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## 1.0 Introduction

The Florida Department of Environmental Protec tion (FDEP) is currently developing a rule to establish numeric nutrient criteria for a number of water body types. The following summarizes FDEP view on the development of numeric nut rient criteria in Florida (FDEP website, 2011).
The overarching FDEP goal is to “manage nutrients in surface water and groundwater at loadings or concentrations that result in protection and maintenance of healthy, well-balanced aquatic communities”. Absent site-specific analyses , water quality criteria should be based on a quantifiable linkage between anthropogenic nutrient en richment and a biological response (such as seagrass growth) that can be used to numerically in terpret the narrative nutrient criteria. There is value in knowing whether nutrient concentrati ons are potentially elevated to environmentally harmful levels, but conversely, it is equally impo rtant to identify adverse biological effects and determine if they are linked to nutrients before deci ding that nutrient reductions should be pursued.
The Department recognizes the role of site-spe cific factors that affect nutrient responses and proposes to base new standards on establishing a systematic numeric interpretation of the existing narrative criteria. This concept is intended to implement Rule 62-302.530 (47)(b), FAC which states, “in no case shall nutrient concentrations of a body of water be altered so as to cause an imbalance in natural populations of aquatic flora or fauna.”
As envisioned by FDEP, the narrative nutrient crite ria would continue to apply to all waterbodies, and numeric interpretations would be applied when adequate scientific information is available. The narrative would be implemented using a systema tic structure that numerically interprets the narrative nutrient criteria for each waterbody in a hierarchical manner

Numeric interpretations are most accurate when determined as a s ite-specific function. Therefore, nutrient Total Maximum Daily Loads (TMDLs), Site Specific Alternative Criteria (SSAC), and other site specific actions written to achieve the narra tive nutrient criteria should be given preference over more broadly applicable interpretations.
The first tier of the hierarchical approach entails an evaluation of previously established site-specific numeric interpretations of the narrative criteria (in cluding TMDLs, SSACs, and other interpretations embodied in an official Department action. This would be the primary interpretation of the narrative nutrient criteria. Currently, the Saraso ta Bay Estuary Program (SBEP) Policy Board has approved a series of segment-specific numeric nutrie nt criteria. FDEP is represented on the SBEP Policy Board, therefore, Sarasota Bay is presumably a candidate for inclusion in the first tier of the hierarchical approach.

FDEP has also stated that if nutrient TMDLs are expressed as loads instead of concentrations, the loads do not have to be translated into concentra tions to be deemed the numeric interpretation of the narrative nutrient criteria. This document pr esents concentration-based numeric nutrient criteria for the five bay segments of Sarasota Bay.
Finally, FDEP recognizes that futu re TMDL rules may include a resp onse target (chlorophyll a, for example) designed to implement the narrative nutrient criterion. Currently, the recommended2 assessment of the proposed numeric nutrient crite ria for Sarasota Bay is built of assessing the achievement of a series of segment-specific chlorophyll a targets.
Over the past several years, the SBEP has conducte d a series of tasks with the ultimate goal of establishing estuarine numeric nutrient criteria for Sarasota Bay.
This first part of this document provides bac kground information concerning the Sarasota Bay estuarine system, including a brief physical char acterization of the system and a summary of previous work completed with respect to Sarasota Bay estuarine targets. The latter part of this document provides the data and methodology utili zed for developing estuarine numeric nutrient criteria for Sarasota Bay, the targets resulting from application of the met hodology, the proposed numeric nutrient criteria, and the proposed method ology for implementation and compliance of the Sarasota Bay estuarine numeric nutrient criteria.



## 1.1 Sarasota Bay and Its Watershed

Sarasota Bay is a fifty-three mile long coastal lag oon on the southwest coast of Florida (Figure 1-1). It is comprised of one large bay segment (Saras ota Bay proper) and several smaller segments ncluding Palma Sola Bay in the north and a series of three contiguous segments (Roberts Bay, Little Sarasota Bay, and Blackburn Bay) to the south. Sa rasota Bay has four inlets or passes (Venice Inlet, Big Sarasota Pass, New Pass and Longboat Pass). The SBEP area is bounded to the north by Anna Maria Sound, which opens into Tampa Bay and to the south by Venice Inlet. There are numerous, small tidal creeks that enter the bay along the east ern shoreline, ranging in size from Phillippi Creek (drainage area of 36,417 acres) to Palma Sola Creek (drainage area of 900 acres). The watershed is highly developed and consists of agricultural, residential, commercial, and light industrial land

uses.
Circulation is primarily driven by tidal exchange w ith the Gulf of Mexico. Sarasota Bay circulation is forced by the tides at Anna Maria Sound, Longboat Pass, New Pass, and Big Pass. Tidal circulation in Little Sarasota Bay is forced by tid es at Venice Inlet and the Intracoastal Waterway running through the middle of the bay. The closin g of Midnight Pass in Little Sarasota Bay in 1983 permanently altered the circulation in the three lower segments.
Between 1950 and 1988, an estimated 18% of the seagrass acreage in Sarasota Bay was lost due to physical alterations (dredge and fill), and excess nitr ogen loading and higher algae concentrations, which ultimately caused light limitation to the seagrasse s. In 1990, all municipal wastewater plants were required to provide Advanced Wastewater Tr eatment for direct discharges to the bay and tributaries.
In addition to the significant reductions in nitrogen loadings from municipal wastewater treatment plants, stormwater regulations enacted in the 1980 s also resulted in reduced nitrogen loads to the bay. Phillippi Creek (38% of the watershed), Bowlees Creek (7%) and Catfish Creek (3%) have been retrofitted to remediate flooding and provid e treatment. Nitrogen loading from Phillippi Creek has decreased 60% since 1988, while overall nitrogen loading to Sarasota Bay has been reduced by at least 46%. A fertilizer ordina nce prohibiting application during the wet summer months was enacted within 75% of the watershed.The SBE P segment s chloroph seagrass t chloroph targets a r
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a nced ive of4 The SBEP, in cooperation with local scientists and the FDEP, has developed tabular listings of symptoms of nutrient enrichment in Sarasota Bay, along with potential explanations of these expressions, to aid in development of numeric nutrient criteria for the bay. These listing are provided in Tables 1-1 and 1-2 below. These tables provide a summary of potential responses to nutrient enrichment expressed in the bay, and notes whether these responses are currently or historically found in the bay.5Table 1-1. Checklist of nutrient enrichment symptoms in Sarasota Bay.-= Empty cell/no data; N/A = Not availableResponse VariableObservedHistorically orCurrently?ExplanationSourceLow dissolved oxygen(DO) (hypoxia/anoxia)Yes, limited inspace and timeDuring the period 1998-2008, there were no years inwhich the proportion of DO samples exceeded the4.0 mg/l criterion by 10% in any bay segment.JEI in prepReduced clarityNo. Water clarity improved by approximately 0.4m from 1987 to 2000.Recent trend analyses (1998-2008)indicate statistically significant improvements in waterclarity in Palma Sola Bay while no significant trendswere shown in the other four bay segments.MML, 1993a; JEI inprepIncreased chlorophyllaconcentrationsNo.During the period 1998-2008, there were statistically significant reductions in chlorophyllain Palma SolaBay, Sarasota Bay proper, and Blackburn Bay. No significant trends were detectedin either Roberts Bay orLittle Sarasota Bay.JEI, 2010Phytoplankton blooms(nuisance or toxic)Yes, limited inspace and timeRed tide blooms originating in the Gulf of Mexico haveoccasionally entered the Bay. The most recent redtide event affecting Sarasota Bay was in 2005.Heil et al., 2007Problematic epiphytegrowthNoResearch showed that epiphytes reduce the light available to seagrasses by nearly 50% as a yearly average.No problems with excessive epiphyticgrowth have been documented.MML, 1995Problematicmacroalgal growthNo Nuisance macroalgal blooms have not occurred overthe past two years (2009-10). Recent trends showlow macroalgal biomass throughout the Bay.Lapointe, 2009Submerged aquaticvegetation (SAV)community changes orlossYes, historically.Current seagrass acreage in Sarasota Bay is 24% higher than 1950 acreage. Total seagrass acreage (2008)has increased 46% since 1988. Seagrass beds designated as continuous (SWFWMD) have increased 180%since 1988.SWFWMDEmergent or shorelinevegetation communitychanges or lossYes, historically. Historical losses have occurred due to shoreline hardening and development. The SBEP recently updatedits Habitat Restoration Plan to identify, prioritize and implement restoration projects to offset thesehistorical wetland and shoreline vegetative losses.Scheda, 2010Coral/hardbottomcommunity changes orlossUnknownNo coral or natural hard bottom is present in Sarasota Bay.MML, 1993bImpacts to benthiccommunityNo Benthic communities are not routinely monitored inSarasota Bay. Abundant shellfish populations havebeen reported and bay scallops have shown a return to the bay after years of absence.Stephenson & Geiger,2010Fish killsYes, limited inspace and timePeriodic fish kills have occurred from red tide blooms entering the bay; however, no fish kills have beenreported as a direct result of nutrient inputs into the estuary. Fisheries data show high abundances ofjuvenile and adult fish within all bay segments.Serviss and Sauers,2003; MacDonald etal., 20106Table 1-2. Checklist of additional factors in Sarasota Bay .- = Empty cell/no dataNutrient FactorsObserved?ExplanationSourceIncreased nutrientloading orconcentrationsNoNitrogen loading to Sarasota Bay, using the EPAapproved Watershed Management Model, has beenreduced by 46% since 1988. Nitrogen loading from Phillippi Creek, which drains 38% of thewatershed, has decreased 60% since 1988.Tomasko et al., 2005;PBSJ, 2009.Mitigating factors thatcouldprevent nutrientexpressionYesBay hydrologic residence times and circulation; season of nutrient inputs; seasonally high water color.-Other significantstressorsto biologicalcommunitiesNo-Natural high-nutrientepisodes(e.g., seasonal orevent driven)YesElevated nutrient and/or chlorophyllaconcentrations have been observed during periods of extremelyhigh rainfall (e.g., the El Niño event in 1998), tropical storms, and/or domestic/industrial spills.-7 

## 2.0 Objective

The objective of this document is to propose estu arine numeric nutrient criteria specific to the five segments of Sarasota Bay: Palma Sola Bay, Sara sota Bay, Roberts Bay, Little Sarasota Bay, and Blackburn Bay. These criteria are expressed as in-bay TN concentrations. SBEP has also developed loading-based TN criteria and both concentr ation and loading-based TP criteria (Janicki Environmental, 2011) that are summarized in Appendix A.



## 3.0 Development of Seagrass, Light Attenuation, and Chlorophyll

a Targets

This section provides detailed summary of work previously completed by the SBEP in the establishment of segment-specific seagrass targets and the associated water quality (light attenuation and chlorophyll) targets. 

## 3.1 Background and Rationale

As discussed above, between 1950 and 1988, a pproximately 18% of the seagrass acreage in Sarasota Bay was lost due to excess nitrogen lo ading. This excess nitro gen loading resulted in increased algae concentrations which caused light limitation detrimental to seagrass survival and growth. Additional seagrass loss was due to physi cal disturbances and shoreline modifications. Since 1980, all municipal wastewater treatmen t plants were required to provide Advanced Wastewater Treatment (AWT) for discharges directly to the Bay and its tributaries. In addition to the significant reductions in nitr ogen loadings from municipal wastewater treatment plants, stormwater regulations enacted in the 1980s also re sulted in reduced nitrogen loads to the Bay. Overall, nitrogen loading to Sarasota Bay ha s been reduced by at least 46% since 1988.
Sarasota Bay was named an estuary of “national si gnificance” in 1987 which led to its designation as a National Estuary Program by the EPA two year s later. The Sarasota Bay Estuary Program is dedicated to improving and protecting the area’s greatest and most important natural asset - Sarasota Bay. The SBEP strives to improve wate r quality, increase habitat and enhance the natural resources of the area for use and enjoyment by the public.
A Management Conference was formed comprising federal, state, regional and local officials who oversaw the development and implementation of a Comprehensive Conservation and Management Plan (CCMP) for Sarasota Bay. Several priority concerns were identified:

 declines in water and sediment quality,

 loss of wetlands and other coastal habitats,

 loss of seagrasses,

 declines in finfish and shellfish populations, and

 overuse.
The Management Conference also developed plans of action to address these concerns through technical studies and citizen action plans which resulte d in the development of the CCMP in 1995. Progress in the implementation of the CCMP has b een documented. A State of the Bay report (SBEP, 2010) has been recently completed.8 While not explicitly stat ed, the SBEP CCMP recognized the lin kages between nutri ent enrichment, algal productivity, light attenuation, and seagrass gr owth and reproduction in a similar fashion to the Tampa Bay Estuary Program nutrient loading-chlorophyll-seagrass paradigm.



## 3.2 Development of Seagrass Targets

One of the primary SBEP goals is to maintain and/ or restore seagrass coverage to its historic extent. The seagrass target project provided technical ly-defensible quantitative seagrass targets for the Sarasota Bay ecosystem. Establishment of se agrass targets provides a necessary basis for management decisions regarding water quality and ot her issues that can influence the distribution and persistence of this resource. Targets were de fined through an analysis of historic and recent aerial surveys of the study area.
The historic aerial photos used to establish the ba seline extent of seagrass in the study area were dated circa 1950. Recent trends in and pers istence of seagrass throughout the SBEP were determined through analysis of GIS shapefiles ba sed on aerial surveys executed biannually by the Southwest Florida Water Management District since 1988. Due to anthropogenic modifications in the estuary such as shoreline build-out and the dr edging of the Intracoastal Waterway (ICW), certain areas have been altered to the extent that they have no reasonable potential for restoration; these so-called non-restorable areas have been identified and removed from the analyses.
The trend analyses show that Sarasota Bay proper is currently at its highest seagrass level since 1950 and exceeds the baseline extent (see Appendix B). The seagrass coverage in Roberts Bay and Blackburn bays also increased since the baseline pe riod. In Palma Sola Bay, the seagrass coverage has remained very similar over the entire period of record. In contrast, Little Sarasota Bay has shown a decline in seagrasses since 1950.
The definition of the most appropriate seagrass targets was reached with input from the Technical Advisory Committee (TAC). The TAC recommended that the greater of either the historic or recent (2004-2006) seagrass coverages be established as the target in each segment. The sole exception to this rule was Little Sarasota Bay. Since 1984, th e direct connection to the Gulf of Mexico (Midnight Pass) has been closed. This physical alteration ma y have contributed to the difference in seagrass coverage between the historic and current coverag es. However, a recent FDEP decision precludes re-opening of this pass. Therefore, the recent co verage in Little Sarasota Bay has been established as the target. Table 3-1 presents the seagrass ta rgets that were adopted by the SBEP Management and Policy boards.

Table 3-1. SBEP seagrass coverage and targets. Unit of measure=acres.

Bay Segment Historical (1950) Current 2004-2006

Average Seagrass Target

Palma Sola 1,031 1,015 1,031 Sarasota 7,269 7,041 7,269 Roberts 283 348 348 Little Sarasota 883 702 702 Blackburn 273 447 447 Total 9,739 9,552 9,9979 

## 3.3 Development of Chlorophyll a Targets

In October 2009, the SBEP Policy and Management boards directed the TAC to develop numeric nutrient criteria for the estuarine waters of the ar ea. A subcommittee of the TAC began the process by reviewing the existing seagrass and chlorophyll a data and proposing a set of chlorophyll a targets to support the development of the numeric nutrient criteria (Janicki Environmental, 2010).

In line with the goal of restoring and protectin g seagrass populations, the SBEP had a goal of improving water clarity (e.g., by reducing algal abundance). Recent results from ambient water quality monitoring programs indicate that the chlorophyll a concentrations have declined in all segments for the period 1998 to 2009. Further anal ysis of the chlorophyll concentration data using non-parametric trend tests (Kendall-Tau) revealed sign ificant decreasing trends in chlorophyll in all segments except Roberts Bay (Figure 2). This fi nding, in conjunction with the increased seagrass acreage, provide strong evidence that the managemen t efforts of the SBEP are bearing fruit with improved water quality and increased seagrass abundance.

Figure 2. Sarasota Bay monthly chlorophyll a concentrations.10 Given that the recent extents of seagrass cove rages are meeting the established targets, the subcommittee determined that the recent chlorophyll a concentrations and resultant water clarity are protective of the seagrasses in each of the SBEP segments. Upon review of the chlorophyll a concentration data, it was deemed appropriate to include not only the data from the 2004-2005 period but also from several antecedent years (2001-2003). The resultant mean chlorophyll a concentrations from this overall period (2001-2005) were established as the targets for each segment (Table III-2). The subcommittee further reco gnized that there may be years in which these targets may be exceeded without causing significan t reductions in seagrass cover. This means that there is some allowable amount of variation that should not elicit a significant degradation in water quality and therefore seagrass coverage. The subco mmittee defined this level of variation as the standard deviation around the mean annual chlorophyll a concentrations in each segment for the entire period of record. Therefore , a distinction is made between a target , i.e., a desired chlorophyll a concentration and a threshold , i.e., a chlorophyll a concentration above which undesirable chlorophyll a concentrations exist. The chlorophyll a threshold for each segment is the sum of the target and the standard deviation around the mean annual chlorophyll a concentrations in each segment. The thresholds shown in Table 3-2 are used in later sections of this report in the development of the numeric nutrient crite ria in the SBEP estuarine waters.

Table 3-2. SBEP recommended chlorophyll a targets and thresholds ( μ g/L).
Bay Segment Target Chlorophyll a ( μ g/L) Inter-annual Variability (1 SD of Annual Means) Threshold Chlorophyll a ( μ g/L) Palma Sola 8.5 3.3 11.8
Sarasota 5.2 0.9 6.1 Roberts 8.2 2.8 11.0 Little Sarasota 8.2 2.2 10.4 Blackburn 6.0 2.2 8.2



## 4.0 Proposed Criteria

The SBEP employed a stressor-response relationship in a similar manner to that proposed by Janicki Environmental (2010a) and EPA (2010). This appro ach involves the development of a quantitative relationship (i.e., model) between a chlorophyll a and some measure of nutrient condition. In this case, the independent variables used in th e model-building process included loadings, concentrations, and estimates of residence time. The loadings data included monthly hydrologic, TN, and TP loads as well as cumulative total loads extending from two to six months (e.g., 2-month cumulative TN load = TN load current month + TN load one- month prior). The water quality constituents included TN and TP concentrations along with numerous other constituents. A complete presentation of the process followed to develop the proposed criteria for SBEP can be found in Janicki Environmental (2010b). The resu lts of the analyses for each segment are as follows.11 

## 4.1 Roberts Bay

TN concentration was identified as the variable that makes that greatest contribution to explaining the variability in chlorophyll a concentrations in Roberts Bay. As can be seen in Figure 4-1, there is a pattern of increasing chlorophyll a concentrations with increasing TN concentrations.

Figure 4-1. Relationship between monthly average chlorophyll a and TN concentrations in Roberts Bay.

A regression model between chlorophyll a and TN concentrations was developed and the residuals from this model were examined to identify any othe r explanatory variables that might contribute to the overall variance accounted for by the model. The residual analysis revealed a seasonal difference in residuals. Specifically, given the same TN concentrations, higher chlorophyll a concentrations can be expected during the wette r, warmer summer months (July-October) than during the remainder of the year. Therefore, a se asonal term was added to the regression equation. The season term is a dummy variable which equals one during July-October and zero other months of the year. The final regression equation is:
[Chlorophyll a ] = -5.73 + (27.07 * [TN]) + (3.41 * season)

The model was fit with 133 observations and resulted in an R 2 value of 0.62. The regression was highly significant with a probability of a greater |F| value of < 0.0001. The slope and parameter coefficients were also highly significant. A plot of predic ted versus observed chlorophyll a concentrations is presented in Figure 4-2.12 Figure 4-2. Predicted versus observed chlorophyll a concentrations for Roberts Bay (R 2 =0.62).


## 4.2 Little Sarasota Bay

TN concentration was identified as the variable that makes that greatest contribution to explaining the variability in chlorophyll a concentration. As can be seen in Figure 4-3 there is a pattern of increasing chlorophyll a concentrations with increasing TN concentrations.

Figure 4-3. Relationship between monthly average chlorophyll a and TN concentrations in Little Sarasota Bay.
A regression model between chlorophyll a and TN concentrations was developed and the residuals from this model were examined to identify any othe r explanatory variables that might contribute to the overall variance accounted for by the mode l. As was found in Roberts Bay, the residual analysis revealed a seasonal difference in residuals. Specifically, given the same TN concentrations, chlorophyll a concentrations can be expected to be higher in Little Sarasota Bay13 during the wetter, warmer summer months (July-Octobe r) than during the remainder of the year. Therefore, a seasonal term was added to the regression equation. The season term is a dummy variable which equals one during July-October and zero other months of the year. The final regression equation is:
[Chlorophyll a ] = -4.48 + (21.59 * [TN]) + (2.97 * season)

The model was fit with 133 observations and resulted in an R 2 value of 0.57. The regression was highly significant with a probability of a greater |F| value of < 0.0001. The slope and parameter coefficients were also highly significant. A plot of predic ted versus observed chlorophyll a concentrations is presented in Figure 4-4.

Figure 4-4. Predicted versus observed chlorophyll a concentrations in Little Sarasota Bay (R 2 =0.57). 

## 4.3 Blackburn Bay

TN concentration was identified as the variable that makes that greatest contribution to explaining the variability in chlorophyll a concentration. As can be seen in Figure 4-5, there is a pattern of increasing chlorophyll a concentrations with increasing TN concentrations.
A regression model between chlorophyll a and TN concentrations was developed and the residuals from this model were examined to identify any othe r explanatory variables that might contribute to the overall variance accounted for by the model. Similar to both Roberts Bay and Little Sarasota Bay, this analysis revealed a seasonal difference in residuals. Specifically, given the same TN concentrations, chlorophyll a concentrations can be expected to be higher in Blackburn Bay during the wetter, warmer summer months (July-October) than during the remainder of the year. Therefore, a seasonal term was added to the regression equation. The season term is a dummy variable which equals one during July-October and zero other months of the year. The final regression equation is:
[Chlorophyll a ] = -3.55 + (23.72 * [TN]) + (1.73 * season)14 Figure 4-5. Relationship between monthly average chlorophyll a and TN concentrations in Blackburn Bay.

The model was fit with 133 observations and resulted in an R 2 value of 0.73. The regression was highly significant with a probability of a greater |F| value of < 0.0001. The slope and parameter coefficients were also highly significant. A plot of predic ted versus observed chlorophyll a concentrations is presented in Figure 4-6.

Figure 4-6. Predicted versus observed chlorophyll a concentrations in Blackburn Bay (R 2 =0.73). 

## 4.4 Sarasota Bay

TN concentration was identified as the variable that makes the grea test contribution to explaining the variability in chlorophyll a concentrations in Sarasota Bay. As expected, there is a pattern of increasing chlorophyll a concentrations with increasing TN c oncentrations (Figure 4-7). As in the regressions developed for Roberts Bay, Little Sa rasota Bay, and Blackburn Bay, initial efforts15 revealed a seasonal difference in residuals. This i ndicates that given the same TN concentration, higher chlorophyll a concentrations are expected during the summer months. Therefore, a seasonal term was added to the regression equation. The season term is a dummy variable which equals one during the wet season (June-Octobe r) and zero other months of the year.
Unlike Roberts Bay, Little Sarasota Bay, and Black burn Bay, Sarasota Bay is a substantially larger system with greater spatial variability in TN concen trations. Specifically, TN concentrations in the northern portion of the bay (Figure 4-8) are greater than those in the southern portion (Table 4-1). In contrast, there is less spatial variability in the chlorophyll a concentrations. As a result, given the same TN concentrations, higher chlorophyll a concentrations are found in the southern portion of the bay. Therefore, a region term was added to the regression equation to account for the spatial differences in TN and chlorophyll a concentrations in Sarasota Bay.

Figure 4-8. Northern and southern portions of the Sarasota Bay segment.

Table 4-1. Comparison of water quality within Northern and Southern Sarasota Bay. Values represent medians for the period 1998-2009. Variable Northern Sarasota Bay Southern Sarasota Bay Chlorophyll a (μg/l) 5.2 3.4 TN (mg/l) 0.56 0.28 Color (PtCo units) 20 10 Northern Region16 Further analysis of the residuals from the regression model revealed that color also contributed to explaining the variation in chlorophyll a and was added to the regression equation. The final regression equation obtained is:
[Chlorophyll a ] = -1.06 + (3.58 * [TN]) + (0.32 * color) + (2.03 * season) – (4.84 * region)

Figure VI-7. Relationship between monthly average chlorophyll a and TN concentrations in Sarasota Bay.

The model was fit with 156 observations and resulted in an R 2 value of 0.67. The regression was highly significant with a probability of a greater |F| value of < 0.0001. The slope and parameter coefficients were also significant. A plot of predicted versus observed chlorophyll a concentrations is presented in Figure 4-8.

Figure 4-8. Predicted versus observed chlorophyll a concentrations in Sarasota Bay (R 2 =0.67).17 

## 4.5 Palma Sola Bay

The relationship between chlorophyll a concentrations and both TN concentrations and loads were graphically examined. There is no apparent relationship between chlorophyll a concentrations and either TN concentrations or loads. A similar l ack of relationships was found for TP concentrations and loads, as well as a series of cumulative loading.
Several other analytical tools were applied to discern if a quantitative relationship between chlorophyll a concentrations and either TN concentrati ons or loads could be found. Specifically, various applications of logistic regression te chniques, changepoint analyses, and a reference condition approach were investigated.
Logistic regression was used to predict the probability of a chlorophyll a concentration greater than the threshold value of 11.8 μg/L for Palma Sola Ba y as a function of TN concentration. Based on the logistic regression results, a TN concentrati on that is predicted to result in a chlorophyll a concentration exceedance in the wet season is 0.57 mg/L and in the dry season 1.31 mg/L. Using these values and calculating a weighted mean TN concentration results in an annual average annual TN concentration of 1.06 mg/L.
Changepoint analysis was performed to identify a potential threshold TN value that was associated with a non-linear increase in chlorophyll a concentrations. A statistically significant TN changepoint value of 0.76 mg/L was identified as the value that maximized the difference in the distribution of chlorophyll a concentrations between two groups. The average chlorophyll a concentration was 7.69 μg/L for the high [TN] group and 11.6 μg/L for the low [TN] group.
The reference condition approach, in this case a Reference Period of 2001 -2005, identified a TN concentration threshold of 0.93 mg/L. 

## 4.6 Proposed TN Criteria

The SBEP water quality subcommittee of the TAC c onsidered three potential candidate methods for estimating the TN criterion for Palma Sola Bay: The subcommittee recommended the third option – i.e., that based on the 2001-2005 ambient TN data. The full TAC concurred with the subcommittee’s recommendation.
The SBEP Management and Policy committees approved the TAC recommendations for the TN concentration-based numeric criteria (Table 4-2).

Table 4-2. SBEP recommended TN criteria.
Bay Segment TN Criteria (mg/L) Palma Sola 0.93

Sarasota Dependent upon observed color; for the period 1998- 2009 the criteria would vary between 0.28 - 1.34 mg/L Roberts 0.54 Little Sarasota 0.60 Blackburn 0.4318 

## 5.0 Implementation

The proposed compliance assessmen t strategy involves two steps. The initial step is the comparison of the mean annual chlorophyll a concentrations in each bay segment to the established chlorophyll a thresholds. Compliance is achieved if th e threshold is met in that year. If the chlorophyll a threshold is exceeded in more than two years durin g any five-year period, the second step would be an assessment of nitrogen concentrations during that period will be completed. The justification fo r this “2-in-5 year” complianc e assessment has been developed (Janicki Environmental, 2010) and summarized in Appendix C.
The recommendation for compliance assessment also hinges on the av ailability of the appropriate data for a defensible assessment. Thus, how the sp ecific spatial and temporal averaging has been conducted in the development of the targets and th resholds as well as the compliance assessment is critical. The following discusses this aspect of the implementation of the proposed numeric nutrient criteria for Sarasota Bay.

From the initial efforts to establish defensible water quality targets throug h the development of the RA, the primary data sources have been Sarasota and Manatee counties. The Sarasota County program is probabilistic, with five (5) monthly-spec ific initially randomized sampling sites chosen for each sub-segment within Sarasota Bay, Robert s Bay, Little Sarasota Bay, and Blackburn Bay. Sampling and analyses are completed under contract with the County by Mote Marine Laboratory. The Manatee County sampling and analyses are comp leted by County staff at a series of sampling sites that were randomly chosen in both Palma Sola Bay and the Saraso ta Bay. The specific sampling sites are presented in Table 4-1. Maps showing the locations of these sites are presented in Appendix D.
Defensible compliance assessment will depend upon continued mon itoring at the sites described above to ensure that the annual assessment for attainment of the proposed chlorophyll a thresholds and TN criteria. Consideration must also be gi ven to the temporal averaging. The mean annual chlorophyll a concentrations have been calcula ted by first calculating the segment-specific arithmetic monthly means and subsequently calculating an arithmetic annual mean. The compliance would be assessed by calculating th e chlorophyll a threshold i.e., the sum of the arithmetic annual mean and the estimate of the interannual variability (see Table 3-2) The TN compliance would be based on a geometric annual mean for each bay segment.



## 6.0 References

Florida Department of Environmenta l Protection (FDEP) website. 2011. http://www.dep.state.fl.us/water/wqssp/nutrients/index.htm

Heil, C.H., M. Revilla, P.M. Gilbert and S. Murask o. 2007. Nutrient quality drives differential phytoplankton community composition on the s outhwest Florida shelf. Limnol. Oceanogr., 52:1067–1078.
Janicki Environmental, Inc. 2010a. Empirical Appro aches to Establishing Numeric Nutrient Criteria for Southwest Florida Estuaries. Prepared for Tampa Bay Estuary Program, Sarasota Bay Estuary Program, and Charlotte Harbor National Estuary Program.19 Janicki Environmental, Inc. 2010b. Numeric Nutrie nt Criteria for Sarasota Bay. Prepared for Sarasota Bay Estuary Program, Sarasota, FL.
Janicki Environmental, Inc. 2011. Numeric Nutrie nt Criteria: Task 1 - TN and TP Concentration and Loading Based Criteria. Prepared for Sa rasota Bay Estuary Program, Sarasota, FL.
Lapointe, B. 2009. Sarasota Bay Macroalgae Study: Interim Report.

MacDonald, T.C., E. Weather, P.W. Stevens and R. H. McMichael, Jr. 2010. Nekton of Sarasota Bay and a Comparison of Nekton Community Structure in Adjacent Southwest Florida estuaries. Annual Report to the Sarasota Bay Estuary Program. 199 p.
Mote Marine Laboratory. 1993a. Wa ter and Sediment Quality: Trends and Status for Sarasota Bay. Mote Marine Laboratory Technical Report No. 254. 179 pp.
Mote Marine Laboratory. 1993b. Bay Bottom Hab itat Assessment Final Report. Mote Marine Laboratory Technical Report No. 303.
Mote Marine Laboratory. 1995. Light Attenuation with Respect to Seagrasses in Sarasota Bay, Florida. Mote Marine Laboratory Technical Report No. 407. 53 pp.
PBS&J. 2009. Status and trends in pollutant loads and natural resource responses in Sarasota Bay. Final Report to the City of Sarasota. 15 pp.
Sarasota Bay Estuary Program. 2010. State of the Bay 2010. Celebrating Paradise: Staying the Course. Sarasota Bay Estuary Program, Sarasota, FL.
Scheda Ecological Assoc, Inc. 2010. Sarasota Ba y Estuary Program Five Year Habitat Restoration Plan: FY 2010 – FY 2014. Final Report to the Sarasota Bay Estuary Program.
Serviss, G.M. and S. Sauers. 2003. Sarasota Bay Ju venile Fisheries Habitat Assessment. Final Report to the Sarasota Bay National Estuary Program. 212 pp.
Stephenson, S.P. and S.P. Geiger. 2010. Bay S callop Project 2009 Annual Report. Annual Report of the Florida Fish and Wildlife Research Institute. 67 pp.
Tomasko, D.A., C.A. Corbett, H.S. Greening and G.E. Raulerson. 2005. Spatial and temporal variation in seagrass coverage in Southwest Florida: assessing the relative effects of anthropogenic nutrient load reductions and rainfall in four c ontiguous estuaries. Mar. Poll. Bull. 50:797-805.
U.S. Environmental Protection Agency (EPA). 2010. Methods and Approaches for Deriving Numeric Criteria for Nitrogen/Phosphorus Pollution in Florida’s Estuaries, Coastal Waters, and Southern Inland Flowing Waters.

## APPENDIX A. Criteria Expressed as TN and TP Concentrations, and as TN and TP Loads

1.0 Introduction

This appendix provides summaries of the derivation and application of the recommended numeric nutrient criteria for the SBEP. Segment

‐ specific stressor

‐ response relationships between chlorophyll a and nitrogen concentrations were developed, and utilized to derive numeric nutrient criteria in terms of TN concentration. Additional analyses have been completed to provide numeric nutrient criteria in terms of TP concentrations, and in terms of TN and TP loadings, should these be necessary.


## 2.0 Total Nitrogen Concentrations

The SBEP has developed recommended numeric nut rient criteria for Sarasota Bay as segmentspecific mean annual total nitro gen concentrations (Janicki Environmental, 2010). The strategy for developing the numeric nutrient crite ria is based on the chlorophyll a thresholds (Table 2-1) (Janicki Environmental, 2010) and segment-specific qua ntitative relationships between chlorophyll a and nutrients. The following describes the approach used.

Table 2-1. Recommended chlorophyll a targets and thresholds ( μ g/L).
Bay Segment Target Chlorophyll a ( μ g/L) Inter-annual Variability (1 SD of Annual Means) Threshold Chlorophyll a ( μ g/L) Palma Sola 8.5 3.3 11.8
Sarasota 5.2 0.9 6.1 Roberts 8.2 2.8 11.0 Little Sarasota 8.2 2.2 10.4 Blackburn 6.0 2.2 8.2



## 2.1 Roberts, Little Sarasot a, and Blackburn Bays

The stressor-response relationships for Roberts Bay, Little Sarasota Bay, and Blackburn Bay (Janicki Environmental, 2010) indicated very similar responses in chlorophyll a concentrations to changes in nutrients. Specifically, two terms, TN concen tration and season, explained more than 60% of the variation in the chlorophyll a data.
For each of these segments, the segment-spec ific regression was used to estimate the TN concentrations that correspond to the chlorophyll a thresholds. This resulted in an estimated TN concentration for each season. The annual mean of the seasonally-specific TN concentrations was calculated by weighting for the number of months in each season. This weighted annual mean TN concentration is that TN concentrati on that corresponds to the chlorophyll a threshold concentration, and therefore is the proposed numeric nutrient criterion for each segment.Figures 2-1 through 2-3 present co mparisons of the proposed numeric criteria for Roberts Bay, Little Sarasota Bay, and Blackburn Bay, expressed as an annual geometric mean TN concentration, to the recent ambient geometric mean TN concentratio ns measured in each segment. The geometric means are used to achieve consistency with the appr oach used by EPA in the proposed freshwater numeric nutrient criteria.

Figure 2-1. Comparison of the proposed TN criterion to observed TN concentrations (expressed as geometric means) in Roberts Bay.

Figure 2-2. Comparison of the proposed TN criterion to observed TN concentrations (expressed as geometric means) in Little Sarasota Bay. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009[TN](mg/l)

Ambient [TN] (mg/l) [TN] Criterion (mg/l) 

### ROBERTS BAY

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009[TN](mg/l)

Ambient [TN] (mg/l) [TN] Criterion (mg/l) 

### LITTLE SARASOTA BAYFigure 2-3. Comparison of the proposed TN criterion to observed TN concentrations (expressed as geometric means) in Blackburn Bay.



## 2.2 Sarasota Bay

The relationship between chlorophyll a concentrations and nutrients in Sarasota Bay were more complex than those observed in Roberts Bay, Little Sarasota Bay, and Blackburn Bay. This resulted from the spatial variability within Sarasota Ba y and the influence of color on the relationship between chlorophyll a concentrations and TN concentrations in that segment. Therefore, application of the regression derived for Saraso ta Bay to estimate the proposed numeric nutrient criterion is more complex. Since color influ ences the relationship between chlorophyll a concentrations and TN concentrations, the TN concentration that corresponds to the threshold chlorophyll a concentrations for Sarasota Bay will va ry from year-to-year depending upon the ambient color observed in a year.

To estimate the TN concentration that co rresponds to the threshold chlorophyll a concentrations for Sarasota Bay, the mean color for each region and season was calculated for a given year. This mean color was used to calcula te the TN concentration corre sponding to the chlorophyll a threshold for each region and season. The annual mean of these TN concentrations was calculated by weighting for the number of months in each se ason, as was done for Roberts Bay, Little Sarasota Bay, and Blackburn Bay. This weighted annual mean TN concentration is that TN concentration that corresponds to the chlorophyll a threshold concentration for a given year. These mean annual TN concentrations are the proposed numer ic nutrient criterion for Sarasota Bay.
Figures 2-4 presents a comparison of the proposed numeric criteria for Sarasota Bay expressed as an annual mean TN concentration, to the recent ambient geometric mean TN concentrations measured in Sarasota Bay. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009[TN](mg/l)

Ambient [TN] (mg/l) [TN] Criterion (mg/l) 

### BLACKBURN BAYFigure 2-4. Comparison of the proposed TN criterion to observed TN concentrations (expressed as geometric means) in Sarasota Bay.



## 2.3 Palma Sola Bay

No significant quantitative relationshi p was found between chlorophyll a concentrations and either the TN or TP concentrations or loadings in Palma Sola Bay (Janicki Environmental, 2010). Therefore, the approach to derive a numeric nutrien t criterion for Palma Sola Bay necessarily differs from that used for the other SBEP segments.
The SBEP water quality subcommittee of the TAC c onsidered three potential candidate methods for estimating the TN criterion for Palma Sola Bay:

 The logistic regression results suggested wet a nd dry season TN concentrations of 0.57 mg/l and 1.31 mg/l, respectively. The annual mean, weighting for season, gives a result of 1.06 mg/l as a candidate TN criterion. Note th at the logistic model fit was weak – r 2 =0.25.

 The changepoint analysis identified a breakpoint in the TN concentrati ons of approximately 0.76 mg/l.

 Following the logic used by the subcommittee to establish chlorophyll a concentrations for the SBEP segments

- the mean TN concentration for the 2001-2005 period was 0.74 mg/l

- the standard deviation of the annual mean TN concentrations for the period of record was 0.19 mg/l

- therefore, this method results in a candidate TN criterion of 0.93 mg/l. 0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009[TN](mg/l)

Ambient [TN] (mg/l) [TN] Criterion (mg/l) 

### SARASOTA BAYAll three potential candidate methods give relatively similar results.
The subcommittee recommended the third option – i.e., that based on the 2001-2005 ambient TN data. The full TAC concurred with the subcommittee’s recommendation on 23 July 2010.
Figures 2-5 presents a comparison of the proposed numeric criteria for Palma Sola Bay expressed as an annual mean TN concentration, to the rece nt ambient geometric mean TN concentrations measured in Palma Sola Bay.

Figure 2-5. Comparison of the proposed TN criterion to observed TN concentrations (expressed as geometric means) in Palma Sola Bay.



## 3.0 Total Phosphorus Concentrations

As described in Janicki Environmental (2011), multip le analyses were completed in the evaluation of potential TP criteria expressed as in-bay concentra tions. The first set of analyses was performed to evaluate potential methods of deriving TP concentration criteria commensurate with the previously established TN concentration criteria and/or chlorophyll a thresholds. These included:

 examination of the relationships between TN and TP concentrations within each segment, following the rationale that the TN concen tration criteria had already been developed (Janicki Environmental, 2010) and relationships between TN and TP concentrations could provided TP concentration criteria;

 examination of relationships between mont hly TP concentrations with chlorophyll a concentrations, with the potential to derive TP concentration criteria based on chlorophyll a thresholds (Janicki Environmental, 2010); and

 application of a reference period approach to establishing TP concentration criteria. 0 0.2 0.4 0.6 0.8

1 1.2

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008[TN](mg/l)

Ambient [TN] (mg/l) [TN] Criterion (mg/l)The most appropriate method was determined to be the Reference Period approach. No relationships were found between TP and TN concentrations appropriate for developing TP concentration criteria as a function of the TN conc entration criteria. Similarly, no relationships were found between TP concentrations and chlorophyll a concentrations appropriate for developing TP concentration criteria based on chlorophyll a thresholds. The Reference Period approach was utilized, with the period defined as the 2001-2005 period, as utilized for establishing the chlorophyll a thresholds.
Following this approach, numeric nutrient criteri a expressed as TP concentrations for the five bay segments were derived. Targets were based on 2001-2005 reference period data, and the standard deviations in each segment were based on the annual means. The TP concentration target, standard deviation, and threshold for each segment are provided in Table 3-1.

Table 3-1. TP targets and thresholds based on the Reference Period approach. The reference period was 2001-2005.
Segment TP Target (mg/L) Standard Deviation of Long-term Annual TP Concentrations TP Threshold (mg/L) Palma Sola Bay 0.13 0.13 (1996-2008) 0.26 Sarasota Bay 0.15 0.04 (1998-2008) 0.19 Roberts Bay 0.19 0.04 (1998-2009) 0.23 Little Sarasota Bay 0.18 0.03 (1998-2009) 0.21 Blackburn Bay 0.17 0.04 (1998-2009) 0.21
The proposed TP concentration criteria are compared to the observed geometric mean annual TP concentrations in Figures 2-1 through 2-5. The horizontal lines represent the proposed criteria.

Figure 3-1. Comparison of proposed TP concentrat ion criterion for Palma Sola Bay to the annual geometric mean TP concentr ations from 1996 through 2008.Figure 3-2. Comparison of proposed TP concentratio n criterion for Sarasota Bay to the annual geometric mean TP concentrations from 1996 through 2008.

Figure 3-3. Comparison of proposed TP concentratio n criterion for Roberts Bay to the annual geometric mean TP concentrations from 1998 through 2009.Figure 3-4. Comparison of proposed TP concentrat ion criterion for Little Sarasota Bay to the annual geometric mean TP concentr ations from 1998 through 2009.

Figure 3-5. Comparison of proposed TP concentratio n criterion for Blackburn Bay to the annual geometric mean TP concentrations from 1998 through 2009.

## 4.0 Total Nitrogen and Tota l Phosphorus Loadings

As described in Janicki Environmental (2011), the simplest method to propose criteria expressed as loadings to bay segments is based on relationships between segment TN and TP concentrations and TN and TP loadings delivered to each segment. The current proposed TN concentration criteria and TP concentration criteria described above can be compared to TN and TP loadings to the segments. If significant relationships are found between the nutrient loads and their respective segment concentrations, then the proposed numeric nutrient criteria could be expressed as loadings. However, based on the limited relations hips found between the nutrient concentrations and nutrient loads to those segments, the results of these analyses do not provide adequate evidence to support recommendations for TN and TP loading criteria.
A second method evaluated to provide potential TN and TP criteria expressed as loadings to bay segments is based on relationships between segment chlorophyll a concentrations and TN and TP loads delivered to each segment. Chlorophyll a thresholds have been developed for the SBEP (Janicki Environmental, 2010), and may be used to derive loading criteria if appropriate relationships exist. The results of these analys es did not provide adequate evidence to support recommendations for TN and TP loading criteria ba sed on the relationships between chlorophyll a concentrations and TN and TP loadings in the bay segments.
Establishment of loading-based nutrient criteri a should be consistent with the chlorophyll a threshold and concentration-based TN and TP criteria already proposed for Sarasota Bay. The third approach to developing loadings-based numeric nutrient criteria for both TN and TP is the reference period approach. Based on the methods used for development of the chlorophyll a threshold and the proposed TP concentration cr iteria, loading targets and thresholds were developed. As with TP concentrations, the refere nce period was 2001-2005. Because loading data are available through 2008, the period used to cal culate the standard deviation was 1998-2008. These loading criteria (thresholds) are considered to be commensurate w ith reference period TP concentrations proposed as criteria above, and with the referenc e period chlorophyll a thresholds developed previously (Janicki Environmental, 2010). The loading targets a nd standard deviations are presented in Table 4-1. By summing the target loads plus one standard deviation (Table 4-1), the loading criteria (thresholds) are obtained. The loading criteria for TN and TP are presented in Table 4-2 based on the 2001-2005 reference period.

Table 4-1. Reference Period Loading-based TN and TP targets (2001-2005) and standard deviations (1998-2008).

Segment TP Target (tons/yr) TP Standard Deviation (tons/yr) TN Target (tons/yr) TN Stadard Deviation (tons/yr) Palma Sola Bay 6.7 1.0 37.9 6.6 Sarasota Bay 31.8 3.4 215.3 21.9 Roberts Bay 42.4 6.4 213.3 37.5 Little Sarasota Bay 7.3 1.6 40.3 9.2 Blackburn Bay 9.3 2.3 55.4 15.6Table 4-2. Reference Period Loading-based TN and TP criteria. Segment TP Load (tons/yr) TN Load (tons/yr) Palma Sola Bay 7.7 44.5 Sarasota Bay 35.2 237.2 Roberts Bay 48.8 250.8 Little Sarasota Bay 8.9 49.5 Blackburn Bay 11.6 71.0
The proposed TN and TP loading criteria are compared to the observed annual TN and TP loadings in Figures 4-1 through 4-5. The horizontal lines represent the proposed criteria.

Figure 4-1. Comparison of proposed TN and TP lo ad criteria for Palma Sola Bay to annual loads.Figure 4-2. Comparison of proposed TN and TP load criteria for Sarasota Bay to annual loads.Figure 4-3. Comparison of proposed TN and TP load criteria for Roberts Bay to annual loads.Figure 4-4. Comparison of proposed TN and TP load criteria for Little Sarasota Bay to annual loads.Figure 4-5. Comparison of proposed TN and TP lo ad criteria for Blackburn Bay to annual loads.

## 5.0 References

Janicki Environmental, Inc. 2010 . Numeric Nutrient Criteria for Sarasota Bay. Prepared for Sarasota Bay Estuary Program, Sarasota, FL.
Janicki Environmental, Inc. 2011. Sarasota Bay Nu meric Nutrient Criteria: Task 1 – TN and TP Concentration and Loading Based Criteria. Prep ared for the Sarasota Bay Estuary Program, Sarasota, FL.

## APPENDIX B. Data Description and Assessment of Seagrass, Wate r Quality, and Loadings

1.0 Data Description

This appendix provides identification and descrip tion of the seagrass, water quality, and loadings data utilized for establishing numeric nutrient crite ria in the Sarasota Bay Estuary system.



## 1.1 Seagrasses

An estimate of seagrass extent is available for 1950 , based on historic aerial photos. Recent trends in and persistence of seagrass throughout the SB EP were determined through analysis of GIS shapefiles based on aerial surveys executed biannually by the Southwest Florida Water Management District since 1988. Due to anthropogenic modifications in the estuary such as shoreline build-out and the dredging of the Intr acoastal Waterway (ICW), certain areas have been altered to the extent that they have no reasonable potential for restoration; these so-called nonrestorable areas have been identifie d and removed from the analyses.
The definition of the most appropriate seagrass targets was reached with input from the Technical Advisory Committee (TAC). The TAC recommended that the greater of either the historic or recent (2004-2006) seagrass coverages be established as the target in each segment. The sole exception to this rule was Little Sarasota Bay. Since 1984, th e direct connection to the Gulf of Mexico (Midnight Pass) has been closed. This physical alteration ma y have contributed to the difference in seagrass coverage between the historic and current coverag es. However, a recent FDEP decision precludes re-opening of this pass. Therefore, the recent co verage in Little Sarasota Bay has been established as the target. Table 1-1 presents the seagrass ta rgets that were adopted by the SBEP Management and Policy boards.

Table 1-1. SBEP seagrass coverage and targets. Unit of

measure=acres.

Bay Segment Historical (1950) Current 2004-2006

Average Seagrass Target

Palma Sola 1,031 1,015 1,031 Sarasota 7,269 7,041 7,269 Roberts 283 348 348 Little Sarasota 883 702 702 Blackburn 273 447 447 Total 9,739 9,552 9,997

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a andNutrient and hydrologic loads were estimated fo r the period 1988-2008 for the five bay segments within the greater Sarasota Bay watershed. Drai nage basins for each bay segment were delineated based on drainage boundaries provided by Manat ee and Sarasota counties. Nutrient loading estimates were derived for each of the 114 drainage basins using the Spatially Integrated Model for Pollutant Loading Estimates (SIMPLE) which wa s designed and calibrated by Jones Edmunds & Associates, Inc (JEA, 2009).

The SIMPLE model consists of six modules that produce pollutant loading estimates for the primary point and non-point sources within a watershed: 1) Direct Runoff, 2) Baseflo w, 3) Irrigation, 4) Point Source, 5) Septic Tanks, and 6) Atmosphe ric Deposition. We used the SIMPLE model to estimate nutrient loads for the first five sources, but calculated atmospheric deposition externally. Nutrient loads from the SIMPLE model were combined with loads from atmospheric deposition to estimate Total Nutrient Loads to each bay segment.
Rainwater runoff and groundwater flow, both tigh tly coupled to precipitation, are the primary mechanisms by which nutrients are transported from the watershed to Sarasota Bay. In order to estimate nonpoint source nutrient loads related to these hydrologic processes, the Direct Runoff and Baseflow modules reference an externally genera ted table of monthly hydrologic values that estimate rainfall, runoff and baseflow for all combina tions of land use and soil type in the Sarasota Bay watershed.



## 2.0 Assessment of Conditions

The following sections provide segment-specific comparisons of observed conditions to the seagrass targets, and summaries of TN, TP, and chlorophyll a concentrations and hydrologic, TN, and TP loadings for each SBEP segment. Times series of monthly mean values for each bay segment were produced to allow visual inspecti on of trends over time and to allow a comparison between bay segments.



## 2.1 Seagrass

The trend analyses show that in Palma Sola Ba y, the seagrass coverage has remained very similar over the entire period of record (Figure 2-1). Saraso ta Bay proper is currently at its highest seagrass level since 1950 and exceeds the baseline extent (Figure 2-2). The seagrass coverage in Roberts Bay (Figure 2-3) and Blackburn Bay (Figure 2-5) also increased since the baseline period. In contrast, Little Sarasota Bay (Figure 2-4) ha s shown a decline in seagrasses since 1950.Figure 2-1. Annual seagrass acreages in Palma Sola Bay. Horizontal line = seagrass target.

Figure 2-2. Annual seagrass acreages in Saras ota Bay. Horizontal line = seagrass target. 

### 0 200 400 600 800 1000 1200

1950 1988 1994 1999 2001 2004 2006

0 1000 2000 3000 4000 5000 6000 7000 8000

1950 1988 1994 1999 2001 2004 2006Figure 2-3. Annual seagrass acreages in Robe rts Bay. Horizontal line = seagrass target.

Figure 2-4. Annual seagrass acreages in Little Sar asota Bay. Horizontal line = seagrass target. 

### 0 50 100 150 200 250 300 350 400

1950 1988 1994 1999 2001 2004 2006

0 100 200 300 400 500 600 700 800 900 1000

1950 1988 1994 1999 2001 2004 2006Figure 2-5. Annual seagrass acreages in Black burn Bay. Horizontal line = seagrass target.



## 2.2 Total Nitrogen Concentrations

Times series of monthly mean values for each bay segment were produced to allow visual inspection of trends over time and to allow a co mparison between bay segments. Trends in TN concentrations were assessed using graphical plot s and the seasonal Kendall Tau trend test (Hirsch et al., 1982; Hirsch and Slack, 1984). The seasona l Kendall Tau is a nonparametric test that estimates the median slope from all pair-wise comparisons in a timeseries of data. The statistical test accounts for seasonality and serial autocorrelati on prior to evaluating the statistical significance of the trend in the time series. Therefore, the seasonal Kendall Tau is a sophisticated and robust method to evaluate trends in water quality data th at often do not fit the assumptions necessary for the use of parametric statistics (e.g. linear regression).
Time series plots of monthly TN concentrations are provided for each segment in Figures 2-6 through 2-10. In Palma Sola Bay (Figure 2-6), the mean annual TN concentrations for the 1998- 2008 period averaged 0.77 mg/L. The lowest annual concentration (0.59 mg/L) occurred in 2004; the highest annual value of 1.09 mg/L occurred in the drought year of 2001. There was no significant trend in TN concentrations in Palma Sola Bay over this time period.
The mean annual TN concentrations for the 1998 through 2009 period in Sarasota Bay averaged 0.35 mg/L (Figure 2-7). The lowest annual value of 0.28 mg/L occurred in 2006, a moderately dry year. The highest mean annual concentration of 0.41 mg/L occurred in 2004, a somewhat wetter year. There was no statistically significant trend in TN concentrations in Sarasota Bay during this period. 

### 0 50 100 150 200 250 300 350 400 450 500

1950 1988 1994 1999 2001 2004 2006The mean annual TN concentrations for the 199 8 through 2009 period in Roberts Bay averaged 0.43 mg/L (Figure 2-8). There was very little inter-annual variation in TN concentrations. The lowest annual value of 0.36 mg/L occurred in 2007 with the highest mean annual TN concentration of 0.48 mg/L observed in 2002. There was no statistic ally significant trend in TN concentrations in Roberts Bay during this period.

The mean annual TN concentrations in Little Sarasota Bay for the 1998 through 2009 period averaged 0.49 mg/L (Figure 2-9). The lowest mean annual TN concentration of 0.43 mg/L was observed in 1998 while the highest mean annual TN concentration was 0.56 mg/L found in 2001. There was no statistically significant trend in Little Sarasota Bay TN concentrations during this period.

The mean annual TN concentrations in Blackburn Bay during the 1998-2009 period in averaged 0.34 mg/L (Figure 2-10). The lowest annual mean TN concentration of 0.26 mg/L occurred in 1998. The highest annual mean concentration of 0.41 mg/L was observed in 2005. There was a statistically significant increasing trend in TN c oncentrations in Blackburn Bay during this period.

Figure 2-6. Monthly TN concentrations (mg/L) in Palma Sola Bay.Figure 2-7. Mean monthly TN concentr ations (mg/L) observed in Sarasota Bay.

Figure 2-8. Mean monthly TN concentr ations (mg/L) observed in Roberts Bay.Figure 2-9. Mean monthly TN concentrat ions (mg/L) observed in Little Sarasota Bay.

Figure 2-10. Mean monthly TN concentr ations (mg/L) observed in Blackburn Bay.

## 2.3 Total Phosphorus Concentrations

Times series of monthly mean TP values for each bay segment were produced to allow visual inspection of trends over time and to allow a comparison between bay segments. As for TN, trends in TP concentrations were assessed using graphical plots and the seasonal Kendall Tau trend test. Time series plots of monthly TP concentrations are provided for each segment in Figures 2-11 through 2-15.
Mean annual TP for the 1998-2008 period in Palma Sola Bay (Figure 2-11) averaged 0.16 mg/L. The lowest annual value (0.07 mg/L) occurred in 2004, a moderately wet year. The highest annual value (0.33 mg/L) occurred in 20 08. A statistically significant, but slight increasing trend in TP concentrations was observed during the 1998-2008 period.

Mean annual TP concentrations for the 1998-2009 period in Sarasota Bay averaged 0.13 mg/L (Figure 2-12). The lowest mean annual concentration of 0.06 mg/L occurred in 2009, while the highest annual value, 0.22 mg/L, occurred in 2002 . There was a statistically significant but slight decreasing trend in Sarasota Bay TP concentrations during this period.

Mean annual TP concentrations for the 1998 thr ough 2009 period in Roberts Bay averaged 0.17 mg/L (Figure 2-13). The lowest mean annual TP concentration (0.09 mg/L) occurred in 2009 and the highest annual mean annual (0.22 mg/L) wa s observed in 2001. There was a significant decreasing trend in Roberts Bay TP concentrations during this period.

Mean annual TP concentrations in Little Sarasota Bay averaged 0.17 mg/L during the 1998-2009 period (Figure 2-14). The lowest mean annual TP concentration was 0.09 mg/L in 2009; while the highest mean annual concentration was 0.22 mg/L in 1999. There was a statistically significant decreasing trend in Little Sarasota Bay TP concentrations during this period.
The annual mean TP concentrations in Blackburn Bay averaged 0.15 mg/L during the 1998-2009 period (Figure 2-15). The lowest annual mean of 0.06 mg/L occurred in 2009. The highest annual mean TP concentration was 0.21 mg/L and was obs erved in 2001. There was no statistically significant trend in Blackburn Bay TP concentrations during this period.Figure 2-11. Mean monthly TP concentrat ions (mg/L) observed in Palma Sola Bay.

Figure 2-12. Mean monthly TP concentr ations (mg/L) observed in Sarasota Bay.Figure 2-13. Mean monthly TP concentr ations (mg/L) observed in Roberts Bay.

Figure 2-14. Mean monthly TP concentrat ions (mg/L) observed in Little Sarasota Bay.Figure 2-15. Mean monthly TP concentr ations (mg/L) observed in Blackburn Bay.



## 2.4 Chlorophyll a
Times series of monthly mean chlorophyll a values for each bay segment were produced to allow visual inspection of trends over time and to allo w a comparison between bay segments. As for TN and TP concentrations, trends in chlorophyll a co ncentrations were assessed using graphical plots and the seasonal Kendall Tau trend test. Time series plots of monthly chlorophyll a concentrations are provided for each segment in Figures 2-16 through 2-20.
Mean annual chlorophyll a concentrations for the 1998-2008 period in Palma Sola Bay averaged 8.25 μg/L (Figure 2-16). The lowest annual value of 4.89 μg/L occurred in 2005, a significantly wet year. Lower chlorophyll concentrations may be expected if higher volumes of freshwater are reducing residence time on an annual basis. The highest annual value, 11.3 μg/L, occurred in 1998, also a wet year. A significan t decreasing trend in chlorophyll a concentrations was observed during the 1998-2008 period.
Mean annual chlorophyll a for the 1998 through 2009 period in Sarasota Bay averaged 5.0 μg/L (Figure 2-17). The lowest annual value of 3.3 μg/L occurred in 2006, a year of moderate rainfall. The highest annual value, 6.0 μg/L, occurred in 2009, a dry year. There was a statistically significant but slight decreasing trend in chloroph yll a concentrations in Sarasota Bay during this period.
Mean annual chlorophyll a concentrations for the 1998-2009 period in Roberts Bay averaged 7.3 μg/L (Figure 2-18). The lowest mean annual concentr ation of 3.6 μg/L occurred in 2007, a dry year. The highest mean annual concentration, 11.6 μg/L , occurred in the drought year of 2001. Therewas no statistically significant trend in chlorophyll a concentrations in Roberts Bay during this period.
Mean annual chlorophyll a concentrations averaged 7.3 μg/L in Little Sarasota Bay during the 1998- 2009 period (Figure 2-19). The lowest mean annual chlorophyll a concentration of 4.9 μg/L occurred in 2007 and the highest mean annual chlorophyll a concentration, 11.2 μg/L, occurred in 2001. There was a statistically significant but slight decreasing tr end in chlorophyll a concentrations in Little Sarasota Bay during this period.
Mean annual chlorophyll a concentrations for the 1998-2009 pe riod in Blackburn Bay averaged 5.3 μg/L (Figure 2-20). The lowe st annual mean chlorophyll a concentration was 3.0 μg/L and occurred in 2007. The highest annual mean chlorophyll a concentration was 9.3 μg/L and was observed in 2001. There was a significant decreasing trend in chlorophyll a concentrations in Blackburn Bay during this period.

Figure 2-16. Mean monthly chlorophyll a concen trations (μg/L) observed in Palma Sola Bay.Figure 2-17. Mean monthly chlorophyll a con centrations (μg/L) observed in Sarasota Bay.

Figure 2-18. Mean monthly chlorophyll a con centrations (μg/L) observed in Roberts Bay.Figure 2-19. Mean monthly chlorophyll a concentr ations (μg/L) observed in Little Sarasota Bay.

Figure 2-20. Mean monthly chlorophyll a con centrations (μg/L) observed in Blackburn Bay.

## 2.5 Hydrologic, Total Nitrogen, a nd Total Phosphorus Loadings

Hydrologic and nutrient loads were estimated fo r the period 1989-2008 for five bay segments within the SBEP area. The total annual hydrol ogic, TN, and TP loads to each bay segment are presented in Figures 2-21 through 2-23.
The highest annual hydrologic loads to Palma Sola Bay were observed during wetter years (generally >55 inches) of 1992, 1995, and 2003 (Fig ure 2-21). Drier years, in terms of total rainfall (<40 inches), occurred in 1990, 1999, 2000, and 2007 and corresponded to the lowest observed hydrologic loads during those years. As expected, the highest annual nutrient loadings for TN and TP were observed during years of peak hydrolog ic loads in 1988, 1992, 1995, 2003, and 2005 (Figures 2-22 and 2-23)
As with Palma Sola Bay, higher-than-average a nnual hydrologic loads to Sarasota Bay were observed during wetter years of 1992, 1995, and 2003, as well as 2005 (Figure 2-21). Drier years occurred in 1990, 1999, 2000, and 2007 and corresponded to the lowest observed hydrologic loads during those years. The highest annual nutrient loadings for TN and TP were observed during years of peak hydrologic loads in 1992, 1995, 2003, and 2005 (Figures 2-22 and 2-23). Sarasota Bay also had noticeably higher than average TN loadings in 1989 despite an average hydrologic load that year.
The highest annual hydrologic loads to Roberts Bay were observed during wetter years of 1992, 1995, 2003, and 2005 (Figure 2-21). Drier years occurred in 1990, 1999, 2000, and 2007 and corresponded to the lowest observed hydrologic loads during those years. The highest annual nutrient loadings for TN and TP were observed in 1992, 1995, 2003, and 2005 during years of peak hydrologic loads (Figures 2-22 and 2-23). Higher than average annual nutrient loads were also observed during 1997 for Roberts Bay.
Little Sarasota Bay received the highest annual hydrologic loads during 1992, 1995, 2003, and 2005 (Figure 2-21). The highest TN and TP loads were observed during the same years (Figures 2- 22 and 2-23). Lowest hydrologic loads occurred in 1990, 1999, 2000, and 2007.
Blackburn Bay received the highest annual hydrol ogic loads during 1992, 1995, 2003, and 2005 (Figure 2-21). As was the case throughout the greater Sarasota Bay watershed, the lowest hydrologic loads to Blackburn Bay were observed in 1990, 1999, 2000, and 2007. The highest annual loadings for TN and TP were observed in 1992, 1995, 2003, and 2005 (Figures 2-22 and 2- 23). Higher than average annual nutrient loads we re also observed during 1997 for Blackburn Bay.Figure 2-21. Annual hydrologic loads by bay segment. BB=Blackburn Bay, LS=Little Sarasota Bay, PS=Palma Sola B ay, RB=Roberts Bay, SB=Sarasota Bay.

Figure 2-22. Annual TN loadings by bay segm ent. BB=Blackburn Bay, LS=Little Sarasota Bay, PS=Palma Sola Bay, RB=Roberts Bay, SB=Sarasota Bay

.Figure 2-23. Annual TP loadings by bay segmen t. BB=Blackburn Bay, LS=Little Sarasota Bay, PS=Palma Sola Bay, RB=Roberts Bay, SB=Sarasota Bay

. 

## 3.0 References

Hirsch, R.M., J.R. Slack, and R.A. Smith. 1982. Techniques of trend analysis for onthly water quality data. Water Resources Research. 18:107-121.
Hirsch, R.M. and J.R. Slack. 1984. A nonparametr ic trend test for seasonal data with serial dependence. Water Resources Research. 20:727-732.

Jones Edmunds & Associates. 2009. Sarasota County Pollutant Loading Model Development (W552) SIMPLE-Monthly Design Report. Prepared for Sarasota County and Southwest Florida Water Management District. 49pp.

## APPENDIX C. Implementation Issues: Targets, Thresholds, Type I Error and Compliance Assessment for Nu meric Nutrient Criteria Using the Reference Period Approach

Background
Site specific numeric nutrient criteria for indivi dual estuarine water bodies in southwest Florida from Saint Joseph Sound in Clearwater, Florida th rough Estero Bay, Florida are being proposed as alternatives to the application of regional or stat e wide model based standards. These site specific criteria rely on the identification of a respons e endpoint and the endpoi nt defined for these estuaries has been defined as a threshold chlorophyll a concentration in all cases. The temporal scale has been defined as yearly; therefore, the a ssessment statistic is an annual average chlorophyll

a concentration. Generally, the geometric annual average is used; however, in cases where the data are first averaged monthly, a simple arithmetic average is used. These threshold chlorophyll a concentrations for all estuaries have been identifie d using a reference peri od approach. Subsequent numeric nutrient criteria have been developed either by applying a stressor response relationship between nutrients and chlorophyll a concentrations, or by simply calculating an average of the annual averages of the reference period.
The reference period approach re lies on the identification of a period in time when these estuarine water bodies were sufficient to support full aquatic life uses. The overarching logic is that, provided a reliable stressor response model cannot be developed to accurately predict that point at which a system would no longer meet its full aquatic life use, water quality conditions during of a period in time when that waterbody was meeting its designat ed use would be protecti ve of full aquatic life uses. For example, the estuarine open water segmen t of Saint Joseph Sound has been meeting full aquatic life uses according to FDEP water quality standards and the Impaired Waters Rule since 2003. This means that chlorophyll a concentrations (Chla) have been below an annual average of 11 μg/L and dissolved oxygen concen trations (DO) have been above 4 mg/L in at least 90% of the samples. These “endpoints” (i.e., Chla and DO) are typical indicators used to assess the health of estuarine waters. The period of time between 2003 and 2009 was therefore chosen as the reference period for Saint Joseph Sound.
To establish numeric nutrient criteria using the re ference period approach, a period of time would be identified when the waterbody was meeting its de signated use, parameters would be defined to characterize the population of wate r quality values for chlorophyll a , total nitrogen (TN) and total phosphorus (TP) during that time, and then threshold values would be defined to represent a deviation from the expected condition of the reference period. The following paragraphs provide an example of how targets, thresholds and the type I error, and compliance assessment procedures have been established using the reference period approach.
Reference Period Example
To begin this example, a hypothetical reference pe riod distribution of chlorophyll a concentrations was generated. Chlorophyll a concentrations in estuaries typica lly exhibit “lognormal” distributioncharacte r becomes lognorm a and a st a in Figur e correspo n Protectio geometri c chloroph hypothet

Given th product o from the obtain t h selecting close to t know th a annual a v of annua expected r istics. This

normally d

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e 1. Once t

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c means as yll a, we n o i cal evaluati o

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distribution h e geometri c

12 samples t he overall a
a t variability

v erages fro m l averages i s

.

F i implies that d istributed w

n of chloro p t ion of 4.49 he data are normal dist r
n d the Envir o regulatory

o w consider

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and calcula

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m the referen c

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w hich confo r

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natural lo g
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o nmental P r

statistics f o the log tra n transform t h

n of Figure p rogram. F o
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u rther, we c ting the ave r
e re f erence
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u lated distrib u u ral log tran s
r ms to most

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istribution c o g transforme d

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2, we can g
o r example,

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e ry close to

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w as simulate

o mposed of d , the distri the Florida g ency (EPA)

r s with log n ues to repr e
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we can ran d

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e it is a rand o
m ple, in Fig u s can be se e the overall

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n ormal dist r
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n ual averag e domly selec

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average in F

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m ean of 6.7 5

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of Environ m sed using a n
r ibutions su c t a underlyin g
m eans.

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a ch average ple, howev e
a ve generate d 3 , the distri b F igure 2 wh i on, it

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a tions

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Figure 3

. re 2. Distrib u

Distributio n tion of the n a

of 100 annu a tural log tra n

l averages o f sformed val u

f the log tran s per year. u es of the si m

s formed chlo r ulated distri b

ophyll value s ution above.

using 12 sa m plesTo deriv e 3. This d i

Targets a

Once th e targets a n

The refe r geometri c to the r e Howeve r populati o can be c averages value to was fro m Using th e standard deviatio n distributi o exceed t o distributi o e the geome t i stribution is

Figure 4. Ge o

a nd Threshol

e geometric m
n d threshol d

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c means. Th a
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r , it can be

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n of the pop u
o n that defi n
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o metric mea n

ds

m eans have d s for this th e

d target for

a t is, 5.947 μ i od conditi o seen from F

e tween 3.9 μ
a populatio n
e nce period.

n annual av e population a standard st a f the distrib u
u lation of ge o
n es with 95 %

hypothesis

s hold value e a verages, w e g ure 4.

n s taken from

been calcul a
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this distribu t μ g/L is our g o
o n we ident i F igure 4 tha t μ g/L and 8.3

n of values Based on n
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o metric mea % confidenc e

that it bel o
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the simulate d

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t ion can be

o al for the w fied as bei n t the distrib u μg/L. Ther e that repres e
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, only 5% o
u re 4 woul d ns. Therefo r
e what valu e
o nged to th e μ g/L. k transform t

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defined as t

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w ould reject

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o f a normal d d lie above t

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n

.

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o ensure that

e of full aq u

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s tribution of p ected distri b

r y, we can a the hypoth e of annual g d istribution t he mean+ 1

o ld can be c o
e ometric me a

period distr i a verages of F

n s above.

c essary to de v

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it remains s i

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r ic means f o

geometric m b ution of a n
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e sis that the g eometric m with a mea n 1 .65*the sta n
o nstructed f o
a n would n e i bution. Fo F igure

v elop

a ll the

i milar p port.

o r this

m eans
n nual

e shold

value

m eans.

n and

n dard

o r this

e ed to

r thisThe threshold value provides a level of certainty that a future single geometric mean value (i.e. a sample) is not declared to exceed the reference distribution simply due to sampling. As was demonstrated above, we know th at some portion of the reference distribution values will be above the target value. In fact, nearly 50% of the annual geometric means will be higher than the target

. However, only 5% of the values will be higher than the threshold value of 7.59 μg/L due to chance. This construct defines what is known as the Type I error rate or False Positive rate

. That is, we are willing to accept that 1 in 20 times we ma y get a value that is higher than 7.59 μg/L that still belongs to the reference period distribution. As was seen in Figure 4, 5% of the values did exceed 7.59 μg/L even though we know the entire population of values.
Therefore, the type I error rate is a value that is selected base d on judgment and while 5% is typically used, there is no definitive rationale fo r 5% versus 1% or 10%. If we wish to be more conservative, we would select a lower type I erro r rate by choosing a larger value (e.g., 1.96) to multiple against the standard deviation to minimize the potential for falsely declaring that a value was different from the reference distribution. If we have reason to believe that the water body may not fully meet its designated use at values at the higher end of the reference distribution, one could select a higher type I error rate which in turn wo uld equate to a smaller z score to multiply by the standard deviation (e.g., 1). For example, selectin g 1 standard deviation equates to a type 1 error rate of 16%. However, the type 1 error rate doe s have implications on the compliance assessment.
Compliance Assessment
Now that the definition of targets, thresholds, and type 1 error has been identified, there is another consideration that must be taken into acc ount when evaluating criteria exceedances. The exceedance frequency for an exceedance due to chance is defined by the type 1 error rate as described above. However, the exceedance frequency and duration component also must be established to allow for some anomalous condition unrelated to anthropogeni c activity such as an El Niño, or La Niña cycles. There is substantia l evidence that these meteorological anomalies can result in higher chlorophyll a concentrations (e.g., Morrison et al. 2006, Sherwood 2010). Therefore, this type of exceedance is different from the type I error described above. Since we have previously defined the type I error rate, we have already made the choice as to the false positive rate we are willing to accept. The implementati on question is now posed to ask “what is the frequency of exceedances and assessment cycle that allows for occurrences unrelated to local anthropogenic activities but remain s sensitive to capturing population deviations from the reference period due indicative of human adverse effects?”
To address this question the Multivariate ENSO I ndex calculated by Klaus Wolter of the National Oceanic and Atmospheric Administration (see Wolter and Timlin 1993) was used as described in several implementation documents produced by Janicki Environmental regarding implementation of numeric nutrient criteria (Janicki Environmental, In c. 2011a-c). It is easy to see in Figure 5 that when El Niño or La Niña events occur they tend to last more than a single year. In fact, it’s quite possible for many of the single events to cr oss a calendar year. Further information on the frequency and duration of ENSO events can be found at: http://www.esrl.noaa.gov/psd/enso/mei/Figur e

Based o n result in one yea r allowan c 2 exceed

It is poss such tha t exceeda n one mig h to a nat u pursued threshol d criteria a r of the 2 i e 5. Classific a

n this analys

higher annu

r in a 5 ye a
e as an exc e ances in a 5

i ble that the t both the p r
n ce occurs d h t calculate t

u ral anomal y as the impl d values wit h

r e inextrica b n 5 implem e a tion of El Ni ñ

is, it makes al geometri c
a r cycle as

e edance freq

year assess m
type I error

r obability o f ue to anom a he union pr o y . This was ementation h known ty p b ly linked an d

e ntation rule ñ o and La Ni ñ

sense to al l

c average th a

is currentl y uency in a f i

m ent cycle.

rate and th e f an exceed a
a lous condit

o bability of a

beyond the process is r p e 1 error r a d should no t is provided ñ a events as d e

l ow for the

a n the refer e y proposed b

ve year ass e

e probability

a nce occurri n

ions could b

a n exceedan

scope of t h

r efined. Bec a
a tes, the crit t be separat e in Janicki En e scribed in J a
potential th a
e nce period by FDEP.

e ssment cycl e

of an anom a
n g by chan c b e integrate d ce due to c h h is memora n
a use the pr o eria and th e
e d. Further i n
n vironmental a nicki Enviro n

a t an anom a threshold v a Therefore t h

e is to allow

a lous event

c e and the p d . That is, o v h ance and a n
n dum but o

o posed crit e
e 2 in 5 ye a
n formation s (2011). n mental 2011 a

a lous event c
a lue in mor e h e recomm e

for no mor e

can be calc u p robability t h

v er a 5 yea r
n exceedanc e ne that mig

e ria are bas e
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upporting t h a .

c ould

e than

e nded

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e due ht be

e d on t ation h e use

### References

Janicki Environmental, Inc. 2011a. Sarasota Bay Numeric Nutrient Criteria: Task 4 - Implementation Issues. Letter Memorandum Prepared for Sarasota Bay Estuary Program. Sarasota, Florida

Janicki Environmental, Inc. 2011b. Charlotte Ha rbor Numeric Nutrient Criteria: Implementation Issues. Letter Memorandum Prepared for Charlott e Harbor Estuary Program. Ft. Myers, Florida
Janicki Environmental, Inc. 2011c. Tampa Bay Numeric Nutrient Criteria: Task 4 – Implementation Issues. Letter Memorandum. Prepared for: Tampa Bay Estuary Program.

Morrison, G., E.T. Sherwood, R. Boler, and J. Barron. 2006. Variations in water clarity and chlorophyll a in Tampa Bay, Florida, in response to annual rainfall, 1985-2004. Estuaries and Coasts. 29:926-931.
Sherwood, E.T. 2010. 2009 Tamp a Bay Water Quality Assessment. 2010. Technical Report #02-10 of the Tampa Bay Estuary Program. Prepared by the Tampa Bay Estuary Program.
Wolter, K., and M.S. Timlin, 1993: Monitoring ENSO in COADS with a seasonally adjusted principal component index. Proc. of the 17th Climate Diagnostics Workshop , Norman, OK, NOAA/NMC/CAC, NSSL, Oklahoma Clim. Survey, CIMMS and the School of Meteor., Univ. of Oklahoma, 52-57. Download PDF

. http://www.esrl.noaa.gov/psd/pe ople/klaus.wolter/MEI/#ref_wt1

## APPENDIX D. Tables and Maps of Manatee County and Sarasota County Monitoring Sites Utilized for Water Quality Assessment in Each SegmentPalma Sola Bay – Manatee County Monitoring
Station Location Year Month 1 2 3 4 5 6 7 8 9 10 11 12 458 27.49700, -82.65683 1995 X 1996 X X X X 1997 X X X 1998 X X X X 1999 X X X X X 2000 X X X X 2001 X X X 2002 X X X X 2003 X X X X 2004 X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 587 27.47319, -82.64874 1996 X X X X X 1997 X X X X 1998 X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X X X 2003 X X X X 2004 X X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 589 27.48150, -82.68308 1995 X 1996 X X X 1997 X X X X 1998 X X X 1999 X X X X 2000 X X X X 2001 X X X 2002 X X X X 2003 X X X X 2004 X X X 2005 X X X X X 2006 X X X X 2007 X X X X 2008 X X X XPalma Sola Bay – Manatee County Monitoring (continued)
Station Location Year Month 1 2 3 4 5 6 7 8 9 10 11 12 590 27.46900, -82.66367 1995 X 1996 X X X 1997 X X X 1998 X X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X 2003 X X X X 2004 X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X XSarasota Bay – Manatee County Monitoring
Station Location Year Month 1 2 3 4 5 6 7 8 9 10 11 12 464 27.49604, -82.69544 1995 X 1996 X X X 1997 X X X X 1998 X X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X X 2004 X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 597 27.41525, -82.59467 1996 X X X X X 1997 X X X 1998 X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X 2003 X X X X 2004 X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 598 27.43558, -82.63981 1995 X 1996 X X X X 1997 X X X X 1998 X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X X 2004 X X X X 2005 X X X 2006 X X X X 2007 X X X X 2008 X X X XSarasota Bay – Manatee County Monitoring (continued)
Station Location Year Month 1 2 3 4 5 6 7 8 9 10 11 12 600 27.41875, -82.60593 1995 X 1996 X X X 1997 X X X X 1998 X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X X X 2003 X X X X 2004 X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 604 27.40242, -82.58064 1996 X X X X 1997 X X X X 1998 X X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X X 2004 X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 607 27.39904, -82.60052 1995 X 1996 X X X 1997 X X X X 1998 X X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X X 2004 X X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X XSarasota Bay – Manatee County Monitoring (continued)
Station Location Year Month 1 2 3 4 5 6 7 8 9 10 11 12 608 27.38020, -82.59224 1995 X X 1996 X X X X 1997 X X X X 1998 X X X X 1999 X X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X X 2004 X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 658 27.48112, -82.69929 1996 X X X X X 1997 X X X X 1998 X X X 1999 X X X X 2000 X X X X 2001 X X X 2002 X X X X 2003 X X X 2004 X X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 659 27.46650, -82.68750 1995 X 1996 X X X X 1997 X X X X 1998 X X X X 1999 X X X X X 2000 X X X X 2001 X X X X 2002 X X X 2003 X X X X 2004 X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X XSarasota Bay – Manatee County Monitoring (continued)
Station Location Year Month 1 2 3 4 5 6 7 8 9 10 11 12 660 27.45332, -82.67522 1995 X 1996 X X X X 1997 X X X X 1998 X X X 1999 X X X X 2000 X X X X 2001 X X X 2002 X X X X X 2003 X X X X 2004 X X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 667 27.43714, -82.66078 1996 X X X X X 1997 X X X 1998 X X X X 1999 X X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X X 2004 X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 668 27.42152, -82.64686 1996 X X X 1997 X X X X 1998 X X X X 1999 X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X X 2004 X X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X XSarasota Bay – Manatee County Monitoring (continued)
Station Location Year Month 1 2 3 4 5 6 7 8 9 10 11 12 669 27.41117, -82.64317 1995 X 1996 X X X X 1997 X X X 1998 X X X X 1999 X X X X X 2000 X X X X 2001 X X X X 2002 X X X X 2003 X X X 2004 X X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X X 676 27.39768, -82.62686 1996 X X X X X 1997 X X X X 1998 X X X X 1999 X X X X X 2000 X X X X 2001 X X X X 2002 X X X 2003 X X X X 2004 X X X X 2005 X X X X 2006 X X X X 2007 X X X X 2008 X X X XSarasota Bay – Sarasota County Monitoring, January-June
Station Location January February March April May June 10-1 27.34662,

‐ 82.58386 27.35361,

‐ 82.57388 27.35334,

‐ 82.58079 27.34649,

‐ 82.58940 27.34539,

‐ 82.57901 27.35884,

‐ 82.57789 10-2 27.34507,

‐ 82.56928 27.34098,

‐ 82.56656 27.33487,

‐ 82.56876 27.33876,

‐ 82.57272 27.33905,

‐ 82.57277 27.33852,

‐ 82.56789 10-3 27.33112,

‐ 82.58462 27.33438,

‐ 82.57998 27.32844,

‐ 82.58688 27.33883,

‐ 82.58128 27.33421,

‐ 82.57938 27.3252,

‐ 82.58018 10-4 27.31723,

‐ 82.56625 27.31019,

‐ 82.56429 27.32557,

‐ 82.56707 27.32571,

‐ 82.56717 27.32673,

‐ 82.56785 27.32441,

‐ 82.55596 10-5 27.31366,

‐ 82.55307 27.30761,

‐ 82.55623 27.30747,

‐ 82.54939 27.30447,

‐ 82.55538 27.31689,

‐ 82.55359 27.31206,

‐ 82.55169 11-1 27.35841,

‐ 82.56685 27.36783,

‐ 82.56561 27.36094,

‐ 82.57067 27.36327,

‐ 82.56689 27.36058,

‐ 82.57022 27.37032,

‐ 82.56841 11-2 27.35354,

‐ 82.55847 27.34727,

‐ 82.55640 27.34770,

‐ 82.56161 27.35432,

‐ 82.56184 27.34440,

‐ 82.5629 27.35302,

‐ 82.56599 11-3 27.34038,

‐ 82.55334 27.33591,

‐ 82.55658 27.33708,

‐ 82.56055 27.34309,

‐ 82.55347 27.34022,

‐ 82.55848 27.33358,

‐ 82.55878 11-4 27.32574,

‐ 82.55068 27.32305,

‐ 82.54990 27.32567,

‐ 82.54475 27.31827,

‐ 82.54685 27.32823,

‐ 82.54878 27.32388,

‐ 82.54816 11-5 27.30577,

‐ 82.54215 27.31270,

‐ 82.54251 27.31328,

‐ 82.54573 27.30974,

‐ 82.54561 27.30490,

‐ 82.54369 27.31187,

‐ 82.54505 7-1 27.37763, -82.60928 27.38528,

‐ 82.61878 27.37768, -82.60438 27.37572, -82.61023 27.36905, -82.61430 27.37482, -82.60818 7-2 27.36627, -82.59251 27.36948, -82.59851 27.36055, -82.60065 27.36163, -82.59953 27.36777, -82.59883 27.36767, -82.59352 8-1 27.37630, -82.58350 27.38169, -82.57971 27.38850, -82.58593 27.38045, -82.58578 27.37688, -82.58288 27.37815, -82.58418 8-2 27.38346, -82.57163 27.38079, -82.57216 27.37888, -82.57587 27.38017, -82.56623 27.37287, -82.57583 27.37682, -82.57575 US-1 27.38590,

‐ 82.62170 27.36690,

‐ 82.61510 27.38850,

‐ 82.60500 27.37580,

‐ 82.61330 27.38790,

‐ 82.63550 27.38430,

‐ 82.60880 US-2 27.37240,

‐ 82.60940 27.37460,

‐ 82.59480 27.37970,

‐ 82.59840 27.37200,

‐ 82.58750 27.37000,

‐ 82.59940 27.37470,

‐ 82.60490 US-3 27.37630,

‐ 82.58350 27.38169,

‐ 82.57971 27.38850,

‐ 82.58593 27.38045,

‐ 82.58578 27.37688,

‐ 82.58288 27.37815,

‐ 82.58418 US-4 27.38346,

‐ 82.57163 27.38079,

‐ 82.57216 27.37888,

‐ 82.57587 27.38017,

‐ 82.56623 27.37287,

‐ 82.57583 27.37682,

‐ 82.57575 US-5 27.35110,

‐ 82.59440 27.36060,

‐ 82.58990 27.35590,

‐ 82.59880 27.35650,

‐ 82.60500 27.35830,

‐ 82.59430 27.36290,

‐ 82.59770Sarasota Bay – Sarasota County Monitoring, July-December
Station Location July August September October November December 10-1 27.34424,

‐ 82.58265 27.35439,

‐ 82.58502 27.3455,

‐ 82.59462 27.35317,

‐ 82.58200 27.35917,

‐ 82.57619 27.34934,

‐ 82.58149 10-2 27.34924,

‐ 82.56986 27.33525,

‐ 82.56750 27.34412,

‐ 82.57124 27.33932,

‐ 82.56744 27.34747,

‐ 82.56917 27.34128,

‐ 82.56857 10-3 27.33400,

‐ 82.58137 27.33128,

‐ 82.58449 27.33926,

‐ 82.58047 27.33075,

‐ 82.58471 27.33983,

‐ 82.58141 27.33483,

‐ 82.57905 10-4 27.32052,

‐ 82.56628 27.32936,

‐ 82.56975 27.31507,

‐ 82.56589 27.31177,

‐ 82.56508 27.32254,

‐ 82.57461 27.32844,

‐ 82.56228 10-5 27.30575,

‐ 82.54863 27.31240,

‐ 82.55368 27.30965,

‐ 82.55321 27.31382,

‐ 82.55319 27.30675,

‐ 82.55676 27.30528,

‐ 82.54905 11-1 27.36572,

‐ 82.56273 27.36236,

‐ 82.56318 27.36698,

‐ 82.56980 27.36367,

‐ 82.56204 27.35999,

‐ 82.56758 27.36393,

‐ 82.56917 11-2 27.35322,

‐ 82.55562 27.35341,

‐ 82.56227 27.34891,

‐ 82.55895 27.35294,

‐ 82.56188 27.35127,

‐ 82.55658 27.34649,

‐ 82.56710 11-3 27.33725,

‐ 82.55643 27.33979,

‐ 82.55321 27.33112,

‐ 82.55516 27.33770,

‐ 82.55514 27.33695,

‐ 82.55966 27.34055,

‐ 82.55682 11-4 27.32226,

‐ 82.54559 27.31907,

‐ 82.54764 27.32487,

‐ 82.54659 27.32001,

‐ 82.54623 27.32545,

‐ 82.54976 27.32778,

‐ 82.54871 11-5 27.31022,

‐ 82.54121 27.30910,

‐ 82.54346 27.30436,

‐ 82.54324 27.31165,

‐ 82.54243 27.30748,

‐ 82.54224 27.31016,

‐ 82.54557 7-1 27.38025, -82.61425 27.37452, -82.60813 27.36491, -82.60823 27.37973, -82.60183 27.38077, -82.61188 27.37025, -82.60916 7-2 27.36670, -82.59143 27.36647, -82.58840 27.36065, -82.59453 27.37328, -82.59063 27.35581, -82.59526 27.37553, -82.59221 8-1 27.38777, -82.59458 27.37904, -82.58343 27.38414, -82.58860 27.38478, -82.58350 27.37421, -82.58307 27.38191, -82.58427 8-2 27.37907, -82.57290 27.37951, -82.57035 27.37632, -82.57216 27.37123, -82.57739 27.38449, -82.57244 27.37703, -82.57264 US-1 27.38920,

‐ 82.63400 27.38290,

‐ 82.61400 27.36900,

‐ 82.61360 27.38890,

‐ 82.61430 27.38710,

‐ 82.62520 27.38230,

‐ 82.60800 US-2 27.37240,

‐ 82.60210 27.36860,

‐ 82.60780 27.38780,

‐ 82.60130 27.37050,

‐ 82.5940 27.37140,

‐ 82.60100 27.37250,

‐ 82.60790 US-3 27.38777,

‐ 82.59458 27.37904,

‐ 82.58343 27.38414,

‐ 82.58860 27.38478,

‐ 82.58350 27.37421,

‐ 82.58307 27.38191,

‐ 82.58427 US-4 27.37907,

‐ 82.57290 27.37951,

‐ 82.57035 27.37632,

‐ 82.57216 27.37123,

‐ 82.57739 27.38449,

‐ 82.57244 27.37703,

‐ 82.57264 US-5 27.35440,

‐ 82.59760 27.36400,

‐ 82.60310 27.36340,

‐ 82.58760 27.36090,

‐ 82.59400 27.35440,

‐ 82.60680 27.36030,

‐ 82.60260Little Sarasota Bay – Sarasota C ounty Monitoring, January-June
Station Location January February March April May June 14-1 27.23947,

‐ 82.51646 27.23565,

‐ 82.51529 27.23802,

‐ 82.52170 27.23997,

‐ 82.51890 27.23664,

‐ 82.51948 27.23379,

‐ 82.51922 14-2 27.23144,

‐ 82.51299 27.22582,

‐ 82.51452 27.22078,

‐ 82.51332 27.22795,

‐ 82.51073 27.22378,

‐ 82.50552 27.23065,

‐ 82.51085 14-3 27.21773,

‐ 82.51221 27.21638,

‐ 82.50430 27.20581,

‐ 82.50502 27.21258,

‐ 82.50245 27.21720,

‐ 82.50704 27.21136,

‐ 82.50371 14-4 27.19807,

‐ 82.50269 27.19453,

‐ 82.50167 27.20371,

‐ 82.50126 27.20034,

‐ 82.50261 27.20444,

‐ 82.50305 27.19106,

‐ 82.49980 14-5 27.18361,

‐ 82.49477 27.18644,

‐ 82.49840 27.19021,

‐ 82.49503 27.18220,

‐ 82.49668 27.18782,

‐ 82.49620 27.18587,

‐ 82.49337

Little Sarasota Bay – Sarasota C ounty Monitoring, July-December
Station Location July August September October November December 14-1 27.24116,

‐ 82.51845 27.24264,

‐ 82.52177 27.23837,

‐ 82.51715 27.23511,

‐ 82.52013 27.23964,

‐ 82.51495 27.23552,

‐ 82.52132 14-2 27.22550,

‐ 82.51247 27.23152,

‐ 82.50869 27.23177,

‐ 82.51597 27.22613,

‐ 82.51325 27.22335,

‐ 82.51206 27.23039,

‐ 82.50961 14-3 27.22112,

‐ 82.50562 27.20965,

‐ 82.50335 27.21801,

‐ 82.51122 27.21758,

‐ 82.50273 27.21974,

‐ 82.50496 27.20594,

‐ 82.50431 14-4 27.20046,

‐ 82.50259 27.19595,

‐ 82.49653 27.20052,

‐ 82.49762 27.19858,

‐ 82.50238 27.19530,

‐ 82.50244 27.19762,

‐ 82.49727 14-5 27.18120,

‐ 82.49282 27.19005,

‐ 82.49946 27.18391,

‐ 82.49674 27.18990,

‐ 82.49436 27.18770,

‐ 82.49842 27.18353,

‐ 82.49713Roberts Bay – Sarasota County Monitoring, January-June
Station Location January February March April May June 13-1 27.28860,

‐ 82.55061 27.29469,

‐ 82.55020 27.29211,

‐ 82.54466 27.29969,

‐ 82.54459 27.29256,

‐ 82.54665 27.29720,

‐ 82.54781 13-2 27.28192,

‐ 82.54274 27.28420,

‐ 82.54711 27.28039,

‐ 82.54666 27.27881,

‐ 82.54358 27.28469,

‐ 82.54454 27.28119,

‐ 82.54489 13-3 27.27008,

‐ 82.54462 27.26242,

‐ 82.53693 27.26596,

‐ 82.53956 27.26316,

‐ 82.53887 27.26714,

‐ 82.54102 27.26400,

‐ 82.53956 13-4 27.25993,

‐ 82.53610 27.25521,

‐ 82.53337 27.25855,

‐ 82.53496 27.25842,

‐ 82.53482 27.25299,

‐ 82.53107 27.25810,

‐ 82.53460 13-5 27.24447,

‐ 82.52525 27.24812,

‐ 82.52477 27.24532,

‐ 82.52124 27.24532,

‐ 82.52646 27.24572,

‐ 82.52386 27.24374,

‐ 82.52199

Roberts Bay – Sarasota County Monitoring, July-December
Station Location July August September October November December 13-1 27.29164,

‐ 82.54427 27.29592,

‐ 82.54452 27.29030,

‐ 82.54766 27.29178,

‐ 82.55099 27.29051,

‐ 82.54812 27.29347,

‐ 82.54677 13-2 27.27946,

‐ 82.54573 27.28598,

‐ 82.55009 27.28120,

‐ 82.54242 27.28559,

‐ 82.54434 27.28305,

‐ 82.54844 27.28378,

‐ 82.54740 13-3 27.27038,

‐ 82.54272 27.27429,

‐ 82.54398 27.26706,

‐ 82.53928 27.26634,

‐ 82.54074 27.26439,

‐ 82.53892 27.27005,

‐ 82.54504 13-4 27.25470,

‐ 82.53197 27.25259,

‐ 82.52970 27.25750,

‐ 82.53300 27.25707,

‐ 82.53238 27.25946,

‐ 82.53508 27.25612,

‐ 82.53564 13-5 27.25261,

‐ 82.52955 27.24856,

‐ 82.52309 27.25082,

‐ 82.52574 27.24775,

‐ 82.52234 27.25003,

‐ 82.52496 27.25038,

‐ 82.52899Blackburn Bay – Sarasota County Monitoring, January-June
Station Location January February March April May June 16-1 27.17680,

‐ 82.49412 27.17026,

‐ 82.49108 27.17215,

‐ 82.49066 27.17502,

‐ 82.49255 27.17147,

‐ 82.49221 27.16933,

‐ 82.48797 16-2 27.15984,

‐ 82.48401 27.16061,

‐ 82.48471 27.15797,

‐ 82.48304 27.16579,

‐ 82.48645 27.15784,

‐ 82.48231 27.15861,

‐ 82.48309 16-3 27.15160,

‐ 82.47709 27.15301,

‐ 82.48082 27.14596,

‐ 82.47679 27.15453,

‐ 82.48033 27.14884,

‐ 82.47570 27.15340,

‐ 82.48046 16-4 27.14028,

‐ 82.47166 27.14384,

‐ 82.47175 27.13647,

‐ 82.47014 27.14340,

‐ 82.47466 27.14072,

‐ 82.47088 27.13781,

‐ 82.47115 16-5 27.13132,

‐ 82.47028 27.13347,

‐ 82.46917 27.12844,

‐ 82.46978 27.12514,

‐ 82.46936 27.13212,

‐ 82.47122 27.13080,

‐ 82.46968

Blackburn Bay – Sarasota County Monitoring, July-December
Station Location July August September October November December 16-1 27.17459,

‐ 82.49122 27.17770,

‐ 82.49345 27.16960,

‐ 82.49064 27.17174,

‐ 82.49235 27.17467,

‐ 82.49089 27.16986,

‐ 82.48801 16-2 27.16012,

‐ 82.48224 27.16605,

‐ 82.48649 27.16197,

‐ 82.48452 27.16133,

‐ 82.48455 27.16057,

‐ 82.48311 27.16442,

‐ 82.48579 16-3 27.14879,

‐ 82.47383 27.15472,

‐ 82.47897 27.14932,

‐ 82.47496 27.15314,

‐ 82.48020 27.14761,

‐ 82.47785 27.15243,

‐ 82.47850 16-4 27.14008,

‐ 82.47017 27.14343,

‐ 82.47405 27.14075,

‐ 82.47156 27.14298,

‐ 82.47512 27.14051,

‐ 82.47043 27.14054,

‐ 82.47178 16-5 27.12788,

‐ 82.46935 27.13458,

‐ 82.46950 27.13033,

‐ 82.47017 27.13421,

‐ 82.47046 27.13017,

‐ 82.46992 27.13274,

‐ 82.46941Figure 1. January monitoring sites used for assessment of water quality conditions.Figure 2. February monitoring sites used for assessment of water quality conditions.Figure 3. March monitoring sites used for assessment of water quality conditions.Figure 4. April monitoring sites used for assessment of water quality conditions.Figure 5. May monitoring sites used for assessment of water quality conditions.Figure 6. June monitoring sites used for assessment of water quality conditions.Figure 7. July monitoring sites used for assessment of water quality conditions.Figure 8. August monitoring sites used for assessment of water quality conditions.Figure 9. September monitoring sites used for assessment of water quality conditions.Figure 10. October monitoring sites used for assessment of water quality conditions.Figure 11. November monitoring sites used for assessment of water quality conditions.Figure 12. December monitoring sites used for assessment of water quality conditions.