SEPA
United States
Environmental Protection
Agency
EPA/100/R-11/002 | September 2011
www.epa.gov/osa
Landscape
and
Predictive Tools
A Guide to Spatial Analysis for
Environmental Assessment
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United States Environmental Protection Agency
Office of the Science Advisor, Risk Assessment Forum
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[This page intentionally left blank.]
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EPA/1 OO/R-11/002
September 2011
Landscape and Predictive Tools: a
Guide to Spatial Analysis for
Environmental Assessment
Office of the Science Advisor
Risk Assessment Forum
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This document has been reviewed in accordance with U.S. Environmental
Protection Agency policy and approved for publication. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
ABSTRACT
This landscape and predictive tools methods manual, developed collaboratively by
U.S. EPA's Office of Water, Office of Research and Development, Regional Offices and
others, describes the purpose, rationale, and basic steps for using landscape and
predictive tools for Clean Water Act monitoring, assessment and management purposes.
Landscape and predictive tools are needed both to guide efficient filling of monitoring gaps
and to prioritize our protection and rehabilitation actions. This should yield better protection
for high quality waters and quicker, more cost-effective restoration of impaired waters.
We have organized this method guidance document into four sections:
(I) Introduction to Landscape and Predictive Tools; (II) Geographic Frameworks, Spatial
Data, and Analysis Tools; (III) Examples and Case Studies; and (IV) Gaps and Needs for
Research and Applications. In addition, the extensive Toolbox provides links to and short
descriptions of a wide range of easily accessed data sets and analytical tools.
This guidance stresses simultaneous use of matched (or paired) landscape and in
situ data for empirical modeling to enhance our predictive capabilities and encourage
science-based targeting and priority setting. Landscape and predictive tools have a wide
range of current and potential applications including criteria and standards development,
problem identification and prevention, prioritization and targeting of rehabilitation, and
advancing science, education, and society's ability to effectively manage aquatic and
terrestrial resources.
Particularly valuable assets are models that combine in situ field measurements
and landcover—thus, providing us with landscape and predictive tools for many water
quality programs. For example, empirical models that provide a predictive capacity are
ii
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including mapped areas such as ecoregions or other appropriate classification
approaches, to establish realistic areas for analysis and extrapolation, (2) use
"wall-to-wall" landscape and other data to document stress gradients, (3) construct
empirical relationships or models linking landscape and other stress indicators to in situ
response, and (4) use these relationships to extrapolate to places lacking in situ data.
Estimating the condition of places lacking data can greatly expand the usefulness of our
limited site-specific data. Regular use of landscape and predictive tools can support
comprehensive, systematic priority setting and targeting for monitoring, rehabilitation
and prevention actions. Using these tools as a matter of course will require ongoing
commitment to training, continued collaborative development of applications, new
techniques and data, and consistent effort to bring these scientific advances to bear on
our water quality monitoring, assessment, rehabilitation, and protection efforts.
Cover Photo:
Nighttime Lights of the World
Data analysis and digital image creation by:
Christopher Elvidge, Kimberly Baugh, Benjamin Tuttle, Jeff Sarfan, Ara Howard, Patrick Hayes, Edward
Erwin; by the NOAA-NESDIS-National Geophysical Data Center-Earth Observation Group; Boulder,
Colorado USA.
http://dmsp.ngdc.noaa.gov
Source Data: U.S. Air Force Defense Meteorological Satellite Program (DMSP)
Date Range: January 1-December 31, 2003.
Preferred Citation:
U.S. EPA (Environmental Protection Agency). (2011) Landscape and predictive tools: a guide to spatial
analysis for environmental assessment. Risk Assessment Forum. Washington, DC. EPA/100/R-11/002.
iii
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CONTENTS IN BRIEF
SECTION I: INTRODUCTION TO LANDSCAPE ASSESSMENT AND PREDICTIVE
TOOLS
Section I: Summary
Chapter 1: Introduction
Jim Harrison, Robert M. Hughes, Barbara Brown, and Susan Cormier
Chapter 2: The Clean Water Act: Benefits of Landscape Tools (Geospatial Data and
Analysis)
Karl Hermann, Doug Norton, Charlie Howell, Kristen Pavlik, Jim
Harrison, Kelly Kunert, Ellen Tarquinio, Brittany Croll, Robert Hall,
Alfonso Blanco, and Peter Stokely
Chapter 3: Using Geospatial Information in Environmental Assessments
Susan Cormier and Glenn Suter
Chapter 4: Basic Concepts for Using Landscape and Predictive Tools
Jim Harrison and Susan Cormier
SECTION II: GEOGRAPHIC FRAMEWORKS, SPATIAL DATA, AND ANALYSIS
TOOLS
Section II: Summary
Chapter 5: Common Geographic Frameworks
James M. Omernik, Robert M. Hughes, Glenn Griffith, and Greg Hellyer
Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
Susan Cormier and Jeff Hollister
Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
Jeff Hollister and Susan Cormier
SECTION III: EXAMPLES AND CASE STUDIES
Section III: Summary
Chapter 8: Impervious Estimates and Projections—EPA Region 4
Jim Harrison, Linda Exum and Sandra Bird
Chapter 9: Water Temperature Regime Assessments—Umatilla River
Peter Leinenbach
Chapter 10: Nonpoint Source Inventory-Integrated Pollutant Source Identification
(IPSI) Process
Pat Hamlett
Chapter 11: Oostanaula Creek IPSI Case Study
Pat Hamlett
IV
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CONTENTS IN BRIEF cont.
Chapter 12: Nutrient Classification of Streams Using CART
Mike Paul
Chapter 13: Biocriteria and Reference Condition
SECTION IV: GAPS AND NEEDS FOR RESEARCH AND APPLICATION
Section IV: Summary
Chapter 14: Research and Application Gaps and Needs
Naomi Detenbeck and Mary White
SECTION V: TOOLBOX: SPATIAL DATA AND TOOLS DATABASE
Greg Hellyer, Peter Leinenbach, Jeff Hollister, Naomi Detenbeck, Susan Cormier,
Jan Ciborowski, Don Ebert, and Ann Lincoln (Accessible from Risk Assessment
Forum Website)
SECTION VI: GLOSSARY AND SUGGESTED READING
v
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CONTENTS
Page
LIST OF TABLES xv
LIST OF FIGURES xvii
ABBREVIATIONS AND ACRONYMS xxiv
PREFACE xxviii
AUTHORS, CONTRIBUTORS AND REVIEWERS xxxii
ACKNOWLEDGMENTS xxxiv
EXECUTIVE SUMMARY XXXV
SECTION I: INTRODUCTION TO LANDSCAPE ASSESSMENT AND
PREDICTIVE TOOLS l-i
1. INTRODUCTION 1-1
1.1. MONITORING AND LANDSCAPE CONDITION 1-2
1.2. APPLICATIONS FOR LANDSCAPE ASSESSMENT AND
PREDICTIVE TOOLS 1-4
1.3. CONCEPTUAL MODELS 1-14
1.4. ORGANIZATION OF DOCUMENT 1-15
1.5. CONCLUSIONS 1-18
1.6. REFERENCES 1-19
2. THE CLEAN WATER ACT: BENEFITS OF LANDSCAPE TOOLS
(GEOSPATIAL DATA AND ANALYSIS) 2-1
2.1. OVERVIEW: LANDSCAPE ANALYSIS LINKAGES TO THE
PRIMARY GOALS OF THE CLEAN WATER ACT 2-1
2.2. WATER QUALITY STANDARDS SECTION 303(C)(2) 2-2
2.2.1. Determining Designated Uses Through Classification of
Similar Waterbody Types 2-2
2.2.2. Characterizing Background Levels and Achievable
Conditions 2-3
2.2.3. Developing Water Quality Criteria 2-4
2.3. MONITORING AND ASSESSMENT SECTION 305(B) 2-4
2.4. IMPAIRED WATERS (SECTION 303[D]) LISTING AND TOTAL
MAXIMUM DAILY LOAD (TMDL) DEVELOPMENT 2-6
2.4.1. Identifying and Listing Impaired Waters 2-7
2.4.2. Prioritizing Total Maximum Daily Load (TMDL)
Implementation and Optimizing Locations and Types of
Remedial Actions (Risk Characterization and Risk
Management) 2-8
2.4.3. Tracking Progress 2-9
2.5. TOTAL MAXIMUM DAILY LOADS (TMDLs) 2-10
2.6. NONPOINT SOURCE CONTROL SECTION 319 2-13
vi
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CONTENTS cont.
2.7. NATIONAL POLLUTANT DISCHARGE ELIMINATION SYSTEM
(NPDES) PERMITTING 2-15
2.8. WETLANDS PROTECTION 2-16
2.9. THE CLEAN WATER STATE REVOLVING FUND (CWSRF) 2-19
2.10. CROSS-PROGRAM APPLICATIONS 2-20
2.11. REFERENCES 2-20
3. USING GEOSPATIAL INFORMATION IN
ENVIRONMENTAL ASSESSMENTS 3-1
3.1. FOUR CLASSES OF ENVIRONMENTAL ASSESSMENT—AN
INTEGRATED IMPLEMENTATION 3-1
3.2. USING LANDSCAPE AND PREDICTIVE TOOLS IN
INTEGRATIVE ENVIRONMENTAL ASSESSMENT 3-5
3.2.1. Characterizing and Assessing Environmental Condition 3-9
3.2.1.1. Condition Assessment 3-9
3.2.2. Understanding the Causal Pathways that Led to Current
Ecological Conditions 3-11
3.2.2.1. Causal Assessment 3-13
3.2.2.2. Source Assessment 3-15
3.2.3. Predicting the Potential for Environmental Consequences 3-19
3.2.3.1. Risk Assessment 3-19
3.2.3.2. Developing and Using Criteria or Restoration
Benchmarks 3-20
3.2.3.3. Designing Management Options 3-23
3.2.3.4. Management Prioritization Assessment 3-24
3.2.4. Outcome Assessment Evaluating Environmental Progress 3-26
3.2.4.1. Outcome Assessment 3-26
3.3. REFERENCES 3-30
4. BASIC CONCEPTS FOR USING LANDSCAPE AND PREDICTIVE
TOOLS 4-1
4.1. APPROPRIATE TOPICS AND QUESTIONS THAT USE
GEOSPATIAL ANALYSIS 4-1
4.2. APPROPRIATE GEOGRAPHIC SCOPE FOR ANALYSIS AND
APPROPRIATE EXTRAPOLATION 4-3
4.3. USE LANDSCAPE AND OTHER DATA TO DOCUMENT
STRESSOR GRADIENTS 4-4
4.4. CONSTRUCT EMPIRICAL MODELS LINKING GEOGRAPHICAL
ATTRIBUTES (SOURCES AND STRESSORS) WITH IN SITU
RESPONSE INDICATORS 4-5
4.5. USE THE RESULTING RELATIONSHIPS TO EXTRAPOLATE TO
PLACES LACKING IN SITU DATA OR TO IDENTIFY AREAS OF
INTEREST 4-9
vii
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CONTENTS cont.
4.6. PROCESS OUTLINE: ASSESSMENT PLANNING, ANALYSIS,
AND SYNTHESIS 4-10
4.6.1. Planning and Problem Formulation 4-10
4.6.2. Analysis 4-12
4.6.3. Synthesis 4-14
4.7. REFERENCES 4-14
SECTION II: GEOGRAPHIC FRAMEWORKS, SPATIAL DATA, AND
ANALYSIS TOOLS Il-i
5. COMMON GEOGRAPHIC FRAMEWORKS 5-1
5.1. ECOREGIONS 5-2
5.1.1. Description 5-2
5.1.2. Strengths and Limitations 5-3
5.2. SINGLE-PURPOSE FRAMEWORKS 5-8
5.2.1. Description 5-8
5.2.2. Strengths and Limitations 5-9
5.3. WATERSHEDS 5-9
5.3.1. Description 5-9
5.3.2. Strengths and Limitations 5-10
5.4. HYDROLOGIC UNITS (HUS) AND HYDROLOGIC UNIT CODES
(HUCS) 5-10
5.4.1. Description 5-10
5.4.2. Strengths and Limitations 5-11
5.5. GENERAL PRINCIPLES 5-28
5.5.1. Computer Delineation of Ecoregions and Hydrologic
Landscape Regions 5-28
5.5.2. Strengths and Limitations of Using Watersheds and
Ecoregions Together 5-31
5.6. REFERENCES 5-35
6. TYPES OF SPATIAL AND LANDSCAPE DATA AND SAMPLING
DESIGNS 6-1
6.1. SOURCES OF DATA: HISTORIC DATA, FIELD SAMPLING,
REMOTELY SENSED 6-1
6.1.1. Historic Data and Hard Copy Maps 6-3
6.1.2. Field Data 6-4
6.1.3. Remotely Sensed Data 6-5
6.2. TYPES OF SPATIAL AND LANDSCAPE DATA 6-5
6.2.1. Vector 6-6
6.2.1.1. Point 6-8
6.2.1.2. Line 6-9
6.2.1.3. Polygon 6-10
viii
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CONTENTS cont.
6.2.2. Raster 6-10
6.3. OBTAINING DATA: COLLECTION 6-11
6.3.1. Basic Considerations and Planning for Data Acquisition 6-11
6.3.2. Types of Sampling Designs for Field Data 6-14
6.3.2.1. Targeted Sampling 6-14
6.3.2.2. Simple Random 6-14
6.3.2.3. Stratified Random Sampling 6-17
6.3.2.4. Systematic and Grid 6-19
6.3.2.5. Ranked Set 6-19
6.3.2.6. Adaptive Cluster 6-22
6.3.3. Aerial Photos 6-22
6.3.4. Remote Sensors 6-23
6.4. SPATIAL DATA COMMONLY USED IN ENVIRONMENTAL
ASSESSMENT 6-27
6.4.1. Land Use/Landcover (LU/LC) 6-28
6.4.2. Elevation 6-28
6.4.3. Hydrology/Hydrography 6-28
6.4.4. Climate 6-29
6.4.5. Soils 6-29
6.4.6. Sources and Stressors 6-29
6.5. REFERENCES 6-30
7. METHODS AND TOOLS FOR ANALYZING SPATIALLY EXPLICIT
INFORMATION 7-1
7.1. INTRODUCTION 7-1
7.2. PLANNING AND PROBLEM FORMULATION 7-2
7.2.1. What New Information is Sought? 7-2
7.2.2. What is the Regulatory Authority or Social, Political,
Economic Driver? 7-4
7.2.3. What Needs Protecting or Rehabilitating? 7-4
7.2.4. What Type of Analyses Need to be Performed and How
Good do They Need to be to Make a Decision? 7-5
7.3. ANALYSIS 7-6
7.3.1. Software 7-6
7.3.1.1. Commercial 7-6
7.3.1.2. Open Source 7-7
7.3.2. Geographic Information System Tools 7-7
7.3.2.1. Commercial 7-7
7.3.2.2. Open Source 7-8
7.3.3. Analysis of Field-Collected Data 7-8
7.3.3.1. Linking Data and Scale 7-9
7.3.3.2. Classification 7-10
7.3.3.3. Normalization 7-10
ix
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CONTENTS cont.
7.3.3.4. Descriptive/Association 7-11
7.3.3.5. Applications of Statistical Models 7-20
7.3.4. Common Spatial Analysis Methods 7-20
7.3.4.1. Landscape Metrics 7-20
7.3.4.2. Specific Tools 7-21
7.3.4.3. Important Considerations 7-21
7.3.4.4. Spatial Interpolation 7-22
7.3.4.5. Specific Tools 7-24
7.3.4.6. Important Considerations 7-24
7.3.5. Hydrologic Analysis 7-25
7.3.5.1. Specific Tools 7-25
7.3.5.2. Important Considerations 7-26
7.3.6. Overlays and Proximity 7-26
7.3.6.1. Specific Tools 7-26
7.3.6.2. Important Considerations 7-27
7.3.6.3. Applications of Statistical and Spatial Models 7-28
7.4. SYNTHESIS 7-29
7.4.1. Decision Support Systems (DSSs) 7-29
7.4.2. Some Specific Decision Support Systems (DSSs) 7-30
7.5. DECISIONS AND ACTIONS 7-31
7.6. REFERENCES 7-31
SECTION III: EXAMPLES AND CASE STUDIES Ill-i
8. IMPERVIOUS ESTIMATES AND PROJECTIONS—EPA REGION 4 8-1
8.1. INTRODUCTION 8-1
8.2. METHODS AND ANALYSIS 8-5
8.2.1. Analyses Conducted 8-5
8.2.1.1. Multiple Data Source Impervious Estimates 8-5
8.2.1.2. Future Projections of Multiple Data Source
Imperviousness 8-6
8.2.1.3. Grid Point Statistical Sampling of Air Photos for
Error Estimation 8-7
8.2.1.4. Relationships Between Imperviousness and
Biological Response 8-8
8.3. OUTPUT 8-9
8.4. DISCUSSION AND CONCLUSIONS 8-12
8.4.1. Advantages 8-16
8.4.2. Cautions and Caveats 8-16
8.4.3. Future Needs 8-17
8.5. ADDITIONAL RESOURCES AND CONTACTS 8-17
8.5.1. Analytical Tools Interface for Landscape Assessments
(ATtlLA) 8-17
x
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CONTENTS cont.
8.5.2. National Land Cover Dataset (NLCD) 2001
Imperviousness 8-17
8.5.3. Impervious Surface Analysis Tool (ISAT)/Nonpoint
Education for Municipal Officials (NEMO)/Tennessee
Growth Readiness and Other State/Local Efforts 8-18
8.6. SUGGESTED READING AND WEB SITES 8-19
8.7. REFERENCES 8-20
9. WATER TEMPERATURE REGIME ASSESSMENTS—UMATILLA RIVER 9-1
9.1. INTRODUCTION 9-1
9.2. METHODS AND ANALYSIS 9-3
9.2.1. Data Sets, Models and Analytical Software 9-4
9.2.2. Analysis Conducted 9-7
9.2.2.1. Temperature Monitoring and Source
Assessment Development 9-10
9.2.2.2. Riparian Vegetation Restoration Measures 9-14
9.2.2.3. Channel Morphology Restoration Measures 9-14
9.3. OUTPUT 9-17
9.4. DISCUSSION 9-21
9.4.1. Advantages 9-21
9.4.2. Cautions and Caveats 9-21
9.5. REFERENCES 9-22
10. NONPOINT SOURCE INVENTORY—INTEGRATED POLLUTANT
SOURCE IDENTIFICATION (IPSI) PROCESS 10-1
10.1. INTRODUCTION 10-1
10.2. METHODS AND ANALYSIS 10-3
10.2.1. Data and Software Requirements 10-3
10.2.2. Selection and Delineation of the Project Watershed 10-4
10.2.3. Acquisition of Photography 10-4
10.2.4. Field Verification and Photographic Signature 10-6
10.2.5. Photographic Interpretation and GIS Database
Construction for NPS Inventory 10-6
10.3. ANALYSIS 10-14
10.3.1. Construction of Geodatabase 10-14
10.3.2. Pollutant Loading Model 10-14
10.3.2.1. Pollutant Loads from Urban Land Classes 10-15
10.3.2.2. Pollutant Loads from Crop, Pasture, Forest,
Mining, and Disturbed Lands 10-16
10.3.2.3. Pollutant Loads from Unpaved Roads 10-18
10.3.2.4. Pollutant Loads from Eroding Streambanks and
Road Features 10-18
xi
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CONTENTS cont.
10.3.2.5. Pollutant Loads from Beef Cattle, Dairy, and
Horse Operations 10-19
10.3.2.6. Bacteria Load Methodology 10-20
10.3.3. Watershed Prioritization 10-21
10.4. DISCUSSION AND CONCLUSION 10-22
10.5. REFERENCES 10-23
11. OOSTANAULA CREEK IPSI CASE STUDY 11 -1
11.1. INTRODUCTION 11-1
11.2. METHODS AND ANALYSIS 11-2
11.3. SOURCE CHARACTERIZATION 11 -5
11.3.1. Agricultural Land Use 11-5
11.3.1.1. Pasture Land 11-5
11.3.1.2. Cropland 11-8
11.3.2. On-Site Septic Systems 11 -9
11.3.3. Eroding Road Banks 11-9
11.3.4. Streambank Conditions 11-12
11.4. SOURCE ASSESSMENT 11-12
11.4.1. Soil Loss Estimate Summary 11-12
11.4.2. Pollutant Loading Summary 11-17
11.4.2.1. Total Nitrogen 11-20
11.4.2.2. Total Phosphorus 11-20
11.4.2.3. Total Suspended Solids 11 -20
11.5. OOSTANAULA CREEK WATERSHED RESTORATION PLAN
AND IMPLEMENTATION 11-21
11.6. DISCUSSION AND CONCLUSION 11-24
11.6.1. Advantages 11-24
11.6.2. Primary Objectives of an NPS Inventory Include the
Following: 11-25
11.6.3. Caveats 11-25
11.7. REFERENCES 11-26
12. NUTRIENT CLASSIFICATION OF STREAMS USING CART 12-1
12.1. INTRODUCTION 12-1
12.2. METHODS 12-2
12.2.1. Statistical Method and Software 12-2
12.2.2. Data Sets 12-3
12.2.2.1. Watershed Delineations 12-3
12.2.2.2. Runoff 12-3
12.2.2.3. Climate 12-3
12.2.2.4. Land Use 12-3
12.2.2.5. Surficial Deposits 12-4
12.2.2.6. Soil Characteristics 12-4
xii
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CONTENTS cont.
12.2.2.1. Principal Aquifer Types 12-4
12.2.2.8. Water Quality Data 12-4
12.3. ANALYSIS 12-4
12.3.1. Results 12-5
12.4. DISCUSSION AND CONCLUSION 12-5
12.4.1. Advantages 12-5
12.4.2. Cautions and Caveats 12-5
12.4.3. Future Needs 12-8
12.4.4. Other Examples 12-8
12.5. REFERENCES 12-8
13. BIOCRITERIA AND REFERENCE CONDITION 13-1
13.1. INTRODUCTION 13-1
13.2. METHODS 13-2
13.2.1. Statistical Method and Software 13-3
13.2.2. Data Sets 13-4
13.3. ANALYSES 13-4
13.3.1. Results 13-7
13.4. DISCUSSION AND CONCLUSIONS 13-7
13.4.1. Advantages 13-7
13.4.2. Cautions and Caveats 13-9
13.4.3. Future Needs 13-9
13.4.4. Other Examples 13-9
13.5. REFERENCES 13-10
SECTION IV: GAPS AND NEEDS FOR RESEARCH AND APPLICATION IV-i
14. RESEARCH AND APPLICATION GAPS AND NEEDS 14-1
14.1. CONCEPTUAL FRAMEWORK GAPS AND NEEDS 14-1
14.2. GAPS IN POTENTIAL APPLICATION OF GEOGRAPHIC
FRAMEWORKS AND LANDSCAPE TOOLS IN CLEAN WATER
ACT PROGRAMS 14-6
14.3. EPA WATER PROJECTS AND PROGRAMS 14-7
14.4. PROGRAMS WITH A GEOGRAPHIC FOCUS 14-10
14.5. DATA GAPS AND NEEDS 14-12
14.5.1. Landcover and Indices 14-12
14.5.2. Infrastructure 14-15
14.5.3. Land Management Practices 14-16
14.5.4. Digital Elevation Models 14-16
14.5.5. Hydrology 14-17
14.5.6. Ground Water 14-19
14.5.7. Soils 14-19
14.5.8. Climate 14-19
xiii
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CONTENTS cont.
14.5.9. Stressor Specific 14-2C
14.5.9.1. Pathogens 14-2C
14.5.9.2. Nutrients 14-2C
14.5.9.3. Suspended and Bedded Sediment 14-21
14.5.9.4. Toxic Inventories 14-21
14.5.9.5. Hydrology 14-21
14.5.9.6. Temperature 14-21
14.5.9.7. Habitat Alterations 14-2
14.5.9.8. Ionic Strength and Alkalinity (Background) 14-2
14.5.10. Remote Sensing 14-2
14.5.11. Demographics/Socioeconomic Data 14-2
14.5.12. Data Portals and Web Services 14-2
14.5.13. Prioritizing Data Gaps and Needs 14-2
14.6. APPLICATION GAPS AND NEEDS 14-2
14.6.1. Predicting Loadings and Concentrations of Pollutants 14-3
14.6.2. Associating Landscape Characteristics with Ecological
Condition 14-33
14.6.3. Water Quality Standards 14-3
14.6.4. Best Management Practice (BMP) Selection and Siting 14-3
14.6.5. Priority Setting 14-3
14.6.6. Evaluation of Restoration Potential or Recovery Potential.... 14-3
14.7. NEEDS BY USER GROUP 14-3
14.7.1. Summary of Available Tools 14-3
14.7.2. Access to Tools and Data 14-3
14.7.3. Tool/Data Requirements for Different Programmatic
Applications 14-3
14.8. REFERENCES 14-4
SECTION V: Toolbox: Spatial Data and Tools Database V
SECTION VI: GLOSSARY AND SUGGESTED READING V
GLOSSARY VI
SUGGESTED READING VI-
General Landscape-Biological Response Patterns VI-
Predictive Modeling Through Use of Landscape Data VI-
xiv
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LIST OF TABLES
No. Title Page
1-1 Monitoring for CWA Purposes Includes Monitoring Objectives Contained
in Several Parts of the Act, Regulatory Requirements, and Guidance
Describing 10 Key Elements of State and Other Monitoring Programs 1-3
1-2 Spectrum of Uses for Landscape and Predictive Tools 1-5
I-3 Roles for Probability Surveys, Landscape/Predictive Tools and
Targeted Monitoring 1-10
3-1 Site Description and Characteristics of Benthic Invertebrate
Assessments Completed by CT DEP on October 24, 2003. Relative
locations of sites for the Eagleville example are shown in Figure 3-2 3-8
4-1 Initial Analyses Performed During Assessment Planning and Factors to
Be Considered 4-2
4-2 Analysis and Synthesis Steps and Factors 4-3
5-1 Differences in NHD Flowline (rivers/streams and artificial path) Density
between Four 1:100K Quads in South Carolina 5-27
7-1 Activities Associated with Different Assessment Types 7-3
8-1 Relative Risk of Impervious Area on Biological Condition in Streams 8-10
9-1 Spatial Data Types and Associated Applications 9-4
9-2 Factors Associated with Temperature Change 9-12
10-1 Example of the Anderson Hierarchy 10-9
10-2 Livestock Operations Data 10-9
10-3 Drainage Conditions Mapped in an NPS Inventory 10-11
11 -1 Animals per Site Used to Estimate Pollutant Loadings 11-7
11 -2 On-Site Septic System by Classifications 11-10
11 -3 Soil loss Estimates for Select Agricultural Classes 11-15
II-4 Nutrient Loading by Land use as Tons/Acre/Year 11-18
xv
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LIST OF TABLES cont.
No. Title Page
13-1 The Reach-Scale Human Activity Checklist. These data are used for
calculating the reach-scale human disturbance index 13-5
13-2 Site Grading Descriptions for Final Site Grades 13-8
14-1 a Status of Development and Testing of Landscape Frameworks 14-2
14-1 b Status of Development and Testing of Landscape Frameworks 14-3
14-2 Informal Ranking of Data Gaps and Needs by Landscape Predictive
Tools Workgroup 14-25
14-3a Application Gaps and Needs 14-26
14-3b Application Gaps and Needs 14-29
14-4 Frequency of Resources in Microsoft® Access Database by Data or Tool
Type 14-38
14-5 Availability of Data or Tool Resources in Microsoft® Access Database by
Programmatic Application 14-39
xv i
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LIST OF FIGURES
No. Title Page
1-1 Line Diagram Illustrating Hypothetical Differences Between the
Proportion of Impaired Waters as Determined by Targeted and
Statistical Sampling 1-7
1 -2 Examples of Actual Differences between Results of Targeted and
Statistical Sampling 1-8
1-3 Conceptual Model of Extrapolating In Situ Results for Targeting Specific
Potentially Impaired Areas 1-9
1 -4 Integrated Monitoring Flowchart 1-11
1-5 The BCG: Biological Response to Varying Stressor Levels 1-13
1-6 Natural Setting, Human Activities, Stressors, and Biological Response 1-14
2-1 Prioritization of Stream Remediations Based on Severity of Impairment
and Recovery Potential 2-9
2-2 Regional Query Tool for Riparian Restoration with Constrained
Parameters Using What If 2-12
2-3 Reach Scale Thermal Profile Illustrating the Cooling Effect of
Groundwater Recharge and Mixed Temperatures in the River 2-14
2-4 Identification and Spatial Distribution of Isolated Wetlands in Alachua
County, Florida as Determined by Segmentation Analysis of Landsat
TM+ Imagery (Frohn et al., 2009) and Overlay Analysis of Soil,
Hydrology, and Elevation GIS Data Layers (Reif et al., 2009) 2-18
3-1 Environmental Assessment Integration 3-2
3-2 Map Showing Location of Fish and Macroinvertebrate Sampling
Locations along Eagleville Brook. Sites numbers correspond with
Table 3-1 3-6
3-3 Every Type of Assessment has the Same Basic Elements 3-7
3-4 Conceptual Model of Causes Associated with the Force Exerted by
Stormwater that Affects the Abundance and Diversity of Fish and Benthic
Invertebrate Taxa 3-12
3-5 Data Support Photo 3-16
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LIST OF FIGURES cont.
No. Title Page
3-6 Data Support Photo 3 3-17
3-7 Scatter Plot of % IC Upstream of Monitoring Locations and a Measure of
the Macroinvertebrate Community with Respect to Reference Sites 3-19
3-8 Diagram of Activities that Lead to Estimated Risk of Effect or Criterion 3-22
3-9 Map of Connecticut Showing Stream Classes and Management Classes
Based on the Conceptual Model in Figure 3-13-29
5-1 Ohio Ecoregional Patterns in Nutrient Richness and Ionic Strength
Variables in Least-Disturbed Watersheds as Indicated by Principal
Components Axis Scores for Each 5-6
5-2 Eight-Digit HUs of Texas that are True Watersheds 5-13
5-3 Level III Ecoregions and 8-Digit HUs of the Pacific Northwest 5-15
5-4 Eight-Digit HUs that are True Watersheds within the Columbia Basin 5-16
5-5 Four 8-Digit HUs in the Columbia Plateau Level III Ecoregion (10) 5-17
5-6 True Watersheds Associated with Downstream Points in HUs A, B, C,
and D 5-18
5-7 Representative 8-Digit HUs that are True Watersheds within Level III
Ecoregions in the Columbia River Basin 5-19
5-8 Twelve-Digit HUs in South Carolina that are True Watersheds5-20
5-9 Level III and IV Ecoregions of South Carolina 5-21
5-10 Selected 12-Digit HUs in the Carolina Slate Belt and Sand Hills Level IV
Ecoregions of South Carolina 5-22
5-11 True Watersheds Associated with Downstream Points in Two of the
Selected 12-Digit HUs Shown in Figure 5-10 5-24
5-12 Examples of 12-Digit HUs that are True Watersheds Completely within,
and thus Representative of, Specific Level IV Ecoregions of South
Carolina 5-25
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LIST OF FIGURES cont.
No. Title Page
5-13 Examples of Aggregations of 12-Digit HUs that Together Comprise True
Watersheds that are Completely or Nearly Completely within Specific
Level IV Ecoregions of South Carolina 5-26
5-14 Stream Densities on 1:100,000 Scale USGS Topographic Maps in South
Carolina 5-27
5-15 Watershed Areas and Mean Annual Discharges Relative to Stream
Orders 5-29
6-1 Portion of 1853-1854 Map, Columbia River, Including the Hood River to
John Day Area 6-2
6-2 In Situ Field Samples of Actual Stream Temperature and Temperatures
Derived from Thermal Infrared Remotely Sensed Data by River Mile (see
Chapter 9; ODEQ, 2007) 6-2
6-3 Remotely Sensed Imagery from Aerial Video True Color Photo and
Thermal Infrared Aerial Photograph (see Chapter 9; ODEQ, 2007) 6-3
6-4 A Raster (left) and Vector Image of River and Two Parallel Roads6-6
6-5 Physical Model of Inferred Stream Channels Derived from Remotely
Sensed Imagery of Elevation and First Principle Model from General
Knowledge of Physics 6-7
6-6 EnviroMapper, Showing Many Hazardous Waste Sites as Green
Squares 6-9
6-7 Line Map of Streams are Shown with Impaired Stream Segments
Indicated in Red and Segments Meeting Water Quality Criteria in
Turquoise or Not Assessed 6-10
6-8 Example Illustrating Matching Temporal Scale of Inherent Causal
Mechanism 6-13
6-9 Targeted Sampling Followed by Targeted Resampling after Impaired Site
is Identified 6-15
6-10 Adaptive Sampling was Done after Impairments were Found at Filled
Circles Near the Publicly Owned Treatment Works 6-16
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LIST OF FIGURES cont.
No. Title Page
6-11 The Filled Circles are First Order Streams, and Open Circles are Second
Order Streams 6-18
6-12 Patterns of Regularly Spaced Samples. One site nearest the center of
the grid is selected 6-20
6-13 Impervious Area Cover and Impaired Waters Causes for Metropolitan
Atlanta can be Used to Focus Where to Sample for Impaired Waters6-21
6-14 Example of GlobeXplorer GIS Interface 6-24
6-15 Chlorophyll a Concentration Map Developed from the Spectral Index.
Brighter yellow indicates greater concentration of chlorophyll 6-25
6-16 One of the Common Candidate Causes Featured in CADDIS, which
Includes Conceptual Diagrams and Information about Sources, Site
Evidence, Effects, Ways to Measure and Literature Reviews and Criteria
Values 6-30
7-1 Scatter Plots Illustrating Different Patterns Suggesting the Underlying
Form of the Stressor-Response Relationship. (A) linear, (B) quadratic,
(C) exponential, (D) logarithmic 7-13
7-2 Examples of Different Correlations between Two Variables, x and y 7-14
7-3 A Sample Box Plot with Different Components of the Plot Labeled 7-16
7-4 Box Plots Showing Symmetrical or Skewed Data Distribution and
Different Types of Kurtosis, or Relative Spread 7-17
7-5 Scatter Plot with 90th Percentile Quantile Regression and Ordinary Least
Squares Regression 7-18
7-6 Results of CART Analysis of Total Phosphorus Resulting in Different
Phosphorus Groups Using All Environmental Predictors, or Excluding
Land-Use Predictors 7-19
7-7 Example Thiessen/Voronoi Polygon Surface 7-23
7-8 Example Triangulated Irregular Network 7-23
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LIST OF FIGURES cont.
No. Title Page
8-1 Conceptual Model for Hydrologic Stresses for Urban Streams (based on
Causal Analysis/Diagnosis Decision Information System [CADDIS's]
extensive collection of conceptual models) 8-3
8-2 Conceptual Model for Sediment Stresses for Urban Streams 8-4
8-3 Percentage of Degraded Piedmont Sites Versus Total Impervious Area 8-10
8-4 Southeastern United States Impervious Cover for 2000 8-11
8-5 North Carolina Impervious Cover Projected to 2030 8-12
8-6 Percentage of Region 4 HUCs Having Specific Impairments within
Impervious Area Ranges 8-14
8-7 Impervious Area Cover and Impaired Waters Causes for Metropolitan
Atlanta 8-15
9-1 General Model of Stream Temperature Control for the Umatilla River 9-3
9-2 Illustration of LiDAR Images and Aerial Photographs of the Study Site in
Northwestern U.S. Stream 9-6
9-3 Example of Cool Hyporheic Flow. Left is a video image 9-7
9-4 TIR-Derived Longitudinal Temperature Profile (°C) 9-8
9-5 Umatilla River and Confederated Tribes of the Umatilla Indian
Reservation (CTUIR) Tribal Boundaries 9-9
9-6 Stream Temperature Measurements Along the Umatilla River Over a
3-Month Period 9-10
9-7 TIR Inferred Longitudinal Temperature Profile with Locations of
In-Stream Probes 9-11
9-8 River Channel Complexity (measured as RCI and Temperature [°C
measured by infrared imagery (TIR)]) Presented by River Mile and
Sorted by Stream Reaches Where Stream Water is Cooling or Heating.
Temperature increases from upstream to downstream 9-13
9-9 Example Area of Hyporheic Exchange and Temperature Response 9-15
9-10 Derived Tree Height Along the River 9-16
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LIST OF FIGURES cont.
No. Title Page
9-11 Changes in Effective Shade Projected for Restoration Condition 9-17
9-12 Umatilla River (River mile 83.8 to 87.2) 9-18
9-13 Historical Changes in Valley Width, Sinuosity and Meander Belt Width
Since 1949 9-19
9-14 Simulated Changes in the Temperature Regime—Current and
Restoration Conditions 9-20
9-15 Current and Modeled Restoration Thermal Conditions 9-22
10-1 CIR Photograph 10-5
10-2 Cattle Access to Streams 10-10
10-3 Socioeconomic Factors Influencing Potential for Increased and
Decreased Pollutant Loading 10-23
11-1 Front of IPSI Web Site 11-3
11 -2 Land Use and Landcover 11-4
11 -3 Good Pasture 11-6
11-4 Fair Pasture 11-6
11 -5 Poor, Over-Grazed Pasture 11 -6
11 -6 Feed Lot, Loafing Area 11-6
11 -7 Sample View of Animal Access to Streams 11 -7
11 -8 Livestock Sites and Animal Access to Streams 11 -8
11 -9 Septic System—Distinctive Drain Field 11-11
11-10 Paved and Unpaved Roads 11-11
11-11 Eroding Streambanks 11-13
11-12 Shrub-Scrub Riparian 11-13
11-13 Forested Riparian 11-13
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LIST OF FIGURES cont.
No. Title Page
11-14 Riparian Buffer Condition 11-14
11-15 Soil Loss Estimates (tons/year) for Select Land Classes (UT, 2007) 11-17
12-1 Results of CART Analysis of Total Phosphorus Resulting in Different
Phosphorus Groups Using All Environmental Predictors or Excluding
Land-Use Predictors 12-6
12-2 Results of CART Analysis of Total Nitrogen Resulting in Different
Phosphorus Groups Using All Environmental Predictors or Excluding
Land-Use Predictors 12-7
13-1 The Oregon DEQ Reference Site Selection Screening Process 13-3
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%TIA
% IC
ATtlLA
BACI
BCG
BMP
CADDIS
CART
CEC
CIR
CT DEP
CTUIR
CWA
CWSRF
DEM
DEQ
DOQQ
dpi
DSS
EJ
EMAP
EPA
ERDAS
FGDC
GAO
GAP
GIS
GPS
GRASS
GVS&DD
ABBREVIATIONS AND ACRONYMS
total percent impervious area
percent impervious cover
Analytical Tools Interface for Landscape Assessments
before/after and control/impact
Biological Condition Gradient
best management practice
Causal Analysis Diagnosis Decision Information System
classification and regression tree
Commission for Environmental Cooperation
color infrared
Connecticut Department of Environmental Protection
Confederated Tribes of the Umatilla Indian Reservation
Clean Water Act
Clean Water State Revolving Fund
digital elevation model
Department of Environmental Quality
digital orthophoto quarter quadrangle
dots per inch
decision support systems
environmental justice
Environmental Monitoring and Assessment Program
U.S. Environmental Protection Agency
Earth Resource Data Analysis System
Federal Geographic Data Committee
Government Accountability Office
Gap Analysis Program
geographical information system
global positioning system
Geographic Resources Analysis Support System
Greater Vancouver Sewerage and Drainage District
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HDI
HDIgis
HDIreach
HICI
HUC
HU
IBI
IC
IPCC
IPSI
I SAT
Landsat
LDC
LID
LiDAR
LU/LC
MDS
NCBI
NCDWQ
NEMO
NEP
NHD
NUT
NLCD
NOAA
NPDES
NPS
NRCS
NWI
ABBREVIATIONS AND ACRONYMS cont.
human disturbance indices
human disturbance indices based on geographical information
system data
human disturbance indices based on reach-scale measures from
site reconnaissance
high intensity commercial/industrial
hydrologic unit code
hydrologic unit
Index of Biological Integrity
impervious cover
Intergovernmental Panel on Climate Change
Integrated Pollutant Source Identification
Impervious Surface Analysis Tool
land remote sensing satellite
least-disturbed condition
low-impact development
light detection and ranging
land use/landcover
multiple data source
North Carolina Biotic Index
North Carolina Division of Water Quality
Nonpoint Education for Municipal Officials
National Estuary Program
National Hydrography Dataset
National Interagency Technical Team
National Land Cover Dataset
National Oceanic and Atmospheric Administration
National Pollutant Discharge Elimination System
nonpoint pollutant source
Natural Resources Conservation Service
National Wetlands Inventory
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ocw
PRISM
QA
QAPP
RC(BI)
RCI
ReVA
RIVPACS
ROE
RUSLE
SAB
SDE
SOP
SSURGO
STATSGO
STEPL
STORET
TALU
TDEC
TDOT
TIA
TINs
TIR
TMDL
TN
TP
TSS
TVA
UAA
USDA
ABBREVIATIONS AND ACRONYMS cont.
Oostanaula Creek watershed
Parameter-elevation Regressions on Independent Slopes Model
quality assurance
quality assurance project plan
reference condition for biological integrity
River Complexity Index
Regional Vulnerability Assessment
River Invertebrate Prediction and Classification System
Report on the Environment
Revised Universal Soil Loss Equation
Science Advisory Board
spatial data engine
standard operating procedure
Soil Survey Geographic
State Soil Geographic
Spreadsheet Tool for Estimating Pollutant Load
storage and retrieval
tiered aquatic life uses
Tennessee Department of Environment and Conservation
Tennessee Department of Transportation
total impervious area
triangulated irregular networks
thermal infrared
total maximum daily load
total nitrogen
total phosphorus
total suspended solids
Tennessee Valley Authority
use attainability analysis
U.S. Department of Agriculture
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USGS
WATERS
WBD
WFS
WMS
WQS
VWVTP
ABBREVIATIONS AND ACRONYMS cont.
U.S. Geological Survey
Watershed Assessment, Tracking and Environmental Results
Watershed Boundary Dataset
Web file services
Web mapping service
water-quality standards
wastewater treatment plant
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PREFACE
This volume (and Web materials) collects, summarizes, and synthesizes an
extensive compendium of research and applications of wide-area landscape analyses
and predictive tools relating environmental stresses to aquatic ecosystem responses.
We at EPA hope that these examples and discussions inspire and encourage
widespread application of these approaches to water quality monitoring and decision
making.
This interdisciplinary synthesis is prompted by an array of pressing water quality
monitoring, restoration, and protection problems in evidence throughout the United
States. These interrelated challenges include defining and documenting protective
standards, criteria, and reference conditions; fully identifying water quality problems,
causes, and sources; efficiently implementing effective solutions for specific problem
areas; and scientifically sound tracking of aquatic resources condition at multiple
scales—site, watershed, ecoregion, state, region, and nation—over time (Harrison,
1998) to ensure that our water quality investments result in positive results for aquatic
ecosystems and for the wide variety of human uses of our precious waters.
According to Dr. Howard Gardner of the Harvard Business School, one of the
primary motivations for interdisciplinary synthesis is that "A pressing problem emerges,
and current individual disciplines prove inadequate to solve that problem." Dr. Gardner
includes pollution of the environment as one example of this type of problem. He further
states, "Such challenges cannot even be understood, let alone addressed, unless
several disciplines and professions can be brought to bear. And so, even when the
researcher or policymaker would prefer to work within the confines of a single discipline,
it soon becomes evident that one needs to call on other disciplines" (Gardner, 2006).
Comprehending and solving water quality problems in a watershed and
ecosystem context requires collaboration of many diverse disciplines including biology,
chemistry, hydrology, geomorphology, geology, soils, geography, statistics, and
geographic information systems (GIS), among others.
Gardner also posits that synthesizing efforts usually involve the following
five steps:
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A motivating goal—Here, the primary motivation is to bring the most current and
practical scientific tools available to bear on solving our most pressing water
quality monitoring, restoration and protection challenges.
An initial stance taken by the synthesizer(s)—This product constitutes this group
of collaborators' first attempt at recommendations to take advantage of
landscape and predictive tools to attack the water quality monitoring challenges
listed above.
A set of tools or strategies that can be employed—The collection of examples,
approaches, and processes gathered here provide a starting point on which to
find solutions to monitoring, restoration and protection problems especially those
in water quality. Creativity, intelligence and hard work will undoubtedly refine and
add to the available tools.
One or more interim syntheses—This attempt at synthesis constitutes a start, a
beginning foundation of a more substantial bridge between research advances
and practical, everyday applications for state water quality programs and
watershed rehabilitation and protection efforts. As knowledge and experience is
gained through trying these approaches, new and better syntheses will emerge.
At least some criteria by which the success of the synthesis can be evaluated—
The success of this synthesis of landscape and predictive tools for water quality
monitoring can and should be evaluated in several ways. Some potential
measures of success might include increased or improved estimates of the
following:
• Dissemination of the tools to and expanded knowledge of potential users.
• Training of users interested in applying the approaches.
• Application of these tools and approaches for real programs and
decisions, including the full array of proposed uses described in the
Introduction.
• Collaborations between researchers and practitioners.
• Water quality assessments, priority setting, targeting and measurement of
results.
o Measured objectively using water quality assessment information,
o Documented subjectively as judged by the users.
In his recent book, The Price of Government: Getting the Results We Need in an
Age of Permanent Fiscal Crisis (Osborne and Hutchinson, 2004), David Osborne (the
author of Reinventing Government) stresses that government should be in the business
of paying for, and measuring outcomes—end results like clean water. He further
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emphasizes that government entities can be seen as one of two types: those in charge
of steering—setting priorities, and budgeting and paying for outcomes; and those in
charge of rowing—delivering results, or outcomes.
Our hope is that the contents of this document will help to build a strong, reliable
rudder and provide a good sextant so that all who steer and row for clean water can
navigate by the same stars. We believe that this method manual summarizes many of
the scientific tools needed by water quality agencies to set priorities, target resources to
get results, and measure outcomes.
The Price of Government also delves into successful business world tools used
to develop and implement smarter work processes, such as Total Quality Management
(TQM), Performance Management, and Business Process Reengineering (BPR). The
tools and approaches contained in this manual embody a fundamental redesign of key
parts of our water quality monitoring, assessment and results measurement process,
and are, thus, squarely in the realm of BPR.
Successfully implementing these redesigned processes will require phasing in of
additional pilot projects and gradual, wider adoption by more agencies and entities. As
this proceeds, everyone involved should also focus on TQM by making ongoing,
continuous, incremental improvements, always on the basis of facts and data. Those
on the front lines implementing these approaches need to be fully in charge of
improving the process, and those steering and rowing must work collaboratively for
success.
Concluding this preface, I leave you with the following thought, again from
Dr. Howard Gardner. "The synthesis is not the same as a successfully executed
strategy, but it may well be the essential point of departure."
Clearly, we are at the "end of the beginning" of our mission to protect the
environment using wide-area landscape analyses and predictive tools.
Jim Harrison
U.S. EPA Region 4
Susan Cormier
U.S. EPA Office of Research and Development
Ellen Tarquinio
U.S. EPA Office of Water
XXX
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REFERENCES
Gardner, H. 2006. Five Minds for the Future. Harvard Business School Press. Boston,
MA.
Harrison, J.E. 1998. Key water quality monitoring questions: Designing monitoring and
assessment systems to meet multiple objectives. National Water Quality Monitoring
Council Conference. Reno, NV. Available online at
http://acwi.gov/monitoring/conference/98proceedings/Papers/17-HARR.html (accessed
7/31/2009).
Osborne, D. and P. Hutchinson. 2004. The Price of Government—Getting the Results
We Need in an Age of Permanent Fiscal Crisis. Basic Books, New York, NY.
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AUTHORS, CONTRIBUTORS AND REVIEWERS
LANDSCAPE AND PREDICTIVE TOOLS STEERING COMMITTEE
Steering Committee Co-Chairs
Jim Harrison EPA Region 4
Susan Cormier EPA ORD/Cincinnati, OH
Don Ebert EPA ORD/Las Vegas
Ellen Tarquinio EPA HQ/OWOW
Steering Committee Members—EPA
Ann Pitchford
EPA ORD/Las Vegas, NV
Betsy Smith
EPA ORD/Research Triangle Park, NC
Charlie Howell
EPA Region 6
Douglas Norton
EPA HQ/OWOW
Greg Hellyer
EPA Region 1
James Wickham
EPA ORD/Research Triangle Park
Jeff Hollister
EPA ORD/Narraganset, Rl
John Richardson
EPA Region 4
Karl Hermann
EPA Region 8
Laura Gabanski
EPA HQ/OWOW
Mary White
EPA Region 5
Michael Griffith
EPA ORD/Cincinnati, OH
Naomi Detenbeck
EPA ORD/Narraganset, Rl
Patricia Shaw-Allen
EPA ORD/Cincinnati, OH
Peter Leinenbach
EPA Region 10
Randy Comeleo
EPA ORD/Corvallis, OR
Santina Wortman
EPA HQ/ORD
Steve Paulsen
EPA ORD/Corvallis, OR
Susan Holdsworth
EPA HQ/OWOW
Susank Jackson
EPA HQ/OST
Tommy Dewald
EPA HQ/OWOW
Tony Olsen
EPA ORD/Corvallis, OR
Steering Committee
Robert M. Hughes
James M. Omernik
Bruce Jones
Doug Drake
Gerry McMahon
Ian Waite
Jan Ciborowski
Ken Weaver
Lisa Huff
Members—Non-EPA
Amnis Opes Institute & Department of Fisheries & Wildlife,
Oregon State University, Corvallis, OR
USGS, Corvallis, OR
USGS
State of Oregon
USGS, Raleigh, NC
USGS
University of Windsor, Windsor, Ontario
Florida Department of Environmental Protection
Alabama Department of Environmental Management
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AUTHORS, CONTRIBUTORS AND REVIEWERS cont.
Pat Hamlett
Russ Frydenborg
Contractors
Michael Paul
Kristen Pavlik
Maggie Craig
Barbara Brown
REVIEWERS
Internal Review
Brenda Rashleigh
Gretchen Hayslip
Stuart Lehman
Tom Johnson
Joe Williams
Bob Hughes
Barbara Brown
Karl Hermann
Pat Hamlett
Peer Review
Tennessee Valley Authority, Chattanooga, TN
Florida Department of Environmental Protection
Tetra Tech
Tetra Tech
Tetra Tech
Camp, Dresser and McKee
EPA ORD/Athens, GA
EPA Region 10
EPA HQ/OWOW
EPA Region 8
EPA ORD/Ada, OK
Oregon State University, Corvallis, OR
Camp, Dresser and McKee
EPA Region 8
Tennessee Valley Authority
Ecological Oversight Committee
Risk Assessment Forum
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ACKNOWLEDGMENTS
Throughout development of this methods guidance, many individuals contributed
their invaluable knowledge and experience. First the chapter authors, who are listed in
the "Contents in Brief," all contributed copious amounts of time and incredible
dedication, despite their already busy schedules and crush of ongoing responsibilities.
Of particular note, Susan Cormier, PhD, stepped up as lead author for several chapters
at a time when fresh thinking was critical to our progress. Her tireless efforts lent logic
and cohesiveness that would have otherwise been lacking. Greg Hellyer and Peter
Leinenbach distinguished themselves through their intensity and service in finding and
documenting most of the vast array of tools in the "Toolbox." And, finally, James M.
(Jim) Omernik, although officially retired from his EPA career, enriched this document
considerably by sharing his continuing lifetime of learning about the ecosystems and
ecological regions of North America and indeed the world.
Next, the Landscape and Predictive Tools Steering Committee (listed above)
spent countless hours together through meetings and calls to define and refine the
content of the guidance. Their unfailing spirit of collaboration, cooperation, willingness
to build on each others' strengths, and dedication to applying all our scientific tools to
protect and restore the nation's waters made the entire effort enjoyable, even when
inevitable delays and lengthy heated discussions required extra measures of patience,
persistence, and diplomacy by all.
We are indebted to all our reviewers (also listed above) who provided numerous
very helpful, constructive comments and cogent, thoughtful criticisms. Their expertise,
attention to broad concepts and tiny details, and important suggestions and ideas
greatly improved every section of this methods document.
Finally, we extend a special thank you to Susan Holdsworth and Susan Jackson
whose prescient confidence in the usefulness of this effort helped initiate and energize
this project over the long haul and the U.S. EPA Risk Assessment Forum for
recognizing its value and ensuring the finalization the document.
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EXECUTIVE SUMMARY
Water quality monitoring resources will never be adequate to regularly sample
every waterbody across the United States; therefore, scientifically sound landscape and
predictive tools are needed to address and help fill the numerous widespread
monitoring gaps where we lack hard, in situ, site-specific data. Similarly, since limited
resources are available to restore and protect (see also prevent) the Nation's waters,
science based targets and priorities are needed to support effective application of our
restricted means. Thus, landscape and predictive tools are needed both to guide
efficient filling of monitoring gaps and to prioritize our protection and rehabilitation
actions. This should yield better protection for high quality waters and quicker, more
cost-effective restoration of impaired waters.
Landscape and predictive tools collectively encompass two areas of innovation
for aquatic resource monitoring, assessment, and management:
• wider use of easily accessed broad scale landscape information, such as satellite
land use/landcover, aerial photography, demographics and other data, made
possible by recent advances in geographical information system (GIS), remote
sensing, and desktop computing, and
• routine use of readily available scientific analytical tools and approaches to relate
landscape characteristics and stresses to aquatic resource response and
condition measurements.
This guidance emphasizes the simultaneous use of matched (or paired)
landscape and in situ data for empirical modeling to enhance our predictive capabilities
and encourage science-based targeting and priority setting. This is quite different from
current applications of landscape information in process models for watershed total
maximum daily loads (TMDLs), and from best professional judgment use of landscape
information for watershed planning.
Routine use of landscape and predictive tools has considerable potential to
strengthen the scientific underpinnings of water quality monitoring, assessment, priority
setting and management decisions. Together, their appropriate application supports
extension of aquatic resource quality and condition estimates beyond the limited
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number of specific sampling points where chemical, physical, and biological water
quality data are gathered. These results can
support many water protection and
rehabilitation decisions through systematic
priority setting, comprehensive targeting of
problems and monitoring efforts, more
complete problem identification, and
improving the efficiency of limited monitoring
resources to support an array of management decisions for which we now usually lack
adequate data. Combining well designed in situ monitoring data with landscape
information promises to provide a much more complete picture of the status of the
Nation's waters.
AUDIENCE
Foremost, we hope this guidance will foster collaboration between diverse
disciplines engaged in water quality monitoring, assessment and management. Some
of these specialties include biology, chemistry, hydrology, geomorphology, geology, soil
science, geography, statistics, and GIS, among others. Clear communication among
and collaboration between experts in multiple disciplines is recommended to
successfully apply landscape and predictive tools. For example, an expert in stream
biology might have little knowledge of GIS, and vice versa. Thus, this guidance
includes a range of "elementary" and "advanced" material, depending on one's area of
expertise and point of view. A brief introduction for each section recommends
appropriate audiences for that section.
ORGANIZATION
We have organized this methods guidance document into four sections:
(I) Introduction to Landscape and Predictive Tools; (II) Geographic Frameworks, Spatial
Data, and Analysis Tools; (III) Examples and Case Studies; and (IV) Gaps and Needs
for Research and Applications. An extensive Glossary provides clear, consistent
definitions of important terms. Throughout this methods guidance, Glossary terms are
Example/Case Study Highlight
Chapter 8: Impervious Estimates and
Projections—EPA Region 4
• Estimates the current probability of
impairment associated with watershed
imperviousness at unsampled locations.
• Identifies watersheds at risk on the
basis of projected development
pressure.
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in italics, and are linked to the Glossary to facilitate quick access. The Toolbox provides
links to and short descriptions of a wide range
of easily accessed data sets and analytical
tools. Toolbox data sets and tools are used in
the Examples/Case Studies (see Section III).
Section I: Introduction to Landscape and
Predictive Tools
Chapter 1 introduces the wide
spectrum of current and potential uses of landscape and predictive tools including
criteria and standards development (see water quality standards), problem identification
and prevention, prioritization and targeting of rehabilitation actions, and advancement of
science, education and water quality management. Basic steps for incorporating
landscape and predictive tools into water quality management are outlined and include
• Formulating relevant problems and questions.
• Analysis through:
o Use of appropriate geographic frameworks, such as ecoregions and
watersheds, or classifications,
o Incorporation of "wall-to-wall" landscape data and appropriate matched paired
in situ response data, and
o Construction of simple empirical models relating landscape/watershed
pressures and stresses to in situ stressors and responses.
• Synthesis for decision-making by using the resulting relationships to extrapolate
spatially to places lacking in situ data.
Landscape analyses and simple empirical stress/response models can play key
roles in water quality monitoring and assessment by providing scientific connections
between statistically based probability surveys and prioritization of targeted monitoring
of specific sites to support management decisions.
Example/Case Study Highlight
Chapter 11: Oostanaula Creek Integrated
Pollutant Source Identification Study
• Urban sources accounted for more
than 50% of nitrogen and
phosphorus loads, cropland and
pasture accounted for more than
50% of sediment loads.
• A 15-year watershed restoration plan
was developed applying BMPs to the
xxxvii
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Chapter 2 describes actual and potential applications of Landscape and
Predictive Tools for Clean Water Act Programs including water quality standards—
determining designated uses through
classification of similar waters, characterizing
background levels and achievable conditions,
and developing protective water quality criteria;
monitoring and assessment, impaired waters
listing and TMDL development, nonpoint source
control, National Pollutant Discharge Elimination
System permitting, wetlands protection, grants
programs, and cross program applications.
Chapter 3 discusses how to integrate
geospatial information with a wide range of assessments including: condition
assessments to detect impairment, causal pathway assessments to identify causes and
sources, predictive assessments to estimate the environmental, economic and social
benefits and risks associated with different management actions, and outcome
assessments to evaluate the performance and effectiveness of management actions in
achieving environmental results. The general problem-solving strategy is illustrated for
the four basic types of assessments using a TMDL for stormwater developed by the
Connecticut Department of Environmental Protection.
Chapter 4 describes in detail the steps to carry out analyses using landscape and
predictive tools.
Section II: Geographic Frameworks, Spatial Data, and Analysis Tools
This section furnishes additional detail on important considerations for
successfully integrating and using geographic frameworks, spatial data and analysis
tools as essential components of studies incorporating landscape and predictive tools.
Chapter 5 provides an in-depth discussion of interdisciplinary integration of
geographic frameworks at multiple scales. This chapter emphasizes: how to combine
frameworks to enhance our ability to extrapolate, appropriate roles for mapped regions
and for classification approaches, and considerations for using ecological regions and
Example/Case Study Highlight
Chapter 9: Water Temperature
Regime Assessments—Umatilla River
• Assessed temperature criteria
exceedances forsalmonid
protection.
• Identified stream channel
modification leading to reduced
hyporheic recharge of cooler
ground water and reduced
vegetative shading as the source
pathways elevating temperatures.
• Predicted temperature reductions
due to BMP imDlementation.
xxxviii
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watersheds together as one example of appropriate use
of geographic frameworks to apply landscape and
predictive tools. A detailed discussion is also included
explaining the important differences between hydrologic
units (and Hydrologic Unit Codes) and watersheds, their
advantages and disadvantages, and appropriate uses.
Chapter 6 outlines the basics of a wide array of
available spatial data useful for landscape and predictive tools analyses. It also
provides a brief introduction to many common spatial designs for gathering in situ
aquatic condition sampling data including probabilistic and targeted designs. The
spatial data and in situ data can constitute matched (or paired) data essential for
implementing landscape and predictive tools as an integral part of water quality
monitoring, assessment and management processes. Biological data and physical or
chemical data sampled closely in time and space is another common form of paired
data.
Chapter 7 emphasizes combining field and remotely sensed data using an
iterative approach to data gathering and analysis, emphasizes the importance of
matched (or paired) data for stressors and responses, and focuses on decisions
relevant to monitoring and other Clean Water Act programs. Topics covered include:
problem and question formulation, available software and GIS tools, linking data and
analysis scales, data classification and normalization, and descriptive analysis and
associative analysis of relationships (associations) using scatter plots, correlation, box
plots, linear and quantile regression, and classification and regression trees. Common
spatial analysis methods include: landscape factors/metrics, spatial interpolation,
hydrologic analyses using Digital Elevation Models, overlay and proximity tools, and
decision support systems such as U.S. Environmental Protection Agency's (EPA's)
Regional Vulnerability Assessment Program and Causal Analysis Diagnosis Decision
Information System (CADDIS).
Example/Case Study
Highlight
Chapter 13: Biocriteria and
Reference Condition
A systematic screening
process using GIS and reach
level data is used for identifying
reference sites.
xxx ix
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Section III: Examples and Case Studies
This section (Chapters 8 through 13) presents a series of examples of real-world
applications of landscape and predictive tools. These case studies include: impervious
area estimates and projections in EPA Region 4, (assessments of the water
temperature regime for TMDL development in the Umatilla River Basin in Oregon), the
Tennessee Valley Authority's use of an (Integrated Pollutant Source Identification
process) for analysis of aerial photography to identify a wide range of nonpoint sources
in the (Oostanaula Creek watershed of Tennessee) and inform development of a
watershed action plan, use of (Classification and Regression Tree analysis to classify
natural phosphorus and nitrogen regions) of the upper mid-West to aid development of
nutrient criteria, and use of a tiered watershed and reach-scale reference site screening
process in the State of Oregon to (identify candidate reference areas) based on least
disturbed condition.
Section IV: Gaps and Needs for Research and Application
Chapter 14 describes important gaps and needs in the areas of geographic
frameworks, geographically focused studies and rehabilitation efforts, and data for:
landcover, habitats, infrastructure, land management practices, digital elevation models,
hydrology, groundwater, soils, climate, stressors, remote sensing, demographics and
socioeconomics, and for Web-based availability of data and information. Application
gaps were noted for: predictions of pollutant loadings and concentrations, associations
between landscape characteristics and ecological conditions, water quality standards
and criteria development, Best Management Practice (BMP) siting and selection, priority
setting, and evaluation of restoration and recovery potential. An informal ranking of
data gaps and needs by the Landscape and Predictive Tools Steering Committee
includes: improved National Land Cover Dataset (NLCD), complete Level 4 Ecoregions,
intermittent/perennial streams, National Wetland Inventory gaps, true watersheds, gross
domestic product and population, Parameter-elevation Regressions on Independent
Slopes Model (PRISM) support, and numerous others. Two critical gaps were identified
for decision support tools: the lack of interpretive tools that aid in conceptualization,
visualization, problem formulation, and identification of alternative hypotheses, and the
xl
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lack of an integrated framework for applying tools for management decisions at multiple
scales (local to regional) and involving simultaneous optimization of multiple endpoints.
Section V: Toolbox: Spatial Data and Tools Database
This extensive compilation includes
over 200 resources for spatial data and
analysis tools useful for enhancing protection
and restoration or rehabilitation of the nation's
waters. About half provide access to
analytical tools, roughly a quarter provide
point, grid or vector data, while less than 10%
point to geographic frameworks or to decision
support systems. Information collected for
each resource includes (when available):
name, Web site, keywords, description, uses,
ecosystems and stressors, related tools,
examples, additional information, minimum
software requirement, required expertise,
technical support, developer and contact
information. Due to its length, the toolbox is
provided as a separate electronic file.
CONCLUSION
The vital importance of water to all
aspects of modern civilization demands that we
improve our tools for understanding and managing aquatic resources for this and future
generations of humans, and indeed, for all life on planet earth. Water is life.
Incorporating landscape and predictive tools into our water quality toolbox will facilitate
applying all our scientific tools and data to water quality monitoring, assessment,
protection and rehabilitation needs. Deliberate action to "make it so" will help to insure
xli
Data Source Highlights
NLCD—national landcover data for 1992
and 2001,
NHD—national hydrography dataset of
rivers, streams, lakes and other waters,
NED—national elevation data set for
topography,
WBD—watershed boundary dataset,
PRISM—rainfall data based on the
Parameter-elevation Regressions on
Independent Slopes Model, and
Omernik Ecoregions—multiple scales of
ecological region maps covering most of
the continental United States.
Analysis Tool Highlights
ATtlLA—the Analytical Tools Interface for
Landscape Assessment for developing
quick, easy summaries of landscape data,
CADDIS—the Causal Analysis/Diagnosis
Decision Information System which
incorporates a statistical tool kit for
exploring associations between stressors
and response measurements,
AGWA—the Automated Geospatial
Watershed Assessment tool for more
detailed modeling of smaller watersheds,
and Index of Hydrologic Alteration for
assessing changes to surface and
groundwater hydrologic regimes and flows.
;onstantly innovate to refine and
-------
that EPA, State and other water quality programs have science as their backbone,
follow the rule of law, and that our priorities and actions are transparent to all.
xlii
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Section I—Summary
SECTION I: INTRODUCTION TO LANDSCAPE ASSESSMENT AND
PREDICTIVE TOOLS
SUMMARY
Chapter 1: Introduction. (Recommended for: Beginner, Intermediate and
Advanced.) Introduces the wide spectrum of current and potential uses of landscape
and predictive tools including criteria and standards development, problem identification
and prevention, prioritization and targeting of rehabilitation actions, and advancement of
science, education and water quality management.
Basic steps for incorporating landscape and predictive tools into water quality
management are outlined and include formulating relevant problems and questions,
analysis through use of geographic frameworks (such as ecoregions and watersheds,
or appropriate classifications), landscape data and paired in situ response data, and,
construction of simple empirical models relating landscape/watershed attributes to in
situ stressors and chemical, physical, and biological stressors and responses; and
synthesis for decision making by using the resulting relationships to extrapolate
spatially to places lacking in situ data or to perform full assessments.
Landscape analyses and simple empirical stress/response models can play key
roles in water quality monitoring and assessment by providing scientific connections
between statistically based probability surveys and prioritization of targeted monitoring
of specific sites to support management decisions.
Chapter 2: The Clean Water Act: Benefits of Landscape Tools (Geospatial Data
and Analysis). (Recommended for Beginner) Since many of the examples are
related to water resources, this chapter describes actual and potential applications of
Landscape and Predictive Tools for Clean Water Act Programs; however, the way the
information is used may be illustrative to other programs as well. This wide range of
applications includes water quality standards—determining designated uses through
classification of similar waters, characterizing background levels and achievable
conditions, and developing protective water quality criteria. For monitoring and
assessment, applications include impaired waters listing and total maximum daily load
(TMDL) development, nonpoint source control, National Pollutant Discharge Elimination
i
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Section I—Summary
System permitting, wetlands protection, grants programs, and cross program
applications.
Chapter 3: Using Geospatial Information in Environmental Assessments.
(Recommended for Beginner and Intermediate.) Discusses how to integrate
geospatial information with a wide range of assessments including: condition
assessments to detect impairment, causal pathway assessments to identify causes and
sources, predictive assessments to estimate the environmental, economic and social
benefits, and risks associated with different management actions, and outcome
assessments to evaluate the performance and effectiveness of management actions in
achieving environmental results. The general problem-solving strategy is illustrated for
the four basic types of assessments using a TMDL for stormwater developed by the
Connecticut Department of Environmental Protection.
Chapter 4: Basic Concepts for Using Landscape and Predictive Tools.
(Recommended for Beginner and Intermediate.) Describes common situations that
are encountered when working with geographical data and large field data sets.
Because it outlines how field and landscape data are sequentially or iteratively
analyzed, it also provides Advanced Analysts with an outline of the general
approaches that are described in greater depth in the analytical Section II and Case
studies in Section III.
ii
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Section I—Chapter 1: Introduction
1. INTRODUCTION
Jim Harrison, U.S. EPA Region 4, Atlanta, GA
Robert M. Hughes, Amnis Opes Institute and
Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR
Barbara S. Brown, Camp, Dresser & McKee, Boston, MA
Susan Cormier, U. S. EPA NCEA ORD, Cincinnati, OH
A wealth of environmental data and the
tools to analyze them are readily available and
can provide valuable insights for environmental
management. Integrating newly researched
landscape assessment and predictive tools,
methods, and approaches into monitoring
programs takes thoughtfulness, time, and
commitment. However, we believe the
benefits are worth the effort.
This guidance document (and associated Web resources) makes a wide range of
approaches more easily available and is intended to encourage their more widespread
use. Our examples of how to apply and understand the tools should facilitate the task.
Together, this information should enable tribes, states, territories, and others to use
landscape data and predictive tools to assess, communicate, and solve environmental
problems. Distribution and training can further accelerate the testing and adoption of
these tools for environmental problem solving. Additional research and development
can make the job even easier, faster, and more affordable.
To contribute to wider adoption of landscape tools, we have organized the
document into four sections: (I) Introduction to Landscape Assessment and Predictive
Tools; (II) Geographic Frameworks, Spatial Data, and Analysis Tools; (III) Examples
and Case Studies; and (IV) Gaps and Needs for Research and Applications. A highlight
of the electronic version of this document is appended material that provides links and
short descriptions of more than 200 data sets and analytical tools.
What is in this chapter? The purpose,
rationale, and basic steps are described for
using landscape and predictive tools for
monitoring, assessment, and water
resources management with an emphasis
on the Clean Water Act. Next, we
introduce examples of how landscape and
predictive tools can be used to assess
biological condition, extend monitoring
results, and improve ecological
understanding. We conclude with a brief
description of the document's organization.
1-1
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Section I—Chapter 1: Introduction
Adding landscape and predictive tools to our water quality monitoring toolbox
involves three activities in five steps:
• Problem Formulation and Planning
o Clearly formulating the problem and desired information.
• Analysis
o Using appropriate geographic frameworks (Omernik, 2003), including mapped
areas such as ecoregions (Loveland and Merchant, 2004) or other
appropriate classification approaches, and watersheds to establish realistic
areas for analysis and extrapolation.
o Using wall-to-wall landscape and other data (Vogelmann et al., 2001) to
document natural and stressor gradients.
o Constructing relatively simple empirical relationships or models (Van Sickle
et al., 2006) linking landscape to in situ (see ambient monitoring) stressor or
response metrics.
• Synthesis
o Using the resulting relationships to extrapolate to places lacking in situ data or
to inform decision making.
1.1. MONITORING AND LANDSCAPE CONDITION
Monitoring and assessment of the nation's waters is mandated by multiple
sections of the Clean Water Act (CWA) (see Table 1 -1). Historically, water quality
monitoring focused on the most obvious and urgent problems, such as the physical and
chemical conditions of point source effluents. Monitoring needs in recent years have
expanded to encompass additional measures of ecosystem and watershed condition,
including bas/n-wide to national ecological assessments (e.g., Paulsen et al., 2008;
Mulvey et al., 2009).
The U.S. Environmental Protection Agency's (EPA's) Science Advisory Board
(SAB, 2002) recommended using landscape condition as one of six essential ecological
attributes for assessing and reporting on ecological condition. The other five are biotic
condition, chemical condition, hydrology and geomorphology, natural disturbance, and
1-2
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Section I—Chapter 1: Introduction
TABLE 1-1
Monitoring for CWA Purposes Includes Monitoring Objectives Contained in Several Parts of
the Act, Regulatory Requirements, and Guidance Describing 10 Key Elements of State and
Other Monitoring Programs
Clean Water Act
References to Monitoring
Activities and Objectives
CWA Regulatory Requirements for Monitoring
(40 CFR 130.4)
• Establish, review and revise
WQS, TMDL, and establish
appropriate monitoring
methods. (CWA303[c],
303[d])
• Conduct analyses of the
extent to which all navigable
waters attain water quality
standards. (CWA305[b])
• Identify impaired waters.
(CWA 303[d])
• Determine Abatement and
control priorities. (CWA Part
402)
• Support implementation of
water management
programs. (CWA Parts 319,
402, 303, 314)
• Evaluate effectiveness of
water management programs.
(CWA Parts 319, 314, 303,
305, 402)
• Establish appropriate methods and procedures to
monitor the quality of navigable waters and ground
waters
• Devices, methods, systems, procedures for biological
monitoring, and eutrophic conditions
• Compile and analyze data on navigable waters and
ground waters
• Devices, methods, systems, procedures for
o Classification of eutrophic conditions
o Physical, chemical, biological data
Guidance for CWA Monitoring:
Monitoring 10 Elements Document (U.S. EPA,
2003)
• Monitoring Elements
o Develop strategy for all water resource types:
streams, rivers, lakes and reservoirs, coastal
areas (estuaries), wetlands, and groundwater
o Objectives—national and state
o Monitoring designs
o Core and supplemental indicators
o Quality assurance
o Data management
o Data analysis and assessment
o Reporting
o Programmatic evaluation
o General support and infrastructure planning
TMDL = total maximum daily load.
WQS = water quality standards.
1-3
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Section I—Chapter 1: Introduction
Why Use Landscape and Predictive Tools? Landscape Assessment and Predictive Tools promote
the following:
• Systematic priority setting
• Comprehensive targeting of problems and monitoring efforts
• Improved efficiency of limited monitoring resources
• Monitoring strategically—combining best professional knowledge and insight with a
scientifically sound basis for monitoring that suits the purpose of the assessment
• Focusing on measuring results—keeping score of what is really important
ecological processes. The SAB envisioned these six interrelated attributes as a
consistent and comprehensive way to organize and report on indicators of ecological
condition. The SAB emphasized that integrating this full array of attributes and
appropriate condition indicators for each is necessary to understand and address the
broad range of today's ecosystem stressors and responses.
1.2. APPLICATIONS FOR LANDSCAPE ASSESSMENT AND PREDICTIVE TOOLS
Landscape assessment and predictive tools have a wide range of current and
potential applications, particularly for enhancing and expanding the effectiveness of
random (probability) surveys and other monitoring network data. This array of purposes
and uses can include criteria and standards development (see water quality standard),
problem identification and prevention, identification of areas and resources at risk,
prioritization and targeting of rehabilitation, and advancing science, education and
society's ability to effectively manage aquatic and terrestrial resources. Table 1-2
summarizes a spectrum of uses for landscape assessment and predictive tools covering
those broad purposes. Chapter 2, Clean Water Act Programs, discusses many of these
and other potential uses for landscape and predictive tools.
In addition, landscape indicators and predictive tools should be integrated with
today's primary spatial approaches for water quality sampling. Probability surveys are
used increasingly to provide scientifically sound estimates of the fraction of impaired
waters at multiple scales, and to ascertain which stressors are associated with those
impaired waters (Detenbeck et al., 2005; Van Sickle et al., 2006). The national
Wadeable Streams Assessment (U.S. EPA, 2006; Paulsen et al., 2008), regional
probability surveys (Whittier et al., 2002; U.S. EPA, 2000; Stoddard et al., 2005, 2006),
1-4
-------
Section I—Chapter 1: Introduction
TABLE 1-2
Spectrum of Uses for Landscape and Predictive Tools
Purpose
Uses
Criteria and
standards
development
• Identify candidate reference areas (minimally disturbed, least
disturbed).
• Calibrate reference condition at state, multistate, and national
scales.
• Calibrate biological and other condition measures.
• Develop more protective predictive models of biota for
bioassessment/biocriteria and water quality criteria.
• Define and document human disturbance gradients for TALU
and other purposes.
• Assess needs for antidegradation implementation procedures
(e.g., growing suburban areas).
Problem
identification and
prevention
• Extrapolate condition estimates to waters lacking in situ data.
• Identify suspected problem areas (see problem identification).
• Target monitoring to assess likely problems.
• Estimate vulnerability to stressors.
• Target and prioritize areas for prevention or protection.
• Anti-degradation assessments.
Prioritization and
targeting of
rehabilitation
• Assist stressor identification and diagnosis.
• Identify probable causes and sources.
• Prioritize TMDL and rehabilitation or regulatory efforts.
• Prioritize waters for delisting efforts.
• Estimate recovery potential and target restoration actions.
Science, education
and management
• Evaluate landscape sources and activities and associated
processes and causal pathways (pressures) that result in
stressors that impair aquatic condition for large areas.
• Assess relative influence of different stressors/pressures and
scales (site, watershed/catchment).
• Relate human disturbance to effects occurring in waterbodies
(rivers, lakes, wetlands, estuaries).
• Raise awareness of consequences of local land decisions.
TALU = tiered aquatic life uses.
1-5
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Section I—Chapter 1: Introduction
and state-level statistical sampling (Paulsen et al., 1998; Hughes et al., 2000; Ode
et al., 2005, 2008; Lincoln et al., 2007) provide strong evidence of the value and
cost-effectiveness of probability surveys. Probability survey design methods are
undergoing continuous improvements to meet the needs of diverse studies and users
(Theobald et al., 2007).
Traditional targeted monitoring of site-specific problems has helped identify and
resolve numerous impairments as part of the CWA section 303(d) listing and total
maximum daily load (TMDL) development process. However, site-specific, monitoring
is not designed to provide estimates of the fraction of impaired waters at state, regional,
or national scales (see Figures 1-1 and 1-2) or to systematically identify all impaired
waters.
Landscape and predictive tools provide opportunities to fill gaps between
statistical surveys and a limited number of targeted monitoring sites by providing
systematic, comprehensive extrapolations of existing data to predict local water quality
for areas lacking in situ data. Such an approach is useful for determining where waters
are likely to be impaired, and for prioritizing targeted monitoring to specific areas and
stressors (see Figure 1-3, Table 1-3). Explicit incorporation of the statistical power of all
of these approaches will assure that the decisions made based on analysis of
monitoring results are technically sound, appropriate to the stage of water quality
management to which they are applied, and defensible in assessment, regulatory, legal
and scientific settings (Legg and Nagy, 2006; McGarvey, 2007).
Brown et al. (2005, see Figure 1-4) provide an example of how
landscape/predictive tools can be integrated with probability surveys and targeted
monitoring. When comparison of probability survey results to known impaired waters
shows significant gaps in problem identification, predictive models are used to assess
the probability of impairment for waters lacking targeted monitoring. Priorities for
targeted monitoring of unassessed waters are established according to the probability of
impairment. Where in situ monitoring confirms impairments, waters can be added to the
section 303(d) impaired waters list for subsequent development of TMDLs and
rehabilitation actions. The example in Chapter 8: Impervious Estimates and
Projections—EPA Region 4, demonstrates this kind of approach to identify waters that
1-6
-------
Section I—Chapter 1: Introduction
Need for Predictive Screening Systems to Identify Problems
0%
Where are these waters?
(Identify with tiered screening
systems-
landscape and in-stream)
All
Waters
100%
Impaired
Impaired
Documented Problems
from Targeted Sampling
Statistical Sample Estimate
FIGURE 1-1
Line Diagram Illustrating Hypothetical Differences Between the Proportion
of Impaired Waters as Determined by Targeted and Statistical Sampling
1-7
-------
Section I—Chapter 1: Introduction
1 40
.3 35'
g
2
1 25
I; 20
X
oQ 15
g
10
5 5
LLi
^ 0
¦ Nonrandom
D Random
I i
1986
1988
1990 1992
Year
1994 1991-94
Nonrandom
~ Random
teS 30
E
Z-te 20
*
1990 1991 1992 1993 1994 1995 1996
Year
FIGURE 1-2
Examples of Actual Differences between Results of Targeted and
Statistical Sampling
Source: (Hughes et al., 2000).
1-8
-------
Section I—Chapter 1: Introduction
Streamlined Monitoring—Using the Tools Together
Watershed
Overall Condition
icteri sties
Statistically-valid
\/e±\f
ndscape
I di. t
Confirmation of
Impairment and Diagnosis
Prediction of
Habitat
Targeted
FIGURE 1-3
Conceptual Model of Extrapolating In Situ Results for Targeting Specific
Potentially Impaired Areas
1-9
-------
Section I—Chapter 1: Introduction
TABLE 1-3
Roles for Probability Surveys, Landscape/Predictive Tools and Targeted Monitoring
Probability survey
• Predict proportion of all waters in good or poor condition, with
documented confidence limits and major stressors.
• Measure trends in water resource condition and CWA program
effectiveness.
• Support development of new WQS.
• Prioritize targeted monitoring to specific parameters/or
stressors.
Modeling and
landscape analysis
• Determine where water quality is likely impaired (problem
identification).
• Predict localized water quality.
• Prioritize targeted monitoring to specific areas and stressors.
Targeted
monitoring
• Assess WQS attainment for specific segments.
• Measure trends at specific sites.
• Identify sources of pollutants to specific waters.
• Support development of local management measures (TMDL,
NPDES permits, nonpoint source BMPs, WQS).
• Assess effectiveness of individual measures or BMPs.
BMP = best management practice.
NPDES = National Pollutant Discharge Elimination System.
1-10
-------
Section I—Chapter 1: Introduction
States conduct
Probability Survey
Describe condition, with known
State 305(b) As
Repoij^s
sotiated
Nationa
I 305(b),
oiijt oji
State of
D
Li
S-«-
i^e j^lih
f
03|d)
I
TMDL
Development
*— Rem
Integrated
Monitoring
Comparison of
survey results
Accept
303(d)
Inc
Apply Predictive
Models to assess
Waterbody has
high probability of
impairment
Waterbody
has moderate
probability of
Waterfcxlety-
impairment
I
I
Targeted
Waterbody
has low probability
of impairment
Category 1
Wa-
terbody
Continue to
monitor as part of 5-
i
FIGURE 1-4
Integrated Monitoring Flowchart
Adapted from: Brown et al. (2005).
may be impaired by impervious surfaces, but are unmonitored, and to target those
waters for monitoring to confirm actual problems.
This systematic and comprehensive approach using model-based inference for
problem identification and targeting of monitoring activities will aid more complete listing
of impaired waters. It also incorporates and improves the ad-hoc, best professional
judgment approaches for identifying and monitoring potentially impaired waters.
High-resolution landscape data can be very useful at the local watershed or
catchment scale. The Chapter 10 and 11 examples, Nonpoint Source
Inventory—Integrated Pollutant Source Identification (IPSI) Process, and Oostanaula
Creek IPSI Case Study, provide real-world applications of air photo interpretation to
both identify a wide array of nonpoint source pollution sources, and to target and
1-11
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Section I—Chapter 1: Introduction
prioritize rehabilitation opportunities. Chapter 9, Water Temperature Regime
Assessments—Umatilla River, provides another local scale example incorporating
infrared and light detection and ranging (LiDAR); and elevation data for development of
TMDLs for temperature.
Another example of using landscape information is in the Biological Condition
Gradient (BCG) conceptual model. The BCG describes multiple levels of aquatic
community biological condition on the basis of structural, functional, and taxonomic
measures for developing tiered aquatic life uses (Davies and Jackson, 2006) and
biocriteria. Use of landscape indicators facilitates estimates of gradients of landscape
pressures, condition, disturbance, and stressors that help us assess biological
assemblage potentials and condition (see Figure 1-5). The data and tools summarized
in this guidance manual provide ready means to document the full gradient of multiple
levels of stressors, pressures, exposure, or watershed disturbance constituting the
X-axis, or Generalized Stressor Gradient (see Figure 1 -5). The analysis and statistical
approaches are useful for determining the shape, variability, and strength of
relationships between the stressor gradient and the biological response gradient. In
turn, the developed relationships can be used to extrapolate estimated biological
condition to areas with known watershed stressor or disturbance characteristics, but
lacking in situ biological condition samples. Two examples in Section III illustrate the
application of landscape tools for development of water quality criteria and standards:
Chapter 12—Nutrient Classification of Streams Using CART, and
Chapter 13—Biocritera and Reference Condition.
Several pilot projects have demonstrated the usefulness of targeting and
prioritizing waters for rehabilitation (Norton et al., 2009). These projects in Illinois,
Maryland, and the Mid-Atlantic States estimate recovery assessed by an array of
metrics representing ecological capacity to regain function, stressor exposure (past,
present and future), and social context and process factors. This comparative approach
to waters' amenability to rehabilitation makes it easier to prioritize projects and
maximize ecological resource improvement for the least cost.
1-12
-------
Levels of Biological Condition
Natural structural, functional, and
taxonomic integrity is preserved.
Structure and function similar to natural
community with some additional taxa
and biomass; ecosystem level functions
are fully maintained.
Evident changes in structure due to loss
of some rare native taxa; shifts in
relative abundance; ecosystem level
functions fully maintained.
Moderate changes in structure due to
replacement of sensitive ubiquitous taxa
by more tolerant taxa; ecosystem
functions largely maintained.
Sensitive taxa markedly diminished;
conspicuously unbalanced distribution
of major taxonomic groups; ecosystem
function shows reduced complexity and
redundancy.
Extreme changes in structure and
ecosystem function; wholesale changes
in taxonomic composition; extreme
alterations from normal densities.
Level of Exposure to Stressors
Watershed, habitat, flow
regime and water chemistry
as naturally occurs.
Watershed, chemistry, habitat,
and/or flow regime severely
altered from natural conditions.
FIGURE 1-5
The BCG: Biological Response to Varying Stressor Levels
Source: Davies and Jackson (2006),
-------
Section I—Chapter 1: Introduction
1.3. CONCEPTUAL MODELS
Natural landscape-level drivers are critically important for understanding, and
calibrating, the relationships between anthropogenic stressors and aquatic ecosystem
responses. For example, Bryce et al. (1999, see Figure 1-6) simplify and summarize
the complex interactions between inherently variable natural environments, landscape
disturbances by humans, physical and chemical stressors generated by those
anthropogenic activities, and responses of aquatic biological communities.
Human Activities
Changes in Biological Assemblages
Physical Habitat
\Nater Quality
Chemical Habitat
Atmospheric
Deposition
Urbanization/
Residential
Development
Agriculture
Recreation
and
Management
Mining
Forest
Practices/
Silviculture
Stream
Channel
Modification
Natural Stressors/Geographic Setting (Climate, Geology, Latitude, etc.)
NOx
SOx
Air Toxics
Liming
Dams
Channelization
Diversions
Levees
Revetment
Fragmentation
Fertilizers
Pesticides
Roads
Monoculture
Compaction
Sedimentation
HabitatAlt.
Toxic V\feste
Oil
Gravel mining
Heavy Metals
Liming
Incr.population
Roads
Construction
Point Sources
V\festewater
Pets
Fertilizers
Livestock
Pesticides
HabitatAlt.
Irrigation
Compaction
Animal Waste
Roads
Construction
HabitatAlt.
Boating
Fishing
Fish Intro.
Poisoning
Changesin
Vegetation
Chemical
Loading:
Toxics,
Nutrients,
02 Demand,
Acid Base
Mobilization of
Heavy Metals
Changesin
SedimentLoad
Changesin Flow:
Timing,
Amount,
Pathway
FIGURE 1-6
Natural Setting, Human Activities, Stressors, and Biological Response
Adapted from: Bryce et al. (1999).
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Section I—Chapter 1: Introduction
The top level of this model emphasizes the importance of geographic settings.
Geographic frameworks classifying the wide range of ecological systems across North
America are readily available (see Chapter 5) and are useful for stratifying areas of
similarity for natural biological communities, their characteristic array of stressors and
disturbances, and the ecosystem responses typical for different areas. For example,
approaches to classify ecological regions (see ecoregion) are many, ranging from
traditional mapping and integrating distinguishing characteristics that vary from region to
region, to computerized clustering of a limited range of mapped factors (Loveland and
Merchant, 2004). The importance and successful use of ecological regions and other
classifications as landscape and predictive tools lies in their ability to provide
boundaries, limits, and constraints on where particular stressor-response relationships
should be applied. Thus, ecological regions provide convenient frameworks for
defensible extrapolation and application of models relating landscape and other
stressors to in-stream responses. Other appropriate data such as natural gradients in
waterbody size, air temperature, channel slope, and alkalinity are being used
increasingly for developing predictive models of assemblage condition for
macroinvertebrate (Hawkins et al., 2000; Stoddard et al., 2005; Ode et al., 2005, 2008)
and fish (Pont et al., 2006, 2009) assemblages at large spatial scales.
1.4. ORGANIZATION OF DOCUMENT
SECTION I: INTRODUCTION TO LANDSCAPE AND PREDICTIVE TOOLS
• Chapter 2. Describes actual and potential applications for Clean Water Act
Programs.
This section links landscape and predictive tools to critical CWA monitoring,
assessment and other water quality management programs and processes. It
describes potential and (brief) actual uses of landscape and predictive tools for a
variety of CWA programs and watershed planning/restoration needs. Selected,
brief examples highlight how needs can be met. It also describes important
strengths and caveats (e.g., spatial/temporal issues) for landscape and predictive
tools for each program to clarify potential advantages and limitations.
• Chapter 3. Defines appropriate roles for and integration of predictive modeling
tools and monitoring for many types of assessments.
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Section I—Chapter 1: Introduction
This chapter introduces the general types of environmental assessments.
Condition assessments detect chemical, physical, and biological impairments
(i.e., ranges of environmental values that differ from natural, background, or
acceptable conditions). Causal pathway assessments identify causes and their
sources for the characterized condition. Predictive assessments estimate
environmental, economic, and societal risks and benefits associated with
different possible management actions postulated to reduce the intensity,
duration, or frequency of interactions between a causal agent and affected
entities. Outcome assessments evaluate the results of the decisions that have
been made after the condition, causal, and predictive assessments by evaluating
the performance of the management action and the effectiveness of the action in
achieving the environmental goal (Cormier and Suter, 2008, U.S. EPA, 2010). It
also shows how assessments work together to solve environmental problems
using geographic data.
• Chapter 4. Describes common situations that are encountered when working
with geographical data and large field data sets. This section also outlines how
field and landscape data are sequentially or iteratively analyzed, thus providing
an outline of the general approaches that are described in greater depth in the
analytical Section II and case studies in Section III.
SECTION II: GEOGRAPHIC FRAMEWORKS, SPATIAL DATA, AND ANALYSIS
TOOLS
• Chapter 5. Discusses interdisciplinary integration of geographic frameworks
such as ecoregions and watersheds.
This section describes a variety of multi scale geographic frameworks essential
for successful application of landscape and predictive tools including their
strengths and limitations. It covers how frameworks can be combined to
maximize our ability to extrapolate. It also discusses appropriate roles for both
mapped regions and classification approaches. It places emphasis on
considerations for simultaneous use of ecological regions and true watersheds
as one example of appropriate use of geographic frameworks to apply landscape
and predictive tools.
• Chapter 6. Describes a variety of data sets and sampling designs.
This section covers a range of practical, readily available landscape data at
multiple scales that states and others can easily acquire to summarize and apply
as an integral part of their water quality monitoring, assessment, and
management (see water resource management) processes.
• Chapter 7. Illustrates a variety of methods to characterize relationships between
sources, stressors and disturbance, and natural gradients at multiple scales and
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Section I—Chapter 1: Introduction
to use that information to build predictive tools using both simple and more
complex statistical and spatial modeling and extrapolation techniques.
This section collects and describes reliable methods to document gradients of
stressor, sources, pressure, and disturbance factors and to relate those factors to
biological condition and other in-stream factors. These methods support
prediction or extrapolation of response or exposure to areas or times lacking in
situ information. It covers a range of simple to complex predictive tools, including
descriptive, empirical, multivariate, and such statistical approaches that can be
applied to a wide range of single or multiple stressors. The section emphasizes
important considerations for use of each tool/method.
SECTION III: EXAMPLES AND CASE STUDIES
• Chapters 8-13. Provide a useful array of six examples of several types of
assessment applications, full TMDL case studies and specialized applications.
This section illustrates a variety of practical uses of landscape and predictive
tools applied to specific stressors. These examples demonstrate techniques that
can help fulfill critical water quality monitoring needs including identifying
potentially impaired and high-quality waters, identifying reference waters,
calibrating biological indices, planning, targeting and prioritizing monitoring
activities and prevention efforts, and others. Collectively, these examples
encompass a wide range of pressures and sources; urban, agricultural, forestry,
and so on, stressors; sediment, nutrients, biocides, toxics, pathogens, and so on,
and scales; state, ecoregion, basin, watershed, riverscape, and others.
SECTION IV: GAPS AND NEEDS FOR RESEARCH AND APPLICATION
• Chapter 14. Describes ways to improve analytical capabilities to leverage in situ
and remotely sensed data.
This section collects, summarizes, and emphasizes the full spectrum of gaps and
needs identified in the other sections. These can include data, software, and
other tools, training, workforce, program integration, research, and others, as
appropriate. This section also prioritizes important gaps and needs and provides
recommendations from the Steering Committee (see Acknowledgments) to
inform (and hopefully guide) both short and long-term research on landscape and
predictive tools within and outside the Agency.
SECTION V: TOOLBOX
• The electronic database of spatial data and tools contains a wealth of
material that provides links and short descriptions of landscape and
other data sets and analytical tools for working with landscape
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Section I—Chapter 1: Introduction
information. This database can be accessed as a separate electronic
file from the link provided on the Risk Assessment Forum or
Watershed Central websites.
1.5. CONCLUSIONS
The use of landscape predictive tools facilitates applying all our scientific tools
and data to water quality monitoring, assessment, and rehabilitation questions by
improving the efficiency of monitoring resources. Full incorporation of these tools into
our water quality monitoring and assessment systems will provide data and methods to
better inform program development and environmental problem solving including
scientifically sound monitoring, priority setting, and rehabilitation. While we have
focused on CWA applications of these approaches, there are many other applications
beyond the CWA for which these tools might be useful, such as landscape ecology,
biogeography, regional biodiversity, and species impediment (Oberdorff et al., 1995;
Leprieur et al., 2008; Jelks et al., 2008).
When implementing landscape predictive tools, one needs to keep several
caveats in mind. First, data might not be available (or affordable) for all important
stressor or response factors, such as historical land-use information (Harding et al.,
1998; Wohl, 2005). Second, selecting the most important factors out of hundreds can
be difficult (Li and Wu, 2004). Third, scientific outputs using landscape tools are best
augmented with other evidence such as laboratory and mechanistic studies to
confidently establish cause-effect relationships or mechanisms of action linking stress
and response (U.S. EPA, 2011). Fourth, as in all aspects of science, variability,
uncertainty, and statistical power should be quantified (McGarvey, 2007; Legg and
Nagy, 2006; Smith et al., 2006). Finally, decisions based solely on landscape
assessment and predictive tools are unlikely to be accepted without supporting in situ
data to confirm or refute the empirical estimates extrapolated to specific locations
(Brown et al., 2005).
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Section I—Chapter 1: Introduction
1.6. REFERENCES
Brown, B.S., N.E. Detenbeck, and R. Eskin. 2005. How probability survey data can
help integrate 305(b) and 303(d) monitoring and assessment of state waters. Environ.
Monit. Assess. 103(1 —3):41 —57.
Bryce, S.A., DP. Larsen, R.M. Hughes, and P.R. Kaufmann. 1999. Assessing relative
risks to aquatic ecosystems: a Mid-Appalachian case study. J. Am. Water Resour.
Assoc. 35(1 ):23-36.
Cormier, S.M. and G.W. Suter II. 2008. A framework for fully integrating environmental
assessment. Environ. Manage. 42(4):543-556. Available online at
http://www.springerlink.com/content/n56531j12q33776t/fulltext.pdf.
Davies, S.P., and S.K. Jackson. 2006. The biological condition gradient: a descriptive
model for interpreting change in aquatic ecosystems. Ecol. Appl. 16(4): 1251 -1266.
Detenbeck, N.E., D. Cincotta, J.M. Denver, S.K. Greenlee, A.R. Olsen, and A.M.
Pitchford. 2005. Watershed-based survey designs. Environ. Monit. Assess.
103(1-3):59-81.
Harding, J.S., E.F. Benfield, P.V. Bolstad, G.S. Helfman, and E.B.D. Jones III. 1998.
Stream biodiversity: the ghost of land use past. Proc. Natl. Acad. Sci.
95(25): 14843-14847.
Hawkins, C.P., R.H. Norris, J.N. Hogue, and J.W. Feminella. 2000. Development and
evaluation of predictive models for measuring the biological integrity of streams. Ecol.
Appl. 10(5): 1456-1477.
Hughes, R.M., S.G. Paulsen, and J.L. Stoddard. 2000. EMAP-Surface Waters:
multiassemblage, probability survey of ecological integrity. Hydrobiologia
422/423:429-443.
Jelks, H.J., S.J. Walsh, N.M. Burkhead, et al. 2008. Conservation status of imperiled
North American freshwater and diadromous fishes. Fisheries 33(8):372-405.
Legg, C.L. and L. Nagy. 2006. Why most conservation monitoring is, but need not be,
a waste of time. J. Environ. Manage. 78(2):194-199.
Leprieur, F., O. Beauchard, S. Blanchet, T. Oberdorff, and S. Brosse. 2008. Fish
invasions in the world's river systems: when natural processes are blurred by human
activities. PLoS—Biology 6(2): e28. doi: 10.1371 /journal.pbio.0060028.
Li, H., and J. Wu. 2004. Use and misuse of landscape indices. Landsc. Ecol.
19(4):389-399.
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Section I—Chapter 1: Introduction
Lincoln, A.R., R.J. Klauda, and E.K. Barnum. 2007. Maryland Biological Stream
Survey 2000-2004. Volume 12: changes in condition. Maryland Department of Natural
Resources, Annapolis, MD. Available online at
http://www.dnr.state.md.us/streams/pubs/ea05-9_changes.pdf.
Loveland, T.R., and J.M. Merchant. 2004. Ecoregions and ecoregionalization:
Geographical and ecological perspectives. Environ. Manage. 34(Suppl. 1 ):S1 —S13.
McGarvey, D.J. 2007. Merging precaution with sound science under the Endangered
Species Act. BioSci. 57:65-70.
Mulvey, M., R. Leferink, and A. Borisenko. (2009) Willamette Basin rivers and streams
assessment. Oregon Department of Environmental Quality, Portland, Oregon; DEQ 09-
LAB-016. Available online at http://www.portlandonline.com/ohwr/?a=291703&c=52358.
Norton, D.J., J.D. Wickham, T.G. Wade, K. Kunert, J.V. Thomas, and P. Zeph. (2009) A
method for comparative analysis of recovery potential in impaired waters restoration
planning. Environ Manage 44(2):356-368.
Oberdorff, T., J.F. Guegan, and B. Hugueny. 1995. Global scale patterns in freshwater
fish species diversity. Ecography 18:345-352.
Ode, P.R., C.P. Hawkins, and R.D. Mazor. 2008. Comparability of biological
assessments derived from predictive models and multimetric indices of increasing
geographic scope. J. N. Am. Benthol. Soc. 27:967-985.
Ode, P.R., A.C. Rehn, and J.T. May. 2005. A quantitative tool for assessing the
integrity of Southern Coastal California streams. Environ. Manage. 35(4):493-504.
Omernik, J.M. 2003. The misuse of hydrologic unit maps for extrapolation, reporting
and ecosystem management. J. Am. Water Resour. Assoc. 39(3):563-573.
Paulsen, S.G., R.M. Hughes, and D.P. Larsen. 1998. Critical elements in describing
and understanding our nation's aquatic resources. J. Am. Water Resour. Assoc.
34:995-1005.
Paulsen, S.G., A. Mayio, D.V. Peck, et al. 2008. Condition of stream ecosystems in the
US: an overview of the first national assessment. J. N. Am. Benthol. Soc.
27(4):812-821.
Pont, D., B. Hugueny, U. Beier, et al. 2006. Assessing river biotic condition at a
continental scale: a European approach using functional metrics and fish assemblages.
J. Appl. Ecol. 43:70-80.
Pont, D., R.M. Hughes, T.R. Whittier, and S. Schmutz. 2009. A predictive index of
biotic integrity model for aquatic-vertebrate assemblages of western U.S. streams.
Trans. Am. Fish. Soc. 138(2):292-305.
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Section I—Chapter 1: Introduction
SAB (Science Advisory Board). 2002. A Framework for Assessing and Reporting on
Ecological Condition. T.F. Young, and S. Sanzone, Ed. Ecological Processes and
Effects Committee, U.S. Environmental Protection Agency, Science Advisory Board,
Washington, DC.
Smith, E.R., P. McKinnis, L.T. Tran, and R.V. O'Neill. 2006. The effects of uncertainty
on estimating the relative environmental quality of watersheds across a region. Landsc.
Ecol. 21 (2):225-231.
Stoddard, J.L., D.V. Peck, S.G. Paulsen, et al. 2005. An Ecological Assessment of
Western Streams and Rivers. U.S. Environmental Protection Agency, Office of
Research and Development, Washington, DC. EPA 620/R-05/005. Available online at
http://www.epa.gov/emap/west/html/docs/Assessmentfinal.pdf.
Stoddard, J.L., A.T. Herlihy, B.H. Hill, et al. 2006. Mid-Atlantic Integrated Assessment
(MAIA): State of the Flowing Waters Report. U.S. Environmental Protection Agency,
Office of Research and Development, Washington, DC. EPA/620/R-06/001. Available
online at
http://www.epa.gov/emap/html/pubs/docs/groupdocs/surfwatr/MAIAFIowingWaters.pdf.
Theobald, D.M., D.L. Stevens, Jr., D. White, N.S. Urquhart, A.R. Olsen, and J.B.
Norman. 2007. Using GIS to generate spatially balanced random survey designs for
natural resource applications. Environ. Manage. 40:134-146.
U.S. EPA (Environmental Protection Agency). 2000. Mid-Atlantic Highland Streams
Assessment. Environmental Monitoring and Assessment Program, Office of Research
and Development, Washington, DC and Region 3, Wheeling WV. EPA 903/R-00/015
U.S. EPA (Environmental Protection Agency). 2003. Elements of a State Water
Monitoring and Assessment Program. Office of Wetlands, Oceans and Watershed,
Washington, DC. EPA 841/B-03/003. Available online at
http://www.epa.gov/owow/monitoring/elements/elements03_14_03.pdf.
U.S. EPA (Environmental Protection Agency). 2006. Wadeable Streams Assessment:
A Collaborative Survey of the Nation's Streams. Office of Water, Washington, DC.
EPA 841/B-06/002. Available online at
http://www.cpcb.ku.edu/datalibrary/assets/library/projectreports/WSAEPAreport.pdf.
U.S. EPA (Environmental Protection Agency). (2010) Integrating ecological assessment
and decision-making at EPA: a path forward. Results of a colloquium in response to
Science Advisory Board and National Research Council Recommendations. Risk
Assessment Forum, Washington, DC; EPA/100/R-10/004. Available online at
http://www.epa.gov/raf/publications/pdfs/integrating-ecolog-assess-decision-making.pdf.
U.S. EPA (Environmental Protection Agency). (2011) A field-based aquatic life
benchmark for conductivity in Central Appalachian streams (Final Report). U.S.
Environmental Protection Agency, Washington, DC; EPA/600/R-10/023F. Available
online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=233809.
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Section I—Chapter 1: Introduction
Van Sickle, J., J.L. Stoddard, S.G. Paulsen, and A.R. Olsen. 2006. Using relative risk
to compare the effects of aquatic stressors at a regional scale. Environ. Manage.
38(6): 1020-1030.
Vogelmann, J.E., S.M. Howard, L. Yang, C.R. Larson, B.K. Wylie, and N. Van Driel.
2001. Completion of the 1990s National Land Cover Data Set for the conterminous
United States from Landsat Thematic Mapper data and ancillary data sources.
Photogramm. Eng. Rem. Sens. 67:650-652.
Whittier, T.R., S.G. Paulsen, D.P. Larsen, S.A. Peterson, A.T. Herlihy, and P.R.
Kaufmann. 2002. Indicators of ecological stress and their extent in the population of
northeastern lakes: a regional-scale assessment. BioSci. 52(3):235-247.
Wohl, E. 2005. Compromised rivers: Understanding historical human impacts on rivers
in the context of restoration. Ecol. Soc. 10(2):2. Available online at
http://www.ecologyandsociety.org/vol10/iss2/art2/.
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Section I—Chapter 2: The Clean Water Act: Statutory and Programmatic Benefits of
Landscape Applications
2. THE CLEAN WATER ACT: BENEFITS OF LANDSCAPE TOOLS
(GEOSPATIAL DATA AND ANALYSIS)
Karl Hermann, U. S. EPA Region 8, Denver, CO
Doug Norton, U. S. EPA Office of Water, Washington, DC
Charlie Howell, U. S. EPA Region 5, Dallas, TX
Kristen Pavlik, Tetra Tech, Owings Mills, MD
Jim Harrison, U.S. EPA Region 4, Atlanta, GA
Kelly Kunert, Ellen Tarquinio, Brittany Croll, Robert Hall, Alfonso Blanco, and Peter
Stokely, U.S. EPA Office of Water, Washington, DC
2.1. OVERVIEW: LANDSCAPE ANALYSIS LINKAGES TO THE PRIMARY GOALS
OF THE CLEAN WATER ACT
A primary objective of the CWA is to restore
and maintain the chemical, physical, and biological
integrity of our nation's waters. This objective
implies an ecosystem perspective for monitoring,
assessing, and managing watersheds and aquatic
resources. An ecosystem approach is comprehensive by considering all forms of life,
habitat, and stressors at multiple spatial and temporal scales. Landscape tools
(geospatial data and analysis) are very useful for gaining the proper perspective and for
implementing this ecosystem approach.
Many types of CWA-related programs recommend using an ecosystem or
watershed approach for assessment, enforcement, incentives, planning, priority setting,
and outreach. These CWA activities can involve very detailed landscape modeling and
reporting of single waters and watersheds or of large basins or major jurisdictions.
Because of the geospatial aspects of many CWA programs, it can be possible to
measure progress and effectiveness with landscape analytical techniques. Landscape
data and analyses help to inform adaptive management, strategic planning for future
efforts, programmatic activities and improvement.
What is in this chapter? Major
sections of the CWA are listed with
brief descriptions of how landscape
tools currently or potentially can
inform or better enable protection and
remediation of aquatic uses.
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Section I—Chapter 2: The Clean Water Act: Statutory and Programmatic Benefits of
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2.2. WATER QUALITY STANDARDS SECTION 303(C)(2)
Water quality standards (WQS) are the foundation of the water quality-based
control program that the CWA mandates. Water quality standards define the goals for a
waterbody by designating its uses, setting criteria to protect those uses, and
establishing provisions to protect water quality from pollutants. A water quality standard
consists of four basic elements:
1) Designated uses of the waterbody (e.g., recreation, water supply, aquatic life,
agriculture) (http://www.epa.gov/waterscience/standards/about/uses.htm).
2) Water quality criteria to protect designated uses (numeric pollutant
concentrations and narrative requirements)
(http://www.epa.gov/waterscience/standards/about/crit.htm).
3) An antidegradation policy to maintain and protect existing uses and high quality
waters (http://www.epa.gov/waterscience/standards/about/adeg.htm).
4) General policies addressing implementation issues (e.g., low flows, variances,
mixing zones) (http://www.epa.gov/waterscience/standards/about/pol.htm).
Landscape analysis can be used in developing and implementing WQS in
several ways: determining designated uses through classification of similar waterbody
types, characterizing background levels and achievable conditions, and ultimately
developing protective water quality criteria.
2.2.1. Determining Designated Uses Through Classification of Similar
Waterbody Types
Designated uses are ideally based on best achievable conditions in the absence
of pollution. Best achievable conditions are influenced by flow, waterbody type, size,
ecoregion, background water constituents such as naturally occurring metals in the
West, and other naturally occurring factors. For biocriteria (biocriteria are direct
measures of aquatic life use support), regional factors such as elevation, topographic,
climate, and geological factors are commonly used when establishing reasonable
expectations because these factors affect the species and abundance of organisms that
can survive and reproduce. For example, a common classification of streams and rivers
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Section I—Chapter 2: The Clean Water Act: Statutory and Programmatic Benefits of
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involves the division of cold and warm waters for determining the appropriate standards
for aquatic life use. Geographical information system (GIS) data are used to assist in
segmenting waters to account for these different thermal regimes. In general,
geographical information can be used to classify streams with similar achievable
conditions, thus reducing the burden imposed by developing site-specific standards.
Geographical information also is used in the standards review processes
including triennial reviews and use attainability analysis (UAA) for specific waters. More
often, water quality program personnel have begun to develop UAAs on watershed
scales.
2.2.2. Characterizing Background Levels and
Achievable Conditions
Reference sites are typically used to define
best achievable conditions. However, reference
sites do not necessarily represent best achievable
conditions; they represent currently best achieved or least disturbed conditions. Until
fairly recently, water quality program personnel have selected reference sites without
the benefit of much geographic information. As in situ and remotely sensed data,
greater computational power, and better spatial capabilities have become more readily
accessible, there are other alternatives for selecting reference sites. One alternative is
to use probability surveys to identify previously unknown, high-quality waters.
Numerous state and other project personnel have used landscape data to
identify minimally disturbed and least disturbed areas as candidate reference areas
(Stoddard et al., 2006). Some examples include projects in Georgia (see Section III,
Chapter 8) and Oregon (see Section III, Chapter 9). These efforts incorporate multiple
sources of geographic information, often at several scales, to delineate areas and
specific watersheds within ecological regions having minimal anthropogenic influences,
and thus, likely to have the best available biological condition for aquatic communities.
For example, geographic analyses can screen sites for adequate distance downstream
from impoundments and point sources.
Geographic information has also been used extensively to calibrate reference
condition at state, ecoregion, multistate and national scales (e.g., the National
Section 2 of this document has
examples that illustrate how
landscape information has been
applied to address CWA objectives.
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Section I—Chapter 2: The Clean Water Act: Statutory and Programmatic Benefits of
Landscape Applications
Wadeable Streams Assessment) and to calibrate biological and other condition
measures, such as in Florida's development of macroinvertebrate biological monitoring
indices for wadeable streams assessment (Fore et al., 2007). These projects use
geographic information to correlate biological condition with land-use-associated
stressors and identify human disturbance gradient thresholds corresponding to
reference condition, as well as other levels of biological integrity.
Geographic information plays a key role in documenting human disturbance
gradients for development of tiered aquatic life uses. Changes to watershed and
riparian structure are key factors associated with altered aquatic environments. Others
are changes to flow, materials transport, channel structure, and biological activity. The
relationship between sources, stressors, and resulting biological response is
summarized in Davies and Jackson (2006).
2.2.3. Developing Water Quality Criteria
After classifying waterbodies and identifying
biological, chemical, physical reference conditions,
analyses can be used to characterize background
values (natural). Furthermore, analysis that
reveals relationships between the stressor(s) and the assessment endpoints can be
used, in conjunction with laboratory findings, to identify what effects are likely to occur
with different exposures. Criteria can be set at exposures that are not likely to cause
harmful effects to aquatic life or impair designated uses. Some examples for nutrients,
suspended and deposited sediment, and salinity measured as conductivity are included
in the references of this section (U.S. EPA, 2000, 2006a, 2010, 2011a; Cormier et al.,
2008).
2.3. MONITORING AND ASSESSMENT SECTION 305(B)
The National Water Quality Inventory Report to Congress (305[b] report) is the
primary vehicle for informing Congress and the public about general water quality
conditions in the United States. This document characterizes our water quality,
identifies widespread water quality problems of national significance, and describes
See also Chapters 8 and 9, which
describe analyses that use landscape
information to contribute toward
development of nutrient criteria.
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Section I—Chapter 2: The Clean Water Act: Statutory and Programmatic Benefits of
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various programs implemented to restore and protect our waters. CWA section 305(b)
requires a biennial assessment of waters, and this is usually presented as the
attainment or nonattainment of WQS.
Applying landscape tools could improve the efficiency of monitoring, assessment,
and reporting components required for CWA section 305(b). Geographic analysis can
be used to do the following:
1) Evaluate if condition is due to natural, minimally disturbed, or background levels
(described above).
2) Evaluate temporal trends that might indicate emerging threats or project harmful
effects.
3) Develop models that might reveal correlations between environmental
assessment endpoints and environmental stressors and their sources.
4) Predict aquatic condition estimates in waters that lack in situ data.
One of the challenges of trying to satisfy the CWA section 305(b) reporting
requirement is that it must describe the quality of all waters. A census monitoring
approach of sampling all waters is not practical for most states biennially because of the
time and resources needed. Probability-based monitoring designs and rotating basins
are strategies that are reasonable approaches to satisfy the 305(b) requirements.
Although, these approaches can characterize the proportional condition of waterbodies,
they might not indicate clusters or patterns of condition unless sampling densities are
sufficient to detect them.
Perhaps one of the most powerful uses of landscape and predictive tools is
predicting aquatic condition estimates to waters that lack in situ data. While ambient
data cannot be gathered everywhere because of resource constraints, landscape data
are available for many locations throughout the world and for the entire United States
usually at multiple levels of resolution (satellite and air photo for example) (Vogelmann
et al., 2001; Homer et al., 2004). These data are useful to describe gradients of both
general stress such as the Landscape Development Intensity Index (Brown and Vivas,
2005) and specific sources such as imperviousness (Jennings et al., 2004). Prediction
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Section I—Chapter 2: The Clean Water Act: Statutory and Programmatic Benefits of
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from the relationships between landscape factors and aquatic responses can be used to
spatially identify suspected problem areas and to effectively and efficiently target limited
monitoring resources to assess likely problems (Wickham et al., 2000, Bellucci et al.,
2009). This approach can improve estimates of impaired waters as required by CWA
sections 303(b)/303(d) and lists of impaired waters that states develop and U.S.
Environmental Protection Agency (EPA) reviews and approves.
For example, targeted monitoring can complement probability-based monitoring
designs and rotating basins approaches by optimizing characterization of large areas
and specific activities and land uses. Targeting sampling can include monitoring
tributaries and other inflows, impoundments, point sources, and nonpoint sources.
Analysis can indicate the likelihood of whether an entire waterbody segment or only a
portion of the segment is expected to have the same condition.
GIS data are instrumental in summarizing the total miles and acres of
assessment condition classes (integrated reporting categories) as well as miles and
acres of unassessed waters. GIS databases very effectively track changing waterbody
conditions that are associated with a changing landscape. Data systems applications
and geospatial analyses are keys to long term-water quality management efforts that
depend on the 305(b) assessment. Setting priorities is an important component of that
management.
2.4. IMPAIRED WATERS (SECTION 303[D]) LISTING AND TOTAL MAXIMUM
DAILY LOAD (TMDL) DEVELOPMENT
Under section 303(d) of the CWA, states, territories, and authorized tribes are
required to develop lists of impaired waters. These are waters that are too polluted or
otherwise degraded to meet the WQS set by states, territories, or authorized tribes.
The law requires that these jurisdictions establish priority rankings for waters on the lists
and develop total maximum daily loads (TMDLs) for these waters. A TMDL is a
calculation of the maximum amount of a pollutant that a waterbody can receive and still
safely meet WQS.
Geospatial data and landscape analysis techniques play prominent roles in
303(d) listing. These data and methods are essential for listing, managing, and
displaying 303(d) list data and information at statewide and national scales. They are
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invaluable in TMDL model development and implementation planning at the scale of the
impaired waterbody or watershed. They can be very effective during priority setting and
for program results tracking at multiple scales.
In recent reporting cycles, approximately 35,000-40,000 waters have been listed
nationally (U.S. EPA, 2008). Listing is based on state-specific assessment procedures
that identify specific waters (often segments rather than whole waterbodies) that do not
meet standards and the listing cause (usually a pollutant type but also sometimes an
effect with an undefined pollutant cause) of the impairment. More than one cause can
be listed for a single waterbody or segment.
Potential Application of Landscape Analysis
• Identify impaired waters
• Identify causes of impairments
• Model movement of contaminants (exposure characterization)
• Backtrack to an unknown source (source trackdown)
• Apportion nonpoint sources
• Estimate the likelihood and degree of exposure to receptors (analysis of exposure)
• Model the movement of contaminants in the environment and through the food web (exposure
characterization)
• Empirically model stressor response relationships, and intermediate causes that are used to
estimate the level that is likely to restore the designated use
• Design and model effects of locating different management options
• Identify locations appropriate for certain types of management strategies
• Help prioritize TMDL implementation and optimize location and types of remedial actions (risk
characterization and risk management)
• Track progress
• Inform future policies and programs
2.4.1. Identifying and Listing Impaired Waters
Although the data for assessing and listing impaired waters are typically
field-gathered monitoring data (usually section 305[b]), existing regulations at Title 40 of
the Code of Federal Regulations (40 CFR 130.7) also support 303(d) listing on the basis
of predictive models. Segments may be included on 303(d) lists where intensive
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land-use development results in a high probability of impairment (or standards
nonattainment).
Initially, 303(d) list submittals are tabular accounts, but once finalized, statewide
303(d) geospatial data and attribute tables are developed and compiled in EPA data
systems. It is crucial for 303(d) data to identify explicit geographic locations of listed
waters. GIS-based segmentation of waters for referencing and the 303(d) GIS data
sets have improved the tracking of impaired waters. By georeferencing the impaired
waters to the National Hydrography Dataset (NHD), impaired waters become available
as a primary data layer in countless state or national GIS assessments, strategies, and
planning activities. For example, federal land management agencies have examined
the co-occurrence of 303(d) listings and their landholdings to reveal the magnitude and
distribution of their impaired waters challenges and organize their response nationally
and regionally (Norton et al., 2007). The 303(d) GIS data sets are available publicly
through Web site download (http://www.epa.gov/waters/data/downloads.html).
2.4.2. Prioritizing Total Maximum Daily Load (TMDL) Implementation and
Optimizing Locations and Types of Remedial Actions (Risk
Characterization and Risk Management)
States are required to prepare a schedule indicating relative priority for TMDL
development among 303(d)-listed waters. Program managers have generally assigned
priority on a case-by-case basis relying on uneven information and knowledge among
cases rather than systematic methods or comparisons. However, the complexity of
subjectively setting priorities among hundreds or even thousands of waters is daunting,
unless assisted by some uniformity of information and decision criteria. Where
geospatial, statewide, 303(d) data are available, prioritization can be informed by
analysis that combines land use and management options with other data sets and
metrics indicative of ecological condition, exposure of stressors, and numerous social
context indicators that are all relevant to priority decisions among the state's impaired
waters. For example, analysis of numerous landscape metrics using EPA and other
common data sets enabled a cluster analysis of more than 700 Illinois 303(d)-listed
waters as a demonstration of recovery potential screening (Wickham and Norton, 2008,
U.S. EPA, 2011 b) (see Figure 2-1).
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Ecological Index
# 32-4620
o 4621-20194
© 20195-92170
• 92171-1053880
Stressor Index
-16
Social Index
• 75-5016
O 5017-11669
® 11670-26556
• >26557
Sum of Ranks
O 10
O 11
• 12
FIGURE 2-1
Prioritization of Stream Remediations Based on Severity of Impairment
and Recovery Potential
Source: EPA, (2011b).
2.4.3. Tracking Progress
Section 303(d) program evaluation and tracking can benefit from the use of
landscape data, tools, and techniques. A major element of the EPA Strategic Plan's
water program measures and reporting (U.S. EPA, 2006b) approach involves reference
to the 2002 baseline, a national geospatial data set of all impaired waters that provides
an important benchmark from which to track changes and evaluate progress in ensuing
years. Developing this baseline was made possible by the previous effort to finalize
2002 303(d) GIS data for all states. The 2002 baseline provides not only a reference
point, but also opens the door to direct calculation of some tracking measures through
GIS rather than full reliance on state-by-state reporting. Also, the availability of the
baseline allows for additional GIS-based analysis of the related driving factors (e.g.,
watershed landcover patterns, population density, and economic factors) that could help
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explain variations in program progress from place to place, and thereby aid program
improvement.
2.5. TOTAL MAXIMUM DAILY LOADS (TMDLS)
CWA section 303(d) also directs the development of TMDLs, which identify
necessary pollutant loading reductions for each listed waterbody to guide restoration
actions. A TMDL is essentially a quantitative, technical plan that calculates specific
pollutant reductions needed to attain WQS for a specific impaired water (segment or
whole waterbody). Although a single TMDL is defined in terms of a specific waterbody
and pollutant, TMDL development efforts often involve multiple TMDLs by addressing
several pollutants affecting one water or multiple 303(d)-listed waters found in the same
watershed with one or more of the same pollutants. TMDLs usually incorporate
quantitative loading reductions. Point and nonpoint source control implementation can
be greatly aided by comprehensive, collaborative watershed plans. The
recommendations of the TMDL and watershed plan are implemented through a
combination of permits for point sources and best management practices (BMPs) for
nonpoint sources (see also this chapter's sections on National Pollutant Discharge
Elimination System [NPDES] and section 319). The entire TMDL cycle includes listing,
determining causes and sources, allocating loads that are likely to restore the
designated use, developing a management plan, implementing controls and monitoring
the controls' performance and the overall plan's effectiveness to restore the use
(Cormier and Suter, 2008; U.S. EPA, 2011a).
Landscape data and tools are highly applicable in developing TMDLs at
waterbody or watershed scales, as well as in support of TMDL implementation
priority-setting and TMDL program evaluation and tracking at statewide, regional, or
national scales.
TMDLs are watershed-oriented, and they are meant to address all contributing
sources of the targeted pollutant in a plan to allocate pollutant reductions that will help
the water return to meeting WQS. At the heart of most TMDLs is a model. A watershed
model quantifies pollutant sources and loading reduction for more than 90% of impaired
waters that are affected wholly or partly by nonpoint sources alternatives. Thus, as GIS
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tools and landscape data sources have improved in availability and efficiency, they have
played prominent roles in watershed modeling and TMDL development. For example,
relative estimates of contributing sources of nutrients can be estimated from discharge
permittee reports and nonpoint source loading models that use landcover and
geospatial data. Quantifying these watershed source categories is necessary to clarify
the linkages among their relative loadings and the impairment, as well as to plan for
allocating loading reductions among contributors. Effective integration of the watershed
spatial data and aquatic monitoring data in models makes it possible to evaluate
different loading reduction scenarios that could help the water recover but might affect
the sources differently. Further, good geospatial data use in TMDL development also
supports the planning for and placement of post-TMDL monitoring to track recovery.
Developing and implementing multiple TMDL projects throughout watersheds
has proven cost-effective because GIS data sets provide a basis for screening and
identifying groups of listed waters with common problems and potential solutions that
are prime for watershed-based analysis and planning. Like the 303(d) lists, TMDL
tracking and program progress is beginning to be aided by developing geospatial data
representing TMDL completion and implementation, and spatial analysis of factors that
help explain differences in TMDL outputs and outcomes from place to place. In
addition, landscape data have provided transparent, consistent, and repeatable decision
support methods to identify priorities for TMDL implementation among large numbers of
TMDLs.
Watershed, reach, and in-stream habitat landscape information are being used to
develop better, more protective predictive models of biota (such as fish populations) and
biocriteria. One pertinent example is the What If? model, developed on the basis of
landscape, habitat, and biological data from the Mid-Atlantic Highlands Integrated
Assessment (U.S. EPA, 2006a). This tool allows estimation of populations of specific
fish species. It also serves as a management tool by predicting fish population changes
on the basis of different land and riparian management scenarios (see Figure 2-2).
Landscape information can play a large role in developing solutions for
site-specific problems. Lower resolution data, including satellite-derived classifications
such as the National Land Cover Dataset coupled with ecological regions and in situ
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CVI - WHAT IF?
Data View Window Help
EH1 Regional Query
Which Highlands streams are most suitable for riparian restoration?
For the streams that are in the WHAT IF database, identify streams for which the following
variables fall within the indicated ranges.
Include
17
Minimum
Maximum
Results
17
|7
17
17
17
17
% watershed forested
150
90
% watershed urbanized
r
|io"
% of reach with riparian
F
|50
cover
stream width (m)
F
120
% of reach with pools
F~
170
bank slope (degrees)
F
150
% fine substrate
F
50
Number of Streams by State
State
N
~
DE
0
MD
4
NC
2
NJ
0
NY
2
PA
19
VA
10
WV
69
Found 10G
out of 558
sites with data.
Show Sites on Map
A
<< Back to Questions <<
FIGURE 2-2
Regional Query Tool for Riparian Restoration with Constrained
Parameters Using What If
Source: EPA (2006a).
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data, have supported identification of predominant stressors for wide areas (Van Sickle
et al., 2006). Higher resolution data, such as aerial photography, support identification
and documentation of specific causes and sources on the ground (Holcombe and
Malone, 2005; Exum et al., 2005). The Tennessee Valley Authority's IPSI process uses
one meter plus resolution air photo interpretation, mapping and modeling to identify and
prioritize nonpoint sources for cost-effectively implementing BMPs in watersheds at
several scales. These and similar approaches could be used much more frequently to
prioritize and target TMDL development, as well as rehabilitation, regulatory, and
delisting efforts for impaired/303(d)-listed waters.
2.6. NONPOINT SOURCE CONTROL SECTION 319
CWA section 319 is a key program addressing the restoration of waterbodies.
Nonpoint sources of impairment are often assessed through landscape tools. Modeling
landscape contributions of stressors to waterbodies can yield estimated loads, which in
turn can be used in the watershed management plan. Impervious surfaces (see
Chapter 8), mining, and agriculture, including cropland (see Chapters 10 and 11) and
livestock grazing, are some of the major nonpoint sources of sediment, nutrient, and
pathogen loadings. Nonpoint source controls can include reducing impervious surface
area, establishing holding ponds, and protecting riparian zones through fencing or
establishing minimal distances for no disturbance from the water's edge (see the
Oostanaula Creek example—Chapters 10 and 11). Stream temperatures can be
reduced with increased shade from taller riparian vegetation and reconnection with
cooler ground water recharge zones (see the Umatilla River example) (also see
Figure 2-3).
BMPs can be targeted and implemented for optimal chances of restoration
success. Topographic features such as slope and aspect are combined with climatic
and soils information for determining erosion potentials. To reduce sedimentation or
other runoff sources, that knowledge can be used for optimal placement of BMPs, thus
enabling effective and efficient restoration efforts.
Finally, performance and effectiveness monitoring can be correlated with
landscape changes to help evaluate BMP techniques. A performance assessment
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FIGURE 2-3
Reach Scale Thermal Profile Illustrating the Cooling Effect of Groundwater
Recharge and Mixed Temperatures in the River
evaluates how well a BMP reduces pollutants and performs to specifications. For
example, did temperatures in the Umatilla River decrease? An effectiveness
assessment determines if the problem is resolved. Did salmonids return to the Umatilla
River? Comparisons of water quality data and landscape information before and after
implementation of BMPs can help justify section 319 expenditures for the restoration
efforts and inform and refine future efforts.
Of equal importance, landscape and predictive tools can also be used to
estimate vulnerability to stress(es) for specific areas and waters. This ability enables
the targeting and prioritization of places for prevention and protection activities. Some
examples of projects emphasizing prevention and protection include mapping of stream
sensitivity to acid deposition on the basis of surface geology, elevation and other factors
(Sullivan et al., 2007), characterization of streams affected by impervious cover as
illustrated by the Eagleville Brook TMDL (CT DEP, 2007) and Connecticut Rivers of
Hope program (Bellucci et al., 2009) and the variety of methods tested by EPA's
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Regional Vulnerability Assessment program in the Mid-Atlantic region (Smith et al.,
2003).
2.7. NATIONAL POLLUTANT DISCHARGE ELIMINATION SYSTEM (NPDES)
PERMITTING
As authorized by the CWA, the NPDES permit program controls water pollution
by regulating point sources that discharge pollutants into waters of the United States.
Point sources are discrete conveyances such as pipes or man-made ditches. Individual
homes that are connected to a municipal system, use a septic system, or do not have a
surface discharge do not need an NPDES permit. However, industrial, municipal, and
other facilities must obtain permits if their discharges go directly into surface waters.
Confined feeding operations of a certain size also must seek a permit. In most cases,
authorized states administer the NPDES permit program. Permits program can use
spatial databases to manage locations of pipes and outfalls, prioritize permit review, and
track and evaluate programs at state, regional, and national scales. At local scales,
landscape databases are most useful for permit review and evaluation.
There are two basic types of permits. One is an individual permit, which is
tailored for a specific facility on the basis of the information provided in the permit
application. The second type of permit is a general permit covering multiple facilities
within the same category (e.g., stormwater) and within a specific geographic area.
Recently, there has been an increased focus on opportunities for developing
watershed-based permits. This type of permit sets ambient water quality requirements
for an entire watershed and allows for a combination of point source and nonpoint
source controls as determined by a local watershed plan to meet those requirements via
pollutant trading. After these permits have been issued, the information contained in the
permits is entered into the national database Permit Compliance System/Integrated
Compliance Information System. These data (including facility location and
latitude/longitude) are then used to measure how the program is being implemented
and how well it is achieving environmental goals. The databases are also used in
Freedom of Information Act requests regarding the nature of facilities regulated by
NPDES permits and providing accountability to citizens.
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There are technical issues involved in the permitting process that require
predictions or models of loadings to waterbodies (e.g., TMDL development) that could
use GIS data. The Reinventing Environmental Information program includes a data
standard for entering spatial coordinates and the associated information about the
coordinates. These data elements are incorporated into EPA information systems
designed so that the CWA programs can be related to improved environmental quality.
Accurate spatial coordinates accurately attribute individual NPDES dischargers to
receiving waterbodies especially those that are impaired. This is necessary to
determine TMDL implementation levels, to prioritize permit issuance, and to provide
Congress and the public with up-to-date information on waterbody condition. GIS
programs can be essential for obtaining this information and coordinating and sharing
data at the state, regional, and national levels.
Once the geospatial data of permits have been collected, the next goal is to use
the GIS information to link to the NHD. This helps EPA do a better job of assessing
cause and effect relationships between permitted discharges and water quality
information. The GIS data can also be used to link with other water databases such as
Storage and Retrieval (STORET), the National TMDL Tracking System, and the Water
Quality Standards Database. Using these data to create linkages across water
databases will make the results of data requests more accurate and allow for better
oversight of the NPDES program.
2.8. WETLANDS PROTECTION
The protection of our nation's wetlands under the CWA is accomplished through
permits for dredged or fill material (Section 404), WQS, advanced targeting of valuable
wetland resources, mitigation, restoration, monitoring, assessment, and enforcement.
Each one of these program elements can benefit from a better understanding of the
overall landscape and the use of geospatial tools and data at multiple scales, but
particularly in the context of watershed characterization and the watershed approaches
to wetland protection.
A fundamental basis of a watershed approach to wetland protection is the
characterization of wetlands and the surrounding watershed. Landscape tools and
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assessments can be used to estimate wetland extent and change, to assess some
wetland functions, to measure baseline environmental conditions, to prioritize wetlands
for protection, to find suitable sites for restoration or mitigation, and can be used to
monitor progress toward a restoration or ecological condition goal (see Figure 2-4).
The spatial distribution of types of wetlands, land uses, other landcover types,
extent of impervious surfaces, hydrography, topography, and the distribution and cause
of impaired waters are but a few of the many spatial data sets that are commonly
examined with landscape tools. The relationships between these features are also
informative. For example, by examining the size of a wetland, its landscape position,
cover type, outlet restrictions and the size and nature of the surrounding watershed, the
wetlands potential to trap sediments and pollutants running off a watershed can be
estimated. Spatial analyses can help wetland program personnel with actions such as
permitting decisions, mitigation or restoration, and developing monitoring plans at the
individual wetland scale.
By incorporating wetlands data layers such as those available from the National
Wetland Inventory, wetland distribution and statistics can be displayed and baselines
quantified to initially screen for wetlands that might be affected by a proposed major
development or other activity. By factoring in surrounding landscape information, it is
possible to predict likely current stressors affecting ecological health of wetlands in a
watershed and to update conditions as they change. Progress toward protecting and
restoring wetlands can be documented, as can declining trends in number, size,
distribution and condition of stressed wetlands. Similarly, by assessing
landscape/wetland relationships, landscape tools can be used to target exceptional
wetlands in good condition for protection.
Landscape analysis also improves wetland mitigation and restoration site
selection. With landscape data sets and tools, site selection criteria can more readily
include location, soil type, slope, and proximity to water and surrounding land use.
Remote sensing analyses of human-induced changes over time are also used to
develop and present evidence in wetland enforcement efforts, and have aided in
settlement negotiations and courtroom victories.
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Combined GIS and Remote Sensing
Isolated Wetland Result
St. Johns River
Water Management District
Ncte: This map illustrates
the 10-meter USGS
National Elevation Dataset
with a hillshade dramatization.
Miles
FIGURE 2-4
Identification and Spatial Distribution of Isolated Wetlands in Alachua County, Florida as
Determined by Segmentation Analysis of Landsat TM+ Imagery (Frohn et al., 2009) and
Overlay Analysis of Soil, Hydrology, and Elevation GIS Data Layers (Reif et al., 2009)
Source: Combined image provided by Ellen DAmico, Dynamac Corporation,
contractor to EPA.
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2.9. THE CLEAN WATER STATE REVOLVING FUND (CWSRF)
The Clean Water State Revolving Fund (CWSRF) was established with the intent
of providing states with a continuous source of funding for projects that clean and
protect the nation's waters. Each state and Puerto Rico maintains independently
operated revolving loan funds, capitalized with federal government grants and
20% state matching funds. The funds are used to provide low-interest loans for a wide
variety of water quality projects, including treatment/mitigation of contaminated runoff
from urban and agricultural areas, wetlands restoration, ground water protection,
brownfields remediation, estuary management, and wastewater treatment. Loan
repayments are recycled back into the program to fund additional water quality
protection projects.
States have the flexibility to target resources to their environmental needs. EPA
encourages its state partners to use watershed planning and develop integrated priority
setting systems to choose projects that address the greatest remaining environmental
challenges. States and EPA are exploring the role of landscape data and tools in
CWSRF priority setting. There is great potential for state program managers to take
advantage of spatial analysis tools to identify priority projects and direct funding to areas
where it will provide the greatest environmental benefit.
In recent years, the CWSRF program personnel have undertaken an ambitious
effort to add outcome-based performance information to its strong financial record.
Spatial analysis tools can be used by CWSRF program managers to harness the
environmental benefits data for a variety of state-level management activities such as
reporting and outreach. All CWSRF programs have committed to document the
projected environmental outcomes of CWSRF-funded projects in the CWSRF Benefits
Reporting database. The environmental benefits data are now available to states and
the public through EPA's Watershed Assessment, Tracking and Environmental Results
(WATERS) database. WATERS uses landscape data to link CWSRF loans to other
national water quality program data and provides EPA and states with a simple way to
produce comprehensive reports to showcase the environmental benefits of CWSRF
loans to stakeholders. At the state level, the CWSRFs are exploring using landscape
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data and tools to generate state-level maps to highlight their programs' environmental
successes.
To date, the use of landscape data and tools has not been widely implemented
by the CWSRF. EPA is working with states to develop case studies to highlight ways in
which some states have begun to use landscape data for CWSRF program
management. The case studies will serve as a model for CWSRF program managers
to follow to incorporate landscape data and tools into their state programs.
2.10. CROSS-PROGRAM APPLICATIONS
The goal of the CWA is to protect and restore aquatic resources. Thus, targeting
and prioritizing protection and remediation efforts is essential for success. Landscape
analyses are particularly useful for visualizing options and anticipating ancillary benefits
or costs. Some important factors in these efforts include a water-body's ecological
capacity to regain functions, past, current, and future exposure to stressors, and local
political and socioeconomic capacity to address problems (Norton et al., 2009).
Landscape data, GIS analyses, and pre- and post-project monitoring, especially for
biological integrity, will need to go beyond measuring reductions of pollutants and
actually establish the protection or return of the aquatic resource or use (Alexander and
Allan, 2007).
2.11. REFERENCES
Alexander, G.G., and J.D. Allan. 2007. Ecological success in stream restoration: Case
studies for the Midwestern United States. Environ. Manage. 40(2):245-255.
Bellucci, C., C,M. Beauchene, and M. Becker. 2009. Physical, Chemical, and
Biological Attributes of Least Disturbed Watersheds in Connecticut. Connecticut
Department of Environmental Protection, Bureau of Water Protection and Land Reuse
Planning and Standards. Available online at
http://www.ct.gov/dep/lib/dep/water/water_quality_management/ic_studies/least_disturb
ed rpt.pdf (accessed 3/9/09).
Brown, M.T., and M.B. Vivas. 2005. Landscape development intensity index. Environ.
Monit. Assess. 101:289-309.
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Cormier, S.M. and G.W. Suter II. 2008. A framework for fully integrating environmental
assessment. Environ. Manage. 42(4):543-556. Available online at
http://www.springerlink.com/content/n56531j12q33776t/fulltext.pdf.
Cormier S.M., J.F. Paul, R.L. Spehar, P. Shaw-Allen, W.J. Berry, and G.W. Suter, II.
2008. Using field data and weight of evidence to develop water quality criteria. Integr.
Environ. Assess. Manage. 4(4):490-504.
CT DEP (Connecticut Department of Environmental Protection). 2007. A Total
Maximum Daily Load Analysis for Eagleville Brook, Mansfield, Ct. Accessed 6/26/09.
Available online at http://www.ct.gov/dep/lib/dep/water/tmdl/tmdl_final/eaglevillefinal.pdf.
Davies, S.P., and S.K. Jackson. 2006. The biological condition gradient: a descriptive
model for interpreting change in aquatic ecosystems. Ecol. Appl. 16(4): 1251 -1266.
Exum, L.R., S.L. Bird, J. Harrison, and C.A. Perkins. 2005. Estimating and Projecting
Impervious Cover in the Southeastern United States. U.S. Environmental Protection
Agency, Office of Research and Development, National Exposure Research Laboratory,
Athens, GA. EPA 600/R-05/061. Available online at
http://www.epa.gov/athens/publications/reports/Exum600R05061EstimatingandProjectin
glmpervious.pdf.
Fore, L.S., R. Frydenborg, D. Miller, et al. 2007. Development and Testing of
Biomonitoring Tools for Macroinvertebrates in Florida Streams (Stream Condition Index
and Biorecon). Florida Department of Environmental Protection. Tallahassee, FL.
Available online at ftp://ftp.dep.state.fl.us/pub/labs/assessment/sopdoc/sci_final.pdf.
Frohn, R.C., M. Reif, C.R. Lane, and B. Autrey. 2009. Satellite remote sensing of
isolated wetlands using object-oriented classification of Landsat-7 data. Wetlands
29(3):931 -941.
Holcombe, J.B. III., and D. Malone. 2005. The Tennessee Valley Authority's
watershed-based approach to integrated pollutant source inventory. In: Proceedings of
the 25th ESRI International User Conference, San Diego, CA, July 25-29. Available
online at http://proceedings.esri.com/library/userconf/proc05/papers/pap1350.pdf.
Homer, C., C. Huang, L. Yand, B. Wylie, and M. Coan. 2004. Development of a 2001
National Land-Cover Database for the United States. Photogramm. Eng. Rem. Sens.
70(7):829-840.
Jennings, D.B., S.T. Jarnagin, and D.W. Ebert. 2004. A modeling approach for
estimating watershed impervious surface area from National Land Cover Data 92.
Photogramm. Eng. Rem. Sens. 70(11):1295-1307.
Norton, D.J., N. Abdelmajid, S. Mann, and C. Knopp. 2007. Patterns observed by
nationally assessing 303(d) waters in or near National Forest lands. In: Proceedings of
the Water Environment Federation TMDL 2007 Conference, Bellevue, WA, June 24-27,
2007, pp. 1-10.
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Norton, D.J., J.D. Wickham, T.G. Wade, K. Kunert, J.V. Thomas, and P. Zeph. (2009) A
method for comparative analysis of recovery potential in impaired waters restoration
planning. Environ Manag 44:356-368.
Reif, M., R.C. Frohn, C.R. Lane, and B. Autrey. 2009. Mapping isolated wetlands in a
Karst landscape: GIS and remote sensing methods. GISci. Rem. Sens.
46(2): 1548-1603.
Smith, E.R., L.T. Tran, and R.V. O'Neill. 2003. Regional Vulnerability Assessment for
the Mid-Atlantic Region: Evaluation of Integration Methods and Assessment Results.
EPA/600/R-03/082. U.S. Environmental Protection Agency, Office of Research and
Development, Research Triangle Park, NC. Available online at
http://www.epa.gov/reva/docs/vulnerable.pdf.
Stoddard, J.L., D.P. Larsen, C.P. Hawkins, R.K. Johnson, and R.H. Norris. 2006.
Setting expectations for the ecological condition of streams: the concept of reference
condition. Ecol. Appl. 16(4):1267-1276.
Sullivan, T.J., J.R. Webb, K.U. Snyder, A.T. Herlihy, and B.J. Cosby. 2007. Spatial
distribution of acid-sensitive and acid-impacted streams in relation to watershed
features in the Southern Appalachian Mountains. Water Air Soil Pollut.
182(1-4):57-71.
U.S. EPA (Environmental Protection Agency). 2000. Nutrient Criteria Technical
Guidance Manual: Rivers and Streams. U.S. Environmental Protection Agency, Office
of Water, Washington, DC. EPA/822/B-00/002. Available online at
http://www.epa.gov/waterscience/criteria/nutrient/guidance/rivers/rivers-streams-full.pdf.
U.S. EPA (Environmental Protection Agency). 2006a. Watershed Health Assessment
Tools Investigating Fisheries, WHAT IF Version 2, A User's Guide to New Features.
U.S. Environmental Protection Agency, Office of Research and Development,
Ecosystems Research Division, Athens, GA. EPA/600/R-06/109. Available online at
http://www.epa.gov/Athens/publications/reports/Johnston600R06109WatershedHealthA
ssessment.pdf (accessed 03/05/2010).
U.S. EPA (Environmental Protection Agency). 2006b. 2006-2011 EPA Strategic Plan:
Charting Our Course. U.S. Environmental Protection Agency, Washington DC.
Available online at http://nepis.epa.gov/Adobe/PDF/P1001IPK.PDF.
U.S. EPA (Environmental Protection Agency). 2008. Total Maximum Daily Loads:
National Section 303(d) List Fact Sheet. URL:
http://www.epa. gov/owow/tmdl/results/pdf/aug_7_introduction_to_clean.pdf(dated
2009).
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Section I—Chapter 2: The Clean Water Act: Statutory and Programmatic Benefits of
Landscape Applications
U.S. EPA (Environmental Protection Agency). (2010) Integrating ecological assessment
and decision-making at EPA: a path forward. Results of a colloquium in response to
Science Advisory Board and National Research Council Recommendations. Risk
Assessment Forum, Washington, DC; EPA/100/R-10/004. Available online at
http://www.epa.gov/raf/publications/pdfs/integrating-ecolog-assess-decision-making.pdf.
U.S. EPA (Environmental Protection Agency). (2011 a) A field-based aquatic life
benchmark for conductivity in Central Appalachian streams (Final Report). U.S.
Environmental Protection Agency, Washington, DC; EPA/600/R-10/023F. Available
online at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=233809.
U.S. EPA (Environmental Protection Agency). (2011b) Fact sheet: Recovery potential
project. Landscape screening tools and resources for comparing the restorability of
impaired waters. Office of Water, Washington, DC; EPA/841/F-11/002. Available online
at http://www.epa.gov/owow/tmdl/pdf/tmdl-recovery-factsheet.pdf.
Van Sickle, J., J.L. Stoddard, S.G. Paulsen, and A.R. Olsen. 2006. Using relative risk
to compare the effects of aquatic stressors at a regional scale. Environ. Manage.
38(6): 1020-1030.
Vogelmann, J.E., S.M. Howard, L. Yang, C.R. Larson, B.K. Wylie, and N. Van Driel.
2001. Completion of the 1990s National Land Cover Data Set for the conterminous
United States from Landsat Thematic Mapper data and ancillary data sources.
Photogramm. Eng. Rem. Sens. 67:650-652.
Wickham, J.D., K.H. Ritters, R.V. O'Neill, K.H. Reckhow, T.G. Wade, and K.B. Jones.
2000. Land cover as a framework for assessing risk of water pollution. J. Am. Water
Resour. Assoc. 36(6): 1417-1422.
Wickham J.D., and D.J. Norton. 2008. Recovery potential as a means of prioritizing
restoration of waters identified as impaired under the Clean Water Act. Water Pract.
2(1): 1 —11.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
3. USING GEOSPATIAL INFORMATION IN ENVIRONMENTAL ASSESSMENTS
Susan Cormier, U. S. EPA Office of Research and Development, Cincinnati, OH
Glenn Suter, U. S. EPA Office of Research and Development, Cincinnati, OH
Chris Bellucci, Department of Energy and Environmental Protection, Hartford, CT
Spatial information is useful only when it is
analyzed and interpreted so that it can inform
environmental decision making. In the simplest
case, field data from known geographical
locations provide a basis for maps showing the
relative locations of an impaired ecosystem and potential sources. In a more complex
form, spatial information can include many data layers representing different attributes
that are analyzed for mechanistic interactions in a large geographic area. However,
colorful maps are just pretty pictures until they are interpreted in the context of
environmental assessments that successfully inform environmental management.
Chapter 3 is intended to increase the probability that assessments provide useful
information, that assessments are used, and that environmental problems are resolved.
Landscape information and analysis can be powerful assets for resolving
environmental problems. A landscape component in an assessment can contribute to
analyzing and communicating information, classifying and organizing data,
characterizing exposures and effects, evaluating trends, developing associations,
testing mechanisms, and integrating assessments of environmental conditions, causes,
consequences, and outcomes. In short, analysis of spatially explicit information is a
very useful tool for environmental management.
3.1. FOUR CLASSES OF ENVIRONMENTAL ASSESSMENT—AN INTEGRATED
IMPLEMENTATION
Environmental assessment is the process of providing scientific information to
inform decisions to manage the environment (Suter and Cormier, 2008a). Some
environmental assessments result in better outcomes than others. Although there can
be many reasons, influential assessments almost always follow a clear structure that
What is in this chapter? The general
types of environmental assessments are
introduced and how assessments work
together to resolve environmental
problems using geographic data. The
chapter also describes basic steps for
using landscape and predictive tools.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
guides the planning, analysis, and synthesis which feeds into a decision-making
process (U.S. EPA, 1998; Presidential/Congressional Commission, 1997). When
several types of assessments are required or used sequentially, a framework for
integrating the assessments clarifies thinking, draws attention to the shared purpose
among assessors and environmental managers, helps identify similar or related data
needs, and suggests the most compelling form for the assessment products. Many
U.S. Environmental Protection Agency (EPA) programs recommend frameworks that
can be used for integration (total maximum daily load [TMDL], Superfund, nonpoint
source, Endangered Species Act recovery plan, and so on). Although terminology
differs, these frameworks share similar basic elements that can be generalized to
provide a convenient, cross-framework terminology (Cormier and Suter, 2008;
U.S. EPA, 2010). In this integrated framework for environmental assessments, there
are four broad classes with subtypes of assessments that can benefit from analysis of
landscape information (see Figure 3-1).
Environmental
Epidemiology
Environmental
Management
Problem
Detection
Condition Assessment
Evaluate
chemical, biological, or
physical state
Problem
Solution
Causal Assessment
Identify
cause or source
Problem
Resolution
Evaluate
performance of management
controls and effect on chemical,
biological or physical state
Outcome Assessment
Forecast from
causal relationship
Predictive Assessment
FIGURE 3-1
Environmental Assessment Integration
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
1) Condition assessments evaluate chemical, physical, and biological conditions or
impairments, i.e., environmental situations that differ from natural, background, or
acceptable conditions.
2) Causal pathway assessments identify causes and sources of an existing
condition.
3) Predictive assessments estimate environmental risks and benefits associated
with different possible management actions postulated to reduce the intensity,
duration, or frequency of interactions between a causal agent and affected
entities.
4) Outcome assessments evaluate the results of the decisions from other
assessments by evaluating the performance of the management action and the
effectiveness of the action in achieving the environmental goal (Cormier and
Suter, 2008).
In Figure 3-1, the environmental assessment process is depicted as a matrix of
assessment approaches that address problem detection (left column) and problem
solving (right column). The rows of the matrix are based on the direction of the analysis
eco-epidemiological assessments attempt to determine probable causes of observed
effects (top row), while environmental management assessments predict effects through
modification of purported causes. Each of the assessment approaches can potentially
lead to the cessation of assessment activities and the solution to an environmental
problem, i.e., an end to an assessment process (central "stop-sign" octagon). Condition
assessments evaluate various types of entities and functions of environmental systems.
Causal pathway assessments indentify proximate causes and their sources. Predictive
assessments evaluate options for reducing environmental risks and the advantages,
disadvantages, and priorities of management options in the context of social and
economic considerations. Outcome assessments evaluate whether specific
management actions have reduced pollutants (performance) and achieved
environmental goals (effectiveness) (modified from Cormier and Suter, 2008).
Within each assessment type, causal relationships describe the linkage between
causes and effects (Cormier et al., 2010). The results of an assessment lead either to
the initiation of another assessment or to a final decision and an action that solves the
problem (Suter et al., 2007; Linkov et al., 2006). The assessment types all have a
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
common structure consisting of planning, analysis, and synthesis. This simple common
structure and terminology can improve communication between those performing
different types of assessments and those who used the findings in different
environmental programs. Similarly, common recognition and consensus as to the type
of assessment being performed or discussed (and the specific terms used for that type
of assessment) helps clarify thinking and communication among workgroup members
and, ultimately, improves the recommendations provided to decision makers.
Spatial information can play a useful role in assessments of site-specific
problems. Lower resolution data, including satellite-derived classifications such as the
National Land Cover Dataset, coupled with ecological regions and in situ data, have
supported identification of predominant stressors for wide areas (Van Sickle et al.,
2006). Higher resolution data, such as aerial photography, support identification and
documentation of specific causes and sources on the ground (Holcombe and Malone,
2005; Exum et al., 2005). The Tennessee Valley Authority's Integrated Pollutant
Source Identification (IPSI) process uses one meter plus resolution air photo
interpretation, mapping, and modeling to identify and prioritize particular nonpoint
sources for cost effective implementation of best management practices (BMPs) in
watersheds at several scales (see the IPSI case study). These and similar approaches
can be used much more frequently in the future to prioritize and target TMDL
development, rehabilitation, regulatory, and delisting efforts for impaired/303(d) listed
waters.
General Uses of Geographical Data in Environmental Assessment and Problem
Solving
• Determine where and when to sample to implement informative and efficient data
gathering (planning).
• Classify and normalize data sets so that analyses are relevant (analysis and
planning).
• Characterize location and intensity of environmental attributes with respect to
sources and impairments (characterization of physical, chemical and biological
variables).
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
• Estimate environmental information from scarce, measured data or landscape
attributes (analysis).
• Document seasonal and temporal trends (analysis).
• Develop regional and local stressor-response models (analysis).
• Model multiple causes (analysis).
• Estimate exposure of receptors (analysis).
• Estimate distribution and condition of effects on wildlife (analysis).
• Model movement of contaminants (analysis).
• Communicate during and after the assessment (planning and synthesis).
• Compare, target, and prioritize placement and deployment of management
options (synthesis).
3.2. USING LANDSCAPE AND PREDICTIVE TOOLS IN INTEGRATIVE
ENVIRONMENTAL ASSESSMENT
This section provides a brief overview of typical activities and products that result
from different types of assessments and how they support one another. Assessments
can stand alone, but, for illustrative purposes, we have selected an example where
success depends on the coordination of different types of assessments. More detailed
descriptions of methods are provided in Chapters 5-7. The concepts are illustrated
using the Streams of Hope program developed by the Connecticut Department of
Environmental Protection (CT DEP) as applied to the Eagleville Brook TMDL. A map of
the study site is shown in Figure 3-2 (CT DEP, 2007).
Each example illustrates the three main elements the problem-solving strategy
shared by all types of assessments: planning, analysis, or synthesis (see Figure 3-3).
Various types of landscape or predictive tools can be useful in any of them.
During planning, when the assessment questions are developed, spatial
boundaries for analyses and the assessment are defined. Based on the assessment
questions, the assessor determines what data, resources, and analytical methods will
be used. These may or may not require the tools described in this document. An
analysis plan is devised.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
agleville Bk
FIGURE 3-2
Map Showing Location of Fish and Macroinvertebrate Sampling Locations
along Eagleville Brook. Sites numbers correspond with Table 3-1,
Adapted from: CT DEP (2007).
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Initiator
Planning
Analysis
Synthesis
Decision/
Action
Assessments are initiated by laws,
regulations, policies or other demands by
society.
Environmental assessments consists of three
steps: planning, analysis and synthesis.
Assessment leads to a management decision
or action that either ends the process or
resolves the environmental problem
FIGURE 3-3
Every Type of Assessment has the Same Basic Elements. Assessments
consist of three elements, planning, analysis, and synthesis. Relevant
assessments are initiated by a societal need for information that are used
for decision making and guiding action.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
TABLE 3-1
Site Description and Characteristics of Benthic Invertebrate Assessments Completed by
CT DEP on October 24, 2003. Relative locations of sites for the Eagleville example are
shown in Figure 3-2.
Site Description
Site
Number
Number
of Taxa
EPT
Taxa
% of
Reference
Condition
Assessment
Eagleville Brook
downstream Hunting Lodge
Rd
1
16
4
25
Impaired
Eagleville Brook upstream
Separatist Rd
2
8
1
20
Impaired
Eagleville Brook upstream
Hillyndale Rd
3
19
9
50
Impaired
Eagleville Brook adjacent N.
Eagleville Rd (above Kings
Brook)
4
22
13
45
Impaired
Eagleville Brook (below
Kings Brook) adjacent N.
Eagleville Rd
5
13
6
45
Impaired
Roaring Brook
Reference
38
23
100
Impaired
Source: CT DEP (2007).
During analysis, the relationships between land uses and physical and chemical
waterbody parameters are modeled. Also, the relationship between proximate causes
and effects are modeled using paired data of sources and stressors, or stressors and
effects. The existing or desired exposure or effect is characterized. The same model
may be used for different types of assessments.
During synthesis, the existing or desired exposure or effect is interpreted in terms
of the relationship models. This may provide an answer in itself, such as the failure to
meet a water quality criterion, evidence that a stressor is sufficient to cause the effect,
or that a reduction to a benchmark will likely restore water quality. However, synthesis
may be more complex when consequences are uncertain or criteria are not defined. In
these cases, the planning, analysis, and synthesis is usually iterative, and different
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
types of analyses and even assessments must be combined. However, the benefits are
worth the effort because the products provide solutions to environmental problems that
have been intractable in the past. One noteworthy example is the use of integrated
environmental assessment for the identification of recoverable ecosystems, that is,
those systems possessing many intact natural features that boost the likelihood that
strategic interventions will be successful. It is like identifying people with high blood
pressure who can be restored to health by a change in diet and exercise. Instead,
water bodies that are impaired or imperiled are identified that are likely to respond to
existing management practices (Norton et al., 2009). Selecting these watersheds for
attention increases success and helps buffer other nearby or downstream watersheds.
This is the basis of the Streams of Hope program developed by the CT DEP.
The CT DEP examined the association between stream aquatic life and
impervious cover. The aquatic life standard for the state was rarely met in watersheds
with greater than 12% IC (percent impervious cover). However, there seemed a
possibility that streams between 5 and 12% IC finds might be recoverable if best
management practices were implemented. The Eagleville Brook fell into this category
with 12% IC and sampling in the stream indicated that aquatic life was impaired.
3.2.1. Characterizing and Assessing Environmental Condition
3.2.1.1. Condition Assessment
How do we know if there is a problem or anticipate that one is emerging? How
do we find impaired sites that are likely to respond to existing management practices?
A condition assessment interprets environmental data to evaluate how existing
conditions compare to a predefined condition. The predefined condition can be
motivated by a need to identify many high or poor quality sites or evaluate a single site.
First, observations of the physical, chemical, or biological conditions are compiled.
These parameters are used to develop a model of natural, minimally disturbed, or
acceptable conditions. This is done by comparing the condition and rates of change at
a location with natural or acceptable ranges or engineered constraints. Deviations from
that defined condition can be used as a benchmark to determine if other observed
conditions are outside the range of typical variation. Alternatively, current conditions
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
can be compared to historical baseline conditions. Finally, the condition assessment
can be performed by comparing observations to a standard such as water quality
criteria (WQC).
Characterization of environmental conditions does not become a condition
assessment until one of those comparisons to a benchmark is performed. Those
comparisons provide a basis for interpreting the significance or relevance of
environmental conditions.
Some Uses of Spatial and Temporal Trends in Environmental Information for
Condition Assessment
• Evaluate if conditions deviate from natural, minimally disturbed, or background
levels, historical baselines, or environmental quality standards.
• Estimate proportion and location of exposure to receptors that exceed water
quality criteria or benchmarks.
• Model areas likely to exceed criteria and enable efficient targeting for monitoring.
• Evaluate temporal trends that might indicate emerging threats or project harmful
effects.
• Develop models that might reveal correlations between environmental
assessment endpoints and environmental stressors or sources (for later use in
causal pathway or predictive assessments).
• Estimate the proportion or an area or set of ecosystems that have an acceptable
condition.
• Estimate co-occurring attributes for later estimation of risk or vulnerability.
• Select sites for intensive or targeted monitoring.
• Estimate conditions where none are measured in situ for 305(b).
Condition Assessment of Eagleville Brook
Planning: Routine monitoring is an on-going process for CWA 305(b) reporting to
Congress using standard state protocols. Geographically distributed, paired data had
been previously developed as an index of biological integrity that was used to evaluate
the condition of Eagleville Brook.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Analysis: The condition of aquatic life in Eagleville Brook, CT was assessed by
comparing benthic invertebrate assemblages in the stream to a State standard.
CT DEP characterizes the condition of aquatic life by using an index based on a
200 count benthic invertebrate sample identified to the lowest possible taxon. Aquatic
life is impaired when the composited invertebrate assemblage index score from the site
is <54% of the reference. Reference is defined based on comparison of balanced
indigenous aquatic communities of acceptable reference streams to stressed
communities of impaired streams.
Synthesis: All sites sampled in Eagleville Brook scored <54%. The condition of the
watershed was assessed as impaired.
3.2.2. Understanding the Causal Pathways that Led to Current Ecological
Conditions
How can someone determine what is causing an ecological problem or factors
that have safeguarded ecosystem attributes? The environmental condition at a site is
the result of a sequence of cause and effect events that are part of a complex network
of other cause-effect interactions (see Figure 3-4). For example, impervious surfaces
(first cause) increase the power of stormwater (environmental effect). Powerful flow
(intermediate cause) erodes streambank (environmental effect). Increased sediment
supply and settlement (proximate cause) buries fish eggs and fry that die (biological
effect). A direct effect can also occur as when stormwater flushes organisms from the
stream and they are swept away. Such a cause that acts directly on the environmental
endpoint, in this case, stream life, is termed a proximate cause. A sequence of causal
events is often termed a causal pathway and used descriptively in this case, but can be
the basis for structural equation modeling. Using even simpler statistical methods,
landcover data and data from in-stream monitoring can be paired to develop empirical
models between steps in the causal pathway. These models can be used to develop
evidence that a candidate cause is or is not a probable cause of stream condition. It
can also be used in predictive assessments and condition assessments, as is discussed
later in this section.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Impervious
surfaces
Increased
force of
storm water
Bank
. erosion,
Key
o Source Mechanism
~ Causes Impairment
Increased
amount of
fine particles
Reduced
in-stream
habitat
\ Fine /
Decreased
\ particles fill /
interg ravel
\ interstitial / *
dissolved
\ space /
oxygen
r
r
k Asphyxiation,
physical
work
Burial
physical
work
Reduced predator
avoidance,
reproduction,
& foraging
Loss of fish and
invertebrate taxa
Swept
away
FIGURE 3-4
Conceptual Model of Causes Associated with the Force Exerted by
Stormwater that Affects the Abundance and Diversity of Fish and Benthic
Invertebrate Taxa
Whereas, causal assessments deal with proximate causal agents, events, or
processes that directly interact with a component in the environment resulting in an
effect, source assessments get at the first cause in the causal pathway. Activities
associated with landscape features, such as roads, agricultural and industrial land uses,
and homes, can be the sources of the causes that affect ecological processes, entities,
and ecosystems. Source assessments attempt to identify the various sources of causal
agents and quantify the amounts contributed by each source to an impairment of
interest. For example, although stormwater may lead to physical burial with
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
bank-eroded sediment, another plausible source, not depicted in the figure, is direct
deposition of mining spoils or construction fill.
3.2.2.1. Causal Assessment
How can someone discover the cause of an impairment? The proximate cause
is identified in a causal assessment by compiling and evaluating scientific evidence.
There are many types of evidence that can support a hypothesized cause (U.S. EPA,
2010). Evidence demonstrates characteristics of the relationship between the cause
and the effect. The six fundamental characteristics of causation are time order,
co-occurrence, preceding causation, sufficiency, interaction, and alteration (Cormier
etal., 2010).
• The cause precedes the effect (time order).
• The cause co-occurs with the unaffected entity in space and time
(co-occurrence).
• Causes and their effects are the result of a Web of causation (preceding
causation).
• The intensity, frequency, and duration of the cause are adequate and the
susceptible entity exhibits the type and magnitude of the effect (sufficiency).
• The cause effectively interacts with the entity in a way that induces the effect
(interaction).
• The entity is changed by the interactions with the cause (alteration).
Evidence of causal characteristics can form the basis for assessments of
epidemiological studies and can structure an explanatory narrative that is causally
relevant and substantive; for example, those that led to low dissolved oxygen. A
probable cause is identified when the body of evidence is credible, coherent, strong,
and diverse.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Credibility—The body of evidence is based on relevant and high quality information.
Coherence—The body of evidence is internally consistent, consistent with scientific
knowledge and theory, and logically explains the facts in the case.
Strength—The body of evidence includes pieces of evidence that are logically
compelling (e.g., to refute because the effect occurred before the cause) or that
present quantitatively strong relationships (e.g., to affirm with high correlation
coefficients or relative likelihoods) (see section above).
Diversity—Many sources of evidence and characteristics of causation are
represented in the body of evidence.
In this process, evidence is weighed and used to build a case for the plausible
causes or combination of causes (Suter and Cormier, 2011). Identifying one credible
cause does not preclude other possible causes. This is why causal assessments are
best conducted as comparisons across all plausible candidate causes.
Some Uses of Spatial Information and Temporal Trends for Causal Assessment
• Map the distribution of potential sources of stressors relative to the distribution of
effects.
• Map the distribution of environmental attributes (physical, chemical and biological
variables) that can influence the distribution and effects of stressors and the
susceptibility of receptors.
• Develop data layers that can be used to develop a list of causal hypotheses.
• Provide surrogates for missing physical, chemical, or biological data.
• Model movement of materials and energy that can characterize the cause as a
process or event.
• Demonstrate that the cause did or did not occur before the effect, co-occur, have
an opportunity to physically interact, or arise from a credible causal pathway.
• Empirically model stressor response relationships and intermediate causes that
are used to evaluate if a candidate cause is capable of causing the effect.
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3.2.2.2. Source Assessment
How can someone find the source and control points of detrimental causes? A
source assessment is needed when the source of the proximate or intermediate
stressor is uncertain or unknown, or when the relative contribution of multiple causal
agents must be described. This is termed source apportionment. The information is
valuable because it is more effective and efficient to control releases at the source than
to contain or remove a causal agent once it has dispersed into the environment. For
example, it is more difficult to remove carbon dioxide from the atmosphere than to limit
its release.
Some Uses of Spatial and Temporal Information for Source Assessment
• Model movement of contaminants (transport and fate modeling).
• Backtrack to an unknown source (receptor modeling).
• Apportion loads among nonpoint sources.
• Document location of sources relative to the affected resource.
Causal Assessment of Eagleville Brook
Planning: Information from the condition assessment and the literature was used to
develop a list of candidate causes and potential sources. Maps were used to locate
sampling sites with potential sources of stressors. Sources included a landfill, possible
water diversions, stormwater associated with impervious surfaces, and altered
vegetative cover. Causes included increased sedimentation, temperature, chemical
contamination, habitat loss, and low dissolved oxygen. The analysis plan included the
development of an empirical model of impervious cover as a surrogate for the mix of
stressors within stormwater. The plan called for the assessment of causal pathways
rather than proximate causes alone. The EPA Stressor Identification process was used
to guide development of evidence (U.S. EPA, 2010).
Analysis: Evidence included measurements and photographs from the stream at
baseflow (see Figures 3-5 and 3-6). Evidence was also developed by comparing
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FIGURE 3-5
Data Support Photo. Excessive sedimentation observed in Eagleville
Brook upstream of Separatist Road (Site 2). Photos taken by CTDEP field
staff
Source: CT DEP (2007).
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
FIGURE 3-6
Data Support Photo 3. Channel down cutting and bank erosion observed
at Site 1, Eagleville Brook downstream of Hunting Lodge Road on July 6,
2005. Photo taken by DEP field staff
Source: CT DEP (2007).
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observations from the stream with causal associations reported in the literature.
Landcover information was used to evaluate the potential importance of impervious
cover in altering hydrologic flow, both reduced low flow that limited habitat and extreme
high flows that were destructive of channel profiles and sediment regimes. Percent
impervious cover was used as a surrogate for causes associated with stormwater
including increased chemical contamination (copper), substrate impacts due to
sedimentation, habitat loss due to channel down cutting, high peak flow rates, and
potential pulses of warm water during stormwater events. A model of % IC and
biological condition that was previously developed was used to evaluate if the level of
% IC was sufficient to limit aquatic life (Center for Watershed Protection, March 2003).
For greater detail consult (CT DEP, 2007).
• Watershed delineation;
• Mapping or estimation of total impervious cover;
• Establishment of % IC target for unimpaired conditions based on State, Region,
and National information;
• Comparison of estimated % IC to the % IC target for un-impaired conditions; and
• Calculation of % IC reduction from current conditions (TMDL implementation
objective) needed to attain water quality.
Percent of reference community was plotted against % IC upstream of the
biological sampling location. At sites greater >12% IC, State WQC for aquatic life were
not met (see Figure 3-7). Spatial analysis estimated that % IC accounted for 12% of
landcover in Eagleville River Watershed. The amount of impervious cover was judged
to be sufficient to cause the physical and biological effects in the stream.
Synthesis: The CT DEP obtained and compared evidence from the % IC model and
other evidence from the site and from the literature. They determined that, "The weight
of evidence supports several different contributions from stormwater flows as being the
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Streams with <50 sqmiles drainage upstream
100
90
80
70
60
50
40
30
20
10
0
n = 125
—
~~~~
Meet WOC
~V
•j ~—
»• ~ r
~ ~
~~
~
rr
~
t
railWQC
~ ~ ~
10V 12^14 16 18 20
% IC Upstream
22 24 26 28 30
FIGURE 3-7
Scatter Plot of % IC Upstream of Monitoring Locations and a Measure of
the Macroinvertebrate Community with Respect to Reference Sites.
Points plotted above the horizontal red line meet Connecticut's WQC to
support aquatic life. Points below the horizontal red line do not meet
Connecticut's water quality criteria to support aquatic life. Sites with
greater than 12% IC fail to meet aquatic life water quality criteria.
Adapted from: CT DEP (2007).
most probable cause of the observed biological impairment (few EPT taxa and reduced
fish abundance)."
3.2.3. Predicting the Potential for Environmental Consequences
3.2.3.1. Risk Assessment
How does someone predict what will happen to the structure and function of
ecosystems under different scenarios? How does someone estimate future exposures
or effects? Risk assessments determine the likelihood that a stressor will reach a level
is relaxed or tightened. Alternatively, the goal could be to determine the probability that
the biological condition (response variable) of a location could become impaired or
enhanced. Risk assessments are sometimes called assessments of future condition if
they do not imply specific risks or benefits.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
The assessment involves characterizing the exposure and determining the
relationship between possible exposure to the potential stressor and the resulting
effects. The predicted risk is estimated from the stressor-response model using the
exposure value to solve for the probable effect (see Figure 3-1) (Suter and Cormier,
2008b).
Some Uses of Spatial and Temporal Information in Conventional Risk
Assessment
• Estimate the likelihood and degree of exposure of receptors (exposure
characterization).
• Model the movement of contaminants in the environment and through the food
Web (exposure characterization).
• Empirically model stressor-response relationships and intermediate causes
that are used to estimate the probability that an effect will occur
(stressor-response characterization).
• Quantify and report differential risks across a geographic area (risk
characterization).
• Identify and report the seasons during which risks are greatest (risk
characterization).
• Determine relative risks or relative benefits of contrasting proposed actions
that can help prioritize remediation locations (risk characterization and risk
management).
3.2.3.2. Developing and Using Criteria
or Restoration Benchmarks
How does someone know the
threshold exposure level when recovery
may occur? Conversely, how does
someone estimate an exposure beyond
which environmental degradation is likely to
occur? This question calls for a form of
predictive assessment that estimates
thresholds of exposure termed criteria or benchmark values. These estimates can then
Many environmental relationships are
nonlinear. The value of the response (Y)
variable can change suddenly once a specific
value of the stressor (X) variable is reached or
exceeded. In this document, we refer to the
point on the Y-axis corresponding to the
change as the breakpoint. In some cases, the
breakpoint is irreversible, such as in extinction.
The corresponding point on the X-axis is the
threshold. When this pattern occurs, the
threshold is especially important in providing
guidance for setting stressor criteria or
describing performance characteristics of a risk
reduction option.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
be used in a risk assessment (see above) to determine the risks of the undesirable
effect or to increase beneficial ones. In contrast to a causal assessment, which
identifies the stressor causing the effect, a threshold assessment predicts the intensity
of the stressor that will produce the effect of interest (see Figure 3-8). Using global
warming as an example, the question is not, "Is atmospheric carbon a problem?"
Rather, it predicts, "What concentration of atmospheric carbon dioxide will maintain a
climate suitable for human life?"
In a criterion assessment, the desired environmental effect is characterized and
used to predict the exposure that is likely to cause the desired environmental effect from
a stressor-response model. For example, the Intergovernmental Panel on Climate
Change (IPCC) estimated that an 80% reduction in current carbon levels is needed to
return to pre-1990 climate (IPCC, 2007). Note that this form of predictive assessment
estimates the exposure that is associated with an environmental effect.
Criterion assessments are used routinely to develop criteria and standards for
air, soil, food, and water quality (NRC, 1972; U.S. EPA, 2000a,b, 2003, 2004, 2006;
Posthuma et al., 2001; Stephan et al., 1985; Cormier et al., 2008, U.S. EPA, 2011).
They are used to evaluate or to recommend management options for implementing
TMDLs and for remediation of hazardous waste sites. Criterion assessments are given
many different names, but all are founded on the same underlying logic and all use
similar methods of analysis and assessment (Suter and Cormier, 2008a). The criterion
or benchmark is estimated from the stressor-response model using the probable effect
to solve for the exposure value (Suter and Cormier, 2008b).
Some Uses of Spatial and Temporal Information for Developing and Using Water
Quality Criteria or Benchmarks
• Empirically model stressor-response relationships and intermediate causes that
are used to estimate the level of stressor that is likely to cause an effect.
• Determine if life stages have different tolerance (effects characterization).
• Set water quality criteria and TMDL goals (risk characterization).
• Prioritize remediation locations (risk characterization and risk management).
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Risk, Benefit, and Criterion Assessments
r \
Planning
Stressor of Concern and
Assessment Endpointor Environmental Goal
i ~i
Exposures of
Concern or Effec
that Match Goa
r >
Analysis
^ J
ts Exp
1
losure-Response
Relationship
\
Estima
Expecte
f >
Synthesis
ted Risk, Criter
d Change in th
f
¦ion, or
le Effect
FIGURE 3-8
Diagram of Activities that Lead to Estimated Risk of Effect or Criterion
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
3.2.3.3. Designing Management Options
How does someone identify which management options can reduce exposures to
levels that achieve environmental goals within acceptable risks without excessive costs
or social disruptions? For example, to manage flashy flows and low summer flow
caused by an interruption in ground water recharge, should stormwater catchments be
built or should tax subsidies be increased for installing pervious pavement, green roofs,
water gardens, and rain barrels?
Management options that reduce exposures are needed to implement the
findings of the predictive assessments. The development of options is considered to be
a design function rather than an assessment. The parameters that describe the
exposure are used by environmental planners, managers, and engineers to develop
options that are expected to reduce the exposure. Options could include regulatory,
economic, engineered, educational, or social incentives (SAB, 2000). The description
of the option includes the expected uncertainty and estimated reduction of the causal
agent, the costs of construction and maintenance, and ancillary effects associated with
implementation. Potential social and economic costs, risks and benefits are
summarized and conveyed to the decision makers and stake holders.
Again using global warming as an example, management options can include
(1) increasing the efficiency of activities that consume fossil fuels, such as those in the
transportation, industrial, and building sectors, (2) conversion to nuclear power,
(3) regulated requirements for automobile gasoline efficiencies, (4) tax incentives to
convert or build energy efficient homes and businesses, and (5) trading of carbon
emission rights (Cox and Allemano, 2003). Some of these ideas have been
implemented in other countries and at state and local levels in the United States.
Some Uses of Spatial and Temporal Information for Designing, Evaluating, and
Prioritizing Options
• Design and model effects of locating different management options.
• Identify locations appropriate for certain types of management strategies.
• Prioritize remediation locations (risk characterization and risk management).
• Communicate locations of planned and existing management actions.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
3.2.3.4. Management Prioritization Assessment
How does someone choose a plan of action? A management assessment
evaluates each management option on its ability to meet the environmental objective,
as well as economic and social preferences and legal requirements. Then, the overall
predicted performance and effectiveness of each option is compared among the
alternatives. This can be done qualitatively (e.g., using a checklist of considerations) or
by quantitative methods such as net benefit analysis, cost-benefit analysis, and
multiattribute decision analysis (Efroymson et al., 2004; Hanley and Spash, 1993;
Linkov et al., 2006). Management assessments can result in a list of actions that are
satisfactory, a ranking of alternative actions, or an optimum management action. The
final objective is to select the option that reduces harmful exposures with the least cost
to social and economic objectives and that managers and society are willing to
implement. If there is no decision, the status quo, a de facto unacceptable
environmental state, persists.
Because costs are associated with environmental management alternatives,
implementation can be stymied unless environmental goals are balanced with economic
and social preferences in a management assessment. A formal management
assessment is needed when the management decision itself is complex, the
acceptability of risks is unclear, the risks and benefits are numerous or heterogeneous,
the decision is controversial, or the success of a management action depends on its
political or social acceptance (SAB, 2000). And yet, there is also a strong incentive to
avoid transparency or to omit a management assessment altogether, because not
everyone's preferences can be accommodated, and managers prefer to avoid
confrontation. The process can become emotionally charged and politicized. It is the
assessor's role to perform the assessment and to ensure that costs and benefits are
estimated for decision making. In this way, decisions are more likely to be informed by
facts that lead to the greater good.
For example, before the 2007 report by the IPCC, there was strong evidence that
carbon emissions, if left unchecked, could potentially cause a catastrophic increase in
global temperatures. And yet, the costs and complexity of the management alternatives
immobilized some governments into a state of denial and inaction (Hertsgaard, 2007).
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Entities already facing impacts from the effects of weather, however, began to move to
action for adjusting to changes, if not to attempt to remedy the problem. Insurance
companies changed policies regarding coastal properties (Mills, 2004, 2006). Western
states signed agreements on the distribution of the dwindling supply of water (Powers
and Boxall, 2007). The Netherlands adapted its policies for managing rising sea levels
(Hertsgaard, 2007). After the IPCC's 2007 report was released, there was more
widespread recognition of the seriousness of global forcing of the climate.
Some Uses of Spatial and Temporal Information for Prioritizing and Targeting
Management Actions
• Interpret the relationships among very different data layers such as
environmental effects, costs, demographics, political boundaries, different
community preferences, and so on.
• Prioritize remediation locations.
• Communicate the rationale and decision process.
Predictive Assessment of Eagleville Brook
Planning: The impervious cover model developed during the causal assessment was
used to estimate reductions necessary to meet aquatic life standards. Public meetings
and other outlets were used to involve stakeholders, in particular the Town of Mansfield,
the University of Connecticut, and the Willimantic River Alliance were noteworthy
participants.
Analysis: The same empirical model of % IC and biological condition was used to
estimate the reductions in % IC necessary to improve the aquatic life and meet State
WQC. Spatial analysis estimated the % IC for three management areas, 5, 14, and
27% IC. Based on the empirical model of biological condition and % IC, a target of
11 % IC equivalence in stormwater was set for the stream segments with 14 and
27% IC. Citing the anti-degradation clause of the Clean Water Act (CWA), no change in
stormwater was allowed for the segment with 5% IC.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Synthesis: As emphasized earlier in this document, impervious cover was used in this
TMDL as a surrogate for the pollutants and other stressors from stormwater that affect
aquatic life in streams. The goal of the TMDL was to reduce impacts from stormwater
on the aquatic life in Eagleville Brook. In the absence of actual impervious cover
reduction, it was recommended that stormwater management techniques be
implemented that would offset the negative effect of impervious cover in the Eagleville
Brook watershed.
An adaptive management strategy was proposed that included (1) reducing
impervious cover where practical, (2) disconnecting impervious cover from the surface
waterbody, (3) minimizing additional disturbance to maintain existing natural buffering
capacity, and (4) installing engineered BMPs to reduce the impact of impervious cover
on receiving water hydrology and water quality. The University of Connecticut Campus
Sustainable Design Guidelines 9 (e.g., see page 11, Goal 1), 2004 Connecticut
Stormwater Manual 10, and Stormwater TMDL Implementation Support Manual 11
provide good background information for new site design, as well as technical guidance
for stormwater BMPs for existing sites. It will be necessary to choose the appropriate
strategies to reduce stormwater runoff on a case-by-case basis.
3.2.4. Outcome Assessment Evaluating Environmental Progress
3.2.4.1. Outcome Assessment
How does someone know if the assessments are correct and management
actions successful? Outcome assessments determine whether the performance of the
management actions (e.g., implementing water quality standards, planting trees,
providing educational programs) reduced exposure and whether they have been
effective in achieving environmental goals. The assessors compare exposures and
effects before and after the management action. They evaluate if changes in the
exposure are due to natural variability or the management action. They determine if
changes achieve the desired goal due to the reduction in exposure or some other factor.
These factors can be coincidental, confounding events, such as a rainy season, that
dilute a contaminant or decrease light and temperature, reducing effects of
eutrophication. Outcome assessments can also evaluate other types of assessments.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
• Were the real causes identified?
o If someone removes or reduces a cause, the effect should change. That is
an outcome assessment.
• Were the risks as great as predicted?
o Someone measures the effects to determine if they were as severe or as
improved as expected.
• Were environmental conditions compromised by using the least expensive
exposure control or by accommodating stakeholders?
o Someone measures the effects to determine what happened and compares
them with results where a different control was used.
Some Uses of Spatial and Temporal Information for Outcome Assessment
• Help identify differential successes that could be related to natural and
remediation factors.
• Determine if the performance of management actions has reduced the intensity,
frequency, and duration of the causal agents, events, or interactions.
• Determine the effectiveness of the management actions toward achieving
environmental objectives and solving environmental problems.
• Inform and improve future assessments, policy making, and management
actions.
Outcome Assessment of Eagleville Brook
Planning: Progress towards attainment of water quality standards will be evaluated by
monitoring the macroinvertebrate community and assessing surface water chemistry
according to an existing rotating basin sampling schedule. Meeting the TMDL will be
assessed by directly measuring the aquatic life. Tracking the impervious cover
elimination/disconnection or equivalent impervious cover reduction in the watershed
during BMP implementation may be used as an interim measure to assess progress.
Although not mentioned in the TMDL, the performance of BMP can be assessed by
observing trends in the hydrograph, such as greater baseflow indicating better ground
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
water recharge and slower rise and fall of the hydrograph during storms, indicating
better retention.
Analysis and Synthesis: The outcome assessment remains unfinished. When aquatic
life uses are not impaired the problem will be resolved. For an example of a completed
outcome assessment, see Bellucci et al. (2010).
Other Potential Applications
Using the State's monitoring database, stream segments that do not meet
aquatic life uses can be identified. If % IC is <5%, it is likely that other causes are
acting and if removed, the stream has a reasonable likelihood of recovering.
Geographical analysis can also identify streams having 6-12% IC for strategic sampling
and remediation planning. The CT DEP has already created such a map (see
Figure 3-9) and is working with stakeholders to develop means to address the impacts
of stormwater.
Summary
The case example illustrates that environmental assessments that use spatial,
geographic, and monitoring information and data have the potential to provide novel
information and perspective that cannot be obtained in any other way. The information
can create compelling evidence that motivates action that avoids and resolves
environmental problems.
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
Best-Preservation
Streams of Hope-Active Management
llrban-Mitigation
50 Kilometers
FIGURE 3-9
Map of Connecticut Showing Stream Classes and Management Classes
Based on the Conceptual Model in Figure 3-1. Categories were based on
using percent impervious cover calculated using the Impervious Surface
Analysis Tool from 2002 landcover data and the relationship with
macroinvertebrate multimetric index scores. Best of streams for
preservation is 0-4.99% impervious cover, streams of hope-active
mitigation is 5-11.99% impervious cover and urban-mitigation is
>12% impervious cover.
Source: CT DEP (2007).
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Section I—Chapter 3: Integrating Assessments with Geospatially Explicit Information
3.3. REFERENCES
Bellucci, C., G. Hoffman, and S. Cormier. 2010 An iterative approach for identifying the
causes of reduced benthic macroinvertebrate diversity in the Willimantic River,
Connecticut. U.S. Environmental Protection Agency, Office of Research and
Development, National Center for Environmental Assessment, Cincinnati, OH;
EPA/600/R-08/144. Available online at
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201947.
Cormier, S.M. and G.W. Suter II. 2008. A framework for fully integrating environmental
assessments. Environ. Manage. 42:543-556. Open Access at
http://www.springerlink.com/content/n56531j12q33776t/fulltext.pdf
Cormier S.M., J.F. Paul, R.L. Spehar, P. Shaw-Allen, W.J. Berry, and G.W. Suter, II.
2008. Using field data and weight of evidence to develop water quality criteria. Integr.
Environ. Assess. Manage. 4(4):490-504.
Cormier S.M., G.W. Suter II, and S.B. Norton. 2010. Causal characteristics for
ecoepidemiology. Human Ecol. Risk Assess. 16(1):53-73.
Cox, P. and G. Alemanno. 2003. Directive 2003/87/Ec of the European Parliament and
of the Council of 13 October 2003 establishing a scheme for greenhouse gas emission
allowance trading within the Community. October 10, 2003. Official J. Eur. Union.
275:32-46. Available online at http://eur-
lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2003:275:0032:0046:EN:PDF.
CT DEP (Connecticut Department of Environmental Protection). 2007. A Total
Maximum Daily Load Analysis for Eagleville Brook, Mansfield, Ct. Accessed 6/26/09.
Available online at http://www.ct.gov/dep/lib/dep/water/tmdl/tmdl_final/eaglevillefinal.pdf.
Center for Watershed Protection. 2003. Impacts of impervious cover on aquatic
systems. Center for Watershed Protection Ellicott City, MD 21043. 150 pp. Accessed
06/08/11. Available on line from http://www.cwp.org/
Efroymson, R.A., J.P. Nicollette, and G.W. Suter, II. 2004. A framework for net
environmental benefit analysis for remediation or restoration of contaminated sites.
Environ. Manage. 34(3):315-331.
Exum, L.R., S.L. Bird, J. Harrison, and C.A. Perkins. 2005. Estimating and Projecting
Impervious Cover in the Southeastern United States. U.S. Environmental Protection
Agency, Office of Research and Development, National Exposure Research Laboratory,
Athens, GA. EPA 600/R-05/061. Available online at
http://www.epa.gov/athens/publications/reports/Exum600R05061EstimatingandProjectin
glmpervious.pdf.
Hanley N, and C.L. Spash. 1993. Cost-Benefit Analysis and the Environment. Edward
Elgar Publishing, Cheltenham, UK.
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Hertsgaard, M. 2007. On the front lines of climate change. Time Magazine. March 29,
2007.
Holcombe, J.B. III., and D. Malone. 2005. The Tennessee Valley Authority's
watershed-based approach to integrated pollutant source inventory. In: Proceedings of
the 25th ESRI International User Conference, San Diego, CA, July 25-29. Available
online at http://proceedings.esri.com/library/userconf/proc05/papers/pap1350.pdf.
IPCC (Intergovernmental Panel on Climate Change). 2007. Climate Change 2007:
Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change. Core Writing Team.
Pachauri, R.K. and A. Reisinger, Ed. IPCC, Geneva, Switzerland, 104 pp.
http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_
synthesis_report. htm.
Linkov, I., F.K. Satterstrom, G. Kiker, etal. 2006. Multicriteria decision analysis: a
comprehensive decision approach for management of contaminated sediments. Risk
Anal. 26:61-78.
Mills, E. 2004 Insurance as an adaptation strategy for extreme weather events in
developing countries and economies in transition: new opportunities for public-private
partnerships. Lawrence Berkeley National Laboratory Report No. 52220. Available on
line at http://evanmills.lbl.gov/pubs/pdf/miti-mills-2007.pdf.
Mills, E. 2006. Synergisms between climate change mitigation and adaptation: an
insurance perspective. Mitig Adapt Strat Glob Change (2007) 12:809-842. Available
on line at http://evanmills.lbl.gov/pubs/pdf/miti-mills-2007.pdf.
Norton, D.J., J.D. Wickham, T.G. Wade, K. Kunert, J.V. Thomas, and P. Zeph. 2009. A
method for comparative analysis of recovery potential in impaired waters restoration
planning. Environ Manag 44:356-368.
NRC (National Research Council). 1972. Water Quality Criteria 1972. A report of the
Committee on Water Quality Criteria at the request of the EPA. U.S. Gov. Printing
Office, Washington, DC. EPA/R3-73/033.
Posthuma, L,, G.W. Suter, II., and T.P. Traas, Ed. 2001. Species Sensitivity
Distributions in Ecotoxicology. Lewis Publishers, Boca Raton, FL.
Powers, A. and B. Boxall. 2007. Colorado River water deal is reached. Los Angeles
Times. December 14, 2007. Available online at
http://articles.latimes.com/2007/dec/14/nation/na-colorado14.
Presidential/Congressional Commission. 1997. Risk Assessment and Risk
Management in Regulatory Decision-Making. Final report, volume 2. The
Presidential/Congressional Commission on Risk Assessment and Risk Management,
Washington, DC. Available online at http://www.riskworld.com/Nreports/1997/risk-
rpt/vo I u m e2/pdf/V2 E PA. pdf.
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SAB (Science Advisory Board). 2000. Toward Integrated Environmental
Decision-Making. Science Advisory Board, U.S. Environmental Protection Agency,
Washington, DC. EPA-SAB-EC-00-011.
Stephan, C.E., D.I. Mount, D.J. Hanson, J.H. Gentile, G.A. Chapman, and W.A. Brungs.
1985. Guidelines for Deriving Numerical National Water Quality Criteria for the
Protection of Aquatic Organisms and Their Uses. U.S. Environmental Protection
Agency, Office of Research and Development, Washington, DC. Available online at
http://www.epa.gov/waterscience/criteria/library/85guidelines.pdf.
Suter, G.W., II., S.M. Cormier, and S.B. Norton. 2007. Ecological epidemiology and
causal analysis. In: Ecological Risk Assessment. 2nd ed. G.W. Suter, II, Ed. CRC
Press, Boca Raton, FL. pp 39-68
Suter, G.W. II, and S. M. Cormier. 2008a. A theory of practice for environmental
assessment. Integr. Environ. Assess. Manag. 4(4)478-485.
Suter, G.W. II, and S. M. Cormier. 2008b. What is meant by risk-based environmental
quality criteria? Integr. Environ. Assess. Manag. 4(4) 486-489.
Suter, G.W., II, and S.M. Cormier. 2011. Why and how to combine evidence in
environmental assessments: weighing evidence and building cases. Sci Total Environ
409(8):1406-1417.
U.S. EPA (Environmental Protection Agency). 1998. Guidelines for Ecological Risk
Assessment. Risk Assessment Forum. EPA/630/R-95/002F. April 1998 Published on
May 14, 1998, Federal Register 63(93):26846-26924). Available on line at
http://www.epa.gov/raf/publications/pdfs/ECOTXTBX.PDF
U.S. EPA (Environmental Protection Agency). 2000a. Methodology for Deriving
Ambient Water Quality Criteria for the Protection of Human Health (2000). Office of
Water, Washington, DC. EPA/822/B-00/004. Available online at
http://www.epa.gov/waterscience/criteria/humanhealth/method/complete.pdf.
U.S. EPA (Environmental Protection Agency). 2000b. Nutrient Criteria Technical
Guidance Manual: Rivers and Streams. U.S. Environmental Protection Agency, Office
of Water, Washington, DC. EPA/822/B-00/002. Available online at
http://www.epa.gov/waterscience/criteria/nutrient/guidance/rivers/rivers-streams-full.pdf.
U.S. EPA (Environmental Protection Agency). 2003. Guidance for Developing
Ecological Soil Screening Levels. Office of Solid Waste and emergency, Washington,
DC. OSWER Directive 9285.7-55. Available online at
http://www.epa.gov/ecotox/ecossl/pdf/ecossl_guidance_chapters.pdf.
U.S. EPA (Environmental Protection Agency). 2004. Draft Ambient Aquatic Life Criteria
for Selenium—2004. Office of Water, Washington, DC. EPA/822/R-04/001. Available
online at http://www.epa.gov/waterscience/criteria/selenium/pdfs/complete.pdf.
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U.S. EPA (Environmental Protection Agency). 2006. Framework for Developing
Suspended and Bedded Sediments Water Quality Criteria. Office of Water,
Washington, DC. EPA/822/R/06/001. Available online at
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=164423.
U.S. EPA (Environmental Protection Agency). 2010. Integrating ecological assessment
and decision-making at EPA: a path forward. Results of a colloquium in response to
Science Advisory Board and National Research Council Recommendations. Risk
Assessment Forum, Washington, DC; EPA/100/R-10/004. Available online at
http://www.epa.gov/raf/publications/pdfs/integrating-ecolog-assess-decision-making.pdf.
U.S. EPA (Environmental Protection Agency). 2010. CADDIS (The causal
analysis/diagnosis decision information system). Available online at
www.epa.gov/caddis (accessed 6/6/11).
Van Sickle, J., J.L. Stoddard, S.G. Paulsen, and A.R. Olsen. 2006. Using relative risk
to compare the effects of aquatic stressors at a regional scale. Environ. Manage.
38(6): 1020-1030.
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Section I—Chapter 4: Basic Concepts for Using Landscape and Predictive Tools
4. BASIC CONCEPTS FOR USING LANDSCAPE AND PREDICTIVE TOOLS
Jim Harrison, U.S. EPA Region 4, Atlanta, GA
Susan Cormier, U. S. EPA Office of Research and Development, Cincinnati, OH
4.1. APPROPRIATE TOPICS AND QUESTIONS THAT USE GEOSPATIAL
ANALYSIS
Not all studies require geospatial
analysis. Many do not require sophisticated
analyses and a map will suffice. In some cases,
such as with existing aquatic life criteria,
landscape information was used in their
derivation, but a new analysis is not necessary. However, when models need to be
developed using field data, choosing a larger data sets makes sense because there is
usually more statistical power. Furthermore, they encompass a wider range of
conditions for predictive purposes than a data set limited to a single lake or stream
segment. Also, the spatial distribution of watershed attributes such as dams, point
sources, landcover, etc. are usually best analyzed with geographic information systems.
This chapter describes the use of landscape and predictive geospatial tools to inform
water quality monitoring, to perform program tasks, or to inform decisions.
The planning phase of an assessment is the time to decide whether or not to
include geospatial data sets and related analyses. In order to decide, define the
problems and questions in the context of the geographic area of concern. For example,
would it be helpful to identify candidate reference areas or suspected problem areas? If
so, then landscape information may be able to narrow the possibilities. Study questions
should be very specific, as in, "What watersheds within an ecological region have the
least pressure from human activities," or "What areas in my state are most likely to have
water quality problems due to urban/suburban development?" These questions are
asked in the planning stage of assessments, and are particularly relevant to condition
assessments. Other suggested concepts for considerations and applications are listed
in the Tables 4-1 and 4-2 and also in the process outline at the end of this chapter.
What is in this chapter? Common
situations are described that are
encountered when working with
geographical data and large field data
sets. This chapter sets the stage for
more detailed descriptions in Section II.
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Section I—Chapter 4: Basic Concepts for Using Landscape and Predictive Tools
TABLE 4-1
Initial Analyses Performed During Assessment Planning and Factors to Be Considered
Planning and Problem Formulation
Factors to Consider
Describe problems/questions
(see Table 1-2: Spectrum of Uses
for Landscape and Predictive Tools)
Criteria and standards development
Problem identification and prevention
Prioritization and targeting of rehabilitation
Science, education, and management
Select areas of interest
Scales
Geographic frameworks
Appropriate areas for analysis and extrapolation
Identify source, stressors, and
response endpoints and attributes
and the measurements that will be
used to evaluate them
Landscape
Habitat/channel/geomorphology
Chemistry
Hydrology
Biology
Others
Find available data
Geographic frameworks/classifications
Landscape
Ambient stressor response
• Gradient of sites covering full range of stressors
• Probability survey data: biological response and stressors
• BACI designs
Assure data quality and define data
quality objectives
Are existing data sufficient to answer questions with required power?
Map the study areas
Simple GIS and statistical approaches
Describe stressor and response gradients
Derive simple stressor/response relationships (if needed/possible)
Use exploratory analysis to ensure
measurements will capture the
range of values needed to
characterize exposures, effects, or
the causal relationship
Develop an analysis and
communication plan
Identify gaps
Plan next steps
BACI = before/after and control/impact; GIS = geographical information system.
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TABLE 4-2
Analysis and Synthesis Steps and Factors
Analysis and Synthesis Steps
Factors to Consider
Refine basis of analysis
Problems/questions
Areas/scales of interest
Geographic frameworks
Pressure/stressor/response parameters of interest
Identify analysis methods and data
requirements
GIS analyses
Statistical approaches
Refine data quality objectives
Develop QAPP/SOP/Study Plan
Peer review (if desired/needed)
Gather additional site and landscape data
to fill gaps
Gradient of sites covering full range of stressors
Probability survey data
Derive landscape stressor/disturbance
factors
Delineate watershed boundaries and buffers for sites
Watershed
Riparian buffer
Proximity buffer
Other appropriate landscape factors
Apply analysis methods
Describe stressor and response gradients
Reduce number of variables (if needed)
Derive robust stressor/response relationships
Extrapolate stressor/response models to
area of interest
Estimate response for areas lacking in situ data
Evaluate power of results
Balance false negative vs. false positive
Refine analyses if needed
Go back to refine basis for analysis
Report results
Peer review (if needed or desired)
Other reviews
Publish results
Use analyses to recommend options or
actions
Targeting
Priorities
Other critical water quality monitoring and management
decisions
QAPP = quality assurance project plan; SOP = standard operating procedure.
4.2. APPROPRIATE GEOGRAPHIC SCOPE FOR ANALYSIS AND APPROPRIATE
EXTRAPOLATION
Inherent to most study questions are bounded geographic areas of concern. A
combination of geographic coverages can be useful, for example, combining political
boundaries and watershed boundaries. Ecological regions or other classifications
defining areas of similar important ecological attributes are needed to delimit where
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extrapolations are feasible. Watersheds are needed to define areas of up-gradient
landscape influence on aquatic resources and responses. Also, political boundaries
might be necessary to formulate voluntary or regulatory responses to problems and to
communicate results to relevant parties. All geographic frameworks embody multiple
scales. Examples of ecoregion classifications include the Omernik ecoregions at
Levels 2, 3, and 4 ranging from broader to more specific. Watersheds (catchments) can
be defined at all scales, and hydrologic units (often used as surrogates for
watersheds—but only about 40% are true watersheds) range from 14- to 2-digit
hydrologic unit codes, from smaller to larger. Political boundaries include cities,
counties, substate regions, states, federal regions, nations, and many others such as
water management districts.
4.3. USE LANDSCAPE AND OTHER DATA TO DOCUMENT STRESSOR
GRADIENTS
A wide range of source, pressure, stressor, and response parameters can be
considered for analysis, but it is likely that a small subset will dominate in an area of
interest. Landscape pressures could include a single predominant source such as
urban or agricultural land use; multiple sources such as the full range of land
use/landcover classifications of the National Land Cover Dataset; or universal causal
indicators such as the index of human influence (U-lndex: sum of anthropogenically
dominated classes) or the energy-based Landscape Development Intensity index. More
proximate stressors could include nutrients; sediment; bacteria; salts, toxic chemicals
and biocides; flow and other hydrologic measures; and riparian, habitat, channel, and
geomorphic parameters. Biological responses could include attributes of populations or
assemblages of periphyton, plankton, benthic invertebrates or fish (Noss, 1990; Hughes
and Noss, 1992).
Geographic frameworks are used in conjunction with other available data to
perform spatial analyses or to classify, landscape and in situ stressor and response
parameters. The range of geographic frameworks includes ecological region and
hydrologic region maps, classifications statistically derived from natural or a
combination of natural and anthropogenic factors, watersheds derived from National
Hydrography Dataset (NHD) or National Elevation Data, and others. Sources of
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landscape data are numerous, at many levels of spatial, temporal, and spectral
resolution including Moderate-resolution Imaging Spectroradiometer (~1 hectare), Land
Remote Sensing Satellite (~30 m), Systeme Probatoire d'Observation de la Terre SPOT
(~<10 m), aerial photography (~<3 m), Light Detection and Ranging (LiDAR) (~<1 m),
and others such as airborne hyperspectral scans. While in situ stressor and response
parameters have already been listed, the most important consideration for these
indicators is that they include a gradient of sites incorporating the full range of stressors
within the area of interest for one's project or study. A gradient of purposely selected
sites can be chosen on the basis of available data. Data from randomly selected sites
based on probability surveys are preferred because any response relationships
developed using the probability data can have known confidence. Another option
includes use of data from before/after and control/impact (BACI) study designs. Again,
the combination of sites chosen should incorporate a gradient covering the full range of
stressor exposures.
Data quality objectives are used to determine whether the existing data (and
subsequent analyses) are sufficient to answer the study questions with required power.
Statistically, formal confidence intervals might not be possible when using data from
nonprobability surveys; nevertheless, relationships based on this type of data might be
sufficient for nonregulatory planning purposes such as targeting and priority setting to
guide additional in situ monitoring. Where the data do allow development of confidence
intervals, alpha, beta, and power, rigorous data quality objectives can be defined.
4.4. CONSTRUCT EMPIRICAL MODELS LINKING GEOGRAPHICAL ATTRIBUTES
(SOURCES AND STRESSORS) WITH IN SITU RESPONSE INDICATORS
Initial, exploratory analyses should focus on relatively simple geographical
information system (GIS) and statistical approaches that support descriptions of
stressor or response gradients for watersheds in a relatively homogeneous area
(multiple scale watershed, riparian and point buffers should be employed if easily
available and should definitely be considered for more detailed analyses), derivation of
simple stressor-response relationships and extrapolation of relationships to answer the
project's questions. The basic step of describing stressor gradients can provide much
useful information even in the absence of paired in situ response data. Basic GIS
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approaches might include simple sum and best/worst quantiles (Smith et al., 2003).
Routine statistical approaches might include descriptive statistics, box plots of classes,
simple linear regressions (U.S. EPA, 2010) and relative risk (Bryce et al., 1999; Van
Sickle et al., 2006). Some factors to consider in analysis and synthesis of these types
of analyses are listed in Table 4-2.
The final recommended steps for initial analysis are to evaluate the results in
terms of how well they answer the study questions, meet the established data quality
objectives, identify any gaps in the data or otherwise, and to plan next steps, including a
more detailed analysis if needed. These screening analyses using existing data are
sufficient in some cases. More often, they are simply part of the planning step for a
more rigorous analysis based on data collected for the purpose of the assessment.
Steps for a detailed analysis are similar to those for an initial analysis but can
involve considerably more effort in the following areas: careful planning for more
intensive GIS and statistical analyses, gathering landscape and in situ data specifically
tailored to the project and analysis methods, deriving a wider array of landscape factors,
additional statistical analyses to reduce the number of variables and to develop robust
stressor/response relationships, more formal analysis of the statistical power (and
likelihood of false negatives and false positives—alpha and beta) of the results, peer
review and publishing of results, and wider and more formal use of extrapolations to
make targeting, priority, and other water quality management decisions.
Refining the basic parameters of the analysis might include revisiting the
areas/scales of interest, geographic frameworks, problems/questions, and
stressor/response parameters of interest. Here are some examples. Analyses might be
applied to additional waterbody types including wetlands (Mack, 2006; Reiss and
Brown, 2007) and estuaries (Rodriguez et al., 2007). The variation within a Level 4
ecoregion might be reduced further through classification of natural factors, such as
surface geology and soils, showing considerable variation within a region (Hopkins,
2003). Nutrients and sediment could be included because existing models are available
to predict nutrient and sediment loadings for the area of interest (Jones et al., 2001).
Research results often yield wedge-shaped plots depicting considerable variation
in the response indicator (e.g., Index of Biological Integrity [IBI]) at the lowest level of
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disturbance, and consistently poor scores at high levels of disturbance. The variability
at low levels of disturbance (e.g., urbanization) could result from unmeasured factors
limiting the biota (e.g., single development intensity variables might not capture all
aspects of development that alter aquatic ecosystems), or inaccurate estimates of
urbanization intensity, which become less important at high levels of urbanization (Karr
and Chu, 2000; Tate et al., 2005). Alternative explanations also include larger
measurement error for predictive variables at low levels of disturbance or greater
disturbance-response scale mismatches at the lower levels. For instance, Wang et al.
(2006) found that assemblages are largely influenced by in-stream habitat but that the
importance of in-stream habitat declines as riparian and watershed disturbance
increase. In addition, Van Sickle et al. (2004) found that considering the condition of the
entire upstream riparian corridor provided better fit with biological responses than either
entire watershed or local reach land uses.
Others have attempted to improve the performance of predictive variables by
creating more integrated measures of development. Brown and Vivas (2005) used
embedded energy signatures to quantify various types of landscape development at
reach and watershed scales. McMahon and Cuffney (2000) and Tate et al. (2005)
developed an urban intensity index that is based on multiple metrics including land use
at watershed and reach scales, infrastructure density, housing/population density, and
socioeconomic indices. Alberti et al. (2007) reported that the configuration or pattern
and the amount of urbanization, were significantly related to macroinvertebrate IBI
scores. These estimates combine more distinct types of information about complex
patterns of development and better account for the numerous aspects of disturbance
that may affect aquatic ecosystems (Tate et al., 2005). As a result, they hold promise
for improving our ability to predict aquatic ecosystem responses to development,
particularly at lower intensities.
Alternative or more intense GIS and statistical approaches and gathering of
additional data can be considered. The Great Lakes sampling design and integrated
measures of anthropogenic stressor provide an example of how this was done for the
Great Lakes shoreline areas and drainages (Danz et al., 2005, 2007).
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Data quality objectives can be refined on the basis of the statistical tools
employed and the volume of data to be used, including development of a formal quality
assurance project plan/project study plan, standard operating procedures, and a
peer-review process for the study plan and products if needed or desired.
Gaps in landscape and in situ data can be filled through gathering additional site
and landscape data, particularly to fill in gradients of the full range of stressors and to
employ the inherent power of statistical survey data. Here, the probability sampling of
the Savannah River Basin (Nash et al., 2005a) and the Great Lakes sampling design
and integrated measures of anthropogenic stressor provide instructive examples.
A wider array of innovative landscape pressure or stressor factors can be
derived, and more temporal and spatial scales can be considered. Watershed
boundaries and multiple buffers (riparian, proximity, hydrologically active areas) for all
sites can be generated. This previously difficult and time-consuming process has been
greatly facilitated by new data sources and tools such as NHD+ and Analytical Tools
Interface for Landscape Assessments (ATilLA). Where comparable landscape data
from several time periods are available, change analysis can be conducted.
Other innovative landscape and stressor factors can also be considered. The
Normalized Difference Vegetation Index could be used to derive additional landscape
indicators for agricultural areas of the Great Plains (Griffith et al., 2002). Hydrologic
alteration indicators (Richter et al., 1996) can be derived to better understand the
mechanisms affecting urban areas (Paul and Meyer, 2001). Relevant soil factors can
be determined (Van Remortal et al., 2005) to better understand sedimentation stressor
gradients in agricultural areas.
Applying refined analysis methods to a more complete set of paired
stressor/response data allows us to fully describe stressor and response gradients,
reduce the number of variables (if needed) to those critical for a successful analysis
(Nash et al., 2005b), and to derive robust stressor/response relationships that can be
applied with known confidence by extrapolating to places without in situ data. Applying
a range of simple to complex statistical relationships and predictive modeling
techniques will yield stressor/response relationships with known error characteristics
and confidence. Techniques that can be used include descriptive, empirical, and
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multivariate statistics, judicious use of multistep statistical processes, and, where
available, process models.
4.5. USE THE RESULTING RELATIONSHIPS TO EXTRAPOLATE TO PLACES
LACKING IN SITU DATA OR TO IDENTIFY AREAS OF INTEREST
Once stressor/response relationships have been developed, they can be applied
throughout the area of interest to estimate response for areas without on-the-ground,
in-stream, in situ data. Ideally, assuming the relationships are based on probability
survey response data, these estimates will have known confidence. The estimated
responses provide a sound basis for recommendations to target and prioritize future
actions for specific areas.
Documenting the estimates' error characteristics, confidence, alpha, and beta
(false negative and positive) should support evaluation of the power of the results and
recommendations and to determine how well the data quality objectives of the project
have been met. At this point, as in any science-based process, it is possible to go back
to the first step, refine the basis of the analyses, and proceed to refine subsequent
steps, if needed and warranted by the importance of the work. Analysis methods,
results and recommendations should be documented in a report and peer reviewed by
independent, knowledgeable reviewers (if needed or desired to maintain scientific
credibility). Finally, the ultimate evaluation of the usefulness of landscape and
predictive tools is how everyone uses the results of the analyses to make decisions.
Using the targeting and priorities developed to guide important decisions and take real
actions makes the effort a success.
The following outline briefly summarizes the general process of using geospatial
data and analyses in environmental assessments. Given the wide range of possible
applications, we do not imply that it is comprehensive and are certain that other
considerations and approaches are possible. In the chapters that follow, some of the
commonly encountered geographic frameworks, data sets, and analytical approaches
are described in more detail. The last chapter provides links to these data sets and
tools. See also the Geospatial Toolbox accessed from the Risk Assessment Forum or
Watershed Central websites.
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4.6. PROCESS OUTLINE: ASSESSMENT PLANNING, ANALYSIS, AND
SYNTHESIS
4.6.1. Planning and Problem Formulation
1) Clearly formulate the problem and desired information.
What are the objectives?
• Determine if the physical, chemical, or biological condition of a place or region is
due to anthropogenic causes.
• Identify impaired waterbodies.
• Determine the cause of a biological impairment.
• Find the sources of a water quality pollutant or stressor.
• Estimate the amount of stressor contributed by each source.
• Estimate the risk of a stressor on a valued resource.
• Estimate the amount of stressor that can occur while also maintaining aquatic
uses.
• Develop water quality criteria and standards.
• Evaluate options for preventing or remediating water quality.
• Target areas to prevent water quality degradation.
• Prioritize among options and targeted areas for management action.
• Develop communication materials for education and partnership programs.
• Evaluate the performance of management actions and best management
practices (BMPs) to reduce stressor loads.
• Evaluate the effectiveness to protect and restore aquatic uses.
(Also see Table 1-2 Spectrum of Uses for Landscape and Predictive Tools.)
What will be protected or restored?
• Define the geographic scope and context.
• What type of waterbody are you interested in?
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Section I—Chapter 4: Basic Concepts for Using Landscape and Predictive Tools
• What environmental entities and their attributes need to be evaluated?
o Habitat/channel/geomorphology.
¦ e.g., sinuosity, embeddedness, clarity, depth, suitability,
o Chemistry.
¦ e.g., toxicity,
o Hydrology.
¦ e.g., diversions, flashiness, low flow, overland flow, ground water,
o Biology.
¦ e.g., species richness, abundance, health, reproduction,
presence/absence.
o Temperature.
¦ e.g., extremes, climate change,
o Others.
• Consider the place and surrounding area.
• Choose and identify a geographic framework and temporal scale,
o For the assessment question of interest.
o For developing models of causal relationships.
What is the regulatory authority or social, political, economic driver?
• CWA.
o 305(b).
o 303(d).
o TMDL.
o 319.
o Other.
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What type of analyses need to be performed, and how good does it need to be to
make a decision?
• Will a screening assessment suffice?
• Can adaptive management approaches be reasonably used?
• Are existing data sufficient to answer questions with required power?
• Is a plan needed to obtain additional data?
• What analyses will be done?
How will the analyses be done?
• Data sets and study designs.
o Gradient of sites covering full range of stressors,
o Laboratory studies.
o Probability survey data: biological response and stressors,
o BACI designs.
o See also Chapter 6 and Toolbox.
• Analytical methods and tools.
o See Chapter 7 and Toolbox.
4.6.2. Analysis
2) Select appropriate geographic frameworks including mapped areas such as
stream reach, catchment, ecoregions, or other appropriate classification
approaches, to establish realistic areas for analysis and extrapolation. (See
Chapter 5.)
Develop the data set
• Match data for relevant spatial and temporal scale, quality, and comparability.
• Identify inherent assumptions about the appropriateness of measurements,
o Mean, extremes, rates, other.
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• Perform exploratory analysis for collocation, co-occurrence, correlation, range,
stressor-response associations.
• Refine analysis question and scope and types of assessments,
o Identify gaps and plan next steps.
• Gather additional in situ, remotely sensed, or composite data for analysis.
3) Use wall-to-wall landscape and other data to document natural and stressor
gradients.
• Delineate watershed boundaries and buffers for sites,
o Watershed.
o Riparian buffer,
o Proximity buffer.
o Other appropriate landscape factors.
• Map landcover, sources, the waterbody, road, and other relevant information.
• Model movement of biological entities.
4) Construct empirical relationships or models linking landscape to in situ stressor
or response metrics.
• Characterize the exposure effect that is occurring or is an objective.
• Develop stressor-response relationship.
• Use the information to develop evidence of a cause or source.
• Use the information to develop predictive models of effects or models of
exposures that are estimated to yield desired effects.
• Use models to extrapolate to areas lacking in situ data.
• Consider using more sophisticated models.
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o Structural equation modeling,
o Propensity scores.
4.6.3. Synthesis
5) Use the resulting relationships to extrapolate to places lacking in situ data or to
inform decision making.
• Combine and interpret finding to answer question posed in planning
formulation using the results from the analysis.
o Assess ecosystem condition.
o Assess cause of impairment.
o Assess sources of stressors.
o Assess risks from known or expected exposures.
o Assess exposure that will be protective.
o Assess and optimize type, placement, and deployment of management
actions and BMPs.
o Assess performance of BMPs and controls.
o Assess effectiveness of management actions and controls to resolve the
environmental problem.
• Evaluate uncertainties and potential for false negatives and false positives
(Type 1 and Type 2 errors).
• Report findings and inform decisions.
o Perform stakeholder and peer review (also at beginning of program),
o Publish results.
4.7. REFERENCES
Alberti M., D. Booth, and K. Hill, et al. 2007. The impact of urban patterns on aquatic
ecosystems: an empirical analysis in Puget lowland sub-basins. Landsc. Urban Plan.
80(4):345-361.
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Bryce, S.A., J.M. Omernik, and D.P. Larsen. 1999 Ecoregions - a geographic
framework to guide risk characterization and ecosystem management. Environ. Pract.
1 (3): 141 —155.
Brown, M.T. and M.B. Vivas. 2005. Landscape development intensity index. Environ.
Monit. Assess. 101:289-309.
Danz, N.P., R.R. Regal, G.J. Niemi, etal. 2005. Environmentally stratified sampling
design for the development of Great Lakes environmental indicators. Environ. Monit.
Assess. 102(1 —3):41 -65.
Danz, N.P., G.J. Niemi, R.R. Regal, R.R., etal. 2007. Integrated measures of
anthropogenic stress in the U.S. Great Lakes Basin. Environ. Manage. 39(5):631 -647.
Griffith, J.A., E.A. Martinko, J.L. Whistler, and K.P. Price. 2002. Interrelationships
among landscapes, NDVI, and stream water quality in the U.S. Central Plains. Ecol.
Appl. 12(6): 1702—1718.
Hopkins, E.H. 2003. Using a geographic information system to rank urban intensity of
small watersheds for the Chattahoochee, Flint, Ocmulgee, and Oconee River basins in
the Piedmont Ecoregion of Georgia and Alabama. In: Proceedings of the 2003 Georgia
Water Resources Conference, University of Georgia, Athens, GA, April 23-24.
Available online at http://water.usgs.gov/nawqa/urban/pdf/Hopkiins-GWRC2003.pdf.
Hughes, R.M., and R.R. Noss. 1992. Biological diversity and biological integrity:
current concerns for lakes and streams. Fisheries 17:11-19.
Jones, K.B., A.C. Neale, M.S. Nash, et al. 2001. Predicting nutrient and sediment
loadings to streams from landscape metrics: A multiple watershed study from the
United States Mid-Atlantic region. Landsc. Ecol. 16:310-312.
Karr J.R., and E.W. Chu. 2000. Sustaining living rivers. Hydrobiologia 422/423:1-14.
Mack, J.J. 2006. Landscape as a predictor of wetland condition: an evaluation of the
landscape development Index (LDI) with a large reference wetland dataset from Ohio.
Environ. Monit. Assess. 120(1-3):221-241.
McMahon, G. and T. F. Cuffney. 2000. Quantifying urban intensity in drainage basins
for assessing stream ecological conditions. J. Am. Water Res. Assoc.
36(6):1247-1261
Nash, M.S., D.J. Chaloud, and S.E. Franson. 2005a. Association of landscape metrics
to surface water biology in the Savannah River basin. J. Math. Stat. 1 (1 ):29-34.
Nash, M.S., D.J. Chaloud, and R.D. Lopez. 2005b. Applications of Canonical
Correlation and Partial Least Square Analyses in Landscape Ecology. U.S.
Environmental Protection Agency. Office of Research and Development. Las Vegas NV.
E PA/600/X-05/004.
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Noss, R.R. 1990. Indicators for monitoring biodiversity: a hierarchical approach.
Conserv. Biol. 4(4):355-364.
Paul, M.J., and J. Meyer. 2001. Streams in the urban landscape. Annu. Rev. Ecol.
Syst. 32:333-365.
Reiss, K.C., and M.T. Brown. 2007. Evaluation of Florida Palustrine Wetlands:
Application of U.S. EPA levels 1, 2, and 3 assessment methods. EcoHealth
4(2):206-218.
Richter, B.D., J.V. Baumgartner, J. Powell, and D.P. Braun. 1996. A method for
assessing hydrologic alteration within ecosystems. Conserv. Biol. 10(4): 1163-1174.
Rodriguez, W., P.V. August, Y. Wang, J.F. Paul, A. Gold, and N. Rubinstein. 2007.
Empirical relationships between land use/cover and estuarine condition in the
Northeastern United States. Landsc. Ecol. 22(3):403-417
Smith, E.R., L.T. Tran, and R.V. O'Neill. 2003. Regional Vulnerability Assessment for
the Mid-Atlantic region: Evaluation of Integration Methods and Assessment Results.
EPA/600/R-03/082. U.S. Environmental Protection Agency, Office of Research and
Development, Research Triangle Park, NC. Available online at
http://www.epa.gov/reva/docs/vulnerable.pdf.
Tate, C.M., T.F. Cuffney, G. McMahon, E.M.P. Giddings, J.F. Coles, and H. Zappia.
2005. Use of an urban intensity index to assess urban effects on streams in three
contrasting environmental settings. In: Effects of Urbanization on Stream Ecosystems.
L.R. Brown, R.H. Gray, R.M. Hughes, M.R. Meador, Ed. Symposium 47. American
Fisheries Society, Bethesda, MD.
U.S. EPA (Environmental Protection Agency). 2010. Causal Analysis/Diagnosis
Decision Information System (CADDIS). Available online at http://www.epa.gov/caddis.
Van Remortel, R.D., R.W. Maichle, D.T. Heggem, and A.M. Pitchford. 2005.
Automated GIS Watershed Analysis Tools for RUSLE/SEDMOD Soil Erosion and
Sedimentation Modeling. U.S. Environmental Protection Agency, Office of Research
and Development, Washington, DC. EPA 600/X-05/007.
Van Sickle J., J. Baker, A. Herlihy, et al. 2004. Projecting the biological condition of
streams under alternative scenarios of human land use. Ecol. Appl. 14(2):368-380.
Van Sickle, J., J.L. Stoddard, S.G. Paulsen, and A.R. Olsen. 2006. Using relative risk
to compare the effects of aquatic stressors at a regional scale. Environ. Manage.
38(6): 1020-1030.
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Wang, L., P.W. Seelbach, and R.M. Hughes. 2006. Introduction to landscape
influences on stream habitat and biological assemblages. In: Landscape Influences on
Stream Habitat and Biological Assemblages. American Fisheries Society Symposium
48. R.M. Hughes, L. Wang, and P.W. Seelback, eds. American Fisheries Society,
Bethesda, MD. pp. 1-23.
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Section II—Summary
SECTION II: GEOGRAPHIC FRAMEWORKS, SPATIAL DATA, AND
ANALYSIS TOOLS
SUMMARY
This section furnishes additional detail on important considerations for
successfully integrating and using geographic frameworks, spatial data, and analysis
tools for landscape assessments and predictive tools.
Chapter 5: Common Geographic Frameworks (Recommended for Beginner,
Intermediate, and Advanced) provides an in-depth discussion of interdisciplinary
integration of geographically explicit data sets designed for different purposes at
different scales, that is, different frameworks for geographic information and analysis.
This chapter emphasizes appropriate ways to combine and extrapolate mapped regions
for classification and analyzing associations among ecological and environmental
factors. For example, Chapter 5 describes how to rectify ecological regions and
watersheds. A detailed discussion is also included explaining the important differences
between hydrologic units (and Hydrologic Unit Codes) and watersheds, their
advantages and disadvantages, and appropriate uses.
Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
(Recommended for Beginner and Intermediate) outlines the basics of a wide array of
available spatial data useful for landscape and predictive tools analyses. It also
provides a brief introduction to many common spatial designs for gathering in situ
aquatic condition sampling data. These constitute the two kinds of data essential to
implement landscape and predictive tools as an integral part of water quality monitoring,
assessment, and management programs.
Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
(Recommended for Intermediate to Advanced) emphasizes combining field and
remotely sensed data using an iterative approach to data gathering and analysis, points
out the importance of paired data for developing associations between stressors and
responses, and focuses on decisions relevant to monitoring and other Clean Water Act
programs. Topics include problem and question formulation, available software and
Geographic Information System tools, linking data and analysis scales, data
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Section II—Summary
classification and normalization, and descriptive analysis of associations using scatter
plots, correlation, box plots, linear and quantile regression, and classification and
regression trees. Common spatial analysis methods covered include landscape
factors/metrics, spatial interpolation, hydrologic analyses using Digital Elevation Models
overlay and proximity tools, and decision support systems such as U.S. Environmental
Protection Agency's Regional Vulnerability Assessment Program and Causal Analysis
Diagnosis Decision Information System (CADDIS).
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Section II—Chapter 5: Common Geographic Frameworks
5. COMMON GEOGRAPHIC FRAMEWORKS
James M. Omernik, U. S. Geological Survey, Corvallis, OR
Robert M. Hughes, Amnis Opes Institute and Department of Fisheries and
Wildlife, Oregon State University, Corvallis, OR
Glenn E. Griffith, U. S. Geological Survey, Corvallis, OR
Greg M. Hellyer, EPA-New England Regional Lab, North Chelmsford, MA
In this section, we describe the geographic
frameworks most commonly used to research,
monitor, assess, and manage aquatic resources.
The success of these activities depends on a sound
understanding of the underpinnings, strengths, and
limitations of the geographic frameworks used. We
also discuss how frameworks can be combined to
maximize the ability to extrapolate. We emphasize
simultaneous use of ecological regions and
watersheds as an example of an appropriate
application of using differing geographic frameworks
as landscape and predictive tools.
The most commonly used geographic frameworks (or spatial classifications) in
aquatic resource science are ecoregions, watersheds/basins, and hydrologic units (HU)
(often referred to as HUCs because of the assigned hydrologic unit code assigned by
the U.S. Geological Survey [USGS]). All three types of tools provide maps or
geographic spatial models of areas or parts of the Earth's surface sharing common
characteristics expected to be useful for understanding patterns in the structure,
function, and responses of ecosystems—especially in cases where quantitative,
site-specific data are insufficient.
The three common geographic frameworks discussed below are widely and
frequently used to do the following:
What is in this chapter? A review
of geographic frameworks commonly
used in landscape analysis with
examples and discussions of their
strengths and weaknesses. A
geographic framework is the
organizing structure for a database
or analytic process having spatially
linked attributes such as latitude and
longitude, relative position as along
a stream, or designated class such
as a county, watershed, or
waterbody size. Different
geographic frameworks are
designed for different purposes at
different spatial extents and levels of
resolution, and therefore combining
them and interpreting results
requires logical rigor.
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Section II—Chapter 5: Common Geographic Frameworks
• Predict patterns or thresholds in natural ecosystem gradients and in
anthropogenic stressor/disturbance gradients.
• Plan and target monitoring to maximize efficient use of limited fiscal resources.
• Develop conservation plans to protect highly valued natural resources.
• Identify and select ecologically appropriate reference sites.
• Interpret patterns in ecological data rationally.
• Extrapolate monitoring results to areas lacking in situ data.
• Develop regional adaptations of national water quality standards that are based
on ecological units versus political units.
• Focus rehabilitation efforts in a cost-efficient and ecologically responsive manner.
5.1. ECOREGIONS
5.1.1. Description
Ecoregions (or ecosystem regions) are defined as areas of relative homogeneity
in ecosystems or relationships among organisms and their environments. Ecoregion
maps depict areas within which the aggregates of all terrestrial and aquatic ecosystem
components are different from or less variant than those in other areas (Omernik and
Bailey, 1997). In explaining the process of defining ecoregions, Wiken (1986) stated:
Ecological land classification is a process of delineating and classifying
ecologically distinctive areas of the earth's surface. Each area can be
viewed as a discrete system which has resulted from the mesh and
interplay of the geologic, landform, soil, vegetative, climatic, wildlife, water,
and human factors which may be present. The dominance of any or a
number of these factors varies with the given ecological land unit. This
approach to land classification can be applied incrementally on a scale
related basis from very site specific ecosystems to very broad
ecosystems.
Although few ecoregions specifically follow Wiken's vision, the following
geographic frameworks have been called or used as ecoregions:
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Section II—Chapter 5: Common Geographic Frameworks
• Ecoregions (Bailey, 1976, 1998).
• Ecoregions (Omernik, 1987; U.S. EPA, 2011).
• Major Land Resource Areas (USDA, 2006).
• ECOMAP terrestrial ecological units (Keys et al., 1995; Cleland et al., 2007).
• Aquatic Ecological Units (Maxwell et al., 1995).
• Ecological Regions (Commission for Environmental Cooperation [CEC], 1997).
• Ecoregions (Hargrove and Hoffman, 1999, 2004).
• Terrestrial Ecoregions (Ricketts et al., 1999).
• Freshwater Ecoregions (Abell et al., 2000, 2008).
• Common Ecological Regions (McMahon et al., 2001).
• Terrestrial Ecosystems (Sayre et al., 2009).
For in-depth discussions and comparisons of these and other ecoregion-type
frameworks, see McMahon et al. (2001), Gallant et al. (2004), Loveland and Merchant
(2004), and McMahon et al. (2004). The last three of these papers and other articles on
ecoregions appeared in a special issue of Environmental Management titled Ecoregions
for Environmental Management.
5.1.2. Strengths and Limitations
Most of the above frameworks were compiled by different individuals or groups,
using different methods, and for different purposes, which has led to misuse and
misunderstanding of ecoregions. Frustrated by a lack of conformity among resource
management agencies regarding a national framework of ecological regions, the
U.S. Government Accountability Office (GAO, 1994) stated that a common framework is
necessary for management of ecosystems versus specific resources. Recognizing this
problem identified by the GAO report, an interagency group called the National
Interagency Technical Team (NITT) was formed, and, together with an interagency
steering committee, created a Memorandum of Understanding focusing on developing a
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Section II—Chapter 5: Common Geographic Frameworks
common framework of ecological regions of the United States (McMahon et al., 2001).
Many involved in this interagency effort were reluctant to delve into the reasons for the
differences in the existing frameworks and to evaluate them against the broader
purpose of a common framework. However, the NITT did agree on a goal that fit the
objectives of the GAO report—to identify regions of similarity in the mosaic of biotic,
abiotic, terrestrial, and aquatic ecosystems with humans considered a part of the biota
(McMahon et al., 2001; Omernik, 2004). The purpose of this common framework is
identical to that of the CEC ecological regions and the U.S. Environmental Protection
Agency (EPA) ecoregions—to provide a geographical tool to aid environmental
resource managers with different responsibilities for the same geographic areas to
integrate their research, monitoring, assessment, and management activities.
Because of the breadth of this purpose (addressing ecosystems in the broadest
sense of the word), frameworks that were developed for specific objectives are likely to
be more effective for management concerns regarding those specific purposes and less
effective for integrating activities of all ecosystems (aquatic, terrestrial, biotic, and
abiotic). For example, the World Wildlife Fund terrestrial ecoregions, which used EPA
ecoregions that were split or merged to distinguish particular assemblages of species or
unique habitats, might be more appropriate for assessing terrestrial biodiversity issues.
Another framework, the Natural Resources Conservation Service (NRCS) Major Land
Resource Regions (USDA, 2006)—which is based primarily on soil characteristics from
state general soil map units, and secondarily on land use, climate, physiography,
vegetation, water resources, and geology—was intended to address soil capacities and
agricultural potential and is ideally suited for those subjects. However, many in the
NRCS recognize the value of the NITT, EPA, and CEC frameworks and have
collaborated with EPA and other agencies in developing Level III and IV ecoregion
maps for states (e.g., McGrath et al., 2002; Thorson et al., 2003; Daigle et al., 2006).
Some organizations, including the U.S. Department of Agriculture (USDA) Forest
Service (Keys et al., 1995; Maxwell et al., 1995), and the World Wildlife Fund
(Ricketts et al., 1999; Abell et al., 2000, 2008), have developed separate frameworks for
terrestrial and aquatic ecosystems. Although these mapped frameworks are useful for
specific aspects of ecosystems in some areas, none is ideally suited for the monitoring,
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Section II—Chapter 5: Common Geographic Frameworks
assessment, research, inventory, and management of entire ecosystems. The
frameworks of Maxwell et al. (1995) and Abell et al. (2000, 2008), which were designed
to address aquatic ecosystems, are problematic because they are based on hydrologic
units. As such, these maps might be useful in identifying potential patterns in
distributions of some fish species (Jelks et al., 2008), but they are of little help in
addressing true fish species pools (e.g., McGarvey and Hughes, 2008; McGarvey and
Ward, 2008), or patterns in physical and chemical habitat or benthic
macroinvertebrates. Aquatic insects, for example, spend their lives in both the aquatic
and terrestrial environments, and unlike fish, their distributions and abundances are not
restricted by drainage basins.
Misunderstanding the general purpose or
ecosystem nature of ecoregions has led to the
development of frameworks that are called
ecoregions but were in fact designed for
addressing a specific ecosystem component.
True ecoregions are not intended to replace
mapped frameworks of vegetation, geology, soil,
water quality, fish distributions, climate,
landcover, or physiography, nor should they
replace frameworks that consider some but not
all these characteristics. Hence, patterns in
characteristics such as nutrient concentrations in
streams are better explained by spatial differences in land use (Omernik, 1977) than by
ecological regions (Wickham et al., 2005). Likewise, patterns in fish and benthos
distributions might be better explained by patterns in current and historical river basins
(Hocutt and Wiley, 1986; Hughes et al., 1987) or by habitat gradients (Hawkins et al.,
2000b; Hughes et al., 2006). However, when assessing and managing aquatic
ecosystems, one must consider all aspects of aquatic ecosystems, including the biota
and chemical and physical habitat (Karr, 1993, 1995; Yoder, 1995). EPA ecoregions
have proven useful for many of these purposes (e.g., Larsen et al., 1988; Hughes et al.,
1994; Davis et al., 1996). For example, Tennessee has used Level III and IV EPA
Reasons for Disagreement About
How to Delineate Ecoregions
• Different definitions of
ecosystems.
• Failure to embrace ecosystem
holism.
• Ecoregion and boundary
complexity.
• Bias toward a single
characteristic.
• Rule-based vs. weight-of-
evidence approach.
• Whether watersheds and
hydrologic units are ecoregions.
• Investment in existing frameworks
and resistance to change.
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Section I!—Chapter 5: Common Geographic Frameworks
ecoregions to assess habitat quality of least disturbed streams (Arnwine and Denton,
2001b), develop regional nutrient criteria (Denton et al., 2001), and develop regional
biological criteria (Arnwine and Denton, 2001a).
Because of the nature of their development, ecoregions depict general patterns
in combinations of ecosystem components rather than the degree to which a single
component matches each region. For example, Larsen et al. (1988) found that the
patterns of single chemical parameters in Ohio streams were seldom associated with
EPA ecoregions. However, a principle components analysis of combinations of all the
chemical characteristics sampled, with a combination of components comprising
nutrient richness on one axis and a combination of components comprising ionic
strength on the other, revealed a strong ecoregion pattern (Larsen et al., 1988; see
Figure 5-1).
FIGURE 5-1
Ohio Ecoregional Patterns in Nutrient Richness and Ionic Strength
Variables in Least-Disturbed Watersheds as Indicated by Principal
Components Axis Scores for Each. Each square color corresponds to a
site in an ecoregion of the same color on the index map.
Source: Larsen et al. (1988), Griffith et al. (1999).
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Section II—Chapter 5: Common Geographic Frameworks
Ecoregions are effective for integrating ecosystem management activities among
resource management agencies and programs because all agencies and programs are
using the same pieces of the puzzle, but might merely be aggregating them differently.
The Tennessee Department of Environment and Conservation, for example, did not use
the same groups of Level III and IV EPA ecoregions for each of their applications; they
used only those useful for explaining patterns of their water quality data (Arnwine and
Denton, 2001b). Other states such as Iowa have used both Level III and IV EPA
ecoregions to locate long-term monitoring sites to monitor water quality trends as
pollution control practices are implemented, farming practices evolve, and watersheds
undergo urban and industrial development (IDNR, 2001). However, Iowa fish
distributions and assemblage conditions showed little relationship to EPA ecoregions
(Heitke et al., 2006). On the other hand, Arkansas fish assemblages showed distinct
ecoregional patterns (Rohm et al., 1987). Arkansas also found Level III EPA
ecoregions useful for developing and evaluating water quality standards (Omernik and
Griffith, 1991) and has subsequently adopted the framework for its multiagency
Comprehensive Wildlife Conservation Strategy (http://www.wildlifearkansas.com/
strategy.html). Other organizations have found EPA ecoregions useful for applications
that require consideration of aquatic and terrestrial ecosystems. The North American
Bird Conservation Initiative uses EPA ecoregions as a framework for biological
conservation research and planning (U.S. NABCI Committee, 2000). The Nebraska
Game and Parks Commission and Georgia Department of Natural Resources used
Level III and IV EPA ecoregions to classify areas of bird occurrence (Farrar, 2004;
Schneider et al., 2010).
Ecoregions are increasingly being used for national-level research and
assessment projects of environmental resources. The U.S. Geological Survey uses
Level III EPA ecoregions to stratify its land cover project that evaluates the rates,
trends, causes, and consequences of land use and land cover change in the
conterminous United States (Drummond and Loveland, 2010; Napton et al., 2010;
Sleeter et al., 2011). The EPA has adapted Level II ecoregions to report the results of
the Wadeable Streams Assessment (U.S. EPA, 2006; Paulsen et al., 2008) and the
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Section II—Chapter 5: Common Geographic Frameworks
National Lake Assessment (U.S. EPA, 2009). Other examples of ecoregion
applications can be found online at http://www.epa.gov/wed/pages/ecoregions/links.htm.
5.2. SINGLE-PURPOSE FRAMEWORKS
5.2.1. Description
Unlike ecoregions, which were designed to identify areas in which there is
similarity in the mosaic of all ecosystem components (aquatic, terrestrial, biotic, and
abiotic), a number of single-purpose frameworks have been developed to delineate
areas in which there is similarity in a specific geographic phenomenon or subject of
interest to environmental resource management. Examples of such maps include those
that have been developed for acid sensitivity of surface waters (Omernik and Powers,
1983; Omernik and Griffith, 1986; Omernik et al., 1988a) stream nutrient levels
(Omernik, 1977), lake trophic state (Omernik et al., 1988b; Rohm et al., 1995), land
use/landcover (Loveland et al., 1991; Vogelmann et al., 2001), and agricultural practices
and products (USDA, 1999).
As with ecoregions, pattern analysis is necessary in developing many of these
single-purpose maps. For example, in mapping alkalinity of surface waters, one must
examine the patterns of alkalinity values together with maps of spatial characteristics
(including soil, geology, vegetation, and land use) that can be associated with regional
differences in alkalinity. At the same time, one must ensure that the watershed sizes
used to help detect patterns are consistent with the spatial heterogeneity and
homogeneity of the spatial characteristics affecting regional differences. For instance,
by evaluating patterns of surface water alkalinity values in very small watersheds in the
mountainous western United States, abrupt changes in values can be detected that
follow differences in geology. On the other hand, because of soil buffering capacity in
most cropland regions, alkalinity values tend to be consistently high across broad areas.
In such areas, the land use/alkalinity value association can be detected from patterns in
water quality data associated with watersheds of varying sizes.
Some single-purpose maps, such as those showing agricultural census data, use
dot patterns and county frameworks to show patterns in agricultural characteristics (e.g.,
USDA, 1999). In the central and eastern United States, where most counties are small
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Section II—Chapter 5: Common Geographic Frameworks
and of relatively similar size, general patterns in characteristics shown by county can be
seen. However, illustrating these patterns through use of dots is often more effective if
the dots are in the agricultural parts of each county. This is especially true in much of
the western United States where there are very large counties, many of which include
small areas of intensive agriculture and much larger areas of forest, rangeland, or
desert. A similar dichotomy occurs with maps of fish species occurrences between dot
maps (e.g., Lee et al., 1980) versus HU or basin maps (e.g., Hocutt and Wiley, 1986).
As with agriculture, the dot maps more accurately illustrate the presence (or probable
absence) of a species than do basin or HU maps that imply that a species is present
throughout the larger map unit, rather than just the headwaters or tidal reaches.
5.2.2. Strengths and Limitations
The obvious value of single-purpose maps is that they illustrate spatial patterns
of particular aspects of interest to resource managers and scientists, thereby allowing
them to structure their management and research according to regional patterns and
trends in those aspects. The limitations of these frameworks are the same as with any
map, whether of land use, alkalinity of surface waters, rock type, or vegetation—they
are only spatial representations of particular characteristics at a scale smaller than 1:1.
Moreover, many if not most of the characteristics mapped vary temporally. Hence, each
of these maps should be considered as representations during a particular time.
5.3. WATERSHEDS
5.3.1. Description
Watersheds (also basins and catchments) are topographic areas in which
surface and ground water drain to a specific point (Omernik and Bailey, 1997; Griffith
et al., 1999). Webster's Dictionary (Merriam-Webster, 1986) defines a watershed as "a
region or area bounded peripherally by a water parting and draining ultimately to a
particular watercourse or body of water." Both definitions above are essentially the
same and are unambiguous.
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Section II—Chapter 5: Common Geographic Frameworks
5.3.2. Strengths and Limitations
As defined above, watersheds have been, and will continue to be, powerful tools
for water resource managers and scientists for associating natural and anthropogenic
characteristics with water quality and quantity. Where watersheds can be defined and
are in mesic and hydric areas, their downstream points reflect the aggregate of the
characteristics upgradient from each point.
Nonetheless, in many parts of the country, watersheds are either difficult or
impossible to define (Hughes and Omernik, 1981). Regions of karst topography,
continental glaciation, extremely flat plains, deep sand, and xeric climates fall into this
category (Omernik and Bailey, 1997; Currens and Ray, 2001). In many xeric regions of
the country where apparent watersheds can be defined and influent streams
predominate (where streams feed the ground water rather than where the ground water
feeds the streams), topographic watersheds do not always encompass the same
integrating processes as in mesic and hydric areas (Strahler, 1975; Omernik and Bailey,
1997). Such streams not only lose water, but they also become markedly more saline
and enriched than effluent streams as a result of evapotranspiration, and they often flow
through surficial mineral deposits that are remnants of pluvial periods. Also, the
usefulness of true topographic watersheds is lessened in places where water has been
diverted from one drainage basin to another and where flows are dominated by
wastewater effluents or irrigation return flows, which is common in xeric areas of the
western United States. Although one could argue that the watershed boundaries could
be modified to account for inputs from diversions, this can be difficult to impossible
because water diversions are rarely constant, can be reversed by pumping, and are
inconsistent with precipitation.
5.4. HYDROLOGIC UNITS (HUS) AND HYDROLOGIC UNIT CODES (HUCS)
5.4.1. Description
Hydrologic units were developed initially from the USGS digital framework
(Seaber et al., 1987), and the EPA River Reach File. The framework is hierarchical and
shows "drainage hydrography, culture, and political and hydrologic unit codes (HUCs)"
(Seaber et al., 1987). The system divides the United States into 21 major regions,
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Section II—Chapter 5: Common Geographic Frameworks
which are subdivided into 222 subregions and 352 accounting units, and finally
subdivided into 2,149 cataloging units. These hydrologic units are commonly known as
HUCs, although the hydrologic unit codes are merely identifiers for the units at each
hierarchical level. In this section, all hydrologic units are called HUs. The 21 major
regions contain either the drainage area of a major river (only 2 cases: Ohio, Upper
Mississippi) or the combined drainage areas of a series of rivers (19 cases, 3 of which
are based on political units: Alaska, Hawaii, Puerto Rico). Each "subregion includes the
areas drained by a river system, a reach of a river and its tributaries in that reach, a
closed basin(s), or a group of streams forming a coastal drainage area" (Seaber et al.,
1987). The accounting units nest within or are the same as subregions, and each of the
cataloging units (8-digit HUs) is an area representing part or all of a drainage basin, a
combination of drainage basins, or a distinct hydrologic feature (Seaber et al., 1987).
Subsequent to the development of the framework by Seaber et al. (1987), the
USDA-NRCS, in collaboration with other federal agencies and many state resource
management agencies, began an effort to delineate smaller, more detailed, 10- and
12-digit HUs (Federal Geographic Data Committee, 2004; Berelson et al., 2004; Eidson
et al., 2005). Whereas 2,146 8-digit HUs cover the United States, it is estimated that
roughly 22,000 10-digit and 160,000 12-digit units will be delineated for the country
(Federal Geographic Data Committee, 2004).
5.4.2. Strengths and Limitations
The major strength of HUs that are also watersheds is, of course, the same as
that previously stated for watersheds. The value of HUs is that such a framework
provides a wall-to-wall national set of terrestrial polygons of roughly comparable size at
each subdivision. As such, the framework is often used for depicting spatial pattern of
resources (e.g., NatureServe, 2005; Jelks et al., 2008), if not connectivity. However,
equally useful and meaningful polygons include hexagons (Rathert et al., 1999) and
quadrangles (McAllister et al., 1986). It is essential to understand that usually fewer
than half of HUs comprise true watersheds regardless of the level of subdivision.
A major limitation of HUs lies in the mistaken statement of their intended use,
which according to Seaber et al. (1987) is to provide "a standard geographic and
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Section II—Chapter 5: Common Geographic Frameworks
hydrologic framework for water-resource and related land-resource planning." The logic
of using these units for these purposes is questionable because they are largely
unrelated to patterns of geographic characteristics that are associated with spatial
differences in water quality and quantity (Omernik and Bailey, 1997; Griffith et al., 1999;
Omernik, 2003; Brenden et al., 2006; Hollenhorst et al., 2007). For example, HUs tend
to include dissimilar ecoregions, as do watersheds.
A second major limitation of HUs is the common misconception that they are
watersheds. Although Seaber et al. (1987) did not define HUs as true watersheds,
many believe that watersheds and HUs are synonymous (e.g., USFWS, 1995; Jones
et al., 1997; Ruhl, 1999; Alexander et al., 2000; Graf, 2001). However, most HUs are
not true watersheds. Because streams are linear features rather than spatial features,
it is impossible to map the watersheds of an area such as a continent, country, or state
so that it is completely covered with watersheds of similar size (Omernik, 2003).
Omernik (2003) reported that national maps of HUs (6-digit, 8-digit, or any other
hierarchical level) contain only about 45% true watersheds (10% in the case of major
regions). This inherent problem with the HU framework at the 8-digit level has also
been demonstrated for Texas (see Figure 5-2, Omernik, 2003). Most HUs are
watershed segments, or watershed groups that do not serve the critical purpose of true
watersheds. For these reasons, the terms HUs and watersheds should not be used
interchangeably. Attempts have been made to code HUs to allow one to distinguish
true watersheds (Verdin and Verdin, 1999). However, none allows the determination of
true watersheds at all levels (e.g., 6-, 8-, 12-, or n-digit) directly, including those of
Verdin and Verdin (1999), Seaber et al. (1987), the Federal Geographic Data
Committee (2004), and Eidson et al. (2005). Instead, one must first employ data from
digital elevation maps, the national elevation data set, or models (Brenden et al., 2006;
Hollenhorst et al., 2007). Unfortunately, even some developers of the 12-digit HUs
(e.g., Berelson et al., 2004), who recognized the inaccurate perception and relationship
that is perpetuated by labeling HUs as watersheds, have not attempted to rectify this
problem by appropriate labeling, thereby furthering the inaccurate perception.
Because of the importance of rectifying this common misperception, we illustrate
the limitations of HUs by also examining the 8-digit HUs within the Columbia basin of
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Section I!—Chapter 5: Common Geographic Frameworks
I I True watersheds
Eight digit HU
State boundary
Stream
FIGURE 5-2
Eight-Digit HUs of Texas that are True Watersheds. Note, only 48% of the
HUs are watersheds.
Source: Omernik (2003).
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Section II—Chapter 5: Common Geographic Frameworks
the Pacific Northwest (see Figure 5-3). Only 58% of these HUs are true watersheds
(see Figure 5-4). If all HUs were true watersheds one would expect the water quality
and flow regime at downstream points of HUs within the same ecoregion to be similar
as compared to those of adjacent ecoregions where conditions are markedly different.
For example, four 8-digit HUs lie completely or nearly completely within the Columbia
Plateau ecoregion which is steppe, and flanked by forested, mountainous ecoregions
(see Figure 5-5; Thorson et al., 2003). However, only two (B and C) of the four 8-digit
HUs are true watersheds (see Figure 5-6). One of the HUs (A) is a downstream
segment of the Columbia River and drains large parts of northeastern Washington,
northern Idaho, northwestern Montana, and southeastern British Columbia. The other
HU (D) is a downstream segment of the Snake River that drains eastern Oregon, most
of Idaho, and parts of Nevada and western Wyoming. Like watersheds, the quality and
quantity of water at the downstream segments of HUs A and D (which drain large
forested ecoregions) will be vastly different from those of HUs B and C (which drain
largely sagebrush steppe). There are 8-digit HUs that are true watersheds completely
or nearly completely within specific Level III ecoregions of the Columbia Plateau (see
Figure 5-7). Data from these types of HUs are useful for determining patterns of water
quality and flow regime in the Columbia Plateau.
The problem with HUs can also be seen at the 12-digit level, using South
Carolina as an example. Of the 986 12-digit HUs completely or partially in the state,
only 47% are true watersheds (see Figure 5-8). If all 12-digit HUs were true
watersheds, one would expect the water quality and flow regime at the downstream
point of all HUs within an ecoregion to be relatively similar and somewhat different than
those of HUs within other ecoregions where factors affecting flow and water quality
differ (see Figure 5-9). Several 12-digit HUs are within the Carolina Slate Belt Level IV
ecoregion or the Sand Hills Level IV ecoregion (see Figure 5-10). Streams in the
Carolina Slate Belt with watershed areas <20 km2 tend to dry up annually because the
region contains some of the lowest water-yielding rock in the Carolinas (Griffith et al.,
2002). In the Sand Hills, on the other hand, such streams rarely dry up or flood
because of the high storage capacity of the sand aquifer (Griffith et al., 2002). Hence,
the 12-digit HUs that are true watersheds completely within one of these ecoregions will
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Section I!—Chapter 5: Common Geographic Frameworks
8-digit HUs
Ecoregion boundary
State boundary
FIGURE 5-3
Level III Ecoregions and 8-Digit HUs of the Pacific Northwest
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Section I!—Chapter 5: Common Geographic Frameworks
l l True Watersheds
Columbia River Basin
8-digit HUs
State boundary
FIGURE 5-4
Eight-Digit HUs that are True Watersheds within the Columbia Basin.
Note that only 58% of the HUs are True Watersheds,
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Section I!—Chapter 5: Common Geographic Frameworks
HI) Codes
A =17020014
B = 17020013
C= 17060109
D= 17060107
Columbia River Basin
8-digit HUs
HUs in Ecoregion 10
Ecoregion boundary
State boundary
FIGURE 5-5
Four 8-Digit HUs (A, B, C, and D) in the Columbia Plateau Level ill
Ecoregion (10)
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Section I!—Chapter 5: Common Geographic Frameworks
HII Codes
A= 17020014
B = 17020013
C= 17060109
D= 17060107
Columbia River Basin
8-digit HUs
True Watersheds
Keoregion boundary
State boundary
FIGURE 5-6
True Watersheds Associated with Downstream Points in HUs A, B, C, and
D. Note that B and C are true watersheds, whereas A and D are merely
downstream segments of vast watersheds, respectively, of the Columbia
(which drains a similar area in Canada) and Snake Rivers.
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Section I!—Chapter 5: Common Geographic Frameworks
Representative true watersheds
Level III Ecoregion boundary
Columbia River Basin
8-digit 11 Us
State boundary
Streams
FIGURE 5-7
Representative 8-Digit HUs that are True Watersheds within Level III
Ecoregions in the Columbia River Basin
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Section I!—Chapter 5: Common Geographic Frameworks
] True watersheds-466 of986 (47%)
- Twelve digit HU
¦ State boundary
FIGURE 5-8
Twelve-Digit HUs in South Carolina that are True Watersheds. Note that
only 47% are true watersheds.
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cn
¦
M
; Greenville
tolumbii
Clj&fleston
Level III ecoregion
Level IV ecoregion
State boundary
> ) A
45 Piedmont
45a Southern Inner Piedmont
I 145b Southern Outer Piedmont
¦H 45c Carolina Slate Belt
M )J 45g Triassic Basins
145i Kings Mountain
63 Middle Atlantic Coastal Plain
^3 63g Carolinian Banner Islands and Coastal Marshes
I 1) 63h Carolina Flatwoods
i ii 63n Mid-Atlantic Floodplains and Low Terraces
65 Southeastern Plains
^65c Sand Hills
651 Atlantic Southern Loam Plains
Si 65p Southeastern Floodplains and Low Terraces
66 Blue Ridge
66d Southern Crystalline Ridges and Mountains
75 Southern Coastal Plain
75i Floodplains and Low Terraces
^3 75j Sea Islands/Coastal Marsh
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OCEAN
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FIGURE 5-9
Level III arid IV Ecoregioris of South Carolina
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Section I!—Chapter 5: Common Geographic Frameworks
45 Piedmont
_ 45c Carolina Slate Belt
65 Southeastern Plains
_ 65c Sand Hills
12 Digit IIUs
Selected HUs
Level III ecoregion
Level IV ecoregion
State boundary
FIGURE 5-10
Selected 12-Digit HUs in the Carolina Slate Belt and Sand Hills Level IV
Ecoregions of South Carolina
5-22
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Section II—Chapter 5: Common Geographic Frameworks
be very dissimilar to those with true watersheds in the other ecoregion. And 12-digit
HUs with topographic watersheds extending far beyond the HU boundary are likely to
be dissimilar to those that are true watersheds and are within a single ecoregion (see
Figure 5-11). Consequently, the quality and quantity of water at the downstream points
of the HUs that are not true watersheds (see A and B in Figure 5-11) will reflect the
aggregate of all the characteristics in the different ecoregions they drain. Data from
12-digit HUs that are true watersheds completely or nearly completely within specific
Level IV ecoregions (see Figure 5-12) are useful for determining patterns of water
quality and flow regime in South Carolina. Variability among these types of reference
watersheds will normally be less for those representing Level IV ecoregions than those
of the larger Level III ecoregions. Data from aggregations of 12-digit HUs that comprise
true watersheds that are completely within Level IV ecoregions (see Figure 5-13) are
useful for determining water quality and flow regime characteristics in larger streams
representative of those ecoregions.
A third limitation of HUs occurs when study sites are located other than at the
downstream boundary of an HU or on a stream in an HU that includes multiple parallel
and disconnected streams. When study sites are linked to such HUs in geographic
analyses, much of the landscape is often downstream of the site or on the disconnected
streams. The landscape conditions downstream of a site and on different streams have
far less influence on the site than does the true watershed upstream of the site
(Brendan et al., 2006; Hollenhorst et al., 2007).
Although not specifically an HU issue, there are two additional pitfalls in the use
of the National Hydrography Dataset (NHD) deserving brief discussion: stream density
and stream order. The NHD hydrography for South Carolina indicates that stream
densities change abruptly at some 1:100,000 USGS topographic quadrangle
boundaries (see Figure 5-14 and Table 5-1). In South Carolina, this change in densities
is about double. This phenomenon occurs in many parts of the United States and can
be traced to the differences in mapping specifications (quality control) among USGS
mapping projects. The density differences can greatly affect sampling efforts and data
interpretation in EPA's regional and national stream assessments (Stoddard et al.,
5-23
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Section I!—Chapter 5: Common Geographic Frameworks
Selected 12-digit HU (A, B)
Watershed of HU A
Watershed of HU B
12-digit HU
Level III ecoregion
Level IV ecoregion
State boundary
FIGURE 5-11
True Watersheds Associated with Downstream Points in Two of the
Selected 12-Digit HUs Shown in Figure 5-10. Note that HU-A is a
downstream segment of the Broad River watershed and HU-B is a
downstream segment of an even larger area comprising over 200 12-Digit
HUs making up the watershed of the Broad and Saluda Rivers, and
draining three Level III ecoregions. The remaining selected HUs shown
Figure 5-10 are true watersheds.
5-24
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Section I!—Chapter 5: Common Geographic Frameworks
I I True watershed within Level IV ccorcgion
— Twelve digit HU
Level III ecoregion
Level IV ecoregion
— - ¦ State boundary
FIGURE 5-12
Examples of 12-Digit HUs that are True Watersheds Completely within,
and thus Representative of, Specific Level IV Ecoregions of South
Carolina
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Section I!—Chapter 5: Common Geographic Frameworks
i ] True watershed HU
I I Non watershed HU
CO True watershed of aggregated HUs
— Twelve digit HU
Level III ecoregion
Level IV ecoregion
— - ¦ State boundary
FIGURE 5-13
Examples of Aggregations of 12-Digit HUs that Together Comprise True
Watersheds that are Completely or Nearly Completely within Specific
Level IV Ecoregions of South Carolina
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Section I!—Chapter 5: Common Geographic Frameworks
\ SPARTANBURG
GREENVILLE
iiiiiii
CAMDEN
FIGURE 5-14
Stream Densities on 1:100,000 Scale USGS Topographic Maps in South
Carolina. Note that the densities on the Greenville and Camden maps are
about twice those on the Spartanburg and Newberry maps.
TABLE 5-1
Differences in NHD Flowline (rivers/streams and artificial path) Density between
Four 1:100K Quads in South Carolina.
100K Quads
Total NHD Flowline (mi.)
NHD Flowline Density (mi/sq. mi.)
Greenville
2,468.43
1.26
Spartanburg
1,174.02
0.60
Newberry
1,357.81
0.69
Camden
2,634.83
1.34
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Section II—Chapter 5: Common Geographic Frameworks
2005; U.S. EPA, 2000, 2006), which selected sites on different order streams on the
basis of NHD stream traces.
Not only does the NHD lead to errors in determining stream order, but stream
order itself is a poor estimator of stream size or volume for several reasons. These
include natural variation in the watershed area required to generate a channel and a
permanent stream, inaccurate and imprecise field annotation, inconsistent mapping
between xeric and humid regions, inconsistent map scales, and the degree to which
ephemeral streams are included (Hughes and Omernik, 1981, 1983; Oberdorff et al.,
1995; see Figure 5-15). In addition, stream order is confounded with influent streams,
distributaries, spatially intermittent streams, streams flowing from other water or ice
bodies, and streams in karst and glaciated regions. Instead of stream order, we
recommend using a more accurate and meaningful indicator of stream size—such as
estimated discharge (e.g., McGarvey and Hughes, 2008) or potential volume, estimated
from the product of mean annual runoff and watershed area (Pont et al., 2009) or direct
field measures of width or depth (Hughes et al., 2011). Pont et al. (2009) reported that
potential stream volume was a significant predictor for vertebrate species richness,
benthic species richness, and fish tolerance index in western U.S. streams. McGarvey
and Hughes (2008) found a highly significant relationship between fish species richness
and discharge for three large Oregon rivers. Further support for using a predictor of
flow comes from Stanford et al. (1996) who argued that altered flows were the most
pervasive threat to the world's rivers, and Poff et al. (1997) who deemed the flow regime
as the master variable governing river character.
5.5. GENERAL PRINCIPLES
5.5.1. Computer Delineation of Ecoregions and Hydrologic Landscape Regions
There have been three noteworthy attempts to map ecological or hydrological
regions through use of geographic information systems (GISs) and multivariate
statistical analyses (Hargrove and Hoffman, 1999, 2004; Wolock et al., 2004; Sayre
et al., 2009). The main rationale for these quantitative methods is that they are "more
explicit, repeatable, transferable, and defensible than subjective models based on
5-28
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Section I!—Chapter 5: Common Geographic Frameworks
100
10
<
in
GC
<
Q
Ui
0.01
0.001
0.01 0.1 1 10 100
MEAN ANNUAL DISCHARGE, m3/«
FIGURE 5-15
Watershed Areas and Mean Annual Discharges Relative to Stream
Orders. Numbers are stream orders, each of which represents a study
stream site in the United States with published results. Note that streams
of any given order may vary by an order of magnitude in discharge or
watershed area.
Source: Hughes and Omernik (1983).
—
5 43
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4 4
1
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4 5
—
4 5
4 5
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. 5
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4 4
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4 4 5
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Section II—Chapter 5: Common Geographic Frameworks
human expertise" (Hargrove and Hoffman, 2004). Computer methods are explicit in that
the developer/mapper can spell out which factors, source maps and data sets, and
methods were used and how they were used. However, decisions concerning the
characteristics to include and the data sets and maps used to portray those
characteristics involve judgments by the mapper. Other scientists might disagree with
those judgments and, if they were to start the same process from scratch without
knowledge of the previous quantitative approach, would likely make very different
judgments that would affect the final product. The use of qualitative, weight of
evidence, methods (Omernik, 2004) for mapping ecological regions, on the other hand,
recognizes that (1) the factors that are more or less important in giving each ecological
region its identity, and how these factors are interrelated, vary from one region to
another regardless of the level of detail (or hierarchical level) of mapping, and (2) the
accuracy, level of generality, and relevance of the classification used in the maps
representing each characteristic (e.g., soil, geology, physiography, and vegetation), vary
from one map to another and from one area to another, even for maps published at the
same scale (Omernik, 1995).
Omernik's experience in mapping military geographic regions reveals the
importance of evaluating the accuracies, relevance of classifications, and levels of
generality of all data sources including maps and numerical data (Omernik and Gallant,
1990). Understanding basic interrelationships and associations among geographic
phenomena is key to filtering the data sources and ultimately mapping regions that fit
the purpose of each map. Each mapper, if he or she understands these geographic
interrelationships, will be able to adjust for these aspects and sketch areas where there
is coincidence in combinations of characteristics that give each region its identity. Even
if two or more individuals using this method are mapping an area independently, the
resultant maps are usually similar. Here, the test of the regions is not in how they were
compiled, but how useful they will be. Had only computer methods been employed, this
filtering would have been difficult to impossible because of the infinite number of ways
geographic characteristics are associated with one another and the many ways each
can be represented on maps. Because human lives were at stake in mapping military
geographic regions, it was vitally important to be generally right rather than precisely
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Section II—Chapter 5: Common Geographic Frameworks
and consistently wrong. The same should be true for mapping ecological regions
because of the importance of maintaining and rehabilitating ecosystem condition.
Quantitatively developed maps of ecoregions or hydrologic regions often identify
the same region in vastly different ecological settings. The map by Wolock et al. (2004)
is made up of about 2,000 groups of HUs each of which is assigned one of
20 hydrologic region numbers. Each of these 20 regions comprises a particular
combination of land-surface form, geologic texture, and climatic characteristics, but
these noncontiguous hydrologic regions can be found in markedly different
environmental conditions. For example, the map shows that one of the 20 regions
occurs in southeastern Maine, the Cross Timbers region of Oklahoma, and south
central Minnesota. Another region can be found in the Mississippi Alluvial Plain of
southeastern Louisiana, the Lake Agassiz Plain in northern Minnesota and North
Dakota, and the Subarctic Coastal Plain of southwest Alaska. Although such regions
display hydrological similarities, their ecological similarities are minimal, and
consequently it is named a hydrologic region map, not an ecoregion map.
Nonetheless, computer mapping exercises such as those of Hargrove and
Hartman (2004), Wolock et al. (2004), and Sayre et al. (2009) are useful in locating
areas with particular combinations of geographic characteristics and should prove
useful in helping to explain the nature of ecoregions and ecoregion boundaries that
have been developed more qualitatively. As has been previously noted, characteristics
that distinguish ecological regions and the order of importance of the different
characteristics vary from one region to another regardless of the hierarchical level of
regionalization. Once these primary distinguishing characteristics have been
determined, quantitative techniques can be employed to help illustrate how conditions
vary within and between regions, particularly as these conditions relate to management
issues and projected land use changes.
5.5.2. Strengths and Limitations of Using Watersheds and Ecoregions Together
Ecoregions and watersheds have very different purposes, but they can be
complementary if used together correctly. An ecoregion provides a spatial framework in
which the quantity and quality of environmental resources and ecosystems in general
5-31
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Section II—Chapter 5: Common Geographic Frameworks
can be expected to exhibit a particular pattern (Omernik and Bailey, 1997). It bears
repeating that watersheds, where definable, are areas where water drains to a point.
Compared with an ecoregion of similar size,
a watershed tends to be more dissimilar in
factors that influence water quality and
quantity because watersheds tend to cross
ecological regions. However, watersheds
completely within an ecoregion will tend to
be similar to one another and dissimilar to
those in other ecoregions. Sets of these
watersheds and their downstream points
have been termed reference watersheds,
and reference sites can be used to
determine the central tendencies of ecoregic
and abiotic conditions of ecoregions (Hughes etal., 1986; Hughes, 1995; Bryce etal.,
1999; Stoddard et al., 2005; Herlihy et al., 2008; Whittier et al., 2006, 2007). (Note the
term reference is not used here as preferred condition but as existing condition).
Comparisons of the natural and anthropogenic characteristics of such watersheds aid
us in determining the factors associated with ecosystem differences in each ecoregion.
Water quality of watersheds crossing more than one ecoregion will reflect
characteristics of each of the ecoregions occupied. For example, watersheds
completely within the Southeastern Plains of the Carolinas will be relatively similar to
one another, as will watersheds completely within the Piedmont ecoregion in those
states. Downstream points of watersheds that drain both of these ecoregions will reflect
the characteristics of both ecoregions. Likewise, streams that originate in the Blue
Ridge ecoregion and flow through the Piedmont, Southeastern Plains, and Atlantic
Coastal Plain ecoregions will reflect contributions from all four of these regions. Water
quality measured for ecoregion reference watersheds for each of these ecoregions will
help in estimating these relative contributions (Omernik and Bailey, 1997).
Watersheds that are completely within each of the Level III ecoregions in the
Columbia Basin (see Figure 5-7) can be used to determine the ecoregion effect on
Watershed/Ecoregion Key Points
• Watersheds are imperative for
understanding associations between
human and nonhuman characteristics
and water quality and quantity.
• Watersheds rarely correspond to
areas within which there is similarity in
characteristics affecting water quality
and quantity.
• Most hydrologic units (HUs) are not
watersheds.
• In some areas, watersheds are
difficult to impossible to delineate or
vary in their relevance.
• Watersheds and ecoregions are
complementary frameworks.
(Omernik, 1995) or the potential biotic
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Section II—Chapter 5: Common Geographic Frameworks
points on those streams, such as for HU-D (see Figures 5-5 and 5-6). Obviously,
ecoregions closer to the downstream point of HU-D will have a greater effect on the site
than more remote ones such as ecoregion 17 (Middle Rockies). Much of the water
draining from the Lost River and Teton Ranges (in ecoregion 17) is lost through
evaporation and infiltration into the lava fields of the Snake River Plain or is used and
reused for irrigation. However, if water were diverted outside the basin or if irrigation
ceased, the quantity and quality of water measured at the downstream point of HU-D
would be affected. Hence, when used together correctly, ecoregions and watersheds
can provide a useful mechanism for meeting resource management goals outlined in
the Clean Water Act, as well as for broader ecosystem management concerns of state
and federal agencies, such as threatened and endangered fish species.
When using ecoregion reference sites or reference watersheds, it is important to
recognize that these terms have many meanings (Stoddard et al., 2006). These
meanings range from representing historic, natural or pristine condition (before human
disturbance), to best attainable condition, to realistically attainable condition, to least
disturbed condition, to minimally impacted condition, to disturbed condition (Stoddard
et al., 2006; Bryce et al., 1999). There has been a tendency by some people to map
ecological regions using naturally occurring characteristics and considering nature as if
humans were not part of it. Such an approach is unrealistic. The same is true for
expecting that it is possible to find stream sites or watersheds that reflect pristine
conditions. It is now generally accepted that if humans were removed from North
America, patterns in ecosystem components would not revert to those that existed
before Europeans arrived or before Native Americans inhabited the continent. Too
many plants and animals have been removed or introduced, and the land and water
have been drastically altered through anthropogenic activities such as agriculture,
urbanization, mining, and channelization (Omernik et al., 2000). In addition, there is
considerable natural variation among least-disturbed reference sites (Whittier et al.,
2006; Stoddard et al., 2008; Pont et al., 2009). However, least-disturbed reference
conditions in ecoregions that are intensively used by humans are comparable to
most-disturbed conditions in ecoregions that are less anthropogenically altered (Whittier
et al., 2006; Pont et al., 2009). Even in the latter, there are clear disturbance gradients
5-33
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Section II—Chapter 5: Common Geographic Frameworks
indicating ample room for marked improvement in ecological condition (Hughes et al.,
2004; Pont et al., 2009).
It is unlikely that any single predictor variable, especially a class variable, will
explain any continuous and complex response variable, such as an ecosystem or
assemblage. In some recent research, watersheds and ecoregions have been found to
explain less biological variability than local, quantitative environmental measures or GIS
variables (Hughes et al., 2006). For example, researchers have used such data as
stream depth, velocity and conductivity to predict the assemblage condition of aquatic
macroinvertebrates (Moya et al., 2007) or their taxa richness (Clarke et al., 1996;
Reynoldson et al., 1997; Davies et al., 2000; Hawkins et al., 2000b; Bailey et al., 2004).
Others have used GIS data (geology, elevation, slope, mean annual air temperature,
catchment area, runoff) for predicting the richness offish assemblages (Joy and De'ath,
2002) or their assemblage condition (Oberdorff et al., 2001, 2002; Tejerina-Garro et al.,
2006; Pont et al., 2006, 2009). Also see Snelder and Biggs (2002) and Seelbach et al.
(2006) for weight-of-evidence classification of rivers based on catchment, valley, and
channel characteristics. Cluster analysis of habitat data, biological data, or both is also
being used in conservation planning as more digital data become available (Belbin,
1993; Ferrier et al., 2002, 2007; Snelder et al., 2007). In addition, basins and
ecoregions were reported to account for no more site-scale assemblage variability than
political or null polygons (Hawkins et al., 2000a; Van Sickle and Hughes, 2000;
McCormick et al., 2000; Waite et al., 2000; Herlihy et al., 2006). Finally, both HUs and
ecoregions include markedly different size rivers, and river size is a major determinant
of ecosystem character. For this reason, Pflieger (1971) classified the great rivers of
Missouri as separate regions, but whatever their locations, expectations for many
ecological variables must be calibrated against river size (Fausch et al., 1984;
McGarvey and Hughes, 2008; Pont et al., 2009). We therefore reemphasize that
ecoregions should not be used alone in quantitative prediction of individual aquatic
ecosystem components, but in combination with other quantitative and continuous
environmental data if they are available.
Nonetheless, when used together correctly, ecoregions and watersheds are
useful for designing research and monitoring programs and for interpreting biological
5-34
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Section II—Chapter 5: Common Geographic Frameworks
patterns over large areas (Hocutt and Wiley, 1986; Hughes et al., 1987, 1994; Stoddard
et al., 2005; U.S. EPA, 2000, 2006; Frimpong and Angermeier, 2010) or where natural
gradients are strong (e.g., mountains versus plains, Omernik and Griffith, 1991; Pinto
et al., 2009; Moya, 2011). We, therefore, suggest using ecoregions and watersheds to
do the following:
• Predict ecosystem conditions when other data or models are unavailable.
• Predict stressors and likely stressor-response patterns.
• Examine patterns in, and reporting aspects of, ecosystem services at different
scales nationally and regionally.
• Evaluate patterns in and threats to both terrestrial and aquatic biodiversity.
• Select and calibrate reference sites.
• Interpret patterns in ecological data.
• Report patterns in ecological condition.
• Forecast where climate change effects will likely be greatest.
• Plan management strategies and tactics.
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Cleland, D.T., J.A. Freeouf, J.E. Keys, G.J. Nowacki, C.A. Carpenter, andW.H. McNab.
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Currens, J.C. and J.A. Ray. 2001. Discrepancies Between HUC Boundaries and Karst
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Daigle, J.J., G.E. Griffith, J.M. Omernik, et al. 2006. Ecoregions of Louisiana. (2 sided
color poster with map, descriptive text, summary tables, and photographs). U.S.
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stream habitat features using large-scale catchment characteristics. Freshwater Biol.
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Davis, W.S., B.D. Snyder, J.B. Stribling, and C. Stoughton. 1996. Summary of State
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Washington, DC. EPA/230/R-96/007.
Denton, G.M., D.H. Arnwine, and S.H. Wang. 2001. Development of Regionally Based
Interpretations of Tennessee's Narrative Nutrient Criterion. Tennessee Department of
Environment and Conservation. Nashville, TN. 58 pp. Available online at
http://tennessee.gov/environment/wpc/publications/pdf/nutrient_final.pdf.
Drummond, M.A. and T.R. Loveland. 2010. Land-use pressure and a transition to
forest-cover loss in the eastern United States. Bioscience 60(4): 286-298.
Eidson, J.P., C.M. Lacy, L. Nance, W.F. Hansen, M.A. Lowery, N.M. Hurley, Jr. 2005.
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Carolina, 2005. USDA Natural Resources Conservation Service. Available online at
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Fausch, K.D., J.R. Karr, and P.R. Yant. 1984. Regional application of an index of biotic
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Federal Geographic Data Committee. 2004. Federal standards for delineation of
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Ferrier, S., M. Drielsma, G. Manion, and G. Watson. 2002. Extended statistical
approaches to modeling spatial pattern in biodiversity in northeast New South Wales. II.
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Ferrier, S., G. Manion, J. Elith, and K. Richardson. 2007. Using generalized
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Frimpong, E.A., and P.L. Angermeier. 2010. Comparative utility of selected
frameworks for regionalizing fish-based bioassessments across the United States.
Trans. Am. Fish. Soc. 139(6): 1872-1895.
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Gallant, A.L., T.R. Loveland, T.R. Sohl, and D.E. Napton. 2004. Using an ecoregion
framework to analyze land-cover and land-use dynamics. Environ. Manage.
34(Suppl. 1 ):S89-S110.
Graf, W.L. 2001. Damage control: restoring the physical integrity of American rivers.
Ann. Assoc. Am. Geogr. 91:1 -27.
Griffith, G.E., J.M. Omernik, J.A. Comstock, et al. 2002. Ecoregions of North Carolina
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
6. TYPES OF SPATIAL AND LANDSCAPE DATA AND SAMPLING DESIGNS
Susan Cormier, U. S. EPA Office of Research and Development, Cincinnati, OH
Jeff Hollister, U.S. EPA Office of Research and Development, Cincinnati, OH
Spatial data are data that include
information about location in addition to
other attributes of interest. Thus, it is
possible to think of all field data as spatial
since they were collected at some location
(see Figures 6-1, 6-2, and 6-3). More
specifically, landscape data refer to a subset
of spatial data that represent a continuous
or categorical surface describing some aspect of the land surface (e.g., elevation, soils,
landcover). In the context of environmental assessment, this distinction could be
somewhat cumbersome because nonlandscape and landscape data are usually
combined to conduct landscape-level analyses.
For the purposes of this chapter, we describe both sources of data but often refer
to them interchangeably or collectively as geographic information whether they are
remotely sensed or from field samples. We also describe the general forms of spatial
data (raster and vector), common sampling designs and methods for collecting spatial
data, and categories of data often used when conducting environmental assessments.
Lastly, we include a detailed listing of several data sets that have proven to be useful for
conducting environmental assessments.
6.1. SOURCES OF DATA: HISTORIC DATA, FIELD SAMPLING, REMOTELY
SENSED
Collecting spatial and landscape data requires collection of the attributes of
interest as well as the spatial information. We focus more on the later and assume
standard methods are used for collecting the former. In this section, we describe
three common sources of spatial and landscape data including historic/hard copy maps
What is in this chapter? General types of
geographic information are described
• Sources of data and creating coverages
• Sampling designs
• Remote sensors
• Commonly encountered landscape data
Detailed descriptions of data sets and
their potential use in water quality programs are
available in the Toolbox.
6-1
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
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In Situ Field Samples of Actual Stream Temperature (red circles) and
Temperatures Derived from Thermal Infrared Remotely Sensed Data (blue
line) by River Mile (see Chapter 9; ODEQ, 2007).
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
FIGURE 6-3
Remotely Sensed Imagery from Aerial Video True Color Photo and
Thermal Infrared Aerial Photograph (see Chapter 9; ODEQ, 2007).
(see Figure 6-1), field data (see Figure 6-2 and others), and remotely sensed data (see
Figure 6-2 and others). These data are depicted in many formats including tables and
graphs (see Figure 6-2) or converted to landscape coverage (see Figure 6-3 and
others).
6.1.1. Historic Data and Hard Copy Maps
As geographic information systems (GIS) were being developed in the early
1980s, the most common source of data for entry into a GIS were existing hard copy
map products (e.g., U.S. Geological Survey [USGS] Topographic Quadrangle Maps) or
historic data files with some associated spatial information. These data were manually
converted from the hard copy analog form to digital data (i.e., digitizing) by a technician.
Today, this is less common because many historic sources of data have already been
digitized, and new data are collected digitally so the digitization step is not required.
Although spatial data are widely available, the assessment question often requires
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
information that is available only on hard copy maps. Two general methods are used to
convert hard copy, analog maps to digital format:
1) Maps can be manually digitized using a large-format, digitizing tablet and
manually tracing the spatial information on the map to create electronically stored
data. Other attribute data usually needs to be manually entered via a keyboard
and linked with the appropriate spatial feature.
2) A more common method used today is to digitally scan the historic data or hard
copy maps. Spatial and attribute data are then recorded by screen digitizing or
optical character recognition software to translate text data into digital data.
While this is often less labor intensive than the manual approach, scanning
information is still labor intensive.
In spite of the fact that digitizing historic data or hard copy maps is labor
intensive, these data are often invaluable. Knowledge of past conditions enables an
assessor to identify changes on the landscape that can help identify causes or sources
of impairments, and help forecast direction, magnitude, and rate of environmental
changes, risks, and vulnerabilities.
6.1.2. Field Data
Field data sets can consist of chemical, physical, biological measurements taken
at geographical locations (i.e., point data) (see Figure 6-2). Data can also include
information about sources such as outfalls, dams, and other structures. The data can
be tagged with geospatial references from maps or global positioning systems (GPS)
and then converted to data layers for use in a GIS. Samples taken from a site can be
analyzed either in situ or in a laboratory (e.g., chemical analysis, genetic sequencing) or
in toxicity tests, in situ or in a laboratory. In all cases, the geographic location is
maintained as an attribute of the data.
Information to locate position on the earth using GPS is derived from an array of
as many as 34 satellites in the U.S. Global Navigation Satellite System, originally a
military resource that is now available for civilian use. It provides longitude, latitude,
and altitude (elevation) by triangulation typically using 3 or more satellites for reference,
but GPS can use more complex calculations for rapidly moving objects. Horizontal
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
accuracy of GPS units vary widely and range from an accuracy of ±10s of meters to
subcentimeter accuracy. For most water quality applications, an accuracy of
±3-5 meters is adequate and attainable with many widely available GPS units.
6.1.3. Remotely Sensed Data
The scientific literature refers to remote sensing in two distinct ways. In this
document, we consider data remotely sensed when the detector is not in direct contact
with the entity being sampled, such as detectors on a satellite. Stoney (2008) provides
several recent summaries of land imaging satellites. In this document, we consider the
collection of field data collected by a remotely deployed detector in the absence of
personnel as a form of field data, not as remotely sensed data. For example, data from
in-stream temperature probes or hydrologic gauging stations are field data.
There are two kinds of remote sensing: passive and active. For example, family
photos are a form of passive sensing, and sonar by bats is a form of active sensing.
Passive sensors detect natural energy (radiation) that is emitted or reflected by the
object or surrounding area being observed. Reflected sunlight is the most common
source of radiation measured by passive sensors. Aerial photography is an example of
passive remote sensing. Active collection, on the other hand, emits energy to scan
objects. The radiation that is reflected or backscattered from the target is detected and
measured by a sensor. For example, light detection and ranging (LiDAR) is a laser
based active sensor that measures and interprets the time delay between laser pulse
emissions and returns to create elevation models (see Figure 6-4). LiDAR may be used
to develop elevation models of both bare earth and of the surface (i.e., a forest canopy).
For greater detail on remotely sensed data, see sections on Aerial Photos (6.3.3) and
Remote Sensors (6.3.4).
6.2. TYPES OF SPATIAL AND LANDSCAPE DATA
The two most common models for representing spatial data are raster and vector
(see Figure 6-4). For example, elevation data can be represented as a raster (e.g.,
digital elevation models) or as vector (e.g., topography contour lines). Because vector
and raster data are often interchangeable, it is important to understand the
6-5
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
A Raster (left) and Vector (right) Image of River and Two Parallel Roads.
The closer the isopleth lines, the steeper the change in elevation. For
more details, see Chapter 9 case example.
characteristics of each data model and the advantages/disadvantages of using one over
the other. A third data type that is starting to see wider use in ecology is graph models
(Urban et al., 2009). These represent the landscape as a network of connected points
and are based on graph theory. These are beyond the scope of this chapter, but graph
models do hold some promise. Those interested should see Urban et al. (2009) for a
thorough review of graph theory in ecology.
Before the 1990s, the tools and methods for collecting both types of data were
mostly separate. That is no longer the case because most tools and methods take
advantage of both types of data and are capable of converting from one to the other
There are no hard-and-fast rules that determine which model is best suited for a given
category of data because many examples exist with both data models being used.
6.2.1. Vector
Most people are familiar with vector data as displayed on Google Maps and
MapQuest or U.S. Environmental Protection Agency's (EPA's) EnviroMapper site.
These sites display several features over a base map. The legend allows the user to
toggle on and off several sets of vector features such as rivers and National Pollutant
Discharge Elimination System permittee locations (see Figure 6-5). The data format
Elevation Contours
FIGURE 6-4
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
UmaU"'
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Physical Model of Inferred Stream Channels Derived from Remotely
Sensed Imagery of Elevation and First Principle Model from General
Knowledge of Physics. (CTUIR [Confederated Tribes of the Umatilla
Indian Reservation] 2005.)
6-7
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
stores spatial information about features of the land surface as points, lines, or
polygons. This information is stored as a collection of XY coordinates that mark the
location or boundary of a given feature. Each point, line, or polygon feature represents
a single homogenous unit. Vector data format is best suited for data that can be
classified into discrete units (e.g., an individual sampling location, a stream reach, or an
individual pond). The vector data model is a compact data structure. It efficiently
implements the concept of topology (i.e., spatial relationships of multiple features) and
often provides a more realistic representation of real-world features (Aronoff, 1995).
More recent advances in the storage of spatial data (e.g., ESRI Geodatabase Schema
and Data Models) build upon the idea of points, lines, and polygons but allow for more
detailed descriptions of how different data types interact. This is beyond the scope of
this chapter, but readers are directed to Zeiler (2010) for more information.
6.2.1.1. Point
Point data are the simplest type of vector data and have spatial information
stored as a single pair of latitude and longitude coordinates, orXY location (see
Figure 6-6). For example, sampling stations in freshwater streams are usually stored as
point data. The coordinates are the only required information to describe a point;
however, additional information (i.e., wastewater treatment plant outfall, field
measurements for a sampling station such as dissolved oxygen,) can be associated
with each coordinate.
In addition to representing discrete information about a given point, it is not
unusual for point data to be used as a source of information for the creation of raster
data sets through various spatial modeling and interpolation approaches (e.g., kriging).
For example, before satellite imagery, elevation maps were created by collecting and
connecting point data collected from on the ground measurements. Examples include
climate data, ocean currents, and many others. There are many caveats and
requirements that must be understood when performing interpolations from point data.
For details on interpolation methods, common problems, and interpretation, see
Chapter 7.
6-8
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
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6.2.1.2. Line
Stream networks and road networks are commonly stored as line vector data
Line data is used to represent linear features and each feature is stored as a collection
of XY locations at each point (i.e., nodes) where the linear feature changes direction.
The location of each node of a line feature and the length of the line is required to fully
describe a feature. Additionally, each individual line feature can have information
associated with it that describes its various components (e.g , name of the stream,
biological or water quality, direction of flow/travel) (see Figure 6-7).
6-9
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
» Bioassessment Stes /\/ Impaired River I I Watershed
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FIGURE 6-7
Line Map of Streams are Shown with Impaired Stream Segments
indicated in Red and Segments Meeting Water Quality Criteria in
Turquoise or Not Assessed. Point map indicates sampling points labeled
sites 1-4 (IDNR, 2005).
6.2.1.3. Polygon
Lakes and ponds are examples of polygon features. Polygon data represent
areal features and are described by a boundary (i.e., a closed line feature). The
location of each node in the polygon boundary, the perimeter (i.e., length of the
boundary), and the area of the polygon are required to fully describe a polygon feature.
Individual polygon features can have additional Information that further describes the
feature (e.g., lake name, trophic status of the lake, average depth).
6.2.2. Raster
Raster data represent features with equally sized cells (i.e., pixels) that subdivide
the area being mapped. Each individual pixel is treated as a homogenous unit, and a
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
single value is recorded for the cell representing the data being mapped. For instance,
a raster of a continuous surface such as elevation would have an elevation value for
each pixel that represents the integrated elevation of the entire pixel or a raster of a
categorical map of land use/landcover (LU/LC) would have categorical codes
representative of each LU/LC class. Digital image formats and most modern remotely
sensed images are collected and stored in a raster format. The raster data model has
the advantage of being relatively intuitive for overlay operations. It is also able to
adequately represent data with high spatial variability (Aronoff, 1995).
6.3. OBTAINING DATA: COLLECTION
Before collecting or mining for data, the intended use and assessment question
that are being addressed must be considered. In particular, the assessment question
influences the selection of assessment endpoints, the entities and attributes of concern,
and measurements used to estimate them. For example, the attribute might be
presence and abundance of steelhead salmon or percent of riparian cover.
Measurements of salmon abundance might include synoptic surveys or estimates from
creel surveys. Riparian habitat can be estimated by ground surveys or by landcover
maps generated from remotely sensed imagery.
To properly interpret environmental data, it is necessary to know how
environmental samples are collected and data limitations due to such issues as logistics
that constrained the sampling in the field or detection limits of the method. The type of
assessments and the analyses that will be performed also influence the kind of data that
are needed and determine an appropriate sampling design.
6.3.1. Basic Considerations and Planning for Data Acquisition
Because assessments depend on causal relationships, it is important to be able
to match data concerning causal agents and affected or susceptible entities. When
relationships between two or more variables are analyzed, it is important that these data
are appropriately matched and the process for matching data and interpreting results is
documented. At the simplest level, data that are collected at the same time and place
can be matched. However, measurements from environmental assessments often
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
cannot be collected on the same date, and the relevance of that variability must be
taken into account when matching data. For example, hydrologic flow changes rapidly
during and after a storm. Data needed for analysis might be available only from satellite
images created months apart. The mode of action by which environmental parameters
affect environmental features or organisms and how organisms might respond to
changing conditions influences the relevance of variability and how it is addressed.
In general, landcover data changes occur over long time scales and need not be
matched closely in time to water quality and biological data. Large woody debris occurs
in localized areas and changes relatively little over time. Conversely, water chemistry
parameters such as total suspended solids are relatively constant throughout a
waterbody but could be very different when measured at different times and under
different flow conditions. Spatial heterogeneity and temporal stability should be
considered when deciding how data should be matched.
Salmon and other species that regularly move long distances require special
consideration when analyzing spatial associations. Thoughtful and strategic analysis at
the proper scale is needed for modeling relationships so that the results are spatially
and temporally relevant. In evaluating spatial associations, the assessor must consider
changes in causes and factors affecting exposure. For example, potentially affected
organisms could have moved since exposure. The movement of a few individual
organisms from contaminated reaches to upstream reaches might diminish but
generally do not conceal the contrast or gradient among reaches. A qualitative or
quantitative estimate of uncertainty helps to weight the information for decision making
or suggest ways to improve future assessments. However, extensive experience with
bioassessment offish and invertebrate communities has demonstrated that the
movements of these organisms usually are not so great as to prevent the observation of
spatial associations.
The mechanism by which a stressor exerts its effect will determine the
appropriate temporal scale. For example, instantaneous stream temperature collected
at the same time as a biological sample is likely to be less relevant than maxima and
minima or some long-term average stream temperature unless the fish are exhibiting
symptoms of thermal stress (see Figure 6-8). Dissolved oxygen, on the other hand, is
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
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Example Illustrating Matching Temporal Scale of Inherent Causal
Mechanism. Temperatures at Either Time A or C Would Not Inform
Effects at B or C. Temperatures and observations at Time B are most
informative. The mean temperature is more relevant, albeit less precise or
discriminating.
best measured when it reaches its diurnal extremes to determine if critical
concentrations occur. The potential for time lags between exposure and effects also
should be considered. For example, if a stressor, such as a diversion of water flow,
prevents salmon from reaching the sea on their out-migration, the effect (i.e.,
destruction of the salmon run) might not be observed for several years. In preparing a
project plan and analysis plan, it is important to ensure that there is an explicit
statement of the objective of the study and how the assessment endpoints inform the
assessment. The measurements need to be able to characterize the attributes of the
entities. The information needs to be relevant to the spatial and temporal scale of the
environmental problem and known scientific processes and mechanism. Exposure and
effects data must be appropriately matched. And, whenever possible, the assessment
endpoints, the reporting, and overall design should be in a format preferred by those
using the assessment. For example, some decision makers prefer maps supported by
graphical data, while others prefer tables. Color-blind individuals might prefer isopleth
depictions rather than multicolored maps.
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
6.3.2. Types of Sampling Designs for Field Data
6.3.2.1. Targeted Sampling
Targeted sampling is particularly useful
for the characterization of conditions
associated with a particular land use, activity,
or other source. The selection of sampling
units (i.e., the number and location or timing
of collecting samples) is based on logic and
knowledge of the feature or condition under
investigation and on professional judgment, for example, sampling upstream and
downstream of a point source (see Figure 6-9). For an example, see Bellucci et al
(2010) in which a point source was discounted as the cause of stream impairment and
another source was identified (see Figure 6-10). Targeted sampling is distinguished
from probability-based sampling in that inferences are based on knowledge about the
region or system. Therefore, conclusions about the target population are only as good
as that knowledge. Proportional statements about parameters are not possible. As
described in subsequent sections, expert judgment can also be used in conjunction with
probability sampling designs to obtain the benefits of both approaches. Targeted
sampling is useful for ensuring that extremes of exposures are not underrepresented
and to bracket a source.
6.3.2.2. Simple Random
Simple random sampling is useful for characterizing a resource or a relationship
in which extremes or rare events are not as important to the assessment as common
occurrences. For example, what is the range of surface temperatures in a lake or set of
lakes? In simple random sampling, particular sampling units (for example, locations or
times) are selected using random numbers, and all possible selections of a given
number of units are equally likely. For example, a simple random sample of a set of
wastewater treatment plants can be taken by numbering all the facilities and randomly
selecting numbers from that list. Similarly, an area can be randomly sampled by using
pairs of random coordinates. This method is easy to understand, and the equations for
This section describes six sampling designs
and one sampling protocol (i.e.. composite
sampling). Most of these designs are
commonly used in environmental data
collection. Some are designs that are not as
commonly used but have great potential for
improving the quality of environmental data.
Each design is briefly described, and some
information is provided about the type of
applications for which each design is
especially appropriate and useful.
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
A
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FIGURE 6-9
Targeted Sampling Followed by Targeted Resampling after Impaired Site
is Identified.
Target Sites Below Permitted Sources
Impaired Target Sites
Sites Selected to Isolate Sources
Streams
Watershed
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
Staffordville Resovoir
MR1
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FIGURE 6-10
Adaptive Sampling (open circles) was Done after Impairments were Found
at Filled Circles Near the Publicly Owned Treatment Works. An illicit
discharge was found in the Middle River (Bellucci et al, 2010).
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
determining sample size are relatively straightforward. Simple random sampling is most
useful when the population of interest is relatively homogeneous (i.e., no major patterns
of contamination or hot spots are expected).
The main advantages of this design are as follows:
• It provides statistically unbiased estimates of the mean, proportions, and
variability.
• It is easy to understand and easy to implement.
• Sample size calculations and data analysis are very straightforward.
However, in some cases, a simple random sample is difficult to obtain because
of geographical barriers or unsafe conditions. Implementation of a simple random
sample can be more difficult than some other types of designs (for example, grid
samples) because of the difficulty of precisely identifying random geographic locations.
Additionally, simple random sampling can be more costly than other plans if there are
additional costs to obtain samples because of private land ownership or other reasons.
6.3.2.3. Stratified Random Sampling
A relatively well-known program that uses a stratified random sampling design is
EPA's Environmental Monitoring and Assessment Program (EMAP). The EMAP uses
stratified random sampling to estimate the various attributes of stream quality and
aquatic life. Sampling sites are randomly selected from a sample of streams from
several groups of streams of the same general size, on the basis of Strahler order, in an
ecoregion (see Figure 6-11). This ensures that a representative sample of several
groups of streams is characterized and is proportional. The same principle can be used
to sample similar assessment endpoints using time, areal area, location, or other
quality. Specifically, the target population is separated into nonoverlapping sample
populations, or subpopulations that are known or thought to be more similar relative to
other subpopulations, so that there tends to be less variation among sampling units in
the same subpopulation than among sampling units in different subpopulations.
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
FIGURE 6-11
The Filled Circles are First Order Streams, and Open Circles are Second
Order Streams.
This design is useful for estimating a parameter when the target population is
heterogeneous and the area can be subdivided on the basis of expected differences.
Advantages of this sampling design are that it has potential for achieving greater
precision in estimates of the mean and variance, and that it allows computation of
reliable estimates for population subgroups of special interest. Greater precision can be
obtained if the measurement of interest is strongly correlated with the variable used to
make the strata. As this is a random design it also suffers from some of the same
drawbacks as a simple random design. In particular it is not the most appropriate
design for capturing extreme values or rare events.
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
6.3.2.4. Systematic and Grid
In systematic and grid sampling, samples are taken at regularly spaced intervals
over space or time. An initial location or time is chosen at random, and then the
remaining sampling locations are defined so that all locations are at regular intervals
over an area (grid), a linear feature such as a stream (systematic—see Figure 6-12) or
time (systematic). Examples of grids include square, rectangular, triangular, or radial
grids (see Section 16.6.2.0 of Myers [1997]). In random systematic sampling, an initial
sampling location (or time) is chosen at random, and the remaining sampling sites are
specified so that they are located according to a regular pattern (Cressie, 1993).
Systematic and grid sampling is used to search for hot spots and to infer means,
percentiles, or other parameters, and it is useful for estimating spatial patterns or trends
over time (e.g., useful design for spatial interpolation). This design provides a practical
and easy method for designating sample locations and ensures uniform coverage of an
area or process.
6.3.2.5. Ranked Set
Ranked set sampling strategically uses information gained from inexpensive,
logically ranked, or screening sampling to focus intensive or more costly sampling. For
example, existing field data and land-use data can be used to predict areas more likely
to exceed criteria for pathogens (Smith et al., 2001). Bracketed sampling can help
home in on a point source. In the impervious surface example in Chapter 8, sites at risk
are targeted for sampling relative to those areas not likely to show impairments with
static landcover (see Figure 6-13). Relative to simple random sampling, this design
results in more representative samples and so leads to more precise estimates of the
population parameters. In practical terms, it can more efficiently identify locations in
need of remediation or rehabilitation.
Ranked set sampling is useful when the cost of locating and ranking locations in
the field is low compared to laboratory measurements. It is also appropriate when an
inexpensive auxiliary variable (selected on the basis of expert knowledge or
measurement) is available to rank population units with respect to the variable of
6-19
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• Sample Sites
Streams
C3 Watershed
FIGURE 6-12
Patterns of Regularly Spaced Samples. One site nearest the center of the grid is
spacing along a Mainstem is another type of design (right).
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
Legend
Total Impeivioiis Area by HUC12/
Atlanta Area Total Impervious Area by HUC
and Impaired Waters by Type
FIGURE 6-13
Impervious Area Cover and Impaired Waters Causes for Metropolitan
Atlanta can be Used to Focus Where to Sample for Impaired Waters, The
relative amount of impervious surface can help to target areas that require
monitoring (red, orange, yellow) compared to those that are less likely to
be impaired (shades of green).
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
interest. To use this design effectively, it is important that the ranking method and
analytical method are strongly correlated.
6.3.2.6. Adaptive Cluster
In adaptive cluster sampling, n samples are taken using simple random
sampling, and additional samples are taken at locations where measurements exceed
some threshold value or are used to zero in on a target. Several additional rounds of
sampling and analysis might be needed. Adaptive cluster sampling tracks the selection
probabilities for later phases of sampling so that an unbiased estimate of the population
mean can be calculated despite oversampling of certain areas. An example application
of adaptive cluster sampling is delineating the borders of a plume of contamination.
Initial measurements are made of randomly selected primary sampling units using
simple random sampling. Whenever a sampling unit is found to show a characteristic of
interest (e.g., contaminant concentration of concern or ecological effect), additional
sampling units adjacent to the original unit are selected, and measurements are made.
Adaptive sampling is useful for estimating or searching for rare characteristics with in a
population and is appropriate for inexpensive, rapid measurements. It enables the
boundaries of hot spots to be delineated, while also using all data collected appropriate
weighting to give unbiased estimates of the population mean (Johnson et al., 2009).
6.3.3. Aerial Photos
Air photos are photographs collected in the air from a camera mounted on an
airplane, helicopter, or satellite. Aerial photographs are produced by exposing film or a
digital sensor to solar energy reflected from Earth. Photographic media have been used
for aerial reconnaissance since the middle of the 1860s; color film became widely used
in the 1950s. Black-and-white and color-infrared films are used today in both high- and
low-altitude aerial photography. Natural-color film is used more rarely because it is
often affected by atmospheric haze. Color-infrared film, which records energy from
portions of the electromagnetic spectrum invisible to the human eye, near-infrared light
reflected from the scene appears as red, red appears as green, green as blue, and blue
as black. Color-infrared film is useful for distinguishing between healthy and diseased
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
vegetation, for delineating bodies of water, and for penetrating atmospheric haze. New
digital airborne cameras provide high-quality airborne imagery at any user defined
resolution. Film-based (analog) air photos must be scanned to convert them to a digital
format before their incorporation into subsequent modeling or landscape analysis.
Obtaining Images—USGS supports air photo development and distribution
through several programs, which are described in detail at
http://www. usgs. gov/pubprod/aerial. htm l#aerial.
Recent computer and Web-based developments have made disseminating air
photos much more convenient to the general public. It is often the case that recently
collected high-resolution ortho-photos are available for download from state or federal
Web sites. In addition, several Web-based services are available to provide
high-resolution imagery for instant downloading in the GIS environment. Two examples
are GlobeXplorer (www.globexplorer.com) and TerraServer (www.terraserver.com).
Additionally, Web services (e.g., Web map services [WMS] and Web feature services
[WFS]) are quickly becoming the preferred method for GIS and Web applications to
interact with large spatial datasets. Google Earth (www.googleearth.com) as an open
source site and a professional version provide another option for obtaining imagery (see
Figure 6-14).
6.3.4. Remote Sensors
Remotely sensed imagery can be collected from reflected energy from objects on
the earth or the earth itself. Most energy is reflected from the sun; however, as
mentioned previously, active sensors both emit a signal and capture the reflected
energy. Energy from fires, electric lights, organisms, vehicles, geothermal sites, and
other sources can also be captured. Detectors are designed to capture or can be
programmed to capture only one band or up to hundreds of bands of wavelengths.
Many passive sensors are a kind of spectrophotometer similar to the laboratory
instruments used to measure concentration of a solution. Passive remote sensors
measure reflected energy, which is akin to transmittance on a laboratory bench-top
spectrophotometer. For example, chlorophyll absorbs red and blue and reflects green.
The specific wavelengths and amount of signal compared to a black reference on the
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
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ground (usually asphalt pavement) can be used to estimate presence and
concentration. For example, suspended sediment, phosphorous and nitrogen levels
have been estimated from vegetation and phytoplankton (see Figure 6-15) (Senay
et al., 2001). Spectral libraries have been created to interpret imagery including
information about geological and biological materials. These often require that signals
from different wavelengths are measured and compared from the same object. The
same concept is applied to bench-top spectrophotometry. For example, a colorimetric
assay for protein reads a band around 640 nm or the uncolored protein concentration
can be estimated from ultraviolet at 280 nm. Because colorimetry using dyes is
impractical for most landscape applications except tracer dye studies, a greater range of
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
|lilOro|>hyll-a
Distribution
FIGURE 6-15
Chlorophyll a Concentration Map Developed from the Spectral Index.
Brighter yellow indicates greater concentration of chlorophyll. Note that
wastewater treatment plant discharge to a ditch dilutes the algae in the
mainstem.
Source: Senay et al. (2001).
information can be gained by hyperspectral imagery, Hyperspectral sensors collect
signals from many wavelengths similar to scanning spectrophotometry in a laboratory.
These data can be used to distinguish chlorophylls from phaeopigments, for example, to
estimate photosynthetic systems from nonfunctioning plant pigments. The three main
types of passive sensors are described below. Note these can be hand-held for
ground-truthing and calibration or attached to an aircraft or satellite.
Panchromatic Imagery—A panchromatic image consists of only one band. It is
usually displayed as a grayscale image (i.e., the displayed brightness of a pixel is
proportional to the pixel digital number, which is related to the intensity of solar radiation
reflected by the targets in the pixel and detected by the detector.) Thus, a panchromatic
image can be similarly interpreted as a black-and-white aerial photograph of the area.
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
Multispectral Imagery—A multispectral image consists of several bands (e.g.,
~10 or less) of data. Most multispectral imagers measure reflectance at a few wide
wavelength bands separated by spectral segment where no measurements are taken.
For visual display, each band of the image can be displayed, one band at a time, as a
grayscale image, or in combination of three bands at a time as a color composite image.
Interpreting a multispectral color composite image require knowledge of the spectral
reflectance signature of the targets in the scene. In such cases, the spectral information
content of the image is used in the interpretation. Land remote sensing satellite
(Landsat) data is an example of multispectral imagery and is discussed later in this
chapter.
Hyperspectral Imagery—Most hyperspectral sensors measure reflected radiation
as series of narrow and contiguous wavelength bands. Although it is true that most
hyperspectral sensors measure hundreds of bands, it is not the number of measured
wavelength bands that qualifies a sensor as hyperspectral, but rather the narrowness
and contiguous nature of the measurements that designates the imagery as
hyperspectral. Ultimately, hyperspectral image analysis provides an opportunity to
develop a much more detailed description of surface conditions than is available
through standard multispectral image processing techniques. As a result of relatively
high costs and very high processing and data storage requirements, hyperspectral
image analysis is often associated with large multi-institutional programs (for example,
www.cicore.org) or is limited to small geographic areas.
Interpreting remotely sensed imagery can be relatively simple but often requires
significant preprocessing before information can be obtained from the raw spectral data.
For example, aircraft fly in straight paths when collecting imagery. Because rivers often
are sinuous, more than one flight path is needed to capture the feature. Images
generated by flight paths or a series of photographs need to be composited and
georeferenced. Furthermore, remotely sensed images are affected by the orientation of
the camera or sensor, topographic relief, earth curvature, and position of the sun. Haze
and clouds can also affect the signal. In the case of aircraft flight paths, data are
collected regarding the yaw, pitch, and roll of the aircraft. Geometric errors associated
with the collected data are corrected by a process called ortho-rectification. Be aware
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
that remotely sensed images are sold without being ortho-rectified, and correcting this
takes time and can be costly.
If a high degree of accuracy is needed, such as with hyperspectral imagery or
topographies with abrupt or a high degree of changes in elevation, ortho-rectification is
necessary.
Finally, four distinct types of resolution are associated with remotely sensed data:
Spatial Resolution—Spatial resolution is a measure of the smallest area that can
be resolved by the sensor or the area on the ground represented by each pixel. A
related concept is the minimum mapping unit which generally corresponds to a 3 * 3
pixel area. The altitude from which an image is taken and the physical characteristics of
the sensor, such as the lens focal length in the case of cameras, largely determine the
area covered and the amount of detail shown. In general, the level of detail is greater in
low-altitude photographs that cover relatively small areas, while satellite images cover
much larger areas but show less detail. Landsat has a swath width of 185 km and
captures seven bands. Bands 1-5 and 7 have 30-meter resolution; whereas band 6, a
thermal infrared band, has a resolution of 120 meters.
Spectral Resolution—Spectral resolution refers to the specific wavelength
intervals in the electromagnetic spectrum that a sensor can record and is very important
in determining the usage of the imagery.
Temporal Resolution—Temporal resolution refers to the frequency of image
acquisition for the same location. Landsat, for example, has a repeat cycle of 16 days.
Other satellites geosynchronous orbit the earth such as GPS satellites or spy satellites.
Sampling at different times is necessary to determine changes such as vegetative
landcover assessments.
Radiometric Resolution—Radiometric resolution refers to the number of data files
within each band of data. For example, with 8-bit data values can range from 0 to 255
for each pixel, and with 16-bit data values can range from 0 to 32,767.
6.4. SPATIAL DATA COMMONLY USED IN ENVIRONMENTAL ASSESSMENT
Short descriptions for several commonly used spatial data are presented below.
This is not intended to be a complete list of available data sets and data sources but a
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
short list of commonly used base maps. Many of the data sets presented below were
used as base layers in producing several of the examples presented earlier in this
chapter. For example, Landsat data were used, along with other data sets, to create
the National Land Cover Dataset (NLCD, MRLC, 2007). A short description, internet
links, and other information for selected data sets are provided in the Geospatial
Toolbox accessible from the Risk Assessment Forum or Watershed Central websites.
Many of these data sets are available through the Seamless Data Distribution System
(http://seamless.usgs.gov) which offers seamless data for a user-defined area, in a
variety of formats, for online download or media delivery.
6.4.1. Land Use/Landcover (LU/LC)
There are many data sets available that represent recently produced landcover
data sets at a variety of scales that incorporate consistent methodologies across the
nation. Each data set describes a different spatial component: contiguous United
States (lower 48) states (MRLC, 2007); coastal (Coastal Change Analysis Program),
and near-shore environments (Benthic Mapping). For details see the Geospatial
Toolbox accessible from the Risk Assessment Forum or Watershed Central websites.
6.4.2. Elevation
Elevation data is available in a variety of resolutions and from a variety of
sources. One of the most commonly used elevation data sets for the United States is
the USGS National Elevation Dataset, which has been developed by merging the
highest-resolution, best-quality elevation data available across the United States into a
seamless raster format (see Geospatial Toolbox accessible from the Risk Assessment
Forum or Watershed Central websites).
6.4.3. Hydrology/Hydrography
Hydrology is the study of the movement, effects, distribution, quantity, and quality
of water throughout the Earth, and thus addresses both the hydrologic cycle and water
resources. Several national Web-based databases and data sets are available (see
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
Geospatial Toolbox accessible from the Risk Assessment Forum or Watershed Central
websites).
6.4.4. Climate
Climate involves the study of past meteorological records to reveal patterns and
trends in weather over long periods of time. Climate GIS data sets are modeled from
point data and describe climate conditions across the landscape (see Toolbox).
6.4.5. Soils
The NRCS has developed two primary soil geographic databases representing
kinds of soil maps: (1) Soil Survey Geographic database, and the (2) State Soil
Geographic database (see Toolbox). The maps are produced from different intensities
and scales of mapping. Each database has a common link to an attribute data file for
each map unit component.
6.4.6. Sources and Stressors
Sources in the context of this chapter broadly refer to landcover or land use
changes, which can affect water quality, quantity and biology of nearby and downstream
waters. Stressors refer to the pollutant or other agent. Often this type of data is
developed on the regional/local scale. Descriptions of sources associated with
commonly encountered stressors are described in text and conceptual diagrams in the
Causal Analysis Diagnosis Decision Information System (CADDIS) in the section
describing common candidate causes (see Figure 6-16).
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Section I!—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
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Section II—Chapter 6: Types of Spatial and Landscape Data and Sampling Designs
Bellucci, C., G. Hoffman, and S. Cormier. 2010 An Iterative Approach for Identifying
the Causes of Reduced Benthic Macroinvertebrate Diversity in the Willimantic River,
Connecticut. U.S. Environmental Protection Agency, Office of Research and
Development, National Center for Environmental Assessment, Cincinnati, OH.
EPA/600/R-08/144. Available online at
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201947.
Cressie, N. 1993. Statistics for Spatial Data. Revised Edition. Wiley, NY, 928 p.
CTUIR (Confederated Tribes of the Umatilla Indian Reservation). 2005. Confederated Tribes of
the Umatilla Indian Reservation Total Maximum Daily Load for Temperature and Turbidity.
Department of Natural Resources. Issued by U.S. EPA Region 10. Available online at
http://www.epa.gov/waters/tmdldocs/12245_CTUIR%20TMDL%20%20July%2005.pdf
(Accessed 03/05/2010).
IDNR (Iowa Department of Natural Resources). 2005. TMDL & Water Quality
Assessment Section. Total Maximum Daily Load for Sediment and Dissolved Oxygen,
Little Floyd River, Sioux and O'Brien Counties, Iowa. IDNR, TMDL and Water Quality
Assessment Section. Available online at
http://www.epa.gov/Region7/water/pdf/littlefloyd_riv_sediment_do_final060605.pdf.
Johnson, M.S., M. Korcz, K. von Stackelberg, and B.K. Hope. 2009. Spatial analytical
techniques for risk based decision support systems. In: Decision Support Systems for
Risk-based Management of Contaminated Sites. A. Marcomini, G. W. Suter II, and A.
Critto, Ed. pp. 1-19.
Lambert, J. (Topographer). 1859. 1853-1854 Map, Columbia River, including the Hood
River to John Day area (section of original) 1:1,200,000. Notes: From the U.S. War
Department, Explorations and Surveys for a Railroad Route from the Mississippi River
to the Pacific Ocean, Topographical Maps, to Illustrate the Various Reports, U.S. Library
of Congress American Memories Reference "LC Railroad Maps #156" (1959), U.S.
Library of Congress, American Memories Website, 2004. Available online at
http://vulcan.wr.usgs.gov/LivingWith/Historical/LewisClark/Historical/Maps/loc-
archives_map_hood_river_john_day_1854.jpg.
Myers, J.C. 1997. Geostatistical Error Management: Quantifying Uncertainty for
Environmental Sampling and Mapping. John Wiley and Sons, New York. 571 p.
MRLC (Multi-Resolution Land Characteristics Consortium). (2007) National Land
Cover Data (NLDC) for 1992, 2001, and 2006. Available online at
http://www.epa.gov/mrlc/nlcd-2006.html.
ODEQ (Oregon Department of Environmental Quality). 2007. Coordinating the
Temperature Water Quality Standard and Umatilla Subbasin TMDL: Practical
Considerations and Cumulative Effects Analysis. 20 p. Available online at
http://www.deq.state.or.us/wq/tmdls/docs/umatillabasin/umatilla/coordtemperature.pdf.
Accessed 11/08/2008.
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Senay, G.B., N.A. Shafique, B.C. Autrey, F. Fulk, and S.M. Cormier. 2001. The
selection of narrow wavebands for optimizing water quality monitoring on the Great
Miami River, Ohio using hyperspectral remote sensor data. J. Spatial Hydrol.
1 (1): 1 —22.
Smith, J.H., J.D. Wickham, D.P. Norton, T.G. Wade, and B.K. Jones. 2001. Utilization
of landscape indicators to model potential pathogen impaired waters. J. Am. Water
Resour. Assoc. 37(4):805-814.
Stoney, W.E. 2008. Guide to Land Imaging Satellites. American Society for
Photogrammetry and Remote Sensing. Available online at
http://www.asprs.org/Satellite-lnformation/Guide-to-Land-lmaging-Satellites.html.
Urban, D.L., E.S. Minor, E.A. Treml, and R.S. Schick. 2009. Graph models of habitat
mosaics. Ecol. Lett. 12:260-273.
Zeiler, M. 2010. Modeling Our World: The ESRI Guide to Geodatabase Concepts.
Second Edition. Esri Press, Redlands, CA. pp. 308.
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
7. METHODS AND TOOLS FOR ANALYZING SPATIALLY EXPLICIT
INFORMATION
Susan Cormier, U. S EPA Office of Research and Development, Cincinnati, OH
Jeff Hollister, U.S EPA Office of Research and Development, Cincinnati, OH
Environmental decisions are not made
using raw data. Data must be analyzed to
make it useful and informative. Descriptive
statistics characterize the population or area
from which data is obtained and can be used
to track changes over space or time.
Extrapolation from first principle and
empirical models can be used to estimate
stressors and their sources, target
monitoring, project likely outcomes of
remediation, and help target and prioritize remedial effort. In a nutshell, analyses that
demonstrate causation can be used to understand why a watershed has changed and
predict how it can change in the future and decide what to do to protect and rehabilitate
environmental quality.
7.1. INTRODUCTION
Spatially explicit data can be used in any assessment and any facet of an
assessment. As previously discussed in Chapter 3, assessments are composed of
three activities: planning, analysis, and synthesis. Furthermore, assessments often
depend on other assessments to be successful. A condition assessment that discovers
a problem is simply a witness to environmental decline. It does little to inform action
unless there are subsequent assessments. Similarly, a remedial action plan that is
directed at the wrong cause or source will probably fail. Seeing the bigger picture from
a landscape, watershed, or regional perspective helps to remind us that integrating
assessments is often necessary. Also, the imagery provided by maps, charts, and other
What is in this chapter? Several common
approaches are described for analyzing
geographical information from field or
remotely sensed data. The chapter is
organized by the three common elements of
assessment: planning, analysis, and
synthesis. Highlights include general
analytical considerations for descriptive and
associative analyses, example methods and
interpretations for in situ data used to
understand causal relationships, and
analysis of spatial data using overlays, map
algebra and other spatial analytical methods.
Statistical and spatial analysis tools and their
potential use in water quality programs are
available in the Toolbox.
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
outputs are wonderful communication tools that can make environmental problem
solving a community affair.
This section expands on planning, analysis, and synthesis with a greater focus
on using the data and combining the data from the field with remotely sensed data.
Because of the breadth of this topic, we refer to other sources for more detailed
explanations of statistical, modeling, and spatial analysis. Instead, we try to provide a
basic primer for different types of statistical analysis using spatially linked data.
The steps recommended here emphasize an iterative approach to analysis and
data gathering, the importance of paired stressor and response data, and focus on
decisions relevant to regulatory monitoring and assessment. Most examples presented
here are related to the Clean Water Act (CWA), however, successful Comprehensive
Environmental Response, Compensation, and Liability Act (CERCLA) programs also
depend heavily on very similar assessment approaches.
As more layers of data and information are brought together, there is more effort
in the activities involved in the assessment. These include careful planning for more
intensive geographic information system (GIS) and statistical analyses, gathering
landscape and in situ data specifically tailored to the project and analysis methods, and
deriving a wider array of landscape factors. Additional statistical analyses are needed
to reduce the number of variables and to develop robust stressor/response
relationships, and more formal analysis of the statistical power (and likelihood of false
negatives and false positives—alpha and beta) of the results. Successful assessments
often require peer reviewing and publishing the results, and wider and more formal use
of extrapolations to make targeting, priority, and other water quality management
decisions.
7.2. PLANNING AND PROBLEM FORMULATION
7.2.1. What New Information is Sought?
Four major types of assessments are described in detail in Chapter 3. Here we
remind you that it is essential to know what you are trying to do and the sequence of
activities you might need to perform. Some activities and products are listed in
Table 7-1. Organization is essential. If you know the cause and the source and want to
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
TABLE 7-1
Activities Associated with Different Assessment Types
Type of
Assessment
Activity Performed by the Assessment
Condition
Identify impaired waterbodies or other ecosystems
Identify impaired waterbodies or other ecosystems that were not
sampled in situ
Causal Pathway
Determine the cause of a biological impairment
Find the sources of a pollutant or stressor
Estimate the amount of stressor contributed by each source
Predictive
Estimate the risk to a valued resource from a stressor
Estimate the amount of stressor that can occur while also
maintaining designated uses
Evaluate options for preventing degradation or remediating
ecosystem quality
Develop protective or remedial benchmarks such as water quality
criteria and standards
Target areas to prevent degradation
Prioritize among options and targeted areas for management action
Outcome
Evaluate the performance of management actions and BMPs to
reduce stressor loads
Evaluate the effectiveness of actions to protect or rehabilitate
ecosystem services
All
Develop communication materials for education and partnership
programs
BMP = best management practice.
Note: Also see Table 1-2: Spectrum of Uses for Landscape and Predictive Tools.
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
regulate the release of the substance, you do not need a condition or causal pathway
assessment. Instead, you need a predictive assessment to develop mitigation or
protective benchmarks or perhaps even water quality criteria. If you want to
cost-effectively place a limited number of on-the-ground controls in a watershed, that is
also a predictive assessment; however, it is done differently from a criterion assessment
(Suter and Cormier 2008b).
Also, beware that many costly remediation efforts have fixed the wrong problem
because an ecosystem condition was natural or because the cause or source was
assumed. Even more studies have been done that never informed a single
environmental decision. Being acutely aware of the objectives and the implementation
team is the difference between work that is interesting and work that is interesting and
guides environmental protection and rehabilitation.
7.2.2. What is the Regulatory Authority or Social, Political, Economic Driver?
Assessments are necessary to implement all other types of regulations. For a
review, see U.S. Environmental Protection Agency (EPA, 2010a) and Chapter 2 for
some of the CWA sections and related programs. However In some cases, the analysis
is prescribed and others they are not. In all cases, a sound scientific argument is
allowed, but it must be more thoroughly defended. Therefore, using some of the newer,
more powerful statistical and spatial analysis tools requires a bit more explanation in
reporting.
7.2.3. What Needs Protecting or Rehabilitating?
All environmental assessments require that the assessment endpoint be defined.
The more precisely defined the assessment endpoint, the more likely that the analyses
will meet the needs of the needs of the assessment (Suter, 2007, Cormier and Suter,
2009). Assessment endpoints might be physical, chemical, or physiological attributes of
entities such as organisms, water, habitat, ecosystem functions, or ecosystems in their
entirety. Some typical examples are listed below:
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
• Habitat/channel/geomorphology
o e.g., sinuosity, embeddedness, clarity, depth, suitability
• Chemistry
o e.g., concentrations relative to criteria
• Hydrology
o e.g., removal, flashiness, low flow, overland flow, ground water
• Biology
o e.g., species richness, abundance, reproduction, presence/absence, invasive
species
• Temperature
o e.g., extremes, climate change
7.2.4. What Type of Analyses Need to be Performed and How Good do They
Need to be to Make a Decision?
Aim for scientific elegance, parsimony. A precise value might not be needed to
make an environmental decision. Qualitative information or a range often can get things
moving in the right direction. Easier, faster, cheaper assessments mean that more
assessments can get done (Suter and Cormier, 2008a). Consider the type of analyses
and the data that is absolutely necessary versus what would be nice to have. Some
examples of useful data are:
• Gradient of sites covering full range of stressors
• Laboratory studies
• Probability survey data: biological response and stressors
• Before/after and control/impact designs
• Remotely sensed data
• Preprocessed spatial information
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
Will a screening assessment suffice? Can adaptive management approaches be
reasonably used? Are existing data sufficient to answer questions with required
sensitivity? If not, is additional data needed? The next step is to draw up a project plan
and assemble the data sets and tools (see Geospatial Toolbox).
7.3. ANALYSIS
Because of the range of potential types of assessments, places, and objectives,
we provide some general and common methods and tips but must rely on the many
statistical, spatial analysis, and decision support publications and Web sites for greater
detail. The examples in Section III illustrate different types of analysis, but the
associated reports also provide much more detail. The intent is to provide a primer and
show the connections needed to complete a wide variety of assessments and analyses.
The list of methods in Chapter 7, while not exhaustive, will provide a basis for
conducting assessments that use spatially distributed data to inform assessments. The
statistical methods were largely taken from the Analyzing Data section of the Casual
Analysis Diagnosis Decision Information System (CADDIS) Web site
(http://cfpub.epa.gov/caddis/analytical_tools.cfm) and are associated with CADStat
(http://cfpub.epa.gov/caddis/analytical_tools.cfm?Section=144), an interface to easily
perform these analyses in R, which is an open-source statistical package.
7.3.1. Software
There are numerous sources of software available for conducting statistical
analyses. Many of the software tools are general use tools that provide access to a
range of statistical tools, while others are designed for a specific suite of analytical
methods. The list below discusses both and presents examples of commercial and
open-source solutions.
7.3.1.1. Commercial
Some commonly available commercial software includes S-plus, SAS, SPSS,
and Matlab. However, even commercial spreadsheets such as Microsoft Excel have
some statistical computing capability.
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
7.3.1.2. Open Source
R (http://www.r-project.org) is a free software environment for statistical
computing and graphics. It compiles and runs on a wide variety of UNIX platforms,
Windows, and MacOS. It is capable of running all the statistical analyses offered by
commercial products; however, it is somewhat more difficult to master. For commonly
performed analyses described in this chapter, a user-friendly front end can provide the
statistical code thus simplifying analysis.
One is CADStat (http://www.epa.gov/caddis/da_software_cadstat.html), a
menu-driven package of several data visualization and statistical methods. It is based
on a Java Graphical User Interface to R (http://www.xmarks.com/site/
jgr.markushelbig.org/JGR.html). Methods in this package include scatter plots, box
plots, correlation analysis, linear regression, quantile regression, conditional probability
analysis, and tools for predicting environmental conditions from biological observations.
Another is CProb 1.0, which is a tool written using R as the back-end statistical
processor and Microsoft Excel as the front end interface to calculate conditional
probabilities and bootstrapped estimates of confidence intervals. CProb 1.0
(http://www.epa.gov/emap/nca/html/regions/cprob/) is a Microsoft Excel Add-in,
developed with the R language and environment for statistical computing, the R(D)Com
Server and Visual Basic for applications, intended to aid in conditional probability
analysis. It offers options to generate scatter plots, cumulative distribution functions,
and conditional probability plots (Hollister et al., 2008).
7.3.2. Geographic Information System Tools
7.3.2.1. Commercial
Arclnfo and ArcView, marketed by Environmental Systems Research Institute,
are the dominant commercial GIS analysis software systems. These high-quality
systems are heavily licensed. IDRISI was one of the first raster-based systems and
was developed by Clark University, Worcester, Massachusetts, to provide access to
GIS tools in less developed countries. As such, it is simple to use and master. ERDAS
(Earth Resource Data Analysis System) Imagine® is proprietary software for GIS and
image processing. ENVI is often used to interpret remotely sensed imagery. For more
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
commonly used proprietary software, see
http://en.wikipedia.org/wiki/List_of_GIS_software.
7.3.2.2. Open Source
There are many open-source GIS analysis and visualization software packages.
They all require time and effort to use and master. Geographic Resources Analysis
Support System, commonly referred to as GRASS, is a GIS that has been in existence
for many years and provides many of the capabilities most environmental assessors
can use such as geospatial data management and analysis, image processing,
graphics/maps production, spatial modeling, and visualization (http://grass.itc.it/).
However, there are many other systems, and users should consider the objectives and
the capabilities of these systems before investing the time it takes to organize a spatially
explicit database. Chameleon (http://en.wikipedia.org/wiki/Chameleon_%28GIS%29)
provides a front end for building applications with MapServer
(http://en.wikipedia.org/wiki/MapServer), a very commonly used Web-based mapping
server, developed by the University of Minnesota. For an extensive list, see
http://opensourcegis.org/.
7.3.3. Analysis of Field-Collected Data
As a part of planning, the waterbody or region should already be chosen. In the
analysis phase, the appropriate geographic frameworks and temporal scale are
explicitly described and selected for a variety of field and remotely sensed data and
analysis. They can include mapped areas such as stream reach, catchments,
ecoregions, or other appropriate classifications to establish realistic areas for analysis,
extrapolation, and assessment. Existing frameworks have been described in Chapter 5,
but in some cases, the data will need to be classified and normalized before analysis
can be done. This is described in Sections 7.3.3.2 and 7.3.3.3 of this chapter. The
order can vary. For example, a general geographical framework can be chosen, data
sets assembled, and then the geographic framework revisited so that data sets can be
matched to the proper temporal and geographic scale or classified and normalized for
other reasons.
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It might be possible to prepare preliminary wall-to-wall landscape and other data
coverages to document natural and stressor gradients and exposure. These can be
helpful for other steps in the analyses and are useful to refine and plan analyses. Some
activities are the delineation of watershed boundaries and riparian proximity buffers for
sites and other appropriate landscape factors. Early in the analysis it is useful to map
landcover, sources, waterbody, road, and other relevant information. For some
predictive assessments, it is helpful to model patterns of movement and migration of
biological entities.
7.3.3.1. Linking Data and Scale
As mentioned previously, assessments involve analyzing data that are based on
causal relationships (Suter and Cormier, 2008a). Evidence of causal relationships is
developed by demonstrating an association between two or more variables that include
the cause and the effect (Cormier et al., 2010). The data that are used must be
appropriately matched in time and space. The process for matching data and
interpreting results also must be documented to ensure quality and transparency.
However, measures from biological assessments cover a spectrum of temporal and
spatial possibilities, and the relevance of that variability must be taken into account
when matching data. The mechanisms by which environmental parameters affect
organisms and how organisms can respond to stressful conditions influences the
relevance of variability and how it is addressed. Relevant spatial and temporal scales
should be considered when deciding how data should be matched. Consider the logic
of the situation and possibly use a GIS for modeling spatial relationships. For details
and perspectives on scale in ecological analyses, see Wu et al. (2006).
Because matched data and the resultant data sets are usually synthesized from
various sources, it is of vital importance to explicitly address data management
concerns. These concerns range from choosing a database management system (e.g.,
simple flat text files, spreadsheets, relational databases) to documenting details about
the synthetic data sets (i.e., with metadata). Without adequate documentation of all
data management decisions, it would be difficult, if not impossible, to repeat or defend
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
the analysis. For more information on ecological and environmental data management
and metadata standards, see Michener (2000, 2006).
General considerations when developing a data set for analysis include:
• Matching data for relevant spatial and temporal scale, quality, and comparability.
• Identifying inherent assumption about the appropriateness of measurements,
such as mean, extremes, rates, or other endpoint.
• Exercising caution when merging datasets especially when sampling schemes or
methods are different.
7.3.3.2. Classification
During the Analysis Phase, field data are analyzed by assessors to classify
ecosystems into ecologically similar classes in a way that reduces the influence of
natural variables (see Chapter 5). In this way, the remaining differences among
locations are more likely to be due to the variable of interest rather than other factors.
For ways to deal with potential confounding that may not be related to geographically
related variables, see Appendix B in EPA (2011). Aquatic systems that have
comparable exposure-response relationships are used to classify system. For example,
low-gradient coastal systems have different substrate compositions, flow regimes,
climate, soils, and landcover than high-gradient, montane streams. Classification is
also used to assure that the assessment endpoints are comparable across the region
being assessed and to assure that the region used to develop models is representative
of the local area being studied. For example, species native to Alaska would not be
used to develop temperature tolerances for Florida. Assessors might also classify the
data sets for analysis on the basis of tiered aquatic life uses (U.S. EPA, 2005; Davies
and Jackson, 2006).
7.3.3.3. Normalization
Normalizing data allows different types of data to be compared and analyzed
(U.S. EPA, 2010a). For data that is strongly influenced by a known natural factor, the
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
association is quantified and then removed from the signal of the variable of interest.
For example, stream size affects the diversity of fish. Regressing basin area against
species richness generates a fairly linear curve. The residual can be removed so that
the effect of stream size is normalized.
For measurements that are qualitatively different, the values can be converted to
proportions or placed in categories with or without weighting. Another option is to
convert values to utilities (Efroymson et al., 2004; Hanley and Spash, 1993; Linkov
etal., 2006).
7.3.3.4. Descriptive/Association
After the data set is characterized, information can be extracted by discovering
possible patterns and associations between factors that can interact or that act as
surrogates and are measured from satellite imagery (Senay et al, 2001). This is done
with univariate or multivariate procedures. The methods indicate how to perform and
interpret exploratory analysis for collocation, co-occurrence, correlation, range, and
stressor-response associations.
Some can be used directly for assessments or combined with spatial coverages
in more complex analyses. The resulting information can be used to refine the analysis
and scope and types of assessments. They can be used to identify gaps and plan next
steps for gathering additional in situ, remotely sensed, or composite data for analysis.
7.3.3.4.1. Scatter plots
Scatter plots are graphical displays of matched data plotted with one variable on
the X-axis and the other variable on the Y-axis. Data are plotted with measures of an
influential parameter on the X-axis (independent variable) and measures of an attribute
that can respond to the influential parameter on the Y-axis (dependent variable).
Scatter plots are a useful first step in any analysis because they help the analyst
to choose which relationships to model and to select models. A scatter of points that
suggests the attribute responds to changes in the independent variable can be analyzed
further using correlation (http://cfpub.epa.gov/caddis/
analytical_tools.cfm?section=147&step=22&parent_section=143) or regression methods
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
(http://cfpub.epa.gov/caddis/analytical_tools.cfm?section=149&step=22&parent_section
=143). However, a scatter of points without any apparent relationship is unlikely to
provide insights into relationships, even using multivariate analyses. The distribution of
points in a scatter plot can suggest whether the relationship is, for example, (A) linear,
(B) a higher-order polynomial (quadratic shown), (C) exponential, or (D) logarithmic (see
Figure 7-1). The distribution of points also can reveal apparent thresholds or
discontinuities in the relationship.
7.3.3.4.2. Correlation
Correlation is a method for measuring the degree of association between
two variables in a matched data set. The Pearson product-moment correlation
coefficient (r) is a unitless value between -1 and 1 measuring the degree of linear
association between variables. The corresponding nonparametric analysis calculates a
Spearman rank-order correlation coefficient (p, rho—pronounced "row") which is
computed using the ranks of the data and does not assume that the relationship is
linear. Kendall's tau (r) has the same underlying assumptions as Spearman's
rank-order correlation coefficient but represents the probability that the two variables are
ordered nonrandomly.
A value of r, p or t is interpreted as follows:
• A coefficient of 0 indicates that the variables are not related.
• A positive coefficient indicates that as one variable increases the other also
increases (see Figure 7-2, D).
• A negative coefficient indicates that as one variable increases, the other
decreases (see Figure 7-1, A).
• Larger absolute values of coefficients indicate stronger associations (e.g., see
Figure 7-2, A vs. C).
However, such correlations do not prove causation and could be due to
confounding or error. Thus, correlation coefficients are only suggestive. In addition,
small Pearson product-moment coefficients can be due to nonlinearity (see
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
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Notes: (A) with an rvalue of -0.8, the band of points indicates a decrease
in y with an increase in x; (B) with an rvalue of 0.1, points are diffusely
scattered throughout the plot area; (C) with an rvalue of 0.3, the points
indicate a weak increase in y with an increase in x, or perhaps a nonlinear
relationship; and (D) with an rvalue of 0.8, the band of points indicates an
increase in y with an increase in x.
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
Figure 7-2, C) rather than to a lack of association (see Figure 7-2, B). Therefore,
scatter plots should be examined for nonlinearity and to identify outliers or unduly
influential data.
7.3.3.4.3. Box plots or box-and-whisker plots
Box plots, or box-and-whisker plots, depict the distribution of observations within
a data set by dividing it into four sections (see Figure 7-3). The box indicates the
spread of the central 50% of the data; the median is denoted by a horizontal line
through the box. The portion of the box above the median line denotes the 50th-75th
percentile range. Likewise, the portion of the box below the median denotes the
25th-50th percentile range. If all data lie within 1.5 times the interquartile range
(75th percentile minus the 25th percentile) from either end of the central box, the
whiskers represent the full range of the data. If not, the whiskers extend to 1.5 times
the interquartile range, and more extreme data are plotted as points. These
conventions are not always followed. Box plots generated by different software can
differ in the percentiles used to denote the box-and-whiskers and other features.
Because box plots depict the distribution of observations, they can be useful for
identifying appropriate statistical analyses and deciding whether data should be
transformed (see Figure 7-4). For example, box plots can show whether the shape of
the data distribution is symmetrical or skewed. If the upper box and whisker are
approximately the same length as the lower box and whisker (see Figure 7-4, A), the
data are distributed symmetrically. If the upper box and upper whisker are longer than
the lower box and whisker (see Figure 7-4, B, and C), the data are skewed to the right.
If the upper box and upper whisker are shorter than the lower box and whisker (see
Figure 7-4, D, and E), the data are skewed to the left. Box plots also reveal the
kurtosis, or relative spread, of a distribution. The smaller the length of the box is relative
to the whiskers and points, the tighter the distribution (see Figure 7-4, B, and D).
Skewed distributions indicate that the data are not normally distributed and that the
variances might not be homogeneous (see Figure 7-4, B, C, D, and E). When analyzing
such data, it is generally recommended that nonparametric methods and regression
models are used that accommodate nonlinear data. If parametric methods or linear
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
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Types of Kurtosis, or Relative Spread
regression are used anyway, the data transformation approach should accommodate
the type of data (continuous, count, proportion), the skewness of the distribution, and
any zero or negative values in the data set.
7.3.3.4.4. Regression
Linear regression quantifies the relationship between a dependent (response)
variable and one or more independent (explanatory) variables by minimizing the sum of
the squared residuals (the difference between the predicted and observed values). The
resulting line models the average value of the dependent variable for each level of the
independent variable. For example, if the resulting line models the average relative
losses of Ephemeroptera, Plecoptera, and Trichoptera species richness for each level
of percent fines, the model can be used to predict how many species are likely to be
present at 5, 10, 20, or 30% fines (see Figure 7-5).
Quantile regression models the relationship between a specified quantile of a
dependent (response) variable and an independent (explanatory) variable (U.S. EPA,
2006). The resulting line represents the upper value of dependent variable that would
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
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be expected for each level of the independent variable, which is an estimate of the
effect when the independent variable is the limiting factor (12.5) (see Figure 7-5).
Classification and regression tree (CART) recursively partitions a matched data
set of categorical variables (for classification trees) or continuous variables (for
regression trees) into progressively smaller groups, using binary splits based on single
independent or predictor variables (De'ath and Fabricius, 2000; Prasad et al., 2006).
CART analysis constructs a set of decision rules with the independent variables.
During each recursion, splits for each independent variable are examined, and the split
that maximizes the homogeneity of the two resulting groups with respect to the
dependent variable is chosen. A typical output from these analyses is shown in
Figure 7-6.
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
A. Including all environmental factors
No (70)
Clay > 26 Percent
No (177 sites)
Yes(107)
Forest > 30 Percent
No(32)
Yes (57 Sites)
Runoff > 12.3 inches per year
Yes (25)
Group 1
Group 2
Group 3
Group 4
B. Excluding land-use characteristics
No(52)
Runoff > 10.3 inches per year
No (83 sites)
Yes (31)
Till > 59 Percent
No(55)
Yes (151 Sites)
Clay > 26 percent
Yes (96)
Group 1
Group 2
Group 3
Group 4
FIGURE 7-6
Results of CART Analysis of Total Phosphorus Resulting in Different
Phosphorus Groups Using (A) All Environmental Predictors, or
(B) Excluding Land-Use Predictors
Source: Robertson et al. (2001; Figure 19).
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
In general, CART can be applied effectively to classification or normalization of
geographical data and in the development of stressor-response relationships from other
field study data. Be aware that sampling bias can be an important classifying variable
and responsible for some splits. Of course, exposing these biases sooner rather than
later is best. Splits in the biological response variable can also identify inflection points
or nonlinearities in a stressor-response relationship. Generally, the first few splits of the
data are the most reliable.
7.3.3.5. Applications of Statistical Models
Data from remotely sensed imagery can also be analyzed using statistical
methods. Effect levels and exposure levels can be extracted from these statistical
analyses and used to estimate risks, estimate benchmarks or goals, or provide
evidence of one sort or another. They can be used to construct relatively simple
empirical relationships or models linking landscape to in situ stressor or response
metrics or used to develop wall-to-wall landscape coverages and other data to
document natural and stressor gradients and exposure levels.
7.3.4. Common Spatial Analysis Methods
7.3.4.1. Landscape Metrics
Landscape metrics are used to quantify the spatial structure of categorical
landscape data, primarily land use/landcover (LU/LC) data. Landscape metrics are
most commonly applied to landscapes made up of homogenous patches where a patch
is defined as a polygon representing the boundary of a given LU/LC class (e.g., forest,
urban, agriculture). From these data, landscape metrics can be calculated for each
individual patch, summarized for each LU/LC class, or summarized for the landscape as
a whole. In the context of water quality applications, class level metrics (e.g., total
urban area or proportion of stream buffer that is forested) tend to have the widest use.
As such, much of the following discussion focuses on class level metrics. For a detailed
review of all types of metrics and background on landscape pattern metrics, see
McGarigal et al. (2002).
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
Class level landscape metrics quantify two aspects of landscape structure:
composition and configuration. Composition refers to the amount of a given class and
measures aspects such as class abundance (e.g., total area or total proportion) or class
diversity (e.g., total number of patches). Configuration describes the spatial character
of individual patches and the spatial arrangement of those patches. Metrics of
configuration measure aspects such as the distribution of patch size, core area,
isolation, and connectivity (McGarigal et al., 2002).
7.3.4.2. Specific Tools
Many tools exist that help calculate hundreds of different landscape metrics.
Two good examples of these software tools are FRAGSTATS 3.3 and ATtlLA
(Analytical Tools Interface for Landscape Assessments).
• FRAGSTATS 3.3—FRAGSTATS is an open source, stand-alone tool that
calculates hundreds of different metrics and accepts raster input data sets in
many formats including Arc Grid, ASCII, binary, and ERDAS.
• ATtlLA—is an ArcView 3.1 extension that calculates many common metrics via a
graphical user interface. ATtlLA requires ArcView 3.1 and the Spatial Analyst
extension.
7.3.4.3. Important Considerations
Calculating these metrics requires a basic level of skill with some software tools
or GIS experience; however, the most important consideration in using landscape
metrics is selecting appropriate metrics and interpreting the ecological and biological
meaning of the metric. For a comprehensive list of caveats, see McGarigal et al.
(2002).
Selecting appropriate metrics—Although it is possible to calculate hundreds of
metrics, it is not necessarily desirable to use all those metrics because many of the
metrics are highly correlated. Many papers have been devoted to methods for
evaluating and choosing appropriate landscape metrics (e.g., Riitters et al., 1995;
Gustafson, 1998).
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
Interpreting the ecological/biological meaning of the metrics—When
calculating landscape metrics and associating those metrics with ecological or biological
endpoints, it is imperative to have a sound conceptual basis for making those
associations. As is often the case with water quality applications, landscapes, and the
patterns of those landscapes are not the causes of impairment or even the source of the
cause. Rather, sources of pollutants and other causes of impairment tend to be
associated with certain land uses. For instance, total urban land itself might not
necessarily increase sediment loads to streams, yet the configuration of the urban lands
(e.g., proximity to streams), the intensity of the urbanization, typical current practices,
infrastructure such as storm drains, and physiographic setting interact to determine the
amount of sediment that ultimately reaches a stream.
7.3.4.4. Spatial Interpolation
Spatial interpolation is a technique that predicts values at unsampled locations
on the basis of the values at sampled locations. Our ability to interpolate is based on
Tobler's law, which simply states that features close to one another are more alike than
features far apart (for an interesting review of Tobler's law, see Miller, 2004). There are
numerous techniques for both vector and raster formats that span a range of
sophistication. Some of the more commonly encountered vector interpolation methods
are briefly described below.
• Thiessen/Voronoi Polygons—ThiessenA/oronoi polygons are relatively simple
vector based methods to estimate a polygon surface from point data. Point data
with a sampled value is required to create ThiessenA/oronoi polygons.
Boundaries for each polygon are the halfway point to all other neighboring points
and the value for the resulting polygon is assumed to be that of the central point
(i.e., ThiessenA/oronoi polygons make the assumption that any point on a
surface will have the value of the closest sampled point). For an example of a
ThiessenA/oronoi polygon surface, see Figure 7-7.
• Triangulated Irregular Networks (TINs)—TINs are vector-based data models
that capture information about a surface with points, connections of points and
the resultant triangular face (see Figure 7-8). The entire TIN is represented by a
complex network of triangles. The most common usage of TINs has traditionally
been capturing elevation data, but any continuous surface can be represented by
a TIN. The triangular faces (i.e., facets) provide information such as the slope,
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Section I!—Chapter 7: Methods arid Tools for Analyzing Spatially Explicit Information
Original Point Data
Point Data Interpolated as
Thiessen Polygons
FIGURE 7-7
Example Thiessen/Voronoi Polygon Surface
Source: ESRI (2006).
Original Point Data
Point Data Interpolated as a
Triangulated Irregular
Network (TIN)
FIGURE 7-8
Example Triangulated Irregular Network
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
aspect, and surface area of the facet as well as interpolated values of any point
within the facet.
Raster-based interpolation techniques are generally classified into two broad
categories:
• Deterministic—Deterministic functions use values from sampled points and
predict at unsampled locations on the basis of smoothing functions, similarity
between sample points, or the relative locations of those points. Common
deterministic techniques are Inverse Distance Weighting, Radial Basis Functions
(e.g., spline), and polynomial functions.
• Geostatistical—Geostatistical methods, also known as Kriging, use statistical
properties (i.e., spatial autocorrelation) of the sampled points to predict at
unsampled locations. Furthermore, because geostatistical methods are based
on statistical models, it is possible to generate prediction error surfaces that give
an indication of accuracy across the predicted surface.
7.3.4.5. Specific Tools
Although many tools exist to facilitate different interpolations, two of the more
commonly used are ArcGIS and Geostatistical Analyst.
• ArcGIS—ArcGIS contains very widely used commercial GIS data and provides
tools for conducting both vector and raster interpolation techniques. Additional
extensions (e.g., Geostatistical Analyst) provide more complete and robust
interpolation capabilities.
• Geostatistical Analyst—Geostatistical Analyst is an ArcGIS extension that was
created specifically for raster interpolations and focuses on the family of
geostatistical methods (e.g., ordinary kriging, universal kriging, indicator kriging,
cokriging). In addition, Geostatistical Analyst includes numerous deterministic
methods and useful tools for exploratory data analysis and evaluating
interpolated surfaces.
7.3.4.6. Important Considerations
Spatial interpolations are useful methods for extending the spatial coverage of
sampled data and for visualizing variation in spatial data. In a water quality context,
spatial interpolations might be used to provide an estimated coverage of precipitation on
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
the basis of rain gauge locations or to predict certain water quality parameter throughout
a waterbody (e.g., a lake or estuary) on the basis of sampling locations. It is important
to remember that conducting spatial interpolations correctly depends on a number of
very important considerations.
Appropriate sampling designs are imperative—Although any data set with
continuous data can be interpolated, not all sampling designs provide sufficient
information for the resultant interpolation to have much meaning. Any sampling design
can be used, but uniform sampling designs are often preferred. However, the most
important aspect of the design is not necessarily the points' selection but the density of
those points. The density must adequately capture the spatial variation of the
phenomena being measured.
Interpolated surfaces are predictions and have error—Interpolated surfaces
are relatively easy to create and display with modern geospatial tools. The display and
use of these products can unintentionally imply a certain degree of certainty about the
data they represent. In some cases, that might be fair, and in other cases, not. It is
imperative that users and analysts of interpolated data understand the data used to
generate the surface and, even more importantly, understand that there is error
associated with the final predicted surface.
7.3.5. Hydrologic Analysis
Hydrologic analysis is the analysis of topographic data, usually with raster digital
elevation models (DEMs), to delineate and quantify hydrologic features and networks.
Hydrologic modeling represents a broad set of techniques that examine elevation data
to determine how water flows across a landscape, delineate watershed boundaries, and
identify likely flow paths.
7.3.5.1. Specific Tools
Tools and methods for conducting terrain analysis are standard features of most
GIS software. There are numerous examples of applications using commercial
software (i.e., ArcGIS) and open source software (i.e., GRASS GIS).
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
A related set of tools analyze networks. A primary example of a hydrologic
network is the National Hydrography Dataset (NHD). The NHD is a vector data set that
combines information about hydrology with the NHD vector data to form networks that
follow hydrologic flow paths and allows for complex hydrologic modeling.
7.3.5.2. Important Considerations
Hydrologic analysis is based on analysis of DEMs; thus, the quality of the
resultant models and features is directly tied to the resolution of the elevation models
and the variability of the terrain that is being modeled. For instance, modeling
hydrology in mountainous regions with significant relief might successfully be
accomplished with coarser-resolution DEMs because slope, aspect, and flow direction
might be adequately estimated. In relatively flat areas (e.g., coastal plains),
coarse-resolution DEMs will fail to capture the fine-scale variation that drives hydrology
in those regions.
7.3.6. Overlays and Proximity
Overlay and proximity analyses are good examples of traditional GIS analytical
approaches. Overlay analysis examines spatial relationships between features
represented in multiple layers. For instance, an overlay analysis could be conducted to
determine how ecoregions and watersheds spatially interact. An analyst could
determine which watersheds occur in only a single ecoregion or which watersheds span
multiple ecoregions (for more information on ecoregions and watersheds, see
Chapter 5). Proximity analysis simply examines the proximity of one feature to another.
A common use of proximity analysis is to identify nearest neighbors or to conduct a
buffer analysis. For example, an analyst could use proximity analysis to identify point
sources of a pollutant that are closest to a water quality sampling station.
7.3.6.1. Specific Tools
Many overlay and proximity analyses can be conducted with either vector or
raster data. Although most GIS applications will have the capability to conduct these
analyses, the tools listed below are specific to ArcGIS (ESRI, 2006).
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
Overlay and Proximity Tools—There are numerous types of overlay and
proximity methods. The most common vector methods are the following:
• Union: Merges all features of two input data sets into one output data set.
Features from both data sets are preserved.
• Intersect: Merges intersecting features of two input data sets into one output data
set. Only features common to both data sets are preserved.
• Identity: Merges intersecting features of two input data sets into one output data
set. All features of the input data set and intersecting features of the identity data
set are preserved.
• Buffer: Generates a polygon that represents the area contained within a given
distance from an input feature.
• Near/Point Distance: Calculates the distance from point features in one data set
to features in another data set.
Some common raster methods are the following:
• Map Algebra: Raster overlay methods are mostly accomplished by using map
algebra. Map algebra is a general language that allows the cell-by-cell analysis
of multiple input rasters. A common example would be the addition of multiple
rasters to create an output raster representing the sum of the two raster data
sets.
• Distance tools: Most raster-based proximity tools are contained in the broad
class of distance tools that include Euclidean distance and cost distance.
Euclidean distance calculates the straight line distance of each pixel in the output
data set to all feature or pixels in an input data set. Cost distance is similar, but
distances are constrained by a cost surface that results in paths that are not
necessarily straight lines (e.g., topography as a cost surface would define paths
along hydrological flow paths).
7.3.6.2. Important Considerations
Two considerations with conducting overlays are the quality of the data and error
propagation.
Data quality—As with all spatial analysis techniques, the quality of the analysis
is a function of the quality of the input data; however, this is especially true with overlay
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
techniques because spatial error in the location of line and point features and polygon
boundaries will have a profound effect on how features overlay. On a similar vein, the
scale of each input data set will affect the result and should be as closely matched as is
possible. Poor spatial accuracy or mismatched scales would preclude conducting any
overlay analyses.
Error propagation—Related to data quality is how error in multiple input data
sets propagates to the final output data set. For example, in studies of LU/LC change,
the general rule is that the output change data set can be no more accurate than the
product of the input LU/LC data sets. For instance, two data sets with accuracies of 90
and 85%, would create an output LU/LC change data set with accuracy no better than
76.5%. Furthermore, it would be unlikely that the accuracy of the output data would be
consistent across the full extent of the data and that the error would also vary spatially.
It is often difficult to fully quantify how error propagates, but the magnitude of error in the
results is certainly a concern in interpreting data resulting from one or more overlay
analyses.
7.3.6.3. Applications of Statistical and Spatial Models
Alone or in combination, spatial coverages can be used in many ways as
illustrated in the examples in Section III of this document and the many Web sites
provided in Appendices A and B. A causal relationship is at the core of each one. The
spatial and statistical models attempt to characterize that relationship to better
understand what is going on in the environment. The information can be used to do the
following:
• Develop evidence of a cause or source.
• Predict effects or exposures that are estimated to yield desired results.
• Extrapolate to areas lacking in situ data.
• Evaluate performance and effectiveness.
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
7.4. SYNTHESIS
Synthesis brings together the results from the analysis to generate the findings of
the assessment in a useful form for the decision. Synthesis is devoted to producing a
coherent output that integrates all evidence and endpoints to inform the decision maker.
This includes deriving endpoint estimates and associated uncertainties from the results
of the analysis, integrating multiple forms of evidence, comparing the management
alternatives, and deriving overall results. The methods and criteria for syntheses vary
with the type of assessment, but they share similar processes of estimation, integration,
comparison, and characterization (U.S. EPA, 2006; 2011, Cormier et al, 2008, 2010;
Suter and Cormier, 2008a; Suter and Cormier, 2011).
7.4.1. Decision Support Systems (DSSs)
Decision support systems (DSSs) can help characterize the exposure or effect
that occurred, is occurring, could occur, or is desired to occur. The can help organize
information and thinking so that the resulting relationships extrapolate to sites lacking in
situ data or to inform decision making. They help combine and interpret finding to
answer questions posed in planning and formulation that assess ecosystem condition;
causes of impairment; sources of stressors; risks from known or expected exposures;
exposure that will be protective; optimization of type, placement, and deployment of
management actions and best management practices (BMPs); performance of BMPS
and controls; and effectiveness of management actions and controls to resolve the
environmental problem. They can also help to evaluate and articulate uncertainties and
potential for false negatives and false positives (Type 1 and Type 2 errors). They are
particularly helpful for reporting findings and ultimately compelling decisions and
actions. They can be used to perform stakeholder and peer review of the products but
these activities should also occur at the beginning of a project or program. DSSs can
be used to communicate results to the decision maker and publish results.
A DSS is a system for helping to choose among alternatives. An environmental
DSS can be described as providing a complete project management system for
environmental decision problems that is composed of the following components:
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
• Guidance for each aspect and function of the DSS. This includes interpreting
results and explanation of technical terms and methods.
• Access to and integration of project-specific knowledge bases with further access
to the wealth of information available on the Web.
• Database management including SQL queries and GIS access.
• Environmental modeling capability using risk assessment, and statistical and
decision analysis tools.
• Expert system components that help the user navigate the technical choices
available within the DSS analysis tools (e.g., risk assessment, financial and
social options, or statistical and decision analysis options).
• A presentation system that can be tailored to the specific needs of the users.
• A document production system that can be tailored to any form of computational
output (e.g., Web-based, PDF, Office products).
• Quality assurance (QA) that is continuously measured and evaluated through
user supplied feedback as well as more traditional QA techniques.
• Interactive training in each aspect of the DSS (Black and Stockton, 2009).
In addition to DSS targeted for environmental assessment there are many open
source DSS software. GIS can be used in DSS to facilitate integrating different types of
analyses and different types of environmental assessments thus leading to
environmental problem solving.
7.4.2. Some Specific Decision Support Systems (DSSs)
The EPA's Regional Vulnerability Assessment (ReVA) program is a DSS for
regional-scale, priority setting being developed by EPA's Office of Research and
Development. Extensive effort has been made to evaluate environmental condition and
known stressors within the Mid-Atlantic region, but predicting future environmental risk
to prioritize efforts to protect and rehabilitate environmental quality efficiently and
effectively is still difficult. ReVA is being developed to identify those ecosystems most
vulnerable to being lost or permanently harmed in the next 5 to 25 years and to
determine which stressors are likely to cause the greatest risk. The goal of ReVA is not
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Section II—Chapter 7: Methods and Tools for Analyzing Spatially Explicit Information
exact predictions but, rather, identification of the undesirable environmental changes
expected over the coming years. The ReVA program extends environmental
assessments for the region by using integrative technologies to predict future
environmental risk and support informed proactive decision making and prioritization of
issues for risk management.
CADDIS, is another type of DSS that provides an organization process for
performing causal assessments along with information about commonly encountered
causes of ecological impairments, statistical software and guidance for use and
interpretation in the context of causal assessment.
Some DSSs are developed for a particular geographic area. For example, the
Tennessee Valley Authority developed The Integrated Pollutant Source Identification, a
geographic database and set of tools for designing and implementing water quality
improvement and protection projects within a watershed. See Chapter 10 for details
and Chapter 11 for an example of its implementation.
7.5. DECISIONS AND ACTIONS
At the end of each assessment, a decision is made. The decision can be (1) to
stop the assessment process because there is no further problem; (2) to perform an
assessment informed management action; (3) to initiate the next assessment in the
sequence; or (4) to bypass the next assessment and proceed to a another type of
assessment. Alternatively, although not a preferred option, a decision can be made
without using the information offered by the assessment (e.g., if the assessment results
suggest a politically or economically unacceptable conclusion [NRC, 2005]) (Cormier
and Suter, 2008).
7.6. REFERENCES
Black, P. and T. Stockton. 2009 Basic steps for the development of decision support
systems. In: Decision Support Systems for Risk-based Management of Contaminated
Sites. A. Marcomini, G.W. Suter II, A. Critto, Ed. Springer, New York. pp. 1-28.
Cormier S.M., G.W. Suter II, and S.B. Norton. 2010. Causal Characteristics for
Ecoepidemiology. Hum. Ecol. Risk Assess. 16(1): 53-73.
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Cormier S.M., J.F. Paul, R.L. Spehar, P. Shaw-Allen, W.J. Berry, and G.W. Suter, II.
2008. Using field data and weight of evidence to develop water quality criteria. Integr.
Environ. Assess. Manag. 4(4):490-504.
Cormier, S.M. and G.W. Suter II. 2008. A framework for fully integrating environmental
assessment. Environ. Manage. 42(4):543-556. Available online at
http://www.springerlink.com/content/n56531j12q33776t/fulltext.pdf.
Davies, S.P., and S.K. Jackson. 2006. The biological condition gradient: a descriptive
model for interpreting change in aquatic ecosystems. Ecol. Appl. 16(4): 1251 -1266.
De'ath, G., and K.E. Fabricius. 2000 Classification and regression trees: a powerful yet
simple technique for ecological data analysis. Ecology. 81(11):3178—3192.
Efroymson, R.A., J.P. Nicollette, and G.W. Suter, II. 2004. A framework for net
environmental benefit analysis for remediation or restoration of contaminated sites.
Environ. Manage. 34(3):315-331.
ESRI (Environmental Systems Research Incorporated). 2006. ArcGIS 9.2 Desktop
Help. ESRI, Redlands, CA. Available online at
http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=welcome. Last
modified March 15, 2007.
Gustafson, E.J. 1998. Quantifying landscape spatial pattern: What is the state of the
art? Ecosystems 1:143-156.
Hanley N, and C.L. Spash 1993 Cost-Benefit Analysis and the Environment. Edward
Elgar Publishing, Cheltenham, UK.
Hollister, J.W., H.A. Walker and J.F. Paul. 2008. CProb: A Computational Tool for
Conducting Conditional Probability Analysis. J. Environ. Qual. 37(6):2392-2396.
McGarigal, K., S.A. Cushman, M.C. Neel, and E. Ene. 2002. FRAGSTATS: Spatial
Pattern Analysis Program for Categorical Maps. Computer software program produced
by the authors at the University of Massachusetts, Amherst. Available online at
www.umass.edu/landeco/research/fragstats/fragstats.html.
Linkov, I., F.K. Satterstrom, G. Kiker, etal. 2006. Multicriteria decision analysis: a
comprehensive decision approach for management of contaminated sediments. Risk
Anal. 26:61-78.
Michener, W. 2006. Meta-information concepts for ecological data management. Ecol.
Inform. 1(1):3-7.
Michener, W.K. 2000. Metadata. In: Ecological Data: Design, Management, and
Processing. W.K. Michener and J.W. Brunt, Eds. Oxford Press, Oxford, UK.
p. 92-116.
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Miller, H.J. 2004. Tobler's First Law and Spatial Analysis. Ann. Assoc. Am. Geogr.
94(2):284-289.
NRC (National Research Council). 2005. Superfund and mining megasites: lessons
from the Coeur D'Alene river basin. National Academies Press, Washington, DC.
Prasad, A.M., L.R. Iverson, and A. Liaw. 2006. Random forests for modeling the
distribution of tree abundances. Ecosystems 9:181 -199.
Riitters, K.H., R.V. O'Neill, C.T. Hunsaker et al. 1995. A factor analysis of landscape
pattern and structure metrics. Landsc. Ecol. 10:23-39.
Robertson, D.M., D.A. Saad, and A.W. Wieben. 2001. An alternative regionalization
scheme for defining nutrient criteria for rivers and streams. United States Geological
Survey (USGS) Water Resources Investigations Report 01-4073. United States
Department of the Interior, USGS, Middleton, Wl. Available online at
http://wi.water.usgs.gov/pubs/wrir-01 -4073/wrir-01 -4073.pdf.
Senay, G.B., N.A. Shafique, B.C. Autrey, F. Fulk, and S.M. Cormier. 2001. The
selection of narrow wavebands for optimizing water quality monitoring on the Great
Miami River, Ohio using hyperspectral remote sensor data. J. Spatial Hydrol.
1 (1): 1 —22.
Suter, G. W., II. 2007. Ecological Risk Assessment, Second Edition. Taylor and Francis,
Boca Raton, FL.
Suter, G.W. II, and S.M. Cormier. 2008a. A theory of practice for environmental
assessment. Integr. Environ. Assess. Manag. 4(4)478-485. .
Suter, G.W. II, and S.M. Cormier. 2008b. What is meant by risk-based environmental
quality criteria? Integr. Environ. Assess. Manag. 4(4) 486-489.
Suter, G.W. II and S.M. Cormier. 2011. Why and how to combine evidence in
environmental assessments: weighing evidence and building cases. Sci Total Environ
409(8):1406-1417.
U.S. EPA (Environmental Protection Agency). 2005. Use of Biological Information to
Better Define Designated Aquatic Life Uses in State and Tribal Water Quality
Standards: Tiered Aquatic Life Uses. U.S. EPA Office of Water, Washington, DC.
EPA/822/R-05/001. Available online at http://www.epa.gov/bioiweb1/pdf/EPA-822-R-
05-001 UseofBiologicallnformationtoBetterDefineDesignatedAquaticLifeUses-
TieredAquaticLifeUses.pdf. Accessed 2 August 2008.
U.S. EPA (Environmental Protection Agency). 2006. Framework for Developing
Suspended and Bedded Sediments Water Quality Criteria. Office of Water,
Washington, DC. EPA/822/R/06/001. Available online at
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=164423.
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U.S. EPA (Environmental Protection Agency). 2010a. CADDIS (The causal
analysis/diagnosis decision information system). Available online at
www.epa.gov/caddis (accessed 6/6/11).
U.S. EPA (Environmental Protection Agency). 2010b. Integrating Ecological
Assessment and Decision-Making at EPA: A Path Forward: Results of a Colloquium in
Response to Science Advisory Board and National Research Council
Recommendations. Risk Assessment Forum, Washington, DC. EPA/100/R-10/004.
Available online at http://www.epa.gov/raf/publications/pdfs/integrating-ecolog-assess-
decision-making.pdf.
U.S. EPA (Environmental Protection Agency). 2011. A Field-Based Aquatic Life
Benchmark for Conductivity in Central Appalachian Streams. Office of Research and
Development, National Center for Environmental Assessment, Washington, DC.
EPA/600/R-10/023F. Available online at
http://cfpub.epa. gov/ncea/cfm/recordisplay.cfm?deid=233809.
Wu, J., K.B. Jones, H. Li, and O.L. Loucks. 2006. Scaling and Uncertainty Analysis in
Ecology: Methods and Applications. Springer, Dordrecht, The Netherlands.
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Section III—Summary
SECTION III: EXAMPLES AND CASE STUDIES
SUMMARY
Chapters 8 through 13 (Recommended for: Beginner, Intermediate, and
Advanced) present a series of examples of real-world applications of landscape and
predictive tools. All readers can benefit from one or more examples. Each case is
organized similarly. The text box ("What's interesting about this case?") lists a highlight
and then indicates what types of assessments are addressed. For the most part,
condition, predictive, and outcome assessments are emphasized. Also at the beginning
of each example chapter are a set of key words related to the case such as
geographical scale and location, data sources, environmental relationships, and Clean
Water Act relevance. These case studies include:
Chapter 8: Impervious Estimates and Projections—U.S. Environmental
Protection Agency (EPA) Region 4. Impervious area estimates and projections in EPA
Region 4.
Chapter 9: Water Temperature Regime Assessments—Umatilla River.
Assessments of the water temperature regime for total maximum daily load
development in the Umatilla River Basin in Oregon.
Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
Identification (IPSI) Process. The Tennessee Valley Authority's IPSI process.
Chapter 11: Oostanaula Creek IPSI Case Study. Use of the IPSI process for
analysis of aerial photography to identify a wide range of nonpoint sources in the
Oostanaula Creek watershed of Tennessee and inform development of a watershed
action plan.
Chapter 12: Nutrient Classification of Streams Using Classification and
Regression Tree analysis (CART). Use of CART to classify natural phosphorus and
nitrogen regions of the upper mid-West to aid development of nutrient criteria.
Chapter 13: Biocriteria and Reference Condition. Use of a tiered watershed and
reach scale reference site screening process in the State of Oregon to identify
candidate reference areas based on least disturbed condition.
Ill-i
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
8. IMPERVIOUS ESTIMATES AND PROJECTIONS—EPA REGION 4
Jim Harrison, U.S. EPA Region 4, Atlanta, GA
Linda Exum, U. S. EPA Office of Research and Development, Athens, GA (Retired)
Sandra Bird, U. S. EPA Office of Research and Development, Athens, GA (Retired)
Key words:
AirPhoto Interpretation: 1 m Digital
Orthophoto Quarter Quadrangles (DOQQs), U.S.
Geological Survey (USGS) Aerial Photographs
12- to 14-digit Hydrologic Unit Codes (HUCs)
Multiple Data Source (MDS) Impervious
Estimates: 12-digit HUCs, Census block level
population, National Land Cover Dataset (NLCD,
1992, 1993) high-intensity commercial/industrial and
mining classes, major roads/highways
Future Projections of Imperviousness: MDS,
states' county population projections; Census block
level population, vacant/seasonal housing
Relationship of Imperviousness and Ecology:
MDS, North Carolina benthic biology, Piedmont
Ecoregion watershed boundaries
Clean Water Act (CWA): Identify impaired waters (303[d]); Determine abatement and control
priorities (402)
8.1. INTRODUCTION
A method was developed and tested for estimating and projecting impervious
cover for 12- and 14-digit hydrologic unit codes (HUCs) over a large area. These
methods were then applied in U.S. Environmental Protection Agency (EPA) Region 4's
eight southeastern states to provide it with a screening tool to guide monitoring and
educational efforts for high-risk areas. Imperviousness is related to biological response
for the Piedmont Level 3 Ecoregion using simple regression, box plots, and relative risk.
Urban/suburban land use is the most rapidly growing land-use class in many
areas of the United States. Increased development inevitably increases impervious
What is interesting about this case
study? Impervious area estimates can be
used to strategically design efficient water
quality monitoring programs. This example
integrates three types of assessment:
Condition Assessment. Estimates the
current probability of impairment
associated with imperviousness at
unsampled locations.
Causal Pathway Assessment. None
Predictive Assessment. Uses models
developed in the condition assessment to
predict risk of biological impairment to sites
not previously field sampled and identifies
catchments areas at risk on the basis of
projected development pressure.
Outcome Assessment. Sets the stage for
documenting protection of streams rather
than rehabilitation of streams.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
surface—areas preventing infiltration of water into the underlying soil. The extensive
hydrological alteration of watersheds associated with increased impervious cover is very
difficult to control and correct. Development practices that reduce impervious area and
include preventative strategies to protect water quality are more effective and less costly
than remedial restoration efforts.
Increased imperviousness causes a well-known cascade of damaging results to
streams (Wolman, 1967; Caraco et al., 1998). Detrimental hydrologic changes cause
more frequent, higher peak flows (Jennings and Jarnagin, 2002) and lower water tables
and baseflows, which can influence both riparian (Groffman et al., 2003) and aquatic
communities (see Figure 8-1). Because of lowered baseflows, streams have reduced
resilience (see restoration potential) to recover from drought conditions. Watershed
runoff can increase by two to more than five times normal for forested catchments as
impervious area increases from the 10-20% range to 75-100%, respectively (Arnold
and Gibbons, 1996). Altered high-flow regimes also increase streambank erosion and
channel enlargement producing significant sedimentation from the stream channel itself
(see Figure 8-2). The few available quantitative studies of channel changes due to
urbanization indicate that from one-half to three-quarters of stream sediment load
originates from channel erosion (Trimble, 1997; Dartiguenave and Maidment, 1997;
Corbett et al., 1997) rather than upland sources. The resulting unstable channel often
indicates highly degraded aquatic habitat, largely due to unstable substrates. The end
result of these stresses is usually severe biological impairment and poor aquatic
community integrity. (For comprehensive reviews of impacts of impervious area on
aquatic systems, see both Paul and Meyer [2001] and Center for Watershed Protection
[2003].)
In addition to extremely deleterious ecological and water quality effects, flooding
is also a devastating result of the urban hydrologic alteration (Inman, 2000, 1995), a
stress that is only sporadically regulated at the local level. Hydrologic (Poff et al., 1997;
Richter et al., 1996) and physical stresses (Gaff, 2001), as well as chemical
contamination, must be addressed to protect and restore urban water resources.
Often, other ecological stresses compound hydrologic effects from
imperviousness. Summer stream temperatures can be elevated because of runoff from
8-2
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FIGURE 8-1
Conceptual Model for Hydrologic Stresses for Urban Streams (based on Causal Analysis/Diagnosis
Decision Information System [CADDIS's] extensive collection of conceptual models). (U.S. EPA CADDIS.)
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
pavement and structures, placing additional stress on the biological communities.
Riparian alterations regularly exacerbate stream channel erosion and further increase
stream temperatures. Additional habitat degradation often ensues from reduced input
of large woody debris and from increased stream crossings by roads, sewers, and other
structures that create barriers to fish movement (Paul and Meyer, 2001). Impervious
surfaces channel pollutants directly into waterways, preventing processing of these
pollutants in soils. Higher pollutant loads—particularly oils, other petroleum products,
and metals—are typically associated with roadways, while biocides (pesticides and
herbicides) are generally associated with managed landscapes (Center for Watershed
Protection, 2003).
8.2. METHODS AND ANALYSIS
The multiple data source (MDS) impervious estimates and projections use simple
empirical relationships based on available literature and on relationships developed
during the project. Geographic information systems (GIS) were used for calculations
and analyses including developing ArcView/Avenue scripts for the MDS work and
ArcView/Avenue extensions for the airphoto interpretation work. MDS scripts were
developed with ArcView and Spatial Analyst and air photo interpretation were performed
with ArcView and Image Analyst.
8.2.1. Analyses Conducted
8.2.1.1. Multiple Data Source Impervious Estimates
(Data from Georgia and eight Southeastern States, EPA Region 4)
In the MDS approach, three different data types—population density from block
level Census data, the commercial-industrial and quarrying-mining landcover category
from National Land Cover Dataset (NLCD, 1992), and interstate and major U.S.
highway coverages—were combined to estimate impervious cover. The MDS approach
uses the different data types to represent components of imperviousness most
appropriate to the specific data source. The residential contribution to imperviousness
was estimated on the basis of population density using the Greater Vancouver
Sewerage and Drainage District (GVS&DD) method (Hicks and Woods, 2000;
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
GVS&DD, 1999) discussed in detail in Section 3.1 of EPA's report, Estimating and
Projecting Impervious Cover in the Southeastern United States (Exum et al., 2005). We
used U.S. Census 2000 block level data to estimate population density in individual
HUCs. Both population data and vacant housing were used to develop an effective
population density in the watershed. Many areas of the Southeast, specifically the
coastal and mountain areas, have high rates of vacation and seasonal housing, which is
not reflected in the resident census count. The two NLCD (1992) categories add
information on the contributions to imperviousness from major manufacturing,
commercial, and quarrying areas that can be detected by satellite imagery. These latter
categories are assumed to be 90% impervious (Caraco et al., 1998). Impervious area
from major highways was calculated on the basis of the total length of interstate and
other major U.S. highways arcs (USDOT, 2001) in a watershed (HUC), times the
number of lanes for an individual road arc multiplied by an assumed lane width of
12 feet. Where highway arcs overlap with the NLCD categories, we extracted, that is,
the road arcs were removed to prevent double accounting. Total percentage
impervious area (%TIA) for the HUC was calculated by summing the impervious area
contributed by major highways, commercial, and mining in each HUC, dividing by the
total HUC area and multiplying by 100 to convert to percentage, and adding to the %TIA
calculated for the residential component from the GVS&DD equation.
8.2.1.2. Future Projections of Multiple Data Source Imperviousness
(Data from eight Southeastern States, EPA Region 4)
Impervious cover projections based on the MDS approach are analogous to
those described for current condition, but they incorporate projected population growth.
Population projections by county for the eight Southeastern states in Region 4 were
obtained from each individual state. Projected growth in a county was distributed on the
basis of the 2000 population in the blocks. Distributing growth proportional to the 2000
population will tend to underestimate growth in rural areas of a county and overestimate
growth in urban areas. For the 12- and 14-digit HUCs, the environmentally significant
subdivision, the coarser political (county) scale projections were apportioned to the finer
HUC scale. The first step was to apportion the growth to the 2000 Census block level.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
With this done, the population could be apportioned to the individual blocks on the basis
of the most recent block level population density.
The critical assumption for the high intensity commercial/industrial (HICI) area
future projections was to maintain in an HUC the HICI (in square miles)/10,000
population ratio constant for future periods. The implications of this assumption for the
projection are that the historical pattern of commercial growth with respect to population
for any area (HUC) will continue into the projection periods. Thus, areas with high
HICI/population ratios are projected to experience high commercial growth with
population increases; areas with low HICI/population ratios are projected to experience
low commercial growth with population increases; and those areas with intermediate
ratios will be in-between.
The only roadways included in this component were interstate highways and
major U.S. highways. This is a minor component in the impervious cover estimation,
and very little new connector highway construction is proposed. Projected construction
information is in a variety of formats and not easily obtainable from individual states.
Because updated values will have negligible effects on projections, we used estimates
of the current status in projection estimates.
8.2.1.3. Grid Point Statistical Sampling of Air Photos for Error Estimation
(Data from Frederick County, Maryland and Atlanta, Georgia)
A number of approaches can be used for measuring impervious cover. The most
accurate and costly are ground-based surveys. Ground-based methods are
prohibitively expensive to use where developing a data base from numerous
watersheds as required in this study. The use of manual interpretation of aerial
photography is commonplace in accuracy assessments of automated interpretation
remote sensing techniques (Slonecker et al., 2001) and for other applications, including
watershed management and tax assessment (Lee, 1987; Kienegger, 1992). Manual
interpretation of aerial photography was chosen for development of our test data sets
because it allows collecting data in a sufficient number of watersheds with an adequate
degree of accuracy. (See Chapters 10 and 11 this volume for a case example and
typical approaches to air photo interpretation processes using numerous characteristics
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
for an entire watershed. Also see TVA 2003 for an online example.) Test data were
collected from aerial photographs in two separate locations: fifty-six 14-digit HUCs in
Frederick County, Maryland, covering 1,728 km2, and in thirteen 12-digit HUCs in the
Atlanta, Georgia, area covering 888 km2 Manual analysis was done on digital
orthophoto quarter quadrangles (DOQQs) obtained from the U.S. Geological Survey
(USGS). DOQQs are digital versions of aerial photographs that have been
orthorectified, so they represent true map distances and are available for any area of
the country from the USGS. The DOQQs have 1 m2 resolution, and their analysis can
provide a high level of accuracy in the determination of impervious cover at a
subwatershed scale (Zandbergen et al., 2000). Rather than delineating individual
impervious features for this study, we estimated impervious cover in HUC areas using a
point sampling technique. A grid of points was overlaid on the HUC area and the %TIA
was estimated as the percentage of the points sampled in the HUC classified as
impervious. The selected software, sampling and analysis systems yielded accurate
and reproducible results and allowed efficient collection of data that was stored in a
georeferenced data format. Ground features were identified and categorized by human
analysts with the help of GIS software and with a cover tool extension designed
specifically for this data collection effort.
8.2.1.4. Relationships Between imperviousness and Biological Response
(Data from Southern Piedmont—Omernik Level 3 Ecoregion #45)
While complete descriptions of the range of aquatic responses to imperviousness
are not available for all areas of the Southeastern United States, extensive biological
sampling of benthic macroinvertebrates by the North Carolina Division of Water Quality
(NCDWQ) covering the wide gradient of impervious area throughout the Southern
Piedmont ecological region (Griffith et al., 2003) provides the best existing data to begin
building such relationships.
Cursory descriptive examination of a portion of these data allows us to glimpse
the potential for using existing and new data to construct robust relationships valid for
the entire Southeast. Benthic data for more than 300 Piedmont sites were provided by
Trish MacPherson of the NCDWQ, along with point watersheds delineated for those
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
sites shared by Dr. Halil Cakir and Dr. James Gilliam of North Carolina State University.
Their detailed, rigorous statistical examination of this data is in preparation.
For 159 of these sites with nonoverlapping watersheds, MDS (described in
Section 3.3 of Exum et al. [2005]) impervious area estimates were produced. The MDS
imperviousness of these watersheds ranges from 1 to 60%. Simple box plots illustrate
the benthic biological condition response of streams to increasing impervious area
(using both 5 and 10% ranges) for that gradient of Piedmont sites based on the North
Carolina Biotic Index (NCBI), a tolerance based metric used for benthic community
assessments and aquatic life use support determinations by NCDWQ (NCDENR, 2006).
Assuming NCBI scores above 6.54 (worse than fair on the state's scale of: excellent,
good, good-fair, fair, fair-poor, and poor) indicate degraded conditions, progressively
greater fractions of degraded sites are evident as impervious area increases. For
watershed %TIA greater than 10%: 62% (32/52) of sites are degraded; for %TIA > 15%,
78% (25/32) of sites are degraded; for %TIA > 20%, 83% (19/23) of sites are degraded;
and for %TIA > 30%, 91 % (10/11) of sites are degraded. In contrast, for watersheds
with %TIA < 10%, 10% (11/107) of sites were degraded. Figure 8-3 also provides
percentages and numbers of sites for individual 5 and 10% ranges of impervious area.
The appendix to the report (Exum et al., 2005) contains descriptions of the
analytical procedures, as well as the Avenue (ArcView) scripts used for the analyses.
8.3. OUTPUT
MDS estimates and projections of imperviousness were produced for
~10,700 12- and 14-digit HUCs covering the Southeastern United States (see
Figures 8-4 and 8-5). Impervious cover is presented as %TIA (percentage of total
impervious area) by 12- and 14-digit HUC using the MDS approach. Data sources used
in the calculations included 1993 NLCD commercial and industrial landcover, 2000
Census data, and U.S. Department of Transportation data for interstates and other
major highways. These estimates demonstrate an inexpensive method of determining
impervious cover with known accuracy at the watershed and subwatershed scales plus
characterization of the change in imperviousness overtime.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
Percent
Degraded"
Worst Condition
10%
45%
Box and Whisker Plot
83%
(5-10%)
(10-15%) (15-20%)
(20-30%)
(>30%)
Percent
Impervious ^79
Number of sites
20 9
zu TIA_Ctass 7
159 cases
FIGURE 8-3
Percentage of Degraded Piedmont Sites Versus Total Impervious Area
TABLE 8-1
Relative Risk of Impervious Area on Biological Condition in
Streams
%TIA Range
% Degraded
Relative Risk
<5
9
1.0
5-10
14
1.5
10-15
35
3.9
15-20
67
7.4
20-30
75
8.3
>30
91
10.1
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
U S. BMranmertd Rcleci on Agarey
jAfuni. Georja Nwuitw 2001
FIGURE 8-4
Southeastern United States Impervious Cover for 2000
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
GIS shape files, by state, including base data, impervious estimates and
projections are available at http://wwvv.epa.gov/athens/research/impervious
/shapes, html,
8.4. DISCUSSION AND CONCLUSIONS
These estimates and methods are important tools that can be used to promote
appropriate monitoring, protection, and rehabilitation of urban/suburban stream
systems. Accurate, inexpensive impervious area estimates constitute an important
landscape screening tool for designing efficient, effective water quality monitoring
programs. State monitoring programs have limited resources and thus cannot sample
everywhere. One solution is to use GIS benchtop analysis to locate those areas that
are more likely to be impaired so that monitoring is efficient. The same information can
ij S ErwimrortsiFiMcienwiritv mnj» iWa
FIGURE 8-5
North Carolina Impervious Cover Projected to 2030
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
then be used to project where impairments are likely to occur as development continues
and how that might differ with proactive management.
Figure 8-6 summarizes actual impairment information for all of the
~10,700 12-digit HUCs in EPA Region 4 by level of imperviousness. This analysis
demonstrates the following
• Far more impairments are being found at >10% impervious than <10%
impervious (74 vs. 41%).
• Most of the documented impairments at >10% impervious are due to
pathogens/bacteria (53%, more than two-thirds of the total).
• Far fewer documented impairments at >10% impervious are due to habitat
(15%), sediment (23%), and unknown/biological (6%).
• And most importantly, far fewer aquatic life use impairments have been
documented than would be expected given the known relationship between
imperviousness and biological response: ~10* less than expected for areas with
greater than 20% impervious area (~6 vs. ~>80%).
This last observation could likely be from incomplete monitoring of urban streams
for aquatic life use impairments.
Figure 8-7 provides a spatially explicit summary of impairments for the Atlanta
area, demonstrating that none of the >20% impervious area HUCs have been listed for
aquatic life use impairments, even though such impairments should be highly likely.
This provides strong evidence that aquatic life use impairments should be
monitored and documented by the states wherever high levels of imperviousness are
known. Region 4 continues to work closely with the eight states in the region to target
and prioritize monitoring of high and medium risk watersheds to document aquatic life
impairments. Confirmed impairments due to runoff from impervious surfaces will be on
the states' Section 303(d) impaired waters lists. Follow-up actions for rehabilitation
planning such as total maximum daily load (TMDL's, allocations, MS4 permit
improvements, and watershed plans for example) can then be undertaken to support of
both site and watershed scale implementation of storm water management. Monitoring
of at risk "growing" watersheds will also be undertaken
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
~ >than 10
¦ >than 20
All Impairments Pathogens Habitat Alteration Sediment Unknown -
Biological
Impairment
*Based on 2002 section 303(d) Impaired Waters lists and year 2000 estimated impervious area. Analysis
by Jon Becker
FIGURE 8-6
Percentage of Region 4 HUCs Having Specific Impairments within
Impervious Area Ranges
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
Atlanta Area Total Impervious Area by HUC
and Impaired Waters by Type
"Based on 2002 Section 303(d) Impaired Waters lists. Map by Jon Becker
FIGURE 8-7
Impervious Area Cover and Impaired Waters Causes for Metropolitan
Atlanta
to help target and prioritize protection of waters vulnerable to ongoing expansion of
impervious surfaces.
Thus, these scientifically sound landscape screening processes provide
workable, defensible methods to do the following: extrapolate condition estimates to
waters lacking in-stream data; identify suspected problem areas (likely impaired
waters); target additional monitoring to confirm problems and thus, aid listing of
impaired waters under Section 303(d) of the Clean Water Act (CWA).
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
Similar application of the same information for fast growing areas with
imperviousness in the ranges of 10-15% and 5-10% can also be used to target
prevention activities to specific threatened areas where watersheds are at risk of
changing rapidly from a system with relatively pristine streams to one with significant
symptoms of degradation.
These applications emphasize that the analyses conducted provide a rational
basis to evaluate landscape stresses and causes of water quality problems for large
areas. Future applications recommended include prioritizing TMDL development and
rehabilitation planning efforts and providing reliable scientific information to local
authorities and interests to energize sound local planning and land-use decisions.
8.4.1. Advantages
• Improves accuracy of impervious estimates in the <10% range compared to
simpler approaches (such as landcover only estimates).
• Allows future projections of impervious area where population projections (by
county) are available.
• Uses readily available, free data sources—population, NLCD, roads, and
DOQQs.
• GIS techniques (avenue code and extensions) can be readily applied to other
areas.
8.4.2. Cautions and Caveats
• Effective Impervious Area, impervious areas directly hydrologically connected to
water courses, is not incorporated in this approach.
• Small watershed/catchment scale projects should supplement these impervious
estimates with high-accuracy, project-specific estimates. (Liu et al. [2011]
provide a good example of using high resolution land use data and GIS to
estimate runoff coefficients [confirmed with site-specific flow data] for 18 small
watersheds in Milwaukee, Wl.)
• Users should be aware of the population/area apportionment and
population/commercial area assumptions used in these analyses.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
8.4.3. Future Needs
• Cost-effective methods are needed to incorporate effective impervious area.
• The population/commercial area assumptions should be tested as new
population and satellite land use/landcover (LU/LC; NLCD, 2000) data become
available.
• Additional research will be needed to describe and explain differences in
sensitivity to impervious cover and hydrologic stormwater stress in different
areas. (For example; Washburn and Sanger [2010] demonstrate that first order
tidal creeks [vs. second and third order] show the strongest negative effects on
macrobenthic communities due to runoff from impervious land cover.)
• Cost-effective storm water management approaches need to be developed and
widely implemented for both individual sites and watershed-wide. Schueler
(2005) offers a comprehensive planning and implementation approach for
rehabilitating small urban watersheds. Roy et al. (2008), using examples from
Australia and the United States, propose a comprehensive suite of solutions to
overcome barriers to sustainable storm water management which could result in
better implementation of low impact development and water sensitive urban
design at a watershed scale.)
8.5. ADDITIONAL RESOURCES AND CONTACTS
8.5.1. Analytical Tools Interface for Landscape Assessments (ATtlLA)
Use of NLDC data with the ATtlLA landscape factor extension tool can provide
very rapid analysis for impervious estimates and can also identify most potentially
degraded watersheds. However, the NLCD-only approach appears to have limitations
for identifying imperviousness in the 5 to 10% range. This range, particularly in areas
with significant growth, likely incorporates the most critical areas where prevention of
stormwater problems might be most effective.
8.5.2. National Land Cover Dataset (NLCD) 2001 Imperviousness
The NLCD 2001 database contains three primary elements—landcover,
impervious surface, and canopy density (whereas NLCD 1992 was primarily a
landcover data set.) The impervious surface data provides per-pixel percentage
estimates of urban impervious cover. Values range from 0 to 100%. Impervious
surface should be thought of as urban impervious surface. Earth surface features such
as rock outcrops and talus slopes might not have high impervious values.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
Subpixel urban impervious surface was mapped using a regression tree model at
30-meter resolution. Techniques and error prediction are described in detail in Yang
et al. (2003). The impervious surface data set has been completed for the
conterminous United States and is available.
8.5.3. Impervious Surface Analysis Tool (ISAT)/Nonpoint Education for
Municipal Officials (NEMO)/Tennessee Growth Readiness and Other
State/Local Efforts
The Impervious Surface Analysis Tool (ISAT) (National Oceanic and
Atmospheric Administration [NOAA] ISAT http://gcmd.nasa.gov/records/
NOAA_ISAT.html) is used to calculate the percentage of impervious surface area of
user-selected geographic areas (e.g., watersheds, municipalities, subdivisions). The
NOAA Coastal Services Center and the University of Connecticut Nonpoint Education
for Municipal Officials (NEMO) Program (http://nemo.uconn.edu/) developed this tool for
coastal and natural resource managers. ISAT is available as an ArcView 3.x, ArcGIS
8.x or an ArcGIS 9.x extension and requires the Spatial Analyst extension and the
following inputs: landcover grid, polygon data set of areas for which percentage of
impervious surface is to be calculated, set of landcover impervious surface coefficients
calibrated for low-, medium-, and high-population densities, and an optional population
density theme. ISAT produces the following outputs: shapefile that includes green,
yellow, and red polygons to represent conditions of potentially good, fair, and poor water
quality, and an attribute table that includes a calculated value for the percentage of
impervious area and total impervious surface area of each selected polygon. ISAT also
incorporates the ability to produce landcover change (or buildout) scenarios to examine
how changes influence impervious surfaces, and thus, potential future water quality.
NEMO was created in the early 1990s to provide information, education and
assistance to local land-use boards and commissions on how they can accommodate
growth while protecting their natural resources and community character. The program
was built upon the basic belief that the future of our communities and environment
depend on land use, and, because land use is decided primarily at the local level,
education of local land-use officials is the most effective and most cost-effective way to
bring about positive change.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
There are 32 charter members of the NEMO Network in 31 states across the
country. Each of the programs is modeled after the original University of Connecticut
program and shares five key elements:
• The issue is the effect that land use has on natural resources, particularly water
quality.
• The solution offered is natural resource based planning.
• The target audience is local land-use decision makers.
• Unbiased, research-based education is the method used.
• GIS technology is used to enhance the educational message.
While sharing these five core principles, each program is its own independent
entity adapted to its state's own unique geography, structure, and sensibilities with a
different structure, approach, focus, funding source and partner. In-depth profiles of
each of these programs are at http://nemonet.uconn.edu/programs/profiles.html.
One pertinent example of these efforts is the Tennessee Growth Readiness program,
which also partners with local planners, local officials, and others for training and
projects (more information is at http://watershed-assistance.net/resources/files/
tngrowthreadiness.pdf). A full report on the approaches, experience, successes, and
plans of this program are at http://swan.southeastwaterforum.org/resources/files/
tngrowthreadiness.pdf.
8.6. SUGGESTED READING AND WEB SITES
Brown, L.R., R.H. Gray, R.M. Hughes, and M.R. Meador. 2005. Effects of Urbanization of
Stream Ecosystems. American Fisheries Society Symposium 47. American Fisheries Society,
Bethesda, MD. 423 pp.
Center for Watershed Protection. 2003. Impacts of Impervious Cover on Aquatic Systems.
Watershed Protection Research Monograph No. 1, Ellicott City, MD.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
Exum, L.R., S.L. Bird, J. Harrison, and C.A. Perkins. 2005. Estimating and Projecting
Impervious Cover in the Southeastern United States. U.S. Environmental Protection Agency,
Office of Research and Development, National Exposure Research Laboratory, Athens, GA.
EPA/600/R-05/061. Available online at
http://www.epa.gov/athens/publications/reports/Exum600R05061EstimatingandProjectinglmper
vious.pdf and http://www.epa.gov/athens/publications/downloadable.html.
Paul, M.J. and J. Meyer. 2001. Streams in the urban landscape. Ann. Rev. Ecol. System.
32:333-365.
Yang, L., C. Huang, C. Homer, B. Wylie, and M. Coan. 2003. An approach for mapping large-
area impervious surfaces: Synergistic use of Landsat 7 ETM+ and high spatial resolution
imagery. Can. J. Remote Sens. 29(2):230-240.
Yang. L., G. Xian, J.M. Klaver, and B. Deal. 2003. Urban land-cover change detection through
sub-pixel imperviousness mapping using remotely sensed data. Photogramm. Eng. Rem. Sens.
69(9):1003-1010.
NOAA's impervious area tool: ISAThttp://www.csc.noaa.gov/digitalcoast/tools/isat/.
Nonpoint Education for Municipal Officials http://nemo.uconn.edu/.
Key Contact(s)
Jim Harrison, 404-562-9271, harrison.jim@epa.gov
Linda Exum, retired
Sandra Bird, retired
8.7. REFERENCES
Arnold, C.L., Jr. and C.J. Gibbons. 1996. Impervious surface coverage: The
emergence of a key environmental indicator. J. Am. Plan. Assoc. 62(2):243-258.
Caraco, D., R. Claytor, P. Hinkel et al. 1998. Rapid Watershed Planning Handbook: A
comprehensive guide for managing urbanizing watersheds. Center for Watershed
Protection. Ellicott City, MD.
Center for Watershed Protection. 2003. Impacts of Impervious Cover on Aquatic
Systems. Watershed Protection Research Monograph No. 1. Ellicott City, MD.
Corbett, C.W., M. Wahl, D.E. Porter, D. Edwards and C. Moise. 1997. Nonpoint source
runoff modeling: A comparison of a forested watershed and an urban watershed on the
South Carolina coast. J. Exper. Marine Biol. Ecol. 213(1): 133—149.
Dartiguenave, C.M. and D.R. Maidment. 1997. Water quality master planning for
Austin. Center for Research in Water Resources (CRWR). University of Texas at
Austin, Bureau of Engineering Research, Austin, TX. CRWR Online Report 97-6.
Available online at http://www.crwr.utexas.edu/reports/pdf/1997/rpt97-6.pdf.
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Exum, L.R., S.L. Bird, J. Harrison, and C.A. Perkins. 2005. Estimating and Projecting
Impervious Cover in the Southeastern United States. U.S. Environmental Protection
Agency, Office of Research and Development, National Exposure Research Laboratory,
Athens, GA. EPA 600/R-05/061. Available online at
http://www.epa.gov/athens/publications/reports/Exum600R05061EstimatingandProjectin
glmpervious.pdf.
Gaff, W.L. 2001. Damage control: Restoring the physical integrity of America's rivers.
Ann. Assoc. Am. Geogr. 91(1): 1-27.
Griffith, J.A., S.V. Stehman and T.R. Loveland. 2003. Landscape Trends in
Mid-Atlantic and Southeastern United States Ecoregions. Environ. Manage.
32(5):572-588.
Groffman, P.M., J.J. Bain, L.E. Band et al. 2003. Down by the riverside: urban riparian
ecology. Front. Ecol. Environ. 1(6):315-321.
GVS&DD (Greater Vancouver Sewerage and Drainage District). 1999. Assessment of
Current and Future GVS&DD Area Watershed and Catchment Conditions. Prepared for
Liquid Waste Management Plan, Stormwater Management Technical Advisory Task
Group, Vancouver, BC.
Hicks, R.W.B. and S.D. Woods. 2000. Pollutant Load, Population Growth and Land
Use. Progress: Water Environment Research Foundation. 11: p. 10.
Inman, E.J. 1995. Flood-Frequency Relations for Urban Streams in Georgia -1994
Update. U.S. Geological Survey. Water Resources Investigations Report 95-4017.
Atlanta, GA. 27 pp.
Inman, E.J. 2000. Lagtime Relations for Urban Streams in Georgia. U.S. Geological
Survey. Water Resources Investigations Report 00-4049. Atlanta, GA. 12 pp.
Available online at http://pubs.usgs.gov/wri/wri00-4049/.
Jennings, D.B. and S.T. Jarnagin. 2002. Changes in anthropogenic impervious
surfaces, precipitation and daily streamflow discharge: a historical perspective in a
mid-atlantic subwatershed. Landsc. Ecol. 17(5):471-489.
Kienegger, E.H. 1992. Assessment of a wastewater service charge by integrating
aerial photography and GIS. Photogramm. Eng. Rem. Sens. 58(11): 1601 -1606.
Lee, K.H. 1987. Determining impervious area for storm water assessment. Paper read
at American Society of Photogrammetry and Remote Sensing/American Congress on
Surveying and Mapping Annual Convention, 1987, at Baltimore, Maryland.
Liu, Y., P. Soonthornnonda, J. Li, and E.R. Christensen. 2011. Stormwater runoff
characterized by GIS determined Source areas and runoff volumes. Environ. Manage.
47(2):201-217.
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
NCDENR (North Carolina Department of Environment and Natural Resources). 2006.
Standard operating procedures for benthic macroinvertebrates. Biological Assessment
Unit, Raleigh, NC. 21 pp. Available online at
http://h2o.enr.state.nc.us/esb/BAUwww/benthossop.pdf.
NLCD National Land Cover Data. 1992. Available online at http://www.mrlc.gov/.
NLCD National Land Cover Data. 1993. Available online at http://www.mrlc.gov/.
NLCD (National Land Cover Data). 2000. Available online at http://www.mrlc.gov/.
Paul, M.J. and J. Meyer. 2001. Streams in the urban landscape. Annu. Rev. Ecol.
Syst. 32:333-365.
Poff, N.L., J.D. Allan, M.B. Bain et al. 1997. The natural flow regime. A paradigm for
river conservation and restoration. BioSci. 47(11):769-784.
Richter, B.D., J.V. Baumgartner, J. Powell and D.P. Braun. 1996. A method for
assessing hydrologic alteration within ecosystems. Conserv. Biol. 10(4): 1163-1174.
Roy, A.H., S.J. Wenger, T.D. Fletcher, et al. 2008. Impediments and solutions to
sustainable, watershed-scale urban stormwater management: Lessons from Australia
and the United States. Environ. Manage. 42(2):344-359.
Schueler, T. 2005. An integrated framework to restore small urban watersheds.
Version 2.0. Urban Subwatershed Restoration Manual No. 1. Center for Watershed
Protection, Ellicott City, MD. 76pp. Available online at
http://www.dep.wv.gov/WWE/Programs/stormwater/MS4/guidance/Documents/Manual
%201%20Framework%20to%20Restore%20Small%20Urban%20Watersheds%202005.
pdf
Slonecker, E.T., D.B. Jennings and D. Garofalo. 2001. Remote sensing of impervious
surfaces: a review. Rem. Sens. Rev. 20:227-255.
Trimble, S.W. 1997. Contribution of stream channel erosion to sediment yield from an
urbanizing watershed. Science. 278(5342): 1442-1444.
TVA (Tennessee Valley Authority). 2003. Blount County and Little River Basin
Nonpoint Source Pollution Inventories and Pollutant Load Estimates. Tennessee Valley
Authority, Knoxville, TN. Available online at http://216.119.90.50/asp/pdf/IPSIreport.pdf.
U.S. Department of Transportation. 2001. National Transportation Atlas Databases,
National Highway Planning Network (NHPN) Version 3.0. Bureau of Transportation
Statistics, Federal Highway Administration, Office of Intermodal and Statewide
Programs (HEPS-20), Washington, DC.
8-22
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Section III—Chapter 8: Impervious Estimates and Projections—EPA Region 4
U.S. EPA (Environmental Protection Agency). 2008. Causal Analysis/Diagnosis
Decision Information System (CADDIS). Available online at http://www.epa.gov/caddis.
Accessed November 21, 2008.
Washburn, T., and D. Sanger. 2010. Land use effects on macrobenthic communities in
southeastern United States tidal creeks. Environ. Monit. Assess. Published online: 02
December 2010. DOI 10.1007/s10661 -010-1780-1
Wolman, M.G. 1967. A cycle of sedimentation and erosion in urban river channels.
Geogr. Annaler. 49A(2-4):385-395.
Yang. L., G. Xian, J.M. Klaver, and B. Deal. 2003. Urban land-cover change detection
through sub-pixel imperviousness mapping using remotely sensed data. Photogramm.
Eng. Rem. Sens. 69(9): 1003-1010.
Zandbergen, P., J. Houston and H. Schreier. 2000. Comparative Analysis of
Methodologies for Measuring Watershed Imperviousness. Watershed Management
2000 Conference, Institute for Resources and Environment, University of British
Columbia, Vancouver, British Columbia, Canada, July 2000.
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
9. WATER TEMPERATURE REGIME ASSESSMENTS—UMATILLA RIVER
Peter Leinenbach, U.S. EPA Region 10, Portland, OR
Key words:
Analysis: Historic aerial photos, Digital
Elevation Model, light detection and ranging
(LiDAR), Thermal Infrared Remote Sensing,
Change simulation models
Clean Water Act total maximum daily
load (TMDL)
9.1. INTRODUCTION
Stream temperature is an aspect
of water quality that affects every
aquatic organism. Placing a
thermometer in a stream and recording the reading is simple enough. The problem is
that the result does not represent the stream, whose temperatures vary markedly over
both time and location. Instead of a single measurement, what is needed is a set of
measures that describes a stream's temperature regime. Even then, the process is
complicated. Many factors affect the temperature regime, including climate, riparian or
streambank vegetation, and channel form and structure. The factors with the strongest
influence vary from time to time and place to place. What's more, patterns of variation
in stream temperature differ depending on the time scale of observation and the size of
the area in which temperatures are measured. For instance, variation in stream
temperatures over a single day differs from variation over an entire year. Similarly, the
patterns of temperatures observed in a single pool or riffle in a stream differs from the
patterns averaged over the entire stream course.
Research suggests that temperature regimes in many streams in the United
States are now different from those that existed before Euro-American settlement.
Evidence further shows that a variety of human activities often are responsible for
changes in temperature regimes and that the effects of human activities often are
What is interesting about this case study? This
example illustrates how field monitoring data,
remotely sensed data, and modeling are used to
integrate different types of assessments.
Condition Assessment. Temperature exceedances
of criteria to protect salmonids.
Causal Assessment: None.
Source Assessment. Spatial modeling showed that
stream channel modification leading to reduced
hyporheic recharge of cooler ground water and
reduced vegetative shading was the source
pathway elevating temperatures.
Predictive Assessments: Temperature reductions
by increasing hyporheic recharge and encouraging
taller tree species and growth were estimated.
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
cumulative—individual land-use activities that alone would not substantially alter stream
temperature can do so when combined with other activities or with natural disturbances.
Alteration of these temperature regimes has been shown to be a factor in the
decline in the family offish known as salmonids, which until recently has successfully
adapted to historical variations in stream temperature. For example, in many streams,
especially in the western United States, where large salmon runs once were typical, the
temperature regimes now appear inhospitable. Thus, restoring temperature regimes
compatible with desired populations is an important factor in their recovery. In the
Umatilla Basin in Oregon, indigenous salmonids include summer steelhead and resident
redband trout (Onchorhynchus mykiss), bull trout (Salvelinus confluentus), fall and
spring Chinook salmon (Onchorhynchus tshawytscha), and coho salmon
(Onchorhynchus kisutch). The U.S. government has listed bull trout (Salvelinus
confluentus) and summer steelhead (Onchorhynchus mykiss) in the Umatilla Basin as
threatened species. While laboratory evidence suggests adult salmon can generally
survive a week or more at constant temperatures of 21 °C and can often tolerate
temperatures as warm as 18°C for prolonged periods under controlled experimental
settings, constant temperatures above 16°C have been shown to be intolerable for
species such as bull trout (U.S. EPA, 2001). Oregon's temperature criteria are 18°C
during summer and 13°C, October to May (ODEQ, 2007).
Riparian vegetation, stream morphology, hydrology, climate, and geographic
location influence stream temperature. While climate and geographic location are
outside of human control, changing land-use activities can affect riparian condition,
channel morphology, and hydrology. Specifically, the elevated summertime stream
temperatures attributed to anthropogenic sources in the Umatilla Basin result from
several landscape factors. Riparian vegetation disturbance reduces stream surface
shading via decreased riparian vegetation height, width or density, thus increasing the
amount of solar radiation reaching the stream surface. Channel widening (increased
width-to-depth ratios) increases the stream surface area exposed to solar radiation.
Near-Stream Disturbance Zone widening decreases potential shading effectiveness of
shade-producing near-stream vegetation, and reduced summertime baseflows can
result from in-stream withdrawals.
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Human activities that contribute to degraded water quality in the Umatilla Basin
include timber harvesting, roads, agriculture, and other activities that disturb riparian
zones. The relationships between percentage of effective shade, channel morphology,
hydrology, and stream temperature are illustrated in Figure 9-1.
Figure A-21. Stream Heating Processes in the Umatilla Basin
Near-Stream
Vegetation
Channel
Morphology
Effective
Width :Depth
Shade
NSDZ
Heat Energy
Hydrology
j~BankStability j j
*~L J ~Bank Erosion ~
~Sinuosity
Gradiant
*
*
Groundwater
Width: Depth
Connectivity
NSDZ
Stream
Surface Area
+
+
Cool-Water
Assimilative
Sources
Capacity
^Stream Temperatures
FIGURE 9-1
General Model of Stream Temperature Control for the Umatilla River
9.2. METHODS AND ANALYSIS
Several state agencies and tribal nations have implemented detailed water
temperature (1) monitoring, (2) source identification and allocation, and (3) modeling
efforts as part of their temperature total maximum daily load (TMDL) program,
emphasizing analysis of nonpoint sources that increase stream temperatures. This
work has included a mixture of continuous, in-stream and remotely sensed temperature
9-3
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
sampling, riparian landcover data collection, and geographic information system (GIS)
data set processing. This chapter focuses on temperature monitoring along with GIS
efforts associated with temperature source assessment development, and only briefly
discusses water quality modeling associated with these TMDL efforts.
9.2.1. Data Sets, Models and Analytical Software
GIS was extensively used during these analyses. Several ArcView/Avenue
extensions for riparian and channel characterization work were used. The system
requirements for the analysis were ArcView 3.x, ArcGIS, and Spatial Analyst.
Field-collected data often include continuous temperature data, flow rates (gage
and instantaneous data), stream morphology surveys, and effective shade
measurements. While these ground-level data are useful, their limited spatial
distribution necessitates the development of spatially continuous data. Exclusive use of
ground-level data forces extrapolation and introduces errors. The widespread use of
spatial data is required for a methodology that captures the thermal uniqueness and
variability inherent to rivers. These data sets represent advanced environmental
monitoring. Several spatial data types are presented in Table 9-1, along with the
application for which they are used.
TABLE 9-1
Spatial Data Types and Associated Applications
Spatial Data Type
Application
DEM (10-Meter):
Measure far-field topography
LiDAR:
Measure valley/channel morphology, landcover height and
density, and sediment depth (when paired with well data)
Aerial Imagery:
Map landcover, stream position and morphology
Thermal Infrared:
Surface water temperatures, direct observation, quantify
surface and subsurface inflows
DEM = digital elevation model
LiDAR = light detection and ranging
9-4
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Digital Elevation Model (DEM) data files are representations of cartographic
information in a raster form and consist of a sampled array of elevations for a number of
ground positions at regularly spaced intervals. Using an available ArcGIS extension
(T-Tools), DEMs can be used to determine stream elevation, stream gradient, valley
gradient, valley shape/landform and topographic shade angles. The T-Tools extension
and associated user's manual can be downloaded from the Oregon Department of
Environmental Quality (DEQ) TMDL Tools Web site at http://www.deq.state.or.us/wq/
TMDLs/tools.htm. An ArcGIS version of this extension is available for download from
the Washington Department of Ecology Web site at http://www.ecy.wa.gov/programs/
eap/models.html.
Light Detection and Ranging (LiDAR, see Figure 9-2) is measured from an
airplane with an instrument that emits laser pulses toward the ground that are then
reflected back to a sensor. Floodplain elevation and channel bathymetry data sets have
been derived from LiDAR and surveyed cross-sections. The first return (reflected from
a surface and received by the sensor) is from the closest object to the sensor (likely the
top of vegetation). The last return received by the sensor is usually from the object
farthest from the sensor (likely the ground—Bare Earth LiDAR). The bare earth sample
density can decrease in dense vegetation. Bare Earth LiDAR data refers to the lowest
return for a sample point, and raw data refers to the highest return for a sample point.
Typically, bare earth data record ground elevations and raw data record landcovertop
elevations.
However, LiDAR cannot effectively penetrate water; therefore, the wetted surface
of the channel appears smooth. To derive underwater bathymetry, additional field data
is needed. These data are then inserted into the LiDAR bare earth data set as points
and combined to generate a triangulated irregular network data set that interpolates
elevations between sample points. It is important to point out that green LiDAR has
initially been shown to be a promising method to map shallow channel areas; however,
water transparency issues associated with suspended soils and turbidity in the water
column are still a major complicating factor effecting sensor efficiency.
9-5
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
First Return liDAR
Elevation Contours
FIGURE 9-2
Illustration of LiDAR Images and Aerial Photographs of the Study Site in
Northwestern U.S. Stream
Rectified aerial photos can produce extremely high-resolution (less than
one-meter) and multispectral (color) imagery useful for mapping streams, identifying
near-stream vegetation, locating unmapped features such as diversions, small dams,
and so on.
Thermal infrared (TIR) data is a passive sensor measurement of surface
temperatures at a very high spatial and thermal resolution. TIR images acquired from
airborne platforms have recently become an important data source in stream
temperature monitoring and analysis programs. TIR imagery is useful for detecting and
quantifying warm and cool water sources, calibrating stream temperature models, and
identifying thermal processes. For example, the image in Figure 9-3 illustrates TIR and
9-6
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
FIGURE 9-3
Example of Cool Hyporheic Flow. Left is a video image. Right image
shows cooler water from tributary TIR Derived Temperatures (°C)
the corresponding video image at the confluence of two streams with strong
temperatures differences.
Most importantly, TIR data provide a spatially continuous map of temperatures
within a watershed, which complements the temporally continuous, but spatially limited,
point monitors traditionally employed to assess stream temperatures (see Figure 9-4).
TIR data has been used extensively in developing TMDL allocation for nonpoint sources
in the Pacific Northwest. The next section briefly introduces an example application of
this TIR data in the TMDL development process.
9.2.2. Analysis Conducted
The Confederate Tribes of the Umatilla Indian Reservation staff recently
developed a temperature TMDL for the Umatilla River within Tribal Nation Boundaries
(see Figure 9-5). This effort focused on assessing and quantifying the various factors
associated with observed elevated temperatures in the river.
9-7
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
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FIGURE 9-4
TIR-Derived Longitudinal Temperature Profile (°C)
Source: CTUIR (2005).
9-8
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Umatilla RiverSubbasin
CTUIR Lands
S Miles
Image 1:250,0GG Seals
FIGURE 9-5
Umatilla River and Confederated Tribes of the Umatilla Indian Reservation
(CTUIR) Tribal Boundaries
Source: CTUIR (2005).
9-9
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Many other temperature TMDLs in the Pacific Northwest implement similar
analysis. Detailed descriptions of methods, along with user manuals associated with
the tools used in these efforts, are available on the Oregon DEQ Web site
(http://www.deq.state.or.us/wq/TMDLs/TMDLs.htm), and the Washington Department of
Ecology Web site (http://www.ecy.wa.gov/programs/wq/tmdl/index.html). The full report
and the figures used in this example can also be found in the Confederated Tribes of
the Umatilla Indian Reservation (CTUIR, 2005); Confederated Tribes of the Umatilla
Indian Reservation Total Maximum Daily Load for Temperature and Turbidity (CTUIR,
2005).
9.2.2.1. Temperature Monitoring and Source Assessment Development
In-stream continuous temperature measurements indicated that current
conditions were well above the 17.8°C criterion (criterion at time of assessment prior to
revision) protective of sensitive salmonid species within this section of river throughout
most of the summer period (see Figure 9-6).
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Sub-Optimal
FIGURE 9-6
Stream Temperature Measurements Along the Umatilla River Over a
3-Month Period
Source: CTUIR (2005).
9-10
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
TIR data were collected for the Umatilla River, which was used to assess and
evaluate potential factors responsible for observed, elevated temperatures. Figure 9-7
illustrates the longitudinal temperature profile derived from the TIR data. The red dots
indicate locations of the in-stream temperature probes listed above. These data
demonstrate that temperature increased in a downstream direction, but it also illustrates
that temperatures were highly spatially variable, with areas experiencing large
temperature change within a very short distance.
River Miles
FIGURE 9-7
TIR Inferred Longitudinal Temperature Profile with Locations of In-Stream
Probes (red dots)
Source: CTUIR (2005).
Subsequently, environmental characteristics/factors were derived from various
spatial data sets using GIS data processing and analysis. These calculated factors
were used to investigate potential associations with the temperature profile
(i.e., temperature regime) established from the TIR data. Detailed methods used to
develop the various environmental characterizations used in this source assessment
9-11
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
analysis are provided in the TMDL document at http://water.epa.gov/lawsregs/
lawsguidance/cwa/tmdl/upload/2009_01_09_tmdl_draft_handbook.pdf. Table 9-2
presents several of these environmental characteristics that were shown to be
significantly associated with stream temperature change in the Umatilla River. For
illustration purposes, results for the River Complexity Index (RCI) are presented in
Figure 9-8.
TABLE 9-2
Factors Associated with Temperature Change
Parameter
Cooling Gradient
(<-1/2°C per km)
Heating Gradient
(<-1/2°C per km)
Statistically
Significance
Difference
Sinuosity
1.23
1.14
Highly Significant
Meander Belt Width
170 m
125 m
Highly Significant
Divergence from
Valley Direction
o
CNl
19°
Highly Significant
Slope
0.51%
0.55%
Significant
Historic Valley Width
Still Active
76%
68%
Significant
Wetted Width to
Depth Ratio
17
24
Highly Significant
River Complexity
Index (RCI)
8.6
4.5
Highly Significant
RCI = River Complexity Index
9-12
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FIGURE 9-8
River Channel Complexity (measured as RCI and Temperature [°C measured by infrared imagery (TIR)])
Presented by River Mile (on left) and Sorted by Stream Reaches Where Stream Water is Cooling or
Heating. Temperature increases from upstream to downstream. Temperatures are cooler where RCI
indicates greater channel complexity.
Source: CTUIR (2005).
-------
Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
These results indicate that for this section of the Umatilla River mainstem, many
channel features were significantly associated with stream temperature change.
Stream reaches over which cooling gradients are observed possessed increased
connectivity with the valley and greater variability within the active flood plain. Also,
areas with cooler water were associated with hyporheic exchange, as indicated by the
statistical data analysis and the TIR data (see Figure 9-9).
Using these observations supported by scientific literature, (1) riparian vegetation
and (2) channel morphology-based restoration measures were developed for the
Umatilla River.
9.2.2.2. Riparian Vegetation Restoration Measures
Measurements of current riparian vegetation conditions indicated poor
conditions. Accordingly, riparian vegetation restoration measures were developed to
eliminate anthropogenic solar loading. In other words, restored vegetation conditions
result in increased shading conditions and thus a decreased solar load.
Shade conditions at both current and target riparian vegetation conditions were
developed from a combination of available GIS landcover data sets and the LiDAR data
(see Figure 9-10). Using the derived riparian vegetation information (i.e., height,
density, and location) as input parameters into a shade model, energy loading to the
stream was calculated (illustrated in Figure 9-11). As can be seen in this figure,
effective shade conditions are much higher at the restored riparian vegetation condition.
These results were input conditions for a temperature model, which was developed for
the system and used to estimate resulting temperature response to the riparian
vegetation restoration. It is important to point out that the channel length was longer at
the restoration condition than at current conditions. This is a consequence of increased
sinuosity established during channel morphology restoration measures described in the
next section.
9.2.2.3. Channel Morphology Restoration Measures
Measurements of current morphology parameters indicated poor conditions.
Accordingly, several channel morphology rehabilitation measures were developed to
9-14
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
TlR Longitudinal Profile
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-------
Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
FIGURE 9-10
Derived Tree Height Along the River
Source: CTUIR (2005).
I I eight
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Transverse
9-16
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Changes iri Effective Shade
Restoration Condition
Effective
Shade
I ¦100%
Hso%
Current Condition
FIGURE 9-11
Changes in Effective Shade Projected for Restoration Condition
Source: CTUIR (2005).
reduce the energy loading resulting from the poor conditions. The Umatilla River valley
width was measured (using the composite LiDAR data) and was found to have many
artificial valley width constrictions. Subsequently, the Umatilla River has experienced a
corresponding decrease in both sinuosity and meander belt width. Figures 9-12 and
9-13 provide examples of the decreased sinuosity observed between 1949 and 2000.
On the basis of these findings, several channel-based targets were developed for the
Umatilla River and served as surrogate measures for temperature allocations:
• River sinuosity of 1.3 or greater.
• Meander belt width appropriate to meet sinuosity target.
• Stream gradient appropriate to meet sinuosity target.
• Valley width and floodplain connection appropriate to meet sinuosity target.
9.3. OUTPUT
The graphs in Figure 9-14 illustrate estimated temperature conditions for the
(1) riparian vegetation, (2) channel morphology, and (3) combination restoration
scenarios. Temperature modeling results show that all the factors responsible for
9-17
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Umatilla River (River Miles 63.8 to 67.2)
Image Shown is a 1949 D00 (Courtesy CTUIR Staff)
1949 Sinuosity = 1.30 2000 Sinuosity = 1.18
FIGURE 9-12
Umatilla River (River mile 83.8 to 87.2). Image shown is a
1949 Digital Orthophoto Quadrangle.
Note: 1949 Sinuosity = 1.30, and 2000 Sinuosity =1.18
Source: CTUIR (2005).
9-18
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
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-------
Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Vegetation Restoration
Dashed Lines Represent Restoration Condition
Hyporheic Restoration
Dashed Lines Represent Restoration Condition
Combined Restoration
Upstream Bound an/ Condition
Dashed Lines Represent Restoration Condition
FIGURE 9-14
Simulated Changes in the Temperature Regime—Current and Restoration Conditions
Note: Dashed lines indicate restored condition.
Source: CTUIR (2005).
9-20
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
elevated (i.e., disturbed) temperature conditions must be addressed simultaneously to
protect and restore the temperature regime of the Umatilla River. Figure 9-15 illustrates
the resulting temperature regime for the combination restoration scenario.
9.4. DISCUSSION
The integration of landscape information in these assessments led to a TMDL for
the Confederate Tribes of the Umatilla Indian Reservation for promoting the protection
and restoration of temperature regimes in streams suitable for aquatic life. Tools and
methods implemented as part of these efforts are primarily developed from available
spatial data sets (DEM, Digital Ortho-photos). Additional remote data sets (i.e., TIR,
LiDAR) have been shown to greatly increase the sensitivity and resolution of the
analysis, but note that the assessment could have been done without them. Similar
efforts have successfully used other types of data sets, depending on availability.
These efforts have consistently shown that the temperature regime can only be
accurately evaluated using a landscape perspective. The tools can be downloaded
from the state TMDL Web sites provided earlier.
9.4.1. Advantages
• Many of the data sets and tools used in the analysis are widely available.
• The methods are malleable. Tools and techniques can be applied to a variety of
data sets, and stream systems.
• The temperature regime cannot be evaluated without accounting for cumulative
effects of landscape-derived conditions. This method provides a means to
estimate and evaluate such conditions.
• Many example applications are available in TMDL documents.
9.4.2. Cautions and Caveats
• Nonpoint source heat loading is inherently difficult to quantify and is often very
interrelated with pollutants and therefore requires careful analysis. For example,
sediment that settles on streambeds can also alter stream channels and
hyporheic flows, but they were not considered in this example. Although,
sediment was a component of the TMDL developed by Oregon DEQ.
9-21
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
Restoration Condition
7-Day Moving
Ave. of Daily
Maximums
26°C
22°C
18°C
14°C
1Q°C
FIGURE 9-15
Current and Modeled Restoration Thermal Conditions. The projected
restored condition has greater stream area below 26°C and 18°C.
Source: CTUIR (2005).
• These analyses are logical and well communicated using graphical
representations of stream processes, but performing the analysis requires some
computational sophistication. The analyses require the use of multiple data sets
and analysis tools. Additional data collection could be required if appropriate
data are not available.
• The software used in this example is proprietary and could be costly if few
applications are anticipated.
9.5. REFERENCES
U.S. EPA (Environmental Protection Agency. 2001. Final Technical Synthesis Paper.
Scientific Issues Relating to Temperature Criteria for Salmon, Trout and Char Native to
the Pacific Northwest (August 2001) EPA/910/R-01/007. Available online at
http://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P1004J0T.txt. Accessed 11/08/2008.
9-22
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Section III—Chapter 9: Water Temperature Regime Assessments—Umatilla River
ODEQ (Oregon Department of Environmental Quality). 2007. Coordinating the
Temperature Water Quality Standard and Umatilla Subbasin TMDL: Practical
Considerations and Cumulative Effects Analysis. 20 p. Available online at
http://www.deq.state.or.us/wq/tmdls/docs/umatillabasin/umatilla/coordtemperature.pdf.
Accessed 11/08/2008.
CTUIR (Confederated Tribes of the Umatilla Indian Reservation). 2005. Confederated
Tribes of the Umatilla Indian Reservation Total Maximum Daily Load for Temperature
and Turbidity. Department of Natural Resources. Issued by U.S. EPA Region 10.
Available online at
http://www.epa.gov/waters/tmdldocs/12245_CTUIR%20TMDL%20%20July%2005.pdf
(Accessed 03/05/2010).
9-23
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
Identification (IPSI) Process
10. NONPOINT SOURCE INVENTORY—INTEGRATED POLLUTANT SOURCE
IDENTIFICATION (IPSI) PROCESS
Pat Hamlett, Tennessee Valley Authority, Chattanooga, 77V
Key words:
Analysis: Photo interpretation, thermal
infrared imagery, aerial photography
Clean Water Source identification for
impaired waters, watershed plans, total maximum
daily load (TMDL) implementation, site and
prioritize abatement and control
10.1. INTRODUCTION
Watershed rehabilitation projects can be more cost effective if the contributors to
the watershed environmental problems are first identified and quantified. Tennessee
Valley Authority (TVA) has developed land use and land activity geographic information
system (GIS) databases to provide a way to effectively prioritize and target watershed
remediation and rehabilitation efforts, allowing for the achievement of the greatest level
of pollutant reduction for the least amount of funding. Although not designed for total
maximum daily load (TMDL) development, nonpoint pollutant source (NPS)
assessments are used to determine the most cost-effective best management practices
(BMPs) for reaching the established TMDL of a stream or to plan the necessary
watershed improvements to retain the water quality of nonlisted streams.
Detailed NPS inventory and land-activity GIS databases developed by the TVA
quantify environmental conditions and provide an effective means of prioritizing pollution
sources for stream remediation. The comprehensive data—details such as eroding
road and streambanks, riparian zone conditions, livestock activity and effects, illegal
dumps, and suspect septic systems along with a comprehensive detailed land
use/cover—extracted from stereo photographs provides a means of determining the
source of pollution problems and their relationship to the landscape. Developed by
photographic interpretation of high-resolution aerial photography, the unique database
of the study area provides necessary information to screen areas by land activities and
What is interesting about the Nonpoint
Source Inventory? This system uses aerial
photography and loading models to identify and
characterize sources.
Source Assessment: Nonpoint sources are
identified and quantified.
Predictive Assessment: Multiple applications
including development of remedial action plans.
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conditions that are indicative of nonpoint sources for pollution. Data analysis transforms
a dispersed, area-wide concern into a defined, site-specific problem by identifying
subwatersheds that are the greatest contributors to the pollution problem, and then
determining in each priority subwatershed the specific sites that contribute the greatest
pollutant loads. The inventory data can be used to indicate potential stream and
terrestrial effects associated with NPS activities and land uses. A cost-effective tool for
developing and implementing NPS corrective actions, the process documents the
location and magnitude of all nonpoint pollution sources in the watershed and provides
an impetus for agencies, industries, interest groups, and landowners to work together
toward a common goal. Primary objectives of an NPS inventory include the following
• Completing a detailed NPS inventory to identify and quantify the nonpoint
pollution sources in a watershed.
• Completing a highly detailed and accurate land use/cover inventory of the
watershed.
• Calculating pollutant loadings, on the basis of NPS inventory.
• Prioritizing subwatersheds and sites on the basis of the pollutant loadings.
• Developing GIS database queries to prioritize sites for corrective actions.
The process generates a unique database for screening land activities and
conditions that affect stream quality. In the absence of stream water quality data, the
inventory data can be used as surrogate indicators for potential stream effects
associated with NPS activities. By coupling remotely sensed data with a GIS, the data
can be incorporated into decision-making and problem-solving processes.
A total NPS assessment uses a watershed-based approach to determine the
pollutant source and needed remedial action to reduce pollutant loads to streams. The
components of the process include selecting the watershed, completing an NPS
inventory, calculating pollutant loads, and analyzing the inventory data to target and
prioritize the nonpoint sources in those watersheds.
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Watershed Targeting
Pollution Problem
I
Land-use Drivers
I
Priority Subwatersheds
I
Priority Sites
10.2. METHODS AND ANALYSIS
10.2.1. Data and Software Requirements
Photography is usually acquired at a scale of 1:24,000. The actual resolution of
the photographs varies with many factors including atmospheric conditions, film type,
camera optics, altitude, and stereo viewing capability. However, for the purpose of
photo-interpretation, the resolution is considered the smallest object that can be
identified and measured. Digital scanning of the 1:24,000 photographs at 1,200 dots
per inch (dpi) will produce a digital image with an approximate pixel resolution
0.5 meter. Using original color infrared (CIR) positive transparencies for the detailed
photographic interpretation results in a functional resolution of about 0.10-0.15 meters
(4-6 inches). Viewed in stereo, these transparencies allow the interpreter to
discriminate details critical to watershed pollution problems such as eroding
streambanks, eroding roads, on-site septic systems, detailed livestock activities,
impervious surfaces and detailed land use/landcover (LU/LC).
NPS Inventory data are collected in a spatial data engine (SDE) geodatabase
and are delivered to the end users in either a personal geodatabase or in shapefile
format. ArcGIS is the preferred software; however, the data can be used in any GIS
software that accepts these formats.
The inventory data are stored on TVA's enterprise GIS within SDE. This desktop
tool runs on a personal computer with ArcMap 9.x software. The simple interface allows
users to view all the inventory data in tables, generate maps or plots, or external data
sets such as landowner information, and perform in-depth spatial and data analyses.
The most unique feature of this tool is the ability for scenario generation. A typical
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query might be, "If we fence cattle out of streams and stabilize eroding streambanks in a
particular subwatershed, what realistic improvements in water quality can we expect?"
10.2.2. Selection and Delineation of the Project Watershed
The first step in an NPS project is to meet with the group or organization
requesting an NPS inventory. The scope of the project is outlined and the project
watershed is defined. In most instances, these requesters have identified pollutant
problems in their watershed and intend to use the NPS inventory to support grant
proposals or other funding sources to obtain funds to implement remediation steps in
the watershed. When funding is obtained, the inventory is used to target the most
effective, on-ground BMP to meet the needed water quality improvements to impaired
streams, or to maintain high-quality water.
10.2.3. Acquisition of Photography
After delineating the watershed and identifying the hydrologic unit boundaries for
subwatersheds, the next step in developing an NPS inventory is acquiring aerial
photography. CIR aerial photography at a scale of 1:24,000 is acquired of the project
area (see Figure 10-1). Key to the photo-interpretation process is imagery acquisition
before spring leaf-out for maximum visibility of small or subtle landscape features
without excessive interference from vegetation. Photographs are processed and
delivered as CIR transparencies.
CIR photography is used for several reasons
• Near-infrared wavelengths are totally absorbed by water, resulting in the ability to
assess sediment conditions in waterbodies.
• Healthy vegetation has a high infrared reflectance. Healthy and vigorous
vegetation appears as red, while stressed or dormant vegetation appears in
shades ranging from pink to blue to green to gray.
• Near-infrared light penetrates atmospheric haze because of the sensitivity of the
emulsion layers.
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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FIGURE 10-1
CIR Photograph
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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The film is scanned at a project-specific resolution (usually 800-1,200 dpi) to
create a digital image for use in the GIS database. The images are orthorectified using
the U.S. Department of Agriculture (USDA) Farm Service Agency's National Agricultural
Inventory Program imagery, the U.S. Geological Survey (USGS) 7.5 Minute
Topographic map series and USGS Digital Ortho Quarter Quads as the X,Y control and
USGS 30-meter or 10-meter digital elevation model as the elevation control.
10.2.4. Field Verification and Photographic Signature
A significant component of an NPS inventory is knowledge of the natural and
cultural characteristics of the study area. This knowledge can then be correlated to the
signatures on the imagery used to identify NPS features. The remote sensing
scientist's primary role is to use a limited amount of field work to verify what to interpret
and determine what inferences can reliably be made from the imagery. A site visit
accomplishes two things. First, the scientist observes the relationships, terrain, and
land uses in the study area. Second, the interpreter correlates the photography
signature to the ground features. This correlation enables the interpreter to produce
photo keys that can be extrapolated to the study area.
10.2.5. Photographic Interpretation and GIS Database Construction for NPS
Inventory
After field reconnaissance to develop photo interpretation keys, the aerial
photographs are interpreted for the following
• Hydrologic unit and subwatersheds
• Stream order
• Detailed land use and landcover
• Livestock, dairy, and poultry operations
• Drainage features
• Road features
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• On-site septic conditions
• Riparian zone conditions
• Impervious surface percentages
• Dump sites
• Drainage points
• Geologic features
The photo interpretation is done in stereo using mirror stereoscopes with 3* and
8* magnification. The level of detail extracted in the photo interpretation process
enables TVA's NPS Inventory process to be highly successful. When the database is
completed, the information includes such features as locations where cattle are entering
streams, homes and commercial buildings with potentially stressed on-site septic
systems, livestock operations without proper waste management, and the riparian buffer
condition of streams. This precise spatial data identifies key targets for abatement
measures and planning of BMPs.
Hydrologic Unit and Subwatershed Mapping. A hydrologic unit or
subwatershed is a hydrologically correct area within the project area. The unit defines a
topographically correct area contributing to the surface runoff at a defined point on a
stream. The point on the stream might be a tributary intersection, sampling site, stream
gage, or accessible point for future sampling.
Stream Network and Order. The stream network is based on the blue-line
streams from USGS/TVA 7.5 Minute Topographic Maps. The streams are entered into
the GIS either by loading existing digital data or by digitizing the stream network from
the maps. This base level of streams is then enhanced, according to the
photo-interpretation. Streams are added or alignment modified, as appropriate, to
accommodate loading of the photo-interpreted information. The order of streams is
based on the blue-line stream network on the map base. Stream order is a number
representing the streams relationship to the overall stream network of a watershed.
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Headwater tributaries are first-order streams. The convergence of two first-order
streams creates a second-order stream. A third-order stream results when
two second-order streams converge; this numbering continues until all the streams of a
watershed are ordered.
Road Conditions. Base information for road coverage is the road network on
the USGS 7.5 Minute Topographic maps. The road network is updated to the date of
the project photography. Road conditions interpreted for the NPS inventory are the
surface type and the significant erosion features associated with the road. Road
surface is either paved (impervious) or unpaved. Unpaved roads are all classes of
unpaved surface from well-maintained gravel to off-road vehicle trails. The significant
erosion features associated with the road include eroding cuts and fills and eroding
ditches along the road.
Land Use and Landcover. The study area is divided into unique polygons, on
the basis of LU/LC as interpreted from the aerial photograph. Each polygon is assigned
an LU/LC code. Such mapping provides a baseline characterization of the watershed
and allows relationships between land use and water quality impairment to be
evaluated. The classification scheme used is a hierarchical system based in part on the
classification developed by the USGS for use with remotely sensed data (see
Table 10-1, Anderson et al., 1971). The classification system is tailored to the study
area while maintaining the ability to aggregate the landcover to Anderson Level 1 or
2 classes (see Table 10-1). For example, the LU/LC classification scheme can be
modified to further identify lands that contribute sediment to streams. Such LU/LCs
include crop fields with different residue cover and conservation tillage practices.
Livestock Operations. Livestock and poultry operations are mapped by
interpretation of facilities and their relationships or associations with the landscape (see
Table 10-2). Examples of the relationships are soil compaction, soil staining, soil
moisture content, size and presence of barns and other structures, presence of hay
bales, animal trails, water sources, fencing, and feedlots. These relationships and
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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TABLE 10-1
Example of the Anderson Hierarchy
1. Urban and built-up
11. Residential
110. Single family, high density (more than 6/acre)
1101. Under construction (roads and some house construction)
1102. Predominately forested
1103. Predom inately cleared
111. Single family, medium density (2-5/acre)
1111. Under construction (roads and some house construction)
1112. Predominately forested
1113. Predom inately cleared
TABLE 10-2
Livestock Operations Data
¦ Type of Operation
• Beef cattle, dairy, swine, horse, or poultry
¦ Location
• Adjacent or nonadjacent to stream
¦ Size of Operation
• Small, medium, or large for livestock
• Number and square feet of houses for poultry
¦ High Potential Impact Sites
¦ Drainage Pathway
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associated landcover are used to determine the relative size and type of livestock
operation. Other potential effects identified include proximity to streams; whether a site
has critical impact factors, such as a large concentration of animals, poor or no waste
management; presence of waste management ponds or lagoons; and whether the
animals are confined (see Figure 10-2)
Drainage Conditions - Animal Access
No Animal Access
Animal Access
| Animal access
I Potential animal access
I Probable animal access
FIGURE 10-2
Cattle Access to Streams
Drainage Conditions. Drainage conditions associated with the various land
uses and livestock operations are mapped. The drainage conditions mapped are listed
in Table 10-3.
On-Site Septic Systems. Stressed on-site septic systems can contribute
contaminants to the surface water through overland flow, particularly when saturated
soil conditions exist. The NPS inventory identifies signatures on the aerial photography,
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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TABLE 10-3
Drainage Conditions Mapped in an NPS Inventory
Feature
Description
Perennial Stream
Water is present throughout most years. Stream usually has a
baseflow.
Intermittent Stream
Water is not present at all times. Stream does not have a
baseflow throughout most years. The stream has a well
defined channel.
Ephemeral Stream
Drainage ways which flow during an individual storm event.
There is not a well defined channel.
Channelized Stream
Perennial or intermittent stream channel altered by
straightening or dredging.
Eroded Streambank
Stream segments that are eroding with visible collapsed banks.
Grassed Waterway
Stream channel that has been planted in vegetation as an
erosion control.
Animal Access
Stream segments where livestock have direct constant access.
Animals are not restricted from the stream by natural or
artificial constraints, and there is evidence that animals are
entering the stream. Such segments can be small sites where
the animals drink or longer segments such as streams through
confined feedlots.
Probable Animal
Access
Stream segments through areas where there is direct evidence
of presence of animals and no physical barrier to the stream.
Barriers could be fences or high banks. Livestock have access
to the entire segment of the stream.
Potential Animal
Access
Stream segment through areas that exhibit no direct evidence
of current animal activity. An example is a hay field that might
be used in pasture rotation. The stream has no physical
barrier to livestock.
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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which might be associated with on-site systems and can indicate the conditions of a
stressed or potentially stressed system. The four conditions identified are:
1) Distinctive moisture pattern: effluent plume associated with visible drain field
pattern.
2) Suspicious moisture pattern: visible plume pattern but no drain field visible;
condition can be a straight-pipe from septic system, greywater disposal, system
breakout with no drain field showing, roof drainage, or natural seepage/spring.
3) Distinctive drain field area: drain field pattern but no plume visible; can indicate
slow leaching of a seasonally or hydraulically stressed system or
evapotranspiration characteristics of a functioning system or newly installed
system.
4) Suspect location: no plume or drain field visible; homesites on very steep slopes,
small lots, visible rock outcrops, in close proximity to streams or reservoirs, or
heavily wooded lots.
Dump Sites. Small undocumented dumping locations can have an adverse
effect on the environment and serve as nonpoint sources. Dump sites, usually along
roads and often adjacent to streams, are identified from the aerial imagery.
Riparian Buffer Condition and Features. The riparian condition in the NPS
inventory is a characterization of the landcover buffer adjacent to a stream. As the area
of interaction between the land and streams, riparian zones can control both erosion
and runoff. The width of the riparian zone, as well as the types and amounts of
vegetation, can influence the quantity of pollutants entering a stream. While woody
vegetation, such as trees and shrubs, contribute to the control of runoff, the root
systems of grasses help to hold the soil in place and prevent erosion. The surface
organic layer that has developed from the vegetation litter allows water to infiltrate the
soil. As a general rule, the flow of water and the erosion rate increase with the
steepness of the slope of the streambank. Thus, steeper slopes could require wider
riparian buffers. Riparian zone conditions are interpreted for both the left and right
streambank.
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The riparian conditions are mapped in two landscape categories. The first is an
open landscape referring to areas lacking appreciable woody vegetation; the stream is
adjacent to grass, bare ground, or urban landcover. The second is a closed landscape,
that is, dominated by woody vegetation. The following riparian buffer features are
mapped for the perennial and intermittent streams: vegetative type, the percentage of
coverage of the vegetative type, the quality of the vegetative cover, and width of the
vegetation. Vegetative type is identified as either woody, grass, or bare. Percentage of
coverage is identified as 0 to 33%, 34 to 66%, or 67 to 100% for woody vegetation, and
grass cover quality is rated as poor, moderate, or good. The width of vegetation is
identified as 0 to 15 , 16 to 30, 31 to 50 , 51 to 100 feet, or greater than 100 feet. All the
attributes of the streams can be modified according to the unique conditions and
requirements of the watershed.
Drainage Points. Points where water emerges or submerges are important
attributes of the hydrology of a watershed. Drainage points identified in the NPS
inventory include the following
• Sinking Points: Points where surface water enters the ground water system.
• Springs: Points where water surfaces from ground water.
• Catch Basins: Man-made feature designed to catch and hold or redirect flow of
surface water.
• Emerging Points: Points where previously subsurface flow emerges and
continues on the surface.
Impervious Cover. The natural surface runoff characteristics of a watershed
can be altered by impervious surfaces. Impervious surfaces include roads, parking lots,
sidewalks, rooftops, and other impermeable surfaces of the urban landscape.
Imperviousness is defined as the percentage of the total area of the mapped unit that is
covered by impervious surface. A percentage of imperviousness, excluding paved
roads, is assigned to each LU/LC polygon on the basis of the photograph interpretation.
For example, a low-density residential area might have an imperviousness of 5%, on
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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the basis of the estimated coverage of structures, driveways, and sidewalks. The
percentage of area covered by paved roads is calculated from the roads' coverage layer
in the database. The percentage of imperviousness for each watershed is calculated by
summing the percentage of imperviousness area of each polygon and the roads in the
watershed.
Geologic Features. The term geologic features are better described as
geomorphology. The NPS inventory identifies subsidence features. Subsidence
features could be a well-defined sinkhole or subtle feature only a few feet or inches in
depth. Sinkholes provide additional routes for excess nutrients and other pollutants to
enter the ground water. These subtle features are photo-identifiable because of soil
moisture characteristics or vegetation changes.
10.3. ANALYSIS
10.3.1. Construction of Geodatabase
The database software is Environmental Systems Research Institute's ArcMap
using custom proprietary tool palates specifically designed for the NPS inventory.
Photo-interpreted features are digitized into the GIS in a logical sequence. The
database components are interrelated and are populated in the database in a defined
process. This process also serves as a working edit as each image is used to input the
features outlined in the previous section. The database is designed so that each related
layer is coincident.
10.3.2. Pollutant Loading Model
A pollutant loading model was developed to estimate the nonpoint source
pollutant loads from the sources identified by the NPS inventory. The model can be
used to estimate pollutant loads for total suspended solids, five-day biochemical oxygen
demand, total nitrogen, and total phosphorus from the following sources: residential,
commercial, industrial, transportation, nurseries, cropland, pasture, beef cattle, dairy
cattle, swine, horses, and poultry. To help evaluate total loading, an estimate of
significant point source loads are also included in the model. Designed as an ArcGIS
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extension, the model allows user input of parameters and coefficients, performs the
calculations, and displays the results in tabular and graphical format.
The interactive pollutant loading model is a geodatabase application that uses
the inventory data and soil loss information from Natural Resources Conservation
Service (NRCS) to estimate pollutant loadings by subwatershed and by source. The
model enables the user to conduct What //"analyses. What if BMPs were installed in a
subwatershed? How much reduction in pollutant loading would be realized? Outputs
may be in the form of tables and graphs.
10.3.2.1. Pollutant Loads from Urban Land Classes
The pollutant load from the urban land uses within the study areas are estimated
using a method described by Schueler (1987). This method uses the following equation
(see Eq. 10-1):
M = RainV * Rv * Area * Cone * 0.0001135 (Eq. 10-1)
where
M = mass load (tons)
RainV = rainfall amount (inches)
Rv = runoff coefficient (unitless)
Area = drainage area (acres)
Cone = average concentration in runoff (mg/L) 0.0001135 = unit
conversion factor
This equation is used to estimate the annual pollutant load for the following land
classes: residential, commercial, industrial, and transportation. The areas used for each
land class are those generated by the NPS inventory. Annual rainfall estimates are
usually obtained from the NRCS District Conservationist for the study area. Runoff
coefficients for the different land classes were estimated using the following equation
(see Eq. 10-2):
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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Rv = 0.050 + 0.009 (PI) (Eq. 10-2)
where
PI = percentage of imperviousness estimated from the remote
sensing process
Pollutant concentrations are taken from the U.S. Environmental Protection
Agency (EPA's) National Urban Runoff Program report (U.S. EPA, 1982), Schueler
(2000), and the PLOAD User's Manual (U.S. EPA, 2001). These literature values are
modified by the modeler's professional judgment and experience in similar projects
(TVA, 2003). These event mean concentrations are assumed to represent delivery to
the storm drainage system; therefore, no delivery factor is used for urban loads.
10.3.2.2. Pollutant Loads from Crop, Pasture, Forest, Mining, and Disturbed Lands
The first step in estimating pollutant loads from pasture, crop, forest, mining, and
disturbed lands is determining the soil loss for each class using the Revised Universal
Soil Loss Equation (RUSLE; see Eq. 10-3):
A = RxKxLS*CxP (Ecl- 1 °"3)
where
A =
= soil loss (tons/acre/year)
R ¦¦
= rainfall energy factor
K =
= soil erodibility factor
LS =
= slope-length factor
c =
= cropping management factor
P :
= erosion control practice factor
The soil loss factors used for the various land classes are usually provided by the
District Conservationist for the project area.
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The pollutant loads from these lands are estimated using the soil loss values
calculated from RUSLE (see Eq. 10-3) and the following equation (see Eq. 10-4):
M = A x Area * DR * PC (E9- 1 °"4)
where
M = pollutant loading (tons/year)
A = soil loss (tons/acre/year)
Area = land class area (acre)
DR = sediment delivery ratio (unitless)
PC = pollutant coefficient (ton pollutant/ton soil)
The acreage used for the various land classes are determined by the NPS
inventory. The sediment delivery ratios are estimated from the USDA, National
Engineering Handbook, Section 3—Sedimentation, Chapter 6—Sediment Sources,
Yields and Delivery Ratios (USDA, 1978). The delivery ratios are based on the
following equation (see Eq. 10-5):
DR= .0417762/T0134958-0.127097 (E9- 10"5)
where
DR = delivery ratio (unitless)
A = area (square miles)
Nutrient characteristics are based on literature values and calibration to water
quality data in previous similar studies (TVA, 2003).
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10.3.2.3. Pollutant Loads from Unpaved Roads
The pollutant load from unpaved road surfaces is estimated using the following
equation (see Eq. 10-6):
M = L x W x E x PC x DR (Eq. 10-6)
where
L = length of unpaved road (feet)
W = width of unpaved road (feet)
E = estimated average erosion rate (tons/acre/year)
PC = pollutant coefficient (ton pollutant/ton soil)
DR = sediment delivery ratio (unitless)
The NPS inventory provides a measurement of length of unpaved roads.
Estimates of average road width and average erosion rates are provided by the District
Conservationist for the study area. Pollutant coefficients for nonagricultural land uses
are applied. The delivery ratio is calculated from the watershed area (see Eq. 10-5).
10.3.2.4. Pollutant Loads from Eroding Streambanks and Road Features
The pollutant load from eroding road banks and streambanks identified in the
study area is estimated using the following equation (see Eq. 10-7):
M=L*RxHxyxPC*DRx 0.0005 (Eq. 10-7)
where
M = pollutant loading (tons/year)
L = length of feature (feet)
R = recession rate or vertical erosion rate (feet/year)
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
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H
= height of feature
y
= soil bulk density (pounds/feet3)
PC
= pollutant coefficient (ton pollutant/ton soil)
DR
= sediment delivery ratio (unitless)
0.0005 =
= conversion factor
The NPS inventory provides the length of the eroding stream and road banks.
The district conservationist for the study area provides estimates of recession rate, bank
height, and soil density. Pollutant coefficients for nonagricultural land uses are applied.
The sediment delivery ratio is calculated on the basis of the watershed size (see
Eq. 10-5). Although eroding road features include banks, ditches, and fill/cut areas, the
model uses the assumption that sediment loading from all these features can be
adequately estimated using this equation.
10.3.2.5. Pollutant Loads from Beef Cattle, Dairy, and Horse Operations
The pollutant load from the livestock operations identified within study area was
estimated using the following equation (see Eq. 10-8):
M = NA x WT x PR x 0.0001825 x DR * NSn (Eq. 10-8)
where
M
= pollutant loading (tons/year)
NA
= number of animals (number/site)
WT
= minimal weight (pounds)
PR
= pollutant production rate (lbs/day/1,000 lb live weight)
0.0001825 =
= unit conversion factor
DR
= delivery ratio (unitless)
NSn
= number of sites of type n
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The number and type of cattle, dairy, and horse sites in the study area are
identified by the NPS inventory. An estimate of the average number of livestock for
each of these operation sizes is provided by the district conservationist for the study
area. The (as excreted) pollutant production rates for total nitrogen and total
phosphorus are obtained from the NRCS Agricultural Waste Management Field
Handbook (USDA, 1996). The production rate for total suspended solids was based on
values derived from Livestock Manure Characterization Values from the North Carolina
Database (Barker et al., 1990).
The delivery of pollutants from these operations varies greatly from operation to
operation. Factors that influence delivery of pollutants to the stream include type and
amount of confinement, management of lagoons or waste storage ponds, proximity of
cattle to streams, and timing and amount of land application of wastes. Because of the
limitations of the remote-sensing process, waste treatment facilities are not considered
in this model. For both dairy cows and beef cattle, only stream access and additional
pasture loadings are included in the model. While estimated time spent in stream
provides a basis for estimation of the direct delivery of waste.
10.3.2.6. Bacteria Load Methodology
This bacteria loading model for LU/LC is a simple washoff model (see Eq. 10-9):
M = RainV * Rv * Area * Cone * CF (Eq. 10-9)
where
M
RainV =
Rv
Area =
Cone =
CF
mass load
rainfall amount (inches)
runoff coefficient (unitless)
drainage area (acres)
average concentration in runoff (mg/L)
unit conversion factor
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
Identification (IPSI) Process
Bacteria loads can be generated by urban or rural land uses, so a runoff factor
based only on imperviousness is inadequate. For this application, the Curve Number
Equation (USDA, 1972) is adapted to estimate annual runoff. The curve number
equation calculates runoff on the basis of LU/LC type and condition, hydrologic soil
group, and precipitation. The factors for this equation can account for impervious
surfaces, and therefore the equation can be used for rural or urban land uses.
The curve number formula is intended to calculate the runoff from a single storm,
so in order to develop an estimate of annual runoff from a particular land use, a
representative average storm is calculated using (see Eq. 10-10)
/ = API + RG (Eq. 10-10)
where
/ = the representative storm in inches
A = average annual precipitation (inches)
R = the number of days with precipitation (rain days)
P = the decimal portion of rain days on which precipitation is greater
than 5 mm
G = the decimal portion of rain days on which runoff is generated
This approach is based on the method used in the STEPL model (short for
Spreadsheet Tool for Estimating Pollutant Load) (Tetra Tech, 2005), and values for P,
R, and G can be obtained from the STEPL database. Values for A can be obtained
from rainfall maps or data from local weather stations.
10.3.3. Watershed Prioritization
The data are used to prioritize subwatersheds on the basis of severity of pollutant
loads. Understanding the pollution problem (using information from the watershed
representatives and from the pollutant loading model) is the first step in watershed
prioritization. Next, using the land-use drivers for the pollution will isolate priority
10-21
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
Identification (IPSI) Process
subwatersheds. At this stage, priority sites can be identified. Working in conjunction
with the customer, BMPs tailored specifically to identified problems can be developed.
This detailed NPS inventory can help city, state, and county governments; nonprofit
groups; and EPA develop cost-effective targeting of improvement efforts. This, in turn,
can lead to the development of manageable watershed programs.
10.4. DISCUSSION AND CONCLUSION
The NPS inventory can also offer TMDL support. By using the land-use
database, a TMDL Watershed Characterization can be developed. The database can
also aid in locating where to place controls to meet TMDL requirements.
Source water protection analysis is another benefit of the NPS database. Both
surface and ground water sources can be analyzed. Knowing land use and activities
within buffer zones combined with identifying potential spill sites are beneficial. When
combined with time of travel studies, the data can help planners with regulation
information as well as cost analysis. Overall, this leads to the development of long-term
surface water protection strategies and programs. This analysis ability is vital to
agencies such as state governments, water suppliers, and EPA.
The database can also be used to test changes in economic growth and the
environment. As illustrated by Figure 10-3, a rising economy leads to an increase in
growth potential. This also leads to increased pollution potential, which could lead to
moratoriums; the final result being a decrease in growth potential. By understanding
and analyzing future trends and growth plans, water quality effects can be assessed.
This can help the study of protection costs and implementation of programs and
regulations. The ability to do this enables planners to determine the best growth
alternatives as well as cost-effective prevention strategies.
NPS inventories have been used to provide a baseline assessment for
brownfields cleanup and redevelopment planning.
10-22
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
Identification (IPSI) Process
Economic Growth and the Environment
^ Economy = Growth Potential ^
f Growth Potential = Pollution Potential tt
^Pollution Potential = Moratoriums^
ttMoratoriums = Growth Potential J.
FIGURE 10-3
Socioeconomic Factors Influencing Potential for Increased and Decreased
Pollutant Loading
10.5. REFERENCES
Anderson, J.R., E.E. Hardy, and J.T. Roach. 1971. A Land-Use Classification System
for Use with Remote Sensor Data. Geological Survey Circular 671. U.S. Geological
Survey, Reston, VA.
Barker, J.C., J.P. Zublena, and C.R. Campbell. 1990. Livestock Manure
Characterization Values from the North Carolina Database. North Carolina Cooperative
Extension Service.
Schueler, T.R. 1987. Controlling Urban Runoff: A Practical Manual for Planning and
Designing Urban BMPs. Metropolitan Washington Council of Governments,
Washington, DC.
Schueler, T.R. 2000. Sources of Urban Stormwater Pollutants Defined in Wisconsin.
In: the Practice of Watershed Protection. Article 7. Center for Watershed Protection.
Ellicott City, MD. Available online at
http://www.stormwatercenter.net/Library/Practice/7.pdf.
Tetra Tech, Inc. 2005. User's Guide: Spreadsheet Tool for Estimating Pollutant Load
(STEPL) Version 3.1. Tetra Tech, Inc., Fairfax, VA. Available online at
http://it.tetratech-ffx.com/steplweb/STEPLmain_files/STEPLGuide310.pdf.
TVA (Tennessee Valley Authority). 2003. Blount County and Little River Basin
Nonpoint Source Pollution Inventories and Pollutant Load Estimates. Tennessee Valley
Authority, Knoxville, TN. Available online at http://216.119.90.50/asp/pdf/IPSIreport.pdf.
10-23
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Section III—Chapter 10: Nonpoint Source Inventory—Integrated Pollutant Source
Identification (IPSI) Process
USDA (Department of Agriculture). 1972. Hydrology. In: National Engineering
Handbook, Section 4. Department of Agriculture, Soil Conservation Service,
Washington, DC.
USDA (Department of Agriculture). 1978. National Engineering Handbook. U.S.
Department of Agriculture, Soil Conservation Service, Washington, DC.
USDA (Department of Agriculture). 1996. Agricultural Waste Management Field
Handbook. U.S. Department of Agriculture, Natural Resources Conservation Service,
Washington, DC.
U.S. EPA (Environmental Protection Agency). 1982. NURP Priority Pollution
Monitoring Program -Volume 1: Findings. Monitoring and Data Support Division, Office
of Water, Washington, DC.
U.S. EPA (Environmental Protection Agency). 2001. PLOAD version 3.0. An ArcView
GIS Tool to Calculate Nonpoint Sources of Pollution in Watershed and Stormwater
Projects. User's Manual. Available online at
http://www.epa.gov/waterscience/BASINS/b3docs/PLOAD_v3.pdf.
10-24
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Section III—Chapter 11: Oostanaula Creek IPSI Case Study
11.OOSTANAULA CREEK IPSI CASE STUDY
Pat Hamlett, Tennessee Valley Authority, Chattanooga, TN
Key words;
Analysis: Historic aerial photos, Digital
Elevation Model, Light Detection and Ranging
(LiDAR), Thermal Infrared Remote Sensing,
Change simulation models
Clean Water Act: Total Maximum Daily
Load (TMDL), Nonpoint Source Inventory
11.1. INTRODUCTION
Oostanaula Creek Watershed
(OCW), in McMinn and Monroe counties
in the Hiwassee River watershed, extends
approximately 23 miles along the eastern
side of I-75 between Knoxville and Chattanooga, Tennessee. The city of Athens,
encompassing approximately 14 square miles, is the primary municipality in the OCW.
Covering 70 square miles (44,864 acres), the watershed is composed of 18 tributaries
or subwatersheds ranging in size from 125 to 6,260 acres. As a largely rural area
dominated mainly by beef cattle and dairy operations, water quality issues have
historically been limited to those resulting from agricultural practices. However, recent
population growth, particularly in the area surrounding Athens has resulted in an
increase in wastewater related pollution problems. Athens Utility Board's Oostanaula
Creek Wastewater Treatment Plant (WWTP) is a known major point source discharger
contributing to phosphate, Escherichia coli and siltation (TDEC, 2006b). The plant is in
the process of a $16 million upgrade (UT, 2007). With a current population of
approximately 14,000, Athens, as well as McMinn County, has been experiencing a
growth surge since 2000 that is expected to continue with a 20% increase by 2025
anticipated by the Tennessee Center for Business and Economic Research (2003).
In 1998 when Oostanaula Creek was placed on Tennessee's 303(d) list as
unable to support designated uses due to pathogens and suspended solids, the
What is interesting about this case study?
An Application: Integrated Pollutant Source
Identification (IPSI) developed by the
Tennessee Valley Authority (Chapter 6).
Condition Assessment. Impairments were listed
due to exceedance of criteria for pathogens and
suspended solids, but other stressors were also
implicated.
Source Assessment: Urban sources accounted
or more than 50% of nitrogen and phosphorus
loads. Cropland and pasturage accounted for
more than 50% of suspended solids.
Predictive Assessments: Effects were not
predicted. BMPs were applied to the highest
priority sources in a 15-year restoration plan.
Outcome Assessment none yet, but planned.
11-1
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Section III—Chapter 11: Oostanaula Creek IPSI Case Study
Tennessee Department of Environment and Conservation (TDEC) gave Oostanaula
Creek high priority for total maximum daily load (TMDL) development (TDEC, 1998).
Because of a clear need for stakeholder participation in restoration efforts, a strong
partnership was developed. Members included Athens, Athens Utility Board, McMinn
and Monroe counties, Soil and Water Conservation Offices of McMinn and Monroe
counties, Tennessee Department of Agriculture, TDEC, Tennessee Wildlife Resources
Agency, Tennessee Department of Health, the University of Tennessee and University
of Tennessee Extension, Agriculture Extension agents of McMinn and Monroe counties,
Tennessee Valley Authority (TVA), U.S. Environmental Protection Agency (EPA), U.S.
Department of Agriculture—Natural Resources Conservation Service (NRCS), and local
landowners and residents. This partnership was built to work together on monitoring
and restoration of the OCW (Hagen and Walker, 2007).
11.2. METHODS AND ANALYSIS
Using historic and recent water quality data they collected, TDEC developed
TMDLs for fecal coliform (TDEC, 2002), pathogens (TDEC, 2005), and siltation and
habitat alteration (TDEC, 2006a). To help stakeholders identify pollutant sources and
prioritize subwatersheds and sites for remediation, TVA provided a nonpoint pollutant
source (NPS) Assessment developed using Integrated Pollutant Source Identification
(IPSI; see Chapter 10), including a desktop geographic information system (GIS), an
NPS inventory in the form of a land-use geodatabase, and pollutant loading models for
total nitrogen (TN), total phosphorus (TP), and total suspended solids (TSS), and soil
loss estimates (see Figure 11-1). A detailed explanation of the capabilities of IPSI is at
www.tva.gov/environment/pdf/ipsi.pdf.
The NPS inventory was developed from 1999 aerial photography. The
photographic interpretation identified the dominant land use in the OCW to be
forest/scrub, covering 47.6% of the watershed land area, primarily in the upland areas.
Dominating the valley, pasture covers 30.7% of the watershed, with row crops
occupying 5.3% of the watershed. Urban areas, primarily in the Athens vicinity,
represent 14.3% of the watershed, with 12.5% residential and 2.3% commercial or
industrial use. Figure 11-2 shows the land use and landcover.
11-2
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
Integrated
PollutantSource
Identification
2
^ , v.-'-*
Overview
Integrated Pollutant Source Identification (IPSI) is a geographic
database and set of tools designed to aid citizens and planners in
implementing water quality improvement and protection projects
within a watershed. IPSI is also
designed to aid water quality
agencies in implementing the
water-quality-based approach to
pollution control. The
geographic database consists of
information on watershed
features, such as land use and
land cover, stream bank erosion
sites, and livestock operations,
that are known or suspected
Successful Use of IPSI Reopens Floatway SOUrceS Of nonpolnt pollution.
The information is generated by
interpretation of low-altitude, color infrared, aerial photography. The
data is managed using commercially available geographic information
-
TENN ESSEE VALLEY AUTHORITY
ResourceManagement
FIGURE 11-1
Front of IPSI Web Site
11-3
-------
Land Use and Land Cover
Oostanaula Creek Watershed
FIGURE 11-2
Land Use and Landcover
LEGEND
] Single Family Residential. High Density (More than 6/acre)
H Single Family Residential. Medium Density (2 - 5/acre)
_J Single Family Residential. Low Density (Fewer than 2/acre) Under Construction
I Single Family Residential, Low Density (Fewer than 2/acre)
H Apartment / Condiminium Complex
Mobile Home Park
Farmstead with Accompanying Structures
Commercial, Service, Institutional
Athletic Field
Commercial. Service, Institutional (Under Construction)
\Nater Treatment Facility
Sewage Treatment Facility
| | \Afcter Tank
Dump Site
| Religious Facility
~ Cemetery
m Industrial
~j Major Highway Right-of-Way
| Electric Transmission Right-of-Way
Substation
| Row Crop, Low Residue (0 -10%)
| Row Crop. High Residue (> 30%)
Strip Cropping
| Row Crop, Medium Residue (10 - 30%)
Good Pasture: Well Maintained
Fair Pasture: Uneven Growth and Condition, Minimal Maintenance
Woodland Pasture: 10% or Greater Crown Cover
| Heavily Overgrazed Pasture
m Feedlot or Loafing Areas
Orchards. Vineyards, and Nurseries
I Poultry Operations
| Shrub and Brush Old Field with Volunteer Woody Growth
Forestland
Clearcut Forestland
[ Water
Palustrine Forested Wetlands
Palustrine Forested'Scrub-Shrub Wetlands
yZ/'j Palustrine Scrub-Shrub Wetlands
[ X | Stripmines, Quarries, or Borrow Areas
Yj/j// Active Quarry
| Flooded Quarry
Disturbed Area Little or No Cover. Non-agricultural Area
an
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-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
11.3. SOURCE CHARACTERIZATION
11.3.1. Agricultural Land Use
Agricultural land is 85% pasture and 15% cropland (including 42.5 acres of
orchards). Of the land identified as pasture, 84% (11,726.57 acres) is classified as fair
pasture with uneven growth and minimal maintenance. 14.3% of the pasture land was
considered overgrazed and good pasture, woodland pasture, and feedlots combined
totaled 1.7% of the OCW pasture land. The quality/conditions of the pasture are used
for calculating soil loss estimates. Figures 11-3 through 11-6 illustrate the differences
among four of the pasture types.
11.3.1.1. Pasture Land
The pasture supports a variety of livestock operations including 150 beef cattle
farms, 11 dairies, and 16 horse farms. Livestock in OCW also include 1 swine
operation and 2 poultry operations (one with 5 poultry houses and the other with
2 houses). Additional animals reported to be kept in the watershed include sheep,
donkeys, hogs, and llamas (UT, 2007). Of the cattle sites, 2 are large, 38 medium in
size, and 110 are considered small cattle farms. Forty-nine (2 large, 25 medium and
22 small) are adjacent to a stream. Two of the dairies are large, 8 are considered
medium and one is small. Five of the dairies are adjacent to a stream (2 large,
3 medium). None of the horse farms (1 medium, 15 small) are adjacent to streams.
The only swine operation, medium in size, was adjacent to a stream. Table 11-1 shows
the estimated number of animals for each size livestock operation (UT, 2007).
Photographic interpretation of the streambanks identified 13,151 feet of banks
where animal access was occurring, and an additional 138,989 feet of bank where
probable animal access to the stream was indicated. Another 85,653 feet of
streambank had no fencing or other barrier to prevent animals from entering the stream.
Because there were no animals visible at these locations in the imagery, these sites
were identified as having potential access. Figure 11-7 is a color infrared photograph
showing animal access sites to streams.
The Figure 11-8 map is part of the OCW, showing the livestock sites by type and
size and their association with animal access to streams. These data are important to
11-5
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
FIGURE 11-4
Fair Pasture
FIGURE 11-5
Poor, Over-Grazed Pasture
FIGURE 11-6
Feed Lot, Loafing Area
11-6
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
TABLE 11-1
Animals per Site Used to Estimate Pollutant Loadings
Number of Animals per Site
Beef Cattle
Dairy Cattle
Horse
Swine
Large
110
150
20
200
Medium
50
100
10
60
Small
15
35
5
12
Drainage Conditions - Animal Access
— No prospect of animal access
Animal Access
Point of animal access
Probable animal access
Potential animal access
FIGURE 11-7
Sample View of Animal Access to Streams
11-7
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
LEGEND
LIVESTOCK SITES
Type. Size
Cattle, Large
41' Cattle, Medium
# Cattle. Small
Dairy, Large
4I1 Dairy, Medium
4$ Dairy, Small
© Horse, Medium
© Horse, Small
4I1 Swine, Medium
41' Poultry
ANIMAL ACCESS
No animal access
Animal Access
Point of animal access
Probable animal access
Potential animal access
| Subwatersheds
FIGURE 11-8
Livestock Sites and Animal Access to Streams
pollutant loading calculation because of both runoff of nutrient rich material from
pastures to streams and direct deposition of animal waste into the streams where there
is direct access (UT, 2007). Delivery ratios for pollutant loading calculations differ
among sites adjacent to a stream and the estimated time the animals spend in the
streams if they have access, management of waste lagoons, and the amount of land
applications of waste. The location of the livestock sites in the watershed, as well as
the fate of the pollutant when entering the environment, affects pollutant delivery to the
stream (UT, 2007).
11.3.1.2. Cropland
The two common cropping practices in the OCW are strip cropping and row
cropping. Strip cropping, the practice of growing crops in a system of alternating strips
across the field such as a strip of erosion resistant cover and a strip of tilled soil or less
Animal Sites and
Animal Access to Streams
Oostanaula Creek Watershed
11-8
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
dense, more erodable crop, is a conservation practice that reduces soil erosion and
sediment transport. Strip cropping often includes the practices of tilling with the
contours, crop rotation, and field borders. Row cropping involves tillage practices that
range from clean tillage, or burying all crop residue at the end of the growing season, to
conservation tillage or no tillage, leaving all or most of the vegetation in place. Helping
to reduce erosion and retain soil moisture, the amount of crop residue or the lack
thereof, is important in determining the C coefficient for the revised universal soil loss
equation (RUSLE) for use with cropland soil loss estimates. With the OCW strip
cropping, one of the most recommended conservation practices was used on 147 acres
or 6% of the cropland. High-residue (>30%) row cropping, the other recommended
conservation practice, was used for 492 acres, or 20% of the cropland. Most of the row
crop fields, 1,409 acres, or 58%, were left with a medium (10-30%) residue. The
remaining 352 acres, 15% of the cropland in the OCW, was left with less than
10% residue covering the soil.
11.3.2. On-Site Septic Systems
As mentioned previously, wastewater pollution including phosphate, E. coli, and
siltation is causing water quality problems in the OCW. While a key contributor of the
contamination is the WWTP, stressed on-site septic systems can also contribute to
pollutant loads, specifically fecal coliform loading. Looking for specific moisture patterns
and other clues in the aerial photographs, analysts identified 104 potentially stressed or
failing septic systems. Table 11-2 identifies the specific patterns observed, the potential
implications of each, and the number of each identified in the OCW.
Figure 11-9 is a color infrared photograph of a residential subdivision showing
signs of stressed septic systems. Notice the distinct lines of the drain fields visible in
the photograph. These indicate Class 3 septic system conditions.
11.3.3. Eroding Road Banks
In the OCW 244 miles of paved roads and 138 miles of unpaved roads with
critically eroding road-side ditches or eroding cuts or fills were identified (see
Figure 11-10). The total length of eroding paved roads was 21.4 miles or nearly 9% of
11-9
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
TABLE 11-2
On-Site Septic System by Classifications
Condition
Observation(s)
Description/Implication
# Systems
Identified
Class 1
Distinctive
moisture pattern
Effluent plume from visible drain field
pattern, or prominent ponding down
slope from drain field.
1
Class 2
Suspicious
moisture pattern
Visible plume pattern, but no drain field
apparent; can be straight-pipe from
septic system, roof drainage, or natural
seepage/spring.
12
Class 3
Distinctive drain
field area
Visible drain field pattern, but no plume
evident; may indicate a seasonally or
hydraulically stressed system.
7
Class 4
Suspect locations
No plume or drain field visible; home
sites on very steep slopes, small lots,
visible rock outcrops, or in close
proximity to streams or reservoirs,
especially those on heavily wooded lots.
84
11-10
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
FIGURE 11-9
Septic System—Distinctive Drain Field
Paved and Unpaved Roads
Oostanaula Creek Watershed
Roads
- Unpaved
Paved
FIGURE 11-10
Paved and Unpaved Roads
11-11
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
the total paved road miles. Similar erosion features affected 52.7 miles (38.3%) of the
unpaved roads while the unpaved surfaces of the roads also contributed to erosion. A
large percentage of the paved roads (44%) were in the subwatersheds in the urban
areas around Athens, and a larger percentage (60%) of eroding unpaved roads were in
the six most southern subwatersheds.
11.3.4. Streambank Conditions
The stream network in the OCW was mapped and classified as perennial,
intermittent, or ephemeral. Perennial and intermittent streams were further interpreted
for eroding banks. Forty miles (231,740 feet) of streambank were shown to be eroding,
resulting in sediment pollution downstream. Figure 11-11 illustrates the unstable,
eroding banks in the watershed.
Riparian condition was recorded for both the left and right streambanks (as
viewed looking downstream) of all perennial streams (e.g., see Figures 11-12 and
11-13).
The features included in the analyses were vegetation type, density, and the
width of the riparian buffer. Although riparian buffer information was not used in the
pollutant loading model for the OCW, the data, providing detailed locations of
inadequate riparian, will be used to support management activities (see Figure 11-14).
11.4. SOURCE ASSESSMENT
11.4.1. Soil Loss Estimate Summary
Using the RUSLE, the estimated total soil loss for the OCW was 61,220 tons per
year, or an average of 1.36 tons per acre per year. Accounting for 31 % of the soil loss
in the watershed, eroding streambanks were the greatest contributors to soil loss. The
land classes responsible for the next largest percentages of soil loss were croplands
and pasture lands at 23 and 21%, respectively. Forests contributed 7%, and disturbed
areas contributed 5%.
Table 11 -3 and Figure 11-15 show soil loss estimates for each subwatershed by
landcover classification. Taking into account the total tons per acre per year and the
total acres in the watershed for each class, heavily overgrazed pasture and medium
11-12
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
Eroding Stream Banks
Oostanaula Creek Watershed
Eroding Streams
Not eroding
Eroding bank
~ Subwatersheds
1.25 2.5
Miles
FIGURE 11-11
Eroding Streambanks
FIGURE 11-12
Shrub-Scrub Riparian
FIGURE 11-13
Forested Riparian
11-13
-------
Section III—Chapter 11: Oostanaula Creek IPSI Case Study
Woody Riparian
Inadequate
Marginal
Adequate
Non-Woody Riparian
Inadequate
Marginal
Good
Riparian Classfication
Using Vegetation Condition and Cover
(Left Bank - Looking Downstream)
Oostanaula Creek Watershed
FIGURE 11-14
Riparian Buffer Condition
11-14
-------
cn
TABLE 11-3
Soil loss Estimates for Select Agricultural Classes
Sub ID
t/ac/yr
t/yr
Row Crop
Pasture
Forest/Scrub/Shrub
Min./Dist.
Low
Res.
High
Res.
Strip
Crop
Med.
Res.
Good
Fair
WL
OG
FL/L
Orch.
S/S
Forest
HFL
Min.
Dist.
Areas
01
0.165
227
0
0
0
0
0
38
0
77
0
0
3
46
35
0
26
02
0.911
1,158
323
0
0
490
0
158
3
155
0
0
2
18
7
0
0
0201
0.385
1,821
177
0
0
142
0
183
3
234
0
0
16
136
881
0
51
03
0.967
4,095
0
94
0
1,554
0
356
0
1,063
200
0
9
80
496
0
244
04
0.545
3,022
0
28
0
96
0
306
0
770
326
0
16
139
1,188
0
153
0401
0.513
1,611
238
34
0
102
0
412
3
606
0
2
6
47
131
0
28
05
0.330
315
96
3
0
0
0
67
0
0
0
0
7
22
65
40
16
0501
0.540
880
269
118
0
0
1
96
12
175
2
1
18
30
109
0
51
06
1.210
576
0
0
0
0
0
8
1
10
0
0
1
15
37
451
53
0601
1.196
1,081
279
48
0
0
0
69
0
595
29
0
3
15
25
0
17
07
0.894
792
0
0
0
358
0
42
2
225
31
0
3
21
56
0
64
08
0.122
6
0
0
0
0
0
3
0
0
0
0
1
1
1
0
0
0801
0.228
78
0
0
0
0
0
10
0
15
0
0
2
10
41
0
0
09
1.695
5,391
1,124
114
133
2,167
0
319
3
917
481
0
2
43
89
0
0
10
1.726
4,827
450
329
0
1,257
0
324
0
1,669
121
0
1
29
16
632
0
1001
0.877
1,483
21
0
0
873
0
172
0
237
123
0
3
30
23
0
0
11
1.808
5,442
924
466
237
1,328
0
259
0
814
87
0
2
46
56
1,224
0
1101
0.870
1,459
9
246
0
158
0
200
2
512
0
0
1
22
309
0
0
CO
CD
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0)
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-------
TABLE 11 -3 cont.
Sub
ID
t/ac/yr
t/yr
Row Crop
Pasture
Forest/Scrub/Shrub
Min./Dist.
Low
Res.
High
Res.
Strip
Crop
Med.
Res.
Good
Fair
WL
OG
FL/L
Orch.
S/S
Forest
HFL
Min.
Dist.
Areas
t/yr
34,264
3,909
1,479
371
8,528
1
3,021
31
8,074
1,399
3
94
750
3,566
2,347
691
Q)
O
0.904
11.115
3.006
2.521
6.052
0.061
0.262
0.262
4.034
15.129
0.061
0.061
0.040
3.026
20.172
20.172
CO
CD
O
o
=3
o
=7
0)
T3
CD
O
o
CO
ST
=3
0)
c_
0)
O
—s
CD
CD
7T
T3
CO
O
0)
w
CD
CO
c"
Q.
•<
Dist. = Disturbed
FL/L = Feedlot/Loafing
HFL = Harvest Forestland
Med. = Medium
Min. = Mining
OG = Overgrazed
Orch. = Orchard
Res. = Residue
S/S = Scrub/Shrub
^ t = tons
_i^ WL = Woodland
CD
Adapted from: UT (2007).
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
19262.1
i
14
I
285
1
12528
4413
l 1 3038
3521 4173
n i
n n
Row crop Pasture/Livestock Forest Mining/Disturbed Streambank Roadbank Unpaved Roads
FIGURE 11-15
Soil Loss Estimates (tons/year) for Select Land Classes (UT, 2007)
residue cropland were the dominant land classes for agricultural soil loss, contributing
13 and 14%, respectively.
Estimated soil loss for road banks (3,521 tons per year) was only 6% of all soil
loss for the watershed.
11.4.2. Pollutant Loading Summary
Using water quality data collected between December 1982 and September
1999, at mile 28.4, TDEC developed the 2002 TMDL on the basis of a required 96.5%
reduction in pathogens (TDEC, 2002). Analysis of recent pathogen data showed a
significant decline in E. coli and fecal coliform at the mile 28.4 and other monitoring
stations. The new pathogen TMDL for Oostanaula Creek requires a reduction at
mile 28.4 of 67.7% (TDEC, 2005).
Using the NPS inventory data to calculate pollutant loadings, total estimated
loading from OCW was 22.13 tons TP per year, 81.66 tons TN per year, and
8,877.65 tons TSS per year (see Table 11-4). Urban areas contributed greater per-acre
11-17
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
TABLE 11-4
Nutrient Loading by Land use as Tons/Acre/Year
TP
TN
TSS
(t/yr)
(% of total)
(t/yr)
(% of total)
(t/yr)
(% of total)
Urban
Residential
3.439
15.5
22.598
27.7
818.782
9.2
Commercial
2.301
10.4
10.740
13.2
383.579
4.3
Industrial
0.649
2.9
5.332
6.5
278.215
3.1
ROW
0.010
<0.1
0.101
0.1
5.061
0.1
Cropland
Low Residue
0.169
0.8
1.686
2.1
589.990
6.6
High Residue
0.063
0.3
0.634
0.8
221.809
2.5
Strip Crop
0.015
0.1
0.154
0.2
53.852
0.6
Medium Residue
0.361
1.6
3.609
4.4
1,263.229
14.2
Pasture
Good Pasture
0.000
<0.1
0.001
<0.1
0.189
<0.1
Fair Pasture
0.128
0.6
1.277
1.6
446.809
5.0
Woodland
0.003
<0.1
0.008
<0.1
4.837
0.1
Overgrazed
0.683
3.1
3.413
4.2
1,194.422
13.4
Feedlot
0.023
0.1
4.254
5.2
198.512
2.2
Forest
Orchard
0.000
<0.1
0.001
<0.01
0.374
<0.1
Scrub/Shrub
0.002
<0.1
0.022
<0.1
14.076
0.2
Forest
0.013
0.1
0.172
0.2
109.467
1.2
Clearcut
0.057
0.3
0.777
0.9
494.514
5.6
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
TABLE 11 -4 cont.
TP
TN
TSS
(t/yr)
(% of total)
(t/yr)
(% of total)
(t/yr)
(% of total)
Other
Mine
0.041
0.2
0.560
0.7
356.414
4.0
Disturbed
0.011
0.1
0.156
0.2
99.463
1.1
Streambank
0.317
1.4
4.365
5.3
1,587.262
17.9
Road Bank
0.058
0.3
0.796
1.0
289.605
3.3
Unpaved Road
0.069
0.3
0.954
1.2
346.901
3.9
Livestock
Beef Cattle
1.786
8.1
5.897
7.2
59.817
0.7
Dairy
0.652
2.9
4.788
5.9
50.425
0.6
Horse
0.001
<0.1
0.002
<0.1
0.362
<0.1
Swine
0.001
<0.1
0.002
<0.1
0.025
<0.1
Poultry
0.018
0.1
0.057
0.1
1.038
<0.1
Wildlife
0.003
<0.1
0.006
<0.1
0.116
<0.1
VWVTP
11.257
50.9
9.302
11.4
8.504
0.1
Total
22.129
81.663
8,877.646
A detailed report on the OCW NPS inventory and pollutant load estimates can be downloaded at
http://ocw.ag.utk.edu/ResRep/OCW-NPSI.pdf.
Adapted from: UT (2007).
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
loads of TP and TN than agricultural areas in the watershed. As expected, as pasture
conditions worsened, the pollutant load per acre increased. Annual per-acre estimates
of TP, TN, and TSS loads were lowest for forested areas and good and fair pastures.
11.4.2.1. Total Nitrogen
• Urban areas accounted for 47% of the TN load in the OCW.
• Cropland contributed 7% of the TN load.
• Pastures contributed nearly 11 %.
• The Athens WWTP contributed 11 % of the TN to the OCW.
• Livestock operations were responsible for 13% of the watershed TN.
• Forests contributed less than 1% of the TN.
11.4.2.2. Total Phosphorus
• The Athens WWTP contributed 51 % of the TP to the OCW.
• Urban areas accounted for 29% of the TP load.
• Livestock operations were responsible for 11 % of the watershed TP.
• Forests contributed less than 1% of the TP.
11.4.2.3. Total Suspended Solids
• Cropland contributed 24% of the TSS in the OCW.
• Pastures contributed nearly 21 % TSS.
• Eroding streambanks contributed nearly 18% of TSS.
• Urban sources accounted for 17% of TSS.
• TSS from livestock was less than 1 %.
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
11.5. OOSTANAULA CREEK WATERSHED RESTORATION PLAN AND
IMPLEMENTATION
In 2007 development of the Oostanaula Creek Watershed Restoration Plan was
completed. The plan is based on a 15-year timeline with a three-phase schedule.
Comprehensively designed to meet the TMDL target reductions in pollutant loads and to
restore the water quality in Oostanaula Creek to full designated use support levels, the
core of the plan is directed toward the use of best management practices (BMPs)
(Hagen and Walker, 2007). Using the NPS Assessment to identify priority sites for
remediation and running what if scenarios to estimate the amount of reduction in
pollutant load, BMPs will be designed and installed to remove pathogens from livestock
sources, siltation from urban and agricultural sources, and phosphorus from urban
sources (Hagen and Walker, 2007).
With planning based on the water quality and NPS Assessment data, along with
support from TVA, NRCS' Environmental Quality Incentive Program grant funds, and
EPA Section 319 grant funds, more than 65 BMPs were installed or implemented by
2007 (UT, 2007). Examples of agricultural BMPs installed in OCW in fiscal year 2004
include stream crossings (4), stream fencing to protect banks (10,985 feet),
cross-fencing for rotational grazing (6,235 feet), manure transfer system pipeline
(9,520 feet), pump and pipeline (6,512 feet), watering tanks (8); feeding pads for heavy
use protection (7), cropland conversion (25 acres), roof water management, and travel
lane for livestock (565 feet) (Hagen and Walker, 2007).
In 2008, over 40 community decision makers in McMinn and Meigs County,
Tennessee, participated in a series of facilitated "Growth Readiness" workshops
provided by TVA and the Southeast Watershed Forum. During the workshops, County
and City leaders and staff, as well as representatives of nonprofit organizations and
state and federal agencies learned about the benefits of utilizing specific Smart Growth
principles to preserve local values and character, protect the natural resources, and
support economic growth and development. The City of Athens immediately focused in
new directions, revisiting and improving codes and ordinances to support proactive
enforcement of better site design plans that effectively manage stormwater and
minimize impacts to receiving water bodies.
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
Working with TVA and other partners, a strong public awareness campaign was
launched to raise awareness of the causes of the flooding and the sources of identified
water quality issues. Athens Public Works Department then initiated a proactive
campaign to 1) reduce the impacts of flooding by requiring effective on-site stormwater
detention and reductions in amounts of impervious surface installed on new
construction sites, and 2) improve the water quality so as to have the listed segments of
both Oostanaula and North Mouse Creeks that flow through Athens removed from the
state's list of impaired waters.
TVA trained Athens Utility Board and Athens staff in benthic identification and
monitoring, and they now incorporate it into their educational outreach efforts as well as
to identify hotspots.
In 2008, Athens applied for and received its first Low Impact Development grant.
Through cooperative, creative pooling of resources and the $30,000 Green
Development Grant cosponsored by TDEC, Tennessee Stormwater Associations, and
TVA, plans for a traditional public parking lot on vacant land between City Hall and the
Athens McMinn Family YMCA were scrapped. On the same site, a totally Green
Parking facility was designed and built, demonstrating five Low Impact Development
techniques to infiltrate stormwater on site and minimize stormwater runoff, while
providing needed parking space. Pervious concrete, pervious coal combustion product
pavers, and geoblock green paving materials were used for driving surfaces. A rain
garden, planted with native trees, shrubs, and flowering plants, allow the rainwater to
percolate through six inches of stone to the underlying soil. Clay berks help keep water
in the garden's subbase longer. A local Eagle Scout candidate led the building of an
educational kiosk with the first green roof in McMinn County.
Interest generated by this project has led to several churches, local businesses
and property owners to initiate projects utilizing these same materials and techniques in
order to address their own stormwater management issues. This project was awarded
the 2009 Tennessee Governor's Environmental Stewardship Award for Aquatic
Resource Preservation.
Athens completed the Visual Stream Survey Protocol, S.O.P. and Analysis,
including aerial photography, November 2009-March 2010 on Oostanaula and North
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
Mouse Creeks. The information provided now helps guide Athens' proactive focus in
stream corridor restoration and protection.
During the last three years, City of Athens has completed and is in the process of
completing a number of projects in the public realm that not only improve residents'
sense of place, but also provide environmental and economic benefits, while well
developed educational signs and programs deliver effective environmental messages to
all who enjoy these public spaces and community improvements. These have been
accomplished though grants awarded and matched by the labor of the Athens Public
Work Department, thousands of hours of labor contributed by community residents,
contributions of partners, businesses, local industries and utilities. Grants acquired
2009 through 2011 include the:
• Tennessee Department of Transportation (TDOT) Pedestrian Enhancement
Phase II Pervious Sidewalk grant, used to build the first mile of pervious paver
sidewalks in Tennessee;
• Tennessee Department of Agriculture 319 North Mouse Creek Restoration grant,
that is paying to restore a section of North Mouse Creek in Regional Park and
fence cows out of North Mouse Creek,
• EPA Wetland Program Development grant, used to restore a 6+ acre wetland at
the E. G. Fisher Library and complete the Native Tree Arboretum,
• TDEC-604B Oostanaula Lake Restoration Planning grant, partnering with Athens
Utility Board,
• TVA Water Quality Improvement grant to create pervious paver amphitheaters
with underground detention at two public facilities.
In addition, Athens Public Works Department continues to build detention ponds
in neighborhoods prone to localized flooding that also negatively impacts water quality;
is working to obtain TDOT Mitigation Highway 30 funding for restoration projects on
Oostanaula Creek; has initiated a large-scale rain garden program that will reduce
runoff from properties of cooperative landowners; continues to plant hundreds of live
stakes on creek banks and clean ditches of debris each year, with the help of
community volunteers.
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
Agricultural impacts continue to be addressed in the Oostanaula Creek
Watershed through the ongoing support of Tennessee Department of Agriculture, USDA
Natural Resources Conservation Service, University of Tennessee Extension, and the
McMinn County Soil Conservation Service. University of Tennessee Extension has
taken a leadership role in this effort, acquiring major funding to:
• hire a Watershed Coordinator to continue implementation of the watershed action
plan,
• support bacteriodes studies to identify animal waste markers as well as several
other research studies,
• identify and address failed septic systems in the watershed, and
• fund specific best management practices not covered by other programs.
The complete Oostanaula Creek Watershed Restoration Plan is available at
http://ocw.ag.utk.edu/ResRep/OCW_WRP.pdf.
11.6. DISCUSSION AND CONCLUSION
11.6.1. Advantages
Detailed NPS inventory and land-activity GIS databases, developed by the TVA,
provide a means to effectively prioritize and target watershed remediation and
restoration efforts, allowing for the achievement of the greatest level of pollutant
reduction for the least amount of funding. The comprehensive data—details such as
eroding road and streambanks, riparian zone conditions, livestock activity and effects,
illegal dumps, and suspect septic systems along with a comprehensive detailed land
use/cover—extracted from stereo photographs provides a means of determining the
source of pollution problems and their relationship to the landscape. Developed by
photographic interpretation of high-resolution aerial photography, the unique database
of the study area provides a means to screen areas by land activities and conditions
that are indicative of nonpoint sources for pollution. Data analysis transforms a
dispersed, area-wide concern into a defined, site-specific problem by identifying
subwatersheds that are the greatest contributors to the pollution problem and then
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
determining in each priority subwatershed the specific sites that contribute the greatest
pollutant loads.
11.6.2. Primary Objectives of an NPS Inventory Include the Following:
• Completing a detailed NPS inventory to identify and quantify the nonpoint
pollution sources in a watershed.
• Completing a highly detailed and accurate land use/cover inventory of the
watershed.
• Calculating pollutant loadings on the basis of the NPS inventory.
• Prioritizing subwatersheds and sites on the basis of the pollutant loadings.
• Developing GIS database queries to prioritize sites for corrective actions.
While the primary emphasis of the NPS GIS databases is to develop
cost-effective environmental restoration plans and increase the ability to secure funding,
several other benefits are inherent in the process.
• Better understanding and consensus among stakeholders on the sources and
effects of land-based pollutants on environmental quality.
• Increased ability to meet the requirements of the Clean Water Act.
• Decrease in the contaminants that affect the consumptive use of water.
• Ability to make real-time decisions during emergency environmental events.
11.6.3. Caveats
A NPS Inventory is not necessary or even correct for every watershed. The
necessary scale and detail of geospatial data is dependent on the application as well as
the available budget of the user. While the highly detailed database developed from
aerial photographs during the NPS inventory is necessary for identifying
cause-and-effect relationships between land use and water quality impacts, it is not
necessary for general watershed assessments. For these applications more
generalized, computer-generated land use/landcover (LU/LC) data, such as those
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
developed from satellite imagery may be sufficient. GIS LU/LC data derived from
30 meter resolution Landsat data are readily available at little or no cost. The fine
points of an NPS Inventory are not needed until it is time to identify and prioritize the
pollutant problems and begin planning specific restoration efforts.
An NPS Assessment may be needed if a watershed meets one or more of the
following criteria:
• Significant and documented environmental impacts (305B or other watershed
assessment report).
• The need to target specific pollutants.
• A need for help identifying what actions are necessary, appropriate, and most
cost effective.
• The need to determine and prioritize for remediation the most significant
contributors to stream impairment.
• Includes more than one jurisdictional entity (city, county, state, or Federal and
State lands).
• A need to build consensus among the stakeholders regarding the causes,
solutions, and water quality responsibilities in the watershed.
• The need and desire to secure watershed improvement implementation funds.
Finally, in order to have a successful watershed restoration project such as that
in Oostanaula Creek Watershed, there are two primary requirements:
• A strong coalition or partnership that is ready to develop and implement an
effective water quality strategy.
• The significant public interest and involvement necessary to implement BMPs.
11.7. REFERENCES
CBER (Center for Business and Economic Research). 2003. Population Projections for
Tennessee, 2005 to 2025. The University of Tennessee, Center for Business and
Economic Research.
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Section III— Chapter 11: Oostanaula Creek IPSI Case Study
Hagen, J., and F. Walker. 2007. Oostanaula Creek Watershed Restoration Plan,
University of Tennessee Extension, Biosystems Engineering and Soil Science.
Available online at http://ocw.ag.utk.edu/ResRep/OCW_WRP.pdf.
TDEC (Tennessee Department of Environment and Conservation). 1998. Final 1998
303(d) List, June 1998 (Rev. September, 1998). State of Tennessee, Department of
Environment and Conservation, Division of Water Pollution Control.
TDEC (Tennessee Department of Environment and Conservation). 2002. Total
Maximum Daily Load for Fecal Coliform in Oostanaula Creek, Hiwassee River
Watershed, McMinn & Monroe Counties, Tennessee. Available online at
http://www.tennessee.gov/environment/wpc/tmdl/approvedtmdl/OostF2.pdf.
TDEC (Tennessee Department of Environment and Conservation). 2005. Total
Maximum Daily Load for Pathogens in the Hiwassee River Watershed, Bradley,
Hamilton, McMinn, Meigs, Monroe and Polk Counties, Tennessee. Available online at
http://tennessee.gov/environment/wpc/tmdl/approvedtmdl/HiwasseePath.pdf.
TDEC (Tennessee Department of Environment and Conservation). 2006a. Total
Maximum Daily Load for Siltation and Habitat Alteration in the Hiwassee River
Watershed, Bradley, Hamilton, McMinn, Meigs, Monroe and Polk Counties, Tennessee.
Available online at
http://www.state.tn.us/environment/wpc/tmdl/approvedtmdl/HiwasseeSed.pdf (Accessed
03/15/10).
TDEC (Tennessee Department of Environment and Conservation). 2006b. Year 2006
303(d) List, Final Version. State of Tennessee, Department of Environment and
Conservation, Division of Water Pollution Control.
UT (University of Tennessee). 2007. Oostanaula Creek Watershed Nonpoint Source
Pollution Inventory and Pollutant Load Estimates, University of Tennessee Extension,
Biosystems Engineering and Soil Science.
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
12. NUTRIENT CLASSIFICATION OF STREAMS USING CART
Mike Paul, Tetra Tech,
Key words:
Analysis: Classification and Regression
Tree (CART)
Clean Water Act: Water Quality Standards
12.1. INTRODUCTION
Nutrients are one of the leading
causes of stream impairment nationwide
(U.S. EPA, 1996). In 1998, U.S. Environmental Protection Agency (EPA) developed a
strategy for reducing nitrogen and phosphorus pollution that included the development
of nutrient criteria (U.S. EPA, 1998). These criteria are intended to be numeric limits on
the concentration of nitrogen, phosphorus, and chlorophyll as well as water clarity. EPA
developed a series of guidance documents to define the technical approaches for
developing these criteria by waterbody type (U.S. EPA, 2000a, b, 2001, 2007).
Nutrients vary across large regions as a function of many factors, including
geology and climate. A critical step in developing nutrient criteria, therefore, is
classifying waterbodies into relatively homogeneous groups of sites for the purpose of
minimizing this natural regional variability. Accurate classification reduces the natural
variability and allows for a focus on principally human-caused nutrient enrichment.
EPA developed initial nutrient ecoregions as a first step toward regional
classification (Omernik, 2008). EPA then worked on developing recommended regional
nutrient criteria by these nutrient regions (e.g., U.S. EPA, 2000c). It released a series of
recommended nutrient criteria that are based on percentage of nutrient, chlorophyll, and
water clarity distributions within each region (e.g., U.S. EPA, 2000a). However, in its
guidance, it encourages states to pursue their own classification schemes to further
refine the larger scale nutrient regions (U.S. EPA, 2000a, b).
The U.S. Geological Survey (USGS), in collaboration with several upper
Midwestern states, pursued a refined regional classification scheme for nutrient criteria
Inc., Owings Mills, MD
What is interesting about this case study? A
statistical process is applied to classify nutrient
regimes in streams in the upper Midwest.
Condition Assessment. Provides a better basis
for classifying sites as impaired with respect to
background nutrient conditions.
Causal Assessment: none
Source Assessment, none
Predictive Assessments none
12-1
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
development (Robertson et al., 2001). The purpose of this effort was to further refine
the nutrient regions and identify smaller homogeneous regions for nutrient criteria
development. The effort included the application of classification and regression tree
(CART) analysis to water quality data using landscape predictors to identify new stream
classes.
12.2. METHODS
A statistical method for classifying streams into relatively homogeneous water
quality classes on the basis of landscape predictors was applied in the upper
Midwestern United States. CART analysis was used to develop a classification model
of reach level nutrient conditions using landscape predictors. Different schemes were
compared to the nutrient regions for the degree to which they reduced model variance.
12.2.1. Statistical Method and Software
Regression tree analysis is a form of recursive partitioning, or a method for
classifying elements into groups using multiple predictors (Breiman et al., 1984; Harrell,
2001). CART models start with all the data in one group and identify all possible binary
subgroups split on every possible predictor value. They then calculate a statistical
criterion (e.g., least squares) around the mean of each subgroup and identify the split
along that predictor that optimizes the value of the criterion. The models then continue
on the resultant subgroups and proceed until a stopping criterion is reached. Methods
exist to then prune back the trees to identify parsimonious models (i.e., the shortest
model that is not overly fit). The approach has several advantages over multiple linear
regression: it makes few assumptions about the relationships between predictors and
responses, it does not assume additivity of predictors, it incorporates interactions, and it
can flexibly deal with missing values. However, CART models also suffer from some
disadvantages—namely that the power of continuous variables is not fully used, and
they have a high potential for over-fitting (Harrell, 2001).
Most standard software applications will now run some form of CART modeling,
and the functions are available in the R open-source statistical programming platform.
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
12.2.2. Data Sets
The predictors used in the CART models of nutrients were generated using
landscape-level data. The sections below describe the predictors used.
12.2.2.1. Watershed Delineations
Watersheds were delineated using a combination of 1:100,000 digital coverages
of USGS hydrologic unit maps, 1:100,000 digital stream coverages from the National
Hydrography Dataset and 1:24,000 scale USGS quadrangle topographic quadrangle
maps.
12.2.2.2. Runoff
Runoff data were taken from digital coverages of annual runoff data for the
conterminous United States (Gebert et al., 1987). Watershed midpoint runoff values
were used as watershed runoff estimates.
12.2.2.3. Climate
Average annual air temperature was taken from digital coverages constructed by
the National Climatic Data Center (USGS, 2000a as cited in Robertson et al., 2001).
Watershed area-weighted average mean annual temperature for each study watershed
was calculated.
Precipitation data was based on digital coverages created by the
Parameter-elevation Regressions on Independent Slopes Model (PRISM)of the Climate
Mapping Program (Oregon Climate Service, 2000 as cited in Robertson et al., 2001).
12.2.2.4. Land Use
Land-use data were based on 1:250,000 scale digitized maps of land use/land
cover interpreted from high-altitude aerial photographs (Feagus et al., 1983 as cited in
Robertson et al., 2001). Some of these data were updated with 1990 human population
census data.
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
12.2.2.5. Surficial Deposits
The percentage of different surficial deposit data were calculated for each
watershed and were derived from a digital coverage of Quaternary sediments of the
eastern United States (Soller and Packard, 1998 as cited in Robertson et al., 2001).
12.2.2.6. Soil Characteristics
Soil data were based on a digital coverage of the State Soil Geographic
(STASGO) database (USGS, 2000b as cited in Robertson et al., 2001). Soil data were
compiled as area-weighted watershed averages.
12.2.2.7. Principal Aquifer Types
Principal aquifer types were derived from digital coverages of principal underlying
aquifers of the 48 conterminous states (USGS, 2000c as cited in Robertson et al.,
2001).
12.2.2.8. Water Quality Data
For each site in the study, multiple nutrient concentration observations were used
and medians estimated from monthly chemistry values.
12.3. ANALYSIS
As stated earlier, the landscape predictors described above were placed into a
CART analysis model to predict in-stream nutrient concentrations across the upper
Midwest region. Models were run including and excluding land-use characteristics,
which are obviously influenced by human activity.
The output of these models is an algorithm for placing watersheds into
appropriately homogeneous classes for nutrient criteria development. Sites in each
class would be expected to have relatively similar natural nutrient conditions—more so
than sites between classes. Therefore, nutrient expectations should be relatively similar
within classes, and criteria could be set for each class.
12-4
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
12.3.1. Results
The output from these analyses is a series of regression tree diagrams and
algorithms for classifying new sites into one of the resultant classes (see Figures 12-1
and 12-2). These tree diagrams serve as a visual representation of the binary recursive
partitioning process and the values for each predictor at each branch represent
thresholds that divide statistically distinct groups.
12.4. DISCUSSION AND CONCLUSION
The purpose of this analysis is to further refine classification of streams for
developing nutrient criteria. EPA nutrient criteria guidance recommends classification of
waterbodies before developing criteria to reduce the variability associated with natural
differences in stream nutrient concentrations resulting from differences in geology and
climate, among other factors (U.S. EPA, 2000a, b). Reducing natural variability
maximizes the ability to identify appropriate reference sites for distribution-based
methods and to detect principally human-influenced nutrient effects in subsequent
stressor-response analysis. Resultant nutrient criteria would then apply within each
class.
12.4.1. Advantages
The advantages of CART were discussed earlier (see Chapter 7 Methods).
Classification is an essential part of many criteria development efforts, including nutrient
and biological criteria (U.S. EPA, 2000 a, b). Classification reduces the variability
associated with natural differences in constituents across broad areas that can obscure
anthropogenic influences. Without classification, therefore, it becomes harder to detect
human effects and to set realistic and appropriately protective criteria.
12.4.2. Cautions and Caveats
Some of the disadvantages of CART were described above (see Methods).
Classification can only reduce natural variability so far, and there could be substantial
remaining natural variability in stressor-response relationships. Variability can be
reduced with further classification; however, at smaller scales, data limitations ultimately
12-5
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
A. Including all environmental factors
No (70)
Clay> 26 Percent
No (177 sites)
Yes(107)
Forest > 30 Percent
No (32)
Yes (57 Sites)
Runoff > 12.3 inches per year
Yes (25)
Group 1
Group 2
Group 3
Group 4
B. Excluding land-use characteristics
No (52)
Runoff > 10.3 inches per year
No (83 sites)
Yes (31)
Till > 59 Percent
No (55)
Yes (151 Sites)
Clay> 26 percent
Yes (96)
Group 1
Group 2
Group 3
Group 4
FIGURE 12-1
Results of CART Analysis of Total Phosphorus Resulting in Different
Phosphorus Groups Using (A) All Environmental Predictors or
(B) Excluding Land-Use Predictors
Source: Figure 19 in Robertson et al. (2001).
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
A. Including all environmental factors
No (9)
Precipitation > 30.1 inches per year
No (81 sites)
Yes (72)
Forest >9 Percent
No (39)
Yes (71 Sites)
Forest > 33 percent
Yes(32)
Group 1
Group 2
Group 3
Group 4
B. Excluding land-use characteristics
No (20)
Air Temperature > 43°F
No (130 sites)
Yes(110)
Soil slope > 11 Percent
No(5)
Yes (22 sites)
Nonglacial sediments or exposed bedrock > 99 percent
Yes(17)
Group 1
Group 2
Group 3
Group 4
FIGURE 12-2
Results of CART Analysis of Total Nitrogen Resulting in Different
Phosphorus Groups Using (A) All Environmental Predictors or
(B) Excluding Land-Use Predictors
Source: Figure 20 in Robertson et al. (2001).
influence analysis options. In addition, these approaches cannot eliminate the effect of
collinear predictors, and it is possible that a local rather than global predictor set is
applied.
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
12.4.3. Future Needs
Further classification applications are certainly needed. Most states are relying
on the existing national nutrient regions, which are large resolution, might not reduce
natural variability sufficiently, and might not coincide with site classes consistent with
use designations. As a result, further refinement can result in improvements to the
criteria models developed, as evidenced in this analysis.
12.4.4. Other Examples
EPA Nutrient Regions
EPA developed the original nutrient ecoregions as a preliminary classification
and developed recommended criteria for each of these regions. The initial map of these
ecoregions reflects this classification and is still widely used (Omernik, 2008).
Key Information Source(s)/Web Sites
The USGS report is at
http://wi.water.usgs.gov/pubs/wrir-01-4073/wrir-01-4073.pdf (accessed November 7,
2008).
Key Contact(s)/Researcher(s)
Dale Robertson, USGS,
http://wi.water.usgs.gov/professional-pages/robertson.html (accessed November 7,
2008)
12.5. REFERENCES
Breiman, L, J.H. Friedman, R.A. Olshen, and C.J. Stone. 1984. Classification and
Regression Trees. Wadsworth and Brooks-Cole Statistics - Probability Series.
Chapman & Hall, London.
Feagus, R.G., R.W. Claire, S.C. Guptill, K.E. Anderson, and C.A. Hallam. 1983. Land
Use and Land Cover Data—U.S. Geological Survey Digital Cartographic Data
Standards: U.S. Geological Survey Circular 895-E, 21 p.
Gebert, W.A., Graczyk, D.J., and W.R. Krug. 1987. Average Annual Runoff in the
United States, 1951-80: U.S. Geological Survey Hydrologic Investigation Atlas HA-170,
1 sheet, scale 1:2,000,000.
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
Harrell Jr., F.E. 2001. Regression Modeling Strategies: With Applications to Linear
Models, Logistic Regression, and Survival Analysis. Springer, New York.
Omernik, J.A. 2008. Draft Aggregations of Level III Ecoregions for the National
Nutrient Strategy. Available online at
http://www.epa.gov/waterscience/criteria/nutrient/ecoregions/. Accessed November 7,
2008.
Oregon Climate Service. 2000. Climate mapping with PRISM. Oregon State
University. Available online at http://www.prism.oregonstate.edu/.
Robertson, D.M., D.A. Saad, and A.W. Wieben. 2001. An alternative regionalization
scheme for defining nutrient criteria for rivers and streams. United States Geological
Survey (USGS) Water Resources Investigations Report 01-4073. United States
Department of the Interior, USGS, Middleton, Wl. Available online at
http://wi.water.usgs.gov/pubs/wrir-01 -4073/wrir-01 -4073.pdf.
Soller, D.R., and P.H. Packard. 1998. Map Showing The Thickness and Character of
Quaternary Sediments in the Glaciated United States East of the Rocky Mountains:
U.S. Geological Survey Digital Data Series DDS-38. U.S. Geological Survey, Reston,
VA. Available online at
http://geo-nsdi.er.usgs.gov/metadata/digital-data/38/qsurf.faq.html.
U.S. EPA (Environmental Protection Agency). 1996. National Water Quality Inventory:
1996 Report to Congress. Office of Water, Washington, DC. EPA-R-97-008. Available
online at http://www.epa.gov/305b/96report/index.html.
U.S. EPA (Environmental Protection Agency). 1998. National Strategy for the
Development of Regional Nutrient Criteria. Office of Water, Washington, DC.
ERP-822-R-98-002. Available online at
http://www.dwaf.gov.za/projects/eutrophication/Website%20Survey/United%20States/N
%20criteria.pdf.
U.S. EPA (Environmental Protection Agency). 2000a. Nutrient Criteria Technical
Guidance Manual: Rivers and Streams. U.S. Environmental Protection Agency, Office
of Water, Washington, DC. EPA/822/B-00/002. Available online at
http://www.epa.gov/waterscience/criteria/nutrient/guidance/rivers/rivers-streams-full.pdf.
U.S. EPA (Environmental Protection Agency). 2000b. Nutrient Criteria Technical
Guidance Manual: Lakes and Reservoirs. Office of Water, Washington DC.
EPA-822-B-00-001. Available online at
http://www.epa.gov/waterscience/criteria/nutrient/guidance/lakes/lakes.pdf.
U.S. EPA (Environmental Protection Agency). 2000c. Ambient Water Quality Criteria
Recommendations: Information Supporting the Development Of State and Tribal
Nutrient Criteria, Rivers and Streams in Nutrient Ecoregion VI. Office of Water,
Washington DC. EPA-822-B-00-017 Available online at
http://www.epa.gov/waterscience/criteria/nutrient/ecoregions/rivers/rivers_6.pdf.
12-9
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Section III— Chapter 12: Nutrient Classification of Streams Using CART
U.S. EPA (Environmental Protection Agency). 2001. Nutrient Criteria Technical
Guidance Manual. Estuarine and Coastal Marine Waters. United States Environmental
Protection Agency, Office of Water, Washington, DC. EPA-822-B-01-003. Available
online at http://www.epa.gov/waterscience/criteria/nutrient/guidance/marine/.
U.S. EPA (Environmental Protection Agency). 2007. Nutrient Criteria Technical
Guidance Manual. Wetlands. United States Environmental Protection Agency, Office of
Water, Washington, DC. EPA-822-R-07-004. . Available online at
http://www.watershedinstitute.biz/files/Executive_Summary.pdf.
USGS (United States Geological Survey). 2000a. Climate divisions. Available online
at http://water.usgs.gov/GIS/metadata/usgswrd/XML/climate_div.xml (Accessed
October 6, 2000).
USGS (United States Geological Survey). 2000b. State Soil Geographic (STATSGO)
data base for the conterminous United States. Available online at
http://water.usgs.gov/GIS/metadata/usgswrd/XML/ussoils.xml (Accessed October 6,
2000).
USGS (United Stated Geological Survey). 2000c. Principal Aquifers—map layer
description file. Available online at http://www-atlas.usgs.gov/aquifersm.html (Accessed
October 6, 2000).
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Section III— Chapter 13: Biocriteria and Reference Condition
13.BIOCRITERIA AND REFERENCE CONDITION
Mike Paul, Tetra Tech,
Key words:
Analysis: Reference Site Selection,
Reference Condition Assessment
Clean Water Act: Water Quality Standards
13.1. INTRODUCTION
Biological indicators are
increasingly being used in aquatic life use
assessments and form the core of aquatic
life use criteria development (U.S. EPA,
2002). Bioindicators based on algae, invertebrates, and fish assemblages are
developed as a part of state/tribal, regional, and federal monitoring and assessment
programs. Developing bioindicators (e.g., Index of Biological Integrity or River
Invertebrate Prediction and Classification System)(RIVPAC) is a multistep process, but
it relies critically on identifying reference sites, which are used to construct and validate
the resultant bioindicators (Barbour et al., 1999; Stoddard et al., 2006).
Reference sites—specifically regional reference sites—are used to build
bioindicator tools. In a very simple sense, they represent the best approximation of
biological integrity. The accuracy of this approximation depends on the type of
reference site used (Stoddard et al., 2006). The true reference condition or the
reference condition for biological integrity (RC[BI]) refers to the biological condition in
the absence of human disturbance. This has been referred to as the pristine condition,
and in many regions where historic disturbance has occurred for centuries, this
condition likely no longer exists. The current best approximation of the RC(BI) is the
minimally disturbed condition. This is the condition in the absence of significant human
disturbance and is relatively unchanging except for large climatic or geologic changes.
It recognizes that there are large-scale changes that have affected biota (e.g.,
atmospheric chemistry) that have altered the condition of all waterbodies to some
Inc., Owings Mills, MD
What is interesting about this case study? A
systematic screening process using geographic
information system (GIS) and reach level data is
used for identifying least disturbed reference
sites.
Condition Assessment. Identifies reference sites
for building tools for assessing aquatic life use
condition.
Causal Assessment: Reference sites are
important in causal assessments.
Source Assessment, none.
Predictive Assessments: Reference sites are
used for building predictive models.
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Section III— Chapter 13: Biocriteria and Reference Condition
extent. The next reference condition is the least-disturbed condition (LDC) and that is
the "best available physical, chemical, and biological habitat conditions given today's
state of the landscape" (Stoddard et al., 2006). The LDC is the most commonly used
reference condition for bioindicator development, and LDC sites are commonly
identified using a series of landscape and local reach-based filters such as
landcover/land use, habitat condition, and water chemistry. The criteria vary by region
and can change through time, so the LDC varies.
Identifying LDC commonly relies on a combination of landscape and local
features. Pertinent to this application is the use of landscape features. A variety of
common geographic information system (GIS) coverages and applications are used for
reference site identification. Most common are the delineation of study site catchments
and characterization of land use/landcover (LU/LC) at a variety of scales (e.g.,
watershed, riparian buffer). Less common, but still often used, are human census data,
specific coverages (e.g., grazing, forest fragmentation measures), the interpretation of
aerial photographs, and thematic mapping (Drake, 2004).
For this example, the Oregon Department of Environmental Quality (DEQ),
Watershed Assessment Section's Selection of Reference Condition Sites method is
used to describe the application of a landscape tool for reference site identification
(Drake, 2004).
13.2. METHODS
The Oregon DEQ developed the Selecting Reference Condition Sites tool as a
systematic method for identifying least-disturbed sites for the purposes of biocriteria
development and watershed assessment with predesignated waterbody classes. The
classifications were largely based on ecoregions and primary natural gradients
(elevation, stream size, and geology) (Drake, 2004). The reference site selection tool is
similar to approaches used by many states/tribes and federal agencies (e.g., Collier
et al., 2007; Whittier et al., 2007). It consists of a series of filters (see Figure 13-1)
beginning with GIS-based landscape prescreening filters and best professional
judgment to identify candidate sites, followed by site visits and calculating a human
disturbance index on the basis of GIS-derived and reach-derived measures, and finally
13-2
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Section III— Chapter 13: Biocriteria and Reference Condition
All Streams
Step 1
Candidate Area
Prescreening
Step 2
Site Visits
& the Human
Disturbance Index
Step 3
Site
Verification
& Grading
Identify region
Natural gradients
GIS prescreening
BPJ prescreening
•Reconn or Sampling
•HDI Reach
•HDI GIS
Review Bio/PHAB/WQ
Verify Flagged sites
Grade Sites A-F
Reference Sites
FIGURE 13-1
The Oregon DEQ Reference Site Selection Screening Process
Source: Drake (2004).
verification and site grading. Only the highest rated sites are selected as reference
sites.
13.2.1. Statistical Method and Software
The model applied is a fairly simple set of decisions and metrics based on
GIS-derived measures, best professional judgment, and reach-scale measures. The
metrics involved include calculating a human disturbance index on the basis of GIS and
reach-scale measures scaled from 0 to 5 and site grades (Drake, 2004). Most of the
analyses can be performed with any standard GIS software and a spreadsheet tool.
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Section III— Chapter 13: Biocriteria and Reference Condition
13.2.2. Data Sets
The data needed for running the Oregon DEQ reference site selection were the
following:
Landscape level—Watershed delineations, site elevations, stream size, geology,
LU/LC from the Oregon National Land Cover Data, aerial photos, thematic
mapping, road density, human population density, forest fragmentation, and
grazing allotments.
Reach level—Subjective visual assessment and grading of 30 different human
disturbance activities along the reach (see Table 13-1).
13.3. ANALYSES
The analyses consist of a best professional judgment of sites combined with GIS
prescreening, a standardized human disturbance index based on reach and GIS scale
measures, and a final site grading (A-F) using professional judgment of the data
considered.
The GIS prescreening decisions were not detailed in Drake (2004) but were
described as being used to identify watersheds, "where one might expect to find
streams with minimal human activity." For this, a combination of LU/LC information,
road density, human population density, forest fragmentation, and grazing allotments
were used, but aerial photographs and thematic mapping imagery were also
recommended.
After candidate watersheds were identified, two human disturbance indices (HDI)
were calculated, one based on GIS data (HDIgis) and one based on reach-scale
measures from site reconnaissance (HDIreach). The HDIgis was based on scores of
road density, urban and agricultural land use, and forest fragmentation. Values for road
density were continuously scored from 0 (road density = 0%) to 5 (road density = 40%).
Percentage of watershed urban and agricultural landcover (low- and high-intensity
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Section III— Chapter 13: Biocriteria and Reference Condition
TABLE 13-1
The Reach-Scale Human Activity Checklist. These data are used for calculating the
reach-scale human disturbance index.
Appendix 1
Oregon DEQ Watershed Assessment Section
Human Disturbance Index Reach Checklist
Street Name:
SITE ID/STATION KEY: DATE:
Crew:
Comments
: (Reconn or Sampling)
Activity Checklist: Circle all that apply
Agriculture-Urban
Silviculture
CAFOs (Cattle, Poultry)
0
1
3
5
Logging Ops - Active
0
1
3
5
Channelization
0
1
3
5
Logging Ops - Recent (< 5 years
ago)
0
1
3
5
Chemical treatment/Liming
0
1
3
5
Logging Ops - History (> 5 years
ago)
0
1
3
5
Construction/storm water
0
1
3
5
Other:
0
1
3
5
Cropland
0
1
3
5
Miscellaneous (Mining, recreational,
etc.)
0
1
3
5
Dams
0
1
3
5
Angling pressure
0
1
3
5
Industrial plants/commercial
0
1
3
5
Dredging
0
1
3
5
Irrigation equipment
0
1
3
5
Dumping/garbage/trash/litter
0
1
3
5
Maintained Lawns/run-off
0
1
3
5
Exotic Plant species
0
1
3
5
Orchards, Tree farms
0
1
3
5
Fish stocking
0
1
3
5
Pavement/cleared lot
0
1
3
5
Hiking trails
0
1
3
5
Power plants/oil/gas wells
0
1
3
5
Mines/Quarries
0
1
3
5
Residences/buildings
0
1
3
5
Parks, campgrounds
0
1
3
5
Riprap/Wall/Dike
0
1
3
5
Primitive parks, camping
0
1
3
5
Sewage/pipes/outfalls/drains
0
1
3
5
Surface films/Oders
0
1
3
5
Water level Fluctuations
0
1
3
5
Other:
0
1
3
5
Other:
0
1
3
5
Natural Disturbance
Rangeland
Fire
0
1
3
5
Cattle, Livestock use
0
1
3
5
Flood Effects
0
1
3
5
Pasture/Range/Hayfield
0
1
3
5
Mass Wasting (landslides)
0
1
3
5
Other:
0
1
3
5
Other:
0
1
3
5
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Section III— Chapter 13: Biocriteria and Reference Condition
TABLE 13-1 cont.
The Reach-Scale Human Activity Checklist. These data are used for calculating the
reach-scale human disturbance index.
Appendix 1
Oregon DEQ Watershed Assessment Section
Human Disturbance Index Reach Checklist
Street Name:
SITE ID/STATION KEY: DATE:
Crew:
Comments: (Reconn or Sampling)
Roads
Legend - Proximity Score
Bridges/culverts/RR crossings
0
1
3
5
Activity absent
O
0
Railroads
0
1
3
5
Activity presenting watershed but > 10
meters from bank
P
1
Roads paved/gravel/dirt
0
1
3
5
Activity present within 10 meters from bank
c
3
Other:
0
1
3
5
Activity present on streambank (or channel)
B
5
Rank score calculation (For each category, enter maximum proximity score)
Disturbance Category
Agriculture & Urban
Maximum proximity score
—>
Rangeland
Maximum proximity score
—>
Roads
Maximum proximity score
—>
Silviculture
Maximum proximity score
—>
Miscellaneous (Mining, recreational, etc.)
Maximum proximity score
—>
| HDIreach Score (sum) —
"*»
Reference Site Candidate Category
Stream a candidate reference site? (Circle One) If no, state reason why
YES
NO
Best Professional Judgment Grade (Check one):
A = Ideal watershed & stream conditions -
disturbance.
wilderness area or watershed with virtually no human
B = Good watershed & stream conditions:
BMPs are well implemented.
some human disturbances but not extensive, and/or
C = Marqinal watershed & stream conditions for a reference site. Human disturbance is present, site
is best available for basin/region.
D = Sub-marainal stream & watershed conditions. Considerable human disturbance is present at
reach or in large portions of watershed.
E = Poor stream & watershed conditions. Considerable human disturbance is present at reach
and in large portions of watershed.
F = Verv poor stream & watershed conditions.
Completely unraveled stream and watershed.
Methods/Forms/HDIreach Checklist Apr03
BMP = best management practice.
Adapted from: Drake (2004; Appendix 1).
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Section III— Chapter 13: Biocriteria and Reference Condition
residential, commercial/industrial, quarries/strip mines/gravel pits, orchards/vineyards,
pasture/hay, row crops, small grains, fallow and urban/recreational grasses) were
combined and scored continuously from 0 (0%) to 5 (100%). Finally, forest
fragmentation was split into three classes: high (>67% of a polygon), medium (33-67%)
and low (<33%). Categories were weighted as 5 * percent high, 3.34 * percent
medium, and 1.67 * percent low. The sum of these values was the forest fragmentation
score. The three GIS-based metrics were averaged to get the HDIgis score.
The HDIreach score was based on scoring the information collected at the
reach-scale using Table 13-1. Scores are based on how close each activity was to the
streambank as 0 = not observed, 1 = observed in watershed, 3 = within 10 meters of
streambank, and 5 = observed on the streambank. The highest value for each category
of Table 13-1 (Agriculture + Urban, Rangeland, Roads, Silviculture, and Miscellaneous)
are then averaged to come up with the HDIreach score. The sum of the HDIgis and
HDIreach scores are the overall HDI score.
After the prescreening and HDI calculations, data are verified and a final site
grade is assigned presumably on the basis of an interpretation of the narrative language
in the site grading table (see Table 13-2) relative to the prescreening, HDI values, and
other available data (Drake, 2004). No quantitative method for site grading was
provided. Reference site are those receiving A-C grades and reflect minimally
disturbed (A) to least disturbed (C) conditions (Stoddard et al., 2006).
13.3.1. Results
The output from the selection process is a set of regional reference sites for use
in biocriteria development and, ultimately, aquatic life use attainment determinations.
13.4. DISCUSSION AND CONCLUSIONS
13.4.1. Advantages
The advantages and necessity of regional reference sites for developing
biological indicators and biocriteria are well described in the literature (Hughes et al.,
1986; Barbour et al., 1999; Hawkins et al., 2000). They provide the benchmark around
which bioindicators are built and define, essentially, the desired condition against which
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Section III— Chapter 13: Biocriteria and Reference Condition
TABLE 13-2
Site Grading Descriptions for Final Site Grades
Grade
Description—Reference Sites
A
Site represents ideal watershed and stream conditions, wilderness area or
watershed with virtually no human disturbance. These sites represent
"natural" conditions and characterize biological integrity.
B
Site represents aood watershed and stream conditions; some human
disturbances but not extensive, and/or BMPs are well implemented. These
sites represent "minimally disturbed" conditions and may characterize
biological integrity.
C
Site represents marginal watershed and stream conditions for a reference
site. Human disturbance is present, but the site was the best available for
basin/region. These sites represent "least disturbed" conditions, and
generally do not characterize biological integrity. These sites will be
replaced if better quality reference sites are located.
Description—Nonreference Sites
D
Site represents submarainal stream and watershed conditions.
Considerable human disturbance is present at reach or in large portions of
watershed.
E
Site represents poor stream and watershed conditions. Considerable
human disturbance is present at reach and in larae portions of watershed.
F
Site represents verv poor stream and watershed conditions. Human
disturbance is extensive throughout stream and watershed.
Adapted from: Drake (2004; Table 3).
sites are compared for aquatic life use attainment decision using traditional regional
biocriteria. The approach described here provides an example of the mixture of
objective site screening and scoring tools and best professional judgment that is
commonly employed in reference site selection. Objective approaches are more
repeatable and the more defensible. More subjective approaches are vulnerable to bias
that can affect the variability of assessments through time. Last, the use of
nonbiological measures in defining reference sites removes circularity from the
biological indicator development process.
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Section III— Chapter 13: Biocriteria and Reference Condition
13.4.2. Cautions and Caveats
The least-disturbed reference condition is not fixed (Stoddard et al., 2006).
However, this condition is most commonly used in developing state bioindicators and in
setting biocriteria and making aquatic life use determinations. As such, decisions for
some sites can change as indicators are revised or recalibrated as new better quality
reference sites are identified through time or as reference sites degrade through time.
This effect must be taken into account and is most often dealt with by sampling the
population of reference sites regularly to quantify these changes through time.
13.4.3. Future Needs
The population of reference sites should be revisited regularly, and
improvements should be made as better quality sites are identified or if reference sites
become degraded. The continuous development of different GIS tools and remote
sensing tools (e.g., light detection and ranging [LiDAR]), satellite imaging) will improve
the quality of data used to screen and identify potential sites, saving resources and
improving the accuracy of site identification.
13.4.4. Other Examples
There are a number of states/tribes and federal agencies that use reference site
selection approaches in bioindicator development and assessment. Also see Chapter 5
of this document.
EPA Environmental Monitoring and Assessment Program West Reference
Site Selection
Environmental Monitoring and Assessment Program West selected reference
sites using reach-scale physical and chemical measures alone, deliberately avoiding the
use of land-use data (Stoddard et al., 2005).
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Section III— Chapter 13: Biocriteria and Reference Condition
Coupling GIS and Multivariate Approaches to Reference Site Selection for
Wadeable Stream Monitoring
Describes the use of GIS to identify reference sites and the use of multivariate
analyses to refine waterbody classification in New Zealand (Collier et al., 2007).
The Reference Condition Approach
Describes a methodology of assessment whereby sites are compared
biologically to reference sites using a multivariate technique and significant distances
from the reference condition are used as the basis for impairment decisions. Reference
site selection is a critical part of the process and uses a variety of approaches, including
the incorporation of landscape-level data (Bailey et al., 2004).
Key Information Source(s)/Web Sites
The Oregon DEQ report is at
http://www.deq.state.or.us/lab/techrpts/docs/WSA04002.pdf (Accessed November 12,
2008).
Key Contact(s)/Researcher(s)
Oregon DEQ Water Quality Division,
http://www.oregon.gov/DEQ/WQ/contact_us.shtml (Accessed November 12, 2008)
13.5. REFERENCES
Bailey, R.C., R.H. Norris, andT.B. Reynoldson. 2004. Bioassessment of Freshwater:
Using the Reference Condition Approach. Kluwer Academic Publishers, Boston, MA.
Barbour, M.T., J. Gerritsen, B.D. Snyder, and J.B. Stribling. 1999. Rapid
Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic
Macroinvertebrates and Fish. 2nd Edition. EPA/841/B-99/002. Office of Water, US
Environmental Protection Agency, Washington, DC. Available online at
http://www.epa.gov/owowwtr1/monitoring/rbp/index.html.
Collier, K.J., A. Haigh, and J. Kelly. 2007. Coupling GIS and multivariate approaches
to reference site selection for wadeable stream monitoring. Environ. Monit. Assess.
127(1 -3):29-45.
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Section III— Chapter 13: Biocriteria and Reference Condition
Drake, D. 2004. Selecting Reference Condition Sites: An Approach for Biological
Criteria and Watershed Assessment. Oregon Department of Environmental Quality,
Watershed Assessment Section, Portland, OR. WAS04-002. Available online at
http://www.deq.state.or.us/lab/techrpts/docs/WSA04002.pdf.
Hawkins, C.P., R.H. Norris, J.N. Hogue, and J.W. Feminella. 2000. Development and
evaluation of predictive models for measuring the biological integrity of streams. Ecol.
Appl. 10(5): 1456-1477.
Hughes, R.M., D.P. Larsen, and J.M. Omernik. 1986. Regional reference sites: a
method for assessing stream potentials. Environ. Manage. 10(5):629-635.
Stoddard, J.L., D.V. Peck, S.G. Paulsen, et al. 2005. An Ecological Assessment of
Western Streams and Rivers. U.S. Environmental Protection Agency, Washington, DC.
EPA/620/R-05/005. Available online at
http://www.epa.gov/emap/west/html/docs/Assessmentfinal.pdf.
Stoddard, J.L., D.P. Larsen, C.P. Hawkins, R.K. Johnson, and R.H. Norris. 2006.
Setting expectations for the ecological condition of streams: the concept of reference
condition. Ecol. Appl. 16(4):1267-1276.
U.S. EPA (Environmental Protection Agency). 2002. Summary of Biological
Assessment Programs and Biocriteria Development for States, Tribes, Territories, and
Interstate Commissions: Streams and Wadeable Rivers. Office of Water, Washington,
DC. EPA/822/R-02/048. Available online at
http://www.epa.gov/bioiweb1/html/program_summary.html.
Whittier, T.R., J.L. Stoddard, D.P. Larsen, and A.T. Herlihy. 2007. Selecting reference
sites for stream biological assessments: best professional judgment or objective criteria.
J. North Am. Benthol. Soc. 26(2):349-360.
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Section IV—Summary
SECTION IV: GAPS AND NEEDS FOR RESEARCH AND APPLICATION
SUMMARY
Chapter 14: Research and Application Gaps and Needs (Recommended for:
Advanced) covers important gaps and needs in the areas of geographic frameworks,
geographically focused studies and rehabilitation efforts, and data for: landcover,
habitats, infrastructure, land management practices, digital elevation models, hydrology,
groundwater, soils, climate, stressors, remote sensing, demographics, and
socioeconomics, and for Web-based availability of data and information.
Application gaps discussed include: predictions of pollutant loadings and
concentrations, associations between landscape characteristics and ecological
conditions, water quality standards and criteria development, best management practice
siting and selection, priority setting, and evaluation of restoration and recovery potential.
An informal ranking of data gaps and needs by the Landscape and Predictive Tools
Steering Committee includes: improved National Land Cover Dataset, complete Level 4
Ecoregions, intermittent/perennial streams, National Wetland Inventory gaps, true
watersheds, Gross Domestic Product and population, Parameter-elevation Regressions
on Independent Slopes Model (PRISM)support, and numerous others.
Two critical gaps were identified for decision support tools
• the lack of interpretive tools that aid in conceptualization, visualization, problem
formulation, and identification of alternative hypothesis tests, and
• the lack of an integrated framework for applying tools for management decisions
at multiple scales (local to regional) and involving simultaneous optimization of
multiple endpoints.
IV-i
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Section IV—Chapter 14: Research and Application Gaps and Needs
14. RESEARCH AND APPLICATION GAPS AND NEEDS
Naomi Detenbeck, U. S. EPA Office of Research and Development, Narraganset, Rl
Mary White, U. S. EPA Region 5, Chicago, IL
14.1. CONCEPTUAL FRAMEWORK GAPS AND NEEDS
There are numerous approaches to classification at the ecosystem to landscape
scales, and many of these classifications are available as digital maps (see Chapter 3;
see also reviews in Detenbeck, 2001; Kurtz et al., 2006). Classification schemes can
be characterized as being geographically dependent or geographically independent
(Detenbeck et al., 2000). Geographically dependent schemes identify spatially coherent
regions with similar landscape attributes, and usually they are developed for multiple
applications. Geographically independent classification schemes are developed on the
basis of one or more environmental drivers expected to affect a specific type of
response. Units of the same class might or might not be spatially contiguous
(Detenbeck et al., 2000).
Despite the wide variety of classification schemes developed, there have been
relatively few studies testing and comparing the performance of different schemes in
explaining background variation in condition, sensitivity, or restoration potential of
ecological systems (see Table 14-1a,b). Biological monitoring programs have
traditionally applied a classification framework such as Omernik's ecoregions (Omernik,
1987; US EPA, 2011) to characterize variation in reference condition of streams
(U.S. EPA, 2006). More recently, expansion of biological monitoring to other aquatic
systems such as wetlands and estuaries has encouraged the testing of alternate
classification schemes such as marine biogeographic provinces for estuaries (Gibson
et al., 2000) and a combination of geographically dependent schemes and
hydrogeomorphic or habitat-based schemes for wetlands (Detenbeck, 2001;
Galatowitsch et al., 2000). Habitat-based schemes historically have been applied for
inventory purposes (Dahl and Johnson, 1991), with occasional evaluation of the
implications of shifting habitat mosaics for wildlife populations (Johnson et al., 1987) or
metapopulations (Gibbs, 1993). Conservation planning initiatives such as the aquatic
National Gap Analysis Program (GAP) (http://gapanalysis.nbii.gov) are fostering the
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Section IV—Chapter 14: Research and Application Gaps and Needs
TABLE 14-1 a
Status of Development and Testing of Landscape Frameworks3
Application
Geographically Based
Frameworks
Environmentally Based
Frameworks
Regional
Scale
Watershed
Scale
Omernik
Ecoregions (1,1a)b
Bailey's
Ecoregions (2)
Ecological Units
(3,4)
Hydrogeologic
Units (5)
Wetland
Hydrogeomorphic
Profiles (6,7)
Hydroclimatic
Zones (8)
Wetland
Ecoregions (9)
Hydrologic
Threshold
Classification (10)
Hydrologic
Regime Type (12)
Describing Reference Condition
Biological
13,14
-
15,16
-
17
-
9
15
12
Water Quality
18-20
-
21-23
24
-
-
-
21-23
-
Hydrology
-
12
11
25
26
8
-
11
12
Ecosystem/Habitat
27
-
-
-
6,7
-
-
-
-
Climate
28
-
-
-
-
-
-
-
-
Predicting Sensitivity of Response
Biological
-
-
15,29
-
-
-
-
-
12
Water Quality
19
-
21-23
-
-
-
-
-
-
Hydrology
-
12
11
25
-
8
-
-
-
Ecosystem/Habitat
-
-
-
30
-
-
-
-
-
Climate
-
-
-
-
-
-
-
-
-
Predicting Restoration Potential
Habitat
-
-
-
-
-
-
-
-
-
aSee Table 14-1 b for ecosystem/habitat scale.
b1) Omernik (1987), 1a) US EPA (2011), 2) Bailey (1976), 3) Maxwell et al. (1995), 4) Keys et al. (1995),
5) Wolock et al. (2004), 6) Detenbeck et al. (1999), 7) Bedford (1996), 8) Saco and Kumar (2000),
9) Lane (2000), 10) Detenbeck et al. (2000), 11) Detenbeck et al. (2005), 12) Poff (1996), 13) Hawkins
et al. (2000), 14) Mazor et al. (2006), 15) Brazner et al. (2005), 16) Sowa et al. (2006), 17) Bennett
(1999), 18) Rohm et al. (2002), 19) Robertson and Saad (2003), 20) Jenerette et al. (2002),
21) Detenbeck et al. (2000), 22) Detenbeck et al. (2003), 23) Detenbeck et al. (2004), 24) Winter (2001),
25) Yadav et al. (2007), 26) Cole et al. (2002), 27) Shirazi et al. (2003), 28) Thompson et al. (2004),
29) Brazner et al. (2004), 30) Winter (2000), 31) Brinson (1993), 32) FGDC. (2010), 33) Engle et al.
(2007), 34) Cowardin et al. (1979), 35) Shaw and Fredline (1956), 36) Gorham et al. (1983), 37) Winter
(1977), 38) Eilers et al. (1983), 39) McKee et al. (1992), 40) Herdendorf et al. (1981), 41) Moffett et al.
(2007), 42) Keough et al. (1999), 43) Albert et al. (2005), 44) Mine (1997), 45) Rosgen (1996), 46) Roper
et al. (2008), 47) Montgomery and Buffington (1997), 48) Montgomery and Buffington (1993),
49) Higgins et al. (1998), 50) Osgood (1988), 51) Euliss et al. (2002), 52) Mine (1997), 53) Harris and
Heap (2003).
- = no value.
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Section IV—Chapter 14: Research and Application Gaps and Needs
TABLE 14-1 b
Status of Development and Testing of Landscape Frameworks3
Environmentally Based Frameworks
Ecosystem/Habitat Scale
Application
Wetland Hydrogeomorphic
Classes (31 )b
Natureserve Marine/Estuarine
Ecosystems (32)
Estuarine Hydrogeomorphic
Classes (33)
Wetland and Deepwater
Habitat (34)
Wetland Classes (35)
Lacustrine Types (36)
Lake Hydrologic Setting (37)
Great Lakes Wetlands (39
Stream/River Channel Type
(45
Valley Segment Scheme (49)
Lake Morphometry (50)
Describing Reference Condition
Biological
-
-
-
51,21
-
-
-
52
-
16
-
Water Quality
-
-
-
-
-
36
37
-
-
-
-
Hydrology
-
-
-
-
-
-
-
-
-
-
-
Ecosystem/Habitat
31
32,53
53
Climate
-
-
-
-
-
-
-
-
-
-
-
Predicting Sensitivity of Response
Biological
-
-
-
-
-
-
-
-
12
-
-
Water Quality
-
-
-
-
-
-
38
-
-
-
50
Hydrology
-
-
-
-
-
-
-
-
-
-
-
Ecosystem/Habitat
-
53
53
Climate
-
-
-
-
-
-
-
-
-
-
-
Predicting Restoration Potential
Habitat
-
-
-
-
-
-
-
-
45
-
-
aSee Table 14-1 a for coarser scales.
bSee Table 14-1a, footnote b.
- = no value.
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Section IV—Chapter 14: Research and Application Gaps and Needs
development and application of nested classification systems (e.g., Sowa et al., 2006;
Higgins et al., 1998), which recognize the operation of landscape filters at multiple
scales (Poff, 1997). Recognition of the need to account for the net gain or loss in
wetland function as opposed to tracking simple acreage of wetlands for wetland
permitting and mitigation programs has led to development of a functionally based
classification system for wetlands, the Hydrogeomorphic Approach (Brinson, 1993).
Shifts in wetland type distributions can now be evaluated within a functional framework
by examining landscape profiles and functional equivalence of wetland types (Bedford,
1996; Detenbeck et al., 1999). Similarly, the need to evaluate channel condition and
restoration potential of stream habitat has fostered development of a channel
classification framework and approach to habitat restoration for streams (Rosgen,
1996).
Historically, the U.S. Environmental Protection Agency (EPA)-sponsored
monitoring of aquatic systems for chemical parameters has focused mainly on tracking
compliance for National Pollutant Discharge Elimination System (NPDES) permits or
providing background information for developing total maximum daily loads (TMDLs).
More recently, attention has been focused on the need to assess success of restoration
projects on a site-specific scale (319 program database). In large part because of the
site-specific scale of decision making for these regulatory, management, and restoration
programs, little attention has been paid to the opportunity to use broader classification
schemes and landscape-level information to inform decision makers. Occasionally,
broader-scale monitoring programs have been carried out to assess the success of
regulatory actions such as implementing deposition standards for nitrates and sulfates
(Church et al., 1989). Although ecological risk assessments of acid rain effects have
applied classification schemes to evaluate the relative sensitivity of different lake types
(e.g., Eilers et al., 1983), developers of broad-scale lake sampling programs have not
applied these same principles. In cases where a geographic framework has been
applied retroactively to understand variation in historic water quality (nutrient reference
condition), states and regions have found significant gaps in information for specific
regions and waterbody types that have limited their ability to assess existing data.
Proactively applying classification schemes to monitoring can yield dual benefits. For
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Section IV—Chapter 14: Research and Application Gaps and Needs
streams, a combination of geographically dependent and watershed classification
schemes has been successfully applied within a probabilistic sampling framework to
simultaneously perform a regional assessment of biological, chemical, and habitat
condition, as well as to evaluate the relative risk of forest-clearing and other land-use
activities to streams or coastal wetlands associated with different watershed classes
(Detenbeck et al., 2000, 2003, 2004, 2005, 2007; Brazner et al., 2004, 2005).
Conceptual frameworks explaining variation in condition, sensitivity, and
restorability of aquatic systems exist for many scales, issues, and system types (see
Table 14-1 a,b; Norton et al. 2009). Unfortunately, testing of individual schemes against
a null model or comparison of alternate models is rare (see Hawkins et al., 2000); more
objective testing is sorely needed. Techniques exist to test alternate classification
strategies either prospectively or retrospectively in an objective fashion (Burnham and
Anderson, 2002); enhanced training or development of user-friendly interfaces for these
methods in common statistical packages could facilitate their broader use. Historic
conventions in monitoring or regulatory programs and the historic scale at which
decisions are made have limited the degree to which the existing scientific frameworks
are applied (Omernik and Bailey, 1997). In some cases, testing and comparison of
alternate schemes could be facilitated by development of digital maps or methods to
produce digital maps reflecting different classification schemes at a regional scale. For
example, national maps are not yet available to illustrate the distribution of streams and
rivers by Rosgen channel type (Rosgen, 1996) or the distribution of wetlands by
hydrogeomorphic type (Brinson, 1993). In the case of Rosgen channel types, some
classification variables are available only through local surveys, but others, such as
channel slope (Detenbeck et al., 2003) and valley bottom morphometry (Williams et al.,
2000) can be derived from hydrography coverages and digital elevation models (DEMs).
Likewise, landscape position relative to other waterbody types and topographic position
could be used to map wetland hydrogeomorphic types. In the absence of national or
regional maps, developing geographic information system (GIS) tools to facilitate local
or regional mapping exercises would facilitate wider use of these classification systems.
Regulatory needs can be expected to force the wider application of classification
systems in the United States, as is being done in other countries. For example, New
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Section IV—Chapter 14: Research and Application Gaps and Needs
Zealand agency staff has proposed to use a nationwide eco-hydrological classification
of rivers to explain spatial patterns in flow variation (Snelder et al., 2005). In one
application, variation in flood frequency is being used to help predict trophic state
(periphyton biomass) as a function of nutrient concentration, a necessary step in the
development of nationwide nutrient criteria (Snelder et al., 2004a,b). A similar approach
is being explored to support the development of nutrient criteria for streams and rivers in
EPA Region 5.
It is likely that emerging needs to evaluate the success of regulatory and
nonregulatory approaches to environmental protection at broader scales will drive the
application of classification frameworks both for retrospective and prospective analyses.
The drive for improved decision support systems (DSS) to support development and
implementation of watershed management at the local scale (U.S. EPA, 2005) should
provide support for the expanded use of classification frameworks as a tool to put the
behavior of local systems into a regional or national context. For example, a Web site
developed by the Australian government (http://www.ozestuaries.org/) provides a map
of classified estuary types for the entire continent, along with conceptual models,
databases, and models. Information on similar systems can be used to inform
management decisions for estuaries with more limited data (Harris and Heap, 2003;
Ryan et al., 2003). Likewise, New Zealand agency staff members have proposed to
use ecological classification to define units for assessment and management to help
inform local decisions (Snelder and Hughey, 2005).
14.2. GAPS IN POTENTIAL APPLICATION OF GEOGRAPHIC FRAMEWORKS AND
LANDSCAPE TOOLS IN CLEAN WATER ACT PROGRAMS
The offices and regions are held accountable for implementing and fulfilling the
National Program Measures. The Office of Water fiscal year 2010 National Program
measures are at http://water.epa.gov/aboutow/goals_objectives/waterplan/upload/
FY2011_nwpg_appendix_1 -6-10_508.pdf in table format. Probably the most serious
analysis gaps in determining appropriate program measures were identified in
association with the 2007 U.S. EPA Report on the Environment (ROE), Science Report
(U.S. EPA, 2008) submitted to EPA's Science Advisory Board in draft form. Program
measures and ROE indicators are not the same, but ROE indicators should be able to
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Section IV—Chapter 14: Research and Application Gaps and Needs
measure changes in the environment due to program activities. Location-specific
indicators identified in the ROE as not available but that could inform the National
Program Measures are the following
• Nationally consistent information to characterize stressors to fresh surface water
condition—specifically pollutant loadings from point and nonpoint sources.
• Information on the condition of large rivers. The Nitrogen and Phosphorus
Discharge from Large Rivers indicator describes nutrient loads at the mouth, but
does not address conditions upstream.
• Information on the condition of lakes. A nationally consistent indicator of lake
trophic state could bring together several aspects of condition (e.g., physical,
chemical, and biological parameters) related to eutrophication—a problem facing
many of the nation's lakes.
• Information about toxic contaminants in freshwater sediments. Sediment
contaminants can accumulate through the food web, and can ultimately affect the
health of humans who consume fish and shellfish.
• Information on the condition of fish communities, which can be affected by many
different stressors.
14.3. EPA WATER PROJECTS AND PROGRAMS
The definition of programs within the EPA organizational structure takes on
various meanings; however, in this context we are referring to those listed on EPA's
Web site (http://www.epa.gov/aboutepa/organization.html). Many of the programs
already have embedded links to databases, maps, and GIS Web servers. The following
is an updated list with indications of those program sites with data and interactive GIS
links.
• *National Aquatic Resource Surveys
(http://www.epa.gov/owow/monitoring/nationalsurveys.html)—conduct a series of
national aquatic resource surveys. Often referred to as probability-based
surveys, these studies report on core indicators of water condition using
standardized field and lab methods. The surveys include a national quality
assurance program and are designed to yield unbiased,
statistically-representative estimates of the condition of the whole water resource
(such as rivers and streams, lakes, ponds and reservoirs, wetlands, etc).
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Section IV—Chapter 14: Research and Application Gaps and Needs
• American Heritage Rivers Initiative
(http://water.epa.gov/type/watersheds/named/heritage/initiative.cfm)—provides help to
communities to restore and revitalize waters and waterfronts, integrating the
economic, environmental and historic preservation programs and services of
federal agencies to benefit communities engaged in efforts to protect their rivers.
• Beach Program (http://www.epa.gov/waterscience/beaches/)—provides grants to state,
tribal, interstate, and local agencies to establish effective monitoring and public
notification programs for beaches. This was first federal site to contain
information about local beach closings and conditions.
• *Clean Lakes Program (http://www.epa.gov/0W0W/LAKES/index.htrnl)—information
about the quality of America's lakes and technical resources for management of
lakes.
• Coastal and Ocean Programs (http://www.epa.gov/OWOW/oceans/index.html)—access
to information on ocean discharges, ocean dumping, marine debris efforts.
• **Drinking Water and Ground Water Protection Programs
(http://water.epa.gov/drink/)—a collection of information about efforts to educate and
communicate about water issues.
• Fish Consumption Advisories (http://www.epa.gov/waterscience/fish/)—several
resources that include all available information describing state-, tribal-, and
federally-issued fish consumption advisories in the United States for the
50 states, the District of Columbia, four U.S. territories, and the 12 Canadian
provinces and territories.
• *Great Lakes National Program (http://www.epa.gov/glnpo/)—brings together federal,
state, tribal, local, and industry partners in an integrated, ecosystem approach to
protect, maintain, and restore the chemical, biological, and physical integrity of
the Great Lakes.
• Gulf of Mexico Program (http://www.epa.gov/gmpo/)—works with many partners from
Alabama, Florida, Louisiana, Mississippi, and Texas to protect the 1.8 million
square miles that make up the waters of the gulf.
• *Mobile Sources (http://www.epa.gov/OMSWWW/)—carries out a broad range of
activities to reduce pollutants emitted from motor vehicles, marine vehicles
(boats) and their fuels.
• *National Estuary Program (NEP, http://www.epa.gov/nep/)—information about this
effort to protect and restore the health of estuaries while supporting economic
and recreational activities.
• **Nonpoint Source Pollution Control (http://www.epa.gov/0W0W/NPS/)—access
information about polluted runoff and exchange information about methods for
reducing the effects of this environmental issue.
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Section IV—Chapter 14: Research and Application Gaps and Needs
• Oil Spill Program (http://www.epa.gov/oilspill/)—information about EPA's program for
preventing, preparing for, and responding to oil spills that occur in and around
inland waters of the United States.
• *Pollutant Load Allocation (Total Maximum Daily Loads)
(http://www.epa.gov/OWOW/tmdl/index.html)—provides information on EPA's TMDL
Program under section 303(d) of the Clean Water Act.
• Superfund Information (http://www.epa.gov/superfund/)—locates, investigates and
cleans up the worst hazardous waste sites throughout the United States.
• *Toxic Release Inventory (http://www.epa.gov/tri/)—the source of information about
toxic chemicals that are being used, manufactured, transported, or released into
the environment.
• *Volunteer Monitoring Program
(http://water.epa.gov/type/rsl/datait/waters/georef/epasvmp.cfm)—addresses methods and
tools to monitor, assess, and report on the health of America's water resources,
and software and automated information systems to manage monitoring data.
• Wastewater Management (http://www.epa.gov/OWM/)—access a range of programs
contributing to the well-being of our nation's waters and watersheds.
• Water Efficiency (http://www.epa.gov/watersense/water_efficiency/)—EPA's water
efficiency program is focusing on creating a market enhancement program for
water efficient products. This site also provides a wide variety of information on
other water efficiency topics, publications (many in downloadable format), and
links to other very useful water efficiency Web sites.
• **Water Quality Standards, Criteria, and Methods
(http://www.epa.gov/waterscience/)—this program is responsible for developing sound,
scientifically defensible standards, criteria, advisories, guidelines, limitations and
standards guidelines for the Office of Water.
• *Watershed Management (http://www.epa.gov/0W0W/watershed/)—this program
encourages solutions to water quality and ecosystem problems at the watershed
level rather than at the individual waterbody or discharger level.
• Wetlands Program (http://www.epa.gov/owow/wetlands/)—encourages and enables
others to act effectively in protecting and restoring the nation's wetlands and
associated ecosystems.
*Data available from site
**Web-based GIS embedded in link
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Section IV—Chapter 14: Research and Application Gaps and Needs
14.4. PROGRAMS WITH A GEOGRAPHIC FOCUS
In addition, there are multimedia programs that focus on specific geographic
areas at http://www.epa.gov/epahome/places.htm. Most of these programs include
collaboration with other agencies, states and nations, and they all contain links to data
and GIS data layers and displays.
• Chesapeake Bay Program
(http://www.epa.gov/region03/chesapeake/index.htm)—this site provides
information about the restoration of the Chesapeake Bay and data and
information about this unique ecosystem.
• Columbia River Basin
(http://yosemite.epa.gov/r10/ecocomm.nsf/Columbia/Columbia)—designated as
a critical ecosystem in EPA's strategic plan, this 260,000-square-mile watershed
is targeted for wetland restoration, contaminated sediment cleanup, and an
overall reduction in contaminant concentrations in water and fish tissue.
• Great Lakes National Program Office (http://www.epa.gov/glnpo/)—the site is
dedicated to communicating information about the Great Lakes ecosystem and
the human health, monitoring, and sediment issues present there.
• Great Lakes Information Network (http://www.great-lakes.net/)—this site is a
partnership that provides one place online for people to find information relating
to the binational Great Lakes region of North America.
• Gulf of Mexico Program (http://www.epa.gov/gmpo/)—this site is a
developmental effort designed to make Gulf of Mexico ecosystem data and
information readily available and to facilitate communications among parties
involved in Gulf of Mexico projects and programs.
• Lake Champlain Basin Program (http://www.lcbp.org/)—a federal, state, and
local initiative to restore and protect Lake Champlain (Vermont and New York)
and its surrounding watershed for future generations.
• Long Island Sound Program (http://www.epa.gov/region01/eco/lis/)—this site
offers access to information about the Long Island Sound and this program to
research and monitor conditions in the sound, identify priority problems, and
develop strategies to address those problems.
• Mid-Atlantic Integrated Assessment (http://www.epa.gov/emap/maia/)—a
research, monitoring, and assessment initiative to develop high-quality scientific
information on the condition of the natural resources of the Mid-Atlantic region of
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Section IV—Chapter 14: Research and Application Gaps and Needs
the eastern United States, including the watersheds of the Delaware and
Chesapeake bays, Albemarle-Pamlico Sound, and the Delmarva Coastal Bays.
• National Estuary Program (http://www.epa.gov/nep/)—a program to protect and
restore the health of estuaries while supporting economic and recreational
activities. To date, 28 local NEPs are demonstrating practical and innovative
ways to revitalize and protect their estuaries.
• Puget Sound Georgia Basin (http://www.epa.gov/region10/psgb/)—a critical
marine ecosystem in the United States and Canada, the Puget Sound portion
has been designated as a critical ecosystem in EPA's strategic plan. A
comprehensive environmental indicators report is one of the tools used to assess
our progress toward improving water quality and shellfish harvesting, remediating
contaminated sediments, wetland restoration and diesel emission reductions.
• Puget Sound Initiatives (http://www.psp.wa.gov/)—this site provides information
about an effort by state and provincial governments to address, plan for, and
resolve the environmental problems associated with population growth in Puget
Sound and the Georgia Basin.
• Regional Geographic Initiative
(http://www.epa.gov/regional/highlightsfin.htm)—this program provides grants for
projects that are identified as high priority by an EPA Region, state, or locality
that pose a high human health or ecosystem risk, and have significant potential
for risk reduction.
• U.S.-Mexico Border 2012 (http://www.epa.gov/usmexicoborder/)—a binational,
interagency program aimed at protecting and improving the environment and
environmental health while fostering sustainable development in the U.S.-Mexico
border area.
• U.S.-Mexico Border Environmental Health Program
(http://www.epa.gov/orsearth/)—this site offers information about this interagency
committee including the program strategy and information about ongoing and
planned projects.
Many of the programs for which measures are necessary are voluntary, such as
Adopt your Watershed and National Nonpoint Source Management Program (all
voluntary water programs are at http://www.epa.gov/partners/programs/
index.htm#water). A fundamental problem with voluntary programs is the lack of
sensible environmental measures of progress that can be attributed to them. Recently,
the Office of Inspector General has commented on this and issued an evaluation report
(U.S. EPA, 2007). The tools and models described in this document, particularly as are
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Section IV—Chapter 14: Research and Application Gaps and Needs
illustrated in Section V, could be used to evaluate volunteer programs to quantify
ecosystem services and restoration progress.
14.5. DATA GAPS AND NEEDS
For all the base coverages described below describing attributes that change
over time, frequent and timely updates are needed. This is critical for National Land
Cover Dataset (NLCD) coverages, which in the past have been produced several years
after the remote-sensing imagery was acquired. National Wetland Inventory maps have
not all been digitized, and many of those that are digitized are out-of-date. Even basic
hydrography shifts over time because of both natural processes (e.g., river channel
migration) and anthropogenic effects (e.g., dredge-and-fill activities).
14.5.1. Landcover and Indices
A widely used data set is the NLCD produced by the Multi-Resolution Land
Characteristics Consortium (http://www.mrlc.gov/index.asp). This group of federal
agencies has produced three national landcover data sets—for 1992, 2001, and 2006.
In addition, estimates of percentage imperviousness and percentage canopy cover have
been generated for 2001 and 2006, and land-use change estimates derived for
1992-2001 and 2001 -2006 (see Homer et al., 2004). It is imperative that the funding
and staffing be dedicated to this project by federal agencies so that future landcover
data sets can be generated timely and more frequently.
Some land-use activities are poorly represented in the current NLCD database,
for example, mining activity. Needs include both current activity above- and
below-ground, mapping extent of valley fill activity, and mapping the extent of
abandoned mine lands. In addition, some land-cover classes have limited accuracy
(e.g., vernal pool wetlands in forested landscapes). Once regional accuracy
assessments have been completed for the most recent NLCD 2001 coverages, effort
should be placed on improving methods to reduce the most significant uncertainties.
Many land-use changes express effects following a significant lag in time
(Harding et al., 1998), e.g., related to the travel time for polluted ground water to reach
stream beds and physical changes in habitat structure. Although presettlement maps
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Section IV—Chapter 14: Research and Application Gaps and Needs
have been developed for some regions of the country on the basis of early survey
records (Manies and Mladenoff, 2000), in general, historic land use/landcover maps are
limited and inconsistent in spatial and temporal coverage. Potential uses of historic land
use/landcover maps include defining reference condition, analyzing land-use change
effects with time lags, and identifying potential wetland restoration sites, among others.
Although many efforts have been made to relate land use/landcover directly to
biological effects, the underlying conceptual models and accuracy of such predictions
could be improved by mapping indices of land use/landcover that are related more to
mechanism of impact than to economic sector (agriculture, transportation, and such;
Novotny et al., 2005). Such indices could include change in runoff and yield coefficients
from regional background values (based on natural landcover) or change in hydrologic
indices. In addition, although methods to calculate land use/landcover values weighted
by hydrologic distance are available, they are not easy or efficient to apply (Stark et al.,
2001).
Some types of land use/landcover data are needed at the parcel scale to
facilitate local land-use planning and alternatives analysis. These include data on
ownership class, use restrictions, zoning, and conservation status. Quantifying types of
development (e.g., low-impact development [LID]) would help researchers and
managers to assess the effect of zoning decisions and smart growth planning.
Similarly, inventories of best management practices (BMPs) are needed to facilitate
assessment of the effectiveness of such measures. Ideally, these would be linked to
information on funding programs to allow analysis of effectiveness by program.
As management interest is focused more and more on protecting remaining
habitat both for aquatic life uses and to maintain downstream water quality, high-quality
habitat coverages become even more critical. Habitat has been defined in multiple
ways. It has been used to refer to biotopes, or vegetation community types which
support specific animal species. The U.S. National Vegetation Classification, a
component of the International Classification, was developed by The Nature
Conservancy and NatureServe in collaboration with partners from the academic,
conservation, and government sectors, and has been adopted for use by all federal
agencies by the Federal Geographic Data Committee (FGDC, Maybury, 1999). This
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Section IV—Chapter 14: Research and Application Gaps and Needs
system has been applied in specific regions, e.g., 250 national parks, but has not yet
been applied nationwide. NatureServe is developing a coarser ecosystem classification
system, with ecological systems defined as, "biological communities that occur in similar
physical environments and are influenced by similar dynamic ecological processes,
such as fire or flooding" (http://www.natureserve.org/prodServices/ecomapping.jsp).
Aquatic habitat types of interest are inventoried by the National Wetlands
Inventory (NWI) for freshwaters and some coastal systems. Wetland coverages in
NLCD can be supplemented with digital information in the NWI. However, the NWI is
not yet complete or completely digital, and even those quadrangles that have been
completed require updating to support local and regional trends analysis. Wetland
coverages could also be made more useful by combining these with DEMs and
hydrography to derive wetland hydrogeomorphic classes, ultimately to allow wetland
functions and values to be mapped (Detenbeck, 2001). Coastal and marine habitat
mapping has not progressed as far as the NWI. Efforts are underway through the
FGDC to develop mapping standards for the Coastal and Marine Ecological
Classification System (FGDC 2010; Chris Madden, FL DEP, personal communication),
which will provide an oceanic complement to inland and nearshore NWI maps
seamlessly. Once mapping standards are established, it will be necessary to create
digital maps based on this system. There might be a need to supplement raw data
used for habitat mapping in the shallow near-shore zone because there are technical
limitations to some sensors and collection techniques in shallow water.
Suitable habitat for specific animal taxa or community types is also of interest. In
September 2007, EPA awarded NatureServe a seven-year, enterprise-wide, fixed-fee
contract providing access to endangered species data and associated scientific
expertise. Terrestrial community types have been mapped through GAP programs as
joint ventures between the U.S. Geological Survey (USGS), states, and private
partners. State GAP programs have been completed for all states except Alaska, and
regional GAP analyses have been conducted for the Southeast, Southwest, and
Northwest. However, relatively few aquatic GAP analyses have been conducted.
Exceptions include riverine and coastal GAP analyses conducted in the Great Lakes
states (http://www.glsc.usgs.gov/main.php?content=research_GAP_national&title=
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Section IV—Chapter 14: Research and Application Gaps and Needs
Aquatic%20GAP0&menu=research_NCE_GAP). The purpose of the GAP is to provide
broad geographic information on the status of ordinary or common species (i.e., those
not threatened with extinction or naturally rare) and their habitats to provide land
managers, planners, scientists, and policy makers with the information they need to
make better informed decisions.
The National Fish Habitat Board (www.fishhabitat.org ) has released a status of
fish habitats in the United States report as described in the National Fish Habitat Action
Plan, an effort to protect, restore and enhance our nation's aquatic habitats. The report
titled THROUGH A FISH'S EYE: The Status of Fish Habitats In The United States 2010
summarizes the results of an unprecedented, nationwide assessment of the human
effects on fish habitat in the rivers and estuaries of the United States (National Fish
Habitat Board, 2010). An interactive online version of mapped habitat quality is
available at: http://www.nbii.gov/far/nfhap/.
Finally, many applications require the mapping of natural or altered
physicochemical habitat features. While some aquatic classification schemes exist
(e.g., Rosgen's stream channel classification system), none have been mapped
extensively. Even point data for habitat assessments are only beginning to be made
available in georeferenced form in national databases such as Storage and Retrieval
(STORET) and the USGS National Water-Quality Assessment data warehouse.
14.5.2. Infrastructure
An initiative is underway to update the accuracy of national road coverages in the
TIGER data set. However, the U.S. Census Bureau's TIGER® System automates the
mapping and related geographic activities required to support the decennial census and
sample survey programs of the U.S. Census Bureau starting with the 1990 decennial
census, and it does not necessarily focus on logging roads in rural areas, which can
have significant effects.
Stormwater infrastructure and effluent locations are generally mapped at the
local scale by townships and municipalities and are not available at regional or national
scales. Stormwater infrastructure often occurs in areas originally occupied by
headwater streams; in some cases, access to these maps has encouraged restoration
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Section IV—Chapter 14: Research and Application Gaps and Needs
practices such as daylighting in which underground channels are restored as surface
drainages.
14.5.3. Land Management Practices
Most land management activities are either not inventoried, or inventories are
summarized and published at coarse (e.g., county) scales that are inadequate to
support predictive modeling or watershed management as a policy to protect landowner
privacy. Examples of data sets typically aggregated at the county level include
Agricultural Census data, the U.S. Forest Service Forest Inventory Analysis data, and
the U.S. Department of Agriculture (USDA) Natural Resource Inventory data.
Agricultural practices and activities such as confined animal feeding operations,
irrigation, and tile drainages are not mapped at regional or national scales; yet these
activities have significant effects on water quality. Likewise, information is needed on
the location of restoration activities, and BMPs, including green infrastructure projects
such as LID, to assess their effectiveness and aid in watershed management planning.
14.5.4. Digital Elevation Models
Most assessment and management needs are well served by existing DEM data.
However, in coastal areas, the completion of nearshore bathymetry and topography
mapping with light detection and ranging (LiDAR) through the U.S. Army Corps of
Engineers' SHOALS program (http://shoals.sam.usace.army.mil/) is essential for both
Great Lakes and ocean shores. Once completed, these data need to be seamlessly
merged with existing DEM data for landward zones, as has been done with existing
bathymetry data (http://www.ngdc.noaa.gov/mgg/coastal/crm.html). LiDAR data for
inland zones is critical for some applications such as updating flood map hazard zones,
characterizing fluvial hydraulic habitat, physical stream channel morphometry and
sediment movement, and riparian zone structure (Vierling et al., 2008).
The functionality of existing DEMs can be enhanced greatly by modifications to
create hydrologically conditioned DEMs. The USGS Elevation Derivatives for National
Applications program has produced tools for this purpose and conducted pilots to create
hydrologically corrected DEMs in much of West Virginia and the Lake Michigan Basin
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Section IV—Chapter 14: Research and Application Gaps and Needs
(http://edna.usgs.gov/; http://edna.usgs.gov/Edna/pubs/KostEDNA.pdf). These models
can provide the basis for accurate, automated, watershed boundary delineation at
scales even finer than headwater 12-digit hydrologic unit codes (HUCs) without the
need for burning in streamlines and coarser existing HUC boundaries as has been done
for the National Hydrography Dataset Plus (NHDPIus). The type of problems that can
be addressed by the hydrological conditioning tools include (1) blockage of flow by
apparent obstructions (e.g., bridges, highway overpasses), (2) areas of low relief, in
which neither the accuracy nor the resolution of the existing digital elevation data is
adequate to accurately delineate the streamlines that pass through, and (3) the need to
clear spurious elevation sinks.
14.5.5. Hydrology
Certified watershed maps by state are online to be completed (for the status of
the National Watershed Boundary Dataset (WBD) within each state, see
http://www. ncgc.nrcs.usda.gov/products/datasets/watershed/status-maps. htm I). These
boundaries are the limiting feature for ensuring accuracy of the NHDPIus catchment
boundaries, which are forced to coincide with WBD and streamline coverages. When
the WBD coverages are available, all indices, such as Landscape Development
Intensity Index, that are calculated by HUC, should be recalculated by watershed. The
use of the WBD would be enhanced by adding coding to distinguish headwater units
from mainstem HUCs at each scale (Verdin and Verdin, 1999), allowing a set of full
watersheds to be identified at each scale. Although NHDPIus provides coding to
identify headwater NHDPIus catchments, it provides information at only the sub-HUC-12
scale.
USGS is in the process of checking and correcting high-resolution National
Hydrography Dataset (NHD) coverages (1:24,000 or finer scale; for the status, see
http://viewer.nationalmap.gov/viewer/nhd.html?p=nhd), while individual states are
developing even finer-scale, local-resolution NHD coverages. High-resolution NHD
streamlines and polygons are needed to identify and protect temporary waters at risk
(e.g., headwater streams, temporary wetlands). NHD coverages at all scales can be
improved by ensuring consistency in hydrographic density across quadrangle
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Section IV—Chapter 14: Research and Application Gaps and Needs
boundaries and accurate assignment of permanent versus intermittent versus
ephemeral flow attributes. The latter are critical because of current jurisdictional issues
related to the definition of waters of the United States. Historical differences in aerial
photo interpretation have resulted in inconsistencies in flow line density for adjacent
quads. Other potential improvements include addition of calculated hydrologic metrics
for peak flows and low flows, as is being done via Stream Stats (http://water.usgs.gov/
osw/streamstats/) through state-USGS partnerships. Although the statewide analyses
provide a useful starting point, these applications would be more powerful if done on a
regional scale using a consistent set of parameters. Addition of flow-exceedance curve
parameters to NHD reaches would facilitate application of load-duration curves for
TMDL analyses. Overall, these types of information would facilitate integrated planning
for water resources (supply, hazard analyses, in-stream habitat quality) and water
quality issues. Addition of Rosgen stream type to NHD reach attribute tables also would
facilitate planning for protection of in-stream habitat. Value-added attributes for lakes
could include mean depth, shoreline development indices, and mixing ratios, among
others. Finally, although the NHD structure and terminology technically supports
identification of estuarine polygons, these have not been included in current coverages.
EPA supported the development of an estuarine inventory to plan and carry out
Environmental Monitoring and Assessment Program's National Coastal Assessment
(Coastal EMAP), but this coverage has not been incorporated into NHD. Estuarine
boundaries from the Coastal EMAP monitoring design, and their associated watersheds
will soon be available through EPA's Estuary Data Mapper application (Detenbeck
etal., 2009; http://badger.epa.gov/rsig/).
Finally, most attention at the nationwide scale has been focused on improving
coverages for upland hydrography, with less effort focused on hydrography and
bathymetry for coastal resources. To support coastal zone management, seamless
DEM/bathymetry coverages are needed (for coverages at 3 arc-second resolution, see
http://www.ngdc.noaa.gov/mgg/coastal/crm.html). Coastal HUCs need to be extended
to the nearshore zone (http://gis.esri.com/library/userconf/proc01/professional/papers/
pap492/p492.htm), and linkages between estuaries and associated watersheds made
more transparent. Ultimately, direct linkages between hydrography and associated
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Section IV—Chapter 14: Research and Application Gaps and Needs
monitoring stations, dams, water withdrawals, and interbasin transfers will facilitate the
integration of monitoring and modeling. The NHD dam initiative, to incorporate dams in
the national U.S. Army Corps of Engineers' coverage as events in NHD, is working east
to west with the eastern half of the country now complete. However, identification of
even smaller dams, e.g., with a normal storage capacity of less than 2,400 acre-feet, is
also needed.
14.5.6. Groundwater
Although a geospatial coverage of the principal aquifers of the United States is
available (http://water.usgs.gov/lookup/getspatial7aquifers_us), digital maps of water
level changes within aquifers are only available for portions of the country (e.g.,
http://water.usgs.gov/GIS/metadata/usgswrd/XML/ofr00-96_wlc80_97.xml).
14.5.7. Soils
While State Soil Geographic (STATSGO) data are available for the entire United
States, Soil Survey Geographic Database (SSURGO), data are not yet complete in all
regions. SSURGO data are particularly critical for informing local land-use decisions,
particularly determining appropriateness of building sites with respect to environmental
effects (erosion potential, septic tank performance, flooding potential) as well as
identifying areas critical for preservation.
14.5.8. Climate
Although the National Climatic Data Center has calculated numerous climate
indices that are critical for both water resource planning and evaluation of climate
change effects on aquatic systems (e.g., Drought Palmer Index, rainfall
intensity-duration-frequency statistics), most of these are not yet in digital form or are in
digital form for only restricted areas of the country (http://hdsc.nws.noaa.gov/hdsc/pfds
/pfds_gis.html). Climate normals have been digitally mapped for the conterminous
United States by the Parameter-elevation Regressions on Independent Slopes Model
(PRISM) project but need to be updated to account for climate change. Weather data
are available online and, for coastal regions, have been linked to digital coverages of
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Section IV—Chapter 14: Research and Application Gaps and Needs
station locations in National Oceanic and Atmospheric Administration's (NOAA's)
nowCOAST Web site (http://nowcoast.noaa.gov/). nowCOAST is being converted to a
Web Mapping Service (WMS) with long-term goal of converting to a Web File Service
(John Kelley, NOAA, personal communication). It would be very useful to extend the
geographic coverage of these features to the inland United States.
14.5.9. Stressor Specific
14.5.9.1. Pathogens
Animal waste sources are not available in geospatial maps except at a very
coarse, county scale (e.g., http://water.usgs.gov/GIS/metadata/usgswrd/XML/
manure.xml) which is unsuitable for watershed planning, TMDL development, and
source tracking.
14.5.9.2. Nutrients
EPA's Office of Water has developed some databases of nutrient concentrations
for a limited set of waterbody types (http://www.epa.gov/waterscience/criteria/
nutrient/database/). As the Water Quality Exchange framework for automated data
import into a national data warehouse is established and applications developed for
extracting data, these should be served in a spatially explicit framework to allow
calculation of regional baselines and trends by waterbody type
(http://exchangenetwork.net/exchanges/water/wqx.htm). Maps of background nutrient
potential based on soils and geology would be helpful in describing regional reference
condition. A consistent set of nutrient load estimates is also needed at finer than the
HUC-8 scale represented in SPARROW (http://water.usgs.gov/nawqa/sparrow/). A
consistent set of regionally calibrated yield coefficients by land use/landcover type and
retention coefficients for landscape sinks would facilitate both load modeling and
alternative scenario analysis. Geospatial maps of septic tank locations are also needed
for watershed management planning; although the percentage unsewered population
was inventoried in the 1990 Census and reported at the block-group scale, this attribute
was dropped from the 2000 Census.
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Section IV—Chapter 14: Research and Application Gaps and Needs
14.5.9.3. Suspended and Bedded Sediment
An inventory of suspended sediment data has been compiled by USGS,
illustrating many gaps in coverage (http://co.water.usgs.gov/sediment/). USDA had
supported calculations of regional sediment loadings with Soil and Water Assessment
Tool as input to the Office of Water's Indices of Watershed Integrity, but raw loading
estimates were not provided to the end user. Regionally calibrated yield coefficients,
rating curves and loading estimates (for both suspended and bedded sediments) are
needed.
14.5.9.4. Toxic Inventories
EPA's recent delivery of toxic inventory data through WMSs and Web file
services (WFS) has made these data much more readily available to managers and
researchers (http://www.epa.gov/geospatial/data.html). Tools to aggregate NPDES
loading data by watershed would make these data much more accessible and useful.
Geospatial maps of nonpoint sources of toxics (urban pesticide applications, natural
hot-spots for heavy metals) are also needed.
14.5.9.5. Hydrology
Methods for calculating numerous (natural and altered) flow statistics are
available, but for the most part, these data have not been provided in digital maps or
digital file services.
14.5.9.6. Temperature
Numerous thermal indices have been calculated for streams, and to a lesser
extent, for lakes, to predict effects on fish populations (Eaton and Scheller, 1996; Eaton
et al., 1995; Fang et al., 2004). Automated calculation of these indices for USGS
gauging stations with continuous temperature monitoring would facilitate evaluation of
regional baseline values, and land use or climate change effects. Given digital retrieval
of air temperature and some watershed characteristics, prediction equations for thermal
indices could also be applied at a regional or national scale to assess problem areas in
a more systematic fashion (Stefan and Preud'homme, 1993). In addition, mapping of
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Section IV—Chapter 14: Research and Application Gaps and Needs
critical ground water recharge and discharge areas and mapped longitudinal thermal
profiles for large rivers and coldwater habitats from thermal infrared imagery would
facilitate planning activities to maintain in-stream temperature profiles (e.g., Baker et al.,
2003).
14.5.9.7. Habitat Alterations
Some components of the Rosgen stream classification system can be calculated
from GIS coverages and could be provided to facilitate local mapping and inventory
efforts. In additions, regional curves of expected channel morphometry (mean channel
depth, width, cross-sectional area) as a function of watershed area (Sherwood and
Huitger, 2005) would facilitate identification of flow-related habitat degradation, and
facilitate calculation of streambank and riparian shading.
14.5.9.8. ionic Strength and Alkalinity (Background)
Existing maps of background levels of alkalinity (Omernik and Powers, 1983;
Omernik et al., 1988) for lakes, streams, and wetlands have been used for assessing
risks from acid rain and acid mine drainage including the sampling design of the EPA's
National Surface Water Survey. Higher resolution predictions of background ionic
strength and conductivity based on soils and geology might be helpful locally in
determining effects from and determining reference conditions for total dissolved solids.
14.5.10. Remote Sensing
Remote sensing needs include improved LiDAR coverage for coastal regions to
improve spatial resolution of the existing 3-arc-second coastal elevation data coverage,
expanded LiDAR coverage for inland regions to facilitate description of physical habitat
and floodplain mapping, improved algorithms for deriving water quality variables
(salinity, light attenuation, chlorophyll a, harmful algal bloom pigments) particularly for
shallow waters (lakes, estuaries), and Web services to facilitate extraction of subsets of
remotely sensed data for specific tools at low or reduced cost.
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Section IV—Chapter 14: Research and Application Gaps and Needs
14.5.11. Demographics/Socioeconomic Data
While urban population density data are available at a relatively fine scale
(block-level), because of the lower density in rural regions, rural population is provided
at a relatively coarse scale by the U.S. Census Bureau. Dasymetric techniques are
available to allow interpolation of rural population density at finer scales but have not
been widely applied. Even in urban regions, the patterns of growth (e.g., urban sprawl
indices or identification of LID areas) are not readily apparent from raw census and
housing data.
Socioeconomic data needs include gross domestic product values mapped at a
community or neighborhood level, and derived indices supporting transparent
assessment, visualization of environmental justice (EJ) issues. EPA program offices
and the Regions have developed EJ Action Plans to address the following issues:
reduction in asthma attacks and exposure to air toxics, fish and shellfish safe to eat and
safe drinking water, consistent enforcement and compliance, revitalization of
brownfields and contaminated sites, and a reduced incidence of elevated blood lead
levels. The EJView system is under development with a consistent set of environmental
justice indicators to identify census tracts at high risk for environmental justice issues on
the basis of demographic, health, compliance, and environmental data. Information is
provided to the public through the EJView (http://www.epa.gov/compliance/
environmentaljustice/mapping.html), which allows summary of block-level data by
population density, per capita income and percentage of population below the poverty
level as well as regulated facilities in the block and air exposures. Health data broken
down by geographic area and ethnic group are still pending from the Centers for
Disease Control and Prevention. Health risk assessments, epidemiological analyses,
and linkages between ecological and public health would be greatly facilitated by
geospatial reporting and mapping of hospital admissions by age, gender, and diagnostic
code.
14.5.12. Data Portals and Web Services
With the accelerated development of digital data portals, WMS, and WFS, there
is a need to coordinate among federal, regional, state, and tribal agencies to ensure
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Section IV—Chapter 14: Research and Application Gaps and Needs
delivery of data cost-effectively and nonredundantly. With distributed data, a user does
not need to personally maintain every data set; that function can be distributed to the
appropriate data stewards. Data access can also be facilitated by use of open-source
GIS or visualization packages linked to WMS or WFS sites, e.g., Google Earth or the
National Aeronautics and Space Administration's WorldWind frameworks
(http://worldwind.arc.nasa.gov/java/). EPA has recently started providing toxics
inventory data (starting with Superfund sites) in Extensible Markup Language XML
format (http://www.epa.gov/enviro/html/frs_demo/geospatial_data/resources.html). For
example, many state managers do not have access to ArcMap or training in its use.
14.5.13. Prioritizing Data Gaps and Needs
An informal ranking exercise of the data gaps and needs described above was
undertaken involving a group of twelve EPA program office, Regional, and Office of
Research and Development staff, academics, and private consultants active in
geographic analysis. Participants were asked to rank each need with a score of 1 -5
(1 as the lowest rank and 5 as the highest rank) and scores were summed for each gap
(see Table 14-2). Consideration was given to the scale of need (national rather than
local), feasibility of filling the gap, and programmatic relevance. The top five ranking
needs included improved and updated NLCD coverage, completing Level IV Omernik
ecoregions, accurately mapping stream flow permanence, completing and updating
digital NWI maps, and ready identification of true watersheds (complete drainage
basins) in existing data sets.
14.6. APPLICATION GAPS AND NEEDS
The state of the science for meeting specific applications can be mapped along a
research and development continuum (see Table 14-3a,b). Before work on any
application can proceed, a conceptual model is required to define the critical elements
of a problem and their interrelationships (U.S. EPA, 1998). The availability of a
geographic framework to explain regional or system-level differences in response often
determines the extent to which a method or results can be extrapolated. In some
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Section IV—Chapter 14: Research and Application Gaps and Needs
TABLE 14-2
Informal Ranking of Data Gaps and Needs by Landscape Predictive
Tools Workgroup
Rank Score
Data Gap/Need
48
Improved NLCD
20
Complete Level 4 ecoregions
15
Intermittent/perennial streams
12
NWI gaps
11
True watersheds
11
Gross Domestic Product, Population by polygon
10
PRISM support
7
WBD completion
7
Fertilizer application rates
7
Full LIDAR
5
Hydrologically corrected DEM's
5
Biocide application rates
5
Fly over temperature data
4
Flow/distance weighted LULC
4
USGS models using old rainfall data
3
Fish communities
3
Stormwater/wastewater outfalls
3
Stream stats completion
3
Completed SSURGO
2
Forest roads
1
Quantitative channel assessments
1
Good dam data
1
Historic LULC
1
BMP locations
LULC = land use/land cover.
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I
K>
CD
TABLE 14-3a
Application Gaps and Needs3
Application
Conceptual Model for
Cause-Effect
Relationships?
Example Model
Geographic Framework?
Data Limited?
a. Predicting loadings and
concentrations
Y
Jones et al., 2001; Smith
et al., 2003b
Applied for concentrations (e.g.,
nutrient ecoregions) but not
explicitly for loadings
Databases for validation
limited for some surface water
types and/or regions
b. Associating landscape
characteristics with
ecological condition
Y
Bryce et al., 1999;
Paul and McDonald,
2005
Ecoregions, etc.
c. Priority setting for management actions
i. Exposure risk
Y
Smith et al., 2003a
Stressor-specific
ii. Sensitivity for given
exposure
Y
(varies by system and
stressor)
Detenbeck et al., 2000;
Eilers et al., 1993
Ecological unit or
ecoregion * watershed class;
Hydrogeoclimatic zones;
Estuarine classification systems
Data need to be compiled by
class for testing models
iii. Ecosystem
valuation/ecosyste
m services
Y
(varies by
system, stressor)
Beier et al., 2008
Needed
Yes
d. Water quality standards
Y
(varies by system)
U.S. EPA, 2000a,b,
2001,2006
Robertson et al., 2006; Rohm
et al., 2002;
Detenbeck et al., 2003, 2004
Databases for validation
limited for some surface water
types and/or regions
(wetlands, estuaries)
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TABLE 14-3a cont.
Application
Conceptual Model for
Cause-Effect
Relationships?
Example Model
Geographic Framework?
Data Limited?
e. Restoration potential
Y
(varies by system
type)
Monitoring data for validation
limited
i. Historic
reference/potential
distribution
Y
(varies by system
type)
Bedford, 1996,
Galatowitsch and van
derValk, 1994;
Detenbeck et al., 1999;
Rosgen, 1996
Wetland landscape profiles
(Bedford, 1996; Detenbeck et al.,
1999); Rosgen, 1996;
Hydrogeomorphic classification
(Brinson, 1993)
Detailed soil survey data
(SSURGO) limited in some
regions for assessing historic
wetland extent; analyses
limited to coarse
(county-level) scale with
STATSGO data
ii. Exposure to
stressors
Y
Smith et al., 2003a
Depends on media/multimedia
nature; limits for
groundwatershed, airshed
mapping, multimedia
Stressor-specific
iii. System capacity to
recover
Y
(varies by system
type)
Niemi et al., 1990;
Detenbeck et al., 1992;
Norton et al., 2009
iv. Socioeconomic
constraints
See Moss, 1985; DINAS COAST
(http://sustainabilityscience.Org/c
ontent.html?contentid=676)
f. Recovery potential
i. Historic reference
Y
Rosgen, 1996;
Bedford, 1996
Ecoregions (Omernik, 1987);
GAP models (Higgins et al.,
2003; Sowa et al., 2006)
Coverage of historic aerial
photos and maps is patchy
ii. Exposure to
stressors
Y
Smith et al., 2003a
Depends on media/multimedia
nature; limits for
groundwatershed, airshed
mapping, multimedia
Stressor-specific
CO
CD
O
O
=3
o
=7
0)
T3
CD
7J
CD
V)
CD
0)
—s
O
=7
0)
=3
Q_
>
T3
¦o
O
CD
o
=3
CD
0)
T3
V)
0)
=3
Q_
CD
CD
Q.
V)
-------
TABLE 14-3a cont.
Application
Conceptual Model for
Cause-Effect
Relationships?
Example Model
Geographic Framework?
Data Limited?
iii. System capacity to
recover
Y
(varies by system
type)
Niemi et al., 1990;
Detenbeck et al., 1992;
Norton (personal com.)
No known examples of
applications
Meta-analysis possible with
historic data (e.g., Niemi et al.,
1990) but no systematic data
collection; few mappable
indicators not identified
iv. Socioeconomic
constraints
See Moss, 1985; DINAS COAST
(http://sustainabilityscience.Org/c
ontent.html?contentid=676)
g. BMP selection and
siting
Y
(varies by system
type)
Riparian Eco-system
Management Model
(Lowrance et al., 2000)
No known examples of
applications
Monitoring effectiveness data
for validation limited for most
BMPs and regions
CO
CD
O
o
=3
o
=7
0)
T3
CD
7J
CD
V)
CD
0)
—s
O
=7
0)
=3
Q_
>
T3
¦o
O
o
=3
CD
0)
T3
V)
0)
=3
Q_
aSee ancillary information in Table 14-3b.
K>
oo SSURGO = Soil Survey Geographic database; com. = communication; Y = yes.
CD
CD
Q_
v>
-------
I
K>
CD
TABLE 14-3b
Application Gaps and Needs3
Application
Method Limited?
Limited Geographic
Application?
Needs User Interface?
Parameter Limited or Other
Limitation
High-Endb
Intermediate0
Visualization for
Community Groups
a. Predicting loadings and
concentrations
Parameter Limitation:
regional loading coefficients
Other Limitation: selectively
applied
Widely available
Public availability
for 8-digit HUCs,
at finer scale for
only selected
regions
Some Web tools
available
b. Associating landscape
characteristics with
ecological condition
Methods available but not
applied to all system
types x stressor type in all
regions
Statistical
methods
available
Not readily
available
Not broadly available
c. Priority setting for management actions
i. Exposure risk
Selective application by
region
Methods
available
Available for some
regions
Available for some
regions
ii. Sensitivity forgiven
exposure
Selective application by
region
Needs
distribution and
user interface
Needs distribution
and user interface
Needs distribution and
user interface
iii. Ecosystem
valuation/ecosystem
services
Y
(more mappable
functional indicators
needed)
Site-specific
methods
available for
specialists
Needed
Needed
CO
CD
O
o
=3
o
=7
0)
T3
CD
7J
CD
V)
CD
0)
—s
O
=7
0)
=3
Q_
>
T3
¦o
O
CD
o
=3
CD
0)
T3
V)
0)
=3
Q.
CD
CD
Q_
V)
-------
I
CO
o
TABLE 14-3b cont.
Application
Method Limited?
Limited Geographic
Application?
Needs User Interface?
Parameter Limited or Other
Limitation
High-Endb
Intermediate0
Visualization for
Community Groups
d. Water quality
standards
Testing in progress
Methods
available but
could be
facilitated by
broader
availability of
GIS coverages
and data
Methods available
but could be
facilitated by
broader availability
of GIS coverages
and data
Needed
e. Restoration potential
i. Historic
reference/potential
distribution
Methods
available with
some
data/scale
limitations
Needed
Needed
ii. Exposure to
stressors
Methods
available
Needed for some
regions
Needed for some
regions
iii. System capacity to
recover
Needed
Needed
Needed
iv. Socioeconomic
constraints
Needed
Needed
Needed
f. Recovery potential
i. Historic reference
Methods
available but
data limited
Needed
Needed
ii. Exposure to
stressors
Methods
available
Needed for some
regions
Needed for some
regions
CO
CD
O
O
=3
o
=7
0)
T3
CD
7J
CD
V)
CD
0)
—s
O
=7
0)
=3
Q_
>
T3
¦o
O
CD
o
=3
CD
0)
T3
V)
0)
=3
Q.
CD
CD
Q_
V)
-------
TABLE 14-3b cont.
Application
Method Limited?
Limited Geographic
Application?
Needs User Interface?
Parameter Limited or Other
Limitation
High-Endb
Intermediate0
Visualization for
Community Groups
iii. System capacity to
recover
Needed
Needed
Needed
iv. Socioeconomic
constraints
Needed
Needed
Needed
g. BMP selection and siting
Some tools
available but
most with
limited
applications
Needed
Needed
CO
CD
O
o
=3
f
o
=7
0)
T3
CD
7J
CD
V)
CD
0)
—s
O
=7
0)
=3
Q_
>
T3
¦o
O
o'
=3
CD
0)
T3
V)
0)
=3
Q_
aSee ancillary information in Table 14-3a.
be.g., consultants.
ce.g., supporting rapid decision making by agency staff.
CD
CD
Q_
v>
-------
Section IV—Chapter 14: Research and Application Gaps and Needs
cases, the scientific framework might be well developed, but data availability limits our
ability to develop and test models or methods. Alternatively, the conceptual framework
and supporting data might be available, but methods have not been developed to
analyze or assess the data. Ultimately, an application could be developed but will be
geographically limited in scope, or the user interface could limit the suite of potential
users because of training or specialized software needs.
14.6.1. Predicting Loadings and Concentrations of Pollutants
Of the applications described in Table 14-3a,b, the methods and models to
predict loadings and concentrations of common pollutants are the most completely
developed, particularly for higher-end users. Simple regression models for predicting
background concentrations and yields of nutrients in streams and rivers are also
available for reference basins across the conterminous United States (Smith et al.,
2003b). Outputs from spatially referenced regression models to predict total nutrients
are available at the 8-digit HUC scale nationwide (Schwarz et al., 2006) and at the
reach-scale for specific regions (Moore et al., 2004). User-friendly applications for
watershed groups to predict nitrogen and phosphorus loads using spreadsheet or
Web-based tools are available (Lim et al., 1999). However the availability of regionally
specific loading coefficients, which could improve the performance of generic tools to
estimate effects of land-use change (Lim et al., 2006), is limited (Wickham and Wade,
2002). For modeling of loadings within small watersheds for TMDL development,
availability of GIS coverages mapping fine-scale land-use and in situ wastewater
treatment applications is a limiting factor (Howes et al., 2001). Methods are best
developed for common nonpoint source pollutants such as nutrients and dissolved
oxygen, less well developed for some conventional toxics (e.g., heavy metals [Caruso
and Ward, 1998; Xiao and Ji, 2006], mercury [Shanley et al., 2005], pesticide transport
potential [Lerch and Blanchard, 2003]), and not developed at all for emerging
compounds of concern such as pharmaceuticals. For some common pollutants such as
pathogens, better modeling of fate is needed, while for others such as suspended and
bedded sediment, better modeling of in-stream sources, transport, and fate are needed,
along with the incorporation of BMP effectiveness into watershed models (Nietch et al.,
14-32
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Section IV—Chapter 14: Research and Application Gaps and Needs
2005). Exposure metrics used in development of stressor-response models for bedded
sediments (e.g., percentage embeddedness) are not commonly incorporated into
sediment transport models (but for an example of regional landscape-scale model
development, see Sable and Wohl, 2006). For cases in which only predictions of
relative risk are needed, indicators of exposure risk are well developed for some
stressors for specific regions and readily available through Web-based interfaces or
maps (Smith et al., 2003a).
Methods are also available to predict peak flows, baseflows, annual runoff
(Bhaduri et al., 2000), a limited suite of hydrologic indices, and the spatial distribution of
ground water discharge sites (Baker et al., 2003) on the basis of landscape-scale
attributes. The USGS has developed regression relationships to predict peak flows for
a range of recurrence intervals for both rural and urbanized watersheds on a
state-by-state basis (Jennings et al., 1994). Although these regression relationships are
publicly available, user-friendly interfaces have been developed for only a subset of
states (http://water.usgs.gov/osw/streamstats/index.html), and the scale at which
relationships have been developed could limit applications to watersheds that cross
state boundaries because of inconsistencies in models across state borders.
Regression relationships to predict baseflow are available for only selected states, while
regressions to predict indices along a flow-duration curve have been systematically
developed only for Michigan (Holtschlag and Croskey, 1984). Extension of predictions
for baseflow to additional states would help in establishing flow criteria in areas with
significant water resource issues (and associated water quality problems). Extension of
predictions of flow-duration statistics for other regions would facilitate the application of
the load-duration curves as a diagnostic tool for the TMDL program
(http://www.kdheks.gOv/tmdl/basic.htm#data).
14.6.2. Associating Landscape Characteristics with Ecological Condition
Standardized methods have been developed to identify potential reference sites
for biological monitoring programs (Bryce et al., 1999). These map-based indicators are
available for higher-end users familiar with GIS but have not yet been packaged into a
user-friendly tool for routine application. Likewise, several map- and statistically-based
14-33
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Section IV—Chapter 14: Research and Application Gaps and Needs
methods have been developed to predict sites that are biologically impaired (Brooks
et al., 2004; Potter et al., 2004; Volstad et al., 2003; Kapo and Burton, 2006; Mack,
2006). Some of the methods (e.g., regression [Potter et al., 2004], landscape
development indices [Mack, 2006] or synoptic approaches [Brooks et al., 2004]) are
readily transferable to a broad user community, but more specialized techniques
(logistic regression [Volstad et al., 2003; Kapo and Burton, 2006], conditional probability
analysis [Paul and McDonald, 2005], geostatistical modeling [Peterson and Urouhart,
2006]) are not necessarily available to local, tribal, state, and regional staff for routine
application. Once the methods have been applied, however, results could be displayed
in map form in ArclMS interfaces. Tools or interfaces to facilitate application of
nonstandard statistical methods would help to facilitate more routine application of these
methods.
14.6.3. Water Quality Standards
The percentile approach has been suggested as one method for deriving
regional nutrient criteria for aquatic systems (U.S. EPA, 1998). Limited testing of
alternative regional or nested classification frameworks (Detenbeck, 2001) has been
performed to find the best combination to describe background variation in nutrient
concentrations (e.g., Robertson et al., 2006) and more is needed. For some
combinations of system type and region, historic data on nutrient concentrations are not
readily available to support these tests, and additional monitoring or compilation of
existing data into publicly accessible databases is needed (http://water.epa.gov/scitech/
swguidance/standards/upload/2008_11_24_criteria_nutrient_guidance_wetlands_wetla
nds-full.pdf). Even less research has been completed to validate classification
frameworks to explain differences in sensitivity of different systems to a given nutrient
concentration or loading (Detenbeck et al., 2007) although some work is in progress
(U.S. EPA, 2004; Engle et al., 2007). Public availability of proposed classification
schemes in digital maps would help to facilitate additional testing and comparison of
different frameworks.
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Section IV—Chapter 14: Research and Application Gaps and Needs
14.6.4. Best Management Practice (BMP) Selection and Siting
The application of landscape tools and frameworks to inform the selection and
siting of BMPs can be broken down into five types of questions: (1) What ecosystem
processes must be restored to maintain good quality habitat and historic hydrologic
regimes within a watershed?, (2) How does the effectiveness of different types of BMPs
vary regionally or by watershed class?, (3) What is the potential for BMP effectiveness
across watersheds within a given region?, (4) What is the predicted effectiveness or
sustainability of specific BMPs in different landscape positions within a watershed?, and
(5) What is the optimal distribution of protected lands, pollutants sinks, and pollutant
sources within a given watershed? Little attention has been paid to the issue of regional
variation in BMP effectiveness based on differences in climate, soils, slope, or ground
water flow paths (Vidon and Hill, 2004). Test data for stormwater BMPs and
constructed wetlands (Knight et al., 1993) have been compiled, the former into a
publicly available database (Clary et al. 2002); these data could be further analyzed to
evaluate regional variation in BMP performance.
Conceptual frameworks are available to inform questions 1 (Bohn and Kershner,
2002; Fischenich, 2006), 3 (Vellidis et al., 2003), 4 (Qui, 2003; Van Lonkhuyzen et al.,
2004; Newbold, 2005; Hipp et al., 2006; Zhen et al., 2006), and 5 (Agnew et al., 2006).
One DSS for phosphorus management allows simultaneous evaluation of regional and
temporally variable climate effects, critical source areas, and BMP effectiveness (Djodjic
et al., 2002). While some simulation model approaches are available to evaluate BMP
effectiveness, these tend to be focused on management of single endpoints (Djodjic
et al., 2002) or BMP types (Walker, 1987; Qui, 2003; Hipp et al., 2006). Optimization
methods are needed to facilitate decisions at multiple scales to protect multiple
endpoints, such as competing human and ecological needs for water resources
(Yates et al., 2005).
14.6.5. Priority Setting
As discussed above, models are available to evaluate exposure risk
quantitatively, but these vary in performance for different stressors. Methods are
available to evaluate relative exposure risk for some regions. Conceptual models and
14-35
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Section IV—Chapter 14: Research and Application Gaps and Needs
classification frameworks are available to evaluate system sensitivity for a given
exposure level of some stressors (Detenbeck et al., 2000, 2005, 2007), but these have
been tested only in specific regions. In general, landscape classification frameworks or
tools are not available to evaluate effects on ecosystem services, although general
methods are available to calculate both direct use values and nonuse values (NRC,
2004; http://www.cwp.org/your-watershed-101/wetlands-and-watersheds/). For direct
use valuation, it might be possible to use existing functionally based classification
systems (e.g., Brinson, 1993) to derive inventories of functional units for priority setting,
but research is needed to develop and test mappable indicators of ecosystem function
(Schweiger et al., 2002). The Natural Resources Conservation Service (NRCS) will be
producing regional predictive functional condition indicator models to identify site and
landscape factors (i.e., variables) that influence wetland ecosystem service estimates in
major agricultural regions of the country and the effect of conservation practices on
those services (http://www.nrcs.usda.gov/Technical/nri/ceap/wetlands.html). For
nonuse valuation methods, classification frameworks are needed to identify
homogeneous regions to facilitate application of benefit transfer functions (Djodjic et al.,
2002) and tools are needed to identify user regions for given ecosystem units.
14.6.6. Evaluation of Restoration Potential or Recovery Potential
Evaluation of restoration potential or recovery potential requires information on
historic reference condition, exposure to stressors, system capacity to recover, and
socioeconomic constraints (Norton et al., 2009). Restoration efforts can be informed by
tools to predict the historic distribution of wetland community types (Galatowitsch and
van der Valk, 1994; Bedford, 1996; Detenbeck et al., 1999) or supporting hydrology
(Sorensen et al., 2006; NRCS, 1995 [http://www.wcc.nrcs.usda.gov/
climate/wets_doc.html]). Use of historic soils data for local planning can be limited by
the resolution of available soil maps (Soil Survey Geographic database [SSURGO]
versus STATSGO coverages). Use attainability analysis can be informed by Rosgen
channel assessments (Rosgen, 1996) with respect to habitat condition and by
development of River Invertebrate Prediction and Classification System-type observed
to expected scores (Wright et al., 2000; Hawkins, 2006), or aquatic GAP models
14-36
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Section IV—Chapter 14: Research and Application Gaps and Needs
(Higgins et al., 2003; Sowa et al., 2006; Steen et al., 2006) for potential species or
community type presence/absence.
Conceptual frameworks are available to support an analysis of system capacity
to recover (Niemi et al., 1990; Detenbecket al., 1992; Norton etal.,2009;
http://hudson.tetratech-ffx.com/RECOVERY_POTENTIAL/home.html). A conceptual
framework is needed to evaluate socioeconomic constraints to system recovery; there
could be an opportunity to synthesize information gained from research supported by
the Watersheds component of the EPA Science to Achieve Results grants program on
socioeconomic factors affecting success of watershed management efforts (Walbeck
et al., 2005).
14.7. NEEDS BY USER GROUP
14.7.1. Summary of Available Tools
A total of 196 Web sites with landscape data or tools were identified and
compiled into a Microsoft Access database. Approximately half of these sites provide
access to analytical tools or models, while another quarter consist of data portals or
model gateways. A quarter provides point, grid, or vector data. Slightly less than 10%
of sites provide access to classification frameworks with associated data or tools and
less than 10% provide access to DSSs (see Table 14-4). More tools are available for
analysis of lotic waters (streams and rivers, approximately 140), as compared to lakes,
estuaries, or wetlands (approximately 100).
14.7.2. Access to Tools and Data
Access to publicly available tools and data can be constrained by cost, training
requirements (both science background and technical expertise) and ancillary software
requirements. In compiling the list of accessible tools and data, we focused on
resources that were free or low cost; thus, the majority of objects in the database
(approximately 180) are available at no cost. Approximately half of the data or tool sites
provide applications requiring moderate levels of expertise (some scientific background,
some computer/GIS/programming skills), while approximately one-quarter were suitable
for stakeholders with more limited expertise (general understanding of issues and basic
14-37
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Section IV—Chapter 14: Research and Application Gaps and Needs
TABLE 14-4
Frequency of Resources in Microsoft® Access Database by
Data or Tool Type
Frequency
Category
56
Analytical tool
42
Model
35
Gateway/Portal
25
Model gateway
23
Grid data
21
Point data
19
Raw tool
18
Classification framework data/tool
18
Vector data
13
Decision support system
7
Statistical tool
5
Visualization (mapping) tool
2
Geodatabase model
1
Review tool
computer skills). Between 60 and 70% of applications requiring basic or moderate
levels of expertise were stand-alone, requiring no additional software, while another
10 applications required ArcView 3.x, with or without Spatial Analyst.
14.7.3. Tool/Data Requirements for Different Programmatic Applications
Approximately three-quarters of the applications were judged suitable for
supporting watershed planning, with about one-half providing support for
monitoring/assessment or TMDL development (see Table 14-5). Tools supporting
emergency response programs are the least well represented in our database.
Cost and expertise constraints are probably most critical for applications to
support watershed planning and community outreach, with stakeholders having a wider
14-38
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Section IV—Chapter 14: Research and Application Gaps and Needs
TABLE 14-5
Availability of Data or Tool Resources in Microsoft® Access
Database by Programmatic Application
Resources
Programmatic Application
149
Watershed planning
105
Monitoring/Assessment
90
TMDL
58
Community outreach
56
Permitting
50
Standards
43
Water resources management
31
Modeling
15
Integrated reporting (303d/305b)
8
Emergency response
range of expertise and available resources. One-fifth of resources for watershed
planning (29 of 149) are stand-alone, requiring basic scientific and computer skills, with
another one-third stand-alone resources requiring intermediate science and computer
skills (42). For community outreach, applications are more scarce, with only
11 stand-alone resources available for stakeholders with basic science and computer
skills, and 12 stand-alone resources available for stakeholders with intermediate skills.
Two critical gaps in the range of decision-support tools reviewed, particularly for
watershed or coastal management organizations with either limited or mixed scientific
training are the following
• The lack of interpretive tools that aid in conceptualization, visualization, problem
formulation, and identification of alternative hypothesis tests.
• The lack of an integrated framework for applying tools for management decisions
at multiple scales (local to regional) and involving simultaneous optimization of
multiple endpoints. The Australian Catchment Modeling Toolbox is one notable
exception.
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Section IV—Chapter 14: Research and Application Gaps and Needs
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Water and Watershed Grants. Prepared by Perot Systems Government Services
(formerly Technology Planning and Management Corporation), Pembroke, MA, for the
U.S. Environmental Protection Agency, Office of Research and Development, National
Center for Environmental Research, Science to Achieve Results Program.
Walker, W.W. 1987. Phosphorus Removal by Urban Runoff Detention Basin. In: Lake
and Reservoir Management Volume 3, North American Lake Management Society,
pp. 314-238.
Wickham, J.D. and T.G. Wade. 2002. Watershed level risk assessment of nitrogen and
phosphorous export. Comput. Electron. Agric. 37:15-24.
Williams, W.A., M.E. Jensen, J.C. Winne, R.L. Redmond. 2000. An automated
technique for delineating and characterizing valley-bottom settings. Env. Monitor.
Assess. 64:105-114.
Winter, T.C. 1977. Classification of the hydrogeologic settings of lakes in the
north-central United States. Water Res. 13(4):753-767.
Winter, T.C. 2000. The vulnerability of wetlands to climate change: A hydrologic
landscape perspective. J. Am. Water Resour. Assoc. 36(2):305-311.
Winter, T.C. 2001. The concept of hydrologic landscapes. J. Am. Water Resour.
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Wolock, D.M., T.C. Winter, and G. McMahon. 2004. Delineation and evaluation of
hydrologic-landscape regions of the United States using geographic information system
tools and multivariate statistical analyses. Environ. Manage. 34(Suppl 1):S71-S88.
Wright, J.F., R.J.M. Gunn, J.H. Blackburn, N.J. Grieve, J.M Winder, J. Davy-Bowker.
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Xiao, H. and W. Ji. 2006. Relating landscape characteristics to non-point source
pollution in mine waste-located watersheds using geospatial techniques. J. Environ.
Manage. 82(1 ):111 -119.
Yadav, M., T. Wagener, and H. Gupta. 2007. Regionalization of constraints on
expected watershed response behavior for improved predictions in ungauged basins.
Adv. Water Resour. 30:1756-1774.
Yates, D., J. Sieber, D. Purkey, and A. Huber-Lee. 2005. WEAP21 - A demand-,
priority-, and preference-driven water planning model. Part I: Model characteristics.
Water 30(4):487-500.
Zhen, J., L. Shoemaker, J. Riverson, K. Alvi, and M.S. Cheng. 2006. BMP analysis
system for watershed-based stormwater management. J. Environ. Sci. Health A. Tox.
Hazard Subst. Environ. Eng. 41 (7): 1391-1403.
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SECTION V: TOOLBOX: SPATIAL DATA AND TOOLS DATABASE
Greg Hellyer, US EPA New England Regional Lab, North Chelmsford, MA
Peter Leinenbach, US EPA Region 10, Portland, OR
Jeff Hollister, US EPA Office of Research and Development, Cincinnati, OH
Naomi Detenbeck, US EPA Office of Research and Development, Narraganset, Ri
Susan Cormier, US EPA Office of Research and Development, Cincinnati, OH
Jan Ciborowski, University of Windsor, Windsor, Ontario
Don Ebert, US EPA Office of Research and Development, Las Vegas, NV and
Ann Lincoln, Tetra Tech Inc., Owings Mills, MD
SUMMARY
Recommended for Beginners to Advanced. This extensive compilation
includes over 200 resources for spatial data and analysis tools useful to enhance
protection and restoration or rehabilitation of the nation's waters. About half provide
access to analytical tools; roughly a quarter provide point, grid, or vector data; while less
than 10% point to geographic frameworks or to decision support systems. Information
collected for each resource includes (when available) name, Web site, keywords,
description, uses, ecosystems and stressors, related tools, examples, additional
information, minimum software requirement, required expertise, technical support,
developer, and contact information.
The Geospatial Toolbox can be accessed from the Risk Assessment Forum or
Watershed Central websites. The entire database is searchable.
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Section VI—Glossary and Suggested Reading
SECTION VI: GLOSSARY AND SUGGESTED READING
GLOSSARY
Aerial Photo Interpretation—The practice and methods of identifying and classifying
ground features by examining photographs of the earth's surface taken from airplanes,
balloons or other at altitude platforms, including space/satellite-based imagery (see also
orthorectification).
Alteration—Characteristic of causation: the entity is changed by the interactions with
the cause.
Ambient Monitoring—Sampling and evaluation of receiving waters. Equivalent to in
situ data collection and analysis.
Anderson Level Classes—Land cover/use classification developed by Anderson et al.
(1976) to permit more uniform classification of land cover for remotely sensed imagery.
Assemblage—An association of organisms that belong to the same major taxonomic
group. Examples of assemblages used for biological assessments include algae,
amphibians, birds, fish, macroinvertebrates and vascular plants.
Analysis—Investigation and examination of data to determine its essential features and
meaning.
Associative Analysis examines the connections or relationships between sets
of data that may have plausible affiliations or potential mechanisms of action
between them. Linear regression is one example of statistical association.
Descriptive Analysis characterizes, depicts, classifies, or summarizes data to
clearly picture or illuminate the details and meaning of the full range or a portion
of a data set or sets. Box plots and percentiles are examples of descriptive
statistics.
Assessment—(1) A process for generating and presenting scientific information to
inform environmental management decisions. (2) The product of an environmental
assessment process.
Condition Assessments detect impairment or degree of impairment with
respect to an appropriate reference condition.
Causal Pathway Assessments identify causes and sources, and can aid
identification of potential mechanisms of action.
Predictive Assessments estimate the environmental, economic and social
benefits and risks associated with different management actions. Through
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associations or process models predictive assessments can also extend
estimated condition assessments spatially and temporally.
Outcome Assessments evaluate the performance and effectiveness of
management actions in achieving environmental results.
Attribute—A measurable or observable part or process of biological, ecological, or
physical systems.
Basin—A large watershed (see watershed).
Best Management Practice (BMP)—An engineered structure or management activity,
or combination of these that eliminates or reduces an adverse environmental effect of a
pollutant.
Biological Assessment or Bioassessment—An evaluation of the biological condition
of a waterbody using surveys of the structure and function of a community of resident
biota.
Biological Condition Gradient (BCG)—Model describing ambient biological response
to increasing levels of stressors. The BCG is analogous to a single species/single
stress dose-response curve, but is rather the in situ response of the biota (one or more
communities) to the cumulative influence of multiple stresses. Biological condition is the
Y axis of the BCG.
Census Sampling Design—Monitoring of all members of a population of interest, such
as all stream segments within a defined geographic area.
Classification—The grouping of entities based on similarity in common attributes.
Causal Characteristics—Characteristics of causal relationships including time order,
co-occurrence, preceding causation, sufficiency, interaction and alteration.
Causal Relationship—The connection between a cause and an effect.
Coherence—Characteristic of the body of evidence indicating internal consistency,
consistency with scientific knowledge and theory, and logical explanation of the facts in
the case.
Community—An association of interacting assemblages in a waterbody—the biotic
component of an ecosystem.
Conceptual Model—A graphic depiction of the causal pathways linking sources and
effects that ultimately is used to communicate why some pathways are unlikely and
others are very likely.
Condition/Status—The current state of a resource compared to reference standards
for physical, chemical, and biological characteristics.
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Co-occurrence—Characteristic of causation: the cause happens or is present in the
same space and time with the affected entity.
Credibility—Characteristic of a body of evidence indicating relevant and high quality
information.
Criteria—Limits on a pollutant or condition of a waterbody presumed to support or
protect the designated use or uses of a waterbody. Criteria may be narrative or
numeric; chemical, physical and biological.
Data Quality Objectives (DQOs)—Qualitative/quantitative statements that clarify
objectives, define appropriate data, and specify tolerable levels of decision error for
monitoring programs. They are used to determine the quality and quantity of data
appropriate to specific assessments and decisions.
Disturbance—Any temporary change in the environment that causes a long-term
change in ecosystem, community, or population structure. A disturbance is a human or
other exogenous influence on an ecosystem, as opposed to any natural disturbance
regime within the ecosystem.
Diversity—Characteristic of the body of evidence indicating that many sources of
evidence and characteristics of causation are represented in the body of evidence.
Ecological Integrity—The condition of an unimpaired ecosystem as measured by
combined chemical, physical, and biological attributes.
Ecosystem Services—The benefits people obtain from ecosystems including
provisioning services such as food and water; regulating services such as regulating
floods, drought, land degradation, and disease; supporting services such as soil
formation and nutrient cycling; and cultural services such as recreational, spiritual,
religious, and other nonmaterial benefits.
Ecoregion—A relatively homogeneous ecological area defined by similarity of climate,
landform, soil, potential natural vegetation, hydrology, or other ecologically relevant
characteristics.
Effectiveness—The ability of a management action to achieve a management
objective (e.g., increase ecological integrity to meet state water quality standards).
Extrapolate—To infer values of a variable in an unobserved range from values within
an already observed range.
Functions—The physical, chemical and biological processes that characterize
ecosystems.
Generalized Stressor Gradient (GSG)—Representation of the cumulative influence of
multiple stressors as the X axis of the Biological Condition Gradient. Components of
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Section VI—Glossary and Suggested Reading
the GSG can include flow, habitat, water quality, toxics, energy and biotic interaction
factors.
Geographic Frameworks—Organizing structure for a database or analytic process
having spatially linked attributes such as latitude and longitude, relative position as
along a stream, or designated class such as a county, watershed, or size waterbody.
Maps or geographic spatial models sharing common characteristics useful for
understanding patterns in the structure, function and responses of ecosystems, and that
facilitate monitoring, assessment, and decision making for aquatic and terrestrial
ecosystem management. Common useful frameworks include ecoregions and
watersheds for scientific understanding, and political boundaries for understanding
social and economic influences.
Gradient—The full range of stress(es), pressure(s) or disturbance(s) potentially
affecting aquatic ecosystem response(s).
Gradient Assessments—Assessments that are focused on determining the strength
and direction of responses to the full range of magnitude of specific stressors.
Hydric—Requiring an abundance of moisture.
Hydrologic Unit (HU)—A terrestrial polygon having a hydrologic basis and comparable
size at each subdivision. HUs are not synonymous with watersheds. Roughly half of
HUs are true watersheds at any scale.
Hydrologic Unit Code (HUC)—A numeric designations for a Hydrologic Unit at
different levels (2 digit to 12 digit).
Impairment—A detrimental effect on the biological integrity of a waterbody that
prevents attainment of the designated use.
Impervious Area—Areas preventing infiltration of water into the underlying soil such as
roads, parking lots, and structures. Increases in impervious area can cause extensive
hydrologic alteration of watersheds.
Indicator—An environmental entity and its attribute whose presence or magnitude is
indicative of specific environmental conditions.
Integrity—The ability of an ecosystem to support and maintain a balanced, adaptive
community of organisms having a species composition, diversity and functional
organization comparable to that of natural habitats within a region.
Interaction—Characteristic of causation: the cause effectively influences the entity in a
way that induces the effect.
Karst—An irregular limestone region with sinks, underground streams and caverns.
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Landscape Data—A subset of spatial data that represents a continuous or categorical
surface describing some aspect of the land surface such as elevation, soil type, land
use/land cover, or other characteristics.
Landscape Indicators—Estimates of gradients of landscape activities, condition,
disturbance, and stressors that help us assess biological assemblage potentials and
condition.
Landscape and Predictive Tool—Analytical capabilities for combining in situ field
measurements and geographic attributes that contribute information toward or directly
predict environmental conditions, causes, effects, or outcomes.
Linear Response—A statistical relationship where one factor changes with another
factor in a way that can be characterized with a straight line equation.
Matched (or paired) Data—Values for at least two parameters potentially capable of
characterizing a causal relationship that are matched in a data set, because they are
spatially and temporally associated. For example, storm water power and bank erosion,
or temperature and presence of salmonids.
Mesic—Requiring a moderate amount of moisture.
Mitigate—To make less severe or harmful.
Monitoring—Sampling, measurement, or observation of characteristics of aquatic or
terrestrial ecosystems.
Multiple Linear Regression—Statistical model of the relationship between two or more
explanatory variables and a response variable by fitting a linear equation to observed
data.
Multivariate Analysis—Statistical methods (e.g., ordination or discriminant analysis)
for analyzing physical and biological community data using multiple variables.
Orthorectification—The process of accurately assigning imagery to ground
coordinates and geometrically correcting it to remove distortions that occur during
image capture. Orthorectification enables viewing, queries, and analysis with other
geographic data.
Performance—The ability of a management practice to achieve its design
specifications (e.g., reduced nutrient delivery to a waterbody by a specified amount).
Performance Characteristics—Quantitative and qualitative descriptors of data quality,
such as precision, accuracy, bias, representativeness, or completeness. Can also
include terms such as selectivity, interferences, or others; other terms can be unique to
particular methods or indicators.
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Planning—Development of detailed methods and programs of action for achieving an
end, such as a plan for performing an assessment or a watershed plan to restore water
quality to a specified level in a particular area.
Preceding Causation—Characteristic of causation: causes and their effects are the
result of a web of causation.
Predict—To project future conditions or events on the basis of observation, experience,
or scientific reason.
Predictive Tool—A scientific method, model, procedures and data enabling spatial or
temporal extrapolation to infer conditions, trends, or other important characteristics.
Pressure—The source of a physical, chemical, or biological stress.
Prevent—To anticipate, forestall and protect from deleterious consequences.
Probabilistic Design—A study or sampling characteristic that has randomization as a
key component.
Prioritize—To list or rank in order of precedence or importance.
Probability (random ) Sampling—Drawing a sample unit from a population such that
every unit has an equal probability of selection.
Problem Identification—To spatially locate impaired or potentially impaired waters
through sampling, or through use of predictive tools to extrapolate stress or condition
estimates to unmonitored areas.
Quantile Regression—A statistical technique used to estimate an exposure-response
relationship when more than one agent could be affecting the receptor, but the other
agents are unknown or unmeasured.
Reference Condition—The biological, physical, chemical condition used for a
comparison that may refer to historic, natural or pristine condition (before human
disturbance), to best attainable condition, to realistically attainable condition, to least
disturbed condition, to minimally impacted condition, to disturbed condition.
Reference Condition (Commonly encountered usage)—The condition that
approximates natural, unimpacted conditions (biological, chemical, physical, and such)
for a waterbody. Reference condition (Biological Integrity) is best determined by
collecting measurements at a number of sites in a similar waterbody class or region
under undisturbed or minimally disturbed conditions (by human activity), if they exist.
Because undisturbed or minimally disturbed conditions can be difficult or impossible to
find, least disturbed conditions, combined with historical information, models or other
methods can be used to approximate reference condition as long as the departure from
natural or ideal is understood. Reference condition is used as a benchmark to
determine how much other waterbodies depart from this condition due to human
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Section VI—Glossary and Suggested Reading
disturbance. See also Best attainable condition, Historic condition, Least disturbed
condition, and Minimally disturbed condition. (Note: In general usage, a reference
condition can be either as nearly natural or impaired.)
Best Attainable Condition—A condition that is equivalent to the hypothetical
ecological condition of least disturbed sites where the best possible management
practices are in use. This condition can be determined using techniques such as
historical reconstruction, best ecological judgment, modeling, restoration
experiments, or inference from data distributions.
Historic Condition—Physical, chemical, and biological conditions existing only
in the historical record, in databases, reports, and literature that contribute to
developing reference expectations.
Least Disturbed Condition—The best available existing conditions with regard
to physical, chemical, and biological characteristics or attributes of a waterbody
within a class or region. These waters have the least amount of human
disturbance in comparison to others within the waterbody class, region, or basin.
Least disturbed conditions can be readily found, but can depart significantly from
natural, undisturbed conditions or minimally disturbed conditions. Least
disturbed condition can change significantly over time as human disturbances
change.
Minimally Disturbed Condition—The physical, chemical and biological
conditions of a waterbody with very limited, or minimal, human disturbance in
comparison to others within the waterbody class or region. Minimally disturbed
conditions can change over time in response to natural processes.
Rehabilitate—To reinstate, restore or bring to a condition of health or usefulness. It
implies partial or incomplete restoration to full health.
Remote Sensing—Collection of data using a detector not in direct contact with the
entity being sampled, such as detectors on a satellite. Alternatively, telemetry involves
collection of data by a detector in the absence of personnel, such as from deployed
in-stream temperature probes, and transmission of that data to another location.
Response—The biological result of an exposure. This term is synonymous with effect,
but emphasizes the receptor that responds (e.g., the response of trout) rather than the
agent that acts upon it (e.g., the effect of cadmium).
Restoration—The reestablishment of predisturbance aquatic functions and related
physical, chemical, and biological characteristics. It implies complete rehabilitation to
full ecological quality.
Restoration Potential—Ability of an aquatic ecosystem (and its associated watershed)
to respond physically, chemically and biologically to management measures, both
through natural resilience and the social and economic capacities of the area.
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Section VI—Glossary and Suggested Reading
Riparian Area—Terrestrial ecosystem along the banks of a stream or river representing
a vegetational transition between upland communities and the river.
Riverscape—Measurements, maps and other depictions of the habitat or physical,
chemical and biological attributes of flowing waters that can include both the bank and
the substrate of the stream channel.
Scale(s)—(1) The proportion between sets of dimensions as between multiple maps
with different intervals for distance measure. (2) The relationship between distances or
areas among maps of different scales (or between actual distances or areas).
Spatial Coverage—The area over which something is observed, measured, analyzed,
or reported.
Stress—(1) A situation, event, or factor producing a strain that disturbs or interferes
with the normal equilibrium of a system. (2) The result of exposure to a stressor or
causal agent that adversely affects an entity.
Stressors—Any physical, chemical, or biological agent that can induce an adverse
response in ecosystem condition.
Strength—Characteristic of the body of evidence indicating that it is logically compelling
or that it presents quantitatively strong relationships.
Study Design—Overall plan for an investigation or assessment that includes the spatial
and temporal site selection, methods, number of replicate samples, and intended
analyses. Examples include targeted sampling and random (probability) sampling.
Regional Assessments—Evaluations of the range and average condition of water
resource quality across a broad region for status and trends monitoring.
Site-Specific Assessments—Evaluations of a particular site or small set of sites—
usually for the purpose of assessing the effects of a specific impact (e.g., effluent) or the
effectiveness of a given intervention (e.g., rehabilitation).
Sufficiency—Characteristic of causation: the intensity, frequency, and duration of the
cause are adequate and the susceptible entity can exhibit the type and magnitude of the
effect.
Synthesis—Assembling, combining and integrating multiple types of data and
information to construct a complete, organized and meaningful whole.
Target—To choose specific geographic areas to receive monitoring, rehabilitation
actions, or other management attention.
Targeted Monitoring or Sampling Design—The plan or collection of measurements
from a defined population or subpopulation (e.g., sampling below industrial outfalls for
NPDES permits, above and below an outfall, or along a gradient below an outfall).
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Tiered Aquatic Life Use (TALU)—Refined aquatic life use descriptions incorporating
multiple condition levels responsive to the full range of stresses affecting aquatic
ecosystems.
Time Order—Characteristic of causation: the cause precedes the effect.
Total Maximum Daily Load (TMDL)—(1) The sum of the allowable loads of a single
pollutant from all contributing point and nonpoint sources. (2) The calculation of the
maximum amount of a pollutant a waterbody can receive and still meet water quality
standards and an allocation of that amount to the pollutant's source.
Variability—Differences among entities or states of an entity attributable to
heterogeneity. Variability is an inherent property of nature and cannot be reduced by
measurement.
Vulnerability—Sensitivity to stress(es).
Wall-to-Wall Landscape Data—Information or attributes about the landscape of an
area of study that cover the entire area of study. For example, 30 m resolution
classified land use/land cover (such as the NLCD derived from LandSat data) depicts
an entire watershed, while point in situ water quality measurements depict only the point
sampled.
Water Resource Management (Nonregulatory)—Decisions on management activities
relevant to a water resource such as problem identification, need for and placement of
best management practices, pollution abatement actions and effectiveness of program
activity.
Water Quality Standard—A law or regulation that consists of the designated use or
uses of a waterbody, the narrative or numerical water quality criteria (including
biocriteria) that are necessary to protect the use or uses of that particular waterbody,
and an antidegradation policy.
Watershed—(1) An area of land from which any released or deposited water flows into
the same waterbody. (2) Topographic areas within which surface and ground water
drain to a specific point. Equivalent to catchment (usually a small watershed) or basin
(usually a large watershed).
Xeric—Requiring only a small amount of moisture.
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SUGGESTED READING
General Landscape-Biological Response Patterns
Allan, J.D. 2004. Landscapes and riverscapes: The influence of land use on stream
ecosystems. Annu. Rev. Ecol. Evol. Syst. 35:257-284.
Brown, L.R., R.H. Gray, R.M. Hughes, and M.R. Meador, Eds. 2005. Effects of
Urbanization of Stream Ecosystems. American Fisheries Society Symposium 47.
American Fisheries Society, Bethesda, MD. Available online at
http://water. usgs.gov/nawqa/urban/htm l/otherpublications. htm I
Brown, M.T., and M.B. Vivas. 2005. Landscape development intensity index. Environ.
Monit. Assess. 101:289-309.
Hughes, R.M., L. Wang, and P.W. Seelbach, Eds. 2006. Landscape Influences on
Stream Habitats and Biological Assemblages. American Fisheries Society Symposium
48. American Fisheries Society, Bethesda, MD.
Hunsacker, C.T., and D.A. Levine. 1995. Hierarchical approaches to the study of water
quality in rivers. Bioscience. 45(3): 193-203.
Munafo, M., G. Gecchi, F. Baiocco, and L. Mancini. 2005. River pollution from non-
point sources: a new simplified method of assessment. J. Environ. Manage. 77(2):93-
98.
Paul, M.J. and J. Meyer. 2001. Streams in the urban landscape. Annu. Rev. Ecol.
Syst. 32:333-365.
Rodriguez, W., P.V. August, Y. Wang, J.F. Paul, A. Gold, and N. Rubinstein. 2007.
Empirical relationships between land use/cover and estuarine condition in the
Northeastern United States. Landsc. Ecol. 22(3):403-417.
Roth, N.E., J.D. Allan, and D.L. Erickson. 1996. Landscape influences on stream biotic
integrity assessed at multiple spatial scales. Landsc. Ecol. 11 (3): 141-156.
Steedman, R.J. 1988. Modification and assessment of an index of biotic integrity to
quantify stream quality in southern Ontario. Can. J. Fish. Aquat. Sci. 45:492-501.
Predictive Modeling Through Use of Landscape Data
Bailey, R.C., R.H. Norris, andT.B. Reynoldson. 2004. Bioassessment of Freshwater
Ecosystems: Using the Reference Condition Approach. Kluwer, Boston, MA.
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Clarke, R.T., M.T. Furse, J.F. Wright, and D. Moss. 1996. Derivation of a biological
quality index for river sites: comparison of the observed with the expected fauna. J.
Appl. Stat. 23(2-3):311-332.
Davies, N.M., R.H. Norris, and M.C. Thorns. 2000. Prediction and assessment of local
stream habitat features using large-scale catchment characteristics. Freshw. Biol.
45(3):343-369.
Hawkins, C.P., R.H. Norris, J.N. Hogue, and J.W. Feminella. 2000. Development and
evaluation of predictive models for measuring the biological integrity of streams. Ecol.
Appl. 10(5): 1456-1477.
Joy, M.K., and R.G. De'ath. 2002. Predictive modelling of freshwater fish as a
biomonitoring tool in New Zealand. Freshw. Biol. 47(11):2261-2275.
Oberdorff, T., D. Pont, B. Hugueny, and D. Chessel. 2001. A probabilistic model
characterizing fish assemblages of French rivers: a framework for environmental
assessment. Freshw. Biol. 46(3):399-415.
Oberdorff, T., D. Pont, B. Hugueny, and J.-P. Porchers. 2002. Development and
validation of a fish-based index for the assessment of 'river health' in France. Freshw.
Biol. 47(9): 1720-1734.
Pont, D., R.M. Hughes, T.R. Whittier, and S. Schmutz. 2009. A predictive index of
biotic integrity model for aquatic-vertebrate assemblages of Western U.S. streams.
Trans. Am. Fish. Soc. 138(2):292-305.
Pont, D., B. Hugueny, U. Beier, et al. 2006. Assessing river biotic condition at a
continental scale: a European approach using functional metrics and fish assemblages.
J. Appl. Ecol. 43:70-80.
Reynoldson, T.B., R.H. Norris, V.H. Resh, K.E. Day, and D.M. Rosenberg. 1997. The
reference condition approach: a comparison of multimetric and multivariate approaches
to assess water-quality impairment using benthic macroinvertebrates. J. North Am.
Benthol. Soc. 16(4):833-852.
Tejerina-Garro, F.L., B. de Merona, T. Oberdorff, and B. Hugueny. 2006. A fish-based
index of large river quality for French Guiana (South America): method and preliminary
results. Aquat. Living Resourc. 19:31-46.
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