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

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## Chapter 1: Methodology for Deriving U.S. EPA’s Proposed Criteria for Lakes
This section describes the background, methodol ogy, and results of U.S. EPA’s analysis for deriving the proposed nutrient criteria for Fl orida lakes. The methodology includes developing lake classification and deriving ch lorophyll and nutrient criteria fo r each of the resultant lake classes. All statistical data analysis was base d on Florida’s STORET and GWIS database, as developed and screened by Florida Departme nt of Environmental Protection (FDEP).



### 1a. Methodology for Proposed Lake Classification
i. Background
Previous work on Florida lakes iden tified four lake classes: (1) so ft water (acidic)-clear lakes; (2) acidic-colored lakes; (3) alkaline-clear lakes; and (4) alkaline -colored lakes. That classification system was orig inally proposed by Shannon and Brezonik (1972) on the basis of cluster analysis of lakes in north and central Florida. The classification was subsequently confirmed by Gerritsen et al. (2000) using principal components analysis of a much larger statewide data set compiled by Gri ffith et al. (1997). Among the la ke groups, softwater (acidic) clear lakes of northwestern and central sandhills have been identified as extremely oligotrophic (Canfield et al. 1983).
Water color is due to dissolved organic carbon from decaying plant material in forests and wetlands of a lake’s surface watershed. Color in Fl orida lakes ranges from none (clear) to heavily stained, tea-colored water. Color limits light pe netration into the wate r column, and chlorophyll and algae are limited in heavily colored lakes. FDEP has defined colored lakes as those with color greater than 40 platinum-cobalt units (PCU ). The color classification was based on the earlier analyses (e.g., Shannon and Brezonik 1972; Gerritsen et al. 2000; Paul and Gerritsen 2002).
Total alkalinity is a measure of the total concentration of bases in water expressed as calcium carbonate (CaCO 3 ) in milligrams per liter (mg/L). Thos e bases are usually bicarbonate (HCO 3 - ) and carbonate (CO 3 = ) that act as buffers that prevent dr astic changes in pH. According to a survey of 946 Florida lakes, the total alkalini ty ranges from 0.24 to 552 mg/L with a median value of 40 mg/L (Lazzarino et al. 2009).
Alkalinity of Florida lakes is regulated by the contribution of groundwater that has been in contact with limestone or calcar eous soils (Stauffer and Canfield 1992). For example, acidic sandhill lakes often have no inlet or outlet but receive shallow groundwater from siliceous sand soils, and the shallow groundwater is perched ab ove deeper limestone aquifers (Stauffer and Canfield 1992). Other lakes are not isolated from limestone aquifers, and groundwater contributes alkalinity from calcium carbonate di ssolved from limestone. Alkalinity of Florida clear lakes ranges from zero (acidic lakes with no ability to neutralize acid) to well over 200 mg/L CaCO 3
.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-2 Alkaline lakes are considered to be more productive than acidic lakes, and alkalinity is associated with productivity of undisturbed re ference lakes (Ryder et al. 1974; Oglesby 1977; Vighi and Chiaudani 1985). The effect of alkalinity has also been shown in experimental studies; increasing alkalinity from 13 to > 50 mg/L CaCO 3 increased primary productivity for the same nutrient loading (Arce and Boyd 1975). Alkalinity influe nces the species richness and composition of both aquatic macrophytes and alg ae species (Vestergaard and Sand-Jensen 2000). Alkalinity has also been used to classify lakes. For example, to comply with the European Water Framework Directive, Danish lakes were cla ssified by depth and alkalinity; the alkalinity threshold used was 10 mg/L CaCO 3 (Søndergaard et al. 2005).

On the basis of recommendations from its Technical Advisory Committee (TAC), FDEP classified clear lakes on the basi s of total alkalinity greater th an or less than 50 mg/L (as CaCO 3 ). Because water color limits algal productivity, the TAC did not recommend an alkalinity breakdown in colored lakes. The TAC recomme nded that the clear lakes needed to be subcategorized to capture the differences be tween lakes receiving groundwater input from calcareous aquifer sources (highe r alkalinity), which c ontain natural higher levels of phosphorus from lakes that receive most of their wate r from (low specific conductance) rainfall.
In many cases, historic alkalinity measurements di d not exist to classify a clear lake as either alkaline or acidic. However, Florida has historically collected specific conductance measurements. When alkalinity da ta are unavailable, specific conductance can be used as a surrogate for alkalinity. Specific conductance is a measure of the ionic activity in water. A data analysis performed by FDEP and reexamined by U.S. EPA found that a specific conductance threshold value of 250 microsiemens/centimeter (μS/cm) corresponds to the alkalinity threshold of 50 mg/L (as CaCO 3 ) proposed by the TAC. Of those two m easures, alkalinity is the preferred parameter to measure because it is less variable, and ther efore a more reliable indicator, and also because it is a more direct measure of the presence of underlying geology associated with elevated nutrient levels.



### ii. Data Used for Analysis Water chemistry and chl. a data in Florida lakes were queri ed from the Florida STORET and the GWIS database. The initial data set consisted of 33,622 samples from 4,417 sites distributed within 1,599 lakes. All data were spatially li nked to USGS lake reach codes on the basis of station coordinates. Water chemistry and chl. a data were averaged by parameter, lake reach code, and date. Data from 324 la kes were used to assess chl. a response to nutrients from more than 9,600 paired results. Nutrient concentr ations, chlorophyll concentration, specific conductance, and alkalinity were log-transforme d (natural log) for statistical analyses.



### iii. Data Analysis for Classification FDEP categorized lakes into clear and colored la kes on the basis of the geometric mean color for the period of record. A small number of lakes shifted between color categories over the period. Clear lakes were further s ubdivided on the basis of lo w alkalinity, or acidic ( ≤ 50 mg/L CaCO 3 ), and high alkalinity, or al kaline (> 50 mg/L CaCO 3 ), or specific conductance (using 250 μS/cm as the threshold). Where appropriate, data were lo g-transformed. Comprehens ive nutrient, specificTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-3 conductance, and chl. a data were available for 228 lake years in 78 clear lakes, and data including alkalinity were availabl e for 200 lake years in 89 lakes.



### iv. Other Classification Variables Regional differences among the lakes were eval uated according to FD EP’s stream nutrient regions (see Chapter 5 in USEPA 20 09). Lakes showed similar chl. a responses regardless of location, with some differences in the range of nutrient concentrations (Figures 1-1 and 1-2). Therefore, regional difference were not found to be a significant differentia tor of the relationship of total nitrogen (TN) or to tal phosphorus (TP) with chl. a . Figure 1-3 shows that there are no significant seasonal chl. a differences in the data set used to develop the nutrient criteria, indicating that a yearly average is appropriate.



### v. Color and Alkalinity/Specific Conductance Results FDEP has proposed an alkalinity class threshold at 50 mg/L. The justification for classifying lakes using alkalinity is that alka line lakes appear to have higher natural nutrient concentrations than acidic lakes. If alkalinity differences ex ist among lakes but no related differences are in trophic variables or response, there is no r eason for classifying la kes using alkalinity.
U.S. EPA examined regression relationships between chl. a concentration and TN and TP by FDEP-proposed lake cate gories (USEPA 2009). Chl. a concentrations exhibited statistically significant positive responses to both TP and TN on an annual average basis (e.g., Figure 1-4). On the basis of that analysis, clear lakes in general have lower nutrien t concentrations than colored lakes and also have highe r (50% to 100%) chl. a concentra tions at the same TP or TN concentrations (Figure 1-4). T hose relationships explain a larg e portion of the annual average variability observed in chl. a concentrations ( R2 = 0.68–0.77). Although the slope of the chlorophyll response to nutrients is very similar between clear and colored lakes, the difference in chl. a response is enough to justify keeping the color classes separate.
Similarly, clear, alkaline lakes di splayed higher nutrient and chl. a concentrations than clear, acidic lakes (Figure 1-5). Clear, ac idic lakes have long been iden tified as crystal-clear, highly oligotrophic lakes (e.g., Canfield et al. 1983), and the TAC also r ecommended their separation as a class of lakes. Again, the slope s of the chlorophyll responses to TN and TP are similar between the alkalinity classes.
Accordingly, U.S. EPA concludes that three lake classes are appr opriate, the same as recommended by FDEP and its TAC: clear lakes ( ≤ 40 PCU) that are acidic ( ≤ 50 mg/L CaCO 3 or specific conductance ≤ 250  S/cm); clear lakes that ar e alkaline (> 50 mg/L CaCO 3 or specific conductance > 250  S/cm); and colored lakes (> 40 PCU). U.S. EPA also recognizes that an alternative classification could be equally justified that uses a lower alkalinity/specific conductance threshold and possibly with an addi tional class division of acidic and alkaline colored lakes. For a description of further analyses of color a nd alkalinity, see Section 1f.ii.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

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### 1b. Methodology for Propos ed Chlorophyll a Criteria Because excess algal growth is associated withdegradation in aquati c life and because chl. a levels are a measure of algal gr owth, U.S. EPA is using chl. a levels as indicators of healthy biological conditions, supportive of aquatic life in each of the categories of Florida’s lakes described above. Multiple lines of evidence support chl. a criteria as an effective indicator of ambient conditions that would be protective of Florida’s aquatic lif e use in lakes. Such lines of evidence include trophic state of lakes and aquatic life use, historical reference conditions in Florida lakes, and model results.



### i. Trophic State and Aquatic Life Use
Trophic State One part of recreational use protection is main taining ecosystem integrity, especially with respect to some highly visible attr ibutes of the system. In lakes, important visual attributes for users are water clarity and color. That does not mean that all lakes should be crystal-clear, rather, that the lakes should resemble their natural, expect ed conditions of color, clarity, and productivity. For waters that are exceptional or near ly pristine, they should very closely resemble all aspects of the natural condition.
Waters are typically classified into three trophic classes to refl ect nutrient conditions and overall productivity: oligotrophic, mesotrophic, and eutrophi c. Classification of a lake into one of the classes is commonly done with a Trophic State Index (TSI). The TSI quantifies the trophic state (oligotrophic, mesotrophic, eutrophic) in a wate rbody on the basis of observed measurements of nutrients and chl. a . The trophic classes and the TSI are ex plained below and also in U.S. EPA 2000. For Florida, a TSI of 40–50 corresponds to the boundary between oligotrophic and mesotrophic, and a TSI of 60–70 corresponds to the boundary between mesotrophic and eutrophic conditions (Salas and Marti no 1991; USEPA 2009). Anthropogenically caused eutrophic conditions are gene rally acknowledged to be undesi rable for drinking water, recreation, and aquatic life us es. In the past, FDEP has managed for a maximum TSI of 60 (highest level for mesotrophy in Fl orida), which corresponds to chl. a concentration of 20  g/L. As a primary line of evidence, U.S. EPA reviewed and evaluated TSI information from FDEP in deriving chl. a criteria that are protective of designated aquatic life uses in Florida’s lakes.
FDEP has a long history of using a modification of Carlson’s TSI (Carlson 1977) as a measure of lake trophic state and lake water quality for the state’s 305(b) and 303( d) assessments. Trophic state reflects the biological response to several factors, includ ing nutrient effects on phytoplankton chl. a , which can be modified or mitigated by water retention time, grazing, and macrophyte nutrient uptake. Havens (2000) reported th at the TSI approach provides an effective, low-cost method for tracking long-term changes inpelagic structure and function and has value in monitoring lake ecology and responses to management actions.
Carlson’s original TSI classifi ed lakes on the basis of chl. a levels and nitrogen and phosphorus concentrations, and it included three indicators—secchi depth, chl. a , and TP—to describe a lake’s trophic state. A 10-unit ch ange in the index represents a doubling or halving of secchi transparency, TP, and algal biomass (but note that chl. a does not follow the doubling or halving precisely; Carlson 1977).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-5 The following interpretation scheme fo r the TSI is based on nutrient/chl. a responses in north temperate lakes (Carlson and Simpson 1996):

TSI < 30 Classical Oligotrophy: Clear water, oxygen throughout the year in the hypolimnion, salmonid fisherie s in deep lakes. TSI 30–40 Deeper lakes still exhibit classical oligotr ophy, but some shallower lakes will become anoxic in the hypolimnion during the summer. TSI 40–50 Water moderately clear but increasing probability of anoxia in hypolimnion during summer. TSI 50–60 Lower boundary of classical eutrophy: Pot ential for decreased transparency, anoxic hypolimnia during the summer and macrophyte growth, warm-water fisheries only. TSI 60–70 Dominance of blue-green algae, algal scum s probable, extensive macrophyte problems. TSI 70–80 Heavy algal blooms possible throughout the summer, dense macrophyte beds but extent limited by light penetration. Often woul d be classified as hypereutrophic. TSI > 80 Algal scums, summer fish kills, few macrophytes, dominance of rough fish.

Salas and Martino (1991) proposed an alternativ e TSI categorization based on their work in phosphorus-limited, warm-water tropi cal lakes, which is more dir ectly applicable to Florida conditions. The TSI and chl. a values in Table 1-1 were dete rmined on the basis of the TSI relationship with TP. In the original index, a TSI of 50–60 re presents the lower boundary of eutrophy in temperate lakes, Salas and Martino cons idered that same range of TSI values to be mesotrophic in warm-water lakes.
The TSI equation describes a theore tical relationship between chl. a , TP, and TN. In the revised TSI, chl. a doubles with every 10-point in crease in the TSI (Table 1-2).
As part of the state’s 305(b) assessment, FD EP revised the TSI by (a) replacing secchi depth with TN, and (b) adding equations that adjust the nut rient component of th e TSI to reflect the limiting nutrient. Use of secchi depth in Florida as a contributing measure to TSI is limited because of the high frequency of dark-water lakes (> 40 PCU), where tannins, rather than algae, diminish transparency.

FDEP’s resultant TSI is based on chl. a , TN, and TP concentrations, as follows:

TSI = ( CHLATSI + NUTRTSI ) / 2 [1]

Where,

CHLATSI = 16.8 + 14.4 × LN (CHLA) [2]

NUTRTSI is based on limiting nutrient considerations, as follows: If TN / TP > 30, then lake is considered phosphorus limited and NUTRTSI = TP2TSI [3] TP2TSI = 10 × [2.36 × LN(TP × 1,000) – 2.38] [4] If TN / TP < 10, then lake is considered nitrogen limited and NUTRTSI = TN2TSI [5] TN2TSI = 10 × [5.96 + 2.15 × LN(TN + 0.0001)] [6] If 10 < TN / TP < 30, then lake is co-limited andTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-6 NUTRTSI = (TPTSI + TNTSI)/2 [7] TNTSI = 56 + [19.8 × LN(TN)] [8] TPTSI = [18.6 × LN(TP × 1000)] – 18.4 [9]
Equations 1-9 were determined on the basis of th e analysis of data from 313 Florida lakes and were adjusted so that a chl. a concentration of 20 μg/L was equa l to a TSI value of 60. For the 1998 305(b) report, a TSI threshold of 60 was used to represent “fair” lakes, while lakes above 70 were assessed as “poor.”
During development of the Impaired Waters Rule (IWR) in 1999–2000, the IWR TAC reviewed the TSI used in the 305(b) assessment and r ecommended that it be used to assess lakes for impairment. On the basis of then current U.S. EPA guidance that “fai r” waters should be included on state 303(d) lists, the TAC recommende d that the nutrient impairment threshold for most lakes (for those with a color higher than 40 PCU) should be an annual average TSI of 60.
While the TAC recommended use of the TSI threshold of 60 for most lakes, it also recognized that some lakes are naturally oligotrophic and ha ve significantly lower natural background TSIs. The TAC requested that FDEP evaluate datafrom reference lakes from the FDEP’s Bioassessment Sampling Program using principal com ponents analysis in an attempt to identify different types of lakes on the basis of water qua lity with the goal to establish different TSI thresholds for each type.
While many different parameters were evaluated, the analysis initially focused on a four-part chemical classification of Florida lakes consisting of acid-clear, acid-colored, alkaline-clear, and alkaline-colored. That classi fication system had originallybeen proposed by Shannon and Brezonik (1972) and was subsequently confirmed as part of the development of the Lake Condition Index for Florida (Gerritsen et al. 2000 ). However, the analysis conducted for the IWR indicates that the most significant differences in the TSI and TSI-related parameters (nutrients and chlorophyll) were seen when the lakes were classified by color alone, with lakes with a color of less than 40 PCU having signifi cantly lower TSIs. That color classification system also covered a previously identified ta rget population of oligotrophic lakes that the TAC wanted to address (low color, oligotrophi c lakes in the panhandle region of Florida). The FDEP then recommended, and the TAC agreed, to establish a TSI threshold of 40 for those lakes, which is equivalent to a chl. a of 5 μg/L.

Furthermore, the TAC recommended that clear, but higher specific conduct ance lakes should be held to a 20 μg/L chl. a threshold for aquatic life use protection. The higher threshold acknowledges the fact th at the higher specific conductance la kes would be expected to have higher chl. a levels related to the higher natural phosphorus conditions.

Aquatic Life Use Nearly all Florida lakes are sh allow and, thus, include large ar eas of potential habitat for submerged and floating-leaved plants. If the water is relatively clear, sh allow zones of the lakes can be dominated by native submerged and floating plants. Natural primary production in such shallow lakes includes contri butions from both submerged macrophytes and phytoplankton. If the water is turbid from suspended sediment or excess algae, the submerged plants will not receive sufficient light to survive.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-7 The algae and macrophytes in sh allow lakes have complex dynamics under nutrient enrichment and physical disturbance conditi ons (Scheffer et al. 1993). Su bmerged macrophytes intercept nutrients and incorporate them into plant biomass, where the nutrients are then unavailable to the phytoplankton algae. Shallow, mesotrophic lakes with abundant macrophytes have clear water and relatively low chlorophyll con centrations. Such lakes are meso trophic (intermediate level of productivity), but their water column chlorophyll concentrations are low because a large fraction of primary production is from the macrophytes an d not phytoplankton algae. Such a stable state can be upset by (1) sudden loss of macrophytes because of disturbance (e.g., grass carp, hurricane, other disturbance); or (2) excessive nutrient loading leading to increased epiphytes and phytoplankton, which then shade the macrophytes (e.g., Scheffer et al. 1993, 2001; Bachmann et al. 1999, 2002; Lowe et al. 1999, 2 001). Loss of the macrophytes can further increase TP concentrations (Bachmann et al. 2002), possibly from release from biomass and sediment, and further increasing TP available to the phytoplankton. The new condition can also be stable, and turbidity from al gae or resuspension of bottom sediment prevents macrophytes from reestablishing. Excess plant biomass problem s can occur both ways: dense algal blooms or a lake choked with inva sive floating plants (e.g., Hydrilla
, Myriophyllum , water hyacinth); extremely dense floating plants can also be in a stable state (Scheffer et al. 2003). Substantial evidence from both historical records and paleol imnology shows that several Florida lakes have made the transition from macrophyte-dominate d, mesotrophic clear waters to phytoplanktondominated, eutrophic (or hypereut rophic) turbid waters as the consequence of nutrient enrichment (Lowe et al. 1999, 2001; Kenney et al. 2002).
Submerged vegetation and phytoplankton support different consumer fo od webs. Macrophytes are the origin of a detritus- and periphyton- based food web including macroinvertebrates, amphibians, fish, and birds. The phytoplankton food web supports zooplankton and fish. More species of fish are dependent on littoral, benthic macroinvertebrates for their diet than those that are dependent on zooplankton (e.g., Vadeboncoeur et al. 2002). In addition to supporting a food web, macrophytes also provide habi tat and cover for fish (especia lly smaller forage fish and juvenile fish) and larger macroi nvertebrates (e.g., Wetzel 1975). Vegetated areas of lakes have greater animal diversity (inverteb rates, fishes, birds) than open waters (Havens et al. 1996). Collapse of the submersed vegetation results in an ecosystem change—the overall simplification of food webs and system function; and reduction or elimination of native fish, invertebrate, and bird species dependent on the littoral habitat. Such effects are well-documented from eutrophication effects in the Great Lakes and elsewhere (e.g. , Wetzel 1975; Winfield 2004). Thus, protecting aquatic life use requires maintenance of these system functions.
Because submersed vegetation is dependent on light, maintaining a lake’s historic balance between algae and submerged plants requires main taining overall historic water transparency. Natural transparency varies widely in Florida lakes because of the range in water color from crystal-clear to deeply stained. Re gardless of what the natural transp arency of a lake might be, it is reduced by increased algal growth that results from anthropogenic nutrient enrichment. Carlson’s TSI incorporates an empirical re lationship between secchi transparency and chlorophyll, such that an increase in chlor ophyll concentration by a factor of approximately 2.75 corresponds to a 50% decrease in secchi tran sparency (Carlson 1977). For constant water color, that relationship is expected to hold in Florida lakes. Accordingly, maintaining water column chlorophyll concentrations within traditional oligotrophic or mesotrophic limits reduces the risk that submerged vegetation and system functions will be unacceptably altered. U.S. EPATSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-8 recognizes that total nutr ient loading to a lake might be hi gher than the appare nt trophic state measured in the water column because submer ged macrophytes intercept some fraction of the nutrients.



### ii. Reference Condition The reference condition approach entails iden tifying lakes exhibiting minimally disturbed conditions, collecting and analyzi ng water quality data from the la kes, and calculating an upper percentile of the data distributi on to establish the criteria. Such an approach assumes that minimally disturbed conditions are the target condition, and substantial departure from this condition, as expressed by the per centile, should be prev ented. Several approaches can be used to identify a reference condition: identifying specific reference lakes; paleolimnological investigation to determine historic and prehistoric conditi ons; statistical or th eoretical models to infer reference conditions when th ey are not available everywhere.

Identification of minimally dist urbed lakes (reference approach) FDEP has identified reference lakes throughout the state, defined as lakes with the least amount of land use change (mostly natural land cover) within 500 meters of the lakeshore. An earlier report (Paul and Gerritsen 2002) characterized chl. a in five classes of reference lakes, and more recently, FDEP analyzed the distribution of chl. a in reference colored a nd clear lakes (but did not further break down the analysis by alkalini ty/specific conductance classes, see Appendix A-1).
A reference site approach, coupl ed with other techniques, incl uding contour plot interpolation, was suggested as a method to establish chl. a thresholds in Florida (P aul and Gerritsen 2003). On the basis of the 75 th percentile of reference sites, determined by contour plot interpolation, Paul and Gerritsen (2003) proposed chl. a targets for Florida clear la kes (< 40 PCU) ranging from 2 μg/L to 8 μg/L, and colored lake ta rgets ranging from 9 μg/L to 18 μg/L.

Paleolimnological Studies Another line of evidence that supports U.S. EPA’s proposed chl. a criteria is historical reference conditions as inferred from paleolimnological studies . Diatom frustules can be preserved in lake sediments, and if the sediments are undisturbed can be used to infer historic and prehistoric conditions in the lake. Because species composition of diatoms is highly sensitive to water column nutrient concentrations, nutrient concentra tions and pH can be inferred from the analysis.
Paleolimnological studies in Florida, where pre-human disturbance chl. a values were inferred from an analysis of diatom communities in deep sediment cores, show that historical conditions in clear, acidic panhandle lakes are similar to present-day reference conditions with TP concentrations in the range of 1 to 10  g/L, and historical TP concentrations in alkaline peninsular lakes were in the range of 15–90  g/L (mixture of both clear and colored lakes). The acidic panhandle lakes would be considered to be at the boundary between classical oligotrophy and mesotrophy.
The paleolimnological studies sugges t that most peninsular Florida lakes would be considered to be at (or above) the lower boundary of classica l eutrophy, even before human habitation of theTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-9 state (Whitmore and Brenner 2002; Whitmore 2 003). Paleolimnological st udies conducted at (colored) Lakes Shipp, Lulu, Haines, May, Conine and Bonny in the Florida peninsula suggest that the average chl. a in the lakes would naturally range between 14–20 μg/L. This is one line of evidence for supporting the chl. a threshold of 20 μg/L that was part of the IWR’s TSI threshold. However, paleolimnolgy of (colored) Lakes Waub erg and Hancock suggests that historic chl. a in those lakes naturally were in the ranges of 38–48 μg/L and 74–133 μg/L, respectively. Note that although Lake Hancock might be somewhat atypical, the paleolimnol ogical results suggest that any proposed nutrient criteria will need to allow for site-specific alternative criteria (SSAC) in those lakes with naturally higher or lower nutrient levels.

Model Predictions In addition to present-day reference and paleol imnological reconstructio n, U.S. EPA also used information from a model developed by the St. Johns River Water Management District that predicts nutrient and chl. a concentrations for a lake given its depth, alkalinity, and color to support the proposed chl. a criteria.
The St. Johns River Water Management District developed a statistica l model of reference condition (transparency and chlorophyll) using obse rvations of existing reference lakes (Lowe et al., personal communication). The model is base d on observations that the natural, reference fertility of a lake can be pred icted from its depth and alkalinit y (Ryder et al. 1974; Oglesby 1977; Vighi and Chiaudani 1985). This model is te rmed the Morphoedaphic Index, or MEI, model because it makes use of lake morphometry (depth), and edaphic factors (the effects of alkalinity from influence of deep groundwater). Developmen t of the model is explained in Appendix A-2. Application to Florida lakes also required inclus ion of water color. The model predicts natural, reference chlorophyll con centrations for any lake given its depth, alkalinity, and color. The model predicted chlorophyll concen trations for 10% reduction of wa ter transparency for a set of lakes identified as clear versus colored and acidic vs. alkaline. Such predictions were in accordance with the 20  g/L chl. a concentration limit defined by the TSI mesotrophic-eutrophic boundary and with 5–10  g/L for the oligotrophic-mesotrophic boundary.



### iii. Other Efforts to Establis h Chlorophyll a Thresholds Appendix A-3 contains a review of literature pertaining to estab lishing protective chl. a thresholds, predominantly from the United States . The literature suggests six main approaches for establishing protective chl. a thresholds in lakes:

 Using an upper percentile of the distribution of reference lakes (used by U.S. EPA; above)

 Paleolimnologic studies, where pr eindustrial disturbance chl. a values are inferred from an analysis of diatom communities in deep sediment cores (used by U.S. EPA; above)

 Expert elicitation for determ ining protective TSI or chl. a values (partially used by FDEP; below)

 Direct responses of other bi ological attributes (invertebrate community, vegetation, fish community) (evaluated but not used; below)

 Associating lake user visual perceptions (f or swimming and aesthetics) with simultaneously measured chl. a (evaluated but not used; below)TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-10  Setting the criterion to maintain the existing condition (maintenance strategy) (evaluated but not used; below).

Expert Opinion Several states, including Florid a, have used input from scie ntific advisory committees to establish chl. a or TSI targets. Recommendations of Flor ida’s TAC are discussed in 1b.i, above. Virginia and Iowa queried a panel of experts to establish protective chl. a targets, and the scientists in both states indepe ndently arrived at a recommended level of 25 μg/L as a yearly average (Gregory 2007; Wilton 2008). Virginia’s panel further recommended that chl. a notexceed 50 μg/L as an instantaneous measurement. Arizona, using a TSI and a weight-ofevidence approach, established lake summer time (peak season) chl. a targets at 20–30 μg/L (ADEQ 2008). Maryland established lake summer time chl. a targets using the rationale that a TSI of 50 (10 μg/L chl. a ) would prevent mesotrophic lakes fr om becoming eutrophic and that a TSI of 60 (20 μg/L chl. a ) would protect against excessive eutrophication (Rule 2004). West Virginia used expert opin ion to establish a chl. a threshold of 33 μg/L. Additionally, Iowa has established annual average TMDL ta rgets in specific lakes for th e protection of aquatic life use (Lost Lake) using a chl. a threshold of 33 μg/L (USEPA 2008).

The various thresholds devel oped by expert opinion that w ould protect against excessive eutrophication, expressed as annual or summertim e averages, yields a range of 20–33 μg/L of chl. a . That range of values suggests that Florid a’s IWR TAC recommendation of 20 μg/L in colored lakes is as protective as t hose established by many other states.

Biological Responses The responses of valued ecological attributes, such as benthic macroi nvertebrates, fish, or submerged macrophytes, to various chl. a levels, would provide the most direct method for establishing targets that would protect aquatic life. FDEP established a Lake Condition Index, using benthic macroinvertebrates as a response vari able in lakes (Gerrits en et al. 2000), and a Lake Vegetation Index, using the littoral and shorezone macrophyte community as the response indicator (Fore et al. 20 07). Although initial results were pr omising, FDEP eventually concluded that color was more responsible for explaining benthic response than were human disturbance measures, such as the Landscape Development Intensity Index (Fore 2007). Development and use of the Lake Vegetation Index is explained in Appendices A-4 and A-5.

Other states have used a fisher ies response variable. For example, Virginia conducted an analysis to determine the effect of chl. a levels on the health of fisher ies and concluded that summer average chl. a concentrations of 25 μg/L in cool-w ater lakes and 35–60 μg/L in warm-water lakes were protective of fish health (Gre gory 2007). Minnesota—using multiple lines of evidence, including regional patterns, reference lakes, fish response, lake user perception, paleolimnolgy, and nuisance algal bloom fr equency—established summer mean chl. a targets of 3 to 5 μg/L for designated coldwater trout fish eries, 9–22 μg/L for deep lakes, and 20–30 μg/L for shallow (< 4.5 m) lakes (Heiskary a nd Wilson 2008). Colorado proposed that summer average chl. a be maintained below 25 μg/L to assure high-quality fisheries (Saunders 2009).
FDEP is investigating fish community compositi on data collected from Florida lakes by the Florida Fish and Wildlife Conservation Commission (FWC) for comparison to chl. a data.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-11 Results of initial analyses do not yield a notable response signal in th e data and, thus, do not further inform the determination of chl. a targets.
FDEP has developed a preliminary Lake Vegetation Index, which comprises all submerged, floating, emergent, and woody vegeta tion in a lake’s littoral and shore zones. The index was not developed or calibrated for response to nutrients and, thus, responds primarily to general levels of anthropogenic disturbance and less to nutrien t loading. The general response of submerged aquatic macrophytes to eutrophicati on is well established and is de scribed above in section 1b.i. However, quantitative information is not yet av ailable on submerged macrophytes in Florida to examine potential thresh olds of response.

User Perceptions In Texas, a study of lake user pe rceptions indicated that in reser voirs without inorganic turbidity (> 1 m secchi) chl. a levels below approximately 20–25 μg /L still support full immersion recreational uses, as well as aesth etics (Glass 2006). For this study, lake users were asked to fill out a questionnaire concerning thei r visual perceptions (for swimmi ng and aesthetics) while chl.

a was simultaneously measured. A similar study c onducted in Florida dem onstrated that user perceptions differed depending on th e lake region (Hoyer et al. 2004). In the Florida study, when lake users responded to a question concerning suitab ility of the lake for recreation and aesthetic enjoyment by saying, “beautiful, could not be nicer,” chl. a ranged from approximately 30 μg/L in the Central Valley Lake regi on (generally high co lor) to approximately 3 μg/L in the Trail Ridge Region (generally uncolor ed lakes) (Hoyer et al. 2004). Those studies did not associate chl. a values with public health concerns, only the public perception of whether swimming was desirable. The user perceptions support the concept of different chl. a criteria for clear lakes and colored lakes.

Maintaining Existing Conditions Alabama’s approach to establis hing lake or reservoir chl. a targets could be described as a method designed to “maintain the existing condition” (Macindoe 2006). Alabama’s chl. a targets for specific lakes or reservoirs range from 5 μg/L to 27 μg/L. The Florida IWR TAC’s recommendation of a TSI of 40 for Florida clear reference lakes was based on the concept of maintaining the current condition of panhandle re gion sandhill lakes. A TSI of 40 equates to a chl. a value of 5μg/L.

Relationships between Cyanobacteria Abundance and Chl. a It is well established that cyanobacteria can be come very abundant and completely dominate the phytoplankton community in lakes when condition s are favorable. Some cyanobacteria blooms can be toxic and present a healthrisk to people recrea ting in and on the water. The World Health Organization (WHO) has established recomm endations for recrea tional exposure during cyanobacterial blooms (WHO 1999). Its recomme ndations reflect findings that a chl. a level of 10 μg/L in which cyanobacteria are dominant pr esents a relativel y low probability of mild irritative or allergenic effects, while a chl. a level of 50 μg/L in which cyanobacteria are dominant presents a moderate risk of adverse he alth effects. The WHO gu idance for recreation in waters (WHO 2003, section 8.1) states that 46 sp ecies of cyanobacteria have been shown to cause toxic effects in vertebrates and that any species or genus of cyanobacteria cannot be ruledTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-12 out as potentially toxic. WHO cau tions that, “it is prudent to presume a toxic potential in any cyanobacterial population.”
Because of the potential human health risks associated with cyanobacteria blooms, FDEP considered the possibility of a chl. a threshold that might be associated with a high probability of cyanobacteria blooms. FDEP examined the relationship between chl. a and the percent cyanobacteria in 1,364 phytoplankton samples fr om small and large lakes randomly sampled between 2000 and 2006 in Florida’s probabilistic sampling network. Figure 1-6 shows chl. a values regressed against the percent cyanobacter ia for each sample. According to the graph, no apparent increased probability of cya nobacteria dominance exists as chl. a increases. Samples dominated by one of the 13 harmful algal bloom taxa listed by WHO (WHO 2003, section 8.1) do not show an increasing trend of cyanobacteria dominance with chl. a either. That analysis does not further inform the determination of chl. a targets.

Conclusions Carlson and Simpson (1996) noted that trophic state is not sy nonymous with the concept of water quality. While trophic state is an absolute scale that describes the biological condition of a waterbody, water quality is used to describe the condition of a waterbody in relation to human needs or values, relative to the use of the wate r and the expectations of the user. Water quality standards are created to protec t the designated uses of waterb odies. In Florida lakes, the designated uses are for the protection of healthy, well-balanced populations of fish and wildlife, and for recreation in and on the water. Criteria must provide protection for these sometimes competing interests. For example, an oligotrophic to mesotrophic lake could have water quality deemed desirable for swimming, as well as natural communities of fish, invertebrates, and submerged macrophytes. However this same lake might not be considered to be optimal for bass fishing. For that reason, U.S. EPA has taken a weight-of-evidence approach for establishing protective chl. a thresholds.
Multiple lines of evidence were also used to evaluate the rigor of protection inherent in the IWR TAC’s TSI-based chl. a recommendations, which were adopte d into the IWR in 2002 (Chapter 62-303, FAC). The multiple lines of evidence co nsidered above, including aquatic life use protection, observed and modeled reference condition, and paleolimnology, converge to support the Florida IWR TAC’s original recommendation.

Colored water lakes – On the basis of the above multiple lines of evidence, U.S. EPA proposes that an annual average chl. a of 20 μg/L in lakes with wate r color greater than 40 PCU is protective of designated uses.
Clear, acidic lakes – Some lakes are naturally ol igotrophic and have sign ificantly lower natural background nutrient and chl. a concentrations. Examples include the “sandhill” lakes in the Florida panhandle, which are cl ear and oligotrophic, often wi th chlorophyll as low as 1  g/L. For protection of those and othe r clear, acidic lakes, U.S. EPA recommends a chl. a criterion of 6
 g/L. In contrast to colored lakes and clear, al kaline lakes, clear, ac idic lakes do not receive significant natural nutrient inputs fro m groundwater or other surface water sources. The lakes are, thus, expected to be oli gotrophic. Some of Florida’s clear , acidic lakes, in sandhills in northwestern and central Florida, have been id entified as extremely o ligotrophic with chl. aTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-13 levels of less than 2 μg/L (e.g., Canfield et al. 1983; Griffith et al. 1997). As discussed above, the warm-water TSI studies suggest that the ol igotrophic-mesotrophic boundary occurs at chl. a concentrations of approximately 7 μg/L. FDEP’s Nutrient Criteria TAC recommended a chl. a concentration of 9 μg/L. The TAC based its reco mmendation largely on an analysis of lake data that showed lack of association between an inde x of benthic macroinvertebrate health and chl. a in the range of 5–10 μg/L chl. a . Within such a small range at the oligotrophic-mesotrophic boundary, lack of association is not surprising because the stressor (chl. a ) range is small and because the invertebrate indicator is responsive to numerous aspects of natural conditions and stressors.
However, some evidence of meaningful distinc tions exists within th e range based on endpoints more directly responsive to nutrients. In this case, the Florida MEI m odel predicted reference chl. a concentrations in the range of 1.4–7.0 μg/L (w ith seven of the eight values below 5 μg/L) for a set of clear, acidic lakes in Florida and predicted a 10% loss of transparency when chl. a concentrations in the range of 5.6–11.8 μg/L (with five of the eight values below 7 μg/L). All but one of the clear, acidic lakes ha d predicted natural or referenc e conditions below 6 μg/L chl. a and the clear majority (six of eight) of clear, al kaline lakes had predicted 10% transparency loss above 6 μg/L chl. a. Given the available information on refe rence condition and predicted effect levels, U.S. EPA adjusted the approximate o ligotrophic-mesotrophic bounda ry value of 7 μg/L slightly downward to 6 μg/L as the proposed chl. a criterion.

Clear, alkaline lakes – Earlier, in 2002, FDEP’s IWR TAC had recommended a chl. a criterion of 5 μg/L in clear lakes that was based on a “mai ntain existing condition approach” and that was primarily targeted at a specific geographic regi on of Florida (the panhandle). The TAC also suggested that different nutrient and chl. a expectations should be established for high specific conductance (> 100 μS/cm) clear lakes because of the naturally higher, aquifer-derived phosphorus levels in that subset of clear lakes. U.S. EPA proposes the TAC suggested nutrient thresholds in clear, high-specific conductance lakes be based on preventing the annual average chl. a from exceeding 20 μg/L.



### 1c. Methodology for Proposed TP and TN Criteria in Lakes
Paired nutrient and chl. a data were available for 129 colored lakes. Initial analyses revealed statistically significant (p < 0.001) ye t weak relationships between chl. a and TP ( R2 = 0.38) and TN (R2 = 0.47). Other factors influencing the chl. a response were then investigated in an attempt to improve the relationship of chl. a with nutrients. Despite initial lake subcategorization by color, a significant inve rse relationship (Spearman R = –0.25) remained between color and chl. a , with the influence of color most pronounced in lakes with color in excess of 150 to 200 PCU. Chl. a level in these highly colored lakes were typically reduced when compared to the levels in less colored lakes, despite similar nutrient concentrations; that is, color in excess of approximately 150 PCU depresses the nutrient response (light limitation).
A multiple regression model (adjusted R2 = 0.507) was constructed between chl. a (dependent variable) and TP and TN (independent variables) to investigate the influence of lake color on chl.

a response in colored lakes (Table 1-3). Mode l residual error was plot ted against both color expressed as a long-term geometric mean (peri od of record) and annual geometric mean color. This evaluation demonstrated that the nutrient regression m odel tended to underestimateTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-14 (positive residuals) chl. a concentrations at lakes with co lor less than approximately 150 PCU and overestimated (negative residuals) chl. a levels at lake with color over approximately 150 PCU (Figure 1-7). Classification and Regression Tree (CART) analys is was used to discriminate a breakpoint in the model residual error. A si gnificant breakpoint that explained 36.4% of the relative error in the residuals was found at a long-term lake color of 143 PCU. Additional breakpoints were found at annual geometric mean colors of 54 and 360 PCU. Those subsequent breakpoints provided only marginal improvements in the amount of explained variance (Figure 1-8). On the basis of the CART analysis, the colored lakes were further subcategorized to longterm ranges of > 40–140 PCU (moderately colore d; n = 100 lakes) and > 140 PCU (highly colored; n = 29 lakes), for purposes of inves tigating nutrient responses to account for the substantial remaining influe nce of color on the chl. a

. Regional differences among the moderately colore d lakes (color between 40 and 140 PCU) were evaluated, but U.S. EPA found that thos e colored lakes show similar chl. a responses regardless of location, although there were diffe rences in the range of nutrien t concentrations (Figures 1-9 and 1-10). Chl. a exhibited statistically si gnificant positive responses to both TP and TN on an annual average basis in the moderately colore d lakes (Figure 1-11). Su ch relationships are sufficiently robust to develop scientifica lly defensible and protective criteria.
The relationships between TP and TN and chl. a in the highly colored lakes (greater than 140 PCU) were significant but weak (Figure 1-12). The relationships demonstrate that nutrients influence chl. a response (excess algal growth) in highl y colored lakes and thus provide support for the need to develop numeri c nutrient criteria to protect the designated use. However, the relationships are not sufficiently r obust to directly derive numeric nutrient criteria given the high level of uncertainty and unexpl ained variance. Without a stro ng and robust nutrient-chl. a relationship in the highly colore d (> 140 PCU) lakes, fully prot ective criteria for these systems can be developed on the basis of the response relationships from the moderately colored lakes (40–140 PCU), although the criteria will be somewhat overprotective, given that high color will reduce algal response and biomass.
Regression models describe the relationship between two variable s where the magnitude of one variable (dependent) is assumed to be a function of one or more i ndependent variables; that is, a degree of the variance in the depe ndent variable is explained by th e independent variable(s). The regression line and equation define the average re lationship (i.e., half the data points fall above the line and half below it). Essent ially, there is a 50% probability th at a given level of nitrogen or phosphorus will elicit the chl. a response corresponding to the regression equations shown in Figures 1-11 and 1-13. FDEP concl uded that a simple application of an average response was not adequately protective and that a more comp lex application was ne eded to account for uncertainty in the dose-response relationship.

For nutrient criteria, re sponse uncertainty can be managed by considering nutrient concentrations in a range between a level that is unlikely (e.g., 25% probability) to elicit a given threshold of response and a level that that is likely to elicit a response (e .g., 75% probability). Regression prediction intervals provide a range above and below the regressi on line that incorporate the unexplained variability of the i ndependent variable as well as the uncertainty in the model parameters (slope and intercept). Within the ra nge of nutrient concentrations (between the upper and lower prediction interval), there is less certainty that a response (exceedance of theTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-15 chlorophyll target) will or will not occur. This represents a range of conditions in which nutrients can be managed while considering the potential for Type I (incorrectly identifying a water as impaired) and Type II (f ailing to identify an impaired water) errors.
Nutrient concentrations less than or equal to the lower end (upper pr ediction interval) are unlikely to elicit the response threshold and ther efore can be used as the basis for protective criteria, with a low probability of Type II statistical error but a high potential for Type I error. Conversely, a high likelihood of anundesirable response occurs when the nutrient concentration exceeds the upper end of the range (lower predicti on interval). The probabilities of statistical errors at the upper end of the nut rient range are inverted compared with those at the lower end; that is, there is a low probability of Type I error and a higher probability for Type II error.
Because algal response is influenced by fact ors other than nutrients (grazing, macrophyte nutrient uptake, water retention time), the most scientifically defensible strategy for managing nutrients within the range of un certainty is to verify a biol ogical response before taking management action. If data demonstrate that a lake is biologicallyhealthy and does not experience excess algal growth (e.g., < 20 μg chl. a /L in a colored lake or high specific conductance clear lake) despite ha ving nutrient concentrations with in the range of uncertainty, the existing nutrient concentrations appear to be acceptable. Howeve r, if the lake exhibits excess algal growth or biological impairment within this band of uncertainty, corrective action is warranted. Without chl. a data, FDEP should make decisions with much caution and assume an impaired condition if nutrients ex ceed the lower threshold. If chl. a data subsequently indicate that the designated use is indeed maintained at nutrient levels within the upper and lower prediction interval, those existing levels should be deemed acceptable.
Given this approach and using annual average chl. a values of 20 μg/L for colored lakes and higher-specific conductance clear lakes, and 6 μg /L for clear, low-speci fic conductance Florida lakes, respectively, criteria ranges associated with protection of designate d uses can be defined on the basis of the 50% predicti on intervals depicted in Figures 1-11 and 1-13. The resultant lower and upper thresholds of TN and TP for clear/low specific conductance lakes, clear/higher specific conductance lakes, and colo red lakes are provided in Table 1-4.
Because color (PCU) is important in determin ing the applicable nutrient criteria, FDEP performed an analysis to establis h the most appropriate averaging pe riod for classifying a lake as clear or colored. For this analys is, FDEP obtained color data sets from several example lakes. Color data from multiple stations and years in a lake were then calculated as annual geometric means for each year. A rolling average was then calculated from the annual geometric means using varying periods to evaluate the period over which to averag e to minimize the variance in the resultant data set. Results indicate that a 5-year rolling average was generally sufficient to ensure minimization of the variance (for an example data set, see Figure 1-14).



### 1d. Duration and Frequency fo r Proposed Lakes Criteria Numeric criteria include magnitude (quantity), duration, and frequency components. For the chl.

a , TN, and TP criteria for lakes, the proposed criterion-magnitudes ar e expressed as annual geometric means, which includes duration (one-y ear averaging period). The criterion-frequency, or allowable excursion, is no more than once in a 3-year period. In addition, the long-termTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-16 arithmetic average of annual geometric mean values may not exceed the criterion-magnitude values (concentration values).
Appropriate duration and frequency components of criteria shoul d be based on how the data used to derive the criteria were analyzed and what th e implications are for protecting designated uses given the effects of exposure at the specified criterion concentration fo r different periods and recurrence patterns. For lakes, the stressor-res ponse relationship was based on annual geometric means for individual years at indi vidual lakes. The appropriate period is therefore annual. The key question is whether this annual geometric me an needs to be met every year or if some allowance for a particular year to exceed the ap plicable criterion could still be considered protective.
Nutrient criteria, unlike criteria for toxic pollutant s in most cases, are typically established within the range of natural variability. A temporary spik e in nutrient levels does not necessarily harm the aquatic resource. In fact, natu ral systems have evolved to proce ss variable inputs of nutrients, particularly where there is much natural vari ability in hydrologic conditions and precipitation patterns (e.g., wet years, dry years). Although biological response in the form of algal production, measured by chlorophyll a , can appear very quickly, longe r term shifts in biological conditions, such as loss of underwater grasses, do not occur as the resu lt of a single event or conditions in a single year. If grasses experien ce reduced light during a portion of a year, the effect on them is not expected to be as large or persistent as if reduced light occurs in multiple years. In other words, severe nutrient impacts often result from chronic exposure to elevated nutrients. In addition, extreme incidents of nut rient pollution are not likely to be isolated incidents. Severe exposure to high nutrients is most likely in the context of a water body that has chronic nutrient overenrichment.

Data that contributed to the analysis of TSI, as well as data generated from supporting paleolimnological studies and MEI modeling, typically represent periods greater than a single year. Conceptually, trophic state and the categor ies of oligotrophic, me sotrophic, and eutrophic, are attributes that are long term (longer than 1 year) and change with the long-term loading rates to a system. As an example, the eutrophication of Lake Washi ngton and subsequent nutrient management and recovery of the lake were pr ocesses that took several decades (e.g., Edmondson 1994). Moreover, many of the models and analyses that form the basis of TSI results are designed to represent the “stea dy state,” or long-term stable water quality conditions. Thus, the calculated criteria for lakes already have natural variability factored in to them. They reflect central tendencies (with associ ated variability) of data, w ith decisions made about the distribution and variability surround ing those central tendency values.
However, researchers have suggested caution in applying steady-state assu mptions to lakes with long residence times (Kenney 1990). In other words, the effects of spikes in annual loading could linger and disrupt the steady state in some lakes. As a result, U.S. EPA proposes two expressions of allowable frequency, both of wh ich are to be met. First, U.S. EPA proposes a no more than one in 3 years excursion frequency for the annual geometric mean criteria for lakes. Second, U.S. EPA proposes that the long-term arithmetic av erage of annual geometric means not exceed the criterion-magnitude concentration.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-17 The long-term average chl. a concentration is also associated with the probability of blooms with excessive chl. a . In a study of Vermont lakes (Wal ker 1984), a long-term average chl. a = 20 μg/L was associated with a 40% probability that instantaneous chl. a would be higher than 20 μg/L, and a 15% probability that instantaneous chl. a would be higher than 30 μg/L.
Those frequency and duration components take into account that hydrologica l variability will in turn produce variability in measured nutrient co ncentrations, and indivi dual measurements may exceed the criteria. Furthermore, they balance th e representation of underl ying data and analyses on the basis of central tendency of many years of data (i.e., th e long-term average component) with the need to exercise some caution to ensu re that lakes have sufficient time to process individual years of elevated nut rient levels and avoid the possib ility of cumulative and chronic effects (i.e., the no more than one in 3-year component).



### 1e. Application of Lake-specific Ambient Calculation Provision for Alternative TP and TN Criteria U.S. EPA is proposing a framework that uses both the upper and lower bounds of the 50% prediction interval (Figures 1- 11, 1-13) to allow the derivation of modified TP and TN lakespecific criteria to account for the natural va riability in the relationship between chl. a response to TP and TN. The proposed rule would allow FDEP to calculate ambient modified criteria for TN and TP for a lake if chl. a is below the criterion magnitude ineach of the 3 or more years of available ambient monitoring. Alternative criteria must be based on at least 3 years of ambient monitoring data with (a) at leas t four measurements per year a nd (b) at least one measurement between May and September and one measurement between October and Ap ril each year. If a calculated modified TN and TP criterion is below the lower bound (lower 50% prediction interval), the lower bound is the criteria. If a calculated modifi ed TN and TP criterion is above the upper bound (upper 50% prediction interval), the upper bound is the criteria. Calculated modified TP and TN values may not exceed criteria applicable to streams to which a lake discharges.
The 50% prediction intervals and their derivation in the chl. a regression models are described above in Section 1c. The 50% prediction interval is the range within which one-half of chl. a observations are expected to fall for a given nutri ent concentration (TN or TP), centered on the mean expectation at the regression line. In ot her words, the lower a nd upper bounds approximate the 25 th and 75 th percentiles of expected chl. a response for the given TN or TP, as predicted by the regression equations (Figures 1-11, 1-13).

One technical concern is the extent to whic h the variability in the data relating chl. a levels to TN and TP levels truly reflects differences among la kes, as opposed to temporal differences in the conditions in the same lake. In the data analys is leading up to this TS D, U.S. EPA analyzed individual lake data within the en tire lake data set, to explore m odels of individual lake responses to nutrient enrichment (Appendix A-6). Models that incorporated individual lake response (single-lake models and Hierarch ical Bayesian models that inco rporate many lakes into the same model) verified that a large fraction of the to tal variability was due to “among lake” variability, and a small fraction was due to “within lake ” variability (Appendix A-6, Figures A6-4).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-18 Another technical concern is thata time lag might exist betwee n the presence of high nutrients and the biological response. Lag time for changes of nutrient concentratio ns following change in loading is dependent on retention time, for exampl e, twice the retention time has been observed (e.g., Coveney et al. 2005). Biological responses follow the nutrient lag time. Because of the potential lag time, U.S. EPA based the proposedrange of TN and TP criteria on a 3-year averaging period so the time lag in response w ould not be expected to affect the ambient assessment.
A third technical concern is the presence of tem porary or long-term, site -specific factors that could suppress biological res ponse, such as the presence of grazing zooplankton, excess sedimentation that blocks light penetration, extensive canopy cover, or seasonal herbicide use that impedes proliferation of al gae. If any of those suppressing factors are removed, nutrient levels could result in a spike in algal production above protective levels. Again, the 3-year data and averaging requirement will remove eff ects of temporary site-specific factors.
U.S. EPA proposes to require that the ambient calculation for modified TP and TN criteria be based on at least a 3-year record of observat ion and be based on representative sampling (i.e., four samples per year with at least one betw een May and September and one between October and April) during each year. Such requirements w ould minimize the influence of long-term, sitespecific factors and ensure longer-term, stable conditions. U.S. EPA selected 3 years as a reasonable minimum length of time to appropriately account for anomalous conditions in any year that could lead to errone ous conclusions regarding the true relationship between nutrient levels in a lake and chl. a levels. U.S. EPA anticipates thatthe state would use all recent consecutive years of data on record(i.e., it would not be appropriat e to select three random years within a complete record over the past seven ye ars). U.S. EPA is requiring four measurements within a year to provide seasonal representa tion (i.e., May–September and October–April). Providing seasonal representation is important because nutrient levels can vary by season. In addition, this minimum sample size is conducive to deriving central tendency measurements, such as a geometric mean, with an ac ceptable degree of confidence. The chl. a criterion must be met in each of the three or more years of am bient monitoring that define the record of observation for the lake to be eligible for th e ambient calculation modified provision for TN and TP.



### 1f. Alternatives Cons idered by U.S. EPA
i. Single Value Approach to Derive Lakes Criteria - Derive TN and TP Criteria Using Correlations Associated w ith the Regression Line or Upper Bou n d of the 50 th 

### Percentile Prediction Interval As described in Section 1c, the proposed method uses the entire 50% prediction interval of the chlorophyll-nutrient regressions to derive the criteria (Figures 1- 11, 1-13). The point at which the
upper -prediction bound intersects with th e chlorophyll criterion (6 or 20  g/L chl. a ) corresponds to lower -nutrient concentrations (TP and TN; Figures 1-11, 1-13). In terms of risk, the upper-prediction bound corresponds to an estima ted risk that 25% of lakes with the given nutrient concentration will exceed the chl. a criterion, and the lower bound corresponds to a risk that 75% of lakes with that concentration of nutrient will exceed the chl. a criterion. The defaultTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-19 criterion is the upper bound (25% risk), but if TN or TP exceed that limit yet chl. a remains below the criterion, TN and TP criteria might be higher, up to that co rresponding to 75% risk.

An alternative is to use a si ngle criterion of the upper bound or the regr ession line, with no option of alternative TN or TP criteria. These al ternatives are more cons ervative and fix the TN and TP criteria to an es timated 25% risk of chl. a exceeding the chl. a criterion if the upper bound is used, or an estimated 50% risk of chl. a exceeding the chl. a criterion if the regression line is used. Given the vari ability among lakes (see Appendix A-6), the upper bound criterion could result in TN and TP criter ia more stringent than absolutely necessary for some lakes. Alternatively, the regression line criterion could re sult in TN and TP crit eria that represent a balance of risk.
U.S. EPA’s rationale for propos ing a framework that uses bot h the upper and lower bounds of the 50 th percentile prediction interval to allow th e derivation of modified TN and TP lakespecific criteria rather than either of these single values was to account for the na tural variability in the relationship between chl. a and TN and TP that can exist in lakes.



### ii. Additional Modification to Proposed Lakes Classification U.S. EPA examined alkalinity in clear Florida lakes to identify alternatives to the classification described in Section 1f.i. Single m easurements of nutrients and chl. a re highly variable and can lead to results that cannot be in terpreted, so annual geometric mean concentrations were used as the data units for the analysis. The data requirem ents are: a minimum of four samples per year, and at least one sample in each of the cool seas on (October to April) and the warm season (May to September). That resulted in 492 annual geometric means from 180 lakes. Individual lakes had 1–12 annual means. Alkalinity has been measured le ss frequently than nutrients or chlorophyll. Approximately half of the lakes had associated al kalinity observations, re sulting in a total of 200 annual means from 89 lakes for this analysis.
The distribution of log alkalinityin Florida clear lakes (Figure 1-15) shows a peak of very low alkalinities less than 1 mg/L (n egative log values, Figure 1-15), a range of intermediate, low alkalinities, and a second peak at 50–90 mg/L . Lakes with alkalinity less than 1 mg/L are essentially unbuffered, highly acidic lakes. In this distribution, the alkaline lakes appear as a group with alkalinities above 20 mg/L.
TP, TN, chlorophyll, and specific conductance are all corr elated with alkalinity in the described data set (Figure 1-16). Change- point analysis showed change points at alkalinities of 13–24 mg/L. Associations of chlorophyl l and TN with alkalinity were the strongest, such that acidic lakes were associated with low values of TN a nd chlorophyll. As an alte rnative classification, the alkalinity breakpoint could be se t at 20 mg/L to reflect similar breakpoints in chlorophyll and nutrient concentrations.
As described in Section 1a, the response of chlorophyll to nutrient concentrations was not different between acid and alkaline lakes (Fig ures 1-5, 1-17). The aci dic and alkaline lakes appeared to lie on the same regression rela tionship, although alkaline lakes had higher mean nutrient and chlorophyll concentrat ions. Acidic lakes showed c onsiderably more variability around the regression line, especially in the ch lorophyll-TN regression (Figure 1-4). Figure 1-5TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-20 shows the alkalinity classificat ion breakpoint at 50 mg/L CaCO 3 . If the class breakpoint is at 20 mg/L, as suggested by the change point analysis, the separation of the alkalinity classes is more distinct (Figure 1-17).

Alkalinity and Specific Conductance U.S. EPA examined whether specific conductance can be used as a surrogate for alkalinity in classifying Florida lakes, because alkalinity is no t universally available inhistorical data. Data used were the same data from FDEP desc ribed at the beginning of this section.
In Florida’s lake database, only a limited numbe r of lakes (386) were measured for alkalinity while the majority of the lake s were measured for specific c onductance. Because of its strong correlation with alkalinity and large sample si ze, specific conductance was considered as a surrogate for alkalinity. Figur e 1-18 (a) indicates a str ong correlation between speci fi c conductance and total alkalinity in clear lakes (Spearman R = 0.871). A change point analysis indicated that a change point occurs when specific conducta nce is around 100 μS/cm. Nonlinear median quantile regression was also used to examine the relationship between specific conductance and alkalinity (Figure 1-18 (b)). FL ha s proposed to use 50 mg/L as a threshold for determining alkaline and acidic waters. In the anal ysis, the intercept of 50 mg/L alkalinity with the quantile regression line was around a specifi c conductance value of 200 μS/cm (Figure 1-18 (b)). The alternative alkalinity classification br eakpoint at 20 mg/L total alkalinity yields a specific conductance breakpoint at approximately 100 μS/cm, equi valent to the change point analysis result.



### iii. Modification to Include Upper Percentile Criteria with an Associated Ten Percent Exceedance Frequency
Variance Components In developing nutrient criteria, including fre quency and duration components, U.S. EPA also considered upper percentile crit eria with low exceedance freque ncies in addition to the annual average criteria concentrations. The upper percentile values analyzed here are in addition to the annual averages, as an alternative.
The objective of this analysis was to assign a cr iterion for single nutrient observations that would be protective of an annual mean nutrient con centration while taking into account natural variability. To predict nutrient c oncentrations in any pa rticular lake, two types of variation must be considered: within lake variation and among la ke variation. Because some lakes have been sampled hundreds of times, and others might have been sampled very few times, sampling errors within lakes could be variable.

U.S. EPA calculated within-lake variance fo r each lake and then plotted the cumulative distribution function (Figure 1-19) . The mean of the observed sta ndard deviation distribution was used as the estimated standard deviation for the population. The mean standard deviations of TP for acidic and alkaline lakes were 0.514 and 0.451, respectively (Table 1-5).
A second approach used was hierarchical modeling to determin e population variation. Hierarchical modeling has been applied fr equently to balance within and among groupTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-21 variations. The basic idea of hierarchical modeling (also known as multilevel modeling, empirical Bayes, random coefficient modeling, or growth curve modeling) is to think of the lowest-level units (smallest and most numerous) as organized into a hierarchy of successively higher-level units. For example, TP concentrations are in lakes, lakes are in regions, and regions are in lake classes. Outcomes for individual lake s can be described as a sum of effects for the individual observations, for the observations of the lake, and for the whole region. Each of these effects can often be regarded as one of an ex changeable collection ofeffects (e.g., all regionlevel effects) drawn from a dist ribution described by a variance com ponent. Once a model of this type is specified, inferences can be drawn from available data for the population means at any level (lake, region, class, etc.) (Price et al. 1996).
These estimators, which can be regarded from a Ba yesian perspective as posterior means or from a frequentist perspective as “Best Linear Unbias ed Predictors” often have better properties than simple sample-based estimators using only datafrom the unit in question. This makes them useful in the problem of “small-area estimation,” i.e., making estimates for units or domains for which there is a very limited amount of information (Osborne 2000).
The R package lme4 was used for the hierarch ical mixed modeling. The computed means and standard deviations (SDs) are also summarized in Table 1-5.

Distribution Because the population mean and SD are known (the proposed mean TP criterion for acidic lakes is 10 mg/L, and for alkaline lakes is 30 mg/L), the probability of a random sample occurring within a normal distribution is known (Figure 1-20), and thus values were calculated for exceeding the selected probability. In other words, when a single TP value falls at the right side of the vertical line, there is only less than 5% pr obability that the mean TP concentration at that lake would have a mean of 10 mg/L (acidic) or 30 mg/L (alkalin e) or less. U.S. EPA used both the mean observed SD and modeled SD values as the population SD to generate single observation criteria (Table 1-6) . The modeled SD are similar to the average observed SD-based single criteria most of the time.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-22 Table 1-1. Warm-water TSI Categori es (after Salas and Martino 1991)

TSI Category TP (μg/L) Chl. A (μg/L) 40 Oligotrophic 21.3 5 50 Mesotrophic 39.6 10 70 Eutrophic 118.7 40

Table 1-2. Relationship between Chl. a , TP, and TN, as Described by Florida’s TSI

TSI Chl. a (μg/L) TP (mg/L) TN (mg/L) 0 0.3 0.003 0.06 10 0.6 0.005 0.10 20 1.3 0.009 0.16 30 2.5 0.01 0.27 40 5.0 0.02 0.45 50 10.0 0.04 0.70 60 20 0.07 1.2 70 40 0.12 2.0 80 80 0.20 3.4 90 160 0.34 5.6 100 320 0.58 9.3

Table 1-3. Summary of the Linear Multiple Regression between Ln Transformed Chl. a and Ln Transformed TP and TN in Colored Florida Lakes

Effect Coefficient Std Error Std Coef. Tolerance T P (2 tail) Constant 2.703 0.272 0 9.922 0.0000
LTP 0.347 0.085 0.213 0.457 4.091 0.0000
LTN 1.546 0.149 0.542 0.457 10.395 0.0000

Source Sum-ofsquares DF Meansquare F-ratio P Regression 449.764 2 224.882 205.223 0.0000
Residual 432.839 395 1.096

Note: The regression multiple R 2 and adjusted R 2 were 0.510 and 0.507, respectively.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-23 Table 1-4. TP and TN Criteria Ranges for Cl ear (< 40 PCU) and Colored Florida Lakes (> 40 PCU)
A B C D E F

Baseline criteria b Alternative criteria (within these bounds) c Long-term Average

Lake Color and Alkalinity Chl. a f (μg/L) a TP (mg/L) a TN (mg/L) a TP (mg/L) a TN (mg/L) a
Colored Lakes > 40 PCU 20 0.050 1.23 0.050–0.157 1.23–2.25

Clear Lakes, Alkaline
≤ 40 PCU d and > 50 mg/L CaCO 3 e 20 0.030 1.00 0.030–0.087 1.00–1.81

Clear Lakes, Acidic
≤ 40 PCU d and ≤ 50 mg/L CaCO 3 e 6 0.010 0.500 0.010–0.030 0.500–0.900

Note: The lower and upper thresholds were based on the intersection of chl. a response concentrations with the 50% predictions intervals shown in Figures 1-11 and 1-13.

a Criteria values are based on annual geometric mean not to be exceeded more than once in a 3- year period. (Duration = annual; Frequency = not to be exce eded more than once in a 3-year period).

b Baseline criteria apply unless other data are readily available to calculate and apply as alternative criteria as described below in footnote c and Florida decides to implem ent and publicly certify them as an alternative WQS on the record as documented in an easily accessible and publicly available location, such as an official state Web site.

c If chl. a is below the criterion in column B and there are repres entative data to calculate ambient TP and TN criteria, alternative criteria may be calculated within these boun ds from ambient measurements to determine lake-specific criteria pursuant to CWA section 303(c). The calculated ambient condition TN and TP values must be based on at least three years of ambient monitoring data with (a) at least four measurements per year and (b) at least one measurement between May and Septem ber and one measurement between October and April each year. These same data requirements apply to chl. a when determining whether the chl. a criterion is met for purposes of conducting the ambient calculation. If the calculated TN and TP value is belo w the lower bound, the lower bound is the criterion. If the calculated TN and TP value is above th e upper bound, the upper bound is the criterion. Calculated TP and TN values may not exceed criteria applicable to streams to which a lake discharges. If chl. a is below the criterion in column B and representativ e data to calculate ambient TP and TN criteria are not av ailable, then the baseline TN and TP criteria apply.

d Platinum Cobalt Units assessed as true color free from turbidity. Long-term average color based on a rolling average of up to 7 years using all available lake color data.

e If alkalinity data are unavailable, a specific conductance of 250 micromho s/cm may be substituted.

f Chl. a is defined as corrected chlorophyl l, or the concentration of chl. a remaining after the chlorophyll degradation product, phaeophytin a , has been subtracted from the uncorrected chl. a measurement.

Table 1-5. Summary Statistics (Mean and SD) of Nutrient Variables for Acidic, Alkaline Lakes, and Colored Lakes, Meeting Annual Criteria
Acidic Lakes Alkaline Lakes Colored Lakes

ln TP ln TN ln Chl. a ln TP ln TN ln Chl.

a ln TP ln TN ln Chl.

a Criterion –4.605 –0.693 1.792 –3. 507 0.000 2.996 –2.996 0.182 2.996 Obs Mean –5.045 –1.215 0.123 –4. 212 –0.414 0.872 –3.779 –0.218 1.200 Obs Mean SD 0.518 0.204 0.730 0.451 0.451 0.675 0.416 0.185 0.889 Mod Mean –5.031 –1.221 0.213 –4. 121 –0.387 0.965 –3.790 –0.211 1.263 Mod SD 0.586 0.293 0.884 0. 544 0.201 1.093 0.513 0.262 1.095TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-24 Table 1-6. Single Observation Criteria for Nutr ient Variables Derived Using Both Mean Observed SD and Modeled SD, for Lakes Meeting Respective Criteria
Acidic Lakes Alkaline Lakes Colored Lakes
TP (mg/L) TN (mg/L) Chl. A (μg/L) TP (mg/L) TN (mg/L) Chl. A (μg/L) TP (mg/L) TN (mg/L) Chl. A (μg/L) Obs 90 th 0.019 0.649 15 0.053 1.782 48 0.085 1.521 63 Mod 90 th 0.021 0.728 19 0.060 1.294 81 0.096 1.678 81 Obs 95 th 0.023 0.699 20 0.063 2 61 0.099 1.626 86 Mod 95th 0.026 0.810 26 0.073 1.392 121 0.116 1.846 121 Obs 97.5th 0.027 0.74 25 0.07 2.42 75 0.11 1.72 114
Mod 97.5th 0.032 0.888 34 0.087 1.483 170 0.137 2.005 171 Obs 99th 0.033 0.804 33 0.086 2.855 96 0.132 1.845 158
Mod 99th 0.039 0.989 47 0.106 1.596 254 0.165 2.207 256

Ln Total Phosphorus (mg/L) -7 -6 -5 -4 -3 -2 -1 0Ln Chlorophyll-a (μg/L)

-2 -1 0 1 2 3 4 5 6

Peninsula Panhandle Bone Valley North Central Northeast

Figure 1-1. Relationship between Ln transformed chl. a and TP in clear lakes by nutrient region. Note, over the entire range of data, that the la kes exhibit a similar chlorophyll response to TP independent of region.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-25 Figure 1-2. Relationship between Ln transformed chl. a and TN in clear lakes by nutrient region. Note, over the entire range of data, that the la kes exhibit a similar chlorophyll response to TN independent of region.

Figure 1-3. Box plots of chl. a by season in clear (left panel) and colored (right panel) lakes. Jan.-April, Oct.-Dec. May-Sept.Chlorophylla(μg/L)

0.01 0.1

1 10 100 1000

Jan.-April, Oct.-Dec. May-Sept.Chlorophylla(μg/L)

0.1 1 10 100 1000 10000TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-26 Colored Lakes Clear Lakes -4 -3 -2 -1 0 1 2 3 Ln Total Nitrogen -3 -2 -1 0 1 2 3 4 5 6 7Ln Chlorophyll a 

# a

Colored Lakes Clear Lakes -7 -6 -5 -4 -3 -2 -1 0 1
Ln Total Phosphorus -3 -2 -1 0 1 2 3 4 5 6 7Ln Chlorophyll a 

# b

Figure 1-4. Regression analyses of annual geometric mean chl. a concentration and nutrients in colored and clear lakes, showi ng grouping of the two lake cla sses and the different regression lines.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-27 Clear Lakes

Alkalinity <= 50 Alkalinity > 50 -4 -3 -2 -1 0 1 2 3 Ln Total Nitrogen -3 -2 -1 0 1 2 3 4 5 6Ln Chlorophyll a 

# a

Clear Lakes

Alkalinity <= 50 Alkalinity > 50 -7 -6 -5 -4 -3 -2 -1 0 1
Ln Total Phosphorus -3 -2 -1 0 1 2 3 4 5 6Ln Chlorophyll a 

# b

Figure 1-5. Scatter plot of annual geometric mean chl. a concentration and nutrients of clear lakes, showing acidic (< 50 mg/L CaCO 3 ) and alkaline lakes. Acidic and alkaline groups were combined in a single regression.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-28 Figure 1-6. The relationship between chl. a concentrations (note the log scale) and the percent cyanobacteria in 1,364 lake samples collected fo r Florida’s statewide probabilistic monitoring program.

Period of Record Color (PCU) 0 100 200 300 400 500 600Model Residual

-5 -4 -3 -2 -1 0 1 2 3 y = -7.1x + 0.90
R 2 = 0.384

Annual Geometric Mean Color (PCU) 0 200 400 600 800 1000 -5 -4 -3 -2 -1 0 1 2 3 y = -3.74x + 0.60
R 2 = 0.246 A) B)

Figure 1-7. Relationship between th e residual error in the TP and TN chlorophyll model and (A) period of record geometric mean lake color and (B ) annual geometric mean lake color. Note that both relationships exhibit a significant negative slope.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-29 RESIDUAL

Mean=-0.003

SD=1.058

N=362

Mean=-1.042

SD=0.982

N=99 Mean=0.388 SD=0.787

N=263 POR_COLOR<143.660

Mean=-2.819

SD=0.801 N=8 Mean=-0.886 SD=0.834

N=91 ANNUAL_COLOR<556.976

Mean=0.103 SD=0.785

N=127 Mean=0.654 SD=0.693

N=136 ANNUAL_COLOR<76.207

Split Variable PRE* Improvement 1 POR_COLOR 0.333 0.333 2 Annual_COLOR 0.371 0.039 3 Annual_COLOR 0.409 0.037

Figure 1-8. CART analysis, using a least-square s fitting method, of the re sidual error from the TP and TN model for chl. a response in colored Florida lakes. The analysis demonstrates that colored lakes can be split into two large groups, using the firs t CART split, where the chl. a response to TP and TN differs because of the confounding effect of color. *Proportion reduction in error.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-30 Ln Total Phosphorus (mg/L) -6 -5 -4 -3 -2 -1 0Ln Chlorophyll-a (μg/L)

-2 -1 0 1 2 3 4 5 6

Peninsula Panhandle Bone Valley North Central Northeast

Figure 1-9. Relationship between Ln transformed chl. a and TP in moderately colored lakes by nutrient region. Note, over the enti re range of data, that the la kes exhibit a similar chlorophyll response to TP inde pendent of region.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-31 Figure 1-10. Relationship between Ln transformed chl. a and TN in moderate ly colored lakes by nutrient region. Note, over the enti re range of data, that the la kes exhibit a similar chlorophyll response to TN independent of region.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-32 Annual Geometric Mean TP (mg/L) 0.01 0.1 1Annual Geometric Mean Chl-a (μg/L)

1 10 100

Annual Geometric Mean TN (mg/L) 0.1 1 10Annual Geometric Mean Chl-a (μg/L)

0.1 1 10 100 1000 Ln (y) = 1.128 Ln(x) + 5.729 R2 = 0.581 p <0.001

Ln (y) = 1.965 Ln(x) + 1.995 R2 = 0.645 p <0.001 Regression Line
50% Prediction Interval A)

B)

Figure 1-11. Regression analyses annual geometric mean chl. a concentrations and annual geometric mean (A) TP and (B) TN concentrat ions in moderately colored (> 40–140 PCU) Florida lakes. Note that the x axis and y axis are both expressed on a log-scale.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-33 Annual Geometric Mean TP (mg/L) 0.01 0.1 1Annual Geometric Mean Chl-a (μg/L)

0.01 0.1

1 10 100

Annual Geometric Mean TN (mg/L) 0123Annual Geometric Mean Chl-a (μg/L)

0.1 1 10 100 Ln (y) = 0.534 Ln(x) + 2.610 R 2 = 0.122 p <0.001

Ln (y)= 0.827Ln (x) + 0.985 R 2 = 0.062

p =0.009

Figure 1-12. Regression analyses annual geometric mean chl. a concentrations and annual geometric mean (A) TP and (B) TN concentrations in highly colo red (> 140 PCU) Florida lakes. Note that the x axis and y axis are both expressed on a log-scale.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-34 

### Annual Geometric Mean TP (mg/L) 0.001 0.01 0.1 1Annual Geometric Mean Chl-a (μg/L)

0.1 1 10 100 1000



### Annual Geometric Mean TN (mg/L) 0.1 1 10Annual Geometric MeanChl-a (μg/L)

0.1 1 10 100 1000 Ln (y) = 1.136 Ln(x) + 6.370 R2 = 0.677 p <0.001

Ln (y) = 1.729 Ln (x) + 2.482 R2 = 0.769 p <0.001 Regression Line
50% Prediction Interval

Regression Line
50% Prediction Interval

Figure 1-13. Regression analyses annual geometric mean chl. a concentrations and annual geometric mean (A) TP and (B) TN concentrations in clear Florida lakes. Note that x axis and y axis are both expressed on a log-scale.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-35 Figure 1-14. Variance in Lake Ok eechobee color data from eight pe lagic stations, averaged over varying time periods.

A lkalinit y of Clear Lakes

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 Ln Mean Alkalinity 0 10 20 30 40 50 60 70 80No. of observations

20 50 Alkaline Lakes Softwater Lakes

Figure 1-15. Distribution of average log alkalinityin clear Florida lakes (log scale). Alkalinities of 20 and 50 mg/L shown by arrows.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-36 AlkalinityAvLnTP

0 1 3 10 32 100-6 -5 -4 -3 -2 -1 0Spearman r= 0.49 5~24~38

AlkalinityAvLnTN

0 1 3 10 32 100-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5Spearman r= 0.69 5~13~13

AlkalinityAvLnChla

0 1 3 10 32 100-1 0 1 2 3 4 5Spearman r= 0.65 14~21~25

AlkalinityAvLnCond

0 1 3 10 323.5 4.0 4.5 5.0 5.5 6.0Spearman r= 0.62 1~13~35

Figure 1-16. Scatterplots of mean TP, TN, Chl. a , and specific conductance on lake alkalinity. Vertical solid lines are the estimated change poi nts, and vertical dashed lines denote the 95% confidence intervals for the change points. Wa vy dashed lines are LOESS nonlinear regressions and confidence intervals.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-37 Clear Lakes

Alkalinity <= 20 Alkalinity > 20 -4 -3 -2 -1 0 1 2 3 Ln Total Nitrogen -3 -2 -1 0 1 2 3 4 5 6Ln Chlorophyll a 

# a

Clear Lakes

Alkalinity <= 20 Alkalinity > 20 -7 -6 -5 -4 -3 -2 -1 0 1
Ln Total Phosphorus -3 -2 -1 0 1 2 3 4 5 6Ln Chlorophyll a 

# b

Figure 1-17. Scatter plot of annual geometric mean chl. a concentration and nutrients of clear lakes, showing separation of acidic and alkaline lakes with cla ssification breakpoint at 20 mg/L CaCO 3 . Compare to Figure 1-5. Acidic and alka line groups were combined in a single regression. a: TN; b: TP.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-38 Figure 1-18. Relationship between specific conductance and alkalinity in clear lakes. (a) The curve shows loess fi tted line, and the vertical lines show change point and its 90% con fi dence limits; (b) The curve shows median quantile fi t, and the red dot is the intercept of the quantile regression line with specific conductance = 200 μS/cm.

Figure 1-19. Cumulative frequency di stribution of mean TP concentra tions in acidic and alkaline lakes that meet the annual aver age criteria. A specific conductance > or < 250 μS/cm is applied as a threshold for classifying high- or acidic lakes.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

1-39 Figure 1-20. Density distribution function of norma l distribution for acidic (left) and alkaline lakes (right)