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 Di stinguishing Clear LakesTSD 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 Di stinguishing 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 supp ly, 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 th e 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 developm ent, the sources of al kalinity are relatively invariant. Hence, present day alkalinity can be used as a proxy for natural background phosphorus and inhere nt productivity.

An MEI has promise for Florida lakes, as the predominant natura l background source of phosphorus, dissolution of limestone bedrock, leechi ng 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 gr ouped together in an ecoregional classification approac h. In northeast Florida, the mo st productive lakes are those of the central lake district that are in contact wi th the Eocene-Miocene limes tone, at roughly at 20 m above sea level. In the same lake region wher e these lakes reside, th ere 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 concentra tions about one tenth of these.

In the absence of a fully calibrated MEI for Fl orida, 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 natu rally higher, aquifer-derived phosphorus levels this subset of cl ear 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 furt her development prior to direct criteria application, Lowe et al. (2009) developed an initial MEI for Florida lakes that is suffici ently calibrated to help inform protective chlorophyll thresholds re lated to natural background nutri ent expectations. Lowe et al. (2009) compiled a dataset for northeast Florida la kes with approximately equal representation of the four lake types identified by Shannon and Br ezonik (1972) and Gerritsen et al. (2000): acid colored, acid clear, alkaline colore d, 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 calcu lated for total phosphorus (TN), chl. a , color, and alkalinityTSD 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 draf t criteria as a way to di stinguish lake types.

Because several of the lake classes, most not ably colored and clear high alkalinity lakes, represent highly valued aesthetic , recreational, and productive sy stems, 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 hi storic data, or recons tructed 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 polluta nt load reduction goal (PLRG) process, during which reconstructed na tural background concentrations have been developed. Several of these lakes have also been the s ubject of studies to reconstruct their predevelopment condition (Whitmore and Brenner 2002). For the St. Johns and Ocklawaha River TMDL and PLRG natural background condition r econstructions, a combin ation of contributing watershed modeling to estimate external phospho rus load for the hypothetical pre-development land cover, and receivin g waterbody models to estimate in lake chl. a and TN concentrations, were used (Fulton et al. 2004; Hendrickson et al. 2002). These rec onstructions were corroborated with historic data where available.

Because sufficient examples of unimpacted low al kalinity 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 hereafte r 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, w ith 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 condi tion 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 lim netic P values where available.



### MEI Model The MEI model proposed by Vighi and Chiaudani (1985) related the log 10 

### of mean TN to the log 10 

### 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 centraltendency, as it is less prone to bias from outliers, and it eliminated the need to develop lognormalized means for variables that exhibit skew ed distributions. The median alkalinity, color, and the calculated MEI for the nor theast Florida test lakes are lis ted 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 app ears 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 reconstr ucted 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 th e natural background or chl. a . Model versions were tested with MEI and color annual medians untransform ed, or were based on the log 10 

### transformed values (as was theTSD 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) m odel). This set of regression models are listed in Table A2-3.

All of the models develope d with this test data set were high ly significant. With the exception of color in the model to pred ict 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 a nd observed very close to one, w ith 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 vari ables are independent of one another and hence account for contrasting environm ental 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 R 2 

### = 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 exis ting condition, TMDL rec onstruction for the St. Johns, and inferred limnetic phosphorus (Wh itmore and Brenner 2002), produced the highest adjusted R 2 

### 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 hypothe tical nature and degree of extrapolation inherent in trying to estimate pre-development ch lorophyll concentrations. Use of paleolimnologic techniques to directly estimate historic chlorophyll conc entration, rather than relying upon phosphorus to chlo rophyll relationships employi ng phosphorus concentrations determined by historic reconstruction, could impr ove 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 calib rate such a model. However, the model can be used to inform regulatory decisions related to natural background expectat ions, particularly for TP where the empirical regression for natural TP is statistically very sign ificant. 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 av erage Florida lake depth. Average depths for Florida lakes typically range fr om 2.0 to 4.0 m, and 3.0 m repres ents a centroid of that range.

Background chl. a expectation can either be estimated fr om the MEI-color equation or using the regression models developed by the Florida Department of E nvironmental Protection (FDEP) and described in Chapter 1c. The FDEP regressi on model for clear lake s had a higher adjusted R 2 

### (0.68) than the MEI-color model. Additionally, the relationship between MEI and chlorophyll is indirect and most likely related to the relati onship 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 fo r clear lakes. The FDEP derived regression equation represents the average relationship be tween TP and chlorophyll; however, the currentTSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



### A2-4 application is more concerned with extrapol ating a reference or pr istine condition. A more appropriate analysis in this case would involve an extrapolation of an upper percentile background condition to better contro l for type I statistical erro r. FDEP regression equations already incorporate a 50% predicti on interval. The upper limit of this prediction interval would be analogous to the use of an upper 75 th 

### 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 conservati ve threshold. Lakes acro ss 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 prot ective of all but the lowest color lakes.



### Specific Conductance Surrogate Alkalinity measurements are not currently availa ble 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 al kalinity and specific conduc tance, a data set of 347 paired lake average specific conductance and alkalinity values was assembled. The relationship between the two parameters was inve stigated using both least squares and quantile regression (Figure A2-5). Quantile regression is th e preferred statis tical approach to investigate the outer edges of the relationship because sp ecific conductance is influenced not just by alkalinity but also all dissolve d ions. The analysis suggests th at a specific conductance of 250
μ mhos/cm would provide a high level of certainty that the majority of low alkalinity lakes (naturally oligotrophic) would be characterized as having low al kalinity 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% chan ce of misclassifying a low alkalinity lake as high alkalinity. However, it may result in a misclassi fication 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 CaCO 3 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. clea r 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. clea r R 12,417 1.7 113.5 25 1.344 Lake Beuclair Alk. colo red 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 Recons tructed Natural Background Phosphorus and Chlorophyll Concentrations for th e MEI Example Lake Data Set

Lake DEP Type Method* Current Median TP, μg/L TMDL/ PLRG NaturalBackground TP, μg/L Whitmore Inferred Limnetic

P, μg/L Current Median Chl. a ,

μg/L Est. NaturalBackground 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. clea r R 43.0 43.0 74.6 21.0 Lake Beuclair Alk. color ed 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 back ground. 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 re ference 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 WatersA2-7Table A2-3. Multiple Regression Models Relating Natural Background and Designated UseFailure Threshold Annual Median TP andChl.aFor 26 Northeast Florida Lakes witha Range of Color and AlkalinityVariables Significance Comparison Predicted vs. ObservedDependent Variable ModelRegressionF StatisticP-valueMEIP-valueColor Slope InterceptR2AdjustedR2Natural BackgroundTP Based onReference Lakes orPLRG and TMDLEstimatesLog10(TP) = 0.822 +0.210*(log10MEI) +0.442*(log10Color)71.6 3.85E-06 1.16E-08 0.90 0.82 0.86 0.85Natural BackgroundTP, Based onReference Lakes andInferred LimneticPhosphorus whereavailableTP = 5.25 + 11.00*(log10MEI) +22.48*(log10Color)92.6 4.41E-07 1.53E-09 1.00 0.00 0.89 0.88Natural BackgroundChl.a: Existing forUnimpacted, orTMDL/PLRGReconstructedCHLA = 5.89 + 14.92*(MEI) -0.002*(Color)17.4 9.10E-06 8.49E-01 1.00 0.00 0.61 0.58TSD 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 t u r a l B a c k g r o u n d T P ( mg / L )

C o l o

r P

C

U

Alka l inity (

m

g

/L

) 0.00 0.01 0.02 0.03 0.04 0.05



### Figure A2-2. Plot of the MEI predicted natura l background lake TP for clear Florida lakes ( ≤ 40 platinum-cobalt units (PCU)). A c onstant depth of 3.0 m was applie d 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 80 100 120 140 160 180 N a t u r a l B a c k g r o u n d C h l o r o p h y l l a ( μg / L )

P r e d i c t e d f r o m N a t u r a l B a c k g r o u n d T P

C o l o

r PC U

A

l ka l i ni

t y ( m

g

/ 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 natura l background TP condition. A constant depth of 3.0 m was applied to the MEI calculation to i nvestigate the influen ce 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 200Natural Background Chlorophylla(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 th e 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 600Alkalinity (mg CaCO3/L)

Specific Conductance (μmhos/cm) 75th Quantile
90th Quantile
25th Quantile Linear (Alk)



### Figure A2-5. Relationship between specific conducta nce and alkalinity in clear Florida lakes.