it is a success. If it is not so pervasive, and one can therefore
easily distinguish its presence, then it may be necessary to reevaluate how and why GIS was
used, with particular attention paid to project planning stages. Again, for sake of organization, all
function and utility of a GIS flows from its management role. Transparent presence use and
pervasive use is a direct consequence of a planned, central management role in a project.
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Project Details
All spatial data collected in the investigation processes was managed with ARC/INFO. RGR
managed the extensive analytical sample information using a custom database system with a
web-based front end. AutoCAD Map was used frequently for complex editing, as ARC/INFO is
deficient in this area, and Arcview was used for convenience from time to time. Map finishing
was done in Corel DRAW. While this appears on the surface to add step and an extra layer of
management, and therefore extra time in a time-sensitive project, the trade off was well worth it.
Professional publishing software has far better fine-grained page control than either ARC/INFO
or Arcview, and the quality of the final maps was very important as they were window from the
GIS to professionals, public officials, and the public itself. So while basic layout of the GIS data
was done using ARC/I NFO's layout manager, final text, color, symbolization, map layout, and
printing was done in Corel.
A list of the data collected for this project includes:
• Aerial Photographs
• Transportation (roads, rails)
• Site structures
• Parcel boundaries
• Surface hydrography
• Surface topography
• Sampling locations
• Surveyed wetlands
• Impacted soils areas
HAI completed Phase I Environmental Assessments at all properties identified for potential
delivery to Chrysler. The assessments determined past management and disposal practices for
hazardous substances, areas of potential contamination, and other environmental concerns,
cumulatively making up VAP identified areas. HAI also conducted preliminary evaluations to
determine eligibility of the identified areas for the VAP.
HAI and RGR conducted GPS and total station surveys of identified areas, entered their spatial
attributes and descriptions into a GIS and presented the areas on an aerial photograph that also
included property lines, wetland delineations, initial plant designs and proposed infrastructure
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Soils sampling points and related analytical data
improvements. The aerial photograph
formed a base map that was used to
show proposed sampling locations,
findings from various phases of
investigation and changes to proposed
land use as the project progressed.
HAI developed a scope of work for an
initial Phase II investigation that
stressed characterization of site
hydrogeology and sampling of "hot
spots" within the identified areas (i.e.,
location within identified areas where evidence of contamination was the strongest). Soil
analytical samples were preferentially collected from within two feet of grade - the point of
compliance for direct contact in the context of industrial land use. Provisions were made for
sampling at greater depth based on field observations and screening results.
The laboratory delivered Phase II analytical data directly to RGR, who immediately incorporated
into the GIS integrated sample management system. In general, the data could be exported by
RGR from the GIS and sent to HAI or shared with Chrysler's consultants as spreadsheets or on
maps (e.g., isoconcentration contours for arsenic) within hours of RGR receiving an electronic
report from the laboratory. Due to restrictions on HAI's ability to access some of the properties,
multiple changes in the proposed footprint of the plant (daily at times), tight deadlines for
investigation and remediation of the properties, and the need to present findings to a number of
interested parties, the flexibility and rapid data management capabilities of the GIS proved
invaluable to the ultimate success of the project.
Following an initial Phase II investigation at a given property, HAI evaluated the data and, where
necessary, collected additional samples to delineate areas of concern. Site-specific cleanup
standards were developed based on multiple chemical adjustment for all chemicals of concern
(COCs) detected on all properties combined. Data evaluation was iterative because as
additional properties were investigated new COCs were detected; therefore requiring revised
cumulative adjustments. The GIS database was an essential element in comparing and
manipulating large data sets and mapping areas requiring remediation.
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The initial Phase II investigation identified direct contact with soils containing metals (primarily
arsenic) and semi-volatile organic compounds (SVOCs) as being the driver for cleanup at most
of the properties. At the project's onset, the City of Toledo envisioned that plant foundations and
parking lots, which per Chrysler's design covered approximately 95 percent of the proposed
plant property, would serve as engineering controls for limiting exposure to soils exceeding
direct contact standards. During early phases of data analysis, HAI and RGR worked closely
with Chrysler's engineers to identify final construction grades to map and identify volumes of
soils requiring excavation during construction activities and additional volumes that would be
excavated to preserve a two-foot point of compliance. However, as plant design proceeded,
Chrysler decided that leaving contaminated soils beneath engineering controls would be
unacceptable due to the following reasons:
• Future changes in facility operations and structures over an approximately 100-year
lifetime for the plant could result in contaminated soils being brought to the surface.
• Contaminated soils brought to the surface would require time-consuming and costly
administrative tasks.
• Operations and maintenance requirements within the VAP would create an additional
tier of administrative responsibilities.
Therefore, Chrysler and the City prepared a development agreement specifying that cleanup
would generally conform to VAP standards, but that soils containing COCs at concentrations
exceeding standards be remediated regardless of whether they were below the point of
compliance.
In addition to the soils
investigation, HAI
assessed impacts to
approximately 45 acres of
wetlands, knowing that a
portion of them would be
destroyed. Existing
wetlands were delineated
\
Wetlands impacted by a construction scenario
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using GPS, and a coverage created from that. The wetlands layer was then overlaid repeatedly
on new plant footprints and road designs, trying to find the optimal mix of minimizing wetland
impacts and maximizing plant design.
A by-product of proper data management, in this case unplanned, is the ability of the system to
behave as a comprehensive clearinghouse in projects such as this, where a wide variety of
consultants worked on the project. This is similar to a large Superfund site with a large PRP
group, where each requires a comprehensive view of the site, but cannot achieve this on their
own. A properly managed GIS in that case becomes the de facto repository for information, and
its ability to transform data from format to format and coordinate system to coordinate system
becomes critical.
The Jeep project had a wide variety of professionals working on it, such as the plant process
designers, wetland scientists, construction contractors, and geotechnical consultants. Each has
its own (for lack of a better term) world view, and sees the data, or needs to see the data, in
wildly different ways. In addition, there were three separate coordinate systems in use at the
site, two local ones and State Plane. Any GIS management and analysis system required to be
a clearinghouse in this situation had to be both responsive and nimble to adjust to the continual
requests from different sectors of the project.
Cleveland Urban Setting Designation
Urban Setting Designations are sub-programs within the Ohio VAP that designate areas within
cities and urban townships where
groundwater is not consumed. While
they are frequently sought by individual
property owners similar to a VAP No
Further Action, they are increasingly
being sought by cities a development
incentive, providing blanket coverage
for large areas within their boundaries
to spur redevelopment of brownfield
areas.
A portion of the Industrial Valley section of the
11
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On May 10, 1999, the Ohio Environmental Protection Agency approved a USD submitted by the
City of Cleveland, covering approximately 11,500 acres, or nearly a quarter of the city.
Neighborhood Progress, Inc., and MacLaren-Hart, both of Cleveland, oversaw the project. RGR
provided the GIS support for the project. The final submittal consisted of 6 separate areas
across the city (West, Northeast, Southeast, Inner East Side, Inner West Side, and Industrial
Valley).
For approval, a USD submittal must prove the following (summarized from Ohio Revised Code
3745-300-10 Ground Water Classification and Response Requirements):
(a) The property or properties for which designation is requested is entirely within the
boundaries of a township with a population of twenty thousand or more residents in the
unincorporated area of the township or entirely within the corporation boundaries of a
city;
(b) Not less than ninety percent of the parcels within the city or township where the
property or properties for which designation is requested is located is connected to a
community water system.
(c) The property or properties for which designation is requested is not located in a
wellhead protection area.
(d) Wells installed or used for potable water supply purposes are not located within one-
half mile of the property boundary of the property or properties.
(e) When the property or properties is located over a sole source aquifer in a
consolidated saturated zone or an unconsolidated saturated zone capable of sustaining
a yield greater than one hundred gallons per minute, the certified professional, must
demonstrate that there is not a reasonable expectation that there will be any wells
installed or used for potable water supply purposes within one-half mile of the property
boundary.
All of the above stipulations are spatial phenomenon, and can be addressed in a GIS. (a), (c),
and (e) are relatively simple in that they only require a single overlay, and (b) is often solved by
12
-------
questioning the local water authority, (d) involves a greater amount of effort, as it involves
collection of potential well locations from a number of sources (the Ohio Department of Natural
Resources (ODNR), local health departments, local water authorities, etc.) and a fair amount of
address matching.
GIS proves itself in the well location phase of a USD. The USD requestor must verify that any
possible well location within the half-mile radius is not being used. The records for the well
locations can be very old (pre-1950), and wildly inaccurate in terms of actual location. Without
'pre-location' via GIS, field workers would require an inordinate amount of time sorting and
verifying potential well locations.
Data Sources
The City of Cleveland, from the Cuyahoga County Auditor, provided both the base parcel map
used for the project, and the coverage containing the ward lines. In addition, Cleveland State
University provided a data set based upon the same materials, the difference between the two
being that the CSU data has been re-projected into State Plane while the original was in a local
county coordinate system. RGR addressed-matched the well locations using the Dynamap 2000
product from Geographic Data Technologies (GOT), of Lebanon, New Hampshire. Well
locations were compiled from several sources: parcel ID's from the Cuyahoga County Health
Department, and Ohio Department of Natural Resources (ODNR) wells logs located by address,
coordinate, or standard well logs.
Procedures
RGR compiled the individual USD areas by aggregating parcels from the base materials.
Unlocated potential well locations were address-matched against the GOT Dynamap 2000
database for Cuyahoga County using ARC/INFO. The well screening procedure left field
workers with 83 potential well locations to verify. Of these 83 potential locations, 2 were found to
still be in operation (original estimates had no wells still in operation). The preliminary USD
areas had to be redrawn around the two wells.
Final Products
USD requestors are required to provide an acceptable legal description of the area. There was
considerable debate as to what was an 'acceptable' legal description in this instance. On other
USD projects RGR had been involved with, either the number of parcels was small enough that
13
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a complete legal description was feasible, or the areas were regular enough in shape that a
legal description of the outside boundary was possible. Neither was practical in this case. The
number of parcels involved (@14,000) made individual descriptions impossible, and the outside
boundary of the areas was too complex. The original set of paper maps produced by RGR was
deemed unacceptable to the State, and the State's suggested modifications were deemed too
expensive by the project coordinators. While on the surface it seems that a parcel list compiled
from the GIS would work, in reality a permanent parcel number is not, in fact, permanent, due to
frequent splits and parcel combinations. In the end, the parcel list combined with the actual GIS
data was accepted as the legal description for the area.
References
Kasper, Craig A., Chrysler-Jeep Industrial Redevelopment Project, Toledo, Ohio, The 27th
Annual Conference on Environmental Law, March 12-15, 1998.
14
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Using GIS To Identify Linkages Between Landscapes and Stream Ecosystems
Carl Richards, Lucinda Johnson, and George Host
University of Minnesota, Duluth, Minnesota
Introduction
Factors that operate on a variety of temporal and spatial
scales influence the structural and functional compo-
nents of stream ecosystems (1). Quantifying the effects
of factors that operate across multiple scales has chal-
lenged aquatic scientists over the last several decades.
Recently, scientists have recognized that they cannot
successfully protect or restore ecosystem integrity with-
out taking into account all appropriate scales; therefore,
they are focusing on understanding interactions between
terrestrial and aquatic components of entire watersheds
(2). Although awareness of the importance of watershed
and landscape-scale influences on streams is growing,
the tools to examine these influences are still in their
infancy.
Most watershed and landscape studies to date have
focused on the role watershed-scale parameters play on
water chemistry (3-5). These studies usually examined
nutrient and sediment inputs from various watershed
land covers. Methods for evaluating the patterns in the
terrestrial segment of the watershed were awkward and
laborious, involving use of planimeters or cutting and
weighing maps. More recent watershed studies have
attempted to integrate both longitudinal and lateral influ-
ences of the terrestrial ecosystems on water quality in
streams and wetlands (6-8). This approach takes advan-
tage of newly available tools (geographic information
systems and multivariate statistics) for quantifying land-
scape structure.
Relatively few studies have examined how watershed
features influence biological communities. Most studies
examining stream biota have concentrated on single
land use types (9, 10) or on the relationship between
watershed land use and stream physical habitat (11,12).
Typically, study designs have not addressed questions
concerning variability of stream communities over rela-
tively large geographic scales.
Our own work centers on identifying linkages between
landscape features (watershed scale) and stream reach
environments (physical habitat, chemistry), and relating
these parameters to major patterns of community vari-
ation. In this manner, landscape and reach environment
interactions probably control the influence that specific
landscape components have on biological communities
(13). This is the general premise when using biological
communities to assess watershed status. To represent
stream biota, we examine benthic macroinvertebrates,
which have been used extensively for biomonitoring
numerous environmental stresses (14). Macroinverte-
brates are sensitive to watershed conditions and exhibit
sufficient stability in assemblage structure over time to
make them useful as long-term monitors of stream
health (15).
This paper presents an overview of our attempts to
identify the relative strengths of landscape variables on
macroinvertebrate communities. We classify landscape
variables into two general categories. The first category,
geology and landscape structure (GEOS), considers
variables that are fixed on the landscape and are largely
uncontrollable by management activities. The second
category, land use (LU), includes variables that have
anthropogenic origins and may be influenced by land
management activities. By understanding the relative
strengths these two sets of variables possess in deter-
mining community structure, we hope to identify specific
species groups that can act as land use and land form
indicators. We also hope to identify ways to predict the
outcome of specific large-scale land management activi-
ties (e.g., silviculture, agriculture) or other large-scale
environmental changes (e.g., global warming) on stream
ecosystems.
Study Area
This study was conducted in the Saginaw River basin,
a 22,562-square-kilometer watershed in east-central
Michigan (see Figure 1) that flows into Lake Huron.
The Saginaw River watershed was chosen for this
study because its component drainages range from
heavily affected agricultural to relatively pristine areas.
-------
Figure 1. The Saginaw basin study area.
Dominating the soils in the lake plain are medium- and
fine-textured loams to clays, with sand found in the
outwash plains and channels. Artificial drainage and tile
systems extensively drain the clay regions. Glacial fea-
tures such as ground moraines and outwash plains are
common. The western sector is characterized by rolling
plains with coarse-textured ground moraines. This re-
gion contains a high percentage of the forested land,
while agricultural land use dominates the eastern sector.
The Saginaw basin covers 16,317 square kilometers,
including four major subbasins: the Tittabawassee
(6,734 square kilometers), Shiawassee (3,626 square
kilometers), Flint (3,108 square kilometers), and Cass
(2,331 square kilometers) Rivers. The Tittabawassee
subbasin further divides into three principal water-
courses—the Chippewa, Pine, and Tittabawassee Riv-
ers. Watersheds adjacent to Lake Huron (Kawkawlin
and East basins) are characterized by low topographic
relief and elevations averaging 203 and 206 meters,
respectively. The Flint and Chippewa/Pine basins aver-
age about 278 meters in elevation. These drainages
also exhibit the greatest variation in topography.
Study Design
The analysis covers 45 stream sites within the study
area. These sites reflect a gradient of land use and
physiographic conditions in the Saginaw River drainage.
Researchers obtained biological, chemical, and physi-
cal samples at one 200-meter stream segment at each
site. In addition, a geographic information system (CIS)
database was compiled reflecting a number of land-
scape parameters for the watershed of each stream
segment.
Sampling Methods
Macroinvertebrates
At each sampling site, we deployed Hester-Dendy arti-
ficial substrate samplers (16) for macroinvertebrate
community characterizations twice, in early summer and
during base flow conditions in late summer and fall of
1991 or 1992. We allowed samplers to colonize for 6 to
8 weeks. In the laboratory, macroinvertebrates were
counted and identified to genus whenever possible. A
series of derived variables from the original species
abundance tables was used to describe community
characteristics. We chose metrics based on their rela-
tive utility for examining macroinvertebrate communi-
ties, as suggested by Barbour et al. (17) and Karr and
Kearns (18). Because macroinvertebrate assemblages
are relatively stable through time (15) and preliminary
analysis indicated no significant differences between
sampling years at stations for which we had 2 years of
data (unpublished data), we combined macroinverte-
brate data into one database.
Chemistry
We assessed nutrients and other chemical properties
related to water quality at each stream site during sev-
eral periods in the summer and fall of 1991 or 1992.
Stream flow during fall sampling was typically less than
median flow rates and was considered to represent base
flow levels. We used the maximum values of samples
taken in June and July to represent summer conditions
and the maximum values from September and October
to represent fall base flow conditions. The nutrients
measured were ammonium (NHs), nitrate-nitrogen, total
nitrogen (TN), orthophosphate (PO4), and total phos-
phorus (TP). In addition, we assessed alkalinity (ALK),
conductivity, total dissolved solids, and total suspended
solids (TSS). Standard methods were used for all
measurements (19).
Physical Habitat
We assessed physical habitat during base flow condi-
tions at each stream site in a stream reach that is at least
8 to 12 times the width of the stream segment. A suite
of quantitative habitat structure measurements and ob-
servations was made at each site. We derived values
for six general habitat attributes:
• Substrate characteristics
• Instream cover
• Channel morphology
• Riparian and bank conditions
-------
• Riffle/Run quality
• Pool quality
Landscape Descriptors
Land use patterns, surficial geology, hydrography, and
elevation databases helped to quantify landscape char-
acteristics in the study area (see Table 1). Land use
patterns were derived from existing digital data at the
Michigan Department of Natural Resources (Michigan
Resource Information System [MIRIS] database) (see
Table 2). We based classification of land use/cover cate-
gories on a modified version of the Anderson (20)
scheme, which was constructed specifically for natural
resource applications. The result was the following nine
land use/cover categories:
• Urban
• Row crop/agriculture
• Other agriculture
• Herbaceous range land
• Shrubby range land
• Nonforested wetlands
• Forested wetlands
• Mixed hardwood forests
• Deciduous forests
In this region, nonrow-crop agriculture is largely repre-
sented by pasture, and range lands are predominantly
abandoned fields (old fields).
The U.S. Department of Agriculture (USDA) STATSGO
soils database enabled the compilation of soil data. The
database consists of U.S. Soil Conservation Service
(SCS) soil surveys and includes information on domi-
nant texture and drainage in large landscape units. We
Table 1. Spatial Data Used for Landscape Characterization
Data Layer Source
aggregated soils into simplified categories based on
glacial landform.
We delineated watershed boundaries for each sampling
station on United States Geological Survey (USGS)
topographic maps and digitized them using ARC/INFO
(Environmental Systems Research Institute [ESRI],
Redlands, California). We identified stream order for
each stream segment and coded it as an attribute of the
stream reach file. All databases were transformed into a
common digital format as necessary and projected into
a common coordinate system. We stored data in vector
format and analyzed them in ARC/INFO.
Table 2 lists the landscape variables we derived for each
watershed. Land use/cover values were reported and
analyzed as a percentage of the total watershed area.
Patch heterogeneity measured landscape fragmenta-
tion and was reported as the number of patches per
hectare. We derived slope from elevation data using
ARC/INFO. The standard deviation of elevation was
used as a surrogate measure of topographic variability.
Statistical Analysis
Using redundancy analysis (RDA), a canonical exten-
sion of principal component analysis (PCA), we de-
tected relationships among the individual multivariate
data sets. RDA is a form of direct gradient analysis that
describes variation in a multivariate data set (e.g., habi-
tat variables or macroinvertebrate metrics) based upon
environmental data (21). In RDA, the station scores from
a PCA are regressed on a specified set of environmental
variables with each iteration, and the fitted values of the
regression become new station scores (22). Thus, envi-
ronmental or predictor variables constrain PCA. Two
important outputs from this method are the interset cor-
relations of environmental variables with the RDA axes,
which indicate the environmental variables that have the
strongest influence in the ordination, and the fraction of
Scale
Format Received
Hydrology
Elevation
Land use/cover
Land use/cover
Watershed boundary
Station locality
Soils
Major basin
Quartenary geology
U.S. EPA stream reach
USGS-DEM
USGS
MIDNR
USGS topographic maps
USGS topographic map
USDA SCS STATSGO
USGS topographic
University of Michigan
1:100,000
1 :250,000
1:100,000
1 :24,000
1 :24,000
1 :24,000
1 :250,000
1 :24,000
1 :250,000
ARC/INFO
Digital elevation model
Digital line graph
Intergraph
Manual delineation
Manual digitizing
ARC/INFO
Manual digitizing
Manual digitizing
Key: USGS = United States Geological Survey
DEM = Digital elevation model
MIDNR = Michigan Department of Natural Resources
USDA SCS = United States Department of Agriculture Soil Conservation Service
-------
Table 2. Landscape Variables Measured or Derived for Each
Watershed
Results
Variable
Units
Land use/cover
Slope
Standard deviation elevation
Patch heterogeneity
Soils
Total area
Proportional area
Degrees
Meters
Patches/hectare
Proportional area
Hectares
total variance of each predicted variable that is ex-
plained by the RDA axes (22).
Performing Monte Carlo permutation tests determined
the statistical validity of the association between predic-
tor and predicted variables. Tests were conducted by
random permutation of the site numbers in the predictor
variables. We randomly linked the predictor data to the
predicted data. We conducted 99 simulations to approxi-
mate a normal distribution with which to compare our
data with random combinations.
We first determined which of the reach variables had
strong influences on macroinvertebrate distributions by
conducting separate RDAs with physical habitat and
chemistry variables as environmental descriptors. We
then examined the ability of the landscape data to pre-
dict the variation in the important reach variables.
To determine the relative influences of LU and GEOS
landscape variables on stream chemistry and physical
habitat, we used partial RDA, where one landscape
variable type was held constant and variation due to the
other landscape set was examined independently. Us-
ing this approach, total variation in a multivariate data
set can be decomposed in a manner analogous to
analysis of variance (23, 24). For this analysis, we at-
tributed variation in the reach variables (habitat and
chemistry) to four separate components:
• The variation in reach variables that LU variables
explained independently of GEOS variables.
• The variation in reach variables that GEOS variables
explained independently of LU variables.
• The variation in reach variables that both GEOS and
LU variables shared. This shared variation could
have been due to both the dependence of one type
of variable on the other as well as noncausal rela-
tionships (e.g., the types of soil found in a watershed
determine in large part the types of agriculture that
can be practiced).
• The variation in reach variables that were unexplain-
able. This may have been attributable to sampling
error, stochastic variation, or other variables not
sampled.
Regional Characteristics
Land Use
Land use within the study region was dominated by
row-crop agriculture (see Table 3). Individual water-
sheds ranged from 14 to 99 percent in agricultural land
uses, with the East basin watersheds exhibiting the
greatest proportion of agricultural land use and the Flint
having the lowest proportion of agricultural land use.
The Chippewa/Pine and Kawkawlin watersheds exhib-
ited the greatest diversity of land use and cover types
within the study region.
Wetlands represented a minor land use component with
most watersheds having between 0 and 15 percent land
area. The Cass and Kawkawlin basins had the greatest
proportion of wetlands, with a median of 6.8 percent for
individual watersheds.
Macroinvertebrates
Considerable variation existed among the major basins
with respect to the 15 macroinvertebrate community
metrics during summer (see Table 4). Metric values for
the Flint, Shiawassee, and Chippewa watersheds were
similar. Sites within the Kawkawlin and East basins
differed considerably from the Flint, Shiawassee, and
East basins in several of the metrics. The Kawkawlin
watershed was notable for high shredder and filterer
proportions and a low proportion of detritivores. The
East basin also had a high proportion of shredders. Both
the East and Kawkawlin basins had lower proportions of
strictly erosional taxa and higher proportions of deposi-
tional taxa than the other major basins.
Taxa at the East and Kawkawlin basins also exhibited
lower oxygen tolerance than at other major basins. In
addition, their Hilsenhoff Biotic Index (HBI) scores
(which are sensitive to oxygen availability) were higher
than other basins, and they had the lowest EPT (Ephe-
meroptera, Plecoptera, Trichoptera) richness. Total rich-
ness at Kawkawlin was relatively high, however.
Richness was highest in the Chippewa/Pine watershed
and lowest in the East basin.
In general, fall patterns of macroinvertebrate metrics
resembled those of summer. The Kawkawlin and East
basins had high HBI scores, low EPT scores, low pro-
portions of erosional taxa, and high proportions of depo-
sitional taxa. The proportion of predators was
exceptionally high in the Kawkawlin basin due to the
abundance and trophic classification of one chironomid
genus.
-------
Table 3. Summary of Landscape Metrics in Six Major Basins of the Saginaw River Drainage
Landscape Variables East Basin Cass Flint Shiawassee Chippewa/Pine Kawkawlin
n
Row crops
Other agricultural land
Urban
Deciduous forest
Mixed hardwood
Range: Herb
Range: Shrub
Forested wetlands
Non-forested wetlands
Slope (degrees)
Elevation (meters)
Patch heterogeneity
Watershed area
(hectares)
8
86.4
79.7-98.0
0.2
0.0-1.2
0.8
0.1-2.0
6.4
0.5-12.1
0.03
0.0-0.3
1.0
0.1-3.6
1.3
0.0-2.3
0.1
0.0-0.5
1.7
0.0-5.3
0.15
0.07
206.5
19.8
241.5
102.0
14,968.9
11,132.0
7
58.4
45.1-73.3
0.6
0.4-1.4
1.9
0.4-3.7
16.9
5.8-33.4
0.3
0.01-1.7
6.7
2.1-12.7
4.7
3.2-7.7
0.1
0.0-0.3
6.7
1.9-14.5
0.29
0.12
239.8
6.7
711.4
250.6
38,117.7
58,321.9
8
38.0
25.6-65.5
3.1
0.6-4.1
8.56
1 .4-23.2
17.5
10.2-20.5
2.6
0.0-3.8
13.0
6.6-14.5
6.5
2.6-10.5
2.3
0.3-3.2
3.9
0.4-5.2
0.41
0.11
277.2
22.5
950.4
228.6
28,926.1
19,236.7
5
43.5
26.7-71 .8
0.4
0.1-2.0
10.3
2.4-15.0
19.2
16.4-30.4
0.7
0.4-0.9
12.6
0.9-17.3
7.3
2.2-10.1
0.7
0.0-3.7
3.6
0.7-5.0
0.27
0.10
252.6
47.1
762.7
223.6
46,530.0
50,798.0
15
48.2
13.9-91.3
4.0
0.3-6.6
2.1
0.8-4.0
23.2
3.2-42.1
5.4
0.1-8.5
6.9
1 .0-9.0
5.6
0.7-9.0
1.0
0.0-2.5
4.3
0.1-7.9
0.35
0.10
278.7
34.9
703.6
185.9
53,704.2
44,872.8
3
26.4
18.5-74.9
1.9
0.2-2.3
1.2
1.0-6.8
56.8
13.2-64.5
0.3
0.1-0.3
2.5
0.7-3.1
3.0
2.7-3.2
1.8
0.1-2.2
5.0
1.3-5.9
0.14
.03
203.1
9.8
519.6
156.7
22,240.2
3,848.0
Land use/cover variables
(slope through watershed
(agricultural land through nonforested wetlands) are reported as median and
area) are reported as mean and standard deviation. Land use/cover represents
range; landscape structure variables
proportional areas of each watershed.
Identification of Important
Reach-Scale Variables
Chemistry
RDA showed that chemical variables explained 26 per-
cent of the variation in macroinvertebrate data in sum-
mer and 33 percent in fall. The most important variables
in summer were TN and TSS (see Table 5). Fall
macroinvertebrate communities were influenced by a
greater number of variables, including NHs, TP, ALK,
and TSS.
Physical Habitat
The 13 physical habitat variables explained 37 percent
of the macroinvertebrate data in summer and 46 percent
of the macroinvertebrate data in fall. In summer, the
percentage of deep pools and canopy extent along with
channel dimensions, such as bank full width (BFW) and
bank full depth (BFD), were the most important vari-
ables. In fall, the percentage of fines and deep pools as
well as canopy extent were among the most important
variables (see Table 6).
Landscape Influences on Surface
Water Chemistry
In summer, the landscape data explained 55 percent of
the variation in chemical variables. The proportion attrib-
utable to LU was larger than that attributable to GEOS
(see Figure 2). The two data types shared 12 percent of
100
Physical
Habitat
' Land Use
• Geology/
Structure
Summer Fall
Chemistry Chemistry
[T] Shared
[ | Unexplained
Figure 2. Results of variance decomposition from partial RDA.
-------
Table 4. Mean and Standard Deviation of Macroinvertebrate Metrics Calculated for Summer Collection Periods for Six Major
Basins of the Saginaw River Drainage
n
Chironomidae
Omnivores
Detritivores
Shredders
Gatherers
Filterers
Grazers
Predators
2 Dominants
Total abundance
HBI
Erosional taxa
Depositional
taxa
Species
richness
EPT taxa
richness
East Basin
8
59.1
35.3
19.4
13.7
57.1
34.1
30.3
33.9
59.8
32.9
27.4
38.1
32.2
34.4
1.5
2.2
64.5
25.5
2077
4951
7.1
1.4
25.9
12.4
35.5
13.2
17.2
4.5
5.0
2.7
Cass
7
57.9
20.3
19.1
7.1
69.7
9.7
18.7
5.1
18.7
5.1
23.4
18.5
13.6
16.2
1.2
1.2
54.3
6.1
650
739
5.6
?
36.1
5.5
23.7
9.6
18.3
9.6
5.7
2.8
Flint
8
45.9
27.9
18.1
13.9
75.3
16.0
10.6
6.0
64.3
12.8
22.4
14.1
26.2
21.0
1.5
2.0
50.3
15.2
574
622
5.6
0.8
35.5
9.5
27.5
11.5
22.1
8.2
7.3
3.0
Shiawassee
5
32.2
28.5
14.4
3.8
79.9
6.4
7.7
6.8
65.0
14.1
18.9
12.8
40.1
22.7
1.0
1.0
54.2
9.5
325
91
6.0
0.5
38.9
14.7
27.0
6.6
20.6
4.7
8.0
2.3
Chippewa/Pine
15
45.5
29.6
21.5
9.8
70.4
9.8
14.4
15.6
65.8
17.2
17.6
15.1
25.5
21.1
1.4
0.8
51.9
11.6
497
230
5.1
1.1
36.1
11.0
25.4
10.7
26.6
3.0
10.0
3.7
Kawkawlin
3
67.1
18.6
22.0
16.7
29.0
26.1
51.0
26.9
39.8
30.1
39.9
35.7
25.4
22.9
1.9
0.8
60.0
21.7
433
297
8.1
0.8
14.9
5.3
52.4
6.5
23.3
4.9
3.3
0.5
the variation. The relationship between LU variables and
chemistry was significant (p < 0.05), and the relationship
between GEOS variables and chemistry was not
significant (p > 0.05).
In fall, variation explained by LU was proportionally less
than during summer (see Figure 2). GEOS landscape
variables explained 25 percent of the total variation
while LU variables accounted for less than 10 percent
of the total variation. LU and GEOS variables shared
approximately 8 percent of the variation. In contrast with
summer, GEOS variables were significant and LU vari-
ables were not significant when examined with the
Monte Carlo test (p < 0.05).
The importance of GEOS and LU variables in explaining
variation in the chemistry variables, as well as the total
amount of variation explained, differed considerably
among the chemistry variables. Figure 3 shows only
summer data. For example, the landscape variables
explained almost 80 percent of the total variation in TN
(see Figure 3). The largest proportion of variance was
explained by the shared influences of GEOS and LU.
Alkalinity, which was also well predicted, however, was
much more influenced by variation attributable to LU
variables. In comparison, LU variables explained less
than 45 percent of the total variance in TP, and the
majority of this variance was attributable to GEOS.
To further examine the influence of specific landscape
variables, we compared the various axes in the signifi-
cant partial ordinations (see Figure 4). In summer, when
we observed a significant effect of LU variables on water
chemistry, forested land covers and nonrow-crop agri-
culture had their greatest influence on TSS and ALK. LU
heterogeneity and shrub vegetative cover most strongly
influenced both TN and NHs. In fall, when GEOS vari-
ables had a significant relationship to water chemistry,
ALK and NHs were more influenced by peat land soils
and watershed size. The proportion of sand and gravel
soils, as well as clays, explained much of the variation
in TN and TSS.
-------
Table 5. Chemistry Variables That Had a Correlation (r) of at Least 0.30 With One of the RDA Axes With the Summer or Fall
Ordinations; a Monte Carlo Analysis Indicated That Both Summer and Fall Ordinations Were Significant (p < 0.05)
RDA 1
RDA 2
RDA 3
Variable
Summer
Fall
Summer
Fall
Summer
Fall
TN
NH3
TP
P04
TSS
ALK
0.44
0.05
0.12
-0.04
0.01
-0.08
0.03
0.04
0.40
0.19
-0.31
-0.36
-0.04
0.09
0.07
0.01
0.43
-0.16
-0.07
0.52
0.26
-0.08
0.36
0.09
0.05
0.07
0.25
0.26
0.22
0.15
-0.14
-0.09
0.14
0.34
-0.13
0.2
Table 6. Physical Habitat Variables That Had Correlations (r) Over 0.3 With the Ordination Axes; Results of the Monte Carlo
Simulation Indicated That the Fall but Not the Summer Ordinations Were Significant (p < 0.05)
RDA1
RDA 2
RDA 3
Variable
Percentage of
fines
Percentage of
shallows
Wood
Percentage of
deep pools
Erosion
Maximum
depth
Canopy extent
BFW
BFD
Flood ratio
Summer
0.27
-0.06
0.09
0.50
0.16
0.08
0.75
-0.10
-0.09
0.03
Fall
0.47
0.17
0.34
0.54
0.30
-0.14
0.21
-0.23
-0.13
-0.30
Summer
0.04
-0.28
-0.1
0.16
-0.3
0.08
-0.47
0.52
0.32
0.07
Fall
0.28
-0.11
0.02
0.09
-0.31
0.16
-0.42
0.36
0.02
0.23
Summer
0.18
0.32
0.10
-0.17
0.01
-0.39
0.21
0.07
-0.08
-0.41
Fall
-0.09
-0.38
-0.17
0.26
0.27
0.13
-0.25
0.03
0.22
0.02
Landscape Influences on Physical Habitat
GEOS landscape variables attributed for the largest
portion (22 percent) of the explained variation in physical
habitat variables (see Figure 2). LU variables accounted
for 16 percent of the explained variance. The partial
ordination for GEOS but not LU was significant as the
Monte Carlo procedure determined.
As noted with chemistry variables, there were consider-
able differences in the ability of the landscape variables
to predict individual habitat characteristics. Landscape
variables were best at predicting BFW and least power-
ful for predicting the percentage of deep pools (see
Figure 5). BFWwas influenced predominantly by GEOS
and only minimally by LU. The most influential GEOS
variables for BFW related to watershed area (see Figure 6).
Woody debris was predominantly influenced by LU vari-
ables. The most influential LU variables for woody debris
related to forested wetlands. Flood ratio was intermedi-
ate to these examples. Both sets of landscape variables
shared the largest proportion of explained variance for
this parameter.
Discussion
Our studies demonstrate the distinct influences land-
scape features have on stream macroinvertebrate com-
munities through modifying surface water chemistry and
stream habitat. Land use most strongly influences
stream chemistry during summer months when surface
runoff and soil leaching are greatest. In addition, fertil-
izer application in row-crop agriculture is highest in the
first part of the growing season. The strong relationship
between some aspects of land use and stream water
chemistry were similar to those observed in other stud-
ies (4, 6, 7). The specific mechanism by which stream
chemistry influences macroinvertebrates is not clear.
The addition of nutrients can significantly affect stream
productivity (25-27); however, light often limits primary
production in agricultural areas (28-30). Nutrients may
-------
Summer Chemistry
Fall Chemistry
120
I 100
I 80
8 60
| 40
1 20
o
-------
100
60
.i 40
20
-------
Land Use
Geology/Structure
T3
-------
29. Bushong, S.J., and R.W. Bachmann. 1989. In situ enrichment
experiments with periphyton in agricultural streams. Hydrobiologia
178:1-10.
30. Wiley, M.J., L.L. Osborne, and R.W. Larimore. 1990. Longitudinal
structure of an agricultural prairie river system and its relation-
ship to current stream ecosystem theory. Can. J. Fish. Aquat.
47:373-384.
31. Vannote, R.L., G.W Minshall, K.W. Cummins, J.R. Sedell, and
C.E. Gushing. 1980. The river continuum concept. Can. J. Fish.
Aquat. 37:130-137.
32. Statzner, B., J.A. Gore, and V.H. Resh. 1988. Hydraulic stream
ecology: Observed patterns and potential applications. J. North
Am. Benthological Soc. 7:307-360.
33. Osborne, L.L., and D.A. Kovacic. 1993. Riparian vegetated buffer
strips in water-quality restoration and stream management.
Freshwater Biol. In press.
34. Sedell, J.R., G.H. Reeves, F.R. Hauer, J.A. Standford, and C.P.
Hawkins. 1990. Role of refugia in recovery from disturbances:
Modern fragmented and disconnected river systems. Environ.
Mgmt. 14:711-724.
11
-------
A Watershed Approach to Source Water Assessment and Protection
Utilizing CIS-Based Inventories: A Case Study in South Carolina
James M. Rine, and Elzbieta R. Covington
Earth Sciences and Resources Institute
University of South Carolina
Columbia, SC 29208
ABSTRACT
The Earth Sciences and Resources Institute at the University of South Carolina (ESRI-USC)
developed a watershed -based methodology for conducting contaminant inventories and
susceptibility analyses required under the U.S. Environmental Protection Agency (US EPA)
State Source Water Assessment and Protection Program Final Guidance (SSWAPP Final
Guidance, August 1997). SSWAPP is an offspring of the 1996 Safe Drinking Water Act. The
watershed-based methodology was tested within a medium-size public water system which
utilizes surface water. The project, initiated at the request of the South Carolina Department of
Health and Environmental Control (SCDHEC), helped prepare South Carolina's initial response
to the SSWAPP Final Guidance (US EPA, 1997). In the initial response, US EPA required all
states to submit a source water assessment plan for their state. The three components of that
assessment plan are: 1.) Delineation of the source water area(s); 2.) Inventory of contaminants
within a source area; and 3.) Determination of source water susceptibilities. SCDHEC tasked
ESRI-USC to perform the assessment component #2, An Inventory of contaminants. Following
the termination of the SCDHEC project, ESRI-USC also completed the other components of the
assessment. The ESRI-USC version of the SSWAPP assessment plan, which differs from the
plan adopted by SCDHEC, is presented in this paper.
In the plan proposed by ESRI-USC for South Carolina (or any State), the inventory assessment
methods follow a watershed-approach which is comprised of three key elements. First, a master
database is maintained for each public water supply (PWS) source area by the respective PWS
operator/owner or by the State environmental agency (SCDHEC). Each PWS master database
lists all inventoried sites within the entire source-area watershed(s) upstream of the PWS intake
structure and lists all databases where pertinent environmental data are stored for those
inventoried sites. Second, each watershed database is linked to original databases maintained
and updated by their originating agency (e.g. SCDHEC, US EPA, U.S. Department of
Agriculture, other SC agencies, etc.). This second element will allow routine updating of
-------
inventories without requiring additional programs. Assuming the inventories are updated
routinely, watershed susceptibility analyses can be revised with current and timely information.
Third, the proposed plan has each PWS database linked to a statewide master database under
the supervision of the State environmental agency.
The first step in field testing of the ESRI-USC plan was the development of a contaminant
inventory for the pilot study area, noting all the known and potential release sites of
contaminants listed in US EPA's National Primary Drinking Water Standards. Initial work
concentrated on synthesizing numerous Federal and State electronic databases of known or
reported contaminant releases. These databases proved to be the most complete source of
information regarding the general type of known releases or discharges. However, inventory
development of potential contamination sites utilizing existing databases was difficult due to the
lack of accurate location data, unpopulated databases, and incomplete listings of the types and
amounts of contaminants present at each permitted site. Consequently, field surveys were
needed to complete the contaminant inventories. The field test demonstrated the need for
consistency and coordination in database development and management, both within and
among Federal and State agencies.
Regarding the determination of source water susceptibilities, ESRI-USC proposes that
inventories of the entire topographically-bound watershed are necessary to develop meaningful
relative susceptibilities. With this approach, the contaminant inventory can be joined with other
digital data sets of the watershed, such as hypsography, land use, soils, and infrastructure to
more accurately determine the relative susceptibilities of specific contaminants at specific sites
anywhere in the watershed.
INTRODUCTION
According to the US Environmental Protection Agency's State Source Water Assessment and
Protection Program Final Guidance (US EPA's SSWAPP Final Guidance, August 1997, page A-
25) susceptibility analysis should be conducted with, "a clear understanding where the
significant potential sources of contamination are located." The Earth Sciences and Resources
Institute at the University of South Carolina (ESRI-USC) interprets this US EPA guidelines to
mean that the source water assessment process should fully integrate susceptibility analysis
with the inventory of present and future sources of contaminants, wherever they may be located
within the source area. To have a "clear understanding" of the "significant potential sources of
-------
contaminants" an entire watershed should be inventoried. Or, at the minimum, significant
sources should not be ignored just because their locations lie outside of some arbitrary
boundary.
In 1998, ESRI-USC conducted a comprehensive contaminant inventory of the upper portion of
the watershed for Shaw Creek (HUC3050204) in the Coastal Plain of South Carolina (Fig. 1).
Shaw Creek is the source for the run-of-river withdrawal for the City of Aiken public water supply
(PWS) which has over 14,300 connections to homes and businesses. The pilot study portion of
the Shaw Creek watershed occupies 181 km2 (70.5 sq. miles). South Carolina Department of
Health and Environmental Control (SCDHEC) initiated this project to help develop a plan for
implementing South Carolina's response to US EPA's State Source Water Assessment and
Protection Program Final Guidance (SSWAPP; August 1997). A report on this pilot study can be
viewed via the Internet by visiting http://gisweb.esri.sc.edu and selecting Projects Contaminant
Inventory for Source Water Assessment and Protection Project (SWAP). REPORT:
Development of a Contamination Inventory Methodology for the Source Water Assessment and
Protection Program in South Carolina. In addition, the contaminant inventory can be queried via
the internet at the same location: Projects: Prototype Model of a Interactive GIS Database for
SWAP Contaminant Inventory.
Via a narrative of the pilot study results, this paper will describe how the assessment of the
entire watershed (topographically delineated boundaries) is a superior approach to restricting
the assessment to some arbitrary segment of the watershed. Secondly, a template for
organizing State SSWAPP databases is suggested. Third, it is suggested that watershed wide
assessments are economically feasible, and in the long term more economical than the piece-
meal assessments that require additional surveys and added funding.
DESCRIPTION OF PILOT STUDY
Development of a Contaminant Inventory
The objective of the pilot study was to develop a contaminant inventory for the entire watershed
area upstream of the City of Aiken Public Water Supply (PWS) intake. In this inventory are the
locations and the characteristics of the sites of all known or reported contaminant releases and
the sites of all potential contaminant releases (i.e., sites where hazardous materials are used
but no release has been reported to date). The substances inventoried were restricted to only
-------
Figure 1
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those listed in US EPA's National Primary Drinking Water Standards (NPDWS) with groupings
under the categories of: petroleum; chlorinated solvents and other volatile organic compounds
(VOC's); pesticides/ herbicides; inorganics, mobile inorganics; and pathogens. (Forthe current
National Primary Drinking Water Standards visit www.eps.gov/OGDW/wot/appa.html.)
Survey of Existing Databases for Reported Contaminant Releases
Work concentrated first on synthesizing and cross-referencing numerous Federal and State
electronic database-listings of reported contaminant releases. Cross-referencing between
USEPA databases is relatively straight forward because each site is given a unique Facility ID
number. Within most of the South Carolina (SC) databases examined, however, the absence an
unique ID for each site makes it impossible to easily cross-reference between databases. In
order to accomplish the objectives of this project, ESRI-USC compiled a prototype master
database for the entire pilot study watershed (see Table 1: Master Watershed Site List for
Source Area to City of Aiken PWS). This master database is designed to link each inventoried
site to every database on which the site is listed (Fig. 2).
Figure 2
MASTER SITE LISTIN
Linked /
Auto update
Source Water
Maintained .
by PWS
Source Water
. Maintained
by PWS
Windshield
Survey
DH EC I Dept. Commerce
US EPA
Facilities ID
US DA
Crop data
Organizational chart showing database structure as proposed for South Carolina by ESRI-USC. MASTER
SITE LISTING would include entire state. PWS people would be responsible for updating and correcting
data from within their respective watershed source area. The State agency responsible for SSWAPP
(SCDHEC) would be responsible for overseeing the entire State MASTER SITE LISTING as well as
maintaining liaison with upstream States. Links to routinely updated databases (e.g., US EPA TRIS or the
states underground storage tank database [UST]) will allow routine updating of the source water
assessments.
-------
Federal and State electronic database-listings of reported contaminant releases generally
contain complete information on the general type and location of known releases or permitted
discharges. Federal databases, such as the Toxic Release Inventory (TRIS;
http://www.epa.gov/enviro/html/tris/state/south carolina.html) indicate the reporting period of the
release and list types and amounts of contaminants released. Within the computerized
statewide databases in SC, however, data on amounts of contaminants released are generally
incomplete.
Figure 3 shows the distribution of reported contaminant release sites within the study area.
Survey of Existing Databases for Potential Contaminant Releases
from Commercial and Public Facilities
Regarding potential contaminant release sites, initial efforts centered on researching the
following electronic databases:
1. SCDHEC databases listing owners of various use permits, such as, underground or
above ground storage tanks;
2. The Emergency Planning and Community Right to Know Act database (EPCRA/SARA
Title III; www.state.sc.us/dhec/eqchome.html) which lists facilities within SC that contain
10,000 Ibs. or more of hazardous raw materials;
3. The industrial facilities and land use databases from the South Carolina Department of
Commerce.
For those sites that have no listing of potential contaminants present, an interpreted listing was
made based on the commercial activities attributed to Standard Industrial Code (SIC)
designated to the site (Table 2). For these descriptions refer to OSHA SIC website at
www.osha.gov/cgi-bin .
Figure 4 shows the distribution of potential contaminant release sites within the study area.
-------
Figure 3
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Address |g] 7536.6452836418Jop=3741036.25£:choice=Potent£:bndrv_s1 =on&streams=on8.inds_s=on&surv=on&iistsites=onS:click.x=2838.click..H52^] [ J Lii
SOURCE WATER PROTECTION AND ASSESSMENT PROGRAM
Earth Sciences & Resources Institute
Click on Map to f Zoom In <"" Pan <• Redraw (~ Change Map f Identify
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Choose a Map:
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| Potential Sites of Contamination 7]
Map Layers:
metadata 17 Study Boundary
metadata 17 Streams
metadata 17 Industrial Sites
metadata 17 Windshield Survey
metadata 17
PWS Master Contaminant Site Listing
Reported Sites of TRIS
Underground Storage Tank Sites
General Contaminants and Species
NPDES
Pesticides Loading Rates
Crop Data 1995
US EPA RCRIS SITES
Screen view is of potential sites of contamination derived from databases within SCDHEC. Because of a
lack of correct location data, a field survey (windshield survey) was necessary to complete an accurate
contaminant inventory of the study area. Out of a total of 62 potential sites identified and mapped in the
final inventory of the pilot study area, only 14 of those sites were identified and located by computer
searches of pre-existing data bases. No underground storage tanks sites (UST) could be located in the
study area via computer searches prior to the windshield survey. After the survey, 15 sites were
correlated with SCDHEC USTSITES database (Bureau of Underground Storage Tanks sites). In
summary, a windshield survey was needed identify and locate over 50% of the inventoried sites found in
the pilot study area.
-------
Field (Windshield) Survey
A field survey (i.e., windshield survey) was critical to completing an accurate contaminant
inventory of the study area. Out of a total of 62 potential sites identified and mapped in the final
inventory of the pilot study area, only 14 of those sites were identified and located by computer
searches of pre-existing data bases. No underground storage tank sites (LIST) could be located
in the study area via computer searches prior to the windshield survey. After the survey, 15 sites
were correlated with SCDHEC USTSITES database (Bureau of Underground Storage Tanks
sites). In summary, a windshield survey was needed to identify and locate over 50 % of the
inventoried sites found in the upper Shaw Creek watershed.
Survey of Potential Agriculturally-derived Nonpoint Source Pollutants
"Agriculture accounts for 75% of total pesticide use in the United States and is the primary
source of pesticides to surface waters in most areas" (Larson and others, 1997). In South
Carolina and in many other states, however, there are no established procedures for
determining the extent of the potential problem before it reaches the municipal water intake.
Data available on agricultural use in the study area (and state-wide) are derived from two
primary sources, SPOT Land-use / Land-cover data from the SC Department of Natural
Resources and annual crop use data from the Farm Services Agency of USDA (FSA). The Land
use / Land cover data are based on 1989-1990 multi spectral SPOT images. According to the
Land-use/Land-cover data agricultural/grassland use occupies 32% of the upper Shaw Creek
study area (Fig. 5). More specific agricultural data (i.e., land use according to individual crops)
are available from the FSA with complete crop listings, according to specific fields, up to 1995.
As of 1996, revisions to the Farm Act require farmers to report only a few selected crops. FSA
crop data originate from individual farm holders and consist of reported use for each
cultivated/farmed plot, crop type planted or maintained, and the number of acres used. Each
field has an assigned ID number referenced to aerial photographic survey grids. The current
FSA record of the individual fields consist of hand-drawn field boundaries on hard copies of
aerial photographs. Presently USDA is conducting a nation wide program to convert this
hardcopy mode of recording farm locations into a digital format. Visit
http://www.nhq.nrcs.usda.gov/ITD/gis.html for details on this USDA program.
-------
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metadata F Agricultural Fields
metadata P USDA/FSA Gnd
metadata I? SPOT Landuse
metadata F Simazine
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metadata F Glyphosate
metadata F
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PWS Master Contaminant Site Listing
Evergreen forest
| Mixed forest
Decidous forest
Scrub/shrub
| Bottomland forest
| Non-forestedwetUnds
Barren
| UrtanAuilt-up
[Underground Storage Tank Sites
• General Contaminants and Species
Reported Sites of TRIS
NPDES
Pesticides Loading Rates
Crop Data 1995
US EPA RCRIS SITES
Land use / Land cover plot of the pilot study area. Coverage is ERDAS-generated CIS files utilizing 60
meter SPOT data from the Land Resources group within the SC Department of Natural Resources
(DNR). The ten categories of land use / land cover are shown in key at bottom of image. Grid pattern is of
aerial photographic survey used by FSA to locate farm track and fields, the boundaries of which are hand
drawn on the photos and given has an unique identifying code that is referenced to the crop-use
database. The irregular-shaped polygons in the NW part of the study area are digitized fields. By
transferring the hard copy location data sets into a digital format, individual fields, crop use, and pesticide
use can be spatially correlated with other data sets, such as topography, soils, geology to expedite the
development of susceptibility analyses. USDA is beginning to implement a nationwide conversion of their
data into such a CIS approach. Image is screen save from interactive, web based contaminant inventory
(http://gisweb:esri.sc.edu).
10
-------
The potential value of the crop data is that it presents a reasonable means to estimate the
loading of pesticides/herbicides per farm field based on the maximum recommended application
rates in the 1997 Pest Management Handbook from the Clemson Agricultural Extension (see
Table 3). Digital crop use data that are available from the FSA on a seasonal basis, can be
immediately summarized for each aerial survey grid to arrive at a loading rate for that grid. If
actual cultivated field boundaries were available, then the exact location where the pesticide
was applied ccould be plotted. USDA is moving towards converting field locations into a GIS
format. This USDA initiative should be strongly encouraged because of the benefits to the
SWAP program. Precise delineation of cultivated areas will aid in the estimating the amount of
specific pesticides introduced annually into the watershed. It will also benefit in susceptibility
analyses. Some of these benefits are as follows.
1. Exact routes of any overland flow from a particular application site can be determined
by overlaying field locations on topographic coverage (hypsography). This will allow
accurate targeting of any necessary use restrictions or remediation efforts.
2. Precise positioning of field locations in a digital format greatly facilitates the correlation
of pesticide use with reported soils data to improve reliability of modeling the post-
application fate of contaminants (i.e., transport of contaminant into groundwater or
surface waters).
Design of Database Structure
In the plan proposed by ESRI-USC for South Carolina (or any State), the inventory assessment
methods follow a watershed-approach which is comprised of three key elements. First, a master
database is maintained for each public water supply (PWS) source area by the respective PWS
operator/owner or by the State environmental agency (SCDHEC). Each PWS master database
lists all inventoried sites within the entire source-area watershed(s) upstream of the PWS intake
structure and lists all databases where pertinent environmental data are stored for those
inventoried sites. Second, each watershed database is linked to original databases maintained
and updated by their originating agency (e.g. SCDHEC, US EPA, U.S. Department of
Agriculture, other state agencies, etc.). This second element will allow routine updating of
inventories without requiring additional large survey projects such as required by SSWAPP.
Assuming the inventories are updated routinely, watershed susceptibility analyses can be
revised with current and timely information. Third, the proposed plan has each PWS database
11
-------
linked to a statewide master database under the supervision of the State environmental agency
(Fig. 2).
RELATIONSHIP OF WATERSHED-WIDE CONTAMINANT INVENTORY
TO SOURCE WATER ASSESSMENT
The source water assessment process should fully integrate susceptibility analysis with the
inventory of present and future sources of contaminants, wherever they may be located within
the source area. Following US EPA guidelines, to have a "clear understanding" of the
"significant potential sources of contaminants" an entire watershed must be inventoried, or, at
the very least, significant sources should not be excluded from the inventory just because their
locations lie outside of some arbitrary boundary. This section describes the advantages of a
watershed approach to contaminant inventory and the advantages of susceptibility analyses
using delineations defined by natural boundaries versus fixed set-back or buffers.
Absolute Distances vs. Buffers / Segmentation
Numerous State SSWAPP plans include delineation schemes that consist of offsets or buffers
from the surface water body and segmentation of the watershed at some arbitrary time of travel,
such as the "24 hour travel distance computed for 10% exceedance flow..." (SCDHEC, 1999).
Such a scheme will group potential contamination sites into broad categories. The South
Carolina plan delineates three zones of susceptibility (Fig. 6):
1. the surface water zone of concern (61 m offset from edge of stream or geomorphic
flood plain);
2. the ground water zone of concern (457 m offset from edge of stream or geomorphic
flood plain);
3. the area outside the ground water zone of concern , (The ground water zone of
concern is defined as that area where shallow ground water may migrate into
stream; SCDHEC, 1999).
These three zones are delineated in both the primary and secondary protection areas within a
watershed, areas demarcated by a 24 hour time of travel determination. Consequently, the
SCDHEC plan delineates a total of six segments within the pilot study watershed (Fig. 7).
12
-------
Figure 6
Primary
source-water
protection areas
Secondary source-water
protection area
Primary
source-water
protection areas
EXPLANATION
ZONE OF CONTTHBUflON
WATERSHED BOUNDARY
SUB-WATERSHED BOUNDARY
STREAM
CRITICAL TRAVEL-TIME SEGMENT
24-HOUR TRAVEL DISTANCE
INTAKE
Zones of Contribution in
Source-Water Protection Area
Susceptibility
Determination
Zones
Area outside ground-water _
zone of contribution
457-meter ground-water.
zone of contribution
61 -meter overland flow _
zone of contribution
Surface-water flow system
(Including stream, floodplain, -
impoundments, ana tributaries)
61 -meter overland flow _
zone of contribution
457-meter ground-water _
zone of contribution
Area aursde ground-water _
zone of contribution
^_ Susceptibility
zone 3
^ Susceptibility
zone 2
Intake
f _ Susceptibility
zone 1
_, Susceptibility
zone 2
^_ Susceptibility
zoneS
Proposed delineation plan for surface water systems in South Carolina contains primary and secondary
source water protection areas (SWPA) and susceptibility offset zones 1,2,and 3. The primary SWPA is
defined as "all subwatersheds adjoining the 24-hr travel distance upstream from the PWS intake"
(SCDHEC, 1999). Offset from the stream trace, the SC plan delineates three zones of susceptibility.
Figure is from SCDHEC (1999).
1. The surface water zone of concern consists of a 61 m off set from edge of stream or geomorphic
flood plain.
13
-------
2. The ground water zone of concern is a 457 m off set from edge of stream or geomorphic flood
plain (the ground water zone of concern is defined as that area where shallow ground water may
migrate into stream).
3. The Area outside the ground water zone of concern.
Figure 7
• Pin f Itilw f CicjlMap T T
Undnjg nia-j 1 l^i.r.ijf T:mk Silr:.
WrnddBtid Saray
[Iff EPA H(7RT5 SITES
B
Application of the proposed delineation plan for surface water systems in South Carolina to the pilot study
area segments the watershed into a total of six segments within the pilot study watershed. The black line
denote the 24-hr travel distance upstream from the PWS intake" (SCDHEC, 1999). A. The surface water
zone of concern, which consists of a 61 m off set from edge of stream or geomorphic flood plain, only
includes 12 of the 62 inventory sites. B. The ground water zone of concern, which is a 457 m offset from
edge of stream or geomorphic flood plain, contains 37 of the 62 inventory sites. C. A problem with
arbitrary delineations is that "reality" often ignores their presence. For example, in the pilot study area, the
14
-------
Bridgestone/Firestone (B /F) which lies outside of the SCDHEC designated Surface water Zone of
Concern. This designation, however, did not prevent water used to fight a fire at the facility from
overflowing the site's storm runoff basin and traveling down stream to the PWS intake where it was
introduced into the public water supply.
The spatial analytical capabilities inherent within a GIS, however, allow for a much more
rigorous analysis, which includes the relative ranking of individual sites.
Using Network Analyst ™ the individual flow-path distances from each site to the PWS intake
can be automatically calculated. Figure 8 presents the flow lines, which have flow distances
values as attributes from each survey site to the intake. A major advantage to this approach is
that not only is there a parameter to individually rank each site, but arbitrary, and often
misleading designations are avoided. For example, in the pilot study area, the
Bridgestone/Firestone site lies outside of the SCDHEC designated "Surface water Zone of
Concern" (Fig. 7 C). This designation, however, did not prevent water used to fight a fire at the
facility from overflowing the site's storm runoff basin and traveling the 10.3 km (6.4 miles) down
stream to the PWS intake where it was introduced into the public water supply.
Environmental Fate of Contaminant Species vs. Single Susceptibility Determinations
In SSWAPP, the US EPA suggests that susceptibility determinations "take into account...
inherent characteristic(s) of the contaminants...." One interpretation of this guideline would be
separate susceptibility analyses for each species of contaminant, or, at a minimum, maps made
for groupings of contaminants that share the same environmental fate. Figure 9 compares
hypothetical sensitivity maps for benzene and cadmium. These maps are "stack- unit maps" or
"spatially joined" maps, which sum various ranked environmental parameters. How these
parameters are ranked depends on the environmental fate of the contaminant in question. For
instance, cadmium adsorbs readily onto sediment surfaces, with particles with higher surface
areas (e.g., clays) adsorbing higher amounts of cadmium. Consequently, the higher ranked
areas on the sensitivity map for cadmium are controlled by spatial distribution of finer grained
soils, such as C and D soils which occur as levees and overbank deposits within areas adjoining
rivers (Fig. 10). In contrast, for benzene, proximity to the stream is considered the most
important parameter as far as benzene's impact on a surface water system. Since benzene
volatilizes rapidly, if benzene is released too far from a stream, evaporation combined with
infiltration into soils, may prevent benzene from reaching a stream. Consequently, the buffer
15
-------
delineations are the most prominent parameter in the benzene soil sensitivity map (Fig. 7 and
9).
Figure 8
Boundary
Windshield Survey
Water Intake
A/ Travel distance
" ' n
Roads
Contour Lines
Using Network Analyst ™ (from Environmental Systems Research Institute, Inc.) the individual flow-path
distances from each survey site to the PWS intake can be automatically calculated. Using the stream flow
calculations for overland as well as in-stream distances, time of travel from each site can be calculated
and compared. Time of travel can also be calculated for low flow as well as high flow conditions. Using
this tool, a coverage consisting of intersections of streams with roads, rails, and pipelines would give
PWS operators quick access to time of travel information in case of an emergency spill due to a
transportation accident. This information would be very accessible via the internet.
16
-------
Figure 9
Benzene Surface Water Susceptibility
Cadmium Surface Water Susceptibility
Susceptibility
^| very high
high
moderate
low
^| very low
Susceptibility
r very high
high
moderate
low
B| very low
BENZENE PARAMETERS
1. SURFACE WATER ZONE OF CONCERN (ZOC)
2. SOIL HYDROLOGIC GROUPS
3. GROUND WATER ZOC
4. SOIL SLOPE
CADMIUM PARAMETERS
1. SOIL HYDROLOGIC GROUPS
2. SOIL EROSION INDEX
3. SURFACE WATER ZOC
4. SOIL SLOPE
Two contaminants on the NPDWS list, benzene, a VOC, and cadmium, a phase II inorganic,
behave differently when released into the environment. Consequently, different sensitivity maps
where developed for each. These maps are "stack- unit maps" or "spatially joined" maps, which
sum various ranked environmental parameters. How these parameters are ranked depends on
the environmental fate of the contaminant in question. For instance, cadmium adsorbs readily
onto sediment surfaces, with particles with higher surface areas (e.g., clays) adsorbing higher
amounts of cadmium. Consequently, the higher ranked areas on the sensitivity map for
cadmium are controlled by spatial distribution of finer grained soils, such as C and D soils which
occur as levees and overbank deposits adjoining rivers (see Fig. 10). In contrast, for benzene
proximity to the stream is considered the most important parameter as far as benzene's impact
on a surface water system. Since benzene volatilizes rapidly, if benzene is released too far from
a stream, evaporation combined with infiltration into the soil, may prevent benzene from
reaching a stream. Consequently, the buffer delineations are the most prominent parameter in
the benzene soil sensitivity map (see Fig. 7).
17
-------
Figure 10
Return to Full Extent
Choose a Map.
(Soils jj]
Map Layers:
metadata F Study Boundary
metadata f~ Soil Slopes
metadata V Soil Types
metadata W Soil Hydrologic Group
Soils Hydro logic
Bi A
B
Choose a Table:
PWS Master Contaminant Site Listing
Underground Storage Tank Sites
General Contaminants and Species
Reported Sites of TRIS
NPDES
Pesticides Loading Rates
Crop Data 1995
US EPA RCRIS SITES
Screen view is of the soil hydrologic group distribution within the upper Shaw Creek watershed area.
Hydrologic soil groups are delineated based on their relative run-off versus infiltration characteristics.
Group A soils have a high infiltration rate and low run-off potential, being deep, well-drained, and
sandy/gravelly. The other end member, Group D soils, has a very slow infiltration rate, a high run-off
potential, and have a high-clay-content layer near the surface or are shallow soils over a low-permeability
bedrock or other material. Soil hydrologic group designations are just one of 14 soil attributes from USDA-
NRCS Map Unit Interpretation Database.
The variability of the environmental fates for benzene and cadmium is not unique. Given the
wide variations in the environmental fate and transport characteristics of hazardous materials, it
does not seem scientifically defensible that a single susceptibility analysis should be applied for
all the contaminants listed in the NPDW Standards.
18
-------
General descriptions of the characteristics of hazardous materials, including those listed in the
NPDW Standards, can be obtained through the US EPA web site (for an example visit
http://www.epa.gov/OGDW/dwh/t-soc.html).
Using Natural Spatial Variability vs. Arbitrary Buffers for Susceptibility Analysis
The diverse data sets describing the geographic, hydrogeologic, hydrologic, and land use
characteristics are readily available for watersheds nationwide. These data sets can be easily
integrated utilizing the GIS capability of spatial joining (also known as, polygon overlay or stack
unit mapping).
The process of integrating varied data sets necessary to develop a susceptibility analysis is
relatively straight forward, utilizing a GIS. The next step of developing a logical and defensible
scheme for ranking the relative importance of these data is not a simple process. To develop a
logical ranking scheme will require the incorporation of ideas and experiences from a variety of
sources. To enlist the appropriate expertise, we recommend establishing an advisory board,
having discussions at public meetings, and posting of the plan on a web site for review.
When conducting a susceptibility analysis of a watershed or a specific site within a watershed,
more than the physical characteristics of the area must be considered. The factors ESRI-USC
recommend are considered for a susceptibility analysis of a specific contaminant or groups of
contaminants with similar environmental fate characteristics and toxicity levels are as follows:
1. PHYSICAL CHARACTERISTICS
• soils
• geology
• hydrology
2. SITE STRUCTURAL / PROCEDURAL CHARACTERISTICS
• history of reported releases, complaints, etc.
• BMP or Emergency Plan in place
3. CONTAMINANT LOADING
• % of product that is a hazardous material
• travel distance from release site to PWS intake or well
19
-------
If a susceptibility analysis lumps contaminants with different environmental fate characteristics
and toxicity levels it is suggested that the most conservative environmental fate
(environmentally harmful) and most lethal toxicity level of the contaminant group be used in the
analysis.
The susceptibility analysis proposed by ESRI-USC is a "relative comparison" approach as
described in the USEPA SSWAPP guidelines (1997, p. 2-18). In implementing this approach,
the ranking scheme will be clearly documented for critique and later revision. In addition, such
an analytical approach can be applied to either surface or ground water systems.
CONCLUSIONS
1. In the susceptibility plan proposed by ESRI-USC for South Carolina (or any State), the
inventory assessment methods follow a watershed-approach which is comprised of three
key elements. First, a master database is maintained for each public water supply (PWS)
source area by the respective PWS operator/owner or by the State environmental agency
(SCDHEC). Each PWS master database lists all inventoried sites within the entire source-
area watershed(s) upstream of the PWS intake structure and lists all databases where
pertinent environmental data are stored for those inventoried sites. Second, each watershed
database is linked to original databases maintained and updated by their originating agency
(e.g. SCDHEC, US EPA, U.S. Department of Agriculture, other State agencies, etc.). This
second element will allow routine updating of inventories without with out requiring additional
programs. Assuming the inventories are updated routinely, watershed susceptibility
analyses can be revised with current and timely information. Third, the proposed plan has
each PWS database linked to a statewide master database under the supervision of the
State environmental agency, such as SCDHEC in SC.
2. Given the wide variations in the environmental fate and transport characteristics of
hazardous materials, it does not seem scientifically defensible that a single susceptibility
analysis should be applied for all the contaminants listed in the NPDW Standards.
3. The spatial analytical capabilities inherent within a GIS allow for a rigorous susceptibility
analysis utilizing diverse data sets describing the geographic, hydrogeologic, hydrologic,
and land use characteristics which are readily available for watersheds nationwide. These
20
-------
data sets can be easily integrated utilizing the GIS capability of spatial joining (also known
as, polygon overlay or stack unit mapping).
21
-------
ACKNOWLEDGEMENTS
ESRI-USC wishes to thank SCDHEC for the opportunity to participate in our portion of the pilot
studies for SSWAPP. ESRI-USC also extends it appreciation to the U. S. Geological Survey
(USGS) for their cooperation while we worked coincidentally on our separate portions of this
project. ESRI-USC offers a special thanks to the SC office of the Farm Services Agency of
USDA for helping us examine and analyze crop use information for the study area.
22
-------
CITATIONS
Clemson University Cooperative Extension Service, 1997. 1997 Pest Management Handbook,
Field Crops, Fruits, and Vegetables. Volume 1: Clemson Agricultural Extension,
Clemson, SC, 700 pp.
Larson, S.J., P.O. Capel, and M.S. Majewski, 1997. Pesticides in Surface Waters, Distribution,
Trends, and Governing Factors; Volume 3 of the Series, Pesticides in the Hydrologic
System: Ann Arbor Press, Inc., Chelsea, Ml, 373 pp.
Rine, J.M., R.C. Berg, J.M. Shafer, E.R. Covington, J.K. Reed, C.B. Bennett, J.E. Trudnak,
1998. Development and testing of contamination potential mapping system for a portion
of the General Separations Area, Savannah River Site, South Carolina: Environmental
Geology, vol. 35, no. 4, p. 263-277.
Rine, J.M., E.R. Covington, J. B. Atkins, 1998. Development of a Contaminant Inventory
Methodology for a SWAP Program in South Carolina, Earth Sciences and Resources
Institute, University of South Carolina, ESRI-USC Technical Report 98-10-F140,
Columbia, SC
South Carolina Department of Health and Environmental Control, Bureau of Water (SCDHEC),
1999. South Carolina Source Water Assessment & Protection Program: Final Draft for
Advisory Committee Review January 1999; Columbia, SC
U.S. Environmental Protection Agency, 1997. State Source Water Assessment and Protection
Programs Guidance, Final Guidance: U.S. EPA Office of Water, EPA 816-R-97-009,
Washington, D.C.
23
-------
Table 1.
Master Watershed Site List for Source Area to City of Aiken Public Water Supply
USCWID FAC_NM
1 Beaulieu of America
2 RaceTrac
2 RACETRAC #427
2 RACETRAC #427
3 BP Gas & convenience store
3 BP Gas & convenience store
3 BP Gas & convenience store
4 Amoco VACANT
5 Smile Gas
5 Smile Gas
5 Smile Gas
5 Smile Gas
5 Smile Gas
6 S.E. Peters, Inc.
7 Firestation
8 Pepperidge Farm, Inc.
8 Pepperidge Farm, Inc.
9 Beloit Manhatten MillPro Service
10 Smith Kline Beecham
11 Carlisle Tire
12 Gorham Bronze
13 United Defence LP Ground Systems Div.
13 United Defence LP Ground Systems Div.
13 United Defence LP Ground Systems Div.
14 Aiken Airport BP fuel pump
14 Aiken Airport (fuel pump?)
14 Aiken Airport (fuel pump?)
14 Aiken Airport (fuel pump?)
15 Aiken Airport Sunococ fuel pump
16 Bridgestone/Firestone Special Training
17 Airport Industrial Park building (VACANT)
18 Aiken Co. Drop off Center #3
19 Southeastern Clay Co.
20 Kentucky-Tennessee Clay Co.
21 Shell gas & convenient shore
21 Shell gas & convenient shore
21 Shell gas & convenient shore
22 VACANT (Abandoned Gas station)
23 Morris Quick Lube
24 Jordans Auto Service & Sales
25 Shiloh Baptist Church
26 Quick Stop & Gas
26 QUICK STOP II
26 QUICK STOP II
27 Shell Food Mart & Blimpies
27 Shell Food Mart & Blimpies
27 Shell Food Mart & Blimpies
28 Exxon / Subway (Garvin Oil, Inc.)
28 Exxon/Subway (Garvin Oil, Inc.)
29 Automotive machine Shop
30 B&B Paint & Body Shop
31 Curry's Auto Service
32 Graves Auto Salvage
AKA FAC NM
Beaulieu Fibers
RACETRAC #427
RACETRAC #427
RACETRAC #427
DEPOT FOOD STORE 135
DEPOT FOOD STORE 135
DEPOT FOOD STORE 135
Amoco
SMILE GAS #77
SMILE GAS #77
SMILE GAS #77
SMILE GAS #77
SMILE GAS #77
Beecham Products
Nl Industries MirreY
Gorham Bronze Div. Textron
FMC Corp.
FMC Corp.
FMC Corp.
AIKEN AVIATION ENTERPRISES INC
AIKEN AVIATION ENTERPRISES INC
AIKEN AVIATION ENTERPRISES INC
AIKEN AVIATION ENTERPRISES INC
AIKEN AVIATION ENTERPRISES INC
?AIRPORT STOP AND SHOP?
?AIRPORT STOP AND SHOP?
?AIRPORT STOP AND SHOP?
Abandoned Gas station
QUICK STOP II
QUICK STOP II
QUICK STOP II
I-20 SHELL
I-20 SHELL
I-20 SHELL
KENTSKORNER#15
KENTSKORNER#15
ADDRESS
136 Frontage Rd.
Columbia Hwy (address*?)
2664 N COLUMBIA HWY
2664 N COLUMBIA HWY
2655 Columbia Hwy (US HWY 1 AT I-20)
US HWY1 ATI-20
US HWY1 ATI-20
Columbia Hwy
2645 COLUMBIA HWY I-20 & US 1
2645 COLUMBIA HWY I-20 & US 1
2645 COLUMBIA HWY I-20 & US 1
2645 COLUMBIA HWY I-20 & US 1
2645 COLUMBIA HWY I-20 & US 1
127W. Frontage Rd.
Columbia Hwy
10 Windham Blvd (Verenes Industrial Park)
10 Windham Blvd (Verenes Industrial Park)
25 Beloit St.(Verenes Industrial Park)
65 Windham Blvd (Verenes Industrial Park)
25 Windhan Blvd (Verenes Industrial Park)
45 Windhan Blvd (Verenes Industrial Park)
15 Windhan Blvd (Verenes Industrial Park)
15 Windhan Blvd (Verenes Industrial Park)
15 Windhan Blvd (Verenes Industrial Park)
AIKEN AIRPORT - HWY #1
AIKEN AIRPORT - HWY #1
AIKEN AIRPORT - HWY #1
AIKEN AIRPORT - HWY #1
AIKEN AIRPORT - HWY #1
(Verenes Industrial Park)
REYNOLDS POND RD N
US 1 AT I-20
US 1 AT I-20
US 1 AT I-20
US 1 (Columbia Hwy)
Columbia Hwy
2142 Edgefield Hwy
HWY 19 & 191 NORTH
HWY 19 & 191 NORTH
I-20&HWY19
I-20&HWY19
I-20&HWY19
1925 Edgefield Rd. (survey notes)
1925 EDGEFIELD HWY(I-20 @ SC19)
1625 Edgefield Rd.
599 Hazel Dr
Shilo Hts. Rd.
Edgefield Rd.
CITY
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
-------
Table 1.
Master Watershed Site List for Source Area to City of Aiken Public Water Supply
33 BP Depot
34 Williams Garage & Auto Salvage
35 2 Fine Fellows US 1
36 Gerry's Automotive
37 Speedway/Starvin' Marvin
38 Grace Div. Kaolin Products
39 City of Aiken Waterworks
40 Chave's Auto
41 Noth Side Golf Club
42 Thomas Used Cars
43 Mt. Sinai Baptist Church
44 Satterfield Construction Co., Inc
45 Trenton Quick Stop/Phillip 66
45 Trenton Quick Stop/Phillip 66
45 Trenton Quick Stop/Phillip 66
46 Bill'y Super Store
46 Bill'y Super Store
46 Bill'y Super Store
46 Bill'y Super Store
46 Bill'y Super Store
46 Bill'y Super Store
47 Martin Color Fi
48 Carlisle Tire & Wheel
49 Trenton Correctional Institute
49 Trenton Correctional Institute
50 Edgefield Co. Collection & Recycling center
51 Ebenezer Baptist church
52 Carlisle Engineering Products
53 Moore Craft Cabinet Co.
54 Yonce Field (grass airfield)
55 191 Country Convenience Store
56 Resale shop and Auto service (abandoned gas pumps)
57 Lewis E. Holmes Farms
58 Condrey's Auto and Cycle Shop
59 Aiken Correction Center
60 Ramada Inn
61 Edgefield Co. Water & Sewer Authority
62 Feldspar Production Inc.
Asphalt plant #1
TRENTON QUICK SHOP
TRENTON QUICK SHOP
TRENTON QUICK SHOP
TRENTON CORRECTIONAL CENTER
TRENTON CORRECTIONAL CENTER
US 1 & Rutland Rd.
SCHWY19
1139 Rutland Rd.
Rutland Rd.
US 1 (Columbia Hwy)
US 1 (Columbia Hwy)
1955 US 1 (Columbia Hwy)
US 1 (Columbia Hwy)
State Hwy 191
SC HWY 191
HWY 121
HWY 121
HWY 121
intersection US1 &SC19 & SC 121
CORNER OF HWY 121 & US 25
CORNER OF HWY 121 & US 25
CORNER OF HWY 121 & US 25
CORNER OF HWY 121 & US 25
CORNER OF HWY 121 & US 25
Trenton 121
intersection US1 &SC19 & SC 121
85 OLD PLANK RD
85 OLD PLANK RD
E. Wise Trenton
E. Wise Trenton
off SC HWY 121 connector
177R1. 191
Box 28 Rt. 2 (SC Hwy 191)
ROUTE 4 BOx 494-B US 1 N
110 Frontage Rd.
???
P.O. Box 2455
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
AIKEN
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
AIKEN
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
TRENTON
SC
SC
AIKEN
AIKEN
AIKEN
AIKEN
TRENTON
AIKEN
KEY TO HEADER ABBREVIATIONS:
USCWID
FAC_NM
AKA FAC_NM
DHECUSTID
surv-id
unique ID# for each inventoried site in watershed
facility name
other / previous facility name
UST permit I D#
site ID numbers utilized in windshield survey
DHECSPCCID
DHECNPDES
DHECSTORMW
EPA FACILITY ID
RCRIS CITE
TRIS CITE
-------
Table 2.
Listing of General Types of Potential Contaminants from Industrial/Govermental Facilities as Derived from Facility SIC Descriptions
u
in
0191
1455
1629
2051
2282
2511
2821
2824
2834
2899
2951
3011
3069
3089
3295
3297
3364
3492
3499
3599
3714
3795
4581
4941
4952
5015
5093
5411
5521
5541
5812
6512
7011
7538
7549
7992
8661
9223
9223
SIC DEFINITION
General farms, primary crop
Kaolin and ball clay, mining
Heavy construction not elsewhere classified
Bread and other bakery products, eYcept cookies & crackers, manufacturing
Yarn texturizing, throwing, twisting, and winding mills, manufacturing
Wood household furniture.except upholstered
Plastics materials and resins, manufacturing
Organic fibers, noncellulosic, manufacturing
pharmaceutical preparations
Chemicals and chemical preparations, not elsewhere classifies manufacturing
Asphatt paving matures and blocks
Tires and inner tubes manufacturing
fabricated rubber products, not elsewhere classified, manufacturing
Plastic products, not elsewhere classified, manufacturing
Vlinerals and earths, ground or otherwise treated, manufacturing
Monday refractories
Nonferrous die-castings, eYcept aluminum, manufacturing
Fluid power valves and hose fittings, manufacturing
Vliscellaneous fabricated metal products
Industrial and commercial machinery and equipment, not elsewhere, manufacturing
Motor vehicle parts and accessories
Tanks and tank components
Airports, flying field and airport
Water supply
Sewerage systems
Motor vehicle parts, used (junkyards)
Scrap and waste materials
Grocery stores
Motor vehicle dealers (used only)
Gasoline service station
Eating places
Operators of nonresdential buildings
Hotel and motels
General automoth/e repair shops
Automoth/e services, eYcept repair
Public golf courses
Religuous organizations (church yard wfth cemetery)
Correctional instftutions (w/UST?, vehicle maintenance, small manufacturing)
=ire protection ((Fire station)
SIC GROUP
01
14
16
20
22
25
28
28
28
28
29
30
30
30
32
32
33
34
34
35
37
37
45
49
49
50
50
54
55
55
58
65
70
75
75
79
86
92
92
SIC MAJOR GROUP DEFINITION
Agricultural production crops
Mining & quarrying of nonmetallic minerals, ex+D6cept fuels
Heavy construction other than building construction contractors
Food & kindred products
Textile mill products
Furniture and fixtures
Chemicals and allied products
Chemicals and allied products
Chemicals and allied products
Chemicals and allied products
Detroleum refining and related products
Rubber and miscellaneous plastic products
Rubber and miscellaneous plastic products
Rubber and miscellaneous plastic products
Stone, clay, glass and concrete products
Stone, clay, glass and concrete products
Primary metal industries
Fabricated metal products, eYcept machinery and transportation equipment
fabricated metal products, eYcept machinery and transportation equipment
Industrial and commercial machinery and computer equipment
Transportation equipment
Transportation equipment
Transportation by air
Electric, gas, and sanitary services
Electric, gas, and sanitary services
Wholesale trade-durable goods
Wholesale trade-durable goods
Food stores
Automotive dealers and gasoline service stations
Automotive dealers and gasoline service stations
Eating and drinking places
Real estate
Hotels, Rooming Houses, Camps, And Other Lodging Place
Automotive repair, services, and parking
Automotive repair, services, and parking
Amusement and recreation services
Vlemberchip organizations
Justice, public order, and safety
Justice, public order, and safety
PETROLEUM
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
QJ
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Ethylbenzene
Y
Y
Y
Y
Y
Y
Y
Y
Y
m
2
Y
Y
Y
Y
Y
Y
Y
Tetrachloroethlylene
Y
Dichloromethane
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Benzo(a)pyrene (PAHs)
Phthalate
Y
1 .2 Dichloropropane
Y
Y
1 ,2,4 - Trichlorobenzene
Y
QJ
Q
h1
(M
Y
Y
Y
Y
Y
Total trihalomethanes (Interim)
Y
-------
Table 2.
Listing of General Types of Potential Contaminants from Industrial/Govermental Facilities as Derived from Facility SIC Descriptions
PESTICIDES/HERBICIDES
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Acrylamide
Y
Alachlor
Y
is
•e
8
Y
Atrazine
Y
Carbofuran
Y
Chlorodane
Q
Y
Y
Y
Dibromochloropropane
Y
Y
Epicchlorohydrin
Y
Y
Y
Y
Y
Ethylene dibromide
Y
Hepachlor + metabolites
Lindane
b
0
i
E
Pentachlorophenol
Y
QJ
1
Q_
1—
2,4,5-TP
Y
Y
Y
Dinoseb
Y
"ro
O"
H
g
o
H
Y
S
Y
£
Q.
5
Y
Y
Y
QJ
1
I
Hexachlorocyclopentadiene
O
Y
Picoloram
Simazine
Y
a
Q.
s
Y
Y
S
Q.
1
Y
Y
INORGANICS
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
1
QJ
1
E
1
Y
Y
Y
Y
Cadmium
Y
Y
E
o
Y
Y
Y
Y
E
Y
•o
0)
Y
Y
SB
Q_
§
Y
Antimony
Y
Y
Y
1
I
Y
Y
QJ
•o
I
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Thallium
Y
Y
Selenium
Y
Sulfate (proposed)
Arsenic (Interim)
Y
MOBILE INORGANICS
Y
Y
Y
Y
Y
Y
±
Y
Y
RADIONUCLIDES
Radium (proposed)
1
1
Uranium (proposed)
PATHOGENS
Y
Y
Y
Y
Giardia
,0
o
1
Y
i—
Y
Y
Y
Viruses
Y
-------
Table 3.
Estimated loading rates of specific pesticides/herbicides per acre farmed land based on maximum
recommended application rates in the 1997 Pest Management Handbook from Clemson University
Cooperative Extension Service
2-4pt/1000gal
Conservation Reserve Plan
CUCUMBER
4lb/gal
OATS (SMALL GRAINS)
RYE(SMALL GRAINS)
CORN (SWEET)
SWEET POTATO
UPLAND COTTON
WHEAT (SMALL GRAINS)
-------
Management and Reuse of Contaminated Soil -
The SoilTrak™ Method
Edward Rogers, Jr., BEM Systems, Inc.1
Introduction
In 1991, BEM Systems, Inc. was contracted to conduct environmental investigations along a
20+ mile corridor for development of a new light rail transit system project. During these
investigations, suspicions that widespread and varying degrees of contamination would be
found were confirmed.
At about the same time, a new approach to contamination had recently been developed by the
State regulatory agency wherein low-level contamination could be left in place based on
proposed land-use and the imposition of engineering controls to limit potential exposure to the
contaminants. This would allow for the redevelopment of mildly contaminated properties for
industrial use where the likelihood of exposure to contamination would be far less than that
encountered for residential development, for example. If such an approach were implemented,
the developer would have to negotiate site-specific cleanup standards, design and implement
engineering controls to limit any potential exposure to remaining contamination and map out the
type and extent of contamination allowed to remain on-site. This information would be attached
to the property deed. The document would further stipulate that as long as the land use
remained as proposed, the contamination could remain in-place. Should the land use change,
the issue of contamination and remedial options would have to be revisited. A document stating
the allowable land use and identifying the contaminants, their locations and concentrations
would be attached to the property deed. This document is referred to as a "Declaration of
Environmental Restrictions" (DER).
As part if its investigations, BEM recommended that contaminated soil reuse be implemented as
the preferred remedial alternative and that a Memorandum of Agreement (MOA) should be
negotiated with the State. The proposal involved reuse of over 400,000 cubic yards of
"moderately" contaminated soil using engineering controls to minimize exposure potential to the
contaminants that would remain. This would minimize the costs associated with off-site disposal
1 Edward Rogers, Jr., BEM Systems, Inc., 100 Passaic Avenue, Chatham, NJ 07928;
-------
of soil and would provide much needed engineering fill material. Since the proposed reuse of
the property was a rail corridor, the probability of public exposure to contamination would be
nearly zero. The state agency agreed to this approach with the following stipulations. Reused
soil must not have contaminant levels that are greater than 10 times the State agency's "Non-
Residential Direct Contact Cleanup Standards". The contaminated soil would have to be placed
at least two feet above the seasonal high water-table level and two feet below grade. Another
stipulation of the MOA was that mapping would be required which showed where contaminated
soil was placed, which contaminants traveled with it and at what concentrations.
For the typical property, implementation of such a plan is fairly straightforward. Such properties
are generally no more than a few acres in size and identifying the degree and extent of
contamination is "text book" environmental work. BEM, however, was working with a single
"site" that was 20 miles long, involved most urban land uses and passed through six
municipalities and two counties. BEM would have to develop a detailed system of tracking and
reporting soil movements and identifying their associated contaminants.
As though this task were not complex enough, the client added another twist. It was the
transportation agency's intention to recover the costs of remediation from the owners of the
properties they planned to acquire. Since the transportation agency could approach the corridor
as one site, they would enjoy an economy-of-scale with regard to investigation costs,
remediation costs, and more favorable cleanup standards that no individual property owner
could achieve alone. Using this information, it was reasoned that property owners would settle
on the final sale price of the property sooner if we could demonstrate that the cost of
remediation, which would be deducted from the sale price, was lower than the property owner
would have paid. Timely property acquisition was key to the success of this rail project. It would
be necessary to provide a detailed accounting of the remediation cost, by property owner, to
defend our client's cost recovery efforts.
It quickly became clear that BEM would have to develop a method of tracking soil movement
activity throughout the corridor. In light of the amount of data that would be generated by the
remediation/construction activity, a database of some type would also have to be developed. In
order to meet the objectives of cost recovery and the DER, this database would have to
integrate tightly with other parts of BEM's environmental database management system. This
908-598-2600, x143; erogers@bemsys.com
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would be particularly true for sample analytical data and property owner information, as well as
existing spatial databases of the corridor that included sample locations and tax lot boundaries.
This paper will present the design and implementation of the SoilTrak Method, including the
issues leading to design decisions, the successes and failures of SoilTrak, an evaluation of
whether intended objectives were met, and where SoilTrak is headed now.
Developing the SoilTrak™ Strategy
The first step in developing the SoilTrak Method was determining precisely what information
would be needed and what would have to be tracked. It seemed clear that we would have to
track soil movement itself. Since it was necessary to know not only the contaminants at source
locations, but also the specific source property, a grid system based on tax lot boundaries and
the width of the corridor seemed ideal. This would allow the contractors to report soil movement
based on a source cell and a destination cell. This seemed an ideal solution to everyone except
the first of the contractors we would work with. SoilTrak would be developed and tested on a
small segment of the corridor that would become the largest of the several "Park & Ride"
facilities. "Contractor 1" was hired to do the construction of this site. Contractor 1 stated that the
unusual shapes of the tax lot parcels in this area, many of which had sweeping curves in them,
were too difficult to map and track during field activities. They proposed developing an
irregularly shaped grid system based upon activity and depth of excavation. The cells would
either be "cut" (excavation) cells or "fill" cells. Since anything else would be too complex to
manage in the field, this approach was adopted. The contractor supplied us with a CAD file
mapping the proposed cut, which would later be critical to implementing the system. From this
point, development of the SoilTrak database and application began.
Database Design
As with most database projects, reporting needs would primarily dictate what information was
stored in the database. It was already known that the primary needs were to support cost
recovery by property owner and to report the source, destination and contaminant content of
reused soil. BEM's environmental management information system (BEMIS™) already had a
database and associated GIS coverage for tax lot information along the corridor, so SoilTrak
would be designed to integrate with these. BEMIS also had tables containing analytical results
data and a sampling location table with an associated sample location GIS coverage. It is
important to understand that BEMIS was designed to be data-centric, rather than GIS-centric.
-------
All attribute information is stored in a central database of many related tables with GIS
coverages and applications like SoilTrak acting as data "peripherals". They could draw data
from, or add data to, the specific tables they supported. This strategy allowed SoilTrak to focus
only on the tables needed to track soil movement and cost information. These tables would be
SoilTrak's contribution to BEMIS.
The first, and main, table would track soil movements (SoilMove). A key question on the
contents of a record centered on whether a record should record the source and destination, or
only source or destination. That is, would a record report the cut and subsequent fill activity, or
the cut and fill activities separately. The first approach, and seemingly the most intuitive, could
produce half as many records as the second could. However, it was determined the soil cut on
one day could be temporarily stockpiled and filled on a later date. This would create confusion
during data entry since SoilTrak would require both a source and destination cell. In many
cases, soil would also be cut from a single cell and filled to several cells. This would result in
significant redundancy in the data since the single cut would have to be written for every fill. It
was decided to record the activities as separate records.
The next decision centered on precisely what it was
that should be tracked. Since soil movement was the
focus, it seemed logical to track the movement of
each discreet amount of soil removed from the
ground, which we will refer to as "slugs" of soil.
Every time a soil excavation activity occurred, the
soil removed would now become an entity in the
database and would receive a unique ID, a "Soil
Code". Other information such as excavation date,
volume, a cell ID and an activity (was the record of
this soil reporting a cut or fill?). Fill records would be
virtually identical to cut records. The Soil Code
would be the primary key of SoilTrak, an approach
that would later prove not to be optimal.
This information was adequate for tracking soil
movements but could not meet either of SoilTrak's
Figure 1
Soil volumes would be assigned as a ratio
of the volume to the area a tax lot
occupied within the source cell.
-------
objectives of cost recovery and contaminant tracking. Another piece of information was
necessary, property ID. Since BEM already had a database of tax blocks and lots, It seemed
reasonable to use this existing data. Each cut or fill activity would occur within a tax block and
lot combination identified within BEMIS by a property code. The inclusion of this field would
provide SoilTrak with the needed link into the rest of BEMIS.
Database Design - Cost Recovery
However, the inclusion of prop_code in a soil move record created a dilemma. The cut and fill
grid was drawn without regard to property boundaries. If soil were excavated from a cell that
was subdivided by multiple property codes, which would you report? Since the smallest
geographic unit the contractors could report on was the cut or fill cell, it would be necessary to
report all properties present in the cell. Cost recovery objectives dictated knowing how much soil
was removed from each property, not cut or fill cell. In the case where a cell were divided by
three tax lots, SoilTrak's data approach would require the three cut records be entered for every
cut from that cell.
With this in mind, the next question centered on assigning the soil volume. For all intents and
purposes, soil excavation and reuse was the cost-recoverable remediation activity. If a rate per
cubic yard could be arrived at, cost recovery reporting would be as simple as multiplying that
rate by the total volume of soil removed from a specific tax lot (property code). However, soil
volumes were being reported by cut cell. This problem was solved using BEM's GIS. At the
time, BEM was using ESRI's ArcCAD™ GIS product. This product used AutoDesk's®
AutoCAD™ as its graphics engine and created PC Arclnfo coverages for storing GIS related
data. BEM had polygon coverages of both the tax lot boundaries and the proposed cut cells. In
ESRI's model of the GIS universe, area features are recorded as polygon coverages, with each
polygon having an area and perimeter. These two pieces of information are reported in every
record of an ESRI Arclnfo polygon attribute table. If one were to assume that any soil removed
from a cell was removed from the entire area of the cell and to a constant depth, the area
information could be useful (Figure 1). Since ArcCAD is a true GIS application, it would be
possible to intersect the tax lot coverage with the cut and fill cell coverage. This would produce
a new polygon attribute table containing the property code, cut or fill cell ID and the area of the
polygon (Figure 2). This table, a Dbase file, was lifted from the coverage and put into a
spreadsheet. From there the percent of the total area of a cell present in a tax lot was
computed. This number would be used as a volume multiplier. If an excavation volume were
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reported as 150 cubic yards for a cell, and the cell were subdivided equally by three different tax
lots, three records would be entered into SoilTrak with 50 cubic yards of the soil attributed to
each record, and thus, each lot.
Knowing the volume of soil removed
from a cell alone was not enough to
determine recoverable costs. SoilTrak
was programmed with a default value for
cost per cubic yard. However, a module
was added that allowed for line item
costs to be entered by cell. Thus, while
one truck may have been used for cut
cell C-1, two may have been used for cut
cell C-2. Off-site disposal may have
been required for only a few cells,
though this could add greatly to the cost of remediation within that cell. The cell cost module
allowed for cell-specific rates that could be calculated and applied to cost recovery reports. This
approach also provided a more defensible and accurate picture of the actual remediation costs
for property owners.
I
1
Area
-7142316.0000
Perimeter | Cnf_prop_
0.9918399E+05! 1
].1858992E+04i0.1975570E+03! 2
18201 1 72E+01
/i >! TJ863E+04
j Hjwsu'jt-ib
Jbi'UJ.-'Jbt -Lib
HJ4193E+04
148091 80E +02
0.545/3bOE+02! 3
0.481 61 74E+03! 4
0.7981525E-f02! 5
0. 1 980884E+04! 6
0.5001 563Et03l 7
0.3903281Et02i 8
T"
Cn(_pinp_i
0
1
2
3
4
5
6
7
Cnf real i
0
312
312
313
313
314
315
315
Prop_code
0.00000
526.00000
531.00000
526.00000
0.00000
100.17000
526.00000
0.00000
*
T
- - .-y
Figure 2
Intersecting the tax lot coverage (prop_code) with the
cell coverage (cnf_real_i) yielded a
each fraction of a lot within
table with areas for
a cell. These were used to
find the multipliers needed to assign volumes to
owners.
Database Design - Contaminant Tracking
This aspect of SoilTrak was quite different from the property considerations of cost recovery.
Contaminated soil was to be reused as fill material. In this case, the only information of interest
was what contaminants were present in the reused soil, where the soil was placed and what
volume of soil was placed there. Every soil placed as fill would have to have an origin and a
means of determining which contaminants were present. Soil would be cut from cell C-12 and
placed in cell F-15. Recall, however, that during the data entry phase, if property lines split a
cell, multiple records for the cut activity were entered. That is, multiple "soil codes" were created
for a single cut cell in a single soil move event. Which one do we list as the source? The answer
is all of them. This was facilitated through a utility that allowed mixing of soil. If three soil codes
existed for a cut activity, this module would allow you to mix them together to form a single new
soil code. This would then be the code used for filling activities. A later re-analysis of the
SoilTrak method would reveal that this was far from an optimal approach, though it did work. A
separate table called "SoilMix" was used to store this "fake" mixing information.
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The issue then centered on
determining which contaminants
were present. In an ideal model,
the distribution of contamination
would be known in at least two-
dimensions for the entire corridor.
However, creating such fine
resolution of contaminant data
over 20 miles would require far
more analytical samples, and thus
cost, than could be supported or
justified. Another approach would
find all sample locations within the
cut cells and assume that any
contaminants present at those
locations were present throughout
the cell. An examination of the
data showed that too few samples
were located directly within the cut
cells. Since many of the samples
G01-B04
G01-B07
G01-B05
G01-B08.
Figure 3
The CIS was used to determine in which lots samples had
been collected. This allowed SoilTrakto assign any sample in
a lot to its associated cell. Thus, the contaminants found in
any of the sample locations above would be said to be
present in Cell C-15.
were collected to characterize an entire tax lot, they were spread too thin to use only those that
fell within the cut cells. The final, and selected approach, involved using the GIS to determine all
samples collected by tax lot. Then, any cell that crossed into the tax lot was assumed to have
any contamination found within the lot, regardless of whether the sample was collected within
the cell (Figure 3). This provided a conservative, but reproducible and systematic approach. The
GIS was used to identify samples by tax lot, and tax lots within cells. These data were tabulated
and provided to SoilTrak which then used its connection to the BEMIS database to determine
which contaminants were present, at what concentrations and whether or not they exceeded
State concentration standards.
Recall that the MOA stated that any reused contaminated soil would have to be at least two feet
below grade and two feet above the mean high water table. This was handled by allowing the
starting and finishing elevations for each fill to be reported.
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Implementation - Contractor 1
In order to provide the data that would be needed for the SoilTrak method, forms were
developed for use by field personnel. These essentially reported the date, source, destination
and volumes of soil moved. Cell specific cost sheets were later provided by the contractor and
used in the cell-specific cost module described above.
The contractor then provided a CAD map showing the proposed cut cells. Fill cell mapping was
not provided, as the cells had not yet been determined. The CAD file was converted into an
ArcCAD polygon coverage and later intersected with an existing tax lot coverage to provide
information that SoilTrak would need.
The SoilTrak application was developed primarily in the spring of 1996 and was on-line by July
of the same year. The application was created using Microsoft FoxPro® version 2.6 with all data
tables in FoxPro's native dBase® (*.dbf) format. This choice worked well with our then current
GIS platform, ArcCAD, since it too stores its attribute data as dBase files.
Initial implementation of SoilTrak and the SoilTrak method went well and the required objectives
were met. During implementation however, one issue did arise. As described above, the
contractor provided CAD files of the proposed cut cells, with each cell having a constant depth
of excavation. Fill cells however were not mapped at all. Fill areas were decided and recorded in
the field. Initially, fill cells were so large in areal extent that making any statements about the
locations of specific contaminants within the cells would be meaningless. At BEM's request, the
contractor began to subdivide the fill cells. These new cell boundaries were largely dictated by
"lifts" of soil that were placed, usually from a particular source area. Unexpectedly, however,
these cells began to overlap each other. Recall that filling was occurring in three dimensions.
These cells were not truly overlapping since they were being defined at different depths. To a
two-dimensional GIS however, these cells did overlap. This apparent overlapping made
mapping fill cells in the GIS impossible since the GIS would try to create new polygons every
time a cell was overlapped. This would have made assigning attributes to fill cells
unmanageable, as there would be multiple many-to-one relationships between cell polygons
and cell attributes. At that point in time however, the SoilTrak application was not GIS enabled
and thus the issue of unmanageable fill cell mapping had no impact.
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Implementation - Contractor 2 - 1998
Several months had passed and SoilTrak had been a success. A new and larger phase of the
corridor construction was about to begin and BEM again prepared to use this new tool. Some
lessons had been learned from the Park & Ride construction implementation. Among these
were:
• The first version did not include a module for entry of cell cost details (line-item costs).
Such a module was added by the conclusion of the Park & Ride effort;
• Cells would not be allowed to overlap at all, each cell would be discrete. This would
support GIS mapping;
• All cell mapping was to be completed prior to the start of soil movement activities.
The Park & Ride test area was fairly small, measuring only 14 acres. The next activity would
involve half of the corridor, covering a distance of approximately 11 miles.
Work began in the spring of 1998 with meetings between BEM and the new contractor. The field
reporting form was refined and initial efforts at cell mapping were made. The corridor consisted
of several Park & Ride facilities and a large light rail maintenance facility. Work was to begin at
the large maintenance facility so mapping was first provided for this facility with Park & Ride
facilities close behind.
Cell Mapping
As planning continued one new change became apparent, there would be no "cut" or "fill" cells.
Previously, cell mapping had been based on the depth of excavation and cutting or filling
activity. This contractor chose a systematically based cell system instead, where cells were 200
feet long and 100 feet wide in the aforementioned areas. The effect of this system was that both
cutting and filling could occur within the same cell. Cell IDs were based primarily on station
numbers along the corridor. Each cell would be named using the station number it started at
going from south to north. The prefix "C" or "F" would identify the cell as a cut or fill cell. Since
both activities could occur within a cell, each cell was given both a cut and fill cell ID. This would
be no problem for the SoilTrak application but posed a problem for the GIS, as it could not
handle one (polygon) to many (Cell IDs) relationships. This would be addressed later since the
SoilTrak application did not yet support GIS mapping.
-------
With cell mapping completed for the Park & Ride areas and the large maintenance facility,
construction began. Initially, the SoilTrak Method worked smoothly, but soon, problems began
to surface.
Incomplete Cell Mapping
As stated previously, cell mapping had been received for only certain areas. Eventually,
construction began along the actual corridor portions of the project area for which no cell
mapping had been received. Many requests were made for this information, however Contractor
2 was not as "computer savvy" as Contractor 1 and was slow to respond with any additional
mapping. Maps that were provided often consisted of small-scale paper maps showing only tick
marks every 200 feet along the corridor. However the width of the cells was never provided.
Without this information, it was impossible to create the CAD drawings needed to build a GIS
coverage. It was later stated that the width of the cells was the corridor "right-of-way". Obtaining
CAD files with complete mapping of the right-of-way lines turned out to be more difficult than
one would have anticipated. CAD files existed with complete design information, but right-of-
way lines were hard to identify as many were not labeled and easily lost among many other
unlabeled lines. Recall that a cut-fill cell GIS coverage was needed to provide the cell-to-
property data needed for data entry. While soil movement activities continued, SoilTrak data
entry ground to a halt. Many months would pass before BEM was able to build a usable cell
coverage while questions continued as to the accuracy of the right-of-way width designations.
This delay allowed field data reporting problems to go unnoticed, and thus unaddressed, for
months.
Significant Issues
By this point, flaws in the SoilTrak method and approach began to surface; these centered on
designation of contaminated areas and the practice of soil mixing via stockpiling. Each is
addressed separately below.
Contaminated Cells
Recall that SoilTrak was created to track the reuse of contaminated soil. Inherent to this mission
is the determination of what is, and is not, contaminated. Daily soil movement sheets reported
all soil movement, contaminated or not. There was a place on the form to designate which soil
movements involved contaminated soil. With this information, only entries for contaminated soil
movement were entered into the SoilTrak application. It was later discovered that there was a
10
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lack of clarity between contractors and BEM staff on how to determine if a cell was
contaminated. Different methods were being used, creating data accuracy problems. Some cells
had been reported as being contaminated and later were considered "clean", and vice-versa.
This again brought SoilTrak data entry to a halt while a time-consuming purge of bad data
ensued. This was made additionally painful by the fact that "fake-mixing" and filling using these
"mixes" pervaded the two main database tables. A consistent standard was developed and
necessary data was reentered and bad data expunged. Data entry resumed, but a new and
more serious problem was being identified.
Stockpiles
When the SoilTrak application was designed, some discussions centered on the likelihood of
soil "mixing". The Soil Reuse Plan that was driving the construction stipulated that soil
"blending" (mixing) was not permitted. Clean soil could not be mixed with contaminated to make
it "less" contaminated or to change it's engineering characteristics. It was decided, however, that
mixing of contaminated soil from different cells was possible. Thus, a mixing module was added
to SoilTrak to address the anticipated cases where soil from varying cells had been temporarily
stockpiled together. This type of activity would have no effect on the cost recovery objective of
SoilTrak, but would have a profound impact on the tracking of contaminants.
With contaminant tracking in
mind, a model had to be
developed that could
accurately account for the
addition of "new"
contaminants in a stockpile,
while assuring that any new
contaminants did not show
up in fill records occurring
prior to addition of the new
contaminants. Recall that
SoilTrak tracked "slugs" of
soil removed from the
January 1
January 2
January 3
Cells
Stockpile A
SollCode 102
Stockpile A
SollCode 102
iNCode
102
Cells
Stockpile A
SollCode 104
Figure 4
The new Soil Code created for Stockpile A was required to prevent
Contaminant D from showing up in the fill activity of January 2
,nd
ground via "Soil Code" numbers. It was determined that a mix could simply be a new Soil Code
to which the comprising soil codes were attached. In this way, SoilTrak could identify a record
11
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as a "mix" record, look into the source soil code field of each mix record and identify which
contaminants were present in the source area, and thus, the mix. Filling would be done using
the mix soil code. However, what if soil containing new contaminants is added to an existing soil
mix/stockpile? If SoilTrak used the mix's soil code to identify contaminants, then early fills would
be identified as having contaminants that weren't part of the stockpile until after the filling
occurred. The solution to this was to create a new soil code every time a new soil was added to
an existing mix. The new soil code would always represent the current suite of contaminants.
This model proved effective in the first phase where nearly all of the mixing was "fake" mixing
required for tracking costs. During the second phase, stockpiling (mixing) became common
place. Three serious issues began to appear, the combination of which would prove a fatal flaw
to SoilTrak's DER contaminant tracking objective.
Multiple Additions to Stockpiles
During SoilTrak's programming, soil mixing was not a major consideration since it was not
anticipated to occur. The above model had been anticipated but it was later discovered that the
code could not backtrack more than one mix at the reporting end. The data entry module could
support entry of these records, however, so data entry continued. This flaw in the code could be
addressed at a later time.
Stockpile Nomenclature
Mixing of soil from multiple cells into stockpiles became a common practice of Contractor #2.
Since the mixing was occurring with contaminated soils from within the corridor, no rules were
being broken. However, the degree to which stockpiling was occurring was unanticipated, so no
agreements were reached regarding stockpile naming. Reviews of soil movement reporting
sheets coming from the field were found to have stockpile names not known to those doing data
entry. Upon further investigation, it was discovered that stockpiles often had more than one
"name", one had at least five synonyms.
Upon discovery of this issue, meetings were arranged between BEM, the Contractor and their
field crews to develop a consistent nomenclature system for the stockpiles. Each stockpile
would receive a name that would include the construction area in which it was located, followed
by a sequential number identifying the order in which the stockpile had been created. This
approach worked, but considerable damage had been done to the data requiring a substantial
12
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effort to correct. Most of the corrections had to be done directly to the data since the SoilTrak
application could not edit Soil Code numbers. Since Soil Codes are program assigned and
represent a key field, SoilTrak application users were not permitted to edit these numbers.
Combining Stockpiles
Frequent additions of soil to an existing stockpile created a large number of new soil codes.
Tracking all of these was proving to be a data entry nightmare for the SoilTrak operators.
Extreme care always had to be taken when selecting the stockpile (Soil Code) to mix new soil
into, since each stockpile was represented by many soil codes. While difficult, however, data
entry through SoilTrak was still possible. One more twist was discovered which would break the
SoilTrak application; Contractor #2 was mixing stockpiles into other stockpiles. By this point the
new soil code - source soil code method of tracking contaminants broke down. Stockpiles could
now have multiple layers of mixing which would be very difficult to backtrack to original cut soil
codes. SoilTrak also had no means of allowing data entry of these kinds of mixes. Data entry
was forced to continue directly into the SoilTrak tables. A major code re-write would be required
to support the combining of stockpiles. Since nearly all of the soil filling during this second
phase of construction originated from stockpiles, it would be some time before the SoilTrak
application could meet its soil contaminant-reporting requirement.
Lessons Learned
In the spring of 1999, the SoilTrak process and application were re-evaluated. The first most
important question was whether or not SoilTrak met its two objectives, cost recovery support
and contaminant tracking. The cost recovery support objective was cleanly met. Accurate
reports could be generated for any tax lot, property owner or cut cell. A default value for cost per
cubic yard of soil excavation could be used or calculated from line item actual costs for a cell.
Many difficulties and issues arose during the initial use of SoilTrak, but none of these affected
the cost recovery tracking or reporting. Periodic mapping of soil movements involving both
cutting and filling was greatly simplified by SoilTrak's ability to generate the attribute tables
needed to link-up with BEM's GIS. SoilTrak successfully tabulated this data for reporting as well.
From the contaminant tracking and reporting perspective, SoilTrak fell significantly short of its
goal. The inability to handle complex soil mixing associated with stockpiling crippled both the
data entry and reporting capabilities. This wasn't the only shortcoming of SoilTrak however.
13
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During the evaluation, and subsequent re-design of SoilTrak, many issues were addressed.
Each is discussed below.
Preparing Data for Data Entry was too Complex
SoilTrak's user interface was not difficult to use; the failing was in the amount of preparation
needed to start entering data. In order to enter data, the user first had to know which properties
a cell consisted of. Using the GIS, a spreadsheet was supplied that listed the properties in a cell
and the area multipliers needed to assign soil excavation volumes to owners. Still, this required
the user to take a field entry of "Cut, 500 yards from cell C-15" and divide it into as many entries
as there were tax lots within cell C-15. Every cut typically needed a fake mix from which fill
records could later be completed. A cell having only three tax lot fragments in it required six
record entries for each cut from that cell. While programming could have eliminated much of the
data entry problem, the database model still required that this many records be written to the
database.
Keeping track of stockpile Soil Codes proved to be nearly impossible for both operators and
SoilTrak. The SoilTrak database was re-evaluated and a more fundamental issue was about to
be identified.
Tracking Soil Codes Created Needless Complexity
The initial idea was to track each "slug" of soil, each discreet cutting activity, from cradle to
grave. However, the smallest geographic unit being reported was the cell. Without the ability to
map precisely where within a cell a "slug" of soil came from, no useful information was gained
by tracking soil at this level. What then was the level of detail that should be tracked? It became
evident that what was really being tracked was cells, not soil. All activity occurred at that level,
soil was either removed from, or placed within, a cell. Stockpile tracking was also complicated
by soil codes. Every time a soil was added to a stockpile, a new Soil Code was created. This
was true even if soil were added from a cell that had been previously added.
It was decided that SoilTrak's database should be redesigned to track soil movement by cells
rather than Soil Codes. Soil would either go into or out of a cell. Stockpiles would be treated as
cells into which soil could be added or removed at any time. Dates, rather than Soil Codes,
would identify which contaminants were present in fills made from stockpiles. By dealing with
14
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cells only, no distinctions had to be made as to which lots were in a cell during data entry
eliminating the need for multiple record entries. Fake mixes were also eliminated.
Another benefit of this approach was that by treating stockpiles as cells, a separate "soilmix"
table was no longer required. This paradigm shift promised to greatly simplify the data entry and
programming tasks.
Handle Cell-Property Relationships at the Back-End
Part of SoilTrak's complexity-of-use centered on relating cells to properties at data entry time.
Moving to a cell-based tracking model changed the need for this. These relationships could be
derived at report-time when the information was needed, rather than stored record-by-record.
This would reduce the number of records in the database by 6:1 for cells that had three lots
within them. Also, if real-time embedded GIS were used to determine these relations at run-
time, any updates to the cell or property coverages would be immediately applied to any reports.
BEM had already had success developing embedded GIS applications using Microsoft Visual
Basic and ESRI's Map Objects LT™. At a minimum, the GIS could be used to re-create a table
of the property-cell relationships that could then be stored as a look-up table in the new SoilTrak
database.
Another advantage would be the ability to enter data into a new SoilTrak without the need for
completed cell or property mapping. This could allow data entry at the outset of the project,
assisting in early identification of any field procedural issues.
Identify Contaminated Cells at the Back-End
SoilTrak's fundamental goal was to track reuse of contaminated soil. In order to achieve this
objective, determinations had to be made up-front regarding which cells were, and were not
contaminated. The field sheets used by the contractors had a check box to identify which cuts
occurred in contaminated cells, allowing them to report all soil movement easily. But it was later
discovered that Contractor #2 was using a different means of determining which cells were
contaminated than the BEM staff. Then, during the re-evaluation of SoilTrak, questions were
raised about whether the method we were using was too conservative and perhaps
indefensible. Such questions at the near end of the data entry process were devastating. Since
only contaminated soil movements were supposed to be present in SoilTrak, any change in
15
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which cells were designated as contaminated could force major editing of the data and entry of
new data.
In order to prevent this chaos in the future, it was decided that SoilTrak should store all soil
movement without regard to classifications of "clean" or contaminated. Determination of which
cells were contaminated could then be done at report time. This would also allow for multiple
methods of making this determination and the ability to change the method at-will, with no
changes in the data. Another advantage of this approach was the elimination of the contractor
as a factor in reporting clean versus contaminated soil reuse.
Providing users with this flexibility would require additional data tables and complicate the
programming task, but the cost was deemed well worth it.
Handle Depth to Contaminated Soil at the Back-End
One minor failing of the SoilTrak application was its inability to report the starting and ending
elevations of contaminated soil filling for a cell. Although the database was designed to handle
this information, it was requested from the user for each filling activity. Rarely was this
information available from the field. More typically, construction firms will survey the beginning
elevation, complete all filling activities and then re-survey. The paradigm shift toward cell-based
tracking resulted in a new data table that stored the current net cut-fill volumes of each cell. This
created a convenient place to store the starting and final elevations of the filled soils by cell.
This would also require only one data entry effort for each cell and could be done when final
elevation data was available.
Agree on all Nomenclature Up-Front
BEM and both contractors had agreed at the outset of each phase what name each cell would
have. In Phase 2, stockpiles became a factor but were named in a haphazard fashion. Often
times multiple names were associated with the same stockpile. The discovery of this problem
resulted in substantial data editing. In a new, cell-based model where even stockpiles are
considered a cell, consistent nomenclature would be critical. Any naming schemes would be
decided by all parties up-front.
16
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Summary
The SoilTrak method and application were designed to track and report on contaminated soil
reuse. It used a database and application developed in Microsoft FoxPro and depended heavily
upon data provided by GIS coverages managed in ESRI's ArcCAD GIS. SoilTrak had two
primary objectives. One was to track and report on costs associated with this cost-effective
remedial option for the purposes of recovering these costs from property owners. The second
was to report on the contaminants present in contaminated soils used as fill throughout the
corridor. This would satisfy the state environmental agency's requirements for the Declaration of
Environmental Restrictions that was a foundation of the soil reuse option.
In the initial phase of construction, a large Park & Ride facility, the SoilTrak method and
application were able to meet these objectives. During the second phase, the southern half of
the rail corridor, the complexity associated with soil mixing via stockpiling proved to be too great
for the SoilTrak application to manage, and the contaminant tracking objective could no longer
be met. Cost tracking and cost recovery objectives were unaffected. A later addition to the
SoilTrak application supported regular mapping and reporting of both cutting and filling
activities.
An evaluation of SoilTrak's effectiveness showed that too much data preparation was required
prior to data entry. It was also clear that SoilTrak had failed its contaminant-tracking objective.
An examination of SoilTrak's data model showed that the tracking of individual "slugs" of soil via
Soil Codes was complicating data management and yielding no benefits. The paradigm was
changed to track activity by cell instead. This solved many problems by simplifying both data
entry and management as well as allowing for back-end determination of cell-property
relationships and resulted in many fewer database records.
In order to provide greater flexibility and minimize confusion, the new paradigm also stated that
all soil movements should be logged. Determination of which source (cut) cells were
contaminated would be done at the back end, allowing for multiple methods of determination.
Moving this determination to the back-end also eliminated any need to edit or add soil
movement data if the determination method was changed.
With these changes, and better communication between all involved parties, SoilTrak can meet
all of its objectives with significantly less labor and reduced chance of error. At this writing (June
17
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1999) SoilTrak is still being examined and development of a more powerful version, including
embedded GIS mapping, is scheduled to begin shortly.
18
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Application of a Geographic Information System for
Containment System Leak Detection
Randall R. Ross1, Milovan S. Beljin2 and Baxter E. Vieux3
ABSTRACT
The use of physical and hydraulic containment systems for the isolation of contaminated ground
water associated with hazardous waste sites has increased during the last decade. Existing
methodologies for monitoring and evaluating leakage from hazardous waste containment
systems rely primarily on limited hydraulic head data. The number of hydraulic head monitoring
points available at most sites employing physical containment systems may be insufficient to
identify significant leakage. A general approach for evaluating the performance of containment
systems based on estimations of apparent leakage rates is used to introduce a methodology for
determining the number of monitoring points necessary to identify the hydraulic signature of
leakage from a containment system. The probabilistic method is based on the principles of
geometric probability. A raster-based GIS (IDRISI) was used to determine the critical dimensions
of the hydraulic signature of leakage from a containment system, as simulated under a variety of
hydrogeologic conditions using a three-dimensional ground-water flow model. MODRISI, a set of
computer programs was used to integrate ground-water flow modeling results into the hydraulic
signature assessment method.
INTRODUCTION
Subsurface vertical barriers have been used to control ground-water seepage in the construction
industry for many years. Recently, the industrial and regulatory communities have applied
vertical barrier containment technologies as supplemental or stand-alone remedial alternatives
at hazardous waste sites to prevent or reduce the impact of contaminants on ground-water
resources (Rumer and Ryan, 1995). While subsurface barriers appear to be useful for isolating
long-term sources of ground-water contamination at many sites, the potential exists for leakage
1Hydrologist, U.S. EPA Nat. Risk Mgmt. Res. Lab, Subsurface Protection and Remediation Div., P.O. Box
1198, Ada, OK 74820
Consulting Hydrologist, M.S. Beljin & Associates, 9416 Shadyoak Court, Cincinnati, OH 45231
3Assoc. Prof., School of Civil Eng. and Env. Sci., Univ. of Oklahoma, 202 West Boyd Street, Norman, OK
73019
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of contaminants through relatively high hydraulic conductivity zones ("windows") within the
barriers.
This paper describes the application of a Geographic Information System (GIS) as a tool to help
identify leakage through discrete zones within a subsurface vertical barrier. The proposed
techniques could be useful for evaluating existing containment systems by providing insight as to
how many monitoring points are necessary to determine the approximate locations of discrete
leaks, given specified confidence and constraints.
Containment Systems
Subsurface containment systems may be active (e.g., ground-water extraction to manage
hydraulic gradient), or passive (e.g., physical barriers only) depending on the remedial objectives
and complexity of the hydrogeologic setting (Canter and Knox, 1986). Frequently, containment
systems employ a combination of active and passive components, which commonly incorporate
vertical barriers keyed into underlying low-permeability units. Many containment systems also
include a low permeability cover to reduce the rainfall infiltration, extraction and injection wells,
and trenches for ground-water management.
Soil-bentonite slurry cutoff walls (slurry walls) are the most common type of subsurface vertical
barriers used at hazardous waste sites and are generally installed around suspected source
areas (U.S. EPA, 1984). Construction defects or post-construction property changes are
potential failure mechanisms of subsurface vertical barriers (Evans, 1991). Construction defects
may result in the formation of relatively high hydraulic conductivity windows in a barrier. Some of
the mechanisms responsible for the formation of such windows include emplacement of
improperly mixed backfill materials, sloughing or spalling of in situ soils from trench walls, and
failure to excavate all in situ material when keying wall to the underlying low permeability unit
(U.S.EPA, 1987). Post-construction property changes may result from wet-dry cycles due to
water table fluctuations, freeze-thaw degradation, or chemical incompatibility between the slurry
wall material and groundwater contaminants.
Monitoring of Containment Systems
The performance of hazardous waste containment systems has generally been evaluated based
on construction specifications. Most subsurface vertical barriers are required to maintain a
hydraulic conductivity of 1x10"7 cm/s, or less. The use of appropriate construction quality
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assurance (QA) and quality control (QC) testing during installation is essential to ensure that the
design performance specifications are achieved. The regulatory community recognized the need
to develop procedures to verify post-construction performance and identify unsatisfactory zones
in containment systems (U.S.EPA, 1987). While construction dewatering systems are deemed
successful if the barriers limit ground-water leakage to reasonably extracted quantities, there are
no uniform methods to reliably measure and document the hydrologic performance of existing
and proposed hazardous waste containment systems (Grube, 1992).
The minimum number of monitoring points necessary to determine whether a containment
system is functioning as designed depends on site-specific conditions. For example, in some
cases it may be possible to determine whether leakage has occurred by analyzing the water
level trends in monitoring wells (Ross and Beljin, 1998). Subtle variations in the hydraulic head
distribution associated with leakage through a subsurface barrier may be identifiable if sufficient
hydraulic head data are available for analysis. Such an undertaking would generally be
considered prohibitively expensive due to the high cost of installing a piezometer network
capable of adequately defining the hydraulic head distribution. However, the recent development
of relatively inexpensive installation techniques may make it feasible to install a sufficient
number of small diameter piezometers to identify the hydraulic signatures associated with
containment system leakage.
A New Monitoring Method
The process of locating a leak in a hazardous waste containment system can be analogous to
mineralogical prospecting where a compromise is sought between the cost of exploration and
the thoroughness of the search. For mineral exploration applications, the expected benefit of a
search is the sum of the value of each target multiplied by the probability of finding it, assuming
that the target exists in the search area (Singer, 1972). For containment system leak detection,
the expected benefit of a search is the potential reduction in risk to human health and the
environment associated with the detection and abatement of significant leaks.
Gilbert (1987) presents a methodology based on the work of Savinskii (1965), Singer and
Wickman (1969), and Singer (1972) that can be used to determine the grid spacing required to
detect highly contaminated local areas or hot spots at a given level of confidence, or estimate
the probability of finding a hot spot of specified dimensions, given a specified grid spacing.
Given a specific grid spacing, the probability of detecting a target is determined by the method of
-------
geometric probability, which is a function of the ratio of the area of the target to the area of the
grid cell. The method assumes that the highly contaminated areas are circular or elliptical in
shape, the boundaries of the hot spot are clearly identifiable based on contamination levels, hot
spot orientation is random with respect to the sampling grid, and the distance between grid
points is much larger than the area sampled. In order to address variations in the distribution of
hydraulic head, rather than contaminant concentrations, the assumptions were modified for the
methodology presented in the paper.
METHODOLOGY
The hydraulic signature associated with leakage from a containment system is simulated using a
numerical model for a variety of hydrogeological settings. The modeling results provide the data
on which the hydraulic signature assessment method is demonstrated. A set of computer
programs was developed (Ross and Beljin, 1995) to import modeling data into a raster-based
GIS, for further processing. The GIS was used to generate the input data for the ground-water
model.
Ground-Water Modeling
A model may be defined as a simplified version of a real system that approximates the stimulus-
response relationships of that system (Bear and others, 1992). By definition, the use of a model
requires the application of simplifying assumptions to describe the pertinent features, conditions,
and significant processes that control how the system reacts to stimuli. In this study, one of the
primary objectives of the modeling was to predict the hydraulic head distribution associated with
leakage through discrete leaks in a vertical barrier under different hydrogeologic conditions.
The conceptual model presented in this paper is based on characteristics of several specific
hazardous waste sites that incorporate physical containment as a major component of the
remedy. The sites which influenced the development of the model used in this study include the
Gilson Road Superfund site (Nashua, New Hampshire), the G.E. Superfund site (Moreau, New
York), and the Velsicol/Michigan Chemical Company Superfund site (St. Louis, Michigan). The
conceptual model for the containment system consists of a slurry wall fully penetrating an
unconsolidated surficial aquifer, keyed in to an underlying low permeability aquitard (Figure 1).
Hydraulic head values are assumed to be higher in the interior of the containment system,
simulating a worst-case scenario for potential contaminant losses from the system (Figure 1).
-------
The elevated water levels within the conceptual containment system are assumed to be derived
from deficiencies in the system (i.e., leakage under or through the upgradientwall and infiltration
through the cap), and water levels are assumed to be relatively stable over time. Ground-water
flow is assumed to be horizontal, except in the immediate vicinity of the vertical barrier. Given the
long-term nature of most hazardous waste containment systems, the hydraulic heads are
averaged over long time periods. Consequently, steady-state flow conditions are assumed for all
simulations used in this study.
The hydraulic head distribution associated with a linear segment of a conceptual vertical barrier
was simulated using Visual MODFLOW7 (Guigerand Franz, 1995), a commercial version of the
three-dimensional, finite difference ground-water flow model MODFLOW, developed by the U.S.
Geological Survey (McDonald and Harbuagh, 1988).
Data Processing with a GIS
The hydraulic head data generated by the numerical simulations are extracted, visualized,
sampled, analyzed, and appropriately manipulated using several software packages. Hydraulic
head data from a vertical cross-section parallel to, and immediately down gradient from the
simulated vertical barrier are used throughout this study. The data are extracted from
MODFLOW output files and reformatted as image files for analysis using MODRISI (Ross and
Beljin, 1995). The GIS software used in this study is IDRISI (Eastman, 1995), a raster GIS that
provides numerous analytical capabilities that are directly applicable to this, and other
hydrogeologic studies. The uniform grid spacing facilitates the transfer of data from one software
package to another. The raster format allows import and export of uniform grid model data and
also provides a robust platform for the analysis, visualization and data manipulation.
Model Setup
The model domain consists of 51 rows, 51 columns, and 25 layers (Figure 2) and is discretized
into uniform 1 m3 blocks. This configuration is sufficiently large to reduce boundary effects and
provides sufficient resolution to allow identification of subtle variations in hydraulic heads
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Idealized Contianment System
Hln>rL>HB
Undesirable Conditions
Qin
Aquifer A
'Cap
Slurry Wall
Qc.
Aquitard
Aquifer B
Hh = Hydraulic Head Inside Containment System
HM = Hydraulic Head In Adjacent Aquifer A
HB = Hydraulic Head In Underlying Aquifer B
Q,, = Leakage Out Of Containment System
Q, = Leakage Into Containment System Via Cap
Figure 1. Major components of an idealized hazardous waste containment system
exhibiting unfavorable conditions (e.g., outward hydraulic gradient).
associated with leakage through a vertical barrier. The uniform grid size allows consistent
precision over the entire model domain and simplifies data management and transfer between
software packages.
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he slurry wall is simulated as a one-meter thick barrier with uniform properties, except for the
window. The hydraulic conductivity values for the aquifer and window are scenario dependent.
Leakage through the wall is simulated as a window with dimensions of 2 x 3 cells (6 m2), located
in the approximate center of the vertical barrier (row 25, columns 24-26, layers 12 and 13).
No-Flow Boundary
Constant-Head Boundary
Columns (J) = 51 /
Vertical Barrier (row 25)
Ground-Water Flow
Direction
Layers (K) = 25
v» = 1 m
Sc, = 1 m
Rows (I) = 51
No-Row Boundary
(not to scale)
r,= 1 m
Constant-Head Boundary
Figure 2. Conceptual model domain and boundary conditions.
Boundary conditions are depicted in Figure 2. The upgradient and downgradient sides of the
model are constant-head boundaries, resulting in a horizontal hydraulic gradient across the
model domain of 0.0196 m/m. This value falls within the range of hydraulic gradients commonly
observed in the field. The sides and lower surface of the model oriented parallel to ground-water
flow are simulated as no-flow boundaries.
The applicability of the numerical model for simulating the hydraulic head distribution associated
with leakage from a containment system was demonstrated by comparing model results to data
generated from a laboratory bench scale model of a cutoff wall (Ling, 1995). Simulation results
agreed favorably with the physical model results, indicating that the approach described in this
-------
study is appropriate for simulating the hydraulic head distribution associated with leaking vertical
barriers.
General Simulation Scenarios
Several hypothetical hydrogeologic conditions are evaluated in this study. Different scenarios are
used to better understand the potential variability of the hydraulic signatures associated with
different subsurface conditions and to account for potential uncertainties associated with
predictive modeling.
A range of homogeneous and isotropic conditions were simulated in an effort to provide a
reference case for evaluating the effects of varying average aquifer hydraulic conductivity values
on the hydraulic signature of a simulated leak. The scenarios spanned a wide range of hydraulic
conductivity values with respect to the aquifer material and zone of leakage. The hydraulic
conductivity values for the aquifer range from 1 10"2 cm/s to 1 10"5 cm/s. The hydraulic
conductivity of the vertical barrier is maintained throughout the study at 1 10"7 cm/s. The
hydraulic conductivity values for the window ranged from 1 10"2 cm/s to 1 10"5 cm/s. The
hydraulic conductivity value for the window is assumed to be less than or equal to that of the
adjacent aquifer materials. The scenarios simulate the general effects of layering by varying the
horizontal to vertical hydraulic conductivity ratios of aquifer materials.
One of the primary limitations of using ground-water flow models as a predictive tool results from
the uncertainty associated with input parameters. This uncertainty is directly related to the spatial
variability of hydrogeologic properties of the porous medium (i.e., aquifer material). To account
for some of the spatial variability and uncertainties associated with three-dimensional predictive
flow modeling, several scenarios utilizing heterogeneous distributions of hydraulic conductivity
were assessed. The assumption of lognormally distributed hydraulic conductivity is used for the
heterogeneous, isotropic and heterogeneous, anisotropic simulations. Unique lognormal
hydraulic conductivity distributions were generated for each of the 25 layers using built-in
functions of the GIS software. This approach resulted in the generation of approximately 63,000
hydraulic conductivity values within the model domain.
Hydraulic Signature Assessment Method
The methodology used to address the hydraulic head distribution associated with leakage from a
containment system was developed based on the work of Singer and Wickman (1969) and
-------
Gilbert (1987). The proposed method is directly applicable to determining the grid spacing
necessary to detect the hydraulic signature associated with a discrete leak in a subsurface
vertical barrier. The methodology requires the following assumptions:
• the hydraulic signature of the leak is circular or elliptical;
• hydraulic head data are acquired on a square grid;
• the criteria delineating the hydraulic signature are defined; and
• there are no measurement misclassification errors.
The model results indicate that the hydraulic signatures associated with the simulated leaks
range in shape from approximately circular to elliptical when viewed in vertical cross-section. An
increase in the anisotropy results in the elongation of the signatures in the horizontal directions.
As expected, the greater the anisotropy, the more elliptical the hydraulic signature of the leak.
The criteria for delineating the hydraulic signature of a leak from background noise are based on
the average hydraulic head value ( h) of the model cross-sectional surface. For this study,
hydraulic head values of h+0.05 m and h+0.1 m were identified as critical values (Cv),
indicating the presence of a hydraulic anomaly associated with containment system leakage.
This follows the assumption that any background noise associated with the hydraulic head
measurements is significantly less than 0.05 m. The dimensions of the hydraulic anomalies are
determined using GIS software by image reclassification to delineate nodes exceeding the
average hydraulic head by the specified critical values. The dimensions of the hydraulic
signatures delineated by the two values for Cv are expressed as shape factors (S), defined as
the ratio of the length short axis to the length of the long axis of the hydraulic signature. The
shape factor for a circular feature is 1. An increase in anisotropy results in the elongation of the
feature and a decrease in S, where 0 < S < 1.
The probability tables of Singer and Wickman (1969), were used to determine the probability of
not detecting a leak when a leak is present (P) to the ratio of the semi-major axis to grid size
(L/G). The semi-major axis is defined as one half the length of the long axis of an elliptical
feature. The general procedure for determining monitoring point spacing necessary to detect a
hydraulic anomaly of given dimensions and specified confidence is outlined in Table 1, and in
the following example.
-------
Table 1. General steps for determining monitoring point grid spacing.
1.
Specify the radius or one half the length of the long semi-major axis
(L) of the hydraulic signature (mound) associated with the leak;
Assuming a circular hydraulic signature, let the shape factor (S) equal
one; for elliptical features, S may be calculated using equation (9);
3.
Specify the maximum acceptable probability (P) of not detecting the
hydraulic feature (=0.1);
Knowing L, S and assuming a value for p, determine L/G from Figure
4, and solve for G (minimum grid spacing required to detect the
hydraulic anomaly associated with the leak, given the specified
constraints).
In order to determine the minimum grid spacing necessary to identify a hydraulic feature of
specified dimensions, an acceptable probability of not detecting the feature must be established.
For this example, a value of p= 0.1 is assumed for a leak signature with dimensions of 5 m by 4
m, as delineated by Cv= 0.1 in Figure 3a. From Figure 4, a value of approximately 0.64 is
indicated for the ratio of the length of the semi-major axis to grid size (L/G), given = 0.1 and S =
0.8. Therefore, solving for G using L = 2.5, it is determined that a minimum grid spacing of
approximately 3.9 m is necessary to identify the specified feature with a 90% probability of
success. The resulting grid spacing (G) may be used to determine the minimum number of
block-centered monitoring points required to detect the feature for a specified area by dividing
the total area by the area of one square grid (G2).
The probability tables were also used to generate nomographs relating the probability of not
detecting a leak (P) of specified dimensions (L), for different grid dimensions (G). Figure 5
illustrates this relationship for circular hydraulic signature (S = 1.0). The nomographs may be
used to estimate the dimensions of the smallest hydraulic signature capable of being identified
by a monitoring network of known dimensions within an acceptable level of confidence (P). For
example, given a monitoring point spacing of 20 m, what is the smallest circular hydraulic
anomaly that can be detected with 80% probability of success (P= 0.2). From Figure 5 it is noted
that a circular feature with a radius of approximately 10.1 m can be detected with the specified
10
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probability and grid spacing. The probability of not detecting the anomaly will increase as the
radius of the hydraulic signature decreases.
RESULTS AND DISCUSSION
The dimensions of the hydraulic signatures associated with leakage through a subsurface
vertical barrier are a function of the hydrogeologic properties of the aquifer, vertical barrier, and
zone of leakage. Assuming all other variables remain constant, the magnitude of the hydraulic
signature diminishes significantly as the hydraulic conductivity of the window decreases (Figure
3). The hydraulic signature of leakage through the hydraulic conductivity window becomes less
prominent as its value is reduced by one order of magnitude (Figure 3g). As the value is further
reduced, the hydraulic signature becomes discernable only immediately adjacent to the window
(Figure 3j). The decrease in hydraulic signature corresponds to a decrease in flux through the
window, as the window hydraulic conductivity is reduced (Table 2).
11
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o co UD OT T— -a- r--
§•*• oo (M r-- i— in
i— CM <•* ir> r~ oo
OOQOOOO
LLl III III
- -
3
o
CM CM CM CM CM CM CM C\j CM CM CM ^3
in o
co o
T— CM
Al
••••SDDDD
X
N
2
r^
a IPJI
o ° ^^
S0'*3 M
§ ^ a
S « '>
O O l>
ffi O ^
(0
60 '-
2
O O
ffi O
o o
ffi O
Figure 3. Vertical cross-section of model results illustrating hydraulic signature (head) variations
due to changes in conceptual hydrogeological setting.
12
-------
1 -,
*••" n Q -
D)
ro n ft -
c o?
'(0
^ 06 -
M—
one;
^°-5.
= 04-
ro
o
o n ^
CL
"09-
ro u.z
0)
co n 1 -
Q
^%^
^x\^
\\\\
\\\
VX
\\
\ i
S=1.0 \
^
^^
\ ^^
; \
\ X
\\ X
\S=0.8\ S=0.6 \
\\ \
XXV
^^^
^\
S=0.4
\
\
\
^^^
3=0.2 ^^^^
0 0.5 1 1.5 2
Ratio of Semi-Major Axis to Grid Size (L/G)
Figure 4. Nomograph relating ratio of semi-major axis of elliptical target and grid size to the
probability of missing the target (Beta) for different shape factors using a square grid pattern.
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
G=5
G=10
G=15
XG=20
6 8 10 12
L = Radius of Circular Target (m)
14
16
18
Figure 5. Nomograph relating radius of circular hydraulic signature to probability of not
detecting leak (Beta) for different grid spacings.
-------
Table 2. Simulated flux through windows of varying hydraulic conductivity.
Window
Hydraulic
Conductivity
(cm/s)
1 10'2
1 10'3
1 1C'4
1 10'5
Minimum
Head
Value (m)
24.0293
24.0117
24.0071
24.0063
Maximum
Head
Value (m)
24.2627
24.0826
24.0165
24.008
Range
(m)
0.2334
0.0709
0.0094
0.0017
Flux
Through
Window
(m3/d)
1.31101
3.98
4.961 0'1
5.091 0'2
The effect of varying the horizontal to vertical hydraulic conductivity values is illustrated in Figure
3. For example, the hydraulic signature from leakage through a window under homogeneous
and isotropic conditions forms an approximately circular feature (Figure 3a). However, as the
horizontal to vertical hydraulic conductivity ratio increases, the hydraulic signature of the leak
becomes more elliptical (Figure 3b,c). Similar trends are observed with respect to increasing the
horizontal to vertical hydraulic conductivity ratio for the heterogeneous simulations (Figure 3d,e,f)
and other homogeneous simulations with smaller hydraulic conductivity values for the windows
(Figure 3g-l).
The method was applied to different hydraulic signatures developed from ground-water flow
simulations of leakage through a vertical barrier. The criteria used to differentiate the hydraulic
signature of leakage from background noise are Cv = h+0.05 m and h+0.1 m. Figure 6a
depicts the head distribution associated with hydraulic signature of leakage through a window
located in the approximate center of a vertical barrier in a homogeneous, isotropic aquifer. The
approximate dimensions of the vertical hydraulic mound as defined by Cv= h+0.05 and h+0.1
are 7 m by 6 m, and 5 m by 4 m, respectively.
14
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An increase in the anisotropy of the simulated aquifer by one order of magnitude produces a
vertically compressed and horizontally elongated hydraulic signature (Figure 6b). Similarly,
increasing the anisotropy of the simulated aquifer by two orders of magnitude results in even
greater compression and elongation of the hydraulic signature in the vertical and horizontal
directions, respectively (Figure 6c).
Hydraulic signatures for leakage through a window with a hydraulic conductivity value of 1 10"3
cm/s exhibit similar trends in response to increases in anisotropy (Figure 7a,b,c). However, the
overall hydraulic signature of the window is decreased significantly relative to that of the base
case. This results in a lack of head values greater than the elevation threshold for Cv= h+0.1 for
the homogeneous, isotropic simulations. The hydraulic head values associated with leakage
through windows with hydraulic conductivities < 1 10"3 cm/s were all less than Cv= h+0.05, and
therefore, could not be evaluated as described above.
The grid sizes necessary to identify the hydraulic features described below with a 90%
probability of success ((3=0.1) were obtained using the nomograph in Figure 4. The number of
sampling points (Ns) necessary to identify the hydraulic features within the domain of the model
cross-section is determined by dividing the cross-sectional area of the model (1,275 m2) by the
area of one square grid spacing (G2). The results are listed in Table 3.
The number of monitoring points required to identify the hydraulic signatures of the simulated
leaks using the prescribed constraints and confidence ranges from approximately 40 to over
300. The wide range of values is a function of the variability in the size and shape of the
hydraulic features. This variability results from the use of different critical values to define the
hydraulic signatures of the leaks and the wide range of shape factors resulting from the three
orders of magnitude range of the anisotropy values.
15
-------
a. Homogeneous and feotropic Simulation Results
b. Homogeneous and Arisotropic Results (KhKv=10)
• 2.40000E+01
• 2.40143Ei-01
• 2.4Q28GE+Q1
• 2.40429E+01
• 2.40571E+01
• 2.40714E +01
I 1 2.40857E+01
• 2.41000E+01
• 2.41143E+01
• 2.41286E+01
IS 2.41429E+01
I I 2.41571E+01
I I 2.41714E+01
I I 2.41857E+01
I I >2.42000E+01
Hydraulic Head Values
X
c. Homogeneous and Arisotropic Results (KhKv= 100)
Figure 6. Vertical cross-section of model results illustrating variations in hydraulic head
values due to changes in anisotropy (Kaq=1 10"2 cm/s, Kwin=1 10"2 cm/s). The ellipses
define the approximate boundaries of the hydraulic features defined by specified critical
values (Cv= +0.1 and +0.05).
16
-------
a. Homogeneous and feotropic Simulation Results
b. Homogeneous and Arisotropic Results (KhKv=10)
• 2.40000E+01
• 2.40143E+01
• 2.40286E+Q1
• 2.40429E+Q1
• 2.40571E+01
• 2.40714E+01
I i 2.40857E+01
• 2.41000E+01
• 2.41143E+01
• 2.412SGE+01
I 1 2.41429E+01
I I 2.41571E+01
I I 2.41714E+01
I I 2.41S57E+01
I I >2.42000E+01
Hydraulic Head Values
X
c. Homogeneous and Arisotropic Results (KhKv=100)
Figure 7. Vertical cross-section of model results illustrating variations in hydraulic head
values due to changes in anisotropy (Kaq=1 10"2 cm/s, Kwin=1 10"3 cm/s). The ellipses
define the approximate boundaries of the hydraulic features defined by specified critical
values (Cv= +0.1 and +0.05).
17
-------
Table 3. Parameters and Results Obtained from Hydraulic Assessment Method.
Kwin
(cm/s)
1 10'2
1 10'2
1 10'2
1 10'2
1 10'2
1 10'2
1 10'3
1 10'3
1 10'3
1 10'3
1 10'3
1 10'3
1 10'2*
1 10'2*
1 10'2*
1 10'2*
1 10'2*
1 10'2*
Kh:Kv
1
1
10
10
100
100
1
1
10
10
100
100
1
1
10
10
100
100
Cv
0.1
0.05
0.1
0.05
0.1
0.05
0.1
0.05
0.1
0.05
0.1
0.05
0.1
0.05
0.1
0.01
0.1
0.05
s
0.8
0.85
0.28
0.31
0.13
0.16
BCL
0.67
0.67
0.4
0.4
0.15
0.8
0.85
0.28
0.31
0.13
0.16
L
2.5
3.5
3.5
6.5
7.5
12.5
1.5
1.5
2.5
2.5
6.5
2.5
3.5
3.5
6.5
7.5
12.5
L/G
0.64
0.62
1.64
1.51
3.5
2.9
0.74
0.74
1.17
1.17
3.05
0.64
0.62
1.64
1.51
3.5
2.9
G
3.91
5.65
2.13
4.3
2.14
4.3
2.03
2.03
2.14
2.14
2.13
3.91
5.65
2.13
4.3
2.14
4.31
Ns
84
40
280
69
278
69
311
311
280
280
281
84
40
280
69
278
69
BCL = All head values below critical value threshold.
*Heterogeneous simulations; all other simulations homogeneous
18
-------
CONCLUSIONS
Numerical modeling of ground-water flow through high hydraulic conductivity windows in
subsurface vertical barriers was conducted to provide data sets for use with a probabilistic
method for determining the grid spacing necessary to identify the hydraulic signature associated
with the leaks. The proposed method of combined ground-water modeling and GIS represents a
potential tool that may be used by the regulatory community and others to evaluate the
adequacy of existing and proposed hazardous waste containment systems for identifying
containment system leakage. The utility of the proposed method is demonstrated using
simulated data. Based on the application of the method presented, the following conclusions
were made:
• The number of points necessary to identify the hydraulic signature of a discrete leak
within prescribed constraints is a function of the criteria used to delineate the feature;
• The hydraulic signature associated with a minor leak in a vertical barrier may be difficult
to detect with a realistic number of monitoring points;
• By using the nomographs described above, the probability of failing to detect the
hydraulic signature of a leak can be estimated for a given monitoring well spacing and
specified confidence;
• The dimensions of the smallest hydraulic signature detectable with a given monitoring
point spacing can be estimated, given the appropriate constraints and specified
confidence;
• The monitoring point spacing used at many hazardous waste sites is likely inadequate to
detect the hydraulic signatures of all but the largest leaks; and
• The method for delineating the hydraulic signature of a leak using the average hydraulic
head plus specified values does not appear to be as sensitive to the heterogeneity of the
aquifer as it is to anisotropy.
DISCLAIMER
The U.S. Environmental Protection Agency, through its Office of Research and Development,
funded and managed the research described here through in-house efforts. This information has
not been subjected to the Agency's peer or administrative review and therefore does not
necessarily reflect the views of the Agency; no official endorsement should be inferred. Mention
of trade names or commercial products does not constitute endorsement of recommendation for
use.
19
-------
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Water Issue, U.S. Environmental Protection Agency, Office of Research and Development, R.S.
Kerr Environmental Research Laboratory, Ada, OK, EPA/540/S-92/005.
Canter, L.W., and Knox, R.C. (1986). Ground Water Pollution Control, Lewis Publishers, Boca
Raton, FL.
D'Appolonia, D.J. (1980). A soil-bentonite slurry trench cutoff, Journal of Geotechnical
Engineering, ASCE, Vol. 106, no. 4, pp.399-417.
Eastman, J.R. (1995). IDRISI for Windows: User's Guide, Version 1.0, Clark University,
Worchester, MA.
Evans, J.C. (1991). Geotechnics of Hazardous Waste Control Systems, Chapter 20, Foundation
Engineering Handbook, 2nd ed., H.Y. Fang, ed., Van Nostrad-Reinhold Company, New York.
Gilbert, R.O. (1987). Statistical Methods for Environmental Pollution Monitoring, Van Nostrand
Reinhold, New York, N.Y.
Grube, W.E., Jr. (1992). ASIurry Trench Cut-Off Walls for Environmental Pollution Control, Slurry
Walls: Design, Construction and Quality Control. ASTM STP 1129, David B. Paul, Richard R.
Davidson, and Nicholas J. Cavalli, Eds., American Society for Testing and Materials,
Philadelphia.
Guiger, N., and Franz, T. (1995). AVISUAL MODFLOW, The Integrated Modeling Environment
for MODFLOW and MODPATH, Version 1.1, Waterloo Hydrogeologic, Ontario, Canada.
Ling, K., (1995b). Windows Development and Detection in Soil-Bentonite Cutoff Walls, Ph.D.
Dissertation, University of Cincinnati.
McDonald, M.G., and Harbuagh, A.W. (1988). AA Modular Three-Dimensional Finite-Difference
Ground-Water Flow Model (MODFLOW), U.S. Geological Survey Techniques of Water-
Resources Investigation, Book 6, Chapter A1.
Ross, R.R., and Beljin, M.S. (1995). AMODRISI: A PC Approach to GIS and Ground-Water
Modeling, Proceedings, National Conference on Environmental Problem-Solving with
Geographic Information Systems, Cincinnati, OH, September 21-23, 1994. EPA/625/R-95/004.
Ross, R.R., and Beljin, M.S. (1998). An Evaluation of Containment Systems Using Hydraulic
Head Data, J. Envir. Eng.. 124(6), 575-578.
Rumer, R.R., and Ryan, M.E., eds. (1995). Barrier Containment Technologies For Environmental
Remediation Applications, John Wiley & Sons, Inc., New York.
Savinskii, J.D. (1965). A Probability Tables for Locating Elliptical Underground Masses with a
Rectangular Grid, Consultants Bureau, New York, pp. 110
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Singer, D.A., (1972). AELIPGRID, a Fortran IV program for calculating the probability of success
in locating elliptical targets with square, rectangular and hexagonal grids, Geocon Programs,
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Singer, D.A., and Wickman, F.E. (1969). Probability Tables for Locating Elliptical Targets with
Square, Rectangular and Hexagonal Point-Nets, Mineral Sciences Experiment Station Special
Publication 1-69, Penn. State University, University Park Pennsylvania, 100 p.
U.S. EPA (1987). A Construction Quality Control and Post-Construction Performance
Verification for the Gilson Road Hazardous Waste Site Cutoff Wall, Hazardous Waste
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EPA/600/2-87/065
21
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MODRISI: A PC Approach to GIS and Ground-Water Modeling
Randall R. Ross
Robert S. Kerr Environmental Research Laboratory, U.S. Environmental Protection Agency,
Ada, Oklahoma
Milovan S. Beljin
University of Cincinnati, Cincinnati, Ohio
Abstract
It is widely accepted that ground-water contamination
problems cannot be adequately defined or addressed
until the governing physical, chemical, and biological
processes affecting the transport and fate of contami-
nants are adequately characterized. Recent research
has led to a better understanding of these complex
processes and their effect on the movement of contami-
nants in the subsurface. The compilation and application
of such information has yet to be accomplished at many
hazardous waste sites, however. Too often, copious
quantities of data are collected, only to be stored, ig-
nored, or misplaced, rather than used for problem-solving.
Geographic information systems (GIS) are computer-
based tools that are relatively new to many environ-
mental professionals. GIS allows the manipulation,
analysis, interpretation, and visualization of spatially re-
lated data (e.g., hydraulic head, ground-water velocity,
and contaminant concentration). GIS is more than a
cartographic utility program, however. The analytical ca-
pabilities of GIS allow users to display, overlay, merge,
and identify spatial data, thereby providing the basis for
effective environmental decision-making.
IDRISI is a widely used PC-based raster GIS system
that provides numerous analytical capabilities that are
directly applicable to hydrogeologic studies. Raster sys-
tems are particularly well suited for analysis of continu-
ous data such as elevation (e.g., water table, land and
bedrock surfaces), precipitation, recharge, or contami-
nant concentrations and may be readily integrated with
finite-difference ground-water models. Because the for-
mats for IDRISI and ground-water model input data sets
are different, a need exists for a program to integrate
these two types of robust tools.
MODRISI is a collection of utility programs that allows
easy manipulation and transfer of data files between
IDRISI and ground-water models (e.g., MODFLOW,
ASM, MOC). In addition, MODRISI integrates other
widely used commercial and private domain software
packages, such as SURFER, Geopack, GeoEas, Auto-
CAD, CorelDraw, and various spreadsheet programs.
Two-dimensional arrays of models' input data sets can
easily be created from IDRISI image files. AutoCAD
vector files obtained by digitizing model boundaries, well
locations, rivers and streams, or U.S. Geological Survey
digital elevation model (DEM) files can also be trans-
lated into model input file formats. MODRISI can proc-
ess model output files and prepare GIS image files that
can be displayed and manipulated within IDRISI. Thus,
MODRISI is more than a pre- and postprocessor for
ground-water models; it is a complete GIS/ground-water
modeling interface that is accessible to most ground-
water hydrologists.
Introduction
Hydrogeologists collect and analyze large volumes of
data during a ground-water modeling process. These
data are stored and presented in many different forms
such as maps, graphs, tables, computer databases, or
spreadsheets. To most hydrogeologists, geographic in-
formation systems (GIS) are relatively new tools. They
have been developed and applied in other natural and
social science fields for over two decades, however, and
can also be used in the ground-water modeling process.
GIS represents a new, powerful set of tools that can
significantly improve the usefulness of results obtained
during the ground-water modeling process. Bridging the
disciplines of ground-water modeling, computer graph-
ics, cartography, and data management, GIS represents
a computer-based set of tools to display and analyze
spatial data (e.g., water level elevations, ground-water
quality data, modeling results, ground-water pollution
potential). Efficient use of increasingly large volumes of
-------
data can be achieved only with powerful systems capa-
ble of acquiring information from a variety of sources,
scales, and resolutions.
CIS can be defined as a computer-assisted system for
the efficient acquisition, storage, retrieval, analysis, and
representation of spatial data. Most CIS platforms con-
sist of numerous subsystems that perform the listed
tasks. The subsystems have the ability to query spatially
related information and incorporate statistical analyses
and modeling of relations and their temporal changes
within the database. More than just a mapping system,
CIS allows the user to analyze spatially related data and
visualize results in either paper map form or graphically
on screen. The data to be analyzed are a collection of
spatial information represented by points, lines, and
polygons and their associated attributes (characteristics
of the features such as elevation or concentration). The
cartographic tools of CIS allow the analyst to display,
overlay, measure, merge, and identify the data to sup-
port a particular analysis. By allowing spatial data analy-
sis and display, CIS provides the means necessary for
effective environmental decision-making and implemen-
tation of environmental management plans.
CIS uses two basic map representation techniques:
vector and raster. Vector representations describe fea-
tures with a number of connected points. Raster repre-
sentations subdivide a study area into a mesh of grid
cells, each cell containing either a quantitative attribute
value or feature identifier. Raster systems are well suited
for analysis of continuous data (e.g., water level eleva-
tions, infiltration and recharge rates). This makes raster-
based systems ideal for integration with ground-water
models that use regularly spaced nodes. The objective
of this paper is to illustrate such an integration of MOD-
FLOW (1), a widely used U.S. Geological Survey
(USGS) finite-difference ground-water flow model, and
IDRISI (2), a raster-based CIS.
Previous Studies
To enhance understanding of a hydrogeologic system,
and also to develop a credible ground-water model of
the system, hydrogeologic features such as lithological
logs, recharge and withdraw rates, estimates of spatial
distribution of hydraulic conductivity, or specific storage
can be plotted using CIS capabilities of data retrieval
and overlay options to interactively define an area of
interest (3-6). Two previous studies that combined CIS
and ground-water modeling are briefly described.
Torak and McFadden (7) used CIS to facilitate finite-element
modeling of ground-water flow. Complex aquifer geometry
and irregularly distributed aquifer-system characteristics
that influence ground-water flow affect the design of the
finite-element mesh. CIS systems represent the com-
plex arrangement of nodes and elements and the dis-
tribution of aquifer properties to provide input to the
flow model. Point-data coverages of pertinent aquifer
characteristics are rated from a relational database and
are displayed using CIS.
Contoured surfaces based on point-data coverages pro-
duce triangulated irregular networks (TINs) that are su-
perposed on the finite-element mesh to delineate zones
of elements having similar aquifer properties. Zone
boundaries are identified using the contoured TIN sur-
face and by manually determining where boundaries
align with the element sides. The allocation of well
pumping rates to nodes in the finite-element mesh is
performed efficiently with CIS for model input. Well
pumping rates are accumulated by element from the
combined coverages of the pumping data and the mesh,
and element data are distributed to the node points for
input. CIS is also used to prepare data for model input
and to assess the adequacy of the data priorto simulation.
Three-dimensional perspectives showing TIN cover-
ages of aquifer-property data are used to analyze and
interpret complexities within the flow system before
zonation. Additionally, CIS is used to display computed
hydraulic heads over the finite-element mesh to produce
contour maps of the simulated potentiometric surface.
Because the node points in the finite-element mesh are
not arranged in an orthogonal fashion, such as a finite-
difference grid, a map display of the computed values of
hydraulic head at the nodes is prepared for efficient and
accurate interpretation of simulation results.
Harris et al. (8) conducted the Remedial Investiga-
tion/Feasibility Study (RI/FS) of the San Gabriel basin.
Vast amounts of hydrogeologic data have been gath-
ered, and a comprehensive systematized CIS database
has been developed. The identified hydrologic bounda-
ries, recharge basins, stream locations, well locations,
and contaminant distributions are some of the features
considered in developing a base map. The CIS-gener-
ated base map has allowed development of a finite-ele-
ment grid for the basin. For each finite element, the initial
estimates of the hydraulic conductivity, specific yield,
recharge rates, and other input parameters were provided.
Using simple interfacing programs, the retrieval CIS
nodal and elemental data were converted to required
formats for the input files of the Couple Fluid, Energy,
and Solute Transport (CFEST) code. Simulated ground-
water levels were compared with the CIS-generated
potentiometric surfaces. In areas of wide variations be-
tween simulated and observed data, the zonal distribu-
tion of controlling parameters was reevaluated,
analyzed, and updated. Data processing, development
of input files for computerized analysis of ground-water
flow, and analysis of simulation results with different
alternative conceptualizations is time consuming and
tedious. Efficient use of CIS and CFEST not only eased
the burden of conducting multiple simulations but
-------
reduced the probability of errors as well as the amount
of time and effort required for each simulation.
IDRISI
IDRISI is a grid-based geographic information and im-
age processing system developed by the Graduate
School of Geography at Clark University and supported
by the United Nations Institute for Training and Re-
search (UNITAR) and the United Nations Environment
Programme Global Resource Information Database
(UNEP/GRID) (9). IDRISI is a collection of over 100
program modules that are linked through a menu
system. These programs are organized into several
groups:
• The core modules provide data entry and database
management capabilities.
• The geographic analysis modules provide tools for
database analysis.
• The statistical analysis modules allow statistical char-
acterization of images.
• The peripheral modules provide a series of utilities.
IDRISI and other raster-based systems divide data sets
into map layers; each layer contains data for a single
attribute. For the example of a ground-water model,
these layers could correspond to the MODFLOW two-
dimensional arrays (e.g., initial water levels, transmis-
sivity distribution, IBOUND arrays, computed hydraulic
heads). IDRISI provides many analytical tools that are
useful in hydrogeologic studies.
Three of the most important categories of these tools
are database query, map algebra, and context operator.
Asemihypothetical case described below illustrates the
use of these analytical tools. IDRISI provides an exten-
sive set of tools for image processing, geographic and
statistical analysis, spatial decision support, time series
analysis, data display, and import/export and conver-
sion. In addition, as a set of independent program mod-
ules linked to a broad set of simple data structures, the
system is designed such that researchers may readily
integrate into the system their own modules, written in
any programming language.
IDRISI uses three types of data files: image, vector, and
attribute. Image files contain rasterized information
relating to a spatial variable. Vector files contain the
coordinates of points, lines, and polygonal features. An
attribute file lists the identifiers of features and the asso-
ciated attribute values. Values files can be extracted
from the existing image files, or image files can be
created from existing values files. The values files can
be combined and stored in a dBASE format. Each image,
vector, or attribute file has a corresponding documentation
file that contains information about the data file (e.g.,
title, number of rows and columns).
MODRISI: MODFLOW/IDRISI Interface
MODRISI is a set of utility programs that allows the
transfer of data files between MODFLOW, IDRISI, Gold-
en Software SURFER, GeoEas, and other software.
Preparation of two-dimensional arrays for the MOD-
FLOW input files is generally tedious and time consum-
ing. The arrays can be created easily from the IDRISI
image files, however. Thus, MODRISI serves as a pre-
processor for MODFLOW. For example, when the val-
ues of a variable are available only for irregularly spaced
points, interpolation routines in SURFER or GeoEas
may be used to estimate the values of the variable on
regularly spaced grid-nodes. MODRISI translates
SURFER or GeoEas files into IDRISI image files for
manipulation, analysis, and display. IDRISI recognizes
either latitude and longitude geodetic coordinates or
arbitrary Cartesian plane coordinates. IDRISI assigns
the lower left grid-block of a raster image as a zero-row,
zero-column block.
Vector files, such as model boundaries, well locations,
and rivers, may be created within IDRISI and translated
into a MODFLOW input file format. For example, the
location of a river may be digitized on screen in IDRISI.
The vector-to-raster function may be invoked, assigning
all blocks through which the river passes as river nodes.
Similarly, the positions of wells may be digitized and
translated into the row-column positions and saved as a
MODFLOW input file for the well package. Once the
MODFLOW input files are prepared, MODFLOW simu-
lations may be initiated. The MODFLOW hydraulic head
output files may be read by MODRISI and modified to
create IDRISI image files. Again, the image files may be
displayed and evaluated within IDRISI. Thus, MODRISI
is used as a postprocessor for MODFLOW.
Case Study
The utility of MODRISI was demonstrated at a hazard-
ous waste site. Previous investigations provided site-
specific information, including water level and bedrock
and land surface elevations, which was then analyzed
using GeoEas, a public domain geostatistical software
program. These data were kriged to produce a grid of
regularly spaced data. These data were imported into
MODRISI and converted to IDRISI image files. IDRISI
was used to visualize the surfaces that GeoEas generated.
Several prominent features are obvious upon inspection
of the kriged bedrock topography (see Figure 1). Abed-
rock ridge trending northwest to southeast is flanked by
a minor trough to the east and a major trough to the
southwest. The outline of the site is visible on all figures.
The kriged water level elevation map (see Figure 2)
illustrates the general hydraulic gradient to the west.
Land surface elevations (see Figure 3) range from
greater than 200 feet in the northeastern portion of the
site to a low of 166 feet on the western boundary.
-------
70-80 H
80-90 M
90-100 Mi
100-110 ••
110-120 m.
120-130 :
130-140
140-150 .:.
150-160
(Feet)
310 Feet
Figure 1. Bedrock elevation contour map derived from kriged data and transformed by MODRISI into IDRISI image file format.
168W*
169
170
171 KB
172
173 HI
174 Ml
175 Ml
176 •§
(Feet)
310 Feet
Figure 2. Water level contour map derived from kriged water level data and transformed by MODRISI into IDRISI image file format.
-------
165-170|
170-1751
175-180
180-1851
185-190!
190-1951
195-2001
(Feet)
310 Feet
Figure 3. Land elevation contour map generated from kriged data and transformed by MODRISI into IDRISI image file format.
Additionally, the value (e.g., elevation) and (x,y) coordi-
nates may be queried for any point, line, or area of an
image file.
The analytical capabilities of IDRISI are illustrated in the
following example. The saturated thickness was deter-
mined by subtracting the bedrock surface from the water
level surface. The results illustrate the spatial variability
of saturated thickness of the overburden aquifer (see
Figure 4). The results correspond favorably with the
general bedrock topography, as would be expected
given the relatively low hydraulic gradient across the
site. This OVERLAY (subtract) function may also readily
be used to evaluate the adequacy of model calibration.
For example, predicted values may be compared with
observed values graphically, allowing the modeler to
quickly visualize and identify areas of the model domain
requiring additional consideration and manipulation.
Conclusions
One of the most tedious tasks in a ground-water mod-
eling process is preparing input data and postprocess-
ing model results. CIS allows rapid incorporation and
evaluation of new site characterization information. The
proposed combination of IDRISI, a raster-based CIS
system, and MODRISI, a set of utility programs, could
significantly reduce the amount of time necessary for
entering data in required array formats. The visualization
capabilities of IDRISI in conjunction with MODRISI and
MODFLOW allow project managers to better under-
stand the three-dimensional nature of subsurface envi-
ronmental problems.
Acknowledgments
The authors wish to thank Chet Janowski, U.S. Environ-
mental Protection Agency Region I, for his support on
this project.
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<20
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8. Harris, J., S. Gupta, G. Woodside, and N. Ziemba. 1993. Integrated
use of CIS and three-dimensional, finite-element model: San
Gabriel basin ground-water flow analyses. In: Goodchild, M., B.
Parks, and L. Steyaert, eds. Environmental modeling with CIS.
New York, NY: Oxford University Press.
9. Eastman, J.R., PA. Kyem, J. Tolendano, and W. Jin. 1993. CIS
and decision making. Explorations in geographic information sys-
tems technology, Vol. 4. United Nations Institute for Training and
Research, Geneva, Switzerland.
6
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Integration of EPA Mainframe Graphics and GiS in a UNIX Workstation Environment
To Solve Environmental Problems
William B. Samuels
Science Applications International Corporation, McLean, Virginia
Phillip Taylor
Tetra Tech, Inc., Fairfax, Virginia
Paul Evenhouse
Martin Marietta, Inc., Research Triangle Park, North Carolina
Robert King
Assessment and Watershed Protection Division, U.S. Environmental Protection Agency,
Washington, DC
Abstract
The Assessment and Watershed Protection Division of
the Office of Wetlands, Oceans, and Watersheds has
developed water quality analysis software on the U.S.
Environmental Protection Agency (EPA) mainframe
computer. This software integrates national on-line en-
vironmental databases and produces maps, tables,
graphics, and reports that display information such as
water quality trends, discharge monitoring reports, per-
mit limits, and design flow analyses.
In the past, this graphic software was available only to
users connected to the mainframe with IBM graphics
terminals or PCs with graphics emulation software. Re-
cently, software has been developed that can be used
to: 1) access the EPA mainframe from a UNIX worksta-
tion via the Internet, 2) execute the Water Quality Analy-
sis System (WQAS) procedures, 3) display WQAS
graphics in an X-Window on the workstation, and 4)
download data in a geographic information system (GIS)
format from the mainframe. At the same time, this work-
station can execute ARC/INFO and ARC/VIEW applica-
tions in other X-Windows. This capability allows analysts
to have the power of GIS, the mainframe databases
(e.g., Permits Compliance System [PCS], STORET,
Reach File, Industrial Facilities Discharge File, Daily
Flow File, Toxic Chemical Release Inventory), and the
retrieval/analysis/display software (Environmental Data
Display Manager, Mapping and Data Display Manager,
Reach Pollutant Assessment [RPA], PCS-STORET In-
terface, UNIRAS) available to them on one desktop.
This capability extends the tool set available to GIS
analysts for environmental problem-solving.
This paper discusses application of these tools and
databases to several problems, including EPAs water-
shed-based approach to permitting, and the RPA, an
automated method to identify priority pollutants in water-
sheds.
Introduction
The purpose of this paper is to explore new geographic
information systems (GIS) data integration tools that are
applicable to a wide range of environmental problems,
including the U.S. Environmental Protection Agency's
(EPAs) watershed-based approach to permitting and
the Reach Pollutant Assessment (RPA), an automated
method to identify priority pollutants in watersheds. The
ultimate goal is to make these tools and databases
accessible to a wide range of users.
Understanding aquatic resource-based water quality
management depends on access to and integration of
diverse information from many sources. To date, the
techniques to perform this integration, and thus yield
meaningful analyses supporting environmental deci-
sion-making, are neither fully developed nor docu-
mented. New tools and information resources are now
available, but not used to their full potential, for more
valuable water quality and watershed analyses. EPA
-------
headquarters is responsible for ensuring that integrated
data management tools are available for water quality
analyses and data reporting as well as making national
data systems more useful. EPA will accomplish this by
upgrading and crosslinking systems, developing interac-
tive data retrieval and analysis mechanisms, and provid-
ing easy downloading of data to client workstations.
The Assessment and Watershed Protection Division
(AWPD) of the Office of Wetlands, Oceans, and Water-
sheds (OWOW) has developed water quality analysis
software on the EPA mainframe computer (1). This soft-
ware integrates national on-line environmental data-
bases and produces maps, tables, graphics, and reports
that display information such as water quality trends,
discharge monitoring reports, permit limits, and design
flow analyses. In the past, this graphic software was
available only to users connected to the mainframe with
IBM graphics terminals or PCs with graphics emulation
software. Recently, software has been developed that
can be used to:
• Access the EPA mainframe from a UNIX workstation
via the Internet.
• Execute the Water Quality Analysis System (WQAS)
procedures.
• Display WQAS graphics in an X-Window on the
workstation.
• Download data in a CIS format from the mainframe.
At the same time, this workstation can execute
ARC/INFO and ARC/VIEW applications in other X-Win-
dows. This capability allows analysts to have the power
of CIS, the mainframe databases (e.g., Permits Compli-
ance System [PCS], STORET, Reach File, Industrial
Facilities Discharge File, Daily Flow File, Toxic Chemical
Release Inventory), and the retrieval/analysis/display
software (Environmental Data Display Manager, Map-
ping and Data Display Manager, RPA, PCS-STORET
Interface, UNIRAS) available to them on one desktop.
This extends the tool set available to CIS analysts for
environmental problem-solving. This paper discusses
how these tools and databases have been applied to
two examples: 1) a watershed-based approach to per-
mitting and 2) the RPA, an automated procedure for
identifying watersheds with priority pollutants.
Mainframe Databases and Tools
The EPA IBM ES9000 mainframe computer, located in
Research Triangle Park, North Carolina, contains a
large volume of digital water quality and environmental
data available on-line through a number of data retrieval
and display tools (see Figure 1). Other documents de-
scribe these databases and tools in detail (2, 3).
This effort focused on showing how these databases
and tools can complement CIS activities. In some cases,
data can be directly downloaded to a workstation in CIS
format. An example of this is accessing EPAs Reach
File (Version 1) from GRIDS (4). In other cases, data-
bases are accessed by mainframe tools, the data are
processed, and a CIS data set is produced that can be
downloaded to a workstation. An example of this case
is the RPA (RPA3) tool that integrates data from the
Reach File, STORET, and PCS to identify priority pollut-
ants in watersheds (5).
The mainframe can be accessed through several paths:
Internet, PC dialup, or dedicated line into a terminal
controller (see Figure 2). In the applications presented
here, the Internet connectivity is emphasized because
this is the mechanism that makes these databases and
tools available to CIS analysts at their workstations.
Figure 3 shows the hardware and software require-
ments for Internet access to the water quality data inte-
gration tools. The basic components are a UNIX
workstation with an X-Window manager, the X3270 soft-
ware, and Internet connectivity. The X3270 software is
required to emulate an IBM 3270 full-screen terminal.
This software is publicly available through EPAs Na-
tional Computer Center User Support. In addition, an
account on the mainframe computer is required. Once
this account is established, an additional software mod-
ule, GDDMXD, is required to map IBM host-based
graphics to the workstation's X-Window. The GDDMXD
software resides on the mainframe and is loaded when
the user logs in. Once the hardware and software are
set up, a single UNIX workstation can provide access to
mainframe and workstation tools and databases on one
desktop (see Figure 4).
Applications
To illustrate how these mainframe and workstation
tools/databases can work together to solve environ-
mental problems, we present two applications. The first
shows a watershed-based approach to permitting; the
second describes the RPA.
Watershed-Based Approach to Permitting
The watershed approach is a process to synchronize
water quality monitoring, inspections, and permitting to
support water quality protection activities on a geo-
graphic basis. It is a coordinated and integrated method
to link science, permits, and other pollution control and
prevention activities to meet state water quality stand-
ards. Numerous local, state, and federal agencies have
recognized watershed approaches as the best way to
manage natural resources effectively and efficiently. Es-
tablishing a schedule for data collection, permit issu-
ance, and other elements of this approach affords the
opportunity to coordinate and integrate other natural
resource management efforts to make better use of
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NATIONAL DATABASES
Reach File
STORET
PCS
TRI
IFD
Gage
Drinks
Daily Flow
BIOS
FRDS
USGS DLG
LU/LC
Soils
Population
Dun &
Bradstreet
ODES
MAINFRAME TOOLS
EDDM
MDDM
RPA3
IPS5
UNIMAP
Sitehelp
GRIDS
DFLOW
DESCON
RF3MSTR
Bookmanager
Pathscan
GIS
Figure 1. EPA mainframe databases/tools and linkage to GIS.
INTERNET
DIALUP
EPA MAINFRAME
Databases
Tools
DEDICATED LINE
PC or UNIX workstation
with GIS software
Figure 2. Access to the EPA mainframe databases and tools.
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LOCAL WORKSTATION
HARDWARE
UNIX workstation (with X-Windows) on Internet (i.e., DG Aviion)
SOFTWARE
X3270 Software - creates an X-Window, which emulates an IBM 3270
full-screen session (provided by EPA)
EPA MAINFRAME ACCOUNT
GDDMXD software provided on the mainframe to map GDDM
graphics into X-Windows
Figure 3. Hardware and software requirements for accessing water quality data integration tools via INTERNET.
EPA MAINFRAME
WINDOW- X3270
ARC/INFO
WINDOW
ARC/VIEW
WINDOW
DATA DOWN-
LOADING (FTP)
Figure 4. A UNIX workstation environment provides access to mainframe and workstation tools and databases on one desktop.
-------
limited local, state, and federal financial and human
resources (6).
This application illustrates how the watershed approach
used CIS and EPA mainframe databases and tools. As
an example, a four-step approach (see Figure 5) has
been developed and applied to an impaired watershed
(Saluda River basin) in South Carolina. Steps one and
two identified watersheds of concern through their
nonattainment of designated uses (see Figure 6) and
highlighted the cause of nonattainment, in this case
pathogens (see Figure 7). The data sets used were U.S.
Geological Survey (USGS) hydrologic unit boundaries,
Soil Conservation Service (SCS) watershed bounda-
ries, and data from the EPA waterbody system, which
were indexed to the SCS watersheds.1 These data sets
were integrated into an ARC/INFO arc macro language
(AMI) to allow users to pose queries and prioritize
watersheds for further investigation.
Once priorities were set, the third step was to evaluate,
in detail, the sources and causes of nonattainment. The
Saluda River basin, which had pathogens as its cause
of nonattainment, was selected for further analyses. In
this step, the mainframe tools supplement the worksta-
Clifford, J. 1994. Personal communication with Jack Clifford, U.S.
EPA, Washington, DC.
tion CIS capabilities illustrated so far. A STORET re-
trieval was performed for ambient water quality stations
monitoring for fecal conforms. The STORET stations
were partitioned into three categories (low, medium, and
high) according to the state fecal coliform standard (7),
which reads:
not to exceed a geometric mean of 200/100 ml,
based on five consecutive samples during any 30
day period; nor shall more than 10% of the total
samples during any 30 day period exceed 400/100 ml
The categories in Figure 8 correspond to the standard
as follows:
low: < 200/100 milliliters
200/100 milliliters < medium < 400/100 milliliters
high: > 400/100 milliliters
Figure 8 illustrates the use of ARC/VIEWto visualize the
location of fecal coliform "hot spots" in the Saluda River
basin. Figure 9 focuses on one SCS watershed
(03050109-040) where pathogens cause nonattain-
ment. The locations of industrial and municipal dis-
chargers are plotted, and facilities with fecal coliform
limits and their respective permit expiration dates (cap-
tured from the PCS) are shown in a table included with
the figure. The CIS capabilities used to generate Figure 9
1. IDENTIFY WATERSHEDS OF CONCERN
- Nonattained waters
2. PRIORITIZE WATERSHEDS FOR PERMITTING
• Toxic vs. Nonconventional vs. Conventional
- Point source vs. Nonpoint source
- Existing/Designated use(s)
- Environmental equity; populations; endangered/sensitive species; etc.
3. EVALUATE SOURCES AND CAUSES
- Ambient water quality
- Location of point and nonpoint source discharges
- Existing controls
4. DEVELOP CONTROLS
- TMDL/WLAs
- NPDES permit limits/controls
• Synchronize issuance
Figure 5. Four steps illustrating an example approach to permitting on a watershed basis.
-------
Step 1: Identify Watersheds of Concern
SseMoh 30Sb Clean Water tet Support
Select Use Category
Figure 6. Identification of watersheds of concern—watersheds where at least 10 percent of the reaches are not fully supporting
overall designated use (data and ARC/INFO AMLs provided by Jack Clifford).2
Step 2: Evaluate Priorities
Figure 7. Watersheds where the cause of nonattainment is pathogens (data and ARC/INFO AMLs provided by Jack Clifford).
2 See note 1.
3 See note 1.
-------
Step 3: Evaluate Ambient Water Quality—Low, Medium, and High Levels of Fecal Coliforms
?&|3F
Figure 8. Map of the Saluda River basin showing the location of STORET monitoring stations and fecal coliform levels.
Step 3: Existing Controls—Dischargers That Have Limits for Fecal Coliforms
t>l rhaigm In SCS !ja in 4
Figure 9. Focus on SCS watershed 03050109-040 (shaded in gray). Locations of STORET stations, municipal and industrial dis-
chargers are also shown. The table in the upper right highlights dischargers with fecal coliform limits and their respective
permit expiration dates.
-------
show that permit issuance is not synchronous, which is
a key element in the watershed approach.
Within this local workstation CIS environment, the attrib-
utes associated with the STORE! monitoring stations
and the PCS dischargers are limited with respect to the
large amount of time series sampling data that exists in
these databases. Figures 10 through 12 illustrate how
an EPA mainframe procedure, the Environmental Data
Display Manager (EDDM), can be accessed from an
X-Window on the CIS workstation to query the entire
STORE! and PCS databases and thus provide additional
data analysis and display capabilities to the CIS work-
station. In Figures 10 and 11, a water quality inventory was
performed for a STORET station, and a time series plot
of fecal coliform levels is displayed. In Figure 12, the
limits and discharge monitoring report (DMR) data were
accessed from PCS for a sewage treatment plant. Ex-
cursions beyond the PCS limits for fecal conforms are
easily visualized in the plot.
The fourth and final step in the watershed approach was
to develop controls for achieving water quality stand-
ards. This might include the development of total maxi-
mum daily loads (TMDL), waste load allocations (WLA),
and the synchronization of permit issuance. Another
mainframe tool, the PCS-STORET INTERFACE (re-
ferred to as IPS5 on the mainframe), can be used to
access and compute design flows for TMDL develop-
ment (see Figure 13) and to find all facilities discharging
to a particular reach (see Figure 14), an initial step in the
synchronization of permit issuance.
RPA
The RPA is a procedure on the EPA mainframe that
automates identification of reaches where priority pollut-
ants have been detected. It can be run for a user-
selected state or USGS hydrologic unit.
Section 304(1) of the Clean Water Act (CWA) identifies
water bodies impaired by the presence of toxic substances,
Step 3: Using EDDM To Evaluate Ambient Upstream Fecal Coliform Levels—Regulation
of Upstream Dischargers Is Necessary
WATERSHED BASED APPROACH TO PERMITTING
fecal conform tevels~2D3
SALUDA RIVER WATERSHED - ARCVIEW
MAPPING AND DATA DISPLAY MANAGER (MDDM)
ENVIRONMENTAL DATA OISPLAV MANAGER (EDDM)
PCS-STORET INTERFACE
REACH POUUTANT ASSESSMENT
IMDL DEVELOPMENT -CLARK FORK RIVER
ENVIRONMENTAL RISK ASSESSMENT-BRANDYW1NE RIVER
ENVIRONMENTAL DATA DISPLAY MANAGER (EDDM)
STORET RETRIEVAL S-OS4
PCS FACILITY LEVEL DATA SUMMARY
PCS PIPE LEVEL DATA SUM MARY
Figure 10. Using EDDM to perform a water quality inventory for STORET station 21SC06WQ S-084 in the Saluda River basin, South
Carolina.
-------
Step 3: Using EDDM To Visualize Trends in Ambient Coliform Levels Upstream
of a Discharger
WATERSHED BASED APPROACH TO PERMITTING
SALUDA RIVER WATERSHED -ARCVIEW
fecal culilomi levels-mi
MAPPING AND DATA DISPLAY HAMACER (MDDM)
ENVIRONMENTAL DATA DISPLAY MANACEMEDDH)
PCS-STORET INTERFACE
REACH POLLUTANT ASSESSMENT
TMDL DEVELOPMENT - CLARK FORK RIVER
ENVIRONMENTAL RISK ASSESSMENT-BRAHfWI
DISPLAV MANAGER (EDDM)
STORET RETRIEVAL S-OB4
TIME SERIES- FECAL COLIFORM
PCS FACILITY LEVELDATA SUMMARY
Figure 11. From the EDDM water quality inventory table, the fecal coliform parameter (31616) was selected for a time series plot.
identifying point source dischargers of these substances
and developing individual control strategies for these
dischargers. To meet these requirements, the EPA Of-
fice of Wetlands, Oceans, and Watersheds prepared
guidance identifying criteria to be used in reviewing
state reports.
The RPA was designed to address the requirements
under criterion 7 of Section 304(1): identification of state
waters with likely presence of priority toxic pollutants.
This assessment was accomplished by identifying and
summarizing reaches with point source dischargers of
priority pollutants and water quality stations with priority
pollutant data.
Information on the state's waters is summarized using
the USGS hydrologic unit naming convention and the
Reach Structure File (Version 1). Numerous databases
were accessed and analyzed, including the Reach
Structure and Reach Trace File (Version 1), industrial
facilities discharge (IFD) file, STORET parameter file,
PCS, and the STORET water quality file. The IFD file
and PCS provided the facility information. Comparing
information from both data sources identified active fa-
cilities and generated a complete list of facilities by their
assigned reach numbers. Water quality data from
STORET were summarized on reaches with priority pol-
lutant monitoring data. Stations were retrieved with the
following restrictions:
• Stations located within the state or hydrologic unit of
interest.
• Ambient monitoring stations located on streams,
lakes, or estuaries.
• Stations sampled for at least one priority pollutant in
either water, sediment, or fish tissue on or after January
1, 1982.
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Step 3: Using EDDM To Evaluate Existing Controls—PCS Limits and DMR
3CG023ci0fc|UJCRSA/PIEDnaNT PIT
PFJ-END
PT4-DHR
PF5-STDRET
PFfe-flPERTURE
PF7-LIMITS
PF8-FLUUI
ENVIRONMENTAL DATA DISPLAY MANAGER (EDDM)
DATA SELECTION
REACH MAP
ZOOM LEVELS
PCS FACILITV LEVEL DATA_SUMMARV
PCSJMPE LEVEL DftTA SUHMflRY
PLOT OF LIMITS/DMB
Figure 12. Using EDDM, PCS data is accessed for discharger SC0023906, Piedmont sewage treatment plant. The windows show
plant location (upper left), facility and pipe summary data (upper right) and time series plot (lower left).
Recently, the RPA program was modified to output files
compatible with CIS. An example of this is shown in
Figure 15. The data in the table portion of this figure are
written to two files as follows:
• For each reach in the hydrologic unit, the geographic
coordinates are written to a file in ARC/INFO GEN-
ERATE format.
• The attributes associated with each reach (e.g.,
name, length, number of water quality stations) are
written to a delimited ASCII file.
A third file is also automatically generated. This file is an
AMI that GENERATES the line coverage of reaches,
defines and populates the INFO table of attributes, then
joins the attributes in the INFO table to the line coverage
of reaches. Once these three files are created, they are
downloaded to the CIS workstation (via ftp) and proc-
essed by ARC/INFO. In ARC/VIEW, the user can identify
a reach and determine:
• The number of water quality stations with priority pol-
lutant monitoring data.
• The number and type of industrial facilities with pri-
ority pollutant discharge.
• The number of publicly owned treatment works
(POTW) with and without indirect dischargers (see
Figure 15).
In addition to this reach summary data, other tables
(cross-linked to reaches, water quality stations, and dis-
chargers) are produced that summarize the data by pollut-
ant (see Figure 16). In this figure, each pollutant is cross-
linked to the reach where it was detected, and the source
of detection is also identified (i.e., water column, sediment,
fish tissue, NPDES permit limit, Form 2(c) submittal) or
10
-------
Step 4: Using the PCS/STORET INTERFACE for NPDES Permit Development—Analysis of
Receiving Stream Flow Data
ti' IPFM JFf is| PFIE j PF17 j PF10 tFr^1'^l'p^"n^i^
Figure 13. Using the PCS-STORET INTERFACE to access and compute design flows for a specific reach.
Step 4: Using the PCS/STORET INTERFACE To Determine Other Facilities on a Reach for
Wasteload Allocation Purposes
IHEffi fKt 6 FflCILITlES flSSQCiflTED HUH THIS R£ft£H
«- | ir V8P TFTEH1 NEB F TtE SFBfH MUfcS (Uh 1HE !FE RLE
Figure 14. Using the PCS-STORET INTERFACE to list all facilities that discharge to a specific reach.
11
-------
predicted to be in the discharger's effluent based on the
standard industrial classification (SIC) code. More de-
tailed information is also generated. For example, Fig-
ure 17 shows a detailed report for priority pollutants
detected in the water column (similar reports are gener-
ated for sediment and fish tissue). Each pollutant is
cross-linked to a reach and the specific monitoring sta-
tion where it was detected. Basic summary statistics are
also presented.
Figure 18 shows a detailed report for pollutants detected
in the NPDES permit limit. In this figure, each pollutant
is cross-linked to a reach and the specific NPDES dis-
charger containing a permit limit. In addition, each dis-
charger is identified as a major, minor, or POTW.
Figures 15 through 17 show the RPA output in the
foreground and coverages displayed by ARC/VIEW in
the background. Inspection of these figures shows that
the Bush River is a priority pollutant reach containing
seven industrial facilities, four of which discharge priority
pollutants (one pulp and paper mill, three textile facto-
ries). Further examination of the data shows that cad-
mium has been detected in the water column and is also
contained in the NPDES permit limit. It is also predicted
to be in the discharge effluent based on SIC code. In the
water column, cadmium was measured at 10 u,g/L at two
stations sampled in 1988.
Finally, there is a limit for cadmium in NPDES facility
SC0024490 (Newberry plant), a POTW on the Bush
River. The RPA output, linked to CIS, can be used as a
screening and targeting tool for identifying specific
reaches within watersheds where toxic priority pollut-
ants cause water quality degradation.
Summary and Conclusions
The proliferation of CIS workstations, the expansion of
the Internet, and the development of X-Window-based
graphics emulation software (X3270 and GDDMXD) has
afforded analysts the opportunity to use the powerful
analytical capabilities of CIS and the EPA mainframe
databases and tools together on one desktop. Thus, a
user performing a CIS watershed analysis can also
have immediate and complete access to national on-line
databases such as STORET and PCS by opening up a
"window" to the EPA mainframe. This allows detailed
queries to be performed that supplement the data al-
ready being analyzed at the local workstation. This ca-
pability allows users to easily visualize additional data
without having to spend effort in retrieval, downloading,
transforming, and reformatting to make it useful. By
enhancing existing mainframe programs to create out-
put in CIS format, the time spent importing data to the
CIS is reduced and more time can be spent on analysis.
An example of this capability is the RPA program.
Reach Pollutant Assessment: Priority Pollutant Detection in the Bush River
•• I Dlgch&rgsni ;>| gc§ badn 4
1 "*..,-.-K* • '•'
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E ?. StlLWRV OF SUilEKiCftllrf SOE1TED REACHES VOTh PRIORITY FOUUTSNT SC-JtHES
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1 nHdiWU (.HtFKftLi
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0 3
Figure 15. Using the RPA procedure to identify specific reaches with priority pollutants.
12
-------
Reach Pollutant Assessment: Cadmium in the Bush River
RPAS SUHHWY SEPCRT 0V FdUnfcNT
AMOIENT KQ HPDti thh
1C 5Fn FISH LIMIT ?f SIC ,
PRIOR IT*' POIitlTfl NT SUMMARY REPOfl
EETftlL REPOfif - BESHiT UMiT
bETASL ftEPORT ~ W&TER CuUIHN
DETA i RIPOBT:- SB&IMENT
Figure 16. RPA summary report by pollutant.
Reach Pollutant Assessment: Bush River: Cadmium in the Water Column
cmdlool-/Hln/r=:ti
POL L UTANT PARSM RFWH
MAME CODE NUMBER
RPAS DETCL REPORT fi) B¥ POLLllTAHT
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Figure 17. RPA detail report: pollutants detected in the water column.
13
-------
Reach Pollutant Assessment: Permitted Industries for Cadmium—Bush River
cmdtDol "/bin/rah
CIS: 3BM1fiS
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Figure 18. RPA detail report: pollutants included in the NPDES permit limit.
Two examples were presented as illustrations of how
CIS and the mainframe databases and tools can work
together.
In the first example, EPA's watershed-based approach
to permitting, a four-step approach, was outlined, show-
ing how a combination of local CIS functions and remote
mainframe databases and tools were used in each step
of the process. The end result was the targeting and
prioritizing of watersheds of concern, and a detailed look
at where and why water quality standards were not
being met.
In the second example, the RPA program along with CIS
was used to identify and map toxic priority pollutants and
cross-link them to reaches, media (water column, sedi-
ment, fish tissue), NPDES dischargers, and monitoring
stations. This analysis focused on what the toxic pollut-
ant problems are and where they occur.
Acknowledgments
The authors wish to thank Jack Clifford, AWPD, OWOW,
EPA, for providing data and ARC/INFO AMLs that were
used in the South Carolina watershed example. The
authors also extend their appreciation to Duane More
and Tom Lewis (both with Martin Marietta), who provided
support in setting up the X3270 and GDDMXD software.
References
1. Taylor, P.L., P. Evenhouse, L. Hoelman, T. DeWald, W.B. Samuels,
and O. Hansen. 1988. STORET—Water quality analysis system.
Presented at the State/EPA Data Management Conference,
Raleigh, NC (June).
2. U.S. EPA. 1992. Office of Water environmental and program
information systems compendium, information resources manage-
ment: Tools for making water program decisions. EPA/800/B-
92/001. April.
3. Samuels, W.B., PL. Taylor, P.B. Evenhouse, T.R. Bondelid, PC.
Eggers, and S.A. Hanson. 1991. The environmental display man-
ager: A tool for water quality data integration. Water Resources
Bull. 27(6):939-956.
4. U.S. EPA. 1993. Geographic Resources Information and Data Sys-
tem (GRIDS): User guide. Office of Information Resources Man-
agement, National GIS Program.
5. Samuels, W.B. 1990. Reach pollutant assessment user's guide.
McLean, VA: Science Applications International Corporation.
6. Washington State Department of Ecology (WDEC). 1993. Water-
shed approach to water quality management. F-WQ-93-029.
October.
7. State of South Carolina. 1992. South Carolina code of regulations,
Chapter 61, Regulation 68 and 69. Amended April 24, 1992.
14
-------
You Can't Do That With These Data! Or: Uses and Abuses of
Tap Water Monitoring Analyses
Michael R. Schock
Drinking Water Research Division, U.S. Environmental Protection Agency, Cincinnati, Ohio
Jonathan A. Clement
Black & Veatch, Cambridge, Massachusetts
Introduction
Linkage between human health and drinking water qual-
ity has been an area of interest in the United States for
many years. Over the past approximately 10 years,
drinking water monitoring requirements have expanded
rapidly under the Safe Drinking Water Act (SDWA).
Growing public and governmental interest in this envi-
ronmental area makes the aggregation and consolida-
tion of data on the occurrence and distribution of many
organic and inorganic contaminants and background
constituents of drinking water an important process.
These data can then be made available for systemiza-
tion and visualization to regulators, municipalities, water
utilities, public interest groups, health researchers, con-
sulting engineers, and water treatment scientists.
Given a sufficient number of data points and a conven-
ient computerized database/mapping platform, a wide
variety of maps can be generated to use in research and
decision-making processes. The validity of doing so,
however, rests inseparably upon the basis of the sam-
pling plan and protocols, as well as the precision and
accuracy of the analytical methods used forthe constitu-
ents of interest. The well-known problem of matching the
proper scale of the source data to that employed in the
maps for interpretation is a critical problem with drinking
water sampling, where many unappreciated small-scale
variations render many, if not most, attempts to make
generalizations inaccurate or meaningless.
This paper introduces and describes many concepts re-
lated to what generates or controls the concentrations
of metals and other constituents in drinking water, ways
in which the sampling protocol affects apparent levels of
constituents, and the magnitude of temporal and spatial
variability present in both municipal and private water
supplies. Illustrations from water quality studies show in
practical terms how generalizations must be kept to a
minimum and how the data input into a geographic
information system (CIS) for interpretation and evalu-
ation must be carefully analyzed and screened to deter-
mine the appropriateness for various well-intended
purposes. The discussion and examples show how
many apparently significant trends and assessments of
exposures or occurrences turn out to be merely artifacts
of critical (yet subtle) inconsistencies or errors in the
planning and execution of the sample collection proc-
ess, or inconsistencies caused by the fact that regula-
tory (and not research) requirements govern the origin
of the data.
The concepts this paper covers are equally valid in
many other disciplines using or contemplating the use
of CIS for interpretation of all kinds of "field" data.
Why Maps Are Useful for Drinking
Water Studies
Maps and CIS databases could have wide applicability
to drinking water studies. For example, they could pro-
vide the basis for investigating the occurrence of regu-
latory contaminants or related constituents, either to
estimate the costs of compliance with a regulation or to
estimate human health effects. Mapping could be useful
to utilities and consultants investigating process changes
for a utility or determining the effectiveness of some
existing treatment such as corrosion control orchlorina-
tion. Use of CIS could also assist in assessing the
feasibility and impact of system expansion. Another
promising application would be CIS assistance in devel-
oping and implementing wellhead protection plans.
Many other areas of application may be possible now,
or will be discovered in the future, as CIS technology
and regulatory requirements continue to develop.
-------
Sampling Protocols for Data Usable in GIS
Several SDWA regulations have resulted or will result in
the collection of geographically diverse drinking water
quality data that may interest mappers. The Lead and
Copper Rule, the Surface Water Treatment Rule, the
proposed Information Collection Rule, and the Disinfec-
tion/Disinfection Byproduct Rule are but four examples.
Many states have their own variations on federal drink-
ing water regulations, so their data collection require-
ments may differ somewhat. Considerable data may
also be collected for specific research studies of either
academic or purely practical nature.
Chemical Factors in Constituent Behavior
For the purposes of this discussion, chemical constitu-
ents in drinking water may be classified as being gener-
ally reactive or nonreactive. Reactive constituents may
change concentrations or chemical form for a variety of
reasons, such as:
• A result of interaction with the background composi-
tion of the drinking water.
• By precipitation or dissolution reactions with pipe ma-
terial used for the distribution system.
• By chemical reactions with disinfectants added at
water treatment plants.
• By slow chemical reactions started at water treatment
plants.
Nonreactive constituents may play an important role by
providing a chemical background that indirectly influ-
ences the speed or extent of other chemical reactions
and transformations. Table 1 gives a summary of many
common constituents of drinking water and identifies
whether they function essentially as reactive or nonre-
active constituents.
Reactive Constituents
Clearly, chemical species or compounds that can
change in concentration or transform into other species
or compounds during distribution make mapping on very
large scales difficult to justify. Reactive constituents may
also change concentration in the same place overtime,
such as water standing overnight in a home, school, or
building, which is discussed in a later section. Some
examples of reactions during water distribution follow:
• During lime softening processes at some central
water treatment plants, a supersaturated state is
used for the compound calcium carbonate to remove
calcium (and sometimes magnesium) ions from the
water. This condition is sometimes maintained into
the distribution system as well to assist in maintaining
chemical conditions useful for corrosion control of
lead and copper. Thus, calcium levels, pH, and car-
Table 1. General Reactivity Trends for Common Drinking
Water Constituents
Constituent
General Reactivity Tendency
PH
Dissolved oxygen
Calcium
Magnesium
Total carbonate
Total alkalinity
Chlorine residual
Temperature
Iron
Copper
Lead
Zinc
Silica
Sulfate
Orthophosphate
Polyphosphate
Total phosphate
Nitrate
Chloride
Fluoride
Trihalomethanes
Haloacetic acids
Highly reactive
Reactive
Nonreactive (reactive when cementitious
pipe linings are present)
Nonreactive
Nonreactive
Reactive, particularly with pH changes
Reactive
Either
Reactive
Reactive
Reactive
Reactive
Nonreactive
Nonreactive
Reactive
Reactive
Reactive
Nonreactive
Nonreactive
Nonreactive
Reactive
Reactive
bonate concentrations (and consequently, alkalinity)
drop as water passes away from the plant (1, 2).
• The metals in pipe materials, such as iron, copper,
zinc (in galvanized pipe), and lead, are oxidized by
oxygen, free chlorine, chloramines, ozone, and other
disinfectants, which renders them into a form that
water can transport, unless other chemical conditions
are such that a highly insoluble scale deposits on the
pipe, immobilizing the metal (1, 3).
• Prolonged contact with chlorine disinfectant species
converts a fraction of natural organic matter present
in many distributed waters into regulated "disinfection
byproduct" compounds, such as trihalomethanes,
chloroform, and haloacetic acids (4, 5).
• Following the addition of chlorine or after increasing
pH to enable some corrosion control for copper and
lead, iron present in well waters in dissolved ferrous
(Fe2+) form oxidizes into Fe3+ form, which is much
less soluble. Obnoxious "red water" results, as ferric
oxyhydroxide precipitate forms and clouds the water.
• Polyphosphate chemicals added to "sequester" iron
or manganese in well waters break down into simpler
-------
polyphosphate forms of shorter chain lengths, plus
orthophosphate. The orthophosphate frequently be-
comes present at high enough concentration to aid
in controlling lead or copper (1, 6-8).
• Water passes through newly installed cement mor-
tar-lined pipes, or aggressive water passes through
older asbestos-cement pipes. Because of the particu-
lar chemical nature of the water, calcium carbonate
and calcium hydroxide in the cement dissolve, raising
the pH and hardness of the water (1).
• Free chlorine is added to disinfect water and is such
a strong oxidant that it is unstable in water at normal
concentrations. Additionally, it reacts with miles of
unlined cast iron pipe, accelerating the decomposi-
tion of hypochlorous acid or hypochlorite ion to chlo-
ride. Consequently, the overall redox potential of the
water supply and the effectiveness of disinfection
decrease.
• A concentration of 1 milligram per liter (as PO4) phos-
phoric acid is added to a distributed water at pH 7.5
to control lead corrosion. The orthophosphate reacts
with exposed iron in the distribution main, however,
and the residual concentration of orthophosphate
decreases throughout distribution passage to the
point where the level is no longer adequate to create
the lead orthophosphate passivating film needed (1,
6, 8, 9).
Unless a constituent is known to be nonreactive, maps
may be falsely generated under the premise that the
concentration of a constituent is essentially a constant
over some geographic area. Following the changes in
concentration or chemical form of reactive constituents
would also seem to be a useful application of CIS tech-
nology. One major restriction applies to the viability of
that approach, however. Presuming that the analytical
techniques used can adequately quantify the concentra-
tion and concentration changes observed, the scale of
the variability or concentration change relative to the
scale of the mapping perspective becomes critical to
accurate mapping. A latersection ofthis paper considers
this critical factor in more specific detail.
Nonreactive Constituents
Almost no inorganic constituents in natural or drinking
water are purely chemically inert. Under some condi-
tions, and at some concentrations, significant reactions
can occur. Some constituents that are actually reactive
may act as if they are nonreactive constituents, how-
ever, because they are present in high enough concen-
trations relative to the extent of chemical reactions
taking place that no discernible change in their concen-
tration results. An obvious example is the dissolved
inorganic carbonate (DIG = hteCOs* + HCOs- + COs2-)
concentration (1, 10). Complexation and formation of
passivating basic carbonate solid films of lead and cop-
per by carbonate and bicarbonate ion dominate the
corrosion control chemistry of copper(ll) and lead(ll) (1,
11). The concentration of DIG in water on either a molar
or weight basis, however, is normally a factor of 500 to
10,000 higher than the lead or copper concentrations.
Hence, changes in the DIG content from these reactions
normally are analytically undetectable.
Another example is fluoride ion, which is often used as
a distribution system water flow "tracer" because of its
relative inertness. Actually, fluoride ion can form strong
complexes with aluminum left in water following coagu-
lation treatment with alum. The solubility of fluoride-
containing solids with other major drinking water
components (such as calcium and sodium) is very high,
however, and fluoride reacts only weakly with metallic
plumbing materials in the distribution system. Therefore,
total fluoride concentrations tend to remain constant.
Relatively accurate maps of the occurrence and distri-
bution of nonreactive constituents can be made, but
their usefulness depends on the scale of the mapping
relative to their occurrence and the particular question
under investigation. All of this supports the need to
ensure that the question asked can be answered cor-
rectly at the map scale.
Scale of Drinking Water Constituent
Sources
More than 59,000 public water suppliers exist in the
United States (12). Of these, approximately 660 are
considered large water systems, which serve over
50,000 in population. These municipal systems use
source water supplies that can be ground-water wells,
"surface" waters (i.e., rivers, reservoirs, lakes), or a
combination of both. Some water suppliers perform
minimal water treatment of their own and purchase
water from another water system or systems to satisfy
their needs.
Surface Water Sources
Many water utilities use a single water treatment plant
to treat surface waters, which could satisfy the entire
water demand of the community all year. In many cases,
however, utilities combine several surface water
sources and use a different treatment plant to treat each
water source. The water plants usually discharge into
the distribution system at different points, and system
hydraulics dictate the areas of the system in which
waters mix. This is important because the water quality
characteristics, which often differ among treatment
plants, influence the corrosivity of the waters to various
plumbing materials in the distribution system. Different
water constituents also may affect the disinfection effec-
tiveness of the treatment and the formation of unwanted
disinfection byproducts.
-------
For surface water systems, the chemical composition of
the water depends on the upstream or watershed geo-
chemistry, the seasonal nature of the water body used
as the source, and the characteristics that the treatment
imparts, such as coagulation with ferric sulfate or alum
(aluminum sulfate), lime softening, filtration, pH adjust-
ment, corrosion control treatment, chlorination, etc.
The scale of the source water chemical data, therefore,
is large, driven by the geology, soil nature, land use, and
climate. The chemical nature of the treated water, how-
ever, may differ significantly from that of its source.
Ground-Water Supplies
Many water utilities use multiple ground-water wells. A
water supply of medium to large size usually uses mul-
tiple wells, instead of or in addition to the surface water
supplies. Wells number from only two or three to more
than 100 for very large water systems. Wells normally
operate in different patterns, and only rarely do all wells
operate at the same time. The yield of the wells and their
water quality dictates the combination and number of
wells used at a particular moment. The wells may or may
not be from the same aquifer, and even if they are, local
inhomogeneities frequently exist in water composition
(especially with iron and manganese) that limit the use-
fulness of certain wells without substantial treatment.
Historically, utilities have treated some (but not neces-
sarily all) wells with a chemical such as a polyphos-
phate or sodium silicate to sequester the iron and
manganese from wells. Some utilities install physical
removal processes such as ion-exchange softeners, re-
verse-osmosis plants, aeration systems for iron re-
moval, air stripping towers for volatile organic compound
or radon removal, or "greensand" filters for the removal
of iron and manganese. These facilities sometimes exist
at only certain well sites or at some point where water
from multiple wells is combined.
The scale of chemical controls on ground-water sup-
plies, therefore, becomes only hundreds of feet. Con-
taminants of raw waters, such as arsenic, nitrate, or
chromium, are geologically and geochemically control-
led. Therefore, their occurrence is geographically vari-
able on even a small scale, and the variability exists
vertically in the subsurface as well as horizontally. A
municipality may use wells of different depths into differ-
ent aquifers, or even approximately the same depth
spread out over hundreds of feet to many miles in the
same aquifer or a variety of geologic units.
The variability of individual ground-water wells overtime
(such as seasonally) is usually less apparent than with
surface water sources, but the fact that many wells are
frequently used in different combinations and for differ-
ent lengths of time (hours to days, usually) makes char-
acterizing "influent" water quality complicated. The
same observation applies to water systems that allow
different amounts of water to bypass treatment proc-
esses (e.g., ion-exchange, reverse osmosis) depending
on the levels of targeted undesirable contaminants (e.g.,
nitrate, sulfate, arsenic).
These characteristics of the nature of chemical compo-
sition, use, and treatment of ground-water supplies
clearly show that generalizations over areas such as
states or geographic regions (e.g., New England, Upper
Midwest) are at least very gross and uncertain and at
worst, entirely misleading when decisions are to be
made about risk and health assessments, or estimates
of the necessity for certain treatments or economic im-
pacts of different potential drinking water regulations.
Combination Systems
Some municipalities combine the use of surface water
supplies and ground-water wells. Therefore, general
water chemical characteristics vary throughout the sys-
tem in a regular manner in response to the location and
use of different sources, as well as relative amounts of
water that the different sources produce and deliver.
Distribution System Mains
The next lower level of scale is the distribution system
network of pipes and storage. Common materials used
for distribution system piping include cast iron, ductile
iron, cement mortar-lined iron, iron with organic coat-
ings, asbestos-cement (A-C), and various forms of plas-
tic. Pipe diameters range from about 4 inches to many
feet, depending on size of the water utility and commu-
nity, size of the neighborhood fed by the line, and dis-
tance of travel for the water. Here, because of the large
volume of water involved relative to the pipe diameter,
the major chemical interactions involve such constitu-
ents as hardness (calcium and magnesium) ions, pH,
iron, bicarbonate and carbonate ions, and chlorine re-
sidual species, and possibly microbiological parameters
such as total plate counts, heterotrophic plate counts,
and assimilable organic carbon. Disinfection byproducts
(DBFs) may change in concentration and type because
of the time involved in the water traveling through the
piping from the treatment plant. Trace metal contamina-
tion, such as lead and copper, is usually negligible from
this source, unless it is present when distributed from
the wells or water treatment plants.
Depending on prevalent economics and construction
practices during periods of water system growth, the
materials will not be either randomly or uniformly distrib-
uted geographically within system boundaries. Water
flow often varies greatly within the distribution system,
and water lines sometimes terminate in dead-end areas
with minimal flow rates. Water quality often differs sub-
stantially in these dead ends from that in the fully flowing
parts of the distribution system.
-------
Household Service Lines
Service lines represent the connection between the
house or building and the distribution main. Sometimes,
the service lines are joined to the mains by a flexible,
approximately 2- to 3-foot long pipe called a "goose-
neck" or "pig-tail." Historically, this connector was often
made of lead. Recently, copper has been the most
widely used material, with plastic gaining in acceptance.
Service lines for homes are usually 0.75 to 1 inch in
diameter, with service lines for many commercial build-
ings or multifamily dwellings ranging in size from 1.5 to
3 inches in diameter. Service line for homes and build-
ings have usually been made of lead, brass, copper,
galvanized steel, or plastic. The material used depends
on the age of the water connection and the construction
practices of the area involved. A recent report estimated
that approximately 6.4 million lead connections ("goose-
necks") still exist in the United States, and about 3.3
million lead service lines still exist (13). In many commu-
nities, old lead service lines remain a major source of
lead in drinking water.
Like distribution system materials, service line materials
may vary greatly within a distribution system by space
and time. For instance, in large eastern cities, very old
neighborhoods may have many (or even mostly) lead
service lines. New neighborhoods likely have copper or
plastic service lines. Galvanized steel or copper pipes
may have been installed between the era when lead was
used and modern times. With the exception of Chicago,
where lead service lines were occasionally installed into
the 1980s, the use of lead for service lines generally
stopped in the late 1940s or early 1950s. An example of
nonuniform distribution of service line materials is
shown by Figure 1, a map indicating Cincinnati sampling
sites for Lead and Copper Rule (14-17) monitoring.
Erratic clustering of different service line materials is
evident.
Rehabilitation of old houses or replacement of failed
piping results in a mixture of new and old material in
areas where houses are predominantly old. Following
completion of the construction, maps of service line
material would show many clusters representing preva-
lent plumbing codes and economics.
Interior Plumbing
Interior plumbing of buildings and houses reflects even
more variability than service lines. This is the dominant
contributor to lead and copper levels at most sites cov-
ered under the Lead and Copper Rule (14-17). Interior
plumbing consists of piping, plus a large number of
valves, connectors, fixtures, and perhaps soldered joints
and a water meter. Any or all of these components are
replaced at varying intervals as a result of failures or
remodeling. Therefore, even generalizations within a
small neighborhood are risky, unless the neighborhood
is very new and uniformly constructed. When attempting
to survey the composition of plumbing materials that
might be the source of drinking water contamination,
merely asking for the age of the house or building is
insufficient. Questions must be asked to obtain the nec-
essary precise information on the age and type of
plumbing materials and components in the building.
Typical interior plumbing materials include lead, galva-
nized steel, copper, and different plastics for pipes.
Some brass and black steel have been used for short
times in some areas. Faucets are almost always made
with either brass or plastic internal parts, which differ in
composition from the exteriors, which are usually plated
with chrome or other metal. Interior faucet volumes typi-
cally range from about 30 milliliters to 120 milliliters,
depending upon design. Valves and meters are also
frequently made of brass or bronze, which are cop-
per/zinc alloys usually containing 2 percent to 6 percent
lead. Until recently, solders used to join copper drinking
water pipe sections were usually a tin and lead combi-
nation, containing 40 percent to 60 percent lead. Occa-
sionally, connector lines to fixtures include copper,
stainless steel, aluminum, or flexible plastic sections.
Private Water Systems
The many possible designs of domestic water systems
originating from wells or cisterns are too numerous to
illustrate. Figure 2 gives an example of one such system
layout. Private systems share many features with do-
mestic systems supplied by water utilities, however.
Interior plumbing shares most of the same configura-
tions and materials. For private water systems, addi-
tional plumbing that could cause contamination or water
chemistry changes includes well casing material, sub-
mersible pump casing and fittings, pressure tank feed
and control plumbing, and nonsubmersible pump inte-
rior materials. Therefore, problems with determining the
frequency and distribution of levels of potential contami-
nants include those present for domestic situations in
general, plus those complications arising from cycling of
the pumps, pressure tank system, or both.
Water Samples Representing Distances
One of the most important fundamentals of under-
standing drinking water sampling is that volumes of
water (e.g., 1-liter samples, 250-milliliter samples) rep-
resent the linear distance of plumbing material in contact
with the water sampled. Because of water mixing and
flow during use or sampling, they are also integrated
samples of that volume. This understanding is at the
heart of designing accurate water sampling programs
and making viable interpretations of existing monitoring
data that may be contained in (or mappable by) a CIS.
Table 2 summarizes some interesting and important
relationships between pipes of different inside diameters
-------
California Water
Treatment Complex
• Lead Site
* Copper Site
« W.Q.P. Site
' C.W.W. Facility
-^ Bolton D.S.
*•* California D.S.
• Not C.W.W. D.S.
— Service Limits
Figure 1. Cincinnati Water Works Lead and Copper Rule compliance monitoring, July to December 1992.
Figure 2. Distribution system.
(IDs) and the volumes of water they contain per unit of
length (7). Much domestic interior plumbing has an ID of
approximately 0.5 inches, depending upon the material.
Figure 3 shows schematically what parts of a plumbing
system would likely be represented by samples of differ-
ent volumes taken after water was allowed to stand in
the pipe for many hours. Faucets, bubblers, and other
terminating fixtures vary widely in volume. Kitchen-type
fixtures usually contain from 60 to 120 milliliters of water.
Bathroom-type fixtures may contain only about 30 to 60
milliliters of water. Bubblers, such as those frequently
found on school or office drinking fountains, are smaller
still. As can be seen schematically in Figure 3a, a small
volume such as 125 milliliters captures the faucet and a
short distance of pipe immediately leading to it. In many
plumbing systems, this volume catches water in contact
with numerous soldered joints. On the other hand, if a
single 1-liter first-draw sample is taken, the water in the
bottle represents a much longer distance back into the
plumbing system. In a situation where the source of lead
in drinking water is a new brass faucet, or soldered joints
of lead-tin solder, this larger volume usually gives a
lower lead concentration than the smaller volume be-
cause more water in the sample is not in intimate contact
with materials containing lead.
Other sampling schemes logically follow. For instance,
if examining copper pipe corrosion, discarding the first
6
-------
Table 2. Interrelationships Among Pipe Length, ID, and
Internal Volume for Selected Common Plumbing
Materials and Pipe Sizes
Length
for 1,000
Identification/ True True Milliliters Milliliters
Material Type ID OD (Feet) per Foot
Copper
tubing
Copper
tubing
Copper pipe
Galvanized
steel pipe
Lead pipe
or tube
Lead pipe
or tube
PVCor
CPVC pipe
0.5-inch, type
L, annealed
0.5-inch, type
L, drawn
0.5-inch,
schedule 40
0.5-inch,
schedule 40
0.5-inch ID,
0.25-inch wall
0.75-inch ID,
0.25-inch wall
0.5-inch,
schedule 80
0.545
0.545
0.622
0.616
0.50
0.75
0.546
0.625
0.625
0.840
0.840
1.00
1.25
0.840
22
22
17
17
26
11.5
22
46
46
60
59
39
87
46
Plumbing Represented by Samples
a. 1,000 Milliliters = 22 Feet @ 0.5-Inch ID Cu Type L
Faucet
1,000-Milliliter Sample
Household
Plumbing
Meter
Main
•mi-
Service Line
125 or 250 milliliters of water is likely to give more
accurate information because it minimizes the effects of
the faucet material as well as piping that connects the
faucet to the interior line. Often, this connecting piping
is not copper. If examining the corrosivity of the water to
lead service lines, wasting a volume of water corre-
sponding to the distance from the outlet to the service
line better estimates the effect, although not without
uncertainty (18). Many other sampling schemes are
possible and useful, but users must be aware that the
sampling protocol may have as much or more influence
on the observed metal concentration than water quality
or other variables. Hence, incorporation of monitoring
data into a CIS database must be done only when the
source represents equivalent samples.
Because of turbulent mixing during flow, local high con-
centrations of lead (or other contaminant) may become
broadened and diluted by the time the water to be
sampled reaches the sample collection bottle (18). In
many cases, therefore, numerous small-volume se-
quential samples can be taken and used to profile a
plumbing system to locate brass valves, connectors,
soldered joints, etc. Figure 4 illustrates sequential sam-
pling results for one room of a building. Peaks in the
distribution of samples physically correspond to the lo-
cation of a chrome-plated brass faucet and to a later
concentration of fresh Sn:Pb soldered joints. Unfortu-
nately, even small-volume sequential tap water samples
must pass over other potentially contaminating or alter-
ing surfaces on the way through the sampling tap.
Kuch and Wagner have shown how water can dissolve
large amounts of lead simply by traveling through long
distances in lead pipes with small IDs (9, 19). Although
this study specifically examined lead, the principle ap-
plies to other metallic piping materials. This phenome-
non is inseparable from the aspect of time, which is the
next subject.
Faucet
b. 125 Milliliters = 2.75 Feet @ 0.5-Inch ID Cu Type L
Household
Plumbing
T
Soldered
Joints
125-Milliliter Sample
Main
Meter
1UI-
Service Line
Figure 3. Schematic diagram of plumbing materials repre-
sented by sample volumes of a) 1 liter and b) 125
milliliters.
Samples
1-2 60 Milliliters
3-12 125 Milliliters
1 2 3 4 5 6 7 8 9 10 11 12
Sample Sequence, #
Figure 4. Sequential sampling results from a room on the
ground floor of a building.
-------
Effect of Time
While some chemical reactions are instantaneous,
many dissolution and precipitation reaction steps that
are important in controlling metal levels in water take
many hours to many days to reach equilibrium. In fact,
passivation films and scales on pipes that inhibit corro-
sion and reduce leaching of trace metals may take
months to decades to develop substantially. Some other
chemical transformations, such as creation of triha-
lomethanes from chlorination of natural organic matter
during disinfection, or processes such as inactivation of
pathogens, may occur over hours (20, 21).
Many steps in an overall chemical reaction process
could be rate-limiting. Figure 5 shows how lead levels
increase in 0.5-inch ID pipe given two different assump-
tions. The top curve shows how lead increases and
levels off after about 8 to 12 hours (9, 19). This curve is
closely applicable to any metal, as long as the limiting
factor on the rate of metal migration into the water is the
radial diffusion of the soluble metal species away from
the pipe surface. The second curve shows, schemati-
cally, the effect of a diffusion barrier film (e.g., calcium
carbonate, adsorbed iron hydroxide mixed with organic
matter, aluminosilicate mineral deposits) or inhibition of
metal oxidation rates on lead migration into water after
different amounts of time.
In some water systems, significant chemical changes
can occur while the water is standing that can drastically
affect the oxidation or solubility of the plumbing material.
For example, dissolved oxygen and free chlorine react
quickly in new copper pipe or brass. If the water stands
0.01
Stagnation Level Controlled by
Metal Ion Diffusion
Stagnation in Presence of Diffusion
or Oxidation Barrier Film
10
20
30 40 50
Time, Hours
60
70
Figure 5. Comparison of lead concentrations that would be ob-
served after water stands different amounts of time
given different controlling chemistry factors.
sufficiently long, their concentrations may become neg-
ligible, which would significantly alter the redox condi-
tions governing metal solubility. In the absence of
oxygen or chlorine species, the dominant form of copper
in water and on plumbing material then becomes cop-
per(l) instead of copper(ll), resulting in different solubility
characteristics after consumption of the oxidant than at
initiation of the standing period (11).
Seemingly identical water samples collected from the
same taps in houses, schools, or other buildings yield
different metal concentrations, depending on the time
the water was in contact with the faucet, solder, or piping
material. Similarly, samples taken for disinfection bypro-
ducts after different chlorine contact times may produce
different concentrations and different speciation (e.g.,
trihalomethanes, haloacetic acids). This factor causes
considerable confusion in many investigations of con-
tamination of school or building drinking water taps and
water coolers and complicates estimating human expo-
sure for health-effects studies.
Interconnectedness of Distance and Time
In innumerable situations, the effects of distance and
time are impossible to separate. Some generalizations
and examples follow.
Dead ends and slow rate areas produce long residence
times for the distributed water. This results in long con-
tact times with pipe materials, so reactive constituents
can change considerably in concentration. The process
is totally interactive, in that concentration changes of
reactive constituents are in response to contact with the
pipe materials, and in turn, the materials respond to the
water composition. Water may take hours to days to
reach a particular home or building and may traverse
many miles of distribution system piping of the same or
differing composition. Water thoroughly run through a
household faucet for 5 or 10 minutes to purge the lines
is "fresh" from the resident's perspective, but may be
"old" from the distribution system perspective.
The profile of the water line shown in Figure 4 was made
on the basis of filling small volume (60 or 125 milliliters)
sample bottles, one after another, without wasting any
water. If the objective were to capture only the highest
risk of lead contamination from a lead service line after
some hours of stagnation, then the sampling process
would be different. Instead of collecting all water be-
tween the tap and the service line, the water can be run
until either a target volume is wasted (representing the
linear plumbing distance to the service line) or can be
run at a given rate until, after the appropriate length of
time passes, the sample bottle can "intercept" the slug
of water residing in the service line. Beware that differ-
ences may exist in the peak concentration of the con-
taminant and the "width" of the slug of elevated
-------
contaminant level, depending upon the rate of waterflow
before and during sampling (18, 22, 23).
Other Sources of Variability in Water
Samples
Variability in water samples can stem from many sources
aside from those discussed in this paper (18). The na-
ture of the errors and their likely magnitude may vary
with each episode of sampling and analysis and is far
beyond the present scope of discussion. A brief listing
to consider, however, when drawing conclusions from
"field" data includes:
• Analytical imprecision or bias.
• Flow rate of the water during sampling.
• Temperature.
• Particulate erosion from plumbing materials during
sampling.
• Effect of container material.
• Effect of air contact or other handling effects during
sample collection and shipment.
A Case Study of Easy Misinterpretation
Interpretations of water quality problems based on ag-
gregate monitoring data can be very misleading unless
analysis is performed at the appropriate scale. The situ-
ation of one utility described below provides a good
example of how using a CIS approach could have
helped solve the problem but also highlights how care-
fully data would need to be matched and consolidated
only at the proper scale if CIS were to be employed for
evaluating some kinds of water quality problems.
The utility at Hopkinton, Massachusetts, found very high
lead and copper levels exceeding the regulatory action
levels under the Lead and Copper Rule (14-17). The
90th percentile copper levels even exceeded 6 milli-
grams per liter, compared with an action level require-
ment of only 1.3 milligrams per liter. Some sites with lead
service lines are present in the system. Figure 6 shows
a schematic representation of the distribution system for
this utility. Five wells feed the system, and four (1, 2, 4,
and 5) are used regularly.
The background hardness, alkalinity, and carbonate
concentrations are fairly similar for all wells. The pH of
the ground water from the wells is usually slightly above
6. Chlorine solution is dosed for disinfection. High iron
levels are present in wells 1, 2, and 3, and high manga-
nese is also present in well 3. Generally, high dosages
of a polyphosphate chemical were added to wells 1 and
2 to respond to consumer complaints about the "red
water" that results from iron oxidation and precipitation.
Water from different wells mixes in the distribution sys-
tem, but the water tends to partition into two zones as
WelM
Well 4
Well 5
Figure 6. Schematic representation of the utility's distribution
system.
Figure 6 indicates. The lead and copper levels tended
to be distinctly lower in the section where the polyphos-
phate was dosed, marked as the "treated" part of the
system. The ongoing research study has employed ap-
proximately 22 monitoring sites.
From the information presented thus far, the system
clearly cannot be characterized by a discrete value for
lead or copper contamination, as well as the chemical
background of water throughout the system. Hence,
putting data at the "whole system" scale into a statewide
or countrywide data system would be tempting, but it
could be very misleading in solving the treatment prob-
lem. Having accurate spatially distributed data for back-
ground water qualities, monitoring site characteristics,
and metal levels at the subsystem scale, such as that
which could be integrated into CIS, would have been
extremely convenient, however. Yet, even more informa-
tion at a smaller scale is necessary to understand and
solve the whole treatment problem.
The utility initially observed that because the lowest lead
and copper levels coincided with the area of the system
fed by the polyphosphate chemical, that chemical likely
caused the corrosion inhibition. Median lead levels, for
example, were between about 200 and 300 milligrams
per liter in the "untreated" section, compared with about
10 to 15 milligrams per liter in the "treated" section.
Median copper levels were approximately 4 to 5 milli-
grams per liter in the untreated section, but only about
0.3 to 0.5 milligrams per liter in the treated section. The
utility and the researchers wondered whether the poly-
phosphate chemical should also be added to the other
wells. This is a matter of significant concern because
some studies indicate polyphosphate chemicals can en-
hance lead corrosion (1, 6) and the subject has rarely
been studied under statistically valid controlled conditions.
Additional site-by-site investigation, however, first re-
vealed that the sites with lead service lines all lay in the
untreated area of the distribution system. Because the
-------
research sampling program included two successive
1-liter samples, the additional contamination from the
service lines was confirmed by higher lead levels in the
second 1-liter sample than in the first in many cases.
Therefore, physical reasons, in addition to chemical
ones, explained the discrepancy in the lead levels. Fur-
ther, when considering only the treated system sites, the
lead levels were still high enough to be of concern.
Focusing on the copper sites resulted in the collection
of more important and interesting small-scale informa-
tion. Figure 7 shows the difference between average
copper levels in the two sections of the system. Almost
all sites in both parts of the system had copper interior
plumbing with 50:50 or 60:40 Sn:Pb soldered joints and
faucets with brass-containing internal materials. Though
the chemical added for iron control was ostensibly a
polyphosphate chemical, it also contained an initially
present fraction of orthophosphate and also tended
to partially break down to orthophosphate in the pres-
ence of iron and calcium (as most polyphosphates do).
Figure 8 shows the orthophosphate concentrations in
the two different parts of the system. While the levels of
orthophosphate present in the treated section would be
far too low to significantly inhibit lead leaching at the
background pH (1, 6, 7, 9, 22), the orthophosphate
plausibly may significantly inhibit copper dissolution, in
concordance with recent research projections (11).
Having determined through detailed small-scale sam-
pling and analysis that the chemistry affecting metal
levels in the system is generally consistent with modern
knowledge, a new treatment plan is being implemented
to control copper and lead levels through pH adjustment
in conjunction with iron control through a compatible
sodium silicate/oxidation treatment. Incorporation of
system and monitoring site physical characteristic data,
plus monitoring results, into CIS could have saved con-
siderable investigatory effort. The importance of this
case history, however, is that the data must be of the
appropriate scale and highly documented to be useful in
problem-solving. Failure to use data meeting these re-
Untreated Average
----• Treated Average
0.6 -,
0.5-
0.4-
0.3-
0.2-
0.1 -
J
/
**
^
*»^
*<
/
t j_
— Untreated Average
Treated Average
\
T/ > I
1 T i .
!-!•-•-•
Sept. Oct.
Nov. Dec. Mar. Apr.
Sept.
Figure 7. Average copper levels in treated and untreated sec-
tions of the system.
Figure 8. Average orthophosphate levels in treated and un-
treated sections of the system.
quirements, as well as overgeneralization to a large
mapping scale, can lead to ineffective if not damaging
water treatment choices that could adversely affect pub-
lic health.
Conclusions
The examples and discussion above lead to several
general conclusions about the use of CIS with drinking
water monitoring data:
• Temporal and spatial variability stems from many
causes, down to a very small scale.
• Sampling protocols must be keyed to the precise
questions under investigation.
• Regulatory sampling, whose results are generally
readily available, is usually inappropriate to assess
human exposure to trace metals or other parameters
of interest (such as DBPs).
• Generalizations on a large scale are often impossible
because of the geology and water chemistry variations.
Additionally, some considerations apply to the types of
mapping that could be employed by CIS. For example,
a mapping technique such as contouring may be espe-
cially inappropriate for use with drinking water data.
Major problems could result from:
• Discrete, small-scale (such as within an individual
house) variability in distributions of certain contami-
nants, such as lead and copper.
• Physical constraints of the distribution system network.
• The small number of monitoring sites in relation to
the size of the distribution network.
• Different chemical or hydraulic zones in the distribu-
tion system.
Employing CIS could be very useful in solving a variety
of drinking water problems. Users must be extremely
conscious of the nature of the source information, how-
ever, to avoid abusive extrapolations and generalizations.
-------
Acknowledgments
Some of the examples cited were investigated as part
of a U.S. Environmental Protection Agency cooperative
agreement (Darren A. Lytle, Project Officer) with the
New England Waterworks Association. Jack DeMarco
of the Cincinnati Water Works kindly provided Figure 1
on distribution of sampling sites for lead and copper
monitoring.
References
1. Schock, M.R. 1990. Internal corrosion and deposition control. In:
Water quality and treatment: A handbook of community water
supplies, 4th ed. New York, NY: McGraw Hill.
2. Benefield, L.D., and J.S. Morgan. 1990. Chemical precipitation.
In: Water quality and treatment: A handbook of community water
supplies, 4th ed. New York, NY: McGraw Hill.
3. Glaze, W.H. 1990. Chemical oxidation. In: Water quality and treat-
ment: A handbook of community water supplies, 4th ed. New
York, NY: McGraw Hill.
4. Cotruvo, J.A., and C.D. Vogt. 1990. Rationale for water quality
standards and goals. In: Water quality and treatment: A handbook
of community water supplies, 4th ed. New York, NY: McGraw Hill.
5. Tate, C.H., and K.F. Arnold. 1990. Health and aesthetic aspects
of water quality. In: Water quality and treatment: A handbook of
community water supplies, 4th ed. New York, NY: McGraw Hill.
6. Schock, M.R. 1989. Understanding corrosion control strategies
for lead. JAWWA 81:7:88.
7. AWWARF. 1990. Lead control strategies. Denver, CO: American
Water Works Association Research Foundation and American
Water Works Association.
8. AWWARF. 1994. Corrosion control in water distribution systems,
2nd ed. Denver, CO: American Water Works Association Re-
search Foundation/Engler Bunte Institute.
9. Schock, M.R., and I. Wagner. 1985. The corrosion and solubility
of lead in drinking water. In: Internal corrosion of water distribution
systems. Denver, CO: American Water Works Association Re-
search Foundation/DVGW Forschungsstelle.
10. Pankow, J.F. 1991. Aquatic chemistry concepts. Chelsea, Ml:
Lewis Publishers, Inc.
11. U.S. EPA. 1995. Effect of pH, DIG, orthophosphate and sulfate
on drinking water cuprosolvency. Cincinnati, OH: Office of Re-
search and Development. In press.
12. AWWA. 1993. Initial monitoring experiences of large water utilities
under U.S. EPA's Lead and Copper Rule. Denver, CO: Water
Industry Technical Action Fund/American Water Works
Association.
13. AWWA. 1990. Lead service line replacement: Benefit to cost
analysis. Denver, CO: Water Industry Technical Action
Fund/American Water Works Association.
14. AWWA. 1991. Lead and copper: A working explanation of the
Lead and Copper Rule. Denver, CO: American Water Works
Association.
15. Federal Register. 1991. Lead and copper: Final rule correction.
Fed. Reg. 56:135:32,112 (July 15).
16. Federal Register. 1991. Lead and copper: Final rule. Fed. Reg.
56:110:26,460 (June 7).
17. Federal Register. 1992. Lead and copper: Final rule correction.
Fed. Reg. 57:125:28,785 (June 29).
18. Schock, M.R. 1990. Causes of temporal variability of lead in
domestic plumbing systems. Environ. Monit. Assess. 15:59.
19. Kuch, A., and I. Wagner. 1983. Mass transfer model to describe
lead concentrations in drinking water. Water Res. 17:10:1,303.
20. Ireland, J.C. 1993. Alternatives to chlorine for disinfection of drink-
ing water. In: Strategies and technologies for meeting SDWA
requirements. Lancaster, PA: Technomic.
21. Miltner, R.J. 1993. Pilot scale treatment for control of disinfection
byproducts. In: Strategies and technologies for meeting SDWA
requirements. Lancaster, PA: Technomic.
22. Sheiham, I., and P.J. Jackson. 1981. Scientific basis for control
of lead in drinking water by water treatment. J. Inst. Water Engrs.
and Scientists 35:6:491.
23. Heumann, D.W. 1989. Los Angeles Department of Water and
Power: Solid lead gooseneck slug dispersion in consumer plumb-
ing system. Proceedings of the AWWA Water Quality Technology
Conference, Philadelphia, PA.
11
-------
Evaluating Soil Erosion Parameter Estimates
From Different Data Sources
G. B. Senay1, B. Subramanian2 and S. Cormier2
1 SAIC/PAI, c/o USEPA, Cincinnati, OH
2 US EPA, Cincinnati, OH
Abstract
Topographic factors and soil loss estimates that were derived from three data sources
(STATSGO, 30-m DEM, and 3-arc second DEM) were compared. Slope magnitudes derived
from the three data sources were consistently different. Slopes from the OEMs tended to
provide a flattened surface with large areas having 0.0% values. The 3 arc second DEM
generally produced a lower slope estimate than either the STATSGO or 30-m DEM. Slopes
from the 30-m DEM fell between the 3-arc second and the STATSGO. Thus, the STATSGO
database provided a higher slope estimate than either of the two DEM sources. However,
slopes from the 30-m DEM and the STATSGO were more comparable than the slope estimates
from 30-m and 3-arc second OEMs. For example, 0.0, 10.0, and 20.0% slope classes from the
30-m DEM showed mean values of 0.0, 4.0, and 6.0 %, respectively with the 3-arc second
DEM, and 3.0, 18.0, and 24.0 %, respectively with the STATSGO. Along with the slope
differences, potential erosion estimate trends varied between the data sets although the soil
loss differences were higher than the slope differences. A recommendation was made to
validate slope and erosion estimates using field data; however, it appeared that STATSGO may
be more reliable than the two data sets for smaller slopes and either STATSGO or the 30-m
DEM may be used for higher slopes. Due to the ease of GIS in combining data from various
sources, the importance of a thorough understanding of data accuracy standards and their
limitations and intended use were highlighted.
Introduction
One of the most difficult challenges in ecological hydrology is the paucity and accuracy of
hydrologically relevant data on factors such as soils, topography, vegetation and stream
ecology. The available data sources vary in their accuracy, accessibility, and data formats.
Stream ecology can be affected by pollution caused by the process of erosion and sediment
transport. Erosion estimates are generally made using the Universal Soil Loss Equation (USLE)
-------
from the multiplicative nature of soil erodibility, topography, landcover, conservation practice
and rainfall erosivity factors. Unlike the other factors, the topographic factor can be readily
derived from different digital sources called Digital Elevation Models (OEMs) each with its own
accuracy standard. Another widely used data set for this kind of study is the STATSGO (State
Soil Geographic) data base that is distributed by the Natural Resources Conservation Service
(NRCS).
Digital Elevation Model (DEM) data files are digital representations of cartographic information
in a raster form. OEMs consist of a sampled array of elevations for a number of ground positions
at regularly spaced intervals. These digital cartographic/geographic data files are produced by
the U.S. Geological Survey (USGS) as part of the National Mapping Program and are sold in
7.5-minute (also known as 30 -m DEM), 15-minute, 2-arc-second (also known as 30-minute),
and 1-degree units (3-arc second DEM). The 7.5- and 15-minute OEMs are included in the large
scale category while 2-arc-second OEMs fall within the intermediate scale category and
1-degree OEMs fall within the small scale category (USGS, 1987).
The accuracy of a DEM is dependent upon its source and the spatial resolution (grid spacing) of
the data profiles. One factor influencing DEM accuracy is source data scale and resolution. A
dependency exists between the scale of the source materials and the level of grid refinement
possible. The source resolution is also a factor in determining the level of content that may be
extracted during digitization. For example, 1:250,000-scale topographic maps are the primary
source of 1-degree OEMs while 1:24,000-scale maps are the primary source for 7.5 minute
OEMs (USGS, 1987).Within a standard DEM, most terrain features are generalized by being
reduced to grid nodes spaced at regular intersections in the horizontal plane. This
generalization reduces the ability to recover positions of specific features less than the internal
spacing and results in a de facto filtering or smoothing of the surface during gridding (USGS,
1987) .
STATSGO is a nationwide soils database that consists of soil maps and attributes that are
assembled by generalizing more detailed soil survey data in accordance with the digitizing
standards of the Natural Resources Conservation Service (NRCS). Soil maps were digitized by
line segment format (vector) from a U. S. Geological Survey topographic quadrangle base map
with a scale of 1:250,000. This level of mapping is designed to be used for broad planning and
management uses covering state, regional, and multistate areas. The mapping area of the
-------
smallest soil polygon is about 1, 544 acres, i.e., soil map units less than this area are not
represented in STATSGO (USDA, 1994).
Each STATSGO map is linked to the Soil Interpretations Record (SIR) attribute database. The
attribute database gives the proportionate extent of the component soils and their properties for
each map unit. The STATSGO map units consist of 1 to 21 components each. The Soil
Interpretations Record database includes over 25 physical and chemical soil properties,
interpretations, and productivity. Information that can be queried from the database include: land
slope, available water capacity, soil organic matter, salinity, flooding, water table, bedrock, and
interpretations for engineering uses, cropland, woodland, rangeland, pastureland, wildlife, and
recreation development.
The objective of this study was to compare topographic factors and soil loss estimates that are
derived from three data sources namely STATSGO, 30-m DEM (7.5 minute data), and 3-arc
second DEM (1 degree data). The three spatial data sources differ in data collection techniques,
spatial accuracy, and data formats.
Material and Methods
Study Site
This study compares spatial data sets for the state of Ohio. Although the two data sets
(STATSGO and 3-arc second DEM) had complete coverage for the entire state, the 30-m DEM
only covered a portion of the state since the rest of the area was still under "work-in-progress"
by the USGS. The total area of Ohio is around 120,362.00 km2 of which the 30-m DEM covered
about 56%.
STATSGO
For this study, STATSGO data for the state of Ohio was downloaded from the NRCS web site.
The file was formatted for use in a Geographical Information Systems software called Arc/Info
7.0 (ESRI, 1997). The spatial data was projected in the Albers Equal Area coordinate system.
Two soil/landscape parameters that are essential for soil erosion modeling were derived from
the STATSGO database. These include the soil erodibility (K) and percent land slope (S). Since
the attribute database is more detailed than the spatial mapping, the component percentage in
each map unit was used to produce an area weighted average value that represents the various
-------
map components in a given spatial polygon (map unit). The attribute database contains
information about soil components while the spatial coverage only identifies map units that are
collections of soil components. Thus, the STATSGO attribute database and spatial map units
were joined by a map unit ID called MAPUNIT in Microsoft Access, a relational database
(Microsoft Corporation, 1996). Component percent values were used as a weighting factor to
calculate the weighted average for each soil polygon. In STATSGO, generally, each soil
parameter with a numerical value is represented by two values namely, the "low" and "high"
range found in each soil component. Once the weighted average was calculated for the
individual "low" and "high" range values, the average of the "high" and "low" was taken to
represent a particular soil polygon (map unit).
In order to process the STATSGO arc coverage file in a grid based (raster) GIS (Arc/lnfo:Grid),
together with the OEMs and for convenience during the USLE calculation, the vector data was
rasterized in Arc/Info using the POLYGRID command at 90 m resolution, closer to the spatial
resolution of the 3-arc second DEM. A finer resolution was not chosen in order to avoid
increased file sizes and computation time. It was believed that there would not be a loss of
significant information by rasterizing the data at 90 m since STATSGO is relatively coarse data
with the smallest polygon area occupying 635 hectares which is equivalent to about 772 cells at
the 90-m grid spacing.
DEM
For this study, land slope was also calculated from two Digital Elevation Models (OEMs) with
spatial resolutions of 30-m and 3-arc second. The 30-m DEM (7.5 minute) data correspond to
the USGS 1:24,000 and 1:25,000 scale topographic quadrangle map series for all of the United
States and its territories. The 3-arc second DEM (1-degree) provides coverage in 1- by
1-degree blocks for all of the contiguous United States. The basic elevation model is produced
by or for the Defense Mapping Agency (DMA), but is distributed by the USGS, in DEM data
record format. The 3-arc second DEM data was available after being resampled to a uniform
grid spacing of 102.0815 m in Albers Equal Area coordinate system.
While the 3-arc second DEM was readily available for the state of Ohio in Albers projection, the
30-m DEM required some pre-processing before it was used together with the other data layers.
The 30-m DEM were purchased from the USGS in separate 7.5 by 7.5 minute topographic
quadrangle sizes in Universal Transverse Mercator (UTM) projection. Using an automated
-------
procedure in Arc/Info's Arc Macro Language (AML), the individual data files (507) were first
projected to an Albers projection, and elevation values were checked and converted to meters if
they were not already in meters. Finally, the individual grids were mosaicked using the MOSAIC
command in Grid-Arc/Info.
Generating Topographic Parameters
Land slope is a very important parameter in hydrological processes. Slope plays an important
role in regulating the rate and amount of water flow on land surfaces. Particularly, slope is
embedded in one of the multiplicative variables (topographic factor, LS) in the Universal Soil
Loss Equation (USLE). The USLE is widely used to estimate the average annual soil loss from
agricultural, range and forest lands. Wischmeier and Smith (1978) formulated the USLE as
follows:
A = R*K*LS*C*P (1)
Where,
A = average annual soil loss in tons/acre
R = rainfall and runoff erosivity index for a geographic location
K = soil erodibility factor
LS = slope steepness and length factor
C = cover management factor
P = conservation practice factor
While there are limited sources to obtain the values of most of the variables, the availability of
OEMs at different accuracy levels and GIS overlay techniques allow the determination of the LS
factor from OEMs plus STATSGO. Generally, the R factor is determined from weather data,
requiring inputs of amount and pattern of rainfall for the area; the K factor is largely dependent
on soil types with, for example, soils with high silt content being the most erodible. STATSGO
provides K values for each soil polygon. The cover management factor (C) is generally
dependent upon crop rotations and tillage sequences. For forest, rangeland and non-agricultural
conditions, C is estimated from density of vegetation and the amount of residue on the soil
surface. The P factor mainly accounts erosion control practices such as contouring, terracing
and strip cropping whose effect vary with the land slope. For many applications of the USLE, no
erosion control practice will be used, and the P factor will be just 1.0 (Ward and Elliot, 1995).
-------
The objective of this study was to investigate the effect of using three data sources in the
estimation of the LS factor and to evaluate its impact on the estimation of the average soil loss.
The three data sources were: STATSGO, 30-m DEM and 3-arc second DEM.
The LS factor is a function of both the length (L) and the steepness of the land (slope, S). In
most field applications, both factors are considered as a single topographic factor. LS is the
expected ratio of soil loss per unit area from a field slope to that from a 72.6 ft length field of
uniform 9-% slope under otherwise identical conditions.
The LS factor is calculated from the following formula (Wischmeier and Smith, 1978):
LS = (• •/ 72.6)m * (65.41 * sin2- •+ 4.56 * sin-1- 0.065) (2)
Where,
• = slope length in feet; • = angle of slope; and
m = 0.5 if the percent slope is 5 or more; 0.4 on slopes of 3.5 to 4.5 percent; 0.3 on slopes of 1
to 3 percent; and 0.2 on uniform gradients of less than 1 percent.
For STATSGO soil polygons, slope was obtained from the attribute database. As described
earlier, the component percentages in each soil polygon were used to calculate area weighted
slope values for each soil map unit. The average of the 'low' (slopejow) and 'high' (slope_high)
range values were used to represent each soil polygon.
For the DEM data sources, slopes were calculated using the SLOPE function in Grid Arc/Info.
Slope identifies the maximum rate of change in value from each cell to its neighbors. An output
"slope" layer was calculated as percent slope. Conceptually, the slope function fits a plane to
the "z: elevation" values of a 3x3 cell neighborhood of a center cell. The slope for the cell is
calculated from the 3x3 neighborhood using the average maximum technique (Burrough, 1986).
o/.
\ 2 _j_ /-j_ /-j. ,\2\ 0.5
%-slope =100 * ((dz/dxr + (dz/dy)T (3)
Where the deltas (differences) are using a 3x3 roving window; a through i represent the z values
(elevation) in the window shown below:
a b c
def
g h i
-------
dz/dx = (a + 2d + g) - ( c + 2f + i) / (8 *x-grid_spacing) (4)
dz/dy = (a + 2b + c) - (g + 2h + i) / (8 * y-grid spacing) (5)
Since both the x- and y-grid spacing are the same, the grid spacing can be replaced by the
spatial resolution of the grid, i.e., simply 30-m for the 30-m DEM.
Once the slope layers were calculated from each DEM grid, the LS factor was calculated using
Equation 2. A procedure was written in Arc Macro Language (AML) to execute equation 2 for
each grid cell. The procedure mainly checks for the magnitude of the slope of a particular cell in
order to vary the exponent 'm' in equation 2 accordingly. The length of the slope was taken to
be equal to the length of a cell. For calculating LS from STATSGO, the rasterized, 90-m
STATSGO layer was used; similarly, equation 2 was used to generate the LS values.
For estimating the average annual soil loss, the following assumptions and approximations were
applied. First, the only variables varying from cell to cell were the LS and K factors. The rainfall
and runoff erosivity index (R) was read from a nation wide chart by Wishemeier and Smith
(1978). A value of 150 seemed a reasonable representative value for most of Ohio. On the other
hand, a cover factor (C) of 0.42was used as a conservative estimate for corn and soybean fields
that are managed in a conventional tillage practice in autumn (Cooperative Extension Service,
OSU, 1979).A conservative value of 1.0 was assumed for the conservation factor for lack on
any information on the application of any soil conservation measures. Although such estimates
may not represent the actual soil loss on a cell by cell basis, the most important exercise in the
study was to show the relative differences in such estimates due to using different data sources
particularly for the topographic factor, LS.
The average annual soil loss was simply estimated by using equation 1 in Grid: Arc/Info. To
develop the 'soil loss' GIS layer, two GIS layers (LS, K factors) and two constants ( R and C)
were multiplied on a cell by cell basis. Three soil loss layers were derived from each of the data
sources which produced varying LS factors. Note that the K factor from STATSGO was also
applied on the soil loss estimates from the DEM sources. This was possible since all the three
data sources were projected to the same coordinate system in Albers Equal Area.
-------
Data Manipulation and Extraction in Arc/Info
It was possible to estimate the average soil loss for the entire state of Ohio using STATSGO
and a combination of STATSGO and the 3-arc second DEM. Only about 56% of the state could
be evaluated using a combination of STATSGO and the 30-m DEM. STATSGO remains
essential in all data sets since it also provides the K factor. Due to the limited area of coverage
and its relative spatial accuracy, the 30-m DEM was used as reference data to which the results
of the other two data sources were compared.
A recommended use of the LS equation is in land slopes not exceeding 20% (Wischmeier and
Smith, 1978). A mask (20%-mask) was created to remove all areas with slopes greater than
20% in the 30-m DEM before any overlay analysis was made with the STATSGO and 3-arc
second DEM data. The combined area of slopes with magnitudes greater than 20% covered
14.4 % of the available 30-m DEM coverage.
After creating the 20%-mask on the 30-m DEM, floating point slope values were rounded to the
nearest integer so that distinct slope classes would be established based on which slope values
from the other data layers can be queried and analyzed. For each of the slope classes, i.e., 1, 2,
3,...,20, the corresponding mean and standard deviation slope values were derived from
STATSGO and the 3-arc second DEM using the ZONAL STATS command in Grid Arc/Info. The
extracted slope values were again rounded to the nearest integer values.
Similarly, soil loss estimates from the three data sources were compared using a soil loss layer
derived from the 30-m DEM as a reference. Only slopes less than or equal to 20% were used
for the soil loss estimation as well. Soil loss estimates were first rounded to the nearest whole
number for the reference data set (that derived from the 30-m DEM). Then, for each soil loss
class (tons/ac/yr) the corresponding mean and standard deviation soil loss estimates were
derived from the other soil loss estimate layers, i.e., those based on STATSGO and 3-arc
second DEM. The mean soil loss estimates for the other layers were then rounded to the
nearest whole number.
Scatter plots of class-mean slope and soil loss estimates were plotted using Excel, a
spreadsheet and graphing software (Microsoft Corporation, 1996). For both cases, the
estimates derived from the 30-m DEM were placed in the x-axis (the reference) and estimates
-------
from STATSGO and 3-arc second were placed in the Y-axis. Relative spreads of the data were
quantified by the standard deviation and coefficient of variation of the class values.
Results
Slopes
It was shown that slopes derived from the three data sources were consistently different. The 3-
arc second DEM showed a generally lower slope estimate than the STATSGO or 30-m DEM.
Slopes from the 30-m DEM fell between the 3 arc second DEM and the STATSGO (Table 1,
Figure 1). Thus, the STATSGO database generally provided a higher slope estimate than either
of the two databases.
Generally, lower slope values occupied most of the area with higher slopes constituting a
smaller percentage of the area. The most abundant slope was the 0.0% in both the 30-m and 3-
arc second DEM, covering about 18% of the 30-m DEM coverage (56% of Ohio) and 44.5% of
the study area, respectively. In STATSGO, the most abundant slope was the 2% slope covering
17% of the study area followed by the 4% slope and 1% (minimum for STATSGO) slope
covering 11.4% and 10.6% of the study area, respectively. The maximum slope values were
245%, 61% and 41% for 30-m DEM, 3-arc Second DEM and STATSGO, respectively. However,
the maximum slope values from the OEMs were more likely to represent outliers since they
were only observed in one pixel. For STATSGO, the maximum slope covered about 0.9% of the
study area. An upper quartile comparison of the slope distribution showed that about 75% of the
area in each data set showed that slope values were less than or equal to 14%, 3%, and 16%
for 30-M DEM, 3 arc second DEM, and STATSGO, respectively (data not shown).
Slopes from the 30-m DEM and the STATSGO appeared more comparable than the slope
estimates from 30-m and 3 arc second OEMs, particularly in higher slope classes. For example,
0.0, 10.0, and 20.0% slope classes from the 30-m DEM showed mean values of 0.0, 4.0, and
6.0 %, respectively, with the 3-arc second DEM, and 3.0, 18.0, and 24.0 %, respectively with the
STATSGO based data (Table 1, Figure 1).
One of the unique characteristics of the STATGO database is that the average between the
high and low values will result in a condition where a zero data point will be eliminated. That is
one of the reasons why the STATSGO minimum value does not start at zero. In the case of the
3-arc second DEM, the flat lands were accurately represented. On an average, slopes labeled
-------
as having 0.0% in the 30-m DEM were also labeled as having 0.0% slopes in the 3-arc second
data set. This unique match in the two data sets can be explained by the general tendency of
the 3-arc second to provide a flattened surface. However, the inverse was not true (data not
shown) in that slopes identified as having 0.0% slopes in the 3-arc second DEM were not
necessarily 0.0% in the 30-m DEM. This was because the 30-m DEM had a better capacity for
resolving smaller slopes than the 3-arc second DEM.
Due to the generally depressed slopes observed from the 3-arc second DEM, the data also
appeared less sensitive compared to the 30-m DEM, resulting in a step function pattern (Tablel;
Figure 1). For example, slopes varying from 1 to 3% in the 30-m DEM were labeled as 1% in the
3-arc second DEM. Similar effects are seen in other slope ranges such as6 to 9-% which were
labeled as having 3% slope in the 3-arc second data set. A similar trend was observed in the
STATSGO data, but only at the higher slope ranges. For example, slopes 13 and 14% were
labeled as 21% and slopes ranging from 17 to 19% were labeled as 23 %.
A detailed investigation of the 30-m DEM with only slopes less than or equal to 20% showed
that most of the study area was under smaller slopes with about 63 % of the total area having a
slope of 5 % or lower (Table 1). Areas with 0.0% slope occupied the largest area with about
19% coverage, percentage slope area decreased with increasing slope (Table 1). The relatively
large percentage of land areas with 0-% slopes indicates that even the 30-m DEM may not be
adequate if a more accurate representation of a field is required in relatively flat to mild
topography.
The spread of the slope values in each 30-m DEM slope-class shows that the 3-arc second
DEM was more spread than the STATSGO data set (Table 1); with coefficient of variation (the
ratio of standard deviation to mean) values ranging from 100 to 150% for 3-arc DEM and only
from 37 to 125% for STATSGO.
Soil Loss Estimate
The differences in soil loss estimates between the three data sets were generally a reflection of
differences in slope and slope length estimates since the topographic factor was the major
difference between the different data sources. The combination of slope values and grid cell
sizes (for slope length) were used to calculate the LS factor in the USLE equation. While the ®
= 150) and © = 0.42) factors were constant, the "K" factor was spatially variable as obtained
10
-------
from the STATSGO data set. The "K" values had a minimum and maximum of 0.09 and 0.45,
respectively, with a mean value of 0.348 for the entire state of Ohio.
From the 30-m DEM, the USLE calculation resulted in 119 soil loss classes ranging in values
from 0 to 118 tons/ac/yr. The soils estimates are generally larger (up to 10 times) than is
observed in agricultural fields and generally greater than the allowable soil tolerance limits that
range from 3 to 20 tons/ac/yr, depending on the soil types (Ward and Elliot, 1995). This was
mainly due to the assumption of a poor cover condition (high C factor) at the worst scenario.
Table 2 shows soil loss classes in increments of 10 after the 10th class. The 0.0 soil class is
due to rounding and it only represented values under < 0.5 tons/ac/yr that were obtained in a
relatively small area (0.06%). As the slope extraction, the 30-m DEM soil classes were used to
extract soil loss estimates from the 3-arc second DEM and STATSGO data sets (Table 2). The
3-arc DEM compared well with the 30-m DEM estimates on lower soil loss estimates up to 10
tons/ac/yr before beginning to underestimate by as much as about 3 times lower at higher soil
loss estimates (Table 2, Figure 2). The reason for the better comparison between the two data
sets at lower soil loss estimates was due to relatively small slope difference at the lower erosion
estimates. For example, slopes 1 to 3 in the 30-m DEM were averaged as 1% in the 3-arc
second DEM. Also, the LS factor tends to compensate (higher) for longer slopes as exhibited in
the 3-arc second data set (102 m vs 30 m). Taking average conditions for K, R, and C factors,
10 tons/ac/yr corresponds to about 4% slope for the 30-m DEM. On an average, the 4% areas
on the 30-m DEM are labeled as having 2% and 10% slope on 3-arc second DEM and
STATSGO data sets, respectively (Table 1).
Taking into consideration the sensitivity (exponential type relationship) of the LS factor
(Equation 2) on slope, erosion estimates generally will be increased more than the increase in
slope. For example, the LS factor will be about 6 times higher when a 4% slope of 30-m DEM is
replaced by a 10% slope from STATSGO. Thus, soil estimates from STATSGO were generally
much higher than the 30-m DEM, reaching up to about 7 times higher (Table 2, Figure 2).This is
certainly the result of the higher slopes observed in the STATSGO database even at lower
slopes when compered to the 30-m DEM. In addition, the slope length of the STATSGO was
three times higher than the 30-m DEM which would also lead to higher soil loss estimates.
Soil loss trends for both data sets (3-arc second, STATSGO) were similar to the increasing
values from the 30-m DEM for most of the data set (Figure 2). Irregular trends were observed at
higher soil loss estimates (from 90 to 118 tons/ac/or). This was due to the fewer number of grid
11
-------
cells that were being averaged at high estimates. Table 2 shows that about 95% of the area had
soils estimate values less than or equal to 90 tons/ac/or indicating that soil loss estimates
beyond 90 tons/ac/or were found in smaller areas. This shows that although this kind of multi-
source comparison seems to be reasonable at a regional scale, conclusions from such studies
can be misleading in applying these studies at a local scale in smaller areas.
Conclusions
Slope and soil loss estimates from the three data sources varied greatly. Slope and soil loss
estimates were ranked from high to low for STATSGO, 30-m DEM, and 3-arc second DEM in
descending order. A large percentage (44.5%) of the 3-arc second DEM coverage produced
slope values of 0.0%. While the 30-m DEM also contained a relatively large area (about 18%)
under 0.0% slope, STATSGO showed that the 2% slope was dominant (about 17% of area)
followed by 4 and 1% slope, each occupying about 11% of the area. The high percentage of
0.0% slopes in the DEM data sets indicated that these data sets were unable to resolve small
relief differences between adjacent cells.
Slopes from the 30-m DEM and the STATSGO were more comparable than the slope estimates
from 30-m and 3-arc second OEMs, particularly at higher slope values. This indicated that
although both OEMs tend to underestimate slopes, the 30-m DEM was more reliable than the 3-
arc second DEM at higher slopes.
Along with the slope differences, potential erosion estimates varied between the three data
sources. Soil loss differences between the data sets were higher than the slope differences. An
irregular trend in erosion estimates at the higher end was attributed to smaller class sizes,
indicating the limitations of the this kind of study at smaller scales while suggesting its validity on
larger scales.
A recommendation was made to validate the slope and erosion estimates using field data.
However, it appears that STATSGO may be more reliable than the two other data sets for
smaller slopes and either STATSGO or the 30-m DEM may be used for higher slopes. Further
investigation will be necessary to recommend the applications and limitations of these data at
different scales. This study showed that the three data sources resulted in widely varying
estimates of topographic factors and soil loss estimates. Although GIS makes it easy to
combine data from various sources, the compatibility of the data accuracy standards and their
12
-------
limitations and intended use should be thoroughly understood before scientific conclusions are
drawn.
Table 1: Slope distribution (%-slope) of 3-arc second DEM and STATSGO based on 30-m
DEM slope classes.
Class
30-m
Mean
3-arc
Mean
STAT
Std.
3-arc
Std.
STAT
Cum.
%-Area
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
0
1
1
1
2
2
3
3
3
3
4
4
4
4
4
5
5
5
5
5
6
3
4
5
8
10
11
13
15
16
17
18
19
20
21
21
22
22
23
23
23
24
1
1
2
2
3
3
4
4
4
4
5
5
5
5
5
5
5
6
6
6
6
3
5
6
8
9
10
10
11
11
11
10
10
10
10
10
10
10
9
9
9
9
18.9
31.0
46.2
52.9
58.5
63.2
66.6
70.1
73.5
76.0
78.9
81.6
84.0
86.3
88.6
90.8
92.7
94.7
96.7
98.3
100.0
30-m: 30-M DEM; 3-arc: 3-arc second DEM; STAT: STATSGO; Std.: Standard deviation;
Cum.: Cumulative area occupied by the 30-m DEM class values.
13
-------
Table 2: Soil loss estimate (tons/ac/or) distribution of 3-arc second and STATSGO based
on 30-m DEM slope classes.
Class
30-m
Mean
3-arc
Mean
STAT
Std.
3-arc
Std.
STAT
Cum.
%-Area
0
1
2
3
4
5
6
7
8
9
10
20
30
40
50
60
70
80
90
100
110
118
1
2
3
4
4
6
7
8
8
9
10
16
20
23
25
28
30
30
32
37
18
5
7
7
17
22
25
39
52
51
58
70
72
130
160
179
183
196
206
205
238
175
20
18
1
3
5
8
9
12
15
15
16
18
19
27
31
34
37
39
43
42
48
54
22
2
0
21
35
53
51
69
86
80
89
96
96
123
122
120
116
112
114
113
108
81
19
0
0.1
11.4
24.5
32.2
41.5
46.7
49.6
52.5
55.1
57.2
58.8
71.5
77.9
83.3
87.8
91.4
94.5
97.1
99.0
99.7
100.0
100.0
30-m: 30-m DEM; 3-arc: 3-arc second DEM; STAT: STATSGO; Std.: Standard deviation;
Cum.: Cumulative area occupied by the 30-m DEM class values.
14
-------
24-
22
20-
18-
16-
a 14
12-
10-
8-
6-
4
2-I
0
STATSGO • •
3-arc second DEM
0
8 10 12 14 16 18 20 22 24
%_Slope: 30-m DEM
Figure 1: Percent land slope comparison between three data sources
with 30-m DEM as a reference.
260
240
220
200
180
160
140
120
100
80
60
40
20
0
STATSGO
3-arc second DEM
0 10 20 30 40 50 60 70 80 90 100 110 120 130
Soil Loss (tons/ac/yr): 30-m Dem
Figure 2: Soil loss estimate comparison between three data sources
with 30-m DEM as a reference. Data fluctuations at the end are caused
by a small number of data points.
15
-------
References
Burrough, P. A., 1986. Principles of geographic information systems for land resources
assessment. Oxford University Press, New York, NY, 50pp.
Cooperative Extension Service and the Ohio State University, 1979. Ohio erosion control and
sediment abatement guide. Columbus, OH, 24pp.
ESRI, 1995. Arc/Info version 7.0.
Microsoft Corporation, 1996. Office 97 spreadsheet, graphing and database software.
U.S. Department of Agriculture, 1994. State soil geographic (STATSGO) data base-data use
information, miscellaneous publication number 1492: Fort Worth, TX, Natural Resources
Conservation Service.
U.S. Geological Survey, 1987. Digital Elevation Models. Data Users Guide 5. Reston, VA.
Ward, A. D. and W. J. Elliot, 1995. Environmental Hydrology. Lewis Publishers, NY, 462pp.
16
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Geology of Will and Southern Cook Counties, Illinois
Edward Caldwell Smith
Illinois State Geological Survey, Champaign, Illinois
Introduction
The Silurian dolomite aquifer is the primary source of
ground water in northeastern Illinois. It is overlain by
glacially derived sands and gravels or tills. The sands
and gravels within the glacial drift hydrologically interact
with the fractured and creviced dolomite bedrock.
The purpose of this study was to define the extent of
major glacial drift aquifers and their relationship to the
shallow bedrock aquifer surface. The study succeeded
in identifying two principal sand and gravel aquifers: an
"upper" drift aquifer within the glacial tills and a "basal"
drift aquifer overlying the bedrock. Bedrock topography,
drift thickness, thickness of the Silurian dolomite, and
thickness of major sand and gravel units were mapped
to help define the geologic and hydrologic system and
the interaction of the upper bedrock aquifer and the drift
aquifers.
The data collected to create the various maps came
from well records, engineering borings, oil and gas tests,
and structure tests on file at the Illinois State Geological
Survey (ISGS). Reviewing published reports, manu-
scripts, and unpublished reports on open file at the ISGS
provided an overall perspective of the geology of the
study area. Previously, no detailed studies of the hydro-
geology of the entire area had been conducted. Incor-
porating water well and other data into a computer
database greatly facilitated map construction. Prelimi-
nary maps were developed using Interactive Surface
Modeling (ISM) software and a geographic information
system (CIS).
Past regional geologic studies of the northeastern Illi-
nois area that have encompassed this study area in-
clude Thwaites (1), Bretz (2), Bergstrom et al. (3), Bretz
(4), Suter et al. (5), Hughes et al. (6), and Willman (7).
Bogner (8) and Larsen (9) included interpretive maps of
the surficial geology of the area as a part of planning
studies for northeastern Illinois.
Map Construction
Creating the database used in the construction of the
maps for this project entailed inputting information from
well driller's logs into a PC-based computerized spread-
sheet (Quattro Pro). Well logs were primarily from water
wells and engineering borings. Data items input into the
spreadsheet included:
• Well identification (ID) number
• Owner name
• Location of well
• Thickness of drift
• Depth to top and bottom of the bedrock
• Depth to top and bottom of each sand unit
The ground surface elevation of each well was interpo-
lated from United States Geological Survey (USGS)
7.5-minute quadrangles. Elevations of the top of bed-
rock and top and bottom of sand bodies were calculated
based on the interpolated elevations. Locations were
verified wherever possible using plat books by matching
either landowner names or the address location from the
well log. After compilation, the data were converted to
ASCII text and transferred into an ARC/INFO (Versions
5.0.1 and 6.0) database on a SUN SPARC workstation.
ARC/INFO is a product of Environmental Systems Re-
search Institute, Inc., of Redlands, California.
Of the more than 10,000 records reviewed for this pro-
ject, over 5,100 were input into the database. Sub-
sequently, numerous data quality checks ensured that
duplicate well ID numbers were corrected, locations
were corrected, thicknesses were checked so that the
sand thickness data reported did not exceed drift thick-
ness, and elevations were checked so that elevation of
a sand body was not below the bedrock surface. After
running the data quality checks and removing questionable
data from the database, approximately 5,000 records
remained.
ISM, a contouring package from Dynamic Graphics,
Inc., of Alameda, California, helped to create two di-
mensional grid representations of:
• Surface topography
• Drift thickness
-------
• Bedrock topography
• Bedrock isopach
• Intermediate sand body isopach
• Basal sand isopach
ISM also allowed for the creation of contoured output of
the grids. Grids are regularly spaced rectangular arrays
of data points (nodes) that allow for efficient mathemati-
cal calculations and contouring. ISM uses a minimum
tension gridding technique, allowing for the curvature
(change in slope) of the surface to be spread throughout
the surface rather than being concentrated at the input
data points. The ISM program uses a biharmonic in-
verse cubic spline function (algorithm) to assign data
values to grid nodes. This function assumes that for any
grid node assignment, input data points farther away
from the node being evaluated have less influence on
that node's value than nearer data points. To determine
each grid node value, ISM calculates an average value
from the surrounding scattered input data (up to 15 input
data points) and finds the standard deviation. ISM con-
tinues to refine the values of the grid nodes until the
standard deviation is minimized (10).
Several grid spacings were reviewed to determine which
would best represent the density of the data. The grids
that ISM uses, as described above, determine the fine-
ness to which the data control the resultant contours.
Experimentation was necessary to determine a grid
spacing that adequately represented the data. Too fine
a grid spacing can exaggerate or overly weight individ-
ual points, causing the resultant contours to be overly
jagged. With too large of a grid spacing, the contours
can become overgeneralized and become much less
data dependent because the calculated grids are
ove rave raged.
The two-dimensional grid of the land surface topography
was based on surface topography lines and spot eleva-
tions digitized from USGS 7.5-minute quadrangles. The
linework for each quadrangle was converted to ASCII
files of data points. The ASCII files contained x and y
coordinates and the elevation value of each data
point. After inputting the ASCII files into ISM, a
two-dimensional grid for each quadrangle was created.
ISM also generated contour lines from each grid. Com-
paring plots of the generated lines with USGS 7.5-minute
topographic maps allowed for the correction of errors and
ensured that the grid elevation values were within 10
feet of the elevations shown on the USGS maps. An ISM
two-dimensional grid of the entire area's surface topog-
raphy was created by combining the grids. After creating
a contoured surface of the grid, an ARC/INFO coverage
of the output was produced. ARC/INFO was used to edit
the coverage and produce the final map.
The two-dimensional grid of the bedrock surface topog-
raphy was based on data from water well and engineer-
ing boring logs, ISGS field observations of outcrop
locations, and previous ISGS mapping (9). An ASCII file
of x and y coordinates and the elevation of each bedrock
top was input into ISM. Subtracting the bedrock topog-
raphy grid from the land surface grid produced a grid of
the drift thickness. A contoured output of the grid was
produced, and an ARC/INFO coverage of the output was
created. Again, ARC/INFO was used to edit the cover-
age and produce the final map.
Creating the isopach maps entailed subtracting the top
and bottom elevations of each unit to calculate the
thickness of each unit. ASCII files of the xand y coordi-
nates and the thickness values for each data point were
input into ISM. ISM then created two-dimensional grids
of each isopach. Contoured output of each grid was
produced, which allowed for the creation of ARC/INFO
cove rages of the output. ARC/INFO was used to edit the
coverages and produce the final maps.
Bedrock Geology of the Study Area
All the sedimentary bedrock units are of the Paleozoic
Era. The Paleozoic bedrock comprises sequences of
sandstones, dolomites, limestones, and shales. The
stratigraphic column of Figure 1 illustrates the vertical
succession of the bedrock. Major tectonic activity of the
area includes the formation of the Kankakee Arch in
Ordovician time (11) and faulting along the Sandwich
Fault Zone. Faulting along the Sandwich Fault Zone
(see Figure 2) may have occurred coincidentally with the
formation of the Lasalle Anticlinorum in early Pennsyl-
vanian time (12). No further faulting has been noted
since deposition of glacial sediments. Bedrock units
gently dip to the east (7). The majority of the area lies
on the Niagara cuesta, a south and west facing scarp
that comprises the resistant Silurian strata that have an
eastward dip of roughly 15 feet per mile (13). The Silu-
rian strata are absent west of the Kankakee River as
well as in an area west of the Des Plaines River in
west-central Will County (see Figure 2). This study re-
lates to the hydrogeology of the Silurian strata and the
drift materials, and details only the uppermost bedrock
units. The report, however, does briefly summarize units
below the Maquoketa Group using information from
Hughes et al. (6) and Visockey et al. (14).
Precambrian Bedrock
Granites or granitic rock compose the Precambrian
basement of northern Illinois. Few details about the
nature of the basement rocks are known because few
wells have completely penetrated the sedimentary bed-
rock of the region. The elevation of the top of the Pre-
cambrian basement probably stands at 4,000 feet below
mean sea level in the study area.
-------
SYSTEM
QUATER-
NARY
PENNSYL-
VANIAN
QC
z
u
ORDOV
z
CD
a
PRE-
CAMBRIAN
SERIES
PLEISTOCENE
DESMOINESIAN
NIAGARAN
ALEXANDRIAN
CINCINNATIAN
CHAMPLAINIAN
CANADIAN
CROIXAN
GROUP OR
FORMATION
Spoon and
Carbondale
Racine
Sugar Run
Joliet
Kankakee
Elwood
Wilhelmi
Maquoketa
Galena
Platteville
Glen wood
St. Peter
Shakopee
New
Richmond
Oneota
Gunter
Eminence
Potosi
Franconia
Ironton
Galesville
Eau Claire
Elmhurst
Member
Mt. Simon
AQUIFER
Sands
and
Grave s
Silurian
Galena-
Platteville
Glenwood
St. Peter
"Vairie du
Chien
Eminence
Potosi
:ranconia
ronton-
Galesville
omite aquifer system
•8
&
\
\
\
\
\
ovician aquifer system
•o
C
h
b
d
Elmhurst-
Mt. Simon
aquifer
system
LOG
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"•* • •". " \" •".•
J- '- J* / ' =^=
B&V* rJHrh
/ \'\ *" . /
/ |.v.'/
-/jil'tVT^
— /-]. .** — /
/ L*» " /
/ 1" A /
^ryj-^o.; ^7-
/ I- :/. /
/' \ b * * /
A / Y1^ A/
— /" t •"•-* — /
T^7&'M=r*
-J^sttv^-r
— 7""®i "^^m/
Z7
/ / /
Al A
A 1 A 1
/ /
/ ^A /"
/ /
. | . . | Q. | .
.'./.'.*?..
mm
g-:-.-.v-:^
/ •-•.•/"
/ ' • • • /
/A • • , ^A/
r * * * /
/ / .-.. / /
•7^7 1. ' J. -/
y. i • ./
/ ' ".'. / /
"•-/ -•: •/• ••
"7^:A- vT"
/'Vfej^-
=2?{V •VTs '^.'
G*; .".;-!^i* •.. .^.
^S?S?
y^ — y — Vt — /f •"•/>
y^ y^V r^iisz,
— /• "^/c y. ~ /•*«
S
X>^v
THICKNESS
(FT)
0-260
0 110
-reef
0 100
ftO-250
310-380
125-600
0-410
110- 160
135-235
390 - 570
2200
DESCRIPTION
Unconsolidated glacial deposits-pebbly clay
(till), silt, sand and gravel
Alluvial silts and sands along streams
Shale, sandstone, clay, limestone, and coal
Dolomite, very pure to argillaceous, silly,
cherty; reefs in upper part
Dolomite, slightly argillaceous and silty
Dolomite, very pure to shaly and shale,
dolomitic; white, light gray, green, pink,
maroon
Dolomite, pure top 1 ' - 2', thin green shale
partings, base glauconitic
Dolomite, slightly argillaceous, abundant
layered white chert
Dolomite, gray, argillaceous and becomes
dolomitic shale at base
Shale, red to maroon, oolites
Shale, silty, dolomitic, greenish gray, weak
(Upper unit)
Dolomite and limestone, white, light gray,
interbedded shale (Middle unit)
Shale, dolomitic, brown, gray (Lower unit)
Dolomite, and/or limestone, cherry (Lower
part)
Dolomite, shale partings, speckled
Dolomite and/or limestone, cherty, sandy
at base
Sandstone, fine an^t coarse-grained; little
dolomite; shale at top
Sandstone, fine to medium-grained; locally
cherty red shale at base
Dolomite, sandy, cherty (oolitic); sandstone
Sandstone interbedded with dolomite
Dolomite, white to pink, coarse-grained
cherty (oolitic)
Sandstone, medium-grained, slightly dolomitic
Dolomite, light colored, sandy, thin sandstones
Dolomite, fine-grained gray to brown,
drusy quartz
Dolomite, sandstone and shale, glauconitic,
green to red, micaceous
Sandstone, fine to coarse-grained, well
sorted; upper part dolomitic
Shale and siltstone, dolomitic, glauconitic;
sand»tane, dclomitic. glauconitic
Sandstone, coarse grained, white, red in lower
half; lenses of shale and siltstone, red,
micaceous
Granitic rocks
Figure 1. Generalized stratigraphic column of rock units and aquifers in northern Illinois (prepared by M.L. Sargent, ISGS).
-------
Cambrian
The Elmhurst-Mt. Simon Sandstone comprises the old-
est sedimentary units in Illinois and consists of medium-
grained sandstones. It has a total thickness of
approximately 2,500 feet. The upper part of this unit has
acted as an aquifer in the Chicago region in the past;
ground-water mining of the aquifer (a nonreplenished
lowering of the static water level), however, has led to a
discontinuation of its use for that purpose. The Eau
Claire Formation, the Basal Sandstone Confining Unit
(14), consists of dolomitic shale and siltstone with thin
beds of sandstone. It has a thickness of 300 feet to 400
feet and separates the Elmhurst-Mt. Simon aquifer from
the Ironton-Galesville Sandstones. The Ironton-Galesville
Sandstones have a thickness of 150 feet to 250 feet and
serve as a source of ground water in northern Illinois (6).
The Galesville Sandstone is fine-grained, while the Iron-
ton Sandstone is coarser grained and contains more
dolomite. The Knox Megagroup, the Middle Confining
Unit (14), comprises all the bedrock units between the
Ironton-Galesville Sandstones and the Ancell Group. It
includes the:
• Cambrian Franconia Formation
• Potosi Dolomite
• Eminence Formation
• Jordan Sandstone
• Ordovician Prairie du Chien Group
The Knox Megagroup is primarily dolomitic in compo-
sition, though it contains thin sandstones. Its thick-
ness ranges from 400 feet in the northern portion of
the study area to about 700 feet in the southernmost
tip of Will County. The sandstones tend to be some-
what discontinuous and, where present, offer a local-
ized source of ground water. The group as a whole
acts as a confining unit between the Ironton Sand-
stone and the Ancell Group.
Ordovician
The Ancell Group, which contains the St. Peter Sand-
stone and Glenwood Sandstone, has a thickness of
roughly 200 feet throughout the study area except in
north-central Will County where it is over 400 feet. The
thickness of the Ancell Group varies considerably in
northern Illinois because it rests on an erosion surface.
The Ancell Group is the shallowest aquifer present in
this area below the Silurian dolomite aquifer. The eleva-
tions of the top of the Ancell Group range from just over
sea level in the northwest corner of Will County to 500
feet below mean sea level in the southwestern corner.
The Galena and Platteville Groups provide a sequence
of carbonate rocks that are primarily dolomitic in com-
position. The Platteville Group conformably overlies the
Ancell Group. The two units have a combined thickness
of 350 feet throughout this part of the state. The Galena
and Platteville Groups, combined with the overlying
Maquoketa Shale Group, act as an aquitard between the
Ancell aquifer and the Silurian dolomite aquifer.
Maquoketa Shale Group
The study area has three subaerially exposed bedrock
units. The oldest of these that this report details are
Ordovician-aged strata comprising the Cincinnatian Se-
ries Maquoketa Shale Group. The thickness of the
Maquoketa Group ranges from 260 feet in eastern Will
County to 120 feet in the northwestern corner of Will
County and is unconformably overlain by Silurian strata
(15). The Maquoketa Group comprises four formations:
• Scales Shale
• Fort Atkinson Limestone
• Brainard Shale
• Neda Formation
The Scales Shale forms the lowermost unit and consists
of gray to brown dolomitic shale. Thin layers with phos-
phatic nodules and pyritic fossils occur near the top and
base of the unit. The Scales Shale may attain a thickness
of up to 120 feet in this region (15). The Fort Atkinson
Limestone, a coarse-grained crinoidal limestone to fine-
grained dolomite, may range up to 60 feet thick (15). The
Brainard Shale comprises greenish gray dolomitic shale
and has a thickness of generally less than 100 feet (16).
The Neda Formation, the youngest formation in the
Maquoketa Group, is relatively thin with a thickness of
usually less than 10 feet. In some places, it may attain a
maximum thickness of 15 feet. The Neda is exposed along
the Kankakee River, and the Silurian-aged Kankakee For-
mation typically overlies it. The Neda Formation consists
mostly of red and green shale with interbedded goethite
and hematite oolite beds (7, 16).
Silurian System
Silurian-aged rocks consist almost solely of dolomites
and dolomitic limestones. The Silurian is divided into the
Alexandrian and Niagaran Series. The Alexandrian Se-
ries is about 25 feet thick and is represented by the
Kankakee, Elwood, and Wilhelmi Formations. These for-
mations are a fine- to medium-grained, white, gray to
pinkish gray dolomite. The Kankakee Formation is exposed
along the Kankakee River in southern Will County (17).
The Niagaran Series comprises much of the bedrock
surface of this area and includes three formations. The
Joliet Formation has a lower member of dolomite with
interbedded red and green shale, and two upper mem-
bers with an increasing purity of dolomite toward the
top of the formation (7). The Sugar Run Formation,
formerly termed the Waukesha Formation (17), is an
argillaceous, fine-grained, medium- to thick-bedded,
-------
brownish gray dolomite (7). The Racine Formation is the
thickest unit in the Niagaran Series, attaining a thick-
ness of as much as 300 feet (17). The Racine Formation
contains large reefs that are as high as 100 feet and
consist of vugular gray dolomite. The inter-reef rock
consists of dense, cherty gray dolomite. The Racine
Formation is exposed in the bluffs along the Des Plaines
River from Joliet to Blue Island, Illinois (17).
Figure 2 is an isopach of the Silurian dolomite indicat-
ing the thickness of the unit in the study area and the
boundary of the Silurian rocks. The Silurian dolomite
aquifer has a maximum thickness of just over 500 feet
in the southeast corner of Will County and becomes
thicker to the east and south. It rapidly increases in
thickness from its margin along the western border of
Will County, where it has eroded. The contact be-
tween the Silurian dolomite and the underlying
Maquoketa Shale Group has relatively little relief.
Thus, the major differences in thickness of the unit
result from erosion of the bedrock surface. Joints and
fracture patterns within the upper bedrock have a domi-
nantly northwest-southeast and northeast-southwest
orientation (18).
Pennsylvanian System
Pennsylvanian-aged bedrock is found in the southeast-
ern portion of Will County west of the Kankakee River
with an outcropping at the confluence of the Des Plaines
and Kankakee Rivers. The lowermost unit, the Spoon
Formation, is very thin and consists of clay beds with
scattered occurrences of coal formed in channel-like
depressions (19). The Spoon Formation overlies the
Maquoketa Shale Group. The overlying Carbondale
Formation may attain a thickness of over 100 feet in the
southwestern corner of Will County. The Carbondale
Formation consists of shale with thin limestone beds.
The lowermost unit, the Colchester (Number 2) Coal
Member, outcrops in this area and attains a thickness of
up to 3 feet. It has been extensively mined along the
Will-Grundy-Kankakee County border where large ar-
eas of strip-mined land are evident. Most of the available
coal has been mined out, and numerous gob piles exist
R9E
R10
R15E
Scale of Miles
5
10
Legend
300"' Thickness in Feet
Contour Interval 50 Feet
•"• Western Boundary of
^^ Silurian Dolomite
^""""^ Fault
it
Figure 2. Thickness of the Silurian dolomite.
-------
in the area of Braidwood. The Francis Creek Shale
Member, which overlies the Number 2 coal, constitutes
the remainder of Pennsylvanian units in the study area.
The Francis Creek Shale is gray with numerous flat-
tened concretions that contain the Mazon Creek flora of
Pennsylvanian-aged fossils (19). Weathering of the
mine slag materials may have exposed fossiliferous
concretions in the gob piles (7).
Bedrock Topography
The highest bedrock elevations are in east-central Will
County where the bedrock rises to over 700 feet above
mean sea level (see Figure 3). Bedrock uplands occur
as a broken curved ridge from the southeast to the
northwest with bedrock elevations consistently rising
over 650 feet above mean sea level. The bedrock sur-
face slopes from the bedrock upland high westward to
the Des Plaines River. It also has a regional downward
slope to the south into Kankakee County, Illinois, to the
northeast into the Lake Michigan basin, and to the east
into Indiana. West of the Des Plaines River, the bedrock
surface rises to over 650 feet above mean sea level in
northeastern Kendall County. Elsewhere, the surface
has relatively low relief.
The dominant features of the bedrock surface are the
river valleys. The Des Plaines River valley is better
expressed than the Kankakee River valley. This is true,
in part, because it is older and acted as a drainageway
for glacial meltwater where it may have become en-
trenched in the present valley. The Kankakee River
valley may be less expressed partly because of the
amount of scouring that occurred over a large area
during the Kankakee flood event such that the river is
not entrenched in most places. Also, smoothing of the
study's contour maps has generalized some of the de-
tail.
The buried Hadley Bedrock valley, described initially by
Horberg and Emery (20), probably existed prior to
glaciation and concurrently with the preglacial Des
Plaines River. The valley may have acted as a drainage-
way for glacial meltwaters until the time that glacial
debris buried it. Glacial scouring was originally believed
to have formed the valley, but evidence presented by
McConnel indicated a fluvial origin of the valley (21).
Also, the base of the Hadley valley does not overhang
or lie much below the Des Plaines valley but rather joins
it at a smooth juncture.
The bedrock surface contains a number of sinkholes
or closed depressions that are expressions of karst
*v-x'~~x^
Legend
Bedrock Elevation in Feet
Above Mean Sea Level;
Contour Interval 25 Feet
Western Boundary of
Silurian Dolomite Aquifer
Bedrock Quarry
Figure 3. Topography of the bedrock in the study area.
-------
development that formed prior to continental glaciation.
Karst, a terrain developed on limestone or dolomite by
solution or dissolving of the rock, is characterized by
closed depressions and cavity development along joints
and fractures. Fischer (22) first noted karst features in
the Joliet area where early Pennsylvanian sediments of
shale and clay filled cavities in the upper bedrock.
Buschbach and Heim (23) indicated closed depressions
in the Silurian dolomite surface in their bedrock topog-
raphy map for the Chicago region. They speculated
these depressions were expressions of karst develop-
ment. McConnel (21) demonstrated the existence of
sinkholes in the area of the buried Hadley Bedrock
valley northeast of Joliet by using seismic refraction
survey data.
Glacial Geology
The sediments overlying the bedrock comprise tills,
sands and gravels, lacustrine deposits from glacial
lakes, and surficial eolian deposits of loess and sand.
The unconsolidated deposits are over 150 feet thick
along the crest of the Valparaiso Morainic System.
Figure 4, adapted from Willman (24), indicates the
principal moraines. In the area where the Hadley Bed-
rock valley is present, the deposits attain a thickness of
over 175 feet. Bedrock is mainly exposed along the Des
Plaines River valley and its tributaries. It is also exposed
in isolated areas in southeastern Cook County. The drift
thickness map (see Figure 5) indicates the distribution
of the earth materials overlying the bedrock and the
locations of bedrock outcrops. The bedrock outcrop in-
formation for this map was derived from Piskin (25) and
Berg and Kempton (26).
Erosion of the glacial sediments was a major factor in
controlling the drift thickness of the area. Succeeding
glaciers scraped off previously deposited sediments, but
glacial meltwaters, which came from the east and north
along the river channels, caused much of the erosion.
Both the Kankakee River and Des Plaines River acted
as meltwater channels as the glaciers melted. The Du
Page River acted as a minor drainageway and was most
active during large-scale flooding events. The thickness
of the drift varies in the area also because of the topo-
graphic control that the bedrock on the overlying sedi-
ments exercises. The crest of the Valparaiso moraines
coincides with the topographic high in the bedrock
End Moraines
Scale in Miles
0 10
Figure 4. End moraines (late Wisconsin) in Will and southern Cook Counties, Illinois.
-------
Legend
Drift Thickness in Feet; Contour
Interval 25 Feet Between 0 to 50
Feet and 50 Feet Between 50 to
150 Feet
Bedrock Outcrop Area
Isolated Bedrock Outcrop
Strip-Mined Areas
Figure 5. Thickness of the glacial drift in the study area and bedrock outcrop information (25, 26).
surface. The cross sections in Figure 6 also show this.
The bedrock high may have caused late Woodfordian
glaciers to stall repeatedly in the same area, causing
moraines to build atop one another sequentially (27).
Descriptions by well drillers note few variations in the
character of the unconsolidated sediments; therefore,
we did not attempt to correlate these deposits. The drift
materials present in the study area are late Wisconsinan
or younger. Though this region experienced glaciating
repeatedly prior to the Wisconsinan glaciation, no Illi-
noisan or pre-lllinoisan deposits have been identified
(28). The drift units divide into three main units (29):
• The Lemont Drift
• The Yorkville Drift
• The Wadsworth Drift
The three drift units are all part of the Wedron Formation
of Wisconsinan age. The Lemont Drift has a dolomitic
character because the source material for the diamicton
was glacially eroded Silurian dolomite. The Lemont Drift
is the oldest of the three units and is found only under-
lying the Wadsworth Drift. The Yorkville Drift is the only
drift unit present west of the Valparaiso Morainic System
boundary within the study area. It overlies the bedrock
surface wherever the basal sand unit is not present. The
Wadsworth Drift comprises silty and clayey diamictons
and is the youngest of the drifts (29). It overlies the
Lemont Drift and the upper sand unit. In the cross
sections (see Figure 6), where the upper sand unit is
present, it roughly indicates the boundary between the
Lemont and Wadsworth Drifts. The gradation between
the different drift units at the Valparaiso System bound-
ary is not well defined. The Wadsworth Drift appears to
grade into the Yorkville Drift because they are very
similar in composition near the boundary (9).
The large Kankakee flood left extensive deposits of
sand and gravel and lacustrine sediments along the
Kankakee River and Des Plaines River. The flood oc-
curred as glacial meltwaters built up behind a constric-
tion at the Marseilles Morainic System to the west (30).
Large glacial lakes, which developed during the flood,
subsequently emptied into the Illinois River valley after
a breach in the moraines developed. The force of the
flood waters eroded the glacial deposits along the river
valleys, flattened the surface of the drift, and, in places,
-------
North
800-j £ T36N
750-
700-
O TO 65°-
^
CO g
JU
§ 550-
South
A'
North
500-1
T37N
0)
"S ^J
0) ~Z
^ (^
£= £=
O CO
^ 0)
TO 5
> •=
0) 0)
QJ §
JD
800-
750-
700-
650-
600-
550-
B
I? ^. Valparaiso Moraines
T36N
T33N
Wast
-800
-750
-700
-650
-600
-550
-500
South
B* r 800
Inlersecl
ol D-D' .750
•700
-650
-600
-550
-500
East
Legend
l-rTTT7| Drift, Undifferentiated
PPSJ Sand
Silurian Dolomite
rSOO
-750
Figure 6. Geologic cross sections of the glacial drift and potentiometric profile of the Silurian dolomite aquifer.
-------
exposed the underlying bedrock. The flood event formed
thin, dispersed lake plain deposits of silt, clay, and sand
in southwestern Will County. Some lacustrine deposits
lie between morainic ridges in southern Cook County
where small glacial lakes developed as the Valparaiso
Moraines were being deposited (8).
Figure 7 shows the locations of some of the surficial
materials. Sands and gravels were also deposited along
tributary creeks and in abandoned channels that once
connected the Du Page River and Des Plaines River
north of their present juncture. Wind has reworked the
surficial sand deposits forming low dunes along the
Kankakee River in southern Will County. Masters (31)
classified the sand and gravel deposits of the area by
their origin, indicating that most of the deposits present
in the valley of the Des Plaines River formed as well-
sorted valley train deposits. In the Kankakee River val-
ley, the sands and gravels were primarily deposited as
riverine sediments during the Kankakee flood event.
Sand and Gravel Isopachs
The sand and gravel isopach maps (see Figures 8 and
9) indicate the variations in thickness of the upper and
basal sand and gravel units. The most extensive depos-
its of both exist throughout the area overlain by the
Valparaiso Morainic System. This may be associated
with bedrock control on the formation of the moraines
and associated deposits referred to earlier.
The thickest deposits lie in the buried Hadley Bedrock
valley where thicknesses of both units can exceed over
100 feet. The upper sand unit may be found in the glacial
drift within a wide range of elevations. For mapping
purposes, we defined the upper sand unit as a sand unit
greater than 1 foot thick that occurs between two fine-
grained layers. The basal sand unit includes all coarse-
grained materials that overlie the bedrock surface. Most
of the basal sands present west of the Des Plaines River
were formed as valley train deposits along the river
R9E R10E
Legend
Surficial Glacial Geology
n Equality Fm - Glacial Lake p-' ,1 Wendron Fm - Wadsworth Drift
Plain Deposits lSf':::j Silurian Dolomite
I 'I Strip-Mined Areas
Figure 7. Surficial glacial geology (23).
10
-------
R8E R 1QE
F^^l
R12E
R13E R14E
R15E
T
32
N
!V
Sca\e of Miles
5 10
Legend
Thickness of Upper Sand
0 Isolated areas > 10 Feet
10 to 50 Feet
50 to 100 Feet
> 100 Feet
Figure 8. Thickness of the upper sand unit.
channels as the glaciers melted back. The origin of the
extensive deposits underlying the Valparaiso Moraines
is not clear. They may have been formed during early
Wisconsinan glacial events as outwash plain deposits
or they may have been deposited subglacially. The
cross sections (see Figure 6) can reveal the variability
and complexity of the sand and gravel layers as they
occur within the drift. The sand and gravel deposits very
seldomly act as aquifers in this region because almost
all wells are completed in the Silurian dolomite aquifer.
Clearly, Figures 8 and 9 indicate that some ground-
water resource potential may exist within these depos-
its.
References
1. Thwaites, FT. 1927. Stratigraphy and geologic structure of north-
ern Illinois. Illinois State Geological Survey Report of Investiga-
tions 13.
2. Bretz, J.H. 1939. Geology of the Chicago region, Part I: General.
Illinois State Geological Survey Bulletin 65.
3. Bergstrom, R.E., J.W. Foster, L.F. Selkregg, and W.A. Pryor.
1955. Ground-water possibilities in northeastern Illinois. Illinois
State Geological Survey Circular 198.
4. Bretz, J.H. 1955. Geology of the Chicago region, Part II: The
Pleistocene. Illinois State Geological Survey Bulletin 65.
5. Suter, M., R.E. Bergstrom, H.F. Smith, G.H. Emerich, W.C. Wal-
ton, and T.E. Larson. 1959. Preliminary report on the ground-
water resources of the Chicago region, Illinois. Illinois State
Geological Survey and Illinois State Water Survey Cooperative
Ground-Water Report 1.
6. Hughes, G.M., P. Kraatz, and R.A. Landon. 1966. Bedrock aqui-
fers of northeastern Illinois. Illinois State Geological Survey Cir-
cular 406.
7. Willman, H.B. 1971. Summary of the geology of the Chicago
area. Illinois State Geological Survey Circular 460.
8. Bogner, J.E. 1976. Geology for planning in northeastern Illinois,
Vol. V: Cook County. Illinois State Geological Survey Open-File
Report.
9. Larsen, J.I. 1976. Geology for planning in northeastern Illinois,
Part VI: Will County. Illinois State Geological Survey Open-File
Report.
11
-------
R9E
R 10E
R 14 E
R15E
Scale of Miles
Q 5 10
i i i
Legend
Basal Sand Thickness, Feet
OtolO
10 to so
50 to 100
>100
Figure 9. Thickness of the basal sand unit.
10. Dynamic Graphics, Inc. 1991. Interactive surface modeling users
guide. Alameda, CA.
11. Ekblaw, G.E. 1938. Kankakee Arch in Illinois. Illinois State Geo-
logical Survey Circular 40.
12. Kolata, D.R., T.C. Buschbach, and J.D. Treworgy. 1978. The
Sandwich Fault Zone of northern Illinois. Illinois State Geological
Survey Circular 500.
13. Horberg, L. 1950. Bedrock topography in Illinois. Illinois State
Geological Survey Bulletin 73.
14. Visocky, A.P., M.G. Sherrill, and K. Cartwright. 1985. Geology,
hydrology, and water quality of the Cambrian and Ordovician
systems in northern Illinois. Illinois Department of Energy and
Natural Resources Cooperative Groundwater Report 10.
15. Kolata, D.R., and A.M. Graese. 1983. Lithostatigraphy and depo-
sitional environments of the Maquoketa group (Ordovician) in
northern Illinois. Illinois State Geological Survey Circular 528.
16. Willman, H.B., et al. 1975. Handbook of Illinois stratigraphy. Illi-
nois State Geological Survey Bulletin 95.
17. Willman, H.B. 1973. Rock stratigraphy of the Silurian system in
northeastern and northwestern Illinois. Illinois State Geological
Survey Circular 479.
18. Foote, G.R. 1982. Fracture analysis in northeastern Illinois and
northern Indiana. Master's thesis, University of Illinois.
19. Smith, W.H. 1968. Strippable coal reserves of Illinois, Part 6: La
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21. McConnel, D.R. 1972. Bedrock topography and paleogeomor-
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ronmental Geology Notes 52.
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Illinois. Illinois State Geological Survey Bulletin 94.
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tion in a late Wisconsinan end moraine complex, northeastern
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13
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Application ofGIS for Environmental Impact Analysis in a Traffic Relief Study
Bruce Stauffer
Advanced Technology Solutions, Inc., Lancaster, Pennsylvania
Xinhao Wang
University of Cincinnati, Cincinnati, Ohio
Abstract
This paper presents an application of a geographic infor-
mation system (CIS) in a traffic relief study. Traffic conges-
tion has severely affected the environmental quality and
the quality of life for residents in the study area. A team of
planners, environmental specialists, historians, landscape
architects, traffic engineers, and CIS professionals organ-
ized to solve the problem. The team has evaluated the
environmental and socioeconomic impacts of highway
alignments from the very first step through every major
decision for the duration of the project.
The CIS professionals have played a crucial role in
maintaining constant and active interactions among
members of the project team, federal and state agen-
cies, and the public. CIS has helped to develop a natural
and cultural resource inventory, identify contamination
sources, assess environmental constraints, and evalu-
ate proposed highway alignment alternatives. CIS pro-
vides an ideal atmosphere for professionals to analyze
data, apply models, and make the best decisions. The
high-quality map products that CIS creates enhance the
quality of public presentations and reports. The authors
feel that, as this project has progressed, more people
have realized the benefit of using CIS.
Introduction
A traffic relief study, as one type of transportation
project, aims to resolve traffic congestion problems
through a combined strategy of upgrading existing
infrastructure, building new infrastructure, and con-
trolling traffic demand using congestion management
strategies (CMS). This type of study proceeds through
at least the following steps:
• Problem identification
• Data collection
• Preliminary design
• Environmental impact analysis
• Final design
• Construction
The process heavily involves federal, state, and local
government agencies, as well as the public. The goal of
the project is to develop an environmentally sound so-
lution to the traffic congestion, which also happens to
promote economic development and improve quality of
life for people in the local area and the region. Environ-
mental, social, and economic issues must be equally
addressed from the very first step through the final
design. Federal and state regulations generally require
an environmental impact statement (EIS) when con-
structing new infrastructure or upgrading existing road
systems. Preparing an EIS is a requirement for such a
project and demands a significant commitment of time,
money, staff, and technical resources.
A geographic information system (CIS) has the ability to
process spatially referenced data for particular pur-
poses. Along with the development of computer hard-
ware and software, CIS has progressed from pure
geoprocessing, to management of geographic informa-
tion, to decision support (1). This paper presents the
application of CIS in an ongoing traffic relief study in
Marshalls Creek, Pennsylvania. The CIS function in this
study has had various purposes:
• Inventory data compilation
• Spatial data analysis
• Map production
• Traffic modeling support
• Public presentations
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The study shows that CIS can play an important and
innovative role in transportation studies.
Project Description
The study area is in the Pocono region, located in
northeastern Pennsylvania (see Figure 1). The Pocono
Mountains and Delaware Water Gap National Recrea-
tion Area possess a wealth of natural and cultural re-
sources. The area is famous for providing year-round
vacation activities. Attractions include fishing, canoeing,
and Whitewater rafting in the summer and downhill and
cross-country skiing, snowmobiling, snowboarding, and
ice fishing in the winter. The area includes quiet wood-
land trails past a rushing waterfall and scenic settings
for camping. In the fall, the area is ablaze with the
brilliant colors of foliage. Various scenic sights, recrea-
tional sites, and national historic sites make the area
ideal for attracting people to come for a day, a weekend,
or a longer vacation.
Although tourism brings people to the Pocono area and
promotes economic growth, it also brings a traffic con-
gestion problem to the community. In addition, the influx
of new home owners from New Jersey and New York
adds the problem of commuter traffic to the area. The
most troublesome section is in the vicinity of Marshalls
Creek where U.S. Route 209 intersects with Pennsylva-
nia (PA) Route 402. Two intersections are only about
500 feet apart. The traffic tieups can extend up to
3.5 miles on northbound Route 209, all the way back to
Interstate Highway I-80. Emergency response times on
U.S. Route 209 can be up to 20 minutes during peak
traffic. The heavy traffic volume results in traffic acci-
dents exceeding state averages on secondary roads as
motorists seek alternative routes to avoid congestion.
Through traffic traveling to and from New England using
U.S. Route 209 as a connector between I-80 and I-84
makes the problem even worse. The year-round outdoor
activities perpetuate the constant traffic problems that
have severely affected the quality of life for residents in
and around the Marshalls Creek area.
In response to the problems, the Pennsylvania Depart-
ment of Transportation (PennDOT) selected a project
team in February 1993 to conduct a traffic relief study in
the Marshalls Creek area. The project team consists of
individuals from seven firms and represents a wealth of
experience in the variety of disciplines necessary to
successfully complete this project. The team members
include land use and traffic planners, biologists, histori-
ans, traffic and environmental engineers, surveyors, and
CIS and global positioning system (GPS) professionals.
In addition to PennDOT, the funding agency, several
federal and state regulatory agencies periodically review
the development of the EIS to ensure that it meets
regulations. These agencies are the Federal Highway
Administration (FHWA), the U.S. Environmental Protec-
tion Agency (EPA), the U.S. Army Corps of Engineers,
the Pennsylvania Department of Environmental Re-
sources (DER), the Pennsylvania Historic Museum
Commission, and the Pennsylvania Fish and Game
Commission. Local planning commissions and citizen
representatives also actively participate in advisory ca-
pacities. A series of agency coordination meetings, pub-
lic meetings, and public information newsletters
coordinates the activities of all participants over the
course of the project.
Primary and Secondary Highways
Light Duty Roads
Phase I Project Boundary-
Streams, Ponds, and Reservoirs
5,000
Feet
10,000
Figure 1. Phase I project area.
-------
After collecting traffic data and performing traffic de-
mand modeling, the team realized that adopting strate-
gies to control traffic demand and upgrading existing
roads through widening and intersection improvements
would not suffice to meet demand projections for the
design year 2015. Consequently, the team has deter-
mined a new road is needed to alleviate congestion in
Marshalls Creek.
The study aims to identify alternatives to relieve traffic
congestion along U.S. Route 209, PA Route 402, and
Creek Road, and eliminate backups onto I-80 from U.S.
Route 209. The alternatives should also improve air
quality by reducing fuel consumption and vehicle emis-
sions and facilitate travel through Marshalls Creek for
local and through traffic. The improvements must com-
ply with federal and state regulations. The study team
must consider county and local government goals and
objectives so that the traffic capacity improvements will
be compatible with planned local development.
The project is being conducted in two phases. Phase I,
which is complete, was an investigation broad in scope.
It used inventory of secondary data to describe the
environmental characteristics of the area. A traffic de-
mand model identified the area for detailed study after
a preliminary analysis of a wide range of baseline data.
In Phase II, the team analyzes both primary and secon-
dary data and delineates alternative alignments or trans-
portation upgrading options that meet the need and
minimize impacts. Analysis of environmental and engi-
neering factors assists both in the determination of the
most practical alternative and in preparation of the final
EIS.
The nature of the study requires the analysis of a variety
of data at different scales by different professionals.
Through field investigation, the project team also con-
stantly updates and adds new data to the existing data-
base. The new data may be attribute data about some
geographic features or may be locational data. The
project team has found CIS to be an appropriate tool to
meet the challenge of better conducting the study.
The team has used CIS extensively in both Phase I and
Phase II studies. The two phases vary in data require-
ments, scales, and purposes of spatial analysis. With
the support of CIS, the team has been able to quickly
assemble data at adequate scales and present data in
formats that are familiar to different professionals. GIS's
data manipulation power distinguishes the different re-
quirements of the two phases and at the same time,
clearly depicts the linkage between the two phases. The
following sections describe CIS applications that have
helped facilitate the study and coordinate project team
members, public agencies, and citizens.
GIS Application
CIS contains powerful tools to process spatially refer-
enced data. These processes and their results are
meaningless, however, without a clearly defined objec-
tive. Many professionals point out the importance of
focusing GIS on practical problems. Using GIS is not an
end; it is a means to represent the real world in both
spatial and temporal dimensions. The benefits of using
GIS can be summarized in three aspects:
• GIS helps to portray characteristics of the earth and
monitor changes of the environment in space and
time (2).
• GIS helps us to more deeply understand the meaning
of spatial information and how that information can
more faithfully reflect the true nature of spatially dis-
tributed processes (3).
• GIS helps us to model alternatives of actions and
processes operating in the environment (2), to antici-
pate possible results of planning decisions (4), and
to make better decisions.
This project demonstrates the advantages of applying
GIS to solve practical problems from the above three
aspects. An EIS requires extensive data about natural
resources, land uses, infrastructure, and distribution of
many interrelated socioeconomic factors. The accuracy
and availability of required data depend on the scope of
a study and the size of the study area. Our study shows
that GIS, with its data retrieval, analysis, and reporting
abilities, significantly improves the analysis. GIS helps
to collect data at various scales, store data, and present
data in forms that allow the project team to carry out the
study in an innovative way.
Phase I Study
Phase I of the traffic relief study was completed in 1993.
The goal of Phase I was to acquire understanding of the
general features of the area and to use a traffic demand
model for delineating an area for detailed study. The
study area is approximately 52 square miles. To provide
data for the preliminary analysis and the traffic demand
modeling, the team developed baseline data inventory
with GIS. Data were primarily secondary data that came
from several different sources in different formats. For
example, the U.S. Census Bureau 1990 population data
were in TIGER format, the U.S. Fish and Wildlife Service
National Wildlife Inventory (NWI) files in digital line
graph (DIG) format, the U.S. Soil Conservation Service
Monroe County Soils in DIG format, and the U.S. EPA
Monroe County Natural Areas in ARC/INFO format. The
majority of data sources were at scales between 1:15,000
and 1:24,000. With GIS tools, the team integrated these
baseline data into a common presentation scale and pro-
jection. This process ensured an effective and comprehen-
sive spatial analysis in the study area.
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The team arranged and stored data in layers according
to themes. Examples of the data layers included:
• Road center lines.
• Twenty-foot elevation contours.
• Utility lines.
• Water features.
• Subdivision boundaries.
• 1990 Census population by Census Tracts and Blocks.
• Flood plains.
• Geological formations.
• Public facilities, including schools, churches, and
cemeteries.
• Political boundaries.
• Hazardous waste locations.
• Potential archaeological areas.
With these data layers, the team generated a series
of 17 thematic maps to describe the features of the
study areas. All maps were plotted on E-sized papers
(48 inches x 36 inches) with the same map layout. The
general reference map served as a base map for the
other themes. It included several data layers to provide
geographic references to the study area.
In addition to the base map, each individual theme map
showed only one theme at a time, such as soils, subdi-
visions, and wetlands. Some theme maps showed de-
rived data from the original data layers. For example, in
developing a slope theme map, the team first built a
three-dimensional surface from the 20-foot elevation
contours, then calculated slope in degrees and aggre-
gated areas based on a 10-degree interval. The slope
theme map showed the result from the data processing.
In addition, the project team created summary statistics
tables to help team members gain knowledge about the
study area. Table 1 is an example of the summary
statistics for land use categories.
Table 1. Phase I Statistical Summaries for Land Use
Categories
Land Use
Acres
Urban
Agricultural
Rangeland
Woodland
Water
Wetland
Transitional
10,628
1,183
12
20,957
1,394
1,428
422
During preparation of the Phase I inventory data and
summary statistics, traffic planners performed traffic de-
mand modeling to determine new road connections that
would provide a minimum acceptable level of service in
the year 2015. The modeling result was loaded into the
CIS and converted into the same format and projection
as other inventory data. Figure 2 displays the boundary
that the traffic demand model delineated and the actual
Phase II boundary. The two boundaries were not the
Primary and Secondary Highways
Light Duty Roads
Unimproved Roads
Trails
Phase II Project Boundary
Streams and Ponds
Minimum Acceptable Service Level
N
2,000
Feet
4,000
Figure 2. Phase II project area.
-------
same. The thick line enclosed a Phase II study area that
was delineated based on the traffic demand modeling
and the team's understanding of environmental and
other factors in the project area.
Phase II Study
The Phase II study is still ongoing. The area for the
Phase II study is much smaller than that for Phase I. It
is about 3.2 miles by 2.5 miles, or approximately
4 square miles.
The objective of the Phase II study is to conduct a detailed
analysis for delineating a full range of feasible highway
alignment alternatives. The alternatives must meet the
needs of relieving traffic congestion and minimizing its
impact on environmental and cultural resources.
Because the accuracy of the Phase I data was not
sufficient for the Phase II study, the project team has
collected data using different approaches to develop a
similar set of baseline data at a finer scale. The major
data source has been the photogrammetry data pro-
vided by PennDOT at a 1:2,400 scale. The data include:
• Road cartways
• Five-foot elevation contours
• Utility lines
• Water features
• Buildings footprints
• Bridges
The team directly digitized tax parcel boundaries from
Monroe County Tax Assessor's maps that range in
scales from 1:1,200 to 1:4,800. After digitizing each map
sheet separately, the team merged them together to
create a continuous parcel layer. The data layer has
been adjusted to fit with the PennDOT photogrammetry
data although the two data sets do not seem to match
exactly. In addition, digital orthophotographs at 5-foot
pixel resolution were also obtained for the project.
From these baseline data, the team has constructed
Phase II data layers in four different ways.
The first approach digitizes from compilations on project
base maps. For example, the team creates a land use
data layer from the digital orthophotographs and infrared
photography. The CIS group first plots the digital ortho-
photographs on a set of 1:2,400-scale map sheets.
Road cartway and water features are plotted on top of
the orthophotographs. Then land use specialists deline-
ate land use boundaries with fine color markers and
code land uses on maps according to the Anderson land
use classification. In the end, the CIS group digitizes the
land use boundaries from the compilations to create a
land use data layer. Similarly, the 100-year flood plain
data layer is delineated from compilations on project
base maps with 5-foot contours and digitized.
The second approach derives new data layers from
existing data. Buildings and structures are plotted at a
1:2,400 scale. Both historians and environmental engi-
neers use the plots in their field investigations. After
historians identify historic-eligible buildings on the plots,
the CIS group develops an attribute data file that links
the historic inventory data to the building geometry.
Similarly, field investigations identify buildings and struc-
tures associated with contamination sites. The system
stores types of contamination as building attributes. By
overlaying the building data layer with the tax parcel
data layer, the team can identify properties on which
historic buildings or contamination sites are located.
The third approach constructs data layers by refer-
encing Phase I data. For example, Phase II subdivision
boundaries are derived from the digitized tax parcel
boundaries by referring to the Phase I subdivision
boundaries. Phase I subdivisions were manually com-
piled at 1:24,000 using approximate location, which did
not align very well with the more accurate tax parcel
boundaries. Using a 1:7,200-scale plot that shows both
Phase I subdivision boundaries and the Phase II tax
parcel boundaries, planners can verify and indicate
properties associated with each subdivision. These
properties are dissolved to create new boundaries for
subdivisions that precisely fit with tax parcels. The same
approach is used to refine public parks and private
recreation areas.
The fourth approach obtains spatial data with GPS. The
GPS surveyors collect accurate locational data about
key features, such as boundaries of wetlands, site loca-
tions for hazardous waste, and locations of archaeologi-
cal field samples. GPS data also supplement existing
data, such as delineating footprints of new buildings to
update the PennDOT baseline data. The integration of
CIS and GPS provides the project team with accurate
and up-to-date data.
Phase II data layers have provided much richer informa-
tion for a detailed study of environmental features. They
are merged in many different combinations to show the
spatial distributions of different factors from different
perspectives. Table 2 lists some of the map themes
created for the Phase II study. All maps are plotted at a
1:7,200 scale.
In addition to using CIS as a data library and map
production tool, we use CIS to support decision-mak-
ing in two ways. First, the creation of a composite data
layer has revealed the impact of alternative align-
ments on several composite constraints. The compos-
ite data layer is an overlay of several inventory layers
and shows the various factors coincident at any loca-
tion, and the relative importance of these factors.
-------
Table 2. Selected Phase II Map Themes
Theme Description
General
reference
Parcels
Community
facilities
Flood plains
Land use
Subdivision
Slope
Wetlands
Historic
resources
Hazardous
wastes
Road networks, hydrographic features,
churches, municipal boundaries, and utilities
Tax parcel boundaries
Public parks and private recreation, cemeteries,
and public buildings
100-year flood plains
Current land uses
Approved subdivisions
Areas delineated with 5-degree slope intervals
Wetlands
Historic buildings and properties
Hazardous waste sites and related buildings
These constraints have been identified by citizens,
government agencies, and the project team. The major
composite constraints include wetlands, historic proper-
ties, steep slopes (slope greater than 15 degrees), pub-
lic parks and private recreation areas, 100-year flood
plains, potential archaeological areas, prime agricultural
land, subdivisions, and existing buildings and structures.
Two maps have been created from the composite con-
straints layer. One map shows the number of coincident
constraint layers that occurs in any one location (see
Figure 3). The other map shows the composite relative
importance of coincident features. Both maps present a
"sensitivity surface" view of the project area.
Secondly, the team uses CIS to perform interactive
summary statistics for each alternative alignment. The
project team analyzes the impact of the alignment alter-
natives on each individual constraint layer.
Two approaches define the impact areas for compari-
son. The first set impact areas are the areas enclosed
by the footprint that traffic engineers delineated for each
alternative. The second set impact areas are 300-foot
buffers on both sides of the alignment delineated by the
traffic engineers. The boundaries of the impact areas
overlay on constraint data layers. Figure 4 displays
wetlands crossed by alignment alternative ROW1B.
A set of summary statistics are calculated for each
alignment. In the end, we compare the statistics for each
alignment in a matrix (see Table 3). The matrix arranges
constraints as rows and alignment alternatives as col-
umns. The statistics include acres of selected features
within each impact area, such as wetlands or high-quality
watersheds, and total counts of features, such as his-
toric-eligible buildings. The summary statistics also in-
clude listings of building names for businesses or public
facilities within the impact areas.
The team has repeated the summary statistics several
times as alignments shift. This procedure ensures that
the final selected highway alignment minimizes environ-
mental impacts, best meets project needs, and is the
most cost-effective alignment to construct. The statisti-
cal matrix of impacts versus alignments is one of the
critical evaluation criteria for comparing alignments and
ultimately for selecting the final alignment.
Existing Roads
Pipeline
Phase II Project Boundary
One to Two Constraint Layers
Three to Four Constraint Layers
Five to Six Constraint Layers
o
1,000
Feet
2,000
Figure 3. Composite constraints.
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Table 3. Phase II Summary Statistics by Alternative
Constraints Alignment Alternatives
ROW1A ROW1B ROW2A ROW2B
ROWS
Wetlands
Acres
Count
2.76
13
3.27
14
3.25
17
1.8
15
13.64
17
High-Quality Watersheds
Acres 66.18 85.62 71.92 94.58 82.90
Hazardous Waste Parcels
Acres 99.50 116.77 98.25 120.68 215.38
Count 8 5 11 10 6
Parcels With Historic-Eligible Buildings
Acres 14.20 0.10 0.14 0.14 3.46
Count 31222
Historic-Eligible Buildings
Count 1011 1
Benefit of Using GIS
In recent years, many federal, state, and local agencies
have been actively acquiring and automating digital data
(5). These databases provide various types of informa-
tion at scales that are appropriate for a preliminary study
covering a large area.
A more detailed study, which usually covers a smaller
area, often requires more accurate data to describe the
spatial distribution of relevant factors. GIS is flexible,
allowing use of data at the scale and accuracy appropri-
ate to the study purpose. The team has found that this
feature helps improve the efficiency of the project with-
out sacrificing the accuracy. This study has required
two sets of scales ranging from 1:24,000 scale for
Phase I, which required projectwide socioeconomic
and environmental assessments, to 1:2,400 scale for
Phase II, which requires detailed analysis for design of
alternative alignments.
GIS has served as a digital database manager to as-
semble environmental, traffic, geographic, socioeco-
nomic, and other data into a centralized project
database. Data analyzed in this study originate from a
variety of sources, such as PennDOT, U.S. Geological
Survey (USGS), U.S. Census Bureau, Monroe County,
and field survey. They are in different formats, including
digital data in ARC/INFO, INTERGRAPH, and AutoCAD
formats, GPS data, digital images, paper maps, and
tabular data. Many of the public agencies and private
organizations involved in this project already have digital
data that GIS could easily use for this specific project.
This has helped to reduce the overall costs of data
collection and conversion.
This study demonstrates that GIS can support the infor-
mation needs of many disciplines within a common
framework and provide powerful, new tools for spatial
analysis. Aside from technological considerations, GIS
development initiates a higher-order systematization of
geographic thinking (3), which is crucial to the success
of a transportation project. In this project, GIS helps to
determine the total impacts of alternative alignments on
identified constraints. The team accomplishes this by
superimposing the alternative alignments on constraint
Existing Roads
Alternative ROW1B
Buffered Area Boundary
Pipeline
Phase II Project Boundary
Wetlands
Buffered Area of Alternative ROW1B
1,000
Feet
2,000
Figure 4. Wetlands crossed by an alternative alignment.
-------
data layers to determine the total amount of each con-
straint layer that each alignment encounters. This ap-
proach supports the analysis of multiple alignments
across the constraint surfaces for a variety of alternative
scenarios. The spatial analysis tools and statistical
function embedded in CIS prove to be very useful in
such study.
Digital data that the CIS stores are used to summarize
environmental, social, and economic data in many dif-
ferent ways. These functions include summing total
acreage, listing entities of special interest, and counting
numbers to provide useful baseline statistics for various
alignments. Within a day, CIS accomplished what would
have required several months of staff labor; CIS sum-
marized impacts of 11 alternative alignments on all con-
straint layers. In addition, CIS creates composite
constraints from individual data layers. A composite con-
straint data layer is created through a series of overlays
to illustrate geographic coincidence of inventory themes.
Conventionally, engineers in a project such as this first
delineate alternatives for alignments from the engineer-
ing perspective; they often consider factors such as
steep slopes and costs. Then, other professionals, such
as environmental specialists, historians, and planners,
evaluate the alternatives from each point of view. CIS
makes possible an early integration of environmental
and engineering activities, ongoing communication with
funding agencies and the public, and continual integra-
tion of a multidisciplinary team.
CIS helps to maintain high-quality data for the project.
It allows for error checking and quality control of multiple
data layers that would not be possible with conventional
mapping. The team always compares a new data layer
with other data to check for conflicts. Making check plots
allows for quick identification of errors and missing data.
For instance, in the process of assigning building-use
attributes to existing buildings, the CIS team first plotted
buildings on a map and created a table with building
identifiers. The field team then used the unique identifi-
ers shown on the plots when noting building names and
building uses in data collection tables. After relating the
data table with the building data layer, the team found
some buildings that did not have building use data.
Moreover, some buildings were assigned uses that were
out of range or seemed out of place, such as a residen-
tial building surrounded by several commercial build-
ings. The team highlighted the data for those buildings
and sent them to engineers for verification. Through this
data cleaning process, the team was able to obtain
complete building use information.
Data quality directly affects project quality. Without CIS,
this type of study often involves using and comparing
maps at different scales, which frequently introduces
serious errors. For each phase of this study, project
team members have used a fixed-base map scale for all
compilations. During data entry and data transformation,
the team has kept accurate registration between data
layers, ensuring data of the same resolution. In addition,
the team has used FREQUENCY, one of the tools that CIS
provides, to look for data values that are out of range as
well as missing data. CIS tools have also helped to
derive new relationships for features. For example, dis-
solving parcels has helped to create subdivision outlines,
or overlaying historic buildings with parcels has helped to
find the parcels on which they are located.
Planners, environmental specialists, historians, and
landscape architects on the project team are responsi-
ble for field data collection, verification, and if necessary,
compilation of field data into the standard project data-
base. Wherever possible, the team has used GPS to
eliminate the task of manual compilation and to improve
accuracy of locating data. A fundamental requirement in
applying the technology appropriately is to understand
its capabilities, requirements, and limitations. Because
several members manage inventory attributes, they
need to know how to maintain unique identifiers for
features so they can link up to the geometry. Because
AutoCAD data transfers occur routinely with engineers,
it is necessary to structure how the AutoCAD drawing
files can be organized, as well as how certain attributes
can be transferred by line color, layer name, or line
width. The CIS group coordinates closely with other
team specialists to identify quick, accurate, and cost-
effective methods of data collection, data analysis, and
presentation. In conjunction with the progress of the
project, specialists from different fields have become
familiar with the concept, requirements, and use of CIS.
They now feel comfortable discussing alternatives while
looking at results displayed on a computer screen.
CIS has created high-quality map products for public
presentations and reports. CIS has also been used in
several agency coordination meetings to display data
and alternative alignments. CIS has allowed for different
data layers to be displayed on the screen with specific
combinations of features at various scales. Public agen-
cies and citizens have been impressed by the clear and
friendly graphic response to their questions. They have
expressed interest in using the technology in future
projects.
Once the study is complete, digital data assembled in
this project will be an excellent resource for future pro-
jects in the study area. For these reasons, CIS provides
more cost-effective project support for gathering, man-
aging, and using data than that provided by paper and
mylar maps.
Summary
For this project, the importance of innovation based on
a solid scientific foundation cannot be overstated. In the
-------
current economic and regulatory climate, sound CIS
methods are emerging as the only convincing and cost-
effective means for locating, designing, and gaining
approval for major public and private infrastructure
projects.
The technology offers new and exciting tools for trans-
portation planning.
The methodology that this project team has used can be
successfully applied to other projects that require envi-
ronmental assessment. The team has found CIS to be
an extremely useful tool as users continue to learn its
capabilities and the multiple tools that it offers. The
regulatory agencies have repeatedly made favorable
comments on how CIS can offer interactive viewing in a
show-and-tell environment. This project is one of the first
EIS projects to use CIS in a PennDOT-funded project.
PennDOT appears convinced that CIS is an important
component for conducting EIS for highway studies.
More EIS projects probably will demand CIS services as
part of the project approach, in part, because many
federal and state regulatory agencies are increasingly
using CIS.
References
1. Fischer, M.M. 1994. From conventional to knowledge-based geo-
graphic information systems. Computer, Environment, and Urban
Systems 18(4):233-242.
2. Star, J., and J. Estes. 1990. Geographic information systems: An
introduction. Englewood Cliffs, NJ: Prentice Hall, Inc.
3. Bracken, I., and C. Webster. 1990. Information technology in ge-
ography and planning, including principles of CIS. London, Eng-
land: Routledge.
4. Burrough, P.A. 1986. Principles of geographic information system
for land resources assessment. Oxford, England: Clarendon
Press.
5. Hendrix, W.G., and D.J.A. Buckley. 1992. Use of a geographic
information system for selection of sites for land application of
sewage waste. J. Soil and Water Conserv. 47(3):271-275.
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A Watershed-Oriented Database for Regional Cumulative Impact Assessment
and Land Use Planning
Steven J. Stichter
Division of Coastal Management, State of North Carolina Department of Environment, Health,
and Natural Resources, Raleigh, North Carolina
Introduction
In 1974, North Carolina passed the Coastal Area Man-
agement Act (CAMA) to guide growth and development
in the state's coastal zone. Today, the Division of Coastal
Management (DCM), under the direction of the gover-
nor-appointed Coastal Resources Council, implements
CAMA. DCM's jurisdiction covers the 20 counties that
border either the Atlantic Ocean or the Albemarle-Pamlico
estuary.
This coastal region comprises a diverse set of human,
animal, and plant communities. Abroad array of coastal
plain ecosystems occurs in this area, from the barrier
dunes and maritime forests of the outer banks to cedar
swamps and large pocosin complexes of interior areas.
This area includes some of the state's fastest growing
counties and some that are losing population. Urban
centers such as Wilmington do exist, but the region
remains primarily rural.
In recognition of the 20th anniversary of the passage of
CAMA, the governor designated 1994 as the "Year of
the Coast." Associated celebrations, panels, and studies
highlighted the unique features of the North Carolina
coast, successes of coastal management in the state,
and unresolved problems and concerns. Problems re-
main despite protection efforts by various agencies. For
instance:
• Fish landings have dropped dramatically of late.
• Shellfish Sanitation recently closed a set of shellfish
beds located in outstanding resource waters.
• Shellfish statistics show that the quality of the state's
most productive coastal waters continues to decline.
Because coastal North Carolina as a whole is growing
more rapidly than any other section of the state,
pressures on coastal resources can only continue to
increase.
Declining water quality and associated sensitive habi-
tats, resources, and animal populations have prompted
several state agencies to develop new approaches to
environmental protection that incorporate a broader,
natural systems perspective. The North Carolina Divi-
sion of Environmental Management is developing river
basin plans to guide point and nonpoint water pollution
control efforts. The DCM has begun work to assess and
manage the cumulative and secondary impacts of de-
velopment and other land-based activities by using
coastal watersheds as the basis for analysis. The goal
of this work is to expand the regulatory and planning
programs in order to better address cumulative impacts.
This paper describes the approach that DCM has devel-
oped for cumulative impacts management, with special
emphasis on the use of a geographic information system
(CIS). The project described here is scheduled for com-
pletion by the fall of 1996.
Cumulative Impacts Management
The concept of cumulative impacts management (1) is
not new to North Carolina's coastal program. CAMA
requires the consideration of cumulative impacts when
evaluating development permits within defined areas of
environmental concern. A permit must be denied if "the
proposed development would contribute to cumulative
effects that would be inconsistent with the guidelines.. .."
Cumulative effects are defined as "impacts attributable
to the collective effects of a number of projects and
include the effects of additional projects similar to the
requested permit in areas available for development in
the vicinity" (2). Despite this directive, few permitting
actions have been denied because of cumulative ef-
fects; the existence of limited impact data and a dearth
of viable analysis approaches have restricted applica-
tion of this rule.
Since the passage of the National Environmental Policy
Act (NEPA) in 1969, many attempts have been made to
-------
define and assess cumulative impacts. The Council on
Environmental Quality developed the most familiar defi-
nition in its guidelines for NEPA implementation. It de-
fines cumulative impact as:
... the impact on the environment which results
from the incremental impact of the action when
added to other past, present, and reasonably fore-
seeable future actions, regardless of what agency
or person undertakes such other actions. Cumula-
tive impacts can result from individually minor but
collectively significant actions taking place over a
period of time (3).
Although this definition focuses the discussion of cumu-
lative impacts, it provides little guidance on how to carry
out such an analysis. Selecting both an appropriate time
frame for the assessment (how far into the past and
future to carry the analysis) and appropriate boundaries
for the study (municipal or county boundaries, water-
sheds, ecoregions) are but two of the questions that
require answers to successfully investigate cumulative
impacts. Such decisions become even more complex
when incorporating the limits imposed by available data
and existing management structures.
Rigorous cumulative impact analysis is a difficult propo-
sition. It requires identification of all sources of degrada-
tion that affect a given resource. The next step involves
assigning relative significance to each of these sources
along with any impacts that result from additive or syn-
ergistic interaction between sources. Assessment of the
impacts of a pier on surrounding sea grasses, for in-
stance, must include not only impacts related to the
structure, such as shading and wave or current changes,
but also such ambient impacts as natural wave and wind
effects, upland runoff, and varying salinity.
All these investigations require the availability or collec-
tion of baseline environmental data at an appropriate
spatial and temporal scale. Quantifying all the sources
and causal pathways that affect a resource is extremely
complicated in all but the simplest of systems. Assigning
proof of significant impact is difficult unless the cause is
clear and direct.
Because of the difficulties associated with assigning
cause in cumulative impact analysis, especially in a
regional review, DCM has chosen a different approach.
It is focusing instead on locating areas at high risk to
cumulative impacts. Impacts management studies and
responses can then target the areas at greatest risk of
degradation. Changing the scale of analysis from the
site to the region requires applying some simplifying
assumptions. The first assumes that any existing re-
source degradation results from the cumulative impact
of all sources within the system boundaries. Locating
such areas is relatively straightforward because most
natural resource fields have developed measurements
and indicators for locating degraded resources. The
second assumption claims that a sufficiently intensive
concentration of activities within a limited area will result
in cumulative impacts on the affected system. Determin-
ing a threshold beyond which impacts cause degrada-
tion is much harder than locating already degraded
resources because the level of such a threshold de-
pends upon both the strength or spatial concentration of
the impacts and the sensitivity of the resource.
Working from these simplifying assumptions, DCM's
first step in assessing regional cumulative impacts is to
identify areas within coastal North Carolina that exhibit
symptoms of resource degradation, contain a concen-
tration of activities that affect resources, or contain a
concentration of sensitive resources. The use of catego-
ries of resources and impacts have helped to focus this
search. These eight cumulative impact, high-risk area
categories are:
• Impaired water quality
• High potential for water quality impairment
• Sensitive ground-water resources
• Impaired air quality (present or potential)
• Historical rapid growth
• Anticipated high growth
• High-value resources
• Productive and aesthetic resources
This set of categories is presently under public review. The
next step is to develop indicators of the presence of im-
pacts or resources appropriate to each of these categories.
These indicators, when applied to a database of information
about the study area, will help identify those locations at
high risk as defined by the eight categories.
The regional cumulative impacts assessment approach
that DCM developed is a hybrid of various assessment
techniques. The overall approach is grounded in the
theory and methods of site-specific cumulative impact
assessment. Determination of high-risk categories and
appropriate indicators and indexes is closely associated
with both relative risk assessment procedures and geo-
graphic targeting. By focusing on known causes and
effects of cumulative impacts on terrestrial and aquatic
natural resources instead of attempting to quantify all
impact pathways, available data and analysis techniques
can help assess relative risk of cumulative impacts.
A Watershed Database for Cumulative
Impact Assessment
High-risk categories and indicators of degradation or
sensitivity are useless without information on the location
of sensitive resources and impact sites. Consequently,
a comprehensive database of information about coastal
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North Carolina is central to cumulative impacts manage-
ment in this area. The form of any database determines
what types of questions to ask it; the selection of
boundaries has been central to this study.
County boundaries constitute the most typical reporting
unit in DCM operations. Counties determine the
boundaries of DCM's jurisdiction, and the great majority
of statistics used in planning and assessment are avail-
able primarily or solely by county. County size, hetero-
geneity, and the small number of counties available for
comparison, however, have made county boundaries
inappropriate for this project. Because the study focuses
on impacts on natural resources, clearly the most appro-
priate boundaries would relate more directly to those
resources.
Although using a single set of boundaries may not be
appropriate for assessing impacts on all resources,
management constraints limit the choice to one bound-
ary type. Because the primary resources of concern are
water based, watersheds were considered most appro-
priate. Surface waters receive the integrated effects of
activities within a watershed; such boundaries fit intui-
tively with the concept of cumulative impact assess-
ment. The number of water-related resources of concern
also supported this choice. This analysis used small
watersheds (5,000 to 50,000 acres) delineated in 1993
by the Soil Conservation Service for the entire state of
North Carolina.
The Population, Development, and Resources Informa-
tion System (PDRIS), which was designed for this pro-
ject, is a PC-based, watershed-oriented database that
contains the following information about the coastal
area:
• Natural resources
• Population and housing
• Agricultural activities
• Economic activities
• Development activities
Table 1 includes a list of database fields. The presence
and extent (or absence) of each of the features that this
database represents will be available for each coastal
watershed. The small watershed orientation of this study
is only possible because of the availability of CIS; the
volume and complexity of the watershed boundaries
preclude any other assessment tool. In fact, 348 of these
watersheds fall wholly or partially in the 20-county re-
gion. Figure 1 shows a map of these small watersheds.
This map indicates county boundaries and shorelines in
solid lines and the watershed boundaries in gray.
Data Needs and GIS Analysis
Over the past 5 years, North Carolina has actively col-
lected a large amount of natural resource and base map
information in GIS form. Research and funding associated
with the Albemarle/Pamlico Estuarine Study (APES), a
national estuary program study, spurred much of this
data development in the coastal area. The state main-
tains a central repository for geographic data at the
North Carolina Center for Geographic Information and
Analysis (CGIA). Table 2 lists the general types of infor-
mation available from the state database. The availabil-
ity of data in GIS form is but one criterion for selecting
a data set for use in this analysis. To be useful, the scale
and accuracy of the data must be appropriate to the
analysis.
Data Scale
The majority of data in the state's GIS database was
collected at a scale of 1:100,000. Broader use and
interest will probably urge the development of data lay-
ers at finer scales. A recently released layer of closed
shellfish waters, for instance, was created at 1:24,000
scale. This prompted an update of the associated shore-
line coverage to the same base scale. A handful of state
departments and divisions, including Coastal Manage-
ment, now use global positioning systems to collect
even more precise locational information. This scale
suits DCM's regional cumulative impacts scan, which is
based on summary values for entire watersheds. More
detailed intrawatershed planning and analysis would
require finer scale data. A scale of 1:24,000 delineated
the watershed boundaries in this project.
Mixing these 1:24,000 boundaries with the 1:100,000
data sets, however, can cause problems. For instance,
a number of watersheds were designated for the large
open water areas in Albemarle and Pamlico sounds.
Although these should comprise exclusively water, over-
lay analysis of these watershed boundaries on the Tl-
GER-derived census boundaries (1:100,000 scale)
resulted in the assignment of small population counts to
some of these watersheds. Individually locating and
correcting such discrepancies is necessary.
Database Accuracy
Data layer accuracy problems are difficult to identify and
assess. Because other agencies developed the majority
of data used in this project, these source agencies must
be relied upon for accuracy assessment of the source
data. CGIA, steward of the state GIS database, adheres
to National Map Accuracy Standards for all GIS data that
it maintains. CGIA delivers metadata reports with any
data; these reports include the source agency, collection
date, and scale for the information used to derive the
GIS layer. Descriptions of data lineage (collection and
processing procedures), completeness, and positional
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Table 1. Population, Development, and Resource Information System: Database Fields
Agriculture: Livestock and Poultry
Beef feedlots (< 300 head, > 300 head)
Dairy farms (< 70 head, > 70 head)
Hog farms (< 200 head, > 200 head)
Horse stables (< 200 head, > 200 head)
Poultry farms (< 15,000 birds, > 15,000 birds)
Agriculture: Farming
Land in farms (acres, % of HU)
Land with best mgmt. practices (acres, % of HU)
Land w/o best mgmt. practices (acres, % of HU)
Land in conservation tillage (acres, % of HU)
Land w/o conservation tillage (acres, % of HU)
Harvested cropland (acres, % of HU)
Hay crops (acres, % of HU)
Irrigated land (acres, % of HU)
Pasture land (acres, % of HU)
Row crops (acres, % of HU)
Primary
Estuarine waters (acres, % of HU)
Freshwater lakes
HU name
Receiving HU
Receiving water body
Primary water body
Secondary water body
Shoreline
Waterways w/vegetated buffers (miles, % of HU)
Population 1970
Population 1980
Population 1990
Population growth 1970 to 1980
Population growth 1980 to 1990
Counties
Total HU size
Land area (acres, % of HU)
Water area (acres, % of HU)
Stream length (miles)
Stream order (miles, % of stream length)
Development
Building permits—all residential
Building permits—amusement/recreation
Building permits—multifamily residential
Building permits—one-family residential
Building permits—hotels and motels
Building permits—retail
Building permits—industrial
Highway mileage:
Total (miles)
Primary (miles, % of total)
Secondary (miles, % of total)
Paved (miles, % of total)
Unpaved (miles, % of total)
Rail lines (miles)
Increase of primary & secondary roads (miles, %)
Increase of paved vs. unpaved roads (miles, %)
Economic
Ag-related business (number, employees, income)
Farms (number, employees, income)
Fisheries business (number, employees, income)
Forestry/wood-using business (number, employees, income)
Lodging establishments (number, employees, income)
Manufacturing establishments (number, employees, income)
Marinas (number, employees, income)
Mining establishments (number, employees, income)
Recreation business (number, employees, income)
Restaurants (number, employees, income)
Retail establishments (number, employees, income)
Ground Water
Ground-water contamination incidents
Ground-water class (acres, % of HU)
Ground-water contamination area (acres, % of HU)
Ground-water capacity use areas (acres, % of HU)
Land and Estuarine Resources
Anadromous fish streams (miles, % of streams)
Coastal reserve waters (acres, % of HU)
Coastal reserve lands (acres, % of HU)
Federal ownership:
National parks (acres, % of HU)
National forests (acres, % of HU)
Military reservations (acres, % of HU)
USFWS refuges (acres, % of HU)
Federal ownership—other (acres, % of HU)
State ownership:
Game lands (acres, % of HU)
State parks (acres, % of HU)
State forests (acres, % of HU)
State ownership—other (acres, % of HU)
Natural heritage inventory sites (count)
Primary nursery areas (acres, % of water area)
Private preservation (acres, % of HU)
Secondary nursery areas (acres, % of water area)
Threatened/endangered species habitat
Water supply watersheds (acres, % of HU)
Land Use
Total wetland area (acres, % of HU)
High-value wetlands (acres, % of HU)
Medium-value wetlands (acres, % of HU)
Low-value wetlands (acres, % of HU)
Predominant land cover
Population and Housing
Average seasonal population
Peak seasonal population
Units without indoor plumbing
Units with septic tanks
Units on central water systems
Units on central sewer
Units with wells
Permits
Air emission permits—PSD
Air emission permits—toxic
CAMA minor permits
CAMA general permits
CAMA major permits
CAMA exemptions
CWA Sect. 404/10 permits
Landfill permits—municipal
Landfill permits—industrial
Nondischarge permits
NPDES permits—industrial
NPDES permits—other
NPDES permits—POTW
Stormwater discharge permits
Sedimentation control plans
Septic tank permits
Shellfish
Shellfish waters (acres, % of HU)
Shellfish closures—permanent (acres, % of HU)
Shellfish closures—temporary (acres, % of HU)
Water Quality—Open Water
Class B waters (acres, % of water area)
Class C waters (acres, % of water area)
HQW waters (acres, % of water area)
NSW waters (acres, % of water area)
ORW waters (acres, % of water area)
Swamp waters (acres, % of water area)
SA waters (acres, % of water area)
SB waters (acres, % of water area)
SC waters (acres, % of water area)
WS-I waters (acres, % of water area)
WS-II waters (acres, % of water area)
WS-III waters (acres, % of water area)
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Table 1. Population, Development, and Resource Information System: Database Fields (Continued)
Water Quality—Streams
Class B streams (miles, % of streams)
Class C streams (miles, % of streams)
HQW streams (miles, % of streams)
NSW streams (miles, % of streams)
ORW streams (miles, % of streams)
Swamp water streams (miles, % of streams)
SA streams (miles, % of streams)
SB streams (miles, % of streams)
SC streams (miles, % of streams)
WS-I streams (miles, % of streams)
WS-II streams (miles, % of streams)
WS-III streams (miles, % of streams)
Key
HU = hydrologic unit
PSD = point source discharges
POTW = publicly owned treatment work
NPDES = National Pollutant Discharge Elimination System
HQW = high-quality waters
NSW = nutrient-sensitive waters
ORW = outstanding resource waters
SA = saltwater classification A
SB = saltwater classification B
SC = saltwater classification C
WS1 = water supply classification 1
WS2: water supply classification 2
WS3: water supply classification 3
Water Quality—Use Support
Algal blooms (count, extent/severity)
Fish kills (count, extent/severity)
Streams fully supporting (miles, % of streams)
Streams support threatened (miles, % of streams)
Streams partially supporting (miles, % of streams)
Streams nonsupporting (miles, % of streams)
Waters fully supporting (acres, % of water area)
Waters support threatened (acres, % of water area)
Water partially supporting (acres, % of water area)
Waters nonsupporting (acres, % of water area)
accuracy are not available from these standard metadata
reports, however.
DCM's cumulative impacts analysis also incorporates
information not available from the state CIS database.
Some of this information, such as business locations, is
available from private data providers. Other information,
especially agricultural statistics, does not presently exist
in CIS form. Non-GIS formats include county statistics,
voluntary compliance databases with self-reported co-
ordinates, and other tabular databases. Typically, little
quality control has been performed on any coordinate
information. When the data originate from other state
agencies, DCM is often the first user of the data outside
of the source agency.
CIS Analysis Procedures
This study involves no sophisticated CIS analysis pro-
cedures. CIS helps to generate summary statistics by
watershed for each of the database features. CIS draw-
ing and query operations allow analysis of database
accuracy. If the data are acceptable, the next step re-
quires overlaying the watershed boundaries on the fea-
ture and assigning the appropriate watershed codes to
all features that fall within the study area. Statistics can
then be generated on the number of points, length of
lines, acreage of polygons, or a total of any other nu-
meric field in the feature coverage. Finally, the resulting
summary file is converted to the format that PDRIS
requires. A macro has been developed to complete this
analysis. This macro generates a page-size reference
map, performs the overlay, generates the watershed
summary statistics, and converts the statistics to the
final PC format.
Extra steps are necessary to analyze any information
that does not already exist as a CIS coverage. Typically,
these are tabular summaries associated with a specific
boundary layer, such as county or U.S. Census statis-
tics. These cases entail overlaying the watershed
boundaries on the reporting unit boundaries; the data
are distributed to the watershed in direct proportion to
the percentage of the unit that falls into the watershed.
For instance, if a census tract falls 30 percent into
watershed A and 70 percent into watershed B, 30 per-
cent of the total tract population will be assigned to
watershed A and the remainder to B. After performing all
assignments, summary statistics are again generated
by watershed. This procedure assumes that the distri-
bution of the feature is even across each reporting unit.
Rarely is this a valid assumption, but when the units are
considerably smaller than the watersheds, as is the
case with census tracts and blocks, this assumption
introduces only limited errors. Watershed estimates
based solely on county statistics, however, can be
grossly inaccurate. When working with county informa-
tion, therefore, using covariate information that ties
more precisely to specific locations is necessary. Crop-
land location derived from the LANDSAT land cover
layer, for instance, can be used to better distribute
county-level agricultural statistics.
Database and Analysis Documentation
Data documentation is essential to this project. Given
the large number of fields in the final database and the
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Figure 1. Watersheds in the North Carolina coastal area.
correspondingly large number of data types and
sources, such documentation is key to understanding
the quality of the individual database components as
well as easing future database additions and updates.
Because the results of this cumulative impact analysis
exercise will be used to extend DCM's resource man-
agement efforts, documenting data sources and analy-
ses will be critical if any decisions made based on this
information are disputed.
A metadata database has been developed to document
PDRIS data sources and analysis procedures. For each
Table 2. A Sample of North Carolina GIS Database Contents
Type Examples
database entry, fields exist for a description and contact,
collection methodology, and geographic extent of the
source data. Data selection, overlay, and conversion
procedures are also documented, along with any as-
sumptions made in the analysis. In addition, recording
data source, analysis procedure, and the date facilitates
future database updates. Fields also record accuracy
assessments for positional and attribute accuracy, logi-
cal consistency, and completeness.
The restrictions listed above regarding source data ac-
curacy assessment, however, have limited their use.
Once an entry is made to the PDRIS, all project team
members receive metadata reports along with a refer-
ence map for a final review of completeness of the
source data, data selection, and analysis logic. Figure 2
shows an example of a blank metadata worksheet.
Status of the Cumulative Impacts
Assessment
DCM is presently gathering, verifying, and analyzing
information for entry into the PDRIS. Although each
source data layer was checked for accuracy before use,
the logical consistency of each of the database entries
relative to the other components also needs addressing.
One example of such database inconsistencies is the
watersheds that are covered entirely by water but also,
according to the database, support a resident popula-
tion. These inconsistencies could result from problems
related to scale, differing category definitions, data inac-
curacies, or errors in the GIS conversion or analysis at
DCM. Database precision is essential for an accurate
analysis and for general support of DCM's cumulative
Coverage
Natural resource
Base data
Ownership
Permits, waste sites
Cultural, population
Fishery nursery areas
Natural heritage sites
Detailed soils
Closed shellfish areas
Water quality use classes
Detailed wetlands maps
Hydrography (24K, 100K)
Roads/transportation
County and city boundaries
LANDSAT-derived land cover
Federal and state ownership
NPDES permit site
Landfills, hazardous waste, Superfund
TIGER boundaries, census information
Historic register sites, districts
Coastal North Carolina
Statewide
Varied
Coastal North Carolina
Statewide
Varied
Statewide
Statewide
Statewide
Coastal North Carolina
Statewide
Statewide
Statewide
Statewide
Statewide
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General Information:
Field
Description
Database
Definition
Source Data Description:
Contact
Data
Scale
Sample Method
Geographic Extent
Database Entry:
Procedures
Assumptions
Accuracy Assessment:
Rating
Logic Test
Error Comment
Value Range
Update Procedure:
Next Update
Procedure
Overall
Positional
Units
Attribute
Logical
Consistency
Completeness
Source
Figure 2. Population/Development database: Data dictionary
impacts approach. Because the watershed database pro-
duced for this project will be widely available, errors and
inconsistencies will undermine support for the rest of the
project. Careful documentation of data sources, limita-
tions, and analysis assumptions and procedures will pro-
vide useful support should problems or concerns arise.
Once database development is sufficiently complete
(the database encompasses much dynamic data and
could be constantly updated), indexes describing each
of the cumulative impact, high-risk areas must be final-
ized. Applying these indexes to the database will allow
identification of the watersheds at highest risk to cumu-
lative impacts. Discussions held concurrently with index
development will determine which management re-
sponses are appropriate to each high-risk category.
Possibilities include strengthened land use planning re-
quirements, new permit standards, or the designation of
a new type of environmental critical area.
Although the data-intensive approach that DCM has
chosen relies heavily on a CIS, the greatest challenges
in this project do not lie in the CIS analysis. Applying this
watershed-based analysis to existing political jurisdic-
tions will be a more difficult undertaking. A convincing
demonstration of the importance of including a natural
systems perspective into a development permitting sys-
tem, land use plan, or even economic development
strategy, will ultimately contribute more to environmental
protection in coastal North Carolina than any individual
regulation that emanates from this project.
Summary
Twenty years after the passage of CAMA, DCM has
developed a framework for a consistent approach to the
problem of cumulative impacts of development. The
approach and PDRIS database combine existing natural
resource management techniques to locate areas of the
coast at greatest risk of serious impairment from cumu-
lative impacts. The availability of natural resource data
at an acceptable scale (1:100,000) eases the develop-
ment of the database essential to this analysis. The
simultaneous development of a set of comprehensive
small watershed boundaries for the state, along with the
initial planning of this project, provided the final critical
component to DCM's approach.
Perhaps more importantly, both DCM and individual
local governments will have a large volume of informa-
tion on natural units, which will provide an important,
new perspective on the problems and prospects for local
governmental action.
This project will not solve all problems related to cumu-
lative impacts. The PDRIS will provide little support for
site-specific or within-watershed cumulative impacts
analysis; such an analysis at fine scales requires a much
more precise database. By providing a broader-scale
framework for this discussion, however, DCM's regional
cumulative impacts study will hopefully further discussion,
understanding, and management of cumulative and
secondary impacts on natural systems.
References
1. Wuenscher, J. 1994. Managing cumulative impacts in the North
Carolina coastal area. Report of the Strategic Plan for Improving
Coastal Management in North Carolina. North Carolina Division of
Coastal Management.
2. North Carolina General Statutes (NCGS) 113A-120.
3. 40 CFR §1508.7.
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Wetlands Mapping and Assessment in Coastal North Carolina:
A CIS-Based Approach
Lori Sutter and James Wuenscher
Division of Coastal Management, State of North Carolina Department of Environment, Health,
and Natural Resources, Raleigh, North Carolina
Introduction
The coastal area of North Carolina covers 20 counties
and over 9,000 square miles of land area, about 20
percent of the state (see Figure 1). It also includes over
87 percent of the state's surface water. The North Caro-
lina Coastal Management Program (NC CMP) is re-
sponsible for managing this area to meet the goals set
forth in the Coastal Area Management Act (CAMA)
(North Carolina General Statute [NCGS] 113A, Article
7). These goals provide a broad mandate to protect the
overall environmental quality of the coastal area and to
guide growth and development in a manner "consistent
with the capability of the land and water for develop-
ment, use, or preservation based on ecological consid-
erations" (NCGS 113A-102(b)(2)).
Figure 1. HU and county boundaries in the North Carolina
coastal area.
Much of the North Carolina coastal area consists of
wetlands, which, in many areas, constitute nearly 50
percent of the landscape. These wetlands are of great
ecological importance, in part because they occupy so
much of the area and are significant components of
virtually all coastal ecosystems, and in part because of
their relationships to coastal water quality, estuarine
productivity, wildlife habitat, and the overall character of
the coastal area.
Historically, close to 50 percent of the original wetlands
of the coastal area have been drained and converted to
other land uses (1-3). Although agricultural conversion,
the largest historical contributor to wetlands loss, has
largely stopped, wetlands continue to disappear as they
are drained or filled for development. Conflicts between
economic development and wetlands protection con-
tinue to be a major concern, with many coastal commu-
nities considering wetlands protection to be a major
barrier to needed economic development.
Because wetlands are such a dominant part of the
coastal landscape and are vitally important to many
aspects of the area's ecology, their management and
protection is a major concern of the NC CMP. The State
Dredge and Fill Act (NCGS 113-229) and the CAMA
regulatory program stringently protect tidal wetlands, or
"coastal wetlands" as law and administrative rules call
them. Coastal wetlands are designated areas of environ-
mental concern (AECs), with the management objective
"to give highest priority to the protection and manage-
ment of coastal wetlands so as to safeguard and per-
petuate their biological, social, economic and aesthetic
values; and to coordinate and establish a management
system capable of conserving and utilizing coastal wet-
lands as a natural resource essential to the functioning
of the entire estuarine system" (15A NCAC 7H .0205).
North Carolina law does not, however, specifically pro-
tect nontidal freshwater wetlands. State protection of
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freshwater wetlands is limited to the regulatory authority
provided under federal laws for state agency review of
federal permits; in this case, §404 permits granted by
the U.S. Army Corps of Engineers. Under §401 of the
Federal Water Pollution Control Act (33 USC 1341), a
Water Quality Certification from the North Carolina Divi-
sion of Environmental Management (DEM) is required
for a 404 permit to discharge fill material into wetlands.
Section 307 of the federal Coastal Zone Management
Act (CZMA - 16 USC 1451 et seg.) also requires that
404 permits be consistent with the enforceable rules and
policies of the NC CMP. The standards for consistency
are the use standards for AECs and wetlands policies
stated in the applicable local land use plan. Other than
AECs, the NC CMP has no consistent policies regarding
wetlands. A few local land use plans include policies to
protect freshwater wetlands, but most do not.
Wetlands Conservation Plan
In 1991, the CZMA §309 Assessment of the NC CMP
revealed NC CMP's weakness in protecting nontidal
wetlands (4). The assessment demonstrated that both
opponents and proponents of wetlands protection con-
sidered the current system inadequate. Economic de-
velopment interests found the 404 regulatory program
to be unpredictable and inconsistent, often resulting in
the loss of needed economic growth in coastal counties.
Environmental interests felt that the program allowed
the continued loss of ecologically important wetlands. As
a result, the assessment identified wetlands manage-
ment and protection as one of the primary program
areas in need of enhancement.
The North Carolina Division of Coastal Management
(DCM) developed a 5-year strategy (5) for improving
wetlands protection and management in the coastal
area using funds provided under the Coastal Zone En-
hancement Grants Program established by 1990
amendments to §309 of the federal CZMA. The Office
of Ocean and Coastal Resources Management (OCRM)
in the National Oceanographic and Atmospheric Admini-
stration (NOAA), U.S. Department of Commerce admin-
isters the §309 program. Funds provided under this
program, particularly Project of Special Merit awards for
fiscal years 1992 and 1993, supported the work reported
in this paper. A grant from the U.S. Environmental Pro-
tection Agency (EPA) for a Wetlands Advance Identifica-
tion (ADID) project in Carteret County, North Carolina,
also funded this work.
The key element of DCM's strategy for improving wet-
lands protection is the development of a wetlands con-
servation plan for the North Carolina coastal area. The
plan has several components:
• Wetlands mapping inventory
• Functional assessment of wetlands
• Wetlands restoration
• Coordination with wetlands regulatory agencies
• Coastal area wetlands policies
• Local land use planning
The obvious first step in developing a wetlands conser-
vation plan is to describe the wetlands resource. An
extensive geographic information system (CIS) wet-
lands mapping program is helping to accomplish this
first step by producing a CIS coverage of wetlands by
wetland type for the entire coastal area. The CIS cover-
age allows generation of paper maps for areas within
any boundaries available in CIS format. This is the
subject of the first part of this report.
One weakness of the 404 program is that, for individual
permits, it attempts to apply the same rules and proce-
dures equally to all wetlands, regardless of the wetland
type and location in the landscape. This approach can
result in permits being granted for fill of wetlands of high
ecological significance or permits being denied to pro-
tect wetlands of little significance. Neither outcome is
desirable because the result may be the loss of either
vital wetland functions or beneficial economic activity.
This is an unsatisfactory way to manage wetland re-
sources in an area such as the North Carolina coast,
where:
• A high proportion of the land is wetlands.
• Many of the wetlands are vital to the area's environ-
mental quality.
• Economic stimulation is sorely needed.
To help overcome this weakness in the current wetland
regulatory framework, the Wetlands Conservation Plan
includes an assessment of the ecological significance of
all wetlands to determine which are the most important
in maintaining the environmental integrity of the area.
This will result in a designation of each wetland polygon
in the CIS coverage as being of high, medium, or low
functional significance in the watershed in which it ex-
ists. The procedure by which this occurs is the subject
of the second part of this report.
The remaining components of the Wetlands Conserva-
tion Plan comprise the means by which the results of the
wetland mapping and functional assessment steps will
be used to improve wetland protection and management.
Close coordination with other state and federal agencies
involved in wetlands protection and management has
been an important component of the entire effort.
Agency representatives have been involved in develop-
ing the methods used, and the agencies will receive
copies of the resulting maps for use in their own plan-
ning and decision-making. Policies for protection of wet-
lands of varying functional significance will be proposed
to the Coastal Resources Commission to serve as the
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basis for consistency review of 404 permit applications.
Wetland maps and functional assessment results will
also be provided to local governments for use in local
land use planning, and DCM will work with local govern-
ments to increase local involvement in the wetlands
regulatory structure.
While the wetland maps themselves are useful for land
use planning and helping to find suitable development
sites, simply knowing where the wetlands are located is
insufficient information for many purposes. Any area for
which a 404 permit application is in process has been
officially delineated as a wetland by the Corps of Engi-
neers. The value of wetland maps to the regulatory
review agencies at this stage is limited to determining
the relationship of the site to other wetlands in the area.
While, ideally, all wetlands should be avoided in plan-
ning development, avoiding wetlands completely in the
coastal area is difficult, and avoiding all wetlands in any
extensive development is virtually impossible.
The results of the functional assessment will provide
additional information about the ecological significance
of wetlands. This information will be valuable to wetland
regulatory review agencies in determining the impor-
tance to an area's environmental integrity of protecting
a particular site for which a permit to fill has been
requested. It will also enable development projects to be
planned so as to avoid, at all reasonable costs, the most
ecologically important wetlands. An accurate functional
assessment of wetland significance, then, is the most
valuable component of the Wetlands Conservation Plan.
Wetlands Mapping Inventory
An important, initial step in developing a comprehensive
plan for wetlands protection is to understand the extent
and location of wetlands in the coastal area. When
developing mapping methods, DCM quickly realized
that the more than 9,000-square-mile coastal area was
too large for any mapping effort in the field (see Figure
1). To complete this task in an accelerated timeframe,
DCM needed to use existing data compatible with CIS.
Reviewing the existing data revealed that most are not
applicable for one of two reasons: (1) available wetlands
data are based on older photography, and (2) more
recent data are not classified with the intent of wetlands
mapping. These data types, used independently, are
inappropriate for use in a coastal area wetlands conser-
vation plan. In addition, the classification schemes used
in the existing methods are too complex or not focused
on wetlands.
While several data sets were believed to be inappropri-
ate if used exclusively for wetlands mapping in coastal
North Carolina, each contained useful components.
DCM elected to combine three primary layers of data
and extract the most pertinent information from each
layer. DCM selected the National Wetlands Inventory
(NWI) because its primary purpose is to map wetlands.
Unfortunately, these maps were based on photography
from the early 1980s in coastal North Carolina, and
many changes have occurred in the landscape since
that time. NWI also omitted some managed wet pine
areas from its maps; DCM wished to include these areas
because they are important to the ecology of the North
Carolina coastal area. DCM also selected detailed soils
lines for use in its mapping efforts. While soils alone
should not be used to identify wetlands, soils can be
very useful in identifying marginal areas. Finally, DCM
also employed thematic mapper (TM) satellite imagery
in its methods. This data layer was not developed as a
wetlands inventory; however, the imagery is more recent
than the soils and NWIs. DCM desired to incorporate the
benefits of each of these data sources into its mapping
techniques.
The information provided by this mapping exercise will
be useful to county and municipal planners in helping
guide growth away from environmentally sensitive ar-
eas. For this reason, DCM elected to pursue mapping
on a county by county basis. In addition, a single county
allowed DCM to focus methodology development to a
limited geographic area to refine its methods. Carteret
County was selected as a methods development labo-
ratory because data were available for the area and
because Carteret has a large number of representative
wetlands.
Data Descriptions
The U.S. Fish & Wildlife Service produces the NWI for
all wetlands in the country. For the coastal North Caro-
lina area, these vector data were developed from
1:58,000-scale color infrared photography taken during
the winters of 1981, 1982, and 1983. Photointerpreters
delineated wetland polygons on clear stabilene mylar
taped over the photographs. After an initial scan of the
photographs to identify questions or problem signatures,
the photointerpreters reviewed areas in the field. They
performed approximately one-half to one full day of field
verification per quadrangle (quad) (6). Features were
compared with U.S. Geological Survey (USGS) topo-
graphic maps for consistency. Following completion of
the 'draft' paper maps, the Regional Coordinator re-
viewed the data. After approval as a final map, each
quad was digitized. Initially, the North Carolina Center
for Geographic Information and Analysis (CGIA) digit-
ized the coastal North Carolina NWI maps, and later, the
NWI Headquarters in St. Petersburg, Florida, who sub-
contracted the task, digitized them. Digital maps were
obtained initially from 1/4-inch tape transfer and later
from direct access to NWI via the Internet.
CGIA provided digital, detailed soil lines, which also are
vector data based on 1:24,000 quads. County soil sci-
entists delineated soil boundaries on aerial photographs
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based on slope, topography, vegetative cover, and other
characteristics. This process occurs in any soil survey.
After appropriate personnel approved the lines, a quali-
fied soil scientist recompiled them onto orthophoto
quads. CGIA scanned or manually digitized these lines.
The coverage incorporated databases describing soil
characteristics, which were then released for use.
The Landsat Thematic Mapper (TM) imagery was clas-
sified as part of the Albemarle-Pamlico Estuarine Study
(APES). To provide complete coverage for the southern-
most region of DCM's jurisdiction (Onslow, Render,
Brunswick, and New Hanover Counties), DCM con-
tracted with CGIA and the North Carolina State Univer-
sity (NCSU) Computer Graphics Center to have that
area processed identically to the APES region. These
data provide a raster-based coverage of approximately
30-meter pixel resolution. Some of the imagery was
taken at high tide, which precludes some near-water
wetlands from appearing in certain areas. Using
ERDAS, imagery specialists grouped similar spec-
tral signatures into one of 20 classes. DCM used these
data in two formats: filtered and unfiltered. The unfiltered
information was vectorized with the ARC/INFO GRID-
POLY command. To remove some of the background
noise in the coverage, it was filtered using ERDAS 'scan'
with a Majority filter of 5 by 5 pixels, then vectorized with
the ARC/INFO GRIDPOLY command.
Methods
Within each county, mapping is based on 1:24,000
USGS quads. After completion, each quad is assembled
into a countywide coverage, which eventually is assem-
bled into a coastal area coverage. The initial step in the
mapping process is to ensure completion of the base
layers described previously. Reviewing for errors at
early stages prevents confusion in correction later in the
process; therefore, the importance of the preliminary
techniques cannot be overemphasized. The NWI data
are first inspected to ensure complete coverage. If parts
of the quad are missing, the error is investigated and
corrected. Omissions may be areas of severe cloud
cover on the photography or areas neglected during the
digitization process. Next, the coverage is reviewed for
missing label points. Any omissions are corrected based
on the finalized version of the published NWI paper map.
Appropriate NWI staff are contacted for the necessary
information. At this time, labels are verified for typo-
graphical misentry. If not corrected, these errors could
lead to confusion later in the mapping process.
Once the label errors are detected and corrected, the
polygons are reviewed for completion. Verifying every
line in the areas of coastal North Carolina densely
populated with wetlands is impossible, but the lines are
reviewed for completeness. NWI staff again must pro-
vide necessary information for any omissions. When the
map is approved, technicians ensure projection of the
quad to the State Plane Coordinate System. If this has
not been completed, the ARC/INFO PROJECT com-
mand is employed.
The soils information is prepared in a similar manner to
the NWIs, with questions being directed to the county
soil scientist. Prior to the steps described previously,
soils must be verified for completeness. Because soils
are mapped by county boundaries and DCM maps by
quad, some files must be joined in quads that intersect
county boundaries. At this time, the quad must be
checked for differing abbreviations between counties.
Discrepancies are handled on a case-by-case basis.
When an abbreviation describes different soils in differ-
ent counties, a temporary abbreviation is created for one
of the counties. If a single soil is described by two
abbreviations across counties, both abbreviations are
incorporated into the classification scheme.
The Landsat data do not require additional verification.
Review of this layer is often helpful, however, to ensure
that the geographic boundaries match. Cases where
landforms do not appear to match require investigation
of the discrepancies. If the area is mis registered, this
layer might be omitted from the analyses. To date, no
area has been mapped without this imagery.
The hydrogeomorphology of a wetland is unique in de-
fining the wetland's function (7). Because these maps
serve as the base for additional wetland projects (as
described later in this report), an accurate determination
of this characteristic is essential. Prior to the overlay
procedure, technicians add a new item, hydrogeomor-
phic (HGM), to the NWI coverage. Because DCM con-
siders both vegetation and landscape position in its
classification (discussed later), riverine, headwater, and
depressional wetland polygons are assigned an HGM of
'r,' 'h,' or 'd,' respectively. The digital line graphs (DLGs)
of hydrography are essential in this step of the procedure.
All wetlands that are adjacent to streams or rivers are
considered in the riverine HGM class and are desig-
nated as riverine polygons. This class should include all
bottomland hardwood swamps and some swamp for-
ests. It rarely includes any of the interfluvial wetland
types. If it does, it is a small section of a large interfluvial
flatwood from which a small stream emerges. Only the
polygons adjacent to the stream are considered riverine.
Headwaters are defined as linear areas adjacent to
riverine areas that do not have a stream designated on
the hydrography data layer. Because these unique sys-
tems form the transition between flatwoods and riverine
wetlands, they are treated specially. Finally, polygons
that exist on interfluvial divides are designated as flats
or depressional wetlands. This class should not include
any wetlands along streams.
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The complete data coverages are overlaid to create a
new, integrated coverage that often approaches 100,000
polygons. Each polygon has many characteristics assigned
to it, including the Cowardin classification assigned by
the NWI, the soil series provided by the detailed soil
lines, the unfiltered land use/land cover code, the filtered
land use/land cover code from the Landsat TM imagery,
and the HGM classification assigned in the previous
step.
Based on these characteristics, each polygon is as-
signed to one of DCM's classes through an automated
ARC/INFO model using an arc macro language (AMI).
Personnel from the NWI and the North Carolina Depart-
ment of Environment, Health, and Natural Resources
Division of Soil and Water Resources have reviewed the
classification of the Cowardin types into DCM wetland
types. The classes that DCM currently recognizes are
upland, salt/brackish marsh, estuarine shrub scrub, es-
tuarine forest, maritime forest, pocosin, bottomland
hardwood, swamp forest, headwater swamp, hardwood
flatwoods, piney flatwoods, and managed pinelands.
DCM also classifies soils as hydric or nonhydric based
on List A of the U.S. Soil Conservation Service (SCS)
List of Hydric Soils.
The base of the map is the NWI polygon coverage.
Some NWI polygons are omitted from the DCM maps
because they are temporarily flooded, but on nonhydric
soils or because recent TM imagery indicates these
areas are currently bare ground. The managed pineland
wetland group on DCM maps includes areas that NWI
considers uplands, identified as pine monocultures on
the imagery, and that occur on hydric soil.
In addition, DCM also provides a modifier to some of
these polygons. DCM notes if NWI has determined that
the area has been drained or ditched. Areas designated
as wetlands at the time of the NWI photography that
currently appear as bare ground on the TM imagery are
designated as 'cleared' on the maps. Many of the
cleared areas would no longer be considered jurisdic-
tional wetlands. These modifiers are useful indicators of
the impacts wetlands sustain from human activities.
Initiation of an interactive session follows completion of
the automated procedure. This session considers land-
scape characteristics that are not easily described to
a computer model in correcting the classification. This
is especially important in distinguishing bottomland
hardwood swamps from hardwood flats. Both contain
deciduous, broad leaf species of trees and can be tem-
porarily flooded. The hydrology of these systems, how-
ever, is completely different. All bottomland hardwood
swamps, for example, must be adjacent to a river where
they receive seasonal floodwaters from the channel.
Conversely, hardwood flatwoods should be located on
interfluvial flats and not adjacent to any streams. Water
is not introduced into hardwood flatwoods via a channel;
rather, precipitation and ground water provide the water
for this system. Polygons that are adjacent to rivers or
estuaries but do not have a distinct channel designated
in the hydrography coverage are considered headwater
swamps.
During the course of methodology development, staff
members visited at least 371 sites in the field. As staff
members encountered new Cowardin classes, they
would verify that the polygons were being placed into
the correct DCM categories. If they determined that a
particular Cowardin class was systematically misidenti-
fied, they updated the algorithm for automation. While
this method does not provide for a usable accuracy
assessment, it allowed development of the most accu-
rate methods.
The accuracy of these data is unknown at this time. An
accuracy assessment of the data is anticipated in the
near future. This assessment will allow map users to
understand the strengths and limitations of the data. It
also will provide an overall summary of data error.
Functional Assessment of Wetlands
Certain initial considerations shaped the approach and
methods used in developing a wetlands functional as-
sessment procedure. The procedure needed to fit within
the context and objectives of the Wetlands Conservation
Plan for the North Carolina coastal area as described
above. This context, and the opportunities and limita-
tions it imposed, had considerable influence on the spe-
cific procedure developed.
Because we are dealing with a large geographic area
with many wetlands, we recognized from the outset that
we needed a method we could apply to large land areas
without site visits to each individual wetland. This ruled
out the many site-specific functional assessment meth-
ods that were applied in other contexts. Almost of ne-
cessity, a CIS-based approach was chosen. That meant
we would have to use information available in CIS for-
mat and make use of CIS analytical techniques. The
wetland mapping on which the functional assessment is
based was performed using CIS, so the basic digital
data were available.
The primary objective was to produce information about
the relative ecological importance of wetlands that would
be useful for planning and overall management of wet-
lands rather than to serve as the basis for regulatory
decisions. While we could not visit every wetland, the
goal was to predict the functional assessment value that
a detailed, site-specific method would determine. We
wanted to be able to predict in advance what the wetland
regulatory agencies would determine as a wetland's
significance so that the resulting maps would identify
those wetlands where a 404 permit would be difficult or
impossible to obtain. The resulting information would
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then be useful in determining where not to plan devel-
opment. This would benefit potential permit applicants
by preventing ill-advised plans that would be unlikely to
receive permits and simultaneously serve to protect the
most ecologically important wetlands. The result of the
procedure, then, is not a substitute for a site visit in
making regulatory decisions, but a predictor of what a
site visit would determine.
A primary consideration was that the procedure be
ecologically sound and scientifically valid, based on the
best information available about the functions of wet-
lands. It needed to be based on fundamental principles
of wetlands and landscape ecology rather than on arbi-
trary or subjective decisions.
Finally, the procedure was to be watershed-based. This
requirement was primarily because consideration of a
wetland's role in its watershed is the soundest basis for
determining its ecological significance, but also because
the other components of the Wetlands Conservation
Plan, including wetland mapping and restoration plan-
ning, are based on watershed units. The watersheds
being used are 5,000- to 50,000-acre hydrologic units
(HUs) delineated by the SCS as illustrated in Figure 1.
The North Carolina coastal area comprises 348 of these
HUs. Watershed units of any size, however, could be
used without changing the validity of the watershed-
based considerations used in the procedure.
These initial considerations result in a summary defini-
tion of the functional assessment procedure. It is a CIS-
based, landscape scale procedure for predicting the
relative ecological significance of wetlands throughout a
region using fundamental ecological principles to deter-
mine the functions of wetlands within their watersheds.
The functional assessment procedure is meant to be
used with CIS data for regional application. It is not a
field-oriented, site-specific method that involves visiting
individual wetlands and recording information. A CIS-
based procedure is the only practical approach for deal-
ing with a large geographic area with many wetlands in
a limited amount of time.
This CIS-based approach can make information on wet-
land functional significance available for broad regions
in advance of specific development plans. The informa-
tion is then available for planning to help avoid impacts
to the most ecologically important wetlands. In this
sense, the North Carolina procedure is unlike other
functional assessment techniques that are designed for
use in a regulatory context or that require field data for
each wetland.
Data Requirements
Because the procedure uses CIS analysis, it requires
digital information in CIS format. CIS data layers used
in the procedure include:
• Wetland boundaries and types (the topic of the first
section of this report).
• Soils maps.
• Land use/land cover.
• Hydrography.
• Watershed boundaries.
• Threatened and endangered species occurrences.
• Estuarine primary nursery areas.
• Water quality classifications.
In the North Carolina coastal area, these data layers
either already existed and were available from the CGIA
or were developed as part of the Wetlands Conservation
Plan. Because other projects funded most of the data
acquisition and digitization, developing the necessary
CIS databases was not a major cost.
The soils coverage consists of digitized, detailed county
soils maps produced by SCS and digitized by CGIA. The
soils coverage allows identification of the soil series
underlying a wetland, and the properties of the series
are used to determine soil capacity for facilitating the
wetland's performance of various functions.
The land use/land cover data layer was produced for the
APES from interpretation of satellite TM imagery (8). It
is used to determine land cover and uses surrounding
each wetland and in the watershed.
The basic hydrography coverage consists of 1:24,000-
scale USGS DLGs. Because the functional assessment
procedure uses stream order as an indicator of water-
shed position, stream order according to the Strahler
system was determined manually and added to the DIG
attribute files.
As described previously, the watersheds used in the
procedure are relatively small HUs delineated by SCS.
DCM contracted with CGIA to have these boundaries
digitized for the coastal area. During the digitization
process, the watershed boundaries were rectified to
USGS and DEM boundaries of larger subbasins to en-
sure that the HUs could be combined into larger water-
shed units.
A data layer produced by the North Carolina Natural
Heritage Program is used to identify threatened and
endangered species occurrences. The North Carolina
Division of Marine Fisheries maintains the coverage of
primary nursery areas, and the Division of Environ-
mental Management developed a map of water quality
classifications that was digitized by CGIA.
The ways in which these data layers are used to determine
values for various parameters in the functional assess-
ment procedure are described later in this report. The
CIS procedures have been automated using ARC/ INFO
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AMI on a Sun workstation. The AMI programs are
available from DCM to anyone planning to use the pro-
cedure elsewhere.
Because the assessment procedure was designed for
CIS analysis, the choice and expression of individual
parameters have been shaped to some extent by the
CIS data available and the capabilities and limitations of
ARC/INFO techniques and AMI automation. DCM was
fortunate to have a relatively large amount of CIS data
readily available. For use in other areas, the procedure
could be modified to use different CIS coverages. At
least the first five databases listed above, however, are
essential to its basic propositions.
Classification Considerations
The HGM classification system for wetlands (7) classi-
fies wetlands into categories based on landscape position
(geomorphic setting), water sources, and hydrodynam-
ics (direction of water flow and strength of water move-
ment). It is being increasingly used as the basis for
wetland classification and functional assessment sys-
tems. HGM classification focuses on the abiotic features
of wetlands rather than on the species composition of
wetland vegetation as do most traditional wetland clas-
sification schemes.
Several features of the HGM classification system make
it a useful starting point for an assessment of wetland
functions. Because the HGM system is based on geo-
morphic, physical, and chemical properties of wetlands,
it aggregates wetlands with similar functions into
classes. The HGM class of a wetland, in itself, indicates
much about the ecosystem functions of the wetland. The
HGM approach also forces consideration of factors ex-
ternal to the wetland site, such as water source. This
helps relate the wetland to the larger landscape of which
it is a part and puts consideration of the wetland's func-
tions in a landscape and watershed context.
Three HGM classes are used as the starting point for
the North Carolina functional assessment procedure. All
wetlands are first classified as one of the following:
• Riverine
• Headwater
• Depressional
Riverine wetlands are those in which hydrology is deter-
mined or heavily influenced by proximity to a perennial
stream of any size or order. Overbank flow from the
stream exerts considerable influence on their hydrology.
Headwater wetlands exist in the uppermost reaches of
local watersheds upstream of perennial streams. Head-
water systems may contain channels with intermittent
flow, but the sources of water entering them are precipi-
tation, overland runoff, and ground-water discharge
rather than overbank flow from a stream. Depressional
wetlands, including wet flats and pocosins, generally are
not in direct proximity to surface water. While they may
be either isolated from or hydrologically connected to
surface water, the hydrology of depressional wetlands is
determined by ground-water discharge, overland runoff,
and precipitation.
The functions of wetlands in these different HGM
classes differ significantly. Riverine wetlands regularly
receive overbank flow from flooding streams and, thus,
perform the functions of removing sediment and pollut-
ants that may be present in the stream water and pro-
viding temporary floodwater storage. Headwater and
depressional wetlands cannot perform these functions
because they do not receive overbank flow. Headwater
wetlands occur at landscape interfaces where ground
water and surface runoff coalesce to form streams.
Headwater wetlands provide a buffer between uplands
and stream flow so they can perform significant water
quality and hydrology functions. While depressional wet-
lands do not perform buffer functions, they often store
large amounts of precipitation or surface runoff waters
that otherwise would more rapidly enter streams. Wet-
lands in all HGM classes can perform important habitat
functions.
Because the wetlands in these different HGM classes
are functionally different, their functional significance is
assessed using different, though similar, procedures. If
the same procedure were used for all HGM classes,
depressional wetlands would always be considered of
lower functional significance simply because they are
not in a landscape position to perform some of the water
quality and hydrologic functions of riverine and headwa-
ter wetlands.
In addition to HGM classes, wetland types identified by
dominant vegetation are used at several points in the
functional assessment. This reflects a recognition that
the biologic properties of a wetland site considered to-
gether with its hydrogeomorphic properties can provide
a more detailed indication of its functions than either
taken alone. The HGM class of a wetland, as a broad
functional indicator, determines which assessment pro-
cedure to use. Within each HGM class and correspond-
ing assessment procedure, wetland type determines the
level or extent of specific parameters.
The wetland types used are those typical of the North
Carolina coastal area. They result from a clumping of
the Cowardin classes used on NWI maps into fewer
types with more intuitively obvious type names (e.g.,
swamp forest, pocosin), as described previously. These
wetland types are used in the wetland maps that form
the starting point for the functional assessment.
Wetland types are used in the procedure as indicators
of functional characteristics. Correlations between wet-
land type and wetland functions were determined from
-------
statistical analysis of field data from nearly 400 sites. At
each site, the presence or absence of a list of functional
indicators was recorded. Dr. Mark Brinson of East Caro-
lina University developed the functional indicators lists,
in part. Dr. Brinson served as primary scientific consult-
ant in developing the HGM classification system and the
field sampling methodology.
Wetland types differ in other areas, so their inclusion in
this procedure limits its use in its current form to the
southeastern coastal plain. Adaptation of the procedure
for use in other areas would require either extensive field
sampling as was performed in coastal North Carolina or
a more arbitrary clumping of wetland types based on
best professional judgment. Other methods of wetland
classification could be used, provided wetlands are clas-
sified in such a way that functional characteristics of the
wetland types are constant and can be determined by
field sampling, literature values, and/or professional
judgment. The procedure could be applied directly to
NWI polygons if these are the only wetland map base
available.
In addition to wetland type, several other parameters are
used as indicators of the existence or level of specific
wetland functions. These include both site-specific pa-
rameters, such as wetland size and soil characteristics,
and landscape considerations, such as watershed posi-
tion, water sources, land uses, and landscape patterns.
CIS analysis determines values for these parameters
based on the data layers discussed above. They could
be determined manually, but the process would be very
labor intensive.
Unlike assessment procedures that depend solely on
information that can be collected within a wetland, this
procedure relies heavily on factors external to the wet-
land site itself. Relationships between a wetland and the
landscape within which it exists are integral considera-
tions in determining wetland functional significance.
Characteristics of the landscape surrounding a wetland
are often more important determinants of its functional
significance than are the characteristics of the wetland
itself. Of the 39 parameters evaluated in the procedure,
21 are landscape characteristics, and 18 are internal
characteristics of the wetland itself.
While we believe this emphasis on a wetland's land-
scape context is a more ecologically sound approach to
functional assessment than site-specific methods, it re-
quires a great deal more information than could be
collected within the wetland itself. The procedure is
based on CIS data and analysis, not only to make it
suitable for regional application, but because CIS pro-
vides the most practical way to analyze the spatial rela-
tionships of landscape elements and their properties.
Structure of the Assessment Procedure
The assessment procedure uses a hierarchical structure
that rates individual parameters and successively com-
bines them to determine the wetland's overall functional
significance. The complete hierarchical structure is illus-
trated in Figure 2. It consists of four levels:
• Overall functional significance of the wetland.
• Specific functions and risk of wetland loss.
• Subfunctions.
• Parameters evaluated to determine the level and ex-
tent of functions.
The objective of functional assessment is to determine
an individual wetland's ecological significance in its wa-
tershed and the larger landscape. The highest hierarchi-
cal level, or end result of applying the procedure, then,
is the wetland's overall functional significance.
The second hierarchical level includes the four primary
factors that are considered in determining the wetland's
functional significance (see Figure 3). The overall eco-
logical significance of a wetland is determined by the
degree to which it performs, or has the capacity to
perform, specific functions. The broadest grouping of
wetland functions includes water quality functions, hy-
drologic functions, and habitat functions. The nature of
the landscape and the water characteristics of the wa-
tershed in which a wetland functions also determine
ecological significance to some extent. These factors
determine the potential risk to watershed and landscape
integrity if the wetland functions were lost. Including a
"risk factor" as a basic consideration in functional as-
sessment also provides a means of considering cumu-
lative impacts and the practicality of replacing lost
functions through mitigation in determining a wetland's
overall significance.
Each primary function of wetlands is actually a combi-
nation of separate, more specific subfunctions. Water
quality subfunctions include the removal of nonpoint
source pollutants from surface runoff and the removal of
suspended or dissolved pollutants from flooding
streams. Hydrology subfunctions include storage of pre-
cipitation and surface runoff, storage of floodwater from
streams, and shoreline stabilization. Habitat subfunc-
tions include providing habitat for both terrestrial species
and aquatic life. Several considerations that, while not
truly wetland functions, are called subfunctions for par-
allelism also determine risk factor. The subfunction lev-
els of the assessment procedure are illustrated in
Figures 4 through 7.
Properties of the wetland and its surrounding landscape
determine the extent to which a wetland performs these
different subfunctions. The assessment procedure re-
fers to these properties as "parameters." Parameters
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Overall Significance
1
Functions/Risk III I
Subfunctions • I I • I I •••it
_ iiiiiiihiiiiii i "i MIII'J iiiii
Figure 2. Overall hierarchical structure of the functional assessment procedure.
Wetland's Overall
Ecological
Significance
Hydrology
Functions
Habitat
Functions
Risk
Factor
Figure 3. Assessment level two: Primary wetland functions and risk factor.
Water Quality
Functions
Nonpoint Source
Function
Floodwater
Cleansing Function
Figure 4. Water quality subfunctions.
make up the levels in the hierarchical structure that are
actually evaluated based on fundamental ecological
considerations. Parameter values, in turn, are combined
to produce ratings for the subfunctions. Future reports
will explain in detail all parameters evaluated in the
assessment procedure and document them for scientific
validity. This paper discusses only the parameters under
the nonpoint source removal subfunction of the water
quality function for illustration (see Figure 8).
The first parameter determining a wetland's significance
in removing nonpoint source pollutants from surface
runoff water is whether the water contains sediment,
nutrients, or toxic pollutants in significant quantities. This
is evaluated in the "proximity to sources" parameter
based on the land uses surrounding the wetland. If
agricultural fields or developed areas from which pollut-
ants are likely to enter surface runoff largely surround
the wetland, the wetland's potential for removing non-
point source pollutants is high. If, on the other hand,
natural vegetation from which runoff water is likely to be
largely unpolluted mostly surrounds the wetland, its po-
tential for removing significant pollutants is low.
Proximity to sources is an "opportunity" parameter. That
is, it determines whether a wetland has the opportunity
to remove pollutants from surface runoff by considering
how likely the runoff water is to be polluted. The other
parameters for this subfunction are "capacity" parame-
ters that measure the wetland's ability to perform the
function if the opportunity is present. Opportunity and
capacity parameters are treated differently in determining
a wetland's overall significance to prevent a wetland from
being rated lower simply because present opportunity
does not exist. This is discussed in more detail below.
The second parameter considered in determining a wet-
land's significance in nonpoint source removal is its
proximity to a surface water body. If runoff entering a
wetland would otherwise directly enter surface water,
the wetland's significance as a filter is greaterthan if the
wetland is far removed from surface water. In that case,
pollutants in runoff could either settle out or be removed
by other means before they enter surface water as
pollutants.
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Surface Runoff
Storage
Hydrology
Functions
Floodwater
Storage
Shoreline
Stabilization
Figure 5. Hydrology subfunctions.
Habitat
Functions
Terrestrial
Wildlife
Aquatic
Life
Figure 6. Habitat subfunctions.
Risk
Factor
Landscape
Character
Water
Characteristics
Replacement
Difficulty
Restoration
Potential
Figure 7. Risk factor subfunctions.
Nonpoint Source
Function
Proximity to
Sources
Proximity to
Water Body
Watershed
Position
Site
Conditions
Wetland
Type
Soil
Characteristics
Figure 8. Parameters evaluated under nonpoint source pollutant removal subfunction.
10
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The third parameter is the position of the wetland in its
watershed. Several studies have documented that
headwater wetlands are most effective in removing non-
point source pollutants (9-11). Thus, the higher in its
watershed a wetland is located, the higher is its signifi-
cance in nonpoint source removal.
Two subparameters, wetland type and soil charac-
teristics, determine the value of the fourth parameter,
site conditions. By virtue of their typical microtopogra-
phy, hydrology, and vegetative structure, some wetland
types more effectively retain and filter surface runoff
than do other types. Some soil series are more effective
than others in retaining and chemically transforming
pollutants. Each subparameter is rated, and their com-
bined values produce a rating for the site conditions
parameter.
A similar evaluation of specific parameters is performed
to derive significance ratings for other wetland subfunc-
tions. In all cases, CIS analysis determines parameter
values based on the data layers described above. Some
parameters, such as wetland type in the nonpoint source
illustration, are surrogates or indicators of other wetland
properties that actually determine the wetland's func-
tional capacity. The limitations of CIS data and tech-
niques necessitate the use of indicator parameters.
Evaluation Procedure
The objective of the assessment procedure is to deter-
mine an individual wetland's ecological significance in
the watershed in which it exists. Ecological significance
is divided into three broad classes (high, medium, and
low) rather than attempting to derive a specific numerical
"score." This is partly because of the procedure's initial
application in an EPA ADID project performed by DCM
in Carteret County, North Carolina. Standard ADID pro-
cedure is to classify wetlands into three groups:
• Areas generally unsuitable for the discharge of
dredged or fill material.
• Areas that require a project-by-project determination.
• Possible future disposal sites for dredged or fill material.
These groups correspond to the H, M, and L used in the
assessment procedure.
The approach of classifying wetlands into three broad
functional significance classes is also used, however,
because it is feasible with our current understanding of
wetland function. Attempting to assign a specific value
along a numeric continuum of functional significance
greatly exaggerates the precision with which we can
realistically apply current knowledge. The three signifi-
cance classes used in the assessment procedure pro-
vide the information necessary to meet the procedure's
objectives without going beyond the realm of reasonable
scientific validity.
As explained above, the basic evaluation is performed
at the parameter level. An H, M, or L value is assigned
to each parameter as it relates to the performance of the
wetland subfunction being considered. For example, if
the soils underlying a wetland have properties that are
highly conducive to the function being considered, the
soil characteristics parameter is rated H; if soil proper-
ties are less conducive to performing the function, the
parameter is rated M; and if soil properties are not at all
conducive to the function, the parameter is rated L. All
individual parameters under a given subfunction receive
similar ratings.
The individual parameter ratings are then combined to
give an H, M, or L rating for each subfunction. The
subfunction ratings are combined into a rating of the
wetland's significance in performing each of the primary
wetland functions. Finally, the ratings for primary func-
tions are combined into an overall rating of the wetland's
functional significance.
The process of successively combining ratings up the
structural hierarchy is the most complex aspect of the
assessment procedure. The combining, as well as the
evaluation of individual parameters, is based on funda-
mental ecological principles about how wetlands and
landscapes function. Because the ecological processes
themselves interact in complex ways, combining ratings
is much more complex than a simple summation of
individual ratings. Some parameters are normally more
important than others in determining the level at which
a wetland performs a specific function and, thus, must
be weighed more heavily in determining the combined
value. In some cases, different combinations of individ-
ual parameter ratings result in the same level of func-
tional significance. Each possible combination of
parameters must then be considered.
The automated version of the assessment procedure
maintains all individual parameter ratings and combina-
tions in a database. Because the combining process is
complex, the reason a wetland receives an overall H, M,
or L rating may not be intuitively obvious. The database
makes it possible to trace through the parameter, sub-
function, and primary function ratings that result in a
wetland's overall rating.
This database also allows consideration of specific wet-
land functions individually. For example, in a watershed
targeted for nonpoint source pollution reduction, one
management objective may be to give the highest level
of protection to wetlands most important in performing
this function. The database allows examination of each
wetland for its significance in nonpoint source removal
and production of a map of wetlands rated according to
their significance for this single function.
Individual function ratings in the database can also
be used to improve planning, impact assessment, and
11
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mitigation for development projects that affect wetlands.
If alternative sites are available, such as alternative
corridors for a highway, the alternative with the least
impact on the wetland function considered most impor-
tant in the watershed can be identified. Rather than
simply minimizing acres of wetland impact, the objective
would be to minimize impacts to the most important
wetland functions. Environmental assessment of wet-
land impacts can identify specific functions to be lost.
Mitigation can be improved by giving priority to sites with
the highest potential for performing the same functions.
Future reports will explain detailed procedures for evalu-
ating individual parameters and combining them into
functional ratings. This paper illustrates only the water
quality nonpoint source removal subfunction. The rating
system for this subfunction is summarized in Figure 9.
Four parameters are evaluated to determine the signifi-
cance of the nonpoint source removal subfunction.
Because (d), the site conditions parameter, has sub-
parameters below it, it is first evaluated using a relatively
simple procedure. If conditions typical of the wetland
type and characteristics of the underlying soil are both
highly conducive to removal of pollutants in runoff water
entering the wetland, the site conditions parameter is H.
If either the wetland type or the soil is not at all conducive
to pollutant removal and the other subparameter is no
more than somewhat conducive, the site conditions pa-
rameter is L. Any other combination results in an M.
Parameters
(a) Proximity to Sources
(b) Proximity to Surface Water
(c) Watershed Position
(d) Site Conditions
(1) Wetland Type
(2) Soil Characteristics
Evaluation Procedures
Site Conditions
H Both Parameters H
M Other combinations
L One parameter L and neither H
NPS Subfunction
H (a) & (b) H and (d) at least M or
(c) & (d) H and (b) at least M
M Other combinations
L Two of (b), (c), & (d) L
Figure 9. Parameters evaluated under nonpoint source pollut-
ant removal subfunction.
Following evaluation of all parameters, they are com-
bined to evaluate the significance of the wetland in
removing nonpoint source pollutants. Two combinations
result in the wetland being evaluated as highly signifi-
cant in performing this function. First, if the wetland is
adjacent to both a significant source of polluted runoff
(a = H) and a permanent surface water body into
which the runoff would flow if the wetland were not
there (b = H), and has site conditions that are at least
reasonably efficient in catching, holding, and removing
pollutants from the runoff (d at least M), it receives an
H. Alternatively, even if the wetland is not adjacent to a
pollutant source, it receives an H if it is in the headwaters
of the watershed (c = H), site conditions are highly
conducive to pollutant removal (d = H), and it is at least
close to an intermittent stream (b at least M).
On the other hand, if any two of parameters (b), (c), and
(d) are evaluated L, the significance of the wetland for
nonpoint source pollutant removal is Low. That is, the
wetland is evaluated as L for this function if any of the
following conditions exist:
• The wetland is not close to surface water (b = L) and
downstream in the watershed (c = L).
• The wetland is not close to surface water (b = L), and
its site conditions are poor for pollutant removal (d = L).
• The wetland is downstream in the watershed (c = L)
and has poor site conditions (d = L).
Any combination of parameter evaluations other than
those resulting in an H or L results in the wetland being
evaluated as of moderate significance for removing non-
point source pollutants. This example is typical of evalu-
ation procedures used for all subfunctions. More often
than not, the evaluation procedures are complex and
multifarious in their reasoning and application. Hope-
fully, though, they are scientifically valid based on cur-
rent knowledge of wetland ecology.
Opportunity and Capacity
The concepts of opportunity and capacity for a wetland
to perform a given function were briefly discussed
above. For a wetland to actually perform a function, it
must have both the opportunity and the capacity for the
function. In terms of the nonpoint source example, a
source of potentially polluted runoff must enter the wet-
land to provide an opportunity, and the wetland must
have the internal capacity to hold the runoff and remove
the pollutants before releasing the water. Factors exter-
nal to the wetland usually determine the opportunity to
perform a function, while properties of the wetland itself
along with its landscape position determine the capacity
to perform the function.
Because the assessment procedure is a landscape
scale procedure that evaluates the functions a wetland
12
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performs in relation to its surroundings, essentially every
subfunction includes opportunity parameters. A func-
tional assessment that is too heavily dependent on op-
portunity parameters, however, is static and rapidly
becomes invalid as land uses change. A wetland that is
bordered by natural forest today can be bordered by a
young pine plantation or a subdivision under construc-
tion by next year. The fact that a wetland does not have
the opportunity to perform certain functions today does
not mean that it will not have the opportunity in the
future. If an assessment of wetland significance is to
remain valid overtime in a landscape subject to change,
opportunity parameters alone cannot be determinative.
The evaluation procedure for the nonpoint source sub-
function explained above is an example of how the
assessment procedure handles this situation. The op-
portunity for a wetland to receive polluted runoff water
from surrounding lands (a = H) can result in an evalu-
ation of H for this subfunction if other properties are also
present, but it does not have to be present for a wetland
to be evaluated H. Other parameters (c and d = H, and
b at least M) that give a wetland a high capacity to
remove nonpoint source pollutants can also result in an
H. Conversely, lack of present opportunity (a = L) does
not result in an evaluation of low significance for this
function. At least two of the other parameters must be L
for the wetland to be evaluated as L.
These conventions hold throughout the procedure. A
present high opportunity to perform a function can result
in an evaluation of high significance for the function, but
high capacity can also result in an H evaluation even if
present opportunity is lacking. Lack of present opportu-
nity alone never results in an evaluation of low signifi-
cance for a function. High opportunity is treated
essentially as a "bonus" consideration that can result in
a higher evaluation for a wetland than its capacity alone
would indicate but that will never result in a lower evalu-
ation because of its absence.
Overriding Considerations
Several considerations are of such importance in the
North Carolina coastal area that their presence alone
will result in a wetland evaluation of high significance.
These parameters are evaluated first as either true or
false, and if one or more of them is true, the rest of the
evaluation procedure is not performed.
The first overriding consideration is whether the wetland
is a salt or brackish marsh meeting the definition of
"coastal wetland" as set forth in North Carolina statutes
(NCGS 113-229(n)(3)) and rule (NCAC 7H .0205(a)).
Coastal wetlands in North Carolina are designated by
law as highly significant. Consequently, the assessment
procedure evaluates them automatically as H and in-
cludes no considerations for differentiating among the
functional significance of these wetland types.
The second overriding consideration is whether the wet-
land is adjacent to an officially designated primary nurs-
ery area (PNA). All designated PNAs are included in
"areas of environmental concern" in the NC CMP and
are protected by a specific set of regulations. They are
areas where initial postlarval development of finfish and
crustaceans takes place and, thus, are critical to estu-
arine fish and shellfish populations. Wetlands adjacent
to PNAs are highly important in maintaining water qual-
ity and appropriate salinity gradients in these critical
areas and are automatically evaluated as of high func-
tional significance.
The third overriding consideration is whether the wet-
land contains threatened or endangered species. If a
known threatened or endangered plant or animal spe-
cies on either federal or state lists is present, the wetland
is evaluated as highly significant. The determination is
based on information obtained from the North Carolina
Natural Heritage Program.
The fourth overriding consideration is whether the wet-
land includes all or part of a critical natural area as
designated by the North Carolina Natural Heritage Pro-
gram. If so, the site is considered of high significance.
CIS data layers maintained by the Natural Heritage
Program also help make this determination.
Verification
Throughout the development and initial application of
the assessment procedure, we have checked and veri-
fied its validity. Parameter evaluations and combination
procedures are based on the best wetland science avail-
able in the scientific literature. The validity and accuracy
of the CIS databases used to apply the procedure
have been verified to the extent possible. Following sec-
tions of this report fully document any assumptions
made about wetland ecology, CIS data, or CIS analytical
techniques.
An advisory panel of wetland scientists familiar with the
wetlands of coastal North Carolina and representatives
of several state and federal wetland-related agencies
reviewed every step of the procedure's development.
While their review does not represent an endorsement
of the procedure or its results by the agencies or indi-
viduals included, it does indicate the level of peer review
the procedure has received.
During development of the procedure, field visits were
made to nearly 400 wetland sites to gather data on func-
tional indicators. On these same site visits, a field-based
functional assessment procedure, the Wetland Rating
System developed by the North Carolina Division
of Environmental Management, was applied. This pro-
vides the basis for a field verification of the assessment
procedure.
13
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Discussion
As we continue to understand more about the role of
wetlands in maintaining a healthy environment, the use-
fulness of wetlands locational data continues to grow in
importance. Spatial data can assist county planners in
guiding development away from environmentally sensi-
tive areas. Landowners now have the capability to look
at a map and realize very quickly that wetlands exist on
a given area of land. In addition, economic development
councils can use this information to plan development in
areas attractive to a particular industry. If a new busi-
ness or industry wishes to locate in an area positioned
such that the wetlands permitting process could be
avoided, maps showing lands void of wetlands could be
a significant tool to the economic development council.
The representation of these wetlands' ecological signifi-
cance dramatically increases the utility for these data.
While paper maps can be distributed to all interested
parties, digital data also are available to public agencies
who have CIS capabilities. In Carteret County, for exam-
ple, a publicly installed workstation will be made avail-
able with these data installed. The county government
will be able to view wetlands in the context of cadastral
boundaries that already are on CIS. Information about
sensitive resources made available prior to any devel-
opment will, hopefully, lead development away from
environmentally sensitive areas.
References
1. Hefner, J.M., and J.D. Brown. 1985. Wetland trends in the south-
eastern United States. Wetlands 4:1-12.
2. Dahl, I.E. 1990. Wetlands losses in the United States 1780s to
1980s. U.S. Department of the Interior, Fish and Wildlife Service,
Washington, DC.
3. DEM. 1991. Original extent, status, and trends of wetlands in
North Carolina. Report No. 91-01. North Carolina Department of
Environment, Health, and Natural Resources, Division of Envi-
ronmental Management, Raleigh, NC.
4. DCM. 1992. Final assessment of the North Carolina coastal man-
agement program. Report to the Office of Ocean and Coastal
Resource Management, NOAA, U.S. Department of Commerce,
performed under the Coastal Zone Enhancement Grants Pro-
gram (January 10). Section 309, CZMA, North Carolina Division
of Coastal Management, Raleigh, NC.
5. DCM. 1992. Final strategy for achieving enhancements to the
North Carolina coastal management program. Proposal to the
Office of Ocean and Coastal Resource Management, NOAA, U.S.
Department of Commerce, performed under the Coastal Zone En-
hancement Grants Program (March 25). Section 309, CZMA,
North Carolina Division of Coastal Management, Raleigh, NC.
6. Hefner, J.M., and K.K. Moorhead. 1991. Mapping pocosins and
associated wetlands in North Carolina. Wetlands 2(Special Is-
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7. Brinson, M.M. 1993. A hydrogeomorphic classification for wet-
lands. U.S. Army Corps of Engineers Waterways Experiment Sta-
tion Report WRP-DE-4. Washington, DC.
8. Khorram, S., H. Cheshire, K. Siderelis, and Z. Nagy. 1992. Map-
ping and CIS development of land use/land cover categories for
the Albemarle-Pamlico drainage basin. Report No. 91-08-NC.
Raleigh, NC: North Carolina Department of Environment, Health,
and Natural Resources.
9. Leopold, L.B. 1974. Water: A primer. San Francisco, CA: WH.
Freeman and Co.
10. Whigham, D.F., C. Chitterling, and B. Palmer. 1988. Impacts of
freshwater wetlands on water quality: A landscape perspective.
Environ. Mgmt. 12(5):663-671.
11. Novitski, R.P. 1979. The hydrologic characteristics of Wisconsin
wetlands and their influence on floods, streamflow, and sediment.
In: Greeson, P.E., J.R. Clark, and J.E. Clark, eds. Wetland func-
tions and values: The state of our understanding. Lake Buena
Vista, FL: American Water Resources Association.
14
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A GIS Strategy for Lake Management Issues
Michael F. Troge
University of Wisconsin, Stevens Point, Wisconsin
Abstract
Lake management plans are crucial to the sustained life
of a lake as it experiences pressures from human as well
as environmental activities. As proven in the past, geo-
graphic information systems (GIS) can meet the needs
of most if not all environmental entities. Applying GIS to
lakes and lake management, however, is a fairly new
concept because most previous work focused on the
terrestrial realm. Future studies must address problems
relating to dimension, but adopting certain methods (i.e.,
cross-sectional coverages) can help lake managers
plan for critical lake issues. By using sufficiently planned
coverages, lake quality data management coverages
can increase storage and/or analysis efficiency. After
evaluating certain management criteria, a lake manage-
ment plan can be derived and set up as a coverage.
These criteria can then correspond collectively to form
management zones within a lake. Each of these zones
has its own set of management goals to which all lake
users must strictly adhere.
Introduction
The importance of maintaining lake quality has long
concerned recreationalists and ecologists. The multifac-
eted interrelationships of the lake environment, how-
ever, usually make proper assessment and analysis of
lake quality information difficult. Over the last decade,
assessment has become easier due to the increased
use and acceptance of geographic information systems
(GIS). This computer-based tool has allowed successful
integration of water quality variables into a comprehen-
sible format.
One area of the environmental sciences that has neglected
GIS is lake management. This paper presents an alternative
method for using a traditional two-dimensional GIS for
viewing, querying, and displaying three-dimensional in-
formation—in this case, lake quality information and
lake management criteria. Lakes, unlike geologic enti-
ties, offer a three-dimensional realm that humans can
fully penetrate without a great amount of effort. Lakes
also contain a complete aquatic environment of physi-
cal, chemical, and biological entities that humans can
effectively observe and analyze. This paper does not dis-
cuss the issue of dimension; however, future studies, pri-
marily those relating to the creation of three-dimensional
GIS, should address this issue.
GIS allows incorporation of a multitude of environ-
mental variables (e.g., water chemistry, geologic
strata) into a synergism of the many coexisting vari-
ables of the lake environment. The ability of a GIS to
"capture, manipulate, process, and display spatial or
georeferenced data" is now well known and accepted
(1). Surprisingly though, GIS is rarely used for lake
management databases and associated water quality
analysis.
The few examples that exist include Schoolmaster's
Texas Water Development Board System (2), which
examined water use on a county basis, and RAISON
GIS (3), which is an expert computer system imple-
menting proper application of hydrologic principles to
a particular lake. Many other systems are simply data-
base collection storehouses of lake information, such as
the Galveston Bay National Estuary Program (4).
One notable example is the LAKEMAP program (5).
This extensive and comprehensive GIS spans the
entire United States covering approximately 800,000
lake sampling sites from the U.S. Environmental Pro-
tection Agency's (EPA's) STORET system. LAKEMAP
uses both a database management and mapping dis-
play system, allowing retrieval of information for spe-
cific sites or aggregation of regional areas. This GIS
is unique because it examined the creation of stand-
ards that could be used across the country in data-
base development and the presentation of that data.
Using GIS in a lake management study inspires many
questions because of the lack of existing research and
the absence of any true standards. For example, how
should one create a lake quality database for general
purpose management? Is visualizing the integration of
several variables within the lake ecology possible? Can
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one examine temporal changes in pH? Many technical
and logistical CIS questions therefore existed when the
Legend Lake study began.
Background of the Legend Lake Study
Legend Lake (see Figure 1) is located in Menominee
Reservation, which is in Menominee County in north-
eastern Wisconsin. Legend Lake is a 1,230-acre im-
poundment comprising eight natural drainage lakes that
a single stream once connected. In the late 1960s, a
plan was introduced to convert this area into an im-
poundment/recreational area, and construction soon be-
gan. The ecology and hydrology had not been seriously
evaluated since the development was finalized, hence
the Legend Lake project was designed in cooperation
with EPA, Wisconsin Department of Natural Resources
(WDNR), Menominee Reservation and County, Legend
Lake District, and the Legend Lake Property Owners
Association. This intensive study spanned the qualita-
tive and quantitative aspects of surface water, ground
water, sediment, and aquatic plants, as well as human
influences on the Legend Lake watershed and sur-
rounding areas.
Figure 1. Legend Lake with basin identifiers.
Initially, questions needed to be answered regarding
aquatic plants and sediment and their influences on lake
management strategies. Because the scope of the Leg-
end Lake study included subjects such as surface- and
ground-water chemistry, land use and development,
septic system impacts, and recreational stress, all these
factors needed to be considered in determining optimum
management strategies. In addition, the study ad-
dressed the question of GIS's ability to alleviate some
technical aspects of deriving and presenting a lake man-
agement plan. Given these questions, the goal was to
integrate collected data into a CIS database to create a
prototype standard for future lake studies, as well as to
present new techniques for visualization and analysis of
lake quality data.
CIS/Database Creation
Several techniques were used to best manage the data
for a three-dimensional system: cross-sectional cover-
ages (described later), data summary coverages, and
multidate coverages. The latter two coverage types are
traditional coverages that contain general lake informa-
tion excluding water column data. These techniques
work fairly well for data storage and visualization; how-
ever, they were not sufficient for determining the geo-
graphic areas within the lake that required intensive
management decisions as opposed to areas that
needed little attention.
During formulation of a new coverage design, the study
focused on the littoral zone, which is usually defined as
that area of the lake with a depth of 15 feet or less. Most
management concerns deal with the littoral zone be-
cause most recreational and ecological activities occur
in this zone. One of the most pressing issues in lake
management is aquatic plant growth. The fact that most
aquatic plant growth is confined to the littoral zone rein-
forced the decision to use the littoral zone as the primary
sink for potential management decisions.
To curtail the dimensional problem, depth was basically
ignored. This allowed for easier delineation of areas
within the lake. This, in turn, facilitated classifying areas
into management zones to implement varying degrees
of activity, ranging from casual to intensive efforts. Thus,
management recommendations for a particular zone
were the same at a depth of 2 feet as at a depth of 12
feet. This greatly reduced complication of the model and
centered effort on the areal extent of the lake. It also
facilitated visualization of management decisions by
professionals and lay people.
Problems can arise when combining depths for man-
agement considerations, as this technique did. For ex-
ample, a littoral zone that contains a gentle slope usually
does not receive the same attention as a littoral zone
with a very abrupt slope because the littoral zone with
the gentle slope contains more area. Thus, if a lake
manager recommends restricting boat traffic in a man-
agement zone (a section of the littoral zone) that has a
gentle slope to promote wildlife habitat, the amount of
area available for boaters would decrease significantly.
In this situation, primary activity would be most crucial
in shallow areas where wildlife or waterfowl predominate
rather than in more open water areas where boaters
predominate. Situations like these may require the crea-
tion of management subzones when setting up a lake
management CIS and database to increase efficiency
of lake area use and increase support by lake users.
Many criteria can affect the decisions made for a
management zone. For instance, if the lake manager
recognizes excessive, unhealthy weed growth in a par-
ticular zone, the lake manager may recommend exten-
sive weed harvesting to neutralize the situation. An
adjacent zone may have very little weed growth and
may not require weed harvesting. Criteria such as these
must be recognized before constructing the CIS. Table 1
lists some common criteria to consider. Generally, man-
agement criteria include anything that is influent on the
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Table 1. Criteria To Consider When Creating Lake
Management Plans
Environmental Variables
Artificial Variables
Aquatic plants
Sediment
Ground water
Surface water
Wildlife/waterfowl
Fisheries
Climate
Wind
Geology/geography
Adjacent natural land cover
Natural nutrient loading
Hydrologic characteristics
Adjacent land use
Septic systems
Development
Construction sites
Fuel leaks
Shoreland zoning
Population density
Primary lake use (recreational)
Nutrient loading
Visitor use
shoreline and lake itself and any influence the lake has
on the shoreline or adjacent shorelands.
A primary concern when accessing lake data from a
computer database is being sure to query the correct
lake. For example, searching a database for all data on
"Sand Lake" would be a legitimate action, except that
the database may include close to 200 lakes with the
name of Sand Lake. This is one of the main reasons why
the WDNR developed a system known as the Water
Mileage System (6). Based on logical criteria, each
water body (e.g., lakes, streams, sloughs) receives a
unique six- or seven-digit number called the waterbody
number. Thus, if the number 197900, assigned to Sand
Lake near Legend Lake, is the query subject, then the
output should include all data for this particular Sand
Lake. Because having a unique identifier for each spe-
cific entity in a CIS database is ideal, the waterbody
number was used, and all sampling performed on this
lake will be linked with this number.
Examples of Types of Coverage
Management Zone Coverages
Figure 2 and Table 2 together show how a potential lake
management CIS and plan might work. The lake man-
ager can easily manage and frequently update this sys-
tem if necessary, or the system can serve as a long-term
plan to consult for all decision-making. A plan of this sort
specifically emphasizes areas that need intensive man-
agement over areas that may need frequent monitoring.
It provides specific instructions for plan implementation,
leaving little guess-work forthe manager. This technique
is also visually informative to the lake user because the
user can easily discern areas of concern. This example
is hypothetical, but a plan is being formulated based on
the information collected during the Legend Lake study.
Cross-Sectional Coverages
Another technique currently included in the Legend
Lake study entails the z dimension. Cross-sectional
views of each lake basin, derived from 1992 lake con-
tour maps, provided a more detailed description of the
lake bottom. These cross sections were then digitized
and transformed into CIS coverages. For each lake
basin, 22 tests were conducted on the deepest part of
the lake at several different depth intervals along the
water column. These data provided valuable information
on the way various chemical and biological attributes
react to depth. Using CIS, a point could represent each
depth where data were collected. These points could
actually act as labels for polygons based on depth.
For example, if performing a series of analyses on a lake
(maximum depth of 10 feet) at 3-foot, 5-foot, and 8-foot
depth intervals, the labels on the cross-sectional cover-
age would be placed at these respective depths. Thus,
labels would be positioned at depths of 3, 5, and 8 feet.
Because these labels represent cross-sectional poly-
gons, the 3-foot depth label may represent a polygon
with boundaries at 0 feet and 4 feet. The 5-foot depth
label may represent a polygon with boundaries at 4 feet
and 6 feet, and the 8-foot depth label may represent a
polygon with boundaries at 6 feet and the lake bottom
(10 feet). Those who are familiar with lake ecology
understand that no clear-cut boundaries distinguish
where chemical values jump from one measurement to
another without a gradual transition. All users of a lake
quality CIS must be made aware of these types of
inaccuracies (see Figure 3).
Figure 2. Hypothetical management zones for a section of Legend Lake that correspond to the management plans in Table 2; black
areas indicate depths greater than 15 feet.
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Table 2. Hypothetical Management Plans for a Section of Legend Lake (see Figure 2)
Management Zone Management Plan (brief explanations)
I
IV
V
VI
VII
VIII
IX
X
XI
XII
XIII
XIV
XV
High recreation area, high plant growth, frequent harvesting; frequently monitor water quality
Moderate recreation; manage for fish habitat
Moderate recreation; manage for fish habitat
Open water, high recreation/possible fish habitat; consider subzoning
High-grade wildlife habitat; restrict human contact
Moderate recreation; manage for fish habitat
Open water, high recreation, increased shoreland development; frequently monitor water quality
Adjacent to high recreation area, possible fish habitat; manage for fish and aquatic habitat
Adjacent to high recreation area, shoreland development; monitor water quality
Increased development; frequently monitor water quality
aPrime wildlife/waterfowl habitat adjacent to high recreation area; restrict human presence (hot spot)
Open water, high recreation area; frequently monitor water quality
Open water, high recreation area, possible fisheries and wildlife habitat; consider subzoning
Excessive aquatic plant growth (species listed) choking out preferred species; potential wildlife/waterfowl
habitat, fisheries potential; continual harvesting; restrict human presence
High-grade slope with little plant growth, potential for increased sedimentation; monitor shoreland development
Critical area between good habitat and high recreation area; monitor extensively.
Figure 3 shows a cross section from one of the larger basins,
Basin F, in the Legend Lake system (see Figure 1). The
shades of gray represent ranges of temperature, with the
lightest being the coldest and the darkest being the
warmest. The thermocline can be located roughly in the
middle. Each colored section represents a depth range
where certain chemical attributes were collected. Using
ARC/VIEW, the user can choose these areas with a
pointer (mouse) and gain access to the database that
contains all the sample results for this depth range.
Conclusion
These ideas are still preliminary as the Legend Lake
study analysis concludes. Clear-cut discussions and
recommendations will become available at a later date,
although some observations can be made at this point.
First, future research should focus on creating and de-
veloping a three-dimensional CIS, not to be confused
with three-dimensional or cartographic models. Ideally,
lake management plans should consider depth. This
paper did not include depth because of the dimensional
factor. Depth was not compatible with ourtwo-dimensional
CIS, and the presentation quality was not sufficient to relay
our results in the form of management zones. We must
develop a three-dimensional CIS to address the problems
of the three-dimensional environment in which we live.
Lastly, maps are the best form for communicating this
information to professionals and the public. Maps can
also easily confuse people, however. Proper carto-
graphic techniques are a necessity (7). Significant effort
must be devoted to map creation to ensure a successful
plan and successful relationships between lake manag-
ers and lake users. CIS and map-making are closely
related. Both the planning stages and the database
development phase of the lake quality CIS should em-
phasize this point. At an early stage of the process,
management criteria should be determined, and all play-
ers or potential players must be included. A poorly
planned project can lead to a failed CIS.
Creativity may offer new ideas in map development. For
instance, animation (8) has some unique traits. Trend
analysis using animation may produce the best visual
results. Techniques such as these augment our methods
of communication, and some are very revolutionary.
Remember, however, that cartographic principles still
must apply.
Acknowledgments
I would like to thank Dr. Keith Rice of the University of
Wisconsin, Stevens Point, for his review efforts as well
as Dr. Byron Shaw and Steven Weber for their technical
support on lake management issues. Also, I would like
Figure 3. Cross section from one of the larger basins, Basin F,
in the Legend Lake system.
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to thank members of the University of Wisconsin,
Stevens Point, Geography/Geology Department for their
support and for the use of their equipment.
References
1. Fedra, K. 1993. Models, GIS, and expert systems: Integrated
water resources models. Proceedings of the HydroGIS '93: Appli-
cation of Geographic Information Systems in Hydrology and Water
Resources Conference, Vienna. International Association of Hy-
drological Sciences Publication No. 211.
2. Schoolmaster, F.A., and P.G. Marr. 1992. Geographic information
systems as a tool in water use data management. Water Re-
sources Bull. 28(2):331-336.
3. Lam, D.C.L., and D.A. Swayne. 1993. An expert system approach
of integrating hydrological database, models and CIS: Application
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cation of Geographic Information Systems in Hydrology and Water
Resources Conference, Vienna. International Association of Hy-
drological Sciences Publication No. 211.
4. Rifai, H.S., C.J. Newell, and P.B. Bedient. 1993. CIS enhances
water quality modeling. CIS World 6(8):52-55.
5. Samuels, WB. 1993. LAKEMAP: A 2-D and 3-D mapping system
for visualizing water quality data in lakes. Water Resources Bull.
29(6):917-922.
6. Fago, D. 1988. Retrieval and analysis system used in Wisconsin's
statewide fish distribution survey, 2nd ed. Report 148. Wisconsin
Department of Natural Resources Bureau of Research.
7. Ahner, A.L. 1993. Modern cartography plays essential role in GIS.
CIS World 6(10):48-50.
8. Loucks, D.P. 1993. Interactive multimedia, GIS, and water re-
sources simulation. Proceedings of the HydroGIS '93: Application
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logical Sciences Publication No. 211.
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Sustainable Developments: Definition, Location, and Understanding
Michael E. Troyer, Ph.D.
USEPA, ORD, NRMRL
ABSTRACT
"Sustainable development" is an important concept, but not well defined in an operational
context. In this study, sustainable development was ideally defined as a positive relationship
between ecological integrity and human welfare within a specific area over time. Watersheds
were chosen as the areal unit of interest because human welfare ultimately relies on the natural
resources and life support systems provided by ecological rather than political systems.
Furthermore, the physical boundaries of watersheds are unambiguous relative to many other
ecological systems and regions. Although no scientific consensus on defining and measuring
"ecological integrity" and "human welfare" exists, partial metrics for each were found in the
literature. Data constraints limited the study to one time period (1988-1994). Therefore,
"sustainable watersheds" were operationally defined as those with significant, above average
levels of ecological and human conditions relative to other watersheds. In the analysis, three
such watersheds were identified on the outskirts of a single metropolitan area and their
locations assisted in developing partial explanations about how their "sustainable" conditions
were achieved. Other watersheds with above average human conditions were also clustered
around metropolitan areas, but had only average or below average ecological conditions.
Watersheds with below average human conditions were all located in less accessible or
intensively farmed areas of Ohio. These results suggested that higher levels of well-being in
Ohio (i.e., in terms of educational attainment, employment, income, and lack of poverty), even in
"sustainable watersheds," were tied to metropolitan areas with connections to national and
global economies rather than isolated or "self-sufficient" economies based on local resources. In
other words, modern sustainability appears to depend more on maintaining open and accessible
economic systems rather than closed systems. Theoretically, economic relations between
different places (e.g., cities, watersheds, countries) may range from mutualistic to competitive
interactions, but mutualistic, along with cooperative and commensal relations, are preferred if
sustainable developments are meant to be spatially non-exclusive.
Key words: Sustainable Development, Ecological Integrity, Human Welfare or Well-Being,
Geographic Information System, Watersheds.
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Introduction
Areas on earth are capable of sustaining various durations, frequencies, intensities, spatial
scales and types of human activity. However, in many parts of the world, human activities
continue to over-tax natural processes; degrade aquatic, terrestrial, and atmospheric resources;
cause irretrievable losses of biological diversity; and increase uncertainty about the well-being
of current and future human populations (Vitousek et al. 1986, Goodland 1991, Wilson 1988,
Daly and Cobb 1989). In response, most member countries of the United Nations have pledged
to develop in a more sustainable way (Goodland, et al. 1991), but have not specified what this
means in an operational context.
Although perceived by some as a politically (or intentionally) vague concept, "sustainable
development" does call attention to the critical question of how humans can continue to thrive
on Earth in a more equitable and ecologically viable manner. Definitions of sustainable
development commonly begin with the "Brundtland Report" written by the United Nation's World
Commission on Environment and Development (WCED 1987).1 In 1987, the WCED defined
sustainable development as development that meets the needs of the present without
compromising the ability of future generations to meet their own needs. However, in the
literature, sustainable development embodies many other concepts, objectives, and constraints
such as those listed in Table 1.
The Research Problem
Systems theory suggests that political units (e.g., a particular city, state, or nation) may attempt
to maintain or improve human welfare and environmental conditions within their jurisdictions by
importing or exporting "sustainability," or by making substitutions for depleted local resources. In
other words, political units may increase their reliance upon external sources for energy,
information, or raw materials; transport their wastes and pollution beyond their boundaries;
and/or adopt new innovations in technology in order to sustain themselves over time. Thus,
theoretically, political units at various hierarchical levels may behave or compete in ways which
result in spatial variations of sustainable and unsustainable developments across the
landscape. However, few, if any, attempts have been made to map and analyze such spatial
1 The WCED was established in 1983 by the General Assembly of the United Nations for the
purpose of developing long-term, environmental strategies for achieving sustainable
development by the year 2000 and beyond.
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variations, particularly at sub-national scales or within the context of ecologically-relevant
systems.
Table 1. Examples of Concepts, Objectives, and Constraints
Commonly Found in Studies of Sustainable Development
Concepts
Objectives
Constraints
Survival of humans Biogeophysical system
Welfare of humans Technology
Satisfying needs
Ecological vitality
Biodiversity
Self-imposed laws,
regulations, taxes,
policies, and/or treaties
Economic development
(not "throughput" growth)
Equitable distribution
of wealth within and
between generations
Remaining supplies of
natural resource stocks
Environmental quality
Carrying capacity
Modified from. Braat and Steetskamp 1991
In this light, this research attempts to measure, detect, and understand spatial variations of
sustainable and unsustainable developments at a watershed scale. However, in doing so, it is
recognized that sustainable development remains a complex and difficult concept to quantify,
especially for researchers working within the theoretical confines of one discipline. Thus, an
interdisciplinary approach is used. Several limitations prevent any single researcher from
implementing such a definition. Most important, perhaps, is the lack of scientific consensus on
defining and measuring broad concepts like "ecological integrity" and "human welfare" (e.g.,
consider Figures 1 and 2). However, the literature does suggest several partial metrics which
can serve as a starting point. With the exception of remotely-sensed data, another limitation is
the relative paucity of comparative ecological data at regional scales over time. As a result, this
paper focuses on the relative difference between watersheds at one point in time. The questions
addressed include the following:
• Question 1: Which watersheds had above average ecological and socio-economic (i.e.,
"sustainable") conditions in the early 1990s?
• Question 2: Does the spatial pattern or location of these watersheds suggest how their
"sustainable" conditions were caused?
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Sustainable Development as a
Relationship Between Ecological Integrity and Human Welfare
The field of Ecology offers several terms for characterizing the relationship between two
organisms. For example, positive relationships may be mutual or cooperative, and negative
relationships may be parasitic or competitive. It is suggested here that these same concepts can
also be applied for defining a range of "sustainable" versus "unsustainable" conditions by
characterizing observable relations between ecological integrity and human welfare within
specific areas. In brief, relationships between co-existing or interacting species (or conditions of
ecological integrity and human welfare within a particular place) may be characterized as shown
in Table 2. In general, commensal, cooperative, and mutual relations can be thought of as
successive stages of relatively benign to symbiotic interactions or, in a sense, increasingly
"sustainable" relations. Neutral or non-interactions may exist between specific organisms, or
between humans and specific ecosystems, but neither humans nor ecosystems exist alone.
Finally, amensal, parasitic, and competitive interactions can be viewed as negative associations
which may lead to the ill-health or demise of one or more organisms or ecosystems if internal or
external resources cannot be found to sustain them.
It is important to note that the type of interaction occurring between organisms, or between
humans and their environment may change over time as conditions change or as they develop
through different stages. For example, Odum (1983, 395) suggests that "mutualism seems to
replace parasitism as ecosystems evolve toward maturity, and it seems to be especially
important when some aspect of the environment is limiting" (also see Bormann's 1985
discussion on redundancy in mature forests). In such cases, Odum implies that mutualism has a
strong selective advantage over other relations because it can lead to the emergence of more
resilient properties within a system. Such properties are important, as Arrow, et al. (1995) note,
because without ecosystem resilience: (1) discontinuous changes in ecosystem functions may
result in a sudden loss of biological productivity and a reduced capacity to support human life,
(2) irreversible changes may also occur to the set of options open for sustaining current and
future generations, and (3) changes from familiar to unfamiliar states will tend to increase
uncertainties associated with the environmental effects of economic activities.
Thus, if sustainable development is defined within a context of humans and other species
interacting within a larger biotic and abiotic environment, then an ecological argument can be
made for defining sustainable developments in watersheds, or other ecologically-relevant areas
as instances where mutual, cooperative, or commensal relations between human welfare and
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ecological integrity can be detected over time (Figure 3). In areas with lower human welfare and
higher levels of ecological integrity, nature may be managed as a preserve or be relatively
unaffected by human activity for various reasons such as lying within less navigable or less
economically productive terrain. Conversely, in areas where human welfare has increased or
remained high, and ecological integrity has decreased or remained low, humans will have likely
dominated the area in an amensal or predatory/parasitic manner. Competitive interactions,
where society and nature are both inhibited, can also happen and may occur in areas where
production has degraded or used-up all the local natural resources. From a systems point of
view, these latter three types of interaction result in areas becoming more dependent upon
external resources for sustaining high levels of welfare.
Table 2. Positive, Neutral, and Negative Interactions
Positive Interactions
Mutualism
Cooperation
Commensalism
Non-interactions
Neutralism
Negative Interactions
Amensalism
Predation/Parasitism
Competition
Source: Odum 1983.
Both benefit and become totally dependent on each other.
Both benefit, but are not dependent upon each other.
One benefits and the other remains unaffected or no worse off.
Neither affects the other.
One is inhibited, but the other remains unaffected or no worse off.
One benefits at the expense of the other.
Both inhibit each other directly or indirectly when a common
resource is limited.
Sustainable development, defined as positive interactions between society and nature, appears
to coincide with thoughts for a new paradigm of environmental management which suggests the
following definition for sustainability:
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Sustainability is a relationship between dynamic human economic systems and larger,
dynamic, but normally slower-changing ecological systems, such that human life can
continue indefinitely, human individuals can flourish, and human cultures can develop—
but also a relationship in which the effects of human activities remain within bounds so
as not to destroy the health and integrity of self-organizing systems that provide the
environmental context for these activities (Norton 1992).
However, by formally adding concepts of interaction, it appears that sustainable development
can be defined in a more testable or operational context as:
ecologically-relevant areas (e.g., watersheds, ecoregions, etc.) where ecological integrity
and human welfare both increase (i.e., exhibit mutual or cooperative relations) over time,
or where either the ecological or human welfare condition remains constant (but not in a
degraded state) while the other increases over time (i.e., a commensal relationship)
(Figure 3).
In instances where temporal studies are not possible (as in this study), sustainable
developments may also be defined in a relative sense as:
ecologically-relevant areas with significant, above average levels of ecological integrity
and human welfare with respect to other comparable areas at a specific point in time
(Figure 4).
Conceptual Model and Data
The entire state of Ohio was used as a study area in order to obtain an adequate sample size of
watersheds for statistical analysis. The state of Ohio was also chosen to limit any potential
travel or data collection costs, and because aquatic biological data, not commonly found in other
states, was available. Furthermore, Ohio contains a variety of ecological, economic, cultural,
and physical features which combine to make a diverse geography (Peacefull 1996, Omernik
1987) and thus, a likely landscape for finding variations in both human and ecological conditions
in watersheds across the state.
Watersheds became the ecologically-relevant areal units of interest in this study because: (1) no
consensus on a standard system for classifying habitats, biological communities, nor
ecosystems exists, and developing one would be difficult and contentious (Orians 1993); and (2)
watersheds are aquatic systems with relatively undisputed physical boundaries. Most of Ohio's
watersheds were assumed to be composed of a number of competing local economies, not
"cooperatives" looking out for the best ecological and economic interests of the entire
watershed. It was also assumed that State-level policies may impact or constrain certain
activities at lower economic and ecological levels and so on. Figure 3 Sus. Dev. Over Time
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For purposes of this study, Ohio was assumed to have many ecologically-relevant areas defined
as watersheds and to have many state laws, regulations, and policies that potentially affect the
ecological and socio-economic conditions of these watersheds. Any particular watershed, in
turn, was thought capable of maintaining only a certain level of human and ecological activity at
any given time. Watersheds were also assumed to be composed of open and complex systems
including an interacting mixture of human settlements (each with its own local authorities
governing land use, commerce, and trade), and a variety of terrestrial and aquatic ecosystems.
Ecological components considered in this study, however, only focused on the physical
conditions of rivers and forests (the latter with respect to forest-interior breeding birds), and the
biological conditions of aquatic invertebrates and fish.
Watersheds having available data for this study (Figure 5) were delineated using Arc/Info (ESRI
1992-1998) and the following three coverages:
1) Point coverages derived from OhioEPA monitoring data measuring aquatic invertebrate,
fish, and physical habitat conditions in Ohio rivers and streams during 1988-1992,
2) Line coverages of Ohio streams based on USGS 1:100,000-scale Digital Line Graphs
(Wessex 1997), and
3) 1:250,000-scale Digital Elevation Models (DEM) of Ohio (USGS 1997).
All coverages used in this study were transformed into a consistent Albers Equal Area Conic
projection prior to any overlays and analysis.
Census tracts (U.S. Census Bureau 1992, Wessex 1997) were used to aggregate socio-
economic data at the watershed scale. This is because watersheds are typically composed of
many census tracts, and these tracts represent the best resolution data available for
comparatively assessing the social-economic statistics collected for this study.
Metrics of ecological and of human condition were selected based on the following criteria:
1. Can support for the measure be found in the scientific literature?
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2. Does a data set based on this metric already exist, or can the metric be derived from
existing data for the entire state of Ohio?
3. Are the data relevant to the time period of study?
4. Are the data at an acceptable spatial resolution (i.e., at a watershed or lesser spatial
scale)?
5. Are values comparable across space?
6. Does the metric quantify a unique aspect of ecological or human condition?
Ecological variables designed to measure aquatic conditions and appearing to meet all of the
above criteria included a number of Ohio EPA's aquatic monitoring data (i.e., the Index of
Biological Integrity, the Modified Index of Well-Being, the Invertebrate Community Index, and
the Qualitative Habitat Evaluation Index, see Ohio EPA 1988). In general, these indices focus
on the biological and physical components of aquatic ecosystems and together characterize
several interacting trophic levels. Data limitations included the fact that several sample years of
Ohio EPA data (1988-1992) were necessary to get an adequate spatial coverage of values for
this study. This was a minor concern because biological and physical data tend to reflect longer
term aquatic conditions than chemical measures.
Terrestrial conditions within the watershed were measured in order to supplement the aquatic
indices. Specifically, several landscape metrics of forest conditions relevant to forest-interior
breeding birds were identified from peer-reviewed literature. These metrics included: percent
forest cover, percent core forest, mean forest patch size, fractal dimension, and a measure of
"connectedness" versus fragmentation called the "mean proximity index" (McGarigal and Marks
1995). In brief, these landscape metrics were derived from an existing remote sensing coverage
of Ohio classified by Anderson Level 1 land use codes (Ohio DNR 1994).
Metrics of socio-economic condition were also chosen to represent different aspects of human
well-being. Variables commonly used in the literature for measuring "human development,"
"sustainable economic welfare," or "quality of life" include: infant mortality, longevity, the level of
educational attainment, the degree of economic diversity, real income or gross domestic product
per capita, and income equity (Liu 1975; Daly and Cobb 1989; Cobb and Cobb 1994; Cobb,
Halstead, and Rowe 1995; Miller 1996; and UNDP 1997). Data used in this study for deriving
-------
human welfare metrics at the watershed scale were obtained from the 1990 Census at the
census tract level (as compiled by Wessex 1997). Infant mortality (a general indicator of health)
which is available from "The Vital Statistics Annual Report" for 1990 at the county and city level
(Ohio DOH 1997) was not included because county level data were too spatially coarse for
aggregation to the watershed scale, and using it would constitute an ecological fallacy (i.e.,
"inferring characteristics of individuals from aggregate data referring to a population" (Johnston
et al. 1986, 115)). One approximation considered though was to use the city data and point
interpolation techniques as described by Lam (1983). However, discussions with Ohio's
Department of Health indicated that the data points were limited, predominately urban, not well
distributed throughout the state, and thus, not amenable to such an effort (Dorothy Myers, pers.
comm., 17 April 1998). For the spatial analysis here, final variables selected included:
educational attainment (percentage of 18+ year-olds with a high school degree or higher),
percent employment (where people live, not work), measures of economic diversity and
evenness, the percentage of persons above poverty thresholds (all ages), median household
and per capita income, and several measures of income equity.
Mean values of available aquatic samples were used to estimate watershed scale conditions. All
socio-economic data at the census tract level were aggregated to the watershed scale using an
area-weighted mean procedure. As a result, values of aquatic ecological and human condition
were merely estimates, not actual measurements of the whole. Landscape measures of forest
conditions were calculated using Arc/Info Grid (ESRI, 1992-1998) and Fragstats, a software
program for spatial pattern analysis (McGarigal and Marks 1995).
Spatial Analysis of Watersheds with Varying Ecological and Human Conditions
In brief, each watershed was first ranked against all others with respect to both its aggregate
ecological conditions and aggregate human conditions around 1990. Next, each watershed was
plotted on a graph with coordinate axes and 99% confidence intervals representing average,
aggregate ecological (X) and human (Y) conditions for all watersheds (Figure 6). Third,
watersheds having above average ecological and human conditions were identified as those
lying in the upper right hand (+ +) quadrant of Figure 6. These watersheds were then mapped
and analyzed from a spatial perspective. Each of these steps are described in more detail
below. Watersheds classed as either"+ -,""- +," or"- -" in Figure 6 were also identified and
mapped, and are also discussed in this paper.
• Calculating Aggregate Ecological Conditions and
-------
• Aggregating Human Conditions for Each Watershed
Aggregate scores for each watershed were derived by considering variables which were:
1) not significantly correlated with watershed area, and
2) minimally collinear (i.e., representing different and independent components of
ecological or human watershed conditions).
All variables were converted to ordinal ranks with increasing values indicating better ecological
(or human) conditions within the watershed, and lower ranks indicating poorer conditions. Thus,
watersheds with higher summed ranks of ecological (or human) variables meeting the two
criteria above were assumed to have higher overall ecological (or human) conditions, and vice
versa.
Although not originally intended, principal components analysis (PCA) was explored to help
select a few, independent "components" which explained most of the variance in the ecological
and human welfare data sets. PCA and factor analysis were both considered as possible data
reduction techniques for this purpose. However PCA was chosen because it simply focuses on
data reduction and makes no assumptions about possible underlying causal structures
responsible for covariation within a data set (Hatcher and Stepanski 1994, 503). It should be
emphasized that PCA is a large sample procedure based typically on Pearson correlation
coefficients derived from raw data measured on an interval or ratio scale. However, SAS does
allow for the direct input of correlation matrices, including those derived from Spearman's non-
parametric test. As such, PCA was used to help supplement the visual inspection of matrices in
order to identify variables which empirically "hung together." PCA also helped to identify
minimally collinear and important components, in terms of explained variance, to include in the
aggregate rankings. In order to focus on solutions not affected by watershed area, correlation
matrices derived from a sub-sample of watersheds with similar area (i.e., those with mean
watershed areas + 99% confidence interval) were used in the SAS routine.
After the principal components analyses, the next step was to use the resulting information to
help rank and plot watersheds based on their relative, aggregate ecological and aggregate
human conditions. Given the two criteria stated before, remaining variables for calculating
aggregate scores included the following:
10
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Aggregate Score for
Ecological Condition = Three Components = (MRS + MPI) + MJCI + M_Mlwb
Aggregate Score for
Human Conditions = One Component = (EA + EMP + P_AP + PCI + MEDHHI)
where:
MPS = Mean forest patch size (hectares) within the watershed.
MPI = Mean proximity index of forest patches (search radius = 2 kilometers).
MJCI = Mean Invertebrate Community Index (aquatic invertebrates).
M_Mlwb = Mean Modified Index of Well-Being (fish community).
EA = Educational attainment. Percentage of persons 18 years and older in the
watershed with a high school degree or higher.
EMP = Percent employment in the watershed.
P_AP = Percentage of persons above poverty thresholds, all ages.
PCI = Per capita income in the watershed.
MEDHHI = Median household income in the watershed.
In other words, aggregate scores were summations of independent components consisting of
variables not affected by watershed area and representing the majority of variance within each
of the data sets. Specifically, the rankings and plots incorporate ecological data on terrestrial
forest conditions and two aquatic trophic levels versus one human welfare component
describing inter-correlated aspects of human capital. Because the principal components
analyses were based on correlation matrices rather than raw, interval-ratio data, SAS could not
derive optimally-weighted component scores. Therefore, factor-based scoring (a summation of
ranks of variables loading meaningfully on a given component) was used in the aggregate score
equations above.
Plotting, Classifying, and Mapping Watersheds Based Upon
Their Relative Ecological and Human Conditions
Once aggregate scores were calculated for each watershed, all watersheds were then plotted
and classified based upon which quadrant they occupied in Figure 6. Watersheds falling outside
the 99% confidence intervals of the mean X and Y values were classified as having either:
1. above average ecological and human conditions (+ +),
2. below average ecological and human conditions (- -),
3. above average ecological conditions and below average human conditions (+ -), or
4. below average ecological conditions and above average human conditions (- +).
Maps were then prepared to show where watersheds within each of these classes were located
and how they were spatially distributed in Ohio (see Figures 7 through 10).
11
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Analysis
Results from the spatial analysis showed that watersheds with relatively lower poverty, and
higher levels of education, employment, and incomes were generally located on the outskirts of
metropolitan areas (e.g., Cincinnati, Lima, Columbus, Cleveland, and Akron in Figures 7 and
10). In contrast, watersheds with lesser welfare conditions were typically located in rural or more
inaccessible areas apart from any Metropolitan Statistical Area (MSA) or significant laborshed
(e.g., the Paint Creek and Appalachian watersheds in Figures 8 and 9, respectively). This led to
the conclusion that higher levels of welfare in Ohio in the early 1990s were more dependent
upon larger-scale economies with more extensive connections to the outside world, than small,
self-sufficient economies based on local natural resources. In general, watersheds near
metropolitan areas were well situated for residents to take advantage of employment
opportunities and the goods and services provided by urban and suburban economies. They
were also distant from any potential inner city problems. However, except for the three
"sustainable" watersheds shown in Figure 7, most watersheds with relatively higher welfare
conditions had only average or lower ecological conditions in response to varying intensities of
suburban development and/or agriculture.
Watersheds with the relatively highest ecological conditions measured in this study were
primarily found in less developed areas of Ohio. For example, the unglaciated and rugged part
of Ohio- the Appalachian Plateau Province- where soil fertility is moderate-to-low, pastures and
farmlands are generally limited to hilltops or lower-slope areas, and forest is the predominate
land cover (Figure 9). Relatively lower levels of human condition were measured in this region
and corroborated by supplemental literature reporting on limited economic opportunities due, in
part, to declines in demand for Appalachian reserves of high-sulphur coal (Stephens 1996).
Comparatively better ecological conditions were also observed in three "sustainable"
watersheds (as defined in this study) located within the Chagrin River watershed system in
Northeast Ohio (Figure 7). In contrast to the Appalachian watersheds, human conditions in
these watersheds were relatively high and likely supported by their proximity to Cleveland.
Historically, more focused developments in Cleveland rather than in these outlying watersheds
may be why above average ecological conditions remained there in the early 1990s. Another
interesting feature was their location within a special survey area known as the Connecticut
Western Reserve. In brief, this part of Ohio was originally surveyed into slightly smaller
townships (i.e., 5 by 5 miles rather than 6 by 6 miles found elsewhere), but not subdivided into
sections (Wilhelm and Noble 1996). Also of interest was the emergence of non-government
organizations (NGOs) committed to protecting natural resources within the watershed, and
12
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informing citizens about local land use decisions and economic and environmental policies
affecting the watershed. Similar organizations were also found elsewhere in Ohio where valued
natural resources were experiencing increased anthropogenic stress (e.g., suburban sprawl in
the Big Darby Watershed west of Columbus and in the Little Miami River Watershed east and
northeast of Cincinnati).
Conclusions
"Sustainable development" is not directly observable. It needs to be defined before it can be
visualized and studied. The unique approach in this study was to explicitly define sustainable
development as a positive relationship between "ecological integrity" and "human welfare." In
other words, the thrust was to explore whether mutualistic or cooperative relationships existed
at a watershed scale; to locate watersheds with such relations (if any); and to suggest how
these relations may have been caused.
The methodology was straightforward and made use of only a few select variables which,
although limited in number and scope with respect to Figures 1 and 2, went a long way towards
answering questions like: "Can society and nature thrive together? If so, where and how?"
Overall, the methodology was successful in identifying watersheds with variable ecological and
human conditions, and knowing their locations assisted in developing hypotheses about how
these conditions were achieved. In this paper, the method identified and located a relative range
of "sustainable" to "unsustainable" watersheds for one time period. Watersheds observed with
above average human and ecological conditions (i.e.,"+ +" conditions) were significant because
they differed from the general inverse relationship between society and nature observed in
Figure 6. Their identification in Figures 6 and 7 demonstrated that apparent harmonies of
society and nature may still be found and studied with respect to their environmental
endowments and histories of human occupation.
Few (if any) attempts have been made to map and analyze spatial variations of sustainable
development at sub-national scales or within the context of ecologically-relevant systems.
Therefore, this research differed from others focusing on political units and much larger spatial
scales. Reasons for looking at ecological rather than political areas stemmed from the
assumption that human welfare ultimately depends upon the natural resources and life support
systems provided by healthy ecosystems (WCED 1987, U.S. EPA 1990). It also stemmed from
the fact that aquatic and terrestrial ecosystems are often degraded or destroyed by the
cumulative, seemingly "independent" decisions made by cities, towns, and villages within a
particular watershed (e.g., see Ohio EPA 1996).
13
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State-of-the-art geographic information systems (Arc/Info and Grid), statistical and spreadsheet
software (SAS and Excel), and landscape metric programs (Fragstats) made this study
possible, but most of the work, in terms of time and effort, still centered on data aggregation and
input. Specifically, this involved aggregating point, polygon, and raster data to the watershed
scale, and converting data from their existing form into others which could be transferred among
these different tools of "automated geography" (see Dobson 1993 and Aronoff 1993, 42).
The analysis in this paper could be improved by considering changes over time (e.g., recall
Figure 3). This may be done after the Year 2000 census and would assist in developing a richer
understanding of human and ecosystem responses to stress over time and space. Others may
find the method useful as a "broad-brush" approach for highlighting specific watersheds for
more detailed studies (e.g, studies like those conducted by Braat and Steetskamp 1991, and
Rees 1995). Similarly, the approach could also assist in prioritizing watersheds in need of
conservation, remediation, or other environmental management efforts. Where sufficient data
and interdisciplinary expertise exists, the method could also be improved by: including better or
more comprehensive sets of ecological and human welfare measures (e.g., additional measures
of chemical pollutants or human health), considering other types of ecological systems or
boundaries (e.g., ecoregions), or by developing predictive models of ecological integrity and
human welfare and plotting and mapping their outputs similar to that done in Figures 6 through
10.
Future researchers may have better and more continuous coverages of data than used here for
observing spatial patterns of sustainable development across a landscape. Flows of people,
money, information, and/or natural resources may also become more apparent, as well as, links
between areas of sustainable and unsustainable developments. Furthermore, researchers may
develop or have better tools for modeling or analyzing such flows at multiple scales (e.g.,
multiple political, economic, and ecological scales). However, humility in quantification will
always need to remain, particularly when attempts are made to address the many important,
qualitative aspects of ecological integrity and human welfare (e.g., recall Figures 1 and 2).
14
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Figure 1
Aspects of Aquatic Ecological Integrity
Dissolved
Land Us
Permeable vs. Impermeable Surfaces:
Natural and Man-Made
Temperatures Oxvsen
~x Y
Solubility--^ \ ^
Water Quality/
Chemical Variables
Temporal Distribution
of Floods and Low Flow
Groundwater
Primary & Secondary
Production in Stream
Nutrients-
Flow Regime
Nutrients ^r
(i.e.,NandK)
\_Precipitation
and Runoff
Ecological Integrity
of Water Resource
Heterogeneity
Sunlight
V
SiltationV
Food/Energy
Sources
Habitat Structure
s
Seasonal Patterns of
Available Energy
.
Bank Stability
Biotic Factors/
Interactions
Reproduction
Organic and Inorganic Chemicals:
Natural and Synthetic
-Morphology
Organic Matter Inputs
from Riparian Area
ompetition Riparian
Vegetation
Current
Velocity
Instream
Cover
V^_ Channel
Sinuosity
Canopy
Cover
^•Width/Depth
-Substrate Type
Modified from: Karr et al. 1986, Karr 1991, and Yoder 1995
-------
Figure 2
Aspects of Human Welfare
Persuasion versus Force or Violence
(Interactions between State and Society)
Membership/
Participation
Immigration/Emmigration
Right to an opinion
Wealth
Distribution
Income Wealth
Availability of non-renewable
and renewable natural resources
(Environmental Quantity)
Environmental
Conditions
Right to action to ensure needs
Healthy Spirituality
Positive emotional and
cognitive functioning
Moral/Ethical
Principles
Personal satisfaction
Self-Esteem
Balance or "Centeredness"
Physical Fitness
Pollution, Degradation of
habitats, Loss of ecological
functions or services
(Environmental Quality)
Energy Flows
_ Biogeochemical
Cycling
Assimilation
of wastes
\Indoor Living
""••— nnH \\Tr\r\rvt~\rf
and Working
Environments
Terrestrial and
Aquatic Envrionments
Identity (Individual, Ethnic,
Community, and National)
Development
vs. Growth
Externalities
Intra- and Inter-
generational equity
Psychological Health
Human Welfare
or Weil-Being
Well-being of family, friends,
community, culture, and world
Economic Conditions
?~
Imports/Exports
Availability of
basic material
needs
Individual versus
Social Preferences
Integration versus
Segregation
Disarticulation
Prices, Supply
and Demand
Income
Employment
Life Expectancy
or Longevity
Health Care (e.g., prenatal
physical, mental, nutrition,
and special health care
services).
Educational
Attainment
Scientific Paradigms
and Theories
Socialization Participation
and Life Skills
Freedom from p6verty
racism, sexism, classism, or
loss of culture and language
Family and Home
Conditions
Child Development
Decent burial
Opportunities for recreation,
entertainment, and leisure
Social programs (e.g., welfare,
pregnancy prevention, foster care)
Peace, individual
security, free of
crime, drugs, disease
-------
Figure 3
Hypothetical Sustainable Developments Over Time
ffi
17
Human Dominated
Landscape
Preservation
of Nature
ffi
17
t/i
o
J
'
No Change
[p worse] Ecological Integrity [better! ]
[p worse] Ecological Integrity [better! ]
-------
Figure 4
Hypothetical, Relative Sustainable Development At One Point In Time
M
Relatively More
Human Dominated
Landscapes
,-'''^
Relatively More
Natural Landscapes
[p worse] Ecological Integrity [better! ]
-------
Figure 5
Watersheds Analyzed in this Study
(N = 57)
100
Watershed
Ohio
200 Miles
-------
Figure 6
Plot of Watersheds Based on Their Aggregate
Ecological and Aggregate Human Conditions
(N = 57)
w
"S5
A
C
O
IS'
O
N=10
» I
» i*
*»
|20(.* - -
»
*
1100
* JJOO - -
N=l
"++"
N=3
» i
200
3i 0
»
•
N=16
worse < ecological conditions > better
(MPS, MPI, Mlwb, ICI)
-------
Figure 7
Map of "Sustainable" Watersheds Relative to Others Studied:
Watersheds With Above Average Ecological and Human Conditions, Circa 1990
Eco+, HW+
Ohio
100
0
100
200 Miles
-------
Figure 8
Map of Watersheds Studied With Below Average Ecological and Human Conditions, Circa 1990
100
0
100
200 Miles
-------
Figure 9
Map of Watersheds Studied With Above Average Ecological Conditions
and Below Average Human Conditions, Circa 1990
100
200 Miles
-------
Figure 10
Map of Watersheds Studied With Above Average Human Conditions, and
Average or Below Average Levels of Ecological Condition, Circa 1990
HW+, Eco Avg.
HW+, Eco-
Ohio
100
200 Miles
1
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Enhancing the Spatial Comparison of Multiple Environmental
Databases using the Prototype NY/NJ Harbor Environmental Data
Management System (HEDMS)
Raymond M. Valente, Peggy M. Myre, and David C. Inglin1
Science Applications International Corporation
James Lodge, U. S. Army Corps of Engineers2
1.0 Introduction
The New York/New Jersey harbor area is one of the busiest ports on the East Coast. It has
served as a reservoir of contaminants as a result of decades of pollution from multiple sources,
including runoff from watersheds, shore-based industrial activity, and atmospheric input.
Environmental data have been collected under the auspices of multiple national and regional
monitoring programs, providing some of the information needed for environmental management
of the harbor. The Contaminant Assessment and Reduction Program (CARP) is a new program
involving the collection of multiple data types. This large-scale monitoring effort is intended to
address the distribution of contaminants in the water, sediments and biota of the harbor, with
the ultimate goal of source reduction. However, data comparability problems exist because the
historical data, as well as the new CARP data, have been collected using different
programmatic objectives, sampling protocols and laboratory data quality objectives.
The New York District of the U.S. Army Corps of Engineers has developed a prototype
environmental chemistry database with a geographic-based (GIS) user interface for New
York/New Jersey Harbor. The prototype is the first phase of the Harbor Environmental Data
Management System (HEDMS) that ultimately will include multiple environmental databases
(chemical, geological, biological and physical), as well as tools for querying and analyzing the
data in a geographic context. The primary objective of the HEDMS prototype is to allow the user
to readily select different data types, import them into a geographic interface, and conduct
spatial data analyses while reducing common problems associated with chemical data
1 Raymond M. Valente, Peggy M. Myre, and David C. Inglin, Science Applications International
Corporation, 221 Third Street, Newport, Rl 02840 (rvalente@mtg.saic.com: pmurray@mtg.saic.com:
dinglin@mtg.saic.com)
2 James Lodge, U. S. Army Corps of Engineers, New York District, 26 Federal Plaza, New York, NY
(iames.lodge@nan02.usace.armv.mil')
-------
comparison.
The system provides a user interface which includes utilities to increase comparability of
databases collected for different programs using different analytical methods. One of the key
requirements for HEDMS is the inclusion of extensive data documentation, including laboratory
method and quality control information, spatial metadata, and overall program metadata
(including program objectives and sampling design).
2.0 Description of HEDMS Features
The primary purpose of the HEDMS tool is to allow the user to readily select different data
types, import them into a geographic interface, and conduct spatial data analyses while
reducing common problems associated with chemical data comparison. The current system
provides a graphic user interface (GUI) that includes utilities to increase comparability of
databases collected for different programs using different analytical methods (Figure 1). The
system graphically displays georeferenced sample locations from specific projects, NOAA
coastlines, major rivers and tributaries, and other relative boundaries. Another key feature of
HEDMS is the inclusion of extensive data documentation, including laboratory method and
quality control information, spatial metadata, and overall programs metadata (including program
objectives and sampling design).
Software/Hardware requirements: The HEDMS interface is a development within the ESRI
ArcView® GIS program. The actual HEDMS program acts as an ArcView® Extension and
requires a licensed copy of the ESRI ArcView® program. An Extension (Figure 2) is an add-in to
ArcView® that adds custom capabilities. Extensions allow the newly created functions to be
added to or removed from ArcView® with ease. The database is implemented using Open
Database Connectivity (ODBC), which allows the user to access the database readily without
having the original program that the database was created in, such as MS Access. The system
is delivered on a single CD-ROM and requires either a Microsoft Windows95, 98 or NT platform.
Minimum system requirements are a processor speed of 166 MHz, 32 megabytes of RAM, and
at least 50 megabytes of free hard drive space.
Data types: The prototype HEDMS represents the first phase of a longer-term system
development effort. It has been developed specifically for the NY/NJ harbor estuary area and
includes multiple chemical databases from this area. Presently, the system is populated mainly
-------
with base map data and data on the concentrations of suite of chemical contaminants in surface
sediments. The HEDMS system will eventually hold multiple environmental databases
(chemical, geological, biological and physical) and will allow display of not only surface
sediment chemistry data, but also subsurface sediment chemistry from cores, tissue chemistry,
and biological testing data (e.g., toxicity and bioaccumulation).
The first-phase base map data include NOAA medium-resolution coastline, channels, navigation
information, watersheds of NY/NJ, and jurisdictional boundaries, as well as a limited amount of
supporting data, gleaned largely from the EPA's BASINS database (e.g., locations of pollution
point sources like Superfund sites and NPDES discharge permit holders). Marine sediment
chemistry data from the following monitoring programs also are included in the present system.
• U.S. Environmental Protection Agency (EPA) Environmental Monitoring and Assessment
Program for Estuaries (EMAP-Estuaries): sediment chemistry data from sampling in the
Virginian Province for the years 1990 through 1993. Data from throughout the NY/NJ
Harbor Estuary system and the Hudson River.
• U.S. EPA Regional Environmental Monitoring and Assessment Program (R-EMAP):
sediment chemistry data from throughout the NY/NJ Harbor Estuary system for the
years 1991 and 1993.
• National Oceanic and Atmospheric Administration (NOAA) National Status and Trends
and Program (NS&T). Sediment chemistry data from both the "Mussel Watch" and
"Benthic Surveillance" components of this long-term marine monitoring effort.
• NOAA Study of Sediment Toxicity in the Hudson-Raritan Estuary in 1994.
Data selection: The program was designed to provide users with speed and flexibility in
querying the database for the purpose of choosing what data and which parameters they want
to examine. The "picker screen" utility provides an interactive window to allow the user to select
data by program, data set, date, or data type (Figure 3). Once these basic specifications have
been set, the user can then specify a particular analyte group, such as metals, organic
contaminants such as PAHs, PCBs, or pesticides, or physical parameters such as sediment
grain size (Figure 4). The data query can be further refined using specific numeric qualifiers
(e.g., above or below a specified value). The remaining functionality parameters for this window
allow the user to either view or export data to a table or text format (Figure 5), add data to a
map view as an ArcView® theme, and finally to plot just station locations into a view window.
-------
When the user chooses to export data to a table, they can choose exactly what columns they
want to view in the table by clicking on desired headers (Figure 6).
Data assessment and comparison: The system contains a number of functions to facilitate
environmental assessments and comparisons across data sets.
• Under the "preferences" menu, the user can specify how "below detection limit" (BDL)
chemistry values are to be treated (Figure 7). The system provides the following options
for handling BDL data in any subsequent calculations or comparisons: substitute zero,
substitute one-half of detection limit, substitute detection limit, or ignore.
• A menu function allows the user to sum the results for individual analytes into
commonly-employed "aggregate" classes. For example, the sediment concentrations of
15 to 20 individual PAH compounds commonly are summed to produce an aggregate
value called "total PAHs." Likewise, "total PCBs" are calculated based on summing the
values for individual PCB congeners. Different investigators traditionally have used
slightly different approaches to defining/calculating aggregate values like total PAHs and
total PCBs. In recognition of such differences, the HEDMS program allows the user to
identify which of the individual compounds will be summed to produce the aggregate
value (Figure 7). This ensures a common basis for comparison among different
monitoring programs.
• A menu screen is provided to allow the user to "normalize" the sediment chemistry data
by either grain size or total organic carbon (TOC) content.
• Data on sediment metal concentrations can be normalized by the sediment
concentration of acid volatile sulfide (i.e., SEM/AVS ratio).
• The "compare to reference" function allows the user to calculate and display the results
of comparisons to different, commonly-employed "reference" values (e.g., Effects
Range-Low/Effects Range-Median values (ERL/ERM), EPA Sediment Quality Criteria
(SQC), Threshold/Probable Effects Levels (TEL/PEL)).
Data display: The GIS application to this system is a very useful tool, in that it allows the user
to create a view and choose the area of interest to be displayed. The area is then displayed in
the state plane projection reference. The view can be set for a number of projection changes
such as latitude/longitude or UTM units, and the user can also change the view's distance units.
Once the view has been given the desired projection, the user will add the data of interest and
-------
can start examining the data spatially (Figure 8).
The HEDMS program also contains a preference feature with functions that allow the user to
personalize a work session for future uses. The user can actually save the chosen preference
configuration to an individual preference file that can be re-loaded. The functions for dealing
with "below detection limit" values and for the calculation of aggregate values are included
under the preferences menu (Figure 7). The preferences feature is intended to make the
program a more flexible to a larger group of users. For example, if there were only one
computer that was available with the HEDMS program installed, a number of different users
could have their own preference files containing the features that they want to see, as opposed
to having the user start anew each time they began a work session.
Quality Assurance/Quality Control: Chemistry data typically require a careful assessment of
quality before use in a study or spatial evaluation. Many times this quality assessment is lost
when a data set is incorporated into a larger database. The HEDMS has been designed to
store, access, and use documentation and quality control data provided by the source
laboratory and also provide a framework for documentation of the decisions made in the
evaluation of data quality. For each sample result, the system is capable of producing a QA/QC
report for the associated quality control data such as matrix spikes, duplicates, Standard
Reference Materials, and blanks. In addition, the system allows users to access program-level
metadata for viewing or downloading in simple text file format. The metadata provides a
complete set of documentation on each data set, including information on program
sponsors/participants, objectives, sampling and analytical methods, analyte lists, and data
quality objectives.
3.0 Summary
The HEDMS has been designed to enhance the utility of historical databases ("data mining"),
increase the statistical confidence (power) of environmental data interpretation by integrating
multiple databases, increase the longevity of newly collected data, and heighten comparability
of disparate data sources.
This system is the first step in a plan to create a useful tool to manage large environmental data
sets of varying complexities. The main focus of the existing prototype is chemical data from the
NY/NJ harbor estuary area. However, future versions of the software are not limited to chemical
-------
data nor is the area of interest a limitation. HEDMS incorporates utilities for integration and
comparison of multiple databases, which can be done using a variety of data types. A primary
system function is the automated features included for complex chemical data analysis. The
system provides rapid spatial analysis due to the integration of the program into a GIS platform.
The flexibility of the system and the use of the existing ArcView® platform allow for a efficient,
user-friendly analysis tool designed to save resource managers and other decision-makers
valuable time.
file Edit View Iherne
graphics Window Help
Create an Area of Interest
Add Data to View
Normalize Data
Compare Data to Reference
Preferences
\m\n\
Scale 1:| 352,696
261,332.67 «•
61391.57 t
Figure 1. Graphic User Interface (GUI) of the HEDMS program that users will most commonly start
from when creating a new view.
-------
Extensions
Available Extensions:
J STFATE
J VPF Viewer
J Zoom Tools Extension
MassCZM Custom Controls
HEDMS
ODB Tools Extension
Script Editor Utilities
About:
I
OK
Cancel
Reset
Make Default
The HEDMS extension contains NY Harbor Environmental Data
Management System. This extension has tools for viewing and analyzing
sediment chemistry data.
Figure 2. The HEDMS has been designed to run as an ArcView® Extension.
-------
«, HEDMS Data Query Builder
Programs
3 CARP
3EMAP
3NOAA-EdLong
DFI-EMAP
£LL PRCiGRAMSj
Studies
0EMAPJ992
3EMAPJ993
Mussel_Watch
Benthic_Surveillance
R-EMAP
ALLSTUDIES
Study Date
(• All Dates Within Selected Study(ies)
mm/dd/yy mm/dd/yy
C~ Date Range
C~ Single Event Date
r Year [1984
Data Type
I Sediment Chemistry
Record Count
24584
Exit
Clear Selections
Plot Stations
Select either All Dates Within Study (the default), or a Date Range, Single Date, or a specific year. A Date Range, Single Event Date, or Year
returns all sampling events that fall within the selected time frame and may only be chosen after a Study is selected.
Figure 3. The initial "picker screen" window, which allows the user to select data by program, data
set, date, or data type.
-------
All Dates Within Selected Studies)
mm/dd/vv mm/dd/vv
(~ Date Range
3 CARP
^EMAP
3 NOAA-Ed Long
3EMAPJ992
3EMAPJ993
Mussel_Watch
Benthic_Surveillance
R-EMAP
Phase I (1991)
(" Single Event Date
Year 11984
|7 ALL PROGRAMS
Analyte Groups
ALLSTUDIES
Parameters
Record Count
6798
1 -Methylnaphthalene
1 -Methylphenanthrene
2,3,5-trirnethiilnaphthalene
2,6-Dimethvlnaphthalene
2-Methvlnaphthalene
Acenaphthene
Acenaphthylene
Anthracene
Benio(a)anthracene
Benio(a)pvrene
Pesticide
Physical
Clear Selections
E_xport/view Table
Select 1 Analyte Group only to Refine the Number of Records in the Data Set. The default is all Analyte Groups listed.
Figure 4. After the user chooses the first set of screening options, additional parameters appear to
allow for more specific screening.
-------
Si. HEDMS Data Table HElEil
JL
<
Program Name
NOAA-Ed Long
NOAA-Ed Long
EMAP
EMAP
EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
EMAP
R-EMAP
R-EMAP
R-EMAP
EMAP
EMAP
R-EMAP
EMAP
Study Name
Phase 1(1 991)
Phasel(19S1)
EMAP 1991
EMAP 1990
EMAP 1990
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
R-EMAP
EMAP 1990
R-EMAP
R-EMAP
R-EMAP
EMAP 1991
EMAP 1990
R-EMAP
EMAPJ990
Select individual columns(fields) to be hidden or
displayed.
Event Name
HRE-37
HRE-38
VA91-424
VA90-173
VA90-177
RE102
RB117
BA112
RB106
LS104
LS006
JB114
RB002
VA90-215
JB006
JB104
JB120
VA91-370
VA90-199
LS102
VA90-217
Lat
40.5013888888389
40.4686111111111
41.872
40.647
40.383
40.572783
40.46355
40.297667
40.51405
40.99
41.031447
40.591167
40.570632
41.733
40.6083
40.621767
40.596367
40.511
41.15
41.021333
42
Long
-73.9747222222222
-73.9330555555556
-73.935
-74.053
-73.943
-73.964283
-74.113767
-73.733833
-74.113733
-73.386167
-73.37733
-73.85135
-74.079879
-73.945
-73.77319
-73.8415
-73.312983
-74.3
-73.883
-73.400667
-73.939
Station Name ^
HRE-37
HRE-33 ~
424 Hi!
173
177
RB102
RB117
BA112 II!
RB106
LS104 I
LS006
JB114
RB002
215
JB006
JB104
JB120
370
199
LS102
217
>r
i h[ ide/LI nhide Columns i £xport to File R.e*urn t° Query Builder
Figure 5. One option available from the data picker screen (Figure 4) is export of the selected data
to a table. The user has the ability to select/deselect different table columns (see Figure 6) and
export the table into a text file.
10
-------
. Unhide Columns
Column:
njProqram Name
Study_Name
EventName
] Long
] Station_Name
] Data_Tvpe
] Major_Group
] Chem_Units
CAS_Num
^] Analyte_Name
Result
Close
Figure 6. The "unhide columns" window, which allows the user to choose the data to be
displayed in table format.
11
-------
Preferences
C Choose Areas of Interest
r BDL
(* Aggregates
(? Total PAHs
C Total PCBs
C~ Tni-flinni-;
• 1 Ulal LJU \ i
Apply
Choose Aggregate Components
Benzo(a)pyrene _^_
Dibenz(a,h)anthracene
Benzo(a)anthracene
1 Acenaphthene jl
Phenanthrene
Fluorene
1 4^eth.vlnaphthalene
Naphthalene
2-Methvlnaphthalene
Biphenyl
Anthracene
Pyrene
Dibenzothioohene _ZJ
Figure 7. The preferences menu allows users to choose how below detection limit values are
handled and how aggregates are to be calculated.
12
-------
file Edit View Iheme HEDMS Graphics Window Help
Scale 1: 1,632,541
178,097.39 «•
186:227.79 t
EMAP Program Anthracene (ug/kg) _^
0 - 1001.67
1001.67- 2003.33
2003.33 - 3005
3O05- 40O6.B7
0 1006.67 - 5008.33
5008.33- 6Q1O
None Detected
NOAA Coastline
cn
Figure 8. Example of spatial data display within HEDMS. The user can add or remove as many
themes or layers as they choose in performing spatial evaluation of the data.
13
-------
Nonpoint Pollutant Loading Application for ArcView GIS
Laurens van derTak, Mike Miller, Cody Zook, Cheri Edwards1
I. Pollutant Loading Application Overview
An ArcView GIS tool was developed that calculates nonpoint source pollutant loads for
watersheds and sub-watersheds. The application is called PLOAD. Currently the application is
set up to estimate nonpoint sources (NPS) of pollution on an annual average basis, for any user
specified pollutant. The user has the option to calculate the NPS loads with either the Simple
Method, or with areal export coefficients. The program was designed to be generic so that it can
be applied as a screening tool in typical NPDES stormwater permitting projects, watershed
management projects or reservoir protection projects, and readily modified for special needs. It
is designed to be a useful analytical tool for end users (primarily water resources engineers and
planners). Therefore, it was programmed in a desktop GIS environment, ArcView, using the
ArcView Avenue programming language.
The tool requires the following input data:
• GIS landuse data.
• GIS watershed data.
• GIS BMP site and area data (optional) showing the location as a point coverage with the
BMP type and service area, or as a polygon coverage with BMP type and the polygon
boundaries delineating the service area. This layer is optional, if BMPs are included in
the analysis.
• Pollutant loading rate data tables for each land use type showing either event mean
concentrations (EMCs, in mg/L of a given pollutant), or export coefficients (e.g. pounds
per acre of a given pollutant).
• Impervious data tables.
• Pollutant reduction BMP data tables (optional).
1 Vice President and Sr. Water Resources Engineer, Sr. Gis Analyst, GIS Analyst, and Water Resources
Engineer, CH2M HILL. Address questions to: CH2M HILL, 13921 Park Center Rd., Suite 600, Herndon
VA 20171. Tel.: 703-417-1441.
-------
Several output options are available:
• Tabular summaries of pollutant loads by basin (Ibs.), EMCs by basin (mg/L), aerial
loading rates by basin (Ibs./acre/yr.), pollutant loads by land use (Ibs.).
• Graphical summaries of pollutant loads by basin (Ibs.), EMCs by basin (mg/L), and aerial
loading rates by basin (Ibs./acre/yr.)
This paper presents the theory and application of the PLOAD tool. The PLOAD application input
data, pollutant evaluation equations, products, installation instructions, and general use
guidelines are described in detail below. A case study is presented showing input and output
screens, and illustrating how the tool was used to develop nonpoint source pollutant loads for
NPDES stormwater permit annual reports for the Cities of Chesapeake, Hampton, Newport
News, Norfolk, Portsmouth, and Virginia Beach, Virginia. These six cities are working together
as part of the Hampton Roads Planning District Commission (HRPDC) Regional Stormwater
Management Committee (RSMC).
II. Input Data
A variety of GIS and tabular source data is accessed by the PLOAD application. This section
describes the required and optional input data components. Note that the GIS data must be
developed as either ESRI Arc/Info coverages or ArcView shape files, while the tabular data may
be prepared as Excel, comma delimited text, dBASE, or INFO files.
11.1 GIS Data
Watershed basin and landuse GIS data coverages are required for PLOAD. The basins define
the areas for which the pollutant loads are calculated. The basin coverage must have a code
field containing unique identifiers for each basin. The landuse file is essential for calculating the
pollutant loads. The landuse coverage must also have a code field identifying the landuse types,
but these types need not be unique. Prior to calculating the pollutant loads, PLOAD will spatially
overlay the basin and landuse coverages in order to determine the areas of the various landuse
types for each basin. The landuse coverage should encompass the entire basin coverage.
Digital watershed basin were available for the six Virginia cities. However, current landuse data
were developed for several of the cities by digitizing planning maps, or converting real estate
parcel information and infrared aerial photography.
-------
Best Management Practices (BMPs) serve to reduce pollutant loads using natural processes
(settling, filtration, biological uptake) for the BMP area of influence. PLOAD will account for the
influence of either site or areal BMPs. Site BMPs represented as point GIS files must contain
attribute codes describing the BMP type and area of influence. Areal BMPs must be delineated
as polygon files coded for BMP type only. The polygon boundaries define the area of influence.
BMP input is optional because they may not exist for the area of evaluation or be desired for
analysis.
II.2 Tabular Data
Pollutant loading rate, impervious factor, and BMP efficiency information must be compiled in
tabular files for use in the PLOAD application. The three files of tabular input data can be
provided in one of four formats: Excel spreadsheet, comma-delimited text, dBASE, or INFO
database tables.
The pollutant loading tables consist of the either the event mean concentration (EMC) or the
export coefficient. The user can choose to use either form of pollutant loading rates (EMCs and
aerial export coefficients) because data are typically available for the EMC and export
coefficient tables for urban and rural landuse types, respectively. Pollutants commonly
evaluated include: TSS, Total Nitrogen, TKN, Nitrate plus Nitrite, Lead, Zinc, BOD5, COD,
Ammonia, Total Phosphorus, and Fecal Coliform.
The impervious factor table identifies percent imperviousness for each landuse type. The BMP
table identifies the percent efficiency for reducing pollutant loads for each BMP type. Multiple
versions of each type of table may be generated to simulate alternative conditions. A description
of each lookup table is provided below.
11.2.1 Export Coefficient Table
The export coefficient table lists loading rates for each pollutant type by landuse type. The table
may contain any number of landuse and pollutant types. There should be loading rates for each
landuse and pollutant type in the evaluation area, otherwise the load for the area will be zero.
The rates in the export coefficient table are measured in Ibs./acre/year and are typically used to
calculate the pollutant loads for rural landuse types. An example of an export coefficient table
follows:
-------
Landuse
Type
Agriculture
Pastures
Forests
Total Nitrogen
(Ib./acre/year)
21.2
5.9
2.4
Total
Phosphorus
(Ib./acre/year)
3.5
0.5
0.0
TSS
(Ib./acre/year)
0
0
0
Lead
(Ib./acre/year)
.18
.06
.02
II.2.2 Event Mean Concentration Table
The event mean concentration (EMC) table is identical to the export coefficient table, except the
EMC values are measured in mg/L and they are typically used to calculate the pollutant loads
for urban landuse types. The following is an example of an EMC table:
Landuse Type
1-Family Res
Rural Res
Industrial
Commercial
Total
Nitrogen
(mg/L)
2.00
2.00
1.62
2.77
Total
Phosphorus
(mg/L)
.26
.26
.35
.38
TSS
(mg/L)
52.60
52.60
79.25
56.50
Lead
(mg/L)
.037
.037
.031
.027
II.2.3 Impervious Factor Table
The impervious factor table identifies the percent imperviousness for each landuse type. It is
used to calculate the EMC runoff coefficient. The table may contain any number of landuse
types, but there should be impervious percentages for each landuse type in the evaluation area.
If there is no impervious factor in the table for a particular landuse type, then the EMC runoff
coefficient will default to .05 for areas with that landuse. The names describing the landuse
types must be the same in the table as they are in the GIS landuse file.
The following is an example of an impervious factor table:
Landuse Type
Residential
Commercial
Industrial
% Imperviousness
18
84
71
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11.2.4 BMP Efficiency Table
The BMP table contains percent efficiency multipliers for each BMP type that are used to
calculate pollutant load reductions. The first record (row) of the table identifies the field names
starting with BMP type followed by the pollutants under evaluation. The table may contain any
number of BMP types. The pollutant types without percent efficiency multipliers will not reduce
the pollutant load for the BMP type.
The BMP table was developed by water resource engineers by using literature values, or by
analyzing local monitoring data comparing pollutant loads entering and leaving BMPs.
The following is a simple example of a BMP efficiency table:
BMP Type
Dry Pond
Basin
Grass Filter Strip
Total Phosphorus
(% removal)
30
45
30
Total Nitrogen
(% removal)
20
25
50
TSS
(% removal)
50
60
20
III. Pollutant Loading Calculation Equations
Annual pollutant loads may be calculated for each watershed basin using either the pollutant
export coefficient or simple methods. Optionally, the pollutant loads derived from these methods
may be refined based on the remedial effects of BMPs. Descriptions of the equations used to
calculate the pollutant loads follows:
111.1 Export Coefficient Method
If the export coefficient method is designated for calculating pollutant loads in PLOAD, then the
loads are calculated for each specified pollutant type by basin using the following equation:
LP = EU(|PU * Au)
Where: LP = Pollutant load, Ibs.;
LPU = Pollutant loading rate for landuse type u, Ibs./acre/year; and
AU = Area of landuse type u, acres
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The loading rates are derived from the export coefficient tables, while the landuse areas are
interpreted from the landuse and basin GIS data.
III.2 Simple Method
If the Simple Method is designated for calculating pollutant loads in PLOAD, then two equations
are required to calculate the loads for each specified pollutant type. First, the runoff coefficient
for each landuse type must be derived with the equation:
Rvu = 0.05 + (0.009 * lu)
Where: RVu= Runoff Coefficient for landuse type u, inchesmn/inchesrain
lu = Percent Imperviousness
Percent impervious is extracted from the impervious factor table.
The pollutant loads are then calculated with the following equation:
LP = Eu (P * Pj* Rvu* Cu* Au * 2.72 / 12)
Where: LP = Pollutant load, Ibs.
P = Precipitation, inches/year (default = 40.86)
Pj = Ratio of storms producing runoff (default = 0.9)
RVU= Runoff Coefficient for landuse type u, inchesmn/inchesrain
Cu = Event Mean Concentration for landuse type u, milligrams/liter
AU = Area of landuse type u, acres
The precipitation and storm ratio values are entered by the PLOAD user interactively. The
loading rates are derived from the EMC tables, while the landuse areas are interpreted from the
landuse and basin GIS data.
III.3 BMP Computations
BMPs serve to reduce pollutant loads and PLOAD has an option to calculate loads based on the
remedial effects of the various BMP types. This section describes the equations that are used to
calculate pollutant loads influenced by BMPs. BMP types may be represented as either area or
site features, but the approach for both is similar. After the raw pollutant loads are calculated
-------
using the export coefficient or simple methods, three equations are used to recalculate the
pollutant loads.
First, the percent of the basins area serviced by BMPs are determined using the following
equation:
%ASBMp = ASBMp/AB
Where: %ASBMP = Percent area serviced by the BMP, decimal percent
ASBMP = Area serviced by the BMP, acres
AB = Area of basin, acres
The BMP and basin areas are derived from the BMP and basin GIS data. Next, the pollutant
loads for each BMP are calculated:
LBMP= (Lp* %ASBMP) * [1- %EFFBMP/100]
Where: LBMp= BMP load, Ibs.
LP = Raw basin load, Ibs.
%EFF = Percent load reduction of BMP, percentage
The raw basin pollutant loads are derived from the results of the export coefficient or simple
methods, while the percent load reduction comes from the BMP efficiency tables.
Finally, the total pollutant loads accounting for BMPs are computed by basin. Each basin load is
a cumulative total of areas which are and are not influenced by BMPs.
L = (EBMP (LBMp)) + L P * (AB - (EAS (AS BMP))
IV. Optional User Input Parameters
Many input parameter options have been built into the PLOAD tool that the user must specify.
Several of the more important ones are listed as follows:
• Specify watershed basin and landuse GIS data files.
• Select single, multiple, or all watershed basins from basin file for evaluation.
• Specify either the export coefficients or the simple method for calculating pollutant loads.
-------
• If simple method is specified, then enter annual precipitation and ratio of storms
producing runoff value to override defaults.
• Evaluate pollutant loads with or without BMPs.
• When BMPs are evaluated, identify whether they are derived from point or polygon GIS
data.
• Select output products.
• Save file of input data sources and parameter settings that may be used to rerun PLOAD
at a later date, with or without input modifications.
V. Output Product Options
After the pollutant loads have been determined, PLOAD may be used to generate a variety of
graphic plot and tabular report products. Listed below are the product options. See Appendix I -
Graphic and Tabular Product Examples.
• Total Pollutant Loads by Basin - Map and Table
• Pollutant Loads Per Acre by Basin - Map and Table
• Event Mean Concentration (EMC) by Basin - Map and Table
• Pollutant Loads by BMP, Landuse and Basin - Table
• PLOAD Data Sources and Parameters - Table
VI. Potential Future Enhancements
It is hoped that the PLOAD application will continue to be enhanced as needed on projects with
related needs. Possible future enhancements include:
• Addition of point sources of pollution to account for stream baseflow background
concentrations from groundwater or treatment plant discharges.
• Addition of pollutant transport or sediment delivery ratio concepts, to account for
reductions in loads in-stream due to deposition or other mechanisms.
• Addition of other types of BMPs, such as those in series and source control type BMPs.
• Evaluate simple and export coefficient methods simultaneously to reflect both urban and
rural landuses.
• Account for BMPs whose remediation properties overlap within a basin.
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Appendix I
Graphic and Tabular Product Examples
The following examples are simplified versions of the PLOAD products.
Nonpoint Pollutant Loading Application for ArcView GIS
Hampton Roads Planning District Commission (HRPDC)
Example One: City of Norfolk
Watersheds and BM Ps
Land Use
Total Phosphorus
Areal Loads, pounds/acre/year
Watersheds and BM Ps
Example Two: City of Portsmouth
Land Use
Total Suspended Solids
Total Load, pounds/year
-------
Pollutant Loads Per Acre by Basin - Map
Summer Total Phosphorus Load
/ Basin Area with BMPs
(Ibs/ac/yr)
Total Phosphorus
Loads, Ib/ac/yr
<0.20
0.20 - 0.40
0.40 - 0.60
0.60 - 0.80
>0.80
Plot Date : June 15,1999; j:\ploadappWabeach\w_bmps\summertvb_summer_bmp.apr
10
-------
Event Mean Concentration by Basin - Map
Roanoke
EMC By Basin
Pollutant: TSS
Units: mg/l
Basin
| 1 46.321
.321 - 56.145
56.145- 60.006
60.006- 64.477
64.477- 68.377
11
-------
Pollutant Loads, Event Mean Concentrations, and Pollutant Loads
by Basin Area - Table
Watershed
1
10
11
12
13
14
15
16
Count
10
15
9
10
11
10
8
9
Area
4948
12153
4019
4894
4065
6576
2180
6248
Total Load (lb./yr.)
BOD
102555
150439
37662
106127
74297
74682
37024
56488
TSS
665717
1034060
360819
676898
530243
635766
231704
540617
Event Mean Cone.
(mg/L)
BOD
8
7
4
8
9
4
5
4
TSS
53
49
38
54
65
35
34
41
Areal Load
(Ib./acre/yr.)
BOD
21
12
9
22
18
11
17
9
TSS
135
85
90
138
130
97
106
87
-------
Pollutant Loads by BMP, Landuse and Basin - Table
1
2
3
4
5
6
7
8
"9
10
11
12
13
14
15
16'"
17"
18"
19"
20"
21"
22"
23"
24"
25"
_____
27
_____
~29~
______
_____
_____
1
Roanoke AreaSum.dbf Load in Ibs/yr
Merge
BAG AC no
BAG AP no
BAR OS no
BAR OW no
BAR PP no
BAR R1 no
BAR R12 BASIN
BAR R12 no
BAR R13 no
BAR R14 no
BAR R18 no
BAR R2 no
BAR WF BASIN
BAR WF no
BCA AC no
MAS AC no
MAS AP no
MAS BR DRY POND
MAS BR no
MAS CD DRY POND
MAS CD no
MAS ID no
MAS OS DRY POND
MAS OS no
MAS PP DRY POND
MAS PP no
MAS R1 no
MAS R12 no
MAS R13 no
MAS R14 DRY POND
2
Prefix
BAG
BAG
BAR
BAR
BAR
BAR
BAR
BAR
BAR
BAR
BAR
BAR
BAR
BAR
BCA
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
MAS
J^^^^^.
Land
AC
AP
OS
OW
PP
R1
R12
R12
R13
R14
R18
R2
WF
WF
AC
AC
AP
BR
BR
CD
CD
ID
OS
OS
PP
PP
R1
R12
R13
R14
4
Acres
2732.759
553.5726
193.2443
1 1 .7574
21.3951
21.6817
1.362
473.8863
86.0562
189.1171
18.6515
15.0539
10.0423
1427.735
141.9152
563.3961
76.5106
0.0225
26.5379
2.5089
470.054
60.7858
9.8503
468.2329
2.0393
105.4346
335.8436
105.9188
14.6245
0.2368
___5__^
LD TSS
189519.2
35991 .32
11726.45
0
8067.278
2015.318
61.3881
53397.58
11394.71
31011.06
5045.605
924.0412
226.3437
80449.39
9841.941
39072
4974.446
0.5461
1610.373
380.9389
178426.7
27666.18
239.0944
28413.31
307.577
39755.38
31216.72
11934.95
1936.432
15.532
-------
Appendix II
PLOAD Dialogue Menus
Pollutant Loading Parameters - Dialogue Design Menu
Q Pollutant Loading Parameters
n;
Name Your Study Area
[ Roanoke
Define Basin Coverage
(* I hAdemQsVcd_TQQt\pload_data\FQanoke\Ba£in Prefix
Define Landuse Coverage
& I hAdernQs\cd_rooKplQad_data\fQanoke\Landuse Land
Basins Selected
Count Land Prefix Acres
66 AC BAC 2732.7594
34 AP BAC 553.5726
17 BR BAC 322.2099
4 CD BAC 21.1613
4 ID BAC 23.2747
1 HC BAC 65.2926
9 OS BAC 163.1220
1 OW BAC 2.7257
2 PP BAC 54.8176
3
u"
Calculation Method Setup—
(* I-. • • .• -;':?n Method
Calculation Method I Simple Calculation Method
Use Best Management Practices?
f res r
BMP Coverage | hAdernos\cd_root\pload_dataSroanoke\brnpserv
Basin/Landuse Intersection
(• Intercect withArcview
f~" Preexisting Intersect Coverage
Output Options
r Pollutant Load By Basin
r Pollutant Load I Basin Area
T Basin EMC by Pollutant
<~ Pollutant Load i Landuse Type I Basin
<~ List of Data Sources and Parameters
Load
Session
14
-------
Tabular Data Definition - Dialogue Design Menu
& Tabular Data Definition
Choose a Calculation Method
"Simple" Calculation Method
Load EMC Table | h:\demos\cd_root\pload_data\roanoke\roanlut.xls
[Load imfieryjous T abiej] h:\derrios\ed_root\pload_data\roanoke\roanlut.xls
Landuse Field I |and
Impervious Rating Field I jmp
" Export Coeffiecient" Calculation Method
| Load Export Coefficient Table
Landuse Field
Pollutant Fields [
15
-------
Define Best Management Practices Parameters - Dialogue Design Menu
Load Best Management Practices (BMP) Coverage and Look Up Table
Done
Load BMP Coverage h:\demos\cd_root\pload_data\roanoke\bnnpserv polygon
; 1 hAdem<
Service Area Field
BMP Type Field
f~ i.L.oad.BM.P.L.fiokui5.iab!el h:\demos\cd_root\pload_data\roanokeMubnnp.xls
Pollutant Fields I TSS
BMP Type Field I brnpjype
16
-------
Using GIS to Analyze the Spatial Distribution of Environmental,
Human Health, and Socio-Economic Characteristics in Cincinnati
Christopher Auffrey and Xinhao Wang
School of Planning, University of Cincinnati
ABSTRACT
The hazardous byproducts of commercial processes are frequently released into the
environment at operations sites. There are two ways that such sites may affect the health of
nearby residents. The first is a direct threat to human health from the hazardous materials
released purposely or inadvertently from the sites. The second is an indirect threat, involving the
perception of a hazard that affects both stress levels and property values, which in turn lead to a
lower quality of life for people living nearby. Sites where the likelihood of substantial hazardous
releases is relatively high, or where environmental degradation has taken place have become a
major concern of environmental planners, especially when they are located near densely
populated urban neighborhoods. These urban sites may not be evenly distributed spatially, but
rather, create an uneven pattern of resident exposure. Therefore, assessment of the
environmental effects of such sites is inherently geographic and spatial analysis can be used to
reveal the patterns, and assist efforts to understand the processes by which the patterns have
evolved.
This paper presents a study analyzing the spatial distribution of potential pollution sources,
mortality rate and other socio-economic indicators by census block groups within the city of
Cincinnati, Ohio. Geographic Information Systems (GIS) and statistical analysis tools are
combined to assess the spatial and temporal variations. The sites included are those included
on the Ohio Environmental Protection Agency's (OEPA) Master Sites List (MSL) and the U. S.
Environmental Protection Agency's (USEPA) Toxic Release Inventory (TRI). The annual release
data from TRI sites were also retrieved for analysis. The results show no correlation of the
annual mortality rates with annual amounts of toxic chemical releases into the air from the TRI
sites. The block groups closest to MSL sites were found to have significantly higher age-
adjusted mortality rates than those farther away, though no significant difference was found
etween the census block groups closer to the TRI sites and those farther away. When both the
MSL sites and the TRI sites were included in the spatial analysis, the mortality rates for the
-------
census block groups closest to the sites were higher than those farther away, at higher
significance levels than was found for just the MSL sites. Furthermore, census block groups
closer to the potential pollution sites were found to be poorer, less educated and have a larger
proportion of minority residents. Race and median rent were found to be significant predictors of
the proximity of a census block group to a hazardous material site, after controlling for other
socio-economic factors. After controlling for other socio-economic factors, race and proximity to
MSL sites were significant predictors of age-adjusted mortality, though proximity to TRI sites
was not.
The study demonstrates while the direct impact from hazardous sites on human health was not
observed, the spatial connection can not be ignored. Although the associations do not establish
causality, they provide the basis for further study to assess how the propinquity of poor and
minority communities to hazardous commercial wastes contributes to their significantly higher
mortality rates. Further, an investigation is indicated for the role of institutional factors in siting
low-income and minority housing near environmental hazards. The findings here will support
better design, implementation and evaluation of policy alternatives to improve the quality of life
for urban residents. This study also demonstrates the usefulness of GIS for assessment of
potential environmental hazards.
INTRODUCTION
The social and health implications of environmental degradation have drawn increasing
attention among scholars and policy makers in environmental, social, and human health studies.
Investigations of hazardous waste sites have demonstrated health effects in exposed persons,
including low birth weight, cardiac anomalies, headache, fatigue, miscarriages, respiratory
problems, neurobehavioral problems and higher cancer rates (Berry & Bove 1997, Geschwind
et. al. 1992, Washington 1994, NRC 1991). Clearly, there are reasons to be concerned about
the effect of hazardous waste on human health. Although there is widespread agreement that
exposure to hazardous waste may be adding to our disease burden in significant, although as
yet not always precisely defined, ways (USDHHS 1980) the social and health effects of
exposure to environmental contamination have been the subjects of considerable controversy.
Sites where substantial amounts of hazardous waste are released, or where environmental
degradation has taken place have become a major concern of environmental planners,
especially when they are located near densely populated urban neighborhoods. Spatial analysis
may be used as a tool to investigate if these sites are evenly distributed spatially or create an
-------
uneven pattern of exposure for certain group of people. The spatial pattern, or lack of it, may
provide an important step in describing problems and in formulating and testing hypotheses
about possible links between environment hazards and quality of life and lead to studies that
can help direct action to areas of greatest need.
Beyond the consideration of health effects of hazardous emissions on urban populations
generally, the disproportionate exposure of poor and minority populations raises important
issues of environmental justice. Environmental justice has been defined as the equitable sharing
of the adverse effects of pollution across the spectrum of social, economic and political power
(Bryant & Mohai 1992; Bullard 1993; Xia et al. 1997). This implies that public policies and
regulations, including the siting of polluting industries or the permitting of toxic releases into the
environment, should not disproportionately expose minorities or the poor to environmental
hazards (Bullard 1997). Unfortunately, we still lack full knowledge of many of the relationships
involved, especially spatial relationships. Studies investigating environmental justice issues
have generally concluded that minorities and the poor are likely to have greater exposure to
toxic landfills, waste incinerators, hazardous industrial facilities and other environmentally
detrimental activities (Bullard 1993, Bryant & Mohai 1992, Buntin 1994; Ortolano 1997;
Ringquist 1997; Bullard 1997, Burby 1997, Vos 1997). Other work has found that commercial
hazardous waste treatment, storage and disposal facilities (TSDFs) were not more likely to be
located in poor and minority communities (Oakes et al. 1996; Anderton et al.1994), though a
more recent study came to an opposite conclusion (Boer et al. 1997). Several researchers have
raised questions about the methods used in some of the early environmental justice studies.
Results were found to be different depending on whether a large (county or zip code) or small
(census tract) geographic unit was analyzed (Bowen et al. 1995, Anderton et al.1994). The
effective handling of spatial information is essential to facilitate an appropriate public policy
response. Planners, public officials and residents concerned with quality of life and
environmental justice require analytical tools that enable them to identify and initiate responses
to potential threats.
These tools must allow for determining the possibility of a significant health threat and the need
for more in depth epidemiological, environmental or land use analyses. The combination of
geographic information systems (GIS) and statistical analysis comprises just such a tool to
analyze health, environmental and demographic data. GIS allows the construction of maps,
identification of nearest neighbors, and display of spatial relationships. A series of patterns,
-------
each for a different variable of interest, may be created and combined to reveal
correspondences and disparities. GIS functions, such as the storage, retrieval and manipulation
of spatially related data also allow the aggregation of data collected from varying sources. This
data, in turn, can be used to develop a composite description, and to explore associations, such
as the use of distance buffers to identify how the effects of a potential hazard may change as
one moves father away (Wartenberg 1993).
It is within this context that this study uses GIS and statistical analysis to describe and analyze
the spatial relationships between human health, race, socioeconomic status (SES) and the
proximity to potential environmental hazardous sites in Cincinnati. The potential environmental
hazards used in this study are sites of reported industrial releases for toxic chemicals or where
improper hazardous materials management has resulted in environmental contamination.
Mortality rates calculated at the 1990 census block group level were used as the human health
indicator. Socioeconomic status was also evaluated at the census block group level. This study
describes the spatial distribution of environmental, human health, and socioeconomic
characteristics in Cincinnati. The relationship between the mortality rates, SES and proximity to
hazardous sites is also addressed.
DATA
The study area was the City of Cincinnati, Ohio (1990 city population 364,040; 1990
consolidated metropolitan statistical area population 1,744,124). Census block groups from the
1990 U.S. Census were used as the unit of analysis. To integrate the data for this study, five
types of digital data files were compiled: 1) death records, including location of residence, date
and causes of death for all persons who died in Cincinnati from January 1, 1986 to December
31, 1994; 2) locations of potential environmental hazardous sites; 3) 1990 U.S. census block
group population and housing and other socioeconomic characteristics for Cincinnati; 4) census
block group boundary for Cincinnati; and 5) streets in Cincinnati.
Records of all deaths reported to the Cincinnati Health Department for the years 1979 to 1994
were obtained from the Cincinnati Health Department. The data file contained 35 data items for
each of the death records, including last residence of the diseased, cause of death, birth and
death dates, and limited socioeconomic information such as race, number of years of education,
and state of birth (CHD 1994). Records for Cincinnati residents who may have died outside the
-------
city were not included. Also, we excluded the records for non-Cincinnati residents who died in
Cincinnati since the study area was limited to the City of Cincinnati.
Two types of environmental contamination and hazardous waste release data were collected:
the 1994 Master Sites List (MSL) from the Ohio Environmental Protection Agency's (OEPA)
Division of Emergency and Remedial Response (DERR); and the 1992 Toxic Release Inventory
(TRI) Annual Report, from OEPA's Division of Air Pollution Control. The MSL is a database of
sites in Ohio where there is evidence of, or it is suspected that improper hazardous waste
management has resulted in the contamination of air, water or soil, and there is a confirmed or
potential threat to human health or the environment. The MSL includes a diversity of sites of
varied environmental concerns. In addition to those sites in the USEPA's Comprehensive
Environmental Response, Compensation, and Liability Information System (CERCLIS) prior to
1989, sites were added to the MSL listing by DERR staff based on inter-program referrals,
citizen complaints, or DERR's discovery efforts. The DERR updates the MSL annually and sites
may be delisted if formal remediation has been completed (DERR 1994). Examples of the MSL
sites are chemical companies where spills or improper storage or disposal have taken place,
and closed landfills that pose a threat because of poor or antiquated design and hazardous
contents. The MSL database records contain a field with the street address for each site.
The TRI report contained annually compiled data on the quantity and location of industrial
releases for approximately 300 toxic chemicals and 20 chemical categories (Bowen et al. 1995).
Manufacturing firms subjected to Title III, Section 313 of the federal Emergency Planning and
Community Right-to-Know Act of 1986 are included in the TRI list. These firms are required to
report the location and amount of toxic chemicals released to the air, water, or land. In Ohio, the
TRI Program within the Division of Air Pollution Control of the OEPA coordinates the collection,
digitizing and distribution of TRI data. The TRI sites include a broad range of industrial facilities,
from manufacturing and food processing, to chemical plants. Like the MSL database, the
locations of the TRI sites are stored in a street address field.
Census population data were directly extracted from the U.S. Census Bureau's 1990 Summary
Tape File (STF) 3A. The census data are considered the most reliable source for population
information by geographic area. Data used in this study include population by age group for
each census block groups within the city of Cincinnati and population by age group for the state
of Ohio. The total numbers of deaths in Ohio by age group were obtained from the Ohio
-------
Department of Health (ODH 1992). The Census block group boundary data were extracted from
the First Street geographic data files produced by Wessex Inc. The First Street files were
compiled and enhanced from the U. S. Census Bureau's 1992 Topographically Integrated
Geographic Encoding and Referencing (TIGER) files. The files contained graphic data (maps)
defining census block group boundaries and associated attribute data. There are 417 census
block groups in the City of Cincinnati, of which 22 had no residents in 1990. These were
eliminated, thus providing a total of 395 census block groups for this study. The street address
data used in the study were the 1994 TIGER files. The street files contained street name and
address ranges for street sections in Hamilton County.
METHODS
Data files were integrated based on their spatial location. GIS functions, including geocoding,
buffering, and overlay analysis, were used to complete the tasks. The location of TRI and MSL
sites were identified with GIS geocoding function based on the street addresses in the data file
and the TIGER street file. After the distance from census block groups to the nearest hazardous
sites were calculated, the block groups were divided into six buffer zones based on an 800-
meter (0.5 miles) interval to the nearest MSL and TRI sites, respectively. Then, statistical tools
were used to calculate mortality rates for each census block group and identify significant
predictors of mortality rates from among proximity to hazardous site variables and selected
socioeconomic status (SES) variables. ArcView, a GIS software program (Environmental
System Research Institute, Inc., Redlands, CA) was used for spatial analyses. SPSS, a
statistical analysis software program (SPSS Inc., Chicago, IL), was used for statistical analysis.
To control for the age effects when comparing mortality rates, age-adjusted rates were
calculated for each census block group using the direct age-adjustment method (Friedman
1994) based on nine-year average crude mortality rates and population age cohorts for the state
of Ohio.
Socioeconomic status indicators were selected to reflect known mortality risk factors, as well as
to provide insight to conditions found in each block group. The following indicators were
selected: length of residence in unit (percent of households living in unit more than 10 years);
median household income; median housing unit value (owner-occupied units); median
household rent (renter-occupied units); percent of persons 25 years or older with less than a
ninth grade education; and percent of population that is African American. A table of bivariate
-------
correlation coefficients was constructed for the relationships between age-adjusted mortality
rates, distance of the census block group from a hazardous site and the SES indicators.
Multiple linear regression analysis (ordinary least squares) was used to identify statistically
significant predictors for proximity to MSL and TRI sites. Based on the correlation table of SES
indicators, four were selected for inclusion in the regression analysis (1) percent of residents in
home for more than 20 years, 2) median rent, 3) percent of population African American, and 4)
percent of population over age 25 with less than nine years of schooling). To avoid multi-
collinearity among the predictor variables, median income and median home value were not
included in the regression. Median household income was strongly correlated with median
home value and median rent, and moderately correlated with percent African American. The
Durbin-Watson statistic was calculated and plots of residuals analyzed to check for
autocorrelation and heteroscedasticity. No evidence of these problems was found.
RESULTS AND DISCUSSION
Among the 1979-1994 death records in the Cincinnati Health Department's data file, 96,440
were identified as Hamilton County residents. Of these, 96,235 were geocoded (99.8%) based
on the recorded street addresses. After excluding accidental causes of death, this study used
records for 31,526 decedents who were Cincinnati residents at the time of their death and died
between January 1,1986 and December 31, 1994.
The locations for 75 MSL sites and 131 TRI sites in Hamilton County were geocoded based on
the street addresses of the sites. Only 12 sites were found on both the TRI and MSL lists. The
MSL sites were scattered within the central part of the city while there were no MSL sites in the
northwestern and southeastern portions of the city (Fig. 1). Several MSL sites were located in
the southern part of the city, just to the west of the central business district. Most MSL sites
were near major highways or close to waterways. The census block groups within 800 meters
(approximately 0.5 miles) of the MSL sites, taken as a whole, were almost entirely contiguous,
except for a few in a small area in the western part of the city.
The distribution of TRI sites shows a similar pattern to that of the MSL sites (Fig. 2). There were
only a few TRI sites in the western portion of the city, mostly located along the narrow corridor in
the southwestern corner. Similarly, there were only a few TRI sites in the southeastern portion
of the city. The TRI sites were even more concentrated along the Mill Creek and two major
-------
highways, 1-71 and I-75, than were the MSL sites. It should be noted that a number of TRI sites
were found in the two communities (Norwood and Elmwood Place) surrounded by the city of
Cincinnati. Those sites were included in the analysis since their impact would not stop at the
political boundaries.
The age adjusted mortality rates by census block groups in Cincinnati are displayed in Fig. 3.
Mortality rates are higher in the older, less affluent areas in the center and west portions of the
city. The newer areas to the east generally have lower mortality rates. Figures 4 and 5 show the
percent African American by block group and median household income by block group,
respectively. Most block groups with higher percentage of African American population are in
the center part of the city. To a large degree, the low median household income block groups
correspond to the high percentage of African American population.
Table 1 shows the mean values for age-adjusted mortality and the selected SES characteristics
for each of the six MSL zones. The zones further away from MSL sites tend to have lower
mortality rates, illiteracy and percent African American and higher income, rent, and home
value. Similar pattern can be found for the percent of residents in their home more than 20
years, but to a lesser extent. When the similar comparison is made to the TRI sites, the results
are similar to, though less pronounced than, those for the MSL zones (Table 2).
The regression results for predicting the proximity of a census block group to a MSL or TRI site
are presented in Table 3. Percent African American, median rent and percent adult illiterate
were significant predictors at a 0.05 level or better for proximity to MSL sites. Length of
residence was nearly significant (p. =0.0977). This model predicts a block group that has 75
percent African American residents, and citywide average values for the other SES variables
will be located 0.85 miles from a MSL site, compared to 1.15 miles for a block group that has 25
percent African American residents. Percent African American, median rent and percent of
residents residing for more than 20 years were significant predictors at a 0.05 level or better for
proximity to TRI sites. The model predicts a block group that has 75 percent African American
residents, and citywide average values for the other SES variables will be located 0.8 miles from
a TRI site, compared to 1.05 miles for a block group that has 25 percent African American
residents.
-------
The regression results for predicting age-adjusted total mortality and age 35-64-cancer mortality
are presented in Table 4. Percent African American, median rent, length of residence and MSL
zone were significant predictors at an 0.05 level or better for predicting total mortality. The
model predicts a block group located in MSL Zone 1 will have a 24 percent higher mortality rate
than average. For predicting age 35-64 cancer mortality, percent African American and median
rent were significant predictors at a 0.05 level or better. This model predicts a block group
located in MSL Zone 1 has a 26 percent higher mortality rate than average.
The results show that the proximity of a census block group to a MSL site is a significant
predictor of age-adjusted mortality rates, even after controlling for socioeconomic risk factors.
However, the reasons for this association are not clear. Given that an association has been
confirmed, a natural expansion of the research will be to explain the nature of the relationships
involved. There may be direct causal relationships, or there may be synergistic effects involving
several factors. Also, it is likely there are other confounding factors which are related to both
proximity and health status. The effect of social stress on community mortality differentials has
been receiving increasing attention, and may be a factor here. Closer analysis of the variation in
mortality rates among subgroups may provide additional insight, as may scrutinize other factors
such as property values, health care access and utilization, and health-related personal habits.
Environmental justice can be considered to entail two components: distributive justice that
relates to the status of spatial relationships, and procedural justice that relates to the processes
by which spatial injustices came about. The results here show that elements of distributive
injustice are clearly present in Cincinnati. The market economy in the U.S. dictates where
people live based on their ability to pay. Consequently, it might be argued that the disparity
identified is simply the result of "the invisible hand" of the market. However, there are at least
two reasons to suggest that non-market institutional factors may be major contributors to the
apparent bias against the poor, less educated and minority. First, several of the largest
residential concentrations closest to the hazardous sites are public housing complexes built
over the past forty years. Decision-makers may have determined that the nearby industrial sites
posed no hazards, or, more likely, simply failed to consider the issue. That the real estate was
relatively inexpensive, and separated from existing neighborhoods made these sites politically
attractive for situating public housing. In either case, hindsight suggests this decision making
was flawed and biased against the current public housing residents. Second, many of the
hazardous facilities were built or substantially expanded over the past forty years, often on
-------
existing industrial sites that had been in use for a century of more. While the industry may have
been there first, the residences may have been built when the quantity and toxicity of the
contamination and releases of nearby industry posed a greatly reduced threat. In such a
situation, the residences have remained the same, but the hazards posed by the nearby
industry have increased substantially. In such cases it would seem that environmental
permitting and land-use regulations have failed to adequately protect residents from the
evolving hazards of modern industrial processes. To the residents it makes little difference
whether their increased exposure to environmental hazards was the result of a bias in permit
decisions (Boer et al. 1997) or simply a reflection of the institutional neglect fostered by existing
power structures. Both explanations recommend changes in planning processes that would give
greater consideration to the hazards resulting from the way in which industrial land is used. For
example, performance zoning might be used, based on an enlightened, more comprehensive
view of the hazards of industrial use, rather than the current system that assumes all permitted
hazardous waste releases pose no hazard to nearby residents.
CONCLUSIONS
After examining the spatial distribution of hazardous waste sites, mortality rate and six
socioeconomic indicators at the census block group level in Cincinnati, we found associations
between the distance to hazardous waste sites and the mortality rate and selected
socioeconomic indicators. The study has demonstrated the need for more in-depth
investigations to elucidate the processes by which the offending spatial relationships evolved so
that the residential areas closest to potential environmental hazards are poorer, less educated,
and minority. An interdisciplinary team from urban and environmental planning, public health,
and environmental engineering is needed to address the complex issues involved. Clearly,
developing a better understanding of the fundamental nature of the relationships would be of
great benefit to planners assisting a wide-range of land use-related decisions.
The locations of hazardous waste sites should be considered in making decisions regarding the
appropriate land use for nearby properties, as well as environmental remediation and pollution
prevention. Planners and communities must take steps to ensure that hazardous sites are
managed in effective ways in order to prevent the exposure of nearby residents to dangerous
levels of toxins. Industrial facilities cannot be permitted for hazardous releases, even in small
quantities, if the cumulative effect of multiple small releases creates health hazards. Steps must
be taken to avoid multiple small hazardous releases that are effectively equivalent to the
10
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hazards posed by more seriously contaminated individual sites. Further, while the synergistic
effects of likely chemical combinations are not well understood, explicit consideration of the
potential for such effects would seem prudent, and should become an integral part of standard
land use planning. Also, when resources are allocated for environmental remediation, higher
priority generally should be given to sites that individually pose more serious problems, yet the
impact of other nearby sites must also be considered.
In this study, we integrated GIS and statistical analyses to examine the spatial distribution of
environmental, human health, and socio-economic characteristics in Cincinnati. For this project,
the importance of innovation based on a solid scientific foundation cannot be overly stressed. In
the current economic and regulatory climate, sound GIS methods are emerging as convincing
and cost effective means for analyzing multiple factors in a project like this one. An important
issue in environmental research concerns the development and use of analytic tools that can
provide rapid, reliable and valid assessment for use in planning, implementation and evaluation
of policy alternatives. These tools should give both researchers and policy-makers new insight
into the consequences of environmental interventions, and facilitate a clear view of the social,
economic and political context for which such interventions are proposed. Just as important as
the technological advances is the higher-order systematization of geographical thinking that GIS
development initiates.
11
-------
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14
-------
MSL Sites
MSL Buffer
Zone 1
Zone 2
Zone 3
Zone 4
Zone 5
Tonp 6
A
N
n
42 Mies
Figure 1. Block Groups by MSL Buffers
-------
TRI Sites
TRI Buffer
Zone 1
Zone 2
Zone 3
Zone 4
Zone 5
Zone 6
A
N
£ Mes
Figure 2. Block Groups by TRI Buffers
-------
MSL Sites
TRI Sites
totality Rate/1,000 Rjp
0-5.3
5.3--7.2
7.2-10.0
10.0-37.1
A
N
_L2 Mes
Figure 3 Total Mortality Rate by Block Group
-------
TRISkes
MSLSkes
ilAfrican American
0-13
_l 13 -21.1
U21.1 -77.3
77.3-100
A
-12 Mes
N
Figure 4. Percent of African American by Block Group
-------
TO Sites
IVBLSites
iHH Income
LJ $4,999-$14,107
$14,138-$20,927
$21,083-$28,482
$28,523-$121,716
A
_L2 Mes
N
Figure 5. Median Household Income by Block Group
-------
Table 1 Differences in Mortality and Socioeconomic Characteristics by MSL Zone
Variable
Mortality
Income
Rent
Home Value
Illiteracy
20+ Yrs. Residence
African American
Zone 1
9.9
$18,586
$305
$59,561
14%
16%
47%
Zone 2
9.6
$22,210
$358
$65,792
11%
17%
39%
Zone 3
8.9
$27,667
$376
$88,895
9%
17%
20%
Zone 4
5.0
$34,952
$428
$87,350
8%
24%
17%
Zone 5
6.9
$36,159
$398
$94,880
11%
16%
1%
Zone 6
3.4
$29,639
$389
$69,599
10%
22%
2%
Table 2 Differences in Mortality and Socioeconomic Characteristics by TRI Zone
Variable
Mortality
Income
Rent
Home Value
Illiteracy
20+ Yrs. Residence
African American
Zone 1
9.6
$19,696
$330
$56,753
14%
17%
46%
Zone 2
9.8
$22,199
$352
$75,097
11%
18%
40%
ZoneS
9.4
$24,150
$323
$92,643
12%
13%
35%
Zone 4
7.3
$26,650
$389
$66,381
8%
13%
10%
Zone 5
5.3
$31,408
$367
$68,222
6%
23%
2%
Zone 6
6.5
$27,947
$389
$68,495
8%
23%
1%
Table 3 Multiple Linear Regression Predicting Proximity to Hazardous Sites
Independent Variables
African American
Rent
20+ Years Resident
Illiteracy
Constant
SigF
Adj. R2
N
Dependent Variable:
Proximity to MSL Sites
Coefficient p
-1.2290 0.0000*
-0.0014 0.0284*
0.9121 0.0977
-1.8607 0.0141*
3.1618 0.0000
0.0000
0.1247
389
Dependent Variable:
Proximity to TRI Sites
Coefficient
-0.9243
0.0159
1 .2279
-0.6793
1 .6205
0.0000
0.1555
389
P
0.0000*
0.0068*
0.0122
0.3109
0.0000
Differs from zero at 0.05 level of significance.
20
-------
Table 4 Multiple Linear Regression Predicting Mortality
Independent Variables
Dependent Variable:
Total Mortality
Differs from zero at 0.05 level of significance.
Dependent Variable:
35-64 Cancer Mortality
African American
Rent
20+ Years Resident
MSL Proximity
TRI Proximity
Constant
Sig F
Adj. R2
Sample Size
Coefficient
0.0028
-7.1930E-06
-0.0078
-5.0456E-04
-1.9533E-04
0.01343
0.0000
0.1879
389
P
0.0001*
0.0014*
0.0001*
0.0182*
0.2985
0.0000
Coefficient
9.4241 E-04
-4.4588E-06
1 .5067E-04
-1.0440E-04
-9.2487E-05
0.0042
0.0000
0.0942
392
P
0.0088*
0.0001*
0.8823
0.3467
0.3413
0.0000
21
-------
Verification of Contaminant Flow Estimation With GIS and Aerial Photography
Thomas M. Williams
Clemson University, Georgetown, South Carolina
Abstract
Estimation of contaminant movement in ground water
requires interpolation of data from sampling wells that
represent a very small sample of aquifer volume. Spatial
statistics and kriging provide the best unbiased estima-
tor of interpolated concentrations. Hurricane Hugo
provided an opportunity to compare these estimators
with actual forest mortality caused by saltwater inunda-
tion associated with the tidal surge. During the 9- to
15- month period after the hurricane, salt from the tidal
surge moved within the shallow water table aquifer,
causing widespread tree mortality on Hobcaw Forest in
eastern Georgetown County, South Carolina. A small
watershed (12 acres) was instrumented with 24 multi-
level sampling wells. Piezometric potential and samples
for salt concentration were collected for 12 months
(months 18 to 30 after the tidal surge). These data
produced three-dimensional estimations of flow direc-
tions and two-dimensional maps of chloride concentra-
tion. These maps led to the identification of important
heterogeneities in the water table aquifer. Apparently,
the infiltrated salt water moved to the bottom of the
aquifer (15 feet) and emerged, killing the forest, where
aquifer heterogeneity resulted in upward movements of
ground water.
Georgetown County implemented a geographic infor-
mation system (GIS) for tax mapping in 1988 and pre-
pared 1:400-scale orthophotographs of the entire county
with true ground accuracy of less than 5 feet. Color
infrared aerial photographs were taken from a Cessna 150
platform annually after the hurricane. ERDAS GIS soft-
ware and the accurate photo base allowed removal of
scale irregularities and distortion that resulted from using
a small aircraft. Scanned images, using a 10-square-foot
pixel, were compared with kriged chloride concentration
maps, also using a 10-foot cell size. Grid cells with
estimated chloride concentration of more than 500 mil-
ligrams per liter also exhibited low reflectance in the
infrared-enhanced color band, indicating tree mortality.
Here, a small number of sampling wells accurately pre-
dicted ground-water movement of a contaminant (NaCI),
and GIS and remote sensing verified this movement.
Introduction
Estimating contaminant flow in ground water is difficult
because we cannot "see" the aquifer. We know that
aquifers comprise sediments that vary from place to
place, that changes in hydraulic conductivity determine
the rate of water movement, and that the spatial variabil-
ity of the aquifer sediment determines the hydraulic
conductivity. Our inability to accurately represent spatial
variability of the aquifer limits our ability to predict
ground-water flow and, thereby, contaminant transport.
A large variety of prediction models are available (1-3),
and stochastic methods of estimating spatial heteroge-
neity have been developed (4, 5) and tested (6, 7). On
well-characterized field sites, these techniques can pro-
duce predictions of tracer movements that accurately
predict experimental plumes in terms of mass behavior.
Even at these research sites, the spatial distribution of
hydraulic conductivity is not known well enough to pre-
dict behavior at any particular point.
Ground-water measurements generally derive from
wells that are single points. To understand movement of
an entire plume, these single point samples must be
extended to represent areas. The geostatistical ap-
proach allows quantitative estimation of the spatial vari-
ation of point estimates (8). Kriging is a technique that
uses spatial covariance to estimate values at points
where no measurement exists (9). It produces the best
linear unbiased estimator of nonmeasured points (8).
Following Hurricane Hugo, these techniques were used
to study saltwater movement in the water table aquifer
in forested stands of eastern Georgetown County, South
Carolina. Clemson University received a grant from the
U.S. Forest Service to examine forest mortality and
regeneration success within the forest zone covered by
salt water during the tidal surge. In this study, we used
a small sample of ground water to estimate the direction
-------
and concentration of salt moving in the aquifer. Geo-
graphic information systems (CIS) proved to be a useful
tool to verify conclusions based on the small sample
size. Onsite sampling, aerial photography, vector and
raster CIS, and spatial statistics were combined into one
analysis system. The system estimated and verified
directions of salt movement within the aquifer. CIS and
remote sensing of forest mortality produced an inde-
pendent indicator of salt movement that could be com-
pared with the geostatistical technique.
Problem Statement
The main goal of the research project was to evaluate
problems for forest regeneration in areas covered by salt
water during the hurricane. In many of these areas, the
mature trees died during the summer following the hur-
ricane. These areas have very low elevation, little relief,
and abundant rainfall, causing the water table to remain
near the soil surface. The hypothesis was that salt
movement within the aquifer killed the mature trees and
could limit regeneration success. We divided the prob-
lem into three tasks: to determine if salt concentrations
in the aquifer were high in areas where mature trees
died, to determine pathways of salt movement within the
aquifer that could explain high salt concentrations, and
to predict regeneration success from the pattern of salt
movement.
CIS contributed both to testing the initial hypothesis and
to extending predictions to areas not initially studied.
CIS has been used primarily to store and display spatial
data in a way that preserves and presents the spatial
relationships as well as the data. For this project, we
collected two dissimilar data types. To determine salt
movement, we measured salt concentrations and pie-
zometric pressures in a series of wells. CIS had to
represent the well data and the domain of the kriging
procedures in a coordinate system compatible with the
mortality data. We determined forest mortality data from
infrared-enhanced color aerial photography. CIS also
had to allow separation of the infrared signature of the
photography, transform the signature into data that were
comparable with the well data, and ensure that the
coordinate systems of the well data and the mortality
data represented the same true ground positions.
To use the CIS ability for this project, we needed to
choose several CIS parameters. In this case, we esti-
mated ground-water chloride concentration using
kriging, which produced data on a grid comparable with
mortality interpreted from photographs. A raster CIS
representation could be compared with individual grid
chloride values. Each grid cell was 10 square feet so
that each cell would be within a single tree crown.
Methods
Study Location
The study was located on 12 acres of a small watershed
located on the eastern side of Hobcaw Forest, an ex-
perimental forest managed by Clemson University, De-
partment of Forest Resources. Hobcaw Forest is
located on the end of a peninsula between the Winyah
Bay and the Atlantic Ocean in eastern Georgetown
County, South Carolina. The study watershed is located
immediately west of the salt marsh and barrier island
separating the forest from the Atlantic Ocean and is in
Pleistocene-aged beach sediment. Watershed divides
were created by former low dune lines, and the stream
is within a small depression between these former dune
lines. Divides are from 7 to 8 feet above sea level and
the stream from 4 to 6 feet above sea level.
The study watershed is 50 miles northeast of Char-
leston, South Carolina, where the eye of Hurricane
Hugo struck the U.S. coastline. Along this portion of the
South Carolina coast, the tidal surge was approximately
10 feet above mean sea level (10), covering the entire
watershed. Afterthe hurricane, shallow auger holes con-
tained water with sodium concentrations of 4,000 milli-
grams per liter (11). The hurricane winds did little
damage to the watershed forest, but 25 percent of the
large oaks were windthrown (12). Beginning in the
spring of 1990, however, many hardwoods and pines
began dying. By the winter of 1990 and 1991, a large
portion of the forest on the watershed had died. Tree
mortality did not correspond with high salinity measured
by the initial auger-hole method, suggesting movement
in the water table aquifer.
Well Installation
The water table aquifer is about 20 feet thick, consisting
of fine sand similar to the present beach, with thin beds
of shells 10 feet beneath the stream. The bottom of the
aquifer is a bed of clay up to 3 feet thick over a leaky
artesian aquifer composed of shell and sand. Local
rainfall recharges the water table aquifer. Recharge for
the lower aquifer is provided by leakage from the water
table aquifer beneath the center of the peninsula, about
2 miles west of the watershed, where land elevations
are 15 to 25 feet above sea level. Piezometric potential
in the lower aquifer is generally a few inches above the
water table aquifer, making it only weakly artesian (13).
We installed 24 multilevel ground-water samplers (14)
in the water table aquifer. Five samplers were located
in regeneration measurement plots (15) placed within
the stream. Two samplers, one at each edge of the
hardwood wetland, formed a line perpendicular to the
stream at the regeneration plot. Two more samplers
were located along these lines near the watershed di-
vides on each side of the watershed. The 24 samplers
-------
formed five transects across the stream (see Figure 1).
Piezometric potential and ground-water chloride con-
centrations were measured from these samplers from
March 31, 1991, through April 1, 1991. Williams (16)
provides a complete description of samplers, sampling
procedures, and laboratory analysis.
GIS Implementation and Measures
A GIS system for Hobcaw Forest management was
developed in 1987 using Environmental Systems Re-
search Institute's PC ARC/INFO software (17). The in-
itial system consisted of forest stand boundaries
digitized onto 1:100,000 digital line graphs (DLGs) pur-
chased from the U.S. Geological Survey. These rela-
tively crude maps were combined with stand records
and used for management decisions that did not require
exact locations of stand boundaries. Later, management
of the endangered red-cockaded woodpecker's habitat
required that mapped stand lines be closer to true
ground locations than the original DIG data scale al-
lowed. A program of ground surveys and aerial photog-
raphy was conducted in the late 1980s to locate stand
boundaries more accurately (18).
In 1988, Georgetown County began a program to con-
vert county tax mapping to computer-based systems.
The first step was to acquire survey grade orthopho-
tography. Copies of 1:400-scale orthophotographs, with
guaranteed ground accuracy of plus or minus 5 feet,
Location of Sample Points
554740
554576 -
554412 _|
D)
C
!c
t
554248
554084 -
553920
Transect 5
Transect 4
"I" Transect 3
•+ Transect 2
Transect 1
2548470 2548634 2548798 2548962
Easting (feet) SC State Plane 1983 Datum
Figure 1. Position of sampling wells in rectangle defined for
estimation of salinity movements.
became available in 1990. Roads and stand boundaries
were digitized from these photographs into the PC
ARC/INFO database. The new, accurate map was com-
bined with stand records of previous coverages to create
stand record coverages on a map that was true to the
ground within 5 feet, plus or minus 0.3 percent.
In 1991, the GIS programs ERDAS VGA and LIVE LINK
were obtained. ERDAS VGA programs allow image
processing and raster GIS to be done on personal com-
puters with VGA and some Super VGA monitor adapt-
ers. The LIVE LINK program allows display of both
ARC/INFO and ERDAS images on the same monitor
screen. Orthophotographs were scanned using 5-
square-foot pixels and rectified with less than 1-pixel
mean error, giving accurate ground locations of plus or
minus 8 feet.
Ground surveys from a nearby benchmark provided accu-
rate locations of the sampling wells. PC-TRAVERSE per-
formed coordinate geometry from the survey notes, which
was then plotted on the ARC/INFO forest stand data-
base. Accuracy was checked by plotting the recogniz-
able points on the survey with the scanned
orthophotograph using the LIVE LINK software.
In February 1991, the Hobcaw Forest was photo-
graphed with infrared-enhanced color film. In this film,
the red layer is sensitive to near infrared radiation that
is strongly reflected by chlorophyll. Red colors in result-
ing prints indicate living vegetation. The color photogra-
phy was not corrected for scale variation (from small
fluctuation in aircraft altitude) or for distortion (caused by
slight variations in the aircraft attitude). One photograph
(1:1,320 scale) covering the study watershed was
scanned into the ERDAS program. This image was
rectified to the 1988 orthophotograph image using con-
trol points visible on both. A 10-square-foot pixel was
used to sample individual tree crowns. The mean error
of rectification was 1.5 pixels for a ground location, plus
or minus 15 feet.
Ground-water chloride values at any one point varied
over three orders of magnitude in all three dimensions
and over two orders of magnitude with time. Annual
averages of piezometric potential and chloride concen-
tration, however, yielded interpretable results that were
also statistically significant (16). Averaged values could
then be combined with the surveyed sampler locations
in the ARC/INFO system. The GIS also calculated the
corner coordinates for a rectangle that would include all
the sampler locations.
Geostatistical calculations were performed using the
GS+ software. The data input to this program is an ASCII
file of sample point locations in x,y coordinates and data
values. The program allows calculation of semivario-
grams with various combinations of active and maxi-
mum lag distance and fitting of various model types to
-------
best fit the semivariogram (19). An active lag of 65 feet
and a Gausian model produced the best fit:
i(h) = 0.001 + 1.337 (1 - exp [-h2/20736]), r2 = 0.616
where i(h) is the semivariance at lag distance h.
This best fit model was then used in a block kriging (9)
procedure. The procedure used eight nearest neighbors
and calculated average values for 10- by 10-square-foot
blocks. Values were calculated within the rectangle de-
fined by the corner coordinates from the ARC/INFO
procedure described above.
Finally, the rectangle defined around the sampler posi-
tion formed the region of comparison between the rate
of mortality, as sampled by infrared reflection, and esti-
mated average chloride concentrations. The first com-
parison involved mapping reflection in the red band of
the aerial photograph as a gray-scale map and compar-
ing it with the contoured map of chloride concentration.
The rectangle coordinates were used in the ERDAS
software to create a subset of the scanned aerial pho-
tograph that included only the red band in the 5,145
pixels defined by the rectangle surrounding the sam-
plers. In this subset, the infrared reflection was scaled
as a gray-scale value between 0 and 255 for each
10- by 10-square-foot block defined in the concentration
map. In addition to mapping, a regression of chloride
concentration to gray-scale value was performed using
the individual blocks.
Results
Ground Water
Ground-water chloride data reflected a consistent expla-
nation of salt movement. Initial auger-hole data col-
lected within a month of the hurricane indicated most
salt was near the surface of the pine ridges, where,
presumably, salt water had filled the aquifer to the soil
surface during the hurricane. Data collected 30 months
after the hurricane indicated the bulk of the salt had
moved to the bottom of the aquifer underthe pine ridges.
Figures 2 and 3 represent the most significant results
interpreted from the piezometric potential and chloride
concentration measurements.
Figure 2 represents a cross section of the chloride con-
centrations and directions of ground-water flow in tran-
sect 4, the second most northern transect. The common
information represented in this cross section was the
west to east movement of ground water, representing
the regional flow toward the forest edge. Also, there is
an area of upwelling just east of the stream at the bottom
of the aquifer, representing a leaky spot in the underlying
clay layer. Upwelling causes the west to east stream-
lines to rise toward the surface along the western edge
of the wetland. Chloride concentrations indicate large
Transect 4
Chloride Concentration
[] < 50 Milligrams per Liter • 500 to 750 Milligrams per Liter
PI 50 to 100 Milligrams pel
T Liter
750 to 1,000 Milligrams per Liter
[] 100 to 250 Milligrams per Liter g 1,000 to 2,000 Milligrams per Liter
[J 250 to 500 Milligrams per Liter M > 2,000 Milligrams per Liter
Figure 2. Cross section of aquifer at transect 4; gray scale rep-
resents chloride concentration, boxes at sampler po-
sitions represent 95-percent confidence limits of
average chloride concentration in same gray scale,
and arrows are perpendicular to contours of pie-
zometric potential and represent two-dimensional
vectors of streamlines.
reservoirs of salt beneath each of the pine ridges and
small pockets of fresher water near the surface, prob-
ably the result of rain infiltration during the 30 months
since the hurricane. The water upwelling from the arte-
sian aquifer was consistently fresh. Flow passing
through the concentrated zone beneath the western
ridge was pushed to the surface beneath the stream,
where evaporation caused chloride concentrations to
average more than 1,000 milligrams per liter.
Figure 3 represents two plan views of the site at depths
of 4 and 12 feet below the surface. The 12-foot plan view
indicated that the east to west flow in transect 4 is only
the east vector of a southeast flow. Other areas of
upwelling exist beneath the stream. Chloride concentra-
tions are highest beneath both ridges and lowest in the
stream center. At the 4-foot depth, the east and south-
east flows are also obvious. Also, as deeper flows from
the western ridge are turned to the surface, high con-
centrations of chloride are present near the surface.
High concentrations within the wetland result from water
being carried to the surface due to upwelling within the
wetland.
GIS Evaluations
The ground-water interpretations show a consistent ex-
planation of salt movement. These interpretations are
-------
Plan View 12-Foot Depth
Plan View 4-Foot Depth
Figure 3. Plan views of aquifer at depths of 4 and 12 feet below the surface with same chloride scale and two-dimensional plan
vectors of streamlines.
based on only 24 sample points. Interpolation was linear
using the nearest neighbor. The samples removed from
the aquifer represent only 0.000014 percent of the aqui-
fer volume. Interpretation of a three-dimensional flow
regimen from such small sampling does not produce
great confidence in the validity of the interpretation.
Salt at the 4-foot depth is most likely to interact with tree
roots, and concentrations at this depth were used for
kriging. Kriged results, mapped in the same manner as
the aerial photography, show general agreement of high
mortality and predicted chloride concentrations over 500
milligrams per liter (see Figure 4). A regression of chlo-
ride concentration (chloride) to gray-scale value (G) for
the individual points yielded a significant negative cor-
relation. The regression line G = 169-0.12 (chloride)
explained only 27 percent of the variation in gray-scale
value, however.
Conclusions
CIS was successfully used to verify interpretations of
ground-water flow and salt movements in a shallow
water table aquifer. A variety of computer software com-
bined to create a system of analysis that allowed inte-
gration of field sampling, aerial photography, vector and
raster CIS, and spatial statistics. Using this system, we
compared chloride movement, measured by subsurface
samplers, with remotely sensed tree mortality caused by
soil salinity. The overall pattern of mortality was pre-
dicted by a 500-milligram-per-liter chloride contour esti-
mated by kriging averaged concentrations. Estimation
of mortality on a single tree basis was less successful,
with a regression of chloride to infrared reflection ex-
plaining only 27 percent of the variation in reflection. The
regression did not fit values of high reflection well but
did predict reflection values of 100 or below (regions of
high mortality) for concentrations above 500 milligrams
per liter.
The most important factor in the success of this project
was the availability of large-scale orthophotography.
Georgetown County's investment in accurate mapping
allowed creation of a map base that made scale correc-
tion of less costly aerial photography possible. Without
assurance that pixels on the aerial photograph corre-
sponded to the same locations as the subsurface sam-
plers, correlations would have been meaningless.
Another factor that contributed greatly to the research
was the fact that most of the computer software ex-
ported or imported data from simple ASCII files. The
standard (x coordinate, y coordinate, data value) format
in ASCII allowed files to be manipulated with spreadsheets
-------
Kriged Chloride Concentrations
554740
554576 H
554412
554248
554084 -
553920
2548470 2548634 2548798 2548962
Easting (feet)
[BSU
mi
Forest Mortality (Dark)
554740 j«.,«.i.
554576 J
554412
554248
554084
553920
2548470 2548634 2548798 2548962
Easting (feet)
<200
<500
<700
<1,000
<1,200
Figure 4. Plan view of chloride concentrations from krig analysis and gray scale of infrared reflection from aerial photograph. Lighter
tones represent greater infrared reflection and less forest mortality.
or word processors. Creation of headers, positioning of
columns, or changing order of rows or columns could be
done for import into the next program. Although more
difficult than point and click file transfers of the modern
software, the simple standard format creates freedom to
use the software in ways not anticipated by the software
developers.
Finally, a clear problem statement aided in selecting the
most applicable CIS techniques. CIS software allows
several methods of data representation. In this example,
we chose a raster representation with a cell the size of
a tree crown. Criteria for choosing these parameters
included physical dimensions of the phenomenon of
interest, dimensions of CIS accuracy, and a desire for
automated determination of values for individual com-
parisons. A careful review of the problem to be solved,
data available, and capabilities of the CIS software are
all necessary ingredients for a useful problem statement.
References
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of the one-dimensional convective-dispersive solute transport
equation. U.S. Department of Agriculture Technical Bulletin 1661.
2. Wang, H.F., and M.P. Anderson. 1982. Introduction to ground-
water modeling: Finite difference and finite element methods. San
Francisco, CA: W.H. Freeman.
3. Bear, J., and A. Verruijt. 1987. Modeling ground-water flow and
pollution. Dordrecht, The Netherlands: D. Reidel Publishing Co.
4. Gelhar, L.W. 1993. Stochastic subsurface hydrology. Englewood
Cliffs, NJ: Prentice Hall.
5. Dagan, G. 1987. Theory of solute transport by ground water. Ann.
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6. Garabedian, S.P., D.R. LeBlanc, L.W. Gelhar, and M.A. Celia.
1991. Large-scale natural gradient tracer test in sand and gravel,
Cape Cod, Massachusetts, 2. Analysis of spatial moments for a
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3,324.
8. de Marsily, G. 1986. Quantitative hydrogeology. San Diego, CA:
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9. Krige, D.G. 1966. Two-dimensional weighted moving average
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surge in coastal South Carolina. J. Coastal Res. SI 8:201-208.
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scomb, and W.H. Jefferson. 1991. Ecological impact of Hurricane
Hugo—salinization of a coastal forest. J. Coastal Res. SI 8:301-318.
12. Gresham, C.A., T.M. Williams, and D.J. Lipscomb. 1991. Hurri-
cane Hugo wind damage to southeastern U.S. coastal tree spe-
cies. Biotropica 23:420-426.
13. Nittrouer, PL. 1988. Ground-water flow patterns in a forested
beach ridge-scale system adjacent to a South Carolina salt
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versity of South Carolina.
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14. Williams, T.M., and J.C. McCarthy. 1991. Field-scale tests of col-
loid-facilitated transport. In: National Research and Development
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search Institute.
15. Conner, W.H. 1993. Artificial regeneration of bald cypress in three
South Carolina forested wetland areas after Hurricane Hugo. In:
Brissette, J.C., ed. Proceedings of the Seventh Southern Silvicul-
tural Research Conference. U.S. Department of Agriculture For-
est Service, General Technical Report SO-93. Southern Forest
Experiment Station, Mobile, AL.
16. Williams, T.M. 1993. Salt water movement within the water table
aquifer following Hurricane Hugo. In: Brissette, J.C., ed. Proceed-
ings of the Seventh Southern Silvicultural Research Conference.
U.S. Department of Agriculture Forest Service, General Technical
Report SO-93. Southern Forest Experiment Station, Mobile, AL.
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data retrieval system for forest managers. In: Proceedings of the
American Congress of Surveying and Mapping/American Society
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Modeling Combined Sewer Overflow (CSO) Impact:
The Use of a Regional GIS in Facilities Planning
Michael Witwer, Jeffrey Amero, Steven Benton,
David Bingham, and Molly Hodgson (Metcalf & Eddy)
Betsy Yingling (NEORSD)
ABSTRACT
This paper presents how a regional geographic information system (GIS) was used to assess
the impacts of combined sewer overflows (CSOs) in Cleveland, Ohio during a recent facilities
planning project. A hydraulic model was used to evaluate the performance of the sewer system
at the present time and with alternative facilities in place. A detailed system and receiving water
quality study was also conducted to assess impacts of CSOs and non-point source pollution on
the receiving waters during wet weather. Using GIS for integration of the database and model
allowed a more accurate and detailed analysis. The GIS interface with the hydraulic and
receiving water quality models during facilities planning presented a significant time and cost
savings. The quality of the data generated was improved through the incorporation of accepted
source information that was repeatable and justifiable. Significant planning prior to the initiation
of a GIS-based effort was necessary to ensure that all data needs and quality objectives were
sufficient for the project.
Keywords: Modeling, CSO, water quality, and pollution
BACKGROUND
The Northeast Ohio Regional Sewer District (NEORSD) provides wastewater collection and
treatment services for the greater-Cleveland metropolitan area through its Westerly, Southerly
and Easterly wastewater treatment plants. NEORSD owns and operates the treatment plants
and the major interceptor sewers. The remainder of the collection system is owned and
maintained by the communities in which it resides. Large portions of each of the three districts
consist of combined sewers. The Easterly district, on which this paper will focus, has a total
area of approximately 49,300 acres, of which about 20,000 acres are served by combined
sewers. The study area is shown in Figure 1.
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LEGEND
— — Easterly Facilities Planning Area
Community Borders
Doan Brook Area
Combined Sewer Overflows
LAKE ERIE
SOUTH
Y I EUCLID
UNIVERSITY
HEIGHTS
/l> >*f%
Figure 1. Easterly District Study Area
The Easterly district consists of approximately 2,368,700 linear feet of combined sewers,
ranging in size from 6-inches to 13.5-feet in diameter. Of these, approximately 333,700 feet
(14%) comprise the interceptor sewers and CSO conduits owned by NEORSD. The 224
combined sewer regulator structures, owned and maintained by NEORSD, provide hydraulic
relief and dictate the point at which a CSO event occurs. Facilities planning was initiated to
assess the frequency, volume and magnitude of CSOs and their impact on urban streams, the
Cuyahoga River and Lake Erie.
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The objective of facilities planning activities is to evaluate and recommend alternatives to control
CSOs to meet USEPA CSO Policy and OhioEPA CSO Strategy, and to improve the quality of
receiving waters after wet-weather events. To begin this process, a hydraulic model of the
collection system was created using MOUSE™, produced by the Danish Hydraulic Institute DHI,
and calibrated using data obtained from temporary flow monitors and rainfall gauges. Sampling
of CSO, storm water runoff and receiving waters was conducted during discrete storm events
throughout the temporary flow monitoring period. This was performed to quantify pollutant
loadings to the receiving waters and evaluate receiving water response. The use of a GIS
allowed the efficient creation of the hydraulic model network and establishment of model input
parameters. Having a visual link with the hydraulic model allowed problem areas to be identified
easily and alternatives to be evaluated quickly.
AVAILABLE INFORMATION AND DATA
Many sources of information, in both digital and hard copy formats, were available about the
physical and operational aspects of the collection system. In conjunction with the facilities
planning, NEORSD conducted a complete inspection and evaluation of their facilities within the
Easterly service area. This inspection provided spatial and physical condition data, which was
used to develop interceptor and CSO conduit portions of the hydraulic model. Record drawings
were obtained for sewers owned by individual communities and not inspected by NEORSD.
Spatial data, available from State and Federal agencies, was used to obtain parameters used in
the hydraulic model. TIGER files, produced by U.S. Department of Commerce Bureau of
Census, were used to define population density within sewer catchments. Land use, used to
determine imperviousness of land areas, was obtained from the Ohio Department of Natural
Resources (ODNR) and interpreted using 1981 aerial photography. Watershed boundaries for
urban streams were also delineated using information available from ODNR.
Extensive information was available from Cuyahoga County, which aided facilities planning
efforts. Digital orthophotography (1 inch = 200 feet scale) existed for the entire service area.
From these orthophotos, the Cuyahoga County Engineer's office had created digital planimetric
drawings (AutoCAD format) providing topographic contours (1 foot interval), roads, road names,
buildings and waterways, which were easily converted to drawing exchange format (.dxf), and
used as coverages within the GIS. Additionally, an address referenced parcel coverage was
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available, which allowed the visual distribution and spatial analysis of flooding complaints to be
utilized.
DATA NEEDS AND ACQUISITION METHODS
It was necessary to acquire physical data about the collection system, major storm conveyance
routes, receiving waters and water quality to complete the GIS database for planning efforts.
The data acquired for the collection system consisted of manhole location surveys, pipe and
manhole condition assessment, internal inspection and surveying, CSO and stormwater effluent
sampling. This data was complimented by sewer flow and rainfall data collected over a 55-day
period. Figure 2 illustrates the data sources and interaction used during the project.
Approximately one-third of the sewers to be included in the collection system model were
surveyed and physically inspected. Inspection data was incorporated directly into the GIS by
querying the necessary information into consistent data table structures. Each manhole (node)
was given a unique numeric attribute. This attribute was used to relate the spatial data defining
each node to the endpoints of sewer lines (arcs). Maintaining this strict arc to node topology
allowed network connectivity and other quality control checks to be executed directly from
ARC/INFO.
Incorporating the remainder of the sewer lines and manholes into the GIS presented a unique
challenge. These sewers, not owned by NEORSD, consisted primarily of major trunk sewers
greater than 30 inches in diameter and conveyed flows significantly impacting the hydraulic
capacity of the sewers. Therefore, it became necessary to incorporate approximately 582,000
feet of sewer in an efficient and cost-effective manner. The incorporation of these sewers
proceeded in a three-step process. First, the spatial location of manholes (model nodes), and
the sewer lines connecting them, were created in the GIS by digitizing existing sewer system
plans. These plans typically did not contain invert and ground surface elevations. In some
instances, it was necessary to verify pipe material and size as well. The second step involved
obtaining record drawings from the respective owners. This information served as a redundant
quality control check of pipe size and material. Invert and ground surface elevations were also
obtained from these record drawings. In isolated instances, neither plan information nor record
drawings were available for certain reaches of sewer. In these locations, inspection and survey
crews were dispatched to obtain the necessary information.
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Receiving
Water
Quality
Models
ARC/INFO
&
ArcView GIS
County
Orfltophoto
Backgrounds
PateiData
DATABASE
Co unty
Planimetric
Backgrounds
Watershed
and Land Use
Data
iData ? 4 *t N. ^m^
TIGER
SewerPlans | Population
Complaint Rainfall & Density Data
Data Flow Monitoring
Figure 2. Project Data Sources and Interaction
Flows in the sewer collection system, storm conveyance culverts, receiving streams and the
treatment plant headworks were monitored for a 55-day period to evaluate daily dry-weather
flow patterns and wet-weather hydraulic responses. Each location was equipped with a monitor,
which recorded Doppler velocity, ultrasonic and pressure-based depth at 5-minute intervals.
-------
This allowed the calculation of flow rates and quantities and provided energy and hydraulic
grade line data for model calibration. The spatial location, unique identifier and operational
characteristics of each monitor were entered in the GIS database for planning use.
Efforts to identify sewer system surcharging were made using community flooding complaint
records and address-referenced land parcel GIS coverage data. Using ArcView GIS to view the
spatial distribution of the flooding complaints was the first step. This was accomplished by
joining a database table containing both the complaint address and type of complaint with the
parcel attribute table in ArcView, with the address being the common field. At this point,
complaints with the designation "water in basement" were queried out and viewed
geographically. Three analysis methods were employed to identify flooding areas, with varying
degrees of effectiveness. First, the overall complaint distribution was studied to identify visual
trends or trouble spots. This proved inconclusive, as the complaints had a wide spatial
distribution. Next, complaints adjacent to NEORSD facilities and very large local facilities were
identified and used as quality assurance to verify hydraulic model coverage in such areas.
Results from this analysis, although useful, did not provide a wide distribution throughout the
combined sewer service area. Therefore, a final spatial analysis of the flooding complaints was
performed. The number of complaints per unit area of each sewer catchment basin was
calculated using the GIS by overlaying the basin coverage and summing the complaints then
dividing by the acreage. This served to normalize the complaint totals by area thus creating a
"complaint density." Basins with the highest density of complaints were evaluated for excessive
sewer system surcharging and other operational problems with the hydraulic model.
CSO and storm water discharges to receiving water bodies were also sampled to determine
average pollutant concentrations. During wet-weather events, automated samplers were
equiped to collect water samples at 15-minute intervals for up to six hours, whenever flow was
adequate. Samples were laboratory composited and analyzed for ammonia, BOD, CBOD,
hardness, metals, TSS, total phosphorus, fecal coliform and E. Co//. Analytical results were
incorporated into the database and combined with flow calculations to develop loadings to
receiving waters. Field monitoring for dissolved oxygen and pH was performed, which included
continuous monitoring in selected streams to observe wet-weather dissolved oxygen sag curve
response.
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MODELING
Hydraulic modeling was enhanced and accelerated through the use of GIS. The MOUSE™
hydraulic model has an available extension called MOUSE GIS. This extension allows the user
to import GIS data directly. Once all model nodes and pipes were loaded into MOUSE, it was
necessary to create drainage basins, or catchments. Basins were defined by one of three
downstream control points being either a regulator structure, flow monitoring location or major
sewer junction. Tracing the collection system upstream from each control point delineated basin
areas. Boundary lines were extended outward from the sewer lines to the adjacent basin
boundaries or the edge of the service area.
Geometric and spatial calculations were performed with the GIS when basin delineations were
complete. The area of each basin was calculated along with the coordinates of the basin
centroid. Using the basin centroid coordinates, and the coordinates of each of twenty rainfall
monitors, the rainfall in each basin was approximated by weighting the rainfall by the distance
from the basin centroid to the monitor. This was accomplished using the inverse-distance
method.
Several values for each basin were necessary as beginning values for model calibration.
Population and land-use values were required for each basin. Base population values were
calculated by overlaying 1990 census data with basin boundaries. Census data GIS coverages
are presented as areas of varying population density. A composite basin population was
calculated by weighting the percentage of basin area attributed to each density value and
multiplying by the area of each density. Land-use, as provided by the ODNR, was used to
calculate an average percent imperviousness for the basin. GIS coverages initially identified
approximately 58 different types of land use. Land uses were consolidated into 9 broader
groups based on similar imperviousness values ranging from 0 (large open flat land areas) to 90
(urban industrial land) percent. Imperviousness values were varied by up to 15% during model
calibration to simulate actual rainfall runoff response. Population values were used as a check
for sanitary flow rates. Imperviousness values were varied to calibrate storm flow runoff and
time to concentration.
GIS was also used to enhance water quality modeling. Watersheds for streams receiving CSO
flows, and tributaries receiving separate storm flow, were delineated using watershed
boundaries obtained from the ODNR. However, the Easterly service area has four streams that
-------
are culverted throughout the entire combined sewer service area. The streams not only receive
storm water flows from separate sewer areas, but CSO flows from the combined sewer
regulators. Additional separate storm water inputs to the culverted streams within the combined
sewer area were defined as individual basins.
Once all hydraulic and hydrologic data were incorporated into the GIS and MOUSE, the model
was calibrated to field monitoring data collected during a 55-day period in April and May of
1998. The calibrated model was then used to predict CSO flows and loads for 4-month, 6-
month, 1-year, 2-year and 5-year design storms. Flows were calculated for each permitted CSO
point directly from model output. Loadings for each point were calculated by multiplying the
CSO flow by contaminant event-mean concentration. Contaminant concentrations vary
according to the percentage of flows coming from storm and combined sewers. Therefore,
event-mean concentrations were weighted based on the source of the flows. The weighted CSO
loadings were used as input to water quality models for major receiving streams, the Cuyahoga
River and Lake Erie.
Water quality models were developed for Lake Erie, the Cuyahoga River and major creeks and
streams that receive discharges from Easterly District CSOs. The objective of receiving water
modeling was to provide a tool to evaluate potential improvements in receiving water quality
resulting from the reduction of CSOs. Five streams flow through the Easterly District, four of
which are contained in culverts for most of their length. All five streams flow to Lake Erie. The
Cuyahoga River is the western boundary of the Easterly District, from which it receives a small
number of CSO discharges. Water quality in Lake Erie was modeled using a modification of the
Lake Erie Information System (LEIFS) hydrodynamic model, which had been developed by Ohio
State University. The LEIFS model was modified to provide a finer resolution in near-shore
areas, which will permit a more detailed evaluation of the flow-splitting effect of the breakwall
that surrounds downtown Cleveland at the mouth of the Cuyahoga River. Euclid Creek, which is
located at the eastern end of the Easterly District, is not culverted throughout its length. Euclid
Creek receives CSO and other wastewater discharges from the Easterly District and numerous
upstream sources. Euclid Creek, along with three of the culverted streams, were modeled using
the transport block of the USEPA Storm Water Management Model (SWMM) to route flows to
Lake Erie, and to account for the timing of pollutant loads to the lake. The fourth culverted
stream, Nine Mile Creek, is culverted for most of its length, but has a significant open-water
section at the mouth near Lake Erie. Therefore, continuous modeling of both bacteria and
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dissolved oxygen was performed for Nine Mile Creek using SWMM for transport, and USEPA
Water Quality Analysis Simulation Program (WASPS) to simulate contaminant fate. The
Cuyahoga River was modeled using the Cuyahoga River Navigation Channel Model previously
updated for work on the Westerly District CSO Planning Study conducted by NEORSD. In each
instance, the GIS was used to provide spatial data and physical characteristics used as input to
the respective models and represented a central data warehouse structure of acceptable
quality-controlled data.
GIS data obtained from ODNR was used to define the stream drainage basins, which were
further refined by overlaying two-foot elevation contours and stormwater pipe information
obtained from county planimetric maps and collection system mapping, respectively. Future
facilities planning and alternatives were readily visualized using GIS. The MOUSE GIS module
enabled users to visualize areas where CSO and sewer system surcharging readily occurred.
Alternatives including infiltration/inflow (I/I) reduction, in-system storage, off-line storage,
conveyance tunnels, flow routing and rapid treatment were considered for CSO reduction. GIS
allowed new facility siting to be expedited and visualized quickly.
The GIS database was also a key factor in the automated mapping of the sewer system for the
Easterly area. Approximately 140 1-inch = 200-feet scale sheets of the system, were generated
using ARC/INFO software. The planimetric coverages were used along with the sewer system
database to provide a complete set of sewer maps for the client. Several routines were used to
verify flow direction, pipe type and size, and also for assigning unique manhole identifiers.
These map sheets will be used by NEORSD for planning, system interpretation, and field
maintenance crews, by serving as a reference point for the GIS. As the database is updated,
new maps will automatically reflect sewer system changes in that area.
DATA QUALITY AND REPEATABLE RESULTS
Data quality assurance was a primary goal in the development of the GIS database. This goal
was considered achieved when data sources were documented, accepted and produced
repeatable results. The use of a GIS allowed parameters to be calculated by standard routines
that did not vary according to user or individual interpretation. This allowed the users to focus
efforts on the legitimacy and accuracy of the results, rather than data compilation.
-------
In addition to using documented source information, the GIS was used to check the collection
system spatial data before incorporation into the model. Network connectivity, pipe sizes, and
material of construction was checked through automated routines. Using the ArcNetwork™
module of ARC/INFO™, sewer system tracing was initiated from the treatment plant. Sewer
lines (arcs) are given a direction within ARC/INFO. The tracing routine, developed using Arc
Macro Language (AML), identified all manholes (nodes) with flow connected to the treatment
plant. The result of this routine was to identify sewers not connected to the collection system
within the GIS. Incidentally, CSO outfall pipes, by their nature, were also identified by this
procedure.
Data gaps existed for both pipe size and material of construction even after review of inspection
data, plans and record drawings. In most instances, missing sewer segments were the cause of
data gaps. To address this, another AML routine was developed. Tracing was initiated on a
reach of sewer to identify the missing segment data. The pipes immediately upstream and
downstream of the missing data were examined for the missing parameters. If all sizes and
materials were the same, the missing attributes were automatically populated in the database
for the segment. Segments with generated data were given a qualifier indicating a lower
confidence level than documented record data.
TIME AND COST SAVINGS
The GIS provided a significant time and cost savings in the facilities planning process. These
savings did not only include the calculation of model input parameters, but the initial
development of the model network. While field inspection and survey data would be desirable
for all sewers within the hydraulic model, the physical inspection of urban sewers is expensive.
It was desired to incorporate information about these sewers in a manner that had a better
benefit to cost ratio. Utilizing the data acquisition and quality assurance / quality control
(QA/QC) procedures outlined earlier allowed the creation of a database comparable to complete
inspection data.
PLANNING AND LESSONS LEARNED
Significant planning was necessary before beginning a GIS-based modeling and planning effort.
This ensured that all data needs and quality objectives were sufficient for the project. All
sources of GIS data should be reviewed for accuracy, completeness and acceptability prior to
their use. Choosing the right software was also an integral part of the planning process for both
10
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GIS functions and modeling. In the case of the GIS, ARC/INFO was chosen because of its
broad capabilities as an analytical tool. Network analysis, which could not be performed in
desktop software by cost-effective means, was completed with success. Completed ARC/INFO
coverages easily converted to shapefile format, which were native to ArcView GIS software.
ArcView shapefiles, which the MOUSE hydraulic model can use as data input, were used by
GIS analysts, managers and casual users. The advantages of using ArcView were its user-
friendly windows-based interface, ability to access documents, images, tables, spreadsheets
and CAD drawings, and its simplicity to query the database.
To expedite model network creation, the collection system layout should be created within the
GIS. This allows basin delineation and parameter calculation to be completed once, thus
avoiding changes and recalculations. It is very easy to lose sight of the ultimate goal of facilities
planning if the use of a GIS becomes a project within itself. A GIS is designed to be a tool to
reduce costs, expedite planning and visualize data and alternatives. Using a GIS is a definite
enhancement to the assessment and control of wet weather water pollution.
11
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Building a Shared and Integrated GIS to Support Environmental
Regulatory Activities in South Carolina
Guang Zhao1, Zhumei Qian, Jeannie Eidson, Paul Laymon, Hsiu-hua Liao, Rob Devlin, Dakin
MacPhail, Steve Dennis, and Derek Graves
South Carolina Department of Health and Environmental Control
Abstract
Environmental managers in South Carolina often encounter difficulty in making scientifically
sound decisions involving complex spatial data and analysis without the benefit of GIS
technology. To facilitate the decision making process, an agency-wide shared and integrated
geographic information system (SIGIS) program was developed and an enterprise SIGIS
database was constructed at the Office of Environmental Quality Control (EQC), South Carolina
Department of Health and Environmental Control (SCDHEC). The goal of the project was to
provide managers and policy makers with decision support systems to applications that enable
them to effectively analyze spatial information related to environmental regulation and
management. The SIGIS database contains 30 environmental layers and 32 baseline layers
which are housed in an ESRI's ARC/INFO, ARCVIEW, and MapObject environments. The
majorities of the environmental GIS data layers are state-wide in scope and were collected and
updated annually using GPS equipment. Their accuracies are mostly sub-meter or meters
which satisfy EPA's 25-meter standard. Enterprise applications such as the SIGIS interface to
the environmental facility information system (EFIS) and a Data Dictionary have been
developed using the SIGIS databse. The SIGIS/EFIS interfaces are web-based and built using
ESRI's MapObject and MapObject Internet Map Server in the Microsoft Visual Basic and
NetScape JavaScript environments. Environmental facility information system is a management
information system housed in the Oracle environment. The integration of EFIS with SIGIS
enables environmental managers to easily query and analyze spatial information for
environmental permitting and other regulatory activities. The SIGIS data dictionary was
developed to document standards, procedures, and GIS data layers stored in the database. It
is updated annually and available through the Internet. While these enterprise applications are
1 Contact author's current address: Department of Health and Environmental Control, Environmental
Quality Control Administration, Information Technology Section, 2600 Bull Street, Columbia, SC 29201.
Phone (803) 898-3653, E-mail: zhaog@columb20.dhec.state.sc.us
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being completed, departmental GISs still function to perform front-end data analysis and
develop project-specific GIS applications.
Introduction
Environmental and public health managers in the South Carolina Department of Health and
Environmental Control (SCDHEC) often encounter difficulty in making scientifically sound
decisions when complex spatial problems are involved. Since early 1990s, GIS technology has
been gradually introduced into the agency. In the past nine years, there have been three major
ways in which GIS systems have been developed: project GIS, departmental GIS, and
enterprise GIS. In the project stage, GIS is used to support an individual project's needs. For
example, the community-based environmental project (CBEP) utilized GIS to help delineate
polluted sites and their neighboring demographics. Once the project is finished, GIS support
ends. In the departmental approach, GIS is used within a single department to support the
department's business functions. For example, the Bureau of Water GIS Lab has generated
numerous maps and reports to support the bureau's major permitting and regulatory activities.
The growing project and departmental GISs, however, resulted in an inefficient sharing of
limited resources, distribution of data, management of the integrity of the database, and service
to GIS customers. Moreover, at the agency level there was no consistent GIS support to the
agency's major business functions. GIS activities have not been in alignment with the strategic
directions of the agency. It is preferable to establish a generic, enterprise-wide GIS
infrastructure including hardware, software, network, database, and applications to in order to
meet the needs for multiple application developments at both the department and the agency
levels.
In March 1996, the Research and Planning Division (RPD) of the the Office of Environmental
Quality Control developed and implemented an agency-wide, shared and integrated geographic
information system (SIGIS) program. The goal was to build an enterprise SIGIS database and
platform to provide internal employees of both the environmental management and public health
services with decision support systems that enable them to effectively analyze spatial
information in supporting their duties. Under such an umbrella, departmental GISs still function
to perform data processing and develop project-specific applications, while enterprise
applications are developed at the agency level to support the agency's critical functions. GIS
information, meanwhile, is also available to the general public through the SIGIS Internet
module.
-------
The enterprise GIS approach has been used in recent years by several other state
environmental protection agencies. For example, Florida EPA (http://www.dep.state.fl.us/gis/),
West Virginia DEP (http://www.dep.state.wv.us/mapping.html) and New Jersey DEP
(http://www.state.nj.us/dep/gis/) have developed such an approach. An enterprise method is an
organizational effort to develop and implement a shared and integrated GIS database and
applications to meet the needs of various user groups across organizational boundaries (ESRI,
1996; Dueker, K.J. 1998; Peng etal., 1998). It focuses on long-term consistency of GIS
support, integration of applications for major business functions, and improvement of an
organization's business operations.
This paper examines the general design issues for an enterprise SIGIS database. It describes
the application examples at both the enterprise as well as the department levels for a state
environmental protection agency.
SIGIS Database Development
The development of an enterprise GIS database follows a systematic and functional approach.
That is, the GIS database reflects the functional components of the organization. Generally, the
following four major steps are followed during the initial development: identification of agency's
major business functions, their logical translation into the GIS counterparts, development of
system standards, and development of database standards. Overall, the agency's business
functions are the backbone for the database development. The existing organizational
structures are only considered as indirect factors.
A functional decomposition process is employed to define functional equivalents, which are
directly translated from the current organizational functions. The functional equivalents are then
translated into SIGIS database counterparts (GIS data layers and associated applications) by
combining with constraints such as users' needs, data density, scale, and locational accuracy
(Figure 1).
For example, administratively Environmental Quality Control (EQC) is the entity in SCDHEC
responsibile for regulating air quality, water pollution, drinking water quality, solid and hazardous
waste, and coastal resources in South Carolina. EQC it is organized into five program areas
(bureaus), with each area responsible for a particular set of permitting and regulatory functions.
Table 1 categorizes the program area and their major functions. Through the functional
-------
decomposition process, 30 environmental thematic layers have been developed. These are the
GIS equivalents translated from the EQC's business functions. Table 2 lists these layers and
their descriptions. Cultural, imagery, and baseline data layers are also collected, generated and
incorporated into the databases with similar data standards to allow spatial display and analysis.
The SIGIS database is then integrated using ESRI's standards for database design. The
components and factors below are included and considered during the integration process:
• File system organization
• Naming conventions
• Spatial data automation standards
• Coordinate system and scale
• Thematic layer organization and description
• ARC/INFO database standards
• Standard symbol and legend
• Spatial data management
• Access and security
• Maintenance and updates
• Metadata and Data sharing
After conforming to the same standards, all GIS data stored in the SIGIS database are
consolidated to form a common platform to support all users' needs, instead of only specific
users or applications. Most problems associated with data redundancy, inconsistency,
dissemination, and management are eliminated. Many applications at the departmental as well
as the enterprise levels now share a common database using different hardware and software
particular to their needs. That is, an open and integrated framework is built independent of any
one application.
Enterprise Application Development
a. SIGIS/EFIS Interfaces
Two enterprise GIS applications have been developed since 1996: the SIGIS Intranet and
Internet Interfaces with environmental facility information system (EFIS) and SIGIS Database
Dictionary.
-------
Currently EQC's Environmental Facility Information System (EFIS) is the only management
information system (MIS) that interfaces with the SIGIS database to support EQC's permitting
and regulatory functions. EFIS resides in an Oracle environment to record and process
environmental regulatory information. The integration of EFIS and SIGIS enables EFIS users to
display and analyze spatial information about environmental facilities consistently across EQC's
organizational boundaries.
The SIGIS/EFIS interfaces are web-based, built using ESRI's MapObject and MapObject
Internet Map Server (IMS) in the Microsoft Visual Basic and NetScape JavaScript environment.
The Intranet version of the interfaces is embedded in the EFIS Oracle application in a
client/server environment, while the Internet version uses an HTML setting. EFIS clients
including internal users and public browsers can use the interface to view a map displaying the
locations of permitted facilities associated with facility permit information. The basic functions
provided by the interfaces include: displaying multiple layers, panning and zooming, spatial
query, address matching, buffering, and identification. Since the interface is designed to be an
independent module of the EFIS application, it can be fully applied to other management
information systems at the enterprise level. Figure 2 shows the basic design of the interfaces.
b. SIGIS Data Dictionary
The Data Dictionary is another enterprise product developed through the SIGIS program. Its
purpose is to provide, at the agency level, both internal and external GIS professionals and
database users with the background information, procedures, guidelines, standards, and
metadata used in the creation and maintenance of the SIGIS database and the state-wide
environmental, public health, and baseline GIS data. Specifically, it describes all data layers
included in the database, their intended uses, coverage characteristics, associated files, source
contacts, and a detailed documentation of attribute information. It is updated annually and is
available through the Internet.
The Data Dictionary serves as a general reference for internal GIS developers in the continued
development and use of the SIGIS database. Along with the dictionary, a SIGIS database CD is
also available for those internal users who do not have Internet access. An ArcExplorer
interface was also developed (and is included on the CD) to allow users to browse the database
on their computers without additional software.
-------
Department Application Development
As the enterprise applications may not satisfy all users needs, departmental GISs still have their
advantages in processing data analysis and developing project-specific applications.
Specifically, all applications developed at the project and department levels share the same
enterprise SIGIS database and are integrated into the same framework. Below is an example
application. Table 3 lists additional departmental applications using the enterprise SIGIS
database.
a. Groundwater 305(b) Report
The goal of the GIS project in the Bureau of Water was to develop a statewide groundwater
network of critical sites for monitoring groundwater quality. This network will contain both
locational and quantitative data: 1) locations and types of known groundwater contamination, 2)
sample locations and aquifer-specific background values for selected dissolved natural
chemicals and other water-chemistry characteristics, and 3) locations and water quality for all
operating public water supply wells.
The application will link directly with the SIGIS database containing layers of known
groundwater contamination sites, public water supply wells, and ambient groundwater network
sites. The application facilitates the compilation of summary reports and will be used in well-site
selection and permitting, wellhead protection planning, and basin- or area-wide assessments or
inventories. It also will help identify sources or potential sources of problems at public water
supplies, and will be used in initial and updated assessments of all contamination sites.
Summary
An enterprise-wide Shared and Integrated GIS (SIGIS) has been built to support the
environmental regulatory effort at the South Carolina Department of Health and Environmental
Control (SCDHEC). Enterprise GIS applications such as the SIGIS interfaces to environmental
facility information system (EFIS) and a Data Dictionary have been developed to support the
agency's major business functions. Department GIS applications were also developed using
the enterprise SIGIS database to support individual projects and departmental GIS needs.
-------
Acknowledgement
Special thanks are due to W. Stephen Vassey, Manager, Application Systems and Mike Rowe,
Director, Research and Planning Division, Environmental Quality Control (EQC) Administration
for their strong encouragement, direction, and critical comments through the development of the
SIGIS program.
References
ESRI. 1996. Managing a GIS: planning and implementation. ESRI, Redland, CA.
Dueker, K.J. and J.A. Butler. 1998. GIS-T enterprise data model with suggested
implementation choices. URISA Journal, Vol. 10(1):12-36.
Peng, Z.R., J.N. Groff and K.J. Dueker. 1998. An enterprise GIS database design for agency-
wide transit applications. URISA Journal, Vol. 10(2).
-------
Table 1. EQC's Program Areas and Major Functions
EQC Program Areas
Business Functions
Bureau of Air Quality (BAQ)
Regulates air emissions including:
Air quality permitting
Asbestos
Bureau of Ocean and Coastal
Resources Management
(BOCRM)
Regulates coastal resources including:
Critical area permitting
Coastal zone consistency certification
Bureau of Land and Waste
Management (BLWM)
Regulates solid and hazardous wastes including:
Hazardous waste facility permitting
Hazardous waste transporter permitting
Infectious waste facility permitting
Radioactive waste facility permitting
Solid waste landfill permitting
Solid waste handling facility permitting
Mining and reclamation permitting
Certificate to explore for minerals
Terminal facility registration
Oil and gas exploration, drilling, transportation,
and production
Bureau of Water (BW)
Regulates water pollution and drinking water including:
Waste water discharge permitting (NPDES) and land
application permitting
State construction permitting
Storm water NPDES permitting
Section 401: Water quality certification
State dams and reservoirs safety act permitting
Navigable waters permitting
State storm water management and sediment reduction
act permitting
Shellfish sanitation - certificates and permits
Public water system construction and operation permits
Interbasin transfer permitting program
Underground injection control permitting
Groundwater use permitting
Recreational waters construction and operating
permitting
Bureau of Environmental
Services (BES)
Manages EQC's environmental laboratories
Environmental lab certification program
-------
Table 2. Environmental Thematic Layers in SIGIS Database
Thematic Layer
(Code)
Description
AGWQS
AMS
ARF
BIOSTAT
BTRP
CERCLA
CUW
DAMS
DSWL
ESTAURY
HWTSD
ISWI
ISWL
KGWCS
MRNA
MSWL
NPL
NPDES
ORPM
PORTS
PWSW
RSWL
SMS
SWI
TRI
LISTS
WQMS
SFCLASS
WHPA
NERR
Ambient Ground Water Quality Stations
Air Monitoring Stations
Air Regulated Facilities
Biological Monitoring Stations
Boat Ramps
Comprehensive, Environmental Response, Compensation and Liability Act
of 1980
Capacity Use Wells
Dams
Domestic Solid Waste Landfills
Estuaries
Hazardous Waste Treatment, Storage, and Disposal Facility Sites
Industrial Surface Water Intakes
Industrial Solid Waste Landfills
Known Ground Water Contamination Sites
Marina Locations
Municipal Solid Waste Landfills
National Priority List Sites
National Pollutant Discharge Elimination System Permit/Discharge
Locations
Ocean and Coastal Resources Management Permitted Sites
Port Locations
Public Water Supply Wells
Regulated Solid Waste Landfills
Shellfish Monitoring Stations
Surface Water Intakes
Toxic Chemical Release Inventory Facility Sites
Underground Storage Tank Sites
Water Quality Monitoring Stations
Shellfish Classification (Standard)
Wellhead Protection Area
National Eastuarine Research Reserves
-------
Table 3. Example Departmental GIS Applications Using the SIGIS Enterprise Database.
Application
Department
208 Water Quality Management Plan
303(d) Priority Ranked Waterbodies
305(b) Report - Groundwater
305(b) Report - Surface/Shellfish
Air Monitoring and Regulated Facility Information System
Charleston Harbor Project
Health Resources and Local Economic Development Planning Zones
Linking Vital Health Statistics and Census Data Using GIS
OCRM Natural Dune Baseline Determination Project
OCRM Post-Hurricane Recovery GIS Project
Orthophoto Production Project (OPP)
Savannah River Site Federal Facility Agreement
Shellfish Sanitation Program
Spatial Distribution of Tuberculosis in South Carolina
Targeting Public Health Outreach for Immunization
Vital Health and Census Data Integration System
Vital Records Birth Certificate Data
Watershed Water Quality Management Strategy
Wetland 401 Certification Decision Analysis System
Water
Water
Water
Water
Air
Coastal Resource
Health Service
Coastal Resources
Coastal Resources
Coastal Resources
Coastal Resources
Land and Waste
Water
Health Service
Health Service
Biostatistics
Biostatistics
Water
Water
10
-------
Figure 1. A Functional Decomposition Process for Developing GIS Thematic Layers
EQC
Functions f^
1
Function
decomposition
Feature type
Data density
Accuracy
Air monitoring
Air regulation
Underground storage
tank
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\ /
\^ /
/---*^ ^7
/ /
f~. -,
Scale
Projection
User needs
/
S
/*
^iS
/
GIS
equivalent
Figure 2. An Enterprise SIGIS/EFIS Interface Design
Public Web
Browsers
XSIG
Vv Interne
S/EFISN
1 ^ b
t Interface/^ ^
SIGIS
Internet
Server
i
File Wall
EFIS Facility
Tables
XSIGIS/EFISN ^ ^
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-"
i
INTERNET
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SIGIS
Intranet
Server
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INTRANET
SIGIS
Enterprise
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11
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XGRCWP, a Knowledge- and GIS-Based System for Selection, Evaluation,
and Design of Water Quality Control Practices in Agricultural Watersheds
Runxuan Zhao, Michael A. Foster, Paul D. Robillard, and David W. Lehning
Penn State University, University Park, Pennsylvania
Abstract
The Expert CIS Rural Clean Water Program (XGRCWP)
integrates a geographic information system (CIS), a
relational database, simulation models, and hypertext
mark language documents to form an advisory system
that selects, evaluates, sites, and designs nonpoint
source pollution control systems in agricultural water-
sheds. Its major features include:
• Customized CIS functions to obtain spatial and attrib-
ute data and feed them to a rule-based expert system
for selecting feasible control practices.
• A user interface for examining the field-specific con-
ditions and recommended control practices on the
screen by clicking on the displayed field boundary
map.
• A direct linkage between the CIS spatial data and the
relational attribute data, which allows users to exam-
ine data on the screen interactively.
• A graphic user interface to CIS functions, which en-
ables users to perform routine watershed analyses.
• Linkage to hypertext reference modules viewable by
Mosaic Internet document browser.
• Dynamic access to other models such as the Agricul-
tural Nonpoint Source Simulation Model.
The software environment of XGRCWP is GRASS 4.1
and X-Windows on SUN OS 4.3.1. Its major functions
have been tested for the Sycamore Creek watershed in
Ingham County, Michigan.
Introduction
In 1981, the U.S. Environmental Protection Agency
(EPA) and the U.S. Department of Agriculture (USDA)
initiated the Rural Clean Water Program (RCWP) in 21
agricultural watersheds. This program represents the
most intensive water quality monitoring and implemen-
tation and evaluation of nutrient, sediment, and pesti-
cide reduction practices ever undertaken in the United
States (1). More than a decade of research efforts has
resulted in a wealth of experiences and lessons on
selection, siting, and evaluation of nonpoint source con-
trol practices.
The storehouse of knowledge gained from RCWP is of
little use, however, unless it is properly integrated and
packaged in an easily accessible form. Technology
transfer of this knowledge is therefore critically impor-
tant. To integrate and synthesize the lessons learned
from RCWP, Penn State University initiated an RCWP
expert project. The hypertext-based version of the
RCWP expert system, completed in 1993, can select
and evaluate nonpoint source control systems at a sin-
gle site. Although the hypertext-based version is still
suitable for users who do not have access to geographic
information systems (CIS) data, it is inadequate for the
comprehensive selection and evaluation of control sys-
tems on a watershed basis. It does not provide the user
the spatial reference of a site and requires the user's
subjective judgment for the model input.
The Expert CIS Rural Clean Water Program (XGRCWP)
is the UNIX and X-Window version of the RCWP expert
system, which integrates CIS and the RCWP expert
system to provide decision support at multiple spatial
scales from single fields to subwatersheds to the water-
shed scale. This paper presents the major features of
XGRCWP, including design of the expert system, inter-
face to CIS functions, and linkages to a relational data-
base and simulation models.
Overview
XGRCWP comprises five major components (see
Figure 1):
• An expert system for recommending control practices
based on site-specific information.
-------
HTML Reference
Modules:
Contaminants
Monitoring
Transport
Case Studies
Expert System:
Recommending
Control Practices
X/Motif
Graphic User
Interface
GIS Functions:
Customized and
Existing Ones for
Watershed Analysis
Relational Database:
Relate Spatial and
Attribute Data
Simulation Models:
AGNPS
GLEAMS (To Be
Added)
Figure 1. Major components of XGRCWP and their relationships.
• Custom and existing GIS functions for watershed
analysis and estimation of contaminant loading
potential.
• Linkage to fields, soils, and land use databases.
• Linkage to the Agricultural Nonpoint Source Simula-
tion Model (AGNPS) (2).
• Hypertext mark language (HTML) reference modules.
The X/Motif graphic user interface (GUI) integrates the
five components and allows the user to navigate flexibly
among them. The components are also internally con-
nected in different ways. For example, the expert system
can use the customized GIS functions to retrieve site-
specific information from Geographical Resource Analy-
sis Support Systems (GRASS) (3) data layers and
INFORMIX relational database tables. In addition, the
expert recommendations of control practices can be
displayed and examined using GRASS functions. Fi-
nally, the GIS functions can help generate input to the
AGNPS model, and its output can be converted to GIS
format for additional analyses.
Design of the Expert System
The objective of the expert system is to recommend
feasible control systems (i.e., complementary sets of
control practices to reduce nonpoint source pollution
based on site-specific conditions). One distinct feature
of this system is the combination of two modes of data
acquisition: direct user input and GIS functions.
XGRCWP also has two modes for deriving the expert
recommendations: batch or interactive. This section dis-
cusses these aspects of the expert system as well as its
knowledge base.
Rules for Control Practice Selection
The knowledge base of the expert system includes the
following six site-specific characteristics:
• Contaminant of interest and its adsorption characteristic.
• Potential level of contaminant loading (low, medium,
or high).
• Potential level of contaminant leaching (low, medium,
or high).
• Soil hydrologic group (A, B, C, or D).
-------
• Time of year (during or outside the growing season).
• Type of land use (cropland, animal waste, or critical
area).
The user first chooses a contaminant of interest from a
list consisting of four kinds of pesticides (strongly, mod-
erately, or weakly adsorbed, and nonadsorbed) and
eight other contaminants (ammonia, bacteria, sediment,
total nitrogen, total phosphorus, nitrate, orthophospho-
rus, and viruses). The values of other characteristics,
some of which vary with the contaminant specified, can
then be input either directly by the user or by custom
GRASS functions as discussed in the section of this
paper on data acquisition.
The RCWP used 14 general categories of control prac-
tices (see Table 1). Many suitable conditions were es-
tablished for each general category. For example,
conservation tillage is recommended to reduce runoff for
cropland under conditions otherwise favoring loss
through sediment transport, such as a contaminant
strongly adsorbed to the soil (e.g., total phosphorus), the
nongrowing season, and soils with a relatively high run-
off potential (soil group C or D) (see Figure 2). Each
general category includes several specific control prac-
tices. When a general practice category is recom-
mended, the user must decide which specific practice
within that general category to evaluate further by con-
sulting the nonpoint source database (NPSDB) for the
reported research data about this practice or by running
the AGNPS simulation model.
Data Acquisition
The expert system recommends one or more control
systems based on site-specific conditions that are either
directly input by the user or calculated by the customized
GRASS functions. The user always specifies the con-
taminant of interest and the season, while a GRASS
function (R.HYDRO-GRP) always determines the soil
hydrologic group of each field. For the other factors
(loading potential, leaching potential, and application
Table 1. The Best Management Practices Used in the Rural
Clean Water Program
Source control
practices
Nutrient Management (NUTR)
Pesticide Management (PEST)
Structural control Animal waste systems (AWS)
practices Diversion systems (DIV)
Sediment retention and water control (SED)
Terrace systems (TERR)
Waterway systems (WATW)
Vegetative control
practices
Conservation tillage (CT)
Critical area treatment (CAT)
Cropland protection systems (CPS)
Grazing land protection (GLP)
Permanent vegetative cover (PVC)
Stream protection (SP)
Stripcropping (SCR)
class), however, the user has two alternative ways to
decide input values. For example, after the user selects
a contaminant of interest, the program displays the con-
taminant loading potential window (see Figure 3). The
potential level of the selected contaminant can be indi-
cated if the user knows it. Otherwise, the user can let
the GRASS functions derive loading potential from ex-
isting field data.
The direct input option can also be used to help the user
address "what-if questions. When the user selects the
CIS functions to determine the loading potential,
XGRCWP makes a series of calls to appropriate cus-
tomized GRASS functions according to the current con-
taminant of interest. For example, if the contaminant is
total nitrogen, the functions R.MANURE, R.FERT, and
R.B.CONCENTRATION are called to estimate total ni-
trogen from manure, fertilizer, and soil base concentration,
respectively. Another GRASS function, R.NP.LOADING,
is then called to translate the quantitative measure of
loading potential into the qualitative classification (low,
medium, or high) as input to the expert systems. These
GRASS functions generate the inputs by searching and
converting the data from INFORMIX relational data ta-
bles that are associated with the GRASS spatially refer-
enced data, such as field boundary and soil map. Table
2 lists the customized GRASS functions developed for
data acquisition.
Control System Recommendation
XGRCWP derives the expert recommendations for con-
trol systems in two ways: in a batch mode for every field
in a watershed and in an interactive mode for a user-
specified field.
In batch mode, an existing GRASS function, R.INFER,
is used to create a raster data layer for each general
practice category of control practice according to a rule-
set prepared for that general category. For example, the
contents and formats for the conservation tillage prac-
tice are documented in Table 3. The raster data layer for
representing the conservation tillage recommendations
(CTree) is generated by running R.INFER with the ap-
propriate rule. The category value of CTree is 1 at each
point in the data layer where the conservation tillage is
recommended, or 0 otherwise. The R.INFER function is
similarly called for other general practice categories.
Additional GRASS functions can then display or further
analyze the resulting map layers. The batch mode pro-
vides the user the overall picture with a watershed-wide
view of feasible control systems.
In the interactive mode, the field boundary map is dis-
played and the user can specify any field of interest by
clicking the mouse on it. The recommendations and the
site-specific conditions of the field are displayed on the
right half of the screen. The recommended control prac-
tices are also displayed in a popup window for further
-------
Adsorb
Nonadsorb
Contaminant
A
B
C | D
Soil Group
Low
Medium
High
Loading
Low
Medium
High
Leaching
Growing] Nongrowing
Season
CL
AW
HSA
Application
Figure 2. Dependency network (AND-OR diagram) for site-specific recommendation of conservation tillage.
examination, such as the specific practices within each
general category, the feasible control systems for non-
point source pollution control, and research data on the
practices. The interactive mode is implemented through
the integration of a Bourne shell script, structured query
language (SQL) commands, a customized GRASS
function (R.RCWP.EXPERT), and GRASS display func-
tions with the Motif GUI. Interactive mode is intended for
detailed consideration of a specific farm.
Interface to GIS Functions
XGRCWP provides a GUI to most of the customized
GRASS functions and some of GRASS'S existing func-
tions (see Figure 4). This interface shields the user from
complex syntax so the user can focus on the subject
Hl.it II l!i«J I'UMljJ'liJ
(Jin
v Uedhim
v High
, To be- drtenm'ned" by GIS function 51
Figure 3. The popup window for the potential level of contami-
nant loading.
matter. The GUI makes it easier for the user to perform
routine operations such as estimation of contaminant
loading, identification of critical areas, erosion and runoff
calculation, and other watershed analysis tasks. It also
helps the user make full, effective use of all custom and
some existing GRASS functions.
Linkages to Database and Other Models
Data Structure
The GRASS functions used to generate inputs for the
expert system use the same soils and fields relational
databases as the Water Quality Model/GRASS Interface
under development by the Soil Conservation Service
(SCS) (4). XGRCWP and our custom GRASS functions
were tested forthe Sycamore Creek watershed, Ingham
County, Michigan. In this data structure, spatial data
(e.g., field boundaries, watershed boundaries, soils map
unit boundaries, and elevation data) are saved as
GRASS raster data layers while attribute data (e.g., crop
information, fertilizing schedule, soil information) are
stored in INFORMIX relational database tables. Each
field or soil map unit is assigned a unique identification
(ID) number. The field attribute (INFORMIX) data also
contain this ID number. The linkage between the
GRASS raster map and the INFORMIX data is accom-
plished with a GRASS category label (see Figure 5).
Linkage to Database
To allow the interactive examination of field data from
GRASS raster layers and the associated relational da-
-------
Table 2. Summary of the Customized GRASS Functions Developed by Nonpoint Source Agricultural Engineering Research
Group at Penn State University To Generate Inputs for the RCWP Expert System
Name
Descriptions
R.FERT
R.MANURE
R.B.CONCENTRATION
R.NP.LOADING
R.EROSION
R.LEACHING.P
R.HYDRO-GRP
Produces raster maps of total nitrogen or total phosphorous from the scheduled fertilizer applications for
different crops by dynamically retrieving information from a GRASS data layer and INFORMIX data tables
Calculates the total manure on each farm according to animal numbers and types (e.g., dairy cow, beef
cow, horses, swine), allocates manure to the fields on a farm by a user specified strategy (uniformly
spreading or inverse distance weighted distributing method), and finally estimates nitrogen and
phosphorous loading from manure application rate, conversion factor, percentages of transportation
losses, and volatile losses
Estimates nitrogen and phosphorous concentration in parts per million within different types of soils
according to the organic matter contents
Classifies the loading potential of nitrogen or phosphorous into three categories (low, medium, and high)
based on the actual loading from fertilizer and manure and the N or P concentration in soils
Obtains a relative measure of soil erosion severity by dividing the amount of erosion by the tolerance
values of the soils and then reclassifying them into three categories (low, medium, and high)
Estimates leaching index from soil hydrologic group and annual and seasonal precipitation and classifies
it into three categories (low, medium, and high)
Retrieves soil hydrologic group from the INFORMIX database and reclassifies the soil map into soil
hydrologic groups
tabase tables, XGRCWP calls our custom function,
D.WHAT.FIELD.SH, a Bourne shell script that dynami-
cally links GRASS raster layers and the INFORMIX
database tables. When the user clicks on a field, for
example, this function extracts field-specific information
from INFORMIX tables such as field information, fertili-
zation schedule, crop operation schedule, and soil infor-
mation. The D.WHAT.FIELD.SH function then displays
all related soils and fields information for the given field.
It also marks the field boundary map to remind the user
which fields have already been examined.
Linkage to Reference Modules
At any stage of the selection, evaluation, siting, and
design procedure for control practices, the user can
consult reference modules that provide information,
guidance, and data about contaminant properties, trans-
port variables, and examples of applications from
RCWP projects. Four reference modules are available
in the Macintosh version of RCWP expert system: con-
taminants, monitoring, transport, and case studies. We
are currently converting these reference modules into
Mosaic-viewable HTML documents so that they can be
accessed from XGRCWP. Mosaic is a public domain,
Internet-aware document browser that is available for
X-Windows, Macintosh, and Microsoft Windows.
All four modules use graphics to demonstrate design
procedures and contaminant control processes. The
contaminant module provides information about 11 cate-
gories of contaminants cited in RCWP projects and their
impacts on surface and ground-water resources. The
monitoring module describes different aspects of water
quality sampling and analysis systems. The transport
module describes contaminant pathways in surface
and ground water. The case studies module presents
detailed examples from key RCWP projects. These
examples cover both practice selection and implemen-
tation aspects of control systems. The reference modules
serve as a complementary component of XGRCWP.
Linkage to AGNPS
AGNPS is a distributed-parameter, storm event-based
model that estimates runoff, sedimentation, and nutrient
loss in surface runoff within agricultural watersheds (2).
The prototype version of the Water Quality Model/GRASS
Interface developed by SCS conveniently generates an
AGNPS input file for all cells in a watershed from the
spatial and relational soils and fields databases. The
UNIX version of AGNPS can then use this input file.
XGRCWP can call AGNPS directly from its X-Window
interface and convert standard AGNPS model outputs
for all cells in the watershed into GRASS raster format
for display and analysis.
Discussion
The literature on software systems for managing non-
point source pollution in agricultural watersheds is di-
verse and rapidly growing. With few exceptions (5-7),
these decision support systems are purely model-
based, CIS-based (8), or hybrid systems with models
running within a CIS framework (9-14). The addition of
expert system components can overcome some of the
difficulties in primarily model-based systems:
• Overly intensive input data requirements.
• Inability to handle missing or incomplete data.
• Requirements that all inputs be numerically expressed.
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Table 3. The Rule File for Recommending Conservation Tillage
IFNOTMAP app.class 3
ANDIFMAP contam.feature 2
ANDIFMAP leaching.p 1
ANDIFMAP soil.g 1 2
ANDIFMAP contam.load 1
ANDIFMAP season 2
THENMAPHYP 1 yes, CT
is recommended
i
IFNOTMAP app.class 3
ANDIFMAP contam.feature 2
ANDNOTMAP leaching.p 3
ANDNOTMAP soil.g 1
ANDNOTMAP contam.load 3
ANDIFMAP season 1
THENMAPHYP 1 yes, CT
is recommended
i
IFNOTMAP app.class 3
ANDIFMAP contam.feature 1
ANDNOTMAP contam.load 3
ANDIFMAP season 1
THENMAPHYP 1 yes, CT
is recommended
i
IFNOTMAP app.class 3
ANDIFMAP contam.feature 1
ANDNOTMAP leaching.p 3
ANDIFMAP soil.g 3 4
ANDNOTMAP contam.load 3
ANDIFMAP season 2
THENMAPHYP 1 yes, CT
is recommended
!application class is not high-source area
!contaminant is nonadsorbance
teaching potential is low
!soil groups are A or D
!contaminant loading is low
!nongrowing season
!application class is not high-source area
!contaminant is nonadsorbance
teaching potential is not high
!soil groups are not A
!contaminant loading is not high
!growing season
!application class is not high-source area
!contaminant is strong adsorbance
!contaminant loading is not high
!growing season
!application class is not high-source area
!contaminant is strong adsorbance
teaching potential is not high
!soil groups are C or D
!contaminant loading is not high
!nongrowing season
Nufritrnt I UMJ
Manure Nutrient Laad
, -
j J ** .'II Ijvi": V
i J ' 'iir-r'c -spr'iCi; on-
Pttrti rnftfrs;
I
.-.- £ ^- ruler. » .-ff,-,
r "
•i- ,i i •:-.
Figure 4. The GUI to the R.MANURE function.
• High degree of expertise needed to structure model
input and explain model output relative to the user's
problem context.
The expert system component of XGRCWP also re-
duces the number of model runs needed for decision
support through preliminary, rule-based screening of
control systems at each site of interest in the watershed.
Conclusions
XGRCWP incorporates several kinds of expertise for the
user's benefit:
• Subject matter expertise in siting and selecting non-
point source control systems in agricultural water-
sheds.
• Expertise in configuring AGNPS model input from the
soils and fields databases.
• Expertise in interpreting, explaining, and visualizing
expert system and model input.
The integration of the expert system and the GRASS
CIS makes input to the expert system easier and more
objective. It enhances the expert system's capability
for recommending effective control practices at the
field level to achieve watershed contaminant loading
objectives. XGRCWP is designed as an open structured
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Category Value
1173
Category Label
FDID1173
Fnin
fert_sch_id
FTID01
fert_name
13-13-13
appl_rate
200.0
fert_name
13-13-13
total_n
13.000
total_p
5.700
Figure 5. Data structure of Sycamore Creek watershed, Ingham County, Michigan.
program and has great potential to be improved easily
and continually according to users' feedback. Ongoing
efforts to enhance the program include:
• Developing more rules that incorporate topographical
factors such as slope and slope length for the expert
system so that more site-specific control practices
can be recommended.
• Adding dynamic hypertext-based help and reference
to the program.
• Establishing intelligent linkages among the expert
system, the CIS functions, and other simulation or
design models for nonpoint source control practices.
Acknowledgments
We thank Vicki Anderson, Ruth Shaffer, and Brent Stin-
son of the Michigan USDA SCS and John Suppnick of
the Michigan Department of Natural Resources for their
assistance in working with the underlying databases in
the Sycamore Creek watershed. This project is sup-
ported by U.S. EPA Grant No. X-818240.
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