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

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  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

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                         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

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                 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

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                  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

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                  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

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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

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                  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
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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 .
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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.
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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;
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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,


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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.
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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.
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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 consequences—such 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 conditions—or 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

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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
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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

-------
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

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                                                                               •
                      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

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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  parameters—turbidity, chlorophyll fluorescence,  and
                   salinity—to 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

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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

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                                                                                                     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

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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

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                                                                                              •
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

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                       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

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                                                                                                "
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

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   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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