TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

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

2a. Methodology for Stream Classificati on: 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 Florid a streams and rivers re lied on a distribution approach. U.S. EPA identified that population of streams sampled for biological condition in Florida which currently supports biologically hea lthy conditions. Nutrient conditions consistent with those from this distribution of sites were then estimated and an upper percentile used to indicate a concentration protec tive of biologically healthy cond itions. The process consisted of classifying streams and rivers into relatively naturally homogene ous groups, identifying that population of biologically healthy st ream sites, estimating a distribu tion of nutrient values within the population, and estimating the 75 th percentile of the distribution.



### i. Developing a Regi onalization Scheme
Spatial frameworks are importa nt for structuring the resear ch, assessment, monitoring, and management of environmental resources. Ecor egions are usually defined by patterns of homogeneity in a combination of factors such as climate, physiogr aphy, geology, soils, and vegetation (Griffith et al. 1994) . Classification or regionaliz ation of streams provides a framework on which to develop and base protective nutrient cr iteria. 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 na tural 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 limest one formations that lie near the surface are extremely high in phosphorus. Some of these phos phatic deposits are mined, making Florida one of the larger producers of phosphate. (Florida produces approximately 25% of phosphate used throughout the world.) Proper spatia l classification to capture re gional differences in natural nutrient concentrations is essential.



### ii. Ecoregional and Biological Re gionalization 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 regi onalization 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 (B arbour 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 75a 1), the Northeast (subecoregions 75e and 75f), the Peninsula (subecoregions 75b, 75c, and 75d, and a small part of 75a), and the Evergl ades (subecoregions 76a,TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-2 76b, 76c, and 76d). Similar pattern s of relatively homogeneous groupi ngs in the Peninsula versus the Panhandle have been observed in wetlands macr ophyte, algae, and invertebrate data (Lane et al. 2003).



### iii. Refinement of Regionalization for Nutrient Criteria Development
The stream bioregions developed by FDEP we re based on the observed biological community resemblance within a bioregion, assuming that biol ogical responses to nutr ients would be similar within and not between bioregions. Evaluation of sites across bioregions revealed additional spatial patterns; that is, the bi oregions were not sufficiently homogenous with regard to nutrient concentration. Further evaluations considered attributes of the bi oregions, ecoregions, geological formations (e.g., Bone Valley a nd 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 identi fied 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 20 th century. Mining of phosphate in the outcrop area began in 1888 (Cathcart 1985) and continues to the present.



### iv. Development of the Nu trient Watershed Regions

U.S. EPA used the assessments, evaluations , and supporting informa tion from FDEP on bioregions, nutrient regions, and su b-regions as a point of departur e to further refine and develop a regional stream nutrient cla ssification approach that reflect ed the understanding that upstream water quality affects downstream water qualit y. A watershed-based cl assification should facilitate the ability to address the effects of total n itrogen (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 La ke 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 co llected across Florida and contained within Florida’s Impaired Waters Rule (IWR) database . Data tables of nutri ent 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 draina ge areas (FDAs), and coastal drainage areas (CDAs). WBID centroids were joined to the CA F so that all stations in WBIDs were also mapped to an EDA, FDA, or CDA. Stations th at were identified in the IWR database as Stream

or Blackwater and had corresponding TN and TP data we re identified and aggregated by EDA, FDA, or CDA. This resulted in approximatel y 145,000 paired observations or sampling events distributed in watershed stream ne tworks across Florida. The mean, distributional statistics of allTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-3 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 aggr egated into four nutrient watershed regions (NWRs)––Panhandle, North Central, Bone Valle y, 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 expect ations 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 th an those in the Bone Valley (median TN:TP = 9).



### 2b. The Use of the Stream Condition Inde x 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 implementati on 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 “balance d natural population of flora and fauna” and for defining what this means in terms of the expe cted diversity and abunda nce of aquatic life and structure and function of the aqua tic community. This section describes the factors that need to be considered when developing a quantitative meas ure of biological health in an aquatic system, and it describes the basis for Florida’s definiti on of the biological c ondition that reflects attainment of the designated use of “Recreat ion, Propagation and Main tenance of a Healthy, Well-Balanced Population of Fish and W ildlife” (F.A.C. 62-302.400). These biological thresholds are then used, in conjunction with ot her data, to determine the particular nutrient concentrations that might interfere with designated use protec tion and attainment.

The response of biological communities to human point source pollution initially received attention in Florida during the late 1950s. In 195 8 Bill Beck, a biologist with the Florida State Board of Health, wrote a series of “Biological Letters” in which he in troduced the concept of using invertebrates as biological indicators, especially for dem onstrating the effects of excess organic matter on streams and lakes (the sa probity 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 invertebra tes were routinely sampled with multi-plate artificial substrate samplers (Hester-Dendy samp lers). Hester-Dendy samplers are placed in receiving waters for 28 days, which is a mini mum period for colonization by a representative benthic community (Figure 2- 7). Hester-Dendy data were summarized using the ShannonWeaver diversity index, a biolog ical metric derived from information theory that became aTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-4 popular index to communicate complicated biol ogical results. The Sh annon-Weaver diversity index is based on a combination of taxa richness at a site and evenness of the distribution of individual abundances. Low divers ity scores represent conditions where a few pollution-tolerant organisms are very abundant, to th e exclusion of other taxa. This index is specified in Florida’s water quality standards (WQS) as a measure of biological inte grity (Rule 62-302.530, F.A.C.). It generally has been applied by comparing site-s pecific control sites to nearby test sites.

In 1989, U.S. EPA promoted the concept of rapi d bioassessment (Plafk in 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 he alth that respond in a predictable manner to human disturbance, we re calculated from refe rence site data. A distribution of reference site metr ic values was calculated, and scores were selected to represent expectations for each metric based on the refe rence site population. A va riety of metrics were combined into a dimensionless multimetric inde x by assigning points to individual metrics based on their relative similarity to the refere nce condition and summing across metrics.

To use a biological assessment tool, an understa nding of an ecosystem's biological components and sources of variability is im portant. Biota respond to a wide va riety of factors, natural and anthropogenic (Figure 2-8). As organisms integrat e and respond to these factors over time, a characteristic community structure emerges, with a range of natural variab ility. Florida biologists have determined that a portion of variability at minimally dist urbed sites is explained by random natural events such as floods and drought, whic h determine the relative abundance of inundated substrates available for invert ebrate colonization. These na tural stressors (e.g., flood, drought, natural low substrate diversity, pe riodic 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 (S CI) 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 ensu re that data are produced with the highest quality, both in the field and in the lab. Samplers and lab technicians fo llow detailed standard operating procedures (SOPs), and additional gui dance 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 laborat ory taxonomists are regularly tested and must maintain >95% identification accuracy.

The SCI is composed of 10 metric s, 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, thr ee stream bioregions, in which there are slightly different expectations for the metrics base d on natural differences, were established: the Panhandle, the Northeast, and th e Peninsula (note that th e SCI is not calibrated for ecoregion 76, the Southern Florida Coastal Plai n, where few natural stream s exist) (Griffith et al. 1994; Figure 2-9). To be scie ntifically defensible, stream sy stems being evaluated against theTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-5 SCI should be morphologically identifiable as str eams, so that potential human influences can be discerned. (The reference streams should be comp ared 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 deve lopment of the SCI.



### ii. Establishing Healthy Biological Conditio ns in Streams––Stream Condition Index: Application of the Reference Site Approach

FDEP examined the lower distribution of refe rence site multimetric scores as a principal approach for establishing aquati c life use support thresholds, in combination with the Biological Condition Gradient (BCG) approach, and U.S. EP A used the results of this analysis for identifying the threshold between hea lthy and unhealthy biological conditions.

FDEP conducted statistical inte rval and equivalence tests wi th SCI data from 55 streams considered by Florida to be reference streams (i ncluding 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 impa ired) and type II errors (f ailing to detect that a site is truly impaired) (see A ppendix 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 stream s 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 bala nced Type I and Type II errors.

When calibrating a threshold be tween healthy and unhealthy bi ological conditions using an index, the amount of human distur bance inherent among reference sites is an important issue, and a rigorous reference site se lection process provides increase d confidence that the reference site population is minimally disturbed, thereby si gnificantly reducing Type II errors (classifying unhealthy sites as healthy). This confidence also allows for establishi ng a biological health threshold at a low level of the reference site di stribution to minimize Type I errors (classifying healthy sites as unhealthy). FDEP attempted to scr een reference sites to ge nerate a population as approximate to the minimally disturbed (limited human influence) na ture 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 dete rmined 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 a pproximately 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 th at illustrates how ecological attr ibutes change in response to increasing levels of human distur bance. The BCG is a conceptual model that assigns the relative health of aquatic communities into one of six categories, from natu ral to severely altered (Figure 2-10). It is based on fundamental ecological pr inciples and has been verified by aquaticTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-6 biologists throughout the Un ited States. The BCG uses biological attributes of aquatic systems that predictably respond to incr easing pollution and human disturba nce. Although these attributes are measurable, some are not routinely qua ntified 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, l ong-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 a quatic biologists from across the United States, includi ng Florida. The six tiers are:

1. Native structural, functional, and taxonomic inte grity is preserved; ecosystem function is preserved within range of natural variability.

2. Virtually all native taxa are ma intained 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 replacem ent of some sensitive–ubiquitous taxa by more tolerant taxa, but reproducing populations of some sensitive taxa are maintained; overall balanced distribution of all ex pected major groups; ecosystem functions largely maintained through redundant attributes.

5. Sensitive taxa are markedly diminished; cons picuously unbalanced distribution of major groups from that expected; or ganism condition shows signs of physiological stress; system function shows reduced complexity and redundanc y; increased buildup or export of unused materials.

6. Extreme changes in structure; wholesale changes in taxonomic composition; extreme alterations from normal densities and distri butions; organism conditioning is often poor; ecosystem functions are severely altered.

The six levels described above are used to corr elate biological index scores with biological condition, as part of calibrating an index. FDEP co nducted a BCG exercise to calibrate scores forTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-7 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 revi ewed 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 i ndependently assigned a BCG score to e ach site and then were able to discuss their scores and rationa le. They could opt to change their scores based on arguments from other participants. At th e 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 stil l provided for the propagation and maintenance of a healthy, well-balanced aqua tic community and the BCG category (and higher) representing exceptional conditions. All 22 partic ipants 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 cons idered impaired, while ni ne 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 mee ting a healthy, well balanced community, and the mean expert response corresponded to a BCG tie r 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 re sponses within each BCG category and conducted an additional analysis of BCG resu lts to further inform the decision point for determination of what constituted a healthy, well balanced aquatic community. U.S. EPA calculated a proportional odds logi stic regression model (Guisa n and Harrell 2000) to better describe the relationship between a continuous vari able (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 tie r (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. On ce 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 pr oportional odds logistic regression models are plotted as solid lines. The lines are color-coded and labeled by di fferent 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 remain ing 10% of experts assigned the sample to Tier 5 (purple line). In Figure 2-12, solid circles represent the actua l expert assignments recorded from the workshop for each SCI value. The size of the circle is proportio nal to the number of experts that assigned a sample to a particular tier, and the circ les 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 scor e where there was an approximately equally low probability of assignment to Tier 5 (i.e., unhealthy) and a low probability of assignment to Tier 2TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-8 (i.e., reference conditions). The resultant thresh old balanced the probability of mistakenly assessing a degraded site as representing health y biological conditions w ith the probability of mistakenly assessing a reference si te 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 Hea lthy and Unhealthy Bi ological 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 s upported by the distribution of reference site scores and corre sponds with a BCG category midway between Tiers 3 and 4 using the BCG tool. The proportional odds analysis pr ovides 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 attr ibutes would be considered he althy using this impairment threshold. Since FDEP conducted the SCI ca libration in 2007, the state has collected approximately 700 additional SCI samples from a va riety of sites, includi ng sites considered by FDEP to be minimally disturbed (for nutrient crit eria development), sites located along a nutrient gradient, and randomly chosen sites for the stat us 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 i ndividual 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 st ream condition that the threshold identifies as h ealthy (Table 2-3). Note that alt hough there are moderate differences between metrics associated with exceptional biological communities and those near the range of the healthy conditions threshold, attributes asso ciated with communities near the threshold are still considered indicative of healthy, well bala nced communities by the majority of the Florida stream experts who participated in the BCG exercise.



### 2c. Methodology for Calculating In-str eam Protection Values: The Nutrient Watershed Region Distribution Approach

i. Methods for Calculating Attain ing Site Distributional Statistics
The approach used by U.S. EPA to develop in-str eam protective nutrient criteria values was to first identify biologically healt hy sites as reference sites and then to apply the standard distribution-based approach de tailed in the U.S. EPA guidan ce (USEPA 2000). This involved identifying and compiling data for which SCI sc ores were available, estimating long-term geometric mean nutrient concentrations for thos e sites, identifying the sites that met the SCI criterion of 40 or greater, removi ng 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 obt ained from FDEP in two spreadsheets––one with data from 2004 and before and a second with data from 2004 through 2007. These spreadsheetsTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-9 contained SCI scores for specific sites across the st ate and additional data a bout the sites, as well as associated water chemical and habitat data , including nutrient con centrations from grab samples taken when biological sampling occurred (h erein referred to as grab sample chemistry). These two data sets were merged together into a final overall data se t containing 2,023 samples from 1,115 sites within 614 WBID s statewide. Water chemistry data included TP. TN was calculated by summing total Kjeldahl ni trogen (TKN) and nitrate/nitrite (NO 3 /NO 2 ) concentrations from each sample. Water chemistry da ta 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 th e influence of extreme values t ypical 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 NO 3 /NO 2 ) 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 si tes 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 stat ion-year average). For site-yea r values, there were 23,104 TN and TP observations across the same number of site s and WBIDs as for site averages. Similar to geometric mean, the standard deviations for stati ons and station-years were also calculated using the same natural log-transformed water chemistr y data. Some SCI sites did not have nutrient chemistry beyond the grab sample taken during bi ological 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 nutrien t criterion that might have met the SCI threshold of 40 but were with in 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 conf irmed 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 describe d above, and individual sites within WBIDs listed for nutrient and/or diss olve oxygen impairments were identified and flagged.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-10 

### v. Identifying Biologica lly Unhealthy Site List

Sites were identified that did not meet the SC I 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 onl y one SCI score were considered biologically healthy only if that score was greater than 40. The in-stream pr otective 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 WB ID listed for nutrients and/or DO impairment was removed from consideration as part of the biologically healthy site population for deriving the in-str eam protective criterion. Once this screening selection was completed, the remaining sites were considered the biologically healt hy sites, unimpaired by nutrients and/or DO. Distributio nal 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 averag e nutrient concentrations were combined into a complete table in the referential database and exported as a spreadshee t. 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 av erage data were the principal values used for deriving in-stream protective nu trient criteria. These are de scribed 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 si te in the FDEP IWR database, and these were linked to the same sites for which SCI scores we re available. Each site was represented by only one value––a long-term site av erage––and therefore sites were equally weighted. A spreadsheet was created for the site average data alone; it in cluded SCI data and labels for sites meeting the biological thresholds and impairme nt 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 wa s 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, Penins ula, and North Central. Assuming the nutrient data were log-normally distributed, the 75 th percentile of the l og-normal distribution was estimated for each nutrient parameter in each region using the following standard formula (Figures 2-14 and 2-16):

75 th 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 treat ed as a replicate; ther efore, sites with more samples were weighted relatively more than site s with fewer samples. A spreadsheet was createdTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-11 for the grab sample data alone; it included SCI da ta and labels for sites meeting the biological thresholds and impairment criteria for nutrients and/or DO explained ab ove. Sites not meeting the biological threshold or impaired for nutri ents and/or DO were removed. The mean and standard deviation natural-log TN and TP concen tration 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 75 th percentile of the log-normal distribution was estimated for each nutrient parameter in each re gion 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 s ite-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 th e biological thresholds and impairment criteria for nutrients and/or DO explained above. Site-yea r 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, Pe ninsula, and North Cent ral. The data were assumed to follow a normal distribution, and the 75 th percentile of the log-normal distribution was estimated for each nutrient parame ter in each region using Equation 1.



### viii. 75 th 

### Percentiles for Site Average Data – In-str eam Protective Values
The site average data based on SCI thresholds of 40 were used to derive the in-stream protective nutrient criteria. The characteristic s of these instream protective nut rient criteria are displayed in Table 2-4 and the in-stream protective criteria were the 75 th percentile of site average values from biologically healthy sites (SCI>40, Figures 2-14 and 2-16).



### ix. Distribution of Nutrient Concen trations among Nutrient Regions
To compare concentrations of nutrients across re gions, maps indicating nutrient concentrations based on grab samples from sites where SCI samples were taken we re created. Maps and cumulative function histograms for each region we re 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 con centrations were highest in the Bone Valley and North Central regions, but principally in the nort hern half of the North Central region (Figure 2-13). Within the Panhandle and Peninsula, occasi onal high concentrations of TP were observed but many of these were observed with biologica lly unhealthy sites (SCI threshold < 40), and these concentrations disappeared when only biologi cally healthy sites were plotted (Figure 2-14).

In contrast, TN exhibited generally high concen trations across the regi ons, although the Bone Valley again appeared to have a preponderance of high values (F igure 2-15). Moreover, high-TN sites were distributed across the Panhandle and Pe ninsula regions, but as with TP, many of these were associated with biologically unhealthy sites (SCI threshold < 40), and these concentrations disappeared when only biologically healt hy sites were plotte d (Figure 2-16).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-12 The 75 th 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 75 th percentile, there is a marked decrease in the slope at the cumu lative and empirical frequency distributions.



### 2d. Duration and Frequency fo r Proposed Streams Criteria
Aquatic life water quality criter ia contain three components: ma gnitude, duration, and frequency. The criterion-duration of the streams criteria ta ble as an annual geometric mean. U.S. EPA is proposing two expressions of allowable frequency, bot h 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 arith metic average of annual geometric means not exceed the criterion-magnit ude concentration. U.S. EPA anticipates that Florida will use their standard a ssessment 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 shoul d be based on how the data used to derive the criteria were analyzed and what th e implications are for protecting designated uses given the effects of exposure at the specified criterion concentration fo r different periods and recurrence patterns.

Lotic systems, such as streams and rivers, fl ow unidirectionally and, depending on the gradient and other factors, typically have shorter residence times than le ntic systems for nutrients and a higher probability of scour and removal of al gae downstream. The likelih ood of nutrient effects in these systems is therefore based largely on th e prevailing concentratio n of nutrients over the growing season as opposed to tota l loads. Many algae and macrophyt es exhibit luxury uptake, in which plants are able to store nutrients in ex cess of current requirem ents to support future growth. This complicates the determination of the appropriate averag ing 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 rapi dly and often enough to pr event 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 di rectly 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 n ecessarily translate into excessive algal biomass (Biggs et al. 1998a, b).

Low and stable flow conditions should be cons idered in addition to frequency and timing of floods when considering nutrient criteria for streams. Flood frequenc y and scouring may be greater in steep gradient (steep slope) and/or channelized streams, and in watersheds subject to intense precipitation events or ra pid snow melt. Periods of dryi ng 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. Th e effects of eutrophicati on may be evident during stable low flows. Also, stable flow periods ar e generally associated with low flow conditions, which can result in the highest nutrient concentra tion from point sources discharging relativelyTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-13 constant loads of nutrients. Hence, low-flow pe riods often present ideal conditions for achieving maximum algal biomass.

Given that the data compiled and analyzed for developing streams criteri a represent all seasons and conditions during the year, and the technical reasons stated a bove that effects can manifest themselves with a growing season and can va ry depending on hydrologic condition, it follows that an annual averaging peri od is the appropriate criterion-duration for the proposed streams criteria.

Nutrient criteria are typically established with in the range of natural variability. A temporary spike in nutrient levels does not necessarily ha rm the aquatic resource. In fact, natural systems have evolved to process variable inputs of nutrients, particular ly where there is much natural variability in hydrologic conditions and precip itation patterns (e.g., wet years, dry years). Although biological response in the form of algal produc tion, measured by chl. a , can appear very quickly, longer term shifts in biological co nditions, such as loss of underwater grasses, do not occur as the result of a singl e 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 ba sed 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 fr om the chronic effects described above. This supports a criterion fre quency 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 elev ated year of nutrient loadings.

Those frequency and duration components take into account that hydrologica l variability will in turn produce variability in measured nutrient co ncentrations, and indivi dual measurements may exceed the criteria. Furthermore, they balance th e representation of underl ying data and analyses on the basis of central tendency of many years of data (i.e., th e long-term average component) with the need to exercise some caution to ensure that streams have sufficient time to process individual years of elevated nut rient levels and avoid the possib ility 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 pr otective water quality crit eria 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 st ream criteria setting, however, are uses in downstream lakes. As a result, there is a need to establish whether stream criteria will beTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-14 consistent with downstream lake uses and criter ia. Here a screening analysis is presented to evaluate the protectiven ess of inland stream criteria with respect to receiving lakes.

Vollenweider Model The analysis compares TP criteria in streams a nd rivers to those proposed to protect lakes using the simple phosphorus loading model deve loped by Vollenweider (1975, 1976) based on a nutrient budgeting approach. It has been update d with adjustments ma de 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 protec tive of the lake. Accord ingly, critical stream concentrations derived by loading models to lake s based on proposed lake criteria that are lower than the benchmark stream criteria would indica te potential concern fo r downstream protection.

The Vollenweider model for loading (Vollenw eider 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 

#  

w 1 

 represents the phosphorus settling rate

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



### AL(P) TL(P) 

Where,

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

A is lake surface area, m 2

It is assumed that total streamflow into a lake (Q i , in m 3 y -1 ) cannot be more than the net outflow, or lake volume divi ded by retention time:

w i 

### zA Q

 

or,

w fi zA cQ

  [2]

[3]

[4]TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-15 where c f is the fraction of inflow due to streamflow, 0  c f  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 phosphor us 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 L s 

### c 1 z [TP] [TP]  
  

resulting in: 

#  

w L
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 c f and  w . Because c f is between 0 and 1, the most stringent critical inflow concentrati on will occur when c f is close to 1 (i.e., when a lake’s inflow is al most entirely due to streamflow).

Florida lakes tend to be shallow, and internal lo adings to the water column following a decrease in external loading (e.g., from storm-induced re suspension or diagenetic release of sediment phosphorus) may be substantial. Resuspension or diagenetic release of sediment phosphorus could be modeled to simulate this phenome non 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 genera l regional application, however, EPA used the model described in Equation 5. [5]TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-16 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 th e TP criterion. Stream [TP] s was calculated for re tention time ranging from 0 to 20 years, and for streamflow repr esenting 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 concen trations 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 75 th percentile proposed instream protective value is given by th e dashed line, which represents the 75 th percentile values. Any predicted critical stream concentrations below the benchmark criterion are not protected by the criterion (orange-shade d area in Figure 2-17); the proposed instream protective value is not protective of the lower-bound lake criterion fo r 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 stre am flowing into them (Fernald and Purdum 1998). Both rainfall and groundwater flow are subs tantial in Florida. Lake Jesup, connected to the St. Johns River, is an example of a lake wi th high stream inflow (t he 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 re sidence 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 real istic and representative of la kes 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 la kes are unlikely to be protected by any of the proposed instream protect ive values, however, most lakes of this class have no inflowing streams. Panha ndle TP criteria are likely to be protective of clear, highconductivity lakes a nd colored lakes.

These results confirm the site-specific analyses of the TMDLs in showing that lakes with a high proportion of streamflow and shor t retention times are least likely to be protected by the 75 th or 90 th percentile proposed instream protective values. Peninsula proposed instream protective values are likely to be protective of most co lored lakes (but see Lake Jesup TMDL, Gao 2006), but they might not be protective of clear, high-c onductivity lakes with sh ort retention times and high streamflow input.

For the proposed rule, EPA evalua ted representative values for pe rcent contribution from stream flow (Gao 2006) and hydraulic retention time (Steward and Lowe 2000) and selected 50% stream flow contribution and 0.2 ye ars retention time as realistic and representative preset valuesTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-17 to provide a protective outcome fo r Florida lakes, in the absence of site-specific data. Using the equation developed (Equation 5) the representative values for stream fl ow, retention time, and the appropriate lake criteria, critical stream va lues were calculated. Using these preset values, streams that flow into colored lakes would ha ve a TP criterion of 0.12 mg/L and streams that flow into clear, alkaline lakes w ould 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 waters hed. One approach is based on the U.S. Geological Survey (USGS) SPAtially Re ferenced 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 estuar ies to less than the protective TN load. The protective TN load is the TN loading rate to estu aries 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 estu aries were developed to derive water quality criteria for TN in flowing waters that ensu re protection of designa ted 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 es tuaries but could not be developed for seven estuaries in south Florida becau se required information related to TN loading could not be obtained for those systems in the time available. Similarly, protective lo ads were not developed for three estuaries in central Florida because protective loads and flows necessary to compute downstream protection values require further ev aluation. 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) estimate d 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 redu ction goal (PLRG)-derived loading limits to residence time or hydraulic loading, and (4) leve ls of TN load reductions that have been suggested for estuarine waters in Florida vi a the TMDL or PLRG development processes. Although current TN loads to estu aries in Florida vary approxim ately 25-fold, from ~2 g N m 2 /y to nearly 50 g N m 2 /y, the ratio of the current load to the background load va ries 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 Loadi ng 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 w ithin flow-monitored river and stream reaches. The USGS developed and widely applies the me thods. The computed estimates of TN loadingTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-18 were incorporated in the SPA RROW model (Schwarz et al. 2006). The model estimates TN loading to estuarine waters fr om 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). Th ese estimates agree very well in most cases with monitored load estima tes (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 estu arine WBIDs with docum ented nutrient-related impairments (for further detail, see sections “C ondition 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 fr om 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 Loadi ng to Estuaries in Florida
TN loading to estuaries in Florida has incr eased to varying degrees as a result of known anthropogenic causes. For 16 estuaries in Flor ida, 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, a nd atmospheric deposition. All the loading categories except for atmospheric depos ition are associated with either local anthropogenic TN sources or local watershed factor s associated with increased TN transport to streams (e.g., urban land use). Ther efore, the atmospheric TN load ing category is an estimate of the background TN loading in the watershed. Acro ss the 16 estuaries in Fl orida, atmospheric TN loading levels account for 20% to 60% of the cu rrent TN loading (Table 2-6). Because those loads exclude much of the anthropogenic TN loadi ng, they were used as an estimate of the lower limit of the protective TN load for each of 16 es tuaries in Florida. U.S. EPA believes that a defensible estimate of the protective TN load mu st be between this va lue 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 th e regional rate of atmospheri c deposition and help explain observed TN loading within gaged streams and rive rs. However, the delivered TN load attributed by SPARROW to atmospheric depos ition 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 re flects the fact that a significant fraction ofTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-19 deposited TN is retained on the landscape, and an additional fraction is removed within the stream network. As a result, SPARROW estimates th at the transport of ni trogen in southeastern streams that is attributable to atmospheric nitrogen deposition is much less than the TN deposition measured by NADP. More specifi cally, the mean TN yield resulting from atmospheric deposition is 120 kg/km 2 /y (standard de viation = 37 kg/km 2 /y) whereas the regional NADP estimate is roughly 300–400 kg/km 2 /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 av ailable. Steward and Lowe (2010) found that protective TN loads, which they ca lled “maximum TN loading” or L
m , were highly correlated with both hydraulic residence time (denoted by τ w ) and hydraulic loading (denoted by qs = z/ τ w , where z is mean depth). The regressions that qu antify these correlations as are referred to as “L
m models.” Conceptually, the regression models as sume that the expression of nutrient enrichment is influenced by hydraulic residence time such th at waterbodies with short residence times are likely to be less susceptible to nutrient enrichment than water bodies with long residence time. They also assume that the desired ecosystem state targeted during de velopment of the TMDLs and PLRGs is the same. In general, the objec tive is moderate trophic state, or mesotrophy (Steward and Lowe 2010). That could overestimat e the appropriate protective loads for some estuaries in Florida for which the natural condition might be o ligotrophy, which is defined as having low productivity and usually very low nutrient concentrations.

U.S. EPA applied both the L
m versus τ w and L
m versus q s models to 13 estuaries in Florida. In 10 estuaries, both the L
m versus τ w model and the L
m versus q s 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 pr ovided a value within th e range between current loading and background loading, U.S. EPA considered it a potentially useful reference point for determining protective loading ta rgets. However, the uncertainty associated with these model predictions is sufficiently large that they do not reduce the possi ble 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 L
m 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 an alyses could be required. For this method, establishing an appropriate estim ate of estuarine surface area and hydraulic residence time is a key challenge. Although measuring surface areas is not technically challe nging, determining the appropriate boundaries for the estuar ine 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 reduc tion 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 load ing) and 45% (relative to the 1980–1996 loads) for loading from the Mississippi River to the north ern 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 FloridaTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-20 Estuaries.” In general, larger load reductions are recommende d 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 an thropogenic contribution to TN loading. Consequently, an alternative to the extrapolat ion approach explored in the “Estimate of Background TN Loading to Estuar ies 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 conne ction to the existing TMDLs and PLRGs in Florida. Specifically, a 50% reduc tion in the anthropogenic fraction of TN loads results in a total load reduction of ~25% on averag e 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 ev idence summarized above to determine estimates of the protective TN loading to estuaries in Fl orida. 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 re lationship 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 cr iteria for average TN concentrati on in each stream reach in the estuarine watershed which, if achie ved, are expected to result in average TN loads to estuaries less than or equal to the protectiv e TN load, but appropriately prot ective. 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 con centrations to be higher in stream reaches where significant nitrogen losses occur before reac hing the coast. U.S. EPA’s approach utilizes specific components of the USGS regional SPA RROW model for the South Atlantic, eastern Gulf Coast, and Tennessee River (SAGT) basi ns, 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 prov ided 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 incl udes TN loads from several sources, including sources in the watershed of the es tuary discharging via stream cha nnels into the estuary, diffuse sources discharging into the estu ary (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 protectio n criteria. Of these loads, the approach previously described, addresses only the fractio n of TN loads from the portion of the watershed resolved by the SAGTSPARROW regional watershed mode l. Assuming that the fraction of TN loading allocated to each stream reach discharging into the estuary is proportional to the fr action of freshwater contributed by that stream r each, the protective TN concentration can be computed asTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-21 [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 ente ring 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, F i , 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 kL est without estimating either k or L est separately. The protective TN lo ading estimate resulting from the approach described above, was taken to be equal to kL est . 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 allocati on 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 woul d become necessary to quantify the average flow in each reach. These flows are estimated via a regr ession 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. Th us, it is necessary to cross-reference reachspecific estimates of delivered fraction, F i , from ERF reaches to WB IDs. Unfortunately, the following was observed:

1. WBIDs do not necessarily intersect any reach.

2. Incremental watersheds associated with reach es 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 reach es 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.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-22 To address these problems, a union coverage wa s created by dividing each WBID into separate polygons, each of which resides fully within a sing le ERF1 watershed. The area of each of these sub-WBIDs was computed and then used to creat e a weighted average of the delivered fraction,
F

i , associated with each ERF1 watershed. These averages were stored, along with the minimum and maximum fraction delivered fo r any portion of the WBID. In th e vast majority of WBIDs, the F i for fractions of the WBID associated with di fferent ERF watersheds varied by a very small amount. The result is a statewide map of fr action delivered by WB ID (Figure 2-22) and associated tables showing F i 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 a nd their watersheds by adopting the NOAA CAF (Bricker et al. 1999). This is not the only delineation that coul d have been used. For example, according to the list associat ed with FDEP’s IWR Run 33, th e 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 a nd 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 estu ary and its watershed (F igure 2-23, Table 2-8).

Using the CAF, U.S. EPA recognized 18 EDAs a nd their associated FDAs in Florida. The present analysis excludes five EDAs in s outh 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 So uth Atlantic, eastern Gu lf, and Tennessee River (SAGT) regional SPARROW model a nd was not available otherwise. The CAF does not define EDAs in some shoreline regions of Florida, specif ically those where estuarine basins are not well defined or are not present (Figure 2-23). Thes e 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 estuarin e attributes. Because CDAs generally lack a well-defined semi-enclosed basin, it is di fficult to unambiguously define the boundaries, particularly the seaward boundaries, and subsequen tly quantify the areal ex tent of the estuarine waterbody. The present analysis includes 6 CDAs (Figure 2-23, Ta ble 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 spa tially joined to the CAF covera ge (Figure 2-23) to provide a means of relating them.

Description of Florida’s Es tuaries and their Watersheds Florida’s estuaries and their watersheds span a diverse landscape that contributes to significant differences in ecological function. Florida’s estu aries are both small (e.g., St. Mary’s, Perdido) and large (Tampa Bay, Indian River) and are al most 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 km 2 in three states. The ratio of waters hed area to estuary area, a key metricTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-23 with implications for the coupli ng of watershed and estuary pro cesses, 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 co mpared to the Indian River without having a proportionate eutrophication response.

The watersheds of estuaries in the Florid a panhandle eastward th rough the Big Bend region (Perdido to Suwannee River waters hed) have a large fraction (> 50%) of forest cover, although a significant fraction of forest la nd is managed for timber production, rather than entirely natural forest. The St. Marys River/Cumberland Sound wa tershed in northeast Fl orida also includes a significant fraction of forest co ver (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 Ta mpa Bay, Sarasota Bay, Indian River, and Charlotte Harbor.

Characterizing Nitrogen Loads to Florida Estuaries A synoptic assessment of nutrient loads to all es tuaries 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, Green ing 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 deve lopment (Hoos et al. 2008; Hoos and McMahon 2009). The SPARROW modeling approach uses a multiple regr ession 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 ma ss flux of nitrogen) as a function of upstream sources and watershed attributes. The SAGT-S PARROW model used monitored streamflow and nutrient water quality data from Florida and acr oss the SAGT region for load estimation. SAGTSPARROW also used exte nsive geospatial data sets describi ng topography, land use, climate, and soil characteristics. Nitrogen loading data fo r 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 agricu lture, and population estimates (Hoos et al. 2008; Hoos and McMahon 2009). TN transports estim ated 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 Ba y, largely varying with watershed size. Watershed yields, a measure of the nitrogen inte nsity of the landscape vari ed from ~190 kg/y for low-yield watersheds such as Nassau, St. Ma ry’s, and St. Andrew Bay to as high as 828 kg/km 2 /y for Indian River (Table 2-9). Averag e 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 TNTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-24 concentration in terminal reaches was > 1.0 mg/L in high-nitrogen yield watersheds such as Indian River and Charlotte Harbor, while much lo wer 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 es tuary 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). Thos e point sources were most important in St. Andrew Bay where they are equal to 50% of the watershed load. Th e largest aggregate point source load discharging into estuarine waters is in the St. Johns Rive r 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 lo ading from the watershed into one of five source categories: atmospheric deposition, ferti lizer, manure, urban, and point sources. The sources other than atmospheric deposition, combined with point sources discharging directly to the estuary compose an estimated total anthr opogenic nitrogen loading. Of those sources, fertilizer was consistently an important TN s ource across the state (Fig ure 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 depos ition is sometimes an important source of nitrogen loading to estuaries. Wet nitrogen deposition at NADP moni toring sites in Florida is approximately 300 kg/km 2 /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 co mmonly, but it is often ta ken to be similar in magnitude to wet deposition. Although atmospheric n itrogen deposition in th e 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 leas t 300 and possibly as high as 600 kg/km 2 /y including dry deposition. Moreover, atmosphe ric 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 waters heds across the state between 300 and 400 kg/km 2 /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 load ing from direct atmospheric de position 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 th e 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 non linear regression model that seeks to explain measured TN loads in streams and rivers in terms of a series of explanatory variables. Those includeTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-25 variables representing watershed a nd stream network attributes (e .g., drainage area, slope of the landscape, percent impervious surf ace, land use) as well as nutrie nt 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-s ource inputs such as “per cent urban land” into nitrogen sources because those attributes explai n observed nutrient outputs from the watershed. For model inputs that can be interpreted direct ly as nutrient sources, such as fertilizer application, SPARROW estimates coefficients indicating how mu ch of the applied source is delivered across the landscape to the stream network. They are called “landscape delivery factors.” The atmospheric term accounts for sour ces that can be expl ained 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 fracti on of nitrogen delivered to the stream network that cannot be at tributed explicitly to other anthropogenic sources. Furthermore, atmospheric term includes any anthropogenic co ntribution to the regional nitrogen deposition, but it does not reflect highly localized contribut ions associated with urban land use or even manure application. In that regard, the SPARR OW 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 fo r 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 km 2 of watershed area) from atmospheri c sources to estuarine waters in Florida was generally between 125 and 175 kg/km 2 /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 coas t. The atmospheric sour ce term from SPARROW accounted for ~25% to 75% of TN loads from al l sources in Florida wa tersheds. Such results suggest that anthropogenic sources have increased TN loading from watersheds as much as fourfold, frequently by two-fold, and in a few estu aries by a smaller amount (e.g., St. Marys, Nassau, St. Andrew Bay).

Time Series of Annual TN Loads Streamflow and TN loading have been monitore d at numerous river gage stations throughout Florida watersheds. Ambient water quality monito ring 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 nutri ent loads. USGS uses well-established, multiple regression methods that estimate loads as a f unction of concentration, discharge, and time (Cohn et al. 1989; Schwarz et al. 2006; H oos et al. 2008). Those load es timates are the foundation of the SPARROW modeling approach, which seeks to explain these observati ons 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 interan nual 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 Flor ida estuarine watershed (Table 2-10), with the exception of Sarasota Bay and Indian River, whic h do not have any suitable river gage stations. The longest period of record at the stations was 1975 to 2004, although the record at mostTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-26 stations was shorter. The fracti on of estuary watersheds monitore d via river gage stations is variable from nearly 100% for the Apalachicola Ri ver 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 downstr eam 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 monitore d TN loads, quantified as the co efficient of variation, is 20% to 130% of the mean load over the period of r ecord. The highest variability was observed for relatively small watersheds and for gages with short periods of r ecord (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 us ed 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 influe ncing factor (generally nitrogen loading rate), expression of eutrophic conditions, and future outlook (i.e., potential for symptoms to become better or worse). Of 19 estuarie s 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 susceptibi lity was unknown at the time of the report. FDEP has also assembled a series of Water Qua lity Assessment Reports (e.g., FDEP 2005), each of which presents a range of environmental and regu latory 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 estuar ine waters is Florida’s 1998 303(d) list and verified list of waters identif ied as impaired under the Florida’s IWR (F.A.C. 62-303) (Table 2- 12). The lists document nutrient-related impair ments throughout the st ate either under the narrative standard (1998 303(d) li st) or under the IWR. In gene ral, impairments for chl. a are concentrated in Atlantic coast estuaries and peni nsular Gulf coast estuar ies. 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 concentr ation, 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 N EEA and the 303(d) and IWR im pairments is uneven. NEEA ASSETS ratings of “poor” for Indian River, Ca loosahatchee River, Char lotte 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 PensacolaTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-27 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 includi ng estuaries. However, because nutrient enrichment can lead to signifi cant 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 nut rient-enriched phytoplankton production, which can re sult in periodic blooms. The 90 th percentile of chl. a concentration reflects the high c oncentration 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 90 th 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 th e latter, the highest are the St. Johns River, Indian River Lagoon, and Caloosahatchee Rivers, wh ereas three panhandle es tuaries, 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 th e 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 90 th percentile were also “Poor” or “Bad.”

Analysis of ecological conditions in Florida estuaries and the re lation to nutrient loading is ongoing and will be evaluated in greater detail over the next year.

Evaluating TMDLs, PLRGs and Dose-Res ponse Models for Florida Estuaries

A TMDL is estimated as the maximum nutrient lo ad 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., po unds 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 und er 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 standard s call for a reduction, on a watershed basis, of the pollutant loading from older stormwater system s as needed to protect, maintain, or restore the beneficial uses of the receiving waterbody. The amount of needed polluta nt load reduction isTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-28 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 Rive r. 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 consecu tive 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 pe riod 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 string ent than that required to verify impairment). Once a waterbody is verified as impaire d, preparation of a TMDL is scheduled.

The TMDL thresholds or endpoints that have b een determined are provided in Table 2-15. The endpoints include levels for chl. a , BOD, TN, TP and are also expr essed as percent reduction in TN. The process for formulating TMDLs and the re sultant reports include a description of the waters, the regulatory reasons for computing th e 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 predic tive models. Estimated loads are compared to loads that would result in desi red improvements to develop a TM DL threshold. The analytical methods used to justify this threshold, often called assimilative capacit y, are provided with supporting evidence—often as data summaries for re ference sites, or details of models with assumptions, calibrations, and results. Finally the loading limits (WLA s and LAs) are provided, usually with the required percentage of load re duction over current levels . Examples of some of the TMDLs that have been finalized or drafte d are provided below. The examples give an indication of the range of methods used to dete rmine 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 pe riod. Point source assessments included NPDES permits and MS4s. Nonpoint source loadings be low 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 differ ent land use types in the watershed. Because a statistically significant relations hip between loading and chl. a levels was not found, the assimilative capacity for TN lo ading 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 wh en algal biomass was elevated. Maintaining the TN loading at 2003 levels is expected to pr event annual average chl. a values from exceeding 11 μ g/L and provide aTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-29 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 wa ter quality criteria, w ith excursions 40% to 55% of the time, causing these waters to be verifi ed as impaired. Point source contributions were determined on the basis of NPDES and MS4 stor mwater permitted discharges. Nonpoint source loading was determined using the Lemon Bay Wa tershed Model, which uses site-specific land use, soil characteristics, and attenuation fact ors (land to water ra tios, stormwater, and conveyance considerations) to pred ict loads. The model was also used to estimate loads before anthropogenic influences (natural condition). Lo ading predictions were generated for five reference WBIDs that are meeting water quality cr iteria and are similar in size to the impaired watersheds for comparison purposes. Average pe rcent reductions recomme nded via the reference approach were similar to those base d on the natural condi tion 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 fa il 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 estimate d from NPDES discharge data. The nonpoint source loads were determined from the Pollution Load Sc reening Model (PLSM) or Hydrologic Simulation Progr am-Fortran (HSPF) models. PLSM is a GIS-based stormwater runoff model useful for identifying potential runo ff 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 fr om data that include soil type s, 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 recei ving waters. Such a comprehensive system of models simulates the effects of changes in land use and point or nonpoint source treatments on watershed hydrology a nd associated water qua lity. 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 determinedfrom this regression-based, seagrass depthlimit restoration to a –10% departur e (shoreward) from the load that was predicted to provide full restoration of seagrass beds. The basis for the 10% departure is relate d to Florida’s water transparency standard, which states that the “d epth of the compensation point for photosynthetic activity [seagrass depth limits] sh all not be reduced by more than 10% as compared to the natural background value [the condition of the waters in the absence of man-indu ced alterations on the basis of the best scientific information availa ble].” The areal point a nd 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).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-30 Lower St. Johns River (LSJR) Of the 15 LSJR segments, 11 were verified as im paired by nutrients on th e 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) PL SM—for calculating seasonal runoff and nutrient loads for subbasins, (2) a hydr odynamic model (Environmental Fluid Dynamics Code (EFDC)) to simulate the mixing and transport of nutrien ts in the river as a function of wind, tide and salinity intrusion, and results from EFDC were in corporated into (3) a water quality model (CEQUAL-ICM ) that predicts th e effects of point and nonpoi nt source loading on chl. a , DO, and other water quality parameters. TMDL threshol ds were set on the basis of a site-specific alternative DO criteria establishe d for LSJR (minimum DO concen tration 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 us ed as supporting evidence to determine DO concentrations above which continuous exposur e is not expected to result in unacceptable biological effects. The Criteri on Minimum Concentration (CMC), which is defined as a daily DO concentration below which any exposure (most se nsitive species) for a 24-hour period would result in unacceptable acute effect s (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 relationshi p are seldom available, and when sufficient data are present, they often prove difficult to demonstrate a signifi cant relationship. If a si gnificant correlation is not found, a second tier is applied. The second tie r is basically a reference condition approach, applying, for example, the 25 th percentile of a data set fo r similar non-impaired ecologically balanced marine waters to determine the nutrien t TMDL targets. Commonly, the data set that can be collected is composed of a few member estuar ies, 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 s ources 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 applic ations including NPDES stormwater permitting, watershed management, or reser voir protection projects. While th e model addresses loading on a watershed basis, it was not designed for estuarie s 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 Appr oach and the Calculation of Be nch mark Distribution Approach and Selection of Percentiles fr om the Benchmark Distribution
FDEP developed an alternative nutrient criteria development pr ocess based on percentiles of nutrient concentrations from minimally disturbed benchmark reference sites. This approach is presented as an alternative to the principal a pproach. Text on the technical approach used by FDEP to derive these benchmark-based criteria based on least-disturbe d streams is presented here. This text was principally authored by FDEP and was edited by U.S. EPA for this technical document.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

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### ii. Identification of Le ast-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 st reams 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 even tually selected truly represented minimal human disturbance and full de signated 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 me thod, are logically within the ra nge of nutrient concentrations also protective of the designated use.

FDEP, after deliberating with its technical advisory committee (TAC), selected the 90 th percentile of this benchmark distribution for thre shold purposes in all nut rient regions except the Bone Valley. The 90 th 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 discussi on below). In the Bone Valley, FDEP proposed the 75 th percentile because of the limited number of minimally disturbed streams available.



### iii. Verification Process for Se lection of Benchmark Sites
Selection of candidate reference sites by id entifying sites with a corridor Landscape Development Intensity Index (LDI) score of ≤ 2. (This step alone eliminated the majority of Florida sites from further consideration; Fi gure 2-29.) Two additional benchmark exclusions were ultimately based on a whole-watershed LD I analysis conducted by U.S. EPA (Appendix B10).
A critical component of the benchmark approa ch 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 nitr ate concentrations greater than the 0.35 mg/L proposed springs nitrate-nitrite criterion, which reduced the possibility of incl uding 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 concentra tions to remove potentially erroneous data,TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-32  Conducting a field evaluation pro cess, including a watershed a ssessment 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 we re 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 f ound in Appendix B-18. Each of the above steps is described in more detail below.



### iv. LDI score of ≤ 2

Candidate benchmark sites were in itially selected based on an app lication 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. Applicat ion of the LDI is based on the ecological principle that the intensity of human-dominated land uses in a land scape affects the ecological processes of natural communities. More intense activities will result in greater effects on ecological processes. Natural landscapes with little or no agricultura l 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 valu e 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 develope d from high-resolution aerial photographs, the LDI is calculated as follows:

LDI Total = ∑ (LDC i * %LU i ) [7]

where,

LDI Total = Landscape Development Intensity Index for the area of influence %LU i = percent of total area of influence in land use i
LDC i = 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 approa ch”), was found to be a better predictor of ecological health than the LDI calculated on an entire catchment (F ore 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 relate d to the demonstrated effectiveness of riparian corridor zones in removing pollutants, especially nutrients, from stormwat er 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 reach ing the stream (Peterjohn and Corell 1985).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-33 Additionally, Coastal Plain ripari an corridors have been shown to be effective in retaining nutrients because of gradual sl opes, permeable soils, and the a bundance of roots that enter the shallow ground water zones (Lowrance 1997). Si nce phosphorus is typically found bound to sediments, riparian zones retain most of th e incoming phosphorus by capturing sediments. Other studies have shown that nitrat e in shallow ground water beneat h riparian zones was removed by 85% to 90% due to plant uptake and denitrific ation in riparian zones 50 m to 70 m wide (Lowrance 1992; Jordan et al. 1993; J acobs and Gilliam 1985; Lowrance 1997).

For purposes of benchmark site selection, LDI valu es 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 liter ature 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 calc ulated based on a 100 m corridor is an appropriate method of selecting candi date 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 exclusio n 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 recalcula ted the nutrient distributions, which resulted in values different from earlier calculations (Table 2-16).

As discussed in FDEP’s Nutrient Plan (2009), th e LDI was specifically designed as a measure of human disturbance. LDI values of less than or equal to 2.0 within th e 100 m corridor area are indicative of areas with minimal levels of human disturbance. Other studie s and evaluations have demonstrated, across other waterbody types and taxonomic groups, that the LDI is an accurate predictor of biological h ealth; that is, healthy, well-balanced biological systems are much more likely to occur at sites with low LDIs ( ≤ 2.0) than at higher disturba nce 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 mini mally 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 causativ e parameters, were excluded asTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-34 benchmark sites. Additionally, s ites within WBIDs listed on the Ve rified or Pla nning 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 ma y travel in subsurface aquifers for significant distances to be discharged into streams vi a seeps or springs. FDEP determined that anthropogenic activities are responsible for elevat ed ground water nitrate-ni trite concentrations and proposed a nitrate-nitrite crit erion of 0.35 mg/L for springs and clear streams, and U.S. EPA is proposing a similar criterion for springs. Gi ven confidence that the response-based proposed nitrate-nitrite criterion is pr otective, candidate benchmark s ites that exceed this nitrate concentration (0.35 mg/L) were eliminated from consideration as benchmark sites.



### vii. Verification of Surroun ding Land Use by Examinin g High-Resolution Aerial Photographs
Minimally disturbed conditions of every candida te site were confirmed through a review of recent (2004–2005) high-resolution (1 m ground reso lution) 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 ch annelized 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. Specificall y, they were presente d 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 defi ne the benchmark condition. Ken Weaver has produced the attached table of low LDI peninsul ar 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 woul d 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 dist rict staff. The staff identified additional channelized streams, estuarine sites, and potenti ally disturbed sites. In some cases the staff identified potential point source discharges or locali zed 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 de velopment within the watershed that were outside the 100 m co rridor but, in their opinion, could potentially increase nutrient concentrations. Exclusion of these potentially disturbed si tes represents an additional conservative component of FDEP’s approach designe d to ensure that the benchmark set consists solely of minimally disturbed locations.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-35 Additional sites were excluded because they were potentially estuarine or tidally influenced based on proximity to the coast and a subseque nt review of specifi c conductance data. All potentially estuarine sites routinely had sp ecific 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, Includ ing Watershed Assessment and Biological Appraisal of Benchmark Sites
In 2007 and 2008, experienced FDEP Tallahassee st aff conducted a study of a number of the candidate benchmark sites, selected through th e above process, as a means of providing additional assurance that sites we re 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 mi d-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 se lection of the final be nchmark sites, focusing especially on those with nutri ent concentrations higher than the middle of the distribution.

Sites visited were selected to be representa tive of most of the WB IDs in the candidate benchmark data set. The site with the most ex tensive 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 accessi ble to FDEP, guided by high-resolution aerial photographs taken in 2004–2005 and maps of the entire drainage basin. During the watershed survey, FDEP investigator s made a series of observations regarding potential human disturbances in the watershe d, including potential nonpo int source inputs and hydrologic modifications (using the FDEP hydrologi c scoring system). The hydrologic scoring system was originally develope d 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 dist urbance in a system (Fore 2004; Fore et al. 2007).

Stream Habitat Assessments (HAs) were c onducted following DEP-SOP-001/01 FT 3100 (FDEP 2008). The HA evaluates substrate condition and avai lability, water velocity, habitat smothering (e.g., by sand and silt), channelization, bank stab ility, 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 upstr eam of the site, includi ng potential riparian zone breaches.

At each site, trained and experienced FDEP sta ff also collected and analyzed the biological, chemical, and physical parameters list ed in Table 2-17 following FDEP SOPs (http://www.dep.state.fl.us/labs/sop). Water le vels were evaluated both by reviewing hydrographs from the given stream or other stre ams in the general vicinity and by visually inspecting the stream habitats. Biological samp les (e.g., SCI) were not collected if, based on the judgment of the experienced investigator, cu rrent or antecedent flow conditions wereTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-36 inappropriate, or a majority of the aquatic habi tat was exposed to the air rather than being inundated. Water chemistry samples were collec ted 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 waters hed 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 screeni ng criteria, the results of the stream surveys provide confiden ce that nutrients associated w ith FDEP’s benchmark data set fully support healthy, well-balanced aqua tic communities (see discussion below).



### x. Results of Benchmark Screening Process

The initial set of candidate reference sites identi fied 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 site s through the multi-step screening process, a total of 493 stations in 129 WBID s remained. Due to time and resource considerations, not all of these WBIDs could be visited; therefore, FDEP emphasized field verifi cation 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 conditi ons), 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 Peri phyton Survey was conducted at 54 sites, and periphyton community structure da ta were collected at 60 site s. Summary descriptions and evaluations for each of the benchmark sites are incl uded 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 nutri ent 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 pa rameters 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 acr oss 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 f ound between LDI score and SCI sc ore (Figure 2-33). (Note that SCI scores were averaged for sites with more th an one SCI sampling event.) This indicates that LDI scores of up to 2.2 (the LDI value at one of the Bone Valley site s), when coupled with FDEP’s verification process, are associat ed with healthy biological communities.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-37 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 90 th percentile of the benchmark distribution. Biological communities with SCI ≥ 64 were found at sites with TP concentra tions 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 90 th percentile were protective of the natural populations of flora and fauna in minimally disturbed streams.

Periphyton community compositi on and taxonomy, and the extent and thickness of algae coverage (RPS), were also coll ected at benchmark sites. The periphyton community response to environmental factors is complex, and data an alysis 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 abund ance are, in part, significantly related to nutrients; however, FDEP conclude d that the relationships discerne d were statistically too weak to establish credible numeric nut rient thresholds. Benchmark sites in general exhibited low algal coverage and thickness, except for a few sites wi th limestone outcroppings, where algal growth was moderate. Based on the periphyton analyses and observations made during benchmark site field studies, evidence suppor ts the selection of the 90 th percentile of the benchmark site nutrient distribution for establishmen t of protective criteria.



### xii. Calculation of Benchmark Derived Nutrient Criteria

Data Handling Nutrient data from the benchmark sites were qu eried from Florida STORET, FDEP’s Status and Trends data set (GWIS database), and the benc hmark site verification data set. Data were screened for potential data quality issues (e.g., improper sample preserva tion, analysis performed outside hold time). Additionally, statistical outlier analysis wa s 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:

75 th percentile + 3.0 * Interquartile Range [8]

Data points identified as outliers were ex cluded from the data set (Table 2-19).

A number of the benchmark waterbodies we re 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 envi ronment; 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).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-38 For the reasons discussed above, annual WBID geometric means were calculated for purposes of evaluating the frequency distribution of n itrogen 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 c onsidered, 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 scr eening of candidate benchmark site s) used to derive TN and TP criteria consisted of 565 WBID -years with a minimum of f our TP measurements. The 565 WBID-years of data included 5,999 individual TP measurements colle cted 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 WB ID-years of data included 5,037 individual TN measurements within 127 WBIDs. Upper-percentile thresholds we re calculated based on the lognormal frequency distribution of annual WBID ge ometric mean concentra tions by region (Table 2-16).

Phosphorus criteria were calculated as the 90 th percentile annual geom etric mean concentration separately for the Panhandle, Northeast, North Central, and Peninsula nutrient regions using the equation:

90 th Percentile =e [ln(mean nutrient concentration) + 1.282 x ln(standard deviation)] [9]

The regionalization for this alternative approach di ffered slightly from that used in the principal criteria approach. The phosphorus criterion fo r the Bone Valley was calculated as the 75 th percentile annual geometric mean concentration. A lower percent ile was selected for the Bone Valley due to greater uncertainty in the upper en d of the distribution for this region given the limited number of benchmark sites and WBIDs.

TN criteria were cal culated as the 90 th percentile annual geometric mean concentration for the Panhandle nutrient region and combined Northeas t, North Central, Peninsula and Bone Valley region. Nitrogen concentrations did not exhibit the same regi onal spatial patterns as that observed for phosphorus. The primary regional di fference was between the Panhandle and the remainder of the state. Therefore, a single TN cr iterion was applied for this alternative approach criterion method to the Northeast, North Ce ntral, Peninsula, a nd Bone Valley regions.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters2-39Table 2-1. Distributional Statistics ofStream TP and TN by Estuarine Watershed and Aggregated by U.S. EPA NWRStream TP Conc in EDA/FDA/CDA (mg/L)Stream TN Conc in EDA/FDA/CDA (mg/L)EDA CodeWatershed NameMeanMedian75th%tile90th%tileCountMeanMedian75th%tile90th%tileCountMedianN:P Ratio(mol:mol)NutrientWatershedRegionG140xPerdido Bay EDA0.1140.0260.1220.36012191.550.711.614.35121950PanhandleG130xPensacola Bay EDA0.0300.0200.0350.05424360.770.560.781.23243656PanhandleG120xChoctawhatchee Bay EDA/FDA0.0400.0270.0450.06610870.520.490.650.81108738PanhandleG110xSt. Andrew Bay EDA0.0190.0110.0200.0206780.500.360.550.7567869PanhandleG100xApalachicola Bay EDA/FDA0.0470.0270.0470.07712190.920.751.011.58121958PanhandleG090xApalachee Bay EDA0.1050.0480.0940.15675230.880.630.941.45752327PanhandleG086xEconfina-Steinhatchee CDA0.0910.0690.1040.1657770.910.871.171.4177725PanhandleG080xSuwannee River EDA/FDA0.3020.1300.2110.449144691.131.031.291.621446917North CentralG070xTampa Bay EDA0.5950.2900.6201.270147731.391.131.562.18147739Bone ValleyG060xSarasota Bay EDA0.2930.2500.3700.5103561.221.161.421.6635610Bone ValleyG050wCharlotte Harbor EDA0.5260.3500.6381.05162101.581.281.802.5562108Bone ValleyS190xIndian River/ St. Lucie EDA0.2380.2070.3110.440171821.451.371.662.031718214PeninsulaS183xDaytona St. Augustine CDA0.1170.0880.1300.19021900.980.941.221.52219022PeninsulaS180xSt. Johns River EDA/FDA0.1820.0920.1710.392414281.511.221.672.444142828PeninsulaS175xNassau CDA0.2040.1300.1940.2754551.301.211.511.8145520PeninsulaS170xSt. Marys River EDA0.0640.0420.0600.0805301.141.091.351.6453055PeninsulaG078xWaccasassa CDA0.0740.0640.0800.1003790.900.720.991.4437924PeninsulaG076xWithlacoochee River CDA0.0660.0420.0770.13025551.090.971.301.73255543PeninsulaG074xCrystal-Pithlachascotee CDA0.0760.0310.0900.18044010.770.611.051.49440127PeninsulaG050aCaloosahatchee River EDA0.1140.0800.1330.22056301.051.001.401.71563026PeninsulaF100xKissimmee River/Lake Okeechobee FDA0.3110.1750.4400.706191391.701.531.962.501913920PeninsulaNote: See NWR tables referenced in Appendix B-4 and contained on CD.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-40 Table 2-2. Results of Interval and Equivalen ce Tests Conducted on Reference Sites with 2 SCI Results

Impairment threshold (description) Ref site

mean Impairment threshold (numeric) Impaired Undetermined Reference

2.5 th percentile of reference 65 40 <35 35–44 >44

5 th percentile of reference 65 44 <39 39–47 >47

Note: Shown are site mean, impairment threshold, and ran ge for threshold values defin ed 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 Statisti cs of TN and TP Based on Site Average Values. The Instream Protective Criteria are the 75th Percentile Valu es (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 17TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-41 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 Flor ida Estuaries, Normalized to Estuary Area (g
N m 2 /y; SE: Standard Error)

Estuary name Current TN
load ± SE (g N m 2 /y) Background TN load ± SE

(g N m 2 /y) Protective TN
load ± SE (g N m 2 /y) TN load limit, Lm ~f(qs) ± SE g N m 2 /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 fr om the USGS regional SPARROW model for the SAGT basins. The protective TN load is t he average of the current and background TN load, and the standard error was computed as the standard error of t he average assuming that the errors are independent. The maximum TN load limit estimates were derived as a function of hydraulic load, f(q s ), 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 uni t of estuary area. To express the loading in units of kilogram per year, multiply the values by estuary area, whic h are reported in Table 2-8. The resulting quantity is the estimated loading from the full watershed. To compute t he loading from the portion of the watershed resolved by the Sparrow model, multiply the fraction of the watershed resolved in Sparrow (Table 2-9).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters2-42Table 2-7. Application of Downstream Protection Methodology to Estimate TN Criteria in Stream Reaches that Ensure Protection ofDownstream WatersStream reach category—fraction TN delivered to estuarine waters0.0–0.50.501–0.60.601–0.70.701–0.80.801–0.90.901–1.0Stream reach TN criteria protective of downstream use in estuaryEDAcodeEstuary nameTN(mg/L)TN(mg/L)TN(mg/L)TN(mg/L)TN(mg/L)TN(mg/L)Currentaverage TNin terminalreachesb(mg/L)G140x Perdido Bay NR NR NR NR 0.34 0.30 0.42G130x Pensacola Bay NR NR NR NR 0.48 0.43 0.55G120x Choctawhatchee Bay NR NR NR 0.48 0.43 0.39 0.55G110x St. Andrew Baya0.48 NR NR 0.30 0.27 0.24 0.26G100x Apalachicola Bay 0.91 NR 0.65 0.57 0.51 0.46 0.67G090x Apalachee Bay NRNR NR 0.67 0.59 0.53 0.71G086x Econfina-Steinhatchee NR NR NR NR 0.41 0.37 0.39G080x Suwannee River NR NR 0.78 0.69 0.61 0.55 0.76G078x Waccasassa NRNR NR NR 0.45 0.40 0.50G076x Withlacoochee Riverc1.55G074x Crystal-Pithlachascoteec0.21G070x Tampa Bay 1.11 0.93 0.80 0.70 0.62 0.56 0.92G060x Sarasota Bay NR NR NR NR NR 0.54 0.85G050w Charlotte Harbor NR1.58 1.35 1.18 1.05 0.95 1.52S190x Indian River NRNR NR 0.87 0.77 0.69 1.15S183x Daytona St. Augustinec1.65S180x St. Johns River 1.411.17 1.00 0.88 0.78 0.70 0.97S175x Nassau 0.59 NRNR NR 0.33 0.30 0.35S170x St. Marys RiveraNR NR NR 0.43 0.38 0.34 0.40NR = 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 estuaries. Protection ofdesignated use in these estuaries might require reduction of these point source loads,in addition achieving TN criteria for downstream use protectionb. Terminal reaches are those reaches discharging directly to estuarine waters. These stream reaches, by definition, have fraction TN delivered = 1.0.c.Protective loads and flows necessary to compute downstream protection values require furtherevaluation in these systems.Note: TN criteria or the downstream protection values are presented in stream categories definedon the basis of the fraction of TN transported in the reach that isultimately delivered to estuarine waters.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters2-43Table 2-8. Characteristics of Florida Estuaries and their Watersheds as Delineated by the NOAA CAFEDA/CDAcodeEstuary nameEstuaryarea(km2)Watershedarea(km2)Ratio ofwatershed toestuary areaEstuaryvolume (1,000x m3)Meandepth(m)# StreamreachesG140x Perdido Bay129 2,928 23 197,900 1.53 26G130x Pensacola Bay477 17,650 37 1,439,402 3.02 198G120x Choctawhatchee Bay340 13,496 40 1,291,654 3.80 175G110x St. Andrew Bay252 2,697 11 712,299 2.83 17G100x Apalachicola Bay593 52,215 88 1,073,374 1.81 474G090x Apalachee Bay427 14,288 33 819,840 1.92 87G086x Econfina-Steinhatcheend 2,047 nd nd nd 7G080x Suwannee River164 25,989 159 191,637 1.17 154G078x Waccasassand 2,128 nd nd nd 9G076x Withlacoochee Rivernd 5,133 nd nd nd 16G074x Crystal-Pithlachascoteend 2,993 nd nd nd 3G070x Tampa Bay902 5,703 6 2,705,905 3.00 60G060x Sarasota Bay124 653 5 271,371 2.19 2G050w Charlotte Harbor502 8,134 16 819,015 1.63 73S190x Indian River866 3,093 4 667,081 0.77 5S183x Daytona St. Augustinend 1,882 nd nd nd 14S180x St. Johns River684 23,214 34 1,510,763 2.21 101S175x Nassaund 1,085 nd nd nd 6S170x St. Marys River64 4,386 69 213,760 3.34 29Note: nd = not determinedTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters2-44Table 2-9. Average TN Loads to Florida Estuaries Normalized to 2002Estuary (EDA code)Watershedarea(km2)Fraction ofwatershedresolved inSPARROWaWatershedTN loadb(kg/y)TN yield(kg/km2/y)PointSourceTN Loadc(kg/y)MeanFlowd(m3/s)Runoff(Cm/y)MeanTN(mg/L)Perdido Bay (G140x)2,928 89% 1,335,499 4560 89 108 0.42Pensacola Bay (G130x)17,650 97% 5,796,330 328 161,685 321 59 0.55Choctawhatchee Bay (G120x) 13,496 96% 4,234,121 3140 236 57 0.55St. Andrew Bay (G110x)2,697 65% 514,281 191 254,540 41 74 0.26Apalachicola Bay (G100x)52,21599% 16,257,090 311 1,412 764 46 0.67Apalachee Bay (G090x)14,288 97% 3,451,330 2420 151 34 0.71Econfina-Steinhatchee (G086x)2,047 52% 372,346 182 0 16 47 0.39Suwannee River (G080x)25,989 101% 7,484,483 2880 313 38 0.76Waccasassa (G078x)2,12892% 587,731 276 0 34 55 0.5Withlacoochee River (G076x)5,133 109% 438,142 8550 10 6e1.55eCrystal-Pithlachascotee (G074x) 2,993 28% 1,171,542 391 20,489 50 189e0.21eTampa Bay (G070x) 5,703 84% 2,541,245 446 55,831 73 48 0.92Sarasota Bay (G060x) 653 60% 408,248 625 2,321 9 72 0.85Charlotte Harbor (G050w) 8,134 96% 4,522,351 556 1,998 91 37 1.52Indian River (S190x) 3,093 30%2,560,222 828 18,360 21 71 1.15Daytona St. Augustine (S183x)1,882 95% 660,380 351 45,856 12 21e1.65eSt. Johns River (S180x) 23,214101% 6,726,409 290 609,935 223 30 0.97Nassau (S175(x) 1,085 77%203,714 188 267 14 41 0.35St. Marys River (S170x) 4,38684% 785,284 179 134,053 52 37 0.40a. Fraction of watershed area computed from NOAA coastal assessment framework resolved within SPARROW model. Values greater than 100% reflectdifferences in delineation of watershed boundaries.b. Computed from load delivered to estuarine water estimated inSPARROW divided by the fraction of the watershed resolved by SPARROW. Assumes that theyield of nitrogen per unit of watershed area is the samein 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 orhigher than the expected range and warrant further evaluation.Streamflow estimates affectestimate of average TN concentrationNote: Computed Using Estimates from the SATG regional SPARROW model. Mean flow includes only the flow in streams resolved by SPARROW and is notscaled to reflect diffuse freshwater inputs from other portions of the watershed.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters2-45Table 2-10. Information on Downstream River GageStations in Florida Estuarine WatershedsEDAcodeEstuary nameStation IDStation nameGageDatayearsMean TNload(kg/y)StandarddeviationTN loadCV (%)TNloadG140x Perdido Bay 21FLBFA.33010004 Perdido River at Hwy 90 BridgeSTORET 1992-2004 560,683 252,075 45%G130x Pensacola Bay 21FLBFA.33030005Big Coldwater Creek Hwy 191 NEMiltonSTORET1991,1998,2001-2004 376,742 77,177 20%G130x Pensacola Bay 21FLGW.3545Blackwater River at Hwy 4 NW ofBakerSTORET 1999-2004 110,126 60,745 55%G130x Pensacola Bay 21FLBFA.33020019 Escambia River Hwy 90 BridgeSTORET 1994-2004 3,055,931 1,081,292 35%G130x Pensacola Bay 21FLBFA.33040004Yellow River Hwy 90 West ofCrestviewSTORET 1990-2004 441,776 173,913 39%G120xChoctawhatcheeBay 21FLGW.3536 Alaqua Creek at Nelson RoadSTORET 1999-2004 26,539 14,111 53%G120xChoctawhatcheeBay 21FLGW.3534 Choctawhatchee River near BruceSTORET 1999-2004 3,124,142 1,491,443 48%G110x St. Andrew Bay 21FLGW.3531Econfina Creek at C.R. 388STORET 1999-2004 107,947 41,118 38%G100x Apalachicola Bay 21FLGW.3524Apalachicola River, Sumatra,Brickyard LandingSTORET 1999-2004 12,105,372 5,230,608 43%G090x Apalachee Bay 2327100Sopchoppy River near Sopchoppy, FLNWIS 1975-1997 125,769 51,999 41%G090x Apalachee Bay 21FLA.22050023 Econfina River at U.S. 98STORET 1976-2000 122,209 86,939 71%G090x Apalachee Bay 21FLGW.3527Ochlockonee River at F.R. 13STORET 1999-2004 1,270,712 813,853 64%G090x Apalachee Bay 21FLGW.3528 St.Marks River at Hwy. 98 NewportSTORET 1999-2004 195,965 62,474 32%G086xEconfinaSteinhatchee02324000Steinhatchee River near Cross City,FLNWIS 1976-1995 177,865 111,503 63%G080x Suwannee River 21FLSUW.SUW275C1 Suwannee River at Gopher RiverSTORET 1990-2004 9,263,637 4,111,771 44%G076xWithlacoocheeRiver 21FLGW .3513 Withlacoochee River at Stokes FerrySTORET 1999-2004 837,504 987,564 118%G074xCrystalPithlachascotee 02310300Pithlachascotee River New PortRichey, FLNWIS 1975-2004 21,229 23,124 109%TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters2-46EDAcodeEstuary nameStation IDStation nameGageDatayearsMean TNload(kg/y)StandarddeviationTN loadCV (%)TNloadG074xCrystalPithlachascotee 02310000 Anclote River near Elfers, FLNWIS 1975-2000 63,407 58,884 93%G070x Tampa Bay 2299950Manatee River near Myakka Head, FLNWIS 1975-2004 127,908 58,283 46%G070x Tampa Bay 2300500Little Manatee River near Wimauma,FLNWIS 1975-2000 382,453 210,330 55%G070x Tampa Bay 2300700Bullfrog Creek near Wimauma, FLNWIS 1989-2004 56,016 23,805 42%G070x Tampa Bay 21FLHILL.138 Delaney Creek at 36th Ave & 54th St.STORET 2000-2004 11,576 4,641 40%G070x Tampa Bay 21FLHILL.137Hillsborough River at Columbus DriveBridgeSTORET 2000-2003 300,782 385,943 128%G070x Tampa Bay 21FLIMCA.LM674 Little Manatee Riverat SR 674 STORET 1996-2002 47,621 29,319 62%G050w Charlotte Harbor 2298202Shell Creek near Punta Gorda, FL NWIS1983-1987,1995-2000434,991 244,275 56%G050w Charlotte Harbor 2297100Joshua Creek atNocatee, FLNWIS 1983-2000 419,935 206,728 49%G050w Charlotte Harbor 21FLSWFD.FLO 27 59 0 Horse Creek near Arcadia STORET 1998-2003 511,035 331,985 65%G050w Charlotte Harbor 21FLGW.3499Myakka River -Snook Haven Dock STORET 1999-2004 560,890 233,566 42%G050w Charlotte Harbor 21FLGW .3556 Peace Riverat Arcadia STORET 1999-2004 1,813,241 1,131,516 62%S180x St. Johns River 21FLA.20020070 Oklawaha River at SR 19 STORET 1978-2000 783,762 417,009 53%S180x St. Johns River 21FLVEMD.SJ28 St. Johns River at CM 19STORET1993,1995 5,411,305 1,605,067 30%S170x St. Marys River 21FLSJWM.44731 MPS-Middle Prong St.Marys at Hwy 127 STORET 1998-2001 141,032 158,160 112%Note: Includes the period of record and the average and standard deviation of the loadover the period of record. The CV (%) isthe coefficient of variation(standard deviation/mean), which provides an estimate ofthe interannual variability in TN load at each gage.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-47 Table 2-11. Selected Results for Florida Es tuaries from NEEA (B ricker 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 coast line, starting in northeast Florida. Results for Influencing Factor, Eutrophic Condition, and Future Outlook are (wor st to best) Unknown/Low, Moderate/Low, Moderate, Moderate/High, or High. Results for ASSETS rating are (wor st to best) Unknown, High, Good, Moderate, Poor or Bad.

* the authors of this study do not agree with the NEEA resu lts for Pensacola Bay. Results shown here, however, are as published by Bricker et al. 2007.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-48 Table 2-12. Summary of Nutrient-r elated 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) verifi ed 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 OxygenTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters2-49Table 2-13. Seasonal 90thPercentile (P90) and GeometricMean Surface (depth < 1.5 m) Chl.ain Florida EstuariesSpringSummerFallWinterAnnual AverageEstuary nameNP90GeomeanNP90GeomeanRank(P90)NP90GeomeanNP90GeomeanP90GeomeanRank(P90)St. Marys River*61.001.00 3 6.00 2.04 1741.001.00 3 1.00 1.002.251.2617St. Johns River83211.575.40 998 24.64 10.53 390518.145.95 760 10.37 4.3516.186.564Indian River3,9549.083.46 3,433 16.32 5.43 73,49123.508.16 3,993 13.40 3.8215.575.225Biscayne Bay7641.101.05 795 1.42 1.11 188471.411.11 678 1.00 1.041.231.0819Florida Bay1211.001.02 152 1.40 1.09 191772.001.16 140 1.00 1.041.351.0818S. Ten Thousand Is3127.302.70 320 7.35 2.64 143206.252.91 334 8.70 3.127.402.8513N. Ten Thousand Is.1244.702.37 141 7.10 3.62 151367.803.78 136 5.10 2.466.173.0615Rookery Bay488.003.03 47 8.90 4.16 124711.704.48 49 8.50 3.289.273.7412Charlotte Harbor20341.007.08 194 68.10 9.32 119715.604.63 152 15.40 5.1335.026.541CaloosahatcheeRiver1,20210.703.20 1,101 17.50 5.29 61,19817.405.54 1,126 12.00 3.4614.404.376Sarasota Bay6447.002.85 637 12.57 5.42 863114.675.24 638 6.30 2.6210.134.0410Tampa Bay2,07718.706.51 1,897 31.08 11.64 21,98131.9010.53 2,260 14.43 5.4924.038.542Suwannee River10110.202.29 141 18.70 5.13 511818.203.16 133 5.87 2.1213.243.187Apalachee Bay*411.007.21 10 11.50 4.42 111613.002.8411.834.829Apalachicola Bay34214.703.02 349 12.20 3.76 1040310.203.18 334 13.70 3.6812.703.418St. Andrew Bay4195.003.26 470 8.00 3.78 133768.003.98 401 5.00 3.226.503.5614Choctawhatchee Bay2155.003.27 237 6.48 3.49 161705.003.39 183 5.00 3.065.373.3016Pensacola Bay3388.704.59 362 12.25 5.35 93099.204.70 244 8.14 4.509.574.7811Perdido Bay4016.356.16 15 21.90 10.74 42118.907.12 9 25.80 7.5420.747.893Note: Computed from data in FDEP’s IWR Run 34 database. Data wereused only if the station location is at least 250 m from land. Estuaries are arrangedclockwise around the coast from northeast Florida to the panhandle region. Annual averages are the average of either the seasonal P90 value or the seasonalgeometric mean. Estuary rank for summer chl. a and annual mean chl. a are based on the P90 values, which are presumed to reflect the intensity of blooms whenthey occur.* Statistics for these estuaries might be particularlyunreliable because of the small number of measurements.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-50 Table 2-14. FDEP TMDLs, Marine WBIDs Consid ered, 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-Biscay ne 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-Caloosahatc hee 3240J Nutrients, BO D, DO 3/2006 EPA Region 4
PROPOSED Escambia Bay 548 Nutr ients 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 FDEPTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-51 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 69TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-52 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 75 th %tile 90 th %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 est ablished as the 90th percentile values, with the exception of TP in the Bone Valley, where the 75 th percentile is more defensible (due to small sample size). These final values are the result of all the various verification and ex clusion 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.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-53 Table 2-17. Parameters Monitored Du ring 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)TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-54 Table 2-18. Summary of Informati on Collected from Field-verified Nutrient Benchmark SitesTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-55 Table 2-19. Summary of the Regi onal Data Outlier Analysis a nd Exclusions for the Florida Stream Nutrient Benchmark Data Set
TP (mg/L)

Parameter Region N 25 th

Percentile 75 th

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 0.8TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-56 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.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-57 Figure 2-2. Stream bioregions of Florida.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-58 Figure 2-3. Location of the Peace River and B one Valley Member geologic formations.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-59TN (mg L-1)

0.0 0.5 1.0 1.5 2.0 2.5 6.0 8.0 10.0TP (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 OkeechobeeTN: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.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-60 Figure 2-5. NWRs in Florida.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-61 Figure 2-6. Typical macroinvertebra te response to organic load ing associated with primary wastewater treatment typi cal in the 1950s and 1960s.

Figure 2-7. Photo of Hester-Dendy samplers used for determining the Shannon-Weaver diversity index.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-62 Figure 2-8. Factors affecting biol ogical community composition. To conclude that human factors are primarily responsible for biological degradat ion, 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.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-63 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 1Biological Condition

Level of Exposure to Stressors

Figure 2-10. The BCG model (fro m Davies and Jackson 2006).

Figure 2-11. Regression line with 90% confidence interv al showing the relationship between the mean BCG score and SCI score.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-64 Figure 2-12. BCG tier assignments modeled with a proportional odds logistic regression.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-65 Figure 2-13. Plot of TP concentration of grab samples taken at all site s sampled for biological condition (SCI sites). Concentrations are in milligrams per liter.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-66 Figure 2-14. Plot of TP concentration of grab samples taken from sites sampled for biological condition (SCI sites) that met the SCI threshol d 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 concentra tions in biologically healthy sites by nutrient region.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-67 Figure 2-15. Plot of TN concentr ation of grab samples taken at all sites sampled for biological condition (SCI sites). Concentrations are in milligrams per liter.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-68 Figure 2-16. Plot of TN concentr ation of grab samples taken fr om sites sampled for biological condition (SCI sites) that met the SCI threshol d of 40. Map symbols as in Figure 2-15. Also shown are histograms of TN con centrations and cumulative (hatch ed line) and empirical (solid line) percent frequencies of TN concentration in biologically healthy sites by nutrient region.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-69 Figure 2-17. Critical stream TP concentration fo r 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 resi dence 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 0.5Critical Stream TP (mg/L)

Peninsula Bone Valley

Panhandle 0.775 mg/L

Figure 2-18. Critical stream TP concentrations fo r stream inflows into clear, low-conductivity lakes. Blue (lower curve): stream flow is 100% of lake water inpu t; 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 i nput. Horizontal dashed lines: 75 th percentile proposed instream protective values for TP regions.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-70 

### 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 0.5Critical Stream TP

Peninsula Bone Valley

Panhandle 0.775 mg/L

Figure 2-19. Critical stream TP c oncentrations 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 inst ream 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 1.0Critical Stream TP (mg/L)

Panhandle Peninsula Bone Valley

Figure 2-20. Critical stream TP con centrations for stream inflows in to colored lakes. Blue (lower curve): streamflow is 100% of la ke water input. Red (middle curv e): streamflow is 50% of lake water input. Green (upper curve): streamflow is 10% of lake wa ter input. Horizontal dashed lines: 75th percentile propos ed instream protective values for TP regions.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-71 10000 100000 1000000 10000000 100000000

Nassau Econfina-Steinhatchee

S

ar

a

so

t a Bay Crystal-Pithl a ch

a sc

o tee

St. An

d

rew B a y

W

it h lac o oc

h

e e

Ri

v er

W

a

cc

a sa

ss

a

Day to

na

St . Au g

us

ti

ne

St . Ma r ys

R

iv

e

r

In

d

ian River

Perdido Bay

T

ampa Bay Apalachee Ba y Choctawha tc

he

e B

a y

C

ha

rl

o tte

H

a rb

o

r

Pe

n

sa

c ol

a

B

ay

St . Jo

h

ns

Riv e

r

S

uwan n ee

Riv e

r

A

palac h ico

l a BayTN 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%.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-72 Figure 2-22. Map showing the spa tial 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 netw ork and Florida’s wa terbodies (WBIDs).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-73 Figure 2-23. Map showing Florida estuaries a nd their watersheds as delineated by the NOAA
CAF.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-74 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Perdi do

Bay Pensacola Bay

C

hoct awhat ch

ee

B

ay

S t . A

n dr

ew

B

ay Apalachicol a Bay

A

pal

a chee Bay

Su

w

anne e River

T

a mp

a Bay

Sa

r aso

t a

B

ay

C

harlotte Harbor Indian River

S t . Jo

hns R

i ver St. Marys RiverLanduse (% of total watershed area)

Urban Agriculture Forest Wetland Range

Figure 2-24. Land use characteri stics of Florida estuarine wa tersheds as reported by NEEA (Bricker et al. 2007).

0% 25% 50% 75% 100%

Per d

ido

B

a y Pens a cola B

ay

C

hoc ta

wha tc

hee Bay St. Andrew Bay

Apal achi c ola B

ay Apalachee Bay

Econfi na-

St

ei

nhatch e e Suwannee River Wacc as

ass a

Wi

thl

acooc h ee R i ver

C

rys

ta

l-P

i thla c hasc otee

Ta

mp

a B a y

Sar as

o ta Bay Charlotte Harbor
Indi a n Ri v er

D

ay

tona St. Augus ti

ne
St. J ohn s R iv

e r

N

as

s au

S

t. M arys R

i ver% 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, fe rtilizer, manure, and urban land us e) 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).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-75 0 1 2 3 4 5 6 7 81983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007

YearAtm 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

P

en

s acol a B

a y

Cho c ta

wha tch

e e B

ay St. Andrew Bay

A

p al

a c hi

col

a B

a y Apalachee Bay

Eco

nfi

n a-

S

t ein

h

a tc

he

e

Su

w

a n nee Rive r

W

acca sa

ss

a

W

it h laco o ch

e e R

i ver

Crys t al-Pithlachascote e

Ta

m

pa

B

ay

Sa

ras

o ta

B

ay

C

h arlo

t te Harbor

In

di

a n River

D ayt

o n a S

t

. Augu s t i ne

St

. J

oh

n s Ri v er

N

as

sa

u

St.

M

arys R i verTN Yield fromAtmospheric Sources (kg/km2/y)

0.00 0.25 0.50 0.75 1.00Fraction of Atmosphericto Total N Load TN Yield

Fraction

Figure 2-27. TN yield (i.e., per km 2 of watershed area) from atmo spheric sources (left axis) and the fraction of loads from all sources contri buting to watershed lo ading contributed by atmospheric deposition (right ax is). All data were derived fr om the SAGT regional SPARROW watershed model (Hoos and McMahon 2009).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-76 0% 25% 50% 75% 100%

Perdi d o Bay Pensacola Bay

C

h oct a w h at

ch

ee

Bay St. Andrew B

ay Apalach i co

l a B a y Apal a chee Bay

Suwannee Ri

v er Tampa B a y Sarasota Bay
Char l ott

e

Har b

or Indian River
St. J o hns R i ve

r St. Marys RiverPercent Watershed AreaGaged 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 furthe r consideration (except for a single site in the Bone Va lley Region that, due to the sparse ness 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 76cLDI

1 2 3 4 5 6 7 8 9 10TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-77 Figure 2-30. Relationship between nutrient loadi ng (nitrogen in panel (a), phosphorus in panel (b)) and the LDI in the St. Marks watershed, Flor ida (after Brown and Vivas 2003). The arrow is the LDI value at 2.0, the be nchmark site threshold.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-78 Figure 2-31. Depiction of land use area (light yellow) included in an LDI calculation.

Figure 2-32. Relationship between the LDI and St ream Condition Index across the entire range of LDI.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-79 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 2.4Average SCI Score

LDI Score 

### Benchmark Site LDI vs. Average SCI Score

Figure 2-33. Benchmark site LDI vs. Average SCI sc ore. 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 in dicates 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 (aft er QA review) were excluded from the benchmark data set for calculation of the fina l nutrient distribution.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

2-80 Figure 2-35. Benchmark site SCI vs. TP. Note the lack of correlation between SCI and TP throughout the range of benchmark sites. This in dicates 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 (aft er QA review) were excluded from the benchmark data set for calculation of the fina l nutrient distribution.