1 

DEP Responses to EPA’s 9/16 Comment Letter

10-9-09

Overall, FDEP found EPA’s comments very comprehensive and detailed. However, it was not always 

clear whether the comment was offered as a suggestion/observation, or whether the comment was an 

alert to the State that, if adopted, a disapproval action would be forthcoming. As the State has already 

invested a significant amount of public resources in the current draft, including a briefing of the 

Environmental Regulatory Commission (ERC), it would be helpful for EPA to identify those comments 

that alert the State to an impending disapproval and provide the rationale so that we can fully address 

the issue and brief FDEP leadership.

Definitions

1) EPA suggested edits to the definition of lakes to help differentiate from wetlands (use depth, 

vegetation or water residence time to distinguish), and noted that they do not want to exclude lakes 

based on size.

Response: The primary intent of the definition is to differentiate lakes from wetlands, and was 

developed based on input from our wetlands permitting staff (Environmental Resource Permitting 

Program). While the definition reflects their input (and is described below), we would like to 

emphasize that any waters that are defined as wetlands, rather than a lake, based on size will still be 

protected from nutrient impairment under the current narrative criteria and under a numeric 

nutrient criteria, once numeric criteria are available for wetlands. 

 

As part of Florida’s adopted methods for delineating wetlands and determining the extent of State 

waters established under Section 373.4211, Florida Statues, DEP’s Environmental Resource 

Permitting Program has established a threshold of two acres of open water to distinguish lakes from 

wetlands. Florida, with over 7,800 lakes of two acres or greater, has more lakes than any other 

state. Florida has extremely rich wetland resources as well, with over 10% of the land surface 

classified as wetlands. It is DEP’s stated mission to protect both resources, however, for practical 

purposes, it is necessary to classify systems appropriately to ensure the correct assessment tools

(Lake Vegetation Index versus Wetland Condition Index) are applied to the proper waterbody type. 

In Florida, lakes with 2 acres or less of open water are generally associated with water depths of < 2 

feet (conditions closely associated with wetlands). A literature search indicated that most entities 

consider a “pond” to be defined by an open water area of 5 acres or more, making DEP’s lake 

definition more inclusive than other lake definitions. For the purposes of lakes nutrient and 

biological criteria, the “two acres of open water” definition has a long, legally accepted history of 

use, and is an appropriate way to distinguish lakes from wetlands. 

2) EPA asked about the term “free-flowing” in the definition for streams.

Response: The definition is based on a statutory (Chapter 373, F.S.) definition for streams, but DEP 

will propose to delete “free”.
2 

3) EPA suggested we edit the definition of “water quality standards” to include “policies” or 

“procedures” in front of mixing zones so that we would not have to submit individual mixing zones, 

and asked for clarification on what “exemptions” the rule referred to.

Response: We agree to propose the addition of “procedures” and deletion of “exemptions.”

62-302.531 Numeric Nutrient Criteria: General

1) EPA suggested some wordsmithing to the paragraph that mentions SSACs established based on 

adopted TMDLs, noted that the SSAC should only be based on the nutrient addressed in the TMDL, 

and recommended a separate paragraph addressing the process for adopted TMDLs as SSACs.

Response: DEP will review at a later date.

2) EPA suggested some wordsmithing about the applicability of the narrative criteria, which would 

make paragraph (a) of the narrative criterion apply to all waters.

Response: DEP recognizes the benefit of continuing to apply the narrative. However, it was our 

impression that EPA is no longer considering narratives as acceptable nutrient criteria in the State of 

Florida (next comment). FDEP will consider continuing to apply the narrative criterion if that is still 

acceptable. 

3) EPA noted several “indications” that the draft criteria are not protective of downstream waters, and 

asked for clarification of “best available scientific information”.

Response: There are equivalent lines of evidence that the draft criteria are inherently protective of 

downstream waters including, Gulf Breeze’s analysis in the Apalachicola River Basin, overlap in 

distribution of “all streams” and benchmark when using a truly probabilistic design, evidence that 

low DO streams exhibit those concentrations in the natural and minimally disturbed conditions, and 

natural condition modeling in Fisheating Creek revealed similar TP values that those represented by 

the draft criteria. However, there will be circumstances when the draft criteria may not provide the 

full level of attention. In those cases, FDEP has proposed narrative criteria for protection of 

downstream waters to account for site specific circumstances. FDEP would like to note that where 

exact science is limited, narrative criteria are fully approvable under current federal regulations [40 

CFR 131.3(b)]. 

62-302.533 Numeric Nutrient Criteria: NO2/3 in Clear Streams

1) EPA requested supporting documentation for:

• Time scale for color?

Response: The instantaneous color of each sample will be used to establish clear versus colored 

conditions, and then any samples that qualify as clear will be assessed against the criterion. This

approach results in a very conservative criterion, since it takes weeks for biological responses to 

nutrients be realized after color is no longer an issue. 
3 

• How are streams with color > 40 protected? 

Response: Due to light limitation, DEP and Tetra Tech could find no cause-effect relationships 

between nitrate and biological response in streams with color >40 PCU, therefore, they are 

inherently protected through the proposed criterion.

• How do 10% exceedances and binomial relate, and what is basis for the 10% exceedance rate? 

Response: 

As explained in DEP’s TSD, the clear streams nitrate criterion was derived based on multiple lines of 

evidence, with the primary lines of evidence being mesocosm dosing experiments and field studies. 

These two main studies were conducted over very different time frames. Stevenson’s mesocosm 

studies were conducted for periods just under one month (i.e., 21 to 28 days), while the algal 

biomass field study from the Suwannee drainage basin was conducted over an eighteen year period 

and was analyzed using four to five year averaging periods. While lab studies indicate that algal 

communities can respond to excess nitrate over a short period of time, the mesocosm and other 

dosing studies indicate that this response occurs on the order of a month, which led DEP to propose 

a monthly expression of the criterion. Further, there is no evidence to suggest that the short-term 

responses observed under controlled lab settings equates to impairment of the designated use in 

conditions experienced in State waters. In fact, the more realistic field surveys demonstrated that 

excess biomass does not become persistent in clear streams until average nitrate exceeds 0.441 

mg/L. The 0.441 mg/L threshold was adjusted to 0.35 mg/L to provide a fully protective criterion 

with a low likelihood of excess algal growth. 

The 10% exceedance frequency was established in recognition that in most cases the monthly 

“median” will actually be based on a single sample. DEP considered the option of setting a 

minimum sample size to calculate the monthly median, but given the potential data limitations 

(most streams are only sampled monthly at the most), decided it was best to not set a minimum 

sample size and instead allow a 10% exceedances frequency. However, it should be emphasized 

that application of a 10% exceedance frequency of the monthly criterion is still highly conservative 

and will necessitate maintenance of a long-term average nitrate conditions well below the 

established response threshold. For example, given the typical variance in the data, a stream with a 

long-tem annual average nitrate concentration at 0.35 mg/L would be expected to exceed the 

criterion during approximately 50% of the months, well in excess of the frequency allowed. Review 

of the month to month nitrate variability at stream benchmark sites and sites within the Suwannee 

drainage basis suggests that average concentration within a stream would need to be below 0.10 

mg/L in order to consistently meet the <10% exceedance requirement.

As for the question about the binomial, DEP concluded that the binomial is well-suited to assess 

compliance with a criteria expressed as not to be exceeded more than 10 percent of the time. DEP

currently uses this approach (and EPA has approved it) for evaluation of the fecal coliform criteria

(the component expressed as not to be exceeded in more than 10% of the samples). 
4 

2) EPA asked what evidence supports the conclusion that keeping NO2/3 concentrations below which 

lyngbya growth/biomass is low support healthy native plant communities, and whether 0.23 mg/l 

would be more appropriate? 

Response: DEP assessed all available scientific information in an effort to develop protective 

nutrient criteria for clear streams. The evidence (as shown in the TSD) indicated that excess algal 

growth was the fundamental issue to address in these systems. DEP has determined that plant 

community shifts, in relation to nutrient enrichment in clear streams, occur only after algal 

smothering is sufficient to interfere with growth and development of the macrophytes. Therefore, 

prevention of the nuisance algal smothering, where unambiguous cause-effect relationships exist, 

would result in inherent protection of the aquatic plant communities. Note that no cause-effect 

relationship has been established between nutrient enrichment and plant communities in clear 

streams (because most aquatic plants receive their nutrients from sediment sources). 

As stated in the TSD, both laboratory and field responses were evaluated to develop a protective 

nitrate criterion. The strength of laboratory experiments includes the ability to determine cause 

effect relationships under controlled conditions (which was how the 0.23 mg/L threshold was 

derived). However, because actual ecological responses to nutrients are influenced by many other 

variables (light penetration, flow, grazing, physical disturbance, etc.), lab studies alone do not 

conclusively demonstrate levels where “real world” adverse responses would be detected. If 

laboratory studies alone were the definitive answer to numeric nutrient criteria, EPA could establish 

nationwide criteria based upon the very lowest level of algal growth that could be detected during a 

nutrient addition experiment. Because nutrients are not toxic, and require an ecosystem response 

to exert actual adverse effects, it is the consensus of the scientific community that a “lab only 

approach” would be an unreasonable, overly restrictive way to derive nutrient criteria. In fact, EPA 

guidance does not recommend lab studies as a stand-alone approach. 

DEP’s method was to use lab studies to demonstrate that nitrate caused nuisance algal growth, and 

then field studies to determine the levels where problems occurred under actual environmental 

conditions. DEP then applied a protective margin of safety to gain 95% confidence that adverse 

responses would not occur if nitrate was maintained below 0.35 mg/L. During extensive public 

input, this approach was widely recognized as a defensible method. 

62-302.354 Numeric Nutrient Criteria: Lakes

1) EPA requested more documentation about use of alkalinity (and specific conductance) as basis for 

distinguishing clear lakes.

Response: 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 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. 
5 

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 meters 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 TAC suggested on August 5, 2009 that 

different nutrient and chlorophyll 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 chlorophyll 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 in the proposed FDEP lake numeric criteria: acid colored, acid clear, alkaline 

colored, and alkaline clear [we only have 3 types?]. The lakes are listed in Table 1, and locations of 

these lakes are shown in the map below. Data sources included the Environmental Data Base of the 

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, 

chlorophyll a, color and alkalinity 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 NE 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 TMDL or 

PLRG process, during which reconstructed natural background concentrations have been developed. 
6 

Several of these lakes have also been the subject of studies to reconstruct their pre-development 

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 

water body models to estimate in lake chlorophyll a and total phosphorus concentrations, were 

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

historic data where available. 

Because sufficient examples of un-impacted 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. 

Locations of Lakes Used to Develop the Florida Morphoedaphic Index.
7 

Lakes used in the Morphoedaphic Index Model

Lake DEP Type Ex/Re* Area, 

ha. 

Mean 

Depth 

(m)

Median

Alkalinit, 

mg/L as 

CaCO3

Median 

Color, 

Pt-Co 

Units

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 ALKALINE CLEAR R 3,161 2.5 100.8 20 0.806

Lake Griffin ALKALINE CLEAR R 2,164 2.0 110.9 30 1.119

Lake Dora ALKALINE CLEAR R 1,788 2.3 122.7 30 1.062

Lake Harris ALKALINE CLEAR R 5,582 3.2 93.1 20 0.581

Lake Yale ALKALINE CLEAR R 1,632 2.8 112.0 20 0.800

Lake Apopka ALKALINE 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). 

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 total phosphorus and chlorophyll a. Two versions of natural 

background models were evaluated: one that used reference condition and TMDL/PLRG reconstructed 

annual mean TP and chlorophyll 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
8 

The MEI model proposed by Vighi and Chiaudani (1985) related the log10 of mean total phosphorus 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 log-normalized means for 

variables that exhibit skewed distributions. The median alkalinity, color and the calculated MEI for the 

NE Florida test lakes are listed in the first table below. Calculated MEIs range from 0.002 to 1.45. For 

the lake data set developed by Vighi and Chiaudani, 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. The existing reference lake or reconstructed natural background 

concentrations for TP and chlorophyll are also provided in the table below. 

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

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

annual medians untransformed, or were based on the log10 transformed values (as was the case for the 

original Vighi and Chiaudani model). This set of regression models are listed in the second table below. 

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 chlorophyll a, both MEI and color individually were highly 

significant factors in determining both background chlorophyll a and TP. All models also produced 

slopes between predicted and observed very close to 1, 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 chlorophyll 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 P (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 predevelopment 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. 
9 

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 P 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. 
10

Multiple Regression Models Relating Natural Background and Designated Use Failure Threshold Annual Median Total Phosphorus and 

Chlorophyll a For 26 NE Florida Lakes With a Range of Color and Alkalinity. 

Dependent Variable

Model Variables Significance 

Comparison Predicted vs. 

Observed

Regressi

on F 

Statistic

P-value 

MEI

P-value 

Color

Slop

e 

Interce

pt R2

Adjuste

d R2

Natural Background TP 

Based on Reference Lakes or 

PLRG & TMDL Estimates

Log10(TP) = 0.822 + 0.210*(log10MEI) + 

0.442*(log10Color)

71.6 3.85E-06 1.16E-08 0.90 0.82 0.86 0.85

Natural Background TP, 

Based on Reference Lakes 

and Inferred Limnetic P 

where available

TP = 5.25 + 11.00*(log10MEI) + 22.48*(log10Color) 92.6 4.41E-07 1.53E-09 1.00 0.00 0.89 0.88

Natural Background 

Chlorophyll 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 0.61 0.58
11

The models developed by Lowe, et al. 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 chlorophyll 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 (see 

below). The depth valued used to calculate the MEI portion of the MEI was held at 3.0 meters, which 

represents a typically average Florida lake depth. Average depths for Florida lakes typically range from 

2.0 to 4.0 meters, and 3.0 meters represents a centroid of that range. 

Plot of the Morphoedaphic Index predicted natural background lake TP for clear Florida lakes ( ≤40 

PCU). A constant depth of 3.0 meters was applied to the MEI calculation to investigate the 

influence of alkalinity on TP.

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

regression models developed by DEP and described in Section 10.3 of the TSD. The DEP regression 
12

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 chlorophyll a would be based on the DEP’s regression equation for clear lakes. The DEP 

derived regression equation represents the average relationship between TP and chlorophyll; however, 

the current 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. DEP 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 percent of the observations are expect to be below the upper limit. 

Natural background chlorophyll 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 chlorophyll a would be expected to exceed 9 μg/L with natural alkalinity

greater than 50 mg/L (see Figure xx); 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 (see below). A threshold of 20 μg/L 

would appear to be protective of all but the lowest color lakes. 
13

Plot of predicted natural background chlorophyll a in clear Florida lakes (≤40 PCU). Chlorophyll a

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

3.0 meters was applied to the MEI calculation to investigate the influence of alkalinity on TP.
14

Plot of predicted natural background chlorophyll a in clear Florida lakes (≤40 PCU) at four levels of color 

represent transects through Figure 3 for better visualization. Chlorophyll a was calculated based on the 

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

calculation to investigate the influence of alkalinity on TP.

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 relationshiop 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 (see Figure below). 

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.

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
15

Relationship between specific conductance and alkalinity in clear Florida lakes.

Literature cited:

Fulton III, R.S. et al. 2004. Pollutant Load Reduction Goals for Seven Major Lakes in the Upper 

Ocklawaha River Basin. SJRWMD Technical Publication SJ2004-5. 

Hendrickson, J.C., Trahan, N., E. Stecker and Y. Ouyang. 2002. TMDL and PLRG Modeling of the Lower St. 

Johns River: Calculation of the External Load. SJRWMD Draft Report. 

Lowe, E., L. Battoe, J.C. Hendrickson, M. Coveney, R. Fulton, E. Marzolf, S.Winkler and J. Di. 2009. A

Morphoedaphic Index to Predict Natural Background and Designated Use Failure Threshold 

Phosphorus and Chlorophyll Concentrations in Florida Lakes. SJRWMD Draft Report.

Rawson D. S. (1951) The total mineral content of lake waters. Ecology 32, 669-672.

Rawson D. S. (1952) Mean depth and the fish production of large lakes. Ecology 33, 513-521. 

Ryder, R. A., S.R. Kerr, K.H. Loftus and H.A. Reiger. 1974. The morphoedaphicindex as a fish yield 

estimator: Review and evaluation. J. Fish. Res. Bd. Can.31: 66-688. 

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)
16

Vighi, M. and G. Chaudani. 1985. A simple method to estimate lake phosphorus concentrations resuling 

from natural background loadings. Water Res 19(8): 987-991. 

Whitmore, T.J. and M. Brenner. 2002 Paleolimnological Characterization of Pre-disturbance Water 

Quality Conditions in EPA-Defined Florida Lake Regions. Final Report to the Department of 

Environmental Protection. 

2) EPA suggested we add “consecutive” in references to 3-year increments.

Response: DEP believes this is not necessary, but will consider the request. 

3) EPA requested additional documentation regarding the selection of chlorophyll a criteria, 

particularly for clear, low conductivity lakes (the comment said data “seem to show greater support 

for a criterion of 5 ug/L” and included specific questions:

• How are TSI/chl a thresholds related to aquatic life use protection, and how are TN and TP 

from TSI related to ALUS?

Response: The analyses conducted by DEP relating chlorophyll a to aquatic life use was 

independent of the TSI. DEP’s analysis resulted in TN and TP to chlorophyll relationships that were

significantly stronger than those used for the original TSI. Multiple lines of evidence were used to 

evaluate the rigor of protection inherent in the proposed 20 ug/L chlorophyll for colored and clear, 

high alkalinity lakes and 9 ug/L for clear lakes (see Table below)

Lines of evidence used in determining support of the chlorophyll a targets.

Line of Evidence Chlorophyll a target State

Paleolimnological studies 14 to 20 μg/L (higher for some 

lakes)

Florida

Expert opinion 20-33 μg/L Virginia, Iowa, West Virginia, 

Maryland

Fisheries responses (warmwater)

Fisheries responses (coldwater 

trout and coolwater)

35-60 μg/L

3-5 μg/L and 25 μg/L, 

respectively

Virginia

Minnesota, Colorado

Lake user perceptions

20-25, up to 30 μg/L in colored 

lakes; as low as 3 μg/L in Florida 

Trail Ridge clear lakes

Texas and Florida

Existing levels approach 5-27 μg/L Alabama

Reference lake approach

2-8 μg/L in clear lakes, 9-18 μg/L 

in colored lakes Florida, using 75th percentile
17

As seen in the table, several lines of evidence, including paleolimnnolgy, fisheries success, and user 

perception, converge to support the conclusion that 20 μg/L of chlorophyll a in colored and clear, high 

alkalinity lakes is protective of designated uses. It has been hypothesized that phytoplankton 

populations may switch to communities dominated by cyanobacteria at chlorophyll a levels above 20 

ug/L, however, this pattern was not observed in an analysis of 1,364 Florida lakes. Cyanobacteria are 

usually an unfavorable food source to zooplankton and many other aquatic animals, and some may even 

produce toxins, which could be harmful to fish and other animals. For this reason, the World Health 

Organization considers it to be a high risk for swimming when waters are dominated by cyanobacteria 

and accompanied by an instantaneous chlorophyll a of 50 μg/L (symptoms such as skin irritation and 

conjunctivitis may be more prevalent). Based upon the above multiple lines of evidence, DEP proposes 

that an annual average chlorophyll a of 20 μg/L in colored lakes is protective of designated uses. See 

below for additional information supporting the 9 ug/L threshold in clear lakes.

• Is 9 protective of all clear lakes, or is sub-classification needed to protect Sand-Hill lakes?

Response: Coldwater trout fisheries (which do not exist in Florida) require chlorophyll a in the 3-5 μg/L 

range. A reference lake approach proposed by Tetra Tech suggests that chlorophyll a values of up to 8 

μg/L in clear lakes represent the 75th percentile of reference lakes. Moreover, the TSI categorization of 

Salas and Martino (1991), based on warm water lakes, would consider a chlorophyll a of 10 μg/L (TSI of 

50) to be mesotrophic. Thus, a multiple lines of evidence approach suggests that a chlorophyll a

concentration <10 μg/L would be a protective threshold for Florida’s clear lakes. DEP solicited input 

from the Nutrient TAC in June, 2009, and the Nutrient TAC also suggested that maintaining chlorophyll a

below 10 μg/L in low alkalinity (<50 mg/L) clear lakes would be protective of the designated use, since a 

value of <10 μg/L would still be categorized as oligotrophic. 

As another line of evidence, FDEP regressed the macroinvertebrate Lake Condition Index values against 

chlorophyll a in clear, low alkalinity lakes to determine if there was a chlorophyll a threshold, below 10 

μg/L, which could be used for criteria development (see below). The Lake Condition Index is a measure 

of lake health based upon a composite of five benthic macroinvertebrate metrics, including taxa 

richness, the number and percent of the Ephemeroptera/Trichoptera/Odonata, Shannon diversity, the 

Hulbert (sensitive) Index, and the percent Diptera (DEP SOP LT 7300, found at: 

ftp://ftp.dep.state.fl.us/pub/labs/assessment/sopdoc/2008sops/lt7000.pdf ).
18

Figure 9-2. The relationship between chlorophyll a concentrations and invertebrate Lake Condition Index.

The results of this regression showed that there was no significant relationship between biological 

health (LCI) and chlorophyll a when chlorophyll a was below 10 μg/L (r2 <0.067). Therefore, DEP has 

established the low alkalinity, clear lake chlorophyll a at a protective threshold of 9 μg/L.

4) EPA suggested we check numbering and rule references.

Response: DEP will check. 

5) EPA had several questions related to the implementation of the lakes criterion.

• How will criteria be documented and available to the public?

• How often can they be modified?

• Suggested we change column heading of second table, which currently reads “Calculated 

criteria may not be lower than.”

• Could a waterbody be listed for exceedances of chl a , TP, or TN, and would it be on planning 

or verified list?

Response: See below. 

6) EPA’s suggested edits to the text that follows the second lakes table indicate that we need to 

provide additional background information about implementation of the rule language. 

Response: The draft FDEP lakes criteria contain a performance based element to the criteria to account 

for the differences between lakes and how they respond to ambient nutrient concentrations. The 

overall criteria are expressed as annual averages that cannot be exceeded more than once in a three 

year period. The criteria are exceeded when there are two excursions of the magnitude and duration 

component of the criteria within a three year period. Since an excursion is applied on an annual 

timeframe and an exceedance is applied on a three year timeframe, the following describes how the 

criteria operate during such timeframes.
19

The underlying criteria are those contained in proposed Rule 62-302.534(1) which, in the absence of 

sufficient chlorophyll a data, are in effect for a given lake. However if there is sufficient Chlorophyll a, 

Total Nitrogen (TN), and Total Phosphorus (TP) data to calculate an annual geometric mean and the 

magnitude of the Chlorophyll a criterion is attained for that year, the applicable magnitude of the TN/TP 

criteria is automatically adjusted based on existing in-lake conditions for that given year, and that given 

year alone, subject to upper and lower boundaries. The duration and the frequency of the TN/TP 

criteria remain the same and the magnitude, duration, and frequency of the criteria for Chlorophyll a is 

not adjusted. If the requisite Chlorophyll a magnitude is not attained for a given year, the TN/TP values 

in Rule 62-302.534(1) are the applicable criteria. 

Under this analysis, an excursion of the numeric nutrient criteria can be analyzed for any given year for 

many different purposes including assessment and permitting. For assessment purposes, the historical 

record of water quality data is analyzed to determine the number of excursions and the frequency of 

which those excursions occur. If more than one excursion has occurred in a three year period, then an 

exceedance of the criteria has occurred. If that exceedance is recent, or is not recent and not been 

followed by multiple years where no excursions have occurred, then the assessment would conclude 

that the waterbody should be placed on the 303(d) list for nutrients. 

For permitting purposes, permit limits will be developed to protect against exceedances during critical 

conditions. This can be done in two ways when sufficient water quality data exists. 

1. Data Assessment: Using multiple years of data, water quality based effluent limits (WQBEL) are 

established using the TP/TN criteria applicable during the most critical condition year. Critical 

condition year is generally defined by the highest Chlorophyll a year, but can also be evaluated 

using other metrics. If the historical record indicates that the Chlorophyll a criterion will not be 

met under critical conditions, then the WQBEL will be set at the TN/TP values in Rule 62-

302.534(1). However, if the historical record provides reasonable assurance that the worst case 

year meets the Chlorophyll a criterion, then the WQBELs will be established at the TN and TP 

levels that have been demonstrated to result in attainment of the criteria. Using data 

assessment alone, this approach provides a margin of safety of preventing the WQBEL from 

causing excursions in any year even though one is allowed in a three year period. 

2. Modeling: Using modeling (either a calibrated and validated water quality model, or an 

empirical regression model), the most critical three year period can be targeted to ensure that 

WQBELs for TN and TP result in attainment of the Chlorophyll a and TN/TP criteria (one or 

fewer excursions during that time period). The models will provide a more site-specific 

relationship between nutrients and chlorophyll a and the resultant WQBELs will be compared to 

both the annual Chlorophyll a and TN/TP criteria applicable during each year to ensure 

attainment.

The following examples help illustrate its operability. It should be noted that, to simplify the discussion, 

the examples only address data and analysis for Chlorophyll a and TP. During actual assessment of lakes 

and development of WQBELs, TN data would, of course, also be evaluated.
20

In the first example, which is a clear lake with alkalinity greater than 50, the lake condition is above the 

applicable chlorophyll a criterion of 20 in years 5 and 6 resulting in two excursions during a three year 

period. As this is more than once in a 3-year period, these chlorophyll a values alone would result in the 

lake being listed as impaired for nutrients due to a recent exceedance of the criteria. In this case, there 

was also an exceedance of the TP criterion because of the excursions in Years 1, 3, 5 and 6 (which results 

in multiple 3-year periods with more than one excursion). Because the historical records indicate 

excursions of the chlorophyll a criterion, WQBELs for discharges to this lake would be set to achieve 

0.030 mg/L and 1.00 mg/L, for TP and TN, respectively. 

In the second example, which is another clear lake with alkalinity greater than 50, there is only one 

excursion of the chlorophyll a criterion (Year 4), but there are two excursions of the TP criterion within 

three years (Years 4 and 5), and as a result, the lake would be listed as impaired for nutrients (TP) due to 

a recent exceedance of the criteria. In this case, there was only one year that was above the magnitude 

of the chlorophyll a criterion, and staff would need to check to see if this year was an extreme condition 

or a potential data error. If the year is representative of worst case conditions, the WQBEL for TP would 

again be 0.030 mg/L. However, if this was determined to be an extreme condition or a potential data 

error, then modeling would be conducted to determine the levels of TN and TP that would not result in 

an exceedance of any of the criteria during critical conditions.

For the third example, which is a colored lake, the lake would qualify for listing based on the excursions 

of the TP criterion in Years 2 and 3, but the lake would not be listed as impaired because the more 

recent data from Years 4-7 indicate that the lake is now meeting the criterion [Rule 62-303.720(2)(j), 

F.A.C., states that waters listed for exceedances of annual average criteria will be delisted if they meet 

the criteria for three consecutive years]. However, the WQBEL would either be set to achieve the TP 

criterion of 0.03 mg/L (based on the high chlorophyll a in Year 3), or be based on model results that 

determined the nutrient concentration that would attain the Chlorophyll a and TP criterion under worst 

case conditions.

In the fourth example, which is another colored lake, there are no excursions of the Chlorophyll a

criterion and only one year (Year 2) with an excursion of the TP criterion. As such, the lake would not be 

listed as impaired for nutrients because the criteria are attained throughout the period of record. While 

there were no excursions of the chlorophyll a criterion, DEP staff would still need to carefully review the 

historical data to determine worst case conditions, and then establish the WQBEL for TP based on model 

results that determined the nutrient concentration that would meet the Chlorophyll a and TP criterion 

under worst case conditions.
21

Example Data Sets and Assessment Resultsi

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 

Example 

1

Lake 

Color 

<40

Alkalinity 

>50

Chl a Annual 

Geomean 

22 18 15 10 27 25 

TP Annual Geomean 0.080 0.040 0.170 0.020 0.180 0.050 

Conclusio

n 

TP 

Standar

d 

0.030 0.040 0.087 0.030 0.030 0.030 

Annual 

Assess

ment

Excursion of Chl a 

and TP

Meets Excursion of 

TP

Meets Excursion 

of Chl a 

and TP

Excursion of Chl a and TP 

Example 

2

Lake 

Color 

<40

Alkalinity 

>50

Chl a Annual 

Geomean

18 10 8 26 9 5 

TP Annual Geomean 0.040 0.070 0.080 0.070 0.110 0.027 

Conclusio

n 

TP 

Standar

d 

0.040 0.070 0.080 0.030 0.087 0.030 

Annual 

Assess

ment

Meets Meets Meets Excursion of 

Chl a and TP

Excursion 

of TP

Meets 

Example 

3

Lake 

Color 

>40

Alkalinity 

Chl a Annual 

Geomean

18 16 21 12 15 8 

TP Annual Geomean 0.060 0.180 0.090 0.060 0.070 0.080 

Conclusio

n 

TP 

Standar

d 

0.060 0. 087 0.030 0.060 0.070 0.080 
22

1 Excursions are highlighted in yellow.

7) EPA requested more documentation for the 7 year averaging period for color, and use of specific 

conductance where there is insufficient alkalinity data.

Response: Analysis of the variance in the available historical data indicate that averaging over a 7 

year period, which is consistent with the IWR assessment period, provides a reliable indicator of 

color, with < 10% variance (see graph below).

62-302.800 Site Specific Alternative Criteria

>50 Annual 

Assess

ment

Meets Excursion of 

T{

Excursion of 

chl a and TP

Meets Meets Meets 

Lake 

Color 

>40

Alkalinity 

>50

Chl a Annual 

Geomean

18 16 18 12 15 8 

TP Annual Geomean 0.060 0.180 0.080 0.060 0.070 0.080 

Conclusion TP 

Stand

ard

0.060 0. 087 0.080 0.060 0.070 0.080 

Annu

al 

Asses

smen

t 

Meets Excursion Meets Meets Meets Meets 
23

1) EPA asked if Type I and II SSAC are available for nutrients, and suggested some guidance on the 

issue in implementation guidance.

Response: Yes, Type I and Type II SSACs will also be available for nutrients. DEP is in the process of 

developing guidance for the appropriate application of each SSAC provision for nutrients. 

2) EPA requested revisions to (2)(d)7., which states “Substances, other than nutrients, in 

concentrations that result in the dominance of nuisance species...”, so that it does not imply that 

nuisance species due to nutrients are allowed for Type II SSACs.

Response: We plan to delete the phrase “other than nutrients” as we agree that nuisance species 

should not be allowed by Type II SSACs. 

3) EPA asked whether the Type III SSAC text means that a water can get a SSAC only if it meets all of 

the criteria for ALUS or is the use of biological health assessments used to demonstrate the criteria 

are fully protective?

Response: Biological health assessments will be used to demonstrate the criteria are fully 

protective of aquatic life use.

4) EPA suggested new text requiring protection of downstream uses/standards, and asked two 

questions about the spatial aspect:

• Do both spatially independent stations have to be in the affected portion of the water?

• How will SSAC address spatial variation in how nutrients express themselves downstream?

Response: Yes, both stations have to be in the affected portion of the waterbody. The SSAC will 

address downstream protection primarily by evaluation of whether downstream waters are 

currently attaining water quality standards related to nutrients, or alternatively, the nutrients 

delivered by the waterbody meet the allocations of a TMDL for a downstream water.

5) In the paragraph (4) about use of BHAs, EPA suggested we change “indicator of designated use of 

support of propagation and maintenance...” to “indicator of biological integrity consistent with

designate use of ...”, and asked if Class II waters are intended to be included.

Response: We would like to discuss this suggestion with EPA. The proposed language could cause 

confusion given our current criterion for biological integrity. As for the reference to Class II waters, 

we will propose deletion of this reference to Class II waters since the current BHA’s do not apply to 

marine waters. 

6) EPA had several comments about the SCI thresholds, including:

• Said significant uncertainty remains whether reference sites truly represent minimally disturbed 

conditions, and asked for more documentation on historical human disturbance and 

past/present use of pesticides and fertilizers, and historical land use.

Response: A DEP literature search and information from EPA’s contractor (Tetra Tech) conclusively

indicate that Florida has used objective, defensible criteria and has produced more documentation 
24

supporting reference site conditions than has any other state. EPA’s own peer review panel 

commented that DEP’s reference selection approach was reasonable and scientifically defensible.

However, DEP will continue to provide information about historical impacts in an effort to alleviate 

EPA’s concerns. 

Historical Land Use Impacts

- Prior to the construction of Flagler’s Railroad and the development of air conditioning (1940s), 

Florida had a relatively low human population, and the interior landscape was largely unimpacted 

(see Figures below from US Census). Logging in the pinewoods, cattle grazing in native prairies, and 

citrus production on high sandy ridges were the most intense land uses. Beginning in the 1940s, 

excessive colonization of the state, resulting in much more intense land uses, became common. As 

seen in the population trend graph, the current conditions in the state reflect the most intense 

potential environmental impacts from humans throughout the state’s history. Therefore, other than 

isolated, small-scale cases (areas surrounding “cattle dip vats” for example), DEP has a high degree 

of confidence that the site has never experienced past adverse human effects if a current site is 

demonstrated to be minimally disturbed. 

Note the exponential increase in population which began during the 1940s. If a modern day site is 

minimally disturbed, there is high confidence that the site has not been adversely affected in the past.

Population

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

18,000,000

20,000,000

1880 1900 1920 1940 1960 1980 2000 2020
25

Note that despite being the fourth most populous state, the majority of human disturbance is located on 

the coastal areas and the I-4 corridor. There are still stream and lake watersheds with minimal 

disturbance for setting reference site expectations.

Pesticide Issue 

To examine the potential for pesticide impacts at reference waters, DEP evaluated available pesticide 

data from the Impaired Waters Rule database. The dataset included 26,732 observations for 43 

pesticides covering 134 WBIDs. Of the 26,732 total observations, only 145 results (0.54% of the 

observations) indicated concentrations that were above the Method Detection Limit (MDL). Applicable 

MDLs may be found in Chapters 62-4 and 62-777 FAC: 

http://www.dep.state.fl.us/labs/library/index.htm

Detectable concentrations were observed for 11 pesticides (Methoxychlor, Aldrin, Tetrachloroethylene, 

Pentachlorophenol, Dieldrin, Cyanide, Chloroform, Malathion, 24D, Phenol, and Endosulfan). Of the 145 

detected concentrations, only 13 observations (0.05%) statewide exceeded the applicable criteria for 

Class III freshwaters. The observed exceedances occurred for 4 pesticides (Endosulfan, Cyanide, 

Dieldrin, and Malathion). 
26

Of the 13 exceedances of criteria, all but 2 occurred in south Florida, where there are no natural 

streams. The 2 exceedances not occurring in South Florida occurred in the Middle St. Johns basin during 

March and April 2004 for cyanide. In addition, none of the exceedances or pesticide detections 

occurred in any of the WBIDs used for derivation of the thresholds for the biological indices. DEP has 

high confidence that the referenced sites represent minimally disturbed conditions, and this pesticide 

data supports that contention.

Fertilizer and Silviculture

While native plant communities were the most common land cover in the reference site watersheds, 

forestry operations did occur in many of the watersheds. DEP provided EPA with a comprehensive 

analysis showing the relative lack of forestry fertilization and the lack of increases in nutrients at sites 

associated with silviculture sites on 10-2-09.

• Asked for our view on comments from peer review that:

• Are SCI and LVI appropriate indicators of nutrient enrichment given that they also respond 

across a gradient of natural factors? 

Response: Bioassessment methods are developed with the knowledge that that biological 

communities respond to all environmental stressors, both natural and anthropogenic (see Figure 

below).

Because biological communities integrate effects of stressors over time (both acute and chronic 

factors) and provide a direct measure of ecosystem health, they are ideal for assessing biological 

integrity. However, here are natural gradients of nutrients throughout Florida, and characteristic 

biological communities have evolved over time to reflect natural physical and chemical dynamics. 

Therefore, to determine the relative influence of human disturbance, considering the “backdrop” of 

natural factors, DEP utilized the Human Disturbance Gradient approach (HDG). The HDG included 

measures for land use intensity, habitat, hydrologic modification, and water quality. Only biological 
27

attributes that responded predictably to objective measures of human disturbance (the HDG) were 

selected for inclusion as metrics in our multi-metric indices. DEP then calibrated the indices using a 

combination of the reference site approach and the Biological Condition Gradient (BCG) model. 

Type I and Type II errors were statistically balanced during the impairment threshold selection 

process and the threshold was “anchored” to the clearly defined endpoints of the BCG. DEP is 

confident that sites that fail the SCI or LVI are indeed impaired by human activities. DEP is also 

confident that sites which pass the SCI or LVI fully meet the designated use of a healthy, well 

balanced aquatic community. However, because TP and TN dose-response relationships were not 

seen in streams, DEP used the reference site distributional approach to derive criteria for streams. 

The LVI and chlorophyll did respond to both TN and TP in lakes, and DEP linked the nutrients to 

aquatic life use endpoints. DEP also provides for Site Specific Alternative Criteria, to address 

instances when statewide criteria are not as appropriate as site specific values.

• SCI might not detect impacts due to acute nutrient loading due to rain

Response: The literature overwhelmingly indicates that there are no “acute” nutrient effects (other 

than toxicity due to un-ionized ammonia, which is addressed by a separate criterion). Experimental 

data show that a minimum of a week of exposure to a given nutrient concentration is required to 

elicit a response in algal growth (different from a control), and that longer time periods would be 

needed to affect aquatic plants. 

From Stevenson et al. (2007). Lyngbya growth rate in a high nitrate stream (Blue Hole, with over 0.7 

mg/L nitrate) after disturbance. Note that after 6 months, Lyngbya thickness had not matched that of 

the control (undisturbed) population.

• EPA noted that given that the SCI threshold is at the low edge of the reference site distribution, 

they “remain concerned” about the risk of missing impaired waters, and requested we take a 

closer look at the variability near the threshold. EPA also asked how we will quantify and 
28

address the risk, if any, of missing impairment. EPA aAsked us to fully explain how the threshold 

protects the designated use of ALUS, and “articulate how any difference in information have 

been reconciled”.

Response: In 2006, the EPA Biological Condition Gradient model was published, offering what DEP 

considers to be an objective, nationally consistent method to calibrate biological indices. DEP 

believes that the BCG approach is a rigorous, scientifically defensible method to calibrate indices 

because it is:

• Based on recognized ecological principles;

• Contains clear definitions to link community structure and function to numeric endpoints;

• Provides a method to ensure that consistent aquatic life use thresholds are established 

nationally; and

• Overcomes the potential criticisms associated with the reference site approach . 

In 2007, DEP calibrated the SCI using primarily the Biological Condition Gradient approach 

(secondarily on the reference site approach), resulting in an impairment threshold of 34 and 

exceptional threshold of 67. During the development of the BCG model at National BCG Workshops, 

each of the break-out groups independently reported that the ecological characteristics 

conceptually described by tiers 1–4 corresponded to how they interpret attainment of the CWA’s 

interim goal for protection and propagation of aquatic life (Davies and Jackson 2006). Two panels of 

Florida experts (one for the SCI, and one for the Lake Vegetation Index) independently arrived at the 

same conclusions as did the national expert groups (BCG category 4 and above supported CWA 

aquatic life use goals). Additionally, the State of Maine has adopted a policy that aquatic 

communities conceptually aligned with BCG Category 4 meets the CWA’s interim goal for protection 

and propagation of aquatic life, and this was subsequently approved by EPA. 

Category 4 on the BCG is equivalent to an SCI score of 34.
29

Despite the clear, objective advantages of using the BCG, EPA requested DEP to revise the SCI 

impairment threshold to be based on the reference site approach as the primary line of evidence. 

DEP believes the reference site approach to be less defensible, as EPA now criticizes the reference 

site selection process and the percentage of reference sites that must “fail, by definition” when 

establishing the SCI threshold based on a lower percentile distribution. 

However, in response to EPA’s request to pursue the reference approach, DEP conducted statistical 

interval and equivalence tests with SCI data from 55 reference streams (predominantly consisting of 

the recently verified nutrient benchmark sites, with additional data from the Fore et al. (2007a) 

analysis). DEP provided site information and taxa lists for the majority of the benchmark streams 

used in this analysis, as well as maps, photos, and a summary of data for each of the verified 

benchmark streams. The analysis of reference streams was performed to determine the lower 

bounds of the reference site distribution of SCI scores, while balancing type I errors (falsely calling a 

reference site impaired) and type II errors (failing to detect that a site is truly impaired). The 

examination of the two most recent visits at 55 reference streams showed that the 2.5th percentile 

of reference data was in the range of 35-44 points (see Table). The middle of this range was 40 

points, which represents an impairment threshold that balances Type I and Type II errors. 

Results of interval and equivalence tests conducted on reference sites with 2 SCI results. Shown are site 

mean, impairment threshold, and range for threshold values defined at the 2.5th and 5th percentile of 

reference sites (p < 0.05; N = 55 reference sites with two SCI values for each site). Reference site values 

from Fore et al. (2007a) and comprehensively verified nutrient benchmark sites.

Impairment threshold 

(description)

Ref site 

mean 

Impairment 

threshold 

(numeric)

Impaired Undetermined Reference

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

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

When calibrating an impairment threshold for an index, the amount of human disturbance inherent 

at the reference sites is a major issue. Some states select reference sites based on the “best 

available condition” (may have substantial disturbance), using a Best Professional Judgment 

approach. This introduces extreme uncertainty as to what level of protection is actually afforded 

by any given reference site population. Florida has employed a rigorous reference site selection 

approach, which objectively demonstrates the “minimally disturbed” (limited human influence) 

nature of Florida’s reference sites. When establishing an impairment threshold using a lower 

distribution of reference sites, a rigorous reference site selection process provides greatly increased 

confidence that the reference site population is minimally disturbed, thereby significantly reducing 

Type II errors (i.e., classifying impaired sites as healthy). This increased confidence also allows for 
30

establishing the impairment threshold at a low level of the reference site distribution to minimize 

Type I errors (classifying healthy sites as impaired).

In the proposed threshold for the SCI, impairment will be determined by the average of two site 

visits, so the threshold determined from the interval and equivalence tests (40, based on an average 

of two site visits) is closely aligned with the assessment methods. An impairment threshold of 40 

would result in approximately 2.5 % of reference sites (known to be minimally disturbed) to be 

deemed impaired. An SCI score of 40 is approximately 3.4 on the BCG scale. DEP believes that this 

threshold is more protective than required by the CWA aquatic life use support goal (BCG of 4). DEP 

requests EPA to recognize that BCG Category 4 is an acceptable national endpoint for index 

calibration. As stated earlier, the reference site approach is subject to subjective application and

uncertainty, since a lower percentage of any given reference site population is not clearly linked to 

objective, numeric aquatic life use endpoints. See table below to show the level of protection 

afforded by the 40 impairment threshold compared to exceptional sites. Note that the individual 

metrics are not too dissimilar from that of the exceptional expectation.

SCI Metric

Metric Average 

at BCG 2 

(Exceptional)

Metric Average 

Near Impairment 

Threshold

Number of Total Taxa 32.0 28.7

Number of Clinger Taxa 5.6 3.3

Number of Long Lived Taxa 1.5 1.1

Percent Suspension Feeders and 

Filterers

22.0 15.8

Number of Sensitive Taxa 5.4 2.7

Percent Tanytarsini 13.3 9.5

Percent Very Tolerant 6.5 14.3

Number of Ephemeroptera Taxa 3.5 2.3

Number of Trichoptera Taxa 4.5 2.6

Percent Dominant 22.6 26.2

Number of Sites in Average 134 64

7) EPA had several comments about the LVI thresholds, including:
31

• Said there is an even higher probability of missing an impaired water

Response: DEP believes the BCG approach to be the most defensible and objective method for 

biological index calibration. A BCG score of 4 is equivalent to an LVI score of 45 (see graph below). 

However, following EPA’s instructions, DEP evaluated data from existing sites to identify benchmark 

lakes that could be used to determine the appropriate threshold for the LVI. To be considered 

benchmark, the watershed-scale landscape development intensity (LDI) index score had to be less 

than 3, and the LDI of the 100-m buffer zone around the lake had to be less than 2. DEP biologists 

also examined aerial photos and conducted an onsite watershed survey to ensure that there were 

no adverse human influences not detected by the LDI, and performed a whole-lake habitat 

assessment. Candidate benchmark lakes were excluded if they had a history of adverse human 

activity (e.g., aquatic plant control, artificial fertilization) or current human activity (e.g., adjacent 

citrus groves). DEP provided site information and taxa lists for the 30 benchmark lakes used in this 

analysis, as well as maps, photos, and a summary of data for each of the verified benchmark lakes.

A BCG Category 4 is equivalent to an LVI score of 45.

DEP conducted statistical interval and equivalence tests with LVI data from these 30 reference lakes 

to determine the lower bounds of the reference site distribution. As was described for the SCI, the 

intent was to identify a threshold for the LVI that balanced type I (falsely calling a reference site 

impaired) and type II (failing to detect that a site is truly impaired) errors. The analysis of the most 

recent LVIs at all 30 sites showed that the 2.5th percentile of reference data was in the range of 33-

48 points, while the analysis of the two most recent visits at 15 lakes showed that the 2.5th

percentile of reference data was in the range of 31-53 points (see below). The middle of this range 

was 46 points, representing an impairment threshold that balances Type I and Type II errors. In the 

proposed water quality threshold for the LVI, impairment will be determined by two site visits, so 

the threshold of 46 is closely aligned with the assessment methods. An impairment threshold of 46 
32

would limit the percentage of reference sites that will be deemed impaired to 2.5%. Note that a 

BCG Category 4 LVI score is 45, which means using an LVI threshold of 46 to be protective.

Results of interval and equivalence tests conducted on reference sites with 2 SCI results. Shown are site 

mean, impairment threshold, and range for threshold values defined at the 2.5th and 5th percentile of 

reference sites (p < 0.05; N = 15 reference sites with two LVI values for each site). 

Impairment threshold (description) Impairment 

threshold (numeric)

Impaired Undetermined Reference

2.5th percentile of reference 46 <31 31–53 >53

5th percentile of reference 50 <37 37–57 >57

EPA’s issue with the LVI calibration appears to be due to the proportional odds logistic regression model 

of the LVI BCG workshop results (see figure below). DEP notes the affect of outliers on the both the 

Category 5 and Category 2 scores , causing additional overlap in the proportional odds distribution 

(resulting in scores of approximately 50-58 to correspond with both a low probability of assignment to 

Tier 5 (i.e., impaired) and a low probability of assignment to Tier 2 (i.e., reference conditions)).

BCG tier assignments based on the Lake Vegetation Index.

DEP has more confidence in using the central tendency approach (calculation of mean values) for 

establishing a robust impairment threshold, since outliers have less effect on the results. As stated 

earlier, a mean BCG Category 4 corresponds to an LVI score of 45, meaning a score of 46 is protective. 

Note that the individual metrics are not too dissimilar from that of the “exceptional” expectation.
33

LVI Metric

Metric Average at 

BCG 2 

(Exceptional)

Metric Average Near 

Impairment 

Threshold

Dominant C of C 5.3 3.7

Percent Sensitive Taxa 21.0 7.4

Percent Invasive Taxa 3.9 14.5

Percent Native Taxa 92.4 78.6

Total Taxa 15.5 19.9

Number of Lakes for 

Average 27 39

• Noted that peer reviewers commented that:

o Presence/absence of individual plant species has little impact on the whole lake species 

composition of other fauna. 

Response: DEP believes that native aquatic plant communities are inherently valuable, and that 

they deserve protection under the CWA in their own right. While DEP agrees that invertebrates, 

fish, and other wildlife also deserve protection, DEP does not believe that these other 

communities must be shown to be adversely affected before protecting the aquatic plant biota.

o Invasive aquatic macrophytes can proliferate in low nutrient waters. 

Response: While this statement is true, this observation indicates that the LIVI is a conservative

measure of nutrient enrichment. In situations where the LVI is impaired due to reasons other 

than nutrients, DEP still believes that management action or restoration are required under the 

CWA.

o Suggested we regress LVI against TSI, chl a, TN, and TP to show stronger relationship 

between the LVI and nutrients.

Response: Lake Vegetation Index samples from 91 clear and 53 colored lakes were paired with 

nutrient data from STORET, DEP’s Ambient Program’s database (GWIS), and Florida’s biological 

database (SBIO). Nutrient data collected during the one year period prior to LVI sample 

collection were averaged using a geometric mean concentration. Only lakes with a minimum of 

three water chemistry samples during the period were analyzed further. Statistically significant 

relationships were found between LVI and TSI and annual geometric mean chlorophyll a

concentrations in both colored and clear lakes (see Figures below). However, the relationships 
34

are weak, especially compared to the relationship between individual nutrients and chlorophyll 

a. 

The additional analyses also show that the selected 9 ug/L and 20 ug/L chlorophyll a thresholds 

for clear and colored lakes, respectively, are protective of the plant communities within these 

lakes. Lakes with chlorophyll a at or below these thresholds typically achieve an LVI of 46 or 

greater. 

It is important to note that LVI responds to many stressors and the observed response to 

chlorophyll may only partially be related to shading effects. It is additionally likely that there is a 

level of inherent non-casual relationship between the two parameters due to the fact that both 

will respond to increased human disturbance, albeit in opposite directions. Increased human 

disturbance tends to increase nutrient loading to lakes, which results in increased chlorophyll 

concentrations and TSI. Conversely, increased human disturbance causes reduction of the LVI 

due to physical habit disruption and the introduction of non-desirable plants including exotic 

species. It is therefore not advisable to use the relationships with LVI to directly derive 

chlorophyll a thresholds. However, it can be concluded that selected thresholds are protective 

of plant communities.
35

Relationship between (A) TSI and (B) annual geometric mean chlorophyll a concentrations and LVI values 

in clear Florida lakes.

TSI

0 10 20 30 40 50 60 70 80 90

LVI

0

10

20

30

40

50

60

70

80

90

100

y = -0.6464x + 76.181

R2 = 0.394

p<0.001

Annual Geomtric Mean Chlorophyll a (μg/L)

0.01 0.1 1 10 100 1000

LVI

0

10

20

30

40

50

60

70

80

90

100

Ln(y) = -7.3888Ln(x) + 61.625

R2 = 0.342

p<0.001

A.

B.
36

Relationship between (A) TSS and (B) annual geometric mean chlorophyll a concentrations and LVI 

values in colored Florida lakes.

8) EPA asked whether 302.800(6), which mentions that “additional relief from criteria... may be 

provided through exemption... or variances” is applicable to nutrients following SSACs or TMDLs.

TSI

0 10 20 30 40 50 60 70 80 90

LVI

0

10

20

30

40

50

60

70

80

90

100

y = -0.5686x + 86.873

R2 = 0.2953

p<0.001

Annual Geomtric Mean Chlorophyll a (μg/L)

0.01 0.1 1 10 100 1000

LVI

0

10

20

30

40

50

60

70

80

90

100

Ln(y) = -5.7317Ln(x) + 69.223

R2 = 0.283

p<0.001

A.

B.
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Response: This subsection does apply to nutrients, but we would not expect to use it in 

conjunction with a SSAC or TMDL. We appreciate EPA pointing out this rule provision, and it is one 

of the reasons why we plan to delete the reference to exemptions in the definition of water quality 

standards.

9) EPA requested that we include a column for concentration in the table of TMDLs, and then noted 

several questions that need to be answered about each TMDL:

• Does target nutrient provide full protection of ALUS in the subject water?

• Is there adequate supporting documentation to demonstrate full protection of ALUS? 

• Does it ensure adequate protection of downstream uses?

• Was public participation provided?

• Response: We do not believe a separate column is needed for concentration, but will consider 

the request. All of the TMDLs included a site-specific evaluation to determine which nutrients 

needed to be addressed to prevent an imbalance in flora or fauna, and we will provide the 

available supporting documentation with the submittal package to EPA. The TMDL analysis also 

examines protection of downstream waters, and supporting documentation will be provided to 

EPA. All TMDLs are adopted by rule, which provides opportunities for public participation 

consistent with federal requirements, however, the TMDLs will need to be re-adopted as SSAC 

as part of the numeric nutrient criteria rulemaking so that the public is notified that they are 

being proposed as SSAC.

EPA provided some details about the above, and requested 

• We delete the text that addresses case where TMDL only addresses one parameter.

Response: While we believe this is an appropriate solution to EPA’s request for criteria 

addressing both TN and TP, we are considering the option of deleting this language and instead 

calculating the appropriate SSAC number for each water prior to rule adoption so that the SSAC 

values can be added to the table. This will make them available to the public and EPA.

• Provide additional documentation, “such as data summaries and site specific reports, 

beyond the approved TMDL, that contain sufficient detail for EPA to review and make a 

decision regarding the appropriateness of the SSAC.” 

Response: We would like clarification on this because it seems like a wildcard that makes the 

previous guidance of limited values.

Impaired Waters Rule

General

1) EPA had a general comment about rule construction, noting that it would be helpful to clarify when 

there are multiple, independent ways to list a water.

Response: We will review the rule language.
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2) EPA noted that 62-303.310 (Evaluation of Aquatic Life Use Support) notes that waters will be listed 

on the planning list if it “exceeds applicable aquatic life-based thresholds”, and asked if they are 

intended to include numeric nutrient criteria.

Response: We did not intend to include numeric nutrient criteria, and will propose revisions to the 

language to exclude the NNC.

Biological Assessment

1) EPA asked if we should add BioRecon to 302 too to be consistent with the IWR.

Response: We do not believe the BioRecon is sufficient as a basis for a SSAC , but plan to use it as 

an additional listing method.

2) EPA asked for clarification on how the BHA thresholds are different for the planning list (PL) and 

verified list (VL), specifically on the use of the averaging. The comment also seemed to suggest an 

additional threshold to list on the VL based on a single failing score. 

Response: The thresholds are different for the PL and VL. Given the documented variability in 

scores (which is small relative to analyses of water chemistry, but still significant) and the potential 

for low scores due to hydrology, DEP concluded that the threshold for the VL should be the average 

of two scores. However, consistent with our overall approach to the PL, we lowered the listing 

threshold for the PL such that waters could be listed on the PL based on only one failed score. This 

allows DEP to prioritize these waters for further study to verify whether they are, in fact, impaired. 

We do not believe an additional single-sample threshold is needed given that there will typically be 

very few BHA scores available for a given waterbody. 

62-303.350 Evaluation of Nutrient Criteria

1) EPA asked if the reference to “annual mean chlorophyll a values” applied to all waterbody types, 

including estuaries.

Response: Yes.

62-303.351 and .352 Nutrients in Freshwater Streams and Lakes

1) EPA asked how long the planning period is, and asked us to ensure the IWR is consistent with the 

language in Chapter 62-302 for both streams and lakes. 

Response: The planning list assessment is 10 years, and the listing threshold is more conservative 

than the criteria in Chapter 62-302, F.A.C., in that exceedances are defined as more than one 

exceedances in three years, rather than 10. However, we intentionally make the thresholds for the 

PL more conservative as way to prioritize our monitoring resources on waters that are potentially 

impaired.

62-303.420 Aquatic Life-Based Water Quality Criteria Assessment
39

1) EPA asked if we meant for the NNC to be included in the ALUS assessment?

Response: No, as noted in the response to a previous comment for the planning list assessment, we 

need to revise the rule language to clarify that nutrients are assessed separately.

2) EPA had several comments about the text in (1)(b), which allows us not to list waters for DO if the 

water passes BHAs and meets numeric nutrient criteria and BOD. Comments include:

• They say benchmark numbers do not represent natural conditions, and say we would need 

to demonstrate that they approximate natural background.

Response: While we have focused much of the discussion about the benchmark sites on the 

fact that they are “minimally disturbed”, we are also confident that the sites are representative 

of natural background conditions. Further, given the extensive requests for additional 

information about the benchmark sites, we would like clarification from EPA on what 

information would be needed to demonstrate a site is “minimally disturbed” versus the 

information needed to demonstrate that a site represents “natural background.” 

• Note that EPA may need to review this provision as a revision to the DO standard.

Response: It is our understanding that EPA already reviewed this provision as a change to 

Florida Water Quality Standards (FWQS), and we do not see how the proposed changes 

substantively change the approvability of the provision. EPA was aware that we previously 

compared nutrient concentrations to the 75th percentile of State waters, and now the text 

references the benchmark-based numeric nutrient criteria, which certainly better 

approximate natural background conditions. That being said, we certainly anticipate that 

EPA will review all of the proposed changes to the IWR as potential changes to FWQS. 

62-303.430 Biological Impairment

1) EPA suggested a minor wordsmithing and then asked why we deleted “Failure of this additional 

bioassessment shall constitute verification that the water is biologically impaired.”

Response: The proposed edits are fine. The sentence was deleted because the VL threshold is the 

average of two BHAs. 

2) EPA asked for examples of “Other scientifically credible methods or procedures” that we might use 

to assess estuaries.

Response: We added this in acknowledgment that we do not currently have a BHA for estuaries, 

and this would allow us to use other methods if they are developed/proposed.

62-303.450 Evaluation of Nutrient Criteria

1) EPA asked several questions about how the section is intended to be used:

• Asked what is meant by “sufficient data” and whether it relates to historical data.

Response: This rule provision relies on the same data sufficiency requirements of the PL, but 

the required data must be available in the verified period (7 1⁄2 years), rather than the slightly 
40

longer planning period (10 years). However, the date limitation does not apply to data used to 

determine historical levels of chlorophyll a. 

• EPA asked what is the difference between the listing thresholds for the PL and VL for 

estuaries?

Response: There are no differences between the listing thresholds for the PL and VL for 

estuaries, other than the length of the planning and verified periods.

• EPA suggested we limit this provision to waters where only narrative applies and place 

reference to the stream NO2/3 criterion in separate section.

Response: We are considering whether to more clearly differentiate between nutrient 

thresholds and nutrient criteria, which would help to clarify that this subsection (1) only applies 

to impairment thresholds.

2) EPA suggested we delete (or limit to estuaries) sentence stating that “the thresholds for impairment 

due to nutrients used under this section are not required to be used during development of WLAs or 

TMDLs.”

Response: We intended this to apply to all “thresholds” as opposed to NNC, and it would apply to 

both the estuaries and stream chlorophyll a thresholds. We will consider whether we need to clarify 

the distinction between a threshold and a criterion.

3) EPA again asked about application of the NO2/3 criterion using the binomial, and asked if the annual 

geometric mean would be a better expression for nutrients given the time needed to cause algal 

growth and eutrophication problems. 

Response: As noted in our previous response regarding the NO2/3 criterion, the studies that were 

the basis for the criterion approximate a monthly time scale, and we believe it is more appropriate 

to select the more conservative monthly median value than an annual mean. 

4) EPA noted that rule does not list waters on VL based on benchmark numbers and asked that we 

revise the rule to do so, noting CWA requires listing waters as impaired based on “any water quality 

standard applicable to such waters.” EPA also noted that the chl a threshold for streams is 

considered “one-sided”, and therefore may not be fully protective. EPA also noted there may be a 

temporal disconnect between stressor and response measurements.

Response: We believe that biological confirmation is needed to list waters on the VL because there 

is insufficient linkage between the benchmark based criteria and impairment. While we are aware 

that EPA considers the chlorophyll a threshold for streams (20 ug/L) as “one-sided”, we would like to 

remind EPA that there are other listing thresholds (50% above historical levels) that are very 

conservative and protective of nutrient impairment in streams. Regardless, we believe it is better to 

retain this additional listing threshold rather than to delete it. We have provided a separate 

submittal regarding the potential temporal disconnect between stressor and response 

measurements.
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