Methods and Approaches for Deriving 

Numeric Criteria for 

Nitrogen/Phosphorus Pollution in 

Florida’s Estuaries, Coastal Waters, and 

Southern Inland Flowing Waters 

November 17, 2010 

Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 provided herein does not substitute 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 requirements on EPA, States, Authorized Tribes, the 

regulated community, or any other party, and might not apply to a particular situation or 

circumstance. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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Contributing Authors

Jim Carleton, U.S. Environmental Protection Agency

Tiffany Crawford, U.S. Environmental Protection Agency

Ed Decker, U.S. Environmental Protection Agency

Edward Dettman, U.S. Environmental 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. Environmental Protection Agency 

Jacques Oliver, U.S. Environmental Protection Agency

Blake Schaeffer, U.S. Environmental 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 Tech 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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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 Nitrogen/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 Selection of Potential Assessment Endpoints and 

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

2.3.1 Selecting Assessment Endpoints and Water Quality Indicator Variables ............ 33

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

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

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

3 Numeric Nutrient Criteria Development in Florida Estuaries .............................................. 45

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

3.2 Selection of Assessment Endpoints 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 ................................................................................................ 65
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 Development 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 ChlRS-a Criteria from Statistical 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 Sources and Assessment Endpoints ............................ 102

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

5.5 Marine Waters: Classification 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 .............................................................................................................. 122

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 Florida Marine Waters .................................................. 126

6.5.1 Binomial Approach for Downstream Protection ................................................ 127

6.5.2 Calculating EAA Downstream Protective Values Based on EvPA Total 

Phosphorus Criteria ............................................................................................. 128
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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

6.7 Example Application: Pensacola Bay Watershed ...................................................... 128

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

6.7.2 Fraction-Delivered in the Pensacola 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 Assessment Endpoints and Water Quality Indicator 

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

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

B.1.1 Scientific Justification 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 Investigations ............................................................................ 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 Estuarine 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 Pollution in Macroalgal Growth ....................... B-18 

B.4.4 Scientific Justification 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/Phosphorus Pollution on Epiphytes .................................... B-24 

B.5.3 Scientific Justification for Not Proposing Epiphytes as an Assessment 

Endpoint ............................................................................................................ B-26 

B.6 Seagrass .................................................................................................................... B-26 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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B.6.1 Effect of Nitrogen/Phosphorus 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/Phosphorus Pollution on Benthic Macroinvertebrates 

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

B.7.2 Scientific Justification for Not Proposing Macroinvertebrate and Fish 

Indices as Assessment Endpoints for Estuarine and Marine Systems .............. B-36 

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

B.8.1 Scientific Justification 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 Justification for Not Proposing Coral as an Assessment Endpoint .. B-46 

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

B.10.1 Scientific Justification for Not Proposing Spartina 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 Hydrodynamics 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-16 

C.6 EFDC: Environmental Fluid Dynamics 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-44 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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

C.13 SPARROW: SPAtially Referenced Regression On Watershed Attributes Model 

Description ............................................................................................................... C-47 

C.14 WAMView: Watershed Assessment Model with an ArcView Interface Model 

Description ............................................................................................................... C-52 

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

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

Formulation ......................................................................................................... D-1 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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Figures

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) ...... 26

Figure 2-1. Pathways for nutrient effects on estuarine 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 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......................................................................................................... 45

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) ..................... 47

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. ........ 48

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). .................................................................. 54

Figure 3-5. Location of 19 drainage areas for which LSPC watershed 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 (Zc-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. ............. 67

Figure 3-7. Location Map of Pensacola Bay ................................................................................ 70
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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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. .. 73

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

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 (ChlRS-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 ChlRS-a coverage of the 91 coastal 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 ChlRS-a distribution. Red points indicate ChlRS-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 ChlRS-a at the 

Tampa Bay coastal segment with and without K. brevis. ........................................... 83

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) ................................... 84

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. ............................................................................................................ 85

Figure 4-7. (A) ChlRS-a (μg/L) for five coastal segments near Tampa Bay. The overall mean 

ChlRS-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) ......................................................................... 86

Figure 4-8. Z-CUSUM plot for (A) ChlRS-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.) .................... 88

Figure 4-9. Field-adjusted ChlRS-a (μg/L) distributions for all coastal segments between 1997 

and 2009 with representations of the minimum, 10th, 25th, 50th, 75th, 90th and 

maximum values. Northwest Gulf Coast segment 1 is located at the Alabama/Florida 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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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.

..................................................................................................................................... 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 Nutrient Watershed Region ................................................................. 94

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

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

Figure 5-5. South Florida Nutrient Region subregions under consideration ................................ 97

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. .. 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 land uses (source: Snyder et al. 

1998) ......................................................................................................................... 103

Figure 5-9. Depiction of land use area (light yellow) included 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 annual geometric mean 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 annual geometric mean 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 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)

................................................................................................................................... 113
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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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].) ............................................ 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 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)) .................... 118

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 ...................... 119

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 window ..................... 119

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 ......... 120

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. . 123

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. ......................... 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 WBIDs that make up the larger open water 

areas of Pensacola Bay. ............................................................................................. 129

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. ............................................. 131

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) ............................................................................... 132

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 (Garćia et al. 2010) .................................... 132
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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, 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 watershed associated with unique terminal reach DPV 

values for TP (panel A) and the resulting 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 Garćia et al. (2010). ... 134
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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Tables

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

advantages and disadvantages. .................................................................................... 34

Table 2-2. Indicator variables for evaluating the magnitude and effects of nutrients, including 

advantages and disadvantages. .................................................................................... 36

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 ........... 50

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. ..................................................................................................................... 51

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. .................................................... 57

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. ................................... 63

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. ............................................................................................................. 65

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. ........................................... 67

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

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

Table 4-1. Number of observations in IWR Run 40 since 1990 for chlorophyll, TN and TP for 

coastal waterbodies ..................................................................................................... 80
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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Table 5-1. Land use classifications and LDI coefficients. Higher values indicate greater intensity 

of human land use (adapted from 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 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) .............................................................................................. 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 Model 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 concentrations for terminal stream reaches. 

Average flows are 1997–2009 averages computed using LSPC. ............................. 130
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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

ChlRS-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 mean 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 

kTN first-order decay rate for total nitrogen 

kTP first-order decay rate for total phosphorus 

L liter 

LDI Landscape Development Intensity Index 

LN natural logarithm 

LM(N) total nitrogen load limit 

LM(P) total phosphorus load limit 

LOESS locally weighted scatterplot smoothing 

log10 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 

NH3 ammonia 

NHDPlus National Hydrography Dataset Plus 

NLCD National Land Cover Data 

NO3-NO2 nitrate+nitrite 

NOAA National Oceanic and Atmospheric Administration 

NODC National Oceanographic Data Center 

NPDES National Pollutant Discharge Elimination System 

NPOESS National Polar-orbiting Operational Environmental Satellite System 

NPP National Polar-orbiting Operational 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 module 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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psu practical salinity units 

qs hydraulic loading 

R2 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 for 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 Regression 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 

Zc colonization depth 

τw water residence time 

μg micrograms 

‰ parts per thousand 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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18

Executive Summary

Excess inputs of nitrogen and phosphorus (nitrogen/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 oxygen levels as the algae and plants decompose, 

and decreased biodiversity. Primary sources of nitrogen and phosphorus to aquatic ecosystems 

include waste water and sewage effluent, atmospheric deposition, landfill leachate, fossil fuel 

combustion, and runoff from commercial fertilizer and manure applications. 

Nitrogen/phosphorus pollution contributes 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 rivers, 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 derivation and implementation of numeric nutrient 

criteria. Numeric criteria and water quality standards are key components of water quality 

assessments and watershed protection management. These numeric criteria will create 

environmental baselines that allow Florida to manage waters more effectively, measure progress, 

and support broader partnerships based on nutrient trading, best management practices (BMPs), 

land stewardship, wetlands protection, voluntary collaboration, and urban stormwater runoff 

control strategies. Establishing numeric criteria in State water quality standards will give Florida 

greater ease and faster development of total maximum daily loads (TMDLs), provide 

quantitative targets to support trading programs, enable permit writers greater flexibility and ease 

in developing National Pollutant Discharge Elimination System (NPDES) permits, and increase 

the effectiveness in evaluating the success of Florida’s efforts to control nitrogen/phosphorus 

pollution throughout the State. 

Chapter 3 describes the approach EPA is considering to develop numeric criteria for Florida's 

estuaries. The approach described would allow the Agency to most fully consider characteristics 

of estuarine ecosystems (e.g., water quality and biological communities in estuaries are affected 

by a combination of basin shape, tides, and the 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 water quality and aquatic life uses, which could 

also result in water quality criteria 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 the waters. Methods for South Florida are described in Chapter 5. 

EPA’s methodology first delineates the estuaries into discrete areas around Florida’s coastline 

for the purpose of organizing the criteria development process. Each of these discrete areas will 

then be evaluated to determine the appropriate “assessment endpoints” and “measurement 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 protection and restoration of 

healthy seagrass communities, balanced communities of benthos, plankton, and nekton, and 

balanced algal biomass and production. Chapter 3 discusses the rationale 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 combination to develop 

numeric criteria for chlorophyll a, total nitrogen (TN) and total phosphorus (TP). 

Chapter 4 provides the rationale and approaches EPA is considering 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 (ChlRS-a) observations. Satellite 

ocean color remote sensing technology has advanced over the past decade and historical ChlRS-a 

data are available for the past ten years. In contrast there is relatively little field monitoring data 

of chemical and biological constituents along the Northwest Gulf Coast and Atlantic Coast of 

Florida. 

Coastal physical forcings such as wind, currents, and tides are known to influence coastal 

chlorophyll dynamics together with nutrient loadings from the land. 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 reflectance on satellite measurements, EPA is not 

considering the derivation of numeric criteria using remote sensing 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 Florida marine and inland flowing waters. EPA is 

defining South Florida inland flowing waters as free-flowing, predominantly fresh surface water 

in a defined channel, and includes, streams, rivers, creeks, branches, canals, freshwater sloughs, 

and other similar water bodies located in the South Florida nutrient watershed 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 waters and numeric chlorophyll a, TN, and TP criteria in South Florida 

marine waters using least-disturbed sites that support balanced natural populations of aquatic 

flora and fauna. Alternative methods of criteria derivation for inland flowing waters that EPA is 

considering include stressor-response 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). 

Pursuant to 40 CFR 131.10(b), water quality standards must ensure the attainment and 

maintenance of downstream water quality standards. Thus, EPA is deriving numeric nutrient 

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 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

20

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 conceptual 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 support balanced natural 

populations of aquatic flora and fauna in estuarine waters. The loading limits will be determined 

as part of the criteria development 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 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 retention of TN and TP within the stream network. Because of the 

complexities associated with the managed flows 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 estuary as protective concentrations or, alternatively, 

protective loads. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

21

1 Introduction

1.1 Purpose of the Document

The United States Environmental Protection Agency (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 revised water quality standards for nutrients 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 flowing waters. In addition, this document describes the approach 

EPA is considering for developing downstream protective values (DPVs) that may be applied to 

Florida streams to ensure the attainment and maintenance of downstream 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 controlling nitrogen/ phosphorus pollution. Chapter 2 

includes the statutory requirements that direct EPA and States to reduce nitrogen/phosphorus 

pollution, the regulatory action that determined numeric criteria are necessary in the State of 

Florida, background information on nitrogen/phosphorus pollution and effects within the aquatic 

environment. 

Chapter 2 provides a description of EPA’s general approach for developing numeric criteria, 

generic conceptual model, and endpoints that form the basis for analysis plans. 

The next three chapters summarize background 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 criteria derivation. Potential uncertainties and data 

gaps associated with the approaches are also included. 

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

estuaries. 

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

derivation in Florida coastal waters. 

Chapter 5 describes the approach EPA is considering for numeric criteria derivation to address 

the unique hydrodynamic conditions found in South Florida. 

Chapter 6 describes an approach EPA is considering to quantitatively derive numeric values for 

streams to protect downstream estuaries. Potential uncertainties and data gaps associated with the 

approach are also included. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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, physical, and biological 

integrity of the Nation's waters.” Under CWA section 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, and wildlife and provides for recreation in and on the water be 

achieved.” This goal is commonly referred to as fishable and swimmable. 

In order to meet the fishable and swimmable goal, 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 standards provide the common 

mechanism by which the other parts of the CWA (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, States adopt water quality standards 

that consist of designated uses of the navigable waters and water quality criteria that are 

protective of those designated uses. The State specifies the designated use that must be achieved 

or protected. When designating uses, the State must take into consideration the use and value of 

water for public water supplies, protection and propagation of fish, shellfish and wildlife, 

recreation in and on the water, agricultural, industrial and other purposes including navigation 

(CWA section 303(c) and 40 CFR 131.10). 

Designated uses can be general, such as aquatic life use protection or primary 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 (Subsection 403.061[10], F.S.). Florida’s surface 

water is protected for five designated use classifications, as follows: 

• Class I Potable water supplies 

• Class II Shellfish propagation or harvesting 

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

• Class IV Agricultural water supplies (large agricultural lands, located mainly 

around Lake Okeechobee) 

• Class V Navigation, utility, and industrial use (there are no state waters 

currently in this class) 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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23

Class I waters generally have the most stringent water quality criteria and Class V 

the least. Class I, II, and III surface waters share water quality criteria established 

to protect recreation and the propagation 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 specifically 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 standards to protect the 

designated uses from the discharge of pollutants. These criteria are expressed as either narrative 

statements or numeric values. In order to protect the designated uses 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, flowing 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 Administrator is then required to promptly prepare and 

publish proposed regulations setting forth new or revised water quality standards. 

In making the determination, the Agency considered (1) the State's documented unique and 

threatened ecosystems, (2) the high number of impaired waters due to existing 

nitrogen/phosphorus pollution, and (3) the challenge associated with growing 

nitrogen/phosphorus pollution resulting from expanding urbanization, continued agricultural 

development, and a significantly increasing population. 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 establish enforceable controls for 

nonpoint source activities. However, the significant 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 Florida’s reliance on a case-by-case interpretation 

of its narrative nutrient criterion in implementing an otherwise comprehensive water quality 

framework of enforceable accountability was insufficient 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 and deriving water quality-based 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 waters1

 and springs within 

 

1

 In the November 2010 rulemaking, EPA did not establish 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 River watershed to the west of Lake Okeechobee and the St. Lucie watershed 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

24

the State of Florida. These promulgated criteria only apply to predominantly fresh surface 

waters classified as Class I or Class III in order 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 waters that have been 

designated Class I, II and III. The methods and approaches 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 waterbody, 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 major 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 combustion) (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 are especially vulnerable to nitrogen/phosphorus 

pollution because they receive nitrogen and phosphorus 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 five 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 well 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. Methods and approaches that EPA is considering 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 loading of N and P, which causes increased organic matter 

production within the ecosystem. 

3

 Some estuaries can be more susceptible to eutrophication 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 all affect an estuary’s response to added nutrients (National Research Council 

2000). 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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25

 

The impacts of nitrogen/phosphorus pollution can adversely affect aquatic life in many different 

ways (see Figure 1-1). The effects of nitrogen/phosphorus pollution include direct changes to 

aquatic systems (e.g., increased algal growth, changes in algal species composition, and 

increased organic matter production) and indirect effects (e.g., loss of submerged aquatic 

vegetation, nuisance algal blooms, and low dissolved oxygen) (USEPA 2006, 2008a). The 

eutrophication process has resulted in large “dead zones” found in many coastal areas, such as 

the Gulf of Mexico and Chesapeake Bay (Ecological Society of America 2009) and reduced 

seagrass beds, a foundation species for many estuarine waters (Hughes et al. 2009; Tomasko et 

al. 2005). The Florida Department of Environmental 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 changes in primary production (e.g., increases in 

phytoplankton) can include increased turbidity and decreased light penetration (Boyer et al. 

2009; Bricker et al. 2007; Bricker et al. 2008; McPherson and Miller 1994). This can reduce light 

availability necessary for the growth of submerged aquatic vegetation. This effect often leads to 

declines in seagrass in estuaries and coastal waters (Lee et al. 2007; Dennison 1987; Duarte 

1991). Seagrass are critical components of many estuarine and coastal systems and are used as 

feeding, spawning, and nursery grounds for many aquatic species (see review 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 entire 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). Because these changes affect natural processes at the lowest 

levels of the ecosystem, they can cause a cascade of problems. 

Eutrophication has also been shown to increase the incidence of disease in aquatic animals and 

wildlife (Johnson et al. 2010). Although nitrogen/phosphorus pollution 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 (Falconer 1999; NOAA 2010). Direct impacts to humans result from 

exposure to HAB toxins or consumption 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 Southern 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 contributes to increased oxygen consumption associated with 

decomposition, potentially reducing oxygen to levels below those needed for aquatic life to 

survive (NOAA 2010; USGS 2010). Low oxygen concentrations, or hypoxia, can occur in 

episodic “events,” which sometimes develop overnight. Migration to avoid hypoxia depends on 

species’ mobility, availability of suitable habitat, and adequate environmental cues for migration. 

For example, mobile species, such as adult fish, 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 animals 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 levels of DO cause a severe decrease in the 

amount of aquatic life in hypoxic zones and affect the ability of aquatic organisms to find 

necessary food and habitat. In extreme cases, anoxic 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 areas to recreational uses. The loss of biological 

resources can also reduce or preclude recreational fishing, shellfish harvest, and diving. Other 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

27

direct impacts to humans include harmful levels of nitrate in drinking water supplies (SENITG 

2009). 

1.5 Purpose of this Effort  

As described above, EPA will propose numeric values to translate the State of Florida’s current 

narrative criteria to protect estuaries, coastal waters and South Florida 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, the numeric values will protect estuaries, coastal 

waters and South Florida inland flowing waters within the State that have been designated as 

Class I, II or III (see Section 1.3), translating the currently applicable narrative criterion that the 

State has established to be protective 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 numeric values to protect Class IV and V waters as 

they are not part of the CWA section 101(a) designations that require water quality that provide 

for fishable and swimmable conditions. As the State of Florida already has numeric criteria for 

dissolved oxygen, EPA is not considering the development of dissolved oxygen criteria. For 

those Class I waters in South Florida (i.e., designated as potable water supplies), the drinking 

water maximum contaminant levels for nitrates (10 mg/L) would continue to apply. EPA is 

currently not considering the modification of any designated uses of waters within the State of 

Florida. 
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2 Approach

2.1 Overview

EPA’s 1976 publication entitled Quality Criteria for Water (also known as the Red Book) 

contains ambient water quality criteria for nitrates and elemental phosphorus. For domestic water 

supplies, the maximum contaminant level for nitrate was set at 10 mg/L to protect human health 

from exposure to this pollutant through domestic drinking water. The phosphorus criterion was 

set at 0.10 μg/L elemental phosphorus to protect against the toxic effects of elemental 

phosphorus to estuarine and marine organisms. Note that neither of these criteria was set to 

reduce the potential for eutrophication, although the Red Book does present a rationale for 

supporting a total phosphorus 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 states, 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-response 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 preventing eutrophication on a national scale. There are a total of 

26 peer-reviewed ecoregional criteria documents in 2001 and 2002 that cover most water body 

types in the United States (12 lakes and reservoirs, 13 rivers and streams, and one wetland). Each 

criteria document presents recommended criteria for causal parameters (total phosphorus and 

total nitrogen) and response variables (chlorophyll a and some form of water clarity, i.e., 

turbidity or Secchi depth). EPA developed the ecoregional criteria values using a distributional 

approach that utilized all available data. This information is intended as a starting point for 

states, authorized tribes and others to develop more refined numeric criteria, as appropriate, 

using EPA waterbody-specific technical guidance manuals and other scientifically defensible 

approaches. These recommended criteria documents can be accessed at the following Web sites: 

• Lakes: http://www.epa.gov/waterscience/criteria/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/nutrient/ecoregions/wetlands/wetlands_13.pdf 

None of these available recommended numeric criteria 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 narrative criterion using the published 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 Southern 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 2000c), and estuaries and coastal 

marine waters in October 2001 (USEPA 2001). These technical guidance documents describe the 

techniques used to derive numeric criteria for use 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 general approaches that could be used 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 data plotted as frequency distributions 

2. Use of predictive relationships (e.g., trophic state classifications, empirical and mechanistic 

models, biocriteria) 

3. Application and/or modification of established nutrient/algal thresholds (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 scientifically defensible numeric nutrient criteria. However, EPA 

recognized that each approach has different data requirements, and these differences should be 

considered in the context of individual situations and available information. The methods and 

approaches described in this document demonstrate that EPA is considering the derivation of 

numeric criteria for estuarine, coastal, and South Florida inland flowing waters using the tools 

and approaches described in these guidance documents, as well as new methods that follow the 

general approach outlined by EPA and are reflective 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 waters 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 Research and Development (staff from the Gulf 

Ecology Division in Pensacola FL and the Atlantic 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, Chapter 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 Southern Inland Flowing Waters 

31

3. Develop a classification scheme – this step establishes a classification scheme for 

subdividing the population of waterbodies for which numeric criteria are developed. For this 

effort, EPA first divided the waters into three groups: (1) estuarine, (2) coastal, and (3) South 

Florida. Chapters 3-5 provide additional details 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 classifications 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 and 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 data and assessment endpoints used to 

translate Florida’s narrative criterion, EPA is considering a variety of specific methods for 

deriving the numeric criteria. Chapters 3-5 describe the 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 nutrient criteria: 

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

• Determination of a reference condition using one of several alternative approaches. 

Remember that the reference condition, however derived, is only one of the three 

approaches of the criteria development process. 

• Use of empirical modeling (or surrogate data sets, where available, in those instances 

where insufficient information exists). This may be the case especially in estuaries with 

insufficient hydrological data, or significantly developed or modified 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 reflect 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 nutrient 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 estuarine waters (see Chapter 3), coastal waters 

(see Chapter 4), and South Florida marine and inland flowing waters (see Chapter 5). 

2.2 Conceptual Model

Ideally, the aquatic life to be protected in a water 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 protected. For this effort, EPA has considered the 

available data and information, which has provided insight into the relative health of various 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

32

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

disturbance. In order to restore 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 suitable surrogates or 

indicators closely correlated with overall system health, and expected to be sensitive to stressors, 

in this case, nitrogen/phosphorus pollution. Figure 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 group of waters in Chapters 3-5. 

Nitrogen/phosphorus pollution can result in excess biomass which can deplete oxygen resulting 

from decay of nutrient-enhanced organic matter production. Recognition of this pathway 

provides direction for setting protective numeric criteria for nitrogen/phosphorus and related 

parameters. In order to support balanced natural populations of aquatic flora and fauna in Florida 

through effective implementation of CWA programs, EPA will develop and establish numeric 

criteria for causal variables, TN and TP, as well as the primary response 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

LightGoal

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

Variable
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 protection of aquatic flora and fauna from 

nitrogen/phosphorus pollution (see Appendix B). 

The true assessment endpoints are the valued ecosystem characteristics that are desired to be 

protected. In a regulatory context, the designated uses and their associated narrative criteria may 

be considered as assessment endpoints. These assessment endpoints (such as shellfish 

propagation and harvesting) are often difficult to predict or measure directly. Therefore, the 

development of water quality criteria usually proceeds through the evaluation of simpler 

endpoints (referred to as indicators or measures) that are measurable 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 environment and their contact or co-occurrence with the 

assessment endpoint,” and measures of ecosystem 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 with 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, the target will be the numeric value of an indicator 

variable that supports a balance natural population of aquatic flora and fauna in Florida’s 

estuaries, coastal areas and southern inland flowing waters. 

Salient aspects of the literature review and EPA’s basis for selecting assessment endpoints and 

the proposed water quality indicator variables to protect those 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 select assessment endpoints that are 

sensitive to nitrogen/phosphorus pollution, so that one can infer that the numeric criteria will 

protect less sensitive receptors from such pollution. Additionally, it is important to choose 

endpoints with sufficient data that would allow quantitative relationships to be developed either 

through stressor-response relationships (e.g., empirical or regression 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 minimum, be qualitatively related to nutrient 

enrichment (e.g., Bricker et al. 2008). EPA searched scientific databases (including: Google 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

34

Scholar, Web of Science, and state research and agency reports) and reviewed more than 800 

documents to investigate assessment endpoints and the likely stressors driving responses in 

estuarine and marine systems. The assessment endpoints examined include phytoplankton, 

macroalgae, epiphytes, seagrass, benthic macroinvertebrate and fish indices, HABs, and coral. 

The literature review also captured the nature and location of the investigations and the 

endpoint’s relationship to nutrients and other causal variables. A detailed bibliography and 

results of the literature review are provided in Appendix A and Appendix B, respectively. The 

major assessment endpoints and indicator variables considered and salient 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 development 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 Disadvantages

Seagrass

• 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 (Zc)

useful indicator

• Once Zc 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 canals
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

35

Importance

Linkages to, or  

Effects of, Nutrients Advantages Disadvantages

Harmful Algal Blooms

• 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 applications

Corals

• 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 enrichment

Epiphytes

• 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 data

Invertebrates

• 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 Southern 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 Disadvantages

Clarity

• 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])

Dissolved Oxygen

• 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 DO  

Chlorophyll a

• 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 canals  

Total Nitrogen

• 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 complex

Total Phosphorus

• 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 systems
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

37

2.3.2 Selected Biological Assessment Endpoints  

Based on the reviewed literature 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 narrative nutrient criterion into numeric values: (1) 

healthy seagrass communities, (2) balanced phytoplankton biomass and production, and 

(3) balanced faunal communities. 

2.3.2.1 Healthy Seagrass Communities

Seagrass can be considered foundation species in aquatic 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 habitat for many fish, birds, and invertebrates, 

and are also an important food source for endangered 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., light availability) is important and related to nutrient enrichment 

(e.g., Dennison 1987; Duarte 1991; Duarte 1995; Lee et al. 2007). Light is a critical variable for 

protecting and restoring seagrass. Water clarity can be negatively affected by an over-abundance 

of phytoplankton because the phytoplankton itself and the detritus associated with phytoplankton 

production absorb and scatter light before it reaches the seagrass. Increases 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 sediments) 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 seagrass bed depth. Numeric criteria for chlorophyll a, 

the indicator variable, can be computed using the relationship between 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 communities 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 Florida 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 estuaries 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 the 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

38

narrative criterion (Giattina 2008). The natural dynamics of chlorophyll a vary among and 

occasionally within aquatic systems. To determine the appropriate water quality criteria, a 

variety of factors influencing the response of chlorophyll a, the assessment endpoint, should be 

considered. 

Shifts in the composition of phytoplankton and zooplankton assemblages have been observed in 

estuarine and freshwater ecosystems in which nutrient loading rates are increased, both in Florida 

and elsewhere in the country (Arhonditsis et al. 2007; Armitage and Fong 2004; Cloern 2001; 

Cloern 1996). Although informative studies of phytoplankton assemblage composition are 

relatively uncommon, unusually high phytoplankton biomass (and thus, chlorophyll a) has been 

associated with proliferation of toxic or otherwise harmful species (Cloern 2001). One reason is 

that such species are not effectively controlled by planktonic grazers, allowing their biomass to 

increase. Species shifts may involve an increase in the abundance 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 occur in response to a change in the relative 

abundance of different nutrients. Increased abundance of nitrogen and phosphorus sometimes 

result in silica limitation, which favors non-diatom species because they do not require silica 

(Cloern 2001). EPA is considering 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 relationship 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 critically on sufficient DO (e.g., Diaz 2001; Diaz and 

Rosenberg 2008). In estuaries and 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 mortality 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 concentration swings characterized by high 

concentrations during the daylight hours, followed by periods of low concentrations (potentially 

hypoxic or anoxic) during the nighttime period. Furthermore, highly productive systems tend to 

have large amounts of detritus that settle to the bottom sediments and are oxidized by bacteria, 

further consuming oxygen and resulting in sediment 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 water from the atmosphere) with 

oxygen-poor bottom waters (where oxygen is consumed 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 and 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 Southern Inland Flowing Waters 

39

2.3.3 Water Quality Indicator Variables for Expressing Criteria  

Based on EPA guidance (USEPA 2001) and an assessment of the available literature, the 

numeric criteria for Florida’s estuaries, coastal 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 μg/L). 

Appropriate numeric criteria for these three variables will help ensure protection of the 

biological assessment endpoints identified in Figure 2-1 are achieved, thereby supporting a 

balanced natural population of aquatic flora and fauna. 

While the conceptual model of eutrophication continues to evolve (Cloern 2001), it is clear that 

nitrogen and phosphorus are the primary macronutrients 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 without sufficient nutrient supply (USEPA 2001). 

Often the addition of both nitrogen and phosphorus will elicit greater phytoplankton biomass 

stimulation than the sum of both nutrients added separately (Fisher et al. 1992). There are 

reported cases where both nitrogen and phosphorus are required to elicit a phytoplankton 

biomass production response in estuaries (Flemer et al. 1998), suggesting 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; Fourqurean et al. 1992a; Fourqurean et al. 1992b). 

2.3.3.1 Total Nitrogen  

Nitrogen is an important limiting nutrient of algal biomass production (USEPA 2001). TN 

consists of organic and inorganic forms. Stimulated algal biomass production is typically 

attributed to inorganic nitrogen (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 estuaries, nitrogen concentrations, especially the 

inorganic forms, typically vary widely seasonally, 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 salinity 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 bottom water), and water column depth. This process 

helps to modulate extreme dissolved inorganic 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 range of salinity in certain 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 organic 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

40

phosphorus (DOP, POP). These compounds may be soluble, in particulates or detritus, or 

incorporated as organic phosphorus in organisms. 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) and some estuaries and coastal marine systems. 

In instances where phosphate is limiting, the discharge of raw or untreated wastewater, 

agricultural drainage, or certain industrial wastes may stimulate 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 utilization except by algae that have the capability to 

break down DOP with alkaline phosphatase (algal and free phosphatases) and utilize the 

phosphate as inorganic phosphate (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 relevancy 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 variety of negative effects (Bricker et al. 

2003b; Vitousek et al. 1997). For example, excess primary production can reduce water clarity, 

resulting in reduced light availability for benthic algae, macrophytes, and seagrasses (Boyer et al. 

2009; Bricker et al. 2008). Seagrass decomposition and destabilization of sediments may then 

result in more nutrient inputs into the water column 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 literature, EPA is not considering using the following nutrient-sensitive 

biological assessment endpoints to translate Florida’s narrative criterion into numeric values: 

(1) HABs, (2) coral, (3) epiphytes, (4) macroinvertebrate 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 because there is either an absence of sufficient 

data to assess the effects of measured nitrogen/phosphorus concentrations, or there is an 

alternative sensitive assessment endpoint available that is a better indicator of 

nitrogen/phosphorus pollution. Appendix B provides additional information regarding the 

scientific rationale for not including these organisms 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 for development of numeric criteria for Florida 

waters. This has been accomplished with the active assistance of state and local governmental 

agencies in Florida, including the Florida Department of Environmental Protection, the Florida 

Fish and Wildlife Research Institute, Florida’s Water Management Districts, and several county 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 and private research institutions. Data have 

been provided to EPA both via existing online data 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 needed (see Section 1.3). The substantial quantity of data also 

reflects ongoing data collection by Federal agencies. 

The many different types of data that EPA is considering 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 that 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 number (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 quality. The largest source of water quality data is 

the Florida Department of Environmental Protection’s Impaired Water Rule Database, version 40 

(IWR40). FDEP maintains the IWR database and updates it quarterly with data it identifies and 

evaluates from trusted sources throughout 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 Biscayne Bay, Florida Bay and the Florida Keys 

(http://serc.fiu.edu/wqmnetwork). Further water quality and other data specific to South Florida 

is available from DBHYDRO, maintained by the South Florida Water Management District 

(http://www.sfwmd.gov/portal/page/portal/xweb%20environmental%20monitoring/dbhydro%20

application). Water quality data for South Florida canals has been provided to EPA by the 

Miami-Dade Department of Environmental Resources Management and Broward County 

Department of Planning and Environmental Protection. Many other counties in Florida conduct 

water quality monitoring and a significant quantity of these data are already integrated by FDEP 

into the IWR database. 

Land Use Data. EPA is considering land use data as inputs to water quality simulation models 

(i.e., mechanistic watershed models), as described in Chapters 3 and 6. EPA is also considering 

land use data as part of a Landscape Development 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 2005 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

42

imagery as reported by FDEP and various Water Management 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 mechanistic watershed models and hydrodynamic and water 

quality models for estuaries, as described in Chapters 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 stations 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.horizon-systems.com/nhdplus), which provides 

subwatershed and flow line delineations that can be used in watershed models (Chapter 3 and 6). 

EPA is considering stream discharge 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 Elevation Dataset is available from the USGS 

(http://ned.usgs.gov/). EPA is also considering water surface elevation data from NOAA tide 

gauges for use as boundary conditions, calibration, 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 coastal areas from the NOAA National 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 Florida’s Waterwater Facility Regulation (WAFR) system 

(http://www.dep.state.fl.us/water/wastewater/facinfo.htm). 

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

satellite-borne ocean color sensors for use in development of numeric criteria for offshore coastal 

waters in Florida (Chapter 4). These sensors include the Sea-viewing Wide Field-of-view Sensor 

(SeaWiFS) and the Moderate Resolution Imaging 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 considering data from the Northeastern Gulf of 

Mexico (NEGOM) project (NOAA National Oceanographic Data Center, 

http://www.nodc.noaa.gov), the Ecology and Oceanography of Harmful Algal Blooms project 

(ECOHAB), as well as collections of data 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 Southern Inland Flowing Waters 

43

Seagrass Coverage Layers. EPA is considering extensive datasets describing historical 

coverage of submerged aquatic vegetation in Florida estuaries (Chapter 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 data, water level data, meteorological data, and 

current velocity data collected for Pensacola Bay in 1996-2000, 2002-2004 and 2009 by EPA’s 

National Health and Environmental Effects Research Laboratory, Gulf Ecology Division. EPA 

has provided the water quality data to FDEP for inclusion in the IWR database. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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3 Numeric Nutrient Criteria Development in Florida Estuaries

An estuary is a part of a stream5or 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 translate Florida’s current narrative nutrient criterion, EPA is considering 

developing numeric criteria for nitrogen/phosphorus pollution in Florida's estuaries on a systemspecific basis. A system-specific approach would 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, and quality of freshwater inflows. The semienclosed basins that define the spatial extent of estuaries areas may also create sub-regions 

within estuaries with differentiated water quality and assessment endpoints. Thus, EPA is 

considering approaches that may result in numeric criteria specific to sub-regions within 

estuaries. 

This chapter describes the approaches EPA is considering to derive numeric criteria for estuarine 

waters in Florida (Figure 3-1), exclusive of marine waters in South Florida, 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 endpoints and indicator variables, and 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 water quality indicator variables for 

which EPA may develop criteria. Finally, we discuss three approaches: (1) reference conditions, 

(2) stressor response relationships (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 stream 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 the 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 

Areas 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 scientific approach, applying the methods and 

approaches most appropriate to each estuary, and ultimately deriving criteria. For estuaries 

outside of South Florida, EPA is considering a delineation approach based on the natural 

geographic limits of estuarine basins and their associated watersheds. Natural constrictions 

between estuarine basins tend to limit water flow and exchange between estuaries, even if 

exchanges are not eliminated entirely. This approach 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 waters in South Florida 

separately (see Chapter 5) because the flows in the waters in that part of the State are heavily 

managed and natural boundaries between basins are less defined. 

EPA is also considering utilizing natural geographic boundaries as an approach for delineating 

sub-segments within estuaries. FDEP has established 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 variety of reasons and could be subject to change 

in the future. Thus, EPA is considering 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 areas 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 Southern 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 WestGulf 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 Southern 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 criteria development are specific, ecologically 

important, and nutrient-sensitive statements of the environmental value to be protected, in this 

case to translate Florida’s narrative nutrient criterion and support a balanced natural population 

of aquatic flora and fauna within Florida estuaries. In environmental risk assessment (ERA) 

guidance these are referred to as “assessment endpoints.” Indicator variables 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 “measurement endpoints”), “measures of exposure,” and 

“measures of ecosystem and receptor characteristics.” The TMDL and Watershed Approach 

literature tends to refer to these measures as “indicators.” 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 value of indicator variables in some way to 

nitrogen/phosphorus concentrations and/or nutrient loadings. Selection of assessment endpoints 

is a critical early step that defines many subsequent aspects of criteria development. EPA has 

reviewed the scientific literature to identify candidate endpoints that are ecologically important, 

widely applicable in Florida estuaries, and sensitive to nutrients. As mentioned in Chapter 2, 

three assessment endpoints that EPA is considering for use in the derivation of numeric criteria 

in estuaries are discussed below. EPA is considering one or more indicator variables for each of 

548E 

548D 

548C 

548B

548A 

548H

548G

915

73

84

548

53
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 related to habitat and encompasses 

seagrass condition (e.g., density), species composition, and distribution approximating local 

historical conditions.7

 EPA is considering the depth of colonization of seagrasses as a readily 

quantifiable indicator of seagrass condition. For a number of estuaries, historical values of the 

depth of colonization can be used to establish values that support the designated use. Moreover, 

depth of colonization and areal extent 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 clarity, which affects the depth of colonization is 

affected8

 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 phytoplankton species composition. EPA is considering this endpoint 

because proliferations of macroalgae, epiphytes, or nuisance algal species may be the most 

apparent nutrient pollution (TN and TP) impacts in some estuaries, particularly those where 

seagrasses are not normally present or where low 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 (drifting organisms), and nekton (swimming organisms, such as 

fish) exhibiting healthy levels of biomass and production. Low dissolved oxygen resulting from 

nutrient pollution is a key mechanism by which nutrients 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 dissolved oxygen), as an indicator of balanced 

communities. Dissolved oxygen is a useful indicator for numeric criteria development because 

ecological modeling approaches can be used to predict the response of dissolved oxygen to 

nutrient pollution loading (TN and TP). 

3.3 Numeric Criteria Approaches

EPA is considering three basic categories of approaches to derive numeric criteria for Florida 

estuaries. These approaches include (1) reference condition approaches, (2) stressor response 

relationships, and (3) water quality simulation models. Associated with each category of 

approach are specific strengths and 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 Florida estuaries. For those estuaries, water clarity 

requirements to support maintenance of seagrasses 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 Southern Inland Flowing Waters 

50

determines which approaches should be used given the ecological details pertinent to each 

estuary as well as the different types and quantities of data available. EPA could consider several 

different types of models and information to derive numeric criteria for different estuaries and 

could consider simultaneously more than one type of information for 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 When

Reference Condition Approaches

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.

Stressor Response Relationships

(Regression Models)

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.

Water Quality Simulation Models

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 Southern Inland Flowing Waters 

51

3.3.1 Reference Condition Approaches  

Reference condition approaches can take a variety of forms, defined by the source of the 

reference condition. EPA has previously recommended (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 number 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 considering a reference condition approach to be most 

applicable when (1) historical data adequately describe water quality conditions 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 pollution and currently 

supporting balanced natural populations of aquatic 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 associated indicators that EPA has identified 

(Section 3.2). EPA has not yet identified any impaired estuaries for which 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 and fauna. 

To evaluate assessment endpoints and associated water quality indicator variables, EPA will 

consider data from FDEP’s Impaired Waters Rule (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 other data sources. A complete listing of the data 

sources EPA is considering can be found in Section 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 observations. These could include (1) an average or 

median concentration and (2) an upper percentile concentration. By simultaneously considering 

both an indicator of central tendency and a measure of higher concentrations, the criteria could 

ensure that future water quality conditions remain similar to present conditions (i.e., the 

conditions associated with support of balanced natural populations of aquatic flora and fauna). 

As an alternative, EPA is also considering whether it should utilize annual geometric mean water 

quality. Water quality observations in the future could be compared 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 Yearsa Count Yearsa Count Yearsb

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 1973–2010 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 1996–2008 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 single 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 explanatory variables), as well as non-linear 

regression models. 

Two major strengths of regression models as approaches that could be used for development of 

numeric criteria are that they are closely grounded in environmental data and, in the case of a 

single explanatory variable, easy to communicate, often by simple 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). Additionally, statistical methods for 

fitting regression models often permit estimation of limits of uncertainty for predictions, even for 

complex regression models (e.g., Hoos and McMahon 2009). Regression models require 

adequate data to develop. Additionally, other environmental variables that covary with 

explanatory variables of interest can introduce uncertainty in estimates of regression model 

parameters. It may also be difficult to find highly predictive regression models for complex 

ecological systems that include many interacting factors that impact the dependent variable. This 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

53

is especially true when important processes occur on differing temporal and spatial scales. 

However, useful regression models do exist and have been applied successfully to quantify 

relationships among water quality indicator variables in estuaries. 

Examples of regression models that could be useful for development 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) models quantifying relationships between other environmental 

variables. Several regression models could be utilized in combination to derive numeric criteria. 

EPA is evaluating existing regression models and is considering work to identify more 

regressions that would be useful for development of numeric criteria in Florida. 

Janicki and Wade (1996) describe a well-known application of regression models for 

development of nutrient loading limits for Tampa 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 balanced natural populations of aquatic flora and fauna. Targets for 

areal extent were based on historical seagrass distributions. Empirical relationships were used to 

quantify the relationship between areal 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 attenuation coefficient needed to support seagrasses at target 

depths. Regressions were developed to quantify the relationship 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 combination, these relationships provide an approach 

for developing numeric criteria for TN loading (causal variable) and average chlorophyll a

(response variable) for each segment of Tampa Bay. As described by Janicki and Wade (1996), 

the approach does not address assessment endpoints other than seagrasses, TP loading, or 

ambient concentrations of either TN or TP in estuarine 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 could 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 another regression-based 

approach that emphasizes the similarity among 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 waterbody types, derived independently 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 residence 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 targets, EPA may not apply this model as the principal evidence for 

developing a nutrient loading target for an estuary because it does not provide estimates of 

protective loads with sufficient precision. This may not be fully evident in Figure 3-4 because of 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

54

the log scale. Although the model has a high coefficient 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 carefully consider the 

appropriate interpretation of the relationships described by Steward and Lowe, which are 

presented as estimates of the loading levels associated with mesotrophy. For some estuaries in 

Florida, a mesotrophic condition may not support balanced natural populations of aquatic flora 

and fauna, or maintain the current chemical and biological integrity. Finally, although the 

outstanding coefficient of determination for the relationships could lead one to conclude that 

they reflect important new insights, the strength of the relationships is a consequence 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 estimates (1 to 300 g N/m2

/y), a strong correlation is likely, regardless of the 

average concentration determined to be associated with a mesotrophic condition. The fact that 

the estimated slopes are generally not equal to -1 (i.e., -0.89) could reflect either the fact that the 

residence time for nutrients is expected 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(τw) (yr)

log L

M(N) (g m-1 yr-1)

log LM(N) = -0.8873(log τw ) + 0.2791

R2

=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 simulation 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 Florida 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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. Coupled watershed-hydrodynamic-water quality 

models are widely accepted and have been utilized 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 processes in estuaries. The most well-known 

hydrodynamic-water quality model application in Florida 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 fundamentally 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 endpoints 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 model can then be used to determine TN and TP 

loading levels or other water quality conditions (e.g., average chlorophyll a, estuarine TN and TP 

concentrations) necessary to ensure water quality that supports balanced natural populations of 

aquatic flora and fauna. As an example, a coupled watershed-hydrodynamics-water quality 

model could be used to simulate the impact of TN and TP loading rates on chlorophyll a, water 

clarity, and therefore support for seagrasses growing to an expected depth of colonization. Water 

quality models could also be used to predict dissolved oxygen concentrations. Criteria, including 

magnitude, frequency and duration, can be derived for the nutrient parameter simulated by the 

model that results in attainment of the quantitative endpoint. 

The process that EPA is considering for development of numeric criteria using water quality 

simulation models would involve estimating the current conditions, characterizing natural 

conditions, and finally developing numeric criteria to translate Florida’s existing narrative 

nutrient criterion. Simulations of observed or “current” water quality conditions are necessary to 

calibrate the watershed and estuarine water quality models. Typically, data from one or more 

years could be used to calibrate the water quality 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 conditions” would be developed to 

estimate the TN and TP loading rates and associated water quality responses that could be 

expected to occur in the absence of anthropogenic 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 condition and removing any point sources of nitrogen/phosphorus 

pollution. The resulting TN and TP loading rates would then be utilized within the hydrodynamic 

water quality model to simulate the water quality conditions that would be expected to occur in 

the estuary if TN and TP loading were returned to background levels. Different numeric criteria 

would be evaluated to determine the highest loading rates that can occur while maintaining 

simulated water quality conditions that will support the assessment endpoints that EPA has 

identified. Because simulation models can provide spatially and temporally-resolved outputs, 

simulated water quality under compliance scenarios could be used to compute spatially-resolved 

(i.e., estuary segment-specific) estimates for criteria magnitude, frequency and duration. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

56

To consider which models could be used to develop numeric criteria, EPA developed an 

inventory of the watershed and estuary models that have been previously applied to estuaries in 

Florida (Table 3-3). EPA’s inventory was based on an inventory of models developed by FDEP 

(Wolfe 2007), with additions based on discussions 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 Program in C++ (LSPC) for simulating freshwater 

flows and nutrient loading from watersheds, the 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 Southern 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 Useda

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 Program
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

58

3.3.3.1 Watershed Models

Of the eleven water bodies for which watershed models have been developed, seven of the 

watershed models were developed using either 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 flow and water quality, but differ in their software 

architecture. LSPC has been updated to relax certain 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 watersheds 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 geospatial data collected by FDEP that 

describes the location and magnitude 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 waters, ground water, marshes, and wetlands in 

this area. 

LSPC is a comprehensive data management system and model that is capable of representing 

water flow, water quality, and pollutant loading from nonpoint and point sources and simulating 

in-stream processes affecting pollutant transport. 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-watersheds. LSPC represents receiving waters as 

one-dimensional, completely mixed stream reaches or reservoirs. 

LSPC is a dynamic watershed model driven by time-variable weather input data. It produces 

time series of flow and pollutants simulating transport in overland flow, the vadose and saturated 

zones, and instream components of the system, 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 Water Quality Modeling Technical Support Center 

(http://www.epa.gov/athens/wwqtsc/). 

Input data for LSPC includes three main categories of information: (1) landscape 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, monitoring data, etc.). The watershed loading component 

of the model divides all land uses into pervious and impervious segments, which are further 

grouped by land use and subbasin. Loads from subbasins 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 Southern 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 processes. Nitrogen/phosphorus pollution loading may 

originate from a wide variety of nonpoint sources, such as agriculture, onsite wastewater 

treatment systems (e.g., septic systems), urban runoff, timber production areas, and atmospheric 

deposition. Only runoff processes are simulated for impervious land 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 water 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

60

body segment, LSPC will simulate hydraulic behavior, 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 approach wherein nutrients accumulate between rain 

events and are mobilized and transported into streams 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 (elevations, slopes, reach lengths, etc.) 

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

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

o Comparable data from Georgia (Georgia Land Use Trends [GLUT] 2005, 

http://narsal.uga.edu/glut.html) and Alabama (National Land Cover Data [NLCD] 

2005, http://www.epa.gov/mrlc/) in applicable watersheds 

• Meteorological data 

o National Climatic Data Center (NCDC) rainfall data – approximately 120 rain gauges 

in Florida (NCDC 2010) 

o Florida State Climatological Office (FL SCO) 

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

• NPDES-permitted point sources and water withdrawals from Florida’s Wastewater Facility 

Regulation (WAFR) system (http://www.dep.state.fl.us/water/wastewater/facinfo.htm) 

Nutrient data from Florida’s IWR database and discharge data from USGS flow gauges will 

provide inventories to select stations to be used in the calibration and evaluation process for 

hydrology and water quality simulation. Sites with 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 depth, 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 the Water Quality Analysis Simulation Program (WASP) for 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

61

water quality have been applied to many water 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 application 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-dimensional surface water modeling system for 

hydrodynamic and reactive transport simulations of rivers, lakes, reservoirs, wetland systems, 

estuaries, and the coastal ocean. The modeling system 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 simulate hydrodynamics (i.e., three-dimensional 

advective transport and mixing) in Florida estuaries. EFDC is run on an orthogonal grid, which 

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

coastal waters to ensure that the domain of interest (i.e., estuarine waters) is not substantially 

affected by artifacts propagating from the seaward 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 temperature 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 considering using Version 7.41 (WASP7) to simulate 

estuarine water quality processes for the purpose 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 capabilities. 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 underlying benthos. The time-varying processes of 

advection, dispersion, point and diffuse mass loading and boundary exchange are represented in 

the basic program. Water quality processes are represented 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 subroutines into the overall package to form problem-specific models. 

WASP comes with two such models—TOXI for toxicants and EUTRO for conventional water 

quality. EPA’s implementation of WASP for criteria development will utilize the EUTRO 

model. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

62

Earlier versions of WASP EUTRO have been used to examine eutrophication of Tampa Bay; 

Lake Okeechobee (James et al. 1997); Neuse River and estuary (Wool et al. 2003); the Great 

Lakes (Thomann et al. 1979; Thomann et al. 1976; Di Toro and Connolly 1980; Thomann 1975), 

and the Potomac Estuary (Thomann and Fitzpatrick 1982), among many others. In addition to 

these, numerous applications are listed in Di Toro et al. (1983). Other applications of the WASP 

in Florida include St. Andrews Bay, Apalachicola Bay, Fenholloway River, Sarasota Bay, 

Charlotte Harbor, and Loxahatchee 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 complex model that explicitly simulates many processes is not 

necessarily better than a simpler model that focuses on the aspects of the ecosystem that are most 

pertinent to the target process (Arhonditsis and Brett 2004). EPA is considering implementations 

of WASP that are guided by the principle that model functions should be activated only if it is 

determined to be necessary to achieve model simulation and performance objectives. 

For the Pensacola Bay application described below, 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 model cells (includes horizontal and vertical advective and nonadvective exchanges, and (4) three-dimensional, time-variable salinity and temperature. State 

variables, water quality processes, and boundary conditions for the Pensacola WASP model are 

shown in Table 3-4. Rates and kinetics were 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 reactivity, or labile. Nitrogen is divided into organic 

and inorganic fractions. Organic nitrogen state variables are dissolved organic nitrogen, labile 

particulate organic nitrogen, and refractory particulate organic nitrogen. Inorganic nitrogen forms 

are ammonia and nitrate (nitrite is implicitly represented). Both NO3

-

 and NH4

+

 are utilized to 

satisfy algal N requirements, with NH4

+

 being preferred. The primary reason for distinguishing 

the two is that ammonia is oxidized by nitrifying bacteria into nitrate. Nitrification can be a 

significant sink of oxygen in the water column. Sediment 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 organic phosphorus, labile particulate 

phosphorus, and refractory particulate phosphorus. Only a single inorganic form, 

orthophosphate, is considered. Orthophosphate exists as several states within the model 

ecosystem: dissolved phosphate, phosphate adsorbed to inorganic solids, and phosphate 

incorporated in algal cells. Equilibrium partition 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 Southern 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 (NH4

+

) 

• Nitrate+nitrite (NO3- + NO2

-

) 

• Inorganic phosphorus (PO4

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 

• NO3

-

 concentrations in rainfall 

WASP is capable of simulating four classes of algae, each targeting a specific ecological “niche” 

defined by distinctive characteristics 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 saline water and by their bloom-forming characteristics in fresh 

water. Being very small, picoplankton do not sink at appreciable rates. Another key feature of 

cyanobacteria is that some species fix atmospheric 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 and are subject to greater grazing pressure than 

are cyanobacteria. The fourth category is macroalgae, which could be expected to increase in 

biomass when nutrients are available but high flushing rates prevent accumulations of planktonic 

algae. The algal community of Pensacola Bay has been characterized as dominated by slowgrowing non-N-fixing picoplanktonic cyanobacteria during summer and diatoms at other times 

of the year. Because the cyanobacteria lack a distinctive functional role (i.e., they are not Nfixers), and the Pensacola Bay phytoplankton community can be modeled effectively with a 

single class of algae, there is not sufficient reason to model the community with more than one 

class. 

WASP is able to simulate sediment-water oxygen 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 EPA 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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is considering using for Pensacola Bay is to specify these oxygen and nutrients fluxes as 

boundary conditions. This may improve model performance 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 nutrient fluxes are an important aspect of the 

response of the ecosystem to changes in nutrient loading, then model 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 would decrease if nitrogen/phosphorus pollution loading rates 

decreased, the model would tend to underestimate the improvement in bottom water oxygen 

condition that could be associated with reduced 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 algorithms, which have been added to allow the model to predict light 

extinction as a function of background light extinction, attenuation by phytoplankton, and 

attenuation by suspended solids (including detritus). These new algorithms allow the model to 

predict changes in light extinction as function of management practices that reduce loading of 

nutrients and suspended solids. The light model in WASP can be used to determine the 

availability of light at the bottom, a key indicator of the likely effect 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 performance against independent 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 environmental modeling (e.g., Wool et 

al. 2003). 

Model calibration for both watershed models proceeds from physical properties, to chemical 

properties and ultimately to biological properties and evaluation of specific model outputs that 

define the model endpoints or objectives. For 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 temperature (EFDC), and proceeds to water quality 

simulations within WASP, including chlorophyll-a or DO, which are modeling endpoints. 

The model evaluation procedure that EPA is considering would involve evaluating models across 

a range of temporal and spatial scales. For example, hydrologic calibration of watershed models 

would begin with ensuring the annual water balance, and then would examine seasonal or 

monthly distributions of discharge, and finally hydrograph components such as base flow and 

storm flow. EPA is considering application of graphical and quantitative tools such as (1) plots 

of hourly, daily and monthly time series, (2) monthly scatter plots and balance plots, (3) seasonal 

plots (i.e., multi-year composites), (4) flow duration curves, (5) flow duration curves, (6) flow 

accumulation curves, (7) cumulative error statistics, and (8) evaluation of hydrograph 

components. EPA is considering similar quantitative evaluation procedures to examine stream 
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velocity and water quality (i.e., TN and TP) across 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 considering overall statistical 

distributions (e.g., cumulative distribution functions), seasonal distributions, and spatial 

distributions of key water quality variables (i.e., state variables). 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 rates (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 Sections 2.3 and 3.2, EPA is considering using healthy seagrass 

communities as an assessment endpoint for numeric criteria in Florida estuaries. Seagrasses are 

present in portions of estuaries throughout Florida, with the exception of estuaries in northeast 

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

habitat and food resources for estuarine biota by providing protection from predators, by 

providing substrate for production of epiphytic algae and algal grazers. Seagrasses themselves 

are highly productive and are grazed directly by a variety of species. 

Although healthy seagrass communities depend on a variety of physical and biological factors in 

addition to water clarity (Koch 2001; Heck and Valentine 2007), management efforts to protect 

seagrass have often focused on improving water clarity because there are clear conceptual and 

empirical linkages between nutrient loading, water clarity and extent of seagrasses. In short, 

increased nutrient loading often contributes to increased phytoplankton production and biomass, 

reduced water clarity, and consequently, degradation and loss of seagrass habitat. Strong 

relationships have been observed between the 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 Region (Iverson and Bittaker 1986); Tampa Bay (Janicki 
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and Wade 1996; Johansson 2002); Indian River (Steward et al. 2005); and Sarasota Bay 

(Tomasko et al. 2001). However, the depth of seagrass 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 more than one point in time. 

EPA is considering using an approach that involves overlaying geospatial seagrass coverage data 

on a spatial data set of bathymetric soundings, then examining the fraction of bathymetric 

soundings that occur within seagrasses as a function of the depth of the soundings (Figure 3-6). 

EPA has assembled the available seagrass coverages, which includes data spanning 1940 to 2007 

(Table 3-6). Seagrasses have has been mapped three or more times in a number of estuaries, 

generally on the Florida peninsula. 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 edge of seagrass beds within an estuary segment 

(Figure 3-6). An approach similar to that of Steward 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 water 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 set characterizing seagrass colonization depths to 

determine targets for seagrass colonization depth. Seagrass restoration targets for Tampa Bay 

were based on the spatial extent of seagrasses that were present circa 1950, less certain areas that 

due to ongoing dredging or other modifications are not considered recoverable habitat. Depth 

targets were then determined based on current bathymetric profiles (Janicki and Wade 1996). 

Similar historical seagrass coverages are available for some estuaries (e.g., St. Andrew Bay, 

Table 3-6), but are not available for all estuaries in Florida. Steward et al. (2005) present 

approaches for determining seagrass depth and associated light targets for protection of 

seagrasses in the Indian River Lagoon. Although the approach they present could not be applied 

in some parts of Florida due to an insufficient number of coverages, EPA could consider 

applying elements of the approach, such as utilizing 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 consider applying the methods presented here for 

computing colonization depth to a union seagrass coverage, as suggested by Steward et al. 

(2005). 
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Depth below MLLW (m)

01234

Percent 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

(Zc‐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 

Zc-max 

Zc50% 
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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 SFWMD 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 Bay9

 is located in northwest Florida in Escambia and Santa Rosa counties. The 

Pensacola Bay system is a complex of sub-estuaries that includes Escambia Bay, Blackwater 

Bay, East Bay, Pensacola Bay and Santa Rosa Sound (Figure 3-7). The combined system is 

medium-sized (370 km2

) and shallow (mean depth ~ 3.0 m) and has been characterized as a 

partially-stratified, drowned river valley estuary (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, Blackwater, and Yellow Rivers. The Escambia River 

discharges into Escambia Bay, whereas the Blackwater 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. Small openings also permit exchanges with Big 

Lagoon through a shallow (3 m), narrow (100 m) channel to the west and Santa Rosa Sound to 

the East through a 1 km opening. Santa Rosa Sound extends 52 km to the east from Pensacola 

Bay and ultimately narrows to 75 m width before opening into Choctawhatchee Bay. Because of 

the small size of these channels, flow through them is expected to be minimal compared to 

Pensacola Pass. 

 

9

 This site description is excerpted from Hagy and Murrell (2007). 
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Figure 3‐7. Location Map of Pensacola Bay

3.5.1 Selection of Assessment Endpoints

EPA evaluated the status of key biological assessment endpoints (Section 3.2) in Pensacola Bay 

and found evidence that (1) considerable seagrass loss has occurred in Pensacola Bay, and (2) 

biotic communities in Pensacola Bay are most likely impacted by low dissolved oxygen. 

However, EPA did not find evidence that harmful algal blooms or proliferations 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 historical coverage, outside of Santa Rosa Sound, 

has been lost (Hagy et al. 2008). Much of the decrease occurred prior to 1980, during a period 

characterized by high point source nutrient (especially 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 seagrasses to Escambia Bay, Pensacola Bay, and 

Big Lagoon. 
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Following the approach that EPA is considering, EPA will develop estimates of the current and 

historical depth of colonization of seagrasses 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 colonization 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 percentage 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 occurs extensively in 

bottom water areas of Escambia Bay, upper Pensacola Bay, East Bay and Blackwater Bay, with 

DO levels being less than 2.0 mg/L on a seasonal basis. Hypoxia is confined to stratified bottom 

waters, whereas surface waters and unstratified areas are well-oxygenated. Limited data are 

available that document the status of the benthic 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 assessments 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-estuarine 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 using 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 concentrations, 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, and develop numeric criteria to translate Florida’s existing 

narrative nutrient criterion. Estimates of current conditions would 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 expected 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 consider establishing TN, TP and chlorophyll 

criteria based on the characterization of natural conditions from LSPC. On the other hand, if 

water quality targets can be achieved, then EPA would with vary nitrogen/phosphorus pollution 

loading rates, ultimately developing a numeric value, which would simulate the highest 

nitrogen/phosphorus pollution loading rate that could occur while maintaining water quality 

targets in the estuarine receiving water. EPA could 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 expressing criteria. For example, time series output 

could be evaluated in the same manner as data from a reference condition. Because the model 

output is highly spatially resolved, EPA could develop criteria for subsegments of Pensacola Bay 

by averaging outputs from model grid cells within the subsegment. 
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3.5.3 Details on the Pensacola Bay Water Quality Model

3.5.3.1 Watershed Model

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

The subwatersheds are based on the NHDPlus catchments 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 reaches discharging directly to the estuary. The 

model will utilize meteorological data from seven locations across the Pensacola Bay watershed. 

The Pensacola Bay LSPC model will be calibrated for 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 are 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 will ensure that the model correctly reproduces 

overall average loading, seasonal patterns of loading, 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 Falls, 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 would 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 observed stream flow. Model parameters that 

would be adjusted include evapotranspiration, infiltration, upper and lower zone storage, 

groundwater storage, and losses to the groundwater system. 
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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 the Pensacola watershed would be used 

to calibrate and evaluate the LSPC watershed hydrology model. There 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 hydrologic parameters would extend from January 1, 
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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 above calibrated hydrologic parameters to other 

subwatersheds of the Pensacola Bay watershed and comparing the simulated flow to measured 

flow from a USGS stream gauging station for the same period of time. Care is taken to select 

watersheds that represent a wide variety of land uses as well as drainage areas. This will help to 

ensure that the hydrological parameters that were calibrated apply to a wide range of conditions. 

3.5.3.1.2 Watershed Water Quality 

Following hydrologic model calibration, water quality 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 contributions. Many components of the water quality 

model would be established during hydrology modeling, including watershed segmentation, 

meteorological data, land use representation, soils, reach characteristics, and point source 

discharges. For application, herein, the water 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 transport from land to streams using the pervious 

quality constituent module (PQUAL) of LSPC and instream processes 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 parameters that can be assigned differently by 

reach group include: sediment bed storage parameters, 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 for 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 temperature. Simulation of soil temperatures is 

accomplished by using three layers. The surface layer is the portion of the land segment that 

determines the overland flow water temperature. The upper subsurface layer determines 

interflow temperature while the groundwater subsurface layer determines groundwater 

temperature. Surface and upper subsurface layer temperatures 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 determine the water temperature of the three different 

flow paths (surface outflow, upper subsurface/interflow 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 decrease the heat content of the water. Mechanisms 

which can increase the heat content of the water are absorption of solar radiation, absorption of 

longwave radiation, and conduction-convection. Mechanisms which decrease the heat content 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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

2004). 

Dissolved Oxygen. LSPC modules for pervious water temperature, 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 concentration are stream temperature and atmospheric reaeration. 

Atmospheric reaeration takes into consideration the initial dissolved oxygen concentration, 

oxygen saturation level for a given water temperature, water depth, water velocity, circulation, 

reaeration rate, and a temperature correction 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 establishing 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 determination of nonpoint source loadings to a waterbody. 

The watershed model must appropriately represent the spatial and temporal variability of 

hydrological characteristics within a watershed. It must also appropriately represent the rate at 

which nitrogen and phosphorus components build-up between rain events and wash off during 

rain events. Key general water quality characteristics include initial storage, wash-off and scour 

potency, accumulation rates, and maximum storage amounts. The water supplied to a stream 

from groundwater and through interflow also plays an important role in loading to a waterbody. 

LSPC allows the user to supply groundwater and interflow concentrations, by hydrologic soil 

group and land use, by month. The accumulation and wash-off and interflow strongly influence 

peak flow water quality while groundwater reflects baseflow water quality. 

3.5.3.1.3 Development and Calibration 

Calibration of the LSPC water quality model includes a sequence of steps that begins with 

temperature, because the other key parameters are regulated by instream temperature. 

Temperature would be calibrated by adjusting surface and interflow slopes and intercepts, and 

groundwater temperature, by land use and hydrologic soil groups until the simulated data closely 

matched observed. Following temperature calibration, dissolved oxygen would be calibrated 

with the observed data by adjusting reaeration, and interflow and groundwater dissolved oxygen 

concentrations. 

Finally, calibration would involve BOD, TN, and TP. These three constituents would be modeled 

by build-up/wash-off and assigned land use-associated concentrations in groundwater and 

interflow. Build-up/wash-off removes constituents from the land and carries them into the 

stream. Adjustments would be made to monthly accumulation rate, monthly storage limit, 

interflow concentration, and groundwater concentration for BOD, TN, and TP until the simulated 

data was in range with the observed field data. 
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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 would 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 through 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 quality 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 water 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 collected on an hourly basis and daily 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 suspended 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 evaluated using the 2003-2004 data from the same 

stations. In general, model evaluation ensures that the range of parameter 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 Southern 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 values 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 for 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 the 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 numeric criteria for coastal 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 satellite-derived chlorophyll a (ChlRS-a) to set numeric criteria for chlorophyll 

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

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

decade. ChlRS-a in the ocean are a routine measurement and available globally (O'Reilly et al. 

1998). Coastal physical forcings such as wind, currents, and tides are known to influence coastal 

chlorophyll dynamics together with nitrogen/phosphorus pollution loadings from the land. It is 

expected that all these processes will be represented when using remote sensing as a reference 

condition approach. Specifically, EPA is considering the use of this method to develop numeric 

chlorophyll criteria for the Northwest Gulf Coast, West Gulf Coast, 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 for 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 

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78

Figure 4‐1. Flow chart for the development of numeric nutrient criteria for coastal waters.

EPA is currently not considering 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 waters 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 ChlRS-a than coastal segments further from 

estuary passes. Examples of coastal chlorophyll responding to nitrogen/phosphorus pollution 

loading and river discharge are included in Walker and Rabalais (2006), where Mississippi River 

nitrogen/phosphorus pollution loads were empirically related to coastal chlorophyll

concentration in the northern Gulf of Mexico. Nababan et al. (In press) also reported river 

discharge was the dominant factor controlling coastal chlorophyll concentration in Mobile Bay, 

Alabama, and the Florida Panhandle. 

Unlike estuaries, which are commonly dominated by freshwater flows and can be organized 

within watershed boundaries, coastal waters have open offshore boundaries and localized 

influences near the estuary/coastal 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 the latter case, EPA recognizes that FDEP’s Water 

Body Identification Numbers (WBIDs) for coastal waters start at land fall 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 

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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 (ChlRS‐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 ChlRS-a: 

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

• Moderate Resolution Imaging Spectroradiometer (MODIS) 

SeaWiFS provides a historical time-series of ChlRS-a back to 1997, whereas MODIS data began 

in 2002. SeaWiFS and MODIS can both provide a continuous temporal measure of ChlRS-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 traditional field samples. Traditional field samples 

account for 1,648 chlorophyll observations from 1998 to 2010. In contrast, satellites measured 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

80

25,549 observations over the same period. When summarized to 8-day averages, a single satellite 

will typically provide a composite ChlRS-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 ChlRS‐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 satellite imaging and processing has resulted in numerous applications 

of remote sensing products to address needs for environmental resource management. The 

following is a brief overview of successful applications of remote sensing toward management 

decision support. 

The NOAA Harmful Algal Bloom Operation Forecast System (HAB-OFS) uses remote sensing 

as an advanced warning system of harmful algal blooms. This system provides information on 

the HAB location and the potential for bloom development along the coastal waters of Florida. 

NOAA Fisheries Service uses remote sensing to characterize sea surface temperature around the 

Monterey Bay National Marine Sanctuary and delineate the marine reserve boundaries. NOAA 

Fisheries Service is also utilizing remotely sensed sea surface temperature, sea surface height, 

and chlorophyll estimates to monitor the recruitment, survival, and productivity of managed fish 

species, and those protected by the Endangered Species and Marine Mammal Protection Acts. 

A) 

B) 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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81

NOAA is currently using remotely sensed sea surface 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 bacterial pathogens in the genus Vibrio. Satellite applications 

pertaining to Vibrio also extend to the management of oysters in the Gulf of Mexico, where sea 

surface temperature and Vibrio levels are used to inform fisheries managers whether oysters are 

harvestable or if shellfish 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 Department of Natural Resources uses SPOT data 

to denote tidal from non-tidal wetlands. Legislation in Maryland requires that wetlands be 

classified, and the State currently uses maps solely generated with SPOT, which are supplied to 

all coastal managers. South Carolina’s Office of Ocean and Coastal Resource Management 

utilizes LIDAR data to determine Beachfront jurisdictional lines, which have proven to be highly 

accurate and adhere to laws requiring the mapping of beach setback in the State. 

The National Aeronautics and Space Administration (NASA) provided remote sensing 

observations during the Deepwater Horizon BP oil spill in the Gulf of Mexico at the request of 

U.S. disaster response agencies. Remote sensing 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 Prevention Control and Countermeasures (SPCC), the 

Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA), and the 

Resource Conservation and Recovery Act (RCRA) (USEPA 2007). 

4.2 Deriving Numeric Criteria

EPA is evaluating the use of remote sensing to provide ChlRS-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 along the Atlantic Coast from the Georgia border 

to Biscayne Bay. Currently, only WBID 8037 (Waccasassa River, north of the West Gulf Coast) 

has been identified as impaired for nutrients and chlorophyll in 2005 according to FDEP’s CWA 

section 303(d) listing. Therefore, EPA is considering applying a reference condition approach to 

derive numeric chlorophyll criteria for Florida’s coastal waters to measure the assessment 

endpoint of balanced phytoplankton biomass and production in order 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 variety of forms, defined by the source of the 

reference condition. EPA has previously recommended (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), could serve as criteria for 

water quality in similar waterbodies. In this case, a reference condition is derived from a 

reference population of waterbodies. As discussed elsewhere in this document (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 flora 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

82

and fauna (i.e., historical reference condition) or (2) when the waterbody is currently minimallyimpacted by nitrogen/phosphorus pollution and currently supporting balanced natural 

populations of aquatic flora and fauna (i.e., current-conditions reference condition). EPA has not 

yet identified any impaired coastal waters for which 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 and 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 satellites provided daily images with 1 km binned spatial 

resolution when not masked with quality control flags. SeaWiFS and MODIS imagery spatially 

covered between 31.0 to 23.0o N and 88.0 to 79.0o W. SeaWiFS Data Analysis System (SeaDAS) 

version 6.1 (Baith et al. 2001) was used to process SeaWiFS and MODIS data from level-1 to 

level-3 composites and derive ChlRS-a concentrations (NASA n.d.). Results within 1 km of the 

shoreline were not included in the averaging of ChlRS-a by using quality control 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 chlorophyll measurements 

was performed using the native resolution 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 corresponding in-situ measurement location. Any 3x 3 

pixel extractions that had greater than 4 pixels flagged were excluded. Satellite data were filtered 

for SeaDAS default flags (i.e., cloud contamination, land, and atmospheric correction failure) 

and any data with viewing angles greater than 60o and solar angles greater than 75o. 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 System (SeaBASS) (Werdell and Bailey 2002; 

Werdell et al. 2003). 

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

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

concluded that “there is no single hypothesis that can account for blooms,” removing data 

collected during bloom events would ensure that the data points used to derive the statistical 

distribution of reference conditions would represent conditions that are supporting balanced 

natural populations of aquatic flora and fauna (See 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 Southern Inland Flowing Waters 

83

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

and Wildlife Conservation Commission FWRI in St. Petersburg can be used to remove the 

chlorophyll signature that resulted from 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 greater than 50,000 cells/L during an 8-day composite 

would be flagged and not averaged into the ChlRS-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

ChlRS‐a distribution. Red points indicate ChlRS‐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 ChlRS‐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 available field data that coincide with ChlRS-a will be assessed 

and considered in EPA’s approach. The ChlRS-a data was paired with 62 field observations 

within the three nautical mile boundary to provide a calibration of ChlRS-a data (Figure 4-5). 

There was a significant positive correlation between field observations and SeaWiFS ChlRS-a 

(slope = 0.85, R2

 = 0.52, p < 0.0001). Therefore, the slope can be applied to the ChlRS-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 Southern 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 ChlRS-a for five coastal segments and two monitoring sites near Tampa 

Bay (Figure 4-6). WBID 8049 is located at the Tampa Bay estuary pass, WBIDs 8047 and 8048 

are located further north, and WBIDs 8050 and 8051 are located 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 ChlRS-a from each of the five WBIDs with two general patterns 

emerging: (1) ChlRS-a demonstrates seasonal variability with higher concentrations in the late 

summer and early fall, and (2) ChlRS-a concentrations are generally higher in the coastal segment 

corresponding to WBID 8049 (located at the Tampa 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, the chlorophyll data from the two sites shown 

in Figure 4-7B trend similar to that of the ChlRS-a concentrations in Figure 4-7A. 

The similarity of the chlorophyll results from remote sensing and field collected data are better 

represented by displaying the time series data with cumulative sum (CUSUM) charts. As used 

herein, cumulative sum charts display the cumulative 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 deviation (i.e., computing z-scores) referred to as Z-CUSUM charts. 

Figure 4-8A is a Z-CUSUM chart for the ChlRS-a from each of the five coastal segments shown 

in Figure 4-7A. Figure 4-8B is a Z-CUSUM 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 average 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 pattern. This indicates that the chlorophyll in the year following 

1998 and 2005 is near average, and the chlorophyll in the second year following 1998 and 2005 

(i.e., 2000 and 2007) is below average. These results provide additional evidence that ChlRS-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 Southern 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 Southern Inland Flowing Waters 

86

Chl-RS (μg/L)

1-98 1-99 1-00 1-01 1-02 1-03 1-04 1-05 1-06 1-07 1-08 1-09 1-10

12

10

8

6

4

2

0

2.34

8047

8048

8049

8050

8051

WBID

A)

Uncorrected Chl-a (μg/L)

1-98 1-99 1-00 1-01 1-02 1-03 1-04 1-05 1-06 1-07 1-08 1-09 1-10

35

30

25

20

15

10

5

0

21FLHILL093

21FLHILL094

STATION_ID

B)

Figure 4‐7. (A) ChlRS‐a (μg/L) for five coastal segments near Tampa Bay. The overall mean ChlRS‐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 Southern Inland Flowing Waters 

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4.2.3 ChlRS­a Criteria from Statistical Distributions

EPA is considering the use of the field adjusted ChlRS-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 considering removing influences from K. brevis blooms from the 

field adjusted ChlRS-a distribution then determining the numeric criteria value from an 

appropriate percentile of the distribution. EPA is considering two approaches for deriving criteria 

using a reference condition approach from a statistical distribution. One approach is to estimate 

the annual geometric mean concentration and the second approach is a binomial test to maintain 

current conditions. 

4.2.3.1 ChlRS­a Annual Geometric Mean Concentration

The geometric mean is preferred to the arithmetic 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 logarithm, and then compute the mean by coastal 

segment and year. The 75th percentile annual geometric mean would be computed as 

ܯܩ଻ହ ൌ ݁ሺ௫ҧା଴.଺଻ସହ௦ሻ

 

where 0.6745 is the inverse of the standard normal cumulative distribution with a probability of 

0.75 and s is the corresponding 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 GM75 would be exceeded, on average, 25 percent of the 

time without any water quality changes. If the annual geometric mean exceeded the GM75 in two 

of three years, there is 84 percent confidence the annual geometric mean of the test data 

exceeded the GM75. If the annual geometric mean exceeded the GM75 in three of five years, there 

is 90 percent confidence the annual geometric mean of the test data exceeded GM75. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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Z-CUSUM

1-98 1-99 1-00 1-01 1-02 1-03 1-04 1-05 1-06 1-07 1-08 1-09 1-10

40

30

20

10

0

-10

-20

-30

8047

8048

8049

8050

8051

WBID A)

Z-CUSUM

1-98 1-99 1-00 1-01 1-02 1-03 1-04 1-05 1-06 1-07 1-08 1-09 1-10

0

-2

-4

-6

-8

-10

-12

-14

21FLHILL093

21FLHILL094

Station

B)

Figure 4‐8. Z‐CUSUM plot for (A) ChlRS‐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 

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Figure 4‐9. Field‐adjusted ChlRS‐a (μg/L) distributions for all coastal segments between 1997 and 2009 with representations of

the minimum, 10th, 25th, 50th, 75th, 90th 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 ChlRS­a Binomial Test

Another method that EPA is considering is to specify numeric criteria applicable 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 C0.50 and C0.25 to the maximum 50th and 75th

percentile concentration for any 3-year period from the supporting data set. This approach 

(1) provides a quantitative evaluation of the probability that the data collected during the 

assessment period are from the same or nearly the same distribution as the observations during 

the baseline period; (2) is resistant 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; and 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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90

(5) tests for changes in both the central tendency and the upper end of the concentration 

distribution. 

4.3 Uncertainties and Data Gaps

Werdell et al. (2009) showed SeaWiFS and MODIS could successfully be used to evaluate 

chlorophyll in coastal environments such as the Chesapeake Bay. CDOM and bottom reflectance 

are typically cited as possible interferences with satellite measurements in coastal waters. 

However, bottom reflectance can be minimized by incorporating the stray light contamination 

flag. This flag excludes any near shore bins where reflected light from land enters the satellites 

field of view. Seagrass may provide interferences 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 2006). However, satellite imagery is not used for 

coastal criteria in these seagrass coverage locations. 

Satellites do not provide direct measurements of chlorophyll as do bottle samples, which are 

commonly considered acceptable across many laboratories and agencies. However, the data 

presented in Section 4.2.2 provide strong evidence 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, there are limited data based on 

field-sampling to support numeric criteria development for all of the State's coastal waters. 

To improve on the proposed approach, continuous monitoring by satellite would be necessary to 

derive more refined criteria. A potential concern is that all satellite missions have a finite 

duration and may not be always available if the State chooses to use satellite data to assess their 

coastal waters. EPA recognizes next generation satellites and algorithms will be developed in the 

future, and criteria values will need to be reviewed and adjusted with improving technologies. In 

addition to SeaWiFS, MODIS and the European Space Agency’s (ESA) Medium Resolution 

Imaging Spectrometer (MERIS) can be used to generate results similar to SeaWiFS. Another 

satellite that proposes similar capacities to measure ChlRS-a is the Visible Infrared Imaging 

Radiometer 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 Fall 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 will have continuity of measurements for longterm assessment using satellite data.
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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91

5 Numeric Nutrient Criteria Development in South Florida Marine and

Inland Flowing Waters

EPA is considering several methodological approaches to derive numeric criteria for 

nitrogen/phosphorus pollution in South Florida marine and inland flowing waters. For the 

purpose of this document, South Florida inland flowing waters have been defined as freeflowing, predominantly fresh surface 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 region. South Florida marine waters include estuarine and coastal 

waters extending three nautical miles offshore. Collectively, 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 considered by this rule making are Class III with 

relatively few Class I waters, while Class II and III waters make up South Florida’s marine 

waters. The same narrative nutrient criterion applies 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 from nitrogen/phosphorus pollution. EPA is not 

deriving criteria that would apply to waters located on the Seminole 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 waters and numeric chlorophyll a, TN and TP criteria in South 

Florida marine waters using least-disturbed sites that support balanced natural populations of 

aquatic flora and fauna. Alternative methods of criteria derivation for inland flowing waters that 

EPA is considering include stressor-response 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 protective of South Florida’s marine waters at the 

terminal reach of each tributary to the 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 displayed in Figure 5-1. 
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Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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by FDEP. In addition to the above analysis, two other analyses are being considered for 

developing IPVs: (1) distributional approach of all sites, and (2) stressor-response approach 

(Sections 5.4.1.1.3 and 5.4.1.2, respectively). Unlike the analysis which targets least-disturbed 

sites, the distributional approach of all sites would rely on data from all sites. The stressorresponse approach would evaluate the relationships between response variables (e.g., chlorophyll

a) and TN or TP. 

5.1.2 Marine Waters

In South Florida, with its highly managed inland flows and open-water dominated systems (i.e., 

Florida Bay and Keys), EPA found that existing information about watershed drainages can be 

supplemented with data from the Southeast Environmental 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 reference condition approach for deriving numeric 

criteria (Section 5.6). The reference condition approach identifies concentrations that are 

inherently protective of the waterbody because those concentrations are associated with currently 

demonstrated healthy balanced populations of aquatic flora and fauna. One approach is to 

estimate criteria based on an inclusive distribution of nitrogen and phosphorus values that are 

temporally and/or spatially averaged and are obtained from least-disturbed reference sites from a 

prescribed region. In this situation, EPA (2001) recommends the selection of a percentile of this 

distribution based on the confidence in identifying least-disturbed reference sites. 

EPA is also considering an approach based on the statistical distribution of raw data and 

evaluated using a binomial test. In this approach, EPA would derive two criteria: (1) an average 

(median) concentration and (2) an upper percentile concentration. By considering an upper 

percentile, this second approach would be sensitive to detecting changes in the distribution of 

higher concentrations that may be “averaged-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 ecoregion (see areas south of blue line in Figure 

5-2). Because soil or substrate type at the bottom 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, along 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 Lake 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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Figure 5‐2. South Florida Nutrient Watershed Region
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

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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 entisol soils in the south and histosol soils in the north. The EAA is 

located south of Lake Okeechobee and includes 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 Peninsula11 and South Florida nutrient watershed regions, EPA 

performed spatial analyses to determine the boundary 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 sub-classifying the Biscayne drainage area and 

analyzing it separately from the rest of the East subregion. Specifically, EPA is considering 

dividing the East subregion just above Canal C-9 as shown in Figure 5-4, thus allowing EPA to 

analyze all canals draining to the Biscayne Bay 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 other issues (e.g., geologic formations, groundwater 

interactions) for developing subregions, the EPA will initially consider, where subregion-specific 

analyses are appropriate, the following five subregions for developing 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 and 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 Southern Inland Flowing Waters 

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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 subregions
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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) established 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 flora or fauna.” To translate this narrative, the 

EFA set a TP criterion of 10 parts per billion (ppb) in the EvPA. In 2005, EPA approved FDEP’s 

implementation of the EFA TP criterion. This “numerical 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 the EvPA, including Water Conservation Areas (WCAs) 2A, 2B, 

3A, and 3B, as well as Everglades National Park 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 Tribe of Indians of Florida vs. United 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. 
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EPA recognizes the aforementioned 10 ppb TP criterion as applicable to the 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 South Florida include free-flowing, predominantly 

fresh surface water in a defined channel, and include streams, rivers, creeks, branches, canals, 

freshwater sloughs, and other similar water bodies. Pursuant to Rule 62–302.200, F.A.C., 

“predominantly fresh waters shall mean surface waters 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 Indian Reservation or the Miccosukee Indian 

Reservation (see Figure 5-2); waters located in stormwater treatment areas (STAs), wetlands, or 

marshes; and Class IV canals.12 

Currently, there does not appear to be an inventory of South Florida inland flowing waters that 

clearly delineates the areas described 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 extensive GIS data on canals and other surface 

water systems; however, these data do not distinguish 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 this coverage matches well with NHDPlus, it appears limited to 

portions of the EAA and areas to the west of the 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 develop an inventory of inland flowing waters for 

the derivation of numeric criteria. The purpose of this inventory is to serve as a tool for screening 

water quality monitoring and other data from South Florida flowing waters that are designated as 

Class I and III from data collected from those waters that are designated as Class IV 

(Agricultural Water Supplies). As mentioned above, EPA is not establishing numeric criteria for 

flowing waters designated as Class IV. An inventory of canals will also provide technical and 

management staff with an improved understanding 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 exceptions to Class III. “Secondary and 

tertiary canals” shall mean any wholly artificial canal or ditch 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. 
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To develop the inventory, NHDPlus flow line coverage (see Figure 5-6) would be intersected 

with the land use data. In general terms, NHDPlus flow line segments with adjoining nonagricultural land uses would be classified 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 canals. EPA considers this second class as potential Class IV 

since there are additional requirements 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 process 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 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. 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 the (green) NHDPlus canals. EPA’s approach for 

identifying potential Class IV canals from NHDPlus would include the canals 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 analyses considered elsewhere in this methods 

document. Based on a preliminary review of data from 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 identify predominately fresh water. Pending these analyses, natural 

streams such as Cocohatchee River, Gordon River, 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 chapter if they are predominantly fresh water. If they are determined 

to be predominantly marine, such waters will analyzed as part of the estuarine criteria derivation 

effort described in Chapter 3. For canals, additional information such as control structure 

locations may be used to confirm these analyses. Additional consideration may be necessary to 

identify sloughs that are in defined channels. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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.
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Figure 5‐7. NHD flowline data near Stormwater Treatment Areas 5 and 6 and Seminole Indian Reservation
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5.4 Inland Flowing Waters: Data Sources and Assessment Endpoints  

EPA has considered a range of approaches for deriving numeric criteria for inland flowing 

waters in South Florida. One approach EPA is considering for developing instream protective 

values (IPVs) for TN and TP is the reference condition approach using least-disturbed sites 

(Section 5.4.1.1). The applicable assessment endpoint for this approach 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 (stream condition index or SCI) modified 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 modified version of FDEP’s SCI bioassessment 

methodology13, adapted to characterize and monitor benthic macroinvertebrates in Dade County 

freshwater canal systems. Dade County’s monitoring 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 results, segregated by land use designations, 

indicated a gradient of biological condition reflected in SCI scores ranging from poorest in 

urban/industrial, suburban, and agricultural canals, to good in canals surrounded by wetlands 

(Figure 5-8). 

Based on the findings in Snyder et al. (1998), EPA is considering a land use-based reference 

condition approach for deriving numeric criteria for canals and other inland flowing waters in 

South Florida. EPA is considering using quantitative land use analyses 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, which 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 

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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 variables (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 identification 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 data 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 using 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 region, respectively. The total number of observations actually 
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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 IWR and removing data from the analysis,such as 

observations from stations located on Class IV canals. 

5.4.1 Deriving Numeric Criteria

5.4.1.1 Reference Condition Approach—Least­Disturbed Sites

As mentioned above, one approach that EPA is considering is the reference condition approach 

(USEPA 2000c). A reference condition for nutrients 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 distribution of nutrient data. The latter 

reference condition approach involves setting criteria based on an inclusive distribution of values 

(e.g., temporally and/or spatially averaged) obtained from least-disturbed reference sites from a 

prescribed region (based on climate 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 aquatic life. This methodology will ensure that 

nutrient concentrations associated with biologically healthy, well-balanced communities would 

be considered acceptable as protective of those 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 reference 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 fauna in a particular set of waterbodies. Developing criteria from 

a concentration distribution of least-disturbed reference condition sites, results in criteria that are 

inherently protective because those concentrations are associated with demonstrated healthy 

biological communities. 

Since the type of soil or substrate can influence the relationships between observed biological 

response and nutrient levels in canals, EPA is considering whether to perform these analyses at a 

subregion level as well as for the entire nutrient region. Also, EPA will continue to investigate 

other hydrogeological issues that may merit 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 separately from other screening criteria, such as 

applicable CWA section 303(d) impairments 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 land 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 coefficient derived by Brown 

and Vivas (2005) (Table 5-1), multiplied by the percent total area occupied by each land use 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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(determined by GIS land use coverage developed from high-resolution aerial photographs), and 

summed using Equation 5-1 (Brown and Vivas 2005) 

LDItotal = Σ %LUi * LDIi Equation 5-1 

where 

LDItotal = LDI ranking for landscape unit 

%LUi = percent of the total area of influence in land use i 

LDIi = landscape development intensity coefficient 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 waterbody types and taxonomic groups, that the LDI 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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is an accurate predictor of biological health; that is, healthy, well-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 biologically significant break 

point that can be used to distinguish benchmark reference conditions 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 similar to FDEP’s stream corridor LDI when 

selecting least-disturbed sites in the South Florida nutrient watershed 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 preliminary results, EPA may need to adjust the size of the canal 

buffer and methods for considering the influence of land beyond the immediate segment. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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5.4.1.1.2 Annual Average Concentration  

Aquatic life water quality criteria include three components: magnitude, frequency, and duration. 

One approach is to consider the annual geometric mean concentration for the magnitude 

component of water quality criteria (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 are log-normally distributed which is common for 

parameters under consideration herein. The annual geometric mean can be computed by taking 

the natural logarithm of the concentrations, and then compute the mean by station and year. 

From these values, an overall mean, , and variability associated with the factors of year and 

station can be estimated with standard statistical software (e.g., Minitab, SAS, R, SYSTAT). 

In past efforts, the 75th or the 90th percentile of this distribution has been selected based on the 

level of confidence EPA has that the sites are, in fact, least-disturbed. Using the 75th percentile 

annual geometric mean (i.e., the criteria magnitude) as an example, EPA would compute the 

result as 

where 0.6745 is the inverse of the standard normal cumulative distribution with a probability of 

0.75 and s is the corresponding standard deviation. (Substitute 1.2816 to compute GM90.) 

Consideration should also be given to how often the test concentration can exceed the criteria 

magnitude and still meet the applicable designated 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 

GM75 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 GM75 in two of three years, one would be 84 percent confident that that annual 

geometric mean of the test data exceeded GM75. If the annual geometric mean exceeded the 

GM75 in three of five years, one would be 90 percent confident that that annual geometric mean 

of the test data exceeded GM75. Several different formulations of the percentile of the annual 

geometric mean and frequency term could be considered 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 impairment of designated uses. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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, distributions 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 represent all waters in the 

subregion and reasonably reflect the full range of ambient conditions with a disturbance gradient 

from least to most impacted, the selection of a lower percentile of this distribution, should 

provide a nutrient level that can be assumed to inherently 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 designated 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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109

use (USEPA 2000b, 2000c). Computationally, the approach is identical to that described in Section 

5.4.1.1 except that the data are not screened for least-disturbed sites. 

5.4.1.2 Stressor­Response Relationships

Another approach described in previously published 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 model (e.g., trophic state 

classification, regional predictive model, nutrient sensitive biocriteria), which in turn would link 

nutrient concentrations to the relative risk of environmental harm. Numeric criteria would be 

based on a correlative relationship between nutrients and biological responses at a level that 

would support balanced natural populations of aquatic flora and fauna. Although the underlying 

relationship certainly exists, in nature it has often 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 equation and 90 percent prediction interval. Figure 5-12 displays the 

residual plots. This regression model explains 49 percent of the variance; the residual plots 

suggest that the residuals have a constant variance, are independent, and are normally distributed. 

Overall, this model might be appropriate provided that a chlorophyll a criterion concentration 

can be selected that supports balanced natural 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 acknowledges the visual appearance of a 

higher variance in the middle of the TN range, but further investigation of the lowest and highest 

chlorophyll a concentrations did not reveal anomalies meriting their exclusion. Because of the 

residual variance heteroscedasticity and low overall R2

 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 nitrogen in these waters and may instead consider 

relying on the reference condition approach described previously to derive TN criteria. 

Alternately, it is possible to use quantile regression. In this application, quantile regression 

computes an estimate of the 90th conditional quantile function of the response, given the stressor 

(TN or TP). The function presumes a linear specification 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 formulated as a linear programming problem. To minimize 

the impact of skewed data, the log10 (logarithm, base 10) transformation can be computed before 

applying the quantile regression algorithm. Figure 5-15 presents bivariate plots of chlorophyll a

as a function of TN and TP (in log10 space) overall. Each plot displays the Spearman’s rho 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 develop 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 nutrient 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 

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111

TP

Chl_50

0.001 0.010 0.100

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

Residual

Percent

-1.0 -0.5 0.0 0.5 1.0

99.9

99

90

50

10

1

0.1

Fitted Value

Residual

0.00 0.25 0.50 0.75 1.00

1.0

0.5

0.0

-0.5

-1.0

Residual

Frequency

-0.6 -0.3 0.0 0.3 0.6 0.9

48

36

24

12

0

Observation Order

Residual

240

220

200

180

160

140

120

100

80

60

40

2

1

0

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 analysis
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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112

TN

Chl_50

0.1 1.0 10.0

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

Residual

Percent

-1.0 -0.5 0.0 0.5 1.0

99.9

99

90

50

10

1

0.1

Fitted Value

Residual

0.2 0.4 0.6 0.8 1.0

1.0

0.5

0.0

-0.5

-1.0

Residual

Frequency

-0.9 -0.6 -0.3 0.0 0.3 0.6

40

30

20

10

0

Observation Order

Residual

240

220

200

180

160

140

120

100

80

60

40

2

1

0

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 analysis
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 estuarine 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). Therefore 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 data 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 South Florida and are appropriate for principal 

component and cluster analysis because they provide a long-term record with consistent 

sampling and analytical methods. EPA is considering use of the SERC water quality monitoring 

data as a primary source for deriving numeric criteria for South Florida’s marine waters, and may 

consider additional data from IWR Run 40, peer-reviewed literature, reports, and other data 

sources as mentioned in Section 2.4. 

 

15 These data are included as part of Florida’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 

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114

The overall segmentation approach recognizes that geomorphologic and geochemical 

characteristics vary throughout a basin and can alter the impact of nitrogen/phosphorus pollution 

on water quality and resident communities. Based on this recognition, numeric criteria can be 

derived for regions with consideration that different areas react differently to 

nitrogen/phosphorus pollution. Overall, the principal component and cluster analysis applied 

here follows a line of research initiated in the 1990s. The current analysis EPA is considering 

consolidates the previous work described below, and uses the most recent data. 

Florida Bay has a history of water quality regionalization. Researchers provided an early 

perspective of community distribution 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 aerial images and consequential ground truthing, and 

subsequently found a correlation between SAV standing crop and biological and water quality 

parameters. Similarly, Philips and Badylink (1996) evaluated the correlation between water 

quality and phytoplankton standing crop, noting that ecologically independent regions were 

dynamic with the changing seasons. The seasonality of nutrient pollution loadings and 

consequential water quality is also highlighted by Boyer et al. (1999). Philips and Badylink 

(1996) subsequently modeled the 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 identify three key processes that independently 

controlled the composition of the studied water column: evaporation-driven concentration of 

dissolved material, delivery of phosphorus through water exchange with the Gulf of Mexico, and 

delivery of freshwater with nitrogen pollution. 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 principal components analysis EPA 

is considering uses a variety of water quality indicator variables. Researchers 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 pollutant loads themselves 

(Boyer and Briceno 2007; Boyer and 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 delineate 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 hierarchical cluster analysis to 

separate benthic SAV habitats into ecologically distinct regions, and subsequent discriminant 

function analysis to predict benthic habitat based on associated water quality indicator variables. 

Evaluation of the resulting predictions 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 underlying water quality differences. EPA is 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

115

considering this approach to determine unique regions via statistical clustering 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 model 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 using 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 determined by local bathymetry, meteorology, and 

hydrology in nearshore environments to divide the Florida Keys National Marine Sanctuary into 

9 sub-regions. These boundaries, which represent transitions between areas with different 

transport activity, are dynamic due to seasonal and other climate changes. The adoption of these 

localized segmentations are supplemented by establishing contour lines between water sampling 

stations following benthic depth 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 detailing 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 current 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 

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116

5.6 Marine Waters: Numeric Criteria Derivation  

EPA is considering two approaches for deriving criteria using a reference condition approach. 

One approach is to estimate criteria from a statistical distribution of water quality indicator 

variable data. Criteria are derived based on an inclusive distribution of nutrient values that are 

temporally and/or spatially averaged 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 considering a second approach based on the statistical 

distribution of raw data and evaluated using a binomial test. In this approach, EPA would derive 

two criteria: 1) an average (median) concentration and 2) an upper percentile concentration. By 

considering an upper percentile, this second approach would be sensitive to detecting changes in 

the distribution of higher concentrations that may be “averaged-out” in annual geometric means. 

The reference condition approach identifies concentrations that are presumed to be inherently 

protective of the waterbody because those concentrations 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 criteria in the form of annual geometric means 

would be the same as Section 5.4.1.1.2. Please see Section 5.4.1.1.2 for a description of this 

approach. 

5.6.1.2 Binomial Test to Maintain Current Conditions

In some situations, it might be ecologically meaningful to specify numeric criteria applicable to 

individual observations rather than annual statistics. For example, short duration increases in 

chlorophyll that are expected during 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 that 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 derivation of a two-number criterion (C0.50 and C0.25) 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 using the binomial test. EPA may also consider 

alternative percent exceedances and confidence levels. 

EPA is considering this formulation of the binomial approach because it (1) provides a 

quantitative evaluation of the probability that the data collected during the assessment period are 

from the same or nearly the same distribution as the observations during the baseline period; (2) 

the approach is resistant to influence of extremely high values; (3) the approach does not require 

or imply an assumption of normality or log normality; (4) the approach is not affected by, or is 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 C0.50 and C0.25 to the maximum 50th and 75th

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 numeric 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 data set includes 11 sites and 14 years (1995-2008) of data. Figure 

5-17 displays TN, TP, and chlorophyll a boxplots as a function of year 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 ranges 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 statistically test whether more than 50 percent or 

25 percent of the results exceed the criteria over a three year period. One method is to set the 

criteria to the to the maximum 50th and 75th percentile concentration for any 3-year period from 

the supporting data set. These results are presented graphically 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 supports aquatic life use, the null and alternate 

hypotheses are 

Ho : p ≤ po

Ha : p > po

where p is the fraction of data exceeding the criteria during the assessment period and po is the 

fraction of data exceeding 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 

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118

YEAR

TN, mg/L

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

SITE

TN, mg/L

AK BC BNP EK FB G1B MN MS RK SC TK

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

YEAR

TP, mg/L

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.05

0.04

0.03

0.02

0.01

0.00

SITE

TP, mg/L

AK BC BNP EK FB G1B MN MS RK SC TK

0.05

0.04

0.03

0.02

0.01

0.00

YEAR

Chl-a, μg/L

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

2.0

1.5

1.0

0.5

0.0

SITE

Chl-a, μg/L

AK BC BNP EK FB G1B MN MS RK SC TK

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 

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119

TN, mg/L

Probability

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

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/L

Probability

0.00 0.01 0.02 0.03 0.04 0.05

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 window
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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120

Chl-a, μg/L

Probability

0.0 0.5 1.0 1.5 2.0

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 South Florida inland flowing waters presents technical challenges 

due to the managed flows of canals that are the majority 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 chemically modified (e.g., fertilized, oxidized), 

can influence inland flowing waters in South Florida. Distinct differences in soil types may 

justify classification of flowing waters by soil type 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 disadvantages to deriving numeric criteria for five subregions 

separately vs. for South Florida as a whole. 

Reference conditions may be developed based on historical data for the system 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 conditions and/or nitrogen and phosphorus 

concentrations during the reference 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 

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6 Numeric Nutrient Criteria Development for the Protection of

Downstream Estuaries

Pursuant to 40 CFR 131.10(b), water quality standards must 

ensure the attainment and maintenance 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 computed such that the TN and TP 

discharged from a stream, after accounting for any expected 

losses during transport, will not contribute a disproportionate 

fraction of the maximum TN or TP loading protective 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 complexities associated with the managed flows in 

South Florida inland flowing waters (Chapter 5), the fraction of TN or TP from the upstream 

tributary reach that eventually reaches the estuary cannot be estimated or predicted. Therefore, 

EPA is considering expressing DPVs at the terminal reach of the tributary into an estuary as 

protective concentrations or, alternatively, 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 support 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 development 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 retention 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 

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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 protective nitrogen and phosphorus concentrations or 

loading rates that result from application of its approach for estuarine criteria development, 

described in Chapter 3 of this document. Although one may estimate a maximum loading rate to 

an entire estuary, it is possible 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 streams to protect downstream estuarine waters. For 

example, water quality responses to nutrient loading could be less in a segment of an estuary that 

exchanges water freely with the coastal ocean than 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 loading rates applicable to different estuarine 

segments. Coupled hydrodynamic-water quality models, segment-specific regression models, or 

similar approaches may be used to develop estimates for the protective loads (Chapter 3). In the 

absence of information regarding the differences in the sensitivity of estuarine segments to 

nitrogen/phosphorus pollution loading, EPA will compute 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 CT = Equation 6-1 

whereCT is average concentration specified as the terminal reach DPV, L is the average loading 

rate determined to be protective of designated use in the receiving water, and Q is the average 

freshwater inflow to the receiving 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 include freshwater inputs 

Estimate 

Protective 

Load 

DPV for 

Terminal 

Reaches 

DPV 

for all Other 

Reaches 

Divide by 

Average 

Streamflow 

Divide by 

Fraction 

Delivered 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 into the estuary. 

Estimates of average streamflow would be obtained 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 the 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.html). LSPC would be implemented on a 

streamflow network derived from the National 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 stream network at approximately the 12-digit HUC 

scale. This approach would provide improved accuracy and spatial resolution afforded by 

NHDPlus yet reduce the computational demand by reducing the number of simulated subwatersheds and stream reaches relative to NHDPlus. LSPC model runs would simulate daily 

average streamflow for 1997–2009. EPA intends to evaluate the performance of the model for 

simulating long-term average streamflow, seasonal average streamflow, high flows, low flows, 

and storm hydrographs using time series of monitored flows at local monitoring stations. EPA 

also expects to calibrate and evaluate LSPC in order to predict 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 Ci is the DPV for an upstream reach and Fi is the average fraction of TN or TP 

transported out of that reach that eventually enters the estuarine receiving water, also called 

average “fraction-delivered.” EPA would compute Fi at a daily time interval (Ft,i) for each reach, 

then compute Fi as the long term average. Daily values would be computed using 

∏ −

= j

k t

t i

t j t j

F e , ,

, Equation 6-3 

where Ft,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 comprise the flow path to estuarine waters from 

reach i. The value kt,j and tt,j are first-order decay rates and reach 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 velocity computed using LSPC watershed 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 

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First-order decay refers to exponential decay where 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 inversely associated with stream depth and mean 

streamflow, the latter of which is likely correlated with stream depth (Alexander et al. 2009; 

Böhlke et al. 2009). First-order decay rates kTN and kTP generally cannot be measured directly at 

the scale of whole watersheds and therefore are often estimated empirically. SPARROW 

watershed models at both regional and national scales are recognized as useful tools for 

obtaining these estimates. The south Atlantic, Gulf and Tenneessee (SAGT) regional 

SPARROW model estimated kTN separately for stream reaches in several stream categories 

based on the magnitude of flow (Table 6-1) (Hoos and McMahon 2009). EPA is considering 

computing Fi (Equation 6-3) using estimates of flow-dependent kTN from SAGT-SPARROW. 

EPA is also considering an alternative and slightly simpler approach in which only a single 

value is used for kTN (e.g., kTN=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 kTN, estimates of Fi would depend on stream reach 

time of travel and potentially stream flow obtained from the 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

(m3/s)

Model A Average Stream Flow

(m3

/s)

Model B

kTN (d-1) kTN (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, which transforms reactive 

N species (e.g., NO3

-

) into N2 gas. Permanent P retention in streams 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. Garćia 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 kTP (d-1) = 0.049/z, where z is mean stream depth (m). 

This approach follows the formulation of Alexander et al. (2008), who addressed N and P 

transport in the Mississippi River basin. Similar to TN, EPA is evaluating whether to use 

estimates of first-order TP loss rates (i.e., retention for TP) for TP in southeastern U.S. streams 

estimated using a SPARROW watershed model (Garćia et al. 2010) or, alternatively, a constant 

value of kTP=0.014 d-1. In order to implement the SPARROW-based estimates of kTP, EPA will 

need estimates of average stream depth, which EPA is considering obtaining 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 kTP would be used in Equation 6-3 to compute Fi with daily 

estimates of time-of-travel computed via LSPC watershed model simulations. Average Fi 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 

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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 approaches. First, EPA acknowledges the existing numeric 

criterion for TP in the Everglades Protection Area (10 ppb), and thus has described its approach 

to ensure that flowing waters upstream of this area assure the maintenance of that criterion (see 

Section 6.5.2). Second, EPA is considering derivation 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 water, based on existing models or measurements. 

This approach diverges from those described above 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 predicted in South Florida due to the region’s altered 

and managed hydrology. For this reason USGS modelers 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 the terminal reach and not at locations farther 

upstream. 

In South Florida a protective load, as an alternative to concentration, 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 concentrations 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 assess downstream protection at both upstream and 

downstream locations. However a downstream estuary would be protected by this approach. 

EPA is considering several strategies for calculating DPVs at a tributary terminal reach. There 

are two steps involved in deriving DPVs: calculating the nitrogen/phosphorus pollution load that 

protects the estuary, and translating this load into DPVs for contributing waters. In South 

Florida, the DPV may be expressed 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 based on an equitable strategy. One 

example is to assign loads or flow-weighted concentrations to each tributary based on its relative 

contribution to the estuary. For example, a flow-weighted concentration is calculated as 

 q q q . q

c q c q c q . c q C 1 2 3 n

1 1 2 2 3 3 n n

+ + + ... +

+ + + ... + = Equation 6-4 

where C is the protective total influent concentration (DPV) for the estuary over the time period 

of interest, ci and qi are the concentration and flow respectively in tributary “i”, and n is the 

number of such tributaries. If there is more than one tributary (i.e., n>1), then there is no unique 

solution to Equation 6-4. In this case, achievement of the DPV may be attained through various 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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127

combinations of individual tributary concentrations. Robust datasets 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 L T Equation 6-5 

where Li is the acceptable load from tributary “i” over the time period of interest, qi is the 

tributary’s flow over the same time interval, qT 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 identical concentrations in all tributaries. 

Alternatively, loads for individual tributaries can be calculated based simply on the acceptable 

summed load to the estuary, irrespective of individual tributary flow considerations. Similar to 

that described for the Mississippi River Basin and Northern Gulf of Mexico (National Research 

Council 2009), a simple load allocation equation can be written as 

L1 + L2 + L3 +....+ Ln = T Equation 6-6 

where Li 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 concentrations that Equation 

6-5 implies. The approach described in Equation 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 response to freshwater inflows and 

nutrients in central and south 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 populations of aquatic flora and fauna, and the objective is to ensure 

that nitrogen and phosphorus concentrations in the tributaries do not increase in order to maintain 

water quality. For situations where EPA is considering 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 further details on this approach). 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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6.5.2 Calculating EAA Downstream Protective Values Based on EvPA Total

Phosphorus Criteria

The State of Florida has adopted, and EPA has approved, a long-term geometric mean TP 

criterion for the EvPA of 10 ppb. This 10 ppb long-term geometric mean criterion applies 

throughout the EvPA (see Section 5.2.1 for more information). In order to ensure the attainment 

and maintenance of downstream water quality standards, 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 well 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 nutrient attenuation that would, in turn, inform 

derivation of DPVs for flowing waters in the EAA. 

6.6 Specifying Frequency­Duration for DPVs

DPV criteria are intended to limit 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 changes in nitrogen/phosphorus pollution inputs, key 

assessment endpoints, such as the depth of colonization of seagrasses, respond relatively slowly 

and cumulatively to sustained patterns of increasing or decreasing nitrogen/phosphorus pollution 

loading (e.g., Greening and Janicki 2006). Moreover, controls on average nitrogen and 

phosphorus concentration and loading at the scale of whole estuarine 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 estimates of protective load limits, it is also 

important to consider the potential impact of systematic bias. EPA will evaluate whether it will 

be practical to specify DPV criteria as flow-weighted concentrations, or as concentrations that 

have been empirically adjusted for season and flow-conditions. If not, EPA will specify an 

alternative approach, potentially simply annual means. EPA is considering the use of arithmetic 

means for this purpose because although geometric means have often been recommended to 

quantify central tendency for log-normally distributed data (TN and TP concentrations are often 

log-normally distributed) the product of geometric mean concentration and average streamflow 

may not provide reliable estimates of the transport of TN and TP (e.g., Cohn et al. 1989). 

6.7 Example Application: Pensacola Bay Watershed

We illustrate the methodology that EPA is considering by providing an example of the 

computation of DPVs for Pensacola Bay using the LSPC watershed model. The Pensacola Bay 

system is an estuarine complex located in Florida's panhandle region. It includes several 

interconnected estuarine bays and 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 stream reaches, 12 of which are terminal reaches 

(i.e., discharge directly into the Pensacola Bay; Figure 6-2, Figure 6-3). 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 

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6.7.1 Protective TN and TP Loading and Terminal Reach DPVs

Because protective loading rates for TN and TP have not yet been determined for Pensacola Bay, 

example calculations are presented here for illustrative purposes only. In this example, the long 

term (1997–2009) average TN and TP loads to Pensacola Bay were estimated from LSPC model 

simulations and used as an estimate of the protective 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 approaches for developing estuarine TN and TP 

numeric criteria (Chapter 3) may be entirely different and would replace 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 respond 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 appropriate 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

(m3

/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 Fraction­Delivered in the Pensacola Bay Watershed

The approach described in section 6.4 was applied 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. Subsequently, long-term averages were 

computed. To reduce the total number of numeric criteria values that would be computed, 

estimates of fraction-delivered were rounded upward to the nearest 0.1. Maps depicting the longterm average TN fraction-delivered for each reach (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 loss rate (Figure 6-5). The average difference in 

the estimate of TN fraction-delivered was 6 percent and the maximum difference was 17 percent. 

Differences between the two approaches decreased as the distance and time-of-travel to the coast 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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131

decreases and all values converged 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-dependent scenario was different 

from that obtained with a constant loss. In particular, areas of more efficient N transport 

extended further into the watershed along major rivers when compared to the other scenario 

(Figure 6-4). Although the spatial scale is much smaller, this pattern is similar to that which has 

been described for N transport in the Mississippi/Atchafalaya River Basin, where N is 

transported with low losses from the upper Midwest to the Gulf of Mexico (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-delivered are greater than 90 percent, indicating 

negligible TP losses. Alternatively, following the approach of Garćia et al. (2010) gives fraction 

delivered estimates for TP between 0.53 and 1.0 (Figure 6-6). EPA is continuing to evaluate 

which 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 

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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 (Garćia 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 K

Flow-Dependent K
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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6.7.3 Computing DPVs for Upstream Stream Reaches

Upstream DPVs were computed following the conceptual approach illustrated 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 Pensacola Bay watershed (Figure 6-7, panel B). In 

several cases, differences between the DPVs were 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 nearest 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 

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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 Garćia et al. (2010).
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

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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 unravels the coupled effects 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-Weaver, C.A. Stow, L.J. Steinberg, and K.H. 

Reckhow. 2007. Application of Bayesian structural 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 modeling of denitrification 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 University, West Palm Beach, FL. 

Boyer, J.N., and H.O. Briceno. 2009. FY2008 Annual 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 Southern Inland Flowing Waters 

137

Boyer, J.N., J.W. Fourqurean, and R.D. Jones. 1997. Spatial characterization 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. Phytoplankton bloom status: 

Chlorophyll a biomass as an indicator of water quality condition in the 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. Dettmann, D. Dunn, J. Ferreira, D. Flemer, P. 

Fong, J. Fourqurean, J. Hameedi, D. Hernandez, 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: Workshop summary and recommendations for 

development of a long-term monitoring and assessment program. Proceedings of a 

workshop September 4-5, 2002, Patuxent Wildlife Research Refuge, Laurel, Maryland. 

NOAA, National Ocean Service, National Centers 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 Assessment, Effects of Nutrient Enrichment in the Nation's 

Estuaries. NOAA, National Ocean Service, Special Projects Office and the National 

Centers for Coastal Ocean Science. Silver Spring, MD, 71. 

Bricker, S.B., J.G. Ferreira, and T. Simas. 2003b. An integrated methodology 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 Reference Conditions for Water Bodies. Technical report submitted to 

the Florida Department of Environmental Protection. University of Florida, Center for 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern Inland Flowing Waters 

138

Environmental Policy, Department of Environmental 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 patterning 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. Statewide 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 locally weighted regression and smoothing scatterplots. Journal of 

the American Statistical Association 74:829-836. 

Cloern, J. 2001. Our evolving conceptual model of the coastal eutrophication problem. Marine 

Ecology Progress Series 210:223-253. 

Cloern, J.E. 1996. Phytoplankton bloom dynamics in coastal ecosystems: A review with some 

general lessons from sustained investigation 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 Verification 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 Southern 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. Pilger, 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. Gruner, 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 caused by toxic blue-green algae (cyanobacteria) 

in drinking and recreational water. Environmental Toxicology 14:5-12. 

FDEP. 2008. Integrated Water Quality Assessment for Florida: 2008 305(b) Report and 303(d) 

List Update. Florida Department of Environmental Protection, Division of Environmental 

Assessment and Restoration, Bureau of Watershed Management, Tallahassee, FL. 

FDEP. 2010. State of Florida Numeric Nutrient Criteria Development Plan. Bureau of 

Assessment and Restoration Support, Division of Environmental Assessment and 

Restoration, Florida Department of Environmental Protection 2009a [cited October 

2010]. Available from http://www.dep.state.fl.us/water/wqssp/nutrients/docs/fl-nutrientplan-v030309.pdf. 

FDEP. 2009b. State of Florida Numeric Nutrient Criteria Development Plan. Bureau of 

Assessment and Restoration Support, Division of Environmental Assessment and 

Restoration, Florida Department of Environmental Protection. 

FDEP. 2010. Integrated Water Quality Assessment for 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 Southern Inland Flowing Waters 

140

Fisher, T.R., E.R. Peele, J.W. Ammerman, and 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. 1998. Potential sediment denitrification rates in 

estuaries of northern Gulf of Mexico. J. Environ. Qual. 27(4):859-869. 

Fore, L.S. 2004. Development and Testing of Biomonitoring Tools for Macroinvertebrates in 

Florida Streams. Final report to the Florida Department of Environmental Protection, 

Tallahassee, FL. 

Fore, L.S., R.B. Frydenborg, D. Miller, T. Frick, D. Whiting, J. Espy, and L. Wolfe. 2007. 

Development and Testing of Biomonitoring Tools for Macroinvertebrates in Florida 

Streams. Florida Department of Environmental 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 changing water quality using monitoring data. 

Ecological Applications 13(2):474-489. 

Fourqurean, J.W., R.D. Jones, and J.C. Zieman. 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 subtropical carbonate environment. Marine Biology

114(1):57-65. 

Frankovich, T., D. Morrison, and J.W. Fourqurean. 2010. Benthic Macrophyte Distribution and 

Abundance in Estuarine Mangrove Lakes and Estuaries: Relationships to Environmental 

Variables. Estuaries and Coasts. 

Garćia, 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 Protection Agency Determination Upon Review 

of Amended Florida Administrative 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 Southern Inland Flowing Waters 

141

Greening, H., and A. Janicki. 2006. Toward reversal of eutrophic conditions in a subtropical 

estuary: Water quality and seagrass response 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 Health and Environmental Effects Research 

Laboratory, Research Triangle Park, 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 Science, Special Report 317, 63 pp. 

Heck, K.L., and J.F. Valentine. 2007. The primacy of top-down effects in shallow benthic 

ecosystems. Estuaries and Coasts 30(3):371-381. 

Hecky, R.E., and P. Kilham. 1988. Nutrient limitation of phytoplankton in freshwater and marine 

environments: A review of recent evidence on the effects of enrichment. Limnol. 

Oceanogr. 33(4, part 2):796-822. 

Heil, C.A., and K.A. Steidinger. 2009. Monitoring, 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 the southeastern United Sates using spatially 

referenced regression on watershed attributes (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 phosphatase activity and utilization 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 Southern 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. Linking 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 requirements of seagrasses 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 framework 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 regression analysis in marine biology. 

Marine Biology 65:13-16. 

Lee, K.-S., S.R. Park, and Y.K. Kim. 2007. Effects 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 Environmental Consulting and Technology, Inc., New Smyrna 

Beach, FL. 

McPherson, B.F., and R.L. Miller. 1994. Causes 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 processes in a Gulf of Mexico estuary: 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 Southern 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 concentration, OC4 algorithm. NASA Goddard Space 

Flight Center, available at 

http://oceancolor.gsfc.nasa.gov/DOCS/MSL12/master_prodlist.html/#prod11. 

National Research Council. 2000. Clean Coastal Waters: Understanding and Reducing the 

Effects of Nutrient Pollution. Report prepared by the Ocean Study Board and Water 

Science and Technology Board, Commission on Geosciences, Environment and 

Resources, National Resource Council, Washington, D.C. 

National Research Council. 2009. Nutrient Control Actions For Improving Water Quality in the 

Mississippi River Basin and Northern Gulf of Mexico. National Academies Press, 

Washington, D.C. 

NCDC. 2010. National Climatic Data Center 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 University, 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/extremeevents/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 chlorophyll 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 Protection 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 variability in phytoplankton 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 Southern 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 tropical estuary with restricted 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. Geology 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 freshwater 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 Innovations 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. Tranvik. 1999. Bioavailability of wetland-derived 

DON to freshwater and marine 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. McGillicuddy, 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 Phytoplankton in Lake Ontario, 1. Model 

Development and Verification. U.S. Environmental Protection 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 District of Columbia, Washington, DC. 
Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s 

Estuaries, Coastal Waters, and Southern 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 Ontario, 2. Simulations Using LAKE 1 Model. U.S. 

Environmental Protection Agency, EPA-600/3-76-065, Grosse 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 (Thalassia 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 rainfall in four contiguous 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 Criteria 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 Guidance Manual, Lakes and Reservoirs. Office of 

Water, EPA-822-B00-001. 

USEPA. 2000c. Nutrient Criteria Technical Guidance 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 Office of Research and Development, EPA-842-

B-06-001. 

USEPA. 2007. Remote Sensing Program 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 Southern 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/definitions/hypoxia.html. 

Vargo, G.A. 2009a. 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 initiation, growth, 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 initiation, growth, maintenance, 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 alteration of the global nitrogen cycle: 

Sources and consequences. Ecological Applications 7(3):737-750. 

Walker, N.D., and N.N. Rabalais. 2006. Relationships among satellite chlorophyll a, river inputs, 

and hypoxia on the Louisiana continental 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. Accelerating loss of seagrasses across the globe 

threatens coastal ecosystems. Proceedings of the National Academy of Sciences of the 

United States of America 106(30):12377-12381. 

Werdell, P.J., and S.W. Bailey. 2002. The SeaWiFS Bio-optical Archive 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. Pietras, K.D. Knobelspiesse, G.C. Feldman, and 

C.R. McClain. 2003. Unique data repository facilitates 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 chlorophyll-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 Southern 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 Support 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 and Macroalgae in Florida Bay. Bulletin of Marine Science

44(1):292-311.