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

Appendix A-2: Documentation about Use of Alkalinity (and Specific 

Conductance) as Basis for Distinguishing Clear Lakes 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-1 

Appendix A-2. Documentation about Use of Alkalinity (and Specific 

Conductance) as Basis for Distinguishing Clear Lakes 

Investigations of inherent fish diversity and abundance, and hence productivity, in Canadian 

lakes (Rawson 1951; Rawson 1952) documented the relationship between lake basin parent 

material, alkalinity and baseline phosphorus supply, and lake depth. Vighi and Chiaudani (1985) 

applied the concepts of Rawson (1951; 1952) and Ryder et al. (1974), and proposed the use of a 

morphoedaphic index (MEI) as a way to quantify the departure from inherent productivity due to 

accelerated eutrophication. The underlying premise of its use in this context is that, while 

phosphorus tends to increase with watershed development, the sources of alkalinity are relatively 

invariant. Hence, present day alkalinity can be used as a proxy for natural background 

phosphorus and inherent productivity. 

An MEI has promise for Florida lakes, as the predominant natural background source of 

phosphorus, dissolution of limestone bedrock, leeching of calcareous soils in the contributing 

basin, and artesian inflow, also impart alkalinity. It also represents a way to distinguish between 

lakes with different inherent productivity that would otherwise be grouped together in an 

ecoregional classification approach. In northeast Florida, the most productive lakes are those of 

the central lake district that are in contact with the Eocene-Miocene limestone, at roughly at 20 

m above sea level. In the same lake region where these lakes reside, there are also many higher 

elevation sand ridge-perched lakes, with contributing basins in deep sands, that are not in contact 

with limestone, and exhibit very low phosphorus and productivity. The former typically exhibit 

calcium and magnesium concentrations above 30 mg/L and 10 mg/L, respectively, while the 

latter typically exhibit concentrations about one tenth of these. 

In the absence of a fully calibrated MEI for Florida, the Technical Advisory Committee (TAC) 

suggested on August 5, 2009 that different nutrient and chl. a expectations should be established 

for high alkalinity (>50 mg/L) clear lakes because of the naturally higher, aquifer-derived 

phosphorus levels this subset of clear lakes. The TAC suggested that nutrient thresholds in clear, 

high alkalinity lakes should be based on preventing the annual average chl. a from exceeding 20 

μg/L. Subsequent work on a Florida lakes MEI provides support for these thresholds. 

Although the index will require further development prior to direct criteria application, Lowe et 

al. (2009) developed an initial MEI for Florida lakes that is sufficiently calibrated to help inform 

protective chlorophyll thresholds related to natural background nutrient expectations. Lowe et al. 

(2009) compiled a dataset for northeast Florida lakes with approximately equal representation of 

the four lake types identified by Shannon and Brezonik (1972) and Gerritsen et al. (2000): acid 

colored, acid clear, alkaline colored, and alkaline clear. The lakes are listed in Table A2-1, and 

locations of these lakes are shown in Figure A2-1. Data sources included the Environmental Data 

Base of the St. Johns River Water Management District (SJRWMD), the Lake Watch data base 

(for mean depth), and several published reports. One river system, the Fresh Tidal St. Johns 

(represented by the center station at Racy Point), was included in the data set, as this broad, slow 

moving reach exhibits residence time and autochthonous productivity similar to lentic systems, 

and has traditionally been assessed in the 305(b) and 303(d) assessments with lake criteria. 

Individual lake medians were calculated for total phosphorus (TN), chl. a, color, and alkalinity 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-2 

concentrations indicative of the natural background, or reference condition. Color was included 

in the data set due to its inclusion in the draft criteria as a way to distinguish lake types. 

Because several of the lake classes, most notably colored and clear high alkalinity lakes, 

represent highly valued aesthetic, recreational, and productive systems, there are few remaining 

examples in undeveloped settings and, unfortunately, in an undegraded condition. For these lake 

types, it was necessary to rely upon either historic data, or reconstructed natural background 

conditions. Since most of these alkaline lakes in northeast Florida are in the well studied and 

intensively managed St. Johns and Ocklawaha chains of lakes, they have been the subject of 

restoration studies under the total maximum daily load (TMDL) or pollutant load reduction goal 

(PLRG) process, during which reconstructed natural background concentrations have been 

developed. Several of these lakes have also been the subject of studies to reconstruct their predevelopment condition (Whitmore and Brenner 2002). For the St. Johns and Ocklawaha River 

TMDL and PLRG natural background condition reconstructions, a combination of contributing 

watershed modeling to estimate external phosphorus load for the hypothetical pre-development 

land cover, and receiving waterbody models to estimate in lake chl. a and TN concentrations, 

were used (Fulton et al. 2004; Hendrickson et al. 2002). These reconstructions were corroborated 

with historic data where available. 

Because sufficient examples of unimpacted low alkalinity clear and colored lakes still remain, 

existing condition data were used for a representative set of lakes that lacked development in the 

contributing watershed. These lakes have hereafter been referred to as “reference” lakes. 

Empirical models were developed from this set of reference (for currently unimpacted lakes) or 

reconstructed (altered, degraded systems) data, with the MEI and color as independent variables, 

predicting the natural background TP and chl. a. Two versions of natural background models 

were evaluated: one that used reference condition and TMDL/PLRG reconstructed annual mean 

TP and chl. a as dependent variables, and another that replaced the TMDL/PLRG estimated 

values of TP with the inferred limnetic P values where available. 

MEI Model 

The MEI model proposed by Vighi and Chiaudani (1985) related the log10 of mean TN to the 

log10 of the MEI, with the MEI calculated as mean alkalinity (milliequivalents/L) divided by 

mean depth (m). The median, rather than the mean, was used in this test as a measure of central 

tendency, as it is less prone to bias from outliers, and it eliminated the need to develop lognormalized means for variables that exhibit skewed distributions. The median alkalinity, color, 

and the calculated MEI for the northeast Florida test lakes are listed in Table A2-2. Calculated 

MEIs range from 0.002 to 1.45. For the lake data set developed by Vighi and Chiaudani (1985), 

values ranged lower, from 0.001 to 0.32, and this appears to be due to the lack of shallow, high 

alkalinity lakes in their analysis, in comparison to the Florida lake data set used by Lowe et al. 

(2009). The existing reference lake or reconstructed natural background concentrations for TP 

and chlorophyll are also provided in Table A2-2. 

A set of simple two-factor multiple regression models were developed that used the MEI and 

color to predict either the natural background or chl. a. Model versions were tested with MEI and 

color annual medians untransformed, or were based on the log10 transformed values (as was the 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-3 

case for the original Vighi and Chiaudani (1985) model). This set of regression models are listed 

in Table A2-3. 

All of the models developed with this test data set were highly significant. With the exception of 

color in the model to predict natural background chl. a, both MEI and color individually were 

highly significant factors in determining both background chl. a and TP. All models also 

produced slopes between predicted and observed very close to one, with intercepts close to zero, 

suggesting very little bias with this data set of wide-ranging lake types. The F-statistic for a color 

= ƒ(MEI) model was not significant, indicating that these two variables are independent of one 

another and hence account for contrasting environmental gradients in the continuum of inherent 

lake productivity. 

The natural background chl. a model also had the poorest fit between predicted and observed 

annual medians (Adjusted R2 = 0.58). This is in contrast to the relatively high fit correlations 

seen for natural background models for TP. The version of the natural background TP model that 

relied on a combination of reference lake existing condition, TMDL reconstruction for the St. 

Johns, and inferred limnetic phosphorus (Whitmore and Brenner 2002), produced the highest 

adjusted R2

 of 0.88. The relatively poor fit and lack of significance of color in the natural 

background chlorophyll model may be due to the somewhat hypothetical nature and degree of 

extrapolation inherent in trying to estimate pre-development chlorophyll concentrations. Use of 

paleolimnologic techniques to directly estimate historic chlorophyll concentration, rather than 

relying upon phosphorus to chlorophyll relationships employing phosphorus concentrations 

determined by historic reconstruction, could improve the natural background chlorophyll model. 

The models developed by Lowe et al. (2009) could ultimately be used to develop nutrient and 

chlorophyll criteria expressed as a continuous function of color and alkalinity as opposed to 

discrete breakpoints in these parameters. The current Federal promulgation schedule does not 

provide sufficient time to further develop and calibrate such a model. However, the model can be 

used to inform regulatory decisions related to natural background expectations, particularly for 

TP where the empirical regression for natural TP is statistically very significant. The MEI-color 

model was used to evaluate the relationship between alkalinity and natural TP and chl. a

expectations. 

The natural background TP model [TP = 5.25 + 11.00*(log10MEI) + 22.48*(log10Color)] was 

used to predict levels of background TP expected within clear lakes for a range of alkalinity 

conditions (Figure A2-2). The depth valued used to calculate the MEI portion of the MEI was 

held at 3.0 m, which represents a typically average Florida lake depth. Average depths for 

Florida lakes typically range from 2.0 to 4.0 m, and 3.0 m represents a centroid of that range. 

Background chl. a expectation can either be estimated from the MEI-color equation or using the 

regression models developed by the Florida Department of Environmental Protection (FDEP) 

and described in Chapter 1c. The FDEP regression model for clear lakes had a higher adjusted R2

(0.68) than the MEI-color model. Additionally, the relationship between MEI and chlorophyll is 

indirect and most likely related to the relationship between alkalinity (parent material) and 

background nutrient conditions. Therefore, the best estimate of natural background chl. a would 

be based on the FDEP’s regression equation for clear lakes. The FDEP derived regression 

equation represents the average relationship between TP and chlorophyll; however, the current 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-4 

application is more concerned with extrapolating a reference or pristine condition. A more 

appropriate analysis in this case would involve an extrapolation of an upper percentile 

background condition to better control for type I statistical error. FDEP regression equations 

already incorporate a 50% prediction interval. The upper limit of this prediction interval would 

be analogous to the use of an upper 75th percentile; that is, 75% of the observations are expect to 

be below the upper limit. 

Natural background chl. a was calculated from the MEI-color predicted natural background TP 

concentrations, within clear lakes for a range of alkalinity conditions, using the equation for the 

upper 50% prediction limit [Ln(Chl-a)=1.137 Ln(TP) + 6.978]. The results of this analysis 

clearly demonstrate that natural background chl. a would be expected to exceed 9 μg/L with 

natural alkalinity greater than 50 mg/L (Figures A2-3 and A2-4); that is, in lakes with contact to 

limestone naturally high in phosphorus. 

Use of a 50 mg/L discrete alkalinity threshold to differentiate between natural oligotrophic and 

mesotrophic lakes appears to be a very conservative threshold. Lakes across the clear lake color 

range would be expected to exceed 9 μg/L at an alkalinity of 50 mg/L (Figure A2-3 and A2-4). A 

threshold of 20 μg/L would appear to be protective of all but the lowest color lakes. 

Specific Conductance Surrogate 

Alkalinity measurements are not currently available for many clear Florida lakes. However, 

specific conductance, which can be used as a surrogate measure for alkalinity, is typically 

available. To evaluate the relationship between alkalinity and specific conductance, a data set of 

347 paired lake average specific conductance and alkalinity values was assembled. The 

relationship between the two parameters was investigated using both least squares and quantile 

regression (Figure A2-5). Quantile regression is the preferred statistical approach to investigate 

the outer edges of the relationship because specific conductance is influenced not just by 

alkalinity but also all dissolved ions. The analysis suggests that a specific conductance of 250 

μmhos/cm would provide a high level of certainty that the majority of low alkalinity lakes 

(naturally oligotrophic) would be characterized as having low alkalinity and thus be ascribed the 

more protective 9 μg/L threshold. Use of the 250 μmhos/cm surrogate threshold represents a step 

that would result in only an approximate 25% chance of misclassifying a low alkalinity lake as 

high alkalinity. However, it may result in a misclassification of a number of high alkalinity sites 

and thus be overprotective for a portion of Florida’s clear lakes. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-5 

Table A2-1. Lakes Used in the Morphoedaphic Index Model 

Lake DEP Type Ex/Re* 

Area 

(ha.) 

Mean 

Depth 

(m) 

Median 

Alknty, 

mg/L as 

CaCO3

Median 

Color, 

PCU MEI 

Lake Sheelar Acid clear E 7 9.9 0.9 10 0.002 

Lake Winona Acid clear E 30 4.9 3.7 10 0.015 

Lake Kerr Acid clear E 1,146 2.2 1.9 10 0.017 

Kingsley Lake Acid clear E 669 7.8 12.0 10 0.031 

Lake Winnemissett Acid clear E 66 4.0 9.4 10 0.047 

Lake Ola Acid clear E 179 3.5 30.8 10 0.178 

Lake Wier Acid clear R 2,332 5.4 14.0 10 0.052 

Lake Annie (Highlands Co.) Acid clear E --- 8.0 3.0 10 0.008 

Cherry Lake Acid colored E 160 3.3 2.0 75 0.012 

Lake Disston Acid colored E 774 2.6 3.2 400 0.025 

Lake Norris Acid colored E 458 2.8 26.0 440 0.189 

Lake Rowan (Ordway) Acid colored E 106 2.5 3.0 200 0.024 

Crescent Lake Acid colored R 6,879 2.5 34.6 300 0.277 

Lake Lochloosa Acid colored R 2,310 1.8 28.0 108 0.304 

Orange Lake Acid colored R 3,478 2.8 23.0 71 0.164 

Newnans Lake Acid colored R 2,400 1.9 12.0 135 0.128 

Lake Eustis Alk. clear R 3,161 2.5 100.8 20 0.806 

Lake Griffin Alk. clear R 2,164 2.0 110.9 30 1.119 

Lake Dora Alk. clear R 1,788 2.3 122.7 30 1.062 

Lake Harris Alk. clear R 5,582 3.2 93.1 20 0.581 

Lake Yale Alk. clear R 1,632 2.8 112.0 20 0.800 

Lake Apopka Alk. clear R 12,417 1.7 113.5 25 1.344 

Lake Beuclair Alk. colored R 440 1.7 120.0 60 1.455 

Fresh Tidal St. Johns River Alk. colored R 17,814 2.9 69.5 161 0.479 

Lake George Alk. colored R 18,623 3.0 68.5 80 0.457 

Lake Jesup Alk. colored R 

*Unimpacted Existing Condition (E) or Reconstructed Natural Background (R). 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-6 

Table A2-2. Existing Condition and Reconstructed Natural Background Phosphorus and 

Chlorophyll Concentrations for the MEI Example Lake Data Set 

Lake DEP Type Method*

Current 

Median 

TP, μg/L 

TMDL/ 

PLRG 

Natural 

Background 

TP, μg/L 

Whitmore 

Inferred 

Limnetic 

P, μg/L 

Current 

Median 

Chl. a, 

μg/L 

Est. 

Natural 

Background 

Chl. a, 

μg/L 

Lake Sheelar Acid clear E 5.4 5.4 2.2 2.2 

Lake Winona Acid clear E 9.0 9.0 1.8 1.8 

Lake Kerr Acid clear E 11.7 11.7 1.4 1.4 

Kingsley Lake Acid clear E 7.0 7.0 2.1 2.1 

Lake 

Winnemissett Acid clear E 9.0 9.0 1.5 1.5 

Lake Ola Acid clear E 16.5 16.5 2.7 2.7 

Lake Wier Acid clear P 13.7 13.0 18.5 9.8 7.0 

Lake Annie Acid clear E 2.4 2.4 5.0 5.0 

Cherry Lake Acid colored E 21.6 21.6 5.2 5.2 

Lake Disston Acid colored E 34.1 34.1 3.0 3.0 

Lake Norris Acid colored E 63.0 63.0 1.3 1.3 

Lake Rowan 

(Ordway) Acid colored E 40.5 40.5 13.4 2.9 

Crescent Lake Acid colored R 52.1 52.1 19.0 11.1 

Lake 

Lochloosa Acid colored R 61.0 50.0 41.5 23.0 

Orange Lake Acid colored R 59.0 28.0 51.0 29.4 22.0 

Newnans Lake Acid colored R 192.9 60.0 54.0 166.1 20.0 

Lake Eustis Alk. clear R 42.0 24.0 29.5 53.9 20.0 

Lake Griffin Alk. clear R 62.0 28.0 28.8 82.8 24.0 

Lake Dora Alk. clear R 65.0 28.0 28.2 125.2 24.0 

Lake Harris Alk. clear R 42.0 23.0 39.6 51.4 18.0 

Lake Yale Alk. clear R 26.0 18.0 34.8 20.9 12.0 

Lake Apopka Alk. clear R 43.0 43.0 74.6 21.0 

Lake Beuclair Alk. colored R 200.0 28.0 54.0 169.4 25.0 

Fresh Tidal St. 

Johns . Alk. colored R 89.0 45.0 27.0 12.3 

Lake George Alk. colored R 68.0 44.0 25.9 15.0 

Lake Jesup Alk. colored R 94.0 

*Methods for Background TP and Chlorophyll Estimates: E = Existing condition; deemed unimpacted and 

representative of natural background. For these lakes, the natural background concentration = the current condition. 

P = For the Ocklawaha Lakes PLRGs, phosphorus loading was determined from watershed models simulating no 

basin development, and from phosphorus concentrations in reference lakes in the same region (Fulton et. al, 2004). 

R = Reconstructed from linked watershed modeling of loads simulating no basin development, and receiving water 

body assimilation and phytoplankton growth. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-7 

Table A2-3. Multiple Regression Models Relating Natural Background and Designated Use Failure Threshold A

Chl. 

a For 26 Northeast Florida Lakes with a Range of Color and Alkalinity 

Variables Significance Comparison Predic

Dependent Variable Model 

Regression 

F Statistic 

P-value 

MEI 

P-value 

Color Slope Intercept

Natural Background TP Based on Reference Lakes or PLRG and TMDL Estimates 

Log10(TP) = 0.822 + 

0.210*(log10MEI) + 

0.442*(log10Color) 

71.6 3.85E-06 1.16E-08 0.90 0.82 

Natural Background TP, Based on Reference Lakes and Inferred Limnetic Phosphorus where available 

TP = 5.25 + 11.00*(log10MEI) + 

22.48*(log10Color) 92.6 4.41E-07 1.53E-09 1.00 0.00 

Natural Background Chl. 

a: Existing for 

Unimpacted, or TMDL/PLRG Reconstructed 

CHLA = 5.89 + 14.92*(MEI) - 0.002*(Color) 17.4 9.10E-06 8.49E-01 1.00 0.00 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-8 

Figure A2-1. Locations of lakes used to develop the Florida morphoedaphic index. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-9 

0.00

0.01

0.02

0.03

0.04

0.05

5

10

15

20

25

30

35

40

20 40 60 80 100 120 140 160 180

N

a

ut ral B

a

c

kgro

u

n

d

T

P mg ( /L)

Color PCU

Alkalinity (mg/L)

0.00 

0.01 

0.02 

0.03 

0.04 

0.05 

Figure A2-2. Plot of the MEI predicted natural background lake TP for clear Florida lakes (≤40 

platinum-cobalt units (PCU)). A constant depth of 3.0 m was applied to the MEI calculation to 

investigate the influence of alkalinity on TP. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-10 

0

5

10

15

20

25

30

35

5

10

15

20

25

30

35

40

20 40 60 80 100 120 140 160 N

180 atural B

a

c

kgro

u

n

d

C

h

ol ro

p

h

yll a (μg/L)

Pre

d

ci te

d fro

m

N

atural B

a

c

kgro

u

n

d

T

P

Color PCU

Alkalinity (mg/L)

Figure A2-3. Plot of predicted natural background chl. a in clear Florida lakes (≤40 PCU). Chl. a

was calculated based on the MEI predicted natural background TP condition. A constant depth of 

3.0 m was applied to the MEI calculation to investigate the influence of alkalinity on TP. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-11 

Alkalinity (mg/L)

0 50 100 150 200

Natural Background Chlorophyll a (μg/L)

0

5

10

15

20

25

30

35

Color=10 PCU

Color=20 PCU

Color=30 PCU 

Color=40 PCU

Figure A2-4. Plot of predicted natural background chl. a in clear Florida lakes (≤40 PCU) at four 

isopleths of color from Figure A2-3 for better visualization. Chl. a was calculated based on the 

MEI predicted natural background TP condition. A constant depth of 3.0 m was applied to the 

MEI calculation to investigate the influence of alkalinity on TP.
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A2-12 

y = 0.3302x - 14.304

R2 = 0.7194

0

50

100

150

200

250

0 100 200 300 400 500 600

Alkalinity (mg CaCO3/L)

Specific Conductance (μmhos/cm)

75th Quantile

90th Quantile

25th Quantile

Linear (Alk)

Figure A2-5. Relationship between specific conductance and alkalinity in clear Florida lakes.