Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

 

 

 

Technical Support Document for

U.S. EPA’s Proposed Rule for Numeric

Nutrient Criteria for Florida’s Estuaries, 

Coastal Waters, and South Florida Inland

Flowing Waters 

Volume 3: South Florida Inland Flowing Waters 

November 30, 2012 
Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

Disclaimer 

This document supports the U.S. Environmental Protection Agency’s (hereafter EPA or the 

Agency) numeric nutrient criteria proposed on November 30, 2012, pursuant to section 303(c)(4) 

of the Clean Water Act (CWA) (Title 40 of the Code of Federal Regulations [CFR] section 

131.43). 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. 

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Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

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Contents 

1.	 Numeric Nutrient Criteria for South Florida Inland Flowing Waters...............................1
 

1.1.	 Deriving Numeric Criteria to Protect Designated Uses in South Florida Inland 

Flowing Waters.................................................................................................................2
 

1.2.	 Background.......................................................................................................................3
 

1.2.1.	 Cultural Setting in South Florida ..........................................................................4
 

1.2.1.1. Agricultural and Urban Development................................................... 4
 

1.2.1.2. Tribal Lands, Federal and State-Managed Areas.................................. 6
 

2.	 Derivation of EPA’s Numeric Nutrient Criteria for South Florida Inland Flowing
 

Waters and the Protection of Downstream South Florida Marine Waters........................7
 

2.1. DPV—Reference Condition Approach.............................................................................7
 

2.1.1.	 Identifying Canal Pour Points...............................................................................8
 

2.1.2.	 Data Selection and Preparation.............................................................................9
 

2.1.3.	 Computational Formula ......................................................................................13
 

2.1.4.	 Criteria Computation Results..............................................................................13
 

2.2. DPV—Dilution Model....................................................................................................14
 

2.3. Ambient Water Quality at each Pour Point.....................................................................15
 

3.	 Alternative Derivation of EPA’s Numeric Nutrient Criteria for South Florida
 

Inland Flowing Waters..........................................................................................................16
 

3.1. Classification of South Florida Inland Flowing Waters .................................................16
 

3.1.1.	 Introduction .........................................................................................................16
 

3.1.2.	 Geology/Soils......................................................................................................18
 

3.1.3.	 Climate/Hydrology .............................................................................................19
 

3.1.4.	 Vegetation...........................................................................................................22
 

3.1.5.	 Synthesis: Proposed Classification .....................................................................23
 

3.2. Numeric Criteria Development—Endpoint Selection and Calculations ........................25
 

3.2.1.	 Conceptual Models .............................................................................................25
 

3.2.2.	 Data Characterization and Additional Classification..........................................26
 

3.2.3.	 Identifying Appropriate and Protective Chlorophyll Endpoints.........................28
 

3.2.4.	 Deriving Alternative Nitrogen and Phosphorus Criteria Using Statistical 

Models and Statistical Distributions ...................................................................33
 

3.2.4.1. Effect of Season .................................................................................. 34
 

3.2.4.2. Multivariate Binary Logistic Regression............................................ 35
 

3.2.4.3. Resulting TN and TP Criteria ............................................................. 39
 

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4. References...............................................................................................................................40
 

Appendix A: South Florida Inland Flowing Waters—Criteria Computations .................. A-1
 

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Figures 

Figure 1-1. Level III Ecoregions of the southeastern United States showing the Southern 

Florida Coastal Plain Ecoregion that contains the South Florida Nutrient
 

Watershed Region ........................................................................................................3
 

Figure 1-2. South Florida Nutrient Watershed Region overlaid with the greater south 

Florida drainage basin (inclusive of Kissimmee River), Everglades
 

Agricultural and Protection Areas, stormwater treatment areas, tribal lands, 

and SFWMD canals......................................................................................................5
 

Figure 2-1. FDEP-established regionalization scheme, delineating Florida into six
 

Nutrient Watershed Regions ........................................................................................9
 

Figure 2-2. Southwest estuary pour points ....................................................................................10
 

Figure 2-3. Biscayne Bay and Florida Bay pour points.................................................................11
 

Figure 3-1. Level IV ecoregions of south Florida..........................................................................17
 

Figure 3-2. Geological strata of south Florida ...............................................................................18
 

Figure 3-3. Soil orders and soil suborders of south Florida...........................................................19
 

Figure 3-4. Physiographic regions prior to human intervention (USGS 2003) .............................20
 

Figure 3-5. 8-digit hydrologic unit codes of south Florida............................................................21
 

Figure 3-6. Major canals in south Florida......................................................................................22
 

Figure 3-7. Five-subregion model for south Florida......................................................................24
 

Figure 3-8. Simplified conceptual model of the causal relationship between nutrients and 

use response in south Florida (after USEPA 2010a)..................................................26
 

Figure 3-9. Monthly chl-a concentration at sites along Tamiami Canal (IWR Run 40 sites
 

21FLSFWMTAMBR105, 21FLSFWMS333, 21FLSFWMS12B, 

21FLSFWMMONROE, and 21FLSFWMBC21). Horizontal bar indicates the 

approximate dry/low flow period. ..............................................................................28
 

Figure 3-10. Mapped results of color and alkalinity analysis of water chemistry in south 

Florida ........................................................................................................................29
 

Figure 3-11. All season geometric mean TN as a function of all season geometric mean
 

TP. Open circles and solid triangles indicate “WBID-years” for which ≤ 10
	

percent and > 10 percent of chl-a observations exceed 30 μg/L, respectively. 

Binary logistic regression model for 10 percent frequency represented by
 

solid and dashed lines for EAA subregion and other subregions grouped 

together, respectively..................................................................................................36
 

Figure 3-12. All season geometric mean TN as a function of all season geometric mean
 

TP. Symbols indicate subregion. Binary logistic regression model for 10 

percent frequency represented by solid and dashed lines for EAA subregion 

and other subregions grouped together, respectively. ................................................37
 

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Figure 3-13. Hypothetical relationship between annual geometric mean TN and annual 

geometric mean TP. Black line represents binary logistic regression equation 

solved for a frequency of 10 percent. Red dashed lines A) and B) represent 

two candidate options for TN and TP criteria. Open circles represent various 

“WBID-year” annual geometric mean TN and TP values. ........................................38 

Figure 3-14. Criteria selection using the all season data and grouped subregions to 

prevent algal conditions exceeding 30 μg/L instantaneous chl-a and limiting 

the frequency to no more than 10 percent ..................................................................38 

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Tables 

Table 2-1. South Florida pour points .............................................................................................12
 

Table 2-2. South Florida pour points and corresponding monitoring sites....................................12
 

Table 2-3. Data excluded for purposes of criteria computation.....................................................13
 

Table 2-4. South Florida pour point DPVs ....................................................................................14
 

Table 3-1. Chl-a (μg/L) distributional statistics computed from samples with matched TN
 

and TP results for south Florida inland waters (AGM = annual geometric
 

mean) ..........................................................................................................................30
 

Table 3-2. Associated average and maximum chlorophyll thresholds for different trophic
 

conditions using fixed and open boundary systems (OECD 1982). The inland 

lake criteria were based on a trophic categorization tailored to Florida waters
 

(Salas and Martino 1991), so this table is used simply as an example of what
 

maxima are typically associated with specific annual average chl-a 

concentrations. ............................................................................................................31
 

Table 3-3. Summary of user perception based chlorophyll endpoints (after Heiskary and 

Walker 1988)..............................................................................................................32
 

Table 3-4. Number of samples with matched chl-a, TN, and TP; number and percentage
 

of samples with chl-a exceeding 30 μg/L using all data ............................................34
 

Table 3-5. Number of samples with match chl-a, TN, and TP; number and percentage of
 

samples with chl-a exceeding 30 μg/L using dry season data....................................35
 

Table 3-6. Binary logistic model summaries for models with TN and TP as independent
 

variables......................................................................................................................35
 

Table 3-7. Selected binary logistic regression model coefficients ................................................36
 

Table 3-8. Selected TN and TP concentrations to prevent algal conditions exceeding 30 

μg/L instantaneous chl-a and limiting the frequency to no more than 10 

percent using the all season data and grouped subregion model................................39
 

Table 3-9. Candidate TN and TP criteria.......................................................................................39
 

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Abbreviations and Acronyms
 

μg microgram 

μM micromoles 

CFR Code of Federal Regulations 

chl-a chlorophyll a 

cm centimeter 

C&SF Central and South Florida (project) 

CWA Clean Water Act 

DO dissolved oxygen 

DPV downstream protection values 

EAA Everglades Agricultural Area 

EPA U.S. Environmental Protection Agency 

EvPA Everglades Protection Area 

F.A.C. Florida Administrative Code 

FDEP Florida Department of Environmental Protection 

IWR Impaired Waters Rule 

km kilometer 

L liter 

LN natural logarithm; also loge 

m meter 

NH4 ammonium 

NO2 nitrite 

NO3 nitrate 

NO3+NO2 nitrate+nitrite 

PCA principal component analysis 

PO4 phosphate 

ppt or ‰ parts per thousand 

ROC receiver operator characteristic 

SFWMD South Florida Water Management District 

TON total organic nitrogen 

TMDL total maximum daily load 

TN total nitrogen 

TP total phosphorus 

USGS U.S. Geological Survey 

WBID water body identification number 

WCA water conservation area 

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1. Numeric Nutrient Criteria for South Florida Inland Flowing Waters 

EPA’s proposed numeric nutrient criteria serve to protect the designated Class I, II, and III uses 

previously defined and established by Florida. The designated uses associated with Class I, II, 

and III waters are Potable Water Supply; Shellfish Harvesting; and Recreation, Propagation and 

Maintenance of a Healthy, Well-Balanced Population of Fish and Wildlife, respectively (Section 

62-302.400, Florida Administrative Code [F.A.C.]). EPA’s effort focuses on implementing the 

narrative criterion, which the state has already adopted into its water quality standards to protect 

those designated uses by establishing numeric criteria for the purposes of the Clean Water Act 

(CWA). Florida’s narrative criterion in Subsection 62-302-530(47)(b), F.A.C. reads, “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.” 

This volume of the Technical Support Document (TSD) presents EPA’s analyses and results for 

developing numeric nutrient criteria for inland flowing waters and the protection of downstream 

marine waters in southern Florida. For the purposes of this document, south Florida inland 

flowing waters refers to inland flowing waters south of Lake Okeechobee and the 

Caloosahatchee River (including Estero Bay) watershed to the west of Lake Okeechobee and the 

St. Lucie watershed to the east of Lake Okeechobee. In EPA’s December 2010 Water Quality 

Standards for the State of Florida’s Lakes and Flowing Waters; Final Rule (Final Rule), EPA 

referred to this area of flowing waters as the South Florida Nutrient Watershed Region (USEPA 

2010b). And, south Florida marine waters refers to predominantly marine waters offshore of 

Collier, Miami-Dade, and Monroe counties with the exception of Atlantic coastal waters north of 

Biscayne National Park and Gulf of Mexico coastal waters of mainland Florida west of Cape 

Romano. 

In establishing numeric criteria for south Florida inland flowing waters, EPA believes that it is 

important that nutrients in the systems be managed to ensure that the water quality standards of 

south Florida inland flowing waters provide for the attainment and maintenance of the water 

quality standards of downstream marine waters [40 CFR 131.10(b)]. 

EPA is proposing a hierarchical approach for deriving TN and TP criteria expressed as 

downstream protection values (DPVs) at the points where inland flowing waters flow into marine 

waters (referred to as pour points). Of the four approaches discussed in Volume 1 of the TSD (see 

Section 1.6 of Volume 1), EPA considered the following three approaches1

 applicable to south 

Florida marine waters: (1) a reference condition approach based on TN and TP concentrations 

from the stream pour point, coincident in time with the data record used to derive downstream 

receiving marine water segment TN and TP criteria that were developed using a reference 

condition approach, (2) a dilution model based on the relationship between salinity and nutrient 

concentration in the receiving segment, and (3) the ambient estuarine TN and TP criteria applied at 

the pour point. The DPV analyses and results for south Florida are presented in Section 2. 

1 Note – EPA did not develop water quality simulation models (the fourth approach used in developing DPVs) for south Florida 

estuaries, so this analytical methodology for developing DPVs was not used in south Florida. In future instances where available 

data and/or resources provide for use of a scientifically defensible and protective system-specific application of water quality 

simulation models that protect the designated uses and meet all applicable numeric nutrient criteria for the downstream water, the 

DPVs for TN and TP can be derived from use of an system-specific application of water quality simulation models. 

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EPA also considered the development of instream TN and TP criteria for Class I and III inland 

flowing waters in the South Florida Nutrient Watershed Region. Inland flowing waters would 

include surface waters with chloride concentrations at the surface less than 1,500 mg/L in a 

defined channel, including streams and canals. 

1.1.	 Deriving Numeric Criteria to Protect Designated Uses in South Florida 

Inland Flowing Waters 

Florida’s current narrative criterion reads, in part, that “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” [Subsection 62-302.530 (47)(b), F.A.C.]. Given those expectations for state numeric 

criteria under the CWA and in accordance with the court decision on EPA’s December 2010 

rule,

2 EPA interpreted FDEP’s (Florida Department of Environmental Protection’s) narrative 

nutrient criterion of causing no imbalance as equivalent to the prevention of a harmful increase 

in levels of nutrients. 

In its decision,3 the court, in discussing numeric criteria translating Florida’s narrative criterion, 

stated that “the right target was a criterion that would identify a harmful increase in a nutrient 

level – an increase that, in the language of Florida’s narrative criterion, would create an 

‘imbalance’ in flora and fauna.” Order at 63. Upon review of the latest scientific knowledge, 

EPA has identified nutrient-sensitive biological endpoints relevant to particular estuarine and 

coastal systems. EPA determined that maintenance of seagrasses, maintenance of balanced algal 

populations, and maintenance of aquatic life are three sensitive biological endpoints, which can 

be measured by water clarity (as it relates to light levels sufficient to maintain historic depth of 

seagrass colonization), chlorophyll a, and DO, respectively, and appropriately used in derivation 

of numeric nutrient criteria that protect the State’s designated uses from harmful increases in 

nitrogen and phosphorus concentrations. 

EPA’s approach to deriving numeric nutrient criteria is consistent with FDEP’s approach to 

interpreting its narrative nutrient criterion and deriving numeric thresholds at the State level. 

FDEP has approached the derivation of numeric nutrient criteria in much the same way as EPA 

by aiming to prevent adverse effects to natural populations of aquatic flora and fauna.4 

The CWA requires water quality standards, consisting of designated uses of the waters and water 

quality criteria based on such uses, to restore and maintain the chemical, physical, and biological 

integrity of the nation’s waters. To derive numeric criteria to protect designated uses, it is 

important to consider the CWA goal of restoring and maintaining biological integrity. This is 

important because the State’s numeric criteria must serve the purposes of the CWA and ultimately 

restore and maintain water quality integrity. The concept of integrity is commonly defined as “the 

2 Case 4:08-cv-00324-RH-WCS, February 18, 2012.
 3 Case 4:08-cv-00324-RH-WCS, February 18, 2012.
 4 State of Florida Numeric Nutrient Criteria Development Plan, Prepared by: Bureau of Assessment and Restoration Support,
 

Division of Environmental Assessment and Restoration, Florida Department of Environmental Protection Tallahassee, FL, March 

2009; Technical Support Document: Development of Numeric Nutrient Criteria for Florida Lakes and Streams. Florida
 

Department of Environmental Protection, Standards and Assessment Section, June 2009; Technical Support Document:
 

Development of Numeric Nutrient Criteria for Florida Lakes, Spring Vents and Streams. Florida Department of Environmental
 

Protection, Standards and Assessment Section, 2012.
 

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capability of supporting and maintaining a balanced, integrated, adaptive community of organisms 

having a species composition, diversity, and functional organization comparable to that of the 

natural habitat of the region” (Frey 1977). EPA uses an operational definition of integrity as “the 

ability of an aquatic community to support and maintain a structural and functional performance 

comparable to the natural habitats of a region” (Frey 1977; Karr 1981). 

As required by 40 Code of Federal Regulations (CFR) 131.11, water quality criteria must be 

based on sound scientific rationale, must contain sufficient parameters or constituents to protect 

the designated use, and for multiple use designations, must support the most sensitive use. 

Because the state has already interpreted what it means to protect designated uses from nutrient 

pollution through its narrative criterion statement, EPA is proposing to prevent the harmful 

increase of nutrient levels by ensuring that the developed numeric values will protect nutrientsensitive aquatic floral and faunal species. 

1.2. Background 

For purposes of the proposed rule, inland flowing waters in south Florida have been defined by 

EPA as surface waters with a defined channel, including streams and canals, and that are 

essentially within the boundaries of the Southern Florida Coastal Plain Ecoregion (Omernik 

1976) (Figure 1-1). The factors used to define ecoregions and subecoregions (e.g., land use, 

topography, native vegetation, soils, climate) were determined to apply directly to the 

development of instream criteria that reflect 

regional conditions (USEPA 2000c). Such 

regionalization is necessary to accurately assess 

nutrient impacts. This region, defined as part of 

the inland Final Rule (USEPA 2010b), is referred 

to in this document as the South Florida Nutrient 

Watershed Region. 

Although EPA considers flowing freshwaters in 

the South Florida Nutrient Watershed Region to 

be distinct, they are nonetheless nested within the 

larger wetland ecosystem of the Everglades. The 

Everglades is hydrologically connected to the 

Kissimmee River-Lake Okeechobee watershed to 

the north, and the estuarine/marine waters of the 

Gulf of Mexico and Atlantic Ocean to the east, 

south, and west. Section 3.1 describes the 

classification of the South Florida Nutrient 

Watershed Region. 

Flowing waters in the south Florida region have 

been highly modified because of human 

settlement. The flowing waters in this Nutrient 

Watershed Region consist of an estimated 

2,600 mi (4,180 km) of engineered canals and 

more than 1,000 water control structures 

Figure 1-1. Level III Ecoregions of the southeastern 

United States showing the Southern Florida Coastal 

Plain Ecoregion that contains the South Florida 

Nutrient Watershed Region 

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(e.g., gates, pump stations) built over the past several decades to control flooding (SFWMD 

2011). Their construction by the U.S. Army Corps of Engineers, stemming from the Central and 

South Florida (C&SF) Project, has altered the historical surface water flows from the Kissimmee 

River-Lake Okeechobee watershed. Today, surface water quantity and flow paths are actively 

managed by the SFWMD. Section 1.2.1 provides an overview of the cultural setting in south 

Florida that includes agricultural and urban development, federal and state-managed lands, and 

the tribal lands of the Miccosukee and Seminole Indians. 

Ecological restoration of these altered flow paths has been the focus of water management over 

the past four decades in an attempt to reestablish natural flow-ways as much as possible while 

still maintaining the uses of freshwater by humans including water supply and flood control. A 

key component of ecological restoration has been the establishment of a target phosphorus 

concentration of 0.01 mg/L applicable to waters in the Everglades Protection Area (EvPA). 

1.2.1. Cultural Setting in South Florida 

1.2.1.1. Agricultural and Urban Development 

The Everglades were drained for the economic development of south Florida with the passing of 

the C&SF Project. Because most people were initially unwilling to live in south Florida because 

of periodic flooding, swamp reclamation became an important part of the state’s history 

(Rizzardi 1999). Residents and land speculators throughout the region were primarily concerned 

with drainage and development in the late 19th and early 20th centuries. After widespread 

destruction associated with the hurricanes of the 1920s and 1940s, citizens petitioned Congress 

to control the floodwaters of the Everglades. In response, the C&SF Project was created in 1948 

to be administered by the U.S. Army Corps of Engineers and the SFWMD. The C&SF Project 

called for creating a system of canals, levees, and roads to provide water and flood protection for 

urban and agricultural lands. The project altered the natural flow of water by dividing the Greater 

Everglades region into fragmented areas. It also led to conserving 15–20 percent of the original 

Everglades wetlands by creating Everglades National Park in 1947 (USDOI 1994). 

The water control infrastructure of the program, along with agriculture and urban development, 

has affected the Florida wetlands over time. The C&SF Project ultimately resulted in changing 

the Everglades from natural “uninhabited wetlands to the home of more than 6 million people by 

the mid-1990s” (Spangler 2004). As a result of the extensive canal system, an average of 

1.7 billion gallons of water per day are delivered to the Atlantic Ocean and the Gulf of Mexico 

(Williams and Gerry 2007). The project also converted 1,094 mi2

 (2,833 km2 [700,000 ac]) of 

wetlands to dry land (USDOI 1994). Canals south of Lake Okeechobee were built to support 

agricultural and urban development, compartmentalize the landscape, shunt flows to the ocean, 

and decrease the total volume of freshwater reaching the Everglades (Krajewski 2008). More 

than 2,600 miles (4,184 km) of canals and levees, about 1,200 water control structures, and 60 

pump stations control the movement of freshwater in south Florida to support the region’s rapid 

growth (SFWMD 2011) (Figure 1-2). 

The region’s 1990 population of 6.3 million people is projected to almost double to 11 million by 

2050. Such growth has heightened demand for dikes to reclaim wetlands, placing additional 

pressure on the water supply of the area (CERP 1999). From 1990 to 2005 alone, the entire state 

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Figure 1-2. South Florida Nutrient Watershed Region overlaid with the greater south Florida drainage basin 

(inclusive of Kissimmee River), Everglades Agricultural and Protection Areas, stormwater treatment areas, 

tribal lands, and SFWMD canals 

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“lost 84,000 acres [34,000 ha]5 of wetlands to development” (Weeks 2008). Residential 

construction and clearing of mangrove forests have threatened the Everglades ecosystem 

(USDOI 1994). Development has also pushed agriculture further into wetlands, increasing the 

amount of water pollution into the aquatic ecosystems. Urban sprawl continues to encourage 

outward growth, loss of habitat, and degradation of surface water. Over the past 200 years, 

approximately 46 percent of the total wetlands in the Everglades has been lost, with 2250 mi2 

(5827 km2 [1.44 million ac]) lost between 1953 and 1973. Not only is less water exiting the 

Everglades ecosystem from increased municipal water use, but more degraded water is also 

entering the system from anthropogenic activities (USDOI 1994). 

SFWMD prepares an annual State of the Environment Report for South Florida (the South 

Florida Environmental Report), which includes additional details on inland waters, including the 

Greater Everglades (SFWMD 2011). 

1.2.1.2. Tribal Lands, Federal and State-Managed Areas 

South Florida is home to the Miccosukee and Seminole Indians, two federally recognized tribes 

(75 FR 60810, October 1, 2010). Their lands are in the central part of the South Florida Nutrient 

Watershed Region and encompass approximately 258 mi2

 (668 km2 [165,000 ac]) of the south 

Florida ecosystem in the Everglades. They are bordered by the Everglades Agricultural Area 

(EAA) to the north, the EvPA to the east, and Big Cypress National Preserve to the south and 

west (Figure 1-2). Surface water flows enter both tribal lands through the network of canals and 

control structures at their borders (Figure 1-2). Both tribes have surface water quality criteria for 

nutrients. The Seminole Tribe’s water quality criteria (SWC Rules, Chapter B, Section 12.3, 

Table 12, 38(a) and 38(b)) and the Miccosukee Tribe’s water quality criteria (Miccosukee 

Environmental Protection Code, Subtitle B, Section 3N) are similar to Florida’s narrative criteria 

at Subsections 62-302.530 (47a, 47b), F.A.C. However, the Miccosukee Tribe’s water quality 

criteria for nutrients also contain a numeric criterion for TP of 0.01 mg/L. 

Several federally managed lands are in the South Florida Nutrient Watershed Region (Figure 

1-2). They include Everglades National Park (2,400 mi2 [6,220 km2]), Big Cypress National 

Preserve (1,200 mi2 [3,108 km2

]), and the Loxahatchee National Wildlife Refuge (221 mi2 

[572 km2])—all of which are managed by the U.S. Department of Interior. These lands are 

managed for a variety of purposes, including habitat conservation, wildlife conservation, and 

recreation (e.g., boating, fishing, wildlife viewing) (FWS 2011; NPS 2011; USDOJ 2010). 

Florida and SFWMD also manage public lands and surface waters in the South Florida Nutrient 

Watershed Region for natural resources purposes. Those include state parks, stormwater 

treatment areas, and water conservation areas (WCA-1, WCA-2, WCA-3) (SFWMD 2011). The 

WCAs encompass approximately 1,400 mi2 (3,630 km2) of shallow water impoundment areas 

surrounded by a network of levees and canals that serve as habitat for wildlife and for flood 

protection and water supply to surrounding agricultural and urban lands (USDOI 1994). WCA-1 

and Loxahatchee National Wildlife Refuge are coincident areas; WCA-1, WCA-2, WCA-3, and 

the Everglades National Park compose the EvPA (Figure 1-2). 

5 84,000 acres is approximately 131 mi2 (340 km2

). 

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2.	 Derivation of EPA’s Numeric Nutrient Criteria for South Florida 

Inland Flowing Waters and the Protection of Downstream South 

Florida Marine Waters 

In establishing numeric criteria for south Florida inland flowing waters, EPA believes it is 

important that nutrients in the systems be managed to ensure that the water quality standards of 

south Florida inland flowing waters provide for the attainment and maintenance of the water 

quality standards of downstream marine waters [40 CFR 131.10(b)]. For south Florida EPA is 

proposing the use of DPVs to manage nitrogen and phosphorus pollution in the inland flowing 

waters and protect the downstream water quality of estuaries and coastal waters. 

The approach for deriving DPVs in south Florida is consistent with the approach used for Florida 

estuaries. In south Florida, EPA is proposing a hierarchical procedure that includes three 

approaches for setting DPVs for TN and TP: (1) a reference condition approach based on TN and 

TP concentrations from the pour point, coincident in time with the data record from which the 

downstream receiving marine water segment TN and TP criteria were developed using the same 

data quality screens and reference condition approach, (2) developing a dilution model to 

statistically estimate a single DPV for each marine segment, and (3) characterizing the water 

quality at each pour point with the 90th percentile annual geometric mean using data that are 

temporally coincident with the data used to compute the downstream receiving marine criteria. 

Each approach in the hierarchy is listed in order of preference. The concept behind this range of 

approaches is to account for the inherent coupling between watershed nutrient delivery and inestuary process while allowing some flexibility for utilizing simpler approaches where data are 

limited. 

EPA’s intention in proposing the three approaches is to provide a range of methods for deriving 

DPVs for TN and TP that reflect the degree of data and scientific information available. 

Mechanistic models were not developed for criteria derivation in south Florida. In the sections 

below, EPA provides examples of DPV calculations using the approaches available in south 

Florida. 

The DPVs calculated are expressed as an annual geometric mean not to be exceeded more than 

once in three years. 

For more details regarding DPVs also see Volume 1, Section 1 of the TSD. 

2.1. DPV—Reference Condition Approach 

EPA is proposing DPVs derived from a reference condition approach, as numeric nutrient 

criteria for south Florida inland flowing waters and the protection of downstream marine 

systems. 

FDEP derived numeric nutrient criteria for TN, TP, and chl-a in south Florida marine waters 

using a “Maintain Healthy Conditions Approach,” which derives criteria reflective of ambient 

water quality conditions (Section 62-302.532, F.A.C.). This approach is akin to EPA’s reference 

condition approach, which is designed to develop numeric nutrient criteria that are protective of 

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applicable designated uses by identifying numeric nutrient criteria concentrations occurring in 

least-disturbed and minimally impacted waters that are supporting designated uses. 

Since the downstream marine water is supporting designated uses during the reference period, 

the nutrient loads passing through the pour points into the marine water during the same period 

of record should be protective of the marine water. Therefore, EPA believes it is appropriate in 

these cases to derive reference condition-based DPVs using water quality data at the pour point 

of the freshwater streams, coincident in time with the data record from which the downstream 

marine water segment TN and TP criteria were derived. Using the reference approach will also 

ensure mathematical consistency between the DPVs and marine water criteria. 

To derive numeric DPVs for TN and TP that would manage nitrogen and phosphorus pollution 

in inland flowing waters and protect downstream marine waters, EPA identified 22 canal pour 

points where freshwater discharges to each marine segment. EPA screened water quality data at 

each pour point to prevent the use of upstream water quality data that coincided with a 

documented downstream impact, and calculated the 90th percentile annual geometric mean over 

the period of record. More details on the criteria computation can be found in Section 2.1.3. 

2.1.1. Identifying Canal Pour Points 

FDEP established a nutrient watershed regionalization scheme based on the unique water quality 

and geologic characteristics of each system and delineated the South Florida Nutrient Watershed 

Region as one of the six Nutrient Watershed Regions in Florida (Figure 2-1). Using geographic 

information systems, EPA integrated a waterway coverage with the estuary segments in the 

South Florida Nutrient Watershed Region and identified the pour point as the location where the 

waterway intersected with the estuary segment. Figure 2-2 (southwest estuary) and Figure 2-3 

(Biscayne Bay and Florida Bay) show 22 pour point locations. The pour point IDs are listed in 

Table 2-1, which also includes the corresponding waterway, nearby control structure, 

downstream estuary segment, and latitude/longitude of the locations shown in Figure 2-2 and 

Figure 2-3. In most cases, the nearby control structure is not co-located with the pour point, but it 

is included as a point of reference for those more familiar with that information. 

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Figure 2-1. FDEP-established regionalization scheme, delineating Florida into six Nutrient Watershed Regions 

2.1.2. Data Selection and Preparation 

To prevent deriving a DPV using upstream water quality data that coincided with a documented 

downstream impact (e.g., CWA section 303(d) listing for nutrients in the marine water segment) 

EPA proposes that data screens be applied to the pour point data, and that DPVs are derived 

using the resulting screened dataset. 

Figure 2-2 and Figure 2-3 also show selected water quality monitoring sites using data from IWR 

Run 40. These sites were selected because of their proximity to the corresponding pour point and 

data availability for computing the 90th percentile annual geometric mean. The list of monitoring 

sites associated with each pour point is presented in Table 2-2. In three cases (NPL-01, GI-01, 

and GI-02), it was necessary to select two monitoring sites, as the nearest sites with data were 

upstream of a confluence. The data from the monitoring sites listed in Table 2-2 were screened 

by sample date using the screening reasons summarized in Table 2-3. 

To obtain a data set that represents balanced, natural populations of aquatic flora and fauna for 

applying the reference condition approach, EPA reviewed the period of record for marine criteria 

derivation and identified two situations that merited exclusion. The first situation is the algal 

bloom documented in Manatee Bay, Barnes Sound and Card Sound in 2005. Chl-a 

concentrations ranged from 8 to 16 μg/L, and TP concentrations reached almost 0.1 mg/L 

(Rudnick et al. 2006). This bloom effect is believed to be a combined result of hurricane-related 

disturbances and construction activity along U.S. Highway 1. The second situation is data 

associated with FDEP’s 303(d) verified list of impairments. In general, 303(d) assessments are 

made with the most recent 7.5 years of data. Table 2-3 summarizes the data excluded as a result 

of these two situations. As a result of this screening process, EPA concluded that the data over 

the selected period of record was supportive of designated uses in South Florida waters. 

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Figure 2-2. Southwest estuary pour points 

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Figure 2-3. Biscayne Bay and Florida Bay pour points 

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Nearby Downstream 

Pour Point Control Estuary 

ID Waterway Structure Segment Latitude Longitude 

COCO-01 Cocohatchee River & North Naples COCO1 COCO 26.28281 -81.79999 

NPL-01 Gordon River & Golden Gate Main Canal GG1 NPL 26.14552 -81.78686 

Drainage Canal 

NPL-02 Haldeman Creek N/a NPL 26.12324 -81.76419 

NPL-03 Lely Canal N/a NPL 26.07987 -81.76723 

NPL-04 Henderson Creek Canal HEN_CR1 NPL 26.04224 -81.71302 

GI-01 Faka Union Canal FU1 GI 25.90439 -81.51134 

GI-02 Barron River (SR-29) SR29_1 GI 25.87191 -81.38201 

MBS-01 Aerojet Canal (C-111) S197 MBS 25.26139 -80.42759 

SCI-01 Mowry Canal (C-103) S20F SCI 25.47020 -80.34650 

SCI-02 Military Canal (G-95) S20G SCI 25.48948 -80.34686 

SCI-03 Princeton Canal (C-102) S21A SCI 25.51942 -80.34623 

SCI-04 Black Creek Canal (C-1) S21 SCI 25.54233 -80.32985 

NCI-01 Cutler Drain Canal (C-100) S123 NCI 25.61074 -80.30683 

NCI-02 Snapper Creek Canal (C-2) S22 NCI 25.65742 -80.26763 

NCI-03 Coral Gables Canal (C-3) G93 NCI 25.70551 -80.25984 

SNB-01 Comfort Canal (C-5) S25 SNB 25.78485 -80.23547 

SNB-02 Tamiami Canal (C-4) S25B SNB 25.79385 -80.26210 

SNB-03 Miami Canal (C-6) S26 SNB 25.80742 -80.26046 

SNB-04 Little River Canal (C-7) S27 SNB 25.84689 -80.17919 

NNB-01 Biscayne Canal (C-8) S28 NNB 25.87138 -80.17294 

NNB-02 Arch Canal G58 NNB 25.89996 -80.16221 

NNB-03 Snake Creek Canal (C-9) S29 NNB 25.92906 -80.15144 

Downstream 

Pour Point ID Estuary Segment Monitoring Site6
 

COCO-01 COCO 21FLSFWMBC13 

NPL-01 NPL 21FLCOLLGORDONRIV 

21FLGW 3495 

NPL-02 NPL 21FLCOLLHALDCRK 

NPL-03 NPL 21FLCOLLLELY 

NPL-04 NPL 21FLCOLLHENDCRK 

GI-01 GI 21FLSFWMBC20 

21FLSFWMFAKA 

GI-02 GI 21FLGW 3494 

21FLSFWMBC18 

MBS-01 MBS 21FLGW 3570 

SCI-01 SCI 21FLDADEMW04 

SCI-02 SCI 21FLDADEMI03 

SCI-03 SCI 21FLDADEPR03 

SCI-04 SCI 21FLDADEBL03 

 

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Table 2-1. South Florida pour points 

Table 2-2. South Florida pour points and corresponding monitoring sites 

6 Pour points NPL-01, GI-01, and GI-02 were assigned DPV criteria values at two monitoring sites as no sites with sufficient data 

were available prior to a major canal bifurcation. 

12 
Downstream 

Pour Point ID Estuary Segment Monitoring Site6 

NCI-01 NCI 21FLDADECD02 

NCI-02 NCI DBHYDRO SP04 

NCI-03 NCI 21FLDADECG07 

SNB-01 SNB DBHYDRO TM02 

SNB-02 SNB 21FLDADETM03 

SNB-03 SNB 21FLDADEMR08 

SNB-04 SNB 21FLDADELR06 

NNB-01 NNB 21FLDADEBS04 

NNB-02 NNB 21FLKWATMIA-ARCHCREE-3 

NNB-03 NNB 21FLDADESK09 

Water Body Reason Data Range Excluded 

Southwest Florida September 2009 303(d) listing 2002–present 

sites: 457, 458, 459, 460, 461, 462, 463, and 479 WBID 3278U 

Southwest Florida September 2009 303(d) listing 2002–present 

site: 464 WBID 3278R 

Biscayne Bay/Florida Bay: Manatee Bay-Barnes Sound (MBS) and 

Card Sound (CS) 

sites: 1, 2, 3, 4, 5, 6, 121, and 135 

Algal bloom 2005–2008 

Biscayne Bay: North Bay—Northern North Bay (NNB) and 

Southern North Bay (SNB) 

sites: 131, 132, and 133 

November 2010 303(d) listing 

WBID 3226H 

2003–present 

Florida Bay 

site 8 

303(d) listing since 2003 

WBID 6005 

1996-present 

Southwest Florida 

sites: 51, 52, 53, 64, 65, 67, 68, 69, 70, 72, 74, 75, and 76 

September 2009 303(d) listing 

WBID 3259M 

2002–present 

 

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Table 2-3. Data excluded for purposes of criteria computation 

2.1.3. Computational Formula 

For each pour point, only data from that pour point is used. Assuming data are natural logarithm 

(loge) transformed, the criteria value for each segment can be estimated as follows: 

Criteria Value = ex + z ∗s [1.1] 

where x is the long-term mean of the reference data (i.e., the mean of the individual annual 

geometric means in loge space), and s is the standard deviation of the annual geometric means in 

loge space. The value of the z-statistic is based on the chosen significance level (α) value. EPA is 

proposing an α equal to 0.10, resulting in z equal to 1.28155. 

2.1.4. Criteria Computation Results 

As discussed previously, under the reference condition approach, proposed criteria can be 

derived from observations collected from reference conditions. As discussed in Section 2.1.2, 

EPA chose to exclude some data from the criteria computations because those data might be 

13 
 

90th Percentile Annual
 

Geometric Mean from Ambient 

Water Quality Data
 

TN TP
 

Pour Point ID (mg/L) (mg/L)
 

COCO-01 1.49 0.108 

NPL-01 1.82 0.271 

0.87 0.028 

NPL-02 1.51 0.222 

NPL-03 1.10 0.149 

NPL-04 1.73 0.142 

GI-01 1.16 0.126 

0.78 0.005 

GI-02 0.67 0.025 

0.90 0.015 

MBS-01 0.66 0.013 

SCI-01 3.99 0.004 

SCI-02 1.12 0.006 

SCI-03 5.39 0.003 

SCI-04 0.97 0.004 

NCI-01 0.59 0.005 

NCI-02 1.02 0.009 

NCI-03 1.12 0.023 

SNB-01 0.90 0.015 

SNB-02 1.13 0.006 

SNB-03 1.73 0.006 

SNB-04 1.46 0.008 

NNB-01 1.42 0.005 

NNB-02 0.99 0.076 

NNB-03 1.19 0.004 

 

 

 

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from periods that are not appropriate for this method. Table 2-4 presents the DPV values with α 

= 0.10 using the method described above. 

Table 2-4. South Florida pour point DPVs 

2.2. DPV—Dilution Model 

In this approach, DPVs are derived using the statistical relationship between site-year mean 

salinity and geometric mean TN and TP concentrations based on the Southeast Environmental 

Research Center water quality monitoring network data. EPA believes this approach is 

appropriate as a method to derive DPVs using dilution models based on the relationship between 

salinity and nutrient concentration where statistical models are used to derive criteria. The 

approach is based on the concept that TN, TP, and salinity in estuaries follow conservative 

mixing principles (i.e., tidal mixing or dilution can be estimated from estuarine salinity) that are 

reported in scientific literature (e.g., Dettmann 2001). As fresh water, carrying nutrients, pours 

into an estuary it mixes with the brackish estuarine water which has less nitrogen and 

phosphorus. As the water flows seaward it continues to mix and the nutrient concentration 

decreases and salinity increases. 

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For some estuarine systems, EPA developed dilution models based on the relationship between 

salinity and nutrient concentration. By plotting TN or TP criteria versus the salinity across a 

range of salinities and fitting a line to these two points, the TN or TP at various levels of salinity 

can be determined. This analysis can then be used to determine the TN or TP concentration at the 

pour point associated with attaining and maintaining the estuarine numeric nutrient criteria 

concentration. Predominantly fresh waters have been previously defined as surface waters in 

which the chloride concentration at the surface is less than 1,500 mg/L (salinity less than ~2.7 

practical salinity units). The DPV can be determined using the salinity associated with the pour 

point of 2.7 practical salinity units and intersecting the line to find the DPV concentration 

associated with, and protective of, the estuarine criteria. 

This approach results in a segment specific DPV that is applicable to all tributaries flowing to an 

estuary segment, but cannot be used to allocate different nutrient concentrations between 

tributaries flowing into a single estuarine segment. This is a notable difference from the 

mechanistic modeling approach. EPA did not use the dilution model approach to derive DPVs in 

south Florida. However, if dilution models are developed in the future, DPVs could be derived 

using the approach described in Volume 1, Section 1.6.2 of the TSD. 

2.3. Ambient Water Quality at each Pour Point 

In the absence of mechanistic models and sufficient data to use a reference or nutrient-salinity 

model, the receiving estuary segment criteria would become the DPV. This is the simplest 

approach and may be appropriate where data are too limited to apply the other approaches. EPA 

did not use the receiving water estuary segment criteria approach to derive DPVs in south 

Florida. However, FDEP’s numeric criteria for south Florida marine waters could be applied as 

an alternative approach for deriving DPVs under this scenario (See Section 62-302.532, F.A.C.). 

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3.	 Alternative Derivation of EPA’s Numeric Nutrient Criteria for South 

Florida Inland Flowing Waters 

EPA also considered the development of instream TN and TP criteria for Class I and III inland 

flowing waters in the South Florida Nutrient Watershed Region. Inland flowing waters would 

include surface waters with chloride concentrations at the surface less than 1,500 mg/L in a 

defined channel, including streams and canals. For the purposes of this document, south Florida 

inland flowing waters refers to inland flowing waters south of Lake Okeechobee and the 

Caloosahatchee River (including Estero Bay) watershed to the west of Lake Okeechobee and the 

St. Lucie watershed to the east of Lake Okeechobee. 

This section provides the classification, analytical methods, and results used to develop the 

alternative criteria for south Florida inland flowing waters. The classification process is 

described in Section 3.1. It identifies canals and streams south of Lake Okeechobee as distinctly 

different from other inland flowing waters of south Florida because of their unique physical, 

chemical and biological characteristics. Channelized waterways (engineered canals) dominate 

the flowing waters in this region. Many were created through historic wetland areas where no 

channelized flow previously existed, leading to unique conditions of primary production and 

nutrients. They are also highly managed systems with an engineered channel shape, little or no 

natural riparian vegetation to provide shade and bank structure, have managed flows for water 

supply and flood control, and are routinely maintained (dredged) and chemically treated to 

control aquatic plant growth. The method used to derive numeric criteria is presented in 

Sections 3.2.1 through 3.2.3. The derivation of the numeric criteria is presented in Section 3.2.4, 

which focuses primarily on the technical procedures used to develop the instream TN and TP 

criteria, while supporting information is provided in Volume 3, Appendix A. 

3.1. Classification of South Florida Inland Flowing Waters 

This section addresses EPA’s approach to classifying south Florida’s inland flowing waters for 

purposes of developing instream TN and TP criteria. In this section, EPA reviews information on 

soils, geology, hydrology, climate, and vegetation (Sections 3.1.2 through 3.1.4) to inform its 

decision to consider five subregions: West, Biscayne Drainage, Palm Beach, EvPA, and EAA. 

3.1.1. Introduction 

Classification of waters is an important part of developing instream TN and TP criteria because it 

allows waters of comparable ecological characteristics to be defined where nutrient dynamics are 

expected to be similar (USEPA 2000c). Because nutrients exert adverse effects through changes 

in primary production, they have regional patterns affected by spatial difference in rainfall, soils, 

topography, vegetation, and land use. The focus of the classification effort is generally on 

identifying natural patterns in physical characteristics that inform the identification of classes 

within which nutrient expectations are reasonably similar. Within each resultant class, criteria 

can be derived that are both specific to and protective of those waters. 

EPA guidance for nutrient criteria development encourages the use of classification and 

describes a variety of approaches for identifying water body classes (USEPA 2000c). EPA has 

previously used a large-scale regional classification of the United States into nutrient ecoregions 

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(USEPA 2000a) and has encouraged states to use this approach or refine these ecoregions (or 

both) as needed to account for finer-scale differences in nutrient expectations. In the inland Final 

Rule for stream criteria (USEPA 2010b), EPA developed a 5-region classification for flowing 

waters: the panhandle west, panhandle east, north central, peninsula, and west central regions. 

Two general strategies are recommended for classifying flowing waters—classification based on 

physical factors and classification based on nutrient gradients (USEPA 2000c). The former is the 

most commonly used approach. Classification based on physical factors focuses on identifying 

general patterns in the major drivers of nutrient dynamics in flowing waters, including climate, 

hydrology, geology, soils, topography, morphology, and vegetation. These factors are used to 

derive EPA ecoregions and used to support this type of classification. EPA has selected the 

physical factor approach for classification of south Florida inland flowing waters. 

The most common existing classification is 

ecoregions, which are based on physical factors 

(Figure 3-1). South Florida has the following 

six level IV ecoregions: Big Cypress, Eastern 

Florida Flatwoods, Everglades, Marine 

Ridge/Atlantic Coastal Strip, Southern Coast 

and Islands, and Southwestern Florida 

Flatwoods. EPA considered these ecoregional 

patterns in its classification of south Florida 

inland flowing waters. Analytically, there are 

also two general classification approaches— 

testing of a priori classification and a posteriori 

development based on analysis of collected 

information. A posteriori analysis consists of 

evaluating spatial patterns in factors that have 

been collected to discern patterns that can 

explain variability in naturally expected 

nutrient dynamics. These analyses ideally rely 

on the use of nutrient data from undisturbed 

sites (often approximated by minimallyimpacted or least-disturbed sites) to develop or 

verify potential classifications. This approach 

was used in the inland Final Rule (USEPA 

2010b) to develop those classifications. This 

approach requires the presence of sufficient data from sites across the entire range of potential 

classes. Given the impact of human disturbance in the western and eastern part of the south 

Florida region from urbanization and in the northern Everglades because of agriculture, 

insufficient data exist from least-disturbed sites across the region. Thus, a physically-based a 

posteriori approach would rely instead on an evaluation of existing spatial patterns within 

ecoregional boundaries. 

The following three main groups of factors were considered for this south Florida classification 

effort: geology and soils, climate and hydrology, and vegetation. The factors are consistent with 

those recommended in the guidance described above (USEPA 2000b, 2000c) and are the 

Figure 3-1. Level IV ecoregions of south Florida 

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principal factors that drive chemical and biological patterns in aquatic systems (USEPA 2000b, 

2000c; Wetzel 2001). 

3.1.2. Geology/Soils 

The geologic parent material underlying a region serves as the major source of chemical 

constituents, including phosphorus, to both soils and receiving waters (Yang et al. 1996). 

Although nitrogen inputs are often associated with atmospheric sources, the underlying geology 

and soil composition remain important to the uptake and storage of nitrogen in soils and its 

subsequent release to surface waters. Therefore, geology and soil composition are important in 

classifying nutrient expectations (USEPA 2000c). 

The geology of south Florida is characterized 

by the following five main lithographic strata 

(Figure 3-2) (Scott et al. 2001): the Shelly 

Pliocene/Pleistocene strata in the north, the 

Tamiami Formation in the west, the Anastasia 

and Miami Ls strata in the east, and the 

Holocene sedimentary strata along the 

southwest coast. Of those, only the Tamiami 

was mentioned as having phosphate present in 

virtually all lithologies as limited quantities of 

sand- to gravel-sized grains. Nevertheless, there 

appears to be both an east-west and north-south 

difference in underlying geology, with the 

western geology noteworthy for its elevated 

phosphorus content (Scott et al. 2001). 

Soils are largely a function of underlying parent 

lithology, which interacts with climate, 

topography, and organisms to influence their 

formation. Not surprisingly, there are also 

differences in soil composition across south 

Florida (Figure 3-3). The central Everglades 

region is typified by Saprist Histosols (soil 

suborder and order). 

To the west of the Everglades, soils are typified by a small region of Aquod Spodosols to the 

north, and minor Aquept Inceptisol patches among a larger Aqualf Alfisol distribution in the 

central and south. To the east of the Everglades, soils are composed of a small Aqualf Alfisol 

northern region and a mixture of Aquent Entisols and Aquod Spodosols in the central portions, 

with a patch of Orthent Entisols to the south. Aquent Entisols characterize the southern and 

southwestern coastal soils. Chemically, the peat/muck Histosols contain a higher content of 

organic matter, phosphorus, and nitrogen than the other major orders in south Florida (Entisols 

and Alfisols) (Chen and Ma 2001; Guo et al. 2006). Alfisols have the lowest organic content of 

the three major soil orders and roughly similar phosphorus content to Entisols. The Alfisol 

nitrogen content overlaps with that of Entisols but has slightly higher maximum nitrogen content, 

Figure 3-2. Geological strata of south Florida 

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depending on soil depth. Spodosols have higher organic matter contents than Alfisols and 

Entisols, but the phosphorus content is generally lower. 

Figure 3-3. Soil orders and soil suborders of south Florida 

3.1.3. Climate/Hydrology 

Precipitation across south Florida exhibits a northwesterly gradient with average annual 

precipitation along the eastern edge of 58–60 in (147–152 cm) and 50–52 in (127–132 cm) in the 

northwest corner (Oregon Climate Service 2010). Temperature is generally uniform, with mean 

annual and maximum annual temperatures similar across the region, but mean annual minimum 

temperature indicates a southeast to northwesterly gradient (NOAA 2011). 

Hydrology and water movement reflect precipitation, geology, soils, and topography. 

Historically, a few predominant flow paths were in south Florida. The Everglades were part of 

the Kissimmee drainage, and large volumes of water flowed southerly through Lake Okeechobee 

with water spilling over the southern margin of the lake. The water moved south as sheet flow 

across a wide swath of the Everglades from the Cypress Swamp/Eastern Flatwoods/Atlantic 

Coastal Ridge in the east to the Immokalee Rise and the Big Cypress/Western Pine Flatwoods 

region in the west. The sheet flow continued due south until southernmost Florida where flow 

became more organized into sloughs and split with some turning southwesterly and emptying to 

the southwest coast via Shark Slough, a portion flowing south into Florida Bay, and a portion 

flowing east into Biscayne Bay through various coastal drainages that transect the Atlantic 

Coastal Ridge (McPherson and Halley 1996; USGS 2003) (Figure 3-4). 

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Figure 3-4. Physiographic regions prior to human 

intervention (USGS 2003) 

Various other smaller easterly coastal flow paths were also likely in the small region outside the 

historic Everglades region (McPherson and Halley 1996). On the western side of the Everglades, 

flow in southern Florida south of the Caloosahatchee River was predominantly south and 

southwesterly through the Big Cypress region, flowing ultimately through fringing mangrove 

forests into the Gulf of Mexico at Ten Thousand Islands. 

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By the 1900s, Lake Okeechobee had been surrounded by a levee and although water still flowed 

along historic flow paths, substantial water was diverted from the Everglades to the east through 

canal systems to the Atlantic Coast to support development of urban and agricultural activities 

(Davis 1943; McPherson and Halley 1996; USGS 2003). Thus, the once continuous wetland 

landscape is now compartmentalized into canals and water management units within which water 

movement is carefully managed. Current flow paths have remained the same since the 

construction of these canals, with some water continuing to flow along historic flow paths, 

partially reflected in the four principal hydrologic unit codes (Figure 3-5). However, substantial 

water movement occurs along the extensive canal systems that characterize the south Florida 

region (Figure 3-6). Major flows west out of Lake Okeechobee occur through the 

Caloosahatchee Canal and River, flows east out of Lake Okeechobee travel through the St. Lucie 

Canal, and major southeasterly flows occur through a series of large canals—including the West 

Palm Beach, Hillsborough, North New River, and Miami canals (SFWMD 2011). 

Figure 3-5. 8-digit hydrologic unit codes of south Florida 

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Figure 3-6. Major canals in south Florida 

Water flow is important in determining the direction in which nutrients travel and therefore, 

where similar water quality conditions can be expected. This allows for continuous cycling along 

the flow path. For those reasons, flow was also considered in developing inland classifications. 

3.1.4. Vegetation 

Patterns in vegetation reflect climate, hydrology, and geology/soils; therefore, similar ecological 

landscapes can be expected to have similar general nutrient dynamics. Further, landscape 

vegetation patterns reflect similarities in nutrient cycling that can help inform the classification 

process. 

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South Florida is split into a few major vegetation types reflected in the major ecoregions (Figure 

3-1). Starting in the west, the western pine flatwoods are part of the large level III Southern 

Coastal Plain ecoregion. This savanna-like region is typified by poor to moderately drained 

sandy, acidic soils with low nutrient and organic matter content. To the south of the western 

flatwoods is the Big Cypress region. It is characterized by higher organic matter, and wetter 

Alfisols that contain more organic matter but low levels of nutrients. This wetland region is less 

inundated and contains less peat accumulation than the Everglades. The Corkscrew Swamp 

contains some of the largest remaining virgin cypress forests in the United States (McPherson 

and Halley 1996). Although cypress associations typify the region, especially in depressional 

areas, pine and hammock forest exist at more elevated locations. Depressional ponds and 

swamps cover large areas and are typified by southern freshwater swamp species. The 

Everglades itself is composed historically of peatland Histosols with higher organic and nutrient 

content than other soils. As noted previously, much of the area has been drained, and the 

northern region converted into extensive agricultural lands. South of Lake Okeechobee was an 

extensive sawgrass region and sawgrass-slough region, though more than 50 percent of that 

habitat has been replaced with agricultural or altered wetland habitat. Further south a more 

complex mosaic of sawgrass, open slough, tree island, and wet prairie developed above and in 

the Shark and Taylor slough areas. This mosaic is repeated along the northeastern, eastern, and 

southeastern boundary that also contained fringing marl marshes, wet prairies with sedge and 

wetland plants, cypress forest, and pine forest on higher elevations. The eastern portion of south 

Florida that includes the Atlantic Coastal Ridge and Eastern Flatwoods has a greater portion of 

Alfisols and Entisols and is now heavily urbanized. Along the southern margin of south Florida, 

a fringing mangrove forest existed and still does in large sections. 

3.1.5. Synthesis: Proposed Classification 

The objective of classification is to define regions of comparable ecological setting within which 

nutrient dynamics are expected to be similar (USEPA 2000b, 2000c). The consideration of the 

principal factors above formed the core of EPA efforts to define appropriate classes. Given the 

patterns in geology/soils, hydrology, and vegetation, EPA is proposing a five-subregion model 

(Figure 3-7). The model has an area west of the Everglades boundary as a distinct class 

containing the Western Flatwoods and Big Cypress areas, and a northern and southern 

Everglades split along the historic Sawgrass/Sawgrass-Slough vegetation boundary. 

The west subregion is typified by the sandier, poor-nutrient Spodosolic western flatwoods to the 

north and the similarly low-nutrient but more organic wetland regions to the south in the 

Big Cypress. Flow in this region is also generally similar, in the southwesterly direction, except 

some small areas that might periodically drain north, west, or east. This region is the only one 

underlain by the Tamiami Formation with its higher phosphorus content. Given similarities in 

geology, soils, flow, and vegetation in this area and its separation from the Everglades by 

elevation, nutrient dynamics are expected to be more similar in this subregion. 

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The Everglades has different soils (Histosols) 

and flow than the western or eastern 

subregions. The Histosols are known to be 

more organic and to contain higher 

concentrations of nitrogen and phosphorus 

than other soils, especially from the 

Spodosols in the western subregion, but also 

the Alfisols and Entisols. Flow is also quite 

distinct from the west and, although diverted 

in large part to the east, still retains a portion 

of southerly flow. The central Everglades 

class was split into northern and southern 

Everglades subregions. The basis of this 

separation was primarily on the differences 

between the two ecoregions defined by the 

sawgrass plains and the ridge and slough, as 

well as the boundary, which essentially 

replicates that ecoregional boundary. 

Although this boundary is, coincidentally, 

similar to the EAA and EvPA boundary, this 

is not surprising given that development 

adhered to the traditionally thicker soiled 

sawgrass plain and was constrained by the 

change in soil, vegetation, and flow 

characteristics to the south. 

The eastern region has less organic, nutrient-poor soils, different hydrology, and more diverse 

historic vegetation than the Everglades subregions, and for this reason is set apart. The proposed 

five-subregion model splits the Palm Beach and Biscayne Bay drainages that reflects (1) 

differences in soils with more Spodosols and Alfisols to the north, and primarily Entisols to the 

south; (2) differences in flow characteristics with the separation between primarily Biscayne 

versus Palm Beach drainage flows; and (3) differences in vegetation with the drier flatwood pine 

associations to the north, and, where not urban, historically wet prairie and Atlantic Coastal 

Ridge associations to the south. 

EPA considered a four-subregion model that retained the west, EAA, and EvPA subregions 

described above but combined the Palm Beach and Biscayne Drainage subregions. EPA also 

considered a three-subregion model that retained the west subregion and combined the EAA and 

EvPA subregions and combined the Palm Beach and Biscayne drainage subregions. EPA is 

proposing the five-subregion model because the physical nature of south Florida most closely 

follows with the inland classification factors of geology and soils, climate and hydrology, and 

vegetation. Note that the four- and three-subregion models can be developed from Figure 3-7 by 

combining the Palm Beach and Biscayne Drainage subregions and the EAA and EvPA 

subregions, respectively. 

Figure 3-7. Five-subregion model for south Florida 

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3.2. Numeric Criteria Development—Endpoint Selection and Calculations 

An alternative method to develop numeric criteria to protect inland flowing waters in south 

Florida from nutrient pollution could use a combination of distributional and stressor-response 

based approaches similar to that used by EPA in the inland Final Rule for deriving numeric 

nutrient criteria for other inland waters throughout the state (USEPA 2010b). The approach 

proceeds in the following four steps: (1) conceptual modeling, (2) data characterization/ 

exploration and classification, (3) identifying appropriate and protective chlorophyll endpoints, 

and (4) deriving nitrogen and phosphorus criteria using statistical models. The first three steps 

are discussed in Sections 3.2.1 through 3.2.3; the last step is discussed in Section 3.2.4. 

3.2.1. Conceptual Models 

Conceptual models are important in deriving numeric nutrient criteria, especially when using 

stressor-response relationships (USEPA 2010a). EPA proposes a conceptual model for nutrient 

pathways in south Florida (Figure 3-8). Nutrients can affect uses in Class III waters through a 

variety of potential causal pathways. Anthropogenic sources of nutrients include both point and 

nonpoint sources and can include both agricultural and urban runoff. These sources generate 

nutrient loads and subsequent other stressors including sediment and toxics that might be 

covariates of interest in any causal models. Nutrients stimulate primary productivity and can 

result in increased autotrophic biomass and respiration reflected both in increased average 

biomass and bloom frequency (Walker 1985). The increased biomass is metabolized by 

heterotrophs, further increasing respiration. Increases in organic matter loading can also affect 

food quality. These changes in DO and food quantity influence aquatic life through stress or 

mortality or both. The changes in food quantity alter competition among taxa, which can also 

contribute to adverse effects on aquatic life community structure. Occurring simultaneously with 

enriched productivity is the effect increased nutrients have on algal species competition, with 

enrichment leading to increased productivity of eutraphentic species (i.e., species that prefer 

nutrient-enriched, eutrophic waters) that increase the propensity for nuisance taxa blooms 

(Tilman 1985; Wetzel 2001). Eutraphentic species include cyanobacteria known to generate 

cyanotoxins that are a threat to both aquatic life and recreation uses (Bigham et al. 2009; Chorus 

et al. 2000). Many nuisance taxa are also less palatable than other taxa, which influences food 

quality and grazers (Wetzel 2001). A variety of mitigating factors influence the relationship 

between nutrients and productivity response, including hydrology/flow, light, alkalinity/pH, and 

temperature, which are, in turn, affected by physical setting. 

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Agriculture 

Nutrients 

Retention 

time 

Canal 

Volume 

Hydro/ 

geology 

Toxics 

Vegetation 

Color 

Light 

Suspended 

Sediment 

Alkalinity 

Temp 

Primary 

Productivity 

Organic 

Matter/Biomass Respiration 

Nuisance 

Algae/Plants 

Oxygen Food Quantity Food Quality Algal Toxins 

Urbanization 

Aquatic Life Recreation 

Contributing 

Factor 

Causal Path 

Figure 3-8. Simplified conceptual model of the causal relationship between nutrients and use response in south 

Florida (after USEPA 2010a) 

3.2.2. Data Characterization and Additional Classification 

Initial data sets were retrieved from IWR Run 407 and, where available, data from DBHydro8 

were added. Site locations were screened to include freshwater locations and Class III waters. 

Screening for freshwater included examining salinity, specific conductivity, and chloride data, as 

well as considering existing control structure locations near the coast. Other data sources used in 

screening for freshwater sites were the delineations of WBID Run 40 designated use 

classifications (Class II and III marine waters) and the head of salt line.9 Water quality site 

locations and descriptions were evaluated, and descriptions indicating that sites were from 

private agricultural pump stations (an indicator of Class IV waters), stormwater treatment areas, 

or marsh were removed. Site locations were also reviewed via geographic information systems 

with satellite imagery to confirm their location on Class III waters in south Florida. 

7 IWR Run 40 is FDEP’s Impaired Waters Rule database of environmental data, version 40.
 8 DBHydro is the South Florida Water Management District’s environmental database, which stores hydrologic, meteorologic,
 

hydrogeologic, and water quality data.
 9 The salt line coverage are data made available from FDEP and is based on an analysis of the National Wetlands Inventory
 

attribute features.
 

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Chl-a, corrected chl-a, TN, and TP were retrieved from sites passing the above analysis. 

Chlorophyll data were consolidated, with preference given to corrected chl-a because this 

variable does not include absorbance by pheophytin, a chlorophyll degradation product that 

interferes with the chlorophyll absorbance; when unavailable, uncorrected chl-a was used. TN, 

TP, and chl-a values were log-transformed and averaged to daily averages. After the data 

preparation steps, and for the binary logistic regression, a total of 4,758 daily averages with 

matched chl-a, TN, and TP data were assembled for analysis. EPA’s focus on matched chl-a, TN, 

and TP data was to enable a logistic regression analysis. EPA’s rationale for using a logistic 

regression analysis is described later in Section 3.2.3). Ultimately, the above data were organized 

by WBID-year, whereby all nutrient data in a specific year were log-transformed and then 

averaged. Paired chl-a data were treated as multiple trials for the same annual geometric mean 

TN and TP. All chl-a and nutrient data were natural log-transformed before analyses and backtransformed to present final values. 

An important element in developing instream TN and TP criteria is to develop spatial or 

temporal classification to reduce natural variability and focus management actions (USEPA 

2010a). Earlier sections of this document describe the regional classification EPA performed to 

identify reasonable spatial patterns; subsequent analyses proceeded with consideration of those 

regions and combinations of regions. In addition to spatial classification, EPA explored seasonal 

variability. On the basis of early investigations of the data, there appeared to be a difference in 

chlorophyll concentrations coincident with the low rainfall period versus higher rainfall periods 

in canals, as evidenced by the increased chl-a concentrations in the latter months (April to May) 

of the dry season was observed in the preliminary analysis of data from the Tamiami Canal 

region (Figure 3-9). That was consistent with an effect of flow on residence time, which is an 

important factor affecting algal growth (USEPA 2000b, 2000c). Chlorophyll increased in these 

canal sites during low-flow/no-flow periods through the late spring with longer residence times, 

reaching maxima right before the wet period. During the wet/flow period, residence time in any 

one of those canal segments was likely decreased, and there was an apparent, coincident decrease 

in algal biomass as a result. This logically led to a consideration of the dry low-flow/no-flow 

period as one for particular focus, because algal biomass accrual would be expected to be 

maximal during that time and the likelihood of impacts would be higher. Therefore, data were 

analyzed from both annual and dry low/flow periods to focus on critical periods for the 

manifestation of nutrient response in canal systems. EPA set the dry period from November to 

May and the wet period, June to October, corresponding to the 5 months over which 65 to 

70 percent of the annual rainfall occurs in south Florida (SERCC 2007). 

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10 987654321 11 12 

Month 

Chl-a (μg/L) 

100 

10 

1 

Figure 3-9. Monthly chl-a concentration at sites along Tamiami Canal (IWR Run 40 sites 21FLSFWMTAMBR105, 

21FLSFWMS333, 21FLSFWMS12B, 21FLSFWMMONROE, and 21FLSFWMBC21). Horizontal bar indicates the 

approximate dry/low flow period. 

3.2.3. Identifying Appropriate and Protective Chlorophyll Endpoints 

The investigation of potential chl-a targets to protect Class III south Florida flowing inland 

waters would provide an important link between nutrients and biology. As noted previously, a 

principal route through which nutrient pollution might cause adverse impacts is the enrichment 

of primary producers, especially algae, and subsequent negative impacts of excess algal growth 

on the two principal uses for Class III flowing waters in south Florida (recreation and aquatic 

life) that occur through alteration of algal assemblage composition and increases in organic 

matter loading (USEPA 2000b, 2000c). Such effects result from both increases in average 

chlorophyll (e.g., change in habitat resulting from water clarity changes that occur through longterm elevation in chlorophyll content) and instantaneous chlorophyll such as adverse aesthetic 

conditions or cyanotoxin production during blooms that are reflected in high instantaneous chl-a 

concentrations). As a result, algal biomass, as indicated by chl-a, is an appropriate sensitive 

endpoint for numeric nutrient criteria development (USEPA 2000b, 2000c). The use of chl-a as a 

water quality target has a long and scientifically defensible history in aquatic ecology (Harding 

1994; Paerl 1988; Piehler et al. 2004) and was used for lakes in the inland criteria effort 

(40 CFR 131.43). The application of algal biomass as an endpoint for criteria derivation in south 

Florida inland flowing waters, including canals, might be appropriate given the following 

observations: (1) reduced flow in these water bodies, leads to long residence times; (2) canopy 

cover is reduced both naturally and through manipulation reducing light limitation; and 

(3) nutrient concentrations are elevated. This combination presents the potential for excess algal 

growth as evidenced by observations of elevated chl-a concentrations (e.g., > 50 μg/L) 

(i.e., Figure 3-9). Following a decision to focus on chlorophyll, EPA explored two options for 

deriving quantitative targets for the biological endpoint chl-a, (1) using inland lake chl-a 

endpoints, and (2) deriving nuisance chl-a avoidance targets. 

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During dry low-flow/no-flow periods, residence time increases, and these systems behave more 

like lentic (lake-like) systems and less like lotic (stream-like) systems. However, there is little 

scientific information on the ecology of canal systems either as lotic or lentic systems, when 

impacts of nutrients can be pronounced. There is, however, more scientific literature on effects 

of nutrients for lakes and they are an analogue to these water bodies during low/no flow periods. 

Therefore, chlorophyll endpoints developed for the numeric lake criteria established by EPA 

were considered. The 20 μg/L associated with colored and clear alkaline lakes would be most 

applicable for south Florida inland waters (locations of clear/high alkalinity and colored lakes are 

shown in Figure 3-10) because these systems exhibit color and alkalinities consistent with this 

lake class defined during the inland Final Rule effort (USEPA 2010b). 

Figure 3-10. Mapped results of color and alkalinity analysis of water chemistry in south Florida 

29 
 

 

 

 

 

WBID-Year 10th Percentile 25th Percentile 50th Percentile 75th Percentile 90th Percentile 

Subregion Observations AGM AGM AGM AGM AGM 

Biscayne 119 0.65 1.14 2.10 3.87 6.74 

Palm Beach 185 1.63 2.85 5.29 9.82 17.17 

EAA 40 1.87 3.37 6.40 12.17 21.89 

EvPA 24 0.90 1.55 2.77 4.98 8.55 

West 117 0.95 1.71 3.25 6.18 11.06 

Number of 

 

 

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The 20 μg/L mean annual chl-a target is consistent with the upper end of the mesotrophic range 

on the basis of the Salas and Martino (1991) interpretation of the trophic state classification 

system for Florida lakes and as converted into chl-a by EPA (40 CFR 131.43). Such 

concentrations are generally well above mean and median concentrations associated with inland 

water systems in regions across south Florida (Table 3-1). Given that some of the WBIDs from 

which the data in Table 3-1 were derived are 303(d) listed for nutrient or chlorophyll 

impairments (or both), an annual average of 20 μg/L might reflect a change in the natural 

balance of flora in south Florida inland waters. The chl-a criterion of 6 μg/L used in the inland 

Final Rule for clear, low alkalinity lakes is more consistent with the 50–75th percentiles of mean 

annual chl-a concentrations across all site populations in south Florida inland waters (Table 3-1) 

(USEPA 2010b). 

Given the different distributions of mean annual chlorophyll data in these systems and the 

different seasonal hydrologic conditions, application of the inland lake chlorophyll endpoints 

were not considered appropriate for south Florida inland waters. Therefore, an alternative 

approach to identify chl-a targets on the basis of avoiding nuisance conditions was evaluated. 

Table 3-1. Chl-a (μg/L) distributional statistics computed from samples with matched TN and TP results for south 

Florida inland waters (AGM = annual geometric mean) 

The temporal scale over which excess algae effects are observed varies by use and causal path. 

For example, to a swimmer or someone fishing, average annual concentration is likely less 

important than conditions on the day they swim or fish (Walker 1985). On the other hand, in 

terms of long-term effects on aquatic life through habitat disturbance, for example, changes in 

clarity that exist over extended periods of time are likely more injurious than any single 

excursion. This is because it is long-term decline in habitat conditions that will ultimately 

decrease growth and survival of expected taxa that will lead to an imbalance of natural 

populations of aquatic flora and fauna. Florida’s narrative nutrient criterion serve to protect all 

Class III uses, which include recreation and propagation and maintenance of a healthy, wellbalanced population of fish and wildlife. Given the effect of nuisance blooms on recreational 

uses and as a reflection of balance of aquatic flora, deriving chlorophyll criteria on the basis of 

reducing the likelihood of increased nuisance algal blooms is a viable approach. 

The concept of deriving protective chlorophyll and associated nutrient targets on the basis of 

reducing nuisance chlorophyll levels has been widely applied in the United States (Walker 

1985), including in Florida (Bachmann et al. 2003; Havens and Walker 2002; Walker and 

Havens 1995). Defining specific chlorophyll concentrations consistent with nuisance conditions 

30 
 

 

 

Fixed Boundary Average Chl-a Maximum Chl-a 

System (μg/L) (μg/L) 

Ultra-oligotrophic < 1.0 < 2.5 

Oligotrophic < 2.5 < 8 

Mesotrophic 2.5–8.0 8–25 

Eutrophic 8–25 25–75 

Hypereutrophic > 25 > 75 

Average Chl-a Maximum Chl-a 

Open Boundary (μg/L) (μg/L) 

System Range Average +/- 1 SD +/- 2 SD Range Average +/- 1 SD +/- 2 SD 

Oligotrophic 0.3–4.5 1.7 0.8–3.4 0.4–7.1 1.3–10.6 4.2 2.6–7.6 1.5–13 

Mesotrophic 0.3–11 4.7 3.0–7.4 1.9–11.6 4.9–49.5 16.1 8.9–29 4.9–52.5 

Eutrophic 2.7–78 14.3 6.7–31 3.1–66 9.5–275 42.6 16.9–107 6.7–270 

Hypereutrophic 100–150 

 

 

 

 

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in this literature was based on trophic state boundaries, user perception studies, and observed 

impacts. 

In addition to commonly applied average annual chlorophyll trophic state boundaries, maximum 

instantaneous chlorophyll concentrations were also developed for the trophic state boundaries 

and allow a comparison of annual maxima associated with specific annual averages (Table 3-2). 

For example, instantaneous maximum annual chl-a concentrations expected for annual averages 

of 20 μg/L (i.e., assuming the criterion was applied for colored, high alkalinity lakes in Florida) 

is in the upper end of the 25–75 μg/L chl-a range. In contrast, annual maxima associated with 

annual mean chl-a of 6 μg/L (i.e., assuming the criterion was applied for acid clear lakes in 

Florida as described above) would be in the range of 8–25 μg/L chl-a. 

Table 3-2. Associated average and maximum chlorophyll thresholds for different trophic conditions using fixed 

and open boundary systems (OECD 1982). The inland lake criteria were based on a trophic categorization 

tailored to Florida waters (Salas and Martino 1991), so this table is used simply as an example of what maxima 

are typically associated with specific annual average chl-a concentrations. 

Equations relating maximum chlorophyll to average annual chlorophyll concentrations have also 

been developed to predict expected maximum conditions for any average condition, and these 

include a range of estimates. One estimate provided in the literature for the maximum is 3.14 × 

the mean annual chlorophyll value (Walker 1985). Another estimate is provided by the following 

formula (Walker 1985): 

Maximum chlorophyll = 2.6(mean annual chlorophyll)1.06 [3.1] 

Other researchers have estimated the ratio of maximum chl-a concentrations to seasonal average 

chl-a concentrations as approximately 1.2 to 2.0. On the basis of the annual equation above, the 

expected maximum chlorophyll level associated with an annual average of 20 μg/L would be 

approximately 62 μg/L; for an annual average of 6 μg/L, it would be approximately 17 μg/L. 

Various authors have published nuisance chlorophyll levels or maximum chlorophyll 

concentrations associated with adverse effects based on surveys. User perception surveys have 

been conducted in various places to identify conditions inimical to recreation and other uses and 

31 
 

Chl-a
 

Location (μg/L) Condition
 

South Africa 0–10 Fine 

10–20 Evident scum 

20–30 Nuisance 

> 30 Severe nuisance 

Louisiana 14 Excellent to good 

30 Good to acceptable 

32 Acceptable to marginal 

Canadian prairie ponds 0–25 Clear, no blooms 

25–100 Moderate blooms 

100–200 Dense colonies and scums 

North Carolina > 15 Unsuitable for trout 

> 40 Severe nuisance 

Wisconsin < 1 Excellent 

1–5 Very good 

5–10 Good 

10–15 Fair 

15–30 Poor 

> 30 Very poor 

 

 

 

 

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a summary of those values are provided in Table 3-3. The threshold nuisance bloom 

concentrations defining serious/severe nuisance concentrations most commonly cited by these 

authors is greater than 30 μg/L chl-a (Heiskary and Walker 1988; Lindon and Heiskary 2009; 

Walker 1985; Walker and Havens 1995; Walmsley 1984); however, nuisance conditions less 

than serious or severe begin to be encountered in the 20–30 μg/L chl-a range (Walker 1985). 

Table 3-3. Summary of user perception based chlorophyll endpoints (after Heiskary and Walker 1988) 

Nuisance chlorophyll levels are associated with toxin-producing nuisance algal species including 

cyanobacteria such as Microcystis, Anabaena, and Aphanizomenon (Chorus et al. 2000). For 

example, studies of microcystin, a cyanobacterial toxin often associated with chlorophyll levels 

that have exhibited toxicity to animals and is presumptively toxic to humans, have shown that 

chlorophyll levels in the 30–40 μg/L chl-a range are also associated with an increased likelihood 

of microcystin production (Chorus et al. 2000; Lindon and Heiskary 2009). The toxin producing 

cyanobacteria prefer warm 77–82 °F (25–28 °C), high light, high pH conditions, such as those 

present in southern Florida (Bigham et al. 2009; Lindon and Heiskary 2009). As noted by local 

scientific experts, a survey of Florida lakes, specifically, indicated that probabilities of 

microcystin concentrations considered risky for consumption by humans and wildlife 

(1–20 μg/L) begin to increase around instantaneous chl-a concentrations above 30 μg/L, but 

concentrations considered risky for recreational physical contact (> 20 μg/L microcystin) only 

begin to exhibit significant risk at instantaneous chl-a concentrations greater than 130 μg/L 

(Bigham et al. 2009). 

On the basis of the available scientific evidence, EPA selected a chl-a concentration of 30 μg/L 

as a potential threshold to define nuisance conditions for use in deriving nutrient criteria in south 

Florida inland flowing waters. This endpoint is supported by the scientific literature that uses 

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both user perception survey information and experimental studies of toxin production that are 

adverse to recreation, consumption, and aquatic life uses. EPA also evaluated an allowable 

probability of chlorophyll levels. EPA considered a zero nuisance probability but concluded that 

there should be some allowable probability of occurrence given the natural cycles of conditions 

that can rarely contribute to nuisance chlorophyll levels but that are minimally manageable 

(e.g., droughts). EPA considered existing literature and selected a value of 10 percent, which is 

consistent with target nuisance chlorophyll levels observed for systems being managed for 

nutrient excess in Florida (Havens and Walker 2002). The use of the 10% value, therefore, 

means that criteria derived using this approach will protect these waters from having nutrient 

concentrations spiking to 30 μg/L less than 10% of the time. Using the equations presented 

earlier, an instantaneous concentration of 30 μg/L can be associated with annual mean chl-a 

concentrations of about 10 μg/L and growing seasonal average chl-a concentrations in the 

vicinity of 15 μg/L. 

On the basis of data from Florida lakes, which is the most informative in the absence of specific 

research on canals, annual average chlorophyll concentrations between 15 and 20 μg/L are where 

nuisance chlorophyll levels greater than 30 μg/L begin to exceed a 10 percent frequency. The 

seasonal averages associated with these same targets are actually the same as the annual averages 

(Bachmann et al. 2003). These values are also similar to those generated by an earlier model 

using data from Vermont, South Africa, and a large national U.S. Army Corps of Engineers lake 

database (Walker 1985). 

3.2.4. Deriving Alternative Nitrogen and Phosphorus Criteria Using Statistical 

Models and Statistical Distributions 

Having identified protective and defensible chl-a levels using an approach similar to that used to 

derive lake criteria for the inland Final Rule (USEPA 2010b), EPA then evaluated the use of 

stressor-response relationship modeling to derive associated nitrogen and phosphorus criteria 

(USEPA 2010a) focusing on the West, Palm Beach, and Biscayne Drainage subregions. EPA 

considered and evaluated data for the EAA and EvPA, but it is not proposing alternative numeric 

TN and TP for those subregions. All calculations were made using natural log-transformed 

values. WBID-year geometric mean (natural log) nitrogen and phosphorus concentrations were 

calculated for each WBID-year combination across south Florida. A WBID-year is all data in a 

WBID for a year. The geometric mean TN and TP values were paired with chl-a observations for 

each corresponding WBID-year where the chl-a data were converted to a binary scale (0 or 1) to 

indicate whether a chl-a observation was greater than 30 μg/L. Using the above data, logistic 

regression models for the probability of a chl-a observation being greater than 30 μg/L as a 

function of geometric mean TN and geometric TP were constructed. The logistic equation is 

given by the following: 

p [3.2] ln ൬ a + b LN(TNGM) + c LN(TPGM) 1 − p

൰ = 

where p is the probability of an instantaneous chl-a observation exceeding the specified threshold 

level; a, b, and c are the regression coefficients; and LN(TNGM) and LN(TPGM) are the logtransformed geometric mean TN and TP, respectively. After the regression coefficients are 

33 
 

 

 

 

 

 

 

 

Percent of Samples 

Samples with Chl-a with Chl-a 

Season Subregion Samples > 30 μg/L > 30 μg/L 

All 1) Biscayne 1,045 14 1.34% 

All 2) Palm Beach 1,288 109 8.46% 

All 3) EAA 398 37 9.30% 

All 4) EvPA 828 15 1.81% 

All 5) West 1,199 70 5.84% 

Totals 4,758 245 5.15% 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

estimated, the value of p can be estimated for various values of TNGM and TPGM using the 

following equation: 

e(a+b LN(TNGM)+c LN(TPGM)) [3.3]

p = 

1 + e(a+b LN(TNGM)+c LN(TPGM)) 

In addition to standard regression statistics, receiver operator characteristic (ROC) curves were 

used to evaluate model performance. ROC curves are used to evaluate models focusing on 

measures of sensitivity and specificity (Gönen 2006). Sensitivity and specificity are also known 

as the true positive and negative rate, respectively. Plots of sensitivity as a function of 

1-specificity are integrated, and the area under the curve is used as a measure of discrimination. 

An area under the curve of 0.5 indicate no discrimination, values from 0.7 to less than 0.8 as 

acceptable discrimination, 0.8 to less than 0.9 as excellent discrimination, and area under the 

curves of 0.9 and greater are outstanding, but extremely rare (Hosmer and Lemeshow 2000). 

Finally, the use of distributional statistics, which is a recommended reference condition approach 

of EPA nutrient criteria guidance (USEPA 2000b, 2000c), was considered. In addition to their 

use to inform criteria setting, distributional statistics are informative for data exploration 

(USEPA 2010a). Distributional statistics of TN and TP were derived for all WBIDs and different 

regional combinations and for different screening rules. 

The results of these analyses are summarized and discussed below. All results and additional 

explanation are provided in Volume 3, Appendix A. 

3.2.4.1. Effect of Season 

EPA first explored the effect of season. The presumption was that nuisance chlorophyll levels in 

dry season samples would exceed those observed when all data throughout a year were 

considered (see Section 3.2.2). Table 3-4 presents the number of samples with matched chl-a, 

TN, and TP organized by subregion for the all-season data set. For example, in Palm Beach 

1,288 matched samples with 109 (8.46%) samples have chl-a greater than 30 μg/L. In all, 

245 samples have chl-a greater than 30 μg/L. Table 3-5 presents the same information for the dry 

(November to May) season with 130 samples having chl-a greater than 30 μg/L. 

Table 3-4. Number of samples with matched chl-a, TN, and TP; number and percentage of samples with chl-a 

exceeding 30 μg/L using all data 

34 
 

Percent of Samples 

Samples with Chl-a with Chl-a 

Season Subregion Samples > 30 μg/L > 30 μg/L 

Dry 1) Biscayne 532 4 0.75% 

Dry 2) Palm Beach 720 44 6.11% 

Dry 3) EAA 180 12 6.67% 

Dry 4) EvPA 472 10 2.12% 

Dry 5) West 711 60 8.44% 

Totals 2,615 130 4.97% 

 

 

 

 

Subregions Area Under 

Model Modeled Nitrogen Phosphorus ROC Curve Notes 

2 EAA, Other P < 0.05 P < 0.05 0.8 

1 5 Subregions P < 0.05 P < 0.05 0.8 EAA significantly different subregion 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

Table 3-5. Number of samples with match chl-a, TN, and TP; number and percentage of samples with chl-a 

exceeding 30 μg/L using dry season data 

Overall, the percent of observations exceeding 30 μg/L chl-a using all data and dry season data 

are the same using a test of two proportions (p > 0.7). The same is generally true of region-byregion comparisons except that the all-season percent of observations in Palm Beach exceeding 

30 μg/L (8.46%) is greater than the corresponding dry-season percentage (6.11%) and the allseason percent of observations in the West subregion exceeding 30 μg/L (5.84%) is less than the 

corresponding dry season percentage (8.44%). The overall similarity of these proportions 

suggests that chl-a values exceed the selected nuisance conditions in similar proportions 

throughout the year as would be the case in the dry season. For this reason, EPA is considering 

that criteria should be developed on the basis of the annual data set. 

3.2.4.2. Multivariate Binary Logistic Regression 

EPA modeled TN and TP simultaneously in multivariate binary logistic regression models, 

seeking to explore the relative effects of each nutrient; those results are provided in Volume 3, 

Appendix A. Two different all-season data set models were constructed and are summarized in 

Table 3-6. The subregions modeled column indicates which subregion combinations were 

modeled. The nitrogen and phosphorus columns indicate whether the nutrient was statistically 

significant (p < 0.05). The area under the ROC curve provides an estimate of the area under the 

curve (for a ROC curve discussion, see Section 3.2.4). The final column provides comments 

about the significance of subregions in the model. For example, the first entry shows that 

proportion of chl-a observations > 30 μg/L was modeled using both TN and TP as predictors, 

and modeled all five subregions separately. The model indicates a significant effect of both TN 

and TP and an area under the ROC curve of 0.8. Of the different subregion models, only the 

EAA subregion was significantly different. As a result, the next model was identical except that 

there were only two subregions, EAA and Other (i.e., all other subregions combined). Each 

independent variable was significant, and no further options were explored. 

Table 3-6. Binary logistic model summaries for models with TN and TP as independent variables 

35 
 

 

Region Model 

Other 

p = e(1.44621 + 0.23991 LN(TNGM)+ 1.28634 LN(TPGM)) 

1 + e(1.44621 + 0.23991 LN(TNGM)+ 1.28634 LN(TPGM)) 

EAA 

e(0.35664 + 0.23991 LN(TNGM)+ 1.28634 LN(TPGM))

p = 1 + e(0.35664 + 0.23991 LN(TNGM)+ 1.28634 LN(TPGM)) 

 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

The first entry shows that proportion of chl-a observations > 30 μg/L was modeled using allseason data, with both TN and TP as predictors, and modeled all five subregions separately. The 

model indicates a significant effect of both TN and TP and an area under the ROC curve of 0.8. 

Of the different subregional models, only the EAA subregion was significantly different. As a 

result, the second model was identical except that there were only two subregions, EAA and 

Other (i.e., all other subregions combined). Each independent variable was significant, and no 

further options were considered. Both models (Table 3-6) show acceptable area under ROC 

diagnostics. Of the subregion models, the EAA region was the only region with significant 

regression coefficients. Therefore, subsequent criteria derivation focused on that model (Table 

3-7). Corresponding contour plots solved for the 10 percent frequency are provided in Figure 

3-11 and Figure 3-12 for the Other and EAA regression model. 

Table 3-7. Selected binary logistic regression model coefficients 

0.001 0.01 0.1 1

10 

1 

0.1 

0.01 

Annual Geometric Mean Total Phosphorus, mg/L 

Annual Geometric Mean Total Nitrogen, mg/L

1) No 

2) Yes 

EAA 

Other 

Legend 

Figure 3-11. All season geometric mean TN as a function of all season geometric mean TP. 

Open circles and solid triangles indicate “WBID-years” for which ≤ 10 percent and > 10 percent 

of chl-a observations exceed 30 μg/L, respectively. Binary logistic regression model for 10 percent 

frequency represented by solid and dashed lines for EAA subregion and other subregions grouped 

together, respectively. 

36 
 

 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

Figure 3-12. All season geometric mean TN as a function of all season geometric mean TP.
 

Symbols indicate subregion. Binary logistic regression model for 10 percent frequency represented
 

by solid and dashed lines for EAA subregion and other subregions grouped together, respectively.
 

Because TN and TP were modeled simultaneously, multiple combinations of TN and TP can be 

substituted into the model and maintain a frequency, p, of 10 percent. That is illustrated in Figure 

3-13. The black line represents a binary logistic regression equation solved for a frequency of 

10 percent and where higher TN values are associated with lower TP values. EPA selected an 

optimal set of TN and TP criteria by maximizing the number of TN and TP observations meeting 

both criteria. This is represented with the red dashed lines A and B. In this example, criteria 

represented by line B would encompass observations 5, 6, 7, and 8; line A would encompass 

observations 2, 3, 4, 5, 6, and 7. Because line A encompasses more observations, the 

corresponding values of TN and TP would be the criteria proposed by EPA. Water bodies with 

concentrations outside line A, but still to the left of the black line, might be appropriate for the 

development of a site-specific alternative criteria. 

Applying this approach to the model results developed earlier is presented in Figure 3-14 with 

values summarized in Table 3-8. The optimal criterion for the Biscayne, Palm Beach, EvPA, and 

West subregions is represented by the red dashed line (2 mg/L TN and 0.052 mg/L TP). The 

optimal criteria for the EAA subregion range from 3.45 mg/L TN and 0.109 mg/L TP to 

4.325 mg/L TN and 0.105 mg/L TP, respectively. These are represented by a pair of solid red 

lines in Figure 3-14. Note that the lower TN value is coupled with the higher TP value for the 

EAA and vice versa. Given the small percent difference in the range of TP values for the EAA, 

relative to the TN range, EPA would likely select 4.325 mg/L TN and 0.105 mg/L TP over 

3.45 mg/L TN and 0.109 mg/L TP. 

37 
0.001 0.01 0.1 1

10

1

0.1

0.01

 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

Annual Geometric Mean Total Phosphorus, mg/L 

Annual Geometric Mean Total Nitrogen, mg/L

A) 

B) 

1 

2 3 

4 

5 

6 7 

8 

equal to 10%. 

exceeding 30 μg/L 

Probability of chl-a 

Figure 3-13. Hypothetical relationship between annual geometric mean TN and annual geometric mean TP. 

Black line represents binary logistic regression equation solved for a frequency of 10 percent. Red dashed lines 

A) and B) represent two candidate options for TN and TP criteria. Open circles represent various “WBID-year” 

annual geometric mean TN and TP values. 

Figure 3-14. Criteria selection using the all season data and grouped subregions to prevent algal conditions 

exceeding 30 μg/L instantaneous chl-a and limiting the frequency to no more than 10 percent 

38 
 

 

TN TP 

Subregion (mg/L) (mg/L) 

1) Biscayne 2 0.052 

2) Palm Beach 2 0.052 

3) West 2 0.052 

Note: EPA is not reporting candidate criteria for the EAA and EvPA. 

Binary Logistic Regression Model: 

Designed to prevent algal conditions 

> 30 μg/L instantaneous chl-a and limiting the 

Method frequency to no more than 10% of the time 

TN TP 

Subregion (mg/L) (mg/L) 

1) Biscayne 2 0.052 

2) Palm Beach 2 0.052 

3) West 2 0.052 

Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

Table 3-8. Selected TN and TP concentrations to prevent algal conditions exceeding 30 μg/L 

instantaneous chl-a and limiting the frequency to no more than 10 percent using the all 

season data and grouped subregion model. 

3.2.4.3. Resulting TN and TP Criteria 

In developing numeric nutrient criteria for south Florida inland flowing waters, EPA focused on 

the grouped region binary logistic regression model for > 30 μg/L chl-a for 10 percent of the 

time using the all-season data to compute TP and TN criteria (see Table 3-9). 

Table 3-9. Candidate TN and TP criteria. 

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Technical Support Document for U.S. EPA’s Proposed Rule for Numeric Nutrient Criteria, 

Volume 3 South Florida Inland Flowing Waters 

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