United States
Environmental Protection
Agency
Region III
Chesapeake Bay
Program Office
Region III
Water Protection
Division
EPA903-R-08-001
CBP/TRS 290-08
September 2008
In coordination with the Office of Water/Office of Science and Technology, Washington, D.C., and the states
of Delaware, Maryland, New York, Pennsylvania, Virginia and West Virginia and the District of Columbia
Ambient Water Quality
Criteria for Dissolved
Oxygen, Water Clarity
and Chlorophyll a for
the Chesapeake Bay
and Its Tidal Tributaries
2008 Technical Support
for Criteria Assessment
Protocols Addendum
September 2008
-------
Ambient Water Quality Criteria for
Dissolved Oxygen, Water Clarity and
Chlorophyll a for the Chesapeake Bay
and Its Tidal Tributaries:
2008 Technical Support for Criteria
Assessment Protocols Addendum
September 2008
U.S. Environmental Protection Agency
Region III
Chesapeake Bay Program Office
Annapolis, Maryland
and
Region III
Water Protection Division
Philadelphia, Pennsylvania
in coordination with
Office of Water
Office of Science and Technology
Washington, D.C.
and
the states of
Delaware, Maryland, New York,
Pennsylvania, Virginia, and
West Virginia and the District of Columbia
-------
-------
Contents
Acknowledgments v
I. Introduction 1
Literature Cited 2
II. 2008 92-Segment Scheme for the Chesapeake Bay
Water Quality Criteria 5
Background 5
Chesapeake Bay Program Segmentation Schemes 5
2008 Chesapeake Bay and Tidal Tributaries 92-Segment Scheme 6
Unresolved Boundary for District of Columbia Upper Potomac River . . 12
Literature Cited 12
III. Refinements to Procedures for Assessing Chesapeake Bay
Dissolved Oxygen Criteria 13
Background 13
Dissolved Oxygen Criteria Assessment: Stations and Accepted Data ... 14
Pycnocline Definition and Boundaries 15
Revising Designated Use Boundaries with Enhanced Pycnocline
Definition Procedure 15
Calculation of Upper and Lower Pycnoclines for Dissolved
Oxygen Designated Use Criteria Assessment 15
Literature Cited 18
IV. Refinements to Procedures for Assessing Chesapeake Bay
Water Clarity and SAV Criteria 19
Background 19
Revision of the Water Clarity Acres Assessment Methodology 20
Clarification of Water Clarity Assessment Procedures 21
Statistical Model Revision 21
Converting Turbidity to Kd for Calculation of Water Clarity Acres . . 21
Interpolation Software and Approach 24
Literature Cited . 24
Contents
-------
V. Chlorophyll a Criteria Assessment Procedures 27
Background 27
Approach and Protocol Application with Examples 29
Types of Output 30
Future Directions 32
Literature Cited 33
Acronyms 34
Appendices
A. Procedure for Assessing Dissolved Oxygen Criteria Attainment:
30-day Criterion, Including Plotting a Bioreference Curve 35
B. Stations Involved in the 2004-2006 303d Listing Assessment for 2008 39
C. A Comparison of Methods for Estimating IQ 44
D. Derivation of Kd Regressions: DATAFLOW Report on the Lumping
vs. Splitting of Regions for MDDNR DATAFLOW Kd vs. Turbidity
Regressions and Calibrations 47
E. Chesapeake Bay Water Clarity Assessment Framework 59
F. Chesapeake Bay Clarity Criteria Attainment Results 66
G. Chlorophyll a Assessment Protocol 70
Contents
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Acknowledgments
This fifth addendum to the EPA April 2003 publication of Ambient Water Quality
Criteria for Dissolved Oxygen, Water Clarity, and Chlorophyll afar Chesapeake Bay
and its Tidal Tributaries (Regional Criteria Guidance) was developed and documented
through the collaborative efforts of the members of the Chesapeake Bay Program's
(CBP) Criteria Assessment Protocols Workgroup and Water Quality Steering
Committee.
PRINCIPAL AND CONTRIBUTING AUTHORS
The document resulted from the collaborative expertise and talents of Chesapeake
Bay Program's state agency, federal agency and academic institutional partners. The
following are principal and contributing authors of this addendum: Peter Tango, U.S.
Geological Survey/Chesapeake Bay Program Office; Jeni Keisman, University of
Maryland Center for Environmental Science/Chesapeake Bay Program Office;
Richard Batiuk, U.S. EPA Region III Chesapeake Bay Program Office, Mark Trice,
Maryland Department of Natural Resources; Frederick Hoffman, Virginia Department
of Environmental Quality; Ken Moore, Virginia Institute of Marine Science; David
Parrish, Virginia Institute of Marine Science; Tish Robertson, Virginia Department of
Environmental Quality; Elgin Perry, Statistics Consultant; Gary Shenk, U.S. EPA
Region III Chesapeake Bay Program Office.
CRITERIA ASSESSMENT PROTOCOL WORKGROUP
Peter Tango, Chair, U.S. Geological Survey; Cheryl Atkinson, United States
Environmental Protection Agency; Harry Augustine, Virginia Department of
Environmental Quality; Mark Barath, United States Environmental Protection
Agency; Tom Barron, Pennsylvania Department of Environmental Protection; Richard
Batiuk, United States Environmental Protection Agency; Stephen Cioccia, Virginia
Department of Environmental Quality; Elleanor Daub, Virginia Department of
Environmental Quality; Thomas Gardner, United States Environmental Protection
Agency; Sherm Garrison, Maryland Department of Natural Resources; Darryl Glover,
Virginia Department of Environmental Quality; John Hill, Maryland Department of
the Environment; Rick Hoffman, Virginia Department of Environmental Quality;
Larry Merrill, United States Environmental Protection Agency; Bruce Michael,
Maryland Department of Natural Resources; Ken Moore, Virginia Institute of Marine
Science; Shah Nawaz, District Department of the Environment; Roland Owens,
Virginia Department of Environmental Quality; Jennifer Palmore, Virginia
Department of Environmental Quality; Tom Parham, Maryland Department of Natural
Acknowledgments
-------
Resources; Elgin Perry, Statistics Consultant; Charles Poukish, Maryland Department
of the Environment; Tish Robertson, Virginia Department of Environmental Quality;
Matt Rowe, Maryland Department of the Environment; John Schneider, Delaware
Department of Natural Resources and Environmental Control; Susan Sciratta, United
States Environmental Protection Agency; Gary Shenk, United States Environmental
Protection Agency; Donald Smith, Virginia Department of Environmental Quality;
Scott Stoner, New York State Department of Environmental Conservation; Matt
Stover, Maryland Department of the Environment; Bryant Thomas, Virginia
Department of Environmental Quality; Mark Trice, Maryland Department of Natural
Resources; Howard Weinberg, University of Maryland Center for Environmental
Science; David Wolanski, Delaware Department of Natural Resources and
Environmental Control.
WATER QUALITY STEERING COMMITTEE
Diana Esher, Chair, United States Environmental Protection Agency; Richard Batiuk,
United States Environmental Protection Agency; Sheila Besse, District of Columbia
Department of the Environment; William Brannon, West Virginia Department of
Environmental Protection Division of Water and Waste Management; Patricia
Buckley, Pennsylvania Department of Environmental Protection; Katherine Bunting -
Howarth, Delaware Department of Natural Resources and Environmental Control;
Monir Chowdhury, District Department of the Environment; Ron Entringer, New York
Department of Environmental Conservation; Richard Eskin, Maryland Department
of the Environment; Carlton Haywood, Interstate Commission on the Potomac River
Basin; David Heicher, Susquehanna River Basin Commission; Ruth Izraeli, United
States Environmental Protection Agency; James Keating, United States Environmental
Protection Agency; Teresa Koon, West Virginia Department of Environmental
Protection; Robert Koroncai, United States Environmental Protection Agency; Bruce
Michael, Maryland Department of Natural Resources; Matt Monroe, West Virginia
Department of Agriculture; Kenn Pattison, Pennsylvania Department of
Environmental Protection; Russ Perkinson, Virginia Department of Conservation and
Recreation; Alan Pollock, Virginia Department of Environmental Quality; John
Schneider, Delaware Department of Natural Resources and Environmental Control;
Ann Swanson, Chesapeake Bay Commission; Robert Yowell, Pennsylvania
Department of Environmental Protection.
The individual and collective contributions from members of the Chesapeake Bay
Program Office are also acknowledged: Holly Davis, University of Maryland Center
for Environmental Science, Howard Weinberg, University of Maryland Center for
Environmental Science/Chesapeake Bay Program Office; John Wolf, National Park
Service, Jacob Goodwin, Chesapeake Research Consortium/Chesapeake Bay Program
Office; Jamie McNees, Chesapeake Research Consortium/Chesapeake Bay Program
Office.
Acknowledgments
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chapter |
Introduction
Since the signing of the multijurisdicational Chesapeake 2000 agreement, the U.S.
Environmental Protection Agency (EPA), in cooperation with its six watershed State
partners and the District of Columbia, has developed a series of water quality criteria
guidance documents in accordance with Section 117b of the Clean Water Act.
Chesapeake Bay regional water quality criteria were developed and adopted into
state water quality standards regulations protective of living resources and their habi-
tats. Five aquatic life tidal-water designated uses were defined by the partners (U.S.
EPA 2003a) apportioning the Chesapeake Bay and its tidal tributaries into appro-
priate habitats:
Migratory fish spawning and nursery habitat;
Open water fish and shellfish habitat;
Deep-water seasonal fish and shellfish habitat;
Deep-channel seasonal refuge habitat; and
Shallow-water Bay grass habitat
Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chloro-
phyll a for the Chesapeake Bay and Its Tidal Tributaries (Regional Criteria
Guidance) April 2003 has been the foundation document defining Chesapeake Bay
water quality criteria and recommended implementation procedures for monitoring
and assessment (U.S. EPA 2003a). The Technical Support Document for Identifica-
tion of Chesapeake Bay Designated Uses and Attainability October 2003 defined the
five tidal water designated uses to be protected through the published Bay water
quality criteria (U.S. EPA 2003b). Six addendum documents have been published
since April 2003 addressing detailed issues involving further delineation of tidal
water designated uses (U.S. EPA 2004a), Chesapeake Bay Program analytical
segmentation schemes (U.S. EPA 2004c, 2005), detailed criteria attainment and
assessment procedures, (U.S. EPA 2004b, 2007a), and Chesapeake Bay numerical
chlorophyll a criteria (2007b).
The detailed procedures are assessing attainment of the Chesapeake Bay water
quality criteria advanced through the collective EPA, States and District of Columbia
partner efforts to develop and apply procedures that incorporate, at the most
advanced state, magnitude, frequency, duration, space and time considerations with
chapter i Introduction
-------
biologically-based reference conditions and cumulative frequency distributions. As
a rule, the best test of any new method or procedure is putting it to work with stake-
holder involvement. Through the work of its Criteria Assessment Protocols
Workgroup, the Chesapeake Bay Program has an established forum for resolving
details of baywide criteria assessment procedure development and implementation.
This addendum document provides previously undocumented features of the present
procedures and refinements and clarifications to the previously published Chesa-
peake Bay water quality criteria assessment procedures.
Chapter 2 documents the most recent Chesapeake Bay 92-segment scheme used for
criteria assessment.
Chapter 3 documents refinements and additions to the procedures for assessing the
previously published Chesapeake Bay dissolved oxygen criteria.
Chapter 4 documents refinements and additions to the procedures for assessing the
previously published Chesapeake Bay water clarity and SAV criteria and deter-
mining attainment of the shallow-water designated use.
Chapter 5 documents refinements and additions to the procedures for assessing the
previously published Chesapeake Bay chlorophyll a criteria.
Appendices to the chapters include more detailed documentation on derivation of the
criteria assessment procedure elements and step-by-step through procedures for
assessing criteria.
This document represents the fifth formal addendum to the 2003 Chesapeake Bay
water quality criteria document; as such readers should regard the sections in this
document as new or replacement chapters and appendices to the original published
report. The criteria assessment procedures published in this addendum also replace
and otherwise supersede similar criteria assessment procedures originally published
in the 2003 Regional Criteria Guidance and the 2004 and 2007 addenda (U.S. EPA
2003a, 2004a, 2007a, b). Publication of future addendums by EPA on behalf of the
Chesapeake Bay Program watershed jurisdictional partners is likely as continued
scientific research and management applications reveal new insights and knowledge
that should be incorporated into revisions of state water quality standards regulations
in upcoming triennial reviews.
LITERATURE CITED
U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries
(Regional Criteria Guidance) April 2003. EPA 903-R-03-002. Region III Chesapeake Bay
Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2003b. Technical Support Document for Identifica-
tion of Chesapeake Bay Designated Uses and Attainability. October 2003. Region III
Chesapeake Bay Program Office. EPA 903-R-03-004. Annapolis, MD.
chapter i Introduction
-------
U.S. Environmental Protection Agency. 2004a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries -
2004 Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office,
Annapolis, MD.
U.S. Environmental Protection Agency. 2004b. Technical Support Document far Identifica-
tion of Chesapeake Bay Designated Uses and Attainability - 2004 Addendum. October 2004.
Region III Chesapeake Bay Program Office. EPA 903-R-04-006. Annapolis, MD.
U.S. Environmental Protection Agency. 2004c. Chesapeake Bay Program Analytical
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. October 2004.
Region III Chesapeake Bay Program Office, Annapolis, MD. EPA 903-R-04-008.
U.S. Environmental Protection Agency. 2005. Chesapeake Bay Program Analytical Segmen-
tation Scheme: Revisions, Decisions and Rationales 1983-2003. 2005 Addendum. December
2005. Region III Chesapeake Bay Program Office, Annapolis, MD. EPA 903-R-05-004.
U.S. Environmental Protection Agency. 2007a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries -
2007Addendum. July 2007. EPA 903-R-07-003. Region III Chesapeake Bay Program Office,
Annapolis, MD.
U.S. Environmental Protection Agency. 2007b. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries
-Chlorophyll a Addendum. October 2007. EPA 903-R-07-005. Region III Chesapeake Bay
Program Office, Annapolis, MD.
chapter iii Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria
-------
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chapter||
2008 92-Segment Scheme
for the Chesapeake Bay
Water Quality Criteria
BACKGROUND
For 25 years, the Chesapeake Bay Program partners have used various versions of a
basic segmentation scheme to organize the collection, analysis and presentation of
environmental data. The Chesapeake Bay Program Segmentation Scheme: Revi-
sions, decisions and rationales provided documentation on the spatial segmentation
scheme of the Chesapeake Bay and its tidal tributaries and the later revisions and
changes over the last 25 years (U.S. EPA 2004b, 2005). This chapter provides
concise information on the historical 1983, 1997, 2003 segmentation schemes and
illustrates the recommended 2008 92-segment scheme for assessing Chesapeake Bay
water quality criteria.
CHESAPEAKE BAY PROGRAM
SEGMENTATION SCHEMES
Segmentation is the compartmentalization of the estuary into subunits based on
selected criteria. The Chesapeake Bay ecosystem is diverse and complex, and the
physical and chemical factors which vary throughout the Bay determine the biolog-
ical communities and affect the kind and extent of their response to pollution stress.
These same factors also influence their response to restoration and remediation. For
diagnosing anthropogenic impacts, segmentation is a way to group regions having
similar natural characteristics so that differences in water quality and biological
communities among similar segments can be identified and their source elucidated.
For management purposes, segmentation is a way to group similar regions to define
a range of water quality and resource objectives, target implementation of specific
actions and monitor responses. It provides a meaningful way to summarize and
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
-------
present information in parallel with these objectives and it is a useful geographic
pointer for data management.
The Chesapeake Bay Program Segmentation Scheme: Revisions, decisions and
rationales 1983-2003 (U.S. EPA 2004b, 2005) contains the following maps and
tables used to document changes to the segmentation scheme from 1983 through
2003 as well as provide the jurisdictions with detailed documentation on the
geographical delineation of each segment's boundaries:
Maps for the 1983, 1997 and 2003 segmentation schemes;
Statistics on the perimeter, surface area and volume of each Chesapeake Bay
Program segment;
Narrative descriptions of each of the coordinates bounding each Chesapeake
Bay Program segment; and
Maps of all the Chesapeake Bay Water Quality Monitoring Program stations
displayed by segment by Maryland, Virginia and the District of Columbia.
A concise history of the original 1983 segmentation scheme, and the 1997 and 2003
revised segmentation schemes is published in Chapter 3 of the U.S. EPA (2004a)
Technical Support Document for identification of Chesapeake Designated Uses and
Attainability, 2004 Addendum. A detailed history of segmentation schemes is
provided in the Chesapeake Bay Program Segment Scheme document at
http://www.chesapeakebay.net/pubs/segmentscheme.pdf and the summary docu-
ments of U.S. EPA 2004b, 2005.
2008 CHESAPEAKE BAY AND TIDAL TRIBUTARIES
92-SEGMENT SCHEME
The 92-segment scheme for the Chesapeake Bay and its tidal tributaries used for
dissolved oxygen and water clarity assessments in the 2008 303d/305b listing efforts
of the four Bay tidal jurisdictions is documented here. The 92-segment scheme was
derived from: 1) the 2003 published 78-segment scheme with the addition of juris-
dictional boundary lines imposed to create 89 segments; then 2) includes only the
split segments agreed upon for the tidal James and Potomac rivers. The result of the
State partners' decisions on the Chesapeake Bay water quality criteria assessment
framework is the 92-segment scheme (Figure II-1), a subset of the 2003 104-segment
scheme that defined boundaries of split segments published in U.S. EPA 2004b.
Table II- lisa complementary reference table that lists the 92 segments definitions
according to their application across the 25 year history of Chesapeake Bay segment
schemes.
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
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Chesapeake Bay 303d list segment
Figure 11-1. 2008 Chesapeake Bay 92-segment scheme.
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
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Table 11-1. Segment acronyms and their historical context to 1983, 1997, 2003 and 2008 Chesapeake Bay
segmentation schemes1.
Chesapeake
Bay Program
Segment-Name
Nomenclature1
ANATF
ANATF_DC
ANATF_MD
APPTF
BACOH
BIGMH
BIGMH1
BIGMH2
BOHOH
BSHOH
C&DOH
C&DOH_DE
C&DOH_MD
CB1TF
CB1TF1
CB1TF2
CB2OH
CB3MH
CB4MH
CB5MH
CB5MH_MD
CB5MH_VA
CB6PH
CB7PH
CB8PH
CHKOH
CHOMH1
Chesapeake Bay Program Segment Scheme Membership
(Y=Yes, N=No)
1985
78 segments
Y
N
N
Y
Y
Y
N
N
Y
Y
Y
N
N
Y
N
N
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
1977
89 segments
N
Y
Y
Y
Y
Y
N
N
Y
Y
N
Y
Y
Y
N
N
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
2003
104 segments
N
Y
Y
Y
Y
N
Y
Y
Y
Y
N
Y
Y
N
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
2008
92 segments
N
Y
Y
Y
Y
Y
N
N
Y
Y
N
Y
Y
Y
N
N
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Tidal Water Body
Anacostia River
Anacostia River, DC
Anacostia River, MD
Appomattox River
Back River
Big Annemessex River
Big Annemessex River, Lower
Big Annemessex River, Upper
Bohemia River
Bush River
C&D Canal
C&D Canal, DE
C&D Canal, MD
Northern Chesapeake Bay
Northern Chesapeake Bay -
Turkey Pt South
Northern Chesapeake Bay -
Susquehanna River and Flats
Upper Chesapeake Bay
Upper Central Chesapeake Bay
Middle Central Chesapeake Bay
Lower Central Chesapeake Bay
Lower Central Chesapeake Bay.
MD
Lower Central Chesapeake Bay.
VA
Western Lower Chesapeake Bay
Eastern Lower Chesapeake Bay
Mouth of Chesapeake Bay
Chickahominy River
Lower Choptank River
'Note: Group acronyms are a combination of river and salinity zone membership. An example is BSHOH where BSH=Bush River and
OH=Oligohaline zone. Salinity zones are TF=Tidal Fresh, OH=Oligohaline, MH=Mesohaline, PH=Polyhaline.
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
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Table 11-1. (continued).
Chesapeake
Bay Program
Segment-Name
Nomenclature1
CHOMH2
CHOOH
CHOTF
CHSMH
CHSOH
CHSTF
CRRMH
EASMH
EBEMH
ELIPH
ELKOH
ELKOH 1
ELKOH2
FSBMH
GUNOH
GUNOH 1
GUNOH2
HNGMH
JMSMH
JMSOH
JMSPH
JMSTF
JMSTF1
JMSTF2
LAFMH
LCHMH
LYNPH
MAGMH
MANMH
MANMH 1
MANMH2
MATTF
Chesapeake
1985
78 segments
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
N
N
Y
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
N
N
Y
Bay Program Segment Scheme Membership
(Y=Yes, N=No)
1977
89 segments
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
N
N
Y
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
N
N
Y
2003
104 segments
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
2008
92 segments
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
N
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Tidal Water Body
Mouth of Choptank River
Middle Choptank River
Upper Choptank River
Lower Chester River
Middle Chester River
Upper Chester River
Corrotoman River
Eastern Bay
Eastern Branch Elizabeth River
Mouth-mid Elizabeth River
Elk River
Elk River, Upper
Elk River, Lower
Fishing Bay
Gunpowder River
Gunpowder River, Upper
Gunpowder River, Lower
Honga River
Lower James River
Middle James River
Mouth of James River
Upper James River
Upper James River - Lower
Upper James River - Upper
Lafayette River
Little Choptank River
Lynnhaven River
Magothy River
Manokin River
Manokin River, Lower
Manokin River, Upper
Mattawoman Creek
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
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10
Table 11-1. (continued).
Chesapeake
Bay Program
Segment-Name
Nomenclature1
MIDOH
MOBPH
MPNOH
MPNTF
NANMH
NANOH
NANTF
NANTF_DE
NANTF_MD
NORTF
PATMH
PAXMH
PAXMH1
PAXMH2
PAXMH3
PAXMH4
PAXMH5
PAXMH6
PAXOH
PAXTF
PIAMH
PISTF
PMKOH
PMKTF
POCMH
POCMH_MD
POCMH_VA
POCOH
POCOH_MD
POCOH_VA
POCTF
POTMH
POTMH_MD
Chesapeake
1985
78 segments
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
Y
N
N
N
N
N
N
Y
Y
Y
Y
Y
Y
Y
N
N
Y
N
N
Y
Y
N
Bay Program Segment Scheme Membership
(Y=Yes, N=No)
1977
89 segments
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
N
N
N
N
N
N
Y
Y
Y
Y
Y
Y
N
Y
Y
N
Y
Y
Y
N
Y
2003
104 segments
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
N
Y
Y
Y
N
Y
2008
92 segments
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
N
N
N
N
N
N
Y
Y
Y
Y
Y
Y
N
Y
Y
N
Y
Y
Y
N
Y
Tidal Water Body
Middle River
Mobjack Bay
Lower Mattaponi River
Upper Mattaponi River
Lower Nanticoke River
Middle Nanticoke River
Upper Nanticoke River
Upper Nanticoke River, DE
Upper Nanticoke River, MD
Northeast River
Patapsco River
Lower Patuxent River
Lower Patuxent River, Lower
Lower Patuxent River, Upper
Lower Patuxent River, Mill Creek
Lower Patuxent River.
Cuckold Creek
Lower Patuxent River.
St. Leonard Creek
Lower Patuxent River, Island Creek
Middle Patuxent River
Upper Patuxent River
Piankatank River
Piscataway Creek
Lower Pamunkey River
Upper Pamunkey River
Lower Pocomoke River
Lower Pocomoke River, MD
Lower Pocomoke River, VA
Middle Pocomoke River
Middle Pocomoke River, MD
Middle Pocomoke River, VA
Upper Pocomoke River
Lower Potomac River
Lower Potomac River, MD
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
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Table 11-1. (continued).
Chesapeake
Bay Program
Segment-Name
Nomenclature1
POTMH_VA
POTOH
POTOHJVID
POTOH_VA
POTOH1_MD
POTOH2_MD
POTOH3_MD
POTTF
POTTF_DC
POTTF_MD
POTTF_VA
RHDMH
RPPMH
RPPOH
RPPTF
SASOH
SASOH1
SASOH2
SBEMH
SEVMH
SOUMH
TANMH
TANMH_MD
TANMH_VA
TANMH1_MD
TANMH2_MD
WBEMH
WBRTF
WICMH
WSTMH
YRKMH
YRKPH
Chesapeake
1985
78 segments
N
Y
N
N
N
N
N
Y
N
N
N
Y
Y
Y
Y
Y
N
N
Y
Y
Y
Y
N
N
N
N
Y
Y
Y
Y
Y
Y
Bay Program Segment Scheme Membership
(Y=Yes, N=No)
1977
89 segments
Y
N
Y
Y
N
N
N
N
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
Y
N
Y
Y
N
N
Y
Y
Y
Y
Y
Y
2003
104 segments
Y
N
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
N
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
2008
92 segments
Y
N
N
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
N
N
Y
Y
Y
N
Y
Y
N
N
Y
Y
Y
Y
Y
Y
Tidal Water Body
Lower Potomac River, VA
Middle Potomac River
Middle Potomac River, MD
Middle Potomac River, VA
Middle Potomac River.
MD Mainstem
Middle Potomac River.
MD Port Tobacco River
Middle Potomac River.
MD Nanjemoy Creek
Upper Potomac River
Upper Potomac River, DC
Upper Potomac River, MD
Upper Potomac River, VA
Rhode River
Lower Rappahannock River
Middle Rappahannock River
Upper Rappahannock River
Sassafras River
Sassafras River, Lower
Sassafras River, Upper
Southern Branch Elizabeth River
Severn River
South River
Tangier Sound
Tangier Sound, MD
Tangier Sound, VA
Tangier Sound, MD, Main Body
Tangier Sound, MD, Deal Island to
Mouth of Nanticoke River
Western Branch Elizabeth River
Western Branch Patuxent River
Wicomico River
West River
Middle York River
Lower York River
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
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12
UNRESOLVED BOUNDARY FOR DISTRICT
OF COLUMBIA UPPER POTOMAC RIVER
This 92-segment scheme is the agreed upon 2008 assessment segmentation. Final
programming adjustments for boundary conditions of the jurisdictions were made in
autumn 2007. During early winter 2007/8, an unresolved upper boundary location
for the District of Columbia segment of the Tidal Fresh Potomac River came to light
due to unresolved station classifications (tidal vs. nontidal) to revise the boundary.
With assessment calculations underway, it was a nontrivial task to revise the map at
this segment boundary which could have affected assessments already completed for
the jurisdictions. The result, coupled with data limitations affected Washington
District of Columbia in 2008 for a "no attainment assessment" result in their
303d/305b listing. This boundary condition will be resolved for the next triennial
review.
LITERATURE CITED
U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries
(Regional Criteria Guidance) April 2003. EPA 903-R-03-002. Region III Chesapeake Bay
Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2003b. Technical Support Document for Identifica-
tion of Chesapeake Bay Designated Uses and Attainability. October 2003. October 2004.
Region III Chesapeake Bay Program Office. EPA 903-R-03-004. Annapolis, MD.
U.S. Environmental Protection Agency. 2004a. Technical Support Document for Identifica-
tion of Chesapeake Bay Designated Uses and Attainability - 2004 Addendum. October 2004.
Region III Chesapeake Bay Program Office. EPA 903-R-04-006. Annapolis, MD.
U.S. Environmental Protection Agency. 2004b. Chesapeake Bay Program Analytical
Segmentation Scheme: Revisions, Decisions and Rationales 1983-2003. October 2004.
Region III Chesapeake Bay Program Office, Annapolis, MD. EPA 903-R-04-008.
U.S. Environmental Protection Agency. 2005. Chesapeake Bay Program Analytical Segmen-
tation Scheme: Revisions, Decisions and Rationales 1983-2003. 2005 Addendum. December
2005. Region III Chesapeake Bay Program Office, Annapolis, MD. EPA 903-R-05-004.
chapter ii 2008 92-Segment Scheme for the Chesapeake Bay Water Quality Criteria
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chapter|||
Refinements to Procedures
for Assessing Chesapeake Bay
Dissolved Oxygen Criteria
BACKGROUND
In 2003, the EPA published detailed criteria for dissolved oxygen tailored to
different habitats within the Chesapeake Bay and its tidal tributaries (U.S. EPA
2003a). Oxygen is critical to most forms of life in the Bay; it must be available in
adequate concentrations to support overall ecosystem health. Minimum concentra-
tions of dissolved oxygen must be present to support the diversity of species and
their various life stages requiring protection.
Dissolved oxygen criteria were established for Chesapeake Bay that varied in space
(e.g., designated uses) and time (e.g., summer) to provide protection for different
species and communities. The criteria were also designed around several durations
(e.g., 30-day, 1-day) to reflect the varying oxygen tolerances for different life stages
(e.g., larval, juvenile, adult) and effects (e.g., mortality, growth, behavior). Thus, the
dissolved oxygen criteria include multiple components. Each component includes a
target of dissolved oxygen concentration, the duration over which the concentration
is averaged, the space (designated-use area) where the criterion applies, and a time
(season, months) when the criterion applies. EPA has published, and the States
adopted into their water quality standards regulations, dissolved oxygen criteria
protective of migratory spawning, open-water, deep-water, and deep-channel desig-
nated-use habitats (U.S. EPA 2003a). These dissolved oxygen criteria include
30-day, 7-day, and 1-day means along with instantaneous minima.
Since the Chesapeake Bay dissolved oxygen criteria were published in 2003, the
capability of fully assessing all the dissolved oxygen criteria for all four designated
uses over all applicable time periods has progressed, however, some limitations
remain. The refined and expanded dissolved oxygen criteria assessment methodolo-
gies documented in this chapter replace the methodologies previous published by
EPA. Work by EPA and its partners will continue to refine these methodologies to
reduce uncertainty further and to increase confidence in the resulting assessments.
chapter iii Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria
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14
Developing, validating and publishing EPA-recommended methodologies for
assessing the full array of Chesapeake Bay dissolved oxygen criteria duration
components will also prove critical. In this chapter and its associated appendices,
details and clarifications regarding data structure and assessment protocols are
provided for completing Chesapeake Bay dissolved oxygen criteria attainment
computations.
DISSOLVED OXYGEN ASSESSMENT:
STATIONS AND ACCEPTED DATA
The EPA water quality criteria assessment methodologies adopted by the Chesa-
peake Bay watershed jurisdictions recommend 3 consecutive years of data to
construct the cumulative frequency distribution function to compare with the biolog-
ical or other recommended reference curve (U.S. EPA 2003a). Step-by-step
procedures of the Chesapeake Bay dissolved oxygen criteria attainment assessment
methodology are provided for in Appendix A. A dissolved oxygen dataset was
developed for a suite of Chesapeake Bay Program monitoring stations, and ancillary
monitoring stations (VA), in the tidal waters of the Chesapeake Bay and its tidal trib-
utaries and embayments (Appendix B) stored on-line in the Chesapeake Information
Management System (CIMS).
A database table was assembled for dissolved oxygen (|ig/L), water temperature (°C)
and salinity (ppt) using all tidal Chesapeake Bay Program Water Quality Monitoring
stations from CIMS. The stations are a composite of Maryland and Virginia's fixed
station water quality monitoring network and the calibration and swapout data (i.e.,
swap out data is data collected when in situ water quality monitoring meters are
switched for maintenance) from their shallow-water monitoring programs (i.e., contin-
uous monitoring and DATAFLOW1 spatially intensive monitoring). The Chesapeake
Bay Program supported monitoring data is relatively extensive in time with a 23-year
history, however, the temporal density of the fixed station network is biweekly to
monthly and spatial distribution of stations is not particularly dense to meet all Chesa-
peake Bay water quality criteria assessment needs. Therefore, ancillary data of
sufficient quality is desirable and recommended for use when available to enhance the
attainment assessments, especially where CBP data are limited or lacking.
Ancillary data derived outside of the Chesapeake Bay Program supported water
quality monitoring program that were considered to have sufficient quality, passing
rigorous quality assurance/quality control standards, were added to the CIMS data.
Examples of additional water quality monitoring data were those data provided by
DATAFLOW: A field sampling technology used on a boat while a watercraft is underway that collects
spatially intensive data (hence DATA) for five environmental parameters (water temperature, salinity.
dissolved oxygen, turbidity (ntu), and fluorescence (used to estimate chlorophyll a) collected from a
flow-through (hence FLOW) stream of water collected near the surface of the water column. The
following website provides additional details about DATAFLOW and water quality monitoring with
DATAFLOW: http://mddnr.chesapeakebay.net/sim/index.cfm .
chapter iii Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria
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Virginia authorities for the 2008 303d/305b listing analyses that were collected from
the Virginia Chesapeake Bay benthic monitoring program and the Alliance for the
Chesapeake Bay's (ACB) Virginia volunteer monitoring program.
PYCNOCLINE DEFINITION AND BOUNDARIES
REVISING DESIGNATED USES BOUNDARIES WITH ENHANCED
PYCNOCLINE DEFINITION PROCEDURE
In U.S. EPA (2003a) Ambient Water Quality Criteria for Dissolved Oxygen, Water
Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries (Regional
Criteria Guidance), EPA identified five habitats (or designated uses) providing a
context for adequately protective Chesapeake Bay water quality criteria. Water
quality criteria and assessment procedures were developed for dissolved oxygen,
water clarity and chlorophyll a, published (U.S. EPA 2003a, 2004a,b, 2007a,b), and
have progressively been adopted into State water quality standards regulations. The
five designated uses were 1) migratory fish spawning and nursery designated use, 2)
shallow-water bay grass designated use, 3) open-water fish and shellfish designated
use, 4) deep-water seasonal fish and shellfish designated use and 5) deep-channel
seasonal refuge designated use (U.S. EPA 2003b). EPA published Technical Support
Document for Identification of Chesapeake Bay Designated Uses and Attainability
(U.S. EPA 2003b, 2004bj which provided further information on the development
and geographical extent of the designated uses to which the criteria may apply.
Refinements to boundary definitions involving open water, deep water and deep
channel have been developed, as described below, to standardize layer definitions.
CALCULATION OF UPPER AND LOWER PYCNOCLINES FOR
DISSOLVED OXYGEN DESIGNATED USE CRITERIA ASSESSMENT
Vertical stratification is foremost among the physical factors affecting dissolved
oxygen concentrations in some parts of Chesapeake Bay and its tidal tributaries. For
the purposes of water quality criteria attainment assessment, three layers are defined
for designated use assessments: 1) an upper mixed layer above the upper pycnocline
boundary; 2) deep water layer constrained by the upper and lower pycnocline bound-
aries; and 3) the lower mixed layer below the lower pycnocline boundary (U.S. EPA
2003a, 2003b). The depths of the upper and lower mixed layers are used to deter-
mine designated use boundaries for the dissolved oxygen assessment. In segments
where deep water and deep channel habitats are applicable, deep channel is defined
as the lower mixed layer, open water is defined as the upper mixed layer, and deep
water is the interpycnocline layer between the upper and lower mixed layers.
Temperature (°C) and salinity (ppt) are used to calculate density which, in turn, is used
to calculate pycnocline boundaries. Density is calculated using the method described in:
Algorithms for computation of fundamental properties of seawater. Endorsed
by UNESCO/SCOR/ICES/IAPSO Joint Panel on Oceanographic Tables and
Standards and SCOR Working Group 51. Fofonoff, N P; Millard, R C Jr.
UNESCO technical papers in marine science, Paris , no. 44, pp. 53. 1983.
chapter iii Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria
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For each vertical column of temperature and salinity data throughout the water
column, the existence of the upper and lower pycnocline boundaries are determined
by looking for the shallowest robust vertical change in density greater than 0.1
kg/m3/m for the upper boundary and deepest change of greater than 0.2 kg/m3/m for
the lower boundary. To be considered robust, the density gradient must not reverse
direction at the next measurement and must be accompanied by a change in salinity
and temperature.
Upper and lower pycnocline boundaries, where present, are interpolated in two
dimensions. The depth to the upper pycnocline boundary tends to be stable across
horizontal space in the estuary and so spatial definition of that boundary using inter-
polation generally works well. However, interpolation of the lower boundary is more
complicated because the results can conflict with 1) the upper boundary definition or
2) with the actual bathymetry of the Bay. As a result, interpolation of the lower
boundary should be performed based on "fraction of water column depth".
In the computations, the lower pycnocline is actually stored as "fraction of water
column below lower pycnocline," and calculated by dividing the lower pycnocline
depth by the total depth and subtracting the product from 1 as follows:
Example: Lower pycnocline depth = 10 m
Total depth = 15 m
% of total depth below lower pycnocline = 1-(10/15) = -.333 or about 33%.
When counting violations, the measures are converted back into an actual depth
before comparing measurements to it. To locate the lower pycnocline, multiply the
total depth at the given measurement location for that day by (1- %below lower
pycnocline), in this example it is 15(1-.33) = 10.01.
This calculation produces essentially the same depth of lower pycnocline. It is
important to proceed in this approach since total depth measurements may differ
across sampling dates. By following this procedure for working with the lower pycn-
ocline calculation it avoids the case where you could have a lower pycnocline value
below the total depth. If no lower boundary is detected then the fraction is zero.
The standardized method for calculating upper and lower boundaries of the pycno-
cline uses water column measurements of water temperature and salinity. Ambient
Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for
Chesapeake Bay and its Tributaries - 2004 Addendum (U.S. EPA 2004a) provided
two basic rules for determination of pycnocline depth:
1. From the water surface downward, the first density slope observation that is
greater than 0.1 kg/m3/m is designated as the upper pycnocline boundary
provided that:
a. That observation is not the first observation in the water column and
b. The next density slope observation is positive.
2. From the bottom sediment-water interface upward, the first density slope
observation that is greater than 0.2 kg/m3/m is designated as the lower pycno-
cline depth provided that:
a. An upper pycnocline depth exists;
chapter iii Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria
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b. There is a bottom mixed layer, defined by the first or second density slope
observation from the bottom sediment-water interface being less than 0.2
kg/m3/m
c. The next density slope observation is positive.
U.S. EPA (2004a, see pg. 87) also provided the procedure for calculation of the
vertical density profile.
These two decision rules remain unchanged. The detailed step-by-step procedure for
applying the two decision rules has been provided here.
Determining the vertical density gradient and defining pycnocline depths requires a
vertical profile of salinity and water temperature measurements collected at multiple
depths and computed as follows:
1. Sort the vertical profile of data from the water surface downwards through the
water column.
2. For each depth at which there are measurements, calculate a water density
value as oT, or "sigma T", using water temperature and salinity measurements
for that depth. Use the following method and equations:
oT = a(T) + b(T)*S, where:
T = temperature (°C)
S = salinity (ppt)
a and b are polynomial functions of T
a(T) = -9.22xlO-3 + 5.59xlQ-2 * T - 7.88xlQ-3 * T2 + 4.18xlQ-5 * T3
b(T) = 8.04X10-1 - 2.92xlO-3 * T + 3.12xlQ-5 * T2
3. Look down through the profile. Wherever the difference between sequential
depth measurements is < 0.19 meters, average the two depth measurements and
their corresponding salinity and density measurements.
4. Look down through the profile again. If there are still any depths (depth,
salinity, temperature and density measurements) < 0.19 meters apart, then
average them again. Continue until there are no depths < 0.19 meters apart.
5. Starting at the surface measurement and continuing until the deepest measure-
ment in the profile, calculate the change in salinity and density between each
sampling depth. For example, for two density values at 1 meter depth (yj) and
2 meters depth (y2) respectively, the change in density, or AoT = y2 -yj. Like-
wise, for salinity measurements AS = y2 -yi.
6. Assign a depth measurement to each pair of A values (AS, AoT) equal to the
average of two depths x2 and xj used to calculate the A values. Thus for the two
measurements y2 and y1; calculate the accompanying depth as (xj + x2)/2. You
should now have a vertical profile of AS and AoT values with an accom-
panying depth.
7. To find the upper boundary of the pycnocline, look at the vertical profile of
AoT, beginning with the second value (from the surface) and excluding the two
deepest values:
a. IF AoT > 0.1,
b. AND IF AoT for the next depth is greater than zero,
chapter iii Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria
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c. AND IF AS > 0.1,
d. Then this depth represents the upper boundary of the pycnocline.
8. Identify whether there is a lower mixed layer: use the same vertical profile but
examine it from the second deepest value upward (exclude the deepest value):
a. IF change in density (AoT) at the second deepest depth < 0.2
b. OR IF AoT at the next depth (moving upwards, i.e. shallower) < 0.2
c. THEN a lower mixed layer (i.e. a layer at depth where the density is not
changing) below the pycnocline exists.
9. If a lower mixed layer exists, then look for the lower boundary of the pycno-
cline. Beginning at the second deepest value, and stepping up to the depth
immediately below the upper pycnocline boundary, for AS and AoT values at
each depth:
a. IF AoT > 0.2,
b. AND IF AS > 0.1,
c. Then this depth is the lower pycnocline boundary.
10. If a pycnocline exists, then the upper and lower (if present) boundaries of the
pycnocline have now been identified.
LITERATURE CITED
U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries
(Regional Criteria Guidance) April 2003. EPA 903-R-03-002. Region III Chesapeake Bay
Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2003b. Technical Support Documentation for Identi-
fication of Chesapeake Bay Designated Uses and Attainability. October 2003. EPA
903-R-03-004. Region III Chesapeake Bay Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2004a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries -
2004 Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office,
Annapolis, MD.
U.S. Environmental Protection Agency. 2004b. Technical Support Document for Identifica-
tion of Chesapeake Bay Designated Uses and Attainability - 2004 Addendum. October 2004.
Region III Chesapeake Bay Program Office. EPA 903-R-04-006. Annapolis, MD.
U.S. Environmental Protection Agency. 2007a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries -
2007Addendum. July 2007. EPA 903-R-07-003. Region III Chesapeake Bay Program Office,
Annapolis, MD.
U.S. Environmental Protection Agency. 2007b. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries
-Chlorophyll a Addendum. October 2007. EPA 903-R-07-005. Region III Chesapeake Bay
Program Office, Annapolis, MD.
chapter iii Refinements to Procedures for Assessing Chesapeake Bay Dissolved Oxygen Criteria
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chapter
Refinements to Procedures for
Assessing Chesapeake Bay
Water Clarity and SAV Criteria
"
BACKGROUND
With the publication of the Ambient Water Quality Criteria for Dissolved Oxygen,
Water Clarity and Chlorophyll a for the Chesapeake Bay and its Tidal Tributaries
(Regional Criteria Guidance) (U.S. EPA 2003a) and the Technical Support Docu-
ment for Identification of Chesapeake Bay Designated Uses and Attainability
(Technical Support Document) (U.S. EPA 2003b), the jurisdictions were provided
with extensive guidance for how to determine attainment of the shallow-water bay
grass designated use. Additional guidance addressing 1) water clarity criteria appli-
cation periods, 2) SAV restoration acreage to shallow-water habitat acreage ratios, 3)
SAV restoration goal acreages and 4) determining attainment of shallow-water bay
grass use was further provided by Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and its Tidal
Tributaries - 2004 Addendum (U.S. EPA 2004). Additional details of water clarity
criteria and SAV restoration acreage attainment assessments were published in the
Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and
Chlorophyll afar the Chesapeake Bay and its Tidal Tributaries - 2007Addendum
(U.S. EPA 2007).
Since publication of the U.S. EPA 2007 Addendum, the following specific revisions
have been agreed upon by the Chesapeake Bay Program partners:
Revision of the water clarity acres assessment methodology;
Clarification on the method for calculation of water clarity acres;
Clarification on the statistical model involved in converting turbidity to Kd; and
Development of the interpolation approach.
Water clarity criteria and SAV restoration acreages are used to define attainment of
the shallow-water bay grass designated use in Chesapeake Bay, its tidal tributaries
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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20
and embayments. EPA provided three measures for assessing attainment of the
shallow-water Bay grass designated use for a Chesapeake Bay segment:
1. measure SAV acreage from overflight data mapping analysis and compare with
the targeted restoration goal acreage of SAV in a given segment;
2. goal attainment may be achieved if sufficient shallow-water area with the water
clarity necessary to achieve restoration of the targeted SAV exists, based on
routine water quality mapping using data from the Chesapeake Bay shallow-
water monitoring program. This measurement concept is defined as "water
clarity acres" (see p. 54, U.S. EPA 2007); and
3. if the water-clarity criteria were attained through the shallow-water designated
use reaching to a specific contour (i.e., segment-specific water clarity criteria
application depth) based on the cumulative frequency diagram assessment
methodology, again based on shallow-water monitoring program data (U.S.
EPA 2003a, 2003b, 2007).
Assessment of either SAV acreage, water clarity acres, or a combination of both,
serves as the basis for determining attainment or impairment of the shallow-water
designated use (U.S. EPA 2007). In the absence of sufficient shallow-water moni-
toring data to determine the available water clarity acres or assess water clarity
criteria attainment using the CFD-based procedure, the EPA recommends that the
states assess shallow-water bay grass designated use attainment based on the acres
of mapped SAV (see Chapter 8 of U.S. EPA 2007).
REVISION OF THE WATER CLARITY ACRES
ASSESSMENT METHODOLOGY
Revision of the water clarity acres assessment methodology involves clarification of
the attainment method previously published in 2007 (U.S. EPA 2007). The 2007
published attainment method recommended assessments to be made from a mean of
annual means for three years of assessments (see p. 54). The revised methodology
evaluates each year in the three-year cycle for a single best year attainment evalua-
tion of segment restoration goals. This attainment assessment framework could be
used when mapped SAV acres alone do not meet its restoration goal and as an alter-
native to the CFD-based water clarity criteria assessment method (Table IV-1).
The detailed standard operating procedures (SOPs) that define the detailed computer
workstation methods used in each State from the import of data through data
processing, regression calculations, interpolations and attainment assessment are
available from Maryland and Virginia (Maryland Department of Natural Resources
2008, Virginia Departments of Environmental Quality 2008). These specific SOPs
are updated with computer coding revisions that maintain the standard baywide
framework of the criteria assessment methodology but acknowledge such State
specific issues as changes with new software and software updates, new data sources
and programming efficiency updates to accomplish the tasks.
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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CLARIFICATION OF WATER CLARITY
ASSESSMENT PROCEDURES
U.S. EPA 2007, on pages 54-55, stated "Calculation of water clarity acres should be
based on spatially intensive shallow-water monitoring turbidity data converted to Kd,
interpolated as described in Chapter 2 and then compared to the corresponding IQ
threshold assigned to each interpolator grid cell". A 2007 review of the published
language, however, found this did not correctly capture the approach to obtaining the
IQ attainment assessment when using water clarity acres. An analysis (Appendix C)
conducted by the Chesapeake Bay Program partners shows the two methods did not
produce dramatically different results for the selected example cruise tracks, but the
analysis did suggest that:
1. The originally published guidance method was simpler to conduct than this
revised method which requires GIS-related software;
2. The revised method predicts with slightly less error; and
3. The revised method allows detection of spatial patterns in the individual
parameters including better depiction of areas of uncertainty due to, for
example, interpolation across land.
The following revisions, which have been made by the Criteria Assessment Protocol
Work Group under the U.S. EPA Chesapeake Bay Monitoring and Analysis Subcom-
mittee, are clarifications of the published methods used by the jurisdictions for
calculating water clarity acres.
STATISTICAL MODEL REVISION
The original publication of the statistical model suggested a multiplicative model of
turbidity, chlorophyll and salinity was appropriate for converting turbidity to IQ. The
regional regressions are, however, additive multiple regression equations. The gener-
alized form of such a model has been provided in Table IV-1 with an expression that
captures the region specific coefficients, exponents involved in the root for turbidity
and recognition of region-specific constants in accordance with what the jurisdic-
tions are using to fulfill their assessments.
Shallow-water habitat area acreage goals have been previously defined for water
clarity acres as 2.5x each SAV acre needed to meet the SAV restoration goal acreage
(p. 54, U.S. EPA 2007). Segment-specific SAV restoration goal acreages were previ-
ously published in U.S. EPA 2003a, 2004 and 2007.
CONVERTING TURBIDITY TO Kd FOR CALCULATION OF
WATER CLARITY ACRES
On pages 54-55, U.S. EPA (2007) recommended "Calculation of water clarity acres
should be based on spatially intensive shallow-water monitoring turbidity data
converted to IQ". To address the issue of converting turbidity measures into IQ
values, multiple regression equations were derived for determining light attenuation
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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22
Table IV-1. Revisions to Water Clarity Acres Attainment Assessment Methodology.
Procedure
2007 Addendum
2008 Addendum
Assessment calculation
U.S. EPA 2007, p54. "Assessment of
attaining a segment's water clarity
restoration acreage should be based on
a calculation of the arithmetic mean of
the year-by-year means of a month-by-
month accounting of water clarity
acres over the three year SAV growing
season assessment period."
Water clarity acres for the segment are
calculated by the taking the annual
mean of the monthly acreage within
the SAV growing season. Single best
year assessments are compared with
segment SAV/Water Clarity restoration
acreage goals.
Assessment calculation and
interpolation
U.S. EPA 2007, p54-55. "Calculation of
water clarity acres should be based on
spatially intensive shallow-water moni-
toring turbidity data converted to Kd.
interpolated as described in Chapter 2
and then compared to the corresponding
Kd threshold assigned to each interpo-
lator grid cell."
U.S. EPA 2007, p80, "The very dense
in situ measurements of turbidity from
each sampling cruise track are first
converted to Kd. The natural log of the
converted Kd values are then interpo-
lated using a standardized ordinary
kriging procedure with ARC/GIS into
a 25-meter square grid over the
segments entire surface area. Once
interpolated, the resultant interpolated
Kd values are transformed back."
Calculation of water clarity acres
should be based on spatially intensive
shallow-water monitoring data for
turbidity, chlorophyll a and salinity in
order to convert results to Kd. Within
each segment, the individually interpo-
lated chlorophyll, turbidity, and
salinity layer grid results are input into
the appropriate equation on a matching
25-m2 cell-by-cell basis. The result of
this cell-specific calculation of Kd is
based on region-specific multiple
regression model equations (Table IV-
2, Appendix D). The result is a new
grid representing the Kd surface. The
Kd grid is compared to the appropriate
Kd threshold on a cell-by-cell basis to
create the attainment grid. The attain-
ment grid results are stored in a
database and used to calculate water
clarity acres by initially converting cell
counts of attainment into acreage of
attainment inside and outside of
current mapped SAV areas for each
segment. As previously defined, attain-
ment evaluations account for any SAV
no grow zones by removing them
before conducting final calculations for
the segment (U.S. EPA 2007). Water
clarity acres for each segment are then
calculated by taking the annual mean
of the monthly acreages.
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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11
Table IV-1. (continued).
Procedure
2007 Addendum
2008 Addendum
Statistical Modeling: Turbidity-to-Kd
conversion
U.S. EPA 2007, p79. Statistical
Modeling - Model definition and
regionally specific models. "A multiple
regression model of Kd vs. 1.5 root of
turbidity [i.e., turbidity1715] x
chlorophyll x salinity provides the best
fit of the Kd-to-turbidity relationship".
A multiple regression model of Kd vs.
1.5 root of turbidity [i.e., turbidity1/L5]
+ chlorophyll a + salinity provides the
best fit of the Kd-to-turbidity
relationship. The general form of the
models then are Kd=(x* turbidity3) +
(y*chlorophyllb ) + (z*salinityc) + C
where:
a,b and c are exponents on
their respective water quality
parameters and a=( 1/1.5), b=l
and c = 1;
x, y and z are region-specific
constant multipliers for the
respective three water quality
parameters defined in Table IV-2;
C is a region-specific constant;
and
Turbidity is measured in NTUs.
chlorophyll a is reported in ug/L
and salinity measures are taken in
parts per thousand (ppt).
(Kd) using in situ IQ calibration measurements and coincident continuous water
quality monitoring data. A single equation for baywide application was not found to
be appropriate (Appendix D). Rather, a series of regionally-specific multiple regres-
sion models for determining light attenuation (Kj) from turbidity, chlorophyll and
salinity data were developed (Table IV-2). Details of the regionally-specific regres-
sion equation derivations supporting their application for turbidity conversion to IQ
throughout Chesapeake Bay and its tidal tributaries and embayments are docu-
mented in Appendix D.
Turbidity conversion to a Kd measure is not a 1:1 unit conversion. On page 79, U.S.
EPA (2007) specifically discussed the multiple regression model approach but
initially provided a multiplicative form of a general equation where Kd =1.5 root of
turbidity x chlorophyll a x salinity as providing the best fit to the IQ-turbidity rela-
tionship. Table IV-2 provides the updated additive form of the regression model and
region-specific groupings of tributaries as defined through State-specific cluster
analyses in Maryland and Virginia. Virginia-specific analyses were the first
completed and published the use of the 1.5 root for turbidity conversion to Kd (U.S.
EPA 2007). Maryland-specific analyses showed that a 1.6 root yielded the lowest
root mean square prediction error and highest r-square value. However, this differ-
ence in root, the associated error and r-square for the 1.5 vs. 1.6 root associated with
turbidity-IQj conversion were so minor (i.e., thousandths-decimal-place differences)
that it was decided for consistency across the jurisdictions to use the results for the
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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24
1.5 root (Appendix D). The regression equations in Table IV-2 provide regional
groupings and their regionally appropriate coefficients.
Note that the equations in Table IV-2 represent regions that pertain to a subset (30)
of the 92 Chesapeake Bay assessment segments. These equations were developed
with the best available shallow-water monitoring data throughout the Chesapeake
Bay. As data becomes available with future monitoring applied to other segments,
the specific groupings and their respective equations can be expected to change in
the future as a result of new data from the unassessed regions.
INTERPOLATION SOFTWARE AND APPROACH
Monthly shallow water monitoring dataflow data can be imported into ArcGIS 9.2
(ESRI 2007) map visualization software as a point dataset or as a layer in ESRI's
ArcMap Geostatistical Analyst Extension. A single point dataset consists of a single
DATAFLOW cruise, typically representing a single Chesapeake Bay segment. Each
point in the dataset has an associated measured value for chlorophyll, dissolved
oxygen, pH, salinity, temperature, and turbidity. A cruise track typically contains
3000-5000 points with a range of approximately 2500-6000 georeferenced locations.
The data are generally collected from April through October with 1-2 cruises per
month. Within a cruise dataset, duplicate data values for a georeferenced point in
time are averaged. This is important for Arclnfo because in the present Arclnfo
workstation environment when kriging is conducted, Arclnfo cannot work with
duplicate points. However, kriging conducted in ArcMap's Geostatistical Analyst has
the capacity to deal with duplicate data and the same step is not necessary. Missing
data are provided with an error code (e.g., Virginia uses a value of -999).
As previously documented in Table IV-1, for the attainment assessment, U.S. EPA
(2007, pp. 54-55) indicated "Calculation of water clarity acres should be based on
spatially intensive shallow-water monitoring turbidity data converted to Kj", but the
discussion further indicates "interpolated as described in Chapter 2, and then
compared to the corresponding Kj threshold assigned to each interpolator grid cell".
Chapter 2 (U.S. EPA 2007 p. 11) provided a step-by-step approach to how the inter-
polation would proceed if only a single parameter is involved in the assessment (e.g.,
dissolved oxygen for dissolved oxygen attainment measures). However, turbidity is
not equivalent to or directly translated into Kd. The regionally-specific multiple
regression model approach (see Table IV-2) requires additional steps to get from
water quality measure to threshold assessment for attainment or impairment.
Details of the water clarity assessment framework, including a step-by-step approach
to assessing attainment, are provided in Appendix E. Appendix F shows 2008 Mary-
land and Virginia 303d/305b Chesapeake Bay water clarity assessment results to
provide examples of water quality criteria attainment assessment output.
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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Table IV-2. Regional Kd regression equations.
State-River segment Group
Regional Kd equation
MARYLAND GROUP 1
Bush River BSHOH,
Gunpowder River GUNOFL
Magothy River MAGOH,
Middle River MIDOH,
St. Mary's River1
Kd = 0.5545 + 0.3172 * Turbidity (1' L5> + 0.0160*Chlorophyll a -
0.0138*Salinity
MARYLAND GROUP 2
Eastern Bay-EASMH
Lower Patuxent River-PAXMH
Lower Potomac River-POTMH
West/Rhode Rivers-WSTMH/RHDMH
Kd = -0.1247 + 0.2820 * Turbidity (1'L5) + 0.0207*Chlorophyll a
0.0515*Salinity
MARYLAND GROUP 3
Fishing Bay/Chicamacomico River-
FSHMH, Severn River-SEVMH
South River-SOUMH
Kd = 1.0895 + 0.4160 * Turbidity (1'L5) + 0.0140*Chlorophyll a -
0.0950*Salinity
MARYLAND GROUP 4
Little Choptank River-LCHMH
Miles/Wye Rivers-EASMH
Kd = -0.8991 + 0.4338 * Turbidity (1 /L5> + 0.0180*Chlorophyll a
0.0912*Salinity
MARYLAND GROUP 5
Upper and Middle Patuxent River-
PAXOH/PAXTF
Kd = 0.8191 + 0.2691 * Turbidity (1'L5) - 0.0084*Chlorophyll a +
0.0384*Salinity
MARYLAND GROUP 6
Lower Chester River-CHSMH
Middle Chester River-CHSOH
Kd = 0.0493 + 0.4658 * Turbidity + 0.0100*Chlorophyll a -
0.0090* Salinity
VIRGINIA GROUP 1
Mattoponi River-MPNOH/MPNTF
Chickahominy River-CHKOH
James River-JMSPH JMSOH
JMSMH JMSTF1 JMSTF2
Appomatox River-APPTF
Kd = 1.192674757 + 0.295620722*Turbidity (1 /L5) -
0.056160407*Salinity + 0.000274598*Chlorophyll a
VIRIGINIA GROUP 2
Upper Middle Pamunkey River-PMKOH PMKTF
Lower York River-YRKPH YRKMH
Lower Piankatank River-PIAMH
Kd = 0.5275793536 + 0.3193475331*Turbidity
0.0176700982*Salinity + 0.0271723238*Chlorophyll a
Source: E. Perry (2006) Appendix D of this document.
'Note: Group acronyms are a combination of river and salinity zone membership. An example is BSHOH where BSH=Bush River and
OH=Oligohaline zone. Salinity zones are TF=Tidal Fresh, OH=Oligohaline, MH=Mesohaline, PH=Polyhaline. Refer to Table II-1, in
Chapter 2 of this document, for the Chesapeake Bay Program segmentation schemes.
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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26
LITERATURE CITED
Perry, Elgin. (2006). Notes on Lumping vs Splitting Kd =/(turbidity) calibration. Appendix
D in this Addendum.
Environmental Systems Research Institute (ESRI). 2007. ArcGIS 9.2. Redlands, CA.
Maryland Department of Natural Resources. 2008. Water Clarity Calculation SOP 2008.
Tidewater Ecosystem Assessment, Annapolis, MD.
U.S. Environmental Protection Agency. 2003a. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries
(Regional Criteria Guidance). April 2003. EPA 903-R-03-002. Region III Chesapeake Bay
Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2003b. Technical Support documentation for identi-
fication of Chesapeake Bay designated uses and attainability. October 2003. EPA
903-R-03-004. Region III Chesapeake Bay Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2004. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries -
2004 Addendum. EPA 903-R-04-005. Region III Chesapeake Bay Program Office,
Annapolis, MD.
U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries -
2007Addendum. July 2007. EPA 903-R-07-003. Region III Chesapeake Bay Program Office,
Annapolis, MD.
Virginia Department of Environmental Quality. 2008. Water Clarity Calculation SOP 2008.
chapter iv Refinements to Procedures for Assessing Chesapeake Bay Water Clarity and SAV Criteria
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"
chapter
Chlorophyll a Criteria
Assessment Procedures
BACKGROUND
Phytoplankton are small often microscopic plants floating in the water. These organ-
isms form the base of the Chesapeake Bay's food web, linking nutrients and sunlight
energy with higher trophic levels such as fish (e.g. menhaden, bay anchovy) and with
bottom dwelling oysters, clams and worms via primary producer and detrital path-
ways. The majority of the Bay's animals feed directly on phytoplankton or on
organisms that directly consume the phytoplankton. Therefore, the Bay's carry
capacity, or its ability to produce and maintain a diversity of species, depends in
large part on how well phytoplankton meet the needs of the consumers.
A primary characteristic of algae is the presence of photopigments. Chlorophyll a is
a primary photosynthetic pigment in algae and cyanobacteria (blue-green algae).
Since chlorophyll a is a measure of photosynthetic activity, it is thus also a measure
of the primary food source of aquatic food webs. Chlorophyll a also plays a direct
role in reducing light penetration in shallow-water habitats, which has a direct
impact on underwater bay grasses. Excess algae, uneaten by higher trophic level
consumers (e.g., zooplankton, filter-feeding fish and shellfish), are decomposed by
bacteria, and in the process, exert a biological oxygen demand upon the system.
Decomposition of the algal organic matter through bacterial respiration can remove
oxygen from the water column faster than it can be replaced and lead to hypoxia and
anoxia, impairing habitat conditions for much of the Bay life. From a water quality
perspective, chlorophyll a is the best available, most direct measure of the amount
and quality of phytoplankton with a relationship to impacts on water clarity and
dissolved oxygen impairments.
The EPA originally provided the States with recommended narrative chlorophyll a
criteria applicable to all Chesapeake Bay and tidal tributary waters:
"Concentrations of chlorophyll a in free floating microscopic aquatic plants (algae),
shall not exceed levels that result in ecologically undesirable consequencessuch as
reduced water clarity, low dissolved oxygen, food supply imbalances, proliferation
of species deemed potentially harmful to aquatic life or humans or aesthetically
chapter v Chlorophyll a Criteria Assessment Procedures
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28
objectionable conditionsor other render tidal waters unsuitable for designated
uses." (U.S. EPA 2003a).
However, the EPA also strongly encouraged states to develop and adopt site-specific
numerical chlorophyll a criteria for tidal waters where algal-related impairments are
expected to persist even after the Chesapeake Bay dissolved oxygen and water
clarity criteria have been attained.
In Ambient Water Quality Criteria for Dissolved Oxygen, Water Clarity and Chloro-
phyll a for the Chesapeake Bay and Its Tidal Tributaries - 2004 Addendum (U.S.
EPA 2004) guidance was developed on determining where numerical chlorophyll a
criteria should apply to Chesapeake Bay and tidal tributary waters. A general recom-
mended methodology was developed by the Chesapeake Bay Program partners for
use by the jurisdictions with tidal waters to determine consistently which local tidal
waters will likely attain the published Chesapeake Bay dissolved oxygen and water
clarity criteria yet show the persistence of algal-related water quality impairments.
Examples of possible salinity-zone-specific, numerical chlorophyll a thresholds
(|ig/L) drawn from a variety of resources and approaches were provided with deri-
vations based in:
1. historical Chesapeake Bay levels;
2. ecosystem trophic status;
3. phytoplankton reference communities;
4. potentially harmful algal blooms;
5. water quality impairments; and
6. user perceptions and State water quality standards (Table IX-1 in U.S. EPA 2004).
From 2004 through 2006, Delaware, Maryland, Virginia and the District of
Columbia promulgated narrative chlorophyll a criteria into their water quality stan-
dards. Virginia promulgated numerical segment- and season-specific chlorophyll a
criteria for the tidal James River. The District of Columbia promulgated numerical
chlorophyll a criteria for its reach for the tidal Potomac River and its remaining
waters, having previously adopted numerical criteria for chlorophyll a criteria for the
protection of the tidal Anacostia River.
Quantitative interpretation of Maryland's narrative criterion for chlorophyll a is cited
in the following excerpt from Maryland Department of the Environment's (MDE's)
"Total Maximum Daily Loads of Nitrogen and Phosphorus for the Upper and Middle
Chester River Kent and Queen Anne's Counties, Maryland" (approved by U.S. EPA
November 2006). The text below also describes MDE's interpretation of this criterion
in terms of quantified goals for application in Total Maximum Daily Loads (TMDLs).
The Chlorophyll a level goals used in this analysis are guidelines set forth by
Thomann and Mueller (1987) and by the EPA Technical Guidance Manual for
Developing Total Maximum Daily Loads, Book 2, Part 1 (1997). The
chlorophyll a narrative criteria ((COMAR 26.08.02.03-3 C (10)) states:
"Chlorophyll a - Concentrations of chlorophyll a in free-floating microscopic
aquatic plants (algae) shall not exceed levels that result in ecologically
undesirable consequences that would render tidal waters unsuitable for desig-
nated uses." The Thomann and Mueller guidelines above acknowledge
" 'Undesirable' levels of phytoplankton [Chlorophyll a] vary considerably
chapter v Chlorophyll a Criteria Assessment Procedures
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"
depending on water body." MDE has determined per Thomann and Mueller
(1987), that it is acceptable to maintain chlorophyll a concentrations below a
maximum of 100 |ig/L, and also to target, with some flexibility depending on
waterbody characteristics, a 30-day rolling average of approximately 50 |ig/L.
Consistent with the guidelines set forth above, MDE's interpretation of narra-
tive criteria for chlorophyll a in the Upper and Middle Chester River consists
of the following goals:
1. Ensure that instantaneous concentrations remain below 100 |ig/l at all
times and
2. Minimize exceedances of the 50 jj.g/1, 30-day rolling average, to a
frequency that will not result in ecologically undesirable conditions.
Further development of numerical chlorophyll a criteria for Chesapeake Bay tidal
waters was advanced with the U.S. EPA 2007b publication Ambient Water Quality
Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll afar the Chesapeake
Bay and Its Tidal Tributaries - 2007 Chlorophyll Criteria Addendum. This 2007
chlorophyll a criteria addendum documented the scientific basis for numerical
chlorophyll criteria based on:
1. historical chlorophyll a reference concentrations;
2. chlorophyll a relationships with dissolved oxygen impairments;
3. chlorophyll a contributions to water clarity impairments; and
4. characteristic chlorophyll a conditions associated with specific impairments
related to harmful algal blooms.
Recommendations on Chesapeake Bay chlorophyll a criteria were provided and
structured, tiered sample collection, analysis and assessment procedures were
recommended. The specific sampling and assessment procedure recommendations
are directed toward a harmful algal bloom (HAB) based chlorophyll a criterion that
could be applied to the Chesapeake Bay tidal fresh and oligohaline waters.
The basic approach used for numerical chlorophyll a criteria assessment procedure
is documented in Table II-l in the July 2007 criteria addendum (U.S. EPA 2007a).
The details of the chlorophyll a criteria attainment assessment are documented
here in Appendix G. The general application example below is illustrated for the
James River.
APPROACH AND PROTOCOL APPLICATION WITH EXAMPLES
The use of spatially and temporally-intensive DATAFLOW data in conjunction with
monthly and semi-monthly fixed station data allowed for the generation of daily
interpolated estimates for each segment. In Virginia, during the 2008 assessment for
example, more than 500,000 data points were used for the assessment of the three-
year period. This monitoring approach produced data that generally resulted in from
1 to 7 individual day-scale interpolation grids in any one month. The day-scale inter-
polation grids were then used to calculate a seasonal average concentration for each
grid cell. This approach ensures that segments are assessed with as much spatiotem-
poral variability as possible while minimizing reliance on weak estimates stemming
from small sample sizes.
chapter v Chlorophyll a Criteria Assessment Procedures
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30
The chlorophyll a criteria attainment assessment procedural steps are as follows:
1. A database was compiled for the three-year assessment period containing the
following:
- Long-term CBP stations (records stored in CIMS database);
- DATAFLOW verification stations (records stored in CIMS database);
- DATAFLOW cruise-tracks (records stored by VIMS, HRSD, MD DNR); and
- VA DEQ stations where applicable (records stored in VA DEQ CEDS database).
2. Only data meeting appropriate QA/QC requirements are used in the assessment.
Cruise-track data flagged with codes related to equipment failure or sampling
artifacts were excluded, while data taken during algal blooms were used.
3. Each segment (e.g., JMSTFL, JMSTFU, JMSOH, JMSMH, and JMSPH -
refer to Chapter II, Table II-1 in this document for segment nomenclature and
water body names) is interpolated separately using only the stations and cruise-
tracks contained in them and directly adjacent. Data from a given day is
interpolated for a segment if: 1) there were two or more fixed stations sampled
on that day in that segment; 2) that segment was targeted by a DATAFLOW
cruise-track on that day; or 3) there was a fixed station sampled in that segment
and an adjacent segment was targeted by a DATAFLOW cruise-track on that
day. The last condition takes advantage of cruise-tracks that cross over into
multiple Chesapeake Bay Program segments.
4. Datasets are imported into the Chesapeake Bay Interpolator and transformed
(natural log) prior to interpolation, as chlorophyll a measurements tend to
follow a log-normal distribution. The program defaults for search area (25 km2)
and maximum sample size (4) are used, and the "2D Inverse-Distance
Squared" algorithm is chosen. The Interpolator automatically back-transforms
interpolated estimates before creating the output files.
5. Interpolator output was organized by segment-season-year. For each interpo-
lator cell in a segment, a season-year (e.g., Spring 2005) average is calculated.
6. For the VA example, grid-cell averages were then assessed against segment-
season criteria specified by the VA DEQ Water Quality Assessment Guidance
Manual for Y2008 303(d)/305(b) Integrative Water Quality Report (VA DEQ
2007). Values over the criteria were assessed as non-attaining; those equal to or
less than were assessed as attaining.
7. Seasonal CFDs are generated for each segment using the steps outlined in
Chapter 2 of Ambient Water Quality Criteria for Dissolved Oxygen, Water
Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries -
2007 Addendum (U.S. EPA 2007a). Assessment curves were compared
against a default reference curve (U.S. EPA 2003). Non-attainment is calcu-
lated by subtracting the area of the reference curve from the area under the
chlorophyll a criteria assessment curve.
TYPES OF OUTPUT
Three types of output were produced for assessment: cumulative frequency
distribution diagrams, maps, and tabular summaries (see Figure V-l, Figure V-2, and
Table V-l for examples).
chapter v Chlorophyll a Criteria Assessment Procedures
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JMS-OH Spring CFD
100
70-
60
CD
~ 50
!40-
30-
20
10-
0
JMS-OH Summer CFD
100
0
20
% space
40 60
% space
100
Figure V-1. Cumulative frequency distribution diagrams for each segment and season (Spring and Summer) showing
the assessment curve (solid blue line) against the default reference curve (dashed black line).
1 ug/l
10 ug/l
20+ ug/l
Figure V-2. Example map graphics. Larger map shows the average chlorophyll a concentration (/jg/L) in the tidal
James River for summer 2006. Dots represent the locations of fixed stations. Inset shows the same data reduced to
the assessment binary (grey=pass, black=fail).
chapter v Chlorophyll a Criteria Assessment Procedures
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32
Table V-1. Example summary pass/fail results for chlorophyll a criteria assessment.
CHLOROPHYLL CRITERIA ASSESSMENT RESULTS (2008 INTEGRATED REPORT)
CBP Segment Season Criteria Attainment % Excess non-attainment
JMSTF1 (James TF Lower)
JMSTF1 (James TF Lower)
JMSTF2 (Jmes TF Upper)
JMSTF2 (Jmes TF Upper)
JMSOH (James Oligohaline)
JMSOH (James Oligohaline)
JMSMH (James Mesohaline)
JMSMH (James Mesohaline)
JMSTPH (James Polyhaline)
JMSTPH (James Polyhaline)
Spring
Summer
Spring
Summer
Spring
Summer
Spring
Summer
Spring
Summer
Fails
Fails
Fails
Fails
Fails
Meets
Fails
Fails
Fails
Fails
26
47
27
26
8
0
17
22
8
29
FUTURE DIRECTIONS
Extractive chlorophyll a has been shown to significantly exceed fluorescent (YSI
probe-based) chlorophyll a measured at verification stations at times (e.g., Virginia
James River example), therefore necessitating calibration between the two measure-
ment methods. While regression coefficients were calculated so as to account for
season and segment-specific idiosyncrasies, the goodness of fit for the different cali-
bration equations varied (Table V-2).
Table V-2. Root mean square errors for segment-season calibration regressions
with extractive and YSI probe-based chlorophyll a measures for tidal James River
segments1.
Segment
JMSTFU
JMSTFU
JMSTFL
JMSTFL
JMSOH
JMSOH
JMSMH
JMSMH
JMSPH
JMSPH
Season
Spring
Summer
Spring
Summer
Spring
Summer
Spring
Summer
Spring
Summer
R2
0.59
0.79
0.84
0.92
0.68
0.23
0.96
0.95
0.69
0.89
RSME
6.21
5.51
4.83
6.80
9.54
4.46
13.28
9.05
1.84
2.18
'JMSTFU=James River, Tidal Fresh Upper Segment; JMSTFL=James River, Tidal Fresh Lower
Segment; JMSOH=James River, Oligohaline Segment; JMSMH=James River, Mesohaline Segment:
JMSPH=James River, Polyhaline Segment
chapter v Chlorophyll a Criteria Assessment Procedures
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In this example, the best predictions overall were obtained in JMSPH and the worse
predictions for JMSMH. As chlorophyll a assessments expand across the tidal
waters, additional environmental parameter(s) may need to be used to increase the
accuracy of estimates similar to the way Kj and turbidity relationships turned to
additive multivariate models.
An additional consideration is that field data are interpolated without respect to land
barriers, which can result in station data having undue influence on distant grid cells.
The use of DATAFLOW data cruise-track points minimizes this because of the high
density of "nearest neighbors." However, it becomes an issue of concern when
cruise-track points are not available in the search radius and the segment of interest
has meandering portions (such as JMSTFU). Interpolating with barriers is not an
option for the Bay Interpolator at this time, but ArcGIS Geostatistical Analyst for
example provides a limited form of this functionality.
LITERATURE CITED
COMAR 26.08.02.03-3 C (10)). Chlorophyll a narrative criteria.
Thomann, R. V. and J. A. Mueller. 1987. Principles of Surface Water Quality
Modeling and Control. Harper & Row, Publ., Inc., New York, NY.
U.S. Environmental Protection Agency. 1997. Technical Guidance Manual for
Developing Total Maximum Daily Loads, Book 2, Part 1. EPA# 823-B-97-002.
Office of Water, Washington, DC.
U.S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its
Tidal Tributaries. April 2003. EPA 903-R-03-002. Region III Chesapeake Bay
Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2004. Ambient Water Quality Criteria for
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its
Tidal Tributaries - 2004 Addendum. October 2004. EPA 903-R-03-002. Region III
Chesapeake Bay Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2007a. Ambient Water Quality Criteria for
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its
Tidal Tributaries - 2007 Addendum. July 2007. EPA 903-R-07-003. Region III
Chesapeake Bay Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2007b. Ambient Water Quality Criteria for
Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its
Tidal Tributaries - 2007 Chlorophyll Criteria Addendum. November 2007. EPA 903-
R-07-005. Region III Chesapeake Bay Program Office, Annapolis, MD.
Virginia Department of Environmental Quality (VA DEQ). 2007. Water Quality
Assessment Guidance Manual for Y2008 303(d)/305(b) Integrative Water Quality
Report. Water Quality Division, Office of Water Quality Programs, Virginia Depart-
ment of Environmental Quality, Richmond, VA.
chapter v Chlorophyll a Criteria Assessment Procedures
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34
Acronyms
ACB Alliance for Chesapeake Bay
°C degrees Celcius
CEDS Comprehensive Environmental Data System
CFD cumulative frequency distribution
CHLA chlorophyll a
CIMS Chesapeake Information Management System
CBP Chesapeake Bay Program
DATAFLOW A field sampling technology that collects spatially intensive data (hence
DATA) for five environmental parameters (water temperature, salinity,
dissolved oxygen, turbidity (ntu), and fluorescence (used to estimate
chlorophyll a) are collected from a flow-through (hence FLOW) stream of
water collected near the surface of the water column.
DE Delaware
DFLO DATAFLOW
EPA U.S. Environmental Protection Agency
GIS Geographic Information System
HRSD Hampton Roads Sanitation District
IQ light attenuation measure
kg/m3/m kilograms per cubic meter per meter
km2 square kilometer
LICOR Company name for a sensor used in water quality monitoring that measures underwater
photosynthetically active radiation (PAR)
MD Maryland
MD DNR Maryland Department of Natural Resources
m2 square meter
mg O2/L milligram dissolved oxygen per liter
NAD North American Datum
NTU nephelometric turbidity units
ppt parts per thousand
QA/QC quality assurance/quality control
RSME root mean square error
SAV submerged aquatic vegetation
SOP standard operating procedures
TMDL Total Maximum Daily Load
|ig/L micrograms per liter
UTM Universal Transverse Mercator
VA Virginia
VA DEQ Virginia Department of Environmental Quality
VIMS Virginia Institute of Marine Science
YSI Yellow Springs Instruments, company producing water quality monitoring sensors
acronyms
-------
appendix
Procedure for
Assessing Dissolved Oxygen
Criteria Attainment
30-day Criterion, Including
Plotting a Bioreference Curve
Currently, dissolved oxygen is assessed using the monthly mean criterion (i.e., 30-
day) for the open-water designated use, the monthly mean criterion for the
deep-water designated use, and the instantaneous minimum criterion for the deep-
channel designated use. The following step-by-step procedure is used to assess the
status of Chesapeake Bay waters with respect to dissolved oxygen.
STEP 1. COMPILING AND FORMATTING THE DATA SET
A three-year dissolved oxygen dataset is compiled (most recently, the 2008 eval-
uation used the 2004-2006 assessment period) with georeferenced stations for
Chesapeake Bay Program mainstem and tributary tidal waters, and included the
date sampled, and coincidently measured water temperature (°C) and salinity
(ppt) covariates. Ancillary data for the same parameters were added by the state
of Virginia where applicable, collected from their benthic monitoring program
and the Alliance for the Chesapeake Bay's Virginia volunteer monitoring
program.
A FORTRAN computer program was developed to reformat this flat file into a
"d3d file" a format that could be input into the Chesapeake Bay Program Inter-
polator.
STEP 2. INTERPOLATION OF WATER QUALITY MONITORING
DATA
For the Chesapeake Bay and its tidal tributaries and embayments, a three-dimen-
sional grid-based spatial interpolator was developed to provide a common spatial
framework for spatial extrapolation of georeferenced water quality monitoring
data (Banner 2001). Spatial interpolation is conducted using an inverse distance
weighting algorithm that extrapolates point data between itself and its nearest
appendix a Procedure for Assessing Dissolved Oxygen Criteria Attainment
-------
36
neighbors in the spatial unit being considered. Further details regarding the basis
of spatial interpolation of water quality monitoring data within the Chesapeake
Bay Program segmentation framework are described in Ambient Water Quality
Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesa-
peake Bay and Its Tidal Tributaries (Regional Criteria Guidance), pp.154-157
(U.S. EPA 2003).
Recent updates to the interpolator software have been made and the present
program is a Visual Basic program, version 4.61 August 2006, with customized
data region and bathymetry files.
STEP 2.1. Vertical Interpolation
Specific to Chesapeake Bay, in areas > 12 meters, where deep-water and deep-
channel designated uses occur, the program uses vertical depth profiles of the
water temperature and salinity data for each Chesapeake Bay Program water
quality monitoring station to calculate the upper and lower boundaries of the
pycnocline.
The program assigns the data from a particular monitoring cruise number by its
date and divides the coincidently measured, georeferenced data into separate files
for dissolved oxygen, salinity, and pycnocline. The result is a set of files for each
parameter that comprise a set of files for each cruise.
The Chesapeake Bay Program interpolator's vertical interpolation function (On
the Data Import screen), is run in batch mode to vertically interpolate each data
file. The program is used with default settings beginning with a 0.5 meter START
DEPTH and applying a 1.0 vertical meter STEP DEPTH.
STEP 2.2. Horizontal Interpolation
After vertical interpolation, interpolated data is available at scales below the more
than 1.0 meter depth-steps from the water quality data collection. To generate a
horizontal interpolation of the vertically interpolated data set, the program uses
the Interpolate screen. Data files are again processed in batch mode presently
using the following settings:
3D inverse-distance squared model
Min # Neighbors = 1
Max # Neighbors = 4
Horizontal Range (max) = 99000 m (essentially only limited by each
segment's data region)
Vertical Range (min) = 0.1 m
Vertical Range (max) = 0.1 m
Vertical step size = 0.1 m
Missing value = -9
A file for each water quality parameter-cruise combination (parameters of
dissolved oxygen, temperature and salinity measured coincidently in space and
appendix a Procedure for Assessing Dissolved Oxygen Criteria Attainment
-------
"
time) is produced containing interpolated values for a set of cells representing the
bathymetry of Chesapeake Bay (with depths in 1-meter increments).
STEP 2.3. 30-Day Average Interpolations by Month
A 30-day average is then calculated for each grid cell, for each parameter-cell
combination. The output is a set of files for each parameter. Each set of files
includes an individual file for each month (e.g., 30-day average interpolation
output per month) of the three-year assessment period.
STEP 2.4. Apportioning Results by Designated Use
Another program uses, in this case, the 30-day average interpolated pycnocline
and salinity files (i.e., salinity data that were originally, coincidently measured at
the same time of the dissolved oxygen measurements) to first divide the interpo-
lated dissolved oxygen data into separate files for each designated use. Second,
the program then applies the appropriate water quality criterion based on the envi-
ronmental parameter and designated use to calculate violation rates for each
Chesapeake Bay Program assessment segment. The result is a file for each Chesa-
peake Bay Program segment-designated use combination. (Note: This procedure
of implementing different criteria over space for a segment that bridges more than
one salinity zone reflects previous documentation in U.S. EPA 2007, Chapter II:
Refinements to Chesapeake Bay Water Quality Criteria Assessment Methodology,
"Step-4 - Pointwise Compliance" (pp. 17-18) and that "the only requirement (of
the assessment) is that the final attainment determination be "yes" or "no" for
each interpolator cell." This procedure assures that salinity-variable criteria
(e.g., 30-day mean = 5.5 mg O2/L where salinity 0-0.5 ppt, and = 5.0 mg O2/L
where salinity > 0.5 ppt in Open Water Designated Use) are appropriately applied
based on measured salinities during the assessment period. The Chesapeake
Bay Program segmentation boundaries (e.g., XXXTF= "Tidal Fresh",
XXOH="oligohaline") are not used as the salinity determinant because they are
based on historical salinity patterns and would not accurately depict salinity
conditions present during individual assessment periods.
STEP 2.5. Water Quality Criteria Assessment, Attainment
and Violations
Output files contain a row for each month of the assessment period (2004 - 2006),
and each row contains the following columns:
"failed volume," "assessed volume," "total volume," and "fraction failed"
(calculated as failed volume/assessed volume).
A final program takes the accumulated violation rates for each segment-desig-
nated use assessment and creates a cumulative frequency distribution (CFD)
curve.
Criteria violation results of the assessment CFD (i.e., non-attainment) are compared
with a standard reference or "bioreference" CFD curve, which represents an "allow-
able" amount of criteria violation that can still represent a healthy habitat. For further
appendix a Procedure for Assessing Dissolved Oxygen Criteria Attainment
-------
38
details with illustrations of the CFD development and comparisons procedure, refer
to Chapter vi. Recommended Implementation Procedures in Ambient Water Quality
Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesapeake
Bay and Its Tidal Tributaries (Regional Criteria Guidance) (U.S.EPA 2003). A
review of the procedure is provided below.
PLOTTING A BIOREFERENCE CURVE
A biological reference curve of acceptable violation rates is generated using a cumu-
lative frequency distribution (CFD) of violation rates for "healthy" designated uses.
The violation rates are sorted in ascending order, ranked in descending order, and
graphed on a quantile plot:
Violation rates are plotted on the x axis, with plotting position on the y axis.
Plotting position represents the probability, i/n, of being less than or equal to a
given violation rate, or x, and is plotted on the y axis as a function of rank, or
"i", and sample size, or "n".
The x axis is labeled "space" because the violation rate represents the fraction
of volume that is in violation.
The y axis is labeled as "time" because "probability" represents the probable
amount of time that a given violation rate will be observed.
The Chesapeake Bay Program currently uses the Wiebull plotting position to
plot the cumulative distribution function. The Wiebull equation for calculating
probability, y, for each violation rate with rank "i" is:
y = i/(n+l); i = rank
In order to generate a graph of the CFD:
Xj , x2, x3,...xn= violation rates provided herein, sorted in ascending order,
with rank (i) assigned in descending order
After plotting the data's violation rates and probabilities, two additional points
should be added to the distribution in order to complete the CFD curve:
- Insert (x0,y0) = (0,1) before the first data point
- Insert (xn+1,yn+1) = (1,0) after the last data point
LITERATURE CITED
Bahner, L. 2001. The Chesapeake Bay and Tidal Tributary Volumetric Interpolator, VOL3D,
Version 4.0. National Oceanic and Atmospheric Administration, Chesapeake Bay Office.
http://www.chesapeakebay.net/cims/interpolator.htm
U. S. Environmental Protection Agency. 2003. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries.
April 2003. EPA 903-R-03-002. Region III Chesapeake Bay Program Office, Annapolis, MD.
U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved
Oxygen, Wlater Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries -
2007 Addendum, July 2007. EPA 903-R-07-003 Region III Chesapeake Bay Program Office,
Annapolis, MD. 21403.
appendix a Procedure for Assessing Dissolved Oxygen Criteria Attainment
-------
.
appendix
Stations Involved in the 2004-2006
303d Listing Assessment for 2008
Table B-1. Stations involved in the 2004-2006 303d listing assessment for 2008.
STATION,UTMJX,UTM_Y
001,332429,4228770
11104,385086,4083611
11109,371365,4089082
11116,352877,4106857
11120,333076,4131339
11124,323799,4129435
11129,372088,4086261
11M03,401227,4087648
11M08,385305,4122864
11M12,396751,4128024
11M16,391726,4150071
11M21,40 8521,4179151
11M25,440944,4199881
11R02.3681 19,4167692
11R08.359773 ,4174837
11R13,355438,4180790
11R19,343916,4194977
11R23,313470,4224856
11R28,368710,4168820
11Y01,362419,4125841
11Y07 ,357536,4129365
11Y12,352900,4139869
11Y16,342801,4150723
11Y20,326135,4174280
11Y24,327153,4159761
11Y28,347570,4145278
12102,333875,4124563
12107,381671,4084565
12113,369573,4086753
12118,346578,4118304
12123,365039,4095140
12128,357036,4101494
12M01,408735,4193275
12M05,385535,4149922
12M 10,38693 1,4095357
12M50.41 0360,41 05475
12M54,385679,4097040
12M58,405548,4181450
12M63,373122,4154180
12R04,338172,4206191
12R08,358718,4179540
12R13,381556,4160738
12R18,366984,4165076
12R23,363648,4172578
12R51,366814,4163862
STATION,UTMJX,UTM_Y
11101,384884,4090174
11105,383882,4079145
11110,367027,4099699
11117,356012,4113447
11121,328208,4140860
11126,297695,4132812
11130,377273,4091426
11M05,399181,4092851
11M09,382035,4130285
11M13,396772,4132609
11M17,402456,4165412
11M22,398699,41 85969
11M26,408315,4168497
11R05 ,365769 ,4166464
11R10.358337 ,4176687
11R14.353987 ,4185245
11R20,336134,4203780
11R24.309333 ,4226458
11R29 ,361047 ,4177106
11Y02,363091,4126804
11Y09,357344,4131741
11Y13,349425,4144222
11Y17,336254,4164699
11Y2 1,334636,41 58658
11Y25,325879,4161838
12,354157,4141353
12104,362548,4078317
12110,365836,4095861
12114,300363,4131717
12119,369509,4095185
12124,356556,4115037
12129,369379,4098616
12M02,409776,4161842
12M06,394259,41 52752
12M1 1,392064,4095135
12M51,410616,4095708
12M55,403856,4144865
12M59,430309,41 94040
12R01,348573,4188859
12R05,356069,4182574
12R09,368179,4165801
12R15,316275,4225666
12R19,351270,4184325
12R26,351570,4187523
12R52,355501,4183241
STATION,UTMJX,UTM_Y
11102,378244,4095294
11106,387988,4077170
11111,360716,4094905
11118,356104,4114426
11122,331320,4122437
11127,321661,4130807
11M01,412946,41 04658
11M06,390360,4108356
11M10,379684,4134839
11M14,408106,4144850
11M19,386402,4171288
11M23,421816,41 88701
11M27,394095,4094153
11R06,364362,4165318
11R11,357392,4177416
11R15,350294,4184286
11R21, 332662,4207607
11R25 ,306676,4229221
11R30,321683,4218631
11Y04,360281,4126313
11Y10,356379,4131943
11Y14,349373,4144445
11Y18,336177,4164605
11Y22,331819,4158177
11Y26,346401,4146943
129,370233,4154327
12105,365478,4099740
12111,358558,4103051
12116,374202,4090672
12120,315601,4130865
12126,377346,4094970
12150,358181,4102588
12M03,393520,4189833
12M07,424142,4170262
12M12,405266,4143616
12M52.40 1836,41 16605
12M56,394538,4161032
12M60,405586,4081623
12R02,357607,4182571
12R06,360703,4169431
12R10,305284,4234599
12R16,356829,4181047
12R21,357887,4182198
12R27 ,356876,4177033
12Y01,369288,4119164
STATION,UTMJX,UTM_Y
11103,380998,4090425
11108,375851,4089230
11114,357043,4100670
11119,356282,4116665
11123,329429,4121882
11128,342192,4117601
11M02,404083,4087758
11M07,401559,41 15322
11M1 1,371533,4136265
11M15,413814,4145848
11M20,413374,4178091
1 1M24,426484,41 92946
11R01, 372854,4163934
11R07 ,361884,4168819
11R12,358075 ,4178093
11R17,347211,4188227
11R22,315369 ,4224203
11R26,334668,4204531
11R31, 364549 ,4164706
11Y06,361003,4129068
11Y11,356647,4135524
11Y15,346502,4145029
11Y19,328309,4173283
11Y23,328370,4157578
11Y27 ,347570,4145278
12101,383101,4085048
12106,363363,4101423
12112,364951,4081518
12117,380489,4088702
12122,308931,4130099
12127,340700,4119303
12151,331988,4138487
12M04,409256,41 12239
12M08,408021,41 16089
12M13,388016,4124145
12M53,399754,41 23033
12M57,408774,4174630
12M62,371283,4137416
12R03,356951,4182406
12R07 ,336923,4199431
12R11,346493,4191397
12R17,381183,4164083
12R22,363586,4174749
12R50,371239,4173901
12Y02,361474,4127273
appendix b Stations Involved in the 2004-2006 303d Listing Assessment for 2008
-------
40
12Y03,347903,4143916
12Y07,366404,4124014
12Y12,364919,4123685
12Y16,364569,4125765
12Y20,354080,4138659
12Y50,373193,4124020
13101,371864,4089215
13106,326134,4124003
13110,357820,4100162
13116,344731,4116590
13120,352809,4114632
13M01,419178,4191194
13M05,403989,4178983
13M10,416700,4165851
13M14,401684,4108566
13R03,326246,4213702
13R08,373787,4165129
13R12,345718,4189617
13R16,363736,4165905
13R21,372805,4164784
13Y01,347639,4145194
13Y05,336033,4159330
13Y09,362918,4128224
13Y15,342473,4152260
13Y19,362071,4126762
13Y24,347034,4147596
13Y28,362930,4128983
1AAUA004.26,294171,4256178
IACOAOOI.44,371078,4204953
IACUTOOO.58,380964,4200362
IAFOUOOO.19,322226,4301101
IAHACOOO.96,383574,4198444
IALIFOOO.19,319656,4286947
IALOGOOI.20,364727,4206164
1AMAO004.08,324217,4229849
1AMON002.60,327477,4235515
1ANOM007.79,349141,4215718
1AOCC006.99,303224,4284377
1APOH002.76,310660,4283722
1APOT040.80,328860,4233805
1APOT041.95,328397,4235329
1APOT080.29,299399,4264955
IASPNOOO.08,378093,4199326
IAWESOOO.41,363387,4210260
1AWLL002.21,319886,4246290
IAYEOOOO.92,365313,4211290
2-BLY000.65,299746,4129267
2-CHK015.50,331924,4138404
2DAPP001.91,295841,4131825
2DSFT001.18,289174,4129111
2-HOF000.44,374870,4083960
2-lMS007.83,374332,4087735
2-1MS021.26,358213,4102625
2-lMS049.00,331863,4120763
2-lMS069.08,311660,4130270
2-lMS074.44,302963,4132363
2-1MS099.30,288286,4142259
2-MIC000.03,345285,4120131
2-PAR000.77,383762,4073440
2-PGN000.80,359708,4095972
2-PGN004.57,355805,4094240
2-PGN008.42,352010,4096905
2-UCK001.23,323578,4122658
2-WWK003.20,360896,4108595
32,355055,4094181
3-CRR000.23,368907,4167865
3-HOK002.74,335485,4197499
3-MLL001.31.374227,4160685
3-RPP007.03,376681,4165415
3-RPP019.80,363048,4174028
3-RPP040.89,341807,4197782
12Y04,358487,4132732
12Y08,349722,4142221
12Y13,355505,4133638
12Y17,356100,4134184
12Y21,369916,4122841
12Y51,363141,4127084
13102,366360,4097666
13107,325862,4123332
13111,325251,4123889
13117,353021,4112633
13121,371166,4090850
13M02,387927,4172675
13M06,430545,4186091
13M11,432827,4196534
13M15,404099,4138941
13R04,365894,4165269
13R09,344053,4193717
13R13,363273,4168836
13R17,377456,4160510
13R22,358895,4180498
13Y02,355822,4133603
13Y06,368749,4122148
13Y11,351738,4138515
13Y16,340652,4153119
13Y20,367784,4123108
13Y25,352660,4146795
15,353572,4129188
IABOMOOO.46,361251,4217014
1ACOA002.06,371715,4204176
IADOUOOO.60,315541,4285375
IAGADOOO.77,358568,4219508
IAHAMOOO.96,361839,4208838
IALIFOOI.09,319144,4288068
1ALOW004.77,354857,4217865
IAMAWOOI.28,308325,4270249
IANEAOOO.40,303460,4274805
1AOCC002.47,306593,4279202
IAPOHOOO.21,313983,4281771
1APOM002.41,296607,4246936
1APOT041.55,328628,4234766
1APOT042.01,328497,4235450
IAPREOOI.58,375458,4201092
IAUMCOOO.96,321083,4242991
IAWESOOI.00,362722,4209838
IAXDWOOO.08,359594,4218429
2-APP001.53,296449,4131891
2CCHK002.40,333212,4126091
2-CHK023.64,328494,4141696
2-DEC000.58,383363,4069224
2-ELI003.98,381324,4081793
2-IND000.98.389897,4076063
2-1MS012.79,368411,4093875
2-lMS025.74,351588,4103152
2-1MS050.57,329334,4121196
2-lMS070.44,309009,4131440
2-lMS077.70,298836,4132820
2-1MS104.16,285959,4147513
2-NANOOO.OO,369794,4086031
2-PAR001.77,382519,4074575
2-PGN001.19,359035,4095644
2-PGN005.46,354865,4094840
2-POW000.60,342253,4121160
2-WBE003.58,375812,4076937
2-WWK003.98,360602,4109706
35,281854,4155933
3-CTM000.63,371248,4173873
3-HOK003.61,334459,4197285
3-MYE000.77.369169,4172488
3-RPP011.58,369683,4164364
3-RPP027.13,355515,4183229
3-RPP042.12,339882,4198504
12Y05,352816,4137062
12Y10,357544,4130728
12Y14,339717,4163580
12 Yl 8,353494,4139214
12Y22,334575,4160901
12Y52,326533,4160948
13104,377311,4088797
13108,356303,4108389
13114,348936,4119193
13118,363950,4102485
13123,370050,4090455
13M03,408469,4093586
13M07,403587,4191220
13M12,391321,4108551
13R01,375597,4162708
13R05,344234,4193818
13R10,366396,4178555
13R14,334998,4204036
13R19,369745,4167979
13R24,342202,4198687
13Y03,355996,4132518
13Y07,357327,4129238
13Y12,352374,4139699
13Y17,340937,4161931
13Y22,368683,4121850
13Y26,339953,4156295
19,371950,4115770
IACHOOOI.57,297049,4264807
1ACOA004.14,369820,4202144
IADOUOOI.40,314689,4286542
IAGLEOOI.50,368393,4205509
IAHUTOOO.01,321740,4295553
1ALIS002.00,388448,4194214
IAMAOOOO.42,327977,4230477
IAMONOOO.96,327987,4233222
IANEAOOO.57,303460,4274805
1AOCC004.52,305326,4281913
IAPOHOOI.56,312384,4282218
IAPOTOOO.00,386637,4204505
1APOT041.65,328608,4234936
1APOT042.03,328424,4235452
1AQUA002.15,299611,4269685
1AUMC002.30,319481,4242280
IAWLLOOO.94,320052,4244917
IAXLDOOO.15,354357,4221173
2-APP009.52.288987,4125505
2-CHK001.27,333288,4124619
2C1MS036.83,349469,4116398
2-DEP000.26,364477,4105050
2-GOR000.35,333898,4126316
2-1MS002.14,380597,4095616
2-1MS014.24.366977,4095460
2-1MS027.31,353262,4110620
2-lMS064.52,315488,4128255
2-lMS071.56,307028,4129956
2-lMS087.01,296140,4137110
2-1MS 109.39,286048,4155684
2-NAN019.14,358661,4067106
2-PGNOOO.OO,360272,4097165
2-PGN002.58,357036,4095863
2-PGN006.65,353359,4094847
2-SBE001.53,384814,407 6792
2-WLY002.03,386308,4090436
2-XQW000.69,289451,4137788
3-BRD000.62,383388,4157650
3-HOKOOO.15,337018,4198921
3-LAN002.81.358359,4185514
3-OCC001.85,330044,4211139
3-RPP013.42.366825,4163831
3-RPP028.20,353232,4182287
3-RPP045.21,335951,4201546
12Y06,353399,4134936
12Y11,344249,4151126
12Y15,342074,4159625
12Y19,360933,4129972
12Y23,355846,4134379
13,363725,4128750
13105,358530,4104593
13109,356704,4107962
13115,381042,4091999
13119,379036,4088121
13124,352987,4116203
13M04.403804,4106992
13M09,399175,4116862
13M13,392173,4187616
13R02,375215,4164139
13R06,362204,4175076
13R11,350199,4185866
13R15,359430,4174611
13R20,321068,4218989
13R25,363120,4168766
13 Y04.357634,4132711
13Y08,356584,4133882
13Y13,354202,4134097
13 Yl 8,327987,4156534
13Y23,370640,4121957
13Y27,346298,4144854
1AAUA003.71,294393,4255383
1ACH0003.65,294112,4266156
IACOCOOO.42,373260,4204208
1ADOU002.01,314781,4286996
IAGLEOOI.76,368034,4204784
IAHUTOOI.72,319410,4296506
1ALIS004.20,385580,4195271
IAMAOOOI.36,326683,4231369
IAMONOOI.91,327798,4234583
IANOMOOS.99,347993,4218267
1AOCC006.64,303729,4284083
1APOH002.32.311316,4283530
1APOT035.00,339535,4225523
1APOT041.80,328504,4235154
1APOT042.72,328360,4235897
1AQUA002.38,299246,4269808
1AUMC004.43,319838,4239783
IAWLLOOI.30,320013,4245454
IAYEOOOO.65,365901,4209979
2-BEN001.42,368193,4080723
2-CHK002.17,333519,4125734
2-CLG000.23,349687,4121280
2-DSC003.19,332348,4140921
2-GOR000.42.334075,4126537
2-lMS006.70,374985,4090059
2-lMS015.70,366809,4098623
2-1MS040.93,342604,4117341
2-lMS066.88,314358,4131632
2-lMS073.08,305386,4132816
2-1MS087.11,295946,4137122
2-lOG000.62,360876,4095527
2-PAROOO. 12,384341,4073277
2-PGN000.76,359256,4096578
2-PGN003.57,355670,4095516
2-PGN007.44,352981,4095871
2-SGL001.00,360456,4067607
2-WWK000.95,362876,4105498
30,309637,4131426
3-CRC000.15,334578,4197167
3-HOK000.74,336555,4198261
3-LIT000.85,366056,4179140
3-PIS000.12,339625,4196605
3-RPP014.38,365692,4166371
3-RPP035.14,345177,4189616
3-RPP060.63,324368,4219682
appendix b Stations Involved in the 2004-2006 303d Listing Assessment for 2008
-------
"
3-RPP067.00,320361,4223860
3-TOT005.11,348728,4198705
752A,346100,4112363
7-BRK004.14,375569,4106430
7-CHE003.49,410480,4105725
7-CHE016.05,399854,4123004
7-CHE025.76.376259,4113531
7-CHE038.32.412691,4159275
7-CHE048.79,389592,4179239
7-COC000.06,386736,4186454
7-COC000.92,387410,4187558
7-CSX001.55,432265,4179838
7-EBL000.01,404331,4082838
7-EST005.56,379462,4144063
7-EST006.91,379155,4145267
7-HAH002.96,384656,4136541
7-HRP001.15,384656,4136541
7-INN001.06,386080,4184450
7-LNC000.68,411015,4080454
7-MES001.34,439853,4195280
7-MJB004.00,381722,4133369
7-NSS001.62,416840,4148332
7-OCB000.18,433619,4173971
7-OCN004.56,432524,4174183
7-ONB000.56,434153,4174596
7-PKS008.53,430317,4194030
7-PNK010.41.373127,4154176
7-PUN000.47,424630,4169872
7-SEN001.35,369671,4131466
7-THF000.62,434933,4185697
7-WAR004.26,370478,4139851
7-WES002.58,400561,4080138
7-XAN000.17,387774,4187007
7-XDN000.27,373648,4142728
8-MPN017.45,332556,4169260
8-MPN039.10.314787,4184137
8-PMK026.98.326537,4160938
8-PMK048.80,311541,4171014
8-YRK001.12,373185,4123996
8-YRK009.39.363157,4127082
ANA0082,331574,4311772
ANA11,329227,4305746
ANA24,325364,4303302
APP001.83,295810,4131789
AQU0037,,
CB2.1,411823,4366119
CB3.3C,382253,4317113
CB4.1E,380969,4297307
CB4.2W,369343,4278281
CB4.4,382750,4252514
CB5.3,397329,4196671
CB6.1,397396,4160791
CB7.1,412737,4171156
CB7.2E,409322,4141018
CB7.4N.411151,4102258
CHE019.38,376475,4119877
CHK015.12,331823,4137970
CHO0490,,
COR11,369341,4172331
COR7,373384,4178324
CYP2,356498,4092680
EBL002.54,405160,4079376
EE3.0.411113,4237754
EE3.4,430437,4195954
ELI2,380690,4082660
ET2.1,429838,4375370
ET4.1,420212,4344230
ET5.2,407846,4270714
ET8.1,428467,4221862
FOCRE,295511,4137654
FOR_4,307247,4229208
3-RPP104.47,288963,4236940
3-URB001.00.361155,4165931
765,302741,4273068
7-BRN000.23,386558,4149956
7-CHE004.52,410536,4095924
7-CHE018.14,385697,4097079
7-CHE027.61,403786,4144760
7-CHE040.53,391319,4165601
7-CHE050.87,405530,4181417
7-COC000.86,387266,4187449
7-COC000.95,387422,4187589
7-CTC001.98,436964,4194453
7-EBL001.15,404314,4081389
7-EST006.33,379845,4145025
7-EST007.06,379086,4145400
7-HKC000.15,386961,4149630
7-HUG001.24,414317,4141554
7-KNS000.40,410434,4126671
7-LOB001.79,405381,4077063
7-MES006.92,445869,4192957
7-MLF002.40,384756,4150243
7-NWB000.34,380322,4106137
7-OCH003.82,422522,4156417
7-OCN004.96,433358,4174109
7-OPC001.68.411815,4122598
7-PNK000.50,384557,4155038
7-PNK014.33,367843,4155429
7-PUN002.12,427134,4168967
7-SWB 001.53,380468,4103652
7-THG000.36,412878,4136123
7-WAR005.77.368149,4140658
7-WET000.60,370359,4129553
7-XAN000.36,388059,4187099
7-XDQ000.27,380211,4145347
8-MPN017.46.332567,4169078
8-PMK006.17,335019,4154703
8-PMK028.43,324933,4161564
8-PMK056.87,307483,4173202
8-YRK004.79,367623,4121449
8-YRK016.88,355361,4135297
ANAOl.331670,4309488
ANA14,328633,4305019
ANA29,324519,4302148
APPOOS.55,291190,4131674
BBY002.88,407532,4083505
CB2.2,398780,4355918
CB3.3E,383418,4317836
CB4.1W,373004,4297091
CB4.3C,374994,4268540
CBS.1,386968,4241940
CB5.4,396587,4184289
CB6.2,397772,4149505
CB7.1N,414166,4181313
CB7.3,400035,4108423
CB8.1,396093,4095002
CHK001.47,334058,4124726
CHK023.96,328514,4141669
CHP,348307,4111102
COR3,370468,4181758
COR9.371191,4172745
EBB01,389035,4077414
EE1.1,391609,4304585
EE3.1,414660,4228468
EE3.5.425661,4183574
ERPJUC ,3 84450,4077457
ET2.2.424761,4368943
ET4.2,394661,4316568
ET6.1,437567,4265248
ET9.1,429117,4212609
FOCRLAG.289043,4138148
FRG0002,378783,4352698
3-RPP107.91,285821,4240552
3-WHSOOO.89,368117,4163379
7-BBY002.88,407537,40 84005
7-BWN000.45,405580,4081622
7-CHE008.90,400213,4103090
7-CHE019.79,409521,4131780
7-CHE033.65,406763,4152802
7-CHE046.24,408823,4174667
7-CHE055.94,399219,4193155
7-COC000.88,387473,4187505
7-COC001.61,387408,4188526
7-DEP001.38,434050,4180587
7-EBL002.54,405159,4079377
7-EST006.41,379742,4145095
7-FER000.92,370130,4152832
7-HKC000.18,386956,4149636
7-HUN001.88,438116,4182039
7-LKNOO1.19,409794,4082039
7-LTH000.14,432103,4173642
7-MIL002.00.384069,4183739
7-MUD002.29,443000,4190304
7-NWB000.38,380192,4106507
7-OCN001.92,429087,4175978
7-ONBOOO.19,433549,4174366
7-OSBOOO.13,433132,4173695
7-PNK001.26,384243,4153636
7-POC001.76,444549,4203301
7-QUE001.23,380963,4149699
7-TAW000.22,418386,4157368
7-WAROOO.88,374244,4136103
7-WES000.62,401362,4083056
7-WHY000.38,384586,4188614
7-XBO001.30,405332,4074845
8-FELOOO. 19,359370,4126447
8-MPN021.07,331050,4173368
8-PMK017.67,331779,4159530
8-PMK039.74,315366,4164047
8-QEN002.47,353684,4129122
8-YRK004.80,368401,4123792
8-YRK021.16,351693,4140831
ANA05,330420,4308527
ANA19,326758,4304276
ANA30,332018,4311226
APP007.58,290915,4128466
BXK0031,460479,4214956
CB3.1,393173,4345077
CB3.3W,379813,4318075
CB4.2C,376622,4278319
CB4.3E,378916,4268479
CB5.1W,379767,4242784
CB5.4W,386042,4185908
CB6.3,397375,4141157
CB7.18,406584,4159881
CB7.3E,406515,4120769
CB8.1E,407851,4089534
CHK006.14,333694,4131169
CHO0367,,
COAN5,367507,4206156
COR5,365898,4179190
CR8,329492,4131109
EBE1,385059,4077977
EE2.1,389081,4278722
EE3.2.418794,4204010
ELDOl.381541,4080783
ET1.1,417720,4381039
ET2.3,422657,4373588
ET5.0,431956,4316775
ET6.2,422828,4243182
FOCR27 A,290992,4139076
FOR_1,282966,4240883
FRG0018,378928,4354684
3-RPP110.57,283925,4244211
SBWNCOIO.02,404533,4072552
7-BLB004.63,446343,4203032
7-CCH000.43,409597,4124691
7-CHE012.06,401819,4116455
7-CHE020.80,389219,4123827
7-CHE037.88,394538,4161031
7-CHE047.16,425565,4176076
7-CHSOOO.84,374251,4116211
7-COC000.89,387448,4187517
7-CRY000.59,411426,4081898
7-DRN003.40,358389,4161011
7-EST002.75,380641,4140383
7-EST006.68,379536,4145280
7-GWR008.89,375180,4192422
7-HLD002.67,446513,4197976
7-IND002.26,380990,4173449
7-LKN002.77,410016,4079663
7-LYN000.03,402732,4085142
7-MIL004.00,381202,4184759
7-NEW001.92,378598,4099666
7-OCBOOO. 10,433477,4173969
7-OCN003.28.430889,4174884
7-ONB000.20,433602,4174369
7-OSB000.25,433342,4173638
7-PNK005.35,378919,4154933
7-POQ004.12,371824,4111076
7-SEN000.19,371294,4131339
7-THA000.76,399755,4078069
7-WAR002.88,371272,4137404
7-WES001.68,401294,4081393
7-WIL001.50,368768,4136705
7-XDB000.08,382365,4180520
8-KNG004.46,357578,4126404
8-MPN024.84,326188,4173993
8-PMK023.12,326164,4156595
8-PMK044.64.313427,4167601
8-SRW000.35,368538,4124653
8-YRK005.67,366800,4122886
8-YRK027.00,345614,4148057
ANA08,329841,4307368
ANA21,326043,4302361
APPOOl.53,296949,4131991
APPO11.04,288246,4123970
CB1.1,407087,4377829
CB3.2,387140,4335727
CB4.1C,378499,4298300
CB4.2E,378169,4278295
CB4.3W.369911,4268621
CB5.2,392384,4221705
CB5.5,395113,4172286
CB6.4,392849,4121795
CB7.2,404455,4141073
CB7.4,409195,4094882
CCM0069,421016,4255255
CHK008.30.334117,4134513
CHO0417,,
COR0056,,
COR6,368533,4175274
CYP,356195,4093196
EBLOOO.01,404330,4082838
EE2.2,385995,4265816
EE3.3,432666,4199634
ELE01,384272,4078834
ET10.1,450335,4215226
ET3.1,423936,4357851
ET5.1,420824,4295761
ET7.1,430776,4235712
FOCRAPP.289197,4129039
FOR_2,282677,4244111
GP1,359159,4095505
appendix b Stations Involved in the 2004-2006 303d Listing Assessment for 2008
-------
42
GWl ,374130,4192478
GW6,375133,4192485
HCWF_PIER,374864,4083967
IH3,304410,4267810
JC1,360878,4093118
JMS042.92.341843,4118873
JMSOSS.94,323512,4126984
JMS075.04,302135,4131894
JMS104.16,286040,4147541
JMSMH_20M,358354,4102271
JMSMH_8M,352893,4104997
JMSOH_26G,342441,4116686
JMSPH_12P(1),383298,4094382
JMSPHJ3P(1 ),376002,4090772
JMSPH_18P(1),381628,4170049
JMSPH_5P,376053,4087673
JMSPH_9P,381693,4092832
KNGOl.331387,4306410
LEI .3,369960,4244663
LE3.1,357659,4180333
LE3.6,386593,4161856
LE4.3,373068,4121782
LES.3,368701,4094829
LFA01,382889,4085496
LKN002.77,410016,4079664
MDR0028,377197,4352657
MPN001.65,342334,4156035
MPN016.28.333757,4169416
MPN028.86,322012,4176989
NOM0007.376691,4351200
PMK006.16,335150,4154852
PMK023.69,327052,4157986
PMK047.41,311137,4171221
PMS29,324518,4302117
PMS51,323525,4293226
PNK013.91,368548,4155161
PTBO 1,323115,4306220
PXT0455.351147,4293900
RET2.4,326093,4247926
RET4.2,341294,4159764
RIC,364854,4090657
SCAUST.290399,4258089
SCSHORE.296472,4253724
SMT02,371683,4230150
SMT08,371055,4224610
SMT12,374363,4224417
TFl.5,352073,4285982
TF2.2,316395,4284566
TF3.1E,296531,4235512
TF4.2,321520,4161136
TF5.3,288218,4142278
TF5.6,323512,4126984
TOT_2,349762,4198971
TRQ0088.413273,4252581
WB1,399240,4079616
WE4.2,377038,4122598
WIW0089,,
WT1.1,393167,4365613
WT5.1,368360,4341015
WT8.2,367033,4304960
XAK7810,442840,4202056
XBE8396,368039,4222175
XBF5231,,
XBF7904,,
XCC4530,,
XCD3596,,
XCD7202,,
XCF2621,371683,4230149
XCH8973,408670,4241343
XCJ5200,,
XDB4544,,
GW2,372523,4194345
GW8,379751,4190651
HOK0005,375698,4352726
IH4,310091,4271218
JMS002.55,377473,4095389
JMS043.78,341072,4121641
JMS062.82,317509,4127379
JMS082.49,300736,4139625
JMS109.62,286771,4156012
JMSMH_23M,353723,4118661
JMSMH_QC_0.1N(20M),368947,4092187
JMSOH_31G,329806,4120138
JMSPH_12P(100),383298,4094382
JMSPH_13P(100),376002,4090772
JMSPH_18P(100),381628,4170049
JMSPH_5P(1),376053,4087673
JMSPH_9P(1),381693,4092832
KNG02,329525,4307251
LEI .4,375738,4241396
LE3.2,363259,4170237
LE3.7,384553,4154548
LE4.3B,369181,4121517
LE5.4,376002,4090771
LFBOl.385815,4083370
LYNOOO.03,402627,4085156
MDR0038,375899,4353341
MPNOOS.04,341256,4160238
MPN018.70,331870,4170504
MPN031.95,317460,4180097
PIA1,374167,4153268
PMK008.92.335637,4158612
PMK034.00,321321,4160784
PMSOl.317531,4309771
PMS35,323814,4299727
PNK002.52,382214,4151860
PNK018.35,363424,4158873
PWC04,324576,4304707
RET1.1,354868,4261571
RET3.1,339830,4198205
RET4.3,341892,4152809
RICE1,304865,4133290
SCDOBE.299028,4247597
SCSPILL.294502,4246711
SMT04,373908,4227728
SMT09,366844,4225233
TF1.2,347994,4297623
TF1.6,353440,4280160
TF2.3,310717,4275538
TF3.2,308359,4227553
TF4.4,321476,4176991
TF5.4,296949,4131991
THA000.07,399520,4078978
TOT_3,348738,4198734
TRQO 146,412737,4257964
WBB05,375524,4076831
WE4.3,378116,4115369
WIW0141,439234,4243948
WT2.1,384454,4360187
WT6.1,372437,4326147
WT8.3,366971,4301260
XBD9558,,
XBE9300,,
XBF6734,373469,4219207
XBF9130,372996,4223562
XCC8346,,
XCD3765,,
XCE1407,,
XCF9029,373013,4241953
XCI3696,,
XCJ6023,,
XDB4877,,
GW3,373221,4192892
GYIOOOl.396196,4327448
IH1,309067,4273070
IH5,304872,4269181
JMS018.23,365742,4101843
JMS048.03,333319,4123129
JMS069.08.311362,4130460
JMS094.45,292157,4139492
JMSMH_16M,373831,4088013
JMSMH_25M,355353,4114480
JMSOH_13G,331043,4120361
JMSOH_3G,352502,4120758
JMSPH_12P(1N),383298,4094382
JMSPH_13P(1N),376002,4090772
JMSPH_18P(1N),381628,4170049
JMSPH_5P(100),376053,4087673
JMSPH_9P(100),381693,4092832
LEI.1,360193,4254200
LE2.2,361327,4225505
LE3.3,370000,4172268
LE4.1,350343,4142679
LE5.1,353723,4118661
LE5.5-W,383145,4095571
Lrr_TOT,348805,4199011
MAT0016,308909,4270769
MNK0146.436651,4225492
MPN008.12,339310,4163162
MPN021.95,329357,4173031
MTI0015,351613,4289677
PIS0033,327238,4285187
PMK012.18,333603,4159929
PMK037.34,316238,4163086
PMS 10,320551,4307974
PMS37,323676,4298959
PNK004.41,380221,4155178
POK0014,444014,4203910
PXT0311,354038,4276642
RET2.1,301859,4253019
RET3.2,349306,4186290
RET5.1A,333694,4131169
SBE2,384911,4074949
SCHARB ,292080,4259256
SGC0041,366758,4225326
SMT06,372996,4223562
SMTIO.375810,4225146
TF1.3,351339,4297128
TFl.7,353648,4271707
TF2.4,302544,4267037
TF3.2A,319860,4220341
TF5.2,284929,4156509
TF5.5,302135,4131894
TOR01,322857,4300088
TOT_4,347298,4194598
TSKOOO.23,348286,4142287
WBE1,378796,4078341
WE4.4,385118,4107873
WIW0144,,
WT3.1,379310,4351015
WT7.1,369723,4318606
WXTOOO 1,351142,4294357
XBE2100,,
XBF0320,,
XBF6843,374772,4219372
XBF9949,375810,4225146
XCC9680,,
XCD5599,,
XCE2643,,
XCH4378,409338,4232794
XCI4078,423925,4232127
XDA0338,,
XDB8278,,
GW4,373207,4191971
HCWF_FORK,625184,4083702
IH2,307992,4269498
IH6,305256,4268604
JMS032.59,353723,4118661
JMSOSO.74,329875,4120262
JMS073.37,304687,4133228
JMS099.00.288718,4142030
JMSMH_1A_M,370074,4094759
JMSMH_4M,368947,4092187
JMSOH_22G,350816,4118845
JMSPH_12P,383298,4094382
JMSPHJ3P,376002,4090772
JMSPH_18P,381628,4170049
JMSPH_21P,383125,4095549
JMSPH_5P(1N),376053,4087673
JMSPH_9P(1N),381693,4092832
LEI .2,368014,4248919
LE2.3,381705,4209059
LE3.4,372510,4165995
LE4.2.360117,4128260
LE5.2,358355,4102271
LE5.6,380766,4085121
LKN001.19,409794,4082039
MAT0078,315480,4273237
MOB006.12,376827,4124418
MPN011.97,335469,4164943
MPN024.65,326202,4174627
NFHFP4.306182,4134236
PMKOOl.29,339868,4156069
PMK018.13,329744,4159301
PMK041.30,312568,4166494
PMS21,322842,4304807
PMS44,323103,4295980
PNK009.96.374119,4152822
POK0087,442045,4210439
PXT0435,352348,4291193
RET2.2,307375,4247179
RET4.1,334971,4154825
RETS.2,341843,4118873
SBE5,384333,4070127
SCRAVEN.294137,4250252
SMTOl.369552,4231904
SMT07,373469,4219207
SMTll.374458,4220311
TF1.4,351454,4292963
TF2.1,321870,4286200
TF3.1B,304567,4235496
TF3.3,332405,4209585
TF5.2A,286040,4147541
TF5.5A.311362,4130460
TOT_1,354273,4197480
TPBOl.328583,4306069
TUK0022,,
WE4.1,380697,4130313
WESOOl.68,401294,4081393
WIW0198,,
WT4.1,374969,4349233
WT8.1,368570,43104 84
WXT0013,350561,4295545
XBE6753,,
XBF3534,,
XBF6903,,
XBG2601,,
XCD0517,,
XCD6674,,
XCF1336,373908,4227728
XCH8097,412074,4239583
XCI4821,415635,4233632
XDA6515,,
XDC3807,,
appendix b Stations Involved in the 2004-2006 303d Listing Assessment for 2008
-------
.
XDE4587,367132,4252270
XEA9461,,
XEG0138,,
XEG4991,,
XEG8519,,
XEH8132,,
XFB1839,317182,4285151
XFB8408,,
XFG3973,,
XFG9210,385759,4297621
XGE0284.367389,4299764
XGE5984,,
XGF1780,366996,4302422
XGG4301,384626,4307155
XGG5959,393038,4309868
XHF0460,378756,4318542
XHG6496,398583,4329224
XHH4822,402236,4326309
XHH6419,401908,4329334
XJF0588,383342,4355530
XJG2340,390817,4358572
XJG7035,390335,4367578
YRKOOl.20,373779,4121033
YRK012.78,357470,4129417
YRK031.24,341562,4152366
ZDM0003,369412,4310776
XDJ9007,428424,4259817
XED0694.353759,4263780
XEG1995,,
XEG5627,,
XEG8593,,
XEI7405,,
XFB1986,324071,4285164
XFD1283,352507,4283330
XFG4620,,
XFH2312,,
XGE2488,368063,4303862
XGE6281,367272,4310889
XGF5404,370593,4309383
XGG4898,398663,4307823
XGG6667,,
XHF0561,378835,4318640
XHH3851,,
XHH4916,,
XIE5748,363143,4346973
XJF0821,387984,4356005
XJG2718,387666,4359928
XJG7856,393266,4368782
YRKOOS.40,367276,4123465
YRKOIS.09,357251,4134595
ZDM0000.369297,4310645
XEA3687,309296,4270145
XEE1502.354949,4265413
XEG2646,,
XEG6966,,
XEH5622,,
XFB0231,315993,4282269
XFB2184,323831,4285612
XFG0809,,
XFG5054,,
XFH7523,402026,4294370
XGE3275,366301,4305255
XGE7059,364063,4312343
XGG2084.396507,4302690
XGG5115,386669,4308606
XGG8251,391935,4314323
XHG0859,393086,4319043
XHH4528,,
XHH4931,,
XIH0077,410272,4335782
XJF2675,381393,4359748
XJG4337.390418,4362370
XJH2362,,
YRK006.77,364805,4123064
YRK023.40,348780,4142550
ZDMOOOl.369325,4310755
XEA6046,,
XEE3604,355325,4269314
XEG3623,,
XEG7539,,
XEH7912,,
XFB0500,,
XFB5581,323512,4292014
XFG0965,,
XFG9164,393576,4297449
XFI1515,,
XGE5492,368823,4309363
XGF0681,381515,4300351
XGG3479.395865,4305355
XGG5932.389110,4310075
XGG9992,397896,4317382
XHG1579,395979,4320319
XHH4742,,
XHH5046,,
XIH3581,410971,4342271
XJF4289,383623,4362249
XJG4451,392592,4363098
XJI1871,,
YRKOIO.59,361480,4128917
YRK028.58,345373,4150519
ZDM0002,369352,4310810
appendix b Stations Involved in the 2004-2006 303d Listing Assessment for 2008
-------
44
appendix
A Comparison of Methods
for Estimating
The light attenuation coefficient (Kd) is used to assess the Chesapeake Bay water
clarity criteria, measured as percent light-through-water (PLW), using the following
equation:
PLW = 100*exp(-KdZ)
where 'exp' is the base of the natural logarithms and 'Z' equals the criteria applica-
tion depth.
Kd is measured in situ at DATAFLOW calibration stations using LICOR and then
related to other measured parametersturbidity, chlorophyll fluorescence, and
salinityto generate a calibration curve that enables the estimation of IQ at cruisetrack
points. The spatially intensive nature of DATAFLOW data support the interpolative
analysis used to produce the cumulative frequency diagram applied in criteria assess-
ment. However, IQ can be interpolated in two ways. In one method, IQ is calculated at
each cruisetrack point using the three simultaneously measured parameters, and then
it is interpolated. This method is described in Chapter 7 of the 2007 Ambient Water
Quality Criteria for Dissolved Oxygen, Water Clarity and Chlorophyll a for the Chesa-
peake Bay and its Tidal Tributaries - 2007 Addendum (U.S. EPA 2007). The other
method calls for first interpolating turbidity, chlorophyll a, and salinity and then using
the resulting estimates of these parameters to calculate Kj. This method was used by
both Virginia Institute of Marine Science (VIMS) and Maryland Department of
Natural Resources (MD DNR) for the 2008 water clarity assessment.
The two methods were compared to determine if they produce similar results.
Between 100-200 "validation" points were randomly selected and removed from
three James River DATAFLOW cruisetracks (Figures C-l, C-2 and C-3). The
remaining cruisetrack points were then analyzed using the two methods (i.e., calcu-
lating Kd from its correlated parameters prior to interpolation versus calculating Kd
after interpolating its correlated parameters). IQ was calculated at each validation
point using the turbidity, chlorophyll, and salinity measured at that point. This value
was then compared to the estimated Kd values generated from the two methods.
The following equation (see Chapter IV, Table IV-2 of this document) was used to
calculate IQ:
Kd = 1.19267 + 0.2956*Turbidity(1/L5) - 0.05616*Salinity + 0.0002746*
Chlorophyll a
appendix c A Comparison of Methods for Estimating
-------
.
5.50
5.30 -
5.10 -
4.90 -
a 4.70 -
"§ 4.50 -\
5
'B 4.30 -
a 4.10 -
3.90 -
3.70 -
3.50
R2=0.972
slope=1.00
SE=0.04
R"=0.922
slope=0.97
SE=0.07
EPA guidance
2008 assessment
1:1
3.50 3.70 3.90 4.10
4.30 4.50 4.70
observed Kd
4.90 5.10 5.30 5.50
Figure C-1. A comparison of the two Kd estimates against values calculated at validation points
(n = 133) in James River tidal fresh-Lower Chesapeake Bay Program segment (JMSTFL) (4/7/2005
cruise).
6.00
5.50 -
5.00 -
4.50-
J> 4.00 -|
o
'B
S. 3.50-
Q.
3.00 -
2.50-
2.00
R2=0.987
slope=0.98
SE=0.09
R"=0.892
slope=0.83
SE=0.22
EPA guidance
2008 assessment
1:1
2.00 2.50 3.00 3.50 4.00 4.50
observed Kd
5.00
5.50
6.00
Figure C-2. A comparison of the two Kd estimates against values calculated at validation points
(n = 200) in tidal middle James River Oligohaline Chesapeake Bay Program segment (JMSOH),
5/22/2006 cruise.
appendix c A Comparison of Methods for Estimating
-------
46
2.00
1.80 -
1.60-
1.40-
1.20 -
1.00-
0.80 -
0.60-
0.40 -
0.20-
0.00
FT=0.768
slope=0.81
SE=0.14
"fa
R =0.766
slope=0.81
SE=0.14
FT=0.766
EPA guidance
2008 assessment
1:1
0.00 0.20 0.40 0.60
0.80 1.00 1.20 1.40
observed Kd
1.60
1.80
2.00
Figure C-3. A comparison of the two Kd estimates against values calculated at validation
points (n = 99) in the lower tidal James River Polyhaline Chesapeake Bay Program segment
(JMSPH) (9/14/2005 cruise).
At least for the three selected cruisetracks, the two methods produced similar esti-
mates, though there is a suggestion that the method used for the 2008 assessment
predicts with less error. The methods come with their own advantages, however. The
2007 U.S. EPA guidance method is faster and easier to do as there are fewer steps
involved. The 2008 assessment method is difficult to do without either Arclnfo or
Arc Spatial Analyst, but it allows one to visualize spatial patterns, particularly areas
of uncertainty, in the individual components of Kd.
LITERATURE CITED
U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved
Oxygen, Wlater Clarity and Chlorophyll a for the Chesapeake Bay and Its Tidal Tributaries
- 2007 Addendum. EPA 903-R-07-003 Region III Chesapeake Bay Program Office,
Annapolis, MD. 21403.
appendix c A Comparison of Methods for Estimating
-------
.
appendix
Derivation of K
-------
48
xCHLA = DATAFLOW measured chlorophyll a, parameter expression
used to differentiate it from lab derived chlorophyll a based
on nutrient samples.
xCHLA*tributary = interaction term of DATAFLOW measured chlorophyll a
with tributary system
xlnSalin = DATAFLOW instrument derived salinity measurement,
parameter used to differentiate this data from routinely meas-
ured salinity with other instrumentation.
Questions regarding IQ -Turbidity relationships:
1. Does the 1.5 root transformation that worked well to linearize the Kd -
Turbidity relation for VIMS data work well for MD DNR data? Yes.
2. Does one Kd -Turbidity model work for all tributaries? No.
3a. Is chla (chlorophyll a) an important predictor? Yes, but contribution is less
than Turbidity.
3b. Is chla effect same for all tributaries? No.
3c. Is it better to use chla or logchla? Chla
4. Is Salinity a useful predictor? Yes
5. Is there a seasonal effect? Not much
6. Can Tributaries be grouped so that calibration terms are uniform within
group? Yes - the 15 tributaries form 6 groups.
The following details provide the supporting analyses for the answers to the ques-
tions above:
1. Does the 1.5 root transformation that worked well to linearize the Kd -
Turbidity relation for VIMS data work well for DNR data? Yes.
To address this question, a series of linear regression analyses were done use root
transformations ranging from the 1.1 root to the 2.9 root. R-square and root mean
square error for this series are reported (Table D-l) as measures of goodness of fit.
Table D-l. R-square and root mean square error from a series of linear regression
models where IQ is the dependent variable and the independent variables include
Tributary, root(turbidity), Tributary*root(turbidity), chlorophyll, Tribu-
tary*chlorophyll. The root transform of turbidity ranges from 1.1 to 2.9. Note:
"chlorophyll" refers to chlorophyll a measurements.
appendix d Derivation of Kj Regressions
-------
49
Table D-1. Comparison of R-square and Root Mean Square for Kd regressions to assist
in determining the best root transformation with turbidity.
root
RSquare RootMSE
1.1
1.2
1.3
1.4
1.5
1.6*
1.7
1.8
1.9
0.692513
0.694958
0.696466
0.697318
0.697708
0.697773
0.697609
0.697286
0.696852
0.777389
0.774293
0.772377
0.771292
0.770795
0.770712
0.770921
0.771333
0.771886
2.0
0.696342
0.772534
2.1
0.695784
0.773244
2.2
0.695196
0.773991
2.3
0.694590
0.774759
2.4
0.693978
0.775535
2.5
0.693367
0.776310
2.6
0.692761
0.777076
2.7
0.692165
0.777829
2.8
0.691581
0.778567
2.9
0.691011
0.779286
* Highest r-square and lowest root mean square error are obtained for the 1.6 root of turbidity. This is very nearly
matched by the results for the 1.5 root which was optimal for the VIMS data. Thus 1.5 root will be employed for
further work.
2. Does one Kd -Tlirbidity model work for all tributaries?
Analysis of covariance (ANCOVA) with an interaction term for Tributaries*turbidity
was used to assess the consistency of the turbidity effect over tributaries (Table D-2).
Table D-2. ANCOVA table showing test for consistency of turbidity (turb) effect over
tributaries. "r1_5turb" is the rootl .5 transform of turbidity measurements.
Source
tributary
rl_5turb
rl_5turb*tributary
DF
16
1
16
Type III SS
19.432
330.819
65.436
Mean Square
1.214
330.819
4.089
F Value
2.04
556.82
6.88
Pr>F
0.0087
<.0001
<.0001
appendix d Derivation of Kj Regressions
-------
50
The evidence is strong (p < 0.0001) that the coefficient for the turbidity term is not
consistent among tributary systems. Thus some splitting of the tributaries into groups
should be explored.
3a. Is chla an important predictor? Yes, but contribution is less than turbidity.
To address this question, the ANCOVA model was expanded to include terms for
chlorophyll (as measured by DATAFLOW) and tributary*chlorophyll (Table D-3).
Table D-3. ANCOVA table showing test for chlorophyll and consistency of chlorophyll
effect over tributaries.
Source
tributary
rl_5turb
rl_5turb*tributary
xCHLA
xCHLA*tributary
DF
16
1
16
1
16
Type III SS
19.290
197.379
25.554
14.771
33.057
Mean Square
1.205
197.379
1.597
14.771
2.066
F Value
2.39
391.14
3.17
29.27
4.09
Pr>F
0.0016
<.0001
<.0001
<.0001
<.0001
Both the chlorophyll term (p<0.0001) and the chlorophyll* Tributary term
(p<0.0001) are statistically significant. However, the mean square for turbidity
(msIII(turb) = 197.2318735) is much greater than the meansquare for chlorophyll
(msIII(chla) = 14.7708689). From this we infer that while chlorophyll is an impor-
tant predictor (p<0.0001) it is much less important than turbidity.
3b. Is chla effect same for all tributaries? No
The interaction statistic for chlorophyll and tributary is significant (p<0.0001) and
this implies that the association of chlorophyll and Kj is not uniform over tributaries.
3c. Is it better to use chla or logchla? Chla
Using the above model, the overall r2(chla) = 0.739471 and the overall r2(logchla) =
0.721274. Thus is appears that the untransformed chla gives better prediction.
4. Is Salinity a useful predictor? Yes
Table D-4. ANCOVA table for model expanded to include salinity terms.
Salinity appears to be an important predictor but its effect is not consistent over
tributaries.
Table D-4. ANCOVA table for model expanded to include salinity terms.
Source
tributary
rl_5turb
rl_5turb*tributary
xCHLA
xCHLA*tributary
xlnSALINITY
x!nSALINIT*tributary
DF
16
1
16
1
16
1
16
Type III SS
16.086
162.130
14.711
9.717
21.609
0.057
18.498
Mean Square
1.005
162.130
0.919
9.717
1.350
0.057
1.156
F Value
2.03
327.82
1.86
19.65
2.73
0.12
2.34
Pr>F
0.0093
<.0001
0.0206
<.0001
0.0003
0.7339
0.0021
appendix d Derivation of Kj Regressions
-------
5. Is there a seasonal effect? Not much.
To address the seasonal issue, we compare models with and without month terms
(Table D-5a,b,c,d).
Table D-5a. Before adding Month and Month*trib.
"
Source DF
tributary 16
rl_5turb 1
rl_5turb*tributary 16
Xchla 1
xCHLA*tributary 16
xlnSALINITY 1
x!nSALINIT*tributary 16
Table D-5b. Fit statistics.
R-Square CoeffVar
0.748631 31.26545
Table D-5c. With Month
Source DF
tributary 16
Month 6
Tributary*Month 87
rl_5turb 1
rl_5turb*tributary 16
xCHLA 1
xCHLA*tributary 16
xlnSALINITY 1
x!nSALINIT*tributary 16
Table D-5d. Fit statistics.
R-Square CoeffVar
0.782748 30.44734
Type III SS
16.086
162.130
14.711
9.717
21.609
0.057
18.498
Root MSE
0.703259
and Month*Trib
Type III SS
14.553
5.092
62.849
93.206
16.690
5.522
19.573
0.125
17.341
Root MSE
0.684857
Mean Square
1.005
162.130
0.919
9.717
1.350
0.057
1.156
Kdl Mean
2.249316
in the model.
Mean Square
0.909
0.848
0.722
93.206
1.043
5.522
1.223
0.125
1.083
Kdl Mean
2.249316
F Value
2.03
327.82
1.86
19.65
2.73
0.12
2.34
F Value
1.94
1.81
1.54
198.72
2.22
11.77
2.61
0.27
2.31
Pr>F
0.0093
<.0001
0.0206
<.0001
0.0003
0.7339
0.0021
Pr>F
0.0144
0.0942
0.0016
<.0001
0.0037
0.0006
0.0005
0.6055
0.0024
Of the two seasonal terms, Month and Trib*Month, the Month term is not significant
(p=0.0942) and the Trib*Month term is significant (p=0.0016). The increase in r2 is
only about 3% which is a not a large increase for the additional 93 degrees of
freedom in the seasonal model. The meansquares for the seasonal terms are small.
appendix d Derivation of Kj Regressions
-------
52
I don't believe there is sufficient gain from adding month to warrant the degree of
splitting of the data that will be required by doing monthly calibration curves.
6. Can Tributaries be grouped so that calibration terms are uniform within group?
At this point, we have established that the model should include three useful predic-
tors: turbidity, chlorophyll, and salinity. These are terms suggested by Chuck
Gallegos of the Smithsonian Environmental Research Center, Edgewater, MD,
(personal communication) as likely to be important. The question now is whether or
not there are groups of tributaries where the intercept and the coefficients for these
three predictors are fairly uniform so that they may be lumped for one calibration
model. The coefficients are shown in Table D-6. Clearly trying to organize these into
uniform groups is complex. To assist with this organization, a cluster analysis was
implemented where the tributaries are the items clustered and the coefficients are the
attributes to cluster by. Note that because some coefficients are large, but not statisti-
cally significant. These data were filtered by statistical significance before clustering
by setting all coefficients with p-value > 0.1 to zero. Note for example the salinity
coefficient for the Potomac. At 4.3, the coefficient is nearly two orders of magnitude
greater than other salinity coefficient and yet it is not even close to being statistically
significant (p=0.74). The sample size for the Potomac is fairly small and the salinity
range for the data collected is also small. These factors contribute to this aberrant
coefficient. This illustrates a hazard of splitting data into subsets that are too small.
The results of the cluster analysis are illustrated by the dendrogram in Figure D-l.
LMLPLEUWGS
Name of Observation or Clustei
Figure D-1. Dendogram illustrating clustering of Maryland Tributaries by model
coefficients.
appendix d Derivation of Kj Regressions
-------
51
Table D-6. Least Square means (LSmean) and model coefficients (upper)
and coefficient p-value (lower) for each Tributary. (TurbSlope, chlSlope and
salSlope = regression coefficients for Turbidity, chlorophyll and salinity
respectively; Turbpv, chlpv, salpv= p-value of model coefficient on
Turbidity, chlorophyll and salinity).
Obs
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Tributary
Bush River
Eastern Bay
Fishing Bay/
Chicamacomico R.
Gunpowder River
Little Choptank
Lower Chester R
Lower Patuxent
Magothy River
Middle River
Miles/Wye River
Potomac River T
Severn River
South River
St. Mary's Rive
Upper Chester R
Upper Patuxent
West/Rhode Rive
LSmean
0.66228760
0.18462797
2.89957195
0.63520230
-0.81415538
0.11402091
-0.19481321
0.83013497
0.86931693
-0.64846088
-0.07347812
1.40514400
1.57989702
0.64822056
0.12031682
0.92850523
0.36220019
TurbSlope
Turbpv
0.34563
0.000000
0.36081
0.000397
0.35825
0.000000
0.29985
0.000000
0.45633
0.003947
0.39440
0.000000
0.29497
0.000000
0.32554
0.002151
0.25333
0.000000
0.43871
0.000000
0.22545
0.010952
0.37330
0.000148
0.19308
0.050066
0.31973
0.000000
0.47757
0.000000
0.26910
0.000000
0.25795
0.000031
chlSlope
chlpv
0.020457
0.00000
0.007857
0.59755
0.019746
0.20928
0.018917
0.02892
0.041194
0.33639
0.010186
0.00000
0.016327
0.02453
0.007115
0.32672
0.020142
0.00732
0.017175
0.00117
0.005387
0.79545
0.007781
0.45731
0.023131
0.00284
0.002694
0.48257
0.021069
0.00006
-0.008369
0.06838
0.023594
0.00047
salSlope
salpv
0.06362
0.37814
0.04053
0.29528
-0.20987
0.00001
-0.01304
0.76646
0.07570
0.32372
0.03962
0.24702
0.06830
0.08088
0.03451
0.80087
0.03057
0.68222
0.09466
0.05894
4.31676
0.74414
-0.08086
0.53781
-0.07961
0.07244
0.00208
0.93330
0.04017
0.50339
0.03845
0.26144
0.03514
0.28732
Least Squares Means at rl_5turb=0.5, xCHLA=3, x!nSALINITY=0
The tributary groups shown in Figure D-l are a starting point for organizing the trib-
utaries into groups with similar coefficients. Tributaries that are joined near the
bottom of the distance scale have similar coefficients and the similarity decreases as
groups are joined further up the distance scale. At the top of the distance scale, all
tributaries are in one group. The question is "How far up the distance scale should
the groups be formed?" For guidance in addressing this question, we implement a
statistical criterion. We try to form tributary groups for which the three predictor
appendix d Derivation of Kj Regressions
-------
54
variables have no statistically significant interaction with tributary. Starting with the
groups shown in Figure D-l and juggling a bit, we arrive at the following groups:
Group 1:
Bush River
Gunpowder River
St. Mary's River
Magothy River
Middle River
Group 2:
Lower Patuxent
Potomac River
Eastern Bay
West/Rhode River
Group 3:
Severn River
South River
Fishing Bay/Chicamacomico
Group 4:
Little Choptank
Miles/Wye River
Group 5:
Upper Patuxent
Group 6:
Lower Chester River
Upper Chester River
These groupings reflect a strong geographical pattern which strengthens their
validity. The Upper Patuxent River falls in a group alone because of the negative
coefficient for chlorophyll. This coefficient seems quite unusual when juxtaposed
with the positive coefficients for all other tributaries. This may be the result of some
spatial pattern that is confounded with chlorophyll and warrants additional study.
Shown below are the ANCOVA tables for each group illustrative that the interaction
terms lack significance (p > 0.01) (Tables D-7 thru D-16). Based on these results, we
infer that the primary independent variables of the calibration equation: turbidity,
salinity, and chlorophyll, have a uniform effect for each tributary group. In some trib-
utary groups, some independent variables, e.g. salinity for group 1, appear to be not
important. The model could be reformulated to omit these variables in these tribu-
tary groups.
The calibration equations for each tributary group are:
Group 1:
Kd = 0.5545 + 0.3172*(rl_5Turb) + 0.0160*(Chlorophyll a) - 0.0138*(Salinity)
Group 2:
Kd = -0.1247 + 0.2820*(rl_5Turb) + 0.0207*(Chlorophyll a) + 0.0515*(Salinity)
appendix d Derivation of Kj Regressions
-------
Group 3:
Kd = 1.0895 + 0.4160*(rl_5Turb) + 0.0140*(Chlorophyll a) - 0.0950*(Salinity)
Group 4:
Kd = -0.8991 + 0.4338*(rl_5Turb) + 0.0180*(Chlorophyll a) + 0.0912*(Salinity)
Group 5:
Kd = 0.8191 + 0.269l*(rl_5Turb) - 0.0084*(Chlorophyll a) + 0.0384*(Salinity)
Group 6:
^ = 0.0493 + 0.4658*(rl_5Turb) + 0.0100*(Chlorophyll a) - 0.0090*(Salinity)
ANCOVA results of Kd-Turbidity regression for tributary groups for Maryland Data
Flow, (run date = December 28, 2006).
Table D-7. Tributaries in Group 1.
Tributary Group
1
Tributaries
Bush River
Gunpowder River
Magothy River
Middle River
St. Mary's River
Table D-8. ANCOVA for tributaries in Group 1 .
Source
Model
tributary
rl_5turb
rl_5turb*tributary
xCHLA
xCHLA*tributary
xlnSALINITY
x!nSALINIT*tributary
Error
Corrected Total
DF
19
4
1
4
1
4
1
4
390
409
Sum of
Squares Mean Square F Value
483.54 25.45 47.13
0.37 0.09 0.17
73.39 73.39 135.91
1.58 0.39 0.73
11.09 11.09 20.53
6.23 1.56 2.88
0.21 0.21 0.40
0.50 0.12 0.23
210.60 0.54
694.13
p-value
0.0000
0.9530
0.0000
0.5710
0.0000
0.0225
0.5294
0.9218
-
appendix d Derivation of Kj Regressions
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56
Table D-9. Tributaries in Group 2.
Tributary Group
Tributaries
2 Eastern Bay
Lower Patuxent
Potomac River
West/Rhode Rivers
Table D-10. ANCOVAfor
Source
Model
tributary
rl_5turb
rl_5turb*tributary
xCHLA
xCHLA*tributary
xlnSALINITY
x!nSALINIT*tributary
Error
Corrected Total
DF
15
3
1
3
1
3
1
3
192
207
tributaries in Group 2.
Sum of
Squares Mean Square F Value
95.66 6.38 27.02
0.34 0.11 0.49
26.26 26.26 111.27
0.61 0.20 0.86
1.86 1.86 7.89
0.78 0.26 1.10
0.06 0.06 0.24
0.28 0.09 0.39
45.31 0.24
140.97
p-value
0.0000
0.6930
0.0000
0.4634
0.0055
0.3512
0.6259
0.7607
-
-
Table D-11. Tributaries in Group 3.
Tributary Group
Tributaries
3
Fishing Bay/
Chicamacomico River
Severn River
South River
appendix d Derivation of Kj Regressions
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Table D-12. ANCOVA for tributaries in Group 3.
Source
Model
tributary
rl_5turb
rl_5turb*tributary
xCHLA
xCHLA*tributary
xlnSALINITY
x!nSALINIT*tributary
Error
Corrected Total
Sum of
DF Squares
11 427.39
2 1.12
1 21.28
2 1.34
1 3.05
2 0.70
1 3.19
2 2.17
141 95.53
152 522.91
Mean Square
38.85
0.56
21.28
0.67
3.05
0.35
3.19
1.08
0.68
-
F Value
57.35
0.82
31.41
0.99
4.50
0.51
4.71
1.60
-
p-value
0.0000
0.4410
0.0000
0.3736
0.0357
0.5997
0.0317
0.2053
-
Table D-1 3. Tributaries in Group 4.
Tributary Group
4
Tributaries
Little Choptank River
Miles/Wye Rivers
Table D-1 4. ANCOVA for tributaries in Group 4.
Source
Model
tributary
rl_5turb
rl_5turb*tributary
xCHLA
xCHLA*tributary
xlnSALINITY
x!nSALINIT*tributary
Error
Corrected Total
Sum of
DF Squares
7 63.05
1 0.03
1 13.03
1 0.01
1 0.90
1 0.15
1 1.71
1 0.02
74 23.98
81 87.04
Mean Square
9.01
0.03
13.03
0.01
0.90
0.15
1.71
0.02
0.32
-
F Value
27.80
0.09
40.20
0.02
2.79
0.47
5.28
0.07
-
-
p-value
0.0000
0.7650
0.0000
0.9010
0.0990
0.4939
0.0244
0.7988
-
-
appendix d Derivation of Kj Regressions
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58
Table D-15. Tributaries in Group 6.
Tributary Group
Tributaries
Lower Chester Rivers
Upper Chester Rivers
Table D-16. ANCOVA for tributaries in Group 6.
Source
Model
tributary
rl_5turb
rl_5turb*tributary
xCHLA
xCHLA*tributary
xlnSALINITY
x!nSALINIT*tributary
Error
Corrected Total
DF
7
1
1
1
1
1
1
1
188
195
Sum of
Squares
319.82
0.01
82.88
0.75
16.64
2.02
0.66
0.00
123.52
443.35
Mean Square
45.69
0.01
82.88
0.75
16.64
2.02
0.66
0.00
0.66
-
F Value
69.54
0.01
126.15
1.15
25.33
3.07
1.00
0.00
-
p-value
0.0000
0.9165
0.0000
0.2854
0.0000
0.0813
0.3175
0.9945
-
appendix d Derivation of Kj Regressions
-------
appendix
Chesapeake Bay Water Clarity
Assessment Framework
STEP 1. WATER QUALITY PARAMETER INTERPOLATIONS
Each water quality parameter in each point dataset involved in the particular region-
ally specific regression model is first interpolated across the segment using the
Ordinary kriging function in the Geostatistical Analyst included in the ArcMap soft-
ware (Figure E-l). All default settings provided by Geostatistical Analyst are used in
the interpolations, except for those specified in Table E-l. STAC 2006 (cited in U.S.
EPA 2007) indicates that of the various types of interpolation algorithms available
and reviewed, ordinary kriging is best positioned to address this issue, i.e., data
density from DATAFLOW cruise tracks.
The results of the interpolations are stored in a grid format, where each cell contains
a value for the associated water quality parameters. For each segment, all grids used
in this analysis are set to the exact same extent (rounded to nearest 25 m) and grid
cell size (25 m x 25 m). This ensures that all segment grids correspond spatially
when overlayed (Figure E-2).
STEP 2. USING PARAMETER INTERPOLATIONS TO
DERIVE Kd SURFACE.
The next step towards calculating water clarity acres is to use the interpolated grids
to calculate a IQ surface. Turbidity, salinity, and chlorophyll were the three parame-
ters used for determining each of the regionally-specific Kd models (see Table IV-2
in Chapter iv, also Appendix D). For each segment, the interpolated chlorophyll,
turbidity, and salinity grids are input into the appropriate equation on a cell by cell
basis. The result of this cell-specific calculation based on the region-specific
multiple regression IQ model is a new grid representing the IQ surface.
,
appendix e Chesapeake Bay Water Clarity Assessment Framework
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60
Figure E-1. Turbidity values from the dataflow point dataset (3/17/06) for the Piankatank
River Mesohaline Chesapeake Bay Program segment (PIAMH) are used to interpolate a
turbidity surface for the entire segment.
Table E-1. Geostatistical analyst settings.
Method Type
Ordinary Kriging
Model Type
Spherical
Max Sample Points
25 / Sector
Min Sample Points
Neighborhood Sectors
appendix e Chesapeake Bay Water Clarity Assessment Framework
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61
Figure E-2. For each segment, all grids are
set to the exact same extent and grid cell s
ize (25 m). When the grids are overlayed to
perform analytical functions, each cell can be
analyzed independently. (Source ESRI 2007)
Kd = 0.5275793536 + 0.3193475331 x 'fTU + 0.0176700982 x SA + 0.0271723238 x CH
Figure E-3. For each segment, the interpolated chlorophyll (CH), turbidity (TU), and
salinity (SA) grids are used to generate a Kj (Kd) grid. Piankatank River, Mesohaline
Chesapeake Bay Program segment (PIAMH) example.
appendix e Chesapeake Bay Water Clarity Assessment Framework
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62
The next step is to calculate a Kd value for each cell and compare this value to a
defined threshold.
STEP 3. ATTAINMENT THRESHOLDS
The following equation defines the relationship between the light attenuation coeffi-
cient (Kd), PLW is the percent light through water, e is the base of the natural
logarithms, Kd is the value of the light attenuation coefficient, and Z is depth
Equation 3: PLW = 100*exp(-KdZ)
This equation can be used to determine the attainment thresholds at different depths
and PLW's (Table E-2).
Table E-2. Kd thresholds.
PLW
0.22
0.13
Segment
Polyhaline, Mesohaline
Oligohaline, Tidal Fresh
0-lm
1.51
2.04
Zones
l-2m
0.76
1.02
0-.5m*
3.03
4.08
Each cell in the Kd grid is evaluated against the appropriate 'segment x zone'
threshold. For each segment, two comparisons are performed, one for each depth
zone. For example, for Piankatank mesohaline segment, each cell must be less than
or equal to the 1.51 threshold for zones where depth is 0-lm, and less than or equal
to 0.76 where depth is 1-2 m. These two comparisons are merged to form an attain-
ment grid (Figure E-4). Each cell in the attainment grid gets a value of one or zero,
one if it meets the appropriate threshold and zero if it does not meet the appropriate
threshold. Also, any designated Chesapeake Bay exclusion zones are removed from
further analysis (Figure E-5).
It is important to identify where this attainment is occurring in relation to other envi-
ronmental factors. A code system is used to identify the presence/absence of historic
and current SAV, and the depth zone for each cell in the grid. To determine the code
for each cell in the grid: bathymetry, historic SAV, and current SAV are overlayed
(Figure E-6). The resulting grid contains a representative, 3-digit code for each cell.
The first digit indicates which bathymetric zone the cell is in, the 2nd digit desig-
nates whether historic SAV is present or absent, and the last digit indicates whether
current SAV is present or absent (Figure E-7). Finally, the attainment grid and zone
codes are combined and the results are exported in table format to an ACCESS data-
base for further analysis (Figure E-8). This method groups the attainment data by
appendix e Chesapeake Bay Water Clarity Assessment Framework
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63
unique zone codes, for example, there may be 463 cells that were in attainment for
cells in 1-2 m of depth, where current and historic SAV are present.
ACCESS is used to calculate water clarity acres by initially converting cell counts of
attainment into acreage of attainment inside and outside of current SAV areas for
each segment. Water clarity acres for the segment are then calculated by the taking
the annual mean of the monthly acreage. Finally, the annual water clarity acreage is
compared with the segment goals as defined in DEQ document 9 VAC 25-260
Virginia Water Quality Standards (2005).
K d Thresholds: < 1.51 (0-1 m), < .76(1-2m)
Figure E-4. The Kj grid is compared to the appropriate Kj threshold on a cell by cell basis
to create the attainment grid.
Figure E-5. Chesapeake Bay exclusion zones are removed from further analysis.
appendix e Chesapeake Bay Water Clarity Assessment Framework
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64
Bathymetry
Figure E-6. Bathymetry, historic SAV, and current SAV are overlayed to determine a unique
code that describes environmental attributes for each cell in the study area.
Bathymetry
210
Historic SAV
Current SAV
Figure E-7. A representative 3-digit code for each cell is used to indicate bathymetric
zone, historic SAV presence/absence, and current SAV presence/absence.
appendix e Chesapeake Bay Water Clarity Assessment Framework
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.
Attainment
Figure E-8. The attainment grid and zone codes are combined and the results are
exported to an access database for further analysis.
LITERATURE CITED
Environmental Systems Research Institute (ESRI). 2007. ArcGIS 9.2. Redlands, CA.
Scientific and Technical Advisory Committee (STAC). 2006. The Cumulative Frequency
Diagram Method for Determining Water Quality Attainment: Report of the Chesapeake Bay
Program STAC Panel to Review Chesapeake Bay Analytical Tools. STAC Publication 06-
003. 9 October 2006. Chesapeake Bay Program Scientific and Technical Advisory
Committee, Chesapeake Research Consortium, Edgewater, MD.
U.S. Environmental Protection Agency. 2007. Ambient Water Quality Criteria for Dissolved
Oxygen, Water Clarity and Chlorophyll afar the Chesapeake Bay and Its Tidal Tributaries -
2007 Addendum, July 2007. EPA 903-R-07-003 Region III Chesapeake Bay Program Office,
Annapolis, MD. 21403.
9 VAC 25-260 Virginia Water Quality Standards. 2005. § 62.1-44.15 3a of the Code of
Virginia. Retrieved February 21, 2007 from http://www.epa.gov/waterscience/standards/
wqslibrary/va/va_3_wqs .pdf
appendix e Chesapeake Bay Water Clarity Assessment Framework
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66
appendix
Water Clarity
Attainment Results
2008 ASSESSMENT MARYLAND WATER CLARITY
ATTAINMENT RESULTS
Analyses were conducted for the 2004-2006 time period, with the exception of the
Magothy and Severn Rivers where DATAFLOW data were evaluated for the
2001-2003 period.
For Maryland, the following segments had no SAV goal, therefore the analysis
was not applicable: BACOH, CHOTF, CHSTF, NANTF, POCOH, POCTF.
For Maryland, the following segments passed their SAV goal in at least one
year between 2004 and 2006: CHSOH, BSHOH, BOHOH, CB2OH, PAXTF,
NORTF, SASOH, C&DOH, PAXOH, MATTF
For Maryland, the following segments failed their SAV goal for each year
between 2004 and 2006 and had incomplete or no data to perform the water
clarity acres assessment: TANMH, CB5MH, MANMH, POCMH, CB4MH,
NANMH, NANOH, POTTF, PISTF, POTOH, CHOMH1, POTMH, LCHMH,
CHOMH2, CHOOH, WICMH
For Maryland, the following segments passed using the water clarity acres
assessment method: GUNOH, FSBMH, SEVMH, RHDMH
For Maryland, the following segments failed using the water clarity acres
assessment method: MAGMH, CHSMH, EASMH, WSTMH, PAXMH,
SOUMH
For Maryland, the following segment failed due to insufficient spatial coverage
of DATAFLOW data during its three year assessment cycle: MIDOH
DETAILED RESULTS FOR WATER CLARITY ASSESSMENT METHOD
The Appendix contains detailed information regarding each segment where the water
clarity assessment method was employed. Monthly pass/fail clarity maps addition to
the annual averages in relation to the SAV goals. Below are short narratives
appendix f Chesapeake Bay Water Clarity Attainment Results
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"
describing any information that was pertinent to the assessment and possible expla-
nation of why a segment passed or failed.
GUNOH
The Gunpowder was very close to meeting its SAV goal (2392.1 out of 2432) for 2004,
so it was relatively easy to obtained the 99.75 water clarity acres needed to pass.
SEVMH
The Severn was assessed for the year 2002. During the inception of the DATAFLOW
program in 2001 and 2002, the data on the Severn and Magothy Rivers were
collected twice monthly from April through October. Most passing water clarity was
observed at the southern shore near the mouth and the SAV margin areas in the
vicinity of Round Bay.
MIDOH
Middle River was assessed with DATAFLOW data for 2004. In all three years of
DATAFLOW assessment, cruise tracks were only conducted within the Middle River
proper. The MIDOH segment however also encompasses Seneca Creek and part of
the main-Bay to the north. During the interpolation process, water clarity acres were
not extrapolated into the unsampled areas of Seneca Creek. This therefore meant that
a smaller assessment area was used to try and obtain the goal for the entire segment.
Using this method resulted in a failure of the threshold. If the water clarity pass/fail
percentage was extrapolated to the entire shallow-water area of the segment, the
segment would pass.
MAGMH
Unlike the Severn, the relatively large shallow areas at the mouth of the Magothy
continuously failed the criteria in 2002, resulting in failure for the entire segment.
Perhaps the orientation of the Magothy makes it more vulnerable to open Bay wave
action and Susquehanna turbid outflow.
CHSMH
It should be noted that the 2004 assessment for the lower Chester relied on data from
two separate cruises for each month. These cruised were interpolated separately. The
general demarcation line between the two cruises was just south of the Corsica River.
CHSMH was only at 25% of its SAV goal of 2928 acres and was therefore difficult
to consistently obtain the large area of water clarity needed for it to pass.
EASMH
Eastern Bay was assessed for 2004 and only had 16.7% of its 6209 acre goal. It
therefore had to achieve an enormous acreage of 12923.5 acres of combined acreage
to pass. It did very well, achieving an average of 9228 acres, but not enough to pass.
appendix f Chesapeake Bay Water Clarity Attainment Results
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68
SOUMH
The South River was assessed for 2004. It had a low SAV acreage and ultimately the
average water clarity did not even meet the SAV goal, let alone 2.5 times the goal.
FSBMH
Fishing Bay passed mainly on the back of its small goal and a few months producing
good water clarity in the shoreline margins of the lower open water portions. These
areas ultimately might not support SAV due to the high wave energy and shifting
sediments of this segment.
PAXMH
Very little SAV is to be found in the lower Patuxent that also has a large SAV goal.
Fifty-four percent of the water clarity goal was achieved.
RHDMH/WSTMH
The Rhode River, much like Fishing Bay, owes some of its passing success to a small
goal (60 acres). No appreciable SAV has been observed in this segment during the
VIMS aerial surveys. Much of the passing water clarity for the West and Rhode were
observed in the downriver portions. This segment also contains shoreline along the
Bay that had better estimated water clarity. The West River failed due to consistently
bad upriver turbidity.
2008 ASSESSMENT VIRGINIA WATER CLARITY
ATTAINMENT RESULTS
Analyses were conducted for the 2004-2006 time period.
For Virginia, the following segments had no SAV goal, therefore the analysis
was not applicable: MPNOH, PMKOH, SBEMH, WBEMH, EBEMH, ELIPH,
LAFMH.
For Virginia, the following segments passed their SAV goal in at least one year
between 2004 and 2006: CHKOH, MPNTF, PMKTF, POTOH, POTTF,
RPPOH, RPPTF
For Virginia, the following segments failed their SAV goal for each year
between 2004 and 2006 and had no data to perform the water clarity acres
assessment: CB5MH, CB6PH, CB7PH, CB8PH, CRRMH, LYNPH, MOBPH,
POCMH, POTMH, RPPMH, TANMH
For Virginia, the following segments passed using the water clarity acres
assessment method: CHKOH, JMSMH, JMSPH, MPNTF, PMKTF
For Virginia, the following segments failed using the water clarity acres assessment
method: APPTF, JMSOH, JMSTF1, JMSTF2. PIAMH, YRKMH, YRKPH
appendix f Chesapeake Bay Water Clarity Attainment Results
-------
"
Table F-l summarizes these results of water clarity attainment results in Virginia
segments for the single best year among the three year period of 2004 through 2006.
Table F-1. 2008 305b/303d list segment water clarity/SAV acres attainment assessment
results.
CBP Segment
APPTF
CB5MH
CB6PH
CB7PH
CB8PH
CHKOH
CRRMH
EBEMH
JMSMH
JMSOH
JMSPH
JMSTF1
JMSTF2
LYNPH
MOBPH
MPNTF
PIAMH
PMKTF
POCMH
POTMH
POTOH
POTTF
RPPMH
RPPOH
RPPTF
TANMH
YRKMH
YRKPH
ND: No shallow-water
Single Best Year Meets
"SAV Acres Criteria"
NO
NO
NO
NO
NO
YES
NO
YES
NO
NO
NO
NO
NO
NO
NO
YES
NO
YES
NO
NO
YES
YES
NO
YES
YES
NO
NO
NO
monitoring DATAFLOW data
Single Best Year Meets
"Water Clarity Acres" Criteria
NO
ND
ND
ND
ND
YES
ND
ND
YES
NO
YES
NO
NO
ND
ND
YES
NO
YES
ND
ND
ND
ND
ND
ND
ND
ND
NO
NO
collected during the assessment period.
appendix f Chesapeake Bay Water Clarity Attainment Results
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70
appendix
Chlorophyll a
Assessment Protocol
STEP 1. CALIBRATING DATAFLOW CRUISE-TRACKS
1. Locate the CPB segments where DATAFLOW cruise-track points are located
using GIS. Although VIMS and Hampton Roads Sanitation District (HRSD)
have cruise-tracks organized by segment, the ends of cruise-tracks "spill" over
into adjacent segments. Points need to be regrouped prior to calibration since
each segment has its own regression equation.
2. Organize records for verification stations by segment and season (spring and
summer). Each verification station should have extracted and YSI chlorophyll for
each sampling date, along with turbidity and temperature data.
3. A calibration equation should be determined for each segment-season combina-
tion by calculating a log-ratio (logExtracted - log YSI) for each verification
event, regressing it over concomitant temperature and turbidity values to deter-
mine a predicted log-ratio, and multiplying the backtransformed predicted
log-ratio by the YSI chlorophyll to estimate the extracted chlorophyll for cruise-
track points.
STEP 2. SETTING UP THE DATA SET
4. Compile a chlorophyll database for the assessment period containing records
from the following stations:
a. Long-term CBP stations (records stored in CIMS database)
b. DFLO verification stations (records stored in CIMS database)
c. VA DEQ stations (records stored in VA DEQ CEDS database)
d. DFLO cruise-tracks (records stored by VIMS/HRSD).
5. Database should contain station name, UTM Easting and Northing coordinates
(NAD83), laboratory-extracted chlorophyll values (ug/1), sampling date,
sampling depth (only depths less than or equal to 1.0 m should be used), and
appendix g Chlorophyll a Assessment Protocol
-------
QA/QC comments. Fields that distinguish the project and source for each record
should also be created, to allow for station filtering. In addition, you may also
want to create a field for segment ID (e.g., OH, TF1, MH, etc.) for each record.
6. In this master dataset, create a field called "input". This will be the field that will
be copied and pasted into the Interpolator.
The Interpolator reads a record with the following format:
EASTING,NORTHING,DEPTH,PARAMETER,STATION
The "input" field should be a concatenation of the pertinent fields in your master
database. A comma is needed between each value, so create a "comma" field that
you reference in the concatenate formula.
7. Replicate samples should be averaged together prior to interpolation if the time
scale you have chosen is greater than a day. This is because the interpolator will
automatically average multiple observations present. If the interpolator is a daily
interpolation, the interpolator will take the replicates on that day and average
them as is appropriate. However, if it is a monthly interpolation, and the daily
replicates have not been previously averaged into a single value, then the repli-
cates will be treated as independent observations and given undue weight in the
monthly average.
8. The QA/QC field should be reviewed and only data meeting appropriate QA/QC
requirements should be used in the following interpolation steps. Cruise-track
data associated with such codes as NQR, NNF, GPF, and GNV are to be
excluded, while data flagged as algal blooms (CAB) should be left in. In Vir-
ginia, consult the table in the Data Disclaimer and Info section of www.vecos.org
for a description of codes.
STEP 3. IMPORTING THE DATA INTO THE INTERPOLATOR
9. Filter the master database so that it only shows data for the specific time period
(e.g., March 1, 2005) and from the type of stations (e.g., long-term CPB stations)
that is desired. Fixed stations alone should be interpolated by month, while fixed
stations + DFLO cruise-tracks should be interpolated by day.
10. Copy and paste the "input" field into a text editor, such as Notepad.
11. The first five lines of this text file are descriptive, providing info to both the
reader and the Interpolator. They should look something like this:
CHL for James March 2005 long-term CPB stations
CHL, Chlorophyll concentration
05/02/2005,05/25/2005
07/10/2007:11:25
127
The first four lines provide general information (which would show if you gener-
ated a map). The third line gives the range of sampling dates for the input data,
and the fourth line gives the current date and time (you can put any date and
time, but it should be formatted as shown). The fifth line is the critical one for
the Interpolator. This is the number of data points in the input. If this number is
appendix g Chlorophyll a Assessment Protocol
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72
larger than the actual number of records, an error message will be generated and
the program will shut down.
12. The analyst should load in all points from a cruise-track, including even the
points beyond a segment's boundary. Fixed station data collected on the date of
a cruise-track should also be included in the file.
13. Save this file with a descriptive title and save it in the same directory as the Inter-
polator .exe. The program will only load files from its directory.
14. Open the Interpolator and follow the radio buttons from left to right. Select
James under Geography and chlorophyll concentration (two decimal places)
under Parameter. Open your text file under Data Import (scroll down to see "All
files"). The fields should populate automatically when the file is loaded. Then,
click on Parameter Transformation and scroll down to "In". Click on the Inter-
polate button and select "2D Inverse-Distance Squared". The defaults should not
be altered. The program should then begin interpolating the data.
15. Using Notepad or Excel, open the ".est" file that has been generated. This "esti-
mates" file gives you the interpolator cells, by segment, and their estimated
chlorophyll values.
16. The ".log" file counts and lists the records used to interpolate each segment.
STEP 4. AVERAGING THE DATA
17. A seasonal average for a specific year should be determined by averaging the
individual interpolations done on data culled from narrower time-frames within
that season. For instance, the interpolations of daily cruise-tracks occurring
between March and May 2005 should be averaged together to create an estimate
for spring 2005.
18. The Interpolator has a Math function that will average the interpolation cover-
ages from individual ".est" files. The advantage of using this function is in its
convenience, but there is one disadvantage: the program is inflexible when it
comes to missing data. If one file has a missing value for a cell (which arises
when there were no data points within the predefined search radius of that partic-
ular cell), the Interpolator ignores the data contained in the other .est files for that
cell, resulting in a missing value (-9) in the average output. The analyst may
choose to bypass the Math function and do the cell-by-cell averaging in a spread-
sheet, so that missing values can be replaced with blanks. After calculating the
seasonal average, values that are still missing should be replaced with a null
character, such as a period or an asterisk.
19. If interpolations are based primarily on daily cruise-tracks, then averages should
be calculated separately for each segment-year. For each segment, the assess-
ment spreadsheet should use only the days of targeted DATAFLOW cruises,
since these dates will provide good estimates for only the targeted segment. The
only other interpolated dates that should be used in the assessment spreadsheet
for a segment's assessment are: 1) those with records for at least two fixed
stations and 2) those in which an adjacent segment was targeted by a DFLO
appendix g Chlorophyll a Assessment Protocol
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cruise AND there is a record for at least one fixed station not located particularly
close to the boundary of the targeted segment.
20. The seasonal averages for each interpolator cell should then be inserted into a
spreadsheet designated for the assessment.
STEP 5. DESIGNING THE ASSESSMENT SPREADSHEET
21. Set up the spreadsheet where the assessment will be done. It should have
columns corresponding to the interpolator cells (or centroids)either as a
unique ID the analyst has created or as the UTMx and UTMy coordinates
assigned to those centroids. A field containing segment identification (e.g.,
"TF1" or "PH") should also be created. The sequence of the centroids should
match exactly with the sequence from the ".est" file, to allow for easy copying
and pasting.
22. Because only the James River main stem requires assessment, certain centroids
need to be excluded from the analysis. It is recommended that the analyst keep
these centroids on the spreadsheet, but that instead of being assigned a segment
ID (e.g., "CHKOH"), they should be marked with a null character, such as a
period or an asterisk. Along with the centroids within Appomattox and Chicka-
hominy segments, individual JMS centroids falling in small embayments and
non-CPB tributaries (like the Pagan River) should be restricted from the assess-
ment. GIS can be used to identify these centroids.
23. Create a field called "chlorophyll". This is where the Interpolator estimates will
be inserted.
24. The next field will contain the assessment binary ("pass" or "fail") for each
centroid. Because each segment has a different criterion, an "IF" statement
similar to the following should be created:
=IF(chlorophyll=".",".",IF(chlorophyll>criteria, "fail","pass"))
where chlorophyll = chlorophyll value for centroid
"." = null value (if centroid has missing data)
criteria = chlorophyll value the centroid is being assessed against
fail = exceeds the criteria
pass = less or equal to the criteria
The statement, reduced to layman's terms, says: "If the chlorophyll value for this
cell is missing, insert a null value. If it's greater than this specified value, insert
a 'fail'. If it's less than or equal to this specified value, insert a 'pass'."
In Virginia, refer to the table on page 35 of the Water Quality Assessment Guid-
ance Manual for Y2008 for the criteria for each segment and season.
25. A table should be created that tallies up the number of "fails" for each segment
and calculates a percentage of "fails" from the total number of cells in a
segment. This percentage will be used to calculate the CFD. In addition, the
analyst should also calculate the percent of area interpolated for each segment
by tallying up the number of null characters in the assessment field.
appendix g Chlorophyll a Assessment Protocol
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74
26. Assessment spreadsheets should be created for each season (i.e., spring 2004,
2005, 2006 and summer 2004, 2005, 2006). Spreadsheets for spring chlorophyll
estimates should have spring assessment criteria; likewise for "summer" spread-
sheets.
STEP 6. CREATING THE CFD
27. The percent non-attainment for each assessed segment, at each season, should be
copied and pasted into another spreadsheet. Organize them into columns corre-
sponding to segment-season. For instance, label column 1 as "TF1 spring" and
insert all the spring percent non-attainment values for TF1 into this column. In
the next field insert all the spring percentages for "TF2 spring". Continue doing
this for all segment-season combinations. These columns correspond to the "%
space" axis on the CFD.
28. Sort them in descending order.
29. To generate the "% time" axis, use the following equation:
= (100* R)/ (N+l)
where R = rank ("1" for the first time point, "2" for the second", "3" for the third
and "4" for the last).
N = number of time points. Since the assessment period consists of three season-
years, this number is equal to 3.
30. For each % space column, insert 100% at the top of the column and 0% at the
bottom. For the % time column, insert 0% at the top of the column and 100% at
the bottom.
31. You can now create the assessment curve.
32. To calculate % space for the 10% reference curve, use the following equation:
% space = [a/(y+b)] - b
where y = % time
b = 0.042995
a = b2 + b
33. You can now create the 10% reference curve.
STEP 7. CALCULATING THE PERCENT EXCESS NON-ATTAIN ME NT
34. Convert the percentage axes of the CFD to fractional axes for this calculation.
35. The trapezoidal rule should be applied to both assessment and reference curves
to determine the area underneath each curve. The following website describes
how to do the calculations using MS Excel: www.montanamath.org/
TMME/v4nl/TMMEv4nl a6.pdf
36. Subtract the area under the assessment curve from the reference curve, looking
only at the parts of the assessment curve that go beyond the reference curve.
37. Multiply the value by 100. This number represents "% excess non-attainment".
appendix g Chlorophyll a Assessment Protocol
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U.S. Environmental Protection Agency
Region III
Chesapeake Bay Program Office
Annapolis, Maryland
1-800-YOUR-BAY
and
Region III
Water Protection Division
Philadelphia, Pennsylvania
in coordination with
Office of Water
Office of Science and Technology
Washington, D.C.
and
the states of
Delaware, Maryland, New York,
Pennsylvania, Virginia and
West Virginia and the District of Columbia
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