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2. 

Chapter 2: Methodology for Deriving U.S. EPA’s Proposed Criteria 

for Streams 

2a. Methodology for Stream Classification: U.S. EPA’s Nutrient Watershed 

Regions (NWRs) 

The U.S. Environmental Protection Agency’s (U.S. EPA’s) principal method for deriving instream protective values for nutrients in Florida streams and rivers relied on a distribution 

approach. U.S. EPA identified that population of streams sampled for biological condition in 

Florida which currently supports biologically healthy conditions. Nutrient conditions consistent 

with those from this distribution of sites were then estimated and an upper percentile used to 

indicate a concentration protective of biologically healthy conditions. The process consisted of 

classifying streams and rivers into relatively naturally homogeneous groups, identifying that 

population of biologically healthy stream sites, estimating a distribution of nutrient values within 

the population, and estimating the 75th percentile of the distribution. 

i. Developing a Regionalization Scheme 

Spatial frameworks are important for structuring the research, assessment, monitoring, and 

management of environmental resources. Ecoregions are usually defined by patterns of 

homogeneity in a combination of factors such as climate, physiography, geology, soils, and 

vegetation (Griffith et al. 1994). Classification or regionalization of streams provides a 

framework on which to develop and base protective nutrient criteria. Proper classification 

ensures homogeneous populations of streams with similar and comparable nutrient regimes and 

biological communities. In addition, it helps ensure that thresholds selected from a benchmark or 

reference approach are truly inclusive of the natural frequency distribution and thus will be 

inherently protective of the natural populations of flora and fauna inhabiting these systems. 

Florida’s geology includes fairly recent sedimentary deposits of marine origin. Certain marine 

clays (e.g., the Hawthorne Formation) and limestone formations that lie near the surface are 

extremely high in phosphorus. Some of these phosphatic deposits are mined, making Florida one 

of the larger producers of phosphate. (Florida produces approximately 25% of phosphate used 

throughout the world.) Proper spatial classification to capture regional differences in natural 

nutrient concentrations is essential. 

ii. Ecoregional and Biological Regionalization Schemes for Florida 

Level IV ecological subregions (Griffith et al. 1994, Figure 2-1) from Florida’s bioassessment 

program were used as a starting point for the regionalization efforts necessary to establish nutrient 

criteria. During development of the bioassessment program, Florida Department of Environmental 

Protection (FDEP) analyzed stream reference site macroinvertebrate community patterns in all nine 

ecological subregions north of Lake Okeechobee (Barbour et al. 1996a). The data indicated the 

presence of four distinct bioregions, within which there were similar biological community 

composition and structure (Figure 2-2). The bioregions were the Panhandle (subecoregions 65f, 

65g, 65h, and the majority of 75a1), the Northeast (subecoregions 75e and 75f), the Peninsula 

(subecoregions 75b, 75c, and 75d, and a small part of 75a), and the Everglades (subecoregions 76a, 
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76b, 76c, and 76d). Similar patterns of relatively homogeneous groupings in the Peninsula versus 

the Panhandle have been observed in wetlands macrophyte, algae, and invertebrate data (Lane et 

al. 2003). 

iii. Refinement of Regionalization for Nutrient Criteria Development 

The stream bioregions developed by FDEP were based on the observed biological community 

resemblance within a bioregion, assuming that biological responses to nutrients would be similar 

within and not between bioregions. Evaluation of sites across bioregions revealed additional 

spatial patterns; that is, the bioregions were not sufficiently homogenous with regard to nutrient 

concentration. Further evaluations considered attributes of the bioregions, ecoregions, geological 

formations (e.g., Bone Valley and Peace River), nutrient levels, geostatistical analysis, and 

drainage basins. A sub-region of the Peninsula bioregion, namely the Bone Valley, with 

exceptionally high natural phosphate levels, was identified by FDEP during initial work to derive 

reference-based total maximum daily loads (TMDLs) for the Northern Lake Okeechobee 

tributaries. The naturally high phosphate areas occurred in portions of Hillsborough, Polk, 

Hardee, Manatee, DeSoto, and Sarasota counties. These areas have been identified geologically 

as the Peace River Formation and the Bone Valley Member (originally the Bone Valley 

Formation of Matson and Clapp 1909, Figure 2-3). The Bone Valley Member is an extremely 

important, unique phosphate deposit, and it provided much of the phosphate production in the 

United States during the 20th century. Mining of phosphate in the outcrop area began in 1888 

(Cathcart 1985) and continues to the present. 

iv. Development of the Nutrient Watershed Regions 

U.S. EPA used the assessments, evaluations, and supporting information from FDEP on 

bioregions, nutrient regions, and sub-regions as a point of departure to further refine and develop 

a regional stream nutrient classification approach that reflected the understanding that upstream 

water quality affects downstream water quality. A watershed-based classification should 

facilitate the ability to address the effects of total nitrogen (TN) and total phosphorus (TP) within 

streams, as well as from streams to downstream lakes or estuaries in the same watershed. U.S. 

EPA classified Florida’s streams north of Lake Okeechobee by separating watersheds, based on 

the molar ratio of TN and TP in streams, into NWRs. The resulting NWRs reflect the inherent 

differences in the natural factors that influence nutrient concentrations in streams (e.g., geology, 

soil composition, and/or hydrology). 

U.S. EPA used nutrient water quality data collected across Florida and contained within 

Florida’s Impaired Waters Rule (IWR) database. Data tables of nutrient water quality sampling 

stations and their associated waterbody IDs (WBIDs) were joined to the National Oceanic and 

Atmospheric Administration (NOAA) Coastal Assessment Framework (CAF) delineation of 

estuarine drainage areas (EDAs), fluvial drainage areas (FDAs), and coastal drainage areas 

(CDAs). WBID centroids were joined to the CAF so that all stations in WBIDs were also 

mapped to an EDA, FDA, or CDA. Stations that were identified in the IWR database as Stream

or Blackwater and had corresponding TN and TP data were identified and aggregated by EDA, 

FDA, or CDA. This resulted in approximately 145,000 paired observations or sampling events 

distributed in watershed stream networks across Florida. The mean, distributional statistics of all 
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observations and the median molar TN:TP ratio were computed for each watershed (Table 2-1, 

Figure 2-4). 

Watershed stream TN:TP molar ratios were aggregated into four nutrient watershed regions 

(NWRs)––Panhandle, North Central, Bone Valley, and Peninsula (Figure 2-5). Across the 

Panhandle NWR, from Perdido to the Econfina-Steinhatchee, the median TN:TP ratio was 37 

mol mol-1. Streams within the Peninsula NWR had median TN:TP ratios ranging from 14 to 55, 

with a median for the entire Peninsula NWR of 28 mol mol-1. The North Central NWR 

comprised the entire Suwannee River watershed, consistent with expectations that upstream 

nutrient concentrations and ratios will likely influence downstream water quality. The 

phosphorus-rich soils and geology in the northern part of the Suwannee River watershed (e.g., 

Hamilton County) resulted in median TN:TP ratios (17 mol mol-1) that were lower than those in 

the Panhandle and Peninsula NWRs but higher than those in the Bone Valley (median TN:TP = 

9). 

2b. The Use of the Stream Condition Index as an Indicator for Establishing 

Biologically Healthy Conditions 

The ability to determine the biological condition of a waterbody’s aquatic community is critical 

to informing decisions related to implementation of state and federal water quality programs. 

Biological assessment measures and tools are very valuable for determining whether, for 

example, a particular waterbody has a “balanced natural population of flora and fauna” and for 

defining what this means in terms of the expected diversity and abundance of aquatic life and 

structure and function of the aquatic community. This section describes the factors that need to 

be considered when developing a quantitative measure of biological health in an aquatic system, 

and it describes the basis for Florida’s definition of the biological condition that reflects 

attainment of the designated use of “Recreation, Propagation and Maintenance of a Healthy, 

Well-Balanced Population of Fish and Wildlife” (F.A.C. 62-302.400). These biological 

thresholds are then used, in conjunction with other data, to determine the particular nutrient 

concentrations that might interfere with designated use protection and attainment. 

The response of biological communities to human point source pollution initially received 

attention in Florida during the late 1950s. In 1958 Bill Beck, a biologist with the Florida State 

Board of Health, wrote a series of “Biological Letters” in which he introduced the concept of 

using invertebrates as biological indicators, especially for demonstrating the effects of excess 

organic matter on streams and lakes (the saprobity index concept). What became known as 

“Beck’s Biotic Index” was developed by sampling invertebrates at control sites located upstream 

of point source discharges and observing which sensitive taxa were eliminated at sites 

downstream of the effluent sources (Beck 1954). Typically, there was a concurrent increase in 

abundance of tolerant taxa, such as “bloodworms” (certain species of chironomid midges), as 

illustrated in Figure 2-6. 

In the early 1970s and 1980s, benthic invertebrates were routinely sampled with multi-plate 

artificial substrate samplers (Hester-Dendy samplers). Hester-Dendy samplers are placed in 

receiving waters for 28 days, which is a minimum period for colonization by a representative 

benthic community (Figure 2-7). Hester-Dendy data were summarized using the ShannonWeaver diversity index, a biological metric derived from information theory that became a 
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popular index to communicate complicated biological results. The Shannon-Weaver diversity 

index is based on a combination of taxa richness at a site and evenness of the distribution of 

individual abundances. Low diversity scores represent conditions where a few pollution-tolerant 

organisms are very abundant, to the exclusion of other taxa. This index is specified in Florida’s 

water quality standards (WQS) as a measure of biological integrity (Rule 62-302.530, F.A.C.). It 

generally has been applied by comparing site-specific control sites to nearby test sites. 

In 1989, U.S. EPA promoted the concept of rapid bioassessment (Plafkin et al. 1989), and 

Florida embraced the concept for establishing biological criteria (Barbour et al. 1996b). Regional 

expectations (generally ecoregions) for biological communities were based on samples from 

reference sites, selected using regional professional judgment on least disturbed or minimally 

disturbed stream locations. Metrics, defined as measures of biological health that respond in a 

predictable manner to human disturbance, were calculated from reference site data. A 

distribution of reference site metric values was calculated, and scores were selected to represent 

expectations for each metric based on the reference site population. A variety of metrics were 

combined into a dimensionless multimetric index by assigning points to individual metrics based 

on their relative similarity to the reference condition and summing across metrics. 

To use a biological assessment tool, an understanding of an ecosystem's biological components 

and sources of variability is important. Biota respond to a wide variety of factors, natural and 

anthropogenic (Figure 2-8). As organisms integrate and respond to these factors over time, a 

characteristic community structure emerges, with a range of natural variability. Florida biologists 

have determined that a portion of variability at minimally disturbed sites is explained by random 

natural events such as floods and drought, which determine the relative abundance of inundated 

substrates available for invertebrate colonization. These natural stressors (e.g., flood, drought, 

natural low substrate diversity, periodic natural low dissolved oxygen) affect all sites, even those 

with minimal disturbance from humans, to a certain degree. 

i. Development of the Stream Condition Index 

The current Florida Stream Condition Index (SCI) was developed in 2004 and revised in 2007. 

The SCI is a multi-metric index that assesses stream health using the benthic macroinvertebrate 

community. FDEP expends great efforts to ensure that data are produced with the highest 

quality, both in the field and in the lab. Samplers and lab technicians follow detailed standard 

operating procedures (SOPs), and additional guidance for sampling and data use is provided 

through a FDEP document entitled Sampling and Use of the Stream Condition Index (SCI) for 

Assessing Flowing Waters: A Primer (DEP-SAS-001/09). Samplers are approved to conduct the 

SCI only after passing a rigorous audit with the FDEP, and laboratory taxonomists are regularly 

tested and must maintain >95% identification accuracy. 

The SCI is composed of 10 metrics, eight of which decrease in response to human disturbance 

while two of which (% very tolerant and % dominant) increase in response to human 

disturbance. Based on reference site community similarity, three stream bioregions, in which 

there are slightly different expectations for the metrics based on natural differences, were 

established: the Panhandle, the Northeast, and the Peninsula (note that the SCI is not calibrated 

for ecoregion 76, the Southern Florida Coastal Plain, where few natural streams exist) (Griffith et 

al. 1994; Figure 2-9). To be scientifically defensible, stream systems being evaluated against the 
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SCI should be morphologically identifiable as streams, so that potential human influences can be 

discerned. (The reference streams should be compared to streams; reference streams should not 

be compared to a system with lake-like or wetland-like conditions.) See Appendix B-2 for a 

description of the development of the SCI. 

ii. Establishing Healthy Biological Conditions in Streams––Stream Condition Index: 

Application of the Reference Site Approach 

FDEP examined the lower distribution of reference site multimetric scores as a principal 

approach for establishing aquatic life use support thresholds, in combination with the Biological 

Condition Gradient (BCG) approach, and U.S. EPA used the results of this analysis for 

identifying the threshold between healthy and unhealthy biological conditions. 

FDEP conducted statistical interval and equivalence tests with SCI data from 55 streams 

considered by Florida to be reference streams (including a portion of recently verified nutrient 

benchmark sites with additional data from the Fore et al. (2007) analysis). These tests were used 

to determine the lower bounds of the reference site distribution of SCI scores while balancing 

type I errors (falsely calling a reference site impaired) and type II errors (failing to detect that a 

site is truly impaired) (see Appendix B-14 for full description of analysis) (Table 2-2). Appendix 

B-15 contains complete taxa data for the samples used in this analysis. The FDEP examination of 

the two most recent visits at 55 reference streams showed that the 2.5th percentile of reference 

data was in the range of 35–44 points. The middle of this range was 40 points, which represented 

an impairment threshold that balanced Type I and Type II errors. 

When calibrating a threshold between healthy and unhealthy biological conditions using an 

index, the amount of human disturbance inherent among reference sites is an important issue, 

and a rigorous reference site selection process provides increased confidence that the reference 

site population is minimally disturbed, thereby significantly reducing Type II errors (classifying 

unhealthy sites as healthy). This confidence also allows for establishing a biological health 

threshold at a low level of the reference site distribution to minimize Type I errors (classifying 

healthy sites as unhealthy). FDEP attempted to screen reference sites to generate a population as 

approximate to the minimally disturbed (limited human influence) nature of streams as possible 

(Fore et al. 2007). However, no method completely removes uncertainty in setting appropriately 

protective thresholds. 

In the proposed assessment methodology for the SCI, biological condition is determined by the 

average of two site visits, so the threshold determined from the interval and equivalence tests 

(40, based on an average of two site visits) is closely aligned with the assessment methods. A 

threshold of 40 would provisionally result in approximately 2.5% of Florida’s reference sites 

(considered by Florida to be minimally disturbed) being deemed unhealthy. 

iii. Biological Condition Gradient Approach 

U.S. EPA has developed a tool that illustrates how ecological attributes change in response to 

increasing levels of human disturbance. The BCG is a conceptual model that assigns the relative 

health of aquatic communities into one of six categories, from natural to severely altered (Figure 

2-10). It is based on fundamental ecological principles and has been verified by aquatic 
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biologists throughout the United States. The BCG uses biological attributes of aquatic systems 

that predictably respond to increasing pollution and human disturbance. Although these attributes 

are measurable, some are not routinely quantified in monitoring programs (e.g., rate 

measurements such as productivity), but may be inferred through community composition data 

(e.g., abundance of taxa indicative of organic enrichment). The following biological attributes 

are considered in the BCG: 

 Historically documented, sensitive, long-lived or regionally endemic taxa 

 Sensitive and rare taxa 

 Sensitive but ubiquitous taxa 

 Taxa of intermediate tolerance 

 Tolerant taxa 

 Nonnative taxa 

 Organism condition 

 Ecosystem functions 

 Spatial and temporal extent of detrimental effects 

 Ecosystem connectance 

The gradient represented by the BCG has been divided into six levels (tiers) of condition that 

were defined by consensus (Davies and Jackson 2006) using experienced aquatic biologists from 

across the United States, including Florida. The six tiers are: 

1. Native structural, functional, and taxonomic integrity is preserved; ecosystem function is 

preserved within range of natural variability. 

2. Virtually all native taxa are maintained with some changes in biomass and/or abundance; 

ecosystem functions are fully maintained within range of natural variability. 

3. Some changes in structure due to loss of some rare native taxa; shifts in relative abundance of 

taxa but sensitive–ubiquitous taxa are common and abundant; ecosystem functions are fully 

maintained through redundant attributes of the system. 

4. Moderate changes in structure due to replacement of some sensitive–ubiquitous taxa by more 

tolerant taxa, but reproducing populations of some sensitive taxa are maintained; overall 

balanced distribution of all expected major groups; ecosystem functions largely maintained 

through redundant attributes. 

5. Sensitive taxa are markedly diminished; conspicuously unbalanced distribution of major 

groups from that expected; organism condition shows signs of physiological stress; system 

function shows reduced complexity and redundancy; increased buildup or export of unused 

materials. 

6. Extreme changes in structure; wholesale changes in taxonomic composition; extreme 

alterations from normal densities and distributions; organism conditioning is often poor; 

ecosystem functions are severely altered. 

The six levels described above are used to correlate biological index scores with biological 

condition, as part of calibrating an index. FDEP conducted a BCG exercise to calibrate scores for 
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the SCI. Twenty-two experts examined taxa lists from 30 stream sites throughout Florida, 10 in 

each ecoregion (Fore et al. 2007), that spanned the range of SCI scores (Appendix B-16). 

Without knowledge of SCI scores, the experts reviewed the macroinvertebrate assemblage data 

from each site sample and assigned a BCG score from 1 to 6, where 1 represents natural or 

native condition and 6 represents a condition severely altered in structure and function from a 

natural condition. Experts independently assigned a BCG score to each site and then were able to 

discuss their scores and rationale. They could opt to change their scores based on arguments 

from other participants. At the conclusion of the workshop, FDEP regressed mean BCG scores 

given to each stream against the SCI score for that site (Figure 2-11). The experts were also 

asked to identify the lowest BCG level that still provided for the propagation and maintenance of 

a healthy, well-balanced aquatic community and the BCG category (and higher) representing 

exceptional conditions. All 22 participants thought category 2 BCG scores should be considered 

exceptional, which corresponds to an SCI score of 64. Eleven of 22 participants thought SCI 

scores associated with category 5 should be considered impaired, while nine participants thought 

category 4 represented an impaired ecological condition and two experts thought that category 4 

was the lowest acceptable condition. 

iv. Evaluation of BCG Calibration Information 

In addition to identifying the thresholds above, experts (Fore et al. 2007) were also asked to 

identify the BCG value they considered meeting a healthy, well balanced community, and the 

mean expert response corresponded to a BCG tier of 4. Based on the relationship between the 

BCG and the SCI, this corresponded to an SCI score of 34. 

U.S. EPA identified an issue with variability in the expert responses within each BCG category 

and conducted an additional analysis of BCG results to further inform the decision point for 

determination of what constituted a healthy, well balanced aquatic community. U.S. EPA 

calculated a proportional odds logistic regression model (Guisan and Harrell 2000) to better 

describe the relationship between a continuous variable (SCI scores) and a categorical variable 

(BCG categories) (Appendix B-13). This model is based on the cumulative probability of a site 

being assigned to a given BCG tier (e.g., Tier 3) or to any higher quality tier (Tiers 1 and 2). 

Thus, five parallel models are fit, modeling the probability of assignment to Tiers 5 to 1, Tiers 4 

to 1, Tiers 3 to 1, Tiers 2 to 1, and Tier 1 only. Once these five models are fit, the probability of 

assignment to any single tier can be extracted from the model results. 

In Figure 2-12, the mean predictions of the proportional odds logistic regression models are 

plotted as solid lines. The lines are color-coded and labeled by different tiers, and each line can 

be interpreted as the proportion of experts that assigned samples with the indicated SCI value to 

a particular tier. For example, approximately 90% of experts assigned a sample with the lowest 

SCI score to Tier 6 (brown line), while the remaining 10% of experts assigned the sample to Tier 

5 (purple line). In Figure 2-12, solid circles represent the actual expert assignments recorded 

from the workshop for each SCI value. The size of the circle is proportional to the number of 

experts that assigned a sample to a particular tier, and the circles are color-coded by tier. Expert 

assignment of BCG scores varied, but there was a central tendency at any given SCI score. 

U.S. EPA identified the threshold at an SCI score where there was an approximately equally low 

probability of assignment to Tier 5 (i.e., unhealthy) and a low probability of assignment to Tier 2 
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(i.e., reference conditions). The resultant threshold balanced the probability of mistakenly 

assessing a degraded site as representing healthy biological conditions with the probability of 

mistakenly assessing a reference site as unhealthy. This score of 42 differed from the threshold 

of 40 determined by FDEP using the reference site approach. 

v. Selecting the Threshold between Healthy and Unhealthy Biological Conditions 

Weighing these multiple lines, U.S. EPA selected an SCI value of 40 as the threshold between 

healthy and unhealthy biological condition. This threshold is supported by the distribution of 

reference site scores and corresponds with a BCG category midway between Tiers 3 and 4 using 

the BCG tool. The proportional odds analysis provides assurance that stream communities 

deemed exceptional (U.S. EPA’s BCG category 2) would not be considered unhealthy at a 

threshold of 40. FDEP evaluated recent data for individual metrics of the SCI to determine what 

range of macroinvertebrate attributes would be considered healthy using this impairment 

threshold. Since FDEP conducted the SCI calibration in 2007, the state has collected 

approximately 700 additional SCI samples from a variety of sites, including sites considered by 

FDEP to be minimally disturbed (for nutrient criteria development), sites located along a nutrient 

gradient, and randomly chosen sites for the status and trends network. Based on the relationship 

described in Figure 2-11, SCI values from this data set were subdivided into increments 

representing half-step BCG Categories, and the individual metrics associated with each half-step 

interval were averaged. Metric data bracketing BCG category 2 were averaged to demonstrate 

metric values associated with exceptional conditions. Data within the range of the impairment 

threshold of 40 were also averaged to provide an example of the stream condition that the 

threshold identifies as healthy (Table 2-3). Note that although there are moderate differences 

between metrics associated with exceptional biological communities and those near the range of 

the healthy conditions threshold, attributes associated with communities near the threshold are 

still considered indicative of healthy, well balanced communities by the majority of the Florida 

stream experts who participated in the BCG exercise. 

2c. Methodology for Calculating In-stream Protection Values: The Nutrient 

Watershed Region Distribution Approach 

i. Methods for Calculating Attaining Site Distributional Statistics 

The approach used by U.S. EPA to develop in-stream protective nutrient criteria values was to 

first identify biologically healthy sites as reference sites and then to apply the standard 

distribution-based approach detailed in the U.S. EPA guidance (USEPA 2000). This involved 

identifying and compiling data for which SCI scores were available, estimating long-term 

geometric mean nutrient concentrations for those sites, identifying the sites that met the SCI 

criterion of 40 or greater, removing sites from WBIDs that were listed as impaired in the most 

recent 303(d) list for dissolved oxygen or nutrients, and then calculating distributional statistics 

from the remaining population that met the selection criteria. 

ii. SCI Data Preparation 

Data on SCI scores for Florida streams were obtained from FDEP in two spreadsheets––one with 

data from 2004 and before and a second with data from 2004 through 2007. These spreadsheets 
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contained SCI scores for specific sites across the state and additional data about the sites, as well 

as associated water chemical and habitat data, including nutrient concentrations from grab 

samples taken when biological sampling occurred (herein referred to as grab sample chemistry). 

These two data sets were merged together into a final overall data set containing 2,023 samples 

from 1,115 sites within 614 WBIDs statewide. Water chemistry data included TP. TN was 

calculated by summing total Kjeldahl nitrogen (TKN) and nitrate/nitrite (NO3/NO2) 

concentrations from each sample. Water chemistry data were natural log (ln) transformed prior to 

analysis to more closely adhere to assumptions regarding normality required of many parametric 

statistical tests and to reduce the influence of extreme values typical of log-normally distributed 

nutrient concentration data on estimates of central tendency. Each site for which SCI data were 

available was linked to a nutrient region using a table of WBID and nutrient regions developed 

using a geographic information system (GIS) when the final classification was set. 

iii. Site Average Water Chemistry 

The FDEP IWR database was used to estimate water chemistry conditions for sites across the 

state. Data on TP and TN (estimated as the sum of TKN and NO3/NO2) from 1990 through 2008 

were organized by station codes. Nutrient data were, again, natural log (ln) transformed for 

reasons stated above. Multiple values for sites were averaged in two ways. Long- term site 

geometric mean concentrations were estimated by averaging all available values across a site 

across all available years (herein referred to as site average). There were 11,761 TN and TP 

observations across 6,342 unique sites across the state in 1,023 WBIDs. Site-year geometric 

mean concentrations were estimated by averaging all available values within a year for a specific 

site (herein referred to as station-year average). For site-year values, there were 23,104 TN and 

TP observations across the same number of sites and WBIDs as for site averages. Similar to 

geometric mean, the standard deviations for stations and station-years were also calculated using 

the same natural log-transformed water chemistry data. Some SCI sites did not have nutrient 

chemistry beyond the grab sample taken during biological sampling. For those sites, long-term 

station geometric means and station-year geometric means could not be calculated. 

iv. Identifying Impaired WBIDs 

As stated, U.S. EPA derived its in-stream protective nutrient criteria for streams and rivers from 

that population of sites with healthy biological conditions (SCI>40). However, U.S. EPA also 

wanted to avoid using sites for estimating an in-stream protective nutrient criterion that might 

have met the SCI threshold of 40 but were within WBIDs listed as impaired for nutrient or 

dissolved oxygen (DO). The most recent 2009 303(d) list of impaired waterbodies was acquired 

from U.S. EPA Region 4. WBIDs that were confirmed as listed for nutrients or DO impairment 

were labeled as such. This effort was performed twice to confirm the listings. A table of WBIDs 

listed as impaired for nutrients and DO was created and imported into a referential database. That 

database contained the nutrient chemical and SCI data described above, and individual sites 

within WBIDs listed for nutrient and/or dissolve oxygen impairments were identified and 

flagged. 
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v. Identifying Biologically Unhealthy Site List 

Sites were identified that did not meet the SCI criterion and were, therefore, not considered 

biologically healthy. Any site with two or more consecutive SCI scores less than 40 was labeled 

as not meeting the SCI criterion. Sites with only one SCI score were considered biologically 

healthy only if that score was greater than 40. The in-stream protective nutrient criteria were 

based on this SCI threshold of 40. Analysis was also run based on SCI thresholds of 44, 50, and 

64. These results are included here as well. 

vi. Removing Sites 

Any site flagged as biologically unhealthy or contained within a WBID listed for nutrients and/or 

DO impairment was removed from consideration as part of the biologically healthy site 

population for deriving the in-stream protective criterion. Once this screening selection was 

completed, the remaining sites were considered the biologically healthy sites, unimpaired by 

nutrients and/or DO. Distributional statistics of TN and TP were then calculated for this 

biologically healthy stream population. 

vii. Calculating Distributional Statistics 

Grab sample, site average, and site-year average nutrient concentrations were combined into a 

complete table in the referential database and exported as a spreadsheet. Biologically unhealthy 

sites and sites within WBIDs listed for nutrient and/or DO impairment were labeled and removed 

as described above. As stated earlier, all nutrient concentrations were natural log (ln) 

transformed before analysis. 

Distributional statistics from long-term site average data were the principal values used for 

deriving in-stream protective nutrient criteria. These are described first followed by the 

alternative grab sample and site-year average data. Long-term site geometric mean data were 

described above, but were the average of natural-log transformed nutrient concentrations at every 

site. Geometric means were calculated for each site in the FDEP IWR database, and these were 

linked to the same sites for which SCI scores were available. Each site was represented by only 

one value––a long-term site average––and therefore sites were equally weighted. A spreadsheet 

was created for the site average data alone; it included SCI data and labels for sites meeting the 

biological thresholds and impairment criteria for nutrients and/or DO explained above. Sites not 

meeting the biological threshold or impaired for nutrients and/or DO were removed. The mean 

and standard deviation natural-log TN and TP concentration was then calculated from the 

remaining biologically healthy and unimpaired for nutrients and/or DO sites for each of the four 

nutrient regions: Bone Valley, Panhandle, Peninsula, and North Central. Assuming the nutrient 

data were log-normally distributed, the 75th percentile of the log-normal distribution was 

estimated for each nutrient parameter in each region using the following standard formula 

(Figures 2-14 and 2-16): 

 75th Percentile =e[ln(mean nutrient concentration) + 0.674 x ln(standard deviation)] [1] 

Grab sample data were those collected in the field along with biological samples and included in 

the data set provided by SCI. Each sample was treated as a replicate; therefore, sites with more 

samples were weighted relatively more than sites with fewer samples. A spreadsheet was created 
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for the grab sample data alone; it included SCI data and labels for sites meeting the biological 

thresholds and impairment criteria for nutrients and/or DO explained above. Sites not meeting 

the biological threshold or impaired for nutrients and/or DO were removed. The mean and 

standard deviation natural-log TN and TP concentration was calculated from the remaining sites 

for each of the four nutrient regions: Bone Valley, Panhandle, Peninsula, and North Central. The 

data were assumed to follow a normal distribution, and the 75th percentile of the log-normal 

distribution was estimated for each nutrient parameter in each region using Equation 1. 

In a similar fashion, percentiles of site-year average data were also estimated. In this case, longterm site data were organized by year and geometric mean nutrient concentrations were 

calculated for each site-year combination. Each site-year average was treated as a replicate; 

therefore, sites with more years of nutrient data were weighted relatively more than sites with 

fewer site-year combinations. A spreadsheet was created for the site-year average data alone; it 

included SCI data and labels for sites meeting the biological thresholds and impairment criteria 

for nutrients and/or DO explained above. Site-year data for any site not meeting the biological 

threshold or impaired for nutrients and/or DO were removed. The mean and standard deviation 

natural-log TN and TP concentration was calculated from the remaining site-years for each of the 

four nutrient regions: Bone Valley, Panhandle, Peninsula, and North Central. The data were 

assumed to follow a normal distribution, and the 75th percentile of the log-normal distribution 

was estimated for each nutrient parameter in each region using Equation 1. 

viii. 75th Percentiles for Site Average Data – In-stream Protective Values 

The site average data based on SCI thresholds of 40 were used to derive the in-stream protective 

nutrient criteria. The characteristics of these instream protective nutrient criteria are displayed in 

Table 2-4 and the in-stream protective criteria were the 75th percentile of site average values 

from biologically healthy sites (SCI>40, Figures 2-14 and 2-16). 

ix. Distribution of Nutrient Concentrations among Nutrient Regions 

To compare concentrations of nutrients across regions, maps indicating nutrient concentrations 

based on grab samples from sites where SCI samples were taken were created. Maps and 

cumulative function histograms for each region were developed for both TP and TN and were 

based on all samples and on those from only sites meeting the biologically healthy SCI threshold 

of 40. 

As noted in the classification section, TP concentrations were highest in the Bone Valley and 

North Central regions, but principally in the northern half of the North Central region (Figure 

2-13). Within the Panhandle and Peninsula, occasional high concentrations of TP were observed 

but many of these were observed with biologically unhealthy sites (SCI threshold < 40), and 

these concentrations disappeared when only biologically healthy sites were plotted (Figure 2-14). 

In contrast, TN exhibited generally high concentrations across the regions, although the Bone 

Valley again appeared to have a preponderance of high values (Figure 2-15). Moreover, high-TN 

sites were distributed across the Panhandle and Peninsula regions, but as with TP, many of these 

were associated with biologically unhealthy sites (SCI threshold < 40), and these concentrations 

disappeared when only biologically healthy sites were plotted (Figure 2-16). 
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The 75th percentiles were selected as the upper bound of the greatest confidence in where 

biologically healthy conditions exist (Figures 2-14 and 2-16). Beyond the 75th percentile, there is 

a marked decrease in the slope at the cumulative and empirical frequency distributions. 

2d. Duration and Frequency for Proposed Streams Criteria 

Aquatic life water quality criteria contain three components: magnitude, duration, and frequency. 

The criterion-duration of the streams criteria table as an annual geometric mean. U.S. EPA is 

proposing two expressions of allowable frequency, both of which are to be met. First, U.S. EPA 

proposes a no-more-than-one-in-three-years excursion frequency for the annual geometric mean 

criteria for lakes. Second, U.S. EPA proposes that the long-term arithmetic average of annual 

geometric means not exceed the criterion-magnitude concentration. U.S. EPA anticipates that 

Florida will use their standard assessment periods as specified in Rule 62-303, F.A.C. (Impaired 

Waters Rule) to implement this second provision. 

Appropriate duration and frequency components of criteria should be based on how the data used 

to derive the criteria were analyzed and what the implications are for protecting designated uses 

given the effects of exposure at the specified criterion concentration for different periods and 

recurrence patterns. 

Lotic systems, such as streams and rivers, flow unidirectionally and, depending on the gradient 

and other factors, typically have shorter residence times than lentic systems for nutrients and a 

higher probability of scour and removal of algae downstream. The likelihood of nutrient effects 

in these systems is therefore based largely on the prevailing concentration of nutrients over the 

growing season as opposed to total loads. Many algae and macrophytes exhibit luxury uptake, in 

which plants are able to store nutrients in excess of current requirements to support future 

growth. This complicates the determination of the appropriate averaging period for nutrient 

concentrations, which may depend on the rates on which algae are able to uptake nutrients. 

Frequent disturbance from floods (monthly or more frequently) and associated movement of bed 

materials can scour algae from the surface rapidly and often enough to prevent attainment of 

high biomass (Peterson 1996). In areas with less stable substrata, such as sandy bottomed 

streams, only slight increases in flow may lead to bed movement and scouring. Scouring by 

movement of rocks has been directly linked to reduction in algal biomass and subsequent 

recovery from floods (Power and Stewart 1987). Larger, more stable rocks can have higher 

periphyton biomass (Dodds 1991; Cattaneo et al. 1997). In cases where there is frequent 

movement of substrata, high nutrients may not necessarily translate into excessive algal biomass 

(Biggs et al. 1998a, b). 

Low and stable flow conditions should be considered in addition to frequency and timing of 

floods when considering nutrient criteria for streams. Flood frequency and scouring may be 

greater in steep gradient (steep slope) and/or channelized streams, and in watersheds subject to 

intense precipitation events or rapid snow melt. Periods of drying can also reduce algal biomass 

to low levels (Dodds et al. 1996). A stream may flood frequently during certain seasons, but also 

remain stable for several months at a time. The effects of eutrophication may be evident during 

stable low flows. Also, stable flow periods are generally associated with low flow conditions, 

which can result in the highest nutrient concentration from point sources discharging relatively 
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constant loads of nutrients. Hence, low-flow periods often present ideal conditions for achieving 

maximum algal biomass. 

Given that the data compiled and analyzed for developing streams criteria represent all seasons 

and conditions during the year, and the technical reasons stated above that effects can manifest 

themselves with a growing season and can vary depending on hydrologic condition, it follows 

that an annual averaging period is the appropriate criterion-duration for the proposed streams 

criteria. 

Nutrient criteria are typically established within the range of natural variability. A temporary 

spike in nutrient levels does not necessarily harm the aquatic resource. In fact, natural systems 

have evolved to process variable inputs of nutrients, particularly where there is much natural 

variability in hydrologic conditions and precipitation patterns (e.g., wet years, dry years). 

Although biological response in the form of algal production, measured by chl. a, can appear 

very quickly, longer term shifts in biological conditions, such as loss of underwater grasses, do 

not occur as the result of a single event or conditions in a single year. If grasses experience 

reduced light during a portion of a year, the effect on them is not expected to be as large or 

persistent as if reduced light occurs in multiple years. In other words, severe nutrient impacts 

often result from chronic exposure to elevated nutrients. In addition, extreme incidents of 

nutrient pollution are not likely to be isolated incidents. 

The data set used to derive these criteria is based on distributional statistics of geometric mean 

values from multiple years of record. Data from a central tendency of multiple years of record 

lend themselves well to expression of a long-term average to protect from the chronic effects 

described above. This supports a criterion frequency expression of the long-term arithmetic 

average of annual geometric means. A no more than one in three year frequency of excursions 

avoids unacceptable effects on aquatic life as it will allow the stream ecosystem enough time to 

recover from the occasionally elevated year of nutrient loadings. 

Those frequency and duration components take into account that hydrological variability will in 

turn produce variability in measured nutrient concentrations, and individual measurements may 

exceed the criteria. Furthermore, they balance the representation of underlying data and analyses 

on the basis of central tendency of many years of data (i.e., the long-term average component) 

with the need to exercise some caution to ensure that streams have sufficient time to process 

individual years of elevated nutrient levels and avoid the possibility of cumulative and chronic 

effects (i.e., the no more than one in 3-year component).

2e. Protection of Downstream Estuaries and Lakes 

i. Downstream Protection of Lakes 

An important component of setting appropriately protective water quality criteria is ensuring that 

the criteria also provide for the attainment and maintenance of the water quality standards of 

downstream waters (40 CFR 131.10(b)). This has been presented in the case of estuarine uses in 

the section below. Also relevant to inland stream criteria setting, however, are uses in 

downstream lakes. As a result, there is a need to establish whether stream criteria will be 
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consistent with downstream lake uses and criteria. Here a screening analysis is presented to 

evaluate the protectiveness of inland stream criteria with respect to receiving lakes. 

Vollenweider Model 

The analysis compares TP criteria in streams and rivers to those proposed to protect lakes using 

the simple phosphorus loading model developed by Vollenweider (1975, 1976) based on a 

nutrient budgeting approach. It has been updated with adjustments made by other authors, 

including some specific to Florida (Larsen and Mercier 1976; Canfield and Bachmann 1981; 

Baker et al. 1985; Steward and Lowe 2010). A similar approach could be used for nitrogen 

criteria comparison using comparable nitrogen loading models developed for lakes (Shannon and 

Brezonik 1972). 

If the benchmark nutrient criterion is higher than the stream concentration necessary to protect a 

lake, the benchmark criterion will not be protective of the lake. Accordingly, critical stream 

concentrations derived by loading models to lakes based on proposed lake criteria that are lower 

than the benchmark stream criteria would indicate potential concern for downstream protection. 

The Vollenweider model for loading (Vollenweider 1976; Larsen and Mercier 1976) follows: 

      w

w

L 1 z TPPL 

  

Where, 

L(P) is loading rate, g m-2 y

-1

[TP]L is lake mean TP concentration, g m-3 

z is mean depth, m 

w is hydraulic retention time, y 

The term   1 w   represents the phosphorus settling rate 

TP load for a lake is the loading rate times the lake surface area: 

TL(P)  AL(P)

Where, 

 TL(P) is total load, in g/y 

 A is lake surface area, m2

It is assumed that total streamflow into a lake (Qi, in m3

 y

-1) cannot be more than the net outflow, 

or lake volume divided by retention time: 

w

i

zA Q





 or, 

w

fi

zA cQ

 

[2] 

[3] 

[4] 
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where cf is the fraction of inflow due to streamflow, 0  cf  1, and includes all streams flowing 

into the lake. 

If there is net evaporation from the lake, this assumption would be untrue; but in Florida direct 

rainfall exceeds evaporation, so Eq. 4 holds. 

It is further assumed that all phosphorus loading is from the stream. Then, 

w

f

i

s zA

c

TL(P)

Q

TL(P) [TP]





Rearranging, 

zAc

TL(P) [TP]

f

w

s

 

Substituting Eq.3 for TL(P): 

zc

L(P) [TP]

f

w

s

 

Substituting Eq. 2 for L(P): 

  z f

w

w

w

s L

c

1 z [TP] [TP] 



  

resulting in: 

  L w

f

s 1[TP]

c

1 [TP]   

By substituting the lake critical TP concentration into [TP]L, derived from chlorophyll-nutrient 

regressions, the maximum stream TP, [TP]s, can be estimated for values of cf and w. Because cf

is between 0 and 1, the most stringent critical inflow concentration will occur when cf is close to 

1 (i.e., when a lake’s inflow is almost entirely due to streamflow). 

Florida lakes tend to be shallow, and internal loadings to the water column following a decrease 

in external loading (e.g., from storm-induced resuspension or diagenetic release of sediment 

phosphorus) may be substantial. Resuspension or diagenetic release of sediment phosphorus 

could be modeled to simulate this phenomenon mechanistically, and it would likely require 

substantial site-specific data for calibration. EPA notes that simpler alternatives, such as 

excluding the settling/loss term from Equation 5, or reversing the sign on the settling/loss term so 

that it becomes a net source term. For general regional application, however, EPA used the 

model described in Equation 5. 

[5] 
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Results of the Vollenweider Model Approach 

Stream critical TP concentrations, [TP]s, were calculated for the lower threshold lake TP 

concentrations (Table 2-5), corresponding to the upper 50% prediction intervals for maximum 

chlorophyll concentrations for the three lake types (Table 2-5). These prediction intervals mean 

that the response model predicts a 25% chance of exceeding the chlorophyll criterion when TP 

concentration is equal to the TP criterion. Stream [TP]s was calculated for retention time ranging 

from 0 to 20 years, and for streamflow representing 100%, 50%, and 10% of total lake water 

inflow. All the phosphorus loading (100%) was assumed to be in the stream. 

Results are shown in Figures 2-17 to 2-20. Critical stream concentrations are lowest (risk is 

highest) when streamflow makes up 100% of the total inflow into a lake, and also when 

residence time is shortest (Figure 2-17). In Figure 2-17, the Panhandle 75th percentile proposed 

instream protective value is given by the dashed line, which represents the 75th percentile values. 

Any predicted critical stream concentrations below the benchmark criterion are not protected by 

the criterion (orange-shaded area in Figure 2-17); the proposed instream protective value is not 

protective of the lower-bound lake criterion for lakes with 100% streamflow input and a 

residence time of less than 3 years. 

The worst-case scenario is where stream flow is 100% of lake inflow and residence time is 0, 

such that the critical stream concentration is the same as the critical lake concentration. 

Approximately 20% of Florida lakes have a stream flowing into them (Fernald and Purdum 

1998). Both rainfall and groundwater flow are substantial in Florida. Lake Jesup, connected to 

the St. Johns River, is an example of a lake with high stream inflow (the St. Johns River and 

several smaller tributaries), estimated at over 61% of the lake water inflow (Gao 2006). Direct 

precipitation is 27%, and groundwater is 12% of total inflow. Steward and Lowe (2010) 

tabulated residence times from 19 lakes, which ranged from 0.0045 year (Lake Hell ‘n Blazes) to 

16.85 years (Lake Weir). Of 19 lakes, 13 had residence times < 1 year (68%). For the high 

conductivity and colored lakes, the case for only 50% of inflow and up to 1 year residence time 

is shown because these are realistic and representative of lakes likely to be affected by 

streamflow. 

In Figures 2-18 and 2-19 it is clear that Bone Valley proposed instream protective values are 

rarely protective of any downstream clear lakes. Clear, low-conductivity lakes are unlikely to be 

protected by any of the proposed instream protective values, however, most lakes of this class 

have no inflowing streams. Panhandle TP criteria are likely to be protective of clear, highconductivity lakes and colored lakes. 

These results confirm the site-specific analyses of the TMDLs in showing that lakes with a high 

proportion of streamflow and short retention times are least likely to be protected by the 75th or 

90th percentile proposed instream protective values. Peninsula proposed instream protective 

values are likely to be protective of most colored lakes (but see Lake Jesup TMDL, Gao 2006), 

but they might not be protective of clear, high-conductivity lakes with short retention times and 

high streamflow input. 

For the proposed rule, EPA evaluated representative values for percent contribution from stream 

flow (Gao 2006) and hydraulic retention time (Steward and Lowe 2000) and selected 50% 

stream flow contribution and 0.2 years retention time as realistic and representative preset values 
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to provide a protective outcome for Florida lakes, in the absence of site-specific data. Using the 

equation developed (Equation 5) the representative values for stream flow, retention time, and 

the appropriate lake criteria, critical stream values were calculated. Using these preset values, 

streams that flow into colored lakes would have a TP criterion of 0.12 mg/L and streams that 

flow into clear, alkaline lakes would have a TP criterion of 0.073 mg/L. 

ii. Downstream Protection of Estuaries 

Protection of Downstream Water Quality Standards 

Pursuant to 40 CFR 131.10(b), WQS must ensure the attainment and maintenance of 

downstream WQS. Two modeling procedures are presented that provide information on the 

protection of downstream WQS in upstream reaches of a watershed. One approach is based on 

the U.S. Geological Survey (USGS) SPAtially Referenced Regression On Watershed Attributes, 

or SPARROW model, which is used to estimate in-stream TN concentration criteria that are 

consistent with limiting TN loading to estuaries to less than the protective TN load. The 

protective TN load is the TN loading rate to estuaries that will ensure that TN loading does not 

cause water quality degradation in estuaries that would impair full support for designated uses. 

Development of Stream TN Criteria to Ensure Protection of Designated Uses in 

Downstream Estuarine Waters in Florida 

Overview 

Protective TN loading estimates for Florida estuaries were developed to derive water quality 

criteria for TN in flowing waters that ensure protection of designated uses in downstream 

estuarine waters per Clean Water Act (CWA) requirements under 40 CFR 131.10(b). Protective 

TN loading levels were determined for 16 estuaries but could not be developed for seven 

estuaries in south Florida because required information related to TN loading could not be 

obtained for those systems in the time available. Similarly, protective loads were not developed 

for three estuaries in central Florida because protective loads and flows necessary to compute 

downstream protection values require further evaluation. The approach used several lines of 

evidence that included (1) estimated current TN loading levels, which are suggested as an upper 

bound for a the protective TN load; (2) estimated background TN loading levels, which are 

suggested as a lower bound for the protective load; (3) TN load limits derived from regression 

models relating TMDL- or pollution load reduction goal (PLRG)-derived loading limits to 

residence time or hydraulic loading, and (4) levels of TN load reductions that have been 

suggested for estuarine waters in Florida via the TMDL or PLRG development processes. 

Although current TN loads to estuaries in Florida vary approximately 25-fold, from ~2 g N m2

/y 

to nearly 50 g N m2

/y, the ratio of the current load to the background load varies only between 

two and four for most estuaries. Thus, for any specific estuary, the range between the upper and 

lower bound is relatively narrow for estimating the protective TN load for each estuary. 

Estimate of Current TN Loading to Estuaries in Florida 

TN loading from gaged portions in the watersheds in Florida estuaries was estimated by simple, 

data-driven procedures that compute loading within flow-monitored river and stream reaches. 

The USGS developed and widely applies the methods. The computed estimates of TN loading 
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were incorporated in the SPARROW model (Schwarz et al. 2006). The model estimates TN 

loading to estuarine waters from both gaged and ungaged portions of watersheds and generally 

estimates loads from most of the watershed of each estuary. Recently published estimates 

computed using a regional implementation of SPARROW developed for the southeast United 

States were used (Hoos et al. 2008; Hoos and McMahon 2009). These estimates agree very well 

in most cases with monitored load estimates (Hoos and McMahon 2009) generally because 

SPARROW relies on the same high density of monitoring data. U.S. EPA computed loading per 

estuary area including loads from stream reaches discharging into the estuary and point source 

loads discharging directly to the estuary. 

Most estuaries examined herein have estuarine WBIDs with documented nutrient-related 

impairments (for further detail, see sections “Condition of estuaries,” “303(d) Listings and the 

IWR Verified List,” “Chl. a in Florida Estuaries”). These nutrient effects are generally wellstudied and widely reported and have been recognized by U.S. EPA via its January 14, 2009, 

determination letter (http://www.epa.gov/waterscience/standards/rules/fldetermination20090114.pdf). Consequently, U.S. EPA suggests that current TN loads are either 

at or above the TN loading level that is protective of designated uses. The estimate of current TN 

loading from the watershed is based on the loading estimated by the SPARROW model. Where 

the SPARROW model does not estimate loading from a portion of the watershed of an estuary, 

U.S. EPA assumed that the yield of TN delivered to estuarine waters per square kilometer in that 

portion of the watershed was the same as in the portion covered by SPARROW. 

Estimate of Background TN Loading to Estuaries in Florida 

TN loading to estuaries in Florida has increased to varying degrees as a result of known 

anthropogenic causes. For 16 estuaries in Florida, the USGS SPARROW model provides an 

empirical means of partitioning the estimated TN loading from watersheds into five loading 

categories: point sources, urban land use, fertilizer, manure, and atmospheric deposition. All the 

loading categories except for atmospheric deposition are associated with either local 

anthropogenic TN sources or local watershed factors associated with increased TN transport to 

streams (e.g., urban land use). Therefore, the atmospheric TN loading category is an estimate of 

the background TN loading in the watershed. Across the 16 estuaries in Florida, atmospheric TN 

loading levels account for 20% to 60% of the current TN loading (Table 2-6). Because those 

loads exclude much of the anthropogenic TN loading, they were used as an estimate of the lower 

limit of the protective TN load for each of 16 estuaries in Florida. U.S. EPA believes that a 

defensible estimate of the protective TN load must be between this value and the current TN 

loading rate. 

Importantly, the atmospheric TN deposition computed by SPARROW is not simply the 

measured atmospheric TN deposition. The SPARROW model includes wet TN deposition data 

from the National Atmospheric Deposition Program (NADP) as an independent variable in the 

regression. Those data reflect the regional rate of atmospheric deposition and help explain 

observed TN loading within gaged streams and rivers. However, the delivered TN load attributed 

by SPARROW to atmospheric deposition is not the same as the NADP wet deposition nor is it 

expected to be the same. Because of the manner in which it is computed (i.e., via the SPARROW 

model) that load implicitly reflects natural TN sources other than wet deposition, such as dry 

deposition. Additionally, the delivered TN load reflects the fact that a significant fraction of 
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deposited TN is retained on the landscape, and an additional fraction is removed within the 

stream network. As a result, SPARROW estimates that the transport of nitrogen in southeastern 

streams that is attributable to atmospheric nitrogen deposition is much less than the TN 

deposition measured by NADP. More specifically, the mean TN yield resulting from 

atmospheric deposition is 120 kg/km2

/y (standard deviation = 37 kg/km2/y) whereas the regional 

NADP estimate is roughly 300–400 kg/km2/y (Hoos et al. 2008). 

Evaluation of “Loading Limit” Models Applied to Estuaries in Florida 

In an effort to determine a protective TN load within the range of values U.S. EPA established, 

the empirical models of Steward and Lowe (2010) were considered. Those models are intended 

to extrapolate estimates of protective TN loads established via the TMDL and PLRG process to 

waterbodies for which the estimates are not available. Steward and Lowe (2010) found that 

protective TN loads, which they called “maximum TN loading” or Lm, were highly correlated 

with both hydraulic residence time (denoted by τw) and hydraulic loading (denoted by qs = z/τw, 

where z is mean depth). The regressions that quantify these correlations as are referred to as “Lm

models.” Conceptually, the regression models assume that the expression of nutrient enrichment 

is influenced by hydraulic residence time such that waterbodies with short residence times are 

likely to be less susceptible to nutrient enrichment than waterbodies with long residence time. 

They also assume that the desired ecosystem state targeted during development of the TMDLs 

and PLRGs is the same. In general, the objective is moderate trophic state, or mesotrophy 

(Steward and Lowe 2010). That could overestimate the appropriate protective loads for some 

estuaries in Florida for which the natural condition might be oligotrophy, which is defined as 

having low productivity and usually very low nutrient concentrations. 

U.S. EPA applied both the Lm versus τw and Lm versus qs models to 13 estuaries in Florida. In 

10 estuaries, both the Lm versus τw model and the Lm versus qs model provided results that were 

between the background and current TN loading, given the error associated with the load 

estimates. For those estuaries where the model provided a value within the range between current 

loading and background loading, U.S. EPA considered it a potentially useful reference point for 

determining protective loading targets. However, the uncertainty associated with these model 

predictions is sufficiently large that they do not reduce the possible range for estimates of the 

protective load. At best, they confirm that TMDLs, PLRGs, and estimates based on SPARROW 

load estimates are at least broadly compatible. For a few estuaries, the Lm models predict a 

protective load that is significantly outside the range established on the basis of current and 

background TN loading. In such cases, further analyses could be required. For this method, 

establishing an appropriate estimate of estuarine surface area and hydraulic residence time is a 

key challenge. Although measuring surface areas is not technically challenging, determining the 

appropriate boundaries for the estuarine ecosystem is more complex. 

Load reductions recommended by TMDLs and PLRGs for estuaries in Florida often describe a 

loading maximum in terms of a percentage reduction relative to a current or reference-year 

loading level. Several well-known examples outside Florida include target loading reductions of 

40% for Chesapeake Bay (relative to 1985 loading) and 45% (relative to the 1980–1996 loads) 

for loading from the Mississippi River to the northern Gulf of Mexico. In Florida, recommended 

reductions from TMDLs and PLRGs range from 3% to 63% (mean 36%, median 38%). For more 

detail see the section, “Evaluating TMDLs, PLRGs and Dose-Response Models for Florida 
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Estuaries.” In general, larger load reductions are recommended where anthropogenic loading has 

increased loads by the largest margin. This means that, in practice, the reduction in TN loading 

required by TMDLs and PLRGs to protect uses is proportional to the anthropogenic contribution 

to TN loading. Consequently, an alternative to the extrapolation approach explored in the 

“Estimate of Background TN Loading to Estuaries in Florida,” section is reducing the 

anthropogenic contribution of TN load to estuaries by a constant fraction. That avoids the 

uncertainty associated with scaling estimates of TN loading to estuary surface area and water 

residence time while retaining a logical connection to the existing TMDLs and PLRGs in 

Florida. Specifically, a 50% reduction in the anthropogenic fraction of TN loads results in a total 

load reduction of ~25% on average across all estuaries (range of 5% to 40%), similar to the 

loading reductions determined via TMDLs and PLRGs in Florida. 

Protective TN Loads for Estuaries in Florida 

U.S. EPA used the observations and lines of evidence summarized above to determine estimates 

of the protective TN loading to estuaries in Florida. Specifically, the TN load was computed by 

reducing the current TN load by one half (i.e., 50%) of the anthropogenic contribution to that 

load (Table 2-6, Figure 2-21). The values are inherently constrained to be between the estimates 

of the background TN loading and current TN loading. Such protective load estimates are 

implicitly related to use attainment via their relationship to the TMDLs and PLRGs in Florida. 

Computation of TN concentrations in flowing waters protective of downstream estuaries (i.e., 

DPVs) given protective TN load for the estuary 

Given estimates of the aggregate protective TN load for downstream estuarine waters, an 

approach is needed to compute criteria for average TN concentration in each stream reach in the 

estuarine watershed which, if achieved, are expected to result in average TN loads to estuaries 

less than or equal to the protective TN load, but appropriately protective. Since it is known that 

some fraction of nitrogen transported in streams is permanently lost (primarily from 

denitrification), rather than being transported downstream to estuarine waters, U.S. EPA’s 

approach accounts for these losses, allowing TN concentrations to be higher in stream reaches 

where significant nitrogen losses occur before reaching the coast. U.S. EPA’s approach utilizes 

specific components of the USGS regional SPARROW model for the South Atlantic, eastern 

Gulf Coast, and Tennessee River (SAGT) basins, and the hydrologic network used for 

SPARROW, the Enhanced River Reach File (ERF1_2) which is based on U.S. EPA’s 1:500,000-

scale Reach File (RF1). USGS has recently provided digital datasets (Hoos et al. 2008) and 

results from the SAGT regional SPARROW model (Hoos and McMahon 2009). 

The protective TN loading to each estuary includes TN loads from several sources, including 

sources in the watershed of the estuary discharging via stream channels into the estuary, diffuse 

sources discharging into the estuary (i.e., not via stream channels), and point sources discharging 

directly into the estuary. Only TN loads from the watershed and discharged via stream channels 

are subject to downstream protection criteria. Of these loads, the approach previously described, 

addresses only the fraction of TN loads from the portion of the watershed resolved by the SAGTSPARROW regional watershed model. Assuming that the fraction of TN loading allocated to 

each stream reach discharging into the estuary is proportional to the fraction of freshwater 

contributed by that stream reach, the protective TN concentration can be computed as 
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 [6] 

where the terms are defined as follows for the i

th stream reach: 

= TN concentration for the i

th stream reach consistent with downstream use protection 

= fraction of the protective TN load entering the estuary via the SPARROW stream 

network 

= protective TN loading rate for the estuary 

= average freshwater discharge from the SPARROW stream network into the estuary 

= fraction of the flux at the downstream node of the i

th reach that is transported through 

the stream network and ultimately delivered to estuary. 

The fraction, Fi, called the “delivered fraction,” ranges from 0 to 100% and is estimated by the 

SAGT SPARROW model, which refers to it as the “Fraction Delivered to Target” or 

FracDelTar. Note that it is possible to compute given an estimate of the product kLest without 

estimating either k or Lest separately. The protective TN loading estimate resulting from the 

approach described above, was taken to be equal to kLest. Although it may seem necessary to 

know the average freshwater flow in the i

th stream reach, this quantity cancels out of Equation 6 

and it in fact does not need to be known as long as the allocation of loading across the stream 

network is based only on the proportion of the overall flow in the reach. If the protective load 

were redistributed in a different manner, it would become necessary to quantify the average flow 

in each reach. These flows are estimated via a regression on stream attributes and are included in 

the Enhanced River Reach File. 

While the quantities in Equation 6 and resulting DPVs are stream reach-specific, the assessment 

and management unit in Florida is the WBID. Thus, it is necessary to cross-reference reachspecific estimates of delivered fraction, Fi, from ERF reaches to WBIDs. Unfortunately, the 

following was observed: 

1. WBIDs do not necessarily intersect any reach. 

2. Incremental watersheds associated with reaches are not nested within WBIDs, nor are 

WBIDs nested within reach watersheds. 

3. When more than one reach is contained within a WBID, these reaches may have dissimilar 

delivered fractions. 

4. Boundaries of incremental watersheds for reaches and WBIDs do not coincide exactly, even 

when they largely coincide. Therefore, a very small area of a WBID may intersect one ERF 

watershed even when nearly all of it is in another. 

5. Among other problems, these factors can cause a WBID to be assigned a fraction delivered 

higher than for another WBID that fully surrounds it. This cannot be correct. 
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To address these problems, a union coverage was created by dividing each WBID into separate 

polygons, each of which resides fully within a single ERF1 watershed. The area of each of these 

sub-WBIDs was computed and then used to create a weighted average of the delivered fraction, 

Fi, associated with each ERF1 watershed. These averages were stored, along with the minimum 

and maximum fraction delivered for any portion of the WBID. In the vast majority of WBIDs, 

the Fi for fractions of the WBID associated with different ERF watersheds varied by a very small 

amount. The result is a statewide map of fraction delivered by WBID (Figure 2-22) and 

associated tables showing Fi for each WBID (Appendix B-18, Table B18-1, and Table 2-7). 

iii. Delineation of Florida’s Estuaries and their Watersheds 

U.S. EPA delineated Florida’s estuaries and their watersheds by adopting the NOAA CAF 

(Bricker et al. 1999). This is not the only delineation that could have been used. For example, 

according to the list associated with FDEP’s IWR Run 33, the FDEP recognizes 70 estuarine 

planning units, 448 estuarine “basins,” and 565 estuarine “waterbodies.” FDEP also subdivides 

its waters into five “groups” for the purpose of rotating assessments. The CAF was selected 

because it delineates estuaries at a scale that is both relevant to their ecological functioning and 

suitable for detailed analysis because of the modest number of basins identified. U.S. EPA 

adopted NOAA’s terminology, in which estuaries and their immediate watersheds are divided 

into EDAs, whereas the remaining upland portion of the watershed is delineated as an associated 

FDA. Together, the EDA and FDA define the estuary and its watershed (Figure 2-23, Table 2-8). 

Using the CAF, U.S. EPA recognized 18 EDAs and their associated FDAs in Florida. The 

present analysis excludes five EDAs in south Florida (Rookery Bay, North Ten Thousand 

Islands, South Ten Thousand Islands, Biscayne Bay and Florida Bay) because requisite 

information related to total nitrogen loading and transport within the highly managed canals and 

waterways cannot be derived from the USGS South Atlantic, eastern Gulf, and Tennessee River 

(SAGT) regional SPARROW model and was not available otherwise. The CAF does not define 

EDAs in some shoreline regions of Florida, specifically those where estuarine basins are not well 

defined or are not present (Figure 2-23). These shoreline areas are instead defined as CDAs. 

Stream networks nonetheless drain to tidal waters in these areas, creating measurable dilution of 

seawater and therefore waterbodies with some estuarine attributes. Because CDAs generally lack 

a well-defined semi-enclosed basin, it is difficult to unambiguously define the boundaries, 

particularly the seaward boundaries, and subsequently quantify the areal extent of the estuarine 

waterbody. The present analysis includes 6 CDAs (Figure 2-23, Table 2-8), but does not address 

2 CDAs in south Florida (Cape Canaveral CDA and Big Cypress CDA). Florida's water bodies, 

identified via WBIDs were spatially joined to the CAF coverage (Figure 2-23) to provide a 

means of relating them. 

Description of Florida’s Estuaries and their Watersheds 

Florida’s estuaries and their watersheds span a diverse landscape that contributes to significant 

differences in ecological function. Florida’s estuaries are both small (e.g., St. Mary’s, Perdido) 

and large (Tampa Bay, Indian River) and are almost always shallow with average depths 

(reported by NOAA) of less than 4 m. Watershed size varies more than 100-fold from small 

watersheds, such as Rookery Bay and Sarasota Bay, to the Apalachicola River, which covers 

more than 50,000 km2

 in three states. The ratio of watershed area to estuary area, a key metric 
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with implications for the coupling of watershed and estuary processes, varies from four for 

Indian River to 159 for the Suwannee River estuary. In general, the ratio of large watershed area 

to estuary area is associated with both large freshwater and nutrient inputs and more rapid 

flushing. The difference helps explain how the Suwannee River estuary can receive a much 

larger TN load per unit of estuary area compared to the Indian River without having a 

proportionate eutrophication response. 

The watersheds of estuaries in the Florida panhandle eastward through the Big Bend region 

(Perdido to Suwannee River watershed) have a large fraction (> 50%) of forest cover, although a 

significant fraction of forest land is managed for timber production, rather than entirely natural 

forest. The St. Marys River/Cumberland Sound watershed in northeast Florida also includes a 

significant fraction of forest cover (Figure 2-24). Agriculture is an important as well as 

significant land use in watersheds across all regions of the state; however, two watersheds (St. 

Marys and St. Andrew Bay) have minimal agricultural land use. Urban land use is most 

prevalent on both coasts of the central peninsula, especially Tampa Bay, Sarasota Bay, Indian 

River, and Charlotte Harbor. 

Characterizing Nitrogen Loads to Florida Estuaries 

A synoptic assessment of nutrient loads to all estuaries in Florida had not been completed; 

although, current nutrient loads and historical loading trends have been characterized in some 

well-studied estuaries (e.g., Tampa Bay, Greening and Janicki 2006). Nitrogen export from 

watersheds to estuaries in Florida was recently estimated by the USGS as part of the SAGT 

regional SPARROW model development (Hoos et al. 2008; Hoos and McMahon 2009). The 

SPARROW modeling approach uses a multiple regression equation to describe the relationship 

between watershed attributes (i.e., the predictors) and measured in-stream nutrient loads (i.e., the 

responses) with stream reaches defined by the ERF1_2, which is based on U.S. EPA’s 

1:500,000-scale RF1. The statistical methods incorporated into SPARROW help explain instream nutrient water quality data (i.e., the mass flux of nitrogen) as a function of upstream 

sources and watershed attributes. The SAGT-SPARROW model used monitored streamflow and 

nutrient water quality data from Florida and across the SAGT region for load estimation. SAGTSPARROW also used extensive geospatial data sets describing topography, land use, climate, 

and soil characteristics. Nitrogen loading data for point sources was obtained from U.S. EPA’s 

Permit Compliance System. Estimates of nitrogen in fertilizer and manure were obtained from 

county-level fertilizer sales, census of agriculture, and population estimates (Hoos et al. 2008; 

Hoos and McMahon 2009). TN transports estimated by SPARROW are detrended to the base 

year of 2002, a year for which much of the data inputs, especially land use and land cover, were 

available. 

The SPARROW-modeled TN load estimates range from ~158,000 kg/y for Nassau River 

watershed to ~16,000,000 kg/y for Apalachicola Bay, largely varying with watershed size. 

Watershed yields, a measure of the nitrogen intensity of the landscape varied from ~190 kg/y for 

low-yield watersheds such as Nassau, St. Mary’s, and St. Andrew Bay to as high as 828 

kg/km2

/y for Indian River (Table 2-9). Average TN concentrations, computed as the 

SPARROW-estimated TN loading divided by the aggregate freshwater flow from the same 

fraction of the watershed, followed, as expected, the trend in nitrogen yield. Average TN 
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concentration in terminal reaches was > 1.0 mg/L in high-nitrogen yield watersheds such as 

Indian River and Charlotte Harbor, while much lower in Nassau, St. Marys, and St. Andrew Bay. 

The SPARROW-modeled TN loading estimates do not include TN sources discharging or 

otherwise entering directly into estuarine waters. They include point sources discharging directly 

to estuaries, atmospheric deposition to the estuary surface and submarine groundwater discharge. 

Quantification of point source TN loads discharging directly to estuaries (McMahon et al. 2007) 

shows that these sources are most often less than 5% of the watershed load and were zero in 

seven estuaries (Table 2-9). Those point sources were most important in St. Andrew Bay where 

they are equal to 50% of the watershed load. The largest aggregate point source load discharging 

into estuarine waters is in the St. Johns River estuary, where the load is just over 600,000 kg/y 

but amounts to 10% of the load from the watershed (Table 2-9). 

The SPARROW model partitioned the delivered TN loading from the watershed into one of five 

source categories: atmospheric deposition, fertilizer, manure, urban, and point sources. The 

sources other than atmospheric deposition, combined with point sources discharging directly to 

the estuary compose an estimated total anthropogenic nitrogen loading. Of those sources, 

fertilizer was consistently an important TN source across the state (Figure 2-25). Urban land use 

was a relatively more important nitrogen source in Peninsula watersheds relative to panhandle 

watersheds. Point sources were most important in a few estuaries where large point sources 

discharge directly into estuarine waters and watersheds are relatively small. 

Direct atmospheric nitrogen deposition is sometimes an important source of nitrogen loading to 

estuaries. Wet nitrogen deposition at NADP monitoring sites in Florida is approximately 300 

kg/km2

/y and has remained nearly stable for more than 20 years (= 3 kg/ha/y, Figure 2-26). Dry 

atmospheric nitrogen deposition is measured less commonly, but it is often taken to be similar in 

magnitude to wet deposition. Although atmospheric nitrogen deposition in the southeast is in the 

lower third of the national distribution (NADP 2008; USEPA 2007), average atmospheric 

nitrogen deposition in Florida is still significant, being at least 300 and possibly as high as 600 

kg/km2/y including dry deposition. Moreover, atmospheric nitrogen deposition is likely to be 

elevated relative to the NADP values within coastal urban areas surrounding estuarine waters. 

With the average nitrogen yield from watersheds across the state between 300 and 400 kg/km2

/y, 

areal rates of atmospheric nitrogen deposition are likely of similar or higher magnitude. Thus, 

atmospheric nitrogen deposition is likely to be important when the ratio of watershed area to 

estuary area is small (e.g., < 10). In Florida, St. Andrew Bay, Tampa Bay, Sarasota Bay, and 

Indian River, all of which have small watersheds relative to estuary area, likely receive more 

than 10% of all nitrogen loading from direct atmospheric deposition to the estuarine water 

surface. 

How is the Atmospheric Deposition term in SPARROW related to Atmospheric Deposition 

measured by the NADP? 

The “atmospheric” source term from the SAGT regional SPARROW model is not equal to the 

atmospheric nitrogen deposition measured by the NADP. To properly interpret the TN load 

attributed to the atmospheric source term in SPARROW, it is useful to understand how 

SPARROW works. SPARROW is a nonlinear regression model that seeks to explain measured 

TN loads in streams and rivers in terms of a series of explanatory variables. Those include 
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variables representing watershed and stream network attributes (e.g., drainage area, slope of the 

landscape, percent impervious surface, land use) as well as nutrient sources such as fertilizer 

applications and measured point sources of nitrogen. One of the variables is wet atmospheric 

nitrogen deposition, measured by the NADP. Using those data, SPARROW estimates the 

fraction of nitrogen exported from each stream reach that can be attributed to each of five 

sources: fertilizer, manure, urban, point sources, and atmospheric. The parameters estimated, 

when the model is so fitted, translate non-source inputs such as “percent urban land” into 

nitrogen sources because those attributes explain observed nutrient outputs from the watershed. 

For model inputs that can be interpreted directly as nutrient sources, such as fertilizer 

application, SPARROW estimates coefficients indicating how much of the applied source is 

delivered across the landscape to the stream network. They are called “landscape delivery 

factors.” The atmospheric term accounts for sources that can be explained by the NADP input 

data and are not explained by the other sources. Because of that, the atmospheric source term is 

not simply equal to the NADP data, but it does represent the fraction of nitrogen delivered to the 

stream network that cannot be attributed explicitly to other anthropogenic sources. Furthermore, 

atmospheric term includes any anthropogenic contribution to the regional nitrogen deposition, 

but it does not reflect highly localized contributions associated with urban land use or even 

manure application. In that regard, the SPARROW atmospheric term is expected to be 

comparable in concept to the all-forest scenario in process-based watershed models. In relating 

those quantities, it is helpful to realize that the nitrogen budget for upland forests often involves a 

balance between atmospheric inputs and generally low nitrogen exports associated with 

particulate and dissolved organic matter exports. 

TN yield (i.e., per km2

 of watershed area) from atmospheric sources to estuarine waters in 

Florida was generally between 125 and 175 kg/km2/y (Figure 2-27). The lower value for the 

Withlacoochee River reflects an extremely high estimate of in-stream loss, resulting in low 

delivery of TN from all sources to the coast. The atmospheric source term from SPARROW 

accounted for ~25% to 75% of TN loads from all sources in Florida watersheds. Such results 

suggest that anthropogenic sources have increased TN loading from watersheds as much as fourfold, frequently by two-fold, and in a few estuaries by a smaller amount (e.g., St. Marys, Nassau, 

St. Andrew Bay). 

Time Series of Annual TN Loads 

Streamflow and TN loading have been monitored at numerous river gage stations throughout 

Florida watersheds. Ambient water quality monitoring programs conducted by federal, state, and 

local agencies measure nutrients in stream reaches either at the river gage stations, or at nearby 

sites, which permits estimation of in-stream nutrient loads. USGS uses well-established, multiple 

regression methods that estimate loads as a function of concentration, discharge, and time (Cohn 

et al. 1989; Schwarz et al. 2006; Hoos et al. 2008). Those load estimates are the foundation of the 

SPARROW modeling approach, which seeks to explain these observations on the basis of 

watershed attributes. Because the SPARROW model provides estimates of loads detrended to a 

single target year (2002), it was of interest to examine interannual variability in loading across 

the state. U.S. EPA examined the time series of annual TN loads for 29 stations that were the 

most downstream river gage stations in each Florida estuarine watershed (Table 2-10), with the 

exception of Sarasota Bay and Indian River, which do not have any suitable river gage stations. 

The longest period of record at the stations was 1975 to 2004, although the record at most 
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stations was shorter. The fraction of estuary watersheds monitored via river gage stations is 

variable from nearly 100% for the Apalachicola River to zero for Sarasota Bay and Indian River 

(Figure 2-28). In most other watersheds, 65% to 97% of the watershed is upstream from a gage 

(Figure 2-28). Monitored TN loads at downstream gages vary by three orders of magnitude 

across Florida, largely reflecting differences in the size of the gaged watershed (Table 2-10). 

Interannual variability in monitored TN loads, quantified as the coefficient of variation, is 20% 

to 130% of the mean load over the period of record. The highest variability was observed for 

relatively small watersheds and for gages with short periods of record (Table 2-10). 

Condition of Estuaries 

Although U.S. EPA is not aware of any systematic and consistent statewide assessment of the 

condition of estuaries in Florida, ample evidence is available to suggest that Florida’s estuaries 

are, in general, not in good ecological condition. The National Estuarine Eutrophication 

Assessment (NEEA) Update (Bricker et al. 2007) may provide the most concise published 

analysis of eutrophication in the state’s estuaries. The NEEA used the ASSETS (ASSessment of 

Estuarine Trophic Status) assessment approach (Bricker et al. 2003), in which local experts 

familiar with a system examine the level of influencing factor (generally nitrogen loading rate), 

expression of eutrophic conditions, and future outlook (i.e., potential for symptoms to become 

better or worse). Of 19 estuaries in Florida examined in the NEEA, no estuary had a “High” 

ASSETS rating, and only two estuaries, Biscayne Bay and Pensacola Bay, had a “Good” 

ASSETS rating (Table 2-11; note that U.S. EPA disagrees with several conclusions related to 

Pensacola Bay and suggest a lower overall rating for the system). In contrast, five Florida 

estuaries had a “Poor” or “Bad” ASSETS rating. Nearly half could not be rated, most often 

because nutrient loading or nutrient susceptibility was unknown at the time of the report. FDEP 

has also assembled a series of Water Quality Assessment Reports (e.g., FDEP 2005), each of 

which presents a range of environmental and regulatory information pertaining to an estuarine 

system in Florida. Although they are very useful, they lack key detail relative to status and 

effects associated with eutrophication. 

303(d) Listings and the IWR Verified List 

One measure of the condition of Florida’s estuarine waters is Florida’s 1998 303(d) list and 

verified list of waters identified as impaired under the Florida’s IWR (F.A.C. 62-303) (Table 2-

12). The lists document nutrient-related impairments throughout the state either under the 

narrative standard (1998 303(d) list) or under the IWR. In general, impairments for chl. a are 

concentrated in Atlantic coast estuaries and peninsular Gulf coast estuaries. The only listing in 

the Florida panhandle related to chl. a is for Escambia Bay (a region of Pensacola Bay) where a 

historical increase in chl. a exceeded the limit specified under the IWR. In the remainder of the 

state, chl. a impairments are mostly for ambient concentration, rather than historical increase. 

One could suspect that in many cases where chl. a levels exceed the IWR threshold, they also 

represent an increase from historical levels that were never documented. 

Overall, agreement of the NEEA and the 303(d) and IWR impairments is uneven. NEEA 

ASSETS ratings of “poor” for Indian River, Caloosahatchee River, Charlotte Harbor and Tampa 

Bay agree well with impairment listings for chl. a and low DO, whereas a “Good” ASSETS 

rating for Biscayne Bay agrees with the lack of impairments within the EDA. On the other hand, 

the NEEA reports low expression of eutrophic condition for Choctawhatchee Bay and Pensacola 
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Bay, each of which has IWR impairments for chl. a, DO, or both. The NEEA reports high 

expression of eutrophic conditions for Perdido Bay, but there are no verified list impairments for 

WBIDs in Perdido Bay. 

Chl. a in Florida Estuaries 

Chl. a is a simple indicator of trophic status in aquatic systems including estuaries. However, 

because nutrient enrichment can lead to significant effects that do not involve elevated chl. a, 

low chl. a is not necessarily a reliable indicator that eutrophic conditions are not present. 

Moreover, average chl. a might not be the only useful indicator of nutrient-enriched 

phytoplankton production, which can result in periodic blooms. The 90th percentile of chl. a

concentration reflects the high concentration values that occur during such blooms. The NEEA 

suggests chl. a categories of low (0–5 μg/L), medium (5–20 μg/L), and high (> 20 μg/L) based 

on the 90th percentile. 

On the basis of the NEEA classification and data from Florida’s IWR 34 database, three estuaries 

(Charlotte Harbor, Tampa Bay, and Perdido Bay) have “high” chl. a levels in surface water, and 

three estuaries have low chl. a (Biscayne Bay, Florida Bay, St. Marys River) (Table 2-13); the 

remaining estuaries have moderate levels. Of the latter, the highest are the St. Johns River, 

Indian River Lagoon, and Caloosahatchee Rivers, whereas three panhandle estuaries, St. Andrew 

Bay, Choctawhatchee Bay and Pensacola Bay have chl. a at the low end of the moderate range. 

These levels generally align with and support the NEEA assessments, in which all three of the 

estuaries with high chl. a have either “Poor” ASSETS ratings or “high” expression of eutrophic 

conditions. ASSETS ratings for systems at the high end of the medium range for 90th percentile 

were also “Poor” or “Bad.” 

Analysis of ecological conditions in Florida estuaries and the relation to nutrient loading is 

ongoing and will be evaluated in greater detail over the next year. 

Evaluating TMDLs, PLRGs and Dose-Response Models for Florida Estuaries 

A TMDL is estimated as the maximum nutrient load that a waterbody can receive and process or 

export while still meeting criteria that protect the designated use. TMDLs are typically expressed 

as the sum of all point source loads (wasteload allocations, or WLAs), and nonpoint source loads 

(load allocations, or LAs), along with a margin of safety (MOS) that takes into account the 

uncertainty surrounding the relationship between effluent limits and water quality. TMDLs can 

be expressed in terms of mass per time (e.g., pounds per day), toxicity, or other appropriate 

measure, such as mass per volume or percent reduction. A total of 11 nutrient-related (chl. a, 

biochemical oxygen demand (BOD), DO, TN, TP or nitrogen) TMDL documents have been 

finalized for Florida estuaries, and 7 have been drafted or proposed, addressing verified 

impairments in more than 55 WBIDs. A listing of those TMDLs, the marine WBIDs considered, 

and the date they were completed or drafted is provided in Table 2-14. 

Additional loading standards were developed under Florida’s stormwater management program, 

FAC 62-40.432(5) as revised in 1990. The program has, as one of many provisions, minimum 

stormwater performance standards. The standards call for a reduction, on a watershed basis, of 

the pollutant loading from older stormwater systems as needed to protect, maintain, or restore the 

beneficial uses of the receiving waterbody. The amount of needed pollutant load reduction is 
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known as a PLRG. To date, stormwater PLRGs have been established for Tampa Bay, Lake 

Thonotosassa, Winter Haven Chain of Lakes, the Everglades, Lake Okeechobee, and Lake 

Apopka and have been proposed for Indian River. These PRLGs are in agreement with TMDL 

thresholds, and proposed percent reduction in nutrient levels. 

The majority of the estuarine WBIDs listed as impaired by FDEP were verified as impaired for 

nutrients and DO on the basis of IWR methods. To be verified as impaired, those waters 

exceeded the IWR listing threshold for annual average chl. a (11 g/L) in at least one year of a 

5-year period. Waters with chl. a levels more than 50% greater than the historical value (e.g., 

10.4 g/L for Tampa Bay) for at least two consecutive years are also verified as impaired. 

Finally, waters with more than 10% of measured DO concentrations falling below 4 mg/L (the 

IWR threshold) over the 5-year period are verified as impaired (The IWR water quality criterion 

for marine waters states that DO should not be less than 5.0 mg/L in a 24-hour period and should 

never be less that 4.0 mg/L, which is more stringent than that required to verify impairment). 

Once a waterbody is verified as impaired, preparation of a TMDL is scheduled. 

The TMDL thresholds or endpoints that have been determined are provided in Table 2-15. The 

endpoints include levels for chl. a, BOD, TN, TP and are also expressed as percent reduction in 

TN. The process for formulating TMDLs and the resultant reports include a description of the 

waters, the regulatory reasons for computing the loading limits, and the water quality data 

available. Assessments of sources include point sources from permitted effluent discharges and 

stormwater contributions (National Pollutant Discharge Elimination System (NPDES) and 

Municipal Separate Storm Sewer System (MS4) permits), and nonpoint sources on the basis of 

land use of the surrounding watershed(s) and predictive models. Estimated loads are compared to 

loads that would result in desired improvements to develop a TMDL threshold. The analytical 

methods used to justify this threshold, often called assimilative capacity, are provided with 

supporting evidence—often as data summaries for reference sites, or details of models with 

assumptions, calibrations, and results. Finally the loading limits (WLAs and LAs) are provided, 

usually with the required percentage of load reduction over current levels. Examples of some of 

the TMDLs that have been finalized or drafted are provided below. The examples give an 

indication of the range of methods used to determine both load estimates and thresholds, varying 

from relatively simple regressions to complex coupled hydrodynamic/process models. 

McKay Bay, Tampa Bay Basin 

This bay was verified as impaired because of average chl. a measurements 14.5–26.1 g/L, well 

above the IWR criterion (11 g/L). In addition, DO measurements were below 4 mg/L more than 

10% of the time (208/1035) in the verified period. Point source assessments included NPDES 

permits and MS4s. Nonpoint source loadings below the SWFWMD Gage S-160 were estimated 

with the Watershed Management Model (WMM), which is based on the imperviousness and 

event mean concentrations (EMCs) from different land use types in the watershed. Because a 

statistically significant relationship between loading and chl. a levels was not found, the 

assimilative capacity for TN loading to the bay was based on the observation that the annual 

average chl. a values in 2003 (5.7 to 8.4 g/L) were less than the IWR threshold (11 g/L), 

unlike previous years when algal biomass was elevated. Maintaining the TN loading at 2003 

levels is expected to prevent annual average chl. a values from exceeding 11 μg/L and provide a 
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MOS for meeting the target. Specifically, the TMDL threshold was set at the annual load 

estimated for 2003 (542,602 pounds). 

Sarasota Bay/Lemon Bay 

Available data indicate that DO concentrations in many of the Sarasota Bay and Lemon Bay 

Harbor WBIDs are frequently below applicable water quality criteria, with excursions 40% to 

55% of the time, causing these waters to be verified as impaired. Point source contributions were 

determined on the basis of NPDES and MS4 stormwater permitted discharges. Nonpoint source 

loading was determined using the Lemon Bay Watershed Model, which uses site-specific land 

use, soil characteristics, and attenuation factors (land to water ratios, stormwater, and 

conveyance considerations) to predict loads. The model was also used to estimate loads before 

anthropogenic influences (natural condition). Loading predictions were generated for five 

reference WBIDs that are meeting water quality criteria and are similar in size to the impaired 

watersheds for comparison purposes. Average percent reductions recommended via the reference 

approach were similar to those based on the natural condition determination. 

Indian River 

Of 16 WBIDs covered in the TMDL document, all but 5 are verified as impaired on the basis of 

DO excursions. All the WBIDs fail to meet TP goals (0.19–0.22 mg/L), and seven fail to meet 

TN goals (11.0–1.6 mg/L), causing the waterbodies to be verified as impaired. In addition, the 

Indian River Lagoon SWIM Plan and associated PLRGs state additional goals for maintaining a 

healthy seagrass ecosystem in the estuary. Three IR subsections were determined (IR North, IR 

Central and Banana River), and point source loads were estimated from NPDES discharge data. 

The nonpoint source loads were determined from the Pollution Load Screening Model (PLSM) 

or Hydrologic Simulation Program-Fortran (HSPF) models. PLSM is a GIS-based stormwater 

runoff model useful for identifying potential runoff problems resulting from current and future 

land use patterns. Estimates of historic, current, and future loads of TN, TP, and total suspended 

solids (TSS) can be calculated from data that include soil types, hydrologic boundaries, rainfall, 

and runoff coefficients. To verify PLSM results, HSPF was used to generate independent loading 

estimates simulating transport and fate on the land and in receiving waters. Such a 

comprehensive system of models simulates the effects of changes in land use and point or 

nonpoint source treatments on watershed hydrology and associated water quality. Finally, linear 

regression models that relate seagrass depth limits to annual TN and TP loading in three 

sublagoons (North, Central Indian River Lagoon (IRL), and Banana River Lagoon) were 

developed. The TMDL target load was determined from this regression-based, seagrass depthlimit restoration to a –10% departure (shoreward) from the load that was predicted to provide full 

restoration of seagrass beds. The basis for the 10% departure is related to Florida’s water 

transparency standard, which states that the “depth of the compensation point for photosynthetic 

activity [seagrass depth limits] shall not be reduced by more than 10% as compared to the natural 

background value [the condition of the waters in the absence of man-induced alterations on the 

basis of the best scientific information available].” The areal point and nonpoint load limits 

(lb/ac/yr) were then multiplied by the acreage of each sub-lagoon to determine the average 

annual loads in pounds/year (lb/yr). 
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Lower St. Johns River (LSJR) 

Of the 15 LSJR segments, 11 were verified as impaired by nutrients on the basis of annual mean 

chl. a concentrations or annual mean TSI values for freshwater WBIDs. To estimate loading, a 

suite of models was employed including (1) PLSM—for calculating seasonal runoff and nutrient 

loads for subbasins, (2) a hydrodynamic model (Environmental Fluid Dynamics Code (EFDC)) 

to simulate the mixing and transport of nutrients in the river as a function of wind, tide and 

salinity intrusion, and results from EFDC were incorporated into (3) a water quality model (CEQUAL-ICM ) that predicts the effects of point and nonpoint source loading on chl. a, DO, and 

other water quality parameters. TMDL thresholds were set on the basis of a site-specific 

alternative DO criteria established for LSJR (minimum DO concentration of 4.0 mg/L, with the 

total fractional exposure to DO levels in the 4–5 mg/L range evaluated over an annual period). 

The “Virginian Province Approach” was used as supporting evidence to determine DO 

concentrations above which continuous exposure is not expected to result in unacceptable 

biological effects. The Criterion Minimum Concentration (CMC), which is defined as a daily DO 

concentration below which any exposure (most sensitive species) for a 24-hour period would 

result in unacceptable acute effects (mortality), was determined. 

Klosterman Bayou, Springs Coast Basin 

A tiered set of considerations formed the basis for TMDL thresholds for Klosterman Bayou 

(Springs Coast Basin, WBID 1508). The first tier is a regression, which attempts to develop a 

multivariate relationship between a response (e.g., low DO) and nutrients. Data to develop a 

statistically supportable relationship are seldom available, and when sufficient data are present, 

they often prove difficult to demonstrate a significant relationship. If a significant correlation is 

not found, a second tier is applied. The second tier is basically a reference condition approach, 

applying, for example, the 25th percentile of a data set for similar non-impaired ecologically 

balanced marine waters to determine the nutrient TMDL targets. Commonly, the data set that can 

be collected is composed of a few member estuaries, so uncertainty values could be large. The 

third tier is a spreadsheet model—U.S. EPA’s Pollutant Loading (PLOAD) spreadsheet. 

PLOAD, a GIS-based model, estimates nonpoint sources of pollution on an annual average basis 

for specified pollutants. The model was designed to be generic so that it can be applied as a 

screening tool in a wide range of applications including NPDES stormwater permitting, 

watershed management, or reservoir protection projects. While the model addresses loading on a 

watershed basis, it was not designed for estuaries or for pollutants like nutrients that do not 

necessarily demonstrate dose-response effects. 

2f. Alternative Approach Considered by U.S. EPA 

i. FDEP’s Benchmark Distribution Approach and the Calculation of Benchmark 

Distribution Approach and Selection of Percentiles from the Benchmark Distribution 

FDEP developed an alternative nutrient criteria development process based on percentiles of 

nutrient concentrations from minimally disturbed benchmark reference sites. This approach is 

presented as an alternative to the principal approach. Text on the technical approach used by 

FDEP to derive these benchmark-based criteria based on least-disturbed streams is presented 

here. This text was principally authored by FDEP and was edited by U.S. EPA for this technical 

document. 
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ii. Identification of Least-Disturbed Streams 

One of the approaches recommended in U.S. EPA’s Nutrient Criteria Technical Guidance 

Manual: Rivers and Streams (USEPA 2000) is the reference condition approach. For this 

approach, which FDEP expanded and called the Nutrient Benchmark Site Distributional 

Approach, U.S. EPA recommends setting criteria based on an inclusive distribution of values 

obtained from reference sites in a designated ecoregion (based on climate and geology, etc.). 

FDEP expanded this approach by identifying streams that were minimally affected by human 

disturbance and nutrients, and also by documenting the existence of aquatic life consistent with 

the designated use (using SCI methods) in a subset of these waters. FDEP developed a multi-step 

process (described below) to help ensure that the reference sites eventually selected truly 

represented minimal human disturbance and full designated use support. Similar to the principal 

distribution-based approach described above, the assumption is that streams documented to be 

minimally affected by humans and characterized by healthy biota, based on the use of an 

effective biological assessment method, are logically within the range of nutrient concentrations 

also protective of the designated use. 

FDEP, after deliberating with its technical advisory committee (TAC), selected the 90th

percentile of this benchmark distribution for threshold purposes in all nutrient regions except the 

Bone Valley. The 90th percentile was justified by FDEP primarily because of the additional 

verification steps, including the documentation that the benchmark site population had healthy, 

well-balanced aquatic communities (see discussion below). In the Bone Valley, FDEP proposed 

the 75th percentile because of the limited number of minimally disturbed streams available. 

iii. Verification Process for Selection of Benchmark Sites 

Selection of candidate reference sites by identifying sites with a corridor Landscape 

Development Intensity Index (LDI) score of ≤ 2. (This step alone eliminated the majority of 

Florida sites from further consideration; Figure 2-29.) Two additional benchmark exclusions 

were ultimately based on a whole-watershed LDI analysis conducted by U.S. EPA (Appendix B10). 

A critical component of the benchmark approach was the multi-step evaluation process through 

which potential benchmark sites were verified by FDEP to ensure they represented minimally 

disturbed conditions. This multi-step evaluation included: 

 Elimination of sites included on the Florida 303(d) list of impaired waters due to nutrients or 

dissolved oxygen related to nutrients. 

 Elimination of sites with nitrate concentrations greater than the 0.35 mg/L proposed springs 

nitrate-nitrite criterion, which reduced the possibility of including sites with far-field human 

disturbance from groundwater inputs, 

 Verification of surrounding land use by examining high-resolution aerial photographs taken in 

2004–2005, 

 Obtaining input from FDEP district scientists knowledgeable of the area, 

 Performing a statistical outlier analysis of nutrient concentrations to remove potentially 

erroneous data, 
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 Conducting a field evaluation process, including a watershed assessment with verification of 

surrounding land use and biological evaluation, of a percentage of remaining waterbodies 

containing benchmark sites, with the emphasis on sites with nutrient concentrations greater 

than the mid-range of the distribution. 

Through this process, candidate reference sites were subjected to a systematic evaluation process 

prior to including them as benchmark sites. Maps, photos, and a summary of the data collected at 

each of the verified benchmark sites can be found in Appendix B-18. Each of the above steps is 

described in more detail below. 

iv. LDI score of ≤ 2 

Candidate benchmark sites were initially selected based on an application of the LDI. Brown and 

Vivas (2003) developed the LDI as an estimate of the intensity of human land uses based on 

nonrenewable energy flow. Application of the LDI is based on the ecological principle that the 

intensity of human-dominated land uses in a landscape affects the ecological processes of natural 

communities. More intense activities will result in greater effects on ecological processes. 

Natural landscapes with little or no agricultural or urban development will likely have intact 

ecological systems and processes. The LDI was developed specifically as an index of human 

disturbance, and it has been shown to provide predictive capability regarding nutrient loading 

(Figure 2-30). 

The LDI is calculated as the area-weighted value of the land uses within an area of influence 

(Figure 2-31). Using the land use coefficients and the percent area occupied by each land use as 

determined by GIS land use coverage developed from high-resolution aerial photographs, the 

LDI is calculated as follows: 

LDITotal = ∑ (LDCi * %LUi) [7] 

where, 

LDITotal = Landscape Development Intensity Index for the area of influence 

%LUi = percent of total area of influence in land use i 

LDCi = landscape development intensity coefficient for land use i 

The LDI calculated on the land uses within a 100 m corridor of a stream, extending 10 km 

upstream from a sample site (“corridor approach”), was found to be a better predictor of 

ecological health than the LDI calculated on an entire catchment (Fore 2004). Sources of 

disturbance near a stream exert greater influence than do far-field human influences (Brown and 

Vivas 2003). Fore (2004) previously demonstrated that LDIs calculated using a 100 m corridor 

were slightly better predictors of biological health (i.e., SCI) than LDIs calculated on the entire 

upstream catchment area (watershed). 

The utility of this corridor approach is related to the demonstrated effectiveness of riparian 

corridor zones in removing pollutants, especially nutrients, from stormwater inputs (both surface 

and subsurface flow). Studies have shown that corridor zone widths of 60 m are sufficient to 

reduce nutrient loads by up to 95% before reaching the stream (Peterjohn and Corell 1985). 
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Additionally, Coastal Plain riparian corridors have been shown to be effective in retaining 

nutrients because of gradual slopes, permeable soils, and the abundance of roots that enter the 

shallow ground water zones (Lowrance 1997). Since phosphorus is typically found bound to 

sediments, riparian zones retain most of the incoming phosphorus by capturing sediments. Other 

studies have shown that nitrate in shallow ground water beneath riparian zones was removed by 

85% to 90% due to plant uptake and denitrification in riparian zones 50 m to 70 m wide 

(Lowrance 1992; Jordan et al. 1993; Jacobs and Gilliam 1985; Lowrance 1997). 

For purposes of benchmark site selection, LDI values were calculated from land uses within a 

corridor area of 100 m on each side of the stream and tributaries within a 10 km radius upstream 

of the sampling point (corridor approach) as shown in Figure 2-31. While numerous studies have 

concluded that corridor widths of 50 m to 70 m are sufficient to reduce storm water nutrient 

loads to streams by as much as 95%, additional corridor width provides additional protection to 

the waterbody. Based on these literature findings and the better correlations with biological 

health described above, FDEP concluded that using a corridor width of 100 m would provide 

adequate protection to Florida’s waterbodies and that a LDI calculated based on a 100 m corridor 

is an appropriate method of selecting candidate benchmark sites with minimal human 

disturbance. 

An additional analysis by U.S. EPA (Appendix B-10) compared the nutrient distributions of sites 

characterized by the LDI corridor approach with LDIs on the same sites calculated on a larger 

watershed area. This analysis suggested exclusion of two additional sites, one from the Bone 

Valley region (South Prong Alafia River) and one from the North Central region (Camp Branch), 

and closer scrutiny of additional sites in the Bone Valley region. The analysis indicated that 

higher TP concentrations observed at the South Prong Alafia River and Camp Branch could be 

explained by human disturbance, based on the higher watershed LDI values found in the WBIDs. 

After exclusion of these two sites, FDEP recalculated the nutrient distributions, which resulted in 

values different from earlier calculations (Table 2-16). 

As discussed in FDEP’s Nutrient Plan (2009), the LDI was specifically designed as a measure of 

human disturbance. LDI values of less than or equal to 2.0 within the 100 m corridor area are 

indicative of areas with minimal levels of human disturbance. Other studies and evaluations have 

demonstrated, across other waterbody types and taxonomic groups, that the LDI is an accurate 

predictor of biological health; that is, healthy, well-balanced biological systems are much more 

likely to occur at sites with low LDIs (≤ 2.0) than at higher disturbance levels (Fore 2004; Niu 

2004; Brown and Reiss 2006; FDEP 2009; Fore et al. 2007). Furthermore, it has been 

demonstrated that an LDI of 2.0 (corridor approach) is an appropriate and biologically significant 

break point that can be used to distinguish benchmark conditions from potentially disturbed 

areas. 

A more detailed discussion of the LDI and its use to select minimally impacted benchmark sites 

is provided in Appendix B-10. 

v. Screening against the 303(d) List of Impaired Waters 

Sites located within WBIDs listed on the state’s Verified 303(d) list as impaired for nutrients or 

dissolved oxygen, where nutrients were identified as the causative parameters, were excluded as 
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benchmark sites. Additionally, sites within WBIDs listed on the Verified or Planning 303(d) lists 

for biological impairments, regardless of cause, were excluded from the benchmark population. 

vi. Screening against the 0.35 mg/L Proposed Nitrate-Nitrite Threshold 

Nitrate-nitrite is mobile in ground water and may travel in subsurface aquifers for significant 

distances to be discharged into streams via seeps or springs. FDEP determined that 

anthropogenic activities are responsible for elevated ground water nitrate-nitrite concentrations 

and proposed a nitrate-nitrite criterion of 0.35 mg/L for springs and clear streams, and U.S. EPA 

is proposing a similar criterion for springs. Given confidence that the response-based proposed 

nitrate-nitrite criterion is protective, candidate benchmark sites that exceed this nitrate 

concentration (0.35 mg/L) were eliminated from consideration as benchmark sites. 

vii. Verification of Surrounding Land Use by Examining High-Resolution Aerial 

Photographs 

Minimally disturbed conditions of every candidate site were confirmed through a review of 

recent (2004–2005) high-resolution (1 m ground resolution) aerial photographs. This review 

consisted of searching the photos for recent land clearing or development, in particular any 

disturbance that encroached into the 100 m corridor area used to calculate the LDI. Many further 

sites were excluded based on the review of aerial photographs, including several that appeared to 

be tidally influenced, within canals, or channelized and therefore were not considered 

representative of a minimally disturbed stream condition. 

viii. Obtaining Input from FDEP District Biologists 

FDEP district scientists familiar with streams in their area were asked to provide feedback on the 

list of candidate benchmark sites. Specifically, they were presented with the following 

information and question: 

“For ongoing nutrient criteria development, we are identifying sites with benign land uses in 

their upstream watershed (LDI < 2) to define the benchmark condition. Ken Weaver has 

produced the attached table of low LDI peninsular benchmark sites. Can you please look over the 

list to determine if there are any human activities at particular sites, which may not have been 

captured by the LDI that would disqualify the site from being used to define benchmark for 

nutrient criteria?” 

More than 20 study sites were excluded from the benchmark set based on feedback and best 

professional judgment comments provided by district staff. The staff identified additional 

channelized streams, estuarine sites, and potentially disturbed sites. In some cases the staff 

identified potential point source discharges or localized disturbances (e.g., cattle in the stream) 

that may not have been captured in the LDI calculation. In other cases, sites were excluded 

because the reviewer was aware of moderate to high levels of development within the watershed 

that were outside the 100 m corridor but, in their opinion, could potentially increase nutrient 

concentrations. Exclusion of these potentially disturbed sites represents an additional 

conservative component of FDEP’s approach designed to ensure that the benchmark set consists 

solely of minimally disturbed locations. 
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Additional sites were excluded because they were potentially estuarine or tidally influenced 

based on proximity to the coast and a subsequent review of specific conductance data. All 

potentially estuarine sites routinely had specific conductance levels above 1,275 μS/cm and 

episodic values above 4,500 μS/cm. A conductivity of 4,500 μS/cm is approximately equivalent 

to a chloride concentration of 1,500 mg/L, which is used in Florida as the threshold between 

predominantly fresh and marine waters. 

ix. Field Evaluation Process, Including Watershed Assessment and Biological 

Appraisal of Benchmark Sites 

In 2007 and 2008, experienced FDEP Tallahassee staff conducted a study of a number of the 

candidate benchmark sites, selected through the above process, as a means of providing 

additional assurance that sites were truly representative of minimally disturbed conditions. The 

population of candidate benchmark sites selected for additional review consisted predominantly 

of WBIDs with nutrient concentrations higher than the mid-range of the distribution. The 

objective of this final in-field verification step, which included a watershed survey and biological 

assessment, was to reinforce confidence in the selection of the final benchmark sites, focusing 

especially on those with nutrient concentrations higher than the middle of the distribution. 

Sites visited were selected to be representative of most of the WBIDs in the candidate 

benchmark data set. The site with the most extensive and longest period of nutrient data was 

selected to represent the WBID. 

Site evaluations included a survey of anthropogenic inputs and surrounding land uses. The 

survey included a driving tour of the portions of the watershed accessible to FDEP, guided by 

high-resolution aerial photographs taken in 2004–2005 and maps of the entire drainage basin. 

During the watershed survey, FDEP investigators made a series of observations regarding 

potential human disturbances in the watershed, including potential nonpoint source inputs and 

hydrologic modifications (using the FDEP hydrologic scoring system). The hydrologic scoring 

system was originally developed to support the development of Florida’s SCI. It is based on 

knowledge of water removal, patterns of drought, and hydrographs for the sites under evaluation, 

and it serves as a rough measure of hydrologic disturbance in a system (Fore 2004; Fore et al. 

2007). 

Stream Habitat Assessments (HAs) were conducted following DEP-SOP-001/01 FT 3100 (FDEP 

2008). The HA evaluates substrate condition and availability, water velocity, habitat smothering 

(e.g., by sand and silt), channelization, bank stability, and the width and condition of riparian 

vegetation. In addition to the 100 m reach of the stream examined during the HA, investigators 

also physically examined a minimum of 200 m upstream of the site, including potential riparian 

zone breaches. 

At each site, trained and experienced FDEP staff also collected and analyzed the biological, 

chemical, and physical parameters listed in Table 2-17 following FDEP SOPs 

(http://www.dep.state.fl.us/labs/sop). Water levels were evaluated both by reviewing 

hydrographs from the given stream or other streams in the general vicinity and by visually 

inspecting the stream habitats. Biological samples (e.g., SCI) were not collected if, based on the 

judgment of the experienced investigator, current or antecedent flow conditions were 
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inappropriate, or a majority of the aquatic habitat was exposed to the air rather than being 

inundated. Water chemistry samples were collected at all sites unless there were only 

discontinuous pools of water, in which case no samples were collected. These sites were, 

however, still included in the benchmark data set. Note that sites with an average score of less 

than 40 (the revised impairment threshold) on the SCI were excluded from the benchmark data 

set for calculation of the final nutrient distribution. 

Information acquired during the site and watershed evaluations was used to provide final 

confirmation that the benchmark sites chosen by FDEP were in fact representative of minimally 

disturbed conditions for the region. Taken together with the screening criteria, the results of the 

stream surveys provide confidence that nutrients associated with FDEP’s benchmark data set 

fully support healthy, well-balanced aquatic communities (see discussion below). 

x. Results of Benchmark Screening Process 

The initial set of candidate reference sites identified by FDEP statewide, with available nutrient 

data of known quality and LDI values less than or equal to 2.0, consisted of 1,171 sites 

distributed among 507 WBIDs. After excluding sites through the multi-step screening process, a 

total of 493 stations in 129 WBIDs remained. Due to time and resource considerations, not all of 

these WBIDs could be visited; therefore, FDEP emphasized field verification of sites with 

nutrient values higher than the mid-range of the distribution. The total number of benchmark 

sites that successfully passed the field verification was 63. Because of hydrologic conditions 

(predominantly low-water conditions), biological sampling was not conducted at all the fieldverified sites; however, these sites were determined to be minimally disturbed through the 

watershed survey and habitat assessment process. As of the date of this document, SCI samples 

were collected at 51 sites, the Rapid Periphyton Survey was conducted at 54 sites, and 

periphyton community structure data were collected at 60 sites. Summary descriptions and 

evaluations for each of the benchmark sites are included in Table 2-18 at the end of this chapter. 

Further screening of sites was based on outliers identified from the near-field watershed LDI 

analysis (Appendix B-10). 

xi. Analysis of Nutrient Benchmark Sites Biological Data 

SCI scores from the list of field-verified nutrient benchmark sites (based on all the verification 

steps previously described) were compared to their corresponding LDI, TP, and TN values in 

order to determine whether the ranges of these parameters were supportive of healthy biological 

communities. The data set includes 66 sampling events at a total of 49 stations across the state 

(13 stations were sampled twice, and two stations were sampled three times). All the SCI values 

were calculated using the 2007 SCI model (Fore et al. 2007). 

As described previously, the SCI is negatively correlated across the whole range of LDI, 

meaning biological health decreases in response to increasing levels of human activities (Figure 

2-32). However, within the low range of LDI (≤ 2) associated with the nutrient benchmark site 

data set, no correlation was found between LDI score and SCI score (Figure 2-33). (Note that 

SCI scores were averaged for sites with more than one SCI sampling event.) This indicates that 

LDI scores of up to 2.2 (the LDI value at one of the Bone Valley sites), when coupled with 

FDEP’s verification process, are associated with healthy biological communities. 
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The graphs of SCI scores versus TN and TP (Figures 2-34 and 2-35) indicate that benchmark 

sites support healthy, well-balanced populations of flora and fauna even at nutrient 

concentrations above the 90th percentile of the benchmark distribution. Biological communities 

with SCI ≥ 64 were found at sites with TP concentrations as high as approximately 600 μg/L and 

TN concentrations as high as approximately 3 mg/L. Therefore, TP and TN concentrations at 

least as high as the 90th percentile were protective of the natural populations of flora and fauna in 

minimally disturbed streams. 

Periphyton community composition and taxonomy, and the extent and thickness of algae 

coverage (RPS), were also collected at benchmark sites. The periphyton community response to 

environmental factors is complex, and data analysis indicates that factors such as pH, 

conductivity, canopy cover, and color appear to exert significant confounding influences on the 

periphyton response to nutrients. The analysis of the periphyton data to date suggests that 

changes in algal community structure and abundance are, in part, significantly related to 

nutrients; however, FDEP concluded that the relationships discerned were statistically too weak 

to establish credible numeric nutrient thresholds. Benchmark sites in general exhibited low algal 

coverage and thickness, except for a few sites with limestone outcroppings, where algal growth 

was moderate. Based on the periphyton analyses and observations made during benchmark site 

field studies, evidence supports the selection of the 90th percentile of the benchmark site nutrient 

distribution for establishment of protective criteria. 

xii. Calculation of Benchmark Derived Nutrient Criteria 

Data Handling 

Nutrient data from the benchmark sites were queried from Florida STORET, FDEP’s Status and 

Trends data set (GWIS database), and the benchmark site verification data set. Data were 

screened for potential data quality issues (e.g., improper sample preservation, analysis performed 

outside hold time). Additionally, statistical outlier analysis was performed on individual data 

points by region to exclude extreme outliers that are likely erroneous and/or not representative of 

normal site conditions. The outlier analysis was performed using standard statistical methods by 

defining the extreme outlier limit as: 

 75th percentile + 3.0 * Interquartile Range [8] 

Data points identified as outliers were excluded from the data set (Table 2-19). 

A number of the benchmark waterbodies were sampled numerous times and by different 

agencies. The sampling sites used by the different samplers are often located within several 

hundred meters of each other. Therefore, to avoid biasing the analyses toward larger waterbodies 

with multiple sampling sites within close proximity to each other, station-level data were 

aggregated by WBID. 

Elevated nutrient levels are not acutely toxic in the aquatic environment; instead, their effects are 

chronic and cumulative. Nutrient concentrations are typically variable over time and exhibit a 

log-normal distribution in the aquatic environment. As a result, the geometric mean, rather than 

an arithmetic mean, is often used to provide an accurate representation of the central tendency of 

positively skewed nutrient data (e.g., log-normal). 
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For the reasons discussed above, annual WBID geometric means were calculated for purposes of 

evaluating the frequency distribution of nitrogen and phosphorus by region for benchmark 

streams. Additionally, to reduce the effect of low sample sizes, while capturing the temporal 

variability and maintaining the maximum amount of data being considered, a minimum of four 

samples per year in each WBID was established as the appropriate requirement for data to be 

included in further analysis of the regional distribution. 

Final Data Set Statistics 

The final data sets (following screening of candidate benchmark sites) used to derive TN and TP 

criteria consisted of 565 WBID-years with a minimum of four TP measurements. The 565 

WBID-years of data included 5,999 individual TP measurements collected from 127 WBIDs 

across the state. Likewise, the final TN data set consisted of 523 WBID-years that had a 

minimum of four TN measurements. The 536 WBID-years of data included 5,037 individual TN 

measurements within 127 WBIDs. Upper-percentile thresholds were calculated based on the lognormal frequency distribution of annual WBID geometric mean concentrations by region (Table 

2-16). 

Phosphorus criteria were calculated as the 90th percentile annual geometric mean concentration 

separately for the Panhandle, Northeast, North Central, and Peninsula nutrient regions using the 

equation: 

90th Percentile =e[ln(mean nutrient concentration) + 1.282 x ln(standard deviation)] [9] 

The regionalization for this alternative approach differed slightly from that used in the principal 

criteria approach. The phosphorus criterion for the Bone Valley was calculated as the 75th

percentile annual geometric mean concentration. A lower percentile was selected for the Bone 

Valley due to greater uncertainty in the upper end of the distribution for this region given the 

limited number of benchmark sites and WBIDs. 

TN criteria were calculated as the 90th percentile annual geometric mean concentration for the 

Panhandle nutrient region and combined Northeast, North Central, Peninsula and Bone Valley 

region. Nitrogen concentrations did not exhibit the same regional spatial patterns as that 

observed for phosphorus. The primary regional difference was between the Panhandle and the 

remainder of the state. Therefore, a single TN criterion was applied for this alternative approach 

criterion method to the Northeast, North Central, Peninsula, and Bone Valley regions. 
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Table 2-1. Distributional Statistics of Stream TP and TN by Estuarine Watershed and Aggregated by U.S. EPA N

Stream TP Conc in EDA/FDA/CDA (mg/L) Stream TN Conc in EDA/FDA/CDA (m

EDA Code Watershed Name Mean Median 

75th

%tile 

90th

%tile Count Mean Median 

75th

%tile 

90th

%tile 

G140x Perdido Bay EDA 0.114 0.026 0.122 0.360 1219 1.55 0.71 1.61 4.35 G130x Pensacola Bay EDA 0.030 0.020 0.035 0.054 2436 0.77 0.56 0.78 1.23 G120x Choctawhatchee Bay EDA/FDA 0.040 0.027 0.045 0.066 1087 0.52 0.49 0.65 0.81 G110x St. Andrew Bay EDA 0.019 0.011 0.020 0.020 678 0.50 0.36 0.55 0.75 G100x Apalachicola Bay EDA/FDA 0.047 0.027 0.047 0.077 1219 0.92 0.75 1.01 1.58 G090x Apalachee Bay EDA 0.105 0.048 0.094 0.156 7523 0.88 0.63 0.94 1.45 G086x Econfina-Steinhatchee CDA 0.091 0.069 0.104 0.165 777 0.91 0.87 1.17 1.41 G080x Suwannee River EDA/FDA 0.302 0.130 0.211 0.449 14469 1.13 1.03 1.29 1.62 G070x Tampa Bay EDA 0.595 0.290 0.620 1.270 14773 1.39 1.13 1.56 2.18 G060x Sarasota Bay EDA 0.293 0.250 0.370 0.510 356 1.22 1.16 1.42 1.66 G050w Charlotte Harbor EDA 0.526 0.350 0.638 1.051 6210 1.58 1.28 1.80 2.55 S190x Indian River/ St. Lucie EDA 0.238 0.207 0.311 0.440 17182 1.45 1.37 1.66 2.03 S183x Daytona St. Augustine CDA 0.117 0.088 0.130 0.190 2190 0.98 0.94 1.22 1.52 S180x St. Johns River EDA/FDA 0.182 0.092 0.171 0.392 41428 1.51 1.22 1.67 2.44 S175x Nassau CDA 0.204 0.130 0.194 0.275 455 1.30 1.21 1.51 1.81 S170x St. Marys River EDA 0.064 0.042 0.060 0.080 530 1.14 1.09 1.35 1.64 G078x Waccasassa CDA 0.074 0.064 0.080 0.100 379 0.90 0.72 0.99 1.44 G076x Withlacoochee River CDA 0.066 0.042 0.077 0.130 2555 1.09 0.97 1.30 1.73 G074x Crystal-Pithlachascotee CDA 0.076 0.031 0.090 0.180 4401 0.77 0.61 1.05 1.49 G050a Caloosahatchee River EDA 0.114 0.080 0.133 0.220 5630 1.05 1.00 1.40 1.71 F100x Kissimmee River/Lake Okeechobee FDA 0.311 0.175 0.440 0.706 19139 1.70 1.53 1.96 2.50 Note: See NWR tables referenced in Appendix B-4 and contained on CD. 
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Table 2-2. Results of Interval and Equivalence Tests Conducted on Reference Sites with 2 SCI 

Results 

Impairment threshold 

(description) 

Ref site 

mean 

Impairment 

threshold 

(numeric) Impaired Undetermined Reference 

2.5th percentile of 

reference 65 40 <35 35–44 >44 

5th percentile of 

reference 65 44 <39 39–47 >47 

Note: Shown are site mean, impairment threshold, and range for threshold values defined at the 2.5th and 5th 

percentile of reference sites (p < 0.05; n = 55 reference sites with two SCI values for each site). Reference site values 

are from Fore et al. (2007) and comprehensively verified nutrient benchmark sites. 

Table 2-3. Average Values for Metrics at an SCI Score Equivalent to BCG Category 2, and 

Average Values for Metrics Near the SCI score of Impairment. 

SCI Metric 

Metric Average at BCG 

2 

Metric Average Near 

Impairment Threshold 

Number of Total Taxa 32.0 28.7 

Number of Clinger Taxa 5.6 3.3 

Number of Long-Lived Taxa 1.5 1.1 

Percent Suspension Feeders and Filterers 22.0 15.8 

Number of Sensitive Taxa 5.4 2.7 

Percent Tanytarsini 13.3 9.5 

Percent Very Tolerant 6.5 14.3 

Number of Ephemeroptera Taxa 3.5 2.3 

Number of Trichoptera Taxa 4.5 2.6 

Percent Dominant 22.6 26.2 

Number of Sites in Average 134 64 

Note: Data were based on FDEP’s data collection effort since 2007 (total n = 696 SCI samples). 

Table 2-4. Distributional Statistics of TN and TP Based on Site Average Values. The Instream 

Protective Criteria are the 75th Percentile Values (in mg/L). Note: The Mean and Standard 

Deviations are in Natural Logarithm Units. 

Bone valley Panhandle Peninsula North central 

TN TP TN TP TN TP TN TP 

SCI=40 

Mean 0.309 -0.752 -0.724 -3.881 -0.240 -2.879 -0.096 -1.847 

Stdev 0.412 0.667 0.788 1.081 0.633 0.957 0.722 1.220 

75th 1.798 0.739 0.824 0.043 1.205 0.107 1.479 0.359 

N 22 22 128 131 82 83 17 17 
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Table 2-5. TP Thresholds for Lakes Used in Vollenweider Model Screening 

Lake Type 

Response (Chl. a

μg/L) 

Proposed TP 

Threshold 

Clear, Low Conductivity 6 0.010 

Clear, High Conductivity 20 0.030 

Colored 20 0.050 

Table 2-6. Summary of TN Load Estimates to Florida Estuaries, Normalized to Estuary Area (g 

N m2

/y; SE: Standard Error) 

Estuary name 

Current TN 

load ± SE 

(g N m2/y) 

Background 

TN load ± SE 

(g N m2/y) 

Protective TN 

load ± SE 

(g N m2/y) 

TN load limit, 

Lm ~f(qs) ± SE 

g N m2/y) 

St. Andrew Bay 2.0±0.3 1.8±0.2 1.9±0.3 5.9±0.57 

Tampa Bay 2.8±0.4 0.6±0.1 1.7±0.3 2.6±0.31 

Indian River 3.0±0.9 0.6±0.2 1.8±0.7 0.7±0.14 

Sarasota Bay 3.3±0.9 0.9±0.2 2.1±0.7 2.5±0.31 

Apalachee Bay 8.1±0.4 4.2±0.8 6.1±0.6 3.5±0.38 

Charlotte Harbor 9.0±2.1 2.2±0.5 5.6±1.5 5.0±0.50 

St. Johns River 9.8±2.9 4.5±1.2 7.2±2.2 8.1±0.77 

Perdido Bay 10.3±2.9 4.4±1.2 7.4±2.2 7.6±0.72 

Pensacola Bay 12.2±3.1 6.9±1.7 9.5±2.5 16±1.8 

St. Marys River 12.3±4.4 8.7±2.6 10.5±3.6 8.4±0.80 

Choctawhatchee Bay 12.5±4.2 5.1±1.7 8.8±3.2 11±1.0 

Apalachicola Bay 27.4±9.7 9.8±3.5 19±7.3 18±2.1 

Suwannee River 45.7±16.3 20±7.1 33±12.6 18±2.0 

Note: Current and background TN load were derived from the USGS regional SPARROW model for the SAGT 

basins. The protective TN load is the average of the current and background TN load, and the standard error was 

computed as the standard error of the average assuming that the errors are independent. The maximum TN load 

limit estimates were derived as a function of hydraulic load, f(qs), from Steward and Lowe (2010). The list excludes 

six CDAs for which estuary area has not been determined. 

Current TN load, and protective TN load are expressed per unit of estuary area. To express the loading in units of 

kilogram per year, multiply the values by estuary area, which are reported in Table 2-8. The resulting quantity is the 

estimated loading from the full watershed. To compute the loading from the portion of the watershed resolved by 

the Sparrow model, multiply the fraction of the watershed resolved in Sparrow (Table 2-9). 
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Table 2-7. Application of Downstream Protection Methodology to Estimate TN Criteria in Stream Reaches that EDownstream Waters 

Stream reach category—fraction TN delivered to estuarine waters 

0.0–0.5 0.501–0.6 0.601–0.7 0.701–0.8 0.801–0.9 0.901–1.0 

Stream reach TN criteria protective of downstream use in estuary 

EDA code Estuary name 

TN 

(mg/L) 

TN 

(mg/L) 

TN 

(mg/L) 

TN 

(mg/L) 

TN 

(mg/L) 

TN 

(mg/L) 

G140x Perdido Bay NR NR NR NR 0.34 0.30 G130x Pensacola Bay NR NR NR NR 0.48 0.43 G120x Choctawhatchee Bay NR NR NR 0.48 0.43 0.39 G110x St. Andrew Baya 0.48 NR NR 0.30 0.27 0.24 

G100x Apalachicola Bay 0.91 NR 0.65 0.57 0.51 0.46 G090x Apalachee Bay NR NR NR 0.67 0.59 0.53 G086x Econfina-Steinhatchee NR NR NR NR 0.41 0.37 G080x Suwannee River NR NR 0.78 0.69 0.61 0.55 G078x Waccasassa NR NR NR NR 0.45 0.40 G076x Withlacoochee Riverc 

G074x Crystal-Pithlachascoteec 

G070x Tampa Bay 1.11 0.93 0.80 0.70 0.62 0.56 G060x Sarasota Bay NR NR NR NR NR 0.54 G050w Charlotte Harbor NR 1.58 1.35 1.18 1.05 0.95 S190x Indian River NR NR NR 0.87 0.77 0.69 S183x Daytona St. Augustinec 

S180x St. Johns River 1.41 1.17 1.00 0.88 0.78 0.70 S175x Nassau 0.59 NR NR NR 0.33 0.30 

S170x St. Marys Rivera NR NR NR 0.43 0.38 0.34 

NR = there are no stream reaches in the category within the estuarine watershed. a. Waste water treatment plant (WWTP) TN loads directly to estuarine waters contribute a significant fraction of the TN load to these estdesignated use in these estuaries might require reduction of these point source loads, in addition achieving TN criteria for downstream u

b. Terminal reaches are those reaches discharging directly to estuarine waters. These stream reaches, by definition, have fraction TN dec. Protective loads and flows necessary to compute downstream protection values require further evaluation in these systems. 

Note: TN criteria or the downstream protection values are presented in stream categories defined on the basis of the fraction of TN trans

ultimately delivered to estuarine waters.
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Table 2-8. Characteristics of Florida Estuaries and their Watersheds as Delineated by the NOAA CAF 

EDA/CDA code Estuary name 

Estuary 

area 

(km2) 

Watershed 

area 

(km2)

Ratio of 

watershed to 

estuary area 

Estuary 

volume (1,000 

x m3) 

Mean 

depth 

(m) 

# S

rea

G140x Perdido Bay 129 2,928 23 197,900 1.53 2G130x Pensacola Bay 477 17,650 37 1,439,402 3.02 19G120x Choctawhatchee Bay 340 13,496 40 1,291,654 3.80 17G110x St. Andrew Bay 252 2,697 11 712,299 2.83 G100x Apalachicola Bay 593 52,215 88 1,073,374 1.81 47G090x Apalachee Bay 427 14,288 33 819,840 1.92 8G086x Econfina-Steinhatchee nd 2,047 nd nd nd G080x Suwannee River 164 25,989 159 191,637 1.17 15G078x Waccasassa nd 2,128 nd nd nd G076x Withlacoochee River nd 5,133 nd nd nd G074x Crystal-Pithlachascotee nd 2,993 nd nd nd G070x Tampa Bay 902 5,703 6 2,705,905 3.00 6G060x Sarasota Bay 124 653 5 271,371 2.19 G050w Charlotte Harbor 502 8,134 16 819,015 1.63 7S190x Indian River 866 3,093 4 667,081 0.77 S183x Daytona St. Augustine nd 1,882 nd nd nd S180x St. Johns River 684 23,214 34 1,510,763 2.21 10S175x Nassau nd 1,085 nd nd nd S170x St. Marys River 64 4,386 69 213,760 3.34 2

Note: nd = not determined 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

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Table 2-9. Average TN Loads to Florida Estuaries Normalized to 2002 Estuary (EDA code) 

Watershed 

area 

(km2) 

Fraction of 

watershed 

resolved in 

SPARROWa

Watershed 

TN loadb

(kg/y) 

TN yield 

(kg/km2/y) 

Point 

Source 

TN Loadc

(kg/y) 

Mean 

Flowd

(m3/s) 

Perdido Bay (G140x) 2,928 89% 1,335,499 456 0 89 Pensacola Bay (G130x) 17,650 97% 5,796,330 328 161,685 321 Choctawhatchee Bay (G120x) 13,496 96% 4,234,121 314 0 236 St. Andrew Bay (G110x) 2,697 65% 514,281 191 254,540 41 Apalachicola Bay (G100x) 52,215 99% 16,257,090 311 1,412 764 

Apalachee Bay (G090x) 14,288 97% 3,451,330 242 0 151 

Econfina-Steinhatchee (G086x) 2,047 52% 372,346 182 0 16 Suwannee River (G080x) 25,989 101% 7,484,483 288 0 313 

Waccasassa (G078x) 2,128 92% 587,731 276 0 34 

Withlacoochee River (G076x) 5,133 109% 438,142 855 0 10 

Crystal-Pithlachascotee (G074x) 2,993 28% 1,171,542 391 20,489 50 Tampa Bay (G070x) 5,703 84% 2,541,245 446 55,831 73 Sarasota Bay (G060x) 653 60% 408,248 625 2,321 9 Charlotte Harbor (G050w) 8,134 96% 4,522,351 556 1,998 91 Indian River (S190x) 3,093 30% 2,560,222 828 18,360 21 Daytona St. Augustine (S183x) 1,882 95% 660,380 351 45,856 12 St. Johns River (S180x) 23,214 101% 6,726,409 290 609,935 223 Nassau (S175(x) 1,085 77% 203,714 188 267 14 St. Marys River (S170x) 4,386 84% 785,284 179 134,053 52 

a. Fraction of watershed area computed from NOAA coastal assessment framework resolved within SPARROW model. Values greater tdifferences in delineation of watershed boundaries. b. Computed from load delivered to estuarine water estimated in SPARROW divided by the fraction of the watershed resolved by SPARRyield of nitrogen per unit of watershed area is the same in the fractions resolved and not resolved by SPARROW. c. Includes only point sources discharging directly to estuarine waters. d. Estimate includes only the mean discharge within stream reaches resolved by stream network used in SPARROW. e. Estimates of average runoff or TN yield appear to be lower or higher than the expected range and warrant further evaluation. Streamflestimate of average TN concentration Note: Computed Using Estimates from the SATG regional SPARROW model. Mean flow includes only the flow in streams resolved by Sscaled to reflect diffuse freshwater inputs from other portions of the watershed.
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Table 2-10. Information on Downstream River Gage Stations in Florida Estuarine Watersheds EDA code Estuary name Station ID Station name Gage 

Data 

years 

Mean T

load 

(kg/y) 

G140x Perdido Bay 21FLBFA.33010004 Perdido River at Hwy 90 Bridge STORET 1992-2004 560,68G130x Pensacola Bay 21FLBFA.33030005 

Big Coldwater Creek Hwy 191 NE Milton STORET 

1991, 1998, 2001-2004 376,74

G130x Pensacola Bay 21FLGW.3545 

Blackwater River at Hwy 4 NW of Baker STORET 1999-2004 110,12

G130x Pensacola Bay 21FLBFA.33020019 Escambia River Hwy 90 Bridge STORET 1994-2004 3,055,93G130x Pensacola Bay 21FLBFA.33040004 

Yellow River Hwy 90 West of Crestview STORET 1990-2004 441,77

G120x 

Choctawhatchee Bay 21FLGW.3536 Alaqua Creek at Nelson Road STORET 1999-2004 26,53

G120x 

Choctawhatchee Bay 21FLGW.3534 Choctawhatchee River near Bruce STORET 1999-2004 3,124,14

G110x St. Andrew Bay 21FLGW.3531 Econfina Creek at C.R. 388 STORET 1999-2004 107,94G100x Apalachicola Bay 21FLGW.3524 

Apalachicola River, Sumatra, Brickyard Landing STORET 1999-2004 12,105,37

G090x Apalachee Bay 2327100 Sopchoppy River near Sopchoppy, FL NWIS 1975-1997 125,76G090x Apalachee Bay 21FLA.22050023 Econfina River at U.S. 98 STORET 1976-2000 122,20G090x Apalachee Bay 21FLGW.3527 Ochlockonee River at F.R. 13 STORET 1999-2004 1,270,71G090x Apalachee Bay 21FLGW.3528 St. Marks River at Hwy. 98 Newport STORET 1999-2004 195,96G086x 

EconfinaSteinhatchee 02324000 Steinhatchee River near Cross City, FL NWIS 1976-1995 177,86

G080x Suwannee River 21FLSUW.SUW275C1 Suwannee River at Gopher River STORET 1990-2004 9,263,63G076x 

Withlacoochee River 21FLGW .3513 Withlacoochee River at Stokes Ferry STORET 1999-2004 837,50

G074x 

CrystalPithlachascotee 02310300 Pithlachascotee River New Port Richey, FL NWIS 1975-2004 21,22
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EDA code Estuary name Station ID Station name Gage 

Data 

years 

Mean T

load 

(kg/y) 

G074x 

CrystalPithlachascotee 02310000 Anclote River near Elfers, FL NWIS 1975-2000 63,40

G070x Tampa Bay 2299950 Manatee River near Myakka Head, FL NWIS 1975-2004 127,90G070x Tampa Bay 2300500 

Little Manatee River near Wimauma, FL NWIS 1975-2000 382,45

G070x Tampa Bay 2300700 Bullfrog Creek near Wimauma, FL NWIS 1989-2004 56,01G070x Tampa Bay 21FLHILL.138 Delaney Creek at 36th Ave & 54th St. STORET 2000-2004 11,57G070x Tampa Bay 21FLHILL.137 

Hillsborough River at Columbus Drive Bridge STORET 2000-2003 300,78

G070x Tampa Bay 21FLIMCA.LM674 Little Manatee River at SR 674 STORET 1996-2002 47,62G050w Charlotte Harbor 2298202 Shell Creek near Punta Gorda, FL NWIS 

1983-1987, 1995-2000 434,99

G050w Charlotte Harbor 2297100 Joshua Creek at Nocatee, FL NWIS 1983-2000 419,93G050w Charlotte Harbor 21FLSWFD.FLO 27 59 0 Horse Creek near Arcadia STORET 1998-2003 511,03G050w Charlotte Harbor 21FLGW.3499 Myakka River - Snook Haven Dock STORET 1999-2004 560,89G050w Charlotte Harbor 21FLGW .3556 Peace River at Arcadia STORET 1999-2004 1,813,24S180x St. Johns River 21FLA.20020070 Oklawaha River at SR 19 STORET 1978-2000 783,76S180x St. Johns River 21FLVEMD.SJ28 St. Johns River at CM 19 STORET 

1993, 1995 5,411,30

S170x St. Marys River 21FLSJWM.44731 MPS-Middle Prong St. Marys at Hwy 127 STORET 1998-2001 141,03Note: Includes the period of record and the average and standard deviation of the load over the period of record. The CV (%) is the coeff(standard deviation/mean), which provides an estimate of the interannual variability in TN load at each gage. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

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Table 2-11. Selected Results for Florida Estuaries from NEEA (Bricker et al. 2007) 

Estuary name 

Influencing 

factor (level of 

nutrient 

influence, 

considering 

susceptibility) 

Eutrophic 

conditions 

Future outlook 

(risk of 

worsening 

symptoms) 

ASSETS rating 

(overall 

assessment of 

estuarine trophic 

status) 

St. Marys River High Moderate/Low High Unknown 

St. Johns River High High Moderate/Low Bad 

Indian River High Moderate High Poor 

Biscayne Bay Low Moderate/Low Moderate Good 

Florida Bay Unknown Moderate Unknown Unknown 

South Ten 

Thousand Islands 

Unknown Unknown Unknown Unknown 

North Ten 

Thousand Islands 

Unknown Moderate/High Unknown Unknown 

Rookery Bay Unknown Moderate/High Unknown Unknown 

Caloosahatchee 

River 

High Moderate High Poor 

Charlotte Harbor High Moderate High Poor 

Sarasota Bay Unknown Moderate/High High Unknown 

Tampa Bay High Moderate/High Moderate Poor 

Suwannee River High Low High Unknown 

Apalachee Bay Moderate Unknown Unknown Unknown 

Apalachicola Bay High Moderate High 

St. Andrew Bay Unknown Unknown Unknown Unknown 

Choctawhatchee 

Bay 

Unknown Low High Unknown 

Pensacola Bay* Low Moderate/Low Moderate Good 

Perdido Bay Unknown High High Unknown 

Note: Estuaries are listed clockwise around the Florida coastline, starting in northeast Florida. Results for Influencing 

Factor, Eutrophic Condition, and Future Outlook are (worst to best) Unknown/Low, Moderate/Low, Moderate, 

Moderate/High, or High. Results for ASSETS rating are (worst to best) Unknown, High, Good, Moderate, Poor or 

Bad. 

* the authors of this study do not agree with the NEEA results for Pensacola Bay. Results shown here, however, are 

as published by Bricker et al. 2007. 
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Table 2-12. Summary of Nutrient-related Listings for Estuarine Waters on Florida’s 1998 303(d) 

List of Impaired Waters 

Estuary name Listed WBIDs 

303(d) list parameters of 

concern IWR 34 verified list 

St. Marys River 2097A, 2097B, 

2124 

Nutrients no impairments. ID for 

historical chl. a. 

St. Johns River 2213E Chl. a

Indian River 2963A-D, 3082, 

3085A, 3098, 

3107, 3129A, 

3135, 5003C-D 

DO, Nutrients Chl. a, DO 

Biscayne Bay 3274 (extreme 

northern Bay) 

not listed Chl. a, Historical Chl. a

Florida Bay no listings 

South Ten 

Thousand Islands 

no listings 

North Ten 

Thousand Islands 

no listings 

Rookery Bay 3278U not listed Chl. a

Caloosahatchee 

River 

3240I, 3240B, 

3240A4 

DO, Nutrients Chl. a, DO 

Charlotte Harbor 2065A, 2056A-C, 

2041A, 2055, 

1991A 

no listings Chl. a

Sarasota Bay 1931, 1936, 1947, 

1951, 1953, 

1968B-E, 1982A, 

1984A, 2009A 

Nutrients, DO (Bayous) Chl. a (creeks, embayments) 

Tampa Bay 63 WBIDs on 1998 

list 

DO, Nutrients Chl. a, DO (creeks and 

embayments only) 

Suwannee River 3422D no listings chl. a, Historical Chl. a

Apalachee Bay 3473A 

(Fenholloway R) 

DO, Nutrients DO 

Apalachicola Bay 1274B, Nutrients Chl. a

St. Andrew Bay 

St. Andrew, 

continued 

1267, 1172, 1170, 

1144, 1141, 1136, 

1131, 1128, 1123, 

1110, 1088, 1053 

(Mostly Bayous) 

DO, Nutrients DO, insufficient data 

reported for Chl. a 

Choctawhatchee 

Bay 

917, 906, 778C-D DO, Nutrients DO 

Pensacola Bay* 846, 548A-C, E, H 

539, 493, 

DO, Nutrients DO, Historical Chl. a

(Escambia Bay) 

Perdido Bay 797, 462A DO, Nutrients no listings 

Note: Also indicated is the status on Florida’s 303(d) verified list of impaired waters under the IWR as shown from the 

IWR Run 34 database. 

* the authors of this study do not agree with the NEEA results for Pensacola Bay. 

ID = insufficient data, DO = Dissolved Oxygen 
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Table 2-13. Seasonal 90th Percentile (P90) and Geometric Mean Surface (depth < 1.5 m) Chl. 

a in Florida Estuar

Spring Summer Fall Winter 

Estuary name N P90 

Geo 

mean N P90 

Geo 

mean

Rank (P90) N P90 

Geo 

mean N P90 

m

St. Marys River* 6 1.00 1.00 3 6.00 2.04 17 4 1.00 1.00 3 1.00 St. Johns River 832 11.57 5.40 998 24.64 10.53 3 905 18.14 5.95 760 10.37 

Indian River 3,954 9.08 3.46 3,433 16.32 5.43 7 3,491 23.50 8.16 3,993 13.40 Biscayne Bay 764 1.10 1.05 795 1.42 1.11 18 847 1.41 1.11 678 1.00 

Florida Bay 121 1.00 1.02 152 1.40 1.09 19 177 2.00 1.16 140 1.00 S. Ten Thousand Is 312 7.30 2.70 320 7.35 2.64 14 320 6.25 2.91 334 8.70 N. Ten Thousand Is. 124 4.70 2.37 141 7.10 3.62 15 136 7.80 3.78 136 5.10 Rookery Bay 48 8.00 3.03 47 8.90 4.16 12 47 11.70 4.48 49 8.50 Charlotte Harbor 203 41.00 7.08 194 68.10 9.32 1 197 15.60 4.63 152 15.40 

Caloosahatchee River 

1,202 10.70 3.20 1,101 17.50 5.29 6 1,198 17.40 5.54 1,126 12.00 

Sarasota Bay 644 7.00 2.85 637 12.57 5.42 8 631 14.67 5.24 638 6.30 

Tampa Bay 2,077 18.70 6.51 1,897 31.08 11.64 2 1,981 31.90 10.53 2,260 14.43 

Suwannee River 101 10.20 2.29 141 18.70 5.13 5 118 18.20 3.16 133 5.87 Apalachee Bay* 4 11.00 7.21 10 11.50 4.42 11 16 13.00 2.84 Apalachicola Bay 342 14.70 3.02 349 12.20 3.76 10 403 10.20 3.18 334 13.70 St. Andrew Bay 419 5.00 3.26 470 8.00 3.78 13 376 8.00 3.98 401 5.00 

Choctawhatchee Bay 215 5.00 3.27 237 6.48 3.49 16 170 5.00 3.39 183 5.00 Pensacola Bay 338 8.70 4.59 362 12.25 5.35 9 309 9.20 4.70 244 8.14 Perdido Bay 40 16.35 6.16 15 21.90 10.74 4 21 18.90 7.12 9 25.80 

Note: Computed from data in FDEP’s IWR Run 34 database. Data were used only if the station location is at least 250 m from land. Estu

clockwise around the coast from northeast Florida to the panhandle region. Annual averages are the average of either the seasonal P90 geometric mean. Estuary rank for summer chl. a and annual mean chl. a are based on the P90 values, which are presumed to reflect the they occur. * Statistics for these estuaries might be particularly unreliable because of the small number of measurements.
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Table 2-14. FDEP TMDLs, Marine WBIDs Considered, and the Date Completed or Drafted 

Final WBID TMDL 

Date 

issued Prepared by 

Tampa Bay 1558 C,D,F,G Nitrogen 6/18/98 

 McKay Bay 1584 Nutrients, DO 9/15/04 FDEP 

 Lower Sweetwater 1570 Nutrients 9/15/04 FDEP 

Sarasota Bay/Charlotte 

Harbor 

1975,1982,1984,1 

984A, 2009A, 2030, 

2039, 2042, 2049 

Nutrients 

DO 

Coliforms 

3/2006 EPA Region 4 

Lower St. Johns River 2213 A-F Nutrients 1/2008 EPA Region 4 

 Lower St.Johns-Moncrief 2228 Nutrients 3/2006 EPA Region 4 

Indian River 2963A,B,C,D, 3082, 

3057A,B, 3085, 

5003, 3098, 3129, 

3135 

DO 

Nutrients 

4/1/07 EPA Region 4 

Hendry Creek, SW Coast 3258B1 DO 8/19/08 FDEP 

Fenholloway-Econfina 3473 A, B Nutrients 2/19/09 EPA Region 4 

Wagner Creek-Biscayne 3288A Nutrients, DO 3/29/07 EPA Region 4 

St. Lucie-Loxahatchee 3193, 3194, 3194B, 

3197, 3200, 3210, 

3210A, 3211, 3218 

Nutrients, DO 10/2008 FDEP 

DRAFT 

Stevenson Creek 1567 DO, Nutrients 10/29/08 FDEP 

Klosterman Bayou, Springs 

Coast Basin 

1508 Nutrients, DO, BOD 10/08 EPA Region 4 

Roberts Bay-Sarasota 1968D Nutrients 10/05 FDEP 

Spruce Creek-Smyrna 2674A DO, Nutrients 4/2008 FDEP 

Caloosahatchee Estuary 3240 A,B,C Nutrients 2/13/2009 FDEP 

Billy Creek-Caloosahatchee 3240J Nutrients, BOD, DO 3/2006 EPA Region 4 

PROPOSED 

Escambia Bay 548 Nutrients 9/2007 EPA Region 4 

EXPECTED 

Palm River, Tampa Bay 1536E Nutrients, DO Exp 2009 FDEP 

Lake tarpon Canal 1541A Nutrients, DO Exp 2009 FDEP 

Horseshoe Creek 1272 DO Exp 2009 FDEP 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

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Table 2-15. TMDL Thresholds, Chl. a, BOD, Nutrient Loads and Percent Reduction 

Final TMDLs 

TMDL 

endpoint 

chl. a (g/L) 

and/or BOD % 

reduction 

TMDL endpoint 

nutrient load 

TMDL endpoint 

% reduction 

Tampa Bay 

 Tampa Bay Upper 8.5 797 T/Yr * 

 Tampa Bay Hillsborough 15.0 1443 T/Yr * 

 Old Tampa Bay (Lower) 5.1 344 T/Yr * 

 Old Tampa Bay (Mid) 9.3 486 T/Yr * 

 McKay Bay 8.4 542,602 lb/yr (246,120 kg/yr) 5.7 

 Lower Sweetwater 11 57,565 lb/yr (26,111 kg/yr) 20 

Hendry Creek, SW Coast 0.60 mg/ L 44 

Sarasota Bay 

 Elligraw 1,799 kg/yr 28.8 

 South Creek 941 kg/yr 48.2 

 Catfish Creek 1,110 kg/yr 51.3 

 North Creek 590 kg/yr 47.1 

 Curry Creek 483 kg/yr 62.9 

Lemon Bay 

 Alligator creek 3,857 kg/yr 28.2 

 Forked Creek 3,387 kg/yr 20 

 Direct Runoff to Bay 640 kg/yr 54.7 

 Gottfried Creek 2,966 kg/yr 2 

Lower St. Johns (marine) 1,376,855 kg/yr 

 Lower St.Johns-Moncrief 0.86 mg/L 14 

Indian River 2.18–3.34 lb/ac/yr 30 

 Indian River, North 389,906 lb/yr 34 

 Banana River lagoon 112,029 lb/yr 63 

 Central Indian River 821,282 lb/yr 55 

Fenholloway-Econfina 5 8920 lb/d 24 

St. Lucie Estuary 0.72 mg/L 21.4 

 N Fork St. Lucie River BOD = 74% 140,134 lb 25 

 N Fork St. Lucie Estuary 103,747 lb 28.8 

 C-24 canal BOD = 33% 348,957 lb 51.8 

 C-23 canal 242,202 lb 51.7 

 S Fork St. Lucie Estuary 24,463 lb 38.4 

 S Fork, St. Lucie River 90,471lb 47.1 

 Bessey Creek 29,981lb 23.9 

 C-44 Canal BOD = 69.7% 242,929 lb 51.2 

Wagner Creek-Biscayne BOD = 54% 31 

DRAFT 

Stevenson Creek 8 39,915 lb/yr 85 (stormwtr) 

Klosterman Bayou, Springs 

Coast Basin 

8 0.53 mg/L 69 
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Final TMDLs 

TMDL 

endpoint 

chl. a (g/L) 

and/or BOD % 

reduction 

TMDL endpoint 

nutrient load 

TMDL endpoint 

% reduction 

Roberts Bay-Sarasota 7.2 679,090 lb/yr 0.6 

Spruce Creek BOD = 25% TP = 27 

Caloosahatchee Estuary 8,986,772 lb/yr 23 

Billy Creek-Caloosahatchee BOD = 29% 

PROPOSED 

Escambia Bay 29 

EXPECTED 

Palm River, Tampa Bay 

Lake tarpon Canal 

Horseshoe Creek 

* Percent TN reduction required in the 1998 TMDL has currently been met 

Table 2-16. Regional TP and TN Criteria for Florida Streams 

Stream Benchmark Distribution Derived Criteria Limits 

Parameter Region WBID Years WBIDS Mean LN Std Dev LN 75th %tile 90th %tile 

TP (mg/L) Panhandle 125 44 -3.662 0.768 0.043 0.069 

 Northeast 54 20 -3.205 0.714 0.066 0.101 

 North Central 180 18 -1.973 0.656 0.216 0.322 

 Peninsula 193 42 -2.746 0.464 0.088 0.116 

 Bone Valley 13 3 -1.211 0.491 0.415 0.559 

TN (mg/L) Panhandle 123 42 -0.76 0.45 0.63 0.82 

 NE-NC-Pen-BV 400 85 -0.03 0.45 1.31 1.73 

TIN (mg/L) Panhandle 120 44 -2.25 0.79 0.18 0.29 

 NE-NC-Pen-BV 379 84 -2.54 0.72 0.13 0.20 

Note: FDEP recommends that stream nutrient criteria be established as the 90th percentile values, with the exception 

of TP in the Bone Valley, where the 75th percentile is more defensible (due to small sample size). These final values 

are the result of all the various verification and exclusion steps mentioned above. TIN values are shown for 

informational purposes only since a nitrate-nitrite criterion has been established for clear streams based on algal 

responses to nitrate-nitrite. 
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Table 2-17. Parameters Monitored During the Benchmark Stream Survey 

Biological Parameters Chemical and Physical Parameters 

Stream Condition Index (SCI) Total Phosphorus 

Rapid Periphyton Assessment Nitrite + Nitrate 

Total Kjeldahl Nitrogen 

Ammonia Qualitative Periphyton Sampling 

(i.e., periphyton taxonomy) Color 

Habitat Assessment Turbidity 

Chlorophyll a Total Suspended Solids 

Phaeophytin Total Organic Carbon 

Hydrologic Modification Scoring Specific Conductance (in situ) 

Linear Vegetation Survey Dissolved Oxygen (in situ) 

Percent Canopy Cover pH (in situ) 

Water Temperature (in situ) 
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Table 2-18. Summary of Information Collected from Field-verified Nutrient Benchmark Sites 
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Table 2-19. Summary of the Regional Data Outlier Analysis and Exclusions for the Florida 

Stream Nutrient Benchmark Data Set 

TP (mg/L) 

Parameter Region N 

25th

Percentile

75th

Percentile 

Outlier 

Limit 

Percent 

Outlier 

Panhandle 1507 0.016 0.050 0.152 2.6 

Northeast 761 0.026 0.096 0.306 1.8 

North Central 2134 0.088 0.280 0.856 5.0 

Peninsula 2421 0.045 0.101 0.269 1.6 

TP 

Bone Valley 176 0.192 0.544 1.60 1.1 

TN (mg/L) Panhandle 1287 0.35 0.70 1.75 2.4 

TN Northeast, North Central, 

Peninsula, and Bone 

Valley 

4645 0.71 1.41 3.49 0.8 
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Alabama

Georgia

Gulf of Mexico

Atlantic

Ocean

Southern Pine Plains and Hills (656)

Dougherty/Marianna Plains (657)

Tifton Upland/Tallahassee Hills (658)

Gulf Coast Flatwoods (751)

Southwestern Florida Flatwoods (752)

Central Florida Ridges and Uplands (753)

Eastern Florida Flatwoods (754)

Okefenokee Swamps and Plains (755)

Sea Island Flatwoods (756)

Everglades (761)

Big Cypress (762)

Miami Ridge/Atomic Coastal Strip (763)

Southern Coast and Islands (764)

Level IV Subecoregions for Florida

Small / Wadeable Streams

N

0 50 100 150 200 Miles

Map prepared April 18, 2002 by: the GIS subsection

Bureau of Watershed Management

Division of Water Resource Management

This map is a representation of ground condition and is

Not intended for delineations or analysis of the features shown.

For more information, contact devan.branscum@dep.state.fl.us

Griffith 1994

Figure 2-1. Level IV subecoregions for Florida’s small/wadeable streams. 
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Figure 2-2. Stream bioregions of Florida. 
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Figure 2-3. Location of the Peace River and Bone Valley Member geologic formations. 
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TN (mg L -1)

0.0

0.5

1.0

1.5

2.0

2.5

6.0

8.0

10.0

TP (mg L -1)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

3.0

4.0

5.0

Perdido Bay

Pensacola Bay

Choctawhatchee Bay

St. Andrew Bay

Apalachicola Bay

Apalachee Bay

Econfina-Steinhatchee

Suwannee River

Tampa Bay

Sarasota Bay

Charlotte Harbor

Indian River

Daytona St. Augustine

St. Johns River

Nassau

St. Marys River

Waccasassa

Withlacoochee River

Crystal-Pithlachascotee

Caloosahatchee River

Lake Okeechobee

TN:TP ratio (mol mol -1)

0

50

100

150

200

Figure 2-4. Box plots of stream TN, TP, and TN:TP molar ratio in Florida watersheds. 
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Figure 2-5. NWRs in Florida. 
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Figure 2-6. Typical macroinvertebrate response to organic loading associated with primary 

wastewater treatment typical in the 1950s and 1960s. 

Figure 2-7. Photo of Hester-Dendy samplers used for determining the Shannon-Weaver diversity 

index. 
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Figure 2-8. Factors affecting biological community composition. To conclude that human factors 

are primarily responsible for biological degradation, reasonable knowledge of the influence of 

natural factors is essential. 

Figure 2-9. Sub-ecoregions of Florida, which were aggregated into three bioregions, based on 

multivariate measures of taxonomic similarity. 
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Chemistry, habitat, and/or flow 

regime severely altered from 

natural conditions.

Watershed, habitat, flow 

regime and water 

chemistry as naturally 

Structure & function similar to 

natural community with some 

additional taxa & biomass; 

ecosystem level functions are 

fully maintained.

Evident changes in structure 

due to loss of some rare native 

taxa; shifts in relative 

abundance; ecosystem level 

functions fully maintained.

Moderate changes in structure 

due to replacement of sensitive 

ubiquitous taxa by more 

tolerant taxa; ecosystem 

functions largely maintained.

Sensitive taxa markedly 

diminished; conspicuously 

unbalanced distribution 

of major taxonomic groups; 

ecosystem function shows 

reduced complexity & 

redundancy.

Extreme changes in structure 

and ecosystem function; 

wholesale changes in 

taxonomic composition; 

extreme alterations from 

normal densities.

Natural structural, functional, 

and taxonomic integrity is 

preserved.

5

6

4

3

2

1

Biological Condition

Level of Exposure to Stressors 

Figure 2-10. The BCG model (from Davies and Jackson 2006). 

Figure 2-11. Regression line with 90% confidence interval showing the relationship between the 

mean BCG score and SCI score. 
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Figure 2-12. BCG tier assignments modeled with a proportional odds logistic regression. 
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Figure 2-13. Plot of TP concentration of grab samples taken at all sites sampled for biological 

condition (SCI sites). Concentrations are in milligrams per liter. 
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Figure 2-14. Plot of TP concentration of grab samples taken from sites sampled for biological 

condition (SCI sites) that met the SCI threshold of 40. Map symbols and units as in Figure 2-13. 

Also shown are histograms of TP concentrations and cumulative (hatched line) and empirical 

(solid line) percent frequencies of TP concentrations in biologically healthy sites by nutrient 

region. 
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Figure 2-15. Plot of TN concentration of grab samples taken at all sites sampled for biological 

condition (SCI sites). Concentrations are in milligrams per liter. 
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Figure 2-16. Plot of TN concentration of grab samples taken from sites sampled for biological 

condition (SCI sites) that met the SCI threshold of 40. Map symbols as in Figure 2-15. Also 

shown are histograms of TN concentrations and cumulative (hatched line) and empirical (solid 

line) percent frequencies of TN concentration in biologically healthy sites by nutrient region. 
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Figure 2-17. Critical stream TP concentration for inflows into clear, low-conductivity lakes to 

meet lake criterion of 0.010 mg/L TP. Area below the curve results in lake TP below the lake 

criterion. Streamflow is 100% of inflow into lake. Dashed line: Panhandle 75th percentile 

proposed instream protective value. For residence times less than 11 years, stream TP 

concentration of 0.042 mg/L will result in lake TP > 0.010 mg/L (shaded area). 

Clear Lakes, Low Conductivity

 100% lake inflow

 50% lake inflow

 10% lake inflow

0 2 4 6 8 10 12 14 16 18 20

Water Residence Time (years)

0.0

0.1

0.2

0.3

0.4

0.5

Critical Stream TP (mg/L)

Peninsula

Bone Valley

Panhandle

0.775 mg/L

Figure 2-18. Critical stream TP concentrations for stream inflows into clear, low-conductivity 

lakes. Blue (lower curve): streamflow is 100% of lake water input; this is the same curve shown 

in Figure 2-17. Red (middle curve): streamflow is 50% of lake water input. Green (upper curve): 

streamflow is 10% of lake water input. Horizontal dashed lines: 75th percentile proposed 

instream protective values for TP regions. 
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Clear Lakes, High Conductivity

 100% Lake Inflow

 50% Lake Inflow

 10% Lake Inflow 0 2 4 6 8 10 12 14 16 18 20

Water Residence Time (years)

0.0

0.1

0.2

0.3

0.4

0.5

Critical Stream TP

Peninsula

Bone Valley

Panhandle

0.775 mg/L

Figure 2-19. Critical stream TP concentrations for stream inflows into clear, high-conductivity 

lakes. Blue (lower curve): streamflow is 100% of lake water input. Red (middle curve): streamflow 

is 50% of lake water input. Green (upper curve): streamflow is 10% of lake water input. Horizontal 

dashed lines: 75th percentile proposed instream protective values for TP regions. 

Colored Lakes

 100% Lake Inflow

 50% Lake Inflow

 10% Lake Inflow

-2 0 2 4 6 8 10 12 14 16 18 20 22

Water Residence Time (years)

0.0

0.2

0.4

0.6

0.8

1.0

Critical Stream TP (mg/L)

Panhandle

Peninsula

Bone Valley

Figure 2-20. Critical stream TP concentrations for stream inflows into colored lakes. Blue (lower 

curve): streamflow is 100% of lake water input. Red (middle curve): streamflow is 50% of lake 

water input. Green (upper curve): streamflow is 10% of lake water input. Horizontal dashed 

lines: 75th percentile proposed instream protective values for TP regions. 
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10000

100000

1000000

10000000

100000000

Nassau

Econfina-Steinhatchee

Sarasota Bay

Crystal-Pithlachascotee

St. Andrew Bay

Withlacoochee River

Waccasassa

Daytona St. Augustine

St. Marys River

Indian River

Perdido Bay

Tampa Bay

Apalachee Bay

Choctawhatchee Bay

Charlotte Harbor

Pensacola Bay

St. Johns River

Suwannee River

Apalachicola Bay

TN Load Estimate (kg/y)

Current Load

Protective Load

Background Load

Figure 2-21. Summary of TN load estimates from watersheds to estuaries in Florida (kg/y). 

Current TN load and the background TN load were derived from the USGS regional SPARROW 

model for the SAGT basins. Protective loads were computed by reducing the anthropogenic 

contribution of the current load by 50%. 
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Figure 2-22. Map showing the spatial distribution of the fraction of TN delivered to estuarine 

waters in watersheds of Florida’s estuaries. The map is based on a spatial join of incremental 

watersheds within the ERF1 stream network and Florida’s waterbodies (WBIDs). 
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Figure 2-23. Map showing Florida estuaries and their watersheds as delineated by the NOAA 

CAF. 
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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Perdido Bay

Pensacola Bay

Choctawhatchee Bay

St. Andrew Bay

Apalachicola Bay

Apalachee Bay

Suwannee River

Tampa Bay

Sarasota Bay

Charlotte Harbor

Indian River

St. Johns River

St. Marys River

Landuse (% of total watershed area)

Urban Agriculture Forest Wetland Range

Figure 2-24. Land use characteristics of Florida estuarine watersheds as reported by NEEA 

(Bricker et al. 2007). 

0%

25%

50%

75%

100%

Perdido Bay

Pensacola Bay

Choctawhatchee Bay

St. Andrew Bay

Apalachicola Bay

Apalachee Bay

Econfina-Steinhatchee

Suwannee River

Waccasassa

Withlacoochee River

Crystal-Pithlachascotee

Tampa Bay

Sarasota Bay

Charlotte Harbor

Indian River

Daytona St. Augustine

St. Johns River

Nassau

St. Marys River

% Other TN Source Loads

Point+WWTP Fertilizer Manure Urban

Figure 2-25. Fraction of delivered anthropogenic TN load (i.e., TN loading delivered to estuaries 

attributed to point sources, fertilizer, manure, and urban land use) resulting from each of those 

sources. Point source loads include those discharging directly to estuaries. Data are from the 

SAGT regional SPARROW model (Hoos and McMahon 2009) and McMahon et al. (2007). 
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0

1

2

3

4

5

6

7

8

1983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007

Year

Atm N Dep (kg/ha)

Quincy, FL Sumatra, FL Kennedy Space Center, FL

Figure 2-26. Time series of atmospheric wet deposition of nitrogen at three NADP monitoring 

sites in Florida. 

0

50

100

150

200

250

Perdido Bay

Pensacola Bay

Choctawhatchee Bay

St. Andrew Bay

Apalachicola Bay

Apalachee Bay

Econfina-Steinhatchee

Suwannee River

Waccasassa

Withlacoochee River

Crystal-Pithlachascotee

Tampa Bay

Sarasota Bay

Charlotte Harbor

Indian River

Daytona St. Augustine

St. Johns River

Nassau

St. Marys River

TN Yield from

Atmospheric Sources (kg/km2/y)

0.00

0.25

0.50

0.75

1.00

Fraction of Atmospheric

to Total N Load

TN Yield

Fraction

Figure 2-27. TN yield (i.e., per km2

 of watershed area) from atmospheric sources (left axis) and 

the fraction of loads from all sources contributing to watershed loading contributed by 

atmospheric deposition (right axis). All data were derived from the SAGT regional SPARROW 

watershed model (Hoos and McMahon 2009). 
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0%

25%

50%

75%

100%

Perdido Bay

Pensacola Bay

Choctawhatchee Bay

St. Andrew Bay

Apalachicola Bay

Apalachee Bay

Suwannee River

Tampa Bay

Sarasota Bay

Charlotte Harbor

Indian River

St. Johns River

St. Marys River

Percent Watershed Area

Gaged and Ungaged

Gaged Ungaged

Figure 2-28. Area of estuarine watershed area (%) accounted for and that unaccounted for by 

river gage stations in Florida estuarine watershed. 

Figure 2-29. LDI results for 5570 stations by sub-ecoregion for initial candidate reference site 

evaluation. Sites scoring above 2 on the LDI were eliminated from further consideration (except 

for a single site in the Bone Valley Region that, due to the sparseness of reference sites, was 

accepted at an LDI of 2.2). 

Minimally 

Disturbed

LDI ≤ 2

Sub-ecoregion

65f 65g 65h 75a 75b 75c 75d 75e 75f 76a 76b 76c

LDI

1

2

3

4

5

6

7

8

9

10
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Figure 2-30. Relationship between nutrient loading (nitrogen in panel (a), phosphorus in panel 

(b)) and the LDI in the St. Marks watershed, Florida (after Brown and Vivas 2003). The arrow is 

the LDI value at 2.0, the benchmark site threshold. 
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Figure 2-31. Depiction of land use area (light yellow) included in an LDI calculation. 

Figure 2-32. Relationship between the LDI and Stream Condition Index across the entire range 

of LDI. 
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y = 4.4938x + 60.839

R2 = 0.0079

0

10

20

30

40

50

60

70

80

90

100

1 1.2 1.4 1.6 1.8 2 2.2 2.4

Average SCI Score

LDI Score

Benchmark Site LDI vs. Average SCI Score

Figure 2-33. Benchmark site LDI vs. Average SCI score. In contrast to Figure 2-32, note the lack 

of correlation between SCI and LDI values of minimally disturbed sites (LDI ≤ 2, with one 

exception where LDI was 2.2). 

Figure 2-34. Benchmark site SCI vs. TN. Note the lack of correlation between SCI and TN 

throughout the range of benchmark sites. This indicates that there are no adverse effects from 

establishing nutrient criteria at the upper 90th percentile of the benchmark distribution. Sites 

scoring less than 40 on the SCI (after QA review) were excluded from the benchmark data set for 

calculation of the final nutrient distribution. 
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Figure 2-35. Benchmark site SCI vs. TP. Note the lack of correlation between SCI and TP 

throughout the range of benchmark sites. This indicates that there are no adverse effects from 

establishing nutrient criteria at the upper 90th percentile of the benchmark distribution. Sites 

scoring less than 40 on the SCI (after QA review) were excluded from the benchmark data set for 

calculation of the final nutrient distribution.