# Methods and Approaches for Deriving

Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and Southern Inland Flowing Waters

November 17, 2010Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 1 

## Disclaimer

This document is intended to provide technical support to EPA in developing and establishing numeric water quality criteria under the Clean Water Act (CWA) pursuant to Section 303(c)(4), in order to support the applicable designated uses in the State of Florida from effects of nitrogen/phosphorus pollution. The information pr ovided herein does not s ubstitute for the CWA or EPA’s regulations; nor is this document a regulation itself. Thus, this document cannot and does not impose any legally binding requirement s on EPA, States, Authorized Tribes, the regulated community, or any other party, and mi ght not apply to a par ticular situation or

circumstance.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 2 

## Contributing Authors

Jim Carleton, U.S. Environmental Protection Agency Tiffany Crawford, U.S. Envi ronmental Protection Agency Ed Decker, U.S. Environmental Protection Agency Edward Dettman, U.S. Envir onmental Protection Agency Erica Fleisig, U.S. Environmental Protection Agency Richard Greene, U.S. Environmental Protection Agency James Hagy, U.S. Environmental Protection Agency Galen Kaufman, U.S. Envir onmental Protection Agency Jacques Oliver, U.S. Environmental Protection Agency Blake Schaeffer, U.S. Envi ronmental Protection Agency Mario Sengco, U.S. Environmental Protection Agency Dana Thomas, U.S. Environmental Protection Agency Timothy Wool, U.S. Environmental Protection Agency Steven Davie, Tetra Tech Jeroen Gerritsen, Tetra Tech Jon Harcum, Tetra Tech John Hochheimer, Tetra Tech Michael Paul, Tetra Tech

Steven Davie, Tetra TechMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 3 

## Contents

Contributing Authors .......................................................................................................... ............ 2

Abbreviations and Acronyms .................................................................................................... ... 15

Executive Summary ............................................................................................................. ......... 18

1 Introduction .................................................................................................................. ......... 21

1.1 Purpose of the Document ............................................................................................. 21

1.2 Document Organization ............................................................................................... 21

1.3 Background: Clean Water Act Requirements and Florida’s Current Water Quality Standard for Nitrogen/Phosphorus Pollution ............................................................... 22

1.4 Nature of the Chemical Stressor: Nitrogen/Phosphorus Pollution ............................... 24

1.4.1 Stressor Source and Distribution .......................................................................... 24

1.4.2 Aquatic Ecosystem Effects of N itrogen/Phosphorus Pollution ............................ 24

1.5 Purpose of this Effort ................................................................................................... 27

2 Approach ...................................................................................................................... ......... 29

2.1 Overview ...................................................................................................................... 29

2.1.1 Nutrient Criteria Development Guidance ............................................................. 30

2.1.2 General Approach ................................................................................................. 30

2.2 Conceptual Model ........................................................................................................ 31

2.3 Review of Scientific Basis and Select ion of Potential Assessment Endpoints and

Indicator Variables ....................................................................................................... 33

2.3.1 Selecting Assessment Endpoints and Wate r Quality Indicator Variables ............ 33

2.3.2 Selected Biological Assessment Endpoints .......................................................... 37

2.3.3 Water Quality Indicator Variable s for Expressing Criteria .................................. 39

2.4 Potential Data Sources .................................................................................................. 40

3 Numeric Nutrient Criteria Devel opment in Florida Estuaries .............................................. 45

3.1 Delineating Estuaries .................................................................................................... 46

3.2 Selection of Assessment Endpoint s and Indicator Variables ....................................... 48

3.3 Numeric Criteria Approaches ....................................................................................... 49

3.3.1 Reference Condition Approaches ......................................................................... 51

3.3.2 Stressor Response Relationships ........................................................................... 52

3.3.3 Water Quality Simulation Models ........................................................................ 54

3.3.4 Evaluating Water Quality Simulation Models ...................................................... 64

3.4 Seagrass Depth Targets ................................................................................................ 65Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 4 3.5 Example Application: Pensacola Bay .......................................................................... 69

3.5.1 Selection of Assessment Endpoints ...................................................................... 70

3.5.2 Numeric Criteria Development ............................................................................. 71

3.5.3 Details on the Pensacola Bay Water Quality Model ............................................. 72

4 Numeric Nutrient Criteria Developm ent for Florida Coastal Waters ................................... 77

4.1 Analysis Plan and Delineation of Coastal Areas .......................................................... 77

4.1.1 Use of Remote Sensing Data ................................................................................ 79

4.2 Deriving Numeric Criteria ............................................................................................ 81

4.2.1 Satellite Chlorophyll Data Evaluation .................................................................. 82

4.2.2 Comparison of Satellite Chlorophyll to Field Collected Chlorophyll .................. 83

4.2.3 Chl RS 

### -a Criteria from Statis tical Distributions ...................................................... 87

4.3 Uncertainties and Data Gaps ........................................................................................ 90

5 Numeric Nutrient Criteria Development in South Florida Marine and Inland Flowing

Waters ........................................................................................................................ ........... 91

5.1 Analysis Plan for South Florida ................................................................................... 91

5.1.1 Inland Flowing Waters .......................................................................................... 92

5.1.2 Marine Waters ....................................................................................................... 93

5.2 Inland Flowing Waters: Classification ......................................................................... 93

5.2.1 Existing TP Criterion for the EvPA ...................................................................... 97

5.3 Inland Flowing Waters: Inventory ............................................................................... 98

5.4 Inland Flowing Waters: Data Source s and Assessment Endpoints ............................ 102

5.4.1 Deriving Numeric Criteria .................................................................................. 104

5.5 Marine Waters: Classifica tion and Data Sources ....................................................... 113

5.6 Marine Waters: Numeric Criteria Derivation ............................................................. 116

5.6.1 Reference Condition Approach—Least Disturbed Sites .................................... 116

5.7 Uncertainties and Data Gaps ...................................................................................... 120

6 Numeric Nutrient Criteria Development for the Protection of Downstream Estuaries ...... 122

6.1 Analysis Plan .............................................................................................................. 12 2

6.2 Estimating Protective TN and TP Loading ................................................................ 123

6.3 Computing DPVs for Terminal Reaches .................................................................... 123

6.4 Computing DPVs for Upstream Reaches ................................................................... 124

6.5 Computing DPVs for South Fl orida Marine Waters .................................................. 126

6.5.1 Binomial Approach for Do wnstream Protection ................................................ 127

6.5.2 Calculating EAA Downst ream Protective Values Based on EvPA Total Phosphorus Criteria ............................................................................................. 128Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 5 6.6 Specifying Frequency-Duration for DPVs ................................................................. 128

6.7 Example Application: Pens acola Bay Watershed ...................................................... 128

6.7.1 Protective TN and TP Loading and Terminal Reach DPVs ............................... 130

6.7.2 Fraction-Delivered in the Pe nsacola Bay Watershed .......................................... 130

6.7.3 Computing DPVs for Upstream Stream Reaches ............................................... 133

7 References .................................................................................................................... ....... 136

Appendix A. Bibliography ...................................................................................................... . A-1

Appendix B. Estuarine and Marine Assessm ent Endpoints and Water Quality Indicator

Variables Literature Review ............................................................................... B-1

B.1 Water Clarity ............................................................................................................. . B-1

B.1.1 Scientific Justifica tion for Not Proposing Water Clarity as a Indicator

Variable ............................................................................................................... B-4

B.2 Dissolved Oxygen ...................................................................................................... B-5

B.2.1 Effects ................................................................................................................. B-5

B.2.2 Mechanisms ........................................................................................................ B-6

B.2.3 Paleoecological I nvestigations ............................................................................ B-9

B.2.4 Potential Measurement Endpoints ...................................................................... B-9

B.3 Phytoplankton ............................................................................................................. B-9

B.3.1 Components of Phytoplankton .......................................................................... B-10

B.3.2 Effects of Nitrogen/Phosphorus Pollution on Phytoplankton ........................... B-10

B.3.3 Effect of Phytoplankton on Estu arine and Coastal Waters ............................... B-14

B.3.4 Potential Assessment Endpoints ....................................................................... B-14

B.4 Macroalgae ............................................................................................................... B-14

B.4.1 Macroalgal Blooms ........................................................................................... B-15

B.4.2 Factors that Control Macroalgal Growth .......................................................... B-17

B.4.3 Role of Nitrogen/Phosphorus Po llution in Macroalgal Growth ....................... B-18

B.4.4 Scientific Justifica tion for Not Proposing Macroalgae as an Assessment Endpoint ............................................................................................................ B-23

B.5 Epiphytes ................................................................................................................. . B-24

B.5.1 Measuring Epiphytes ........................................................................................ B-24

B.5.2 Effect of Nitrogen/Phosphor us Pollution on Epiphytes .................................... B-24

B.5.3 Scientific Justifica tion for Not Proposing Epiphytes as an Assessment Endpoint ............................................................................................................ B-26

B.6 Seagrass .................................................................................................................. .. B-26Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 6 B.6.1 Effect of Nitrogen/Phosphor us Pollution on Seagrasses .................................. B-27

B.6.2 Factors that Affect Seagrass Growth ................................................................ B-28

B.6.3 Habitat Restrictions ........................................................................................... B-28

B.6.4 Potential Assessment Endpoints ....................................................................... B-33

B.7 Benthic Macroinvertebrate and Fish Indices ............................................................ B-33

B.7.1 Effects of Nitrogen/P hosphorus Pollution on Benthi c Macroinvertebrates

and Fish ............................................................................................................. B-33

B.7.2 Scientific Justifica tion for Not Proposing Macroinvertebrate and Fish Indices as Assessment Endpoints for Es tuarine and Marine Systems .............. B-36

B.8 Harmful Algal Blooms ............................................................................................. B-37

B.8.1 Scientific Justifica tion for Not Proposing Harmful Algal Blooms as an Assessment Endpoint ........................................................................................ B-40

B.9 Coral ..................................................................................................................... .... B-41

B.9.1 Corals in Florida ............................................................................................... B-41

B.9.2 Effects of Nitrogen/Phosphorus Pollution on Coral Reefs ............................... B-42

B.9.3 Other Impacts .................................................................................................... B-45

B.9.4 Scientific Justif ication for Not Proposing Coral as an Assessment Endpoint .. B-46

B.10 Spartina ................................................................................................................. .... B-46

B.10.1 Scientific Justifica tion for Not Proposing Sparti na as an Assessment Endpoint ............................................................................................................ B-47

B.11 References ............................................................................................................... . B-47

Appendix C. Modeling Tool Overviews .................................................................................. C-1

C.1 CE-QUAL-ICM Model Description .......................................................................... C-2

C.2 CH3D-IMS: Curvilinear-grid Hydrodynami cs 3D—Integrated Modeling System Model Description ...................................................................................................... C-6

C.3 CH3D-SED (& CH3D-WES ): Curvilinear Hydrodynamics in Three Dimensions Model Description .................................................................................................... C-10

C.4 DYNHYD Model Description .................................................................................. C-15

C.5 ECOMSED: Estuary and Coastal Ocean Model with Sediment Transport Model Description ............................................................................................................... C-1 6

C.6 EFDC: Environmental Fluid Dynami cs Code Model Description ........................... C-20

C.7 HABSIM Model Description ................................................................................... C-28

C.8 HSPF: Hydrologic Simulation Program FORTRAN Model Description ................ C-30

C.9 HYCOM Model Description .................................................................................... C-36

C.10 LSPC: Loading Simulation Program in C++ Model Description ............................ C-38

C.11 PLSM: Pollutant Load Screening Model Description .............................................. C-44Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 7 C.12 POM: Princeton Ocean Model Description .............................................................. C-45

C.13 SPARROW: SPAtially Referenced Regression On Watershed Attributes Model Description ............................................................................................................... C-4 7

C.14 WAMView: Watershed Assessment Mode l with an ArcVie w Interface Model Description ............................................................................................................... C-5 2

C.15 WASP: Water Quality Analysis Simu lation Program Model Description ............... C-56

Appendix D. Division of Estuarine and Coastal Wa ters into Subbasins for Nutrient Criteria

Formulation ......................................................................................................... D-1Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 8 

## Figures

Figure 1-1. The model of primary and secondary symptoms of nitrogen/phosphorus pollution and the potential effects and impairments (Brick er et al. 1999; Bricke r et al. 2003b) ...... 26

Figure 2-1. Pathways for nutrient effects on es tuarine and coastal aquatic life uses. ................... 32

Figure 3-1. Flow chart for the development of numeric criteria for Florida estuaries. The approach EPA is considering will involve delineating the state's estuarine waters into discrete estuarine areas. Following an ove rall framework approach, aquatic life endpoints and specific data and methods will be selected on a system-specific basis. Numeric criteria for each estuary, or sub-segment of each will be derived using those

data and methods......................................................................................................... 45

Figure 3-2. Major areas of estuarin e and coastal waters in Florida that EPA has identified for the purpose of organizing development of numeri c criteria. The approach for developing numeric criteria for offshore coastal waters of the northwest Gulf coast [6], west Gulf Coast [23], and Atlantic Coas t [30] is described in Chapter 4. Approaches for South Florida [17, 18, 19, 20] are described in Chap ter 5. The approach for the remaining 23 areas is described in this chapter. Shading of land areas illustrates watershed areas to be used in EPA’s proposed framework for estimating watershed loadings of TN and TP to estuaries using a water qual ity simulation model (Section 3.3.3) ..................... 47

Figure 3-3. Map of Pensacola Bay illustrating the boundaries associ ated with FDEP’s WBIDs for Pensacola Bay. Shaded segments account for the majority of the estuarine area. Additional segments are identified for smalle r creeks and bayous that adjoin the main part of the Bay. Boundaries for major Bay segment are generally associated with geographic features of the ba sin, with segments occurri ng at regular intervals to ensure relatively homogeneous water quality within sub-segments of the Bay. ........ 48

Figure 3-4. Base-10 logarithms of TN load lim its established for 18 Florida lakes, river segments, bays and lagoons vs. water re sidence time (R63 for lakes and river segments, R99 for bays and estuaries). Th e line is the regression (Equation 3-3) established by Steward and Lowe (2010). .................................................................. 54

Figure 3-5. Location of 19 drainage areas for which LSPC wa tershed models are proposed for development ................................................................................................................ 59

Figure 3-6. An example of the approach used to estimate seagrass depth of colonization, in this case applied to the 1953 seagrass coverage for the East Bay segment (WBID 1061F) of St. Andrews Bay. The estimate of maximum colonization depth (Z

c 

### -max, relative to MLLW) is 1.7 m for continuous seagrass a nd 2.0 m for all seagrass. The depth at which the percent of soundings in seagrass is decreased to 50% of the maximum is 1.35 m, extrapolated linearly between the observations at 1.25 and 1.50 m. ............. 67

Figure 3-7. Location Map of Pensacola Bay ................................................................................ 70Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 9 Figure 3-8. (Upper Panel) Map in dicating the 224 subwatersheds a nd associated flowlines that comprise the Pensacola Bay LSPC watershe d model. (Lower Panel) The model grid

that will be utilized for the EFDC/WASP wa ter quality model for Pensacola Bay. .. 73

Figure 4-1. Flow chart for the development of nu meric nutrient criteria for coastal waters. ....... 78

Figure 4-2. Map of coastal segments used in sa tellite remote sensing analysis. The remote sensing data shown in this figure are a composite result from July 1-August 31, 1999. A remote sensing approach is proposed to develop numeric chlo rophyll criteria for the Northwest Gulf Coast, West Gulf Coast, and Atlantic Coastal Area based on

establishment of a reference condition using remotely sensed chlorophyll a observations (Chl RS 

### - a) as the response variable. EPA’s approach for developing

numeric criteria for South Florida and the coastline from Apalachicola Bay to Suwannee River is described in Chapter 3. ................................................................ 79

Figure 4-3. (A) Satellite Chl RS 

### -a coverage of the 91 coasta l segments between 1998 and 2010. (B) Number of chlorophyll field observations that occur within all Florida coastal

waters ........................................................................................................................ .. 80

Figure 4-4. Example of (A) K. brevis bloom occurrence at the Tampa Bay coastal segment. Black points are included in the Chl RS 

### -a distribution. Red points indicate Chl RS 

### -a values that would be excluded from the distribution because a K.brevis count of 50,000 cells/L or higher was measured. (B ) Cumulative distribution of Chl RS 

### -a at the Tampa Bay coastal segment with and without K. brevis . ........................................... 83

Figure 4-5. Regression of SeaW iFS ChlaRS-a versus field observations from NEGOM, ECOHAB, FWRI, Mote, and SeaBASS which occur within the 3nm coastal boundary (grey dashed line is 1:1 fit and bl ack line is regre ssion slope) ................................... 84

Figure 4-6. Tampa Bay map displaying the locati on of five coastal WBIDs and two monitoring sites near Tampa Bay. WBID 8049 is located at the Tampa Bay estuary pass, WBIDs 8047 and 8048 are to the north, and WBIDs 8050 and 8051 are to the south. The two monitoring sites with uncorrected Chl-a fi eld data are labeled as 21FLHILL094 and

21FLHILL093. ............................................................................................................ 85

Figure 4-7. (A) Chl RS 

### -a (μg/L) for five coastal segments near Tampa Bay. The overall mean

Chl RS 

### -a for the coastal segment correspondi ng to WBID 8049 is 2.34 μg/L. (B) IWR40 field Chl-a (uncorrected) from two st ations in WBID 8049 (note that there are no field data between 1-01 and 1-02) ......................................................................... 86

Figure 4-8. Z-CUSUM plot for (A) Chl RS 

### -a (μg/L) for five coastal segments near Tampa Bay and (B) IWR40 field Chl-a (uncorre cted) from two stations in WBID 8049 (note that there are no field data between 1-01 and 1-02 [see Fi gure 4-7B]. Therefore, the straight line in 4-8B, between 1-01 and 1-02, is an artifact of data availability.) .................... 88

Figure 4-9. Field-adjusted Chl RS 

### -a (μg/L) distributions for all coastal segments between 1997 and 2009 with representati ons of the minimum, 10 th 

### , 25 th 

### , 50 th 

### , 75 th 

### , 90 th 

### and maximum values. Northwest Gulf Coast segment 1 is located at the Alabama/FloridaMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 10 border and segment 17 is at St. Joseph Bay. West Gulf Coast coastal segment 18 is located at Anclote Bay and segment 38 is at Rookery Bay. Atlantic Coast segment 39 is located at northern Biscayne Bay and se gment 77 is at the Fl orida/Georgia border.

............................................................................................................................... ...... 89

Figure 5-1. Flow chart for the development of numeric criteria for South Florida marine and inland flowing waters .................................................................................................. 92

Figure 5-2. South Florida Nutr ient Watershed Region ................................................................. 94

Figure 5-3. Location of EAA and Ev PA relative to STATSGO soil orders for South Florida .... 96

Figure 5-4. Division between Biscayne Drainage and Pa lm Beach subregions ........................... 96

Figure 5-5. South Florida Nu trient Region subregions under consideration ................................ 97

Figure 5-6. NHD flowline data in S outh Florida. Note that the pi nk lines shown south of Lake Okeechobee are from the GIS coverage of Class IV canals provided by FDEP. Most of these “pink lines” coincide with NHDPlus flowlines identified as canal/ditch. .. 100

Figure 5-7. NHD flowline data near Stormwater Treatment Areas 5 and 6 and Seminole Indian

Reservation ............................................................................................................... 101

Figure 5-8. Box plots of SCI total index scores for a variety of la nd uses (source: Snyder et al. 1998) ......................................................................................................................... 103

Figure 5-9. Depiction of land us e area (light yellow) include d in an LDI calculation ............... 106

Figure 5-10. Site locations (n=72) with chlorophyll a data evaluated in this document together

with Florida WBIDs that FDEP has identified as nutrient-impaired ....................... 110

Figure 5-11. Bivariate plot of a nnual geometric m ean chlorophyll a as a function of TP with fitted linear regression and 90% prediction interval ................................................. 111

Figure 5-12. Residual plots from chlorophyll a /TP regression analysis ..................................... 111

Figure 5-13. Bivariate plot of a nnual geometric m ean chlorophyll a as a function of TN with fitted linear regression and 90% prediction interval ................................................. 112

Figure 5-14. Residual plots from chlorophyll a /TN regression analysis .................................... 112

Figure 5-15. Bivariate plot of a nnual geometric m ean chlorophyll a as a function of TN and TP across all regions t ogether with Spearman’s rho correlation coefficient, LOESS smoother (black line, f=0.7), and quantile regr ession results (blue line and blue text)

............................................................................................................................... .... 113Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 11 Figure 5-16. Proposed segmentation (r ed lines) for South Florida. (C riteria derived as a result of this document would only be applicable to wa ters within 3 nautical miles of land [i.e., those waters highlighted in bl ue or further landward].) ............................................ 115

Figure 5-17. Boxplots of TN, TP, and Chl-a for Biscayne Bay’s South Central Outer Bay using SERC water quality monitoring network da ta from 1995-2008 by year and site. (Site key: Adams Key (AK), Biscayne Channel (BC), BNP Marker C (BNP), Elliott Key (EK), Featherbed Bank (FB), Marker G- 1B (G1B), Midbay North (MN), Midbay South (MS), Rubicon Keys (RK), Sa nds Cut (SC), Totten Key (TK)) .................... 118

Figure 5-18. TN CDF for Bisca yne Bay’s South Central Outer Bay using SERC water quality monitoring network data from 1995 -2008 by 3-year rolling window ...................... 119

Figure 5-19. TP CDF for Biscayne Bay’s Sout h Central Outer Bay using SERC water quality monitoring network data from 1995 –2008 by 3-year rolling window ..................... 119

Figure 5-20. Chlorophyll-a CDF fo r Biscayne Bay’s South Centra l Outer Bay using SERC water quality monitoring network data from 1995–2008 by 3-year rolling window ......... 120

Figure 6-1. The major steps involved in developm ent of numeric criteria for TN and TP in streams and rivers protective of water quality standards in downstream estuaries. . 123

Figure 6-2. Map of Pensacola Bay watershed illu strating the 224 sub-watersheds and associated

stream reaches included in the LSPC watershed model. Ten of the twelve terminal reaches discharging into Pensacola Bay are shown with blue dots. ......................... 129

Figure 6-3. Map of Pensacola Bay illustrating where 11 of 12 terminal reaches from the LSPC watershed model enter the bay (not shown is the terminal reach at the eastern end of Santa Rosa Sound). Labels indicate the WB IDs that make up the larger open water areas of Pensacola Bay. ............................................................................................. 129

Figure 6-4. Map of average TN fraction-deliver ed to Pensacola Bay calculated daily for 1997– 2009 using (A) a constant first-order decay rate of 0.14 d -1 

### and (B) using first-order decay rates for TN specified on the basis of two ranges of flow rate in the reach (Table 6-1), following the Model B SPARROW regression parameters reported for southeast U.S. streams by Hoos and McMahon (2009). In both cases , the estimates of fraction delivered were rounded up to the nearest 0.1. ............................................. 131

Figure 6-5. The relationship betw een TN fraction delivered (d -1 

### ) in the Pensacola Bay watershed computed using a constant first-order decay rate of 0.14 d -1 

### (horizontal axis) and flow-dependent values for the first-orde r decay rate for TN, following Hoos and McMahon (2009) (vertical axis) ............................................................................... 132

Figure 6-6. A map showing estimates of the TP fraction delivered to estuarine waters in Pensacola Bay as calculated using a TP rete ntion rate (expressed as first-order decay) that is inversely relate d to stream depth (Gar ć ia et al. 2010) .................................... 132Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 12 Figure 6-7. Regions of the Pensacola Bay watershe d associated with unique terminal reach DPV values for TN (panel A) and the resulting computed values for TN DPVs in all streams (panel B). TN fraction-delivered wa s rounded up to the nearest 0.1, resulting in 12 unique TN DPV values for the enti re watershed. For these maps, fractiondelivered was calculated using stream-flow dependent first-order decay rates for TN, following the results of Hoos and McMahon (2009). ............................................... 133

Figure 6-8. Regions of the Pensacola Bay watershe d associated with unique terminal reach DPV values for TP (panel A) and the resulti ng computed values for TP DPVs. Fractiondelivered was rounded up to the nearest 0.1, resulting in nine unique TP DPV values for the entire watershed. For these calculations, TP fraction-delivered was calculated using stream-depth dependent rates following the results of Gar ć ia et al. (2010). ... 134Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 13 

## Tables

Table 2-1. Assessment endpoints for evaluating the magnitude and effects of nutrients, including advantages and disadvantages. .................................................................................... 34

Table 2-2. Indicator variables fo r evaluating the magnitude and effects of nutrients, including advantages and disadvantages. .................................................................................... 36

Table 3-1. Strengths, weaknesses, indications (situations where appr oach is most applicable), and contraindications (situations where another approach may be needed) for each of the three categories of criteria development approaches that EPA is considering ........... 50

Table 3-2. Estuary water quality data inventor y for causal (TN and TP) and response variables (chlorophyll a ) from FDEP Impaired Waters Rule database. Count is the number of observations; years represent the time pe riod over which the observations were

collected. .................................................................................................................... . 51

Table 3-3. An inventory of models that have be en previously applied to Florida estuaries and their watersheds. The inventory is ba sed upon an inventory compiled by Wolfe (2007) and was developed further based on di scussions with FDEP staff. “fecal only” indicates that although a wa ter quality model was developed, it was only used to

simulate concentrations of fecal coliform bacteria. .................................................... 57

Table 3-4. The state variables included in the implementation of WASP7 for Penscaola Bay. In addition, the key biogeochemical processe s represented in the model and the boundary conditions that must be specified to run the model. ................................... 63

Table 3-5. Performance goals for LSPC hydrol ogy simulations, which are based on previous modeling experience. Values are (average of simulated values - average of observed values) / average of observed values. H ydrologic calibrations for longer averaging periods, including the long-term average flow, are expected to be in closer agreement than for shorter periods, especially su mmer storm volumes, which can depend on

rainfall events. ............................................................................................................. 65

Table 3-6. Seagrass coverages available for Florida estuaries. CC=complete coverage, PC=partial coverage. USGS = United Stat es Geological Survey, FDEP=Florida Department of Environmental Protec tion, SWFWMD=Southwest Florida Water Management District, SF WMD=South Florida Water Management District. SJRWMD=St. Johns River Water Management District. ........................................... 67

Table 3-7. USGS flow gauges and water quality monitoring sites within the Pensacola Bay

watershed .................................................................................................................... 72

Table 4-1. Number of observati ons in IWR Run 40 since 1990 fo r chlorophyll, TN and TP for

coastal waterbodies ..................................................................................................... 80Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 14 Table 5-1. Land use classifications and LDI coeffici ents. Higher values indicate greater intensity of human land use (adapted fr om Brown and Vivas 2005). ..................................... 105

Table 5-2. (A) The calculated probability that the population proportion of years not meeting the criteria is greater than 10%, based on the observed numbers of exceedances, (B) calculated probability that the population pr oportion of years not meeting the criteria is greater than 20%, based on the observed numbers of exceedances, (C) calculated probability that the population proportion of years not meeting the criteria is greater than 25%, based on the observed numbers of exceedances. Values are colored in a stoplight pattern indicating probabilities less than 80% (g reen), 80-90% (orange), and greater than 90% (red) .............................................................................................. 108

Table 6-1. Empirical estimates of the first-order loss rates for TN in southeast U.S. streams from two formulations (Model A and Mode l B) of the SAGT-SPARROW regional watershed model (Hoos and McMahon 2009) .......................................................... 125

Table 6-2. Hypothetical values illustrating the computation of segment-specific protective loading rates and associated DPV concentr ations for terminal stream reaches. Average flows are 1997–2009 averages computed using LSPC. ............................. 130Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 15 

## Abbreviations and Acronyms

BMAP Basin Management Action Plan BMP best management practice BOD biological oxygen demand

CAF Coastal Assessment Framework CBOD carbonaceous biochemical oxygen demand CDA coastal drainage area CDOM colored dissolved organic matter CE-QUAL-ICM three-dimensional eutrophication model CERCLA Comprehensive Environmental Response, Compensation, and Liability

Act CFR Code of Federal Regulations CH3D Curvilinear-grid Hydrodynamics 3d Chl-a chlorophyll a Chl RS 

### -a satellite-derived chlorophyll a cm centimeters

CUSUM cumulative sum

CWA Clean Water Act DO dissolved oxygen DOC dissolved organic carbon DOM dissolved organic matter DOP dissolved organic phosphorus DPV downstream protective values DYNHYD WASP hydrodynamics model EAA Everglades Agricultural Area ECOHAB Ecology and Oceanography of Harmful Algal Blooms project

ECOM-3D Estuarine Coastal Ocean Model EDA estuarine drainage area EFA Everglades Forever Act EFDC Environmental Fluid Dynamics Code EPA United States Environmental Protection Agency EPT Ephemeroptera, Plecotera, and Trichoptera

ERA environmental risk assessment ESA European Space Agency EvPA Everglades Protection Area

F.A.C. Florida Administrative Code FDA fluvial drainage area FDEP Florida Department of Environmental Protection

FL Florida

FWRI Fish and Wildlife Research Institute GIS geographic information system GLUT Georgia Land Use Trends GM geometric meanMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 16 GQUAL generalized quality constituent module HAB harmful algal bloom HAB-OFS Harmful Algal Bloom Operation Forecast System HSPF Hydrological Simulation Program—Fortran HUC hydrologic unit code HYCOM Hybrid Coordinate Ocean Model IPV instream protective value IWR Impaired Waters Rule

km kilometers

k TN 

### first-order decay rate for total nitrogen

k TP 

### first-order decay rate for total phosphorus

L liter LDI Landscape Development Intensity Index LN natural logarithm

L M 

### (N) total nitrogen load limit

L M 

### (P) total phosphorus load limit LOESS locally weighted scatterplot smoothing log 10 

### logarithm, base 10 LSPC Loading Simulation Program in C++ LTER Long Term Ecological Research MERIS Medium Resolution Imaging Spectrometer MGD million gallons per day MODIS Moderate Resolution Imaging Spectroradiometer N nitrogen NASA National Aeronautics and Space Administration NBOD nitrogenous biochemical oxygen demand

NCDC National Climatic Data Center NEGOM Northeastern Gulf of Mexico project

NH 3 

### ammonia NHDPlus National Hydrography Dataset Plus

NLCD National Land Cover Data

NO 3 

### -NO 2 

### nitrate+nitrite

NOAA National Oceanic and A tmospheric Administration NODC National Oceanographic Data Center NPDES National Pollutant Discharge Elimination System NPOESS National Polar-orbiting Operatio nal Environmental Satellite System NPP National Polar-orbiting Operationa l Environmental Satellite System Preparatory Project P phosphorus PAR photosynthetically active radiation PLRG pollutant load reduction goal PLSM Pollutant Load Screening Model

POM Princeton Ocean Model POP particulate organic phosphorus ppb parts per billion PQUAL pervious quality constituent moduleMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 17 psu practical salinity units

q s 

### hydraulic loading

R 2 

### coefficient of regression

RCA Row Column AESOP RCRA Resource Conservation and Recovery Act RTAG Regional Technical Assistance Group SAB Science Advisory Board SAGT USGS regional SPARROW model fo r the South Atlantic, eastern Gulf

Coast and Tennessee River SAV submerged aquatic vegetation

SCI stream condition index SeaBASS SeaWiFS Bio-optical Archive and Storage System SeaDAS SeaWiFS Data Analysis System SeaWiFS Sea-viewing Wide Field-of-view Sensor

SERC Southeast Environmental Research Center SOD sediment oxygen demand SPARROW SPAtially Referenced Regressi on On Watershed attributes model SPCC Spill Prevention Control and Countermeasures SSAC Site-Specific Alternative Criteria

STA stormwater treatment area STATSGO State Soil Geographic Database TKN total Kjeldhal nitrogen TMDL total maximum daily load TN total nitrogen TP total phosphorus TSI Trophic Status Index USEPA United States Environmental Protection Agency USGS United States Geological Survey VIIRS Infrared Imaging Radiometer Suite WAFR Wastewater Facility Regulation

WAMView Watershed Assessment Model WASP Water Quality Analysis Simulation Program WBID waterbody identification number

WCA Water Conservation Area

Z
c 

### colonization depth

τ w 

### water residence time μg micrograms ‰ parts per thousandMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 18 

## Executive Summary

Excess inputs of nitrogen and phosphorus (nitro gen/phosphorus pollution) in surface waters can be harmful in aquatic ecosystems by directly producing excess plant and algal growth, and indirectly leading to reduced clarity, reduced ox ygen levels as the algae and plants decompose, and decreased biodiversity. Primar y sources of nitrogen and phos phorus to aquatic ecosystems include waste water and sewage effluent, atmo spheric deposition, landfill leachate, fossil fuel combustion, and runoff from commercial fe rtilizer and manur e applications.

Nitrogen/phosphorus pollution cont ributes significant loadings of nitrogen and phosphorus to

waters of the United States and is one of the leading causes of water quality degradation. Many of our nation’s waters, including ri vers, canals, lakes, estuaries, and coastal marine waters, are affected by nitrogen/phosphorus pollution. There is increasing evidence of nitrogen/phosphorus pollution in Florida’s waters and clear, widespread indications of the resulting adverse effects on aquatic life in those waters.

EPA is seeking to improve and enhance protection of aquatic life from the detrimental effects of nitrogen/phosphorus pollution through the deri vation and implementation of numeric nutrient

criteria. Numeric criteria and water quality standards are ke y components of water quality assessments and watershed protection management. These numeric criteria will create

environmental baselines that allow Florida to ma nage waters more effectively, measure progress, and support broader partnerships based on nutrient trading, best management practices (BMPs), land stewardship, wetlands prot ection, voluntary collabo ration, and urban stormwater runoff control strategies. Establishing numeric criteria in State water quality sta ndards will give Florida greater ease and faster development of to tal maximum daily loads (TMDLs), provide quantitative targets to support trad ing programs, enable permit writer s greater flexibility and ease in developing National Pollutant Discharge Elim ination System (NPDES) permits, and increase the effectiveness in evaluating the success of Florida’s effort s to control nitrogen/phosphorus pollution throughout the State.

Chapter 3 describes the approach EPA is consider ing to develop numeric criteria for Florida's estuaries. The approach described would allow th e Agency to most fully consider characteristics of estuarine ecosystems (e.g., wate r quality and biological communitie s in estuaries are affected by a combination of basin shape, tides, and th e magnitude, location, and quality of freshwater inflows). In some of Florida’s estuaries, the semi -enclosed basins that define their spatial extent may also create sub-regions with differentiated wa ter quality and aquatic life uses, which could also result in water quality crit eria specific to a particular sub-region. The approach EPA is considering for deriving numeric nutrient criteria for estuarine waters in South Florida differs from that outlined in Chapter 3 because the water systems in South Florida are unique due to the high degree of management of th e waters. Methods for South Florid a are described in Chapter 5.

EPA’s methodology first delineates the estuaries in to discrete areas aroun d Florida’s coastline for the purpose of organizing the criteria developm ent process. Each of th ese discrete areas will then be evaluated to determine the appropr iate “assessment endpoints” and “measurementMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 19 endpoints.” The specific endpoints and indicators that EPA is considering for use in the development of numeric criteria in Florida’s estuaries include protec tion and restoration of healthy seagrass communities, balanced comm unities of benthos, plankton, and nekton, and balanced algal biomass and producti on. Chapter 3 discusses the ra tionale that may be used for selecting specific water quality variables for each of the estuaries. Finally, EPA is considering three approaches: (1) reference conditions, (2) stressor-response relationships, and (3) water quality simulation modeling that could be used independently or in co mbination to develop numeric criteria for chlorophyll a , total nitrogen (TN) and total phosphorus (TP).

Chapter 4 provides the rationale a nd approaches EPA is consideri ng to derive numeric criteria

for Florida’s coastal waters. For much of the Florida’s coastal waters, EPA is considering a reference-based approach with satellite-derived chlorophyll a (Chl RS 

### -a) observations. Satellite ocean color remote sensing technology has advan ced over the past decade and historical Chl RS 

### -a data are available for the past ten years. In contra st there is relatively little field monitoring data of chemical and biological constituents along th e Northwest Gulf Coast and Atlantic Coast of

Florida.

Coastal physical forcings such as wind, current s, and tides are known to influence coastal chlorophyll dynamics together with nutrient loadings from the la nd. Thus, all of these processes will be represented when using remote sensing as a reference condition approach. Specifically, EPA is considering the use of remote sensing data to develop numeric criteria for the Northwest Gulf Coast, West Gulf Coast, and Atlantic Coastal Areas of Florida. Due to interference from colored dissolved organic matter and bottom refl ectance on satellite measurements, EPA is not considering the derivation of nume ric criteria using remote sensi ng data in coastal waters from Apalachicola Bay to Suwannee River (Big Bend) and South Florida.

Chapter 5 outlines the approach EPA is considering to derive numeric criteria for nitrogen/phosphorus pollution in South Florid a marine and inland flowing waters. EPA is defining South Florida inland flowing waters as free-flowing, predominan tly fresh surface water in a defined channel, and includes, streams, ri vers, creeks, branches, ca nals, freshwater sloughs,

and other similar water bodies located in the South Florida nutrient wa tershed region. South

Florida marine waters include estuarine and coastal waters extending three nautical miles

offshore.

EPA is considering a reference-based approach to derive numeric TN and TP criteria for South Florida inland flowing wate rs and numeric chlorophyll a , TN, and TP criteria in South Florida marine waters using least-distur bed sites that support balanced natural populations of aquatic

flora and fauna. Alternative methods of criteria de rivation for inland flowing waters that EPA is considering include stressor-respons e relationships between chlorophyll a and TN and TP, and a distributional approach using all sites. EPA is not establishing new TP criteria for canals in the Everglades Protection Area (EvPA) in deferen ce to the Everglades Forever Act (EFA).

Pursuant to 40 CFR 131.10(b), water quality st andards must ensure the attainment and maintenance of downstream water quality standard s. Thus, EPA is deriving numeric nutrient criteria for streams in Florida in order to protec t the estuarine waterbodies that ultimately receive nitrogen/phosphorus pollution from the watershe d. These criteria, which EPA will refer to asMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 20 Downstream Protection Values, or DPVs, will appl y in place of the stream’s TN and TP criteria if the applicable DPV is more stringent.

The conceptual approach that EPA is considering for developing stream DPV criteria will begin with estimates of limits on TN and TP loading ra tes that are needed to support balanced natural populations of aquatic flora and fauna in estuarin e waters. The loading limits will be determined as part of the criteria development effort for es tuarine waters as described in Chapter 3 of this document. The protective load limits can be scal ed by average streamflow entering the estuary to determine criteria for TN and TP concentrations in streams as they discharge into estuaries. Finally, DPVs can be determined for upstream reaches within watersheds by accounting for expected loss or permanent rete ntion of TN and TP within the stream network. Because of the complexities associated with the managed fl ows in South Florida inland flowing waters (Chapter 5), the fraction of TN or TP from the upstream tributary reach that eventually flows into marine waters cannot be estimated or predicted. Therefore, EPA is considering expressing DPVs at the terminal reach of the tributary into an estu ary as protective concentr ations or, alternatively, protective loads.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 21 

## 1 Introduction

1.1 Purpose of the Document

The United States Environmental Protection Ag ency (EPA) is deriving numeric criteria for nitrogen/phosphorus pollution to protect waters in the State of Florida, in response to the 2009

determination that “new or revi sed water quality standards for nut rients in the form of numeric nutrient criteria are necessary [in the State of Florida] to meet the requirements of Clean Water Act (CWA) (CWA section 303(c)(2)(A) and 40 CFR §131.11(a)(1)).” This document describes approaches EPA is considering for the derivation of numeric criteria in Florida estuaries, coastal waters, and southern inland flow ing waters. In addition, this doc ument describes the approach EPA is considering for developing downstream prot ective values (DPVs) that may be applied to

Florida streams to ensure the attainment and maintenance of dow nstream water quality

standards.



### 1.2 Document Organization

Chapter 1 provides a summary of the background information that serves as a basis for the development of numeric criteria for contro lling nitrogen/ phosphorus pollution. Chapter 2 includes the statutory requiremen ts that direct EPA and Stat es to reduce nitrogen/phosphorus pollution, the regulatory action that determined nu meric criteria are necessary in the State of Florida, background information on nitrogen/phos phorus pollution and effects within the aquatic

environment.

Chapter 2 provides a description of EPA’s genera l approach for developing numeric criteria, generic conceptual model, and endpoints th at form the basis for analysis plans.

The next three chapters summarize backgr ound information on the geography, water quality characteristics, and water quality conditions specific to each water body type and presents approaches EPA is considering for numeric crite ria derivation. Potential uncertainties and data gaps associated with the a pproaches are also included.

Chapter 3 describes the approach EPA is consider ing for numeric criteria derivation in Florida

estuaries.

Chapter 4 provides a description of the appro ach EPA is considering for numeric criteria

derivation in Florid a coastal waters.

Chapter 5 describes the approach EPA is consider ing for numeric criteria derivation to address the unique hydrodynamic conditions found in South Florida.

Chapter 6 describes an approach EPA is consider ing to quantitatively derive numeric values for streams to protect downstream estuaries. Potential uncertainties and data ga ps associated with the approach are also included.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 22 

### 1.3 Background: Clean Water Act Requirements and Florida’s Current Water Quality Standard for Nitrogen/Phosphorus Pollution

The Clean Water Act (CWA) established a basis for water quality protection in section 101(a): “The objective of this Act is to restore and maintain the chemical, phy sical, and biological integrity of the Nation's waters.” Under CWA se ction 101(a)(2) “it is the national goal that wherever attainable, an interim goal of water quality which provides for the protection and propagation of fish, shellfish, a nd wildlife and provides for recreation in and on the water be achieved.” This goal is commonly referre d to as fishable and swimmable.

In order to meet the fishable and swimmable goa l, the CWA defines a structure of interlinked programs and identifies the establishment of water quality standards as the key component necessary to achieve that goal. In many ways , water quality standa rds provide the common mechanism by which the other parts of the CW A (such as NPDES permits and TMDLs) work together to accomplish the overall goals and objective of the CWA.

To assist in achieving the goals and objective of the CWA, Stat es adopt water quality standards that consist of designated uses of the naviga ble waters and water quality criteria that are protective of those designated uses. The State sp ecifies the designated use that must be achieved or protected. When designating us es, the State must take into c onsideration the use and value of water for public water supplies, protection a nd propagation of fish, shellfish and wildlife, recreation in and on the water, agricultural, i ndustrial and other purpos es including navigation (CWA section 303(c) and 40 CFR 131.10).

Designated uses can be general, such as aquati c life use protection or pr imary contact recreation, or more specific. In Florida, waters have been already classified by designated use.

As described by FDEP in the 2010 Integrated Water Quality Assessment for Florida (FDEP 2010):

Florida’s Water Quality Standards Program , the foundation of the state’s program of water quality management, designates the “present and future most beneficial uses” of the waters of the state (Subs ection 403.061[10], F.S.). Florida’s surface water is protected for five designate d use classificati ons, as follows:

• Class I Potable water supplies

• Class II Shellfish propagation or harvesting

• Class III Recreation, propagation, an d maintenance of a healthy, wellbalanced population of fish and wildlife

• Class IV Agricultural water supplies (lar ge agricultural lands, located mainly around Lake Okeechobee)

• Class V Navigation, utility, and industri al use (there are no state waters currently in this class)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 23 Class I waters generally have the most st ringent water quality cr iteria and Class V the least. Class I, II, and III surface waters share water quality criteria established to protect recreation and the propagati on and maintenance of a healthy, wellbalanced population of fish and wildlife. All waters of the state are considered to be Class III, except for those specifica lly identified in Section 62-302.600, F.A.C. All waters of the state are required to meet the “Minimum Criteria for Surface Waters,” as identified in Section 62-302.500, F.A.C.

In general, States adopt water quality criteria into water quality st andards to protect the designated uses from the discharge of pollutants. These criteria are expres sed as either narrative

statements or numeric values. In order to protect the designated us es listed above from nitrogen/phosphorus pollution, the State of Florida has adopted a narrative criterion into their water quality standards, which provides in part:

in no case shall nutrient concentrations of a body of water be altered so as to cause an imbalance in natural populations of aquatic flora or fauna (F.A.C. 62- 302-530(47)(b)).

On January 14, 2009, EPA made a determination under CWA section 303(c )(4)(B) that numeric nutrient water quality criteria for lakes, flow ing waters, estuaries and coastal waters are necessary for the State of Florida to meet the requirements of CWA Section 303(c). Upon making this determination, the EPA Administrato r is then required to promptly prepare and publish proposed regulations setting forth new or revised water quality standards.

In making the determination, the Agency cons idered (1) the State' s documented unique and threatened ecosystems, (2) the high number of impaired waters due to existing nitrogen/phosphorus polluti on, and (3) the challenge a ssociated with growing nitrogen/phosphorus pollution resulting from expanding urbanization, co ntinued agricultural development, and a significantly increasing popula tion. EPA also reviewed the State’s regulatory nutrient accountability system, which represents an impressive synthesis of technology-based standards, point source control authority, and authority to esta blish enforceable controls for nonpoint source activities. However, the sign ificant challenge faced by the water quality components of this system is its dependence upon an approach involving resource-intensive and time-consuming site-specific data collection and analysis to interpret the narrative nutrient criterion. EPA subsequently determined that Fl orida’s reliance on a case-by-case interpretation of its narrative nutrient criterion in implemen ting an otherwise comprehensive water quality framework of enforceable accountability was insu fficient to ensure protection of applicable designated uses.

EPA determined that numeric nutrient criteria would strengthen and expedite the process for identifying impaired waters, preparing TMDLs a nd deriving water quality-ba sed effluent limits in NPDES permits, thus providing the necessary protection for the State’s designated uses. In November 2010, EPA established criteria for Florida’s lakes, flowing waters 1 

### and springs within

1 In the November 2010 rulemaking, EPA did not establis h numeric criteria for inland flowing waters in South Florida. For the purpose of this effort, EPA has distinguished South Florida as those areas south of Lake

Okeechobee and the Caloosahatchee Rive r watershed to the west of Lake Okeechobee and the St. Lucie watershedMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 24 the State of Florida. These promulgated crit eria only apply to predominantly fresh surface

waters classified as Class I or Class III in or der to implement the State’s narrative nutrient criterion mentioned above. EPA will propose numeric criteria for nitrogen/phosphorus pollution to protect estuaries, coastal areas and South Florida inland flowing wa ters that have been designated Class I, II and III. The methods and a pproaches that EPA is considering to establish numeric criteria for estuaries, coastal areas and South Florida inland flowing waters is the subject

of this document.



### 1.4 Nature of the Chemical Stressor: Nitrogen/Phosphorus Pollution

Excess anthropogenic inputs of nitrogen and phosphorus, commonly referred to as nutrient pollution or nitrogen/phosphorus pollution, in surface waters can result in excessive and imbalanced primary production in a wa terbody, referred to as eutrophication. 2



### 1.4.1 Stressor Source and Distribution

Nitrogen/phosphorus pollution in water bodies comes from many point and nonpoint sources, which can be grouped into the following five ma jor categories: 1) urban stormwater runoff, 2) municipal and industrial waste water discharge (e.g. sewage effluent, landfill leachate), 3) row crop agriculture (e.g. commercial fertilizer and manure applications), 4) animal husbandry, and 5) atmospheric deposition (and fossil fuel comb ustion) (SENITG 2009). These sources are often direct inputs to estuaries and coasts because of the large populations that reside very close to

their shores. Estuaries and coastal waters ar e especially vulnerable to nitrogen/phosphorus pollution because they receive nitrogen and phos phorus from multiple natural and anthropogenic

upstream sources. 3



### 1.4.2 Aquatic Ecosystem Effects of Nitrogen/Phosphorus Pollution

The biennially published National Water Quality Inventory Report to Congress indicates that excess nitrogen and phosphorus are consistently a major source of water quality impairment in the Nation’s waters. Since the 1992 report, nitrogen and phosphorus compounds have consistently ranked in the top fi ve causes of U.S. water quality impairment. This pollution causes major impacts to aquatic ecosystems and disrupts the natural populations of flora and fauna (Dodds et al. 2009; Howarth et al. 2002; National Research Council 2000). Imbalances in natural communities can adversely affect aquatic life as we ll as human health. In Florida, nutrients have been reported as the cause of impairment in 120 estuarine water bodies covering approximately 569 square miles (FDEP 2010).

to the east of Lake Okeechobee. Met hods and approaches that EPA is consider ing to establish criteria for the inland flowing waters in South Florida are contained within this document.

2 Eutrophication is defined as an increase in the rate of supply of organic matter to an ecosystem (Nixon 1995). Eutrophication can adversely affect human and aquatic life uses of waters, and may impact human health. Here we are concerned with eutrophication caused by excess loadin g of N and P, which causes increased organic matter production within the ecosystem.

3 Some estuaries can be more susceptible to eutrophicatio n than others. Physiographic setting, primary production base, nutrient load, dilution, residence time, flushing, stratification, hypsography, grazing of phytoplankton, suspended material load and light extinction, denitrification, spatial and temporal distribution of nutrient inputs, and allochthonous organic matter inputs a ll affect an estuary’s response to adde d nutrients (National Research Council 2000).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 25 The impacts of nitrogen/phosphorus pollution can a dversely affect aquatic life in many different ways (see Figure 1-1). The effects of nitrogen/p hosphorus pollution include direct changes to aquatic systems (e.g., increased algal growt h, changes in algal species composition, and increased organic matter production) and indir ect effects (e.g., loss of submerged aquatic vegetation, nuisance algal blooms, and low dissolved oxygen) (USEPA 2006, 2008a). The eutrophication process has resulte d in large “dead zones” found in many coastal areas, such as the Gulf of Mexico and Chesapeake Bay (Eco logical Society of America 2009) and reduced seagrass beds, a foundation species for many estu arine waters (Hughes et al. 2009; Tomasko et al. 2005). The Florida Department of Environmenta l Protection (FDEP) notes that harmful algal

blooms in both fresh and marine waters continue to be a concern that have toxic effects to fish, wildlife, and humans, and indirect effects due to anoxia (FDEP 2010). In 2008 and 2010, FDEP lists nutrient impairment as a special initiative (FDEP 2008, 2010).

Environmental consequences from change s in primary production (e.g., increases in phytoplankton) can include increased turbidity a nd decreased light penetration (Boyer et al. 2009; Bricker et al. 2007; Bricke r et al. 2008; McPherson and Mill er 1994). This can reduce light availability necessary for the gr owth of submerged aquatic vegeta tion. This effect often leads to declines in seagrass in estuaries and coastal waters (L ee et al. 2007; Dennison 1987; Duarte 1991). Seagrass are critical components of many estu arine and coastal systems and are used as feeding, spawning, and nursery grounds for many a quatic species (see revi ew by Waycott et al. 2009).

Imbalances in primary producer dynamics can cause changes in habitat and available food resources that can induce changes affecting an en tire food web (Bricker et al. 2003b; Vitousek et al. 1997). Increased phytoplankton abundance has also been linked to composition shifts to less desirable species (Paerl 1988). Beca use these changes affect natu ral processes at the lowest levels of the ecosystem, they can cause a cascade of problems.

Eutrophication has also been shown to increase th e incidence of disease in aquatic animals and wildlife (Johnson et al. 2010). Although nitrogen/phosphorus pollu tion may not always be the trigger, nutrient overenrichment can contribute to blooms of nuisance or toxic algae (Glibert et al. 2006) or may extend bloom duration (Vargo 2009b). Called harmful algal blooms (HABs), these blooms can damage or clog the gills of fish and invertebrates and cause illness or death to animals and humans (Falcone r 1999; NOAA 2010). Di rect impacts to humans result from exposure to HAB toxins or consum ption of toxic shellfish. Examples of freshwater algal species

that are considered HABs in Florida include Microcyctis

, Arabaena , and Cylindrospermopsis and marine species include Karenia brevis

, Alexandrium monilatum

, Takayama pulvhella

, K. selliforms

, K. mikimotoi

, Karlodinium venificum

, Pyrmnesium parvum , and Chattonella spp

. (FDEP 2010).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 26 Figure 1

‐ 1. The model of primary and secondary symptoms of nitrogen/phosphorus pollution and the potential effects and
impairments (Bricker et al. 1999; Bricker et al. 2003b)



### Excessive algal growth contri butes to increased oxygen c onsumption associated with decomposition, potentially reducing oxygen to levels below those needed for aquatic life to survive (NOAA 2010; USGS 2010). Low oxygen con centrations, or hypoxia, can occur in episodic “events,” which sometimes develop ove rnight. Migration to avoid hypoxia depends on species’ mobility, availab ility of suitable habita t, and adequate environmental cues for migration. For example, mobile species, such as adult fi sh, can sometimes survive by moving to areas with more oxygen availability. Less mobile or immobile species, such as oysters and mussels, cannot move to avoid low oxygen and are often killed during hypoxic events (Ecological Society of America 2009). While certain mature aquatic an imals can tolerate a range of dissolved oxygen (DO) levels, younger life stages of species like fish and shellfish often require higher levels of oxygen to survive (USEPA 2000a). Sustained low le vels of DO cause a severe decrease in the amount of aquatic life in hypoxic zones and affect th e ability of aquatic organisms to find necessary food and habitat. In extreme cases, anox ic conditions occur when and where there is a complete lack of oxygen. Because most plants and aquatic organisms cannot live without sufficient oxygen, hypoxic and anoxic areas are sometimes referred to as dead zones (Ecological Society of America 2009).

Other indirect impacts of algal blooms include restrictions on recreation (such as boating, swimming, and kayaking) due to closures of area s to recreational uses. The loss of biological resources can also reduce or preclude recreatio nal fishing, shellfish harvest, and diving. OtherMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 27 direct impacts to humans include harmful levels of nitrate in drinki ng water supplies (SENITG 2009).



### 1.5 Purpose of this Effort

As described above, EPA will propose numeric values to translate the Stat e of Florida’s current narrative criteria to protect estuaries, coastal waters and South Florid a inland flowing waters from nitrogen/phosphorus pollution. These numeric criteria will establish limits to pollutant concentration levels that will ensure protection of the designated use of the water body that has been determined by the State. Specifically, th e numeric values will protect estuaries, coastal waters and South Florida inland flowing waters wi thin the State that have been designated as Class I, II or III (see Section 1.3), translating the currently applicable narr ative criterion that the State has established to be prot ective of these designated uses:

in no case shall nutrient concentrations of a body of water be altered so as to cause an imbalance in natural populations of aquatic flora or fauna (F.A.C. 62- 302-530(47)(b))

EPA is not considering the establishment of numeri c values to protect Class IV and V waters as they are not part of the CWA s ection 101(a) designations that re quire water quality that provide

for fishable and swimmable conditions. As the St ate of Florida already has numeric criteria for dissolved oxygen, EPA is not considering the de velopment of dissolved oxygen criteria. For

those Class I waters in South Fl orida (i.e., designated as pota ble water supplies), the drinking

water maximum contaminant levels for nitrates (10 mg/L) would continue to apply. EPA is currently not considering the modi fication of any designated uses of waters within the State of

Florida.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 28 This page is inte ntionally blank.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 29 

## 2 Approach

2.1 Overview

EPA’s 1976 publication entitled Quality Criteria for Water (also known as the Red Book) contains ambient water quality cr iteria for nitrates and elemental phosphorus. For domestic water supplies, the maximum contaminant level for nitrat e was set at 10 mg/L to protect human health from exposure to this pollutan t through domestic drinking wa ter. The phosphorus criterion was

set at 0.10 μ g/L elemental phosphorus to protect agai nst the toxic eff ects of elemental phosphorus to estuarine and marine organisms. Note that neither of these criteria was set to reduce the potential for eutr ophication, although the Red Book doe s present a rationale for supporting a total phos phorus criterion.

EPA has published peer-reviewed technical guidance for states to develop numeric nutrient criteria for lakes and reservoirs (USEPA 2000b) , for rivers and streams (USEPA 2000c), for

estuarine and coastal waters (USEPA 2001), and for wetlands (USEPA 2008b). These guidance manuals are intended to help stat es, tribes and others in establishing scientifically defensible

nutrient criteria for classes of water bodies. EPA has also published supplemental peer reviewed technical guidance for states using stressor-resp onse relationships to derive numeric nutrient criteria (USEPA 2010).

Additionally, EPA has recommended CWA section 304( a) water quality criteria for nutrients with the aim of reducing and pr eventing eutrophication on a nationa l scale. There are a total of 26 peer-reviewed ecoregional criteria documen ts in 2001 and 2002 that cover most water body types in the United States (12 la kes and reservoirs, 13 rivers and streams, and one wetland). Each criteria document presents recommended criteria for causal parameters (total phosphorus and total nitrogen) and respon se variables (chlorophyll a and some form of water clarity, i.e., turbidity or Secchi depth). EPA developed the ecor egional criteria values using a distributional approach that utilized all availa ble data. This information is intended as a starting point for states, authorized tribes and ot hers to develop more refined nu meric criteria, as appropriate, using EPA waterbody-specific technical guidance ma nuals and other scientifically defensible approaches. These recommended criteria documents can be accessed at the following Web sites:

• Lakes: http://www.epa.gov/waterscience/crit eria/nutrient/ecoregions/lakes/index.html

• Streams and Rivers: http://www.epa.gov/waterscience/criteria/ nutrient/ecoregions/ rivers/index.html

• Wetlands: http://www.epa.gov/waterscience/criteria/nu trient/ecoregions/we tlands/wetlands_13.pdf

None of these available recommended numeric cr iteria apply to Florida’s estuaries, coastal waters and southern inland flowing waters. Thus, for this effort EPA must derive numeric values to translate Florida’s existing na rrative criterion using the pub lished peer reviewed technical guidance, the best available data, and sound scientific rationale.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 30 2.1.1 Nutrient Criteria Development Guidance

As noted above, EPA published peer reviewed technical guidance for developing numeric

nutrient criteria for rivers and streams in July 2000 (USEPA 2 000c), and estuaries and coastal marine waters in October 2001 (USEPA 2001). Thes e technical guidance documents describe the techniques used to derive numeric criteria for us e in State water quality standards. They provide background information on classifying water bodies , selecting criteria variables, designing monitoring programs, analyzing nutrient and algal data, deriving regional criteria, and implementing management practices. 4



### The documents describe three gene ral approaches that could be us ed to develop numeric nutrient criteria (USEPA 2000c):

1. Identification of reference conditions for each water body type based on best professional judgment or percentile selections of da ta plotted as fre quency distributions

2. Use of predictive relationships (e.g., trophic stat e classifications, empirical and mechanistic models, biocriteria)

3. Application and/or modification of establis hed nutrient/algal th resholds (e.g., nutrient concentration thresholds or algal limits from published literature)

EPA’s technical guidance documents suggest that each of the above analytical approaches was appropriate for deriving scientif ically defensible numeric nut rient criteria. However, EPA recognized that each approach has different data requirements, and these differences should be

considered in the context of i ndividual situations and availa ble information. The methods and approaches described in this document demonstr ate that EPA is considering the derivation of numeric criteria for estuarine, coastal, and S outh Florida inland flowing waters using the tools and approaches described in these guidance docum ents, as well as new methods that follow the general approach outlined by EPA and are refl ective of the latest scientific knowledge.

2.1.2 General Approach

The general approach that EPA followed for each of the water body system types is outlined in

the estuarine and coastal marine wa ters guidance document (USEPA 2001):

1. Establish a panel of technical experts – this group is responsible for developing the numeric criteria; EPA created an internal work group consisting of staff from EPA’s Office of Science and Technology, Office of Resear ch and Development (staff from the Gulf Ecology Division in Pensacola FL and the Atla ntic Ecology Division in Narragansett, RI), and EPA Region 4.

2. Review the scientific and regulatory basis – Chapter 1 of this document describes the scientific and regulatory basis for establishing numeric criteria for Florida estuarine, coastal, and South Florida waters. In addition, Chapte r 1 describes the basis for EPA to establish numeric downstream protective values to protect estuaries from impacts originating

upstream.

4 This and other EPA guidance documents can be accessed from the Office of Science and Technology’s Web site: http://www.epa.gov/waterscience/criteria/nutrient/guidance/index.html.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 31 3. Develop a classification scheme – this step establishes a classification scheme for subdividing the population of waterbodies for whic h numeric criteria are developed. For this effort, EPA first divided the waters into three gr oups: (1) estuarine, (2) coastal, and (3) South Florida. Chapters 3-5 provide additional detail s on the specific classifications for each group

of Florida waters.

4. Select indicator variables – EPA has evaluated causal (e.g., TN and TP) and response (e.g., chlorophyll a , and others) specific to the classificati ons of waters in the groups of estuarine (see Chapter 3), coastal (see Chapter 4), and South Florida (see Chapter 5). Based on the evaluations and analyses, EPA selected appropriate causal an d response variables for each water body system in the development of numeric criteria.

5. Data collection and assessment – based on the classification scheme, EPA will collect available data for each system or group of waters. EPA has reviewed data from STORET, Florida’s IWR data set, and NOAA and other remote sensing data.

6. Establish methodology – based on the availability of da ta and assessment endpoints used to

translate Florida’s narrative cr iterion, EPA is considering a va riety of specific methods for deriving the numeric criteria . Chapters 3-5 describe th e methodologies in detail.

7. Criteria development - the EPA guidance Nutrient Criteria Technical Guidance Manual:

Estuarine and Coastal Marine Waters (USEPA 2001) outlines the following process for developing numeric nut rient criteria:

• Examination of the historical record or pale oecological evidence for evidence of a trend.

• Determination of a reference condition usi ng one of several alte rnative approaches. Remember that the reference condition, how ever derived, is only one of the three approaches of the criter ia development process.

• Use of empirical modeling (or surrogate data sets, where available, in those instances where insufficient information exists). This ma y be the case especia lly in estuaries with insufficient hydrological data, or significan tly developed or m odified watersheds.

• Objective and comprehensive interpretation of all of this information by the established panel of technical experts.

• Finally, the criterion developed for each variable should refl ect the nutrient condition for the waterbody to protect the designated use. Second, it must be reviewed to ensure that the proposed level does not entail adverse nut rient loadings to downstream waterbodies.

EPA is considering this criteria development process for each of the groups of water bodies and explains the process in more detail for es tuarine waters (see Chapter 3), coastal waters (see Chapter 4), and South Florida marine a nd inland flowing waters (see Chapter 5).



### 2.2 Conceptual Model

Ideally, the aquatic life to be protected in a wa ter body needs to be characterized in a way that captures the range and diversity of the inter-related life forms that comprise the biological community that the public expects to be protec ted. For this effort, EPA has considered the available data and information, which has provided insight into the relative health of variousMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 32 systems in Florida, all potentially subject to degrees of stress from various levels of human

disturbance. In order to rest ore and maintain water quality, it is necessary to determine the health of the system, and to understand the range of conditions, both physical and chemical, that support and sustain that health. To accomplish this, one can select su itable surrogates or indicators closely correlated with overall system health, and expected to be sensitive to stressors, in this case, nitrogen/phosphorus pollution. Figu re 2-1 illustrates the conceptual relationship between the objective, which is the support of balanced natural populations of aquatic flora and fauna, appropriate biological assessment endpoints and indicators (the causal and response variables, or measurement endpoints, for numeric criteria). EPA also provides a simple flow chart analysis plan for each gr oup of waters in Chapters 3-5.

Nitrogen/phosphorus pollution can result in exce ss biomass which can deplete oxygen resulting from decay of nutrient-enhanced organic matter production. Recognition of this pathway provides direction for setting prot ective numeric criteria for nitrogen/phosphorus and related parameters. In order to support ba lanced natural populati ons of aquatic flora and fauna in Florida through effective implementation of CWA program s, EPA will develop and establish numeric criteria for causal variables, TN and TP, as well as the primary res ponse variable, chlorophyll a

.

Figure 2

‐ 1. Pathways for nutrient effects on estuarine and coastal aquatic life uses. Biological
Endpoints Balanced
Phytoplankton Biomass

and Production Protection and

Restoration of Healthy

Seagrass Communities Balanced Faunal

Communities Chlorophyll a

Concentration Prior to

Shift in Species

Dominance

Percent Surface
Light Goal Chlorophyll a at

Recommended
Trophic
Boundary Chlorophyll a Criteria Proposed TN/TP Criteria

Water Clarity for

Maintenance of SAV

Habitat Dissolved

Oxygen

Balanced Natural Populations of Aquatic Flora and Fauna Water Quality

Targets

Objective

Causal

Variable

Response

VariableMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 33 

### 2.3 Review of Scientific Basis and Selection of Potential Assessment Endpoints and Indicator Variables

EPA conducted a thorough literature review to evaluate biological, chemical, and physical assessment endpoints appropriate to the prot ection of aquatic flora and fauna from nitrogen/phosphorus pollu tion (see Appendix B).

The true assessment endpoints are the valued ecos ystem characteristics that are desired to be protected. In a regulatory context, the designated uses and their a ssociated narrative criteria may be considered as assessment endpoints. Th ese assessment endpoints (such as shellfish propagation and harvesting) are of ten difficult to predict or meas ure directly. Therefore, the development of water quality criteria usually proceeds throug h the evaluation of simpler endpoints (referred to as indicators or measures) that are m easurable and predictable, and serve as surrogate measures to link stressors and outcomes.

These “measures” include measures of effect (formerly known as “measurement endpoints”), defined as “measurable changes in an attribute of an assessment endpoint or its surrogate in response to a stressor to which it is exposed,” measures of exposure, defined as “measures of

stressor existence and movement in the environm ent and their contact or co-occurrence with the assessment endpoint,” and measures of ecosyst em and receptor characteristics (USEPA 1998). The TMDL and Watershed Approach literature tends to refer to these measures as “indicators.”

A target is simply a value of an indicator that is consistent wi th attaining the assessment endpoint or management objective. In other words, a target is equivalent to a criterion value for protecting a specific use at a given site. For this effort, th e target will be the numeric value of an indicator variable that supports a balance natural popula tion of aquatic flora and fauna in Florida’s estuaries, coastal areas and s outhern inland flowing waters.

Salient aspects of the literature review and EPA’s basis for selecting assessment endpoints and the proposed water quality indi cator variables to protect t hose endpoints are discussed in subsequent sections of this chapter.

2.3.1 Selecting Assessment Endpoints and Water Quality Indicator Variables

Selecting assessment endpoints to protect a balanced natural population of aquatic flora and fauna represents a balance among environmental sensitivity to nitrogen/phosphorus pollution and available data. To develop numeric criteria, it is important to sele ct assessment endpoints that are sensitive to nitrogen/phosphorus pollution, so th at one can infer that the numeric criteria will protect less sensitive receptor s from such pollution. Additionally, it is important to choose endpoints with sufficient data that would allow qua ntitative relationships to be developed either through stressor-response relations hips (e.g., empirical or regr ession models) and/or water quality simulation models, and that would be sensitive to environmental changes that are supported by data.

There are numerous endpoints that can, at a mi nimum, be qualitativel y related to nutrient enrichment (e.g., Bricker et al. 2008). EPA sear ched scientific databa ses (including: GoogleMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 34 Scholar, Web of Science, and state research a nd agency reports) and reviewed more than 800 documents to investigate assessment endpoints an d the likely stressors driving responses in estuarine and marine systems. The assessm ent endpoints examined include phytoplankton, macroalgae, epiphytes, seagrass, benthic macroinv ertebrate and fish indices, HABs, and coral. The literature review also captured the nature and location of the i nvestigations and the endpoint’s relationship to nutrien ts and other causal variables. A detailed bibliography and

results of the literature revi ew are provided in Appendix A and Appendix B, respectively. The major assessment endpoints and indicator variab les considered and sa lient aspects of the

literature review are summarized in Table 2-1 and Table 2-2, respectively. For a discussion on assessment endpoints considered in the developmen t of numeric criteria for South Florida inland flowing waters, please see Chapter 5.

Table 2

‐ 1. Assessment endpoints for evaluating the magnitude and effects of nutrients, including advantages and
disadvantages.

Importance Linkages to, or
Effects of, Nutrients Advantages DisadvantagesSeagrass • Valuable marine habitat

• Primary food source for

many organisms • Spatial extent, density,
growth rates decline

with decreased light

transmittance

• Light transmittance

decreases with

decreased clarity/increased

nutrients

• Light requirement usually 20–25% surface

irradiance • Mechanism of nutrient
impact mostly well

‐ understood

• Colonization depth (Z

c )

useful indicator

• Once Z
c goal
established, can use light requirements to

infer water clarity
requirement and
chlorophyll a criteria

• Historical depth of

colonization could be

used to infer reference

water clarity • Co

‐ factors exist

– salinity stress, food web
change, dredging,
propeller scarring,

sediment loading,

disease

• Response to nutrients

can be slow (especially
recovery)Phytoplankton • Primary producers and
important component

of marine food web

• Excess growth affects
clarity, DO, habitat,

aesthetics • Nutrients are key
limiting factors for algal
growth rate. • Responsive to nutrients,

Well

‐ established basis

for use as indicator

• Biomass data in

estuarine waters are routinely monitored and

data are generally

abundant

• Satellite

‐ derived
chlorophyll data readily

available in many
coastal waters • Other factors can
interfere with
evaluating stressor

‐

response relationships

• Species composition

data limited; differences

in field sample and

taxonomic methods

may increase
uncertainty

• Field

‐ collected biomass

data in coastal
(offshore) waters are
limited

• Most estuaries lack
species composition

models developed for

nutrient response

• Lack of phytoplankton

data in healthy canalsMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 35 Importance Linkages to, or
Effects of, Nutrients Advantages DisadvantagesHarmfulAlgalBlooms • Certain HABs impact

human health/other

marine organisms, and
aquatic ecosystems

• Often associated with

toxins leading to faunal
kills, shellfish

contamination,

economic impacts,

decline in aesthetic
value, environmental

and ecological damage • HAB species other than

K. brevis occur in Florida

marine waters, but are
less studied • Foul odor and reduced

aesthetics can lead to public awareness

• Once driven toward
landfall, there is some
evidence that K. brevis

bloom duration may be

extended by land

‐ based

sources of nutrients • K. brevis initiation

occurs in coastal waters beyond 3 miles

• It is unclear that

reduction in land

‐ based

nutrients would reduce

K. brevis blooms.

Current Gulf of Mexico

K. brevis models (see
Appendix C) are focused

on research applicationsCorals • Highly productive and

valued ecosystem

• High species richness

and diversity • Nutrient

‐ poor habitat

• Nutrients may
contribute to bleaching,
disease, and excess macroalgal growth • Highly valued resource • Role of nutrients on
coral health is mixed

• Method limitations

• Interacting factors are important (dissolved
organic carbon, fish,
etc.)

• May depend on
duration of enrichmentEpiphytes • Excess growth hinders

seagrass growth • Epiphyte biomass

increases with nutrient

enrichment • Responsive to nutrients

• May be more sensitive

than seagrass loss,
especially epiphyte
composition

• Clear linkage to
important aquatic life (seagrass) • Biomass responses

sometimes equivocal

• Confounding factors (light, grazing, etc.)

• Composition difficult to

measure

• Limited dataInvertebrates • Reliable indicator of
biological conditions • Invertebrate community changes from increased
phytoplankton food

base and reduced

benthic food base

• Severe community changes with hypoxia • Established indicator of
biological conditions

• Existing monitoring

programs

• Stream Classification

Index in canals

decreases with
increasing nutrient

concentration • Many confounding

factors (e.g., seagrass
and other habitat loss,

sediment toxicity, overfishing, indirect

effects of nutrients)Fish • Indicator of biological

condition • Nutrient loading may impact habitat quality

for fish (e.g., due to hypoxia or seagrass loss)

HABs can cause fish
mortality or reduced

fish growth.

• Excess nutrients can
also stimulate fisheries
production by increasing

prey abundance.

• • Highly visible

• Substantial public

concern • Many confounding

factors (e.g., overfishing,
stocking, seagrass and

other habitat loss,

indirect effects of
nutrients)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 36 Table 2

‐ 2. Indicator variables for evaluating the magnitude and effects of nutrients, including advantages and disadvantages.

Importance Linkages to, or
Effects of, Nutrients Advantages DisadvantagesClarity • Affects growth of plants

and phytoplankton • Nutrient enrichment

enhances
phytoplankton growth,
reducing clarity • Easy to measure (photosynthetically

active radiation [PAR],
Secchi)

• Clear linkage to important aquatic life (e.g., seagrass)

• Sensitive to nutrient

enrichment

• Responsive to water quality management • Confounding factors (e.g., inorganic particles,

dissolved organic

carbon [DOC])DissolvedOxygen • Hypoxia kills fish and

invertebrates

• Hypoxic or low DO areas
nullified as suitable

habitat • Nutrients affect organic loading through algal
growth, depleting

oxygen

• Nutrients accelerate
decomposition rates by

microbial stimulation,
consuming oxygen • Existing criteria

• Well established basis

for protection of aquatic

life

• Clear linkages to

nutrient enrichment

• Extensive database • Need to model
relationship between

nutrients and DOChlorophylla • Chlorophyll is an
indicator of
phytoplankton
production and biomass • Nutrients are key
limiting factors for algal
growth • Responsive to nutrients

• Biomass is a well

‐ established as indicator

of phytoplankton
production

• Biomass data in

estuarine waters are routinely monitored and

data are generally

abundant

• Satellite

‐ derived
chlorophyll data readily

available in many
coastal waters • Establishing protective

concentrations for non

‐

seagrass uses is less well

studied

• Other factors can
interfere with
evaluating stressor

‐

response relationships

• Field

‐ collected biomass

data in coastal
(offshore) waters are
limited

• Lack of phytoplankton

data in healthy canalsTotalNitrogen • N is typically more limiting of algal growth

than P in estuarine

systems • N directly related to phytoplankton
production in N

‐ limited

systems • Estuarine water quality

best predicted in the

short term by

antecedent TN loading

rates or freshwater
discharge

• TN concentration is

associated with TN loading over the long

term • Nutrient transport and

transformation

processes complexTotalPhosphorus • Algal production can be

P

‐ limited in areas with

less soil P such as in

South Florida • P directly related to phytoplankton
production in P

‐ limited

systems

• P

‐ limitation more

common in spring when

N loading is highest • TP loading best predicts

water quality response

in P

‐ limited systems

• TP concentration is

associated with influent

TP loading over the long

term • Water quality response relationship less strong

in N

‐ limited systemsMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 37 2.3.2 Selected Biological Assessment Endpoints

Based on the reviewed litera ture on potential assessment endpoints for nitrogen/phosphorus pollution in estuarine and marine systems, EPA is considering the following water quality goals to be achieved when translating Florida’s narrat ive nutrient criterion into numeric values: (1) healthy seagrass communities , (2) balanced phytoplankt on biomass and production, and (3) balanced faunal communities.

2.3.2.1 Healthy Seagrass Communities

Seagrass can be considered foundation species in a quatic systems, meaning that they provide the complex physical substrate necessary to support normal growth and reproduction of a wide range of species (Hughes et al. 2009). Seagrass provide ha bitat for many fish, bird s, and invertebrates, and are also an important food source for endangere d species such as manatees and green turtles. Without seagrass, entire habitats and their associated biotic communities can be replaced by less desirable and simplified assemblages that are less ecologically productive and have reduced taxonomic and functional diversity. Although seagrass are widely distributed in Florida, they are not present everywhere (Sargent et al. 1995).

Healthy seagrass communities depend on a variety of physical and biological factors to thrive. Among these, water clarity (i.e., li ght availability) is important and related to nutrient enrichment (e.g., Dennison 1987; Duarte 1991; Duar te 1995; Lee et al. 2007). Light is a critical variable for protecting and restoring seagrass . Water clarity can be negativel y affected by an over-abundance of phytoplankton because the phytoplankton itself a nd the detritus associated with phytoplankton production absorb and scatter light before it r eaches the seagrass. In creases in phytoplankton

abundance as a result of nutrient enrichment cause increases in light attenuation, which can be important to seagrass communities as in estuaries such as Lemon Bay, Florida, where light attenuation due to phytoplankton is estimated to range from 12 to 39 percent (Tomasko et al. 2001). Other factors (e.g., color, suspended sedi ments) in addition to phytoplankton can also produce light attenuation. Seagrass water clarity requirements can be computed as the percent of surface irradiance required at a selected seagra ss bed depth. Numeric criteria for chlorophyll a, the indicator variable, can be computed usi ng the relationship betw een water clarity and chlorophyll a . Subsequently, numeric criteria for TN and TP can be computed using the relationship between chlorophyll a and TN or TP.

2.3.2.2 Balanced Phytoplankton Biomass and Production

In most of Florida's waters, healthy biotic co mmunities depend on normal, balanced levels of phytoplankton abundance (Bricker et al. 1999; Bricker et al. 2003b). Chlorophyll a concentration is the measurement endpoint most often used to indicate balanced phytoplankton biomass and production (Boyer et al. 2009; Hagy et al. 2008 ). Several water quality management organizations (e.g., Tampa Bay Estuary Program, Charlotte Harbor National Estuary Program)

within the state of Fl orida have already begun developing chlorophyll a targets for estuaries based on water clarity or DO goals. FDEP has also established a chlorophyll a monitoring threshold in their Impaired Waters Rule (IWR) of 11 μg/L for all estuarie s of the State (F.A.C. 62-303.353); this value represents conditions above which a water body is identified as impaired,

however waters that are below IWR thresholds are not considered “in attainment” of theMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 38 narrative criterion (Giatt ina 2008). The natural dynamics of chlorophyll a vary among and occasionally within aquatic systems. To determ ine the appropriate water quality criteria, a variety of factors influenci ng the response of chlorophyll a , the assessment endpoint, should be

considered.

Shifts in the composition of phytoplankton and z ooplankton assemblages have been observed in estuarine and freshwater ecosystems in which nutri ent loading rates are increased, both in Florida and elsewhere in the country (Arhonditsis et al. 2007; Armitage a nd Fong 2004; Cloern 2001; Cloern 1996). Although informative studies of phytoplankton assemblage composition are relatively uncommon, unusually high phyt oplankton biomass (and thus, chlorophyll a ) has been associated with proliferation of toxic or otherwise harmful sp ecies (Cloern 2001). One reason is that such species are not effectively controlled by planktonic grazers, a llowing their biomass to increase. Species shifts may i nvolve an increase in the abundan ce of unpalatable, toxic, or otherwise nuisance species that disrupt grazing and may negatively impact the estuarine food chain from the bottom up. Some species shifts o ccur in response to a ch ange in the relative

abundance of different nutrients. Increased a bundance of nitrogen and phosphorus sometimes result in silica limitation, which favors non-diatom species because they do not require silica (Cloern 2001). EPA is cons idering using chlorophyll a as an indicator of the changes in phytoplankton species composition. Subsequently, numeric criteria for TN and TP can be computed using the relati onship between chlorophyll a and TN or TP.

2.3.2.3 Balanced Faunal Communities

The health of estuarine and coastal biological communities, from fish to benthic macroinvertebrates to plankton, depends critic ally on sufficient DO (e .g., Diaz 2001; Diaz and Rosenberg 2008). In estuaries a nd coastal waters, low DO is one of the most widely reported consequences of nitrogen/phosphorus pollution and one of the best predictors of a range of biotic impairments (e.g., Bricker et al. 2003a; Bricker et al. 1999). Low DO causes impacts to marine life ranging from mass cross-species mortalit y to chronic impairment of growth and reproduction. Thus, DO is a measurement endpoint proxy for a wide range of marine life for which DO requirements for survival, growth, and reproduction are known.

Estuaries may exhibit large, diurnal DO c oncentration swings characterized by high concentrations during the daylight hours, followe d by periods of low concentrations (potentially hypoxic or anoxic) during the nigh ttime period. Furthermore, highl y productive systems tend to have large amounts of detritus th at settle to the bottom sedime nts and are oxidized by bacteria, further consuming oxygen and resulting in sedi ment nutrient releases (Cloern 2001).Water column stratification due to salinity and/or temperature gradients reduces the mixing of oxygenrich surface waters (where oxygen is transferred into the wate r from the atmosphere) with oxygen-poor bottom waters (where oxygen is consum ed through sediment diagenesis processes), exacerbating the effects of low DO on bottom-dwelling species.

In the case of DO, the State of Florida has an established a DO standard for estuarine and coastal waters that states the average DO shall not be less than 5.0 mg/L in a 24-hour period and shall never be less than 4.0 mg/L, with normal daily and seasonal fluctuations maintained (F.A.C. 62- 302.530). Subsequently, numeric criteria for TN a nd TP can be computed using the relationship

between DO and TN or TP.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 39 2.3.3 Water Quality Indicator Variables for Expressing Criteria

Based on EPA guidance (USEPA 2001) and an a ssessment of the available literature, the numeric criteria for Florida’s estuaries, coasta l waters and southern inland flowing waters will address the following three indicator variables: TN concentration (as mg/L), TP concentration (as mg/L), and chlorophyll a concentration corrected for pheophytin (chlorophyll a as μ g/L). Appropriate numeric criteria fo r these three variables will he lp ensure protection of the biological assessment endpoints id entified in Figure 2-1 are achieved, thereby supporting a balanced natural population of aquatic flora and fauna.

While the conceptual model of eutrophication con tinues to evolve (Cloer n 2001), it is clear that nitrogen and phosphorus are the primary macronut rients that enrich waters and can cause nuisance levels of algae (Elser et al. 2007; Howarth et al. 2002). Conditions that allow phytoplankton to accumulate (i.e., adequate light , optimum velocity or mixing, low loss to grazing, etc.) will not result in high biomass w ithout sufficient nutrient supply (USEPA 2001). Often the addition of both nitrogen and phosphor us will elicit greater phytoplankton biomass

stimulation than the sum of both nutrients adde d separately (Fisher et al. 1992). There are reported cases where both nitrogen and phosphor us are required to elicit a phytoplankton biomass production response in es tuaries (Flemer et al. 1998), s uggesting that nitrogen and phosphorus supply rates were equally limiting. On the other hand, tropical lagoons, with carbonate sands low in phosphorus and unaffected by human activity, are prone to phosphorus limitation. For example, the seagrass Thalassia testudinum was found to be phosphorus-limited in Florida Bay (Powell et al. 1989; Four qurean et al. 1992a; F ourqurean et al. 1992b).

2.3.3.1 Total Nitrogen

Nitrogen is an important lim iting nutrient of algal bioma ss production (USEPA 2001). TN consists of organic and inorganic forms. S timulated algal biomass production is typically attributed to inorganic nitr ogen (Stepanauskas et al. 1999), although some dissolved organic nitrogen may be used for algal growth (dissolved and particulate organic nitrogen are involved in recycling processes) (USEPA 2001). In estuarie s, nitrogen concentra tions, especially the inorganic forms, typically vary widely seasona lly, interannually, and along salinity gradients (USEPA 2001). In those estuaries where nitrogen has been demonstrated to limit algal biomass production, it typically does so at higher salinities along the salinit y gradient. Denitrification may remove from a few to approximately 50 percent of the TN load entering temperate estuaries annually (Seitsinger 1988; Cornwell et al. 1999 ) depending largely on residence time of the water, sediment biogeochemical conditions (e.g., benthic macrofauna present to maintain irrigation, oxic conditions in the overlying botto m water), and water column depth. This process helps to modulate extreme dissolved i norganic N concentrations (USEPA 2001).

2.3.3.2 Total Phosphorus

Phosphorus is often the nutrient that most limits algal production in tidal fresh to oligohaline

areas of estuaries as well as areas with a wider ra nge of salinity in certai n subtropical to tropical marine systems (USEPA 2001). Phosphorus occurs in natural waters and in wastewaters almost solely as phosphates. These are classified as orthophosphates, condensed phosphates, and organically bound phosphates. Common analytes are TP and dissolved or particulate organicMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 40 phosphorus (DOP, POP). These compounds may be so luble, in particulat es or detritus, or incorporated as organic phosphorus in organism s. Phosphorus is essential to the growth of organisms and can limit phytoplankton biomass production, which is most commonly observed in freshwater systems (Hecky and Kilham 1988) a nd some estuaries and coastal marine systems. In instances where phosphate is limiting, the di scharge of raw or unt reated wastewater, agricultural drainage, or certain industrial wastes may stimulat e the growth of algae (USEPA 2001). Some fraction of phosphorus may be strongly embedded in a mineral matrix, rendering that fraction relatively inert to biological utilizat ion except by algae that have the capability to break down DOP with alkaline pho sphatase (algal and free ph osphatases) and utilize the phosphate as inorganic phospha te (Huang and Hong 1999).

2.3.3.3 Chlorophyll a

Chlorophyll a is a measure of phytoplankton abundance (or biomass) in the water column and can serve as an index of the productivity and trophic condition of waters. Chlorophyll a biomass

reflects the standing stock , which is the balance of growth and loss in pelagic waters. The benefits of chlorophyll a as an indicator variable are its re levancy to conditions of Florida’s ecosystems, its sensitivity to stressors such as nutrients, and ease of monitoring (Boyer et al. 2009). Elevated concentrations of chlorophyll a are indicative of enhanced phytoplankton production. Excess primary production can cause a va riety of negative effects (Bricker et al. 2003b; Vitousek et al. 1997). For example, ex cess primary production can reduce water clarity, resulting in reduced light availa bility for benthic algae, macrophyt es, and seagrasses (Boyer et al. 2009; Bricker et al. 2008). Seagrass decompositi on and destabilization of sediments may then result in more nutrient inputs into the water co lumn from sediments (Boyer et al. 2009). Excess production of chlorophyll a also provides greater loads of reduced carbon, which fuels respiration, decreases DO, and results in hypoxic and anoxic conditions (Vitousek et al. 1997).

2.3.3.4 Not Selected for Numeric Criteria Development

Based on the reviewed l iterature, EPA is not considering us ing the following nut rient-sensitive biological assessment endpoints to translate Florid a’s narrative criterion into numeric values: (1) HABs, (2) coral, (3) epiphytes, (4) macroinve rtebrate and fish indices, (5) macroalgae, (6) Spartina marshes (salt-marshes), and (7) Eastern oysters ( Crassostrea virginica ). In general, these assessment endpoints have not been selected b ecause there is either an absence of sufficient data to assess the effects of measured nitrog en/phosphorus concentrations, or there is an

alternative sensitive assessm ent endpoint available that is a better indicator of nitrogen/phosphorus pollution. Appendix B provi des additional information regarding the scientific rationale for not including these orga nisms in development of numeric criteria for

estuaries and coastal areas.



### 2.4 Potential Data Sources

EPA has assembled a large and diverse resource of environmental data to support the analytical approaches that the Agency is considering fo r development of numeric criteria for Florida waters. This has been accomplished with the ac tive assistance of state and local governmental agencies in Florida, including the Florida Depa rtment of Environmenta l Protection, the Florida

Fish and Wildlife Research Inst itute, Florida’s Water Management Districts, and several countyMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 41 governments. Significant data have also been provided by multiple Federal agencies, including the USGS, NASA, and NOAA, as well as public a nd private research institutions. Data have been provided to EPA both via existing online da ta portals and other means (e.g., e-mail, FTP, mail). The magnitude of data available reflects in part Florida’s substantial investment in data collection and data management, which EPA noted in its 2009 determination that new or revised water quality standards were need ed (see Section 1.3). The substa ntial quantity of data also reflects ongoing data collec tion by Federal agencies.

The many different types of data that EPA is cons idering reflect both the richness of data that are available and the many types of data that could be needed to support the analytical effort that EPA is considering. Because EPA is considering water quality simulation models as one of the analytical approaches, the number of different kinds of data that could be needed is very broad and extends well beyond water quality monitoring data.

The paragraphs below describe in further detail major data sets th at EPA may use, the sources of the data including internet sources for the data or information about the data, and which aspects of criteria development the data may support.

Waterbody Delineation . EPA is considering as a source of information delineating waterbodies in Florida, the Waterbody Identification numbe r (WBID) GIS layer. The WBID layer is available via the FDEP GIS portal ( http://www.dep.state.fl.us/gis/datadir.htm ).

Water Quality Monitoring Data

. EPA is considering water quality monitoring data from a variety of sources. These data may be used in almost every aspect of criteria development and pertain to both freshwater and marine water qualit y. The largest source of water quality data is the Florida Department of Environmental Protec tion’s Impaired Water Rule Database, version 40 (IWR40). FDEP maintains the IW R database and updates it quarterly with data it identifies and

evaluates from trusted sources th roughout Florida. The database is available from FDEP at http://publicfiles.dep.state.fl.us/dear/IWR/ . Additionally for Chapter 5, the Southeast Environmental Research Center (SERC) Water Quality Monitoring Network is a significant source for more than a decade of quarterly or monthly (depending on the location) water quality data for South Florida, including the Bisca yne Bay, Florida Bay and the Florida Keys ( http://serc.fiu.edu/wqmnetwork ). Further water quality and othe r data specific to South Florida is available from DBHYDRO, maintained by th e South Florida Water Management District ( http://www.sfwmd.gov/portal/page/portal/xwe b%20environmental%20monitoring/dbhydro%20 application ). Water quality data fo r South Florida canals has b een provided to EPA by the Miami-Dade Department of Environmental Resources Management and Broward County Department of Planning and Environmental Protec tion. Many other counties in Florida conduct water quality monitoring and a sign ificant quantity of these data are already integrated by FDEP

into the IWR database.

Land Use Data . EPA is considering land use data as i nputs to water quality simulation models (i.e., mechanistic watershed models), as describe d in Chapters 3 and 6. EPA is also considering land use data as part of a Landscape Developm ent Intensity Index calculation in Chapter 5.

Since several Florida watersheds extend into Alabama and Georgia, EPA is considering sources

of land use data from all three states. EPA is considering land use data for Florida based on 2005Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 42 imagery as reported by FDEP and various Water Ma nagement Districts. Data for Georgia are from Georgia Land Use Trends (GLUT) 2005 ( http://narsal.uga.edu/glut.html ). EPA is considering the 2005 National Land Cover Database for data from Alabama ( http://www.epa.gov/mrlc ).

Meteorological Data

, EPA is considering meteorological data, including rainfall and wind speed and direction as inputs to mechanisti c watershed models and hydrodynamic and water quality models for estuaries, as described in Ch apters 3 and 6. EPA is considering data from approximately 120 sites in Florida. These data may be obtained from the National Climatic Data Center, which reports data for more than 1800 stati ons in Florida, Georgia, and Alabama. Further

information is available at http://www.ncdc.noaa.gov

.

General Hydrology . EPA is considering hydrology data , including the National Hydrography Dataset Plus (NHDPlus, http://www.horiz on-systems.com/nhdplus), which provides

subwatershed and flow line delinea tions that can be used in waters hed models (Chapter 3 and 6). EPA is considering stream discha rge data and flow velocity data from the US Geological Survey, available through the National Water Information System (NWIS, http://waterdata.usgs.gov/nwis ). These data could be used to parameterize, calibrate and evaluate mechanistic watershed models (Chapter 3 and 6) and identify canals (Chapter 5). EPA is considering use of the National Elevation Dataset 1/3 arc-second (10 meter by 10 meter) for computing elevations and slopes. The National El evation Dataset is available from the USGS (http://ned.usgs.gov/). EPA is al so considering water surface elevation data from NOAA tide gauges for use as boundary conditions, calibratio n, and evaluation data for hydrodynamic models (Chapter 3). These data are reported by NOAA’s Center for Operational Oceanographic Products and Services ( http://www.tideandcurrents.gov ). EPA obtained bathymetric data for

Florida estuaries and coasta l areas from the NOAA Nationa l Geophysical Data Center (http://map.ngdc.noaa.gov).

NPDES Point-Sources and Water Withdrawals . For use in water quality simulation models (Chapter 3 and 6) EPA is considering data on NPDES-permitted point sources and water

withdrawals obtained from Flor ida’s Waterwater Facility Regulation (WAFR) system ( http://www.dep.state .fl.us/water/waste water/facinfo.htm ).

Ocean Color Satellite Data and Field Validation . EPA is considering data from two NASA

satellite-borne ocean color sensors for use in de velopment of numeric criteria for offshore coastal waters in Florida (Chapter 4). These sensors in clude the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the Moderate Resolution Imagi ng Spectroradiometer (MODIS). The period of record for both sensors is more than 10 years. EPA identified at least six sources of shipboard data to compare with satellite data. EPA is c onsidering data from the Northeastern Gulf of Mexico (NEGOM) project (NOAA Nati onal Oceanographic Data Center, http://www.nodc.noaa.gov ), the Ecology and Oceanography of Harmful Algal Blooms project (ECOHAB), as well as collections of da ta from the Mote Marine Laboratory ( http://www.mote.org ), Florida Fish and Wildlife Research Institute ( http://research.myfwc.com/ ), and SeaWiFS Bio-optical Archive and Storage System (SeaBASS, http://seabass.gsfc.nasa.gov/ ).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 43 Seagrass Coverage Layers . EPA is considering extensive datasets describing historical coverage of submerged aquatic ve getation in Florida estuaries (Cha pter 3). Data were provided to EPA by FDEP, the US Geological Survey Wetland Research Center ( http://sdms.cr.usgs.gov/pub/flsav.html ), Florida’s water management districts, and by the Tampa Bay National Estuary Program (see Table 3-6).

Other Data . EPA may consider other well-documented data obtained from known sources. For example, EPA is considering water quality da ta, water level data, meteorological data, and current velocity data collected for Pe nsacola Bay in 1996-2000, 2002-2004 and 2009 by EPA’s

National Health and Environmental Effects Re search Laboratory, Gulf Ecology Division. EPA has provided the water quality data to FD EP for inclusion in the IWR database.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 44 This page is inte ntionally blank.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 45 

## 3 Numeric Nutrient Criteria Development in Florida Estuaries

An estuary is a part of a stream 5 

### or other body of water that has an unimpaired connection with the open sea and where sea water is measurably diluted with freshwater derived from land drainage. In order to translat e Florida’s current narrative nutrient criterion, EPA is considering developing numeric criteria for nitrogen/phosphorus pollution in Fl orida's estuaries on a systemspecific basis. A system-specific approach woul d allow the Agency to consider the individual characteristics of these estuarine ecosystems in groups with common characteristics. For example, water quality and biological communities in estuaries are affected by a combination of basin shape, tides, and the magnitude, location, a nd quality of freshwater inflows. The semienclosed basins that define the spatial extent of estuaries areas may also create sub-regions

within estuaries with differ entiated water quality and assess ment endpoints. Thus, EPA is considering approaches that may result in numer ic criteria specific to sub-regions within

estuaries.

This chapter describes the approaches EPA is cons idering to derive numeric criteria for estuarine waters in Florida (Figure 3-1), exclusive of mari ne waters in South Flor ida, which are addressed in Chapter 5. We describe the approach that EPA is considering for delineating estuaries into discrete areas for the purpose of organizing the criteria development process. We also discuss the concepts of assessment endpoint s and indicator variables, an d the specific endpoints and indicators that EPA is considering for use in development of numeric estuarine criteria. We discuss the rationale that may be used for selecting specific wate r quality indicator variables for which EPA may develop criteria. Finally, we discuss three appro aches: (1) reference conditions, (2) stressor response relationshi ps (regression models), and (3) water quality simulation modeling that EPA could use independently or in combination to develop numeric estuarine

criteria.

Figure 3

‐ 1. Flow chart for the development of numeric criteria for Florida estuaries. The approach EPA is considering will

involve delineating the state's estuarine waters into discrete estuarine areas. Following an overall framework approach,
aquatic life endpoints and specific data and methods will be selected on a system

‐ specific basis. Numeric criteria for each

estuary, or sub

‐ segment of each will be derived using those data and methods.

5 For the purpose of this effort, a st ream has been defined as free-flowing, predominantly fresh surface water in a defined channel, and includes rivers, creeks, branches, canals, freshwater sloughs, and other similar water bodies. Predominantly fresh waters have been previously defined as surface waters in which th e chloride concentration at

the surface is less than 1500 mg/L (salinity less than ~2.7 psu). EPA is considering alternative definitions which could be based on conductivity or salinity. 

## Identify

Assessment

Endpoints Derive Numeric
Criteria Delineate

Estuarine

AreasMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 46 

### 3.1 Delineating Estuaries

The first step in any approach for developing numeric criteria for nitrogen/phosphorus pollution is delineating the water bodies. Delineating the estuarine waters provides an organizational framework for developing and presenting the sc ientific approach, applying the methods and approaches most appropriate to each estuary, and ultimately deriving criteria. For estuaries outside of South Florida, EPA is consideri ng a delineation approach based on the natural geographic limits of estuarine basins and thei r associated watersheds . Natural constrictions

between estuarine basins tend to limit water flow and exchange between estuaries, even if exchanges are not eliminated entirely. This appr oach results in 23 estuarine areas (Figure 3-2), including five in the Florida panhandle region, six in the Big Bend region, four in southwest Florida, and eight on the Atlantic coast. 6 

### This general approach has been utilized previously in development of the NOAA Coastal Assessment Framework (Bricker et al. 1999). EPA is presenting the approaches that it is considering for delineating marine wa ters in South Florida separately (see Chapter 5) because the flows in th e waters in that part of the State are heavily managed and natural boundaries be tween basins are less defined.

EPA is also considering utilizi ng natural geographic boundaries as an approach for delineating sub-segments within estuaries. FDEP has es tablished a waterbody identification scheme (WBIDs) which defines segments within estuaries (see Figure 3-3). This approach is based to a significant extent on natural geographic boundaries within and among Florida's estuaries, but it has been modified by the State over time for a vari ety of reasons and could be subject to change in the future. Thus, EPA is c onsidering modifying the boundaries of FDEP’s WBIDs to achieve the objective of homogenous water quality within segments while maintaining a reasonable spatial scale for criteria development (e.g., not an excessive number of very small segments).

6 A total of 30 estuarine and coastal ar eas are identified. EPA's approaches for offshore coastal waters and for South Florida waters are presented in separate chapters.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 47 1 Perdido Bay

2 Pensacola Bay

3 Choctawhatchee Bay

4 St. Andrews Bay

5 St. Joseph Bay

6 Northwest Gulf Coast

7 Apalachicola Bay

8 Alligator Harbor

9 Ochlockonee Bay

10 Big Bend/Apalachee Bay

11 Suwannee Sound/Cedar Keys

12 Springs Coast

13 Clearwater Harbor/St. Joseph Sound

14 Tampa Bay Estuary

15 Sarasota

16 Charlotte Harbor

17 Rookery Bay ‐ Southwest Shelf

18 Florida Keys

19 Florida Bay

20 Biscayne Bay, Barnes Sound, Card Sound

21 Lake Worth Lagoon/Loxahatchee

22 St. Lucie

23 West Gulf Coast

24 Indian River Lagoon

25 Halifax River

26 Guana, Tolomato, Matanzas, Pellicer

27 St. Johns River

28 Nassau River/ Big Talbot

29 St. Marys River/Amelia River

30 Atlantic Coastal Area 

## 1 2 3

4

5 6

7 8 9 10

11

12

13

14

15

16

17

18 19 20 21 22

23 24 25 26

27 28 29

30

Figure 3

‐ 2. Major areas of estuarine and coastal waters in Florida that EPA has identified for the purpose of organizing
development of numeric criteria. The approach for developing numeric criteria for offshore coastal waters of the northwest

Gulf coast [6], west Gulf Coast [23], and Atlantic Coast [30] is described in Chapter 4. Approaches for South Florida [17, 18,

19, 20] are described in Chapter 5. The approach for the remaining 23 areas is described in this chapter. Shading of land areas
illustrates watershed areas to be used in EPA’s proposed framework for estimating watershed loadings of TN and TP to

estuaries using a water quality simulation model (Section 3.3.3)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 48 Figure 3

‐ 3. Map of Pensacola Bay illustrating the boundaries associated with FDEP’s WBIDs for Pensacola Bay. Shaded

segments account for the majority of the estuarine area. Additional segments are identified for smaller creeks and bayous

that adjoin the main part of the Bay. Boundaries for major Bay segment are generally associated with geographic features of

the basin, with segments occurring at regular intervals to ensure relatively homogeneous water quality within sub

‐ segments

of the Bay.



### 3.2 Selection of Assessment Endpoints and Indicator Variables

Biological assessment endpoints for numeric crit eria development are specific, ecologically important, and nutrient-sensitive st atements of the environmental va lue to be protected, in this

case to translate Florida’s narrative nutrient crit erion and support a bala nced natural population of aquatic flora and fauna within Florida estu aries. In environmental risk assessment (ERA) guidance these are referred to as “assessment endpoints.” Indicator vari ables are quantitative measures that point to or “indicate” the status of the assessment endpoint in a waterbody, or in ERA terms “measures of effect” (or “measurem ent endpoints”), “measures of exposure,” and “measures of ecosystem and receptor characte ristics.” The TMDL and Watershed Approach

literature tends to refer to these measures as “indi cators.” To be a useful indicator variable, the

indicator must bear a defensible conceptual relationship to the endpoint. Data must be available to measure or otherwise quantify the indicator value in water bodies to which it will be applied. It should also be possible to relate the va lue of indicator variables in some way to nitrogen/phosphorus con centrations and/or nutrient loadings . Selection of assessment endpoints is a critical early step that defines many subse quent aspects of criteria development. EPA has

reviewed the scientific literature to identify candidate endpoi nts that are ecologically important, widely applicable in Florida estuaries, and se nsitive to nutrients. As mentioned in Chapter 2, three assessment endpoints that EPA is consideri ng for use in the derivation of numeric criteria

in estuaries are discussed below. EPA is consider ing one or more indicato r variables for each of 548E

548D 548C 548B 548A

548H 548G

915 73

84

548 53Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 49 these endpoints. Please refer to section 2.3 for more detail regarding these assessment endpoints

and indicator variables.

Healthy seagrass communities. This assessment endpoint is rela ted to habitat and encompasses seagrass condition (e.g., density), species co mposition, and distribution approximating local

historical conditions. 7 

### EPA is considering the depth of co lonization of seagrasses as a readily quantifiable indicator of seagrass condition. For a number of estuar ies, historical values of the depth of colonization can be used to establish values that sup port the designated use. Moreover, depth of colonization and areal ex tent of seagrass are related via the bathymetric profile (e.g., Janicki and Wade 1996). Depth of colonization of seagrasses is a useful assessment endpoint for numeric criteria development because water clar ity, which affects the de pth of colonization is

affected 8 

### by phytoplankton biomass (measured as chlorophyll a ) and nutrient (TN and TP) loading.

Balanced phytoplankton biomass and production. This assessment endpoint is characterized by normal levels of biomass and productivity of phytoplankton communities, with an absence of harmful or nuisance species or other adverse shifts in species dominance, and no unusual proliferations of macroalgae or epiphytic algae. EPA is considering using chlorophyll a as an indicator of the changes in phyt oplankton species composition. EPA is considering this endpoint because proliferations of macroalgae, epiphytes , or nuisance algal species may be the most apparent nutrient pollution (TN a nd TP) impacts in some estuarie s, particularly those where seagrasses are not normally present or where lo w dissolved oxygen is unlikely due to physical factors (e.g., strong tides).

Balanced faunal communities. This assessment endpoint is refers to communities of benthos (bottom-dwellers), plankton (dri fting organisms), and nekton (s wimming organisms, such as fish) exhibiting healthy levels of biomass and production. Low dissolved oxygen resulting from nutrient pollution is a key mechanism by which nut rients may impact this assessment endpoint. Because quantitative indicator variables for this endpoint are lacking, EPA is considering using the absence of hypoxia (i.e., absence of low disso lved oxygen), as an indicator of balanced communities. Dissolved oxygen is a useful indica tor for numeric criteria development because ecological modeling approaches ca n be used to predict the re sponse of dissolved oxygen to nutrient pollution loading (TN and TP).



### 3.3 Numeric Criteria Approaches

EPA is considering three basic categories of appr oaches to derive numeric criteria for Florida estuaries. These approaches include (1) refe rence condition approaches , (2) stressor response relationships, and (3) water quality simulation models. Associated w ith each category of approach are specific strengths a nd weakness, factors indicating that it could be used, and factors that indicate another approach may be needed (Table 3-1). EPA will consider these as it

7 Seagrasses are naturally absent from some northeast Fl orida estuaries. For those estuaries, water clarity requirements to support maintenance of seagrass es are not being considered as an endpoint.

8 Water clarity in estuaries is largely determined by the sum of light attenuation caused by phytoplankton biomass, of which chlorophyll a concentration is an indicator, non-pigmented suspended solids, and colored dissolved organic matter (e.g., Gallegos 1994, 2001).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 50 determines which approaches should be used gi ven the ecological detail s pertinent to each estuary as well as the different t ypes and quantities of data availa ble. EPA could consider several different types of models and information to de rive numeric criteria for different estuaries and could consider simultaneously more than one type of information fo r a single estuary.

Table 3

‐ 1. Strengths, weaknesses, indications (situations where approach is most applicable), and contraindications (situations where another approach may be needed) for each of the three categories of criteria development approaches

that EPA is considering

Strengths & Weaknesses Most Applicable When Least Applicable WhenReferenceConditionApproaches Strengths

• Simple, direct and understandable; provides

information to quantify criteria.

Weaknesses

• Need quantitative data to characterize the

reference condition that reflects support of the
designated use. • Substantial water quality

data are available and the

estuary is minimally
impacted by nitrogen/phosphorus
pollution sources.

• Substantial water quality

data are available from a
historical period when the

estuary was minimally
impacted by nutrients.

• The estuary is very similar

to another estuary to

which one of the above

conditions applies. • The estuary is impacted by nitrogen/phosphorus
pollution sources and is likely impaired by

nutrients.

• Little or no data are
available from a historical
period when the estuary

was not minimally
impacted by nutrients

• The estuary is considered
relatively unique.StressorResponseRelationships(RegressionModels) Strengths

• Easy to understand and visualize; uncertainty

may be quantified, provides linkage between

criteria and aquatic life uses, can quantify
relationships between different criteria values.

Weaknesses

• Regressions can be affected by covariates; may

not address additive or interacting effects of

more than one causal factor. • Extensive data are available, spanning multiple years and
spanning a range of

nutrient loading rates and

water quality response.

• Simple regression relationships exist and
quantify relationships

between nutrient loading and/or nutrient

concentrations and water quality responses.

• Response is consistent

across many estuaries • Little or no data are
available

• Complex relationships

between nutrients and

water quality responses
involve multiple
interacting causes, including physical

‐ biological coupling.

• Key ecological processes
and interactions are
different or unique compared to other

estuaries.WaterQualitySimulationModels Strengths

• Can provide detailed simulation results for many variables, addressing magnitude, frequency and
duration; addresses physical

‐ biological coupling.

Weaknesses

• May not address important ecological

processes; many unknown model parameters including boundary conditions; may not be valid

for unobserved conditions. • Important ecosystem

processes are well

‐ understood

• Available data are from

process studies or other

isolated studies, rather

than consistent
monitoring over multiple

years.

• Interactions are complex,

involve physical

‐ biological

interactions, or are spatially structured.

• Relatively little site

‐ specific data are available. • Mechanisms governing

interaction among

nutrient sources, water quality, and biological

responses are not well

understood.

• Critical inputs to model

are completely unknown (e.g., large open boundaries)

• Linkages between possible

model outputs and use

attainment are not well

‐ defined.

• Adequate data are not

available as model input.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 51 3.3.1 Reference Condition Approaches

Reference condition approaches can take a vari ety of forms, defined by the source of the reference condition. EPA has previously recomme nded (e.g., USEPA 2000c) that a percentile of water quality measurements in a sample of minimally-impacted waterbodies, which are known to be fully supporting designated uses (i.e., not impaired), could serve as numeric criteria in similar waterbodies. In this case, a reference condition is derived from a reference population of waterbodies. In Florida, there are a small nu mber of estuaries (i.e ., 23 estuarine systems compared to >1,000 streams and lakes in Florida) that leads EPA to consider developing systemspecific criteria for estuaries. EPA is consider ing a reference condition approach to be most applicable when (1) historical data adequately describe water quality c onditions when the estuary was minimally-impacted by nitrogen/phosphorus pollution and was supporting balanced natural populations of aquatic flora and fauna (i.e., historical reference condition) or (2) when the estuary is currently minimally-impacted by nitrogen/phosphorus po llution and currently supporting balanced natural populations of aqua tic flora and fauna (i.e., current-conditions reference condition). In either case, interpretation of the status of reference conditions could be based on examining the assessment endpoints and a ssociated indicators th at EPA has identified (Section 3.2). EPA has not yet identified any im paired estuaries for which historical water quality data could provide a suit able reference condition. Therefor e, EPA is largely considering using a reference condition appro ach only in the second case, namely when current water quality conditions are supporting balanced natural populations of aquatic flora and fauna.

To evaluate assessment endpoints and associated water quality indicato r variables, EPA will consider data from FDEP’s Impaired Waters Ru le (IWR) database. The number of TN, TP, and chlorophyll a observations from the IWR are presented in Table 3-2. EPA will also consider data from peer-reviewed literature, reports, and othe r data sources. A complete listing of the data sources EPA is considering can be found in S ection 2.4, Potential Data Sources. To derive criteria from current water quality conditions, EPA is considering computing two statistical reference points from the water quality observati ons. These could include (1) an average or median concentration and (2) an upper percent ile concentration. By simultaneously considering

both an indicator of central tende ncy and a measure of higher con centrations, the criteria could ensure that future water quali ty conditions remain similar to present conditions (i.e., the conditions associated with support of balanced natural populations of a quatic flora and fauna). As an alternative, EPA is also considering whet her it should utilize annu al geometric mean water quality. Water quality observations in the future could be compar ed with current water quality using this binomial approach (see Section 5.6.1.2).

Table 3

‐ 2. Estuary water quality data inventory for causal (TN and TP) and response variables (chlorophyll a ) from FDEP Impaired Waters Rule database. Count is the number of observations; years represent the time period over which the

observations were collected.

Estuary TN TP Chlorophyll a Count Years a Count Years a Count Years b

1 Perdido Bay 241 1999–2010 268 1999–2010 2,591 1974–2010

2 Pensacola Bay 1,530 1998–2010 1,597 1998–2010 9,278 1968–2010

3 Choctawhatchee Bay 1,827 1997–2009 1,972 1997–2009 4,828 1974–2009

4 St Andrew Bay 1,234 2000–2009 1,295 2000–2009 3,910 1973–2010Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 52 5 St Joseph Bay 20 2008–2009 20 2008–2009 100 1974–2009

7 Apalachicola Bay 476 2000–2007 278 2000–2007 728 1974–2007

8 Alligator Harbor N/A N/A 4 1987–1987

9 Ocklockonee Bay 25 2000–2007 27 2000–2007 57 1975–2007

10 Big Bend/Apalachee Bay 158 2000–2009 173 2000–2009 228 1973–2009

11 Suwannee Sound/Cedar Keys 155 2000–2009 166 2000–2009 731 1995–2009

12 Springs Coast 320 2002–2009 322 2002–2009 868 1982–2009

13 Clearwater Harbor/ St. Josephs
Sound 722 2002–2009 695 2002–2009 1,810 1991–2009

14 Tampa Bay 12,844 1996–2009 13,532 1996–2009 58,891 1971–2009

15 Sarasota Bay 5,463 1997–2009 5,460 1997–2009 7,911 1978–2009

16 Charlotte Harbor 19,319 1996–2009 19,177 1996–2009 19,990 1973–2009

21 L. Worth Lagoon/ Loxahatchee Estuary 674 1997–2009 777 1997–2009 1,499 1974–2009

22 St. Lucie Estuary 7,830 1996–2008 7,708 1996–2008 17,097 1973–2009

24 Indian River Lagoon 8,191 1999– 2010 10,912 1999–2010 37,429 1973–2010

25 Halifax River 986 1999–2009 996 1999–2009 6,318 1973–2009

26 Guano/Tolomato/Matanzas/

Pellicer 1,880 1999–2010 1,897 1999–2010 3,941 1973–2010

27 St. Johns River Estuary 4,240 199 6–2008 4,241 1996–2008 7,575 1973–2009

28 Nassau River/ Big Talbot 391 1998–2009 244 1998–2009 193 1973–2008

29 St. Marys River/Amelia River Estuary 78 1998–2004 79 1998–2004 127 1973–2004

a. Data from IWR Run 40 only; data prior to 1996 have not yet been combined with IWR Run 40 data. b. Data from IWR Run 40 combined with data from prior IWR runs. N/A = no data available.



### 3.3.2 Stressor Response Relationships

Regression models usually express a stressor response relationship between one or more explanatory variables and a si ngle response variable. Regression models can also encompass more complex linear statistical models such as analysis of covariance models (i.e., models involving both continuous and categorical expl anatory variables), as well as non-linear regression models.

Two major strengths of regression models as appr oaches that could be used for development of numeric criteria are that they are closely grounded in environmenta l data and, in the case of a single explanatory variable, eas y to communicate, often by simp le graphics (e.g., bi-variate plots). Accordingly, they can be easy to understand and less dependent upon assumptions and other analytical decisions made by investigators (Table 3-1). Add itionally, statisti cal methods for fitting regression models often permit estimation of limits of uncertainty for predictions, even for complex regression models (e.g., Hoos a nd McMahon 2009). Regression models require adequate data to develop. Additionally, othe r environmental variables that covary with explanatory variables of intere st can introduce uncertainty in estimates of regression model parameters. It may also be difficult to find highly predictive regressi on models for complex ecological systems that include many interacting f actors that impact the de pendent variable. ThisMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 53 is especially true when important processes occur on differing temporal and spatial scales. However, useful regression models do exist a nd have been applied successfully to quantify relationships among water quality in dicator variables in estuaries.

Examples of regression models th at could be useful for developm ent of numeric criteria include (1) models relating a “causal variable” such as TN or TP loading or concentration to a response variable such as chlorophyll a , (2) models relating TN or TP loading to average concentration in estuarine waters, and (3) mode ls quantifying relationships between other environmental variables. Several regression mode ls could be utilized in combinat ion to derive numeric criteria. EPA is evaluating existing regres sion models and is consider ing work to identify more regressions that would be useful for deve lopment of numeric criteria in Florida.

Janicki and Wade (1996) describe a well-know n application of regression models for development of nutrient loading limits for Tamp a Bay, which EPA is considering as a useful example of the application of regression models for development of numeric criteria. This approach targets the areal extent of seagrass as an indicator of healthy seagrasses in Tampa Bay

and of overall attainment of bala nced natural populations of aqua tic flora and fauna. Targets for

areal extent were based on historic al seagrass distributions. Empiri cal relationships were used to quantify the relationship between ar eal extent of seagrass and depth of colonization of seagrass in Tampa Bay. Depth of colonization estimates for each segment of Tampa Bay and empirically determined estimates of the light requirements of seagrasses were used to compute segmentspecific limits for average light at tenuation coefficient needed to support seagrasses at target depths. Regressions were deve loped to quantify the relations hip between light attenuation coefficient and average chlorophyll a , and the relationship between average chlorophyll a and TN loading to each segment of the Bay. In combin ation, these relationships provide an approach for developing numeric criteria for TN loadi ng (causal variable) and average chlorophyll a (response variable) for each se gment of Tampa Bay. As described by Janicki and Wade (1996), the approach does not address assessment endpoi nts other than seagra sses, TP loading, or

ambient concentrations of either TN or TP in es tuarine waters. However, EPA is considering this approach as a potentially useful example of the application of regression models for development

of numeric criteria for Florida estuaries that c ould be applied using other types of models, such as water quality simulation models (Section 3.3. 3), to quantify relationships between water quality variables.

Whereas Janicki and Wade (1996) describe an approach for numeric criteria development on a highly system-specific basis, Steward and Lowe (2010) illustrate a nother regression-based approach that emphasizes the similarity am ong estuaries in their response to nutrients. Specifically, Steward and Lowe (2010) found that estimates of protective TN and TP loading rates for a variety of estuaries and other waterb ody types, derived indepe ndently using a variety of approaches could be related via the average residence time for freshwater (Figure 3-4). This relationship could be used to compute an estimate of the protective TN and TP loading rate for any estuary, given only an estimate of reside nce time. Although EPA is considering this approach as a tool that could be applied in combination with other information to develop and evaluate nutrient loading target s, EPA may not apply this model as the principal evidence for developing a nutrient loading target for an es tuary because it does not provide estimates of protective loads with sufficient pr ecision. This may not be fully evident in Figure 3-4 because ofMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 54 the log scale. Although the model has a high coef ficient of determination and narrow limits of uncertainty, accurate predictions of loading limits depend on estimates of average residence time, which can be difficult to estimate precisely. In addition, EPA must car efully consider the appropriate interpretation of the relationshi ps described by Steward and Lowe, which are presented as estimates of the loading levels a ssociated with mesotrophy. For some estuaries in Florida, a mesotrophic condition may not support balanced natural populati ons of aquatic flora and fauna, or maintain the current chemical and biological integr ity. Finally, although the outstanding coefficient of determination for the re lationships could lead one to conclude that they reflect important new insights, the strength of the relationships is a c onsequence of scaling. Given the very narrow range in appropriate values for average TN or TP concentrations (~ 3 to 4-fold variation) compared to a very large range in residence times (~1 day to 3 years) and protective loading estimat es (1 to 300 g N/m 2 

### /y), a strong correlation is likely, regardless of the average concentration determined to be associ ated with a mesotrophic condition. The fact that the estimated slopes are generally no t equal to -1 (i.e., -0.89) could re flect either the fact that the residence time for nutrients is expe cted to be less than for water (i.e., due to denitrification and other nutrient losses; Dettmann 2001) or alternatively, could reflect a slight increase in average depth among estuaries with longer residence times.

-0.5 0 0.5 1 1.5

2 2.5

3

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5



### log( τ w 

### ) (yr)log LM(N) (g m-1yr-1)



### log L M 

### (N) = -0.8873(log τ w 

### ) + 0.2791

R 2 

### =0.92, (Steward and Lowe 2010)

Figure 3

‐ 4. Base

‐ 10 logarithms of TN load limits established for 18 Florida lakes, river segments, bays and lagoons vs. water

residence time (R63 for lakes and river segments, R99 for bays and estuaries). The line is the regression (Equation 3

‐ 3)

established by Steward and Lowe (2010).



### 3.3.3 Water Quality Simulation Models

EPA is considering using water quality simula tion models as tools for developing numeric criteria for estuaries. Specifically, EPA is considering using hydrodynamic models coupled to water quality models to simulate coupled physical, chemical, and biological processes in FloridaMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 55 estuaries. EPA is considering development of water quality simulation models (i.e, mechanistic watershed models) to provide daily estimates of freshwater and nutrient loading to estuaries as inputs to hydrodynamic-water quality models. C oupled watershed-hydro dynamic-water quality models are widely accepted and have been utili zed previously for water quality management purposes. Where EPA utilized simulation modeling, EPA is considering coupling water quality models to state-of-the-art hydrodynamic models because of the often close coupling between water quality processes and physical transport pr ocesses in estuaries. The most well-known hydrodynamic-water quality model application in Flor ida estuaries is the model of the St. Johns River and its watershed (Tillman et al. 2004; Magley and Joyner 2008).

Although water quality models are fundamenta lly different from regression models, the conceptual approach that EPA is considering is very similar to the approach EPA is considering for use with regression models . Specifically, assessment endpoint s and associated biological

indicator variables could be used to determine water quality endpoints that can be predicted by the water quality model. The water quality mode l can then be used to determine TN and TP loading levels or other water qual ity conditions (e.g., average chlorophyll a , estuarine TN and TP concentrations) necessary to ensure water qualit y that supports balanced natural populations of aquatic flora and fauna. As an example, a coupled watershed-hydrodyna mics-water quality model could be used to simulate the impact of TN and TP load ing rates on chlorophyll a , water clarity, and therefore support for seagrasses growing to an expect ed depth of colonization. Water quality models could also be used to predict dissolved oxygen concentrati ons. Criteria, including magnitude, frequency and duration, can be derive d for the nutrient parameter simulated by the

model that results in attainme nt of the quantitative endpoint.

The process that EPA is considering for develo pment of numeric criter ia using water quality simulation models would involve estimating th e current conditions, characterizing natural conditions, and finally developing numeric criteri a to translate Florida’s existing narrative

nutrient criterion. Simulations of observed or “cu rrent” water quality cond itions are necessary to calibrate the watershed and estuarine water qualit y models. Typically, data from one or more years could be used to calibrate the water qualit y models, and data from one or more different years would be used to evaluate the performance of the model. In the case when aquatic life uses are impaired under existing water quality conditions, “natural condi tions” would be developed to estimate the TN and TP loading rates and asso ciated water quality responses that could be expected to occur in the absence of anthr opogenic disturbance. To characterize natural conditions, the watershed model would be run with all anthropogenic sources removed to determine the concentrations of nutrients, absent any human disturbance. This includes returning

all land uses to a natural c ondition and removing any point so urces of nitrogen/phosphorus pollution. The resulting TN and TP loading rates would then be utilized within the hydrodynamic water quality model to simulate th e water quality conditions that w ould be expected to occur in the estuary if TN and TP loadi ng were returned to background le vels. Different numeric criteria would be evaluated to determine the highest lo ading rates that can occur while maintaining simulated water quality conditi ons that will support the a ssessment endpoints that EPA has identified. Because simulation models can provide spatially and temporally-resolved outputs, simulated water quality under comp liance scenarios could be used to compute spatially-resolved (i.e., estuary segment-specific) estimates fo r criteria magnitude, frequency and duration.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 56 To consider which models could be used to develop numeric criter ia, EPA developed an inventory of the watershed and estuary models that have been previously applied to estuaries in Florida (Table 3-3). EPA’s invent ory was based on an inventory of models developed by FDEP (Wolfe 2007), with additions based on discussion s with FDEP staff. EPA reviewed the models

that have been utilized in Florida (Appendix C). Based on the results of its review, EPA is considering using the Loading Simulation Prog ram in C++ (LSPC) for simulating freshwater flows and nutrient loading from watersheds, th e Environmental Fluid Dynamics Code (EFDC) for simulation of estuarine hydrodynamics, and Water Quality Analysis Simulation Program (WASP) for simulation of estuarine water quality.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 57 Table 3

‐ 3. An inventory of models that have been previously applied to Florida estuaries and their watersheds. The inventory

is based upon an inventory compiled by Wolfe (2007) and was developed further based on discussions with FDEP staff. “fecal
only” indicates that although a water quality model was developed, it was only used to simulate concentrations of fecal

coliform bacteria.

Estuary Models Used a Watershed

Model Hydrodynamic

Model Water Quality

Model

Perdido Bay EFDC, HSPF Y Y Y

Pensacola Bay

Escambia Bay EFDC N Y Y (fecal only)

Escambia Bay RCA N Y Y

Choctawhatchee Bay N Y N

St Andrews Bay LSPC, EFDC, WASP Y Y Y
Apalachicola Bay EFDC, WASP N Y Y (fecal only)
Apalachicola Bay POM N Y N
Apalachee Bay

Suwannee River Estuary EFDC, CH3D N Y Y
Fenholloway River EFDC, WASP N Y Y
Fenholloway River RCA N Y Y

Tampa Bay WAMView, HSPF, WASP,

ECOM

‐ 3D, CH3D Y N Y

Sarasota Bay DYNHYD, WASP, CH3D N Y Y

Charlotte Harbor LSPC, EFDC, WASP, CH3D Y Y Y

Caloosahatchee River
Estuary CH3D N Y Y

Caloosahatchee River
Estuary HSPF, EFDC Y Y Y

Myakka River Estuary WAMView Y N N
Naples and Rookery Bay N Y N

North Ten Thousand

Islands

South Ten Thousand

Islands

Biscayne Bay CH3D N Y Y

Indian River Lagoon CH3D N Y Y

Indian River Lagoon PLSM, HSPF, regression approach Y N Y

Indian River Lagoon HSPF, EFDC Y Y Y

Loxahatchee River Estuary WAMView, EFDC, WASP Y Y Y

St. Lucie Estuary EFDC N Y N

St. Johns River Estuary PLSM, HSPF, WAMView, EFDC, CE

‐ QUAL

‐ ICM Y Y Y

St. Marys River Estuary

Florida Bay EFDC, HYCOM, USGS

‐ provided
Everglades flows Y Y Y

a. Model abbreviations: CE

‐ QUAL

‐ ICM ‐ three

‐ dimensional eutrophication model; CH3D ‐ Curvilinear

‐ grid Hydrodynamics 3d;

DYNHYD ‐ WASP hydrodynamics model; ECOM

‐ 3D ‐ Estuarine Coastal Ocean Model; EFDC ‐ Environmental Fluid Dynamics
Code; HSPF ‐ Hydrological Simulation Program—Fortran; HYCOM ‐ Hybrid Coordinate Ocean Model; LSPC ‐ Loading Simulation

Program in C++; PLSM ‐ Pollutant Load Screening Model; POM ‐ Princeton Ocean Model; RCA ‐ Row Column AESOP; WAMView ‐ Watershed Assessment Model; WASP ‐ Water Quality Analysis Simulation ProgramMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 58 3.3.3.1 Watershed Models

Of the eleven water bodies for which watershe d models have been developed, seven of the watershed models were developed using eith er Hydrological Simulation Program—Fortran (HSPF) or Loading Simulation Program in C++ (LSPC). These models are nearly identical in terms of the algorithms used to simulate water fl ow and water quality, but differ in their software architecture. LSPC has been updated to relax ce rtain computation limitations associated with HSPF, making it easier to apply it to larger watersheds. Aside from HSPF and LSPC, the Watershed Assessment Model (WAMView) has been previously used most often.

EPA is considering applying LSPC to 19 watershe ds in Florida (Figure 3-5). These watersheds encompass all of the watershed areas for Florida’ s estuaries, except for those in South Florida.

For watersheds outside of South Florida, EPA can utilize geospati al data collected by FDEP that describes the location and magn itude of flow from springs, to account for water lost to groundwater.EPA is not considering application of LSPC to South Florida due to the complexity of the Everglades canal systems, the high degree of artificial (i.e., human) control of water flow, and the complex interactions between surface wa ters, ground water, marshes, and wetlands in

this area.

LSPC is a comprehensive data management syst em and model that is capable of representing water flow, water quality, and po llutant loading from nonpoint a nd point sources and simulating in-stream processes affecting pollutant transpor t. LSPC is supported by EPA and has been used by FDEP for TMDLs across Florida for estimating watershed loads. LSPC simulates watersheds as a series of hydrologically connected sub-wate rsheds. LSPC represents receiving waters as one-dimensional, completely mixed stream reaches or reservoirs.

LSPC is a dynamic watershed model driven by ti me-variable weather input data. It produces time series of flow and pollutants simulating tran sport in overland flow, the vadose and saturated zones, and instream components of the syst em, using an area-weighted or “lumped” methodology. LSPC can simulate loadings from multiple land uses and represent instream processes that affect the fate of nutrients within the stream network. Model documentation is

available from the EPA Watershed and Wate r Quality Modeling Technical Support Center ( http://www.epa.gov/athens/wwqtsc/ ).

Input data for LSPC includes three main categorie s of information: (1) la ndscape data, including topography, point source locations, locations and connections among streams, etc.; (2) meteorological data, including precipitation, air temperature, humidity; and (3) land use and pollutant-specific data (land use areas, monitori ng data, etc.). The watershed loading component of the model divides all land uses into pervi ous and impervious segments, which are further grouped by land use and subbasin. Loads from su bbasins are routed to receiving waters (representative stream reaches or reservoirs).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 59 Figure 3

‐ 5. Location of 19 drainage areas for which LSPC watershed models are proposed for development



### LSPC can evaluate both point and nonpoint sources and simulate both land-based (e.g., rainfallrunoff) and instream water movement and proc esses. Nitrogen/phosphorus pollution loading may originate from a wide variety of nonpoint source s, such as agriculture, onsite wastewater treatment systems (e.g., septic systems), urban r unoff, timber production areas, and atmospheric deposition. Only runoff processes are simulated for impervious la nd units, whereas interflow and

base flows are simulated in addition to runoff for pervious land units.

For each pervious and impervious land segment, LSPC will estimate the water budget, sediment transport, and general water quality constituents , which will represent TN and TP. In each waterMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 60 body segment, LSPC will simulate hydraulic behavi or, water temperature, transport of TN, TP, and sediments, and transport of BOD. LSPC simulates nitrogen/phosphorus pollution loading from watersheds using a build-up/washoff appro ach wherein nutrients accumulate between rain events and are mobilized and transported into st reams during rain events. The model utilizes an hourly time step and provides daily average outputs. When using this method, nitrogen, phosphorus and other constituents can be applied to the land surface over time so that a mass of the pollutant accumulates and is subsequently removed at a rate correlated to a corresponding quantity of sheet flow on the land surface.

EPA is considering the use of the following model inputs:

• Subwatershed delineations

o National Hydrography Dataset (NHD+) catchments

o Natural Resources Conservation Service (NRCS) 12-Digit HUCs

• Subwatershed information (elevatio ns, slopes, reach lengths, etc.)

o National Elevation Dataset (NED) 1/3 arc-second (10 meter by 10 meter)

• Land use (2005) provided by FDEP a nd Water Management Districts

o Comparable data from Georgia (G eorgia Land Use Trends [GLUT] 2005, http://narsal.uga.edu/glut.html) and Alab ama (National Land Cover Data [NLCD] 2005, http://www.epa.gov/mrlc/) in applicable watersheds

• Meteorological data

o National Climatic Data Center (NCDC) rain fall data – approximately 120 rain gauges in Florida (NCDC 2010)

o Florida State Climatological Office (FL SCO)

• Soils, onsite wastewater treatment syst ems, and other county-level data

• NPDES-permitted point sources and water withdr awals from Florida’s Wastewater Facility Regulation (WAFR) system (http://www.dep.sta te.fl.us/water/wastewater/facinfo.htm)

Nutrient data from Florida’s IWR database a nd discharge data from USGS flow gauges will provide inventories to se lect stations to be used in the calibration and evaluation process for hydrology and water quality simulation. Sites wi th the highest number of observations for parameters such as TN, TP, biological oxygen demand, dissolved oxygen, and water temperature

will be selected for calibration. The LSPC model will be run from 1995 through 2009, with 1995 serving as a “spin-up” period (to minimize effects of initial conditions). Model output will include daily outputs for streamflow, average de pth, average flow velocity, and TN and TP concentrations for each subbasin of the watershed. Outputs from terminal reaches (i.e., those that discharge to tidal waters) will provide estimates of TN and TP loading rates to estuaries.

3.3.3.2 Hydrodynamic and Water Quality Models

Coupled hydrodynamic-water quality models using the Environmental Fluid Dynamics Code (EFDC) for hydrodynamics and th e Water Quality Analysis Si mulation Program (WASP) forMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 61 water quality have been applied to many wate r quality management projects throughout the Southeast United States and Florida (see Table 3- 3). Because of this, EPA is considering using the EFDC and WASP (Version 7.41) models for a pplication to numeric criteria development for

Florida estuaries. EFDC and WASP are both publicly available ( http://www.epa.gov/athens/wwqtsc/ )

.

3.3.3.2.1 EFDC

The EFDC model is an advanced, three-di mensional surface water modeling system for hydrodynamic and reactive transport simulations of rivers, lakes, reservoirs, wetland systems, estuaries, and the coastal ocean. The modeling syst em was originally developed at the Virginia

Institute of Marine Science as part of a long-term research program to develop operational models for resource management applications in Virginia's estuarine and coastal waters (Hamrick 1992). EFDC is currently used by universities, governmental agencies, and engineering consultants.

EPA is considering application of EFDC to si mulate hydrodynamics (i.e., three-dimensional advective transport and mixing) in Florida estu aries. EFDC is run on an orthogonal grid, which

will extend from tidal reaches of Florida’s str eams beyond the ocean and gulf passes, and into

coastal waters to ensure that the domain of inte rest (i.e., estuarine waters) is not substantially affected by artifacts propagating from the seaw ard open boundary of the model. Inputs to the model include freshwater inflows (from LSPC), precipitation and evaporation, wind speed and direction, solar heating, and tidal forcing at the open boundaries. EFDC will simulate threedimensional distributions of water temperatur e and salinity, and will compute advective and diffusive transport in three dimensions. EFDC time-variable outputs will be exported as a hydrodynamic output file, which will be utilized within WASP7 to calculate constituent transports between model elements.

3.3.3.2.2 WASP

The Water Quality Analysis Simulation Program (WASP) is an EPA-developed and supported water quality model that is routinely applied throughout the United States and worldwide to investigate water quality issues. EPA is consid ering using Version 7.41 (WASP7) to simulate estuarine water quality processes for the purpos e of numeric criteria development. WASP

version 7.41 is the newest version of WASP (released June 7, 2010) and has many upgrades to the user interface and to the model cap abilities. WASP can be downloaded at http://www.epa.gov/athens/wwqtsc/html/wasp.html

.

WASP is a dynamic compartment-modeling program for aquatic systems. It can simulate processes in both the water column and underl ying benthos. The time-varying processes of advection, dispersion, point and diffuse mass load ing and boundary exchange are represented in the basic program. Water quality processes are repr esented in special kinetic subroutines that are either chosen from a library or written by the user. WASP is structured to permit easy

substitution of kinetic subroutin es into the overall package to form problem-specific models.

WASP comes with two such models—TOXI for toxicants and EUTRO fo r conventional water quality. EPA’s implementation of WASP for cr iteria development w ill utilize the EUTRO

model.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 62 Earlier versions of WASP EUTR O have been used to examine eutrophication of Tampa Bay; Lake Okeechobee (James et al. 1997); Neuse Ri ver and estuary (Wool et al. 2003); the Great Lakes (Thomann et al. 1979; Thomann et al. 19 76; Di Toro and Connolly 1980; Thomann 1975), and the Potomac Estuary (Thomann and Fitzpa trick 1982), among many others. In addition to these, numerous applications are listed in Di To ro et al. (1983). Other applications of the WASP in Florida include St. Andrews Bay, Apalach icola Bay, Fenholloway River, Sarasota Bay, Charlotte Harbor, and Loxahatche e River Estuary (see Table 3-3).

Many models, including WASP, include code to enable a wide variety of simulations. The

choice of which features should be activated in any particular model implementation will depend upon the target processes, or objectives, of the simulations , which are determined by the questions to be addressed. A co mplex model that explicitly s imulates many processes is not necessarily better than a simpler model that focuse s on the aspects of the ecosystem that are most pertinent to the target process (Arhonditsis and Brett 2004). EPA is cons idering implementations of WASP that are guided by the principle that mode l functions should be ac tivated only if it is determined to be necessary to achieve model simulation and performance objectives.

For the Pensacola Bay application described be low, the EFDC hydrodynamic model was used to perform hydrodynamic simulations, which provide the following information to WASP via a hydrodynamic linkage (HYD) file: (1) time variable exchanges with the Gulf of Mexico (e.g., due to tides), (2) three-dimensional model cell structure and volumes, (3) three-dimensional, time-variable exchanges among mo del cells (includes horizontal and vertical advective and nonadvective exchanges, and (4) three-dimensional, time-variable salinity and temperature. State variables, water quality pro cesses, and boundary conditions fo r the Pensacola WASP model are

shown in Table 3-4. Rates and kinetics we re established through parameter calibration techniques to match water quality observations from Pensacola Bay.

Three organic carbon variables play an equivalent role as BOD, representing organic matter that is relatively refractive, of an intermediate reac tivity, or labile. Nitrogen is divided into organic and inorganic fractions. Organic nitrogen state variables are disso lved organic nitrogen, labile particulate organic nitrogen, a nd refractory particulate organic nitrogen. Inorganic nitrogen forms are ammonia and nitrate (nitrite is implicitly represented). Both NO 3 - 

### and NH 4 + 

### are utilized to satisfy algal N requirements, with NH 4 + 

### being preferred. The primary reason for distinguishing

the two is that ammonia is oxidi zed by nitrifying bacteria into nitrate. Nitrification can be a significant sink of oxygen in the water column. Sedi ment nitrification is represented implicitly via the boundary condition value for sediment oxygen demand. As with carbon and nitrogen, organic phosphorus is considered in three states: dissolved organi c phosphorus, labile particulate phosphorus, and refractory particulate phos phorus. Only a single inorganic form, orthophosphate, is considered. Orthophosphate ex ists as several states within the model ecosystem: dissolved phosphate, phosphate ad sorbed to inorganic solids, and phosphate incorporated in algal cells. Equilibrium partiti on coefficients are used to distribute the total among the three states.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 63 Table 3

‐ 4. The state variables included in the implementation of WASP7 for Penscaola Bay. In addition, the key
biogeochemical processes represented in the model and the boundary conditions that must be specified to run the model.

State Variables Processes

• Ammonia (NH 4 + )

• Nitrate+nitrite (NO 3 - + NO 2 - )

• Inorganic phosphorus (PO 4 3- )

• Dissolved organic nitrogen

• Dissolved organic phosphorus

• Phytoplankton (carbon and chlorophyll a )

• Particulate detritus (C, N, and P)

• Carbonaceous biological oxygen demand 1
(CBOD1)

• Nitrogenous biochemical oxygen demand

• Dissolved oxygen

• Total suspended solids • Phytoplankton production and mortality

• Light and nutrient limitation (N, P, not Si)

• N and P uptake by phytoplankton

• Mineral sediment sinking (but not resuspension)

• Particulate organic matter sinking

• N and P remineralization

• Nitrification

• Denitrification

• Chemical-biological oxygen demand (3 classes)

• Air-sea exchange of dissolved oxygen

Boundary Conditions

• Sediment oxygen and nutrient fluxes

• Gulf of Mexico concentrations of all state

variables

• Inflows of all state variables associated with freshwater inflows and point sources

• NO 3 - concentrations in rainfall



### WASP is capable of simulating four classes of al gae, each targeting a specific ecological “niche” defined by distinctive characterist ics of the class and the role those characteristics play in ecosystem function. Cyanobacteria, commonly called blue-green algae, are characterized by their abundance as picoplankton in sali ne water and by their bloom-fo rming characteristics in fresh water. Being very small, picoplankton do not sink at appreciable rates. Another key feature of cyanobacteria is that some species fix atmo spheric nitrogen and can form harmful blooms. Diatoms are distinguished by their need for silica to form their siliceous tests. Diatoms are can grow quickly given sufficient nutrients, but also sink relatively quickly, being large cells and lacking flagella to actively maintain position in the water column. Planktonic algae that do not fall into the two groups are lumped into the heading of green algae. Green algae settle at a rate intermediate between cyanobacteria and diatoms a nd are subject to greater grazing pressure than are cyanobacteria. The fourth category is macr oalgae, which could be ex pected to increase in

biomass when nutrients are ava ilable but high flushing rates prevent accumulations of planktonic algae. The algal community of Pensacola Bay ha s been characterized as dominated by slowgrowing non-N-fixing picoplankton ic cyanobacteria during summer and diatoms at other times of the year. Because the cyanobact eria lack a distinctive functional role (i.e., they are not Nfixers), and the Pensacola Bay phytoplankton co mmunity can be modeled effectively with a single class of algae, there is not sufficient reas on to model the community with more than one

class.

WASP is able to simulate sediment-water oxyge n and nutrient exchanges by simulating sediment processes using a sediment diagenesis model. This approach entails substantial data requirements, as well as a need for adequate data to calibrate the model. The approach that EPAMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 64 is considering using for Pensacola Bay is to specify these oxygen a nd nutrients fluxes as boundary conditions. This may improve model perf ormance relative to a poorly specified and calibrated simulation of these processes. A limitation of the approach is that if changes in benthic processes such as sediment oxygen demand or nutr ient fluxes are an important aspect of the response of the ecosystem to cha nges in nutrient loading, then mode l will not be able to reflect those responses. For example, if estimates of SOD are based on recent observations (e.g., Murrell et al. 2009), and SOD woul d decrease if nitrogen/phos phorus pollution loading rates decreased, the model would tend to underestim ate the improvement in bottom water oxygen

condition that could be associated with reduc ed nitrogen/phosphorus pollution loading. EPA could address this by modifying the boundary condition.

An important development within WASP7 for numeric criteria development in Florida is the addition of light extinction algorith ms, which have been added to allow the model to predict light extinction as a function of background light extinction, attenuati on by phytoplankton, and attenuation by suspended solids (including detritus). These new algorithms allow the model to predict changes in light extincti on as function of management pr actices that reduce loading of nutrients and suspended solids. The light mode l in WASP can be used to determine the availability of light at the botto m, a key indicator of the likely e ffect of water quality on seagrass growth and survival.

3.3.4 Evaluating Water Quality Simulation Models

Because simulation models are complex computational constructs with many parameters that must be specified, EPA is evaluating systematic and quantitative approaches that the Agency could use to calibrate models, verify their perf ormance against independe nt data, and evaluate uncertainty associated with model predictions. EPA is evaluating a variety of quantitative performance metrics that have been proposed (e .g., Stow et al. 2009) and is also considering metrics that have been previously applied in regulatory environmenta l modeling (e.g., Wool et al. 2003).

Model calibration for both watershed models pro ceeds from physical properties, to chemical properties and ultimately to biological propertie s and evaluation of specific model outputs that define the model endpoints or objectives. Fo r example, the calibration sequence for LSPC watershed modeling begins with water balance and then proceeds to water temperature and finally water chemistry (e.g., TN and TP). The calibration sequence for EFDC/WASP begins with water levels, salinity and water temper ature (EFDC), and proceeds to water quality simulations within WASP, including chlorophy ll-a or DO, which are modeling endpoints.

The model evaluation procedure that EPA is cons idering would involve ev aluating models across a range of temporal and spatial scales. For ex ample, hydrologic calibration of watershed models would begin with ensuring the annual water ba lance, and then would examine seasonal or monthly distributions of discha rge, and finally hydrograph com ponents such as base flow and storm flow. EPA is considering application of gr aphical and quantitative tools such as (1) plots of hourly, daily and monthly time series, (2) mont hly scatter plots and bala nce plots, (3) seasonal plots (i.e., multi-year composites) , (4) flow duration curves, (5) flow duration curves, (6) flow accumulation curves, (7) cumulative error st atistics, and (8) evaluation of hydrograph components. EPA is considering similar quantita tive evaluation procedures to examine streamMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 65 velocity and water quality (i.e., TN and TP) acros s a range of time scales and flow conditions. EPA is considering quantitative performance goals based on prior modeling experience to evaluate if the models are performing well (Table 3-5).

EPA is considering evaluating water quality models by consideri ng overall statistical distributions (e.g., cumulative distribution func tions), seasonal distributions, and spatial distributions of key water qual ity variables (i.e., state variab les). Indicators of ecosystem function may also be considered and could include process rates (e.g., primary productivity, plankton metabolism) and estimates of important controls on process ra tes (e.g., measures of light, nitrogen, or phosphorus limitation of algal growth).

Table 3

‐ 5. Performance goals for LSPC hydrology simulations, which are based on previous modeling experience. Values are (average of simulated values ‐ average of observed values) / average of observed values. Hydrologic calibrations for longer
averaging periods, including the long

‐ term average flow, are expected to be in closer agreement than for shorter periods,
especially summer storm volumes, which can depend on rainfall events.



### Error Category (Simulated-Observed) Recommended Criteria (± %) Total flow volume (i.e., average flow) 10

Lowest 50% of flows 10 Highest 10% of flows 15 Seasonal average flow (spring, summer, fall, winter) 30

Storm volumes 20

Summer storm volumes 50



### 3.4 Seagrass Depth Targets

As was mentioned in Secti ons 2.3 and 3.2, EPA is consider ing using healthy seagrass communities as an assessment endpoint for numeric criteria in Florida estuaries. Seagrasses are present in portions of estuarie s throughout Florida, with the exception of estuaries in northeast

Florida from the St. Johns River northward to th e Georgia border. Seagrasses create valuable

habitat and food resources for estuarine biot a by providing protection from predators, by providing substrate for production of epiphytic al gae and algal grazers. Seagrasses themselves are highly productive and are grazed di rectly by a variety of species.

Although healthy seagrass communities depend on a va riety of physical and biological factors in addition to water clarity (Koch 2001; Heck and Valentine 2007), ma nagement efforts to protect seagrass have often focused on improving water cl arity because there are clear conceptual and empirical linkages between nutrient loading, water clarity and extent of seagrasses. In short, increased nutrient loading ofte n contributes to increased phyto plankton production and biomass, reduced water clarity, and consequently, degr adation and loss of s eagrass habitat. Strong relationships have been observed between th e maximum depth to which seagrasses grow, referred to as the “depth of colonization,” and water clarity (e.g., Duarte 1991; Kenworthy and Fonseca 1996).

Several studies have characterized the depth of colonization of seagrasses in specific Florida estuaries, for example in the Big Bend Regi on (Iverson and Bittaker 1986); Tampa Bay (JanickiMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 66 and Wade 1996; Johansson 2002); Indian River (Steward et al. 2005); and Sarasota Bay (Tomasko et al. 2001). However, the depth of se agrass colonization has not been evaluated in detail in many estuaries. Estimates of depth of colonization may be needed for specific segments

of estuaries as well as for mo re than one point in time.

EPA is considering using an appr oach that involves overlaying geos patial seagrass coverage data on a spatial data set of bat hymetric soundings, then examini ng the fraction of bathymetric soundings that occur within seagrasses as a func tion of the depth of th e soundings (Figure 3-6). EPA has assembled the available seagrass covera ges, which includes data spanning 1940 to 2007 (Table 3-6). Seagrasses have has been mapped th ree or more times in a number of estuaries, generally on the Florida peninsul a. Seagrass coverage has been mapped once in several estuaries in the Florida Panhandle and Big Bend Region.

The statistical deepwater edge will be operationally defined to target the middle of the range of depths that occur at the seaward or deepest e dge of seagrass beds within an estuary segment (Figure 3-6). An approach similar to that of Stew ard et al. (2005) may be utilized for a fraction of

the data to evaluate the method. Initial estimates of the depth of colonization for each segment may be adjusted from a mean lower low wate r datum to mean tide level datum using on tide datums published by NOAA for nearby tide stations.

EPA is considering how to best utilize a data se t characterizing seagrass colonization depths to determine targets for seagrass colonization dept h. Seagrass restoration targets for Tampa Bay were based on the spatial extent of seagrasses that were present circa 1950, less cert ain areas that due to ongoing dredging or other m odifications are not considered recoverable habitat. Depth targets were then determined based on current bathymetric profiles (Janicki and Wade 1996). Similar historical seagrass coverages are availa ble for some estuaries (e.g., St. Andrew Bay, Table 3-6), but are not availabl e for all estuaries in Florida. Steward et al. (2005) present approaches for determining seagrass depth a nd associated light targ ets for protection of seagrasses in the Indian River Lagoon. Although th e approach they present could not be applied in some parts of Florida due to an insuffici ent number of coverages, EPA could consider applying elements of the approach, such as utili zing the depth of colonization within reference segments as “upper restoration depths” and the highest value observed for a specific segment as a minimum target for that segment. EPA could c onsider applying the methods presented here for computing colonization depth to a union seagra ss coverage, as suggest ed by Steward et al. (2005).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 67 Depth below MLLW (m) 01234Percent of Soundings in Seagrass

0 20 40 60 80

Continuous

Discontinuous All Seagrass

Figure 3

‐ 6. An example of the approach used to estimate seagrass depth of colonization, in this case applied to the 1953

seagrass coverage for the East Bay segment (WBID 1061F) of St. Andrews Bay. The estimate of maximum colonization depth (Z

c ‐ max, relative to MLLW) is 1.7 m for continuous seagrass and 2.0 m for all seagrass. The depth at which the percent of
soundings in seagrass is decreased to 50% of the maximum is 1.35 m, extrapolated linearly between the observations at 1.25

and 1.50 m.

Table 3

‐ 6. Seagrass coverages available for Florida estuaries. CC=complete coverage, PC=partial coverage. USGS = United

States Geological Survey, FDEP=Florida Department of Environmental Protection, SWFWMD=Southwest Florida Water
Management District, SFWMD=South Florida Water Management District. SJRWMD=St. Johns River Water Management

District.



### Estuary Year(s) Extent Source

Panhandle Region Perdido Bay 1940, 1992 CC USGS 1992, 2002, 2003 PC FDEP Pensacola Bay 1950 PC Ollinger et al. (1974) 1992, 2001 PC FDEP

1992 CC USGS

2003 CC FDEP Choctawhatchee Bay 1992 CC FDEP

1992 CC USGS St. Andrews Bay 1953, 1964, 1980,

1992 CC USGS

1992, 2003 CC FDEP Z

c-max 

### Z

c50%Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 68 Estuary Year(s) Extent Source St. Joseph Bay – Apalachicola 1992 CC USGS

Apalachicola Bay 1992 CC FDEP

1992 CC USGS

Big Bend Region Apalachee Bay 1992, 2001 PC FDEP

1992 CC USGS

Suwannee River 2001 CC FDEP Cedar Key - Suwannee River 1992 CC USGS

Steinhatchee-Charlotte 1992 CC USGS

Peninsula Gulf Coast St. Joseph Sound - Tampa 1999, 2001, 2004,

2006 CC SWFWMD

Clearwater Harbor - Tampa Bay 1999, 2001, 2004,

2006 CC SWFWMD

Crystal Bay - Tampa Bay 1992 CC USGS Anclote Keys - Tampa 1992 CC USGS Tampa Bay 1950 CC Haddad (1989) 1988, 1990, 1992, 1994, 1996, 1999, 2001, 2004, 2006 CC SWFWMD

2006 CC FDEP Sarasota Bay 1950 CC 1988, 1994, 1996, 1999, 2001 2004, 2006 CC SWFWMD

1990 PC SWFWMD

2006 CC FDEP

Charlotte Harbor 1950 CC 1982 , 1988, 1992, 1994, 1996, 1999, 2001, 2004, 2006 PC SWFWMD

2003, 2004 PC SFWMD 2004, 2006 PC FDEP

Caloosahatchee River -

Charlotte Harbor 1999 PC SFWMD

Lemon Bay – Charlotte 1982 PC SWFWMD 1988, 1994, 1996, 1999, 2001, 2004,

2006 CC SWFWMD

Gasparilla Sound -Charlotte

Harbor 1982, 1988, 1992, 1994, 1996, 1999, 2001, 2004, 2006 CC SWFWMD

Matlacha Pass - Charlotte 1999 CC SFWMDMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 69 Estuary Year(s) Extent Source

Harbor

Pine Island Sound -

Charlotte 1999 CC SFWMD

Estero Bay 1999, 2003, 2004 CC SFWMD San Carlos Bay - Charlotte 1999 CC SFWMD

South Florida Rookery Bay Unknown CC FDEP Ten Thousand Islands 1987, Unknown PC FDEP Biscayne Bay (1991,92,95); 2004 PC FDEP

Atlantic Coast Indian River 1943, 1986, 1989, 1992, 1994, 1996, 1999, 2003, 2005,

2007 CC SJRWMD

2001, 2006 PC SJRWMD

2001 PC SFWMD

2003 CC SFWMD 2005, 2006 PC FDEP



### 3.5 Example Application: Pensacola Bay

This section illustrates how EPA could utilize a coupled watershed-hydrodynamic-water quality model to develop numeric criteria for Pensacola Bay.

System Description Pensacola Bay 9 

### is located in northwest Florida in Escambia and Santa Rosa counties. The Pensacola Bay system is a complex of sub-es tuaries that includes Es cambia Bay, Blackwater Bay, East Bay, Pensacola Bay and Santa Rosa Sound (Figure 3-7). The combined system is medium-sized (370 km 2 

### ) and shallow (mean depth ~ 3.0 m) and has been characterized as a partially-stratified, drowned river valley es tuary (Schroeder and Wiseman 1999). Tides are diurnal and have low amplitude, ranging from 15 cm to 65 cm. The basin includes three major watersheds, which drain via the Escambia, Black water, and Yellow Rivers. The Escambia River discharges into Escambia Bay, whereas the Bl ackwater and Yellow Rivers discharge into Blackwater Bay. Both bays join Pensacola Bay, which connects to the Gulf of Mexico through the narrow (800 m wide) Pensacola Pass. Sma ll openings also permit exchanges with Big Lagoon through a shallow (3 m), narrow (100 m) ch annel to the west and Santa Rosa Sound to the East through a 1 km opening. Santa Rosa S ound extends 52 km to the east from Pensacola Bay and ultimately narrows to 75 m width befo re opening into Choctawhatchee Bay. Because of the small size of these channels, flow through th em is expected to be minimal compared to

Pensacola Pass.

9 This site description is excerpted from Hagy and Murrell (2007).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 70 Figure 3

‐ 7. Location Map of Pensacola Bay



### 3.5.1 Selection of Assessment Endpoints

EPA evaluated the status of ke y biological assessment endpoint s (Section 3.2) in Pensacola Bay and found evidence that (1) consid erable seagrass loss has occurre d in Pensacola Bay, and (2) biotic communities in Pensacola Bay are mo st likely impacted by low dissolved oxygen. However, EPA did not find evidence that harmful algal blooms or prolif erations of macroalgae occur in the Bay.

Historical seagrass coverages (e .g., 1950) reveal that seagrasses were historically abundant throughout Pensacola Bay but that much of the hi storical coverage, outside of Santa Rosa Sound, has been lost (Hagy et al. 2008). Much of th e decrease occurred prior to 1980, during a period characterized by high point source nutrient (especia lly largely nitrogen) loading (Olinger et al. 1975). As of 2003, most of the remaining seagrass area was in Santa Rosa Sound. Historically, low salinity areas such as river deltas were colonized by Vallisneria americana ; Rupppia

maritima has been observed at intermediate salinity, whereas Thallassia testudinum remains the dominant species in areas with high salinity, such as Santa Rosa Sound. FDEP has recently begun experiments involving transplantation of s eagrasses to Escambia Bay, Pensacola Bay, and Big Lagoon.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 71 Following the approach that EPA is considering, EPA will develop estimates of the current and historical depth of colonization of seag rasses in Pensacola Bay (Section 3.3.3.2.2), and subsequently establish targets for depth of colonization. EPA may consider that supporting seagrasses at their colonizati on depth requires a range of 20-25 percent of incident light (as estimate that has been applied to Thallassia ), then compute limits for light attenuation that would be consistent with achieving an average depth of colonization equal to the target value. EPA could consider alternative values for the percen tage of light required at the colonization depth, recognizing that published estimates have included both lower and higher values (e.g., Steward et al. 2005).

Hagy and Murrell (2007) documented that severe low dissolved oxygen o ccurs extensively in bottom water areas of Escambia Bay, upper Pens acola Bay, East Bay and Blackwater Bay, with DO levels being less than 2.0 mg/L on a seasonal basi s. Hypoxia is confined to stratified bottom waters, whereas surface waters and unstratifie d areas are well-oxygena ted. Limited data are

available that document the status of the bent hic infaunal community in Pensacola Bay. These data suggest that biomass and abundance of benthos are reduced in areas that are often affected by low dissolved oxygen. FDEP water quality assessm ents determined that several segments of Pensacola Bay are impaired for low dissolved oxygen.

3.5.2 Numeric Criteria Development

EPA has developed a coupled watershed-estu arine model for Pensacola Bay using LSPC (Section 3.3.3.1) for watershed modeling and EFDC coupled with WASP for modeling estuarine water quality responses (Section 3.3.3.2). EPA is considering usi ng this modeling system to develop numeric criteria for Pensacola Bay. LSPC was used to simulate daily freshwater and nutrient inputs to Pensacola Bay for 1996-2009. Outputs from LSPC are used as inputs to the

EFDC/WASP model. WASP would then be used to simulate the effect of TN and TP loading on

TN and TP concentr ations, chlorophyll a , water clarity, and dissolved oxygen in Pensacola Bay. Following the approach outlined in Section 3.3.3, EPA would estimate current conditions, characterize natural conditions, a nd develop numeric cr iteria to translate Florida’s existing

narrative nutrient criterion. Estim ates of current conditions woul d be used for model calibration

and evaluation. Natural conditions would utilize LSPC to simulate loading rates, with natural variability in flows and load that would be expe cted in the absence of a significant anthropogenic contribution. If water quality targets cannot be met without reducing TN and/or TP loading to below the natural background, then EPA could c onsider establishing TN, TP and chlorophyll

criteria based on the characterization of natu ral conditions from LSPC. On the other hand, if water quality targets can be achieved, then EP A would with vary nitrogen/phosphorus pollution loading rates, ultimately developing a numeric value, which would simulate the highest nitrogen/phosphorus pollution load ing rate that could occur wh ile maintaining water quality targets in the estuarine receiving water. EPA coul d then consider development of criteria for TN and TP loading, estuarine TN and TP concentrations, and chlorophyll a concentrations based on the simulated water quality under these conditions . Using time series outputs from the model, EPA could apply a variety of approaches for expr essing criteria. For example, time series output

could be evaluated in the same manner as da ta from a reference condition. Because the model output is highly spatially resolv ed, EPA could develop criteria fo r subsegments of Pensacola Bay by averaging outputs from model grid cells within the subsegment.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 72 3.5.3 Details on the Pensacola Bay Water Quality Model

3.5.3.1 Watershed Model

In this example, the Pensacola Bay watershe d model includes 224 subwatersheds (Figure 3-8).

The subwatersheds are based on the NHDPlus ca tchments and flowlines, but catchments are aggregated to approximately the HUC12 scale to reduce the number of watersheds in the

simulation. The LSPC model has 12 terminal reach es discharging directly to the estuary. The model will utilize meteorological data from seve n locations across the Pensacola Bay watershed. The Pensacola Bay LSPC model will be calibrated fo r flow at five USGS flow stations (Table 3-7) and eight associated water quality monitoring stations with data obtained from FDEP (IWR Run 40 database) and the Alabama Department of Environmental Management. With the exception of one station, the calibration points ar e located in the lower watershed, close to Pensacola Bay. Flow calibrations will ensure that the model correctly simulates overall average flow, seasonal patterns in flow, magnitudes of low and high flow events, and event scale flow patterns. Similarly, water quality calibrations wi ll ensure that the model correctly reproduces overall average loading, seasonal patterns of load ing, and magnitude of loading across a range of flow magnitudes.

Table 3

‐ 7. USGS flow gauges and water quality monitoring sites within the Pensacola Bay watershed



### River Location Flow Gage IWR Gage Yellow River Milligan, Fl USGS 02368000 21FLNWFDS271

21FLBFA 33040004 Big Coldwater Creek Milton, Fl USGS 02370500 21FLPNS 33030069

Conecuh River River Fall s, AL USGS 02372422 n/a Escambia River Century, FL USGS 02375500 21FLGW 3549

21FLBFA 33020001 Escambia River Molino, FL USGS 02376033 21FLGW 3541

21FLBFA 33020007

3.5.3.1.1 Watershed Hydrology

Initial values for the hydrological parameters woul d be taken from default data sets from similar studies in the region. During the calibration process, model parameters would be adjusted, based on local soil types and groundwater conditions, within reasonable constraints until an acceptable agreement was achieved between simulated and obs erved stream flow. Model parameters that would be adjusted include ev apotranspiration, inf iltration, upper and lower zone storage, groundwater storage, and losses to the groundwater system.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 73 Figure 3

‐ 8. (Upper Panel) Map indicating the 224 subwatersheds and associated flowlines that comprise the Pensacola Bay

LSPC watershed model. (Lower Panel) The model grid that will be utilized for the EFDC/WASP water quality model for

Pensacola Bay.



### Historical and short-term USGS flow stations located in th e Pensacola watershed would be used

to calibrate and evaluate the L SPC watershed hydrology model. Th ere are a total of four USGS

flow stations in the Pensacola watershed that have an overlapping period of record with the model simulation. The calibration of the hydrolog ic parameters would extend from January 1,Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 74 1997, through December 31, 2009. Velocity would also be calibrated by adjusting the instream Manning’s n value. Simulated velocity would be compared to USGS measured velocities that

were taken at the flow calibration stations.

The model would be evaluated by applying the a bove calibrated hydrologic parameters to other subwatersheds of the Pensacola Bay watershed a nd comparing the simulated flow to measured flow from a USGS stream gauging station for the same period of tim e. Care is taken to select watersheds that represent a wide va riety of land uses as well as dr ainage areas. This will help to ensure that the hydrologi cal parameters that were calibrated a pply to a wide range of conditions.

3.5.3.1.2 Watershed Water Quality

Following hydrologic model calibration, water qual ity will be simulated for the watershed. The watershed water quality model would include all point source discharges with permitted flow above 0.1 MGD and nonpoint source contributio ns. Many components of the water quality

model would be established during hydrology modeling, incl uding watershed segmentation, meteorological data, land use representation, so ils, reach characteristi cs, and point source discharges. For application, herein, the wa ter quality model was setup to model water temperature, dissolved oxygen, biochemical oxygen demand, TN, TP, and total suspended solids. EPA is considering modeling TN and TP transp ort from land to streams using the pervious quality constituent module (PQUAL ) of LSPC and instream pro cesses using the generalized quality constituent module (GQUAL).

Reach Group. For the water quality simulations, stream reach processes would be assigned by reach group. Assigning reaches into groups allows for the assignment of unique values, for each reach group, for certain model parameters. The pa rameters that can be assigned differently by reach group include: sediment bed storage para meters, cohesive and non-cohesive suspended sediment variables for in-stream transport, temperature for stream groups, land to stream mapping, variables associated with BOD sinking, decay, and benthic release, variables for oxygen re-aeration, benthic oxygen demand, oxygen scour, all nutrient parameters, and all plankton parameters. In preliminary analyses fo r temperature, it was observed that headwater reaches responded differently than non-headwater reaches. Therefore, headwater reaches were assigned to their own reach group and defined as first order streams.

Water Temperature. Soil temperature and heat exchange/water temperature modules with LSPC would be used to simulate water temp erature. Simulation of soil temperatures is accomplished by using three layers. The surface la yer is the portion of the land segment that determines the overland flow water temperat ure. The upper subsurface layer determines interflow temperature while the groundwater subsurface layer determines groundwater temperature. Surface and upper subsurface layer te mperatures would be estimated by applying a regression equation as a function of measured air temperature and the groundwater subsurface temperature. Soil temperature is used to determ ine the water temperature of the three different flow paths (surface outflow, upper subsurface/in terflow outflow, lower subsurface/groundwater outflow) contributing to stream flow. Once the water is in the stream, the temperature is impacted by mechanisms that can increase or decr ease the heat content of the water. Mechanisms

which can increase the heat conten t of the water are absorption of solar radiation, absorption of longwave radiation, and conduction- convection. Mechanisms which decrease the heat contentMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 75 are emission of longwave radiation, conduction- convection and evaporation (Bicknell et al. 2004).

Dissolved Oxygen. LSPC modules for pervious water temp erature, dissolved gas concentration, and primary DO and BOD balances would be used to simulate dissolved oxygen. Aside from instream transformations, which either consume or produce dissolved oxygen, the primary factors regulating stream DO concentr ation are stream temperature and atmospheric reaeration. Atmospheric reaeration takes in to consideration th e initial dissolved oxygen concentration, oxygen saturation level for a given water temperat ure, water depth, water velocity, circulation, reaeration rate, and a temperature correct ion coefficient for surface gas invasion.

Sediment. Production and removal of sediment, accumulation and removal of solids and behavior of inorganic sediment modules within LSPC would be used to model sediment loading. EPA would rely on initially establ ishing detachment coefficients based on similar studies in the region. Depending on the calibration results, this parameter would be adjusted.

Nutrients. LSPC simulates water quality constituents and wash-off of water quality constituents using simple relationships to model nitrogen and phosphorus loadings. Accumulation and washoff rates play an important role in the determ ination of nonpoint source loadings to a waterbody. The watershed model must appropriately repres ent the spatial and temporal variability of hydrological characteristics within a watershed. It must also a ppropriately repres ent the rate at which nitrogen and phosphorus components build- up between rain events and wash off during rain events. Key general water qua lity characteristics include initial storage, wash-off and scour potency, accumulation rates, and maximum stor age amounts. The water supplied to a stream from groundwater and through interf low also plays an important role in loading to a waterbody. LSPC allows the user to supply groundwater an d interflow concentrations, by hydrologic soil group and land use, by month. The accumulation a nd wash-off and interflow strongly influence peak flow water quality while groundw ater reflects baseflow water quality.

3.5.3.1.3 Development and Calibration

Calibration of the LSPC water quality model incl udes a sequence of steps that begins with temperature, because the other key paramete rs are regulated by instream temperature. Temperature would be calibrated by adjusting su rface and interflow slopes and intercepts, and groundwater temperature, by land use and hydrologic soil groups until the simulated data closely matched observed. Following temperature calib ration, dissolved oxygen would be calibrated with the observed data by ad justing reaeration, and interflo w and groundwater dissolved oxygen

concentrations.

Finally, calibration would involve BOD, TN, and TP. These three c onstituents would be modeled by build-up/wash-off and assigned land use-asso ciated concentrations in groundwater and interflow. Build-up/wash-off removes constituen ts from the land and carries them into the stream. Adjustments would be made to mont hly accumulation rate, monthly storage limit, interflow concentration, and groundwater concentr ation for BOD, TN, and TP until the simulated data was in range with the observed field data.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 76 3.5.3.2 Estuarine Model

As described earlier, EPA proposes to use the EFDC to simulate water surface elevations, currents, salinity and water temperature. The estuarine model grid w ould include the entire Pensacola Bay system, including Santa Rosa Sound, and will also extend to a seaward boundary approximately 18 km seaward of Pensacola Pass (Figure 3-8). Extending the model well into the Gulf limits the potential for boundary conditions to influence model results in the estuary. The proposed model would resolve 998 grid cells and will have five sigma layers (i.e., it will resolve water column profiles into five vertical layers of equal thickness). The model will be calibrated for the period of 2009 and evaluated for 2002 th rough 2005. Model calibrations will utilize data collected by EPA approximately monthly at 15 stations during 2002-2004, as well as a continuous record of water qua lity data recorded in 2009 by an environmental monitoring buoy deployed by EPA. The data include surface and bottom temperature and salinity collected at 15 minute intervals from April to October 2009 at a station in Escambia Bay and May to September 2009 near Navarre Beach. In addition, data on wate r surface elevations (i.e., tide) collected by the NOAA (Pensacola tidal station 8729840) will be used for calibration. These data include

water surface elevation data co llected on an hourly basis and da ily surface water temperature. Evaluation was performed using data from 2002 through 2004 from 16 stations throughout the estuary with salinity and temperature data.

The output from EFDC would be used to drive the hydrodynamic factors that are used in the WASP model for simulating nitrogen/phosphorus pollution (NO3, ammonia, orthophosphate), phytoplankton (chlorophyll), BOD, DO and total su spended solids. A particularly important feature of WASP is its ability to estimate light attenuation in bottom layers of the estuary. The

model would be calibrated with water quality data (nutrients , chlorophyll, and DO) from 2002 from 16 stations throughout the estuary and ev aluated using the 2003-2004 data from the same stations. In general, model evaluation ensures th at the range of parame ter values and seasonal dynamic cycles produced by the model are similar to the observed data.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 77 

## 4 Numeric Nutrient Criteria Development for Florida Coastal Waters

EPA is considering several approaches to derive numeric criteria for Florida’s coastal waters. For the purpose of this document, coastal waters are defined as marine waters up to three nautical miles from shore, but exclude estuaries, which are generally semi-enclosed waters. 10 

### For much of the State’s coastal waters, EPA is considering a reference-based approach with satellite remote sensing chlorophyll a observations to derive numeric valu es that translate Florida’s narrative criteria and ensure support of a natural balanced population of aquatic flora and fauna.



### 4.1 Analysis Plan and Delineation of Coastal Areas

At this time, there are variable amounts of monitoring data fo r Florida’s coastal waters. For example, the Northwest Gulf Coast and Atlantic Coast contain little monitoring data of chemical and biological constituents. In contrast, significant monitoring has taken place in coastal waters

of South Florida and the Florida Keys as part of the Southeast Environmental Research Center (SERC) water quality monitoring network, and the West Gulf Coast of th e Florida peninsula in response to recurring blooms of the toxic dinoflagellate, Karenia brevis

.

Given the variability of coastal water quality data available in Florida, EPA is considering the use of satellite remote sensing data to derive num eric criteria for coasta l waters. EPA’s approach is shown in Figure 4-1. First, EPA will delineate the coastal areas, based on the definition of

waters of the U.S. in the Clean Water Act and the boundaries of defined estuarine waters. Next,

EPA will evaluate satell ite-derived chlorophyll a (Chl RS 

### -a) to set numeric cr iteria for chlorophyll

as the indicator variable usi ng a reference condition approach in these coastal areas.

EPA recognizes that satellite ocean color remote sensing technology has a dvanced over the past

decade. Chl RS 

### -a in the ocean are a routine measuremen t and available globally (O'Reilly et al. 1998). Coastal physical forcings such as wind, curr ents, and tides are known to influence coastal chlorophyll dynamics together with nitrogen/phosphorus pollution lo adings from the land. It is expected that all these processe s will be represented when using remote sensing as a reference condition approach. Specifically, EPA is consider ing the use of this method to develop numeric chlorophyll criteria for the Northw est Gulf Coast, West Gulf Coas t, and Atlantic Coastal Areas (Figure 4-2).

Due to the increased interference from colored dissolved organic matter (CDOM) and bottom reflectance on satellite measurements, EPA is not considering the derivation of chlorophyll criteria using remote sensing data in coastal waters from Apalachicola Bay to Suwannee River (Big Bend) and South Florida. EPA’s approach fo r developing numeric criteria for these waters is described in Chapter 3 and 5, respectively.

10 The Clean Water Act’s definition of “waters of the U.S.” is limited to three nautical miles offshore.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 78 Figure 4

‐ 1. Flow chart for the development of numeric nutrient criteria for coastal waters.



### EPA is currently not consideri ng the derivation of separate TN and TP criteria for Florida’s

coastal waters because EPA believes that numeric criteria derived for estuarine waters (see Chapter 3) will inherently protect coastal wate rs from anthropogenic TN and TP from streams and estuaries. The coastal chlorophyll criteria will be an independent means to assess whether coastal waters are maintaining water quality. Preliminary analysis by EPA indicates that coastal segments adjacent to estuary passes have higher Chl RS 

### -a than coastal segments further from estuary passes. Examples of coastal chlorophyll responding to nitrogen/phosphorus pollution loading and river discharge are included in Walk er and Rabalais (2006), where Mississippi River nitrogen/phosphorus pollution loads were empirically rela ted to coastal chlorophyll

concentration in the northern Gu lf of Mexico. Nababan et al. ( In press ) also reported river discharge was the dominant factor controlling coas tal chlorophyll concentration in Mobile Bay, Alabama, and the Florida Panhandle.

Unlike estuaries, which are commonly dominate d by freshwater flows and can be organized within watershed boundaries, coastal waters have open offshore boundaries and localized influences near the estuary/coas tal boundary (i.e., estuary pass). Numeric criteria for Florida’s

coastal waters could be derived on a regional scale (i.e. Northwest Gulf Coast, West Gulf Coast, and Atlantic Coast) or at a finer scale. In th e latter case, EPA recogni zes that FDEP’s Water Body Identification Numbers (WBIDs) for coastal waters start at land fa ll and extend seaward

for three nautical miles. Coastal WBIDs are also typically centered at an estuary pass. Thus, EPA

will consider the coastal WBID scheme for the basis of organizing defined coastal segments.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 79 Figure 4

‐ 2. Map of coastal segments used in satellite remote sensing analysis. The remote sensing data shown in this figure

are a composite result from July 1

‐ August 31, 1999. A remote sensing approach is proposed to develop numeric chlorophyll

criteria for the Northwest Gulf Coast, West Gulf Coast, and Atlantic Coastal Area based on establishment of a reference

condition using remotely sensed chlorophyll a observations (Chl RS ‐ a) as the response variable. EPA’s approach for developing

numeric criteria for South Florida and the coastline from Apalachicola Bay to Suwannee River is described in Chapter 3.



### 4.1.1 Use of Remote Sensing Data

EPA is evaluating the use of the following satellites to provide measurements of Chl RS 

### -a:

• Sea-viewing Wide Field-of -view Sensor (SeaWiFS)

• Moderate Resolution Imaging Spectroradiometer (MODIS)

SeaWiFS provides a historical time-series of Chl RS 

### -a back to 1997, whereas MODIS data began in 2002. SeaWiFS and MODIS can both provide a continuous temporal measure of Chl RS 

### -a in the Florida coastal waters. Figure 4-3 provides the number of data points that occur within all of

Florida’s coastal waters from satellite and trad itional field samples. Traditional field samples account for 1,648 chlorophyll observations from 1998 to 2010. In contrast, satellites measuredMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 80 25,549 observations over the same period. When summa rized to 8-day averag es, a single satellite will typically provide a composite Chl RS 

### -a observation for 40 of the 91 coastal segments each week. Table 4-1 presents data from IWR Run 40 over a 20-year period from 1990 to present for

three coastal water bodies.

Figure 4

‐ 3. (A) Satellite Chl RS ‐ a coverage of the 91 coastal segments between 1998 and 2010. (B) Number of chlorophyll field

observations that occur within all Florida coastal waters

Table 4

‐ 1. Number of observations in IWR Run 40 since 1990 for chlorophyll, TN and TP for coastal waterbodies

Waterbody Years Chl-a,
uncorrected Chl-a TN TP

Northwest Gulf Coast 1990–2010 311 241 180 156 West Gulf Coast 1990–2009 865 498 1,862 1,932

Atlantic Coastal Areas 1990–2008 51 16 50 73



### The technical advances in satell ite imaging and processing has resu lted in numerous applications of remote sensing products to address needs for environmental resource management. The following is a brief overview of successful applic ations of remote sensing toward management decision support.

The NOAA Harmful Algal Bloom Operation Forecas t System (HAB-OFS) uses remote sensing as an advanced warning system of harmful al gal blooms. This system provides information on the HAB location and the potential for bloom deve lopment along the coastal waters of Florida. NOAA Fisheries Service uses remote sensing to characterize sea surface temperature around the Monterey Bay National Marine Sanctuary a nd delineate the marine reserve boundaries. NOAA Fisheries Service is also utilizing remotely sens ed sea surface temperature, sea surface height, and chlorophyll estimates to monitor the recruitm ent, survival, and productivity of managed fish species, and those protected by the Endangered Species and Marine Ma mmal Protection Acts. A)

B)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 81 NOAA is currently using remotely sensed sea su rface temperature for beach closure applications within Chesapeake Bay. Two specific projects in Chesapeake Bay are (1) forecasting nuisance jellyfish using remotely sensed sea surface temperature and hydrographic models and (2) mapping the occurrence of bacter ial pathogens in the genus Vibrio . Satellite applications pertaining to Vibrio also extend to the management of oyste rs in the Gulf of Mexico, where sea surface temperature and Vibrio levels are used to inform fisher ies managers whether oysters are

harvestable or if shellfis h bed closures are required.

The SPOT 5 satellite (French Space Agency) provides data that are subsequently classified into geospatial imagery of land cover. Maryland’s De partment of Natural Resources uses SPOT data to denote tidal from non-tidal wetlands. Legisl ation in Maryland requires that wetlands be classified, and the State currently uses maps so lely generated with SPOT, which are supplied to all coastal managers. South Carolina’s Offi ce of Ocean and Coastal Resource Management utilizes LIDAR data to determine Beachfront juri sdictional lines, which have proven to be highly accurate and adhere to laws requiring th e mapping of beach setback in the State.

The National Aeronautics and Space Administ ration (NASA) provided remote sensing observations during the Deepwater Ho rizon BP oil spill in the Gulf of Mexico at the request of U.S. disaster response agencies. Remote se nsing was used by NOAA, the U.S. Geological Survey, and the Department of Homeland Security for monitoring the spill. Finally, EPA used remote sensing to support the Spill Preventi on Control and Countermeasures (SPCC), the Comprehensive Environmental Response, Compen sation, and Liability Act (CERCLA), and the

Resource Conservation and Recove ry Act (RCRA) (USEPA 2007).



### 4.2 Deriving Numeric Criteria

EPA is evaluating the use of re mote sensing to provide Chl RS 

### -a distributions for the Northwest Gulf Coast, West Gulf Coast, and Atlantic Coastal Area (Figure 4-2). These coastal waters extend along the Northwest Gulf Coast from the Alabama border to St. Joseph Bay; the West Gulf Coast from Anclote to Rookery Bay; and al ong the Atlantic Coast from the Georgia border to Biscayne Bay. Currently, only WBID 8037 (Waccasa ssa River, north of the West Gulf Coast) has been identified as impaired for nutrient s and chlorophyll in 2005 according to FDEP’s CWA section 303(d) listing. Therefore, EPA is consider ing applying a referenc e condition approach to derive numeric chlorophyll criteria for Florid a’s coastal waters to measure the assessment endpoint of balanced phytoplan kton biomass and production in or der to translate Florida’s

narrative nutrient criteria into numeric values that support balanced natural populations of aquatic flora and fauna.

Reference condition approaches can take a vari ety of forms, defined by the source of the reference condition. EPA has previously recomme nded (e.g., USEPA 2001) that a percentile of water quality measurements in a sample of minimally-impacted waterbodies, which are presumed to be fully supporting designated uses (i.e., not impaired), coul d serve as criteria for water quality in similar waterbodies. In this case, a reference conditi on is derived from a reference population of waterbodies. As discussed elsewhere in this docu ment (Chapter 3), EPA considers a reference condition approach to be most applicable when (1) historical data adequately describe water quality conditions when the waterbody was minimally-impacted by nitrogen/phosphorus pollution and was supporting balanced natural populations of aquatic floraMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 82 and fauna (i.e., historical reference condition) or(2) when the waterbody is currently minimallyimpacted by nitrogen/phosphorus pollution a nd currently supporting balanced natural populations of aquatic flora and fauna (i.e., curr ent-conditions reference condition). EPA has not yet identified any impaired coastal waters for wh ich historical water quality data could provide a suitable reference condition. Therefore, EPA is largely considering using a reference condition approach only in the second case, namely when current water quality conditions are supporting balanced natural populations of aquatic flora a nd fauna. Further discussion of the reference condition approach applied to coastal waters in Florida is discussed in Section 4.2.3.

4.2.1 Satellite Chlorophyll Data Evaluation

Satellite ocean color data were obtained from NASA Ocean Color Web (Feldman and McClain 2010). SeaWiFS and MODIS satelli tes provided daily images with 1 km binned spatial resolution when not masked with quality contro l flags. SeaWiFS and MODIS imagery spatially

covered between 31.0 to 23.0 o 

### N and 88.0 to 79.0 o 

### W. SeaWiFS Data Analysis System (SeaDAS) version 6.1 (Baith et al. 2001) was used to pr ocess SeaWiFS and MODIS data from level-1 to level-3 composites and derive Chl RS 

### -a concentrations (NASA n.d.). Results within 1 km of the

shoreline were not included in the averaging of Chl RS 

### -a by using quality co ntrol flags in the SeaDAS software, and by averaging bins that only fell completely within the coastal segment perimeter line.

Satellite validation of the derived chlorophyll product against field ch lorophyll measurements was performed using the native reso lution of the sensor. Satellite match-ups were evaluated with a Type II geometric mean linear regression (Laws and Archie 1981) between a 3x3 pixel

extraction of satellite data centered at the co rresponding in-situ measur ement location. Any 3x 3 pixel extractions that had greater than 4 pixels flagged were exclude d. Satellite data were filtered for SeaDAS default flags (i.e., cloud contamin ation, land, and atmospheric correction failure) and any data with viewin g angles greater than 60 o 

### and solar angles greater than 75 o 

### . In-situ data were filtered so only samples collected within ±3 hours of the satellite overpass were utilized (Bailey and Werdell 2006). Field chlorophyll data used for satellite validation were from the following sources:

• Northeastern Gulf of Mexico (NEGOM) project (NOAA National Oceanographic Data Center)

• Ecology and Oceanography of Harmful Algal Blooms project (ECOHAB)

• Fish and Wildlife Research Institute (FWRI)

• Mote Marine Laboratory

• SeaWiFS Bio-optical Archive and Storage Sy stem (SeaBASS) (Werdell and Bailey 2002; Werdell et al. 2003).

EPA is considering excluding from the statistical distribution of affected coastal segments any

Chl RS 

### -a measurements taken during Karenia brevis bloom events. While Vargo (2009a) has

concluded that “there is no single hypothesis that can accoun t for blooms,” removing data collected during bloom events woul d ensure that the data points used to derive the statistical

distribution of reference condi tions would represent conditions that are supporting balanced natural populations of aquatic flora and fauna (S ee Appendix B for a more complete review of K.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 83 brevis as an assessment endpoint). K. brevis cell abundance observations from the Florida Fish

and Wildlife Conservation Commi ssion FWRI in St. Petersburg can be used to remove the chlorophyll signature that resulted fr om the onset of K. brevis blooms (Figure 4-4). Satellites

detect K. brevis blooms when cell counts are above 50,000 cells/L (Heil and Steidinger 2009). Coastal segments with an FWRI count greate r than 50,000 cells/L duri ng an 8-day composite would be flagged and not averaged into the Chl RS 

### -a distributions. In addition, the same WBID will be flagged one week prior and after a bloom detection to provide a temporal buffer as blooms are transported along the coast.

Figure 4

‐ 4. Example of (A) K. brevis bloom occurrence at the Tampa Bay coastal segment. Black points are included in the

Chl RS ‐ a distribution. Red points indicate Chl RS ‐ a values that would be excluded from the distribution because a K.brevis count

of 50,000 cells/L or higher was measured. (B) Cumulative distribution of Chl RS ‐ a at the Tampa Bay coastal segment with and

without K. brevis

.



### 4.2.2 Comparison of Satellite Chlorophyll to Field Collected Chlorophyll

There are limited field-collected water quality indicator variable data from throughout the coastal waters of Florida. However, the availa ble field data that coincide with Chl RS 

### -a will be assessed and considered in EPA’s approach. The Chl RS 

### -a data was paired with 62 field observations

within the three nautic al mile boundary to prov ide a calibration of Chl RS 

### -a data (Figure 4-5). There was a significant positive correlation between field observations and SeaWiFS Chl RS 

### -a (slope = 0.85, R 2 

### = 0.52, p < 0.0001). Therefore, the slope can be applied to the Chl RS 

### -a as a correction factor. (A)

(B)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 84 Figure 4

‐ 5. Regression of SeaWiFS ChlaRS

‐ a versus field observations from NEGOM, ECOHAB, FWRI, Mote, and SeaBASS

which occur within the 3nm coastal boundary (grey dashed line is 1:1 fit and black line is regression slope)



### This section compares Chl RS 

### -a for five coastal segments a nd two monitoring sites near Tampa Bay (Figure 4-6). WBID 8049 is located at th e Tampa Bay estuary pass, WBIDs 8047 and 8048 are located further north, and WBIDs 8050 and 8051 are loca ted further south. The two monitoring sites with uncorrected chlorophyll a field data are located in WBID 8049. Figure 4-7A displays the time series of Chl RS 

### -a from each of the five WB IDs with two general patterns emerging: (1) Chl RS 

### -a demonstrates seasonal variability wi th higher concentrations in the late summer and early fall, and (2) Chl RS 

### -a concentrations are generally higher in the coastal segment corresponding to WBID 8049 (located at the Ta mpa Bay estuary pass) relative to WBIDs 8047, 8048, 8050, and 8051. Figure 4-7B displays the two monitoring sites’ uncorrected chlorophyll a results in WBID 8049 from 2000-2006. In general, th e chlorophyll data from the two sites shown in Figure 4-7B trend similar to that of the Chl RS 

### -a concentrations in Figure 4-7A.

The similarity of the chlorophyll results from re mote sensing and field co llected data are better represented by displaying the time series data with cumulative sum (CUSUM) charts. As used herein, cumulative sum charts display the cumula tive sum of deviations between the result (i.e., chlorophyll result) and its overall mean. The data are transformed by subtracting the mean and dividing by the standard deviati on (i.e., computing z-scores) re ferred to as Z-CUSUM charts. Figure 4-8A is a Z-CUSUM chart for the Chl RS 

### -a from each of the five coastal segments shown in Figure 4-7A. Figure 4-8B is a Z-CUSU M chart of FDEP uncorrected chlorophyll a results from two sampling sites located in WBID 8049. In both Figure 4-8A and Figure 4-8B, there is an

increase in the Z-CUSUM in 2005 representing greater than av erage chlorophyll levels. The ZCUSUM values in the year following 1998 and 2005 (i.e., 1999 and 2006) are approximately the same followed by a decreasing patte rn. This indicates that the ch lorophyll in the year following 1998 and 2005 is near average, and the chloroph yll in the second year following 1998 and 2005 (i.e., 2000 and 2007) is below average. These re sults provide additional evidence that Chl RS 

### -a and field collected data provide similar results.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 85 Figure 4

‐ 6. Tampa Bay map displaying the location of five coastal WBIDs and two monitoring sites near Tampa Bay. WBID

8049 is located at the Tampa Bay estuary pass, WBIDs 8047 and 8048 are to the north, and WBIDs 8050 and 8051 are to the

south. The two monitoring sites with uncorrected Chl

‐ a field data are labeled as 21FLHILL094 and 21FLHILL093.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 86Chl-RS (μg/L)

1-10 1-09 1-08 1-07 1-06 1-05 1-04 1-03 1-02 1-01 1-00 1-99 1-98 12

10

8

6

4

2

0 2.34 8047

8048

8049

8050

8051 WBID 

## A)Uncorrected Chl-a (μg/L)

1-10 1-09 1-08 1-07 1-06 1-05 1-04 1-03 1-02 1-01 1-00 1-99 1-98 35

30

25

20

15

10

5

0 21FLHILL093

21FLHILL094 STATION_ID 

## B)

Figure 4

‐ 7. (A) Chl RS ‐ a (μg/L) for five coastal segments near Tampa Bay. The overall mean Chl RS ‐ a for the coastal segment corresponding to WBID 8049 is 2.34 μg/L. (B) IWR40 field Chl

‐ a (uncorrected) from two stations in WBID 8049 (note that

there are no field data between 1

‐ 01 and 1

‐ 02)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 87 4.2.3 Chl RS 

### ­ a Criteria from Statistical Distributions

EPA is considering the use of the field adjusted Chl RS 

### -a to establish chlorophyll distributions (Figure 4-9) for each coastal segment within the Northwest Gulf Coast, West Gulf Coast, and Atlantic Coast regions. EPA is cons idering removing influences from K. brevis blooms from the field adjusted Chl RS 

### -a distribution then de termining the numeric criteria value from an appropriate percentile of the dist ribution. EPA is considering two approaches for deriving criteria using a reference condition approach from a statis tical distribution. One approach is to estimate the annual geometric mean concentration and the s econd approach is a binomial test to maintain

current conditions.

4.2.3.1 Chl RS 

### ­ a Annual Geometric Mean Concentration

The geometric mean is preferred to the arithme tic mean because the geometric mean is a better estimator of the central tendency when the data are log-normally distributed as commonly the case for chlorophyll. One approach to compute the annual geometric mean from a data set is to transform the concentrations to a natural loga rithm, and then comput e the mean by coastal segment and year. The 75 th 

### percentile annual geometric mean would be computed as



## ܯܩ ଻ହ 

## ൌ ݁ ሺ௫ ҧ ା଴.଺଻ସହ௦ሻ



### where 0.6745 is the inverse of the standard normal cumulative distri bution with a probability of 0.75 and s is the correspondi ng standard deviation.

It has been recommended that criteria frequency ranges from one in three years to two in five years would protect designated uses. The GM 75 

### would be exceeded, on average, 25 percent of the time without any water quality changes. If the annual geometric mean exceeded the GM 75 

### in two of three years, there is 84 percent confidence the annual geom etric mean of the test data

exceeded the GM 75 

### . If the annual geometric mean exceeded the GM 75 

### in three of five years, there is 90 percent confidence the annual geomet ric mean of the test data exceeded GM 75



### .Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 88Z-CUSUM

1-10 1-09 1-08 1-07 1-06 1-05 1-04 1-03 1-02 1-01 1-00 1-99 1-98 40

30

20

10

0

-10

-20

-30 8047

8048

8049

8050

8051 WBID 

## A)Z-CUSUM

1-10 1-09 1-08 1-07 1-06 1-05 1-04 1-03 1-02 1-01 1-00 1-99 1-98 0

-2

-4

-6

-8

-10

-12

-14 21FLHILL093

21FLHILL094 Station 

## B)

Figure 4

‐ 8. Z

‐ CUSUM plot for (A) Chl RS ‐ a (μg/L) for five coastal segments near Tampa Bay and (B) IWR40 field Chl

‐ a (uncorrected) from two stations in WBID 8049 (note that there are no field data between 1

‐ 01 and 1

‐ 02 [see Figure 4

‐ 7B].
Therefore, the straight line in 4

‐ 8B, between 1

‐ 01 and 1

‐ 02, is an artifact of data availability.)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 89 Figure 4

‐ 9. Field

‐ adjusted Chl RS ‐ a (μg/L) distributions for all coastal segments between 1997 and 2009 with representations of

the minimum, 10 th

, 25 th

, 50 th

, 75 th

, 90 th and maximum values. Northwest Gulf Coast segment 1 is located at the Alabama/Florida border and segment 17 is at St. Joseph Bay. West Gulf Coast coastal segment 18 is located at Anclote Bay

and segment 38 is at Rookery Bay. Atlantic Coast segment 39 is located at northern Biscayne Bay and segment 77 is at the Florida/Georgia border.



### 4.2.3.2 Chl RS 

### ­ a Binomial Test

Another method that EPA is considering is to sp ecify numeric criteria ap plicable to individual

observations rather than annual statistics. For example, short duration increases in chlorophyll are expected, but these events might not be represented in annual means.

EPA is considering a binomial threshold to set C 0.50 

### and C 0.25 

### to the maximum 50 th 

### and 75 th


### percentile concentration for any 3-year period from the supporting data set. This approach (1) provides a quantitative eval uation of the probability that the data collected during the assessment period are from the same or nearly th e same distribution as the observations during the baseline period; (2) is resist ant to bias by high values; (3 ) does not require or imply an assumption of normality or log normality; (4) is minimally affected by censored data; andMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 90 (5) tests for changes in both the central te ndency and the upper end of the concentration

distribution.



### 4.3 Uncertainties and Data Gaps

Werdell et al. (2009) showed SeaWiFS and MODI S could successfully be used to evaluate chlorophyll in coastal en vironments such as the Chesapeake Bay. CDOM and bottom reflectance are typically cited as possible interferences with satellite m easurements in coastal waters. However, bottom reflectance can be minimized by incorporating the stra y light contamination flag. This flag excludes any near shore bins wher e reflected light from land enters the satellites field of view. Seagrass may provide interfer ences with satellite derived chlorophyll though seagrasses are not present in coastal waters of the Northwest Gulf Coast, Atlantic Coast, and

most of the West Gulf Coast. The northern West Gulf Coast between Cedar Key and Anclote Bay and southwest Florida between Gullivan Bay and Florida Bay have seagrass coverages that occur in coastal segments (Carlson and Madley 2 006). However, satellite imagery is not used for

coastal criteria in these s eagrass coverage locations.

Satellites do not provide dire ct measurements of chlorophyll as do bottle samples, which are commonly considered acceptable across many labor atories and agencies. However, the data presented in Section 4.2.2 provide strong evid ence that remotely sensed chlorophyll is empirically related to field observations and thus exhibits similar trends and patterns as field

observations. Florida's coastal waters have not been monitored comprehensively, and regular monitoring of coastal areas is challenging. As a consequence, th ere are limited data based on field-sampling to support numeric criteria develo pment for all of the St ate's coastal waters.

To improve on the proposed approach, continuous monitoring by satellite w ould be necessary to derive more refined criteria. A potential concer n is that all satellite missions have a finite duration and may not be always avai lable if the State chooses to us e satellite data to assess their coastal waters. EPA recognizes next generation sate llites and algorithms will be developed in the future, and criteria values will need to be revi ewed and adjusted with improving technologies. In addition to SeaWiFS, MODIS and the Europ ean Space Agency’s (ESA) Medium Resolution Imaging Spectrometer (MERIS) can be used to ge nerate results similar to SeaWiFS. Another satellite that proposes sim ilar capacities to measure Chl RS -a 

### is the Visible Infrared ImagingRadiometer Suite (VIIRS), which will be one of five instruments to fly on the National Polarorbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) spacecraft. NPP is scheduled for launch in Fa ll 2011. VIIRS is the successor to MODIS and SeaWiFS for Earth science data product generation. There are multiple missions planned by space agencies and overlapping of sensors that wi ll have continuity of measurements for longterm assessment using satellite data.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 91 

## 5 Numeric Nutrient Criteria Development in South Florida Marine and

Inland Flowing Waters

EPA is considering several methodological appr oaches to derive numeric criteria for nitrogen/phosphorus pollution in South Florid a marine and inland flowing waters. For the purpose of this document, South Florida inland flowing waters have been defined as freeflowing, predominantly fresh surf ace water in a defined channel, and include, streams, rivers, creeks, branches, canals, freshwater sloughs, and other similar water bodies located in the South Florida nutrient watershed regi on. South Florida marine waters include estuarine and coastal waters extending three nautical miles offshore. Co llectively, these waters have been classified by the State of Florida as Class I, II, or III waters as defined in Chapter 2. By and large, most of the inland flowing waters in South Florida consid ered by this rule making are Class III with relatively few Class I waters, while Class II a nd III waters make up South Florida’s marine

waters. The same narrative nutrient criterion a pplies to all of these waters: “in no case shall nutrient concentrations of a body of water be altered so as to cause an imbalance in natural populations of aquatic flora and fauna.” EPA is deriving numeric criteria to interpret this narrative statement to protect these waters fr om nitrogen/phosphorus pollution. EPA is not deriving criteria that would appl y to waters located on the Semi nole Indian Reservation or the Miccosukee Indian Reservation; waters located in stormwater treatment areas (STAs), wetlands, or marshes; and Class IV canals.

EPA is considering a reference condition approach to derive numeric TN and TP criteria for South Florida inland flowing wa ters and numeric chlorophyll a , TN and TP criteria in South Florida marine waters using least-disturbed s ites that support balanced natural populations of aquatic flora and fauna. Alternative methods of cr iteria derivation for inland flowing waters that EPA is considering include stressor-re sponse relationships between chlorophyll a and TN and TP, and a distributional approach using all sites. EPA is not establishing new TP criteria for canals in the Everglades Protection Area (EvPA) in deference to the Everglades Forever Act (EFA) as discussed more in Section 5.2.1. EPA is also considering derivation of TN and TP downstream protective values (DPVs) that are prot ective of South Florida’s marine waters at the terminal reach of each tributary to th e estuary or coast (see Chapter 6).



### 5.1 Analysis Plan for South Florida

EPA is deriving numeric criteria for nitrogen/ phosphorus pollution in South Florida marine and inland flowing waters. The approach that EPA is considering is di splayed in Figure 5-1.Met h

Figure 5

‐ 1. F



### 5.1.1 I n

Section 5

inland fl o subregio n consider i develop a derivatio

that sho u

Finally f o (IPVs) f o 5.4). EP A

approac h h ods and Ap p

F low chart for t h



### n land Flo w

5 .2 describe s

o wing wate r

n s to accou n i ng use of t h

a n inventor y n of numeri c

u ld exclude i

o r inland fl o

o r TN and T P

A is conside r h similar to t h p roaches for D

h e developmen



### w ing Water s

s the first st e

r s into five s u

n t for variab i

h e National H

y of inland f l

c criteria (S e nland water s

o wing water s

P with the r e

r ing identif y h e stream c o D eriving Num e Estua r

t of numeric cri



### s

e p of the an a

u bregions. E

i lity and div e
H ydrograph y l owing wate r

e ction 5.3). T

s not covere

s , EPA is co n

e ference co n

y ing least-di s

o rridor Lan d e ric Criteria f o

r ies, Coastal W

teria for South F



### a lysis that E P

E PA is consi

e rsity of the s

y Dataset ( N

r s that are s u
T his is nece s d by this ru l

n sidering d e

n dition appr o

s turbed sites

d scape Deve l o r Nitrogen/ P

W aters, and S

F lorida marine a



### P A is consi d dering divi d

s e flowing w N HDPlus), la

u bject to thi s

s sary to faci l

l emaking su c

e rivation of i

o ach using l e by integrat i

l opment Int e P hosphorus P
S outhern Inla

a nd inland flow i



### d ering to fur t
d ing South F

w aters. Next

,

nd use, and

s rulemakin g l itate subse q

c h as Class I

nstream pro t

e ast-disturb e i ng land use

e nsity Index ollution in Fl o nd Flowing W

i ng waters



### t her classify

lorida into

, EPA is

IWR data t o

g to assist in

q uent analys e
I V canals.

t ective valu e

e d sites (Sec t

data in an (LDI) deve l o rida’s

W aters



### 92 the

o the

e s

e s

t ion

l opedMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 93 by FDEP. In addition to the above analysis, tw o other analyses are being considered for developing IPVs: (1) distributiona l approach of all sites, and (2) stressor-response approach (Sections 5.4.1.1.3 and 5.4.1.2, respectiv ely). Unlike the analysis wh ich targets least-disturbed sites, the distributional approach of all sites w ould rely on data from all sites. The stressorresponse approach would evaluate the relations hips between response variables (e.g., chlorophyll

a ) and TN or TP.

5.1.2 Marine Waters

In South Florida, with its highly managed inla nd flows and open-water do minated systems (i.e., Florida Bay and Keys), EPA found that existing information about watershed drainages can be supplemented with data from the Southeast E nvironmental Research Center (SERC) water quality monitoring network to further classify marine waters. EPA considers a multivariate statistical approach to identify 30 individual waterbodies throughout the State (Section 5.5).

EPA is considering two methods for using a refe rence condition approach for deriving numeric criteria (Section 5.6). The refe rence condition approach identif ies concentrations that are inherently protective of the waterbody because thos e concentrations are associated with currently demonstrated healthy balanced populations of aquatic flora and fauna. One approach is to

estimate criteria based on an inclusive distri bution of nitrogen and phos phorus values that are temporally and/or spatially averag ed and are obtained from least- disturbed reference sites from a prescribed region. In this situa tion, EPA (2001) recommends the sel ection of a percentile of this

distribution based on the confidence in identif ying least-disturbed reference sites.

EPA is also considering an a pproach based on the statistical distribution of raw data and evaluated using a binomial test. In this approac h, EPA would derive two cr iteria: (1) an average (median) concentration and (2) an upper per centile concentration. By considering an upper percentile, this second approach would be sensit ive to detecting changes in the distribution of higher concentrations that may be “aver aged-out” in annual geometric means.



### 5.2 Inland Flowing Waters: Classification

In determining the geographic scope for South Florida inland flowing waters, EPA initially focused on canals located in the Everglades ecore gion (see areas south of blue line in Figure 5-2). Because soil or substrate type at the botto m of a canal can influence the nutrient cycling and relationships between the observed biological response and nutrient levels in canals, EPA used data on soil types in South Florida, al ong with existing subdivisions (primarily for the Everglades Agricultural Area (EAA) and EvPA) already used for managing area resources to develop subregions. The East subregion was composed of the Southeast Coast—Biscayne monitoring basin and most of the LakeMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 94 Figure 5

‐ 2. South Florida Nutrient Watershed RegionMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 95 Worth Lagoon—Palm Beach Coast monitoring basin; and generally included spodosol, entisol, and alfisol soils (see Figure 5-3). The East subregion was bounded on the west by the EvPA which includes the Everglades National Park as well as additional wetlands to the north. The EvPA is largely composed of entis ol soils in the south and histosol soils in the north. The EAA is

located south of Lake Okeechobee and include s the northern portion of the Everglades monitoring basin not included in the EvPA. Histosol soils make up much of the EAA. The West subregion included much of Collier County and Big Cypress National Preserve, where spodosol and alfisol soils predominate.

Along the border between the Peninsula 11 

### and South Florida nutrient watershed regions, EPA performed spatial analyses to determine the boun dary to be consistent with the monitoring basins, with exception of the area near Estero Bay on the west coast as depicted by the green line shown in Figure 5-2. EPA is also considering su b-classifying the Biscayne drainage area and analyzing it separately from the rest of the East subregion. Sp ecifically, EPA is considering dividing the East subregion just above Canal C-9 as shown in Fi gure 5-4, thus allowing EPA to analyze all canals draining to the Biscayne Ba y waters as one group where subregion-specific analyses are appropriate. Figure 5-5 presents a simplified map of the subregions that EPA is considering.

While the EPA will continue to consider othe r issues (e.g., geologic formations, groundwater interactions) for developing subregions, the EPA w ill initially consider, where subregion-specific analyses are appropriate, the follo wing five subregions for deve loping numeric criteria for South Florida inland flowing waters:

• EAA: Everglades Agricultural Area

• EvPA: Everglades Protection Area

• West Subregion: Areas west of the EAA and EvPA

• Biscayne Drainage Subregion: Areas east of the EvPA and generally representing waters flowing toward Biscayne Bay

• Palm Beach Subregion: Areas east of the EAA a nd EvPA and north of the Biscayne Drainage Subregion

11 EPA established numeric criteria for nitrogen/phosphorus pollution for flowing waters in the peninsula as well as the rest of northern Florida in a November 2010 rulemaking.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 96 Figure 5

‐ 3. Location of EAA and EvPA relative to STATSGO soil orders for South Florida

Figure 5

‐ 4. Division between Biscayne Drainage and Palm Beach subregionsMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 97 Figure 5

‐ 5. South Florida Nutrient Region subregions under consideration



### 5.2.1 Existing TP Criterion for the EvPA

In 1994, the EFA (Florida Statutes, Ch. 373.4592) es tablished that “(i)n no case shall such phosphorus criterion allow waters in the Everglades Protection Area to be altered so as to cause an imbalance in the natural populations of aquatic fl ora or fauna.” To translate this narrative, the EFA set a TP criterion of 10 part s per billion (ppb) in the EvPA. In 2005, EPA approved FDEP’s implementation of the EFA TP criterion. This “numer ical interpretation of the Class III narrative nutrient criterion for phosphorus” is a long-term geometric mean of TP equal to 10 ppb (or 0.010 mg/L) and applies throughout th e EvPA, including Water Conser vation Areas (WCAs) 2A, 2B, 3A, and 3B, as well as Everglades National Pa rk and the Arthur R. Marshall Loxahatchee National Wildlife Refuge (WCA 1) (F.A.C. 62- 302.540(1)) (see Figure 5-2) . In a 2010 ruling in

the case of Miccosukee Tr ibe of Indians of Florida vs. Un ited States (Case 1:04-cv-21448-ASG Document 404), the U.S. District court affirmed the need to support the 10 ppb TP criterion established in the Everglades Protection Area.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 98 EPA recognizes the aforementioned 10 ppb TP criteri on as applicable to th e Class III waters in the EvPA, including canals (as defined at F. A.C. 62-302.400 and F.A.C. 62-303.200(21)), and is not establishing additional numeric TP criteria for this subregion.



### 5.3 Inland Flowing Waters: Inventory

As mentioned above, inland flowing waters in So uth Florida include free-flowing, predominantly

fresh surface water in a defined channel, and include streams, ri vers, creeks, branches, canals, freshwater sloughs, and other similar wate r bodies. Pursuant to Rule 62–302.200, F.A.C.,

“

predominantly fresh waters shall mean surface wa ters in which the chloride concentration at the surface is less than 1,500 milligrams per liter. ” Also, EPA is not deriving criteria that would apply to waters located on the Seminole In dian Reservation or the Miccosukee Indian Reservation (see Figure 5-2); wate rs located in stormwater treatme nt areas (STAs), wetlands, or marshes; and Class IV canals. 12



### Currently, there does not a ppear to be an inventory of South Florida inland flowing waters that clearly delineates the areas desc ribed above. From the Florida Coastal Everglades Long Term Ecological Research (FCE LTER) network web site, a GIS coverage of major canals is available.

Yet there are numerous smaller canals in urbanized areas that may fit the definition of Class I or II designated inland flowing waters, but are not in this coverage. NHDPlus is available from the U.S. Geological Survey. The NHDPlus provides exte nsive GIS data on canals and other surface water systems; however, these data do not distin guish Class IV canals from other waters. FDEP provided EPA a coverage of canals identified as Class IV canals. While initial review of the Class IV canals suggests that th is coverage matches well with NHDPlus, it appears limited to portions of the EAA and areas to the west of th e EAA and thus may not be comprehensive. EPA also has access to the detailed land use data maintained by FDEP and individual Water Management Districts.

EPA is considering integrating these data to deve lop an inventory of inland flowing waters for the derivation of numeric criteria. The purpose of th is inventory is to serve as a tool for screening water quality monitoring and other data from Sout h Florida flowing waters that are designated as

Class I and III from data collected from t hose waters that are designated as Class IV (Agricultural Water Supplies). As mentioned above, EPA is not es tablishing numeric criteria for flowing waters designated as Class IV. An inve ntory of canals will also provide technical and management staff with an improved understandi ng of the complexity associated with canal management. Distinguishing natural streams from canals might allow EPA to assess whether the

numeric criteria for canals should be set differently from natural streams.

12 The definition of Class IV is provided in Rule 62-302 .400: All secondary and tertiary canals wholly within agricultural areas are classified as Class IV and are not individually listed as ex ceptions to Class III. “Secondary and tertiary canals” shall mean any wholly artificial canal or d itch which is behind a control structure and which is part of a water control system that is connected to the works (set forth in Section 373.086, F.S.) of a water management district created under Section 373.069, F.S., and that is permitted by such water management district pursuant to Section 373.103, 373.413, or 373.416, F.S. Agricultural areas shall generally include lands actively used solely for the production of food and fiber which are zoned for agricultural use where county zoning is in effect. Agricultural areas exclude lands which are platted and subdivided or in a transition phase to residential use.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 99 To develop the inventory, NHDPlus flow line co verage (see Figure 5-6) would be intersected with the land use data. In general terms, NHDPlus flow line segmen ts with adjoining nonagricultural land uses would be cl assified as Class III. Canals that coincide with the LTER coverage of major canals would be designated as major canals. Segments classified as canal/ditch, artificial path, or connector with only adjoining agricultural land uses would be classified as potential Class IV canal s. EPA considers this second class as potential Class IV since there are additional requireme nts that must be met to qualify as a Class IV canal such as being behind a control structure. Water quality data from these potential Class IV segments

would not be included in the derivation of the numeric criteria. The proc ess of identifying Class IV segments using the above approach would be compared to the independently provided Class IV canal data coverage. Note that in Figure 5-6, the pink line s shown south of Lake Okeechobee are from the GIS coverage of Class IV canals provided by FDEP. Most of these “pink lines” coincide with NHDPlus flowlines identified as canal/ditch. This comparison is more clearly portrayed in Figure 5-7, which is a smaller scale map that focuses on the area near STAs 5 and 6

and the Seminole Indian Reservation. In the upper portion of Figure 5-7, the (pink) Class IV canals coincide (i.e., lie on top of) with th e (green) NHDPlus canals. EPA’s approach for identifying potential Class IV canals from NHDP lus would include the ca nals where the (pink)

Class IV canals coincide with the NHDPlus canals.

The NHDPlus flow line coverage would also be used to identify natural streams. Based on visual inspection of the NHDPlus flow line coverage, most of the segments classified as streams are near the coastline, which indicates that they might not be predominately fresh water.

As mentioned before, predominantly fresh waters have been defined in this effort as having chloride concentrations less than 1,500 mg/L chloride at the surface, pursuant to Rule 62– 302.200, F.A.C. Thus, waters greater than 1,500 mg/L chloride at the surface will be classified as estuarine or marine waters and subject to an alyses considered elsewhere in this methods document. Based on a preliminary review of data fr om FDEP’s IWR, chloride data are sparse in South Florida. Thus, EPA is also considering the use of the relationship between specific conductivity and salinity to iden tify predominately fresh water. Pending these analyses, natural streams such as Cocohatchee River, Gordon Ri ver, Turner River, and Henderson Creek near Naples, Florida and Halfway Creek in the Ten Thousand Island area might be included in the analyses considered in this chap ter if they are predominantly fres h water. If they are determined to be predominantly marine, such waters will analy zed as part of the estu arine criteria derivation effort described in Chapter 3. For canals, additional information such as control structure locations may be used to confirm these analyses . Additional consideratio n may be necessary to identify sloughs that ar e in defined channels.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 100 Figure 5

‐ 6. NHD flowline data in South Florida. Note that the pink lines shown south of Lake Okeechobee are from the GIS

coverage of Class IV canals provided by FDEP. Most of these “pink lines” coincide with NHDPlus flowlines identified as canal/ditch.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 101 Figure 5

‐ 7. NHD flowline data near Stormwater Treatment Areas 5 and 6 and Seminole Indian ReservationMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 102 

### 5.4 Inland Flowing Waters: Data Sources and Assessment Endpoints

EPA has considered a range of approaches fo r deriving numeric criteria for inland flowing

waters in South Florida. One a pproach EPA is considering for de veloping instream protective values (IPVs) for TN and TP is the referen ce condition approach usi ng least-disturbed sites (Section 5.4.1.1). The applicable assessment endpoint for this a pproach is balanced faunal communities as described in Chapter 2.

Specifically for these waters, EPA is considering the use land use data of a multi-metric index of aquatic macroinvertebrates (str eam condition index or SCI) modi fied for use in these highly managed systems as a means to indicate balance in the natural populations of aquatic flora and fauna. Snyder et al. (1998), described a modi fied version of FDEP’s SCI bioassessment methodology 13 

### , adapted to characterize and monitor be nthic macroinvertebrates in Dade County freshwater canal systems. Dade County’s mon itoring program involved 36 canal sites, and focused on the sampling of potential reference site locations as well as “areas of known impairment.” Dade County canal bioassessment re sults, segregated by land use designations, indicated a gradient of biologi cal condition reflected in SCI scores ranging from poorest in urban/industrial, suburban, and agricultural canals, to good in canal s surrounded by wetlands (Figure 5-8).

Based on the findings in Snyder et al. (1998), EP A is considering a land use-based reference condition approach for deriving num eric criteria for canals and ot her inland flowing waters in

South Florida. EPA is consideri ng using quantitative land use analys es to identify least-disturbed sites (e.g., sites surrounded by wetlands), which in turn can serve as a reference condition population for deriving numeric criteria (see Section 5.4.1.1).

13 Metrics included in Dade County’s canal SCI were a subset of seven of the ten that make up FDEP’s SCI. All metrics except the percent filterers and EPT taxa metrics, wh ich Dade County found to be “very low” at all of their sampled sites. With a maximum possible score of 5 assigned to each metric, Dade County’s canal SCI spans a range of 0 to 35, in contrast with FDEP’s SCI which spans a range of 0 to 100.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 103 Figure 5

‐ 8. Box plots of SCI total index scores for a variety of land uses (source: Snyder et al. 1998)



### The second approach that EPA is considering is a stressor -response approach (Section 5.4.1.2). In this approach, empirical relationships between response vari ables (e.g., chlorophyll a ) and nutrients would be developed. EPA is also considering means to determine a protective chlorophyll a level for canal systems as EPA has found chlorophyll a to be an appropriate response variable for flowing waters and lakes to measure the assessment endpoint of balanced phytoplankton biomass and production. Yet, this may prove difficult since there is limited

evidence to assist in the identificat ion of a specific protective chlorophyll a threshold in canals.

The approach for identifying least-disturbed sites integrates land use data into its assessment. EPA is also considering using da ta from the following sources:

• IWR Run 40

• South Florida Water Management District (i.e ., DBHYDRO)

• Local agencies with water quality monitoring data o Broward County Department of Planning and Environmental Protection o Miami-Dade Department of Environmental Resources Management

EPA is considering applying both approaches usi ng the inventory of inland flowing waters and subregions described earlier in this chapter. Based on IWR Run 40 database, there are more than 50,000, 65,000, and 9,500 observations of TN, TP, and Chl-a from 1990 to 2010 for the South

Florida nutrient watershed re gion, respectively. The total num ber of observations actuallyMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 104 available for analysis of inland flowing waters may be more or less depending on the addition of data from other data sources not already in IW R and removing data from the analysis,such as

observations from stations lo cated on Class IV canals.

5.4.1 Deriving Numeric Criteria

5.4.1.1 Reference Condition Approach—Least

­ Disturbed Sites

As mentioned above, one approach that EPA is considering is the reference condition approach (USEPA 2000c). A reference condition for nutri ents can be determined using a variety of empirical approaches. They include correlative , stressor-response relationships between a biological response variable and TN or TP, or a statistical distribut ion of nutrient data. The latter reference condition approach involve s setting criteria based on an inclusive distribu tion of values (e.g., temporally and/or spatially averaged) obtaine d from least-disturbed reference sites from a prescribed region (based on clim ate and geology). In this case, EPA is considering using an upper percentile value of this distribution to represent a level of nitrogen and phosphorus concentrations that will inherently support a quatic life. This methodology will ensure that nutrient concentrations associated with biolog ically healthy, well-bala nced communities would be considered acceptable as protective of thos e communities. The percentile chosen will be dependent upon the level of confidence that EPA has that these sites are in fact, the leastdisturbed conditions. The referenc e condition approach is an inference model that is used to

derive numeric criteria based on conditions that are known to result in a balance of the natural populations of aquatic flora and faun a in a particular set of water bodies. Developing criteria from

a concentration distribution of leas t-disturbed reference condition site s, results in criteria that are inherently protective because those concentratio ns are associated with demonstrated healthy biological communities.

Since the type of soil or substrate can influe nce the relationships between observed biological response and nutrient levels in can als, EPA is considering whether to perform these analyses at a subregion level as well as for the entire nutrien t region. Also, EPA will continue to investigate other hydrogeological issues that may mer it further refinement of the subregions.

5.4.1.1.1 Selecting Least‐Disturbed Sites

EPA is considering using the LDI to identify a population of sites that are least-disturbed. The LDI may be used in conjunction with or separa tely from other screen ing criteria, such as applicable CWA section 303(d) impairment s to identify least-disturbed sites.

The LDI is a quantitative method to evaluate the intensity of human usage of landscapes, based on nonrenewable energy flow. The application of this method is based on the principle that the “intensity of human-dominated la nd uses in a landscape affects ecological processes of natural communities,” where more intense activities result in greater effects on ecological processes (Brown and Vivas 2005). The LDI is calculated as the area-weighted value of land uses within an “area of influence,” where each land use is assigned a land use coe fficient derived by Brown and Vivas (2005) (Table 5-1), multiplied by the percent total area occupied by each land useMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 105 (determined by GIS land use coverage develope d from high-resolution aerial photographs), and summed using Equation 5-1 (Brown and Vivas 2005)

LDI total 

### = 

# Σ %LU i 

### * LDI i 

### Equation 5-1

where

LDI total 

### = LDI ranking for landscape unit

%LU i 

### = percent of the total area of influence in land use i

LDI i 

### = landscape development intensity coeffi cient for land use i (see Table 5-1 for coefficients).

Table 5

‐ 1. Land use classifications and LDI coefficients. Higher values indicate greater intensity of human land use (adapted

from Brown and Vivas 2005).

Land use LDI coefficients

Natural system 1.00

Natural open water 1.00

Pine plantation 1.58

Recreational / open space

– low

‐ intensity 1.83

Woodland pasture (with livestock) 2.02
Improved pasture (without livestock) 2.77
Improved pasture

– low

‐ intensity (with livestock) 3.41

Citrus 3.68
Improved pasture

– high

‐ intensity (with livestock) 3.74

Row crops 4.54
Single family residential

– low

‐ density 6.9

Recreational / open space

– high

‐ intensity 6.92
Agriculture

– high intensity 7.00
Single family residential

– medium density 7.47
Single family residential

– high density 7.55

Mobile home (medium density) 7.70
Highway (
‐ lane) 7.81

Low

‐ intensity commercial 8.00

Institutional 8.07
Highway (4

‐ lane) 8.28

Mobile home (high density) 8.29

Industrial 8.32

Multi

‐ family residential (low rise) 8.66
High

‐ intensity commercial 9.18

Multi

‐ family residential (high rise) 9.19

Central business district (average 2 stories) 9.42

Central business district (average 4 stories) 10.00



### As discussed in FDEP’s Nutrient Plan (2009a), the LDI was specifically designed as a measure

of human disturbance. LDI values of less than or equal to 2.0 within the 100 m corridor area (see example Figure 5-9) are indicative of areas with less human impact. Other studies and evaluations have demonstrated, across other wa terbody types and taxonomic groups, that the LDIMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 106 is an accurate predictor of biol ogical health; that is, healthy, we ll-balanced biological systems are much more likely to occur at sites with low LDIs ( ≤ 2.0) than at higher disturbance levels (Brown and Reiss 2006; FDEP 2009b; Fore 2004; Fore et al. 2007; Niu 2004). Furthermore, it

has been demonstrated that an LDI of 2.0 is an appropriate and biologi cally significant break point that can be used to dis tinguish benchmark reference conditi ons from potentially disturbed

areas.

Figure 5

‐ 9. Depiction of land use area (light yellow) included in an LDI calculation



### EPA is considering the use of a methodology si milar to FDEP’s str eam corridor LDI when selecting least-disturbed sites in the South Florida nutrient wate rshed region. Using the land use data maintained by FDEP or at individual water management districts, the land use within a 100 m buffer of each canal segment over the length of the canal segment would be tabulated and

converted into a canal LDI. This LDI would be compared to in-canal nitrogen and phosphorus

concentrations to determine if the LDI can be used to explain a significant portion of the variability. Depending on preliminar y results, EPA may need to adjust the size of the canal buffer and methods for considering the infl uence of land beyond the immediate segment.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 107 5.4.1.1.2 Annual Average Concentration

Aquatic life water quality criteria include th ree components: magnitude , frequency, and duration. One approach is to consider the annual ge ometric mean concentration for the magnitude component of water quality criter ia (implicitly using an annual duration). The geometric mean is preferred to the arithmetic mean because the geometric mean is a better estimator of the distribution central tendency when the data ar e log-normally distributed which is common for parameters under consideration herein. The annu al geometric mean can be computed by taking the natural logarithm of the c oncentrations, and then compute the mean by station and year. From these values, an overall mean, , and variability associated w ith the factors of year and

station can be estimated with standard statisti cal software (e.g., Minita b, SAS, R, SYSTAT).

In past efforts, the 75 th 

### or the 90 th 

### percentile of this distributio n has been selected based on the

level of confidence EPA has that the sites ar e, in fact, least-disturbed. Using the 75 th 

### percentile annual geometric mean (i.e., the criteria magn itude) as an example, EPA would compute the

result as

where 0.6745 is the inverse of the standard normal cumulative distri bution with a probability of 0.75 and s is the corresponding standard deviation. (Substitute 1.2816 to compute GM 90 

### .)

Consideration should also be given to how often the test concentration can exceed the criteria magnitude and still meet the applicable desi gnated uses. When considering annual geometric means, most dialogue on frequency ranges from one in three years to two in five years as meeting designated uses. If there are no changes in water quality, one would expect that the

GM 75 

### would be exceeded, on average, 25 percent of the time. Table 5-2 presents the calculated probability that the proportion of years not meeting the criteria is greater than (A) 10 percent, (B) 20 percent, and (C) 25 percent of years not meeting the criteria. If the annual geometric mean

exceeded the GM 75 

### in two of three years, one would be 84 percent confident that that annual geometric mean of the test data exceeded GM 75 

### . If the annual geometric mean exceeded the

GM 75 

### in three of five years, one would be 90 perc ent confident that that annual geometric mean

of the test data exceeded GM 75 

### . Several different formulations of the percentile of the annual geometric mean and frequency term could be co nsidered from a statistical perspective. One potential concern with formulations that would permit the annual geometric mean to exceed the criteria magnitude in two or more consecutive years is that protracted adverse water quality conditions could cause impai rment of designated uses.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 108 Table 5

‐ 2. (A) The calculated probability that the population proportion of years not meeting the criteria is greater than 10%,

based on the observed numbers of exceedances, (B) calculated probability that the population proportion of years not meeting the criteria is greater than 20%, based on the observed numbers of exceedances, (C) calculated probability that the
population proportion of years not meeting the criteria is greater than 25%, based on the observed numbers of exceedances.

Values are colored in a stoplight pattern indicating probabilities less than 80% (green), 80

‐ 90% (orange), and greater than

90% (red)



### (A) Probability that >10% Exceed Criteria

Years Observed Exceedances

0 1 2 3 4 5

2 0.00 0.81 0.99

3 0.00 0.73 0.97 1.00

4 0.00 0.66 0.95 1.00 1.00

5 0.00 0.59 0.92 0.99 1.00 1.00

6 0.00 0.53 0.89 0.98 1.00 1.00

(B) Probability that >20% Exceed Criteria

Years Observed Exceedances

0 1 2 3 4 5

2 0.00 0.64 0.96

3 0.00 0.51 0.90 0.99

4 0.00 0.41 0.82 0.97 1.00

5 0.00 0.33 0.74 0.94 0.99 1.00

6 0.00 0.26 0.66 0.90 0.98 1.00

(C) Probability that >25% Exceed Criteria

Years Observed Exceedances

0 1 2 3 4 5

2 0.00 0.56 0.94

3 0.00 0.42 0.84 0.98

4 0.00 0.32 0.74 0.95 1.00

5 0.00 0.24 0.63 0.90 0.98 1.00

6 0.00 0.18 0.53 0.83 0.96 1.00

5.4.1.1.3 Distribution Approach—All Sites

Another approach that EPA is considering is a distributional approach of all nitrogen and phosphorus concentrations. In this application, di stributions of nitrogen or phosphorus data are assembled from all sites in the subregion (or, alternatively, a random selection of sites). Assuming the pool of sites in the distribution is large enough to represen t all waters in the subregion and reasonably reflect th e full range of ambient conditions with a disturbance gradient from least to most impacted, th e selection of a lowe r percentile of this distribution, should provide a nutrient level that can be assumed to in herently support a balanced natural population of aquatic flora and fauna. Several examples with data from actual reference sites have supported the use of the selection of a lower percentile of this distribution as being protective of the designatedMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 109 use (USEPA 2000b, 2000c). Computationally, the approach is iden tical to that described in Section 5.4.1.1 except that the data are not screened for least-disturbed sites.

5.4.1.2 Stressor

­ Response Relationships

Another approach described in previously publis hed EPA guidance for deriving numeric criteria (USEPA 2000c) is to quantify a stressor-response relationship between nitrogen/phosphorus and adverse effects on aquatic life, and then use that relationship to establish numeric criteria. The observed dose-response relationship could be described by a mode l (e.g., trophic state classification, regional predictive m odel, nutrient sensitive biocrite ria), which in turn would link

nutrient concentrations to the relative risk of environmental harm. Numeric criteria would be based on a correlative relationship between nutrien ts and biological respon ses at a level that would support balanced natural populations of aquatic flora and fauna. Although the underlying relationship certainly exists, in nature it has of ten been difficult to quantify because of the complexity of nutrient dynamics, the influence of interfering factors, and the temporal and/or spatial scales of the data and analyses.

In this analysis, matched chlorophyll a and nutrient data would be analyzed using regression. Using the matched chlorophyll a and nutrient data (see Figure 5-10 for site locations) assembled, Figure 5-11 is a bivariate plot of annual geometric mean chlorophyll a as function of TP together with the fitted regression equa tion and 90 percent pred iction interval. Figure 5-12 displays the residual plots. This regression model explains 49 percent of the varian ce; the residual plots suggest that the residuals have a constant variance, are independent, and ar e normally distributed. Overall, this model might be appr opriate provided that a chlorophyll a criterion concentration can be selected that supports balanced na tural populations of aquatic flora and fauna.

Figure 5-13 is a bivariate plot of annual geometric mean chlorophyll a as function of TN with the fitted regression equation along with the 90 percent prediction interval. Figure 5-14 displays the residual plots. This regression model explains 16. 5 percent of the variance, and the residual plots suggest that the residuals are normally distributed. EPA acknowledge s the visual appearance of a higher variance in the middle of th e TN range, but further investiga tion of the lowest and highest chlorophyll a concentrations did not reveal anomalies meriting their exclusion. Because of the

residual variance heterosced asticity and low overall R 2 

### value, this model is not as strong as the TP model. Using this data, EPA may consider whether there is additional data available to

control for the effects of stressors other than ni trogen in these waters and may instead consider relying on the reference conditi on approach described previous ly to derive TN criteria.

Alternately, it is possible to us e quantile regression. In this application, quan tile regression computes an estimate of the 90 th 

### conditional quantile function of the response, given the stressor (TN or TP). The function presumes a linear speci fication for the quantile regression model, i.e.,

that the formula defines a model that is linear in parameters. The function minimizes a weighted

sum of absolute residuals that can be formul ated as a linear program ming problem. To minimize the impact of skewed data, the log 10 

### (logarithm, base 10) transformation can be computed before applying the quantile regression al gorithm. Figure 5-15 presents bivariate plots of chlorophyll a as a function of TN and TP (in log 10 

### space) overall. Each plot displays the Spearman’s rhoMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 110 correlation coefficient, the locally weighted scatterplot smoothing (LOESS) 14 

### line (black line), and the quantile regression results (blue line and blue text for the regression terms). Should EPA chose to use stressor-response models to deve lop numeric criteria; EPA will use quantitative

metrics for evaluation of these models.

Figure 5

‐ 10. Site locations (n=72) with chlorophyll a data evaluated in this document together with

Florida WBIDs that FDEP has identified as nutrient

‐ impaired

14 We used a locally weighted scatterplot smoothing (LOESS) technique to visually examine possible trends along environmental gradients. The LOESS technique (Cleveland 1979) models nonlinear relationships where linear methods do not perform well, and fits simple models to localized subsets of the data to construct a function that describes, essentially, the central tendency of data along nut rient gradients. We used a smoothing factor of 0.7 to compute LOESS curves.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 111 TPChl_50

0.100 0.010 0.001 10.0

1.0

0.1 S 0.223073
R-Sq 49.1% R-Sq(adj) 48.9% Regression

90% CI

90% PI logten(Chl_50) = 1.452 + 0.5707 logten(TP)

Figure 5

‐ 11. Bivariate plot of annual geometric mean chlorophyll a as a function of TP with fitted linear regression and 90% prediction interval

ResidualPercent

1.0 0.5 0.0 -0.5 -1.0 99.9

99

90

50

10

1

0.1

Fitted ValueResidual

1.00 0.75 0.50 0.25 0.00 1.0

0.5

0.0

-0.5

-1.0

ResidualFrequency

0.9 0.6 0.3 0.0 -0.3 -0.6 48

36

24

12

0

Observation OrderResidual

2 4 0 2 2 0 2 0 0
1 8 0
1 6 0
1 4 0
1 2 0
1 0 0 8 0 6 0 4 0 2 0 1 1.0

0.5

0.0

-0.5

-1.0 Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

Histogram of the Residuals Residuals Versus the Order of the Data Residual Plots for logten(Chl_50)

Figure 5

‐ 12. Residual plots from chlorophyll a /TP regression analysisMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 112 TNChl_50

10.0 1.0 0.1 10.0

1.0

0.1 S 0.279109
R-Sq 16.8% R-Sq(adj) 16.5% Regression

90% CI

90% PI logten(Chl_50) = 0.5238 + 0.5738 logten(TN)

Figure 5

‐ 13. Bivariate plot of annual geometric mean chlorophyll a as a function of TN with fitted linear regression and 90% prediction interval

ResidualPercent

1.0 0.5 0.0 -0.5 -1.0 99.9

99

90

50

10

1

0.1

Fitted ValueResidual

1.0 0.8 0.6 0.4 0.2 1.0

0.5

0.0

-0.5

-1.0

ResidualFrequency

0.6 0.3 0.0 -0.3 -0.6 -0.9 40

30

20

10

0

Observation OrderResidual

2 4 0 2 2 0 2 0 0
1 8 0
1 6 0
1 4 0
1 2 0
1 0 0 8 0 6 0 4 0 2 0 1 1.0

0.5

0.0

-0.5

-1.0 Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

Histogram of the Residuals Residuals Versus the Order of the Data Residual Plots for logten(Chl_50) vs TN

Figure 5

‐ 14. Residual plots from chlorophyll a /TN regression analysisMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 113 Figure 5

‐ 15. Bivariate plot of annual geometric mean chlorophyll a as a function of TN and TP across all regions together with
Spearman’s rho correlation coefficient, LOESS smoother (black line, f=0.7), and quantile regression results (blue line and blue text)



### 5.5 Marine Waters: Classification and Data Sources

The watershed-based approach for identifying estu arine waterbodies described in Chapter 3 is

less suited in South Florida due to its highly managed inland flows and open-water dominated systems (i.e., Florida Bay and Keys). Theref ore EPA considered an approach to enhance waterbody identification with data from the:

• Southeast Environmental Research Center (SERC) water quality monitoring network

The SERC Water Quality Monitoring Network da ta includes monthly to quarterly sampling

results from more than 350 sites collected from 1992 to 2009. 15 

### There are more than 24,000 observations each of TN, TP, and chlorophyll a in the SERC database. In cooperation with EPA, scientists for the National Park Service and Florida International University used principal component and cluster analysis to segment these waters based on their similarities and differences. The SERC data are unique to Sout h Florida and are appropriate for principal component and cluster analysis because they provide a long-t erm record with consistent sampling and analytical methods. EPA is consid ering use of the SERC water quality monitoring data as a primary source for deriving numeric crit eria for South Florida’s marine waters, and may consider additional data from IWR Run 40, peer-r eviewed literature, reports, and other data

sources as mentioned in Section 2.4.

15 These data are included as part of Flor ida’s IWR database or can be accessed from http://serc.fiu.edu/wqmnetwork/

.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 114 The overall segmentation approach recogni zes that geomorphologic and geochemical characteristics vary throughout a basin and can al ter the impact of nitrogen/phosphorus pollution on water quality and resident communities. Base d on this recognition, numeric criteria can be derived for regions with c onsideration that different areas react differently to nitrogen/phosphorus pollution. Over all, the principal component and cluster analysis applied

here follows a line of research initiated in th e 1990s. The current analys is EPA is considering consolidates the previous work described below, and uses the most recent data.

Florida Bay has a history of water quality re gionalization. Researchers provided an early perspective of community distri bution in their calculation of SAV productivity and spatial patterning in Florida Bay (Zieman et al. 1989). In their study, Ziemann et al. (1989) visually determined regional differences through aeria l images and consequential ground truthing, and subsequently found a correlation between SAV sta nding crop and biological and water quality parameters. Similarly, Philips and Badylink (199 6) evaluated the correlation between water quality and phytoplankton standi ng crop, noting that ecologically independent regions were dynamic with the changing seasons. The seas onality of nutrient po llution loadings and consequential water quality is also highlighted by Boyer et al. (1999). Philips and Badylink (1996) subsequently modeled th e relationship of chlorophyll a and nutrient pollution concentrations to conclude the phosphorus is the major limiting nutrient in Florida Bay. Fourquean et al. (1993) also explained the phosphorus limitation of phytoplankton in Florida Bay, using a principal components analysis to id entify three key processes that independently controlled the composition of the studied water column: evaporat ion-driven concentration of dissolved material, delivery of phosphorus through water exchange with the Gulf of Mexico, and delivery of freshwater with nitrogen pollu tion. Boyer and Briceno (2007) conducted trend analysis to further understand such underlying processes that dictate Florida’s coastal water quality.

Key to all of these studies is the approach of sampling many water quality indicator variables to attain a perspective that recognizes the importance of larger , watershed and land use influences on the resulting water quality (Caccia and Boyer 2005). The prin cipal components analysis EPA is considering uses a variety of water quality indicator variables. Researcher s found that a variety of water quality attributes, which affect how coastal and estuarine regions respond to nitrogen/phosphorus pollution, should be considered in addition to the poll utant loads themselves (Boyer and Briceno 2007; Boyer an d Briceno 2009; Boyer et al. 1997; Boyer et al. 1999; Briceno and Boyer 2010; Caccia and Boyer 2005; Fourqurean et al. 2003; Fourqurean et al. 1993; Frankovich et al. 2010; Phlips and Badylak 1996; Zieman et al. 1989).

Other studies employ statistical modeling to de lineate regional boundaries , form relationships, and facilitate habitat prediction based on water quality indicator variables. For example, two researchers (Fourqurean et al. 2003; Frankovich et al. 2010) used hier archical cluster analysis to separate benthic SAV habitats in to ecologically distinct regions, and subsequent discriminant function analysis to predict benthic habitat based on associated water quality indicator variables. Evaluation of the resulting predictio ns showed that these analyses were able to determine habitat with a high level of accuracy. Boyer et al. (1997) used stationary monitoring sites to collect multivariate data, from which cluster analysis determined similar regions. Comparison of data among the regions allowed establishment of und erlying water quality differences. EPA isMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 115 considering this approach to determine unique re gions via statistical clus tering and analysis of significance.

The segmentation EPA is considering refers back to several existing frameworks found in the literature. Cosby et al. (2005) discuss updates to the FATHOM salinity m odel in the Florida Bay to assess the effects of potential management scenarios. The FATHOM model assesses solute fluxes among basins of the Florida Bay, whose limits are defined by a series of naturallyoccurring shallow banks. EPA is considering us ing these existing morphology-based divisions to create GIS-layer polygons from the cluster analysis of Florida Bay.

In the Florida Keys, EPA is considering to use segmentation developed by Klein and Orlando (1994), who considered water circulation dete rmined by local bathymetry, meteorology, and hydrology in nearshore environments to divide th e Florida Keys National Marine Sanctuary into 9 sub-regions. These boundaries, which represen t transitions between areas with different transport activity, are dynamic due to seasonal a nd other climate changes. The adoption of these localized segmentations are s upplemented by establishing contour lines between water sampling stations following benthic dept h contours, collectively providing division of South Florida’s

estuaries and coastal basins and a basis that EPA is considering for numeric criteria assignment. A preliminary, summary-level report detaili ng the proposed segmentation results for South Florida is provided as Appendix D. Figure 5-16 shows the proposed segmentation plan provided by these researchers in comparison to the curren t estuarine and coastal FDEP-defined WBIDs.

Figure 5

‐ 16. Proposed segmentation (red lines) for South Florida. (Criteria derived as a result of this document would only be
applicable to waters within 3 nautical miles of land [i.e., those waters highlighted in blue or further landward].)Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 116 

### 5.6 Marine Waters: Numeric Criteria Derivation

EPA is considering two approaches for deriving criteria using a refere nce condition approach. One approach is to estimate criteria from a st atistical distribution of water quality indicator

variable data. Criteria are derive d based on an inclusive distributi on of nutrient values that are temporally and/or spatially averag ed and are obtained from least- disturbed reference sites from a prescribed region. This is the same approach as for inland flowing waters (see Section 5.4.1.1). For marine waters, EPA is also consideri ng a second approach based on the statistical

distribution of raw data and eval uated using a binomial test. In this approach, EPA would derive two criteria: 1) an average (med ian) concentration and 2) an u pper percentile concentration. By considering an upper percentile, th is second approach would be sens itive to detecting changes in the distribution of higher concentrations that ma y be “averaged-out” in annual geometric means. The reference condition approach id entifies concentrations that are presumed to be inherently protective of the waterbody because those concen trations are associated with demonstrated healthy biological communities.

5.6.1 Reference Condition Approach—Least Disturbed Sites

5.6.1.1 Annual Average Concentration

The approach that would be used to compute cr iteria in the form of annual geometric means

would be the same as Section 5.4.1.1.2. Please se e Section 5.4.1.1.2 for a de scription of this approach.

5.6.1.2 Binomial Test to Maintain Current Conditions

In some situations, it might be eco logically meaningful to specify numeric criteria applicable to

individual observations rather than annual statistics. For exampl e, short duration increases in chlorophyll that are expected dur ing short periods of the summer , might not be well represented in criteria solely based on central tendency measures.

EPA is considering an approach that provides a two-number criterion th at would be evaluated using a binomial test. The two-number threshold is particularly applicable to an indicator variable such as chlorophyll where changes may be gradual, but where there is also a desire to avoid infrequent blooms.

EPA is considering the deriva tion of a two-number criterion (C 0.50 

### and C 0.25 

### ) that should not be exceeded by more than 50 percent and 25 percent of the samples at the 90 percent confidence level over an assessment period of three years us ing the binomial test. EPA may also consider alternative percent exceedan ces and confidence levels.

EPA is considering this formulation of th e binomial approach because it (1) provides a quantitative evaluation of the probability that th e data collected during the assessment period are from the same or nearly the same distribution as the observations during th e baseline period; (2) the approach is resistant to influence of extrem ely high values; (3) the ap proach does not require or imply an assumption of normality or log normali ty; (4) the approach is not affected by, or isMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 117 minimally affected by, censored data, and; (5) the approach tests for changes in both the central tendency and the upper end of the concentration distribution.

Under the assumption that the water body fully supports aquatic life use, one method to computing the criteria magnitudes is to set C 0.50 

### and C 0.25 

### to the maximum 50 th 

### and 75 th


### percentile concentration for any 3-year period from the supporting data set.

5.6.1.3 Example: Binomial Test to Maintain Current Conditions

As discussed in Section 5.6.1.2, EPA is considering the derivation of numeri c criteria that would be evaluated with a binomial test using individual observations. To illustrate this approach, data from the SERC water quality monitoring network for the South Central Outer Bay Segment of Biscayne Bay are used. This da ta set includes 11 sites and 14 y ears (1995-2008) of data. Figure 5-17 displays TN, TP, and chlorophyll a boxplots as a function of y ear and site. TN median ranges from 0.12 mg/L in 2002 and 0.17 mg/L at Marker G-1B to 0.28 mg/L in 2003 and 2004 and 0.30 mg/L at Totten Key. TP median ra nges from 0.003 mg/L in 2004 and 0.0046 mg/L at Midbay South to 0.008 mg/L in 2000 and 0.0057 mg/L at Biscayne Channel. Chlorophyll a median ranges from 0.16 μg/L in 1999 and 0.18 μg/L at Featherbed Bank, to 0.36 μg/L in 2003 and 0.34 μg/L at Biscayne Channel.

Although different percentiles may be considered, the example application in this section is based on deriving criteria that would be used to st atistically test whether more than 50 percent or 25 percent of the results exceed the criteria ove r a three year period. One method is to set the

criteria to the to the maximum 50 th 

### and 75 th 

### percentile concentration for any 3-year period from the supporting data set. These re sults are presented gr aphically in Figure 5-18 for TN, Figure 5-19 for TP, and Figure 5-20 for chlorophyll a

.

Under the assumption that the water body fully s upports aquatic life use, the null and alternate hypotheses are

H
o 

### : p ≤ p o 

### H

a 

### : p > p o



### where p is the fraction of data exceeding th e criteria during the assessment period and p

o 

### is the

fraction of data exceedin g the criteria in its derivation (i.e., 0.50 or 0.25).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 118 YEARTN, mg/L

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

SITETN, mg/L

TK SC RK MS MN G1B FB EK BNP BC AK 0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

YEARTP, mg/L

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 0.05

0.04

0.03

0.02

0.01

0.00

SITETP, mg/L

TK SC RK MS MN G1B FB EK BNP BC AK 0.05

0.04

0.03

0.02

0.01

0.00

YEARChl-a, μg/L

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 2.0

1.5

1.0

0.5

0.0

SITEChl-a, μg/L

TK SC RK MS MN G1B FB EK BNP BC AK 2.0

1.5

1.0

0.5

0.0

Figure 5

‐ 17. Boxplots of TN, TP, and Chl

‐ a for Biscayne Bay’s South Central Outer Bay using SERC water quality monitoring

network data from 1995

‐ 2008 by year and site. (Site key: Adams Key (AK), Biscayne Channel (BC), BNP Marker C (BNP), Elliott

Key (EK), Featherbed Bank (FB), Marker G

‐ 1B (G1B), Midbay North (MN), Midbay South (MS), Rubicon Keys (RK), Sands Cut (SC), Totten Key (TK))Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 119 TN, mg/LProbability

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 C(0.50) C(0.25)

2004-06

2005-07

2006-08 1995-97

1996-98

1997-99

1998-00

1999-01

2000-02

2001-03

2002-04

2003-05 Year

Figure 5

‐ 18. TN CDF for Biscayne Bay’s South Central Outer Bay using SERC water quality monitoring network data from

1995

‐ 2008 by 3

‐ year rolling window

TP, mg/LProbability

0.05 0.04 0.03 0.02 0.01 0.00 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 C(0.50) C(0.25)

2004-06

2005-07

2006-08 1995-97

1996-98

1997-99

1998-00

1999-01

2000-02

2001-03

2002-04

2003-05 Year

Figure 5

‐ 19. TP CDF for Biscayne Bay’s South Central Outer Bay using SERC water quality monitoring network data from

1995–2008 by 3

‐ year rolling windowMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 120 Chl-a, μg/LProbability

2.0 1.5 1.0 0.5 0.0 1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 C(0.50) C(0.25)

2004-06

2005-07

2006-08 1995-97

1996-98

1997-99

1998-00

1999-01

2000-02

2001-03

2002-04

2003-05 Year

Figure 5

‐ 20. Chlorophyll

‐ a CDF for Biscayne Bay’s South Central Outer Bay using SERC water quality monitoring network data

from 1995–2008 by 3

‐ year rolling window



### 5.7 Uncertainties and Data Gaps

Deriving numeric criteria for Sout h Florida inland flowing waters presents technical challenges due to the managed flows of canals that are the majo rity of flowing waters in this region, as well as the generally complex hydrogeology in the region. For example, soil type, particularly in the EAA, where the soils have been physically and ch emically modified (e.g., fertilized, oxidized), can influence inland flowing waters in South Fl orida. Distinct differences in soil types may justify classification of flowing waters by soil ty pe in South Florida. EPA’s proposed approach currently considers up to five subregions to address this issue; however, EPA will need to evaluate the advantages and di sadvantages to deriving numeric criteria for five subregions separately vs. for South Florida as a whole.

Reference conditions may be developed based on hist orical data for the syst em of interest or by comparison with data for other systems. Use of a historical reference condition is based on the availability of sufficient data to document ecological condition s and/or nitrogen and phosphorus concentrations during the refere nce period. Use of inter-system comparison to develop numeric criteria is based on the availability of data for a sufficient number of comparison systems.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 121 This page is inte ntionally blank.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 122 

## 6 Numeric Nutrient Criteria Development for the Protection of

Downstream Estuaries

Pursuant to 40 CFR 131.10(b), wa ter quality standards must

ensure the attainment and ma intenance of downstream water quality standards. Thus, EPA is deriving numeric criteria for streams in Florida in order to protect the estuarine waterbodies that ultimately receive nitrogen/phosphorus pollution from the watershed. These criteria, which EPA will refer to as Downstream Protection Values, or DPVs, will apply in place of the stream’s TN and TP criteria if the applicable DPV is more stringent.

The DPV criteria will be comput ed such that the TN and TP discharged from a stream, af ter accounting for any expected losses during transport, will not contribute a disproportionate fraction of the maximum TN or TP loading protec tive of water quality standards in the estuarine receiving water. The proportionate fraction will be based on the fraction of total freshwater flow contributed by the reach. Because of the complex ities associated with the managed flows in South Florida inland flowing wate rs (Chapter 5), the fraction of TN or TP from the upstream tributary reach that eventually reaches the estu ary cannot be estimated or predicted. Therefore, EPA is considering expressing DPVs at the termin al reach of the tributary into an estuary as protective concentrations or, al ternatively, protective loads.



### 6.1 Analysis Plan

The approach that EPA is considering for developing stream DPV criteria will begin with estimates of limits on TN and TP loading rates that are needed to s upport balanced natural populations of aquatic flora and fauna in estuarine waters (Figure 6-1). The loading limits will be determined as part of the criteria developmen t effort for estuarine waters as described in Chapter 3 of this document. The protective load limits can be scaled by average streamflow entering the estuary to determine numeric criteria for TN and TP concentrations in streams as they discharge into estuaries. These segments or “reaches” of streams and rivers are referred to as “terminal reaches.” Finally, DPVs can be determined for upstream reaches within watersheds by accounting for expected loss or permanent retenti on of TN and TP within the stream network. The fraction of TN or TP transported in a reach that ultimately reaches estuarine waters is

referred to as "fraction delivered." 40 CFR 131.10(b)
In designating uses of a water body and the appropriate criteria for those uses, the State

shall take into consideration the water quality standards of

downstream waters and shall ensure that its water quality standards provide for the attainment and maintenance of the water quality standards of

downstream waters.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 123 Figure 6

‐ 1. The major steps involved in development of numeric criteria for TN and TP in streams and rivers protective of

water quality standards in downstream estuaries.



### 6.2 Estimating Protective TN and TP Loading

EPA is considering using the estimates of prot ective nitrogen and phosphor us concentrations or loading rates that result from application of its approach for estuarine criteria development, described in Chapter 3 of this document. Alt hough one may estimate a maximum loading rate to an entire estuary, it is possibl e that different regions within an estuary may be more or less sensitive to nitrogen/phosphorus pollution loading. These differences could affect the TN and TP

concentrations that would be needed in str eams to protect downstream estuarine waters. For example, water quality responses to nutrient loadi ng could be less in a segment of an estuary that exchanges water freely with the coastal ocean th an in another segment that is relatively more isolated from such exchanges. Thus, EPA will consider for each estuary whether it is appropriate to establish separate protective TN and TP lo ading rates applicable to different estuarine segments. Coupled hydrodynamic-water quality mode ls, segment-specific regression models, or similar approaches may be used to develop estimat es for the protective load s (Chapter 3). In the absence of information regarding the differences in the sensitivity of estuarine segments to nitrogen/phosphorus pollution loading, EPA will co mpute a single protective loading rate for the entire estuary.



### 6.3 Computing DPVs for Terminal Reaches

In this approach, DPVs for terminal reaches could be computed by dividing the average protective loading rate for an estuary or estuary segment by the sum of the average total streamflow from all stream reaches discharging into the estuary or segment (Equation 6-1).



### Q L C

T 

### = Equation 6-1

where
T 

### C is average concentration specif ied as the terminal reach DPV, 

### L is the average loading

rate determined to be protective of de signated use in the r eceiving water, and Q is the average

freshwater inflow to the rece iving water body or segment. For the purpose of computing DPVs, the estimate of the average protective loading would exclude loads resulting from direct atmospheric deposition to estuarine surface waters and point-source loads discharged directly to estuarine waters. Similarly, the estimate of total streamflow would not in clude freshwater inputs 

## Estimate

Protective

Load DPV for

Terminal

Reaches DPV
for all Other

Reaches Divide by Average
Streamflow Divide by

Fraction

DeliveredMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 124 resulting from net deposition onto the surface of the estuary (i.e., precipitation minus evaporation) and point source discharges of freshwater in to the estuary.

Estimates of average streamflow would be obtai ned from a watershed model because streamflow is not monitored in many streams. Moreover, if monitoring of streamflow occurs at some distance upstream from the point of discharge into the estuary, it would not represent the fraction of flow resulting from the ungauged portion of th e watershed. EPA is considering using the LSPC watershed model for this purpose (Loading Simulation Program in C++; http://www.epa.gov/athens/wwqtsc/html/lspc.ht ml). LSPC would be implemented on a

streamflow network derived from the Na tional Hydrography Dataset Plus, or NHDPlus (http://www.horizon-systems.com/ nhdplus/index.php). NHDPlus watersheds and flow-lines will

be abstracted or “dissolved” to achieve a st ream network at approxi mately the 12-digit HUC scale. This approach would provide improved accuracy and spatial resolution afforded by NHDPlus yet reduce the computational dema nd by reducing the number of simulated subwatersheds and stream reaches relative to NHDP lus. LSPC model runs would simulate daily average streamflow for 1997–2009. EPA intends to ev aluate the performance of the model for simulating long-term average streamflow, seasona l average streamflow, high flows, low flows, and storm hydrographs using time series of monito red flows at local monitoring stations. EPA also expects to calibrate and ev aluate LSPC in order to pred ict observed stream velocity. Additional information regarding EPA’s proposed implementation of LSPC is included in

Sections 3.3.3.1 and 3.3.4.



### 6.4 Computing DPVs for Upstream Reaches

In this approach, DPVs for upstream reaches would be computed from DPVs for downstream

terminal reaches via

i T

i 

### F C C = Equation 6-2

where

i 

### C 

### is the DPV for an upstream reach and

i 

### F 

### is the average fraction of TN or TP

transported out of that reach that eventually en ters the estuarine receiving water, also called average “fraction-delivered.” EPA would compute F i 

### at a daily time interval ( F t,i 

### ) for each reach, then compute

i 

### F 

### as the long term average. Daily values would be computed using



## ∏

− 

## =

j t k

i t j t j t 

## e F

, ,

, 

### Equation 6-3

where F t,i 

### is the fraction-delivered for TN or TP on day t from sub-watershed i . The values of j specify the sequence of stream reaches that comp rise the flow path to estuarine waters from

reach i . The value k t,j 

### and t t,j 

### are first-order decay rates and r each time-of-travel, respectively, on day t for reach j . EPA is considering using a flow network based on NHDPlus hydrology and daily estimates of stream velo city computed using LSPC wate rshed models to compute daily

time of travel for each stream reach.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 125 First-order decay refers to exponential decay wher e the instantaneous loss rate does not depend

on the concentration of TN or TP. Permanent losses of TN in streams are generally attributed to denitrification in stream sediments (Alexander et al. 2009). Therefore, loss rates for TN are affected by stream attributes that determine the rate that stream water encounters stream sediments. For example, TN losses are invers ely associated with stream depth and mean streamflow, the latter of which is likely correlated with str eam depth (Alexander et al. 2009; Böhlke et al. 2009). First-order decay rates k TN 

### and k TP 

### generally cannot be measured directly at

the scale of whole watersheds and therefor e are often estimated empirically. SPARROW watershed models at both regional and national scales are recognized as useful tools for obtaining these estimates. The south Atla ntic, Gulf and Tenneessee (SAGT) regional

SPARROW model estimated k TN 

### separately for stream reaches in several stream categories based on the magnitude of flow (Table 6-1) (Hoos and Mc Mahon 2009). EPA is considering computing F i 

### (Equation 6-3) using estimates of flow-dependent k TN 

### from SAGT-SPARROW. EPA is also considering an alternative and sl ightly simpler approach in which only a single

value is used for k TN 

### (e.g., k TN 

### =0.14 d -1 

### ), representing a stream of intermediate size (Alexander et al. 2000; Böhlke et al. 2009). This simplification may be justified by the large range in measured stream N loss rates, even for a stream of a given size or depth (Alexander et al. 2009), Regardless of the approach used to determine k TN 

### , estimates of F i 

### would depend on stream reach time of travel and potentially stream flow obtained from th e LSPC watershed model.

Table 6

‐ 1. Empirical estimates of the first

‐ order loss rates for TN in southeast U.S. streams from two formulations (Model A

and Model B) of the SAGT

‐ SPARROW regional watershed model (Hoos and McMahon 2009)

Average Stream Flow
(m 3 

### / s) Model A Average Stream Flow
(m 3 

### / s) Model B

k TN (d -1 ) k TN (d -1 )
Q > 28 0.00 Q > 28 0.014 2.8<Q<28 0.13 Q < 28 0.14

Q < 2.8 0.23



### Loss rates for TP represent permanent retention (i .e., burial or binding in sediments) since there are no biogeochemical processes for P comparable to denitrification, wh ich transforms reactive N species (e.g., NO 3 - 

### ) into N 2 

### gas. Permanent P retention in str eams is expected to be smaller in streams than N losses, but potentially significant in lakes and especially deeper reservoirs, where sediment-associated P may accumulate more readily. Gar ć ia et al. (2010) model the rate of P

retention in southeast U.S. streams as a first- order loss rate inversely proportional to average stream depth, empirically determining k TP 

### (d -1 

### ) = 0.049/z, where z is mean stream depth (m). This approach follows the formulation of Al exander et al. (2008), who addressed N and P transport in the Mississippi River basin. Sim ilar to TN, EPA is evaluating whether to use estimates of first-order TP loss rates (i.e., retent ion for TP) for TP in southeastern U.S. streams estimated using a SPARROW watershed model (Gar ć ia et al. 2010) or, alternatively, a constant

value of k TP 

### =0.014 d -1 

### . In order to implement the SPARROW-based estimates of k TP 

### , EPA will need estimates of average stream depth, which EP A is considering obtaini ng at a daily time step

from the LSPC watershed models. Estimates of kTP would be scaled up to compute Fi in the

same manner as for TN: estimates of k TP 

### would be used in Equation 6-3 to compute F i 

### with daily estimates of time-of-travel computed via L SPC watershed model simulations. Average F i 

### would be used in Equation 6-2 compute DPVs for TP.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 126 

### 6.5 Computing DPVs for South Florida Marine Waters

To address the protection of marine waters downstream from South Florida inland flowing waters, EPA is considering a range of approach es. First, EPA acknowledg es the existing numeric criterion for TP in the Everglades Protection Ar ea (10 ppb), and thus has described its approach to ensure that flowing waters upstream of this area assure the maintena nce of that criterion (see Section 6.5.2). Second, EPA is considering deriva tion of protective TN and TP values for the marine receiving waters in South Florida based on the South Florida marine waters criteria (see Chapter 5). These values may take the form of either protective concentrations, or loads from tributaries entering the downstream receiving wate r, based on existing models or measurements. This approach diverges from those described ab ove for inland flowing waters outside of South

Florida because the fraction of TN or TP from the upstream tributary reach that eventually reaches the estuary cannot be estimated or predic ted in South Florida due to the region’s altered and managed hydrology. For this reason USGS m odelers excluded South Florida from their SAGT SPARROW model (Hoos and McMahon 2009). For the same reason, EPA is considering the assignment of DPVs for South Florida at th e terminal reach and not at locations farther

upstream.

In South Florida a protective load, as an alternative to concentrat ion, may be considered as a way to express the DPV. One advantage of this approach is that loads may be more directly related to impacts on receiving waters and how the waters are managed during the dry and wet seasons. One advantage of expressing DPVs as concentrati ons is that in upstream reaches a concentration can be directly compared to a stream IPV, and an assessment can be made as to whether the water quality standard is simultaneously being protective of conditions both instream and downstream. A disadvantage of applying DPVs only at terminal reaches is that this approach does not provide an ability to simultaneously as sess downstream protection at both upstream and

downstream locations. However a downstream estu ary would be protected by this approach.

EPA is considering several strategies for calcula ting DPVs at a tributary terminal reach. There are two steps involved in derivi ng DPVs: calculating the nitrogen/ phosphorus pollution load that protects the estuary, and transl ating this load into DPVs fo r contributing waters. In South Florida, the DPV may be expresse d either as a load from, or a concentration at, the terminal reach of each tributary to the estuary.

The assignment or allocation of DPVs to multiple tributaries which may be influenced by different combinations of point and non-point sources can be ba sed on an equitable strategy. One example is to assign loads or flow-weighted conc entrations to each tribut ary based on its relative contribution to the estuary. For example, a fl ow-weighted concentration is calculated as

q

. q q q q c

. q c q c q c C

n 3 2 1 n n 3 3 2 2 1 1

+

... + + + +

... + + +

= 

### Equation 6-4

where C is the protective total influent concentr ation (DPV) for the estu ary over the time period of interest, c i 

### and q i 

### are the concentration and flow respectively in tributary “i”, and n is the

number of such tributaries. If there is more th an one tributary (i.e., n>1), then there is no unique solution to Equation 6-4. In this case, achieveme nt of the DPV may be attained through variousMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 127 combinations of individual tri butary concentrations. Robust datase ts of water quality and flow for each tributary are necessary in order to compute acceptable individual tributary concentrations this way.

Tributary loads can be apportioned most simply according to flows as follows

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

=

t i

i q q T L 

### Equation 6-5

where L i 

### is the acceptable load from tributary “i” over the time period of interest, q i 

### is the tributary’s flow over the same time interval, q T 

### is the summed flow to the estuary from all such tributaries, and T is the total load that the estuary can receive and still meet standards. Calculation of loads in this manner implies id entical concentrations in all tributaries.

Alternatively, loads for individua l tributaries can be calculated based simply on the acceptable summed load to the estuary, irrespective of indivi dual tributary flow considerations. Similar to that described for the Mississippi River Basin an d Northern Gulf of Mexico (National Research Council 2009), a simple load alloca tion equation can be written as

T L

. L L L
n 3 2 1 

### = +

... + + + 

### Equation 6-6

where L i 

### is tributary “i” load over the time period of interest. Because each tributary drains a

different area with a different combination of land uses, pollutant sources, nutrient management practices etc., it is perhaps unrealistic to expect the uniform effluent con centrations that Equation 6-5 implies. The approach described in Equati on 6-6 allows more flexibility in DPV setting, by making the total delivered load to the estuary the only constraint.

Potential models that could inform derivation of a protective load include those of Marshall et al. (2008), who provided mass balance calculations of salinity respons e to freshwater inflows and

nutrients in central and sout h regions of Biscayne Bay, and Cosby et al. (2005), who characterized the hydrologic influences to and water budgets for Florida Bay.

6.5.1 Binomial Approach for Downstream Protection

An alternative approach to that described in the previous section would be to estimate concentrations based on existing monitoring data at terminal reaches of tributaries into the estuaries. This approach would be most applicable in situations where the downstream estuary is currently supporting natural populati ons of aquatic flora and fauna, and the objective is to ensure that nitrogen and phosphorus concentr ations in the tributaries do not increase in order to maintain water quality. For situations where EPA is consid ering setting criteria to maintain current concentrations, EPA is considering developing a two-number criterion based on the binomial distribution (see Section 3.3.2 for furthe r details on this approach).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 128 6.5.2 Calculating EAA Downstream Protective Values Based on EvPA Total Phosphorus Criteria

The State of Florida has adopted, and EPA ha s approved, a long-term geometric mean TP criterion for the EvPA of 10 ppb. This 10 ppb lo ng-term geometric mean criterion applies throughout the EvPA (see Section 5.2.1 for more info rmation). In order to ensure the attainment and maintenance of downstream water quality sta ndards, EPA is considering the derivation of

numeric criteria for TP within the EAA that are supportive of balanced natural populations of aquatic flora and fauna within the EAA as we ll as supportive of the current water quality standards (with a TP criterion of 10 ppb) in the EvPA. To do this , EPA could consider historical water quality and pumping records to characterize nut rient attenuation that w ould, in turn, inform derivation of DPVs for flowing waters in the EAA.



### 6.6 Specifying Frequency

­ Duration for DPVs

DPV criteria are intended to lim it average nitrogen/phosphorus pollution loading rates from watersheds to estuaries. Although water quality in estuaries has been shown to change in relatively short periods in response to change s in nitrogen/phosphorus pollution inputs, key assessment endpoints, such as the depth of coloni zation of seagrasses, re spond relatively slowly and cumulatively to sustained pa tterns of increasing or decrea sing nitrogen/phosphorus pollution loading (e.g., Greening and Ja nicki 2006). Moreover, contro ls on average nitrogen and phosphorus concentration and loading at the scale of whole estuarin e watersheds tend to change slowly in response to cumulative effects of multiple anthropogenic causes, whether beneficial or detrimental. These observations suggest that DPV criteria should be structured to reflect this longer time-scale of impact.

Because DPV criteria would be derived from es timates of protective load limits, it is also important to consider the potential impact of sy stematic bias. EPA will evaluate whether it will be practical to specify DPV crite ria as flow-weighted concentrati ons, or as concentrations that have been empirically adjusted for season a nd flow-conditions. If not, EPA will specify an alternative approach, potentially simply annual means. EPA is c onsidering the use of arithmetic means for this purpose because although geometric means have often been recommended to quantify central tendency for log-normally distribu ted data (TN and TP concentrations are often log-normally distributed) the produ ct of geometric mean concentr ation and average streamflow may not provide reliable estimates of the tran sport of TN and TP (e .g., Cohn et al. 1989).



### 6.7 Example Application: Pensacola Bay Watershed

We illustrate the methodology that EPA is c onsidering by providing an example of the computation of DPVs for Pensaco la Bay using the LSPC waters hed model. The Pensacola Bay system is an estuarine complex located in Florida's panhandle region. It includes several interconnected estuarine bays a nd sounds and has a large watershed that extends well into the state of Alabama (Figure 6-2 and Figure 6-3) . The LSPC watershed model for Pensacola Bay

includes 224 sub-watersheds and associated stre am reaches, 12 of which are terminal reaches (i.e., discharge directly into the Pe nsacola Bay; Figure 6-2, Figure 6-3).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 129 Figure 6

‐ 2. Map of Pensacola Bay watershed illustrating the 224 sub

‐ watersheds and associated stream reaches included in

the LSPC watershed model. Ten of the twelve terminal reaches discharging into Pensacola Bay are shown with blue dots.

Figure 6

‐ 3. Map of Pensacola Bay illustrating where 11 of 12 terminal reaches from the LSPC watershed model enter the bay (not shown is the terminal reach at the eastern end of Santa Rosa Sound). Labels indicate the WBIDs that make up the larger

open water areas of Pensacola Bay.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 130 6.7.1 Protective TN and TP Loading and Terminal Reach DPVs

Because protective loading rates fo r TN and TP have not yet been determined for Pensacola Bay, example calculations are presented here for illust rative purposes only. In this example, the long term (1997–2009) average TN and TP loads to Pens acola Bay were estimated from LSPC model simulations and used as an estimate of the pr otective loads. The long term average load and average streamflow was used to derive the associated estimates of 0.41 mg/L TN and 0.015 mg/L TP for terminal reach DPVs for the entire bay (Table 6-2; last row). The concentrations that would result from application of EPA’s a pproaches for developing estuarine TN and TP numeric criteria (Chapter 3) may be entirely different and would re place these values in the final computations.

To illustrate how DPVs could be computed on a segment-specific basis, four regions of the Pensacola Bay system were hypothesized to resp ond differently to nitrogen/phosphorus pollution (again this is for illustrative purposes only). In this example, slightly lower TN and TP concentrations are assumed to be required to support balanced natural populations of aquatic flora and fauna in the Blackwater-East Bay region , which is further from Pensacola Pass than Escambia Bay and Pensacola Bay proper (Table 6-2) . It was assumed, for example, that slightly higher TN and TP concentrations would be appr opriate in Santa Rosa Sound because the small watershed area limits overall nitrogen/phosphorus pollution loading to this coastal lagoon segment of the bay.

Table 6

‐ 2. Hypothetical values illustrating the computation of segment

‐ specific protective loading rates and associated DPV

concentrations for terminal stream reaches. Average flows are 1997–2009 averages computed using LSPC.

Region TN Load (kg/d) TP Load (kg/d) Avg Flow (m 3 /s) DPV-TN (mg/L) DPV-TP (mg/L) Escambia Bay 7,165 267 193 0.43 0.016 Pensacola Bay 111 4 3.0 0.43 0.016 Blackwater-East Bay 4,657 173 139 0.39 0.014

Santa Rosa Sound 90 3 2.2 0.47 0.018 Total/Average 12,023 447 337 0.41 0.015



### 6.7.2 Fraction

­ Delivered in the Pensacola Bay Watershed

The approach described in section 6.4 was applie d to compute the TN and TP fraction-delivered for each reach in the Pensacola Bay watershed. The computations were completed for each day during the 1997–2009 LSPC simulation period. S ubsequently, long-term averages were computed. To reduce the total number of numeri c criteria values that would be computed, estimates of fraction-delivered were rounded upwar d to the nearest 0.1. Maps depicting the longterm average TN fraction-delivered for each reac h (Figure 6-4) contrast the results obtained when using a constant first-order loss rate of 0.14 d -1 

### (panel A) versus flow-dependent values (panel B) for first-order TN loss based on a SPARROW model B (Table 6-1) (Hoos and McMahon 2009). The estimates obtained using flow -dependent loss rates were almost always higher than those obtained assuming a constant lo ss rate (Figure 6-5). The average difference in the estimate of TN fraction-delivered was 6 perc ent and the maximum difference was 17 percent. Differences between the two approaches decreased as the distance and time-of-travel to the coastMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 131 decreases and all values converg ed to 1.0 (Figure 6-5). Because the flow-dependent loss rates based on the SPARROW model example cause larger rivers to transport N with lower loss to the coast, the spatial distribution of fraction-delivered in the flow-d ependent scenario was different from that obtained with a constant loss. In pa rticular, areas of more efficient N transport extended further into the watershed along major ri vers when compared to the other scenario (Figure 6-4). Although the spatial scale is much sma ller, this pattern is si milar to that which has been described for N transport in the Missi ssippi/Atchafalaya River Basin, where N is transported with low losses from the upper Midwes t to the Gulf of Mexi co (Alexander et al. 2008).

If one assumes a constant first-order loss rate of 0.014 d -1 

### for streams in the Pensacola Bay watershed, then all estimates of TP fraction-de livered are greater than 90 percent, indicating negligible TP losses. Alternatively, following the approach of Gar ć ia et al. (2010) gives fraction delivered estimates for TP between 0.53 and 1.0 (F igure 6-6). EPA is continuing to evaluatewhich are the most defensible parameter values for modeling TP retention in streams as a firstorder decay process. An important consideration is that there are few lakes and reservoirs in the Pensacola Bay watershed. Even though there is at least one reservoir within the watershed, Gantt Lake, located on the Conecuh River, the model was able to adequately simulate hydrology and water quality in the basin without including it. This may not be the case in other watersheds in Florida, where the presence of multiple lakes and reservoirs in the watershed may have more of an effect on TN and TP transport to the coast.

Figure 6

‐ 4. Map of average TN fraction

‐ delivered to Pensacola Bay calculated daily for 1997–2009 using (A) a constant first

‐ order decay rate of 0.14 d ‐ 1 and (B) using first

‐ order decay rates for TN specified on the basis of two ranges of flow rate in the

reach (Table 6

‐ 1), following the Model B SPARROW regression parameters reported for southeast U.S. streams by Hoos and

McMahon (2009). In both cases, the estimates of fraction delivered were rounded up to the nearest 0.1.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 132 Figure 6

‐ 5. The relationship between TN fraction delivered (d ‐ 1 ) in the Pensacola Bay watershed computed using a constant

first

‐ order decay rate of 0.14 d ‐ 1 (horizontal axis) and flow

‐ dependent values for the first

‐ order decay rate for TN, following

Hoos and McMahon (2009) (vertical axis)

Figure 6

‐ 6. A map showing estimates of the TP fraction delivered to estuarine waters in Pensacola Bay as calculated using a

TP retention rate (expressed as first

‐ order decay) that is inversely related to stream depth (Gar ć ia et al. 2010) 

### 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Constant KFlow-Dependent KMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 133 6.7.3 Computing DPVs for Upstream Stream Reaches

Upstream DPVs were computed following the concep tual approach illustra ted in Figure 6-1 and expressed in Equation 6-2 and using the example terminal-reach TN and TP DPV values (Table 6-2). Portions of the watershed draining to each of the respective estuarine regions were determined from the stream network (Figure 6-7, panel A). Using equation 6-2 with estimates of

TN fraction-delivered rounded to the nearest tenth, twelve unique TN DPV criteria were determined for corresponding regions of the Pens acola Bay watershed (Figure 6-7, panel B). In several cases, differences between the DPVs we re quite small. The same watershed regions correspond to the terminal reach TP criteria (Figure 6-8, panel A). Using the estimates of TP fraction delivered (Figure 6-6) rounded to neares t tenth resulted in nine unique TP DPV values for corresponding regions of the watershed (Figure 6-8, panel B), ranging from 0.014 to 0.027 mg/L TP.

Figure 6

‐ 7. Regions of the Pensacola Bay watershed associated with unique terminal reach DPV values for TN (panel A) and

the resulting computed values for TN DPVs in all streams (panel B). TN fraction

‐ delivered was rounded up to the nearest 0.1, resulting in 12 unique TN DPV values for the entire watershed. For these maps, fraction

‐ delivered was calculated using

stream

‐ flow dependent first

‐ order decay rates for TN, following the results of Hoos and McMahon (2009).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 134 Figure 6

‐ 8. Regions of the Pensacola Bay watershed associated with unique terminal reach DPV values for TP (panel A) and

the resulting computed values for TP DPVs. Fraction

‐ delivered was rounded up to the nearest 0.1, resulting in nine unique TP

DPV values for the entire watershed. For these calculations, TP fraction

‐ delivered was calculated using stream

‐ depth
dependent rates following the results of Gar ć ia et al. (2010).Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 135 This page is inte ntionally blank.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 136 

## 7 References

Alexander, R.B., J.K. Böhlke, E.W. Boyer, M. B. David, J.W. Harvey, P.J. Mulholland, S.P. Seitzinger, C.R. Tobias, C. Tonitto, and W.M. Wollheim. 2009. Dynamic modeling of nitrogen losses in river networks unrave ls the coupled effect s of hydrological and biogeochemical processes. Biogeochemistry 93:758-761.

Alexander, R.B., R.A. Smith, and G.E. Schwarz. 2000. Effect of stream channel size on the delivery of nitrogen to the Gulf of Mexico. Nature 403:758-761.

Alexander, R.B., R.A. Smith, G.E. Schwarz, E. W. Boyer, J.V. Nolan, and J.W. Brakebill. 2008. Differences in Phosphorus and Nitrogen Delivery to the Gulf of Mexico from the Mississippi River Basin. Environmental Science and Technology 42:822-830.

Arhonditsis, G.B., and M.T. Brett. 2004. Evaluation of the current state of mechanistic aquatic biogeochemical modeling. Marine Ecology Progress Series 271:13-26.

Arhonditsis, G.B., H.W. Paerl, L.M. Valdes-W eaver, C.A. Stow, L.J. Steinberg, and K.H. Reckhow. 2007. Application of Bayesian stru ctural equation modeling for examining phytoplankton dynamics in the Neuse River Estuary (North Carolina, USA). Estuarine Coastal and Shelf Science 72(1-2):63-80.

Armitage, A., and P. Fong. 2004. Upward cascading effects of nutrients: shifts in a benthic microalgal community and a negative herbivore response. Oecologia 139(4):560-567.

Bailey, S.W., and P.J. Werdell. 2006. A multi-sensor approach for the orbit validation of ocean color satellite data products. Remote Sensing of Environment 102:12-23.

Baith, K., R. Lindsay, G. Fu, and C.R. McClain. 2001. SeaDAS: Data analysis system developed

for ocean color satellite sensors. EOS, Transactions, American Geophysical Union 82(18).

Bicknell, B.R., J.C. Imhoff, J.L. Kittle, Jr ., T.H. Jobes, and A.S. Donigian, Jr. 2004. HSPF

Version 12 User’s Manual . Aqua Terra Consultants, Mountain View, CA.

Böhlke, J.K., R.C. Antweiler, J.W. Harvey, A. E. Laursen, L.K. Smith, R.L. Smith, and M.A. Voytek. 2009. Multi-scale measurements and mode ling of denitrificati on in streams with varying flow and nitrate concentration in the upper Mississippi River basin, USA. Biogeochemistry 93:117-141.

Boyer, J.N., and H.O. Briceno. 2007. 2007 Cumulative Annual Report for the Coastal Water Quality Monitoring Network . Florida International Univer sity, West Palm Beach, FL.

Boyer, J.N., and H.O. Briceno. 2009. FY2008 An nual Report of the Water Quality Monitoring Project for the Florida Keys National Marine Sanctuary. edited by U.S.EPA. Washington,

D.C.: SERC Tech.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 137 Boyer, J.N., J.W. Fourqurean, and R.D. Jones. 1997. Spatial characterizati on of water quality in Florida Bay and Whitewater Bay by multivariate analyses: Zones of similar influence.

Estuaries 20(4):743-758.

Boyer, J.N., J.W. Fourqurean, and R.D. Jones. 1999. Seasonal and long-term trends in the water quality of Florida Bay (1989-1997). Estuaries 22(2B):417-430.

Boyer, J.N., C.R. Kelble, P.B. Ortner, and D.T. Rudnick. 2009. Phyt oplankton bloom status: Chlorophyll a biomass as an indicator of wate r quality condition in th e southern estuaries of Florida, USA. Ecological Indicators 9(6, Supplement 1):S56-S67.

Briceno, H.O., and J.N. Boyer. 2010. Climatic Controls on Phytoplankton Biomass in a Subtropical Estuary, Florida Bay, USA. Estuaries and Coasts 33(2):541-553.

Bricker, S., B. Longstaff, W. Dennison, A. Jones, K. Boicourt, C. Wicks, and J. Woerner. 2007. Effects of Nutrient Enrichment In the Nation’s Estuaries: A Decade of Change. NOAA Coastal Ocean Program, National Centers for Coastal Ocean Science, Decision Analysis Series No. 26, 328, Silver Spring, MD.

Bricker, S., G. Matlock, J. Snider, A. Mason, M. Alber, W. Boynton, D. Brock, G. Brush, D. Chestnut, U. Claussen, W. Dennison, E. Dettma nn, D. Dunn, J. Ferreira, D. Flemer, P. Fong, J. Fourqurean, J. Hameedi, D. Hernand ez, D. Hoover, D. Johnston, S. Jones, K. Kamer, R. Kelty, D. Keeley, R. Langan, J. Latimer, D. Lipton, R. Magnien, T. Malone, G. Morrison, J. Newton, J. Pennock, N. Rabalais, D. Scheurer, J. Sharp, D. Smith, S. Smith, P. Tester, R. Thom, D. Trueblood, and R. Van Dolah. 2003a. National Estuarine Eutrophication Assessment Update: Work shop summary and recommendations for development of a long-term monitoring and assessment program . Proceedings of a workshop September 4-5, 2002, Patuxent Wildli fe Research Refuge, Laurel, Maryland. NOAA, National Ocean Service, National Cent ers for Coastal Ocean Science. Silver Spring, MD, Original edition, Proceedings of a workshop September 4-5, 2002, Patuxent National Wildlife Research Refuge, Laurel, Maryland, 19.

Bricker, S.B., C.G. Clement, D.E. Pirhalla, S.P. Orlando, and D.R.G. Farrow. 1999. National Estuarine Eutrophication Assessm ent, Effects of Nutrient Enrichment in the Nation's

Estuaries . NOAA, National Ocean Service, Spec ial Projects Office and the National

Centers for Coastal Ocean Scie nce. Silver Spring, MD, 71.

Bricker, S.B., J.G. Ferreira, and T. Simas. 2003b. An integrated methodol ogy for assessment of estuarine trophic status. Ecological Modelling 169(1):39-60.

Bricker, S.B., B. Longstaff, W. Dennison, A. Jones, K. Boicourt, C. Wicks, and J. Woerner.

2008. Effects of nutrient enrichment in the nation's estuaries: A decade of change. Harmful Algae 8(1):21-32.

Brown, M.T., and K.C. Reiss. 2006. Proposed Breakpoint of LDI < 2.0 for Determining Minimally Affected Referen ce Conditions for Water Bodies . Technical report submitted to the Florida Department of Environmental Prot ection. University of Florida, Center forMethods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 138 Environmental Policy, Department of Envir onmental Engineering Sciences, Gainesville,

FL.

Brown, M.T., and M.B. Vivas. 2005. Landscape development intensity index. Environmental Monitoring and Assessment 101(1-3):289-309.

Caccia, V.G., and J.N. Boyer. 2005. Spatial patter ning of water quality in Biscayne Bay, Florida as a function of land use and water management. Marine Pollution Bulletin 50(11):1416-

1429.

Carlson, P.R., and K. Madley. 2006. St atewide summary for Florida. In Seagrass Status and

Trends in the Northern Gulf of Mexico: 1940-2002. , edited by L. Handley, D. Altsman and R. DeMay. Washington, DC: US Environmental Protection Agency.

Cleveland, W. 1979. Robust loca lly weighted regression an d smoothing scatterplots. Journal of

the American Statistical Association 74:829-836.

Cloern, J. 2001. Our evolving conceptual m odel of the coastal eu trophication problem. Marine Ecology Progress Series 210:223-253.

Cloern, J.E. 1996. Phytoplankton bl oom dynamics in coastal ecosystems: A review with some general lessons from sustained investig ation of San Francisco Bay, California. Reviews of Geophysics 34(2):127-168.

Cohn, T.A., L.L. DeLong, E.J. Gilroy, R.M. Hirsch, and D.K. Wells. 1989. Estimating

constituent loads. Water Resources Research 25(5):937-942.

Cornwell, J.C., W.M. Kemp, and T.M. Kana. 1999. Denitrification in coastal ecosystems: methods, environmental controls, and ecosystem level controls, a review. Aquatic Ecology 33:41-54.

Cosby, B., W. Nuttle, and F. Marshall. 2005. FATHOM Enhancements and Implementation to Support Development of Minimum Flows and Levels for Florida Bay

.

Dennison, W.C. 1987. Effects of light on seagrass photosynthesis, growth and depth distribution. Aquatic Botany 27:15-26.

Dettmann, E.H. 2001. Effect of water residence time on annual export and denitrification of nitrogen in estuaries: A model analysis. Estuaries 24(4):481-490.

Di Toro, D.M., and J.P. Connolly. 1980. Mathematical Models of Water Quality in Large Lakes,

Part 2: Lake Erie . U.S. Environmental Protection Agency, Office of Research and Development, EPA-600-3-80-065.

Di Toro, D.M., J.J. Fitzpatrick, and R.V. Thomann. 1983. Water Quality Analysis Simulation Program (WASP) and Model Verifica tion Program (MVP) Documentation

. Hydroscience, Inc., Westwood, NY.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 139 Diaz, R.J. 2001. Overview of hypoxia around the world. Journal of Environmental Quality 30(2):275-281.

Diaz, R.J., and R. Rosenberg. 2008. Spreading dead zones and consequences for marine

ecosystems. Science 321(5891):926-929.

Dodds, W.K., W.W. Bouska, J.L. Eitzmann, T.J. P ilger, K.L. Pitts, A.J. Riley, J.T. Schloesser, and D.J. Thornbrugh. 2009. Eutrophication of US Freshwaters: Analysis of Potential Economic Damages. Environmental Science & Technology 43(1):12-19.

Duarte, C.M. 1991. Seagrass depth limits. Aquatic Botany 40(4):363-377.

Duarte, C.M. 1995. Submerged Aquatic Vegetation in Relation to Different Nutrient Regimes. Ophelia 41:87-112.

Ecological Society of America. 2009. Hypoxia . Ecological Society of America 2009 [cited December 2009]. Available from http://www.esa.org/education/edupdfs/hypoxia.pdf

.

Elser, J.J., M.E.S. Bracken, E.E. Cleland, D.S. Gr uner, W.S. Harpole, H. Hillebrand, J.T. Ngai, E.W. Seabloom, J.B. Shurin, and J.E. Smith. 2007. Global analysis of nitrogen and phosphorus limitation of primary producers in freshwater, marine and terrestrial

ecosystems. Ecology Letters 10(12):1135-1142.

Falconer, I.R. 1999. An overview of problems cause d by toxic blue-green algae (cyanobacteria) in drinking and recreational water. Environmental Toxicology 14:5-12.

FDEP. 2008. Integrated Water Quality Assessment fo r Florida: 2008 305(b) Report and 303(d) List Update . Florida Department of Environmental Protection, Division of Environmental Assessment and Restoration, Bureau of Wa tershed Management, Tallahassee, FL.

FDEP. 2010. State of Florida Numeric Nutr ient Criteria Development Plan . Bureau of Assessment and Restoration Support, Divi sion of Environmental Assessment and Restoration, Florida Depart ment of Environmental Pr otection 2009a [cited October 2010]. Available from http://www.dep.state.fl.us/water/w qssp/nutrients/docs/fl-nutrientplan-v030309.pdf

.

FDEP. 2009b. State of Florida Numeric Nutr ient Criteria Development Plan . Bureau of Assessment and Restoration Support, Divi sion of Environmental Assessment and Restoration, Florida Department of Environmental Protection.

FDEP. 2010. Integrated Water Quality Assessment fo r Florida: 2010 305(b) Report and 303(d) List Update . Florida Department of Environmental Protection, Division of Environmental Assessment and Restoration, Tallahassee, FL.

Feldman, G.C., and C.R. McClain. 2010. Ocean Color Web, SeaWiFS and MODIS Reprocessing

2009.1 . NASA Goddard Space Flight Center 20102010]. Available from http://oceancolor.gsfc.nasa.gov/

.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 140 Fisher, T.R., E.R. Peele, J.W. Ammerman, a nd J. L.W. Harding. 1992. Nutrient limitation of phytoplankton in Chesapeake Bay. Mar. Ecol. Prog. Ser. 82:51-63.

Flemer, D.A., E.M. Lores, and C.M. Bundrick. 1 998. Potential sediment de nitrification rates in

estuaries of norther n Gulf of Mexico. J. Environ. Qual. 27(4):859-869.

Fore, L.S. 2004. Development and Testing of Biomonitori ng Tools for Macroinvertebrates in

Florida Streams . Final report to the Florida Depart ment of Environmental Protection, Tallahassee, FL.

Fore, L.S., R.B. Frydenborg, D. Miller, T. Fr ick, D. Whiting, J. Espy, and L. Wolfe. 2007. Development and Testing of Biomonitoring Tool s for Macroinvertebrates in Florida

Streams . Florida Department of Environmen tal Protection, 110 pp, Tallahassee, FL.

Fourqurean, J.W., J.N. Boyer, M.J. Durako, L. N. Hefty, and B.J. Peterson. 2003. Forecasting responses of seagrass distributions to ch anging water quality using monitoring data. Ecological Applications 13(2):474-489.

Fourqurean, J.W., R.D. Jones, and J.C. Ziema n. 1993. Process influencing water column nutrient characteristics and phosphorus limitation of phytoplankton biomass in Florida Bay, FL, USA: Inferences from spatial distributions. Estuarine, Coastal and Shelf Science 36(3):295-314.

Fourqurean, J.W., J.C. Zieman, and G.V.N. Powell. 1992a. Phosphorus limitation of primary production in Florida Bay: Evidence from the C:N:P ratios of the dominant seagrass

Thalassia testudinum. Limnology and Oceanography 37:162-171.

Fourqurean, J.W., J.C. Zieman, and G.V.N. Powell. 1992b. Relationships between porewater nutrients and seagrasses in a s ubtropical carbonate environment. Marine Biology 114(1):57-65.

Frankovich, T., D. Morrison, and J.W. Fourqur ean. 2010. Benthic Macrophyte Distribution and Abundance in Estuarine Mangrove Lakes and Es tuaries: Relationships to Environmental

Variables. Estuaries and Coasts

.

Gar ć ia, A.M., A.B. Hoos, and S. Terziotti. 2010. A regional framework of phosphorus sources and transport in streams of the southeastern United States. Journal of the American Water

Resources Association. In Press.

Giattina, J.D. 2008. United States Environmental Protecti on Agency Determination Upon Review of Amended Florida Admini strative Code Chapter 62-303 Identification of Impaired Surface Waters . U.S. Environmental Protection Agency, Region 4, Atlanta, GA.

Glibert, P.M., J.M. Burkholder, M.W. Parrow, A.J. Lewitus, and D.E. Gustafson. 2006. Direct uptake of nitrogen by Pfiesteria piscicida and Pfiesteria shumwayae, and nitrogen nutritional preferences. Harmful Algae 5(4):380-394.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 141 Greening, H., and A. Janicki. 2006. Toward revers al of eutrophic conditio ns in a subtropical estuary: Water quality and seagrass respons e to nitrogen loading reductions in Tampa Bay, Florida, USA. Environmental Management 38(2):163-178.

Hagy, J.D., J.C. Kurtz, and R.M. Greene. 2008. An Approach for Developing Numeric Nutrient Criteria for a Gulf Coast Estuary . U.S. Environmental Protection Agency, Office of Research and Development, National Hea lth and Environmental Effects Research Laboratory, Research Triangle Pa rk, NC. , EPA 600R-08/004, 44 pp.

Hagy, J.D., and M.C. Murrell. 2007. Susceptibility of a northern Gulf of Mexico estuary to hypoxia: An analysis using box models. Estuarine, Coastal and Shelf Science 74:239-

253.

Hamrick, J.M. 1992. A Three-Dimensional Environmental Fluid Dynamics Computer Code: Theoretical and Computational Aspects . The College of William and Mary, Virginia

Institute of Marine Scie nce, Special Report 317, 63 pp.

Heck, K.L., and J.F. Valentine. 2007. The prim acy of top-down effects in shallow benthic

ecosystems. Estuaries and Coasts 30(3):371-381.

Hecky, R.E., and P. Kilham. 1988. Nutrient limita tion of phytoplankton in fr eshwater and marine

environments: A review of recent ev idence on the effects of enrichment. Limnol. Oceanogr. 33(4, part 2):796-822.

Heil, C.A., and K.A. Steidinger. 2009. Monito ring, management, and mitigation, of Karenia

blooms in the eastern Gulf of Mexico. Harmful Algae 8:611-617.

Hoos, A.B., and G. McMahon. 2009. Spatial analysis of instream nitrogen loads and factors controlling nitrogen delivery to stream in th e southeastern United Sates using spatially referenced regression on watershed attri butes (SPARROW) and regional classification

frameworks. Hydrological Processes 23(16):2275-2294.

Howarth, R.W., A. Sharpley, and D. Walker. 2002. Sources of nutrient pollution to coastal waters in the United States: Implications for achieving coastal water quality goals.

Estuaries 25(4B):656-676.

Huang, B.Q., and H.S. Hong. 1999. Alkaline phosphata se activity and utiliz ation of dissolved organic phosphorus by algae in subtropical coastal waters. Marine Pollution Bulletin

39:205-211.

Hughes, A.R., S.L. Williams, C.M. Duarte, K.L. Heck, and M. Waycott. 2009. Associations of concern: declining seagrasses and threatened dependent species. Frontiers in Ecology

and the Environment 7(5):242-246.

Iverson, R.L., and H.F. Bittaker. 1986. Seagrass distribution in the eastern Gulf of Mexico. Estuarine Coastal Shelf Science 22:577-602.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 142 James, R.T., J. Martin, T. Wool, and P.F. Wang. 1997. A sediment resuspension and water quality model for Lake Okeechobee. Journal of the American Water Resources

Association 33:661-680.

Janicki, A.J., and D.L. Wade. 1996. Estimating critical nitrogen loads for the Tampa Bay estuary: An empirically based approach to setting management targets . Tampa Bay National Estuary Program, Technical publication #06-96, St. Petersburg, FL.

Johansson, J.O.R. 2002. Historical overview of Tampa Bay water quality and seagrass: issues

and trends. In Seagrass Management: It’s Not Just Nutrients! , edited by H. S. Greening. St. Petersburg, FL: Tampa Bay Estuary Program.

Johnson, P.T.J., A.R. Townsend, C.C. Cleveland, P. M. Glibert, R.W. Howarth, V.J. McKenzie, E. Rejmankova, and M.H. Ward. 2010. Linki ng environmental nutrient enrichment and disease emergence in humans and wildlife. Ecological Applications 20(1):16-29.

Kenworthy, W.J., and M.S. Fonseca. 1996. Light re quirements of seagrass es Halodule wrightii and Syringodium filiforme derived from the relationship between diffuse light attenuation and maximum depth distribution. Estuaries 19(3):740-750.

Klein, C.J., and S.P. Orlando. 1994. A spatial fram ework for water-quality management in the Florida Keys National Marine Sanctuary. Bulletin of Marine Science 54(3):1036-1044.

Koch, E.W. 2001. Beyond light: Physical, geological , and geochemical parameters as possible submersed aquatic vegetation habitat requirements. Estuaries 24(1):1-17.

Laws, E.A., and J.W. Archie. 1981. Appropriate use of regressi on analysis in marine biology. Marine Biology 65:13-16.

Lee, K.-S., S.R. Park, and Y.K. Kim. 2007. Effect s of irradiance, temperature, and nutrients on growth dynamics of seagrasses: A review. Journal of Experimental Marine Biology and Ecology 350(1-2):144-175.

Magley, W., and D. Joyner. 2008. Total maximum daily load for nutrients for the Lower St.

Johns River . Watershed Assessment Section, Bureau of Watershed Management, Florida Department of Environmental Protection, Tallahassee, FL.

Marshall, F., W. Nuttle, and B. Cosby. 2008. Biscayne Bay Freshwater Budget and the Relationship of Inflow to Salinity . Project report submitted to South Florida Water Management District by E nvironmental Consulting and Technology, Inc., New Smyrna Beach, FL.

McPherson, B.F., and R.L. Miller. 1994. Caus es of Light Attenuation in Tampa Bay and Charlotte Harbor, Southwestern Florida. Water Resources Bulletin 30(1):43-53.

Murrell, M.C., J.G. Campbell, J.D. Hagy, and J.M. Caffrey. 2009. Effects of irradiance on benthic and water column proce sses in a Gulf of Mexico estu ary: Pensacola Bay, Florida,

USA. Estuarine Coastal and Shelf Science 81(4):501-512.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 143 Nababan, B., F.E. Muller-Karger, C. Hu, and D.C. Biggs. In press . Chlorophyll variability in the

northeastern Gulf of Mexico. International Journal of Remote Sensing

.

NASA. n.d. chl_oc4: Chlorophyll-a c oncentration, OC4 algorithm . NASA Goddard Space Flight Center, available at http://oceancolor.gsfc.nasa.gov/DOC S/MSL12/master_prodlist.html/#prod11

.

National Research Council. 2000. Clean Coastal Waters: U nderstanding and Reducing the Effects of Nutrient Pollution . Report prepared by the O cean Study Board and Water Science and Technology Board, Commissi on on Geosciences, Environment and Resources, National Resource Council, Washington, D.C.

National Research Council. 2009. Nutrient Control Actions For Im proving Water Quality in the Mississippi River Basin and Northern Gulf of Mexico . National Academies Press, Washington, D.C.

NCDC. 2010. National Climatic Data Ce nter Online Climate Data Directory.

Niu, X. 2004. Change Point Analysis of the Chlorophyll a and LDI Data from the IWRM Status Network Cycle 1 Lakes Study . Technical report submitted to the Florida Department of

Environmental Protection. Florida State Universi ty, Department of Statistics, Tallahassee,

FL.

Nixon, S.W. 1995. Coastal marine eutrophication - A definition, social causes, and future

concerns. Ophelia 41:199-219.

NOAA. 2010. Harmful Algal Blooms: Current Programs Overview . Center for Sponsored Coastal Ocean Research, National Oceanic and Atmospheric Administration 2010 [cited September 22 2010]. Available from http://www.cop.noaa.gov/stressors/e xtremeevents/hab/default.aspx

.

O'Reilly, J.E., S. Maritorena, B.G. Mitchell, D.A. Siegel, K.L. Carder, S.A. Garver, M. Kahru,

and C. McClain. 1998. Ocean color ch lorophyll algorithms for SeaWiFS. Journal of Geophysical Research-Oceans 103(C11):24937-24953.

Olinger, L.W., R.G. Rogers, P.L. Fore, T. R.L ., B.L. Mullins, F.T. Bisterfeld, and L.A. Wise.

1975. Environmental and recovery studies of Escambia Bay and the Pensacola Bay system, Florida . United States Environmental Pr otection Agency, EPA 904/9-76-016.

Paerl, H.W. 1988. Nuisance phytoplankton blooms in coastal, estuarine, and inland waters. Limnology and Oceanography 33(4):823-847.

Phlips, E.J., and S. Badylak. 1996. Spatial va riability in phytopla nkton standing crop and composition in a shallow inner-shelf lagoon, Florida Bay, Florida. Bulletin of Marine

Science 58(1):203-216.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 144 Powell, G.V.N., W.J. Kenworthy, and a.J.W. Fourqurean. 1989. Experimental evidence for nutrient limitation of seagrass growth in a tr opical estuary with re stricted circulation. Bulletin of Marine Science 44(1):324-340.

Sargent, F., T. Leary, D.W. Crewz, and C.R. Kruer. 1995. Scarring of Florida's seagrasses: assessment and management options. Fla. Mar. Res. Inst. Tech. Rep 1:43.

Schroeder, W.W., and W.J. Wiseman. 1999. Geol ogy and hydrodynamics of Gulf of Mexico

Estuaries. In Biogeochemistry of Gulf of Mexico Estuaries , edited by T. S. Bianchi, J. R. Pennock and R. R. Twilley. New York: John Wiley and Sons, Inc.

Seitsinger, S.P. 1988. Denitrification in freshwat er and coastal marine ecosystems: ecological and geochemical significance. Limnol. Oceanogr. 33(part 2):702-724.

SENITG. 2009. An Urgent Call to Action: Report of the State-EPA Nutrient Innovations Task Group . State-EPA Nutrient Innovatio ns Task Group, Washington, DC.

SFWMD. 2010. Canals in South Florida: A Technical Support Document . South Florida Water Management District, 86 pp.

Snyder, B., M. Barbour, and E. Leppo. 1998. Development of a watershed-based approach for biomonitoring of fresh surface waters in coastal Florida canal system . Prepared by Tetra Tech, Inc., Owings Mills, MD under contract with Metro-Dade Environmental Resources Management, 201 pp.

Stepanauskas, R., L. Leonardson, and L.J. Tr anvik. 1999. Bioavailability of wetland-derived

DON to freshwater and ma rine bacterioplankton. Limnol. Oceanogr. 44(6):1477-1485.

Steward, J., R. Virnstein, L. Morris, and E. Lowe. 2005. Setting seagrass depth, coverage, and light targets for the Indian River Lagoon system, Florida. Estuaries and Coasts 28(6):923-935.

Steward, J.S., and E.F. Lowe. 2010. General empirical models for estimating nutrient load limits

for Florida's estuaries and inland waters. Limnology and Oceanography 55(1):433-445.

Stow, C.A., J. Jolliff, D.J. Mc Gillicuddy, Jr., S.C. Doney, J.I. Allen, M.A.M. Friedrichs, K.A. Rose, and P. Wallhead. 2009. Skill assessment for coupled biological/physical models of marine systems. J. Marine Systems 76:4-15.

Thomann, R.V. 1975. Mathematical Modeling of Phytopl ankton in Lake Ontario, 1. Model Development and Verification . U.S. Environmental Prot ection Agency, EPA-600/3-75- 005, Corvallis, OR.

Thomann, R.V., and J.J. Fitzpatrick. 1982. Calibration and Verification of a Mathematical Model of the Eutrophication of the Potomac Estuary . Department of Environmental Services, Government of the Distri ct of Columbia, Washington, DC.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 145 Thomann, R.V., R.P. Winfield, D.M. Di Toro, and D.J. O’Connor. 1976. Mathematical Modeling of Phytoplankton in Lake Ontari o, 2. Simulations Using LAKE 1 Model . U.S. Environmental Protection Agency, EPA-600/3-76-065, Gr osse Ile, MI.

Thomann, R.V., R.P. Winfield, and J.J. Segna. 1979. Verification Analysis of Lake Ontario and Rochester Embayment Three Dimensional Eutrophication Models . U.S. Environmental Protection Agency, EPA-600/3- 79-094, Grosse Ile, MI.

Tillman, D.H., C.F. Cerco, M.R. Noel, J.L. Martin, and J. Hamrick. 2004. Three-dimensional eutrophication model of the lower St. Johns River, Florida . Engineer Research and Development Center, US Army Corps of Engineers, ERDL/EL TR-04-13, 310 pp.

Tomasko, D.A., D.L. Bristol, and J.A. Ott. 2001. Assessment of present and future nitrogen loads, water quality, and seagrass (Thalass ia testudinum) depth distribution in Lemon Bay, Florida. Estuaries 24(6A):926-938.

Tomasko, D.A., C.A. Corbett, H.S. Greening, and G.E. Raulerson. 2005. Spatial and temporal variation in seagrass coverage in Southwest Florida: Assessing the relative effects of anthropogenic nutrient load reductions and ra infall in four co ntiguous estuaries. Marine

Pollution Bulletin 50(8):797-805.

USEPA. 1998. Guidelines for Ecological Risk Assessment . Risk Assessment Forum, U.S. Environmental Protection Agency, Washington, DC.

USEPA. 2000a. Ambient Aquatic Life Water Quality Crite ria for Dissolved Oxygen (Saltwater): Cape Cod to Cape Hatteras . Office of Water, PA-822- R-00-012, Washington D.C.

USEPA. 2000b. Nutrient Criteria Technical Gui dance Manual, Lakes and Reservoirs . Office of Water, EPA-822-B00-001.

USEPA. 2000c. Nutrient Criteria Technical Gui dance Manual, Rivers and Streams . Office of Water, EPA-822-B-01-002.

USEPA. 2001. Nutrient Criteria Technical Guidance Manual, Estuarine and Coastal Marine

Waters . Office of Water, EPA-822-B-01-003.

USEPA. 2006. National Estuary Program Coastal Condition Report . U.S. Environmental Protection Agency, Office of Water and Offi ce of Research and Development, EPA-842-

B-06-001.

USEPA. 2007. Remote Sensing Prog ram for EPA FY2006 Program Summary. U. S. Environmental Protection Agency, Washington, DC. EPA/600/X-06/035:pp. 40.

USEPA. 2008a. National Coastal Condition Report III . U.S. Environmental Protection Agency, Office of Water and Office of Research and Development.

USEPA. 2008b. Nutrient Criteria Technical Guidance Manual, Wetlands . Office of Water, EPA822-B-08-001.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 146 USEPA. 2010. Using Stressor-Response Relationships to Derive Numeric Nutrient Criteria

. Office of Water, EPA-820- S-10-001, Washington, DC.

USGS. 2010. Hypoxia . U.S. Geological Survey 2010 [cited September 22 2010]. Available from http://toxics.usgs.gov/de finitions/hypoxia.html

.

Vargo, G.A. 2009a. A brief summary of the physio logy and ecology of Karenia brevis Davis (G. Hansen and Moestrup comb. nov.) red tides on the West Florida Shelf and of hypotheses posed for their initiation, grow th, maintenance and termination. Harmful Algae 8:573-

584.

Vargo, G.A. 2009b. A brief summary of the physiology and ecology of Karenia brevis Davis (G. Hansen and Moestrup comb. nov.) red tides on the West Florida Shelf and of hypotheses posed for their initia tion, growth, maintena nce, and termination. Harmful Algae 8:573-

584.

Vitousek, P.M., J.D. Aber, R.W. Howarth, G.E. Likens, P.A. Matson, D.W. Schindler, W.H. Schlesinger, and D.G. Tilman. 1997. Human a lteration of the global nitrogen cycle: Sources and consequences. Ecological Applications 7(3):737-750.

Walker, N.D., and N.N. Rabalais. 2006. Relationshi ps among satellite chlo rophyll a, river inputs, and hypoxia on the Louisiana continen tal shelf, Gulf of Mexico. Estuaries and Coasts 29(6B):1081-1093.

Waycott, M., C.M. Duarte, T.J.B. Carruthers, R.J. Orth, W.C. Dennison, S. Olyarnik, A. Calladine, J.W. Fourqurean, K.L. Heck, A.R. Hughes, G.A. Kendrick, W.J. Kenworthy, F.T. Short, and S.L. Williams. 2009. Acceler ating loss of seagrasses across the globe threatens coastal ecosystems. Proceedings of the National Ac ademy of Sciences of the United States of America 106(30):12377-12381.

Werdell, P.J., and S.W. Bailey. 2002. The SeaWiFS Bio-optical Ar chive and Storage System (SeaBASS): Current architecture and implementation. . In NASA Tech. Memo. 2002-

211617 , edited by G. S. Fargion and C. R. McClain. NASA Goddard Space Flight Center, 45, Greenbelt.

Werdell, P.J., S.W. Bailey, G.S. Fargion, C. Pi etras, K.D. Knobelspie sse, G.C. Feldman, and C.R. McClain. 2003. Unique data repository fa cilitates ocean color satellite validation.
EOS, Transactions 84(38):377.

Werdell, P.J., S.W. Bailey, B.A. Franz, L.W. H. Jr., G.C. Feldman, and C.R. McClain. 2009. Regional and seasonal variability of chlor ophyll-a in Chesapeake bay as observed by SeaWiFS and MODIS-Aqua. Remote Sensing of Environment 113:1319-1330.

Wolfe, S.H. 2007. An Inventory of Hydrodynamic, Water Quality, and Ecosystem Models of

Florida Coastal and Ocean Waters . Florida Department of Environmental Protection, Tallahassee, Florida.Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and S outhern Inland Flowing Waters



### 147 Wool, T.A., S.R. Davie, and H.R. Rodriguez. 2003. Development of a Three Dimensional Hydrodynamic and Water Quality Model to Su pport the TMDL Decision Process for the Neuse River Estuary, North Carolina. American Society of Engineers Journal of Water Resources Planning and Management 129(129):295-306.

Zieman, J.C., J.W. Fourqurean, and R.L. Iverson. 1989. Distribution, Abundance and Productivity of Seagrasses a nd Macroalgae in Florida Bay. Bulletin of Marine Science 44(1):292-311.