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 , 2012Technical 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 communi ty, or any other party, and might not apply to a particular situation or circumstance.

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

, Volume 3 South Florida Inland Flowing Waters



### 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. Cultura l 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. Criteri a 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. Cla ssification 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). Ho rizontal 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 geom etric 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 percen tage of samples with chl- a exceeding 30 μ g/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 mod els 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 (p roject )

CWA Clean Water Act

DO dissolved oxygen

DPV downstream prote ct ion 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 log

e

m meter

NH 4 ammonium

NO 2 nitrite

NO 3 nitrate

NO 3 + NO 2 nitrate + nitrite

PCA principal component analysis

PO 4 phosphate

ppt or ‰ parts per thousand

ROC receiver operator characteristic

SFWMD South Florida Water Management District

TON total organic nitrogen

TMDL t otal m aximum d aily load

TN total nitrogen

TP total phosphorus

USGS U.S. Geological Survey

WBID water body identification number

WCA w ater conservation a rea

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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 nutri ent 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

Caloosahatche e 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 th is area of flowing waters as the South Florida Nutrient Watershed Region ( USEPA 2010b) . And, south Florida marine waters refers to predomin antly 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 criter ia 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 deriv ing 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 d iscussed in Volume 1 of the TSD (see

Section 1.6 of Volume 1), EPA considered the following three approaches 1 applicable to south

Florida marine waters: (1) a reference condition approach based on TN and TP concentrations from the stream pour point, coincident in tim e with the data record used to derive downstream receiving marine water segment TN and TP criteria that were developed using a referenc e 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

DPV s 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 de cision 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 tha t 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 ch emical, 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 nume ric 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 Criteri a 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 Strea ms . 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 organis ms 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 nutrient

sensitive 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, de fined 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 M exico 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 th e 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, p ump 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 mai ntaining 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 m g/L applicable to waters in the Everglades Protection Are a (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 19 th and early 20 th centuries. After widespread

destruction associated with the hurricanes o f 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 perc ent 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” ( Sp angler 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 mi 2 (2,833 km 2 [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 area s , 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 an d 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 mi 2
(5827 km 2 [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 Nutrien t Watershed Region and encompass approximately 258 mi 2 (668 km 2 [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 m g/L.

Several federally managed lands are in the South Florida Nutrient Watershed Region ( Figure 1-2) . They include Everglades National Park (2,400 mi 2 [6,220 km 2 ]), Big Cypress National Preserve (1,200 mi 2 [3,108 km 2 ]), and the Loxahatchee National Wildlife Refuge (221 mi 2

[572 km 2 ])

— 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 mi 2 (3,630 km 2 ) 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 mi 2 (340 km 2 ).

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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 wate rs, 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 concent rations 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 90 th percentile annual geometric mean using data that are temporally coincident with the data used to compute the dow nstream receiving marine criteria. Each approach in the hierarchy is listed in order of preference. The concept behind this range ofapproaches is to account for the inherent coupling between watershed nutrient delivery and inestuary process while allowin g 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 de rived 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 desig nated 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 f rom 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 t o each marine segment . EPA screened water quality data at each pour point to prevent the us e of upstream water quality data that coincided with a documented downstream impact , and calculated the 90 th 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 system s , 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 po ur 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 co ntrol 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 90 th 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.

T o 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 situat ion 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

11Nearby Downstream

Pour P oint C ontrol Estuary

ID Waterway S tructure S egment Latitude Longitude

COCO

- 01 Cocohatchee River & North Naple s 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 Site 6

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

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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.

12Downstream

Pour Point ID Estuary Segment Monitoring Site 6

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 B ody Reason Data R ange E xcluded

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

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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. A ssuming data are natural logarithm (log

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

Criteria Value = e x + 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 log

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

e 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 , u nder the reference condition approach, proposed crite ria 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

1390th 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

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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 s tatistical 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 develo ped 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 define d 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 crit eria 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 EP A’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 man aged 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

Section s 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 t o 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 specifi c 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 expectation s. In the inland Final

Rule for stream criteria (USEPA 2010b), EPA developed a 5- region classification for flowing waters: the panha ndle 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 p hysical 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 ha s 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 r ely

on the use of nutrient data from undisturbed

sites (often approximated by minimally

impacted 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 sit es across the region. Thus, a physically- based a posteriori approach wou ld 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 e coregions of s outh 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 s erves 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 no teworthy 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 compo sed 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 s outh 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 La ke 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

ca nal 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 code s ( 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 code s 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 )

. Sta rting 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 comp osed 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 fores t 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 spl it 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 s outhwesterly 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 vegetatio n 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 so uth.

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, E AA, 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- an d 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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, Volume 3 South Florida Inland Flowing Waters



### 3.2. **Numeric Criteria Development**

**— Endpoint Selection and Calculations** 

An alternative met hod to develop nu meric 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 num eric

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 a ffe ct uses in C lass 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 m ight be covariates of interest in any causal models. Nutrients stimulate primary productivi ty 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. I ncreases in organic matter loading can also affect food quality. The se 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 produ ctivity is the effect increased nutrients ha ve 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

cyano toxins 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

Food Quality Algal Toxins Oxygen Food Quantity Urbanization

R ecreation Aquatic Life 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**  **C haracterization and**  **A dditional**  **C lassification** 

Initial data sets were retrieved from IWR Run 40 7 and , w here available

, data from DBHydro 8

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

. Screening for freshwater included examin ing salinity, specific conductivity, and chloride data, as well as consider ing existing control structure locations near the coast. Other data sources u sed 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 d escriptions indicating that sites w ere 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 Na tional 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 sp ecific 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 back

transformed to present fin al 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 analys es proceeded with consideration of those regions and combinations of regions. In addition to spatial classification, EPA explored seasonal variability. On the basis of early investigation s of the data, the re 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 C anal region ( Figure 3-9 ). Th at was consistent with an effect of flow on residence time, which is an importa nt 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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12 11 10 987654321

MonthChl-a (μg/L) 100

10

1

Figure 3-9

. Monthly c hl -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**  **A ppropriate and**  **P rotective**  **C hlorophyll**  **E ndpoints** 

The investigation of potential chl -a targets to prot ect 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 m ight cause adverse impacts is the enrichment of primary producers, esp ecially 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 develo pment (USEPA 2000b, 2000c). The use o f 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 , m ight 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 μ g/L ) ( i.e.

, Figure 3-9 ). Following a deci sion to focus on chlorophyll, EPA explored two options for deriving quantitative targets for the biological endpoint chl -a, (1) us ing inland lake chl -a endpoints, and ( 2) deriv ing nuisance chl -a avoidance targets.

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During dry low-flow/no-flow periods, residence time increase s, 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. T he 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 R ule effort (USEPA 2010b).

Figure 3- 10

. Mapped results of color and alkalinity analysis of water chemistry in south Florida

29WBID-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 Technical Support Document for U.S. EPA ’ s Proposed Rule for Numeric Nutrient Criteria

, Volume 3 South Florida Inland Flowing Waters

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 m ight reflect a change in the natural

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

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

annual chl

- a concentrations across all site population s 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 t argets 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 serv e to protect all Class III uses, which include recreation andpropagation 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

, deriv ing 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 lev els 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

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

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in this literature was b ased 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 concentration s expected for annual average s 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 thi s 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 f or the maximum is 3.14 ×
the mean annual chlorophyll value (Walker 1985) . An other estimate is provided by the following

formula (Walker 1985):

Max imum chlorophyll = 2.6(mean annual chlorophyll) 1.06 [3.1]

O ther researcher s 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 1 7 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

31Chl -a

Location ( μg/L ) Condition

South Africa 0

– 10 Fine

10

– 20 Evident s cum

20

– 30 Nuisance

> 30 Severe n uisance

Louisiana 14 Excellent to g ood

30 Good to a cceptable

32 Acceptable to m arginal

Canadian p rairie p onds 0

– 25 Clear, no b looms

25

– 100 Moderate b looms

100

– 200 Dense c olonies and s cums

North Carolina > 15 Unsuitable for trout

> 40 Severe n uisance

Wisconsin < 1 Excellent

1

– 5 Very g ood

5

– 10 Good

10

– 15 Fair

15

– 30 Poor

> 30 Very p oor Technical Support Document for U.S. EPA ’ s Proposed Rule for Numeric Nutrient Criteria

, Volume 3 South Florida Inland Flowing Waters

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 a n 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 consi dered 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 u se s

32Technical Support Document for U.S. EPA ’ s Proposed Rule for Numeric Nutrient Criteria

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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 ar e 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 chlorophy ll concentrations between 15 and 20 μg/L are where nuisance chlorophyll levels greater than 30 μg/L begin to exceed a 10 percen t 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 N**  **itrogen and P**  **hosphorus C**  **riteria U**  **sing Statistical** 

**M odels**  **and S**  **tatistical D**  **istributions**
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 E vPA, 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

- ye ar 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
in dicate 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 lo gistic equation is given by the following :

p [3.2 ] ln ൬

a + b LN ( T N GM ) + c LN ( T P GM ) 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 ( TN GM ) and LN ( TP GM ) are the log

transformed geometric mean TN and TP, respectively

. After the regression coefficients are

33Percent of Samples
Samples w ith Chl -a w ith Chl -a Season Subregion Samples > 30 μg/L > 30 μg/L

All 1) Biscayne 1

, 04 5 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 TN GM and TP GM using the following equation:

e ( a + b LN ( T N GM ) + c LN ( T P GM ) ) [3. 3 ]

p = 1 + e ( a + b LN ( T N GM ) + c LN ( T P GM ) )

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

measu res 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 f unction 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 chlo rophyll 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%) sampl es hav e chl -a gr eater than 30 μg/L. In all,

245 samples hav e 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
exceed ing 30 μ g/L using all data

34Percent of Samples
Samples w ith Chl -a w ith 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 μ g/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

- by

region comparisons except that the all-season percent of observations in Palm Beach exceeding30 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. Multivar iate Binary Logistic Regression
EPA modeled TN and TP simultaneously in multivariate binary logistic regression models, seeking to explore the relative effects of each nutrient ; th o se 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 signific ant 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 varia bles

35Region Model

Other

p = e ( 1

. 44621 + 0

. 23991 LN ( T N GM ) + 1

. 28634 LN ( T P GM ) )

1 + e ( 1

. 44621 + 0

. 23991 LN ( T N GM ) + 1

. 28634 LN ( T P GM ) )

EAA
e ( 0

. 35664 + 0

. 23991 LN ( T N GM ) + 1

. 28634 LN ( T P GM ) )

p = 1 + e ( 0

. 35664 + 0

. 23991 LN ( T N GM ) + 1

. 28634 LN ( T P GM ) ) 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 indicate s 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 wi th significant regression coefficients. Therefore, subsequent criteria derivation focused on th at 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 b inary logistic regression model coefficients

1 0.1 0.01 0.001 10

1

0.1

0.01

Annual Geometric Mean Total Phosphorus, mg/LAnnual 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 whic h ≤ 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.

3 6Technical 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. Th at 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 wo uld 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 Bisc ayne, 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 ar e 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.

371 0.1 0.01 0.001 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/LAnnual 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 T N 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 subregi ons to prevent algal conditions exceeding 30 μg/L instantaneous chl

- a and limiting the frequency to no more than 10 percent

38TN 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.

39Technical Support Document for U.S. EPA ’ s Proposed Rule for Numeric Nutrient Criteria

, Volume 3 South Florida Inland Flowing Waters



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