January 2005 Report of the 

Academic Advisory Committee 

To 

Virginia Department of Environmental Quality: 

Freshwater Nutrient Criteria 

Submitted to:

Division of Water Quality Programs 

Virginia Department of Environmental Quality 

Prepared by:

Carl E. Zipper 

John J. Ney 

Leonard A. Smock 

Eric P. Smith 

John C. Little 

Paul A. Bukaveckas 

Gene R. Yagow 

Jane L. Walker 

Tamim Younos 

Submitted by:

Virginia Water Resources Research Center 

February 7, 2005 
Table of Contents 

Members of the 2004-2005 Academic Advisory Committee i.

Acknowledgments ii.

Acronyms and Abbreviations iii.

Introduction 1

I. Lakes and Reservoirs 1

A. Preliminary Analyses 1

Data Structure 2

Seasonality 4

B. Responses to DEQ Questions 7

Question 1 7

Question 2 10

Question 3 11

Question 4 11

Question 5 15

C. Nutrient Requirements for Fisheries in Virginia’s 

Impoundments 16

Review and Interpretation of Scientific Literature: 

Nutrient Requirements for Virginia Reservoir Fisheries 

16

Virginia DEQ Data Interpretation 23

Synthesis and Candidate Criteria Recommendations 29

Criteria Implementation: TP vs. Chl-a 34

Nutrient Requirements for Fisheries: Perspective and 

Recommendations 

36

II. Rivers and Streams 38

Analysis of USGS and EPA Studies 38

Maryland-USG and Pennsylvania-USGS: Efforts to 

Develop Response-Based Nutrient Criteria 38

The Academy of Natural Sciences-Virginia DEQ Study 

on Periphyton-Nutrient Relationships 41

References 44

Appendices 47

A: Lakes and Reservoirs Data Summary 47

B: Reservoir Dynamics: A Comparison of Smith Mountain 

Lake and Claytor Lake 

60

C. Responses to DEQ Questions Concerning Dissolved 

Oxygen Criteria for Lakes 

68

D: Maryland-USGS Proposal to EPA 91

E. Pennsylvania-USGS Proposal to EPA 103

F. The Academy of Natural Sciences – Virginia DEQ 

Proposal to EPA 

114
i

Members of the 2004-2005 

Academic Advisory Committee 

E. Fred Benfield 

Paul A. Bukaveckas 

Thomas J. Grizzard, Jr. 

Carlton H. Hershner, Jr. 

Howard I. Kator 

John C. Little 

Wu-Seng Lung 

Saied Mostaghimi 

John J. Ney 

Leonard A. Shabman 

Eric P. Smith 

Leonard A. Smock 

Kurt Stephenson 

Eugene R. Yagow 

Tamim Younos 

Carl E. Zipper 
ii

Acknowledgements 

Thank you to the following for your contributions to this report: 

• Academic Advisory Committee members who participated in the meetings 

where the topics covered in this report were discussed. 

• David Johnson at Ferrum College for his assistance in responding to the 

DEQ query regarding Trophic State Index. The text in this report that 

addresses that issue was adapted from his e-mail, with additional detail 

added by AAC members. 

• Virginia Department of Environmental Quality staff for providing data sets 

used in the report. 

• Tom Roberts with the Virginia Department of Conservation and Recreation 

for providing data on the morphometric features of Virginia’s 

impoundments. 

• Fisheries biologists with the Virginia Department of Game and Inland 

Fisheries for contributing their assessments to rate the quality of reservoir 

fisheries. 

• Patricia Donovan, Virginia Tech Department of Crop and Soil 

Environmental Sciences GIS lab manager, and John Frey, Virginia Tech 

undergraduate student, for their work in delineating watershed areas.
iii

Acronyms and Abbreviations 

AAC: Academic Advisory Committee 

ac: acre

AFDM: ash free dry mass 

cfs: cubic feet per second 

Chl-a: chlorophyll-a 

DCR: Virginia Department of Conservation and Recreation 

Dep: depth 

DEQ: Virginia Department of Environmental Quality 

DO: dissolved oxygen 

EPA: U.S. Environmental Protection Agency 

ft: feet

ha: hectare 

Int: intercept 

kg: kilogram 

L: liter 

m: meter 

μg/L: micrograms per liter (1 μg/L = 0.001 mg/L) 

mg/L: milligrams per liter (1 mg/L = 1,000 μg/L) 

mg/m2

: milligrams per square meter 

n: number of observations in a sample (sample size) 

N: nitrogen 

NES: National Eutrophic Survey 

NO2

-

: nitrite

NO3

-

: nitrate 

P: phosphorus 

r: coefficient of correlation for a sample 

r

2

: coefficient of determination for a sample 

R2: coefficient of multiple determination

Res: residence time 

SA: surface area

SD: Secchi depth 

SWCB: State Water Control Board 

TN: total nitrogen 

TP: total phosphorous 

TSI: trophic state index 

TSS: total suspended solids 

USEPA: United States Environmental Protection Agency 

USGS: United States Geological Survey 

VDGIF: Virginia Department of Game and Inland Fisheries 

Vol: volume 
1

Introduction 

This interim report of the Academic Advisory Committee (AAC) to the Virginia 

Department of Environmental Quality (DEQ) contains information intended to aid DEQ’s 

development of nutrient criteria for the state of Virginia in a manner which is compliant 

with EPA requirements. This report builds upon an earlier report prepared by the AAC in 

July of 2004 (AAC 2004), which contains background and justification for the 

procedures employed. The report addresses issues related to freshwater nutrient 

criteria development for lakes and reservoirs (Section I) and rivers and streams (Section 

II). It includes six appendices: a summary of lake data used in this report (Appendix A), 

a paper comparing two reservoirs in Virginia (Appendix B), responses to DEQ questions 

concerning dissolved oxygen criteria for lakes (Appendix C), and three research 

proposals to study nutrient relationships in freshwater streams (Appendices D-F). 

I. Lakes and Reservoirs 

Although referred to as lakes throughout this document, the AAC analyses and 

recommendations in the current report apply only to Virginia’s constructed 

impoundments. The AAC has recommended that DEQ address nutrient criteria for the 

state’s two natural lakes (Mountain Lake and Lake Drummond) separately. 

A. Preliminary Analyses 

Ambient monitoring data from lakes and reservoirs were provided to the AAC by 

DEQ. The data included nutrient parameters (various forms of water-column nitrogen 

and phosphorous, Secchi depths [SD], and chlorophyll-a [Chl-a]), suspended and 

dissolved solids, and context variables such as location, depth, and date/time of 

sampling. Monitoring locations were specified as 8-digit alphanumeric monitoring 

location codes. DEQ personnel supplied the AAC with additional information including 

the lake names and ecoregion locations corresponding with DEQ monitoring location 

codes, and physical parameters for a limited number of lakes. Some of the lakes in the 

database were represented by multiple monitoring locations. 

Only observations from 1 meter or less in depth were used in the data analyses. 

Based on recommendations by DEQ personnel following EPA practice in developing 

guidance criteria, only data from 1990 or later were considered in the analyses that 

follow. Total phosphorous (TP) values listed as equal to or less than a 0.1 mg/L 

detection limit were discarded; all other observations recorded as equal to or less than a 

detection limit were represented in the analyses as the detection-limit value, considering 

that detection-limit values were low compared to the bulk of the recorded observations 

and compared to the concentrations of concern in the current analysis. Analyses that 

considered water-column nutrient concentrations were conducted using total nitrogen 

(TN) and TP. The TN variable was calculated from measured components using the 

following logic: 
2

If TN is measured, TN = measured value. 

Else TN = nitrate-nitrite N (NN) + Total Kjeldahl nitrogen (TKN) 

If NN is measured, NN = measured value. 

Else NN = NO2-N + NO3-N 

The TN variable was calculated for each sampling record for which all necessary 

components were available. 

Data Structure 

Although 112 lakes were represented by over 10,000 observed values of SD, 

Chl-a, TN, and TP (nutrient variables) in DEQ’s data record since the beginning of 1990, 

those observations were distributed unevenly over the monitored lakes. Thirty-eight 

lakes averaged 10 or more observations per each nutrient variable, while another 33 

averaged 5 to 9 observations per variable (Figure 1). 

Those observations are distributed unevenly through time, as DEQ’s approach to 

lake monitoring changed during the period of analysis. Prior to 1999, DEQ rarely 

collected monitoring data throughout the warm-weather season at individual lakes and 

recorded SD infrequently. SD measurements have been routinely collected by DEQ 

(and precursor agency State Water Control Board) at lakes since the early 1970s but 

only in recent years have the data been electronically recorded and readily accessible. 

Of Chl-a observations prior to 1999, 21% are accompanied by SD measurements. In 

recent years, DEQ has increased the frequency and consistency of lake monitoring 

procedures, and the majority of locations monitored are represented by data collected 

throughout the warm-weather season (Table 1). Current lakes’ monitoring procedures 

include 7 months of data collection at each lake monitored during a given year (April – 

October). Since and including 1999, 76% of Chl-a observations are accompanied by SD 

measurements. Sixty nine percent of the total 1990-2003 observations in the database 

represent the 1999-2003 period (Figure 2). The fact that lake conditions in more recent 

years are heavily represented in the database is an asset to the current analyses. 

The analyses were conducted using the EPA-recommended approach of 

considering each lake to be a sampling unit. Within this context, the uneven distribution 

of monitoring observations among lakes creates analytical difficulties. On one hand, it is 

desirable to conduct the analyses using the largest number of lakes possible so as to be 

able to derive conclusions based on water sampling data that are representative of 

conditions across the state and of temporal and regional variations. However, achieving 

such a sample size requires data from lakes with well documented conditions (i.e., with 

sufficient numbers of observations and a distribution through time to represent both 

annual and seasonal variability) be considered as equivalent to those represented by 

many fewer observations. 
3

1

10

100

1000

1 15 29 43 57 71 85 99 113

Number of Lakes

Observations per Lake

Chl-a 

Secchi Depth

TN

TP

Figure 1. Number of observations per lake for the four primary nutrient variables 

considered for nutrient criteria development. Lakes are ordered by number of Chl-a 

observations 

Table 1. DEQ lake monitoring data for nutrient variables available by year, 1990-2003. 

Year 90 91 92 93 94 95 96 97 98 99 00 01 02 03 Sum

 

Lakes with Monitoring Data 

SD 1 2 3 6 9 13 16 5 8 10 10 24 31 25 163

Chl-a 29 12 13 21 13 20 20 10 12 11 15 28 32 28 264

TN 13 9 7 12 13 20 20 9 12 11 15 28 32 28 229

TP 13 10 8 12 13 19 20 8 12 11 15 28 32 28 229

 

Lakes with at least one observation per month, May - Sept 

SD 0 0 0 0 0 0 0 0 0 5 0 9 15 10 39

Chl-a 1 0 0 1 1 1 1 1 1 6 8 17 21 20 79

TN 0 0 0 1 1 1 1 1 1 6 8 17 18 18 73

TP 0 0 0 0 0 0 0 0 0 6 8 17 18 17 66

 

Total observations, all lakes 

SD 1 4 4 12 36 43 96 88 105 212 115 333 437 291 1777

Chl-a 288 57 46 68 76 101 157 144 198 252 324 503 473 366 3053

TN 122 41 24 48 76 99 155 141 143 233 308 496 466 348 2700

TP 105 45 31 38 68 90 139 129 135 228 315 496 466 350 2635
4

0%

20%

40%

60%

80%

100%

120%

90 91 92 93 94 95 96 97 98 99 00 01 02 03

Cumulative % of Total Observations

SD 

Chl-a 

TN 

TP 

Figure 2. Cumulative frequency of observations, 1990-2003. 

Seasonality 

An underlying assumption of nutrient criteria development for lakes and 

reservoirs is that their response to nutrient inputs varies seasonally. Therefore, a 

preliminary analysis was conducted to characterize seasonal variations of nutrient 

variables in Virginia lakes and reservoirs. In addition to the four primary nutrient 

variables, the analysis was also conducted for total suspended solids (TSS) because 

TSS levels can be expected to influence measured TP concentrations, Secchi depths, 

and the algal biomass response to available phosphorous in the water column. 

The analysis was conducted by calculating, for each of the 5 variables of interest 

and for each lake, a median for each month represented by 1 or more observations. For 

each variable, lakes were selected for inclusion in the analysis if observations were 

present in at least 6 of the 7 sampling months (April – October). Using this subset of the 

DEQ monitoring data, a monthly median was calculated and plotted for each ecoregion 

(Figures 3 and 4). 

Results demonstrate that nutrients and related parameters in Virginia 

impoundments vary month-to-month and seasonally, but not necessarily in the manner 

expected based on scientific studies of natural lakes. 

Both TN and TP appear to exhibit summer-season minima in ecoregion 14 lakes 

during August. A similar pattern is evident for ecoregion 9 for TP. We have no ready 

explanation for this pattern but can state that exploratory application of other calculation 

methods generated similar patterns. Thus, the pattern does not appear to be an artifact 
5

of the analysis method. Chl-a tends to exhibit summer maxima, but these occur later in 

the summer season than the July-August time frame that is commonly assumed and 

was expected. SD fails to exhibit summer minima as expected, possibly due to the 

influence of non-algal turbidity caused by TSS. 

Figure 3. Level III ecoregions in Virginia 
6

0

1

2

3

4

5

6

7

Apr May Jun Jul Aug Sep Oct

TSS (mg/L)

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

Apr May Jun Jul Aug Sep Oct

TP (mg/L)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Apr May Jun Jul Aug Sep Oct

TN (mg/L)

0

0.5

1

1.5

2

2.5

3

Apr May Jun Jul Aug Sep Oct

SD (m)

0

5

10

15

20

25

30

Apr May Jun Jul Aug Sep Oct

Chl-a (ug/L)

Eco 9

Eco 11

Eco 14

Figure 4. Seasonal variation of nutrient variables and TSS, by ecoregion. The 

ecoregion 9 plots represent an average of 30 lakes (range = 23 to 36); ecoregion 11 by 

an average of 16 lakes (range = 15 – 17); and ecoregion 14 by an average of 7 lakes 

(range = 5 – 8). 3 mg/L TSS and 0.01 mg/L TP represent commonly applied analytical 

detection limits.
7

B. Responses to DEQ Questions 

Analysis of DEQ monitoring data, information from others sources including 

scientific literature, and professional judgment were applied to answer five questions 

relevant to nutrient criteria development. 

Question 1: What would be the implications of using the reference approach to 

establish nutrient criteria? 

In response to telephone discussions with DEQ personnel in November of 2004, 

this analysis was conducted using procedures described (USEPA 2000a, b, and c) and 

used by EPA to calculate the guidance criteria that were published by the agency in July 

of 2002 (USEPA 2002). In conducting this analysis, we applied minor modifications to 

the EPA method to accommodate DEQ data characteristics. 

The EPA method is to calculate the 25th percentile of medians from all available 

lakes for Chl-a, TN, and TP, and the 75th percentile of SD medians as surrogates for 

reference values. We applied this approach. 

EPA calculated TN 25th percentiles separately for measured and calculated TN 

values. We combined measured TN values and calculated TN values because 

measured values are sparse within DEQ’s database. We then calculated a single 25th

percentile for TN from the combined data. 

EPA recommends that the analysis be conducted by calculating seasonal 

medians for each lake, and calculating a lake median for each lake from the seasonal 

medians. The “guidance criteria” percentiles are then calculated from the distribution of 

lake medians. The EPA documents suggest that a median be calculated for each of the 

4 seasons that is represented by data, and a lake median be calculated only when at 

least 3 seasonal medians are present. We modified the concept of seasons to reflect 

data availability because the DEQ lake-monitoring program operates from April through 

October, a seven-month sampling season. We considered three separate methods for 

aggregating observations from this seven-month sampling period into 4 monitoring 

“seasons”: 

1. April – May, June – July, August – September, October (method S1) 

2. Equal-length periods: 4/1 – 5/23, 5/24 – 7/16, 7/17 – 9/7, and 9/8 – 10/31 

(method S2) 

3. April, May – June, July – August, September – October (method S3). 

We concluded that none of these methods was ideal, since methods S1 and S3 

created seasons of unequal length, and method S2 creates seasons that do not match 

the DEQ monitoring schedule. Furthermore, a preliminary analysis determined that 

calculated lake medians and percentiles were, in some cases, influenced by the 

season-definition method. This influence was greatest for Chl-a and SD, with 25th/75th 

percentiles varying by more than 10 percent as a function of seasonal definition in some 

cases. Therefore, we calculated a lake-median and the desired percentiles for each 
8

nutrient variable using each of the 3 methods, and report the averages of those values 

(Table 2, Figure 5) for each ecoregion and for the state. Lakes with estimated residence 

times of fewer than 5 days were removed from the database prior to analysis (Byllesby, 

Nottoway Falls, Swift Creek Lake, and Banister). The procedure applied to estimate 

residence times and its limitations are described below (Question 4). For comparison 

purposes, the EPA guidance criteria, which were developed for Level III ecoregions at 

the national scale, are also listed. 

Table 2. Results of procedure to calculate reference values for Virginia 

impoundments through application of EPA methods, and EPA reference 

values for each ecoregion that contains portions of Virginia. 

 Chl-a

(μg/L)

SD

(m)

TN

(mg/L)

TP

(mg/L)

Statewide: 

25th percentile 3.65 0.37 0.014

75th percentile 1.78

Count 67 63 67 67

Ecoregion 9: 

25th percentile 3.99 0.44 0.020

75th percentile 1.45

Count 38 36 38 38

EPA Guidance 4.93 1.53 0.36 0.020

Ecoregion 11: 

25th percentile 2.50 0.29 0.010

75th percentile 3.01

Count 19 17 19 20

EPA Guidance 2.79 2.86 0.46 0.008

Ecoregion 14: 

25th percentile 12.82 0.58 0.028

75th percentile 1.23

Count 10 8 10 10

EPA Guidance: 

Entire Ecoregion 2.90 4.50 0.32 0.008

Subregion 63a

 2.10 1.20 0.46 0.020

a

 The portion of Virginia contained within Ecoregion 14 is entirely within Subregion 63. 

Virginia segments of Ecoregions 9 and 11 are within multiple subregions. 
9

0

2

4

6

8

10

12

14

Eco 9 Eco 11 Eco 14 State

Chl-a (ug/L)

Virginia 

EPA Guidance

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Eco 9 Eco 11 Eco 14 State

SD (m)

Virginia 

EPA Guidance

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Eco 9 Eco 11 Eco 14 State

TN (mg/L)

Virginia 

EPA Guidance

0.000

0.005

0.010

0.015

0.020

0.025

0.030

Eco 9 Eco 11 Eco 14 State

TP (mg/L)

Virginia 

EPA Guidance

Figure 5. Virginia 

reference values (25th

percentiles for Chl-a, 

TN, and TP; 75th

percentiles for SD) for 

Virginia Level III 

ecoregions, and for 

the state as a whole, 

and EPA guidance for 

Level III ecoregions at 

the national level. 
10

Question 2: Do lakes' game fish populations vary with the lakes' nutrient status? 

An exploratory analysis was conducted following a modified version of the 

protocol described in the EPA lakes and reservoirs nutrient criteria guidance document 

(USEPA, 2000d), which considers each lake as a sampling unit. For each time period 

considered, the TP and Chl-a status of each lake was characterized as a mean or 

median value of all sampling observations from that lake. 

Indicators of fishery status in Virginia reservoirs were obtained by Dr. John Ney 

from Virginia Department of Game and Inland Fisheries (VDGIF) biologists. Dr. Ney 

attended meetings of the biologists from each VDGIF region and, in a discussion 

format, worked with them to elicit ratings of each lake represented by 5 or more 

chlorophyll-a observations. The question used to evaluate reservoirs’ fishery status was: 

How well does the water body support desirable species that achieve good growth and 

attain desirable size? In response to that question, VDGIF biologists rated lakes on the 

following scale: 

1 = poor: VDGIF biologists would recommend that anglers avoid such lakes. 

2 = fair: VDGIF biologists would recommend that anglers fishing such lakes not 

expect much in the way of fishing success. 

3 = average: the lake supports an adequate fishery. 

4 = good: VDGIF would recommend such a lake for fishing. 

5 = excellent: VDGIF would highly recommend such a lake for fishing. 

Working with the entire data set, both parametric (Pearson) and non-parametric 

(Kendall’s) correlation analyses of fishery status with nutrient status were performed. 

Results are displayed in Table 3: 

Table 3. Results of exploratory correlation analysis of nutrient variables vs. fishery 

status. 

Vari- Aggre- Seasonal Pearson: Kendall’s: 

able gation Period n r p r p 

SD median June - Aug 41 0.005 0.976 0.111 0.354

TSS median June - Aug 48 -0.156 0.291 -0.072 0.545

TP mean April - May 51 0.076 0.598 0.170 0.118

TP median April - May 51 0.054 0.707 0.126 0.259

TP mean April - Sept 53 0.047 0.740 0.130 0.210

TP median April - Sept 53 -0.008 0.954 0.066 0.550

Chl-a mean June - Sept 52 0.008 0.957 0.164 0.117

Chl-a median June - Sept 52 0.016 0.911 0.099 0.343

Chl-a mean July - August 52 0.003 0.984 0.161 0.124

Chl-a median July - August 52 -0.009 0.951 0.099 0.343

Chl-a mean June - August 52 0.020 0.887 0.173 0.099

Chl-a median June - August 52 0.018 0.900 0.110 0.295

Notes: r = correlation coefficient; p = p-value; n = number of lakes considered in each analysis. 
11

This preliminary analysis yielded no useful results. Analysis of data plots 

confirmed that (a) well defined statistical relationships between nutrient variables (Chl-a 

and TP) and fishery status, that would be useful in the development of nutrient criteria, 

are not apparent, and (b) the general form of nutrient variable-fishery status 

relationships is not subject to major influence by the time period selected for calculation 

of nutrient variable medians. Fishery nutrient requirements are addressed through an 

alternative procedure described later in this document (See Section I-C). 

Question 3: Do Virginia lakes demonstrate consistent relationships between watercolumn nutrient levels (TN and TP) and response variables (Secchi depth, and Chl-a)? 

and 

Question 4: Are there detectable influences of factors such as non-algal turbidity, 

suspended solids, retention time, etc. that may be used to classify lakes? 

The first step in the procedure employed to answer these two related questions 

was to estimate residence times for each lake, so as to remove those lakes with 

abnormally low residence times from the data set. As noted by USEPA (2000d. p. 3-1), 

“[m]any studies suggest that phytoplankton do not accumulate at retention times less 

than 7 days.” 

Data describing physical parameters for some lakes (volume, surface area, 

drainage area) were provided to the AAC by DEQ, but this data set was incomplete. An 

additional physical parameter data set for selected lakes was obtained from the Virginia 

Department of Conservation and Recreation (DCR), Division of Dam Safety. Drainage 

areas for lakes with water-quality data not represented in either of the above data sets 

were generated in the Virginia Tech Department of Crop and Soil Environmental 

Sciences GIS lab. Drainage areas, mean daily flows, latitudes, and longitudes for 

gaging stations located within Virginia were also obtained (from USGS) for the purposes 

of developing a model capable of predicting the water-volume yield of each lake’s 

watershed. Average (1961-1990) annual rainfall for all USGS gaging and Virginia DEQ 

lake monitoring stations were generated in the Department of Crop and Soil 

Environmental Sciences GIS lab. Several water-yield prediction equations were 

generated and applied. One problem encountered in application concerned watershed 

sizes: Whereas only 3 of the gaging station watershed areas were less than 10,000 

acres in size, more than 50% of Virginia lakes for which physical parameter are 

available have drainage areas less than 10,000 acres in size. As a result, the “best” 

multivariate prediction equations (i.e., highest R2

) were found to produce anomalous 

results for the small lakes. Therefore, we found it necessary to use a simplified 

procedure with drainage area as the sole predictive variable for inflow, estimating 

separate regression parameters for 3 watershed size ranges: less than 10,000 acres, 

10,000-50,000 acres, and greater than 50,000 acres. These equations were used to 

estimate mean inflow to each Virginia lake, and to calculate a mean residence time, for 

each Virginia lake for which a volume estimate is available. 

The calculated residence times are rough estimates. In addition to the 

inaccuracies resulting from the crudeness of the water-yield estimation procedure, the 
12

accuracy of the physical parameter estimates used in the calculation is also 

questionable. Where both DEQ and DCR provided estimates of physical parameters for 

lakes, these estimates were often in disagreement. Of the 46 lakes for which DCR and 

DEQ both provided volumetric estimates, for example, only 15 of these estimates were 

within 20 percent of one another. 

In EPA’s guidance manual, it defines lakes as having a “mean water residence 

time of 14 or more days” and recommends that states that have not set a size limitation 

defining a lake, determine appropriate size limitation “to eliminate small water bodies 

that, because of their size (and resulting hydrology) or uses (e.g., small agricultural 

impoundments), do not accurately represent typical lake conditions or do not exhibit 

expected responses to stressors” (2000d, p. 3-1). Because of the inherent uncertainty in 

our calculated residence times, we decided to remove from the data set lakes with a 

calculated residence time of less than 5 days. Of the 95 lakes for which we were able to 

calculate residence times, 5 residence-time estimates were below 5 days (these were 

removed from the analysis), and another 8 were between 5 and 14 days. 

The following analysis was performed for TP vs. Chl-a and TN vs. Chl-a, using a 

median value for each variable to represent each lake. The medians used were the 

same as those employed for the fisheries analysis below (Section I-C, Virginia DEQ 

Data Interpretation). Resulting relationships are represented in Figure 6. 

Stepwise regressions were performed for the purpose of determining the effect of 

other variables on the explanatory power of the TN and TP-based Chl-a prediction 

models. The stepwise procedures were performed until all variables left in the model 

were significant at the 0.15 level. Variables considered in stepwise procedure in various 

combinations were TP/TN ratio, depth (ft), volume (ac-ft), surface area (ac), retention 

time (days), median TSS (mg/L, calculated using the same procedure as TN and TP), 

estimated inflow (cfs), and the natural logs of the reservoir parameters (depth, retention 

time, surface area, volume). Complications in the analysis occurred because the data 

set was poorly populated, i.e., variables were not consistently available for lakes. 

Therefore, as stepwise procedures considered increasing numbers of potential 

explanatory variables, the number of observations available to test the resultant models 

declined. The presence of “Outliers” (i.e., data points well outside the range established 

by the bulk of the data in the plots of TN and TP vs. Chl-a) also complicated the 

analysis due to the fact that, in least-squares regression, such points tend to exert a 

disproportional influence on the resultant functional form. 

A number of efforts were employed to apply stepwise regression procedures as 

needed to overcome difficulties with the data set, including selective removal of outliers 

from the data set. Those procedures that yielded the most satisfactory results are 

summarized below (Table 4, Figure 6). As more outliers were removed, the R2

 of 

resultant models tended to increase. Both TN and TP proved to be useful as predictors 

of both Chl-a and SD, and the utility of both could be increased by adding additional 

variables through the stepwise regression procedures. Mean impoundment depth 

proved consistently useful as a predictor of both Chl-a and SD, while median TSS 

concentrations proved consistently useful as a predictor of SD. Residence time was not 

consistently useful, as expected based on previous studies, which may indicate that the 

residence time estimates are insufficiently accurate to aid the modeling procedure. 
13

To summarize the committee’s perspective on the results of analyses performed 

in response to questions 3 and 4: Although the results of these analyses do yield 

statistically significant relationships, we do not consider those relationships to be 

sufficiently robust to justify their use in criteria development. Contributing factors to this 

opinion include the fact that the models explain only 50 to 70 percent of the independent 

variables’ variation, in most cases, and our lack of faith in the accuracy and precision of 

the input variables. Non-log plots of Chl-a vs. TN and TP show a more dispersed 

pattern than the log-plots of Figure 6. 

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16

TP

Ln Chl-a

Eco 9

Eco 11

Eco 14

Outliers Removed

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0.00 0.50 1.00 1.50 2.00

TN

Ln Chl-a

Eco 9

Eco 11

Eco 14

Outliers Removed

Figure 6. Ln Chl-a as univariate functions of TP and TN. Ecoregions are represented in 

the plots but were not considered in the regression analyses represented above.
14

Table 4. Results of stepwise regression procedures. 

Dependent - - - - - - Independent Variables / Regression Coefficients - - - - - - n p Model 

R2

 

Statewide 

 Int TP TN Dep Res Vol TSS SA 

Chl-a 0.64 52.12 62 *** .5266 

0.64 2.21 62 *** .4260 

 

Chl-a 0.52 57.27 55 *** .6176 

 1.29 50.45 -.216 *** .6538 

 2.84 53.11 -.318 .156 *** .6953 

Chl-a 0.50 2.43 55 *** .5237 

 1.60 2.08 -.321 *** .6117 

 1.36 2.01 -.625 0.134 *** .6953 

Chl-a 2.45 44.59 -.227 .174 .104 46 *** .7489 

 0.54 2.15 -.240 .098 .070 45 *** .7506 

SD 1.09 -.166 42 *** .5147 

 1.40 -.973 -.118 *** .7015 

 1.01 -.870 .097 -.107 *** .7211 

 1.00 -.825 .263 -.062 -.095 *** .7590 

 1.16 -.787 .388 -.091 -.101 11E-6 *** .7770 

 

SD 1.10 -31.86 42 *** .5512 

 1.30 -21.14 -.101 *** .6767 

 .899 -18.54 .103 -.092 *** .6991 

 0.86 -16.38 .250 -.053 -.085 *** .7256 

 1.05 -15.99 .277 -.086 -.092 12E-6 *** .7459 

 

Ecoregion 9 

Chl-a .874 41.93 37 *** .3497 

 .882 1.800 37 ** .3335 

 

Ecoregion 11 

Chl-a .290 71.64 17 *** .6932 

 .182 3.048 *** .6808 

 

Ecoregion 14 

Chl-a 1.38 42.88 7 * .7730 

 1.12 2.420 7 .06 .5495 

Notes: TP, TN, and TSS = lake medians; Dep = depth (ft); Res = residence time (days); Vol = 

volume (acre-ft); SA = surface area (ac). All variables in italics (Chl-a, SD, Dep, Res, Vol, and 

SA) are expressed as natural logs. n = number of lakes used; 

p-values for model: *** = p <.0001; ** = .0001< p < .001; * = .001 < p < .01
15

Question 5: Should Carlson's Trophic State Index be considered as a scale to express 

Virginia's nutrient criteria? 

The Trophic State Index (TSI) indicator was developed for application in natural 

lakes (Carlson 1977). The TSI is a good tool for communicating trophic state condition 

to the public because it is an index. Total phosphorus, chlorophyll-a, and Secchi depth 

are all on a common, understandable scale. The problem with using the TSI to express 

nutrient criteria is the lack of spatial and temporal homogeneity among trophic state 

parameters in a reservoir. Suspended sediments delivered to impoundments lead to 

levels of non-algal turbidity that interfere with algal production, especially in the upper 

channel, and thus distort the assumed correspondence between the TSI components. 

As documented in Appendix B for Smith Mountain and Claytor Lakes, sediment-related 

non-algal turbidity varies spatially within reservoirs. Suspended sediment delivery from 

the watershed to impoundments varies temporally in response to weather conditions 

and seasonal cycles, as suggested by the seasonality analysis in Section I-A, 

Preliminary Analyses. The extent to which reservoirs vary in dissolved components that 

affect water clarity (such as tannins) is not known. 

Virginia’s impoundments are highly variable in morphometric characteristics, 

watershed area, retention time, and other factors that can be expected to influence both 

their capability to sustain designated uses at various levels of nutrient enrichment and 

potential correspondence between TSI measures. Given that Virginia impoundments 

are being treated collectively for the purpose of nutrient criteria development, the AAC 

recommends that nutrient criteria be implemented by monitoring nutrient variables 

directly and not through use of TSI, which would add yet another source of variability to 

criteria implementation. 
16

C. Nutrient Requirements for Fisheries in Virginia’s Impoundments 

The following analysis is conducted for the purpose of recommending candidate 

nutrient criteria for Virginia reservoirs that will be protective of aquatic life and of the 

reservoirs’ suitability for recreational fishery use. The committee believes that the status 

of the recreational fishery can be considered as an indicator of the impoundments’ 

suitability for aquatic life. Given that species of recreational fish are generally at the 

upper trophic level, the health of recreational fish populations can be interpreted as an 

indicator of ecosystem health as well as suitability for the aquatic life designated use. 

The candidate criteria that follow were developed expecting that DEQ will seek to 

balance the nutrient requirements of recreational fisheries against those of other 

potential uses, including contact recreation and public water supplies, in defining 

nutrient criteria for implementation. 

This analysis is conducted in three steps: a review of scientific literature prepared 

by Dr. John Ney, graphic analysis of Virginia impoundments’ fisheries and nutrient 

status, and synthesis of these two information sources. 

Review and Interpretation of Scientific Literature: Nutrient Requirements for 

Virginia’s Reservoir Fisheries, by Dr. John Ney. 

Community energetics dictates that the biomass of fish at or near the top of the 

trophic pyramid should be highly dependent on the amount of primary production at the 

base (Lindemann 1942). Primary production in lakes is limited by nutrients, principally 

phosphorus. USEPA (2000d) notes that nitrogen limitation is largely confined to 

subtropical and high altitude/latitude lakes). Nitrogen limited waters have TN:TP < 30 

(Alam and Glecker 1994): the ratio in Virginia reservoirs is much greater. 

However, the productivity of a fishery can be limited not only by insufficient 

energy (food) but also by inadequate habitat. High levels of algal production can cause 

hypolimnetic oxygen deficits to the detriment of coldwater and coolwater fishes. In 

shallow lakes, nutrients can stimulate excessive macrophyte growth, reducing habitat 

for warmwater sportfish species (Wiley et al. 1984). The influence of nutrients and 

resulting primary production on fisheries productivity in lakes and reservoirs should thus 

be parabolic, with low concentrations of nutrients constraining food supply and high 

concentrations limiting suitable habitat. The nutrient (phosphorus) or response 

(chlorophyll-a, Secchi disk water transparency) parameters that promote healthy 

fisheries will vary by waterbody type and the species-specific requirements of the 

desired fishes. 

What concentrations of nutrient or response parameters will ensure the quality of 

Virginia’s reservoir fisheries? To address this question, we conducted a comprehensive 

search of relevant published literature in library data bases and interviewed fisherieswater quality experts to identify further sources. The results are summarized below. This 

report proceeds from a general overview of the fisheries-water quality relationship to a 

consideration of the particular nature of that relationship in reservoirs (vs. natural lakes), 
17

followed by analysis of water quality requirements for Virginia’s three categories of 

reservoir fisheries: coldwater (trout), coolwater, and warmwater. 

Overview

Empiric relationships between fisheries productivity (as measured by fish 

harvest, production, or biomass) and both primary production and phosphorus 

concentration have been developed and published for regional and cosmopolitan sets of 

lakes. Correlations between primary production and fisheries productivity are highly 

positive, the former explaining (r2

) 67-84% of the latter (Table 5). Correlations between 

total phosphorus (TP) concentration and fisheries productivity are equally strong (51-

84%, Table 6). 

Table 5. Predictive relationships between measures of plant and fish productivity in lakes and 

reservoirs, as determined from single-variable regression models. 

Independent 

Variable 

Dependent 

Variable 

Data Set (n) % of Variation 

Explained (r2

) 

Source 

 

Gross 

photosynthesis 

Total fish yield Indian lakes (15) 82 Melack (1976) 

Phytoplankton 

standing stock 

Total fish yield Natural lakes, 

northern 

hemisphere (19) 

84 Oglesby (1977) 

 

Gross 

photosynthesis 

Total fish yield Chinese lakes 

and ponds (18) 

 

76 Liang et al. 

(1981) 

 

Chlorophyll-a Sport fish yield Midwestern U.S. 

lakes and 

reservoirs (25) 

83 Jones and Hoyer 

(1982) 

 

Primary production

 

Total fish 

production 

Cosmopolitan 

lakes (19) 

67 Downing et al. 

(1990) 
18

Table 6. Relationship between total phosphorus concentration (μg/L) as the independent 

variable and various measures of fish production in lakes and reservoirs. 

 

Dependent Variable Data Set (n) % of Variation 

Explained (r2

) 

Source 

 

Total fish yield North American lakes 

(21) 

84 Hanson and 

Leggett (1982) 

 

Sport fish yield Midwestern U.S. 

lakes and reservoirs 

(21) 

52 Jones and Hoyer 

(1982) 

 

Total standing stock Southern 

Appalachian 

reservoirs (21) 

84 Ney et al. (1990) 

 

Piscivore standing 

stock 

Southern 

Appalachian 

reservoirs (11) 

51 Ney et al. (1990) 

 

Total fish production Cosmopolitan lakes 

(14) 

67 Downing et al. 

(1990) 

Water Quality in Reservoirs

Some of the above data sets were limited to natural lakes. Indeed, most of the 

analyses of trophic state (e.g., Carlson’s TSI) are based on the relationships of 

phosphorus, chlorophyll-a, and water transparency (Secchi disk depth) in northern 

natural lakes (USEPA 2000d). These relationships are less robust in reservoirs, which 

comprise 99% of Virginia’s lentic waters. Chlorophyll-a concentrations tend to be lower 

in reservoirs than in natural lakes (Soballe et al. 1992) because higher inorganic 

turbidity and flushing rates in reservoirs may limit the ability of phosphorus to stimulate 

phytoplankton production. In a regression analysis of 80 southeastern U.S. reservoirs, 

Reckhow (1988) reported a fairly strong correlation between transparency and 

phosphorus (r2

 =0.50), a weak relationship between chlorophyll-a and phosphorus (r2 = 

0.10), and virtually no correlation between chlorophyll-a and transparency (r2

 < 0.01). In 

these impoundments, inorganic turbidity largely determined water transparency, and 

although the suspended sediment contained phosphorus, most of the phosphorus was 

not biologically available. In contrast, the r2

 for phosphorus vs. chlorophyll-a has been 

widely reported as ~0.70 (Brown et al. 2000) for sets of natural lakes. Canfield and 

Bachman (1981) examined the National Eutrophic Survey (NES) data set and 

compared nutrient and response parameters between natural lakes and reservoirs. 

They also found that reservoirs usually have substantially lower chlorophyll-a than 

natural lakes at the same phosphorus concentrations. Interpretation of their scatter 

diagram indicates that to produce 10.0 mg/m3 of chlorophyll-a (indicative of marginally 
19

eutrophic conditions) in the average natural lake would require 30 μg/L total 

phosphorus, whereas the average reservoir would require 40 μg/L total phosphorus. 

High flushing rates (low retention times) also limit development of phytoplankton 

biomass. In fact, the Technical Guidance Manual (USEPA 2000d) recommends that 

reservoirs with retention times < 14 days be exempted from nutrient regulation because 

algal biomass buildup is minimal. 

Chlorophyll-a has long been recognized as the single best metric for assessing 

nutrient-induced water quality of lakes because it most directly measures the parameter 

that affects aesthetic value and recreational use (Carlson 1977, Heiskary and Walker 

1988, Bachman et al. 1996). Because water transparency is affected by inorganic 

turbidity and phosphorus concentration is irrelevant in low retention-time impoundments, 

chlorophyll-a would appear to be the parameter of choice as a criterion for nutrient 

standards for reservoirs. 

Reservoirs also differ from natural lakes in that they characteristically exhibit a 

trophic gradient (Soballe et al. 1992). As dammed rivers, reservoirs lose nutrients 

through settling in a downstream direction. Thus a single reservoir may grade from 

eutrophic in its upper reaches to mesotrophic in its mid section to oligotrophic near the 

dam. Such systems can support good fisheries for a combination of warmwater, 

coolwater, and even coldwater fishes. 

Reservoir Fisheries and Water Quality

Because inorganic turbidity and flushing can limit nutrient impacts on reservoir 

productivity, it might be expected that the empiric relationship between phosphorus 

concentration and fisheries would be relatively weak. This does not appear to be the 

case in the southeastern U.S. Ney et al. (1990) examined the relationship between fish 

standing stock and a variety of potential predictors in a set of 21 southeastern, 

Appalachian-region multi-purpose reservoirs for which fishery and water chemistry 

information was available for the same time frame (within 2 years). These reservoirs 

varied greatly in surface area (1,700-132,000 ha), retention time (4-438 d), and total fish 

standing stock (77-2,321 kg/ha). Total phosphorus was easily the best predictor of fish 

standing stock (r2

 = 0.84), followed by Secchi disk depth (negative slope, r2= 0.42) and 

chlorophyll-a (r2 = 0.31). Fish standing stock increased linearly over the range of total 

phosphorus (8-81 μg/L) on a log-log scale, suggesting that maximum fish biomass 

would occur at higher phosphorus concentrations (Ney 1996). Fish production will 

ultimately be limited by habitat loss, resulting in a parabolic relationship with nutrient 

concentrations (Figure 7). 

Total fish standing stock or total fish production may not be indicative of 

sportfishing potential of reservoirs because sport and food fishes usually account for 

less than half the total. For the southern Appalachian reservoir data set, Yurk and Ney 

(1989) found that piscivore (largely game fish) standing stock increased linearly over the 

range of total phosphorus concentrations from 8 to 81 μg/L (r2

 = 0.51). Jones and Hoyer 

(1982) reported that annual sportfish (synonym here for “gamefish”) harvest increases 

linearly with total phosphorus over the range 15-90 μg/L in 25 midwestern U.S. lakes (r2
20

= 0.52) and with chlorophyll-a between 4 and 67 μg/L (r2 = 0.83). In a study of 21 

northern temperate natural lakes, Hanson and Leggett (1982) found that long-term sport 

and commercial annual harvests increased with total phosphorus concentration up to 

500 μg/L (r2 = 0.84). 

Fisheries in Virginia Impoundments

The fisheries of Virginia’s public reservoirs include several, mostly small (<100 

acres) systems managed for trout (coldwater). Some are managed for a combination of 

coolwater (e.g., striped bass, walleye) and warmwater (sunfish, largemouth bass, 

catfish) species; most of these are large (> 500 acres) impoundments. Reservoirs 

managed solely for warmwater fisheries range from large systems (primarily in eastern 

Virginia) to ponds. Many of the smaller impoundments are owned by the Virginia 

Department of Game and Inland Fisheries (VDGIF) and managed primarily for 

sportfishing. 

Within the overall sportfish complex, it has long been recognized that individual 

species respond differently to particular levels of lake fertility. The Technical Guidance 

Manual (USEPA 2000d) uses the work of Oglesby (1977) to predict that as phosphorus 

in natural lakes increases, fisheries will shift from coldwater (TP < 24 μg/L) to coolwater 

(TP = 24-48 μg/L) to warmwater (TP = 48-193 μg/L); total fisheries yield (harvest) 

should progressively rise over this range of phosphorus concentration. However, 

Oglesby’s projections were based on rather limited data that has been supplemented by 

later studies and did not apply specifically to many of the sportfish species of Virginia’s 

Figure 7. Generalized relation of total 

fish and sport fish standing stock with 

total phosphorous concentration in 

temperate latitude reservoirs. Standing 

stock values are representative of 

southeastern U.S. reservoirs to 100 μg/L 

total P, while standing stocks at higher P 

concentrations are hypothetical. The 

vertical line labeled as “clean water” 

represents a TP concentration 

associated with water clarity that could 

be considered as minimally acceptable 

for contact recreational use and is an 

approximate value. The “clean water” 

representation is conceptual and is not 

reproduced here for the purpose of 

suggesting a specific TP criterion value 

(from Ney 1996). 
21

reservoirs. We use this later work, as well as water quality information from some of 

Virginia’s best reservoir fisheries, to recommend water quality limits that will support 

healthy fisheries in our waters. 

Coldwater Fisheries: Trout fisheries in Virginia lakes are maintained by frequent 

stockings from hatcheries, either on a put-and-take (adults) or put-grow-take basis 

(fingerlings). Rainbow, brown, and brook trout are stocked alone or in combination. 

Because stocked put-and-take trout fisheries are seasonal and not habitat-limited 

(swimming pools have been used in other states), this analysis focuses on conditions 

necessary for trout to grow and survive over one or more years to reach harvestable 

size. Essentially, this requires an oxygenated hypolimnion during thermal stratification. 

The relevant water quality literature is sparse. In Minnesota, natural populations of lake 

trout (Salvelinus namayacush) achieve peak abundance at TP = 6 μg/L and 

chlorophyll-a = 1 μg/L (Schupp and Wilson 1993). However, the lake trout requires the 

lowest temperatures of any salmonid and does not occur in Virginia. In Lake 

Windemere, UK, brown trout abundance more than doubled when TP was reduced from 

30 to 11 μg/L and chlorophyll-a declined from 30 to 14 μg/L (Elliott et al. 1996). A 

fertilization experiment in a small mountain lake in British Columbia increased rainbow 

trout growth and interannual survival while raising TP from 4 to 9 μg/L and chlorophyll-a 

from 1 to 6 μg/L (Johnston et al. 1999). 

On the basis of this literature and the concentrations reported from elsewhere for 

successful trout fisheries, it appears that the following concentrations are adequate to 

sustain habitat and promote trout growth: TP ≤ 10 μg/L and Chl-a ≤ 6 μg/L. 

Coolwater Fisheries: Virginia’s coolwater sportfish species are striped bass, 

hybrid striped bass (white bass x striped bass) and walleye. The smallmouth bass is 

sometimes considered a coolwater species, but it has virtually identical temperature 

tolerances to its congener largemouth bass, a warmwater fish considered below (Brown 

1974). All three coolwater species are maintained by the stocking of hatchery-reared 

fingerlings on a put-grow-take basis; the single exception is the striped bass population 

of Kerr reservoir, which is self-sustaining. 

Walleye, striped bass, and hybrid striped bass prefer water temperatures in the 

range of 19-28o

C (Coutant 1985, Hokanson 1990, Kilpatrick 2003). By late summer in 

Virginia reservoirs, this habitat is usually limited to the metalimnion/hypolimnion 

downlake region near the dam (Ney 1988, Kilpatrick 2003). However, all three species 

can tolerate water temperatures of >28o

C for extended periods without observed 

mortality, although growth will likely be impaired (Brown 1974, Wrenn and Forsythe 

1979, Kilpatrick 2003). 

The influence of water quality on walleye abundance has been examined for 

Minnesota lakes (the walleye is the state fish of Minnesota) and Lake Erie, which 

supports the most productive walleye fishery in the world. In Minnesota, walleye 

abundance peaks under mesotrophic conditions: TP of 15-25 μg/L and Chlorophyll-a of 

7-10 μg/L (Schupp and Wilson 1993). Lake Erie’s walleye populations is thriving at 

chlorophyll-a of 5-15 μg/L; it is actually projected to increase if phosphorus loading is 

doubled (Anderson et al. 2001). Walleye do well in lakes that experience occasional 
22

hypolimnetic anoxia, but poorly in lakes with Secchi disk transparency > 4 m (Schupp 

and Wilson 1993). 

Striped bass also fare poorly under oligotrophic conditions. When Lake Mead, 

Nevada, became oligotrophic (TP = 10 μg/L), striped bass became stunted and 

emaciated (Axler et al. 1987). Smith Mountain Lake is Virginia’s premier inland striped 

bass fishery and has a classic trophic gradient. The lower segment of Smith Mountain 

Lake has an oxygenated hypolimnion year-round, providing a summer thermal refuge 

for striped bass. However, striped bass congregate further upstream in summer, where 

prey fish are more abundant (Ney 1988), suggesting that food is more important than 

ideal habitat. 

Virginia’s coolwater sportfishes are fast-growing piscivores dependent on a large 

supply of forage fishes (e.g., gizzard shad, threadfin shad). These planktivores are most 

abundant in fertile systems (Bremigan and Stein 2001, Maceina 2001). In Virginia’s 

large reservoirs, coolwater fishes appear to be more food limited than habitat limited. 

The scientific literature reviewed above indicates that coolwater fisheries can 

prosper in systems where TP >10 μg/L and where Chl-a ≤ 15 μg/L. 

Warmwater Fisheries: Principal warmwater sportfishes are primarily of the 

sunfish family (Centrarchidae) as well as catfishes. Catfishes have higher temperature 

and lower dissolved oxygen (DO) tolerances than centrarchids and are not considered 

further in this review. Virginia’s centrarchids include sunfishes (bluegill, redear, 

redbreast, and pumpkinseed), black and white crappie, smallmouth bass, as well as the 

most-sought freshwater sportfish species, largemouth bass. Centrarchids are littoral and 

epilimnetic fishes that do not require an oxygenated hypolimnion as summer habitat. 

Nutrient-induced habitat limitations occur only in shallow lakes that become choked with 

aquatic macrophytes. In such systems, the cover provided by dense stands of “weeds” 

prevents largemouth bass from preying on sunfish; both largemouth bass and sunfish 

become stunted (Bennett 1962). Virginia has few macrophyte-dominated reservoirs. 

Where they exist, poor watershed practices (erosion) or invasive exotics (e.g., Hydrilla)

are usually responsible. 

For the most part, centarchid populations are food-limited rather than habitatlimited. Higher levels of nutrients translate to more centrarchid biomass. In fact, 

centrarchid lakes devoted primarily to fishing are often fertilized at least annually. 

Auburn University, which pioneered research on centrarchid management, recommends 

fertilization to achieve chlorophyll-a concentrations of 40-60 μg/L (Maceina 2001). The 

VDGIF frequently fertilizes its small fishing lakes to produce robust centrarchid 

populations for anglers. In these small (< 200 acres) lakes, chlorophyll-a in the 40-60 

μg/L range commonly results. 

Obviously, larger reservoirs are not subject to direct fertilization because they 

must accommodate aesthetic and water-contact recreation and (sometimes) coolwater 

fisheries. However, across reservoirs of all sizes, the pattern of higher fertility = better 

centrarchid fishing holds. In Minnesota, Schupp and Wilson (1993) reported that black 

crappie fisheries peak at TP ~60 μg/L and chlorophyll-a ~20 μg/L; white crappie do best 

under hypereutrophic conditions (TP ~100 μg/L; chlorophyll-a ~60 μg/L). 
23

In a study of 30 large Alabama reservoirs, Maceina et al. (1996) found that 

growth of crappie and largemouth bass increased up to ~20 μg/L chlorophyll-a. In fact, 

the potential for an angler to catch a trophy largemouth bass (> 5 lbs.) was about 3 

times greater in eutrophic than mesotrophic lakes. Bachmann et al. (1996) confirmed a 

similar pattern for natural Florida lakes (n = 360): trophy largemouth bass were more 

abundant in highly eutrophic lakes (chlorophyll-a > 40 μg/L), as were populations of 

redear sunfish and black crappie. 

This review indicates that warmwater fisheries can thrive where TP ≤ 50 μg/L and 

Chl-a is 20-40 μg/L. 

Virginia DEQ Data Interpretation 

The above review summarizes scientific studies from throughout the USA, while 

the analysis that follows is focused on Virginia conditions. The purpose of this analysis 

is to determine the maximum nutrient concentrations (TP and Chl-a levels) that sustain 

good-to-excellent recreational fisheries, by fishery type and by ecoregion. 

The status of the recreational fishery in each impoundment was rated on a scale 

of 1 (poor) to 5 (excellent) by VDGIF biologists, in response to requests advanced by 

Dr. John Ney. The rating scale and process are described in Section I-B, Question 2. 

Each reservoir was classified as one of the following types, based on the professional 

knowledge of Dr. John Ney and considering VDGIF biologists’ comments during the 

rating process. 

Coolwater Fisheries: Impoundments that support coolwater fishes in the deep 

bottom waters and warm-water fishes in shallower waters. The largest and 

deepest of the state’s impoundments are included within this category. 

Trout Fisheries: Lakes managed for support of trout, including those managed by 

VDGIF for this purpose. These lakes are generally small in size, and 

predominantly within the state’s mountainous regions. 

Fertilized Fisheries: Lakes managed for centrarchid species, such as sunfishes, 

crappies, and black basses, with fertilizers applied as a management input. 

These lakes are generally quite small, and fish production is the primary use. All 

of lakes identified as fertilized fisheries in the current analysis are owned and 

managed by VDGIF. 

Warmwater Fisheries: The majority of the state’s impoundments; all 

impoundments not explicitly classified as another type. 

Other: Impoundments known to be affected by unique or unusual conditions and 

therefore considered as poorly suited to serve as indicators of how the state’s 

lakes, in general, can be expected to respond to water-column nutrients. 

Conditions that cause lakes to be categorized as “other” include high levels of 

non-algal turbidity (including coloration of waters from watershed geology or 

tannins), low retention times, mechanical aeration, the presence of prolific 

macrophytes and vegetative structure on the lake bottom. 
24

Impoundment classifications and the VDGIF biologists’ ratings are listed in 

Appendix A, Table A-4. 

Data analysis was conducted by plotting fishery status, represented by fishery 

type, against two nutrient variables – Chl-a and TP – for each lake, followed by visual 

interpretation of the resulting graphic plots. As discussed in the AAC July 2004 report, 

the AAC recommends that criteria for lakes and reservoirs not include TN criteria that 

could be applied independently of TP. 

The initial stage of this analysis was exploratory: statewide plots were prepared 

of fishery status vs. a number of seasonal indicators (e.g., April – May, June – August, 

July – August, August – September, April – September) for both Chl-a and TP. This 

exercise was conducted in response to the variety of findings in the scientific literature 

regarding seasonal nutrient-status indicators. Visual analysis of these plots yielded no 

indication of any seasonal nutrient indicator being superior or qualitatively different from 

any other. Major differences among these plots were due to the number of lakes 

represented (because of varying numbers and distributions of water quality 

observations among lakes, the number of lakes with sufficient observations for inclusion 

in each representation varied). This exercise led to the conclusion that development of 

nutrient indicators representing the entire April – October sampling period would be the 

preferred approach, as it would maximize use of available water quality data. 

For the second stage of analysis, Chl-a and TP medians for each lake were 

generated using a procedure that is analogous to the EPA protocol for calculating lake 

medians (see Section I-B, Question 1). However, instead of aggregating values by 

season, we aggregated by month (April – October) so as to generate lake-median 

values that better represent DEQ’s lake monitoring schedule. For each lake and nutrient 

variable, monthly medians were calculated by aggregating all observations for each 

month. Then, a lake median was calculated as the median of the monthly medians for 

all lakes represented by 6 or 7 monthly medians (Appendix A, Table A-2, Mo values). 

Lake median values for lakes that had been rated by VDGIF but were represented by 

fewer than 6 monthly medians for any nutrient variable were calculated as the average 

of the S1, S2, and S3 medians (See Section I-B, Question 1. The S1, S2, and S3 

medians are listed in Appendix A, Table A-2). Each rated impoundment’s fishery status 

was plotted against its TP and Chl-a lake medians (Figure 8). The plots are interpreted 

by identifying those Virginia impoundments that are considered to be representative of 

fishery types (i.e., the fishery in that lake is not known to be influenced by conditions 

that are unusual throughout the ecoregion or state) and able to sustain high-quality 

fisheries with the highest nutrient levels. Nutrient levels in these impoundments are not 

necessarily at the maximum level that would be capable of sustaining such fisheries. 

Limited data from impoundments where fish populations have been impaired by nutrient 

overenrichment prevent direct interpretation of these data to indicate nutrient levels that 

limit fishery success. 

In contrast to previous studies (Yurk and Ney 1989, Ney 1996), the plots of 

fishery status vs. water column TP and Chl-a generated for this report do not yield welldefined relationships. We believe that the reason for this result is size variability. Prior 

studies included only very large impoundments (> 2,400 acres). Lakes vary in nutrient 

response capability due to physical features. Generally, fish populations in small lakes 
25

are more subject to influence by non-nutrient factors than fish populations in large lakes, 

and relatively small lakes are heavily represented in the DEQ database. Non-nutrient 

factors capable of influencing fish populations include inorganic turbidity (suspended 

sediments) and lake physical features and structural elements. 

Statewide

0

1

2

3

4

5

6

0 20 40 60 80

Chl-a (ug/L)

Fishery Status

Coolwater

Warmwater

Trout

Fertilized

Other

Statewide

0

1

2

3

4

5

6

0.00 0.05 0.10 0.15 0.20

TP (mg/L)

Fishery Status

Coolwater

Warmwater

Trout

Fertilized

Other

Figure 8.

Fishery status 

vs. Chl-a 

median and TP 

median, 

statewide (left) 

and ecoregion 

(following 

pages) Lake 

Moomaw is a 

coolwater 

fishery that 

also supports 

trout.
26

Ecoregion 9

0

1

2

3

4

5

6

0 10 20 30 40 50 60

Chl-a (ug/L)

Fishery Status

Coolwater

Warmwater

Fertilized

Other

Ecoregion 11

0

1

2

3

4

5

6

0 5 10 15

Chl-a (ug/L)

Fishery Status

Coolwater

Warmwater

Trout

Other

Lake Moomaw

Ecoregion 14

0

1

2

3

4

5

6

0 20 40 60 80

Chl-a (ug/L)

Fishery Status

Coolwater

Warmwater

Other
27

Ecoregion 9

0

1

2

3

4

5

6

0.00 0.02 0.04 0.06 0.08

TP (mg/L)

Fishery Status

Coolwater

Warmwater

Fertilized

Other

Ecoregion 11

0

1

2

3

4

5

6

0.00 0.01 0.02 0.03 0.04 0.05

TP (mg/L)

Fishery Status

Coolwater Warmwater

Trout Other

Lake Moomaw

Ecoregion 14

0

1

2

3

4

5

6

0.00 0.05 0.10 0.15 0.20

TP (mg/L)

Fishery Status

Coolwater

Warmwater

Other
28

Table 7. Maximum TP and Chl-a lake medians (April – October, μg/L) corresponding with highquality recreational fisheriesa

 (fishery status 4 and 5, if available), by ecoregion. 

Ecoregion Fishery 

Type 

High-quality 

fishery 

maxima 

(μg/L)

Comments (Fishery status)

 

9 Coolwater TP ≤ 30 Kerr (5): TP = 30. 

 Chl-a ≤ 10 Kerr (5): Chl-a = 9.66. 

 

 Warmwater TP ≤ 40 Lake Chesdin (4): TP = 40 

 Chl-a ≤ 25 Diascond Reservoir (5): Chl-a = 25 

 

 Chickahominy Lake (TP = 60 and Fishery Status 

= 5) is atypical of Virginia reservoirs due to 

abundant macrophytes. Despite relatively high 

TP, Chl-a is relatively low (16). 

 

 Fertilized TP ≤ 40 Curtis (4) and Stonehouse (5): TP = 40 

 Chl-a ≤ 60 Curtis (4): Chl-a = 53.9 

 

11 Warmwater, 

Coolwater 

TP ≤ 20 

Chl-a ≤ 10 

South Holston Lake (5): TP = 20, Chl-a = 10. 

Biologically: warm water should not be any more 

sensitive that multipurpose. 

 

 Trout TP ≤ 10 Sugar Hollow (2) is said by DGIF to lose 

coldwater habitat in the summer; TP = 15 in 

Sugar Hollow; TP = 10 in all other trout lakes. 

 Chl-a ≤ 4 Of the single-purpose cold-water fisheries, 

Switzer (3) is the best trout lake (Chl-a = 1.5). All 

other trout lakes rated as (2), and Chl-a > 2 

 Although not a single-purpose fishery, Lake 

Moomaw (4) supports the state’s best trout 

fishery (Chl-a = 3; TP = 10). 

 

14 Warmwater TP ≤ 50 

Chl-a ≤ 30 

Lake Cohoon (4) has TP = 50, Chl-a = 29. Lakes 

with higher TP (> 10) and Chl-a (>60) are Little 

Creek Res. (3) and Lake Smith (3). 

 

 Coolwater TP≤ 20 Lake Prince (5) and Western Branch (5): TP = 20 

 Chl-a ≤ 11 Lake Prince (5): Chl-a = 11 

 

a

 As rated by VDGIF biologists; see Section I-B, Question 2. Fishery status ratings are in 

parentheses after lake names; 5 = best and 1 = worst.
29

Synthesis and Candidate Criteria Recommendations: 

The literature review provided general input for chlorophyll-a (Chl-a) and total 

phosphorus (TP) recommendations based on the performance of coldwater, coolwater, 

and warmwater fisheries. However, most of this work was conducted in other regions of 

the U.S. and beyond. In particular, the literature-based recommendations do not 

correspond to Virginia’s ecoregions as defined by the USEPA. Input for ecoregion-level 

analysis was provided by VDGIF fisheries biologists’ ratings of lake-specific fisheries as 

related to lake-specific nutrient parameters (preceding section, Table 7). This approach 

is inherently subjective and so vulnerable to potential error. 

To develop candidate criteria recommendations by ecoregion, we consider both 

the literature synthesis and the Figure 7 plots for agreement. This exercise resulted in 

the following recommendations. 

Ecoregion 9

Coolwater Fisheries:

Coolwater fishes require an oxygenated hypolimnion during summer 

stratification. The hypolimnia of virtually all Virginia reservoirs become anoxic in their 

upper regions by late summer; those that support good coolwater fisheries retain 

oxygenated hypolimnia downlake, providing thermal refuge for striped bass, hybrid 

striped bass (in ecoregion 11) and walleye. The striped bass is of most concern 

because it is the main coolwater sportfish in Virginia and its temperature preferences 

are slightly lower than those of hybrid striped bass and walleye. However, healthy 

striped bass fisheries are dependent on the supply of forage fish, which increases with 

nutrient concentration. This poses a habitat vs. food tradeoff. The literature review failed 

to identify optimum nutrient concentrations for striped bass. In ecoregion 9, good to 

excellent coolwater fisheries (predominantly striped bass) occur at Chl-a concentrations 

of 2-10 μg/L and TP of 4-40 μg/L. The premier inland striped bass fisheries in Virginia 

are Smith Mountain Lake and Kerr Reservoir, with median Chl-a of 2.6 and 9.7 μg/L, 

respectively, and median TP of 20 and 30 μg/L. Of particular interest is Kerr Reservoir 

because it supports the only reproducing freshwater population of striped bass in 

Virginia. This exceptional resource provides the hatchery supply for stocking other 

Virginia waters. However, higher nutrient concentrations than Kerr now experiences 

could reduce summer habitat for striped bass, impacting survival rates (V. DiCenzo, 

VDGIF, personal communication). We therefore recommend candidate criteria for 

coolwater fisheries in ecoregion 9 of 10 μg/L chlorophyll-a and 30 μg/L total 

phosphorus. 

Warmwater Fisheries:

Nutrient levels in most Virginia reservoirs are not limiting to warmwater fisheries. 

In ecoregion 9, highly-rated warmwater fisheries occur at Chl-a up to 25 μg/L (Diascund 

Reservoir) and TP to 40 μg/L (Lake Chesdin). From a fisheries perspective, a TP limit 
30

below 40 μg/L would be counterproductive. Whether lower Chl-a levels would have the 

same result is less certain. Based on Alabama studies (Maceina et al. 1996, Maceina 

2001), it appears that Chl-a of ~20 μg/L may not be detrimental to centrarchid fisheries 

in larger reservoirs. For ecoregion 9 warmwater fisheries, we recommend candidate 

criteria of 25 μg/L chlorophyll-a and 40 μg/L total phosphorus.

Fertilized Lakes:

The VDGIF fertilizes many of the small lakes it owns to achieve Chl-a of 40-60 

μg/L, which is recommended to achieve optimum sunfish and largemouth bass 

(centrarchid) fisheries. In ecoregion 9, good fisheries results in fertilized lakes with 

chlorophyll-a up to 60 μg/L and TP of 40 μg/L. Inasmuch as these VDGIF-owned lakes 

are managed primarily for sportfishing by professional biologists, we recommend 

candidate criteria of 60 μg/L chlorophyll-a and 40 μg/L total phosphorus. Lakes 

fertilized and managed as centrarchid fisheries by VDGIF in other Virginia ecoregions 

should also be managed based on the same rationale. 

Ecoregion 11

Coolwater Fisheries:

In western Virginia, Claytor (fishery status rating of 4), Flanagan (3) and South 

Holston (5) are the only reservoirs of depth and size to support coolwater fisheries. The 

best of these, South Holston, has median Chl-a of 9.2 μg/L and TP = 20 μg/L. 

Flanagan’s coolwater fishery productivity is limited by low fertility (Chl-a = 2.8 μg/L, TP = 

10 μg/L). Claytor is intermediate in Chl-a (6.2 μg/L) and equivalent to South Holston in 

TP (20 μg/L). As in ecoregion 9, a Chl-a limit of 10 μg/L appears correct to support 

ecoregion 11 coolwater fisheries. A TP limit of 20 μg/L is also sound; higher TP in 

riverine Claytor Lake could endanger summer habitat for striped bass (Kilpatrick 2003). 

For ecoregion 11, we recommend candidate criteria for coolwater fisheries as 10 μg/L 

chlorophyll-a and 20 μg/L total phosphorus. 

Warmwater Fisheries:

Ecoregion 11 lakes that support warmwater fisheries exclusively are generally 

infertile. These lakes have only fair fisheries, with the exception of two small reservoirs, 

Lake Frederick (rating of 5) and Lake Robertson (rating of 4), which are rated more 

highly because they have better centrarchid fisheries than other lakes in the ecoregion. 

From the fisheries perspective, even these waters would benefit from greater nutrient 

inputs (Steve Reeser, VDGIF, personal communication). Our recommendation is the 

same as for ecoregion 9 warmwater fisheries; candidate criteria of 25 μg/L chlorophylla and 40 μg/L total phosphorus. 
31

Coldwater (Trout) Fisheries:

Virginia’s trout lakes are generally small (< 50 acres) and managed by either 

VDGIF or the U.S. Forest Service. Most support rather mediocre fisheries, either 

because they lose habitat in the summer (anoxic hypolimnion) or have low fertility. An 

example of the former is Sugar Hollow Lake (rating of 2), where median Chl-a and TP 

are 3.8 and 15 μg/L, respectively. The latter is represented by Switzer Lake (rating of 3), 

where chlorophyll-a is 1.5 and TP is < 8 μg/L. The exception to this situation is Lake 

Moomaw (2,600 acres; rating 4), which supports a trophy trout fishery, with stocked 

fingerlings growing to 5-12 lbs. over several years. Lake Moomaw has median 

chlorophyll-a and TP concentrations of 4 and 10 μg/L, respectively. These values 

concur with literature reports for productive trout fisheries. Higher levels of either 

parameter could impact critical summer habitat. We therefore recommend for ecoregion 

11 (and all trout lakes in Virginia) candidate criteria of 4 μg/L chlorophyll-a and 10 

μg/L total phosphorus. 

Ecoregion 14

Lakes in southeastern Virginia are principally water supply reservoirs which 

provide some good fishing for the region’s largely urban population. Warmwater 

fisheries predominate. 

Coolwater Fisheries:

The VDGIF has endeavored to establish coolwater fisheries in ecoregion 14 by 

stocking striped bass in larger water-supply reservoirs. Good coolwater fisheries have 

been established in Lake Prince (rating of 5 for combined warmwater and coolwater 

fishery) and Western Branch Reservoir (5). The median TP in each of these reservoirs 

is 20 μg/L; median Chl-a is11 μg/L in Lake Prince and 9 μg/L in Western Branch. 

Nutrient parameter concentrations greater than currently experienced are likely to limit 

habitat in this region, which experiences the longest periods of lake thermal stratification 

in Virginia. For coolwater fisheries in ecoregion 11, we recommend candidate criteria of 

10 μg/L chlorophyll-a and 20 μg/L total phosphorus.

Warmwater Fisheries:

The scatter plots for warmwater fisheries in southeastern Virginia (Figure 7) 

provide a rare dichotomy. Highly rated (4 or 5) lakes have Chl-a of 7-30 μg/L and TP of 

20-50 μg/L, while ratings drop for more eutrophic lakes (Chl-a > 60 μg/L; TP > 100 

μg/L). The nutrient concentrations to produce peak warmwater fisheries may lie 

between these groupings, as indicated by the literature. However, it appears that good 

warmwater fisheries in ecoregion 14 can be sustained with the same candidate criteria 

as in ecoregion 9: chlorophyll-a = 25 μg/L and 40 μg/L total phosphorus. 
32

Summary

To illustrate potential effects of candidate criteria (Table 8), we applied those 

criteria to the Virginia lakes data set used in the above analysis (Table 9). Available 

data indicate that most of the state’s reservoirs satisfy the candidate criteria. Fisheries 

in those reservoirs that have both water quality that satisfies the candidate criteria and 

low fishery status ratings can be presumed to be affected by factors other than nutrient 

overenrichment; comments by VDGIF biologists, in many cases, document these 

effects (Appendix A, Table A-4). Several lakes with good (4) or excellent (5) fishery 

status ratings fail to satisfy the criteria, thus demonstrating the reservoir responses to 

nutrient inputs vary due to differences in morphometric and other factors. Lake 

Chickahominy, for example, supports an excellent fishery despite high phosphorous 

levels (median TP = 60 μg/L). Lake Chickahominy differs from other lakes in the data 

set due to the abundance of macrophytes and structure in the lake bottom. Lake 

Chickahominy also has a very low TN/TP ratio (using lake median values, TN/TP = 12, 

vs. the data set’s mean value of 25). Lake Cohoon (4) and Lake Prince (5) also support 

high-quality fisheries despite marginal exceedance of candidate criteria. 

Table 8. Candidate criteria to accommodate fishery recreation and protect aquatic life.a 

Fishery 

Type 

Warmwater Coolwater Coldwater 

(trout) 

Managed / 

Fertilized 

Warmwater Coolwater Coldwater 

(trout) 

Managed / 

Fertilized 

Ecoregion - - - - - - - - Chl-a (μg/L)- - - - - - - - - - - - - - - - - TP (μg/L)- - - - - - - - - 

 

11 25 10 4 40 20 10 

9 25 10 60 40 30 40 

14 25 10 40 20 a TP and Chl-a are median values representative of the April – October period. 

Table 9. Status of Virginia reservoirs used in analysis relative to candidate criteria. 

(> CC = median concentration higher than candidate criteria). 

Lake Ecoregion Type Chl-a 

(μg/L) 

TP 

(μg/L) 

Fishery 

Status 

Chl-a 

Status 

TP 

Status 

Abel Lake 9 Warm 2 20 2 ok ok 

Banister Lake 9 Warm 5 20 1 ok ok 

Big Cherry Reservoir 11 Warm 3 10 3 ok ok 

Briery Creek Lake 9 Warm 12 13 5 ok ok 

Brookneal Reservoir 9 Warm 4 30 2.5 ok ok 

Byllesby Reservoir 11 Warm 1 40 3 ok ok 

Carvin Cove Reservoir 11 Warm 4 20 3 ok ok 

Chickahominy Lake 9 Warm 16 60 5 ok > CC 

Claytor Lake 11 Cool 6 20 4 ok ok 

Curtis Lake 9 Fert 54 40 4 ok ok 

Diascund Reservoir 9 Warm 25 30 5 ok ok 

Douthat Lake 11 Cold 3 10 2 ok ok 
33

Elkhorn Lake 11 Cold 2 10 2 ok ok 

Emporia Lake 9 Warm 7 25 2 ok ok 

Fairy Stone Lake 11 Warm 2 10 2.5 ok ok 

Fort Pickett Reservoir 9 Warm 15 50 3 ok > CC 

Great Creek Reservoir 9 Warm 4 20 3 ok ok 

Harrison Lake 9 Warm 3 47 2 ok > CC 

Harwoods Mill Reservoir 14 Warm 8 25 4 ok ok 

Hungry Mother Lake 11 Warm 4 10 3 ok ok 

John W. Flannagan 

Reservoir 

11 Cool 3 10 3 ok ok 

Kerr Reservoir 9 Cool 10 30 5 ok ok 

Keysville Reservoir 9 Warm 21 40 3 ok ok 

Lake Albemarle 9 Fert 18 30 4 ok ok 

Lake Anna 9 Cool 3 10 4 ok ok 

Lake Burnt Mills 14 Warm 21 30 4 ok ok 

Lake Chesdin 9 Warm 17 40 4 ok ok 

Lake Cohoon 14 Warm 29 50 4 > CC > CC 

Lake Frederick 11 Warm 5 10 5 ok ok 

Lake Gaston 9 Cool 5 20 4 ok ok 

Lake Kilby 14 Warm 28 45 3 > CC > CC 

Lake Meade 14 Warm 20 40 4 ok ok 

Lake Moomaw 11 Cool 3 10 4 ok ok 

Lake Nelson 9 Warm 7 30 4 ok ok 

Lake Pelham 9 Warm 4 20 3 ok ok 

Lake Prince 14 Cool 11 20 5 > CC ok 

Lake Robertson 11 Warm 3 20 4 ok ok 

Lake Smith 14 Warm 64 150 3 > CC > CC 

Lee Hall Reservoir 14 Warm 14 30 3 ok ok 

Little Creek Reservoir 14 Warm 72 135 3 > CC > CC 

Martinsville Reservoir 9 Warm 3 10 2 ok ok 

Modest Creek Reservoir 9 Warm 10 30 2 ok ok 

Motts Run Reservoir 9 Cool 6 15 3 ok ok 

Mountain Run Lake 9 Warm 11 25 3 ok ok 

North Fork Pound 

Reservoir 

11 Warm 2 10 2 ok ok 

Pedlar River Reservoir 11 Warm 3 10 3 ok ok 

Philpott Reservoir 9 Cool 2 10 4.5 ok ok 

Shenandoah Lake 11 Warm 12 29 2 ok ok 

Smith Mountain Lake 9 Cool 3 20 4 ok ok 

South Fork Rivanna 

Reservoir 

9 Warm 6 25 3 ok ok 

South Holston Lake 11 Cool 9 20 5 ok ok 

Stonehouse Creek 

Reservoir 

9 Fert 26 40 5 ok ok 

Sugar Hollow Reservoir 11 Cold 4 15 2 ok > CC 

Swift Creek Lake 9 Warm 16 37 2 ok ok 

Switzer Lake 11 Cold 1 10 3 ok ok 

Thrashers Creek 

Reservoir 

9 Warm 14 20 4 ok ok 

Totier Creek Reservoir 9 Warm 9 30 2 ok ok 

Waller Mill Reservoir 9 Cool 7 20 4 ok ok 

Western Branch 

Reservoir 

14 Cool 9 20 5 ok ok 
34

Criteria Implementation: TP vs. Chl-a 

A central issue in developing candidate criteria to protect aquatic life and 

recreational fisheries in impoundments is the decision whether they are intended to 

identify sites experiencing nutrient enrichment or the subset of these sites exhibiting 

symptoms of eutrophication (biological impairment arising from excess nutrients). This 

is a key point because it informs subsequent decisions about the types of criteria that 

should be adopted. The committee has adopted the position that the detection of 

eutrophication is the primary goal underlying the development of nutrient criteria for 

Virginia reservoirs. Based on this viewpoint, we recommend that secondary metrics 

(specifically, chlorophyll concentrations) should be the basis for establishing criteria. 

Our rationale is that reservoirs exhibit variable sensitivity to nutrient enrichment based 

on their flushing rate (residence time), critical depth (ratio of optical to mixed depth), 

sediment influx, and other factors (Figure 9). Given their variable response, it is not 

practical to apply a single standard based on nutrient concentration for the purpose of 

identifying impairment or to establish mitigation targets. Because only a subset of 

reservoirs experiencing nutrient enrichment will experience inability to serve designated 

use, setting standards based on nutrient concentrations would result in the classification 

of all nutrient-rich water bodies as impaired irrespective of their sensitivity. Prior 

attempts to address the issue of variable autotrophic potential have focused on the 

exclusion of reservoirs with very short water residence time (e.g., < 14 days). As the 

effects of flushing rate on autotrophic potential are continuous, there is no defensible 

basis for selecting an arbitrary threshold to distinguish sensitive vs. non-sensitive waterbodies. Adopting criteria based on biological attributes (chlorophyll concentration) 

resolves this issue by identifying lakes where the effects of nutrient enrichment have 

exceeded an algal-biomass threshold and avoids mitigation efforts for lakes where 

nutrient concentrations may be high but algal production is constrained by low 

autotrophic potential (fast flushing and low critical depth). Another important advantage 

of this approach is that chlorophyll concentrations are directly related to a number of 

factors that have a direct effect on the water body’s capability to serve designated uses. 

These include: (1) taste/odor/toxicity effects arising from algal blooms in reservoirs used 

as drinking water sources, (2) reductions in water column transparency diminishing 

swimming and other recreational uses during algal blooms, and (3) effects of hypoxia on 

recreational fisheries and biodiversity arising from enhanced algal production. 

We have expressed candidate nutrient criteria as both TP and Chl-a 

concentrations in response to an expectation that EPA may require that criteria be 

expressed as both as nutrient concentrations and as effects-based variables (SD and/or 

Chl-a). However, we believe that candidate criteria expressed as Chl-a concentrations 

of Table 8 provide a more appropriate basis for implementation than those expressed 

as TP concentrations. 
35

0.00

5.00

10.00

15.00

20.00

25.00

30.00

0.000 0.010 0.020 0.030 0.040 0.050 0.060

TP (mg/L)

Chl-a (ug/L)

Eco 9

Eco 11

Eco 14

Figure 9. Lake-median (April – October) Chl-a vs. TP concentrations, by ecoregion, for 

the Virginia impoundments used in this study. Three lakes with Chl-a medians > 50 μg/L 

(outliers) are not represented. 
36

Nutrient Requirements for Fisheries: Perspective and Recommendations

The committee is recommending candidate criteria for fishery recreation and 

aquatic life in recognition of DEQ’s obligation to develop freshwater nutrient criteria in a 

manner that is consistent with EPA’s requirements and timeline. Based on analyses 

described above, the Committee suggests candidate criteria that would be protective of 

fishery recreation and aquatic life in the state’s impoundments (Table 8). We believe 

these criteria are derived from the available data via a thorough analysis using 

defensible methods. The candidate criteria are recommended expecting that DEQ will 

seek to balance the nutrient requirements of recreational fisheries against those of other 

potential uses, including contact recreation and public water supplies, in defining 

nutrient criteria for implementation, and that DEQ’s implementation would include a 

systematic review and evaluation, as discussed below. 

However, when looking at the analyses from a scientific perspective, the 

committee has reservations. For the following reasons and from a scientific perspective, 

we consider the available data to be less than an ideal basis for the analyses 

performed: 

• VDGIF biologists’ recreational fishery assessments, although based on 

professional knowledge of fish populations in each reservoir, are subjective. If 

available, data quantifying populations of recreational fishes and other aquatic 

species that represent a number of impoundments and are comparable across 

impoundments would be more desirable as a basis for conducting this task. 

• Because some impoundments considered in the analysis are represented by 

relatively small numbers of observations, available data are not capable of 

characterizing water quality conditions with a high level of certainty. Most months 

for most lakes are represented only by one observation collected during a single 

year, and thus do not represent interannual variability. Lake nutrient conditions 

can change both throughout any given year and from year to year as a result of 

climatic variations and other factors. 

• Because examples are few within the state’s reservoirs where fisheries are 

known to be impaired due to nutrient overenrichment, the available data do not 

allow for a precise definition of the criteria. 

In the committee’s view, the candidate criteria expressed as Chl-a concentrations 

(Table 8) provide a more appropriate basis for implementation than those expressed as 

TP concentrations. 

The committee recommends that DEQ’s implementation of nutrient criteria 

should include a process that embodies systematic review, evaluation, and refinement 

as recommended by the our July 2004 report (AAC, 2004). We recommend that 

components of that review and evaluation process for lakes and reservoirs should 

include: 
37

• Maintenance of a consistent monitoring approach and development of a more 

complete data record with improved utility for characterizing Virginia lakes’ 

responses to nutrient inputs, which can be used to aid the process of criteria 

evaluation and refinement. 

• A systematic method for developing an improved descriptive database for the 

state’s impoundments. Availability of such a database can be expected to 

improve DEQ’s ability to categorize impoundments based on differences in 

response to nutrient inputs and to understand underlying reasons for those 

differences. Such a database would include information on waterbody usage and 

morphometric/physiographic features (such as surface area, volume, mean 

depth, watershed areas, surface water inflows, and retention time) that influence 

response to nutrient inputs. 

• A process of responding to numeric criteria violations that includes an evaluation 

of whether or not the water body in question is serving its designated use(s), 

combined with an associated process for making similar determinations in water 

bodies that are not in violation of numeric criteria. 

• Review and evaluation of the number and location of monitoring points within 

individual lakes, including development and/or refinement of a rationale for each 

monitoring point’s placement and documentation of that rationale so as to enable 

the process of criteria review, evaluation, and refinement to consider spatial 

variability within impoundments.
38

II. Rivers and Streams

Analysis of USGS and EPA Studies 

Water Quality Academic Committee Report on the Utility of Ongoing Studies in Virginia 

and Neighboring States for Nutrient Criteria Development for Wadeable Streams, 

by Leonard Smock 

Introduction

As part of the process of developing nutrient criteria for wadeable streams, the 

Virginia Department of Environmental Quality (DEQ) determined that it would be useful 

to identify existing or planned studies from neighboring states that might assist Virginia 

in the development of its nutrient criteria. Identifying data or studies focused on the 

relationships between nutrient concentrations and appropriate response variables from 

states that share nutrient ecoregions with Virginia could greatly assist and expedite 

DEQ’s nutrient criteria development process. 

The Academic Advisory Committee was requested to review several ongoing 

studies, identified by DEQ, to determine their potential for aiding the development of 

Virginia’s nutrient criteria. These studies approach the problem with a focus on using 

periphyton as the primary response variable affected by varying nutrient concentrations 

in streams. The studies are reviewed here in terms of their proposed objectives, 

appropriateness of methodologies, data to be produced, and projected outcomes. The 

primary focus of this review thus is to determine the potential value and timeliness of 

these studies in assisting DEQ with the development of nutrient criteria for wadeable 

streams in Virginia. 

Maryland-USGS and Pennsylvania-USGS: Efforts to Develop Response-Based 

Nutrient Criteria 

Overview

Two of the efforts that hold promise for providing useful information are 

coordinated proposals to the EPA by Maryland and Pennsylvania. The initial proposal 

was developed jointly by the Maryland Department of the Environment and the 

Pennsylvania District of the US Geological Survey, titled “A Regional Approach to the 

Development of a Response-Based Nutrient Criterion for Wadeable Streams in Nutrient 

Ecoregions IX, XI, & XIV” (Appendix D). This proposal was funded, with field work 

having been completed during the summer 2004. Samples are presently being 

processed. The Pennsylvania Department of Environmental Protection and the USGS 

subsequently submitted a similar proposal to EPA, titled “A Response-Based Approach 

for Development of Nutrient Criteria in the Mid-Atlantic States” (Appendix E). That work 

also has been funded, with the intent of effectively doubling the sampling effort of the 
39

Maryland-USGS project. Field work for this project is scheduled for completion during 

the summer of 2005. Although data from both projects could be analyzed separately, it 

should be the intent of the agencies to eventually pool their data to increase the 

statistical robustness of their analyses. 

Objectives and Methodology

The overall objective of the two studies is to use an effects-based approach, 

rather than a reference condition approach, to develop nutrient criteria for wadeable 

streams in three nutrient ecoregions shared by Virginia, Maryland, Pennsylvania and 

West Virginia. The studies focus on the linkage of nutrient concentrations with 

chlorophyll-a and minimum dissolved oxygen (DO) concentrations, as well as 

periphyton community attributes. The projected outcome of the studies is the 

establishment of threshold nutrient concentrations that result in a desired minimum DO 

concentration for streams in each of the three nutrient ecoregions. These thresholds 

then could be used either to support EPA’s suggested nutrient criteria based on 

reference conditions or as an alternative to EPA’s approach and threshold 

concentrations. 

Using information in the PA-USGS proposal, which refers to the number of 

streams to be sampled in both studies, there would be 24 streams sampled for each 

study, for a total of 48 streams across the three ecoregions. The MD-USGS work this 

past summer, however, resulted in only 15 streams being sampled and thus something 

less than the projected 48 streams will be included in the overall data base. The 

ecoregions that are the focus of the study are Nutrient Ecoregions IX: Southeastern 

temperate forested hills and plains; XI: Central and eastern forested uplands; and XIV: 

Eastern coastal plain. Site selection is based on a stratified random design, with the 

three strata being ecoregion, stream order (2nd through 4th order), and nutrient 

environment (low, medium and high concentrations). All sampling is conducted during 

the summer months in order to maximize the likely response of periphyton and DO to 

nutrients. 

The focus of the studies is on the effect of both nitrogen and phosphorus 

concentrations on the response variables. Appropriate USGS and EPA sampling, 

sample analysis, and QA/QC protocols are being followed. Single grab samples of 

water are collected and analyzed from each stream to determine the concentrations of 

the various forms of nitrogen and phosphorus, other standard water quality parameters, 

and total organic carbon (TOC), the latter to provide an indication of water column DO 

demand. Data on DO concentrations in each stream are derived using continuous 

recorders over a 48-hour period, allowing the determination of nighttime, minimum DO 

concentrations. Benthic chlorophyll-a concentrations are used as surrogates for 

periphyton biomass. Data also are collected at each stream on periphyton community 

metrics, including species composition of the diatom community. 
40

Analysis of projected outcomes of the studies

The three nutrient ecoregions included in these studies cover a large majority of 

the streams in the Commonwealth, and thus, the studies have the potential for broad 

utility to DEQ. One caveat to this point is that the assumption must be made that there 

is sufficient homogeneity within each ecoregion such that the periphyton communities of 

streams across these geographically rather broad ecoregions react in the same way to 

nutrient concentrations as they do in Virginia. These studies likely will not be sufficiently 

robust to test that assumption, but this probably will not be enough of a problem that it 

limits the usefulness of the studies to Virginia. 

The general design of the studies may provide useful information that could 

assist in the development of nutrient criteria. One potential problem, however, concerns 

sampling replication. Only one sample per stream is being taken for the determination 

of nutrient and chlorophyll-a concentrations, raising the question of how representative 

a sample will be of conditions in the stream. In addition, the grab sample will only 

provide information on existing nutrient concentrations, with no indication of the nutrient 

conditions under which the periphyton grew. These limitations may impact the analysis 

of the relationship between nutrient concentrations and periphyton growth. 

The number of streams to be sampled also will place limitations on the ability of 

the studies to fully reach their goals. The stratified random sampling design indicates 

that there will be three strata (ecoregion, stream order, and nutrient regime), each with 

three categories. Only when data from the two studies are pooled may there be 

sufficient replication of the test categories. The PA-USGS sampling effort for this 

coming year needs to sample as many streams as possible. 

Data analysis focuses on determining the nutrient concentration (either N or P) 

that results in a DO concentration of 5 mg/L, the water quality standard for DO. A 

general problem with the approach of these studies is that they assume that the 

response variables, including DO, are directly reacting to nutrient concentrations. This 

is a tenuous assumption. The proposals do attempt to take into account in the data 

analysis the problem that periphyton growth may be limited by some other factor. For 

example, since many of the low order streams to be sampled probably have partial to 

full canopy cover, it is very likely that during the summer, light will be the factor limiting 

periphyton growth in at least some, if not many, of the streams. Unless a nutrient is the 

limiting factor, differences in periphyton growth and DO concentrations among streams 

will not be in response to differences in nutrient concentrations. 

The proposed data analysis makes an attempt to alleviate this problem, but the 

analysis to be used seemingly employs circular reasoning. In essence, it pre-selects 

points by assuming that the lowest points on the plot of nutrient vs. DO concentrations 

are from streams that are nutrient limited and that other points are from streams where 

periphyton growth is limited by some other factor. Only the lowest points on the plot are 

then used in a regression analysis to mathematically express the nutrient-DO 

relationship. Thus, only those points that are presumed to define the relationship are 

used to quantify the relationship. Unfortunately, there will be no data to allow a 

determination if any of the streams are nutrient limited and thus appropriate for 
41

determining the nutrient-periphyton relationship. Defense of a nutrient criterion from this 

line of data analysis may be difficult. 

Since light is a likely limiting factor during the summer, it could be incorporated 

into the data analysis to remove its effects. Categorizing the extent of canopy cover 

(e.g., open, partial, and full) and using that information in a multiple regression may 

result in a more accurate determination of the nutrient-DO relationship. 

Another point to consider is that the study design and data analysis assume that 

the nighttime minimum DO concentration in a stream is a result of the DO demand 

placed on the stream by periphyton, both through respiration and their decay. Other 

sources of demand and reaeration are not incorporated into the study. The 

concentration of TOC in the water column will be determined, but there is no 

consideration of benthic organic matter, which in shallow streams usually accounts for a 

greater proportion of the DO demand than does organic carbon in the water column. In 

addition, variability in reaeration rates among streams will not be taken into account; 

differences in stream geomorphology and hydrology can have a marked effect on 

reaeration and hence on the minimum DO concentration in a stream. 

The above real and potential problems with the proposed data analysis may limit 

the potential value to DEQ of this aspect of the studies. The studies will provide far 

better information than presently exist on the relationship of nutrients with DO and 

periphyton growth in wadeable streams. The data thus will provide a good next step in 

the development of the criteria, but the limitations of the studies must be fully 

recognized. 

The data on periphyton community structure and species composition will be 

useful for the later development of a periphyton IBI that could be correlated with the 

general nutrient environment and eutrophication status of the streams. Much highly 

useful data will be made available. It is possible that this information, coupled with the 

information derived from The Academy of Natural Sciences-DEQ study, will provide a 

mechanism to classify streams based on their eutrophication status, which could lead to 

better establishment of a nutrient-periphyton relationship and thus nutrient criteria. 

The Academy of Natural Sciences–Virginia DEQ Study on Periphyton-Nutrient 

Relationships 

Overview

The Philadelphia Academy of Natural Sciences and DEQ collaborated on a 

proposal (Appendix F) to EPA’s Environmental Monitoring and Assessment Program 

(EMAP) for funding of a proposal titled “The Development of an Algae-based Water 

Quality Monitoring Tool for Virginia Streams.” That proposal was funded, samples were 

collected during September through November 2004, and the samples are presently 

being processed. A second year of the project, however, has not been funded, and the 

limited number of streams that were sampled may decrease the usefulness of this 

project. 
42

Objective and Methodology

The study focuses on determining the relationship of algal biomass and species 

composition with nutrient conditions in wadeable streams in Virginia. The desire is to 

determine algal indicators of nutrient conditions that are appropriate for streams in 

Virginia, that can be incorporated into biomonitoring programs, and that ultimately can 

distinguish between different states of eutrophication. 

The study uses standard EPA sampling and laboratory protocols. Some number 

less than the 40-70 sites that were proposed for sampling were actually sampled during 

the 2004 field season. Streams were chosen from DEQ’s ongoing probabilistic 

biomonitoring sampling sites. Data will be available for each stream on chlorophyll-a, 

algal biomass, and species composition of the periphyton community. The proposal 

does not indicate that samples were collected to determine nutrient concentrations, but I 

assume that grab samples were taken for analysis of both N and P. Data analysis will 

focus on these parameters as well as periphyton community metrics such as species 

richness and dominance. 

Analysis of Projected Outcomes of the Studies

The study takes a broad effects-based approach to determining the relationship 

of aspects of the periphyton community to nutrient concentrations in wadeable streams. 

The general approach should provide useful information that could be used in the 

development of nutrient criteria, although no indication is given in the proposal as to 

how the data will be analyzed to this effect. At this time, it appears the study is focused 

on data collection, and the consideration of data analysis will be made at a later time. 

Another point is the underlying premise of the study: the extent of periphyton growth in 

the streams is directly dependent on nutrient concentrations, rather than some other 

factor such as light. Until this connection is demonstrated, there will be uncertainty in 

any conclusions on a nutrient-periphyton relationship drawn from the study. 

For the data to be useful in the development of nutrient criteria, DEQ will have to 

be able to adequately characterize the nutrient conditions in the streams or at least 

categorize the eutrophication status of the streams. The former may suffer unless there 

are more data on nutrient concentrations in the stream beyond a grab sample taken at 

the time of the periphyton sampling. The second approach would establish 

eutrophication categories defined by the extent of periphyton growth in streams. 

Nutrient concentrations associated with an unacceptable level of stream eutrophication, 

as defined by periphyton biomass or chlorophyll-a, would assist in setting nutrient 

criteria. The challenge here is to determine what is an acceptable vs. unacceptable 

level of periphyton in a stream. Data tying levels of periphyton growth to water quality 

parameters for which there exists a standard (e.g., DO) or to the degrading of other 

biological characteristics of the stream will be necessary for this approach to be of use 

for nutrient criteria development. 

The results from this study probably can be linked to results on the periphyton 

community in streams being generated from the MD-USGS and PA-USGS studies. 
43

There is sufficient similarity in the methodologies of the studies that the data could 

possibly be pooled, providing a larger data base for analysis. If additional sampling is 

conducted in the future for this or similar studies, attention should be placed on insuring 

compatibility with the existing data base. 
44

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Management 5:83-90. 
47

Appendix A. Lakes and Reservoirs Data Summary 

Table A-1. Correspondence of DEQ monitoring stations with lake names for analysis. 

Station ID DEQ Lake Name Station ID DEQ Lake Name 

1ALOH001.20 Abel Lake 6BLSR008.12 Corder Bottom Lake (Bark Camp L.) 

1ALOH002.20 Abel Lake 6BLSR008.32 Corder Bottom Lake (Bark Camp L.) 

1APOM013.02 Abel Lake 6BLSR008.52 Corder Bottom Lake (Bark Camp L.) 

1APOM013.41 Abel Lake 5ALZT000.12 Crystal Lake 

5ALTD005.10 Airfield Pond 1ALOM007.93 Curtis Lake 

2-XLW000.60 Amelia Lake 2-DSC005.85 Diascund Reservoir 

4ABAN012.46 Banister Lake 2-DSC005.91 Diascund Reservoir 

2-BRC001.55 Bear Creek Lake 2-DSC006.03 Diascund Reservoir 

1ABEE000.40 Beaverdam Creek Reservoir 2-DSC006.46 Diascund Reservoir 

1ABEE001.40 Beaverdam Creek Reservoir 2-DSC006.65 Diascund Reservoir 

7-BEA002.82 Beaverdam Reservoir (coast) 2-DSC007.09 Diascund Reservoir 

7-BEA005.82 Beaverdam Reservoir (coast) 2-DSC008.12 Diascund Reservoir 

7-BEE000.60 Beaverdam Reservoir (coast) 2-WLN007.36 Douthat Lake 

4AXKD003.34 Beaverdam Reservoir (Bedford) 1BNTH045.36 Elkhorn Lake 

4ASCB004.58 Bedford Reservoir 5AMHN053.00 Emporia Lake 

6BPLL012.79 Big Cherry Reservoir 5AMHN053.29 Emporia Lake 

6BPLL012.99 Big Cherry Reservoir 5AMHN057.92 Emporia Lake 

6BPLL013.59 Big Cherry Reservoir 4AGOB003.86 Fairy Stone Lake 

2-BRI010.78 Briery Creek Lake 2-FAC003.85 Falling Creek Reservoir 

2-BRI013.12 Briery Creek Lake 2-CFK004.34 Fluvanna Ruritan Lake 

4APLP000.45 Brookneal Reservoir 5ACDR000.30 Fort Pickett Reservoir 

5ARDC007.30 Brunswick County Lake 5ANTW127.14 Fort Pickett Reservoir 

1ASOH006.66 Burke Lake 5ANTW128.67 Fort Pickett Reservoir 

1ASOH007.26 Burke Lake 5ADBS001.00 Game Refuge Lake 

4ATMA004.60 Burton Lake 9-PKC016.91 Gatewood Reservoir 

9-NEW129.80 Byllesby Reservoir 9-PKC017.71 Gatewood Reservoir 

9-NEW132.86 Byllesby Reservoir 2-XEP000.44 Goodwin Lake 

4ACRV006.19 Carvin Cove Reservoir 1AGOO003.82 Goose Creek Reservoir 

4ACRR008.32 Cherrystone Lake 1AGOO004.89 Goose Creek Reservoir 

2-CHK024.07 Chickahominy Lake 2-GRA000.40 Graham Reservoir 

2-CHK025.15 Chickahominy Lake 5AGTC009.94 Great Creek Reservoir 

2-CHK026.94 Chickahominy Lake 5AGTC011.35 Great Creek Reservoir 

2-CHK029.54 Chickahominy Lake 4AGEO011.38 Gretna Lake 

2-JCB000.80 Chris Green Lake 2-WER002.06 Harrison Lake 

9-NEW087.14 Claytor Lake 7-POQ005.72 Harwoods Mill Reservoir 

9-NEW089.34 Claytor Lake 7-POQ006.84 Harwoods Mill Reservoir 

9-NEW092.66 Claytor Lake 9-HGN001.06 Hogan Lake 

9-NEW098.32 Claytor Lake 9-HGN001.29 Hogan Lake 

9-NEW100.54 Claytor Lake 2-HOL001.05 Holiday Lake (Appomattox) 

9-PKC000.00 Claytor Lake 6CHUN004.76 Hungry Mother Lake 

9-PKC004.16 Claytor Lake 6CHUN005.24 Hungry Mother Lake 

4AHTA003.26 Conner Lake 6CHUN006.13 Hungry Mother Lake 
48

Station ID DEQ Lake Name Station ID DEQ Lake Name 

6ACNR000.00 John W. Flannagan Reservoir 1ABRU017.58 Lake Manassas 

6ACNR001.03 John W. Flannagan Reservoir 1ABRU018.78 Lake Manassas 

6APNR001.82 John W. Flannagan Reservoir 2-LKM001.16 Lake Maury 

6APNR007.67 John W. Flannagan Reservoir 2-LMD000.02 Lake Meade 

6APNR008.15 John W. Flannagan Reservoir 2-LMD000.20 Lake Meade 

4AGRA003.22 Kerr Reservoir 2-LMD000.41 Lake Meade 

4AROA022.52 Kerr Reservoir 2-LMD001.41 Lake Meade 

4AROA028.04 Kerr Reservoir 2-LMD002.07 Lake Meade 

4AROA032.42 Kerr Reservoir 2-JKS044.60 Lake Moomaw 

4AROA043.14 Kerr Reservoir 2-JKS046.40 Lake Moomaw 

4ASRN005.14 Keysville Reservoir 2-JKS048.90 Lake Moomaw 

2-SIN000.44 Lake Albemarle 2-JKS053.48 Lake Moomaw 

8-CON002.32 Lake Anna 2-XLU000.10 Lake Nelson 

8-ELK003.35 Lake Anna 8-CLC003.48 Lake Orange 

8-GMC000.23 Lake Anna 8-CLC004.28 Lake Orange 

8-NAR034.92 Lake Anna 3-MTN025.17 Lake Pelham 

8-NAR037.22 Lake Anna 2-MIC002.44 Lake Powell 

8-NAR043.00 Lake Anna 2-MIC002.84 Lake Powell 

8-NAR044.68 Lake Anna 2-LPR000.02 Lake Prince 

8-NAR047.17 Lake Anna 2-LPR007.55 Lake Prince 

8-NAR047.69 Lake Anna 2-NWB006.56 Lake Prince 

8-NAR054.17 Lake Anna 2-XMW000.72 Lake Robertson 

1BDRI005.55 Lake Arrowhead 7-LAS000.06 Lake Smith 

2-NWB007.04 Lake Burnt Mills 7-LAS001.03 Lake Smith 

2-NWB009.48 Lake Burnt Mills 7-LAS001.44 Lake Smith 

2-NWB010.54 Lake Burnt Mills 2-BRO003.55 Lake Taylor 

2-APP020.23 Lake Chesdin 2-BRO003.95 Lake Taylor 

2-APP023.27 Lake Chesdin 7-LCC005.40 Lake Wright 

2-APP026.67 Lake Chesdin 2-SFT006.10 Lakeview Reservoir 

2-APP028.58 Lake Chesdin 6CLAU001.84 Laurel Bed Lake 

2-APP029.23 Lake Chesdin 6CLAU003.05 Laurel Bed Lake 

2-APP061.02 Lake Chesdin 2-LHR000.96 Lee Hall Reservoir 

2-LCN000.20 Lake Cohoon 2-LHR001.76 Lee Hall Reservoir 

2-LMD004.35 Lake Cohoon 2-LHR002.56 Lee Hall Reservoir 

2-LMD005.55 Lake Cohoon 4AROA140.66 Leesville Lake 

4AROA000.00 Lake Gaston 4AROA145.34 Leesville Lake 

4AROA004.54 Lake Gaston 4AROA153.59 Leesville Lake 

4AROA008.66 Lake Gaston 2-LTL001.20 Little Creek Reservoir (2LTL) 

4AMES007.54 Lake Gordon 2-LTL001.60 Little Creek Reservoir (2LTL) 

6BPWL024.64 Lake Keokee 2-LTL002.46 Little Creek Reservoir (2LTL) 

6BPWL025.20 Lake Keokee 7-LTR000.04 Little Creek Reservoir (7LTR) 

6BPWL025.32 Lake Keokee 7-LTR000.95 Little Creek Reservoir (7LTR) 

2-LKK000.80 Lake Kilby 9-LRV000.44 Little River Reservoir (New) 

2-PKC001.84 Lake Kilby 2-LSL000.16 Lone Star Lake F 

7-LAK000.34 Lake Lawson 2-LSL000.04 Lone Star Lake G 

7-LAK000.41 Lake Lawson 2-LSL000.20 Lone Star Lake I 

1ABRU016.28 Lake Manassas 4ABAU005.34 Martinsville Reservoir 
49

 

Station ID DEQ Lake Name Station ID DEQ Lake Name 

5AMDT004.94 Modest Creek Reservoir 4AROA158.22 Smith Mountain Lake 

3-MOT000.39 Motts Run Reservoir 4AROA163.76 Smith Mountain Lake 

3-MOT001.19 Motts Run Reservoir 4AROA167.34 Smith Mountain Lake 

3-MTN028.68 Mountain Run Lake 4AROA175.63 Smith Mountain Lake 

7-MTL000.20 Mt. Trashmore Lake 4AROA180.21 Smith Mountain Lake 

8-NIR012.99 Ni River Reservoir 4AROA183.64 Smith Mountain Lake 

8-NIR016.09 Ni River Reservoir 4AROA192.94 Smith Mountain Lake 

8-PNB000.05 Ni River Reservoir 4AROA196.05 Smith Mountain Lake 

1BBKN001.81 North Fork Back Creek 4AWTH000.40 Smith Mountain Lake 

6APNK001.26 North Fork Pound Reservoir 2-RRS003.59 South Fork Rivanna Reservoir 

6APNK001.87 North Fork Pound Reservoir 2-RRS005.62 South Fork Rivanna Reservoir 

6APNK002.08 North Fork Pound Reservoir 6CSFH062.93 South Holston Lake 

5ANTW143.06 Nottoway Falls Lake 6CSFH066.16 South Holston Lake 

5ANTW145.30 Nottoway Falls Lake 6CSFH070.80 South Holston Lake 

2-LDJ000.60 Nottoway Lake 2-SPE000.17 Speights Run Lake 

2-POL017.59 Pedlar River Reservoir 2-SPE001.18 Speights Run Lake 

4ASRE046.90 Philpott Reservoir 2-SHS001.00 Stonehouse Creek Reservoir 

4ASRE048.98 Philpott Reservoir 2-MNR014.50 Sugar Hollow Reservoir 

4ASRE052.31 Philpott Reservoir 2-SFT022.14 Swift Creek Lake 

4ASRE056.06 Philpott Reservoir 2-DYC000.19 Swift Creek Reservoir 

2-STG000.21 Powhatan Lake - Lower 2-SFT031.08 Swift Creek Reservoir 

2-STG000.91 Powhatan Lake - Upper 2-SFT031.28 Swift Creek Reservoir 

2-SDY011.08 Prince Edward Lake 2-SFT032.53 Swift Creek Reservoir 

4ARFK000.20 Roaring Fork Reservoir 2-SFT033.42 Swift Creek Reservoir 

9-XBL000.20 Rural Retreat Lake 2-SFT034.38 Swift Creek Reservoir 

9-XBL000.98 Rural Retreat Lake 1BSKD003.18 Switzer Lake 

9-XBL001.02 Rural Retreat Lake 4ADAN196.09 Talbott Reservoir 

2-MBN000.96 Sandy River Reservoir 2-TRO000.40 Third Branch Lake 

2-SDY004.27 Sandy River Reservoir 2-TRH000.40 Thrashers Creek Reservoir 

2-SDY005.85 Sandy River Reservoir 2-TOT001.01 Totier Creek Reservoir 

1BCNG003.13 Shenandoah Lake 4ADAN187.94 Townes Reservoir 

2-TBM000.92 Slate River Dam 4ATWT009.63 Twittys Creek 

1AAUA012.15 Smith Lake 8-QEN007.02 Waller Mill Reservoir 

1AAUA012.55 Smith Lake 8-QEN007.22 Waller Mill Reservoir 

1ABED000.19 Smith Lake 8-QEN008.02 Waller Mill Reservoir 

4ABSA000.62 Smith Mountain Lake 8-QEN008.58 Waller Mill Reservoir 

4ABWR002.50 Smith Mountain Lake 2-NWB002.93 Western Branch Reservoir 

4ABWR010.55 Smith Mountain Lake 2-NWB004.14 Western Branch Reservoir 

4ABWR017.42 Smith Mountain Lake 2-NWB004.67 Western Branch Reservoir 

4ACCK001.80 Smith Mountain Lake 2-NWB006.06 Western Branch Reservoir 

4ACOA000.60 Smith Mountain Lake 6BXAR000.69 Wise Lake 

4AGIL002.39 Smith Mountain Lake 

 
50

Table A-2. Lake median values derived by 4 methodsa and ecoregion locations. 

Lake 

Chl-aS1 Chl-aS2Chl-aS3Chl-aMo SDS1SDS2 SDS3 SDMo 

Abel Lake 2.56 1.93 1.93 2.17 

Banister Lake 5.75 4.66 4.66 0.93 0.90 0.90 

Beaverdam Res. (Bedford) 4.66 4.18 4.10 4.18 2.15 2.00 2.00 2.08

Big Cherry Reservoir 3.72 2.60 3.27 

Briery Creek Lake 10.71 9.54 10.65 12.13 1.38 1.40 1.40 1.35

Brookneal Reservoir 4.27 4.10 5.17 4.27 1.78 1.70 1.45 1.80

Byllesby Reservoir 0.99 1.53 1.17 1.30 0.73 0.70 0.68 0.71

Carvin Cove Reservoir 4.47 4.07 4.07 4.47 0.95 1.00 1.00 1.00

Cherrystone Lake 6.12 5.16 5.16 4.54 1.63 1.60 1.60 1.70

Chickahominy Lake 11.49 15.98 13.61 15.71 0.95 0.93 0.85 0.88

Claytor Lake 6.59 6.28 5.81 6.21 1.90 1.75 1.73 1.75

Crystal Lake 10.06 10.59 10.59 8.85 0.70 0.55 0.55 0.63

Curtis Lake 52.47 54.96 0.60 0.60 

Diascund Reservoir 19.44 19.27 21.75 24.59 0.88 0.88 1.00 0.80

Douthat Lake 2.57 2.07 2.10 2.63 3.74 3.81 3.64 3.68

Elkhorn Lake 3.32 2.20 2.20 2.33 3.11 3.24 3.24 

Emporia Lake 9.35 5.85 6.84 6.73 0.83 0.83 0.80 0.85

Fairy Stone Lake 1.80 1.80 1.90 1.90 1.40 

Falling Creek Reservoir 10.64 10.39 9.98 10.57 1.03 0.93 0.93 1.00

Fort Pickett Reservoir 15.99 14.94 15.08 15.22 0.70 0.68 0.65 

Gatewood Reservoir 2.28 2.28 2.38 2.33 2.55 2.55 2.55 2.40

Graham Reservoir 3.67 4.31 3.48 2.94 1.60 1.65 1.70 1.60

Great Creek Reservoir 4.90 8.67 3.50 3.94 1.20 1.25 1.33 1.30

Gretna Lake 5.56 4.71 4.65 0.80 0.75 

Harrison Lake 14.16 3.01 3.01 3.14 0.65 0.55 0.55 

Harwoods Mill Res. 8.68 9.22 7.69 8.29 1.30 1.23 1.25 1.20

Hungry Mother Lake 4.03 3.70 3.70 3.93 1.95 2.03 2.06 2.20

John W. Flannagan Res. 2.83 3.69 2.71 2.85 4.24 4.44 3.73 3.91

Kerr Reservoir 11.03 10.75 10.17 9.66 1.35 1.40 

Keysville Reservoir 21.12 0.95 0.95 0.95 

Lake Albemarle 29.86 24.51 24.34 18.38 1.47 1.38 1.51 1.32

Lake Anna 2.65 2.65 2.32 1.45 1.45 1.45 

Lake Burnt Mills 17.80 22.41 22.41 1.04 1.14 1.14 1.14

Lake Chesdin 17.56 17.73 17.73 17.38 0.78 0.85 0.85 0.85

Lake Cohoon 33.00 33.00 25.41 29.05 0.71 0.79 0.81 0.70

Lake Frederick 4.83 4.77 4.58 4.78 2.68 2.74 2.63 2.75

Lake Gaston 4.24 5.03 4.74 1.50 1.30 

Lake Kilby 31.41 26.83 23.09 27.63 1.03 1.10 1.08 1.05

Lake Meade 24.91 25.91 26.21 20.29 1.05 1.01 1.06 1.10

Lake Moomaw 3.46 3.13 2.66 2.94 2.68 2.45 2.50 2.75

Lake Nelson 14.68 13.07 13.07 7.19 1.62 1.65 1.65 1.40

Lake Pelham 4.36 3.89 3.98 0.88 0.60 0.60 

Lake Prince 12.10 13.33 13.40 11.19 1.25 1.38 1.45 1.50
51

Lake 

Chl-aS1 Chl-aS2Chl-aS3Chl-aMo SDS1SDS2 SDS3 SDMo 

Lake Robertson 10.93 2.76 3.29 3.29 2.44 3.38 3.00 2.85

Lake Smith 68.76 59.74 63.77 0.50 0.50 

Lee Hall Reservoir 17.76 12.00 12.37 14.17 0.85 0.85 0.95 0.80

Leesville Lake 4.24 3.69 3.67 4.13 1.50 1.43 1.23 1.25

Little Creek Reservoir 67.37 67.03 82.96 0.50 0.50 

Martinsville Reservoir 3.31 3.36 1.05 1.05 1.15 

Modest Creek Reservoir 16.95 6.68 6.68 1.05 1.00 1.00 

Motts Run Reservoir 8.00 7.52 7.52 6.05 

Mountain Run Lake 10.38 10.38 11.56 1.15 1.13 1.20 

North Fork Pound Res. 2.61 2.24 2.47 2.15 

Nottoway Falls Lake 7.10 12.68 12.68 6.48 0.83 0.90 0.90 0.75

Pedlar River Reservoir 8.37 4.23 3.41 2.92 2.43 2.55 2.48 2.35

Philpott Reservoir 2.51 2.33 2.35 1.70 2.60 2.63 2.60 2.70

Roaring Fork Reservoir 3.50 3.01 3.01 3.54 1.38 1.38 1.38 1.40

Shenandoah Lake 11.88 11.15 12.04 0.65 0.60 

Slate River Dam 6.45 3.88 3.88 4.24 0.61 0.65 0.65 0.60

Smith Mountain Lake 2.60 2.50 2.53 2.61 2.30 2.18 2.23 2.20

South Fork Rivanna Res. 6.25 6.25 5.63 5.62 1.46 1.46 1.35 1.53

South Holston Lake 10.44 9.53 8.26 9.24 1.70 1.64 1.49 1.64

Stonehouse Creek Res. 35.00 34.39 34.39 25.89 0.65 0.73 0.73 

Sugar Hollow Reservoir 3.79 3.77 3.78 3.75 2.63 2.61 2.60 2.63

Swift Creek Lake 14.47 17.60 16.43 0.60 0.60 0.60 

Swift Creek Reservoir 14.23 12.93 14.02 15.47 1.00 1.00 1.05 1.03

Switzer Lake 1.87 2.06 1.78 1.48 7.83 7.60 7.88 7.90

Thrashers Creek Res. 11.39 12.57 12.57 13.53 1.00 1.10 1.10 

Totier Creek Reservoir 7.19 8.54 8.54 8.55 1.08 0.93 0.93 0.90

Waller Mill Reservoir 6.49 6.88 6.88 6.86 1.40 1.53 1.50 1.40

Western Branch Res. 10.30 8.76 8.64 8.65 1.28 1.45 1.33 1.25

 

Lake 

TNS1 TNS2TNS3TNMo TPS1TPS2 TPS3 TPMo 

Abel Lake 0.53 0.56 0.53 0.53 0.015 0.020 0.020 0.020

Banister Lake 0.30 0.35 0.35 0.30 0.023 0.025 0.025 0.020

Beaverdam Res. (Bedford) 0.43 0.40 0.38 0.40 0.010 0.010 0.015 0.010

Big Cherry Reservoir 0.25 0.33 0.30 0.010 0.010 0.010 

Briery Creek Lake 0.47 0.45 0.45 0.47 0.015 0.010 0.010 0.013

Brookneal Reservoir 0.35 0.35 0.35 0.35 0.028 0.028 0.025 0.030

Byllesby Reservoir 0.77 0.80 0.79 0.79 0.045 0.035 0.045 0.040

Carvin Cove Reservoir 0.51 0.46 0.46 0.55 0.020 0.020 0.020 0.020

Cherrystone Lake 0.49 0.48 0.48 0.45 0.020 0.020 0.020 0.020

Chickahominy Lake 0.81 0.84 0.81 0.75 0.060 0.063 0.065 0.060

Claytor Lake 0.59 0.60 0.64 0.59 0.020 0.020 0.020 0.020

Crystal Lake 0.69 0.74 0.74 0.74 0.050 0.060 0.060 0.055

Curtis Lake 1.60 1.61 1.28 0.040 0.040 0.040 

Diascund Reservoir 0.75 0.58 0.63 0.60 0.030 0.030 0.030 0.030
52

Lake 

TNS1 TNS2TNS3TNMo TPS1TPS2 TPS3 TPMo 

Douthat Lake 0.29 0.26 0.28 0.25 0.010 0.010 0.010 0.010

Elkhorn Lake 0.24 0.24 0.24 0.25 0.010 0.010 0.010 0.010

Emporia Lake 0.34 0.35 0.36 0.36 0.025 0.025 0.023 0.025

Fairy Stone Lake 0.31 0.22 0.32 0.010 0.010 0.010 

Falling Creek Reservoir 0.61 0.64 0.64 0.62 0.035 0.035 0.035 0.030

Fort Pickett Reservoir 0.58 0.63 0.60 0.56 0.050 0.050 0.045 0.050

Gatewood Reservoir 0.35 0.35 0.35 0.35 0.010 0.010 0.010 0.010

Graham Reservoir 0.38 0.38 0.38 0.35 0.018 0.015 0.015 0.020

Great Creek Reservoir 0.30 0.38 0.30 0.35 0.020 0.023 0.023 0.020

Gretna Lake 0.52 0.53 0.42 0.020 0.020 0.020 

Harrison Lake 1.10 1.05 1.05 0.050 0.045 0.045 

Harwoods Mill Res. 0.58 0.55 0.53 0.55 0.025 0.025 0.025 0.025

Hungry Mother Lake 0.31 0.32 0.30 0.29 0.010 0.013 0.010 0.010

John W. Flannagan Res. 0.34 0.32 0.36 0.35 0.010 0.010 0.010 0.010

Kerr Reservoir 0.51 0.46 0.46 0.47 0.025 0.030 0.028 0.030

Keysville Reservoir 0.93 0.75 0.80 0.85 0.043 0.035 0.035 0.040

Lake Albemarle 0.95 0.92 0.90 0.95 0.028 0.028 0.028 0.030

Lake Anna 0.44 0.43 0.45 0.45 0.010 0.010 0.010 0.010

Lake Burnt Mills 0.69 0.68 0.68 0.025 0.030 0.030 0.030

Lake Chesdin 0.58 0.60 0.60 0.65 0.045 0.045 0.040 0.040

Lake Cohoon 0.92 0.95 0.95 0.91 0.048 0.045 0.050 0.050

Lake Frederick 0.55 0.55 0.55 0.55 0.013 0.010 0.010 0.010

Lake Gaston 0.42 0.43 0.43 0.020 0.020 0.020 

Lake Kilby 0.72 0.70 0.66 0.71 0.043 0.045 0.040 0.045

Lake Meade 0.80 0.80 0.75 0.85 0.035 0.040 0.040 0.040

Lake Moomaw 0.35 0.34 0.35 0.35 0.010 0.010 0.010 0.010

Lake Nelson 0.88 0.88 0.88 0.95 0.035 0.033 0.033 0.030

Lake Pelham 0.68 0.75 0.75 0.65 0.025 0.025 0.028 0.020

Lake Prince 0.46 0.46 0.47 0.46 0.025 0.028 0.030 0.020

Lake Robertson 0.45 0.38 0.35 0.35 0.018 0.015 0.015 0.020

Lake Smith 1.45 1.48 1.40 0.165 0.145 0.140 

Lee Hall Reservoir 0.70 0.70 0.65 0.75 0.030 0.033 0.033 0.030

Leesville Lake 0.58 0.58 0.60 0.60 0.020 0.020 0.020 0.020

Little Creek Reservoir 1.65 1.55 1.65 0.120 0.135 0.150 

Martinsville Reservoir 0.51 0.47 0.44 0.50 0.010 0.010 0.010 0.010

Modest Creek Reservoir 0.75 0.68 0.68 0.75 0.028 0.028 0.025 0.030

Motts Run Reservoir 0.70 0.59 0.59 0.65 0.013 0.015 0.015 0.015

Mountain Run Lake 0.65 0.80 0.80 0.70 0.025 0.025 0.023 0.025

North Fork Pound Res. 0.16 0.21 0.16 0.16 0.010 0.010 0.010 0.010

Nottoway Falls Lake 0.56 0.55 0.55 0.55 0.038 0.038 0.038 0.040

Pedlar River Reservoir 0.28 0.31 0.25 0.25 0.010 0.013 0.010 0.010

Philpott Reservoir 0.27 0.33 0.30 0.25 0.010 0.010 0.010 0.010

Roaring Fork Reservoir 0.35 0.30 0.30 0.35 0.020 0.018 0.018 0.020

Shenandoah Lake 0.65 0.68 0.65 0.030 0.028 0.030 

Slate River Dam 0.47 0.52 0.52 0.49 0.020 0.020 0.020 0.020
53

Lake TNS1 TNS2TNS3TNMo TPS1TPS2 TPS3 TPMo 

Douthat Lake 0.29 0.26 0.28 0.25 0.010 0.010 0.010 0.010

Elkhorn Lake 0.24 0.24 0.24 0.25 0.010 0.010 0.010 0.010

Emporia Lake 0.34 0.35 0.36 0.36 0.025 0.025 0.023 0.025

Fairy Stone Lake 0.31 0.22 0.32 0.010 0.010 0.010 

Falling Creek Reservoir 0.61 0.64 0.64 0.62 0.035 0.035 0.035 0.030

Fort Pickett Reservoir 0.58 0.63 0.60 0.56 0.050 0.050 0.045 0.050

Gatewood Reservoir 0.35 0.35 0.35 0.35 0.010 0.010 0.010 0.010

Graham Reservoir 0.38 0.38 0.38 0.35 0.018 0.015 0.015 0.020

Great Creek Reservoir 0.30 0.38 0.30 0.35 0.020 0.023 0.023 0.020

Gretna Lake 0.52 0.53 0.42 0.020 0.020 0.020 

Harrison Lake 1.10 1.05 1.05 0.050 0.045 0.045 

Harwoods Mill Res. 0.58 0.55 0.53 0.55 0.025 0.025 0.025 0.025

Hungry Mother Lake 0.31 0.32 0.30 0.29 0.010 0.013 0.010 0.010

John W. Flannagan Res. 0.34 0.32 0.36 0.35 0.010 0.010 0.010 0.010

Kerr Reservoir 0.51 0.46 0.46 0.47 0.025 0.030 0.028 0.030

Keysville Reservoir 0.93 0.75 0.80 0.85 0.043 0.035 0.035 0.040

Lake Albemarle 0.95 0.92 0.90 0.95 0.028 0.028 0.028 0.030

Lake Anna 0.44 0.43 0.45 0.45 0.010 0.010 0.010 0.010

Lake Burnt Mills 0.69 0.68 0.68 0.025 0.030 0.030 0.030

Lake Chesdin 0.58 0.60 0.60 0.65 0.045 0.045 0.040 0.040

Lake Cohoon 0.92 0.95 0.95 0.91 0.048 0.045 0.050 0.050

Lake Frederick 0.55 0.55 0.55 0.55 0.013 0.010 0.010 0.010

Lake Gaston 0.42 0.43 0.43 0.020 0.020 0.020 

Lake Kilby 0.72 0.70 0.66 0.71 0.043 0.045 0.040 0.045

Lake Meade 0.80 0.80 0.75 0.85 0.035 0.040 0.040 0.040

Lake Moomaw 0.35 0.34 0.35 0.35 0.010 0.010 0.010 0.010

Lake Nelson 0.88 0.88 0.88 0.95 0.035 0.033 0.033 0.030

Lake Pelham 0.68 0.75 0.75 0.65 0.025 0.025 0.028 0.020

Lake Prince 0.46 0.46 0.47 0.46 0.025 0.028 0.030 0.020

Lake Robertson 0.45 0.38 0.35 0.35 0.018 0.015 0.015 0.020

Lake Smith 1.45 1.48 1.40 0.165 0.145 0.140 

Lee Hall Reservoir 0.70 0.70 0.65 0.75 0.030 0.033 0.033 0.030

Leesville Lake 0.58 0.58 0.60 0.60 0.020 0.020 0.020 0.020

Little Creek Reservoir 1.65 1.55 1.65 0.120 0.135 0.150 

Martinsville Reservoir 0.51 0.47 0.44 0.50 0.010 0.010 0.010 0.010

Modest Creek Reservoir 0.75 0.68 0.68 0.75 0.028 0.028 0.025 0.030

Motts Run Reservoir 0.70 0.59 0.59 0.65 0.013 0.015 0.015 0.015

Mountain Run Lake 0.65 0.80 0.80 0.70 0.025 0.025 0.023 0.025

North Fork Pound Res. 0.16 0.21 0.16 0.16 0.010 0.010 0.010 0.010

Nottoway Falls Lake 0.56 0.55 0.55 0.55 0.038 0.038 0.038 0.040

Pedlar River Reservoir 0.28 0.31 0.25 0.25 0.010 0.013 0.010 0.010

Philpott Reservoir 0.27 0.33 0.30 0.25 0.010 0.010 0.010 0.010

Roaring Fork Reservoir 0.35 0.30 0.30 0.35 0.020 0.018 0.018 0.020

Shenandoah Lake 0.65 0.68 0.65 0.030 0.028 0.030 

Slate River Dam 0.47 0.52 0.52 0.49 0.020 0.020 0.020 0.020

Smith Mountain Lake 0.49 0.47 0.50 0.47 0.020 0.020 0.020 0.020

South Fork Rivanna Res. 0.44 0.44 0.51 0.45 0.023 0.023 0.030 0.025
54

Lake TNS1 TNS2TNS3TNMo TPS1TPS2 TPS3 TPMo 

South Holston Lake 0.47 0.52 0.54 0.47 0.023 0.023 0.023 0.020

Stonehouse Creek Res. 0.75 0.75 0.75 0.88 0.040 0.040 0.040 0.040

Sugar Hollow Reservoir 0.26 0.28 0.28 0.26 0.015 0.015 0.018 0.015

Swift Creek Lake 0.55 0.65 0.65 0.030 0.040 0.040 

Swift Creek Reservoir 0.65 0.63 0.58 0.60 0.025 0.023 0.028 0.020

Switzer Lake 0.67 0.63 0.62 0.65 0.010 0.010 0.010 0.010

Thrashers Creek Res. 0.34 0.35 0.35 0.36 0.028 0.035 0.035 0.020

Totier Creek Reservoir 1.07 1.05 1.05 1.08 0.030 0.033 0.033 0.030

Waller Mill Reservoir 0.50 0.48 0.45 0.45 0.020 0.020 0.020 0.020

Western Branch Res. 0.50 0.53 0.51 0.50 0.023 0.020 0.023 0.020

 

Lake TSSS1 TSSS2TSSS3TSSMo Ecoregion 

Abel Lake 3 3 3 9 

Banister Lake 10 6 6 6 9 

Beaverdam Res. (Bedford) 3 3 3 3 11 

Big Cherry Reservoir 3 3 3 11 

Briery Creek Lake 9 

Brookneal Reservoir 3 3 4 3 9 

Byllesby Reservoir 13 15 15 14 11 

Carvin Cove Reservoir 4 4 3 4 11 

Cherrystone Lake 4 3 3 3 9 

Chickahominy Lake 4 4 4 4 9 

Claytor Lake 3 4 3 3 11 

Crystal Lake 9 

Curtis Lake 6 6 5 9 

Diascund Reservoir 7 5 6 5 9 

Douthat Lake 3 3 3 3 11 

Elkhorn Lake 3 3 3 3 11 

Emporia Lake 7 7 7 7 9 

Fairy Stone Lake 11 

Falling Creek Reservoir 5 6 6 6 9 

Fort Pickett Reservoir 8 8 9 8 9 

Gatewood Reservoir 3 3 3 3 11 

Graham Reservoir 3 3 3 3 9 

Great Creek Reservoir 4 4 4 3 9 

Gretna Lake 9 

Harrison Lake 11 9 9 9 

Harwoods Mill Res. 3 3 3 3 14 

Hungry Mother Lake 3 3 3 3 11 

John W. Flannagan Res. 3 3 3 3 11 

Kerr Reservoir 8 6 6 9 

Keysville Reservoir 7 6 7 7 9 

Lake Albemarle 4 4 4 4 9 

Lake Anna 3 3 3 3 9 

Lake Burnt Mills 14 

Lake Chesdin 8 10 10 9 9 

Lake Cohoon 14 
55

Lake TSSS1 TSSS2TSSS3TSSMo Ecoregion 

Lake Frederick 3 3 3 3 11 

Lake Gaston 9 

Lake Kilby 14 

Lake Meade 4 4 4 4 14 

Lake Moomaw 3 3 3 3 11 

Lake Nelson 4 4 4 3 9 

Lake Pelham 5 6 6 5 9 

Lake Prince 3 3 3 3 14 

Lake Robertson 3 3 3 3 11 

Lake Smith 12 13 15 14 

Lee Hall Reservoir 7 6 6 6 14 

Leesville Lake 4 4 4 5 9 

Little Creek Reservoir 16 16 18 14 

Martinsville Reservoir 9 

Modest Creek Reservoir 5 6 6 6 9 

Motts Run Reservoir 3 3 9 

Mountain Run Lake 3 4 4 4 9 

North Fork Pound Res. 3 3 3 3 11 

Nottoway Falls Lake 7 8 8 7 9 

Pedlar River Reservoir 3 3 3 3 11 

Philpott Reservoir 3 3 3 3 9 

Roaring Fork Reservoir 3 3 3 3 9 

Shenandoah Lake 11 13 13 11 

Slate River Dam 9 

Smith Mountain Lake 3 3 3 3 9 

South Fork Rivanna Res. 9 

South Holston Lake 3 3 3 3 11 

Stonehouse Creek Res. 10 9 

Sugar Hollow Reservoir 3 3 3 3 11 

Swift Creek Lake 10 9 9 9 

Swift Creek Reservoir 5 5 5 5 9 

Switzer Lake 11 

Thrashers Creek Res. 9 

Totier Creek Reservoir 9 

Waller Mill Reservoir 4 3 3 3 9 

Western Branch Res. 4 3 4 3 14 

Note a: 

S1: Seasons defined as April – May, June – July, August – September, October; lake medians 

calculated as median of seasonal medians if 3 seasonal medians are present. 

S2: Seasons defined as equal-length periods: 4/1 – 5/23, 5/24 – 7/16, 7/17 – 9/7, and 9/8 – 

10/31; lake medians calculated as median of seasonal medians if 3 seasonal medians are 

present. 

S3: Seasons defined as April, May – June, July – August, September – October; lake medians 

calculated as median of seasonal medians if 3 seasonal medians are present. 

Mo: Lake medians calculated as median of monthly medians, April – October; if at least 6 of the 

7 months were represented by measured data. 
56

Table A-3. Number of observations per lake. 

Lake Chl-a SD TN TP Lake Chl-a SD TN TP

Abel Lake 14 2 17 17 Lake Arrowhead 1 

Airfield Pond 1 1 Lake Burnt Mills 15 18 15 18

Amelia Lake 1 1 1 Lake Chesdin 45 31 43 58

Banister Lake 8 5 6 6 Lake Cohoon 19 19 16 16

Bear Creek Lake 1 1 1 1 Lake Frederick 63 56 63 63

Beaverdam Crk Res. 2 2 Lake Gaston 26 22 27 27

Beaverdam Res. 13 11 14 14 Lake Gordon 1 

Bedford Res. 1 1 1 Lake Keokee 6 6

Big Cherry Res. 21 21 21 Lake Kilby 14 14 12 12

Briery Creek Lake 17 13 16 16 Lake Lawson 6 4 4 4

Brookneal Res. 7 7 7 7 Lake Manassas 3 3

Brunswick Cnty Lake 1 1 Lake Meade 34 34 35 35

Burke Lake 1 Lake Moomaw 91 84 135 136

Burton Lake 1 1 Lake Nelson 7 7 7 7

Byllesby Res. 8 6 7 7 Lake Orange 2 

Carvin Cove Res. 10 8 11 11 Lake Pelham 5 6 11 11

Cherrystone Lake 7 8 8 8 Lake Powell 2 2

Chickahominy Lake 23 17 21 21 Lake Prince 21 21 21 21

Claytor Lake 197 162 384 325 Lake Robertson 7 7 7 7

Conner Lake 1 Lake Smith 34 31 33 34

Corder Bottom Lake 3 3 3 Lake Taylor 2 

Crystal Lake 8 7 7 7 Lakeview Res. 1 1 1 1

Curtis Lake 7 9 12 12 Laurel Bed lake 2 3 3

Diascund Res. 17 16 16 15 Lee Hall Res. 18 21 21 21

Douthat Lake 7 6 7 7 Leesville Lake 69 73 107 108

Elkhorn Lake 7 5 7 7 Little Creek Res. 22 20 22 21

Emporia Lake 15 14 12 12 Little River Res. 1 2 2

Fairy Stone Lake 5 5 5 5 Martinsville Res. 5 5 7 7

Falling Creek Res. 8 8 7 7 Modest Creek Res. 7 6 9 9

Fort Pickett Res. 24 18 21 21 Motts Run Res. 11 2 13 13

Game Refuge Lake 2 1 1 Mountain Run Lake 5 7 11 11

Gatewood Res. 22 24 26 26 Ni River Res. 3 3 3

Goodwin Lake 2 1 1 1 North Fork Back Crk 1 

Goose Creek Res. 2 2 4 4 North Fork Pound Res. 21 25 25

Graham Res. 7 6 7 7 Nottoway Falls Lake 9 7 9 9

Great Creek Res. 9 8 9 9 Nottoway Lake 1 1 1 1

Gretna Lake 6 5 6 6 Pedlar River Res. 7 7 8 8

Harrison Lake 6 5 5 5 Philpott Res. 97 87 147 145

Harwoods Mill Res. 12 14 14 14 Powhatan Lake Lower 2 1 1 1

Hogan Lake 1 1 1 1 Powhatan Lake Upper 2 1 1 1

Holiday Lake 1 Prince Edward Lake 2 1 1

Hungry Mother Lake 6 7 10 10 Roaring Fork Res. 7 7 6 6

J.W. Flannagan Res. 23 22 60 43 Rural Retreat Lake 3 

Kerr Res. 72 35 61 67 Sandy River Res. 3 2 3 3

Keysville Res. 6 4 8 8 Shenandoah Lake 7 6 7 7

Lake Albemarle 7 7 7 7 Slate River Dam 8 6 7 7

Lake Anna 36 36 67 54 Smith Lake 3 3
57

Lake Chl-a SD TN TP

Smith Mountain 

Lake 

506 504 775 777

S. Fork Rivanna 

Res. 

14 12 14 14

South Holston Lake 21 18 24 24

Speights Run Lake 2 

Stonehouse Creek 

Res. 

6 6 8 8

Sugar Hollow Res. 6 6 6 6

Swift Creek Lake 7 5 5 5

Swift Creek Res. 34 28 32 32

Switzer Lake 7 7 7 6

Talbott Res. 1 1 1

Third Branch Lake 1 

Thrashers Creek 

Res. 

6 6 8 8

Totier Creek Res. 7 7 8 8

Townes Res. 1 1

Twittys Creek 1 1 1 1

Waller Mill Res. 22 22 22 22

Western Branch 

Res. 

28 28 28 28

Wise Lake 2 2
58

Table A-4. Lakes fishery status ratings and classifications for analysis. 

Lake Status Type VDGIF comments 

Abel Lake 2 Warmwater Infertile - Very riverine, steep-sided 

Banister Lake 1 Turbid Inorganic turbidity - Small & riverine 

Big Cherry Reservoir 3 Warmwater OK 

Briery Creek Lake 5 Macrophytes & 

Structure 

OK - VDGIF owned 

Brookneal Reservoir 2.5 Very Small OK 

Burton Lake 1 Other - Impaired by inorganic turbidity; 

Byllesby Reservoir 3 High flush OK - Riverine 

Carvin Cove Reservoir 3 Warmwater OK - Limited access, no mgmt. 

Chickahominy Lake 5 Macrophytes & 

Structure 

OK - Excellent fishery; lots of wood, 

vegetation 

Claytor Lake 4 Coolwater OK - WW, CW; riverine 

Curtis Lake 4 Fertilized OK - VDGIF fertilizes annually 

Diascund Reservoir 5 Warmwater OK 

Douthat Lake 2 Trout lake Infertile - Put and take trout (no 

summer habitat) 

Elkhorn Lake 2 Trout lake Infertile - Supports trout 

Emporia Lake 2 High flush Inorganic turbidity - High flushing rate 

Fairy Stone Lake 2.5 Warmwater Infertile - Limited info 

Fort Pickett Reservoir 3 Mechanically 

Aerated 

Eutrophic 

Great Creek Reservoir 3 Turbid Inorganic turbidity - Built 1994 

Harrison Lake 2 Turbid Inorganic turbidity 

Harwoods Mill Reservoir 4 Warmwater OK - Gets CuSO4 

Hungry Mother Lake 3 Warmwater OK - Frequent drawdowns 

John W. Flannagan 

Reservoir 

3 Coolwater OK - WW and CW 

Kerr Reservoir 5 Coolwater OK - Best crappie lake in USA 

Keysville Reservoir 3 Warmwater OK - No public access 

Lake Albemarle 4 Fertilized OK - VDGIF owned 

Lake Anna 4 Big multipurpose OK - Too warm for CW fish 

Lake Burnt Mills 4 Warmwater OK 

Lake Chesdin 4 Warmwater OK - V. good bass fishery 

Lake Cohoon 4 Warmwater OK - Dendritic, forested PWS 

Lake Frederick 5 Warmwater - 

Lake Gaston 4 Coolwater OK - Hydrilla in upper end 

Lake Kilby 3 High flush Other - High flushing rate 

Lake Lawson 2 Warmwater Eutrophic - Stunted fish 
59

Lake Meade 4 Warmwater OK - Supports warmwater & coolwater 

fishes 

Lake Moomaw 4 Coolwater OK - WW, CW and trout 

Lake Nelson 4 Warmwater OK - Vegetation controlled by grass 

carp; VDGIF owned 

Lake Pelham 3 Warmwater Other - May be CuSO4 treated 

Lake Prince 5 Coolwater OK - Warmwater & Coolwater fish 

Lake Robertson 4 Warmwater OK - Fertilized by VDGIF in the past 

Lake Smith 3 Warmwater OK 

Lee Hall Reservoir 3 Warmwater OK - CuSO4; lots of vegetation 

Little Creek Reservoir (7LTR) 3 Warmwater OK - Treated with CuSO4

Martinsville Reservoir 2 Warmwater Infertile 

Modest Creek Reservoir 2 Warmwater OK -Young, not fully developed fishery 

Motts Run Reservoir 3 Coolwater OK - WW and CW fish; deep 

Mountain Run Lake 3 Warmwater Other 

North Fork Pound Reservoir 2 High flush Infertile - Recovering from AMD 

Pedlar River Reservoir 3 Warmwater Infertile 

Philpott Reservoir 4.5 Coolwater Other 

Shenandoah Lake 2 Turbid Inorganic turbidity - VDGIF lake 

Smith Mountain Lake 4 Coolwater OK 

South Fork Rivanna 

Reservoir 

3 High flush Eutrophic - High flushing rate 

South Holston Lake 5 Coolwater OK - WW, CW and trout 

Stonehouse Creek Reservoir 5 Fertilized OK - VDGIF lake 

Sugar Hollow Reservoir 2 Trout lake Other - Stocked with trout, but lose 

coldwater habitat in the summer; 

stunted crappie 

Swift Creek Lake 2 Turbid Inorganic turbidity 

Switzer Lake 3 Trout lake Infertile - Holds trout over summer 

Thrashers Creek Reservoir 4 Warmwater OK - VDGIF lake 

Totier Creek Reservoir 2 Fluctuating Infertile - PWS/flood control 

Waller Mill Reservoir 4 Coolwater OK - WW, CW 

Western Branch Reservoir 5 Coolwater OK 
60

Appendix B 

RESERVOIR DYNAMICS: A COMPARISON OF SMITH MOUNTAIN LAKE AND 

CLAYTOR LAKE 

Dr. David M. Johnson 

Dr. Caroline L. Thomas 

Life Sciences Division 

Ferrum College 

Ferrum, Virginia 

djohnson@ferrum.edu

KEY WORDS: trophic state, total phosphorus, chlorophyll-a, Secchi depth, nutrient dynamics 

ABSTRACT 

The dynamics and trophic states of Smith Mountain Lake and Claytor Lake are compared with 

each other and with other southern reservoirs studied by William Walker in his work for the 

Army Corps of Engineers (Walker, 1996). 

The Ferrum College Water Quality Lab has collaborated with the Smith Mountain Lake 

Association (SMLA) since 1987 and with the Friends of Claytor Lake (FOCL) since 1996 to 

monitor the trophic status of Smith Mountain Lake and Claytor Lake. 

Both reservoirs are hydroelectric facilities operated by American Electric Power (AEP), but are 

very different impoundments. Smith Mountain Lake has an average hydraulic residence time 

(AHRT) of about a year, while Claytor Lake’s AHRT is about a month. SML receives most of 

its water from two rivers, the Roanoke and the Blackwater, that form the two main channels of 

the lake. The two channels are nearly perpendicular before their confluence produces the lake’s 

“main basin.” Claytor Lake is a “run of the river” reservoir on the New River. In addition to the 

differences in AHRT and geometric configuration, hydroelectric functions also produce 

differences in circulation patterns and water quality dynamics. Both reservoirs produce 

hydroelectric power, but Smith Mountain Lake is a “pump-back” facility. The Pigg River joins 

the Roanoke River just below the dam so that water from the Pigg River is pumped back to mix 

with Smith Mountain Lake water near the dam. The pump-back system also leads to more 

frequent variation in the water level in Smith Mountain Lake, which increases lake mixing and 

destabilizes the shoreline. 

Smith Mountain Lake and Claytor Lake will be compared with each other and with other 

southeastern reservoirs in terms of trophic state parameters (total phosphorus, chlorophyll-a, and 

Secchi depth), non-algal turbidity, and trophic state index. Additional comparisons will be made 

between Smith Mountain Lake and Claytor Lake by considering the rate of change in trophic 

status and variation in water quality with distance from the dam. The presentation concludes by 

considering how the differences might affect strategies for managing water quality in reservoirs. 
61

INTRODUCTION 

With the assistance of citizen volunteers and student interns, scientists from the Ferrum College 

Water Quality Lab have been monitoring the trophic status of Smith Mountain Lake since 1987 

and the trophic status of Claytor Lake since 1996. Both water bodies are reservoirs, owned and 

operated by American Electric Power (AEP), and were constructed for flood control, generation 

of hydroelectric power, and recreation. Claytor Lake, on the New River, dates back to the late 

1930’s and is a “run-of-the-river” reservoir with an average hydraulic residence time (AHRT) of 

about one month. Smith Mountain Lake dates back to the late 1960’s and has an AHRT of about 

a year and receives most of its water from two rivers, the Roanoke and the Blackwater, that form 

the two main channels of the lake. The two channels are nearly perpendicular before their 

confluence produces the lake’s “main basin.” In addition to the differences in AHRT and 

geometric configuration, hydroelectric functions also produce differences in circulation patterns 

and water quality dynamics. Both reservoirs produce hydroelectric power, but Smith Mountain 

Lake is a “pump-back” facility. The Pigg River joins the Roanoke River just below the dam, and 

water from that river is pumped back to mix with SML water in the main basin. The pump-back 

system also leads to more frequent variation in the water level in Smith Mountain Lake, which 

increases lake mixing and destabilizes the shoreline. 

The trophic state of a lake depends on the degree of nutrient enrichment. As nutrients 

accumulate in the lake, algal production increases and, in turn, water clarity decreases. The 

biomass produced by algae settles in the lake, causing the depletion of dissolved oxygen (DO) in 

the hypolimnion, even though algal photosynthesis produces oxygen in the epilimnion during the 

day. The diurnal DO swing becomes more severe because increased algal populations produce 

more oxygen during the day and consume more oxygen at night. 

Trophic status is evaluated by measuring the typical suite of trophic state indicators: total 

phosphorus, chlorophyll-a, and Secchi depth. At both reservoirs, the college has worked 

cooperatively with the local lake association, i.e., the Smith Mountain Lake Association (SMLA) 

and the Friends of Claytor Lake (FOCL). Samples are collected at permanently designated 

stations each two weeks during the period between Memorial Day and Labor Day. Trained 

volunteer monitors collect the samples and measure water clarity from a boat during a one-week 

sampling window, and student interns analyze the samples at the Ferrum College Water Quality 

Lab. 

Bob Carlson developed algorithms to calculate a trophic state index based on algal biomass 

(Carlson, 1977). However, Carlson’s Trophic State Index (TSI) can be calculated from the 

seasonal average value of any one of the three trophic state parameters: total phosphorus 

concentration (TP) as the indicator of nutrient enrichment, chlorophyll-a concentration (CHA) as 

the indicator of algal biomass, or Secchi depth (SD) as the indicator of water clarity. If a lake or 

reservoir were functioning in classic fashion, the three TSI values (TSI-TP, TSI-CHA, and TSISD), calculated from each of the three trophic state parameters, would be similar. The average 

of the three TSI values is the combined trophic state index, TSI-C. 

More recently, William Walker studied 41 southern reservoirs in his work developing a reservoir 

model (BATHTUB) for the Army Corps of Engineers (Walker, 1996). He found that insights 
62

into non-classical behavior could be gained by comparing relative values of TP, CHA, and SD in 

the study set. 

The full trophic state data set from Smith Mountain Lake (1987-2004) and Claytor Lake (1996-

2004) has been summarized, compared with each other, and compared with average values for 

the 41 southern reservoirs included in the Walker study. 

METHODS 

Field Procedures:

Volunteer monitors measure water clarity with a Secchi disk and collect integrated samples of 

the photic zone. The photic zone is operationally defined as twice the Secchi depth (~ 95% light 

extinction) and the integrated sample is collected with a rubber hose that has been conditioned in 

lake water, marked at one-meter intervals, and fitted with a rope and diver’s weight. The water 

sample is mixed in a 4-L polyethylene bucket and an aliquot is placed in a 60-mL polyethylene 

bottle for total phosphorus analysis. A second 100-mL aliquot is filtered through a type-A glass 

filter, and the filter is analyzed for chlorophyll-a. The procedures used by the volunteer monitors 

are described in detail in the Smith Mountain Lake/Claytor Lake Volunteer Monitoring Manual

(Thomas and Johnson, 2003). 

Laboratory Procedures: 

Analytical methods are adapted from Standard Methods for the Examination of Water and 

Wastewater (APHA, 1995). Total phosphorus is measured spectrophotometrically after 

persulfate digestion, and chlorophyll-a is measured fluorometrically after acetone extraction. 

The detailed methods are described in the Ferrum Water Quality Lab Procedures Manual

(Johnson and Thomas, 2004). 

 

RESULTS AND DISCUSSION 

Comparison of Smith Mountain Lake and Claytor Lake to the Reservoirs in Walker’s 

study:

Overall average values for total phosphorus, chlorophyll-a, and Secchi depth are displayed in 

Table 1 for Smith Mountain Lake (SML), Claytor Lake (CL), and the Walker study (WS). 

Trophic state data for SML and CL is grouped by zone, with each zone representing a 5-mile 

length of reservoir. Zone 1 is from 0 – 5 miles from the dam and so on; Smith Mountain Lake 

has 6 zones, and Claytor Lake has 4 zones. In Table 1, the average value for SML and CL is the 

average for all zones; the minimum value is the average value for the zone with the lowest 

average, and the maximum is the average value for the zone with the highest average. Both local 

reservoirs have lower concentrations of total phosphorus and chlorophyll-a and greater Secchi 

depths than the average of the 41 reservoirs included in the Walker study. As expected, the 

range of values in the 41 reservoirs in Walker’s study is larger than the range found in SML and 

CL. 
63

Table 1. Average values for trophic state parameters for Smith Mountain Lake, Claytor 

Lake, and the Walker study. 

min max avg min max avg min max avg

Smith Mtn Lake 20 64 38 Smith Mtn Lake 1.8 11.3 5.3 Smith Mtn Lake 3.2 1.3 2.0

Claytor Lake 30 57 44 Claytor Lake 1.9 8.4 6.5 Claytor Lake 1.0 1.6 1.3

Walker Study 10 274 48 Walker Study 2.0 64.0 9.4 Walker Study 0.2 4.6 1.1

Total Phosphorus (ppb) Chlorophyll-a (ppb) Secchi Depth (m)

As part of the model development, Walker examined several relationships among the three 

trophic state parameters. Three of the relationships are displayed in Figure 2 with brief 

interpretations in the boxes below. Average values calculated for the diagnostics for Smith 

Mountain Lake and Claytor Lake are neither “high” nor “low,” indicating classical behavior “on 

average.” However, the minimum values of CHA*SD and CHA/TP in SML and CL indicate a 

low response to nutrients in some zones of SML and CL. 

Table 2. Diagnostic variables used in the Walker study with values for Smith Mountain 

Lake and Claytor Lake. 

min max avg min max avg min max avg

Smith Mtn Lake 0.27 0.52 0.42 Smith Mtn Lake 5.6 14.0 8.7 Smith Mtn Lake 0.09 0.17 0.13

Claytor Lake 0.40 1.00 0.60 Claytor Lake 1.9 13.6 9.2 Claytor Lake 0.03 0.25 0.2

Walker Study 0.13 5.2 0.61 Walker Study 1.8 31.0 10.0 Walker Study 0.04 0.6 0.2

Low: < 0.4; allocthanous PM unimportant?

High: > 16; algal turbidity dominates

low response to nutrients expect high nutrient response algae limited by phosphorus

mean CHA/mean TP

Algal use of phosphorus supply

Low: < 0.13; low phosphorus response

algae limited by N, light, or flushing rate

High: > 0.40; high phosphorus response

mean CHA * mean SD (mg/m2)

Light extinction; algal & non-algal turidity

Low: < 6; nonalgal turbidity dominates

expect low nutrient response

NAT = 1/SD - 0.025*CHA

Inverse SD corrected for light extinction by CHA

high response to nutrients

High: >1; alloctanous PM important?

Non-algal Turbidity (1/m) CHA*SD (mg/m2) CHA/TP

Temporal Variation of trophic state parameters:

The trophic state of Claytor Lake and Smith Mountain Lake has not changed significantly since 

the Ferrum Water Quality Lab began the monitoring programs. Figure 1 displays the combined 

trophic state index for Claytor Lake. 

 

Figure 1. The combined trophic state index for Claytor Lake (1996 – 2004). 

R2 = 0.2388

30

40

50

60

70

1996

1998

2000

2002

2004

TSI-C
64

Trophic status is evaluated by zone in Smith Mountain Lake because water quality changes 

significantly as it moves down the long Blackwater and Roanoke channels. Figure 2 displays the 

trophic state data for Smith Mountain Lake by year for each of the six zones. 

Figure 2. Combined trophic state index for Smith Mountain Lake by zone (1987-2004). 

R2 = 0.0923

30

40

50

60

70

1986

198819901992199419961998200020022004

TSI - Combined

R2 = 0.1547

30

40

50

60

70

1986198819901992199419961998200020022004

TSI - Combined

R2 = 0.1538

30

40

50

60

70

198619881990

199219941996

199820002002

2004

TSI - Combined

R2 = 0.0768

30

40

50

60

70

198619881990

1992

1994199619982000

20022004

TSI - Combined

R2 = 0.0768

30

40

50

60

70

198619881990

199219941996

19982000

2002

2004

TSI - Combined

R2 = 0.0768

30

40

50

60

70

1986198819901992199419961998200020022004

TSI - Combined

Zone 1 (0-5 MTD) Zone 2 (5-10 MTD)

Zone 3 (10-15 MTD) Zone 4 (15-20 MTD)

Zone 5 (20-25 MTD) Zone 6 ( > 25 MTD)
65

Spatial Variation of Trophic State Parameters:

Variation of the three trophic state parameters by zone in Smith Mountain Lake is shown in 

figures 3, 4, and 5. In each case, the pattern in 2004 is similar to the pattern over the period from 

1987 – 2004. The increase in SD towards the dam can be explained by the settling of silt in the 

upper channels of the lake. Phosphate strongly adsorbs to clay particles in the silt and the 

removal of phosphorus, in turn, leads to reduced CHA. The improved curve fit seen with the 

second-order regression is consistent with a settling process following first order kinetics. 

Figure 3. Total Phosphorus by zone in Smith Mountain Lake. 

R2 = 0.952

0

20

40

60

80

123456

Zone

Total Phosphorus (ppb)

1987-2004

R2 = 0.9448

0

20

40

60

80

123456

Zone

Total Phosphorus (ppb)

2004

R2 = 0.9902

0

20

40

60

80

123456

Zone

Total Phosphorus (ppb)

1987-2004

Figure 4. Chlorophyll-a by zone in Smith Mountain Lake. 

R

2

 = 0.8863

0

2

4

6

8

10

123456

Zone

CHA (ppb)

R2 = 0.9253

0

2

4

6

8

10

123456

Zone

CHA (ppb)

R

2

 = 0.9953

0

2

4

6

8

10

123456

Zone

CHA (ppb)

Figure 5. Secchi depth by zone in Smith Mountain Lake. 

R2 = 0.9107

0

1

2

3

4

123456

Zone

Secchi Depth (m)

R2 = 0.93

0

1

2

3

4

123456

Zone

Secchi Depth (m)

1987-2004

R2 = 0.99

0

1

2

3

4

123456

Zone

Secchi Depth (m)

1987-2004 2004 

2004 1987-2004 1987-2004
66

Figure 6 shows the variation of trophic state parameters with distance from the dam in Claytor 

Lake. Each data point in the figures below represents one of the 12 sampling sites on the lake. 

As in Smith Mountain Lake, settling of silt lowers the trophic status of the water near the dam. 

However, the extremely turbid headwaters of Claytor Lake inhibit algal growth. The three 

stations furthest from the dam have non-algal turbidities above 1 and much lower CHA levels 

than would be expected given the high TP concentration at those sites. 

Figure 6. Variation of trophic state parameter with distance from dam in Claytor Lake. 

R2 = 0.7722

25

35

45

55

65

75

0 5 10 15 20 25

Distance to Dam (mile)

Total Phosphorus (ppb)

R2 = 0.2041

0

5

10

15

20

0 5 10 15 20 25

Distance to Dam (mile)

Chlorophyll- a (ppb)

R2 = 0.7013

0

1

2

3

0 5 10 15 20 25

Distance to Dam (mile)

Secchi Depth (m)

R2 = 0.5776

0.0

0.5

1.0

1.5

2.0

0 5 10 15 20 25

Distance to Dam (mile)

Non-Algal Turbidity (1/m

CONCLUSIONS 

Smith Mountain Lake and Claytor Lake have the long narrow channels typical of reservoirs. As 

a result, the water is not as homogeneous as found in classical lakes. As Virginia develops 

assessment criteria and methodologies for lakes and reservoirs, there is a need to consider the 

down-channel point at which the riverine channel becomes the lake and should meet state water 

quality criteria. AHRT of the reservoir should also be considered because “run-of-the-river” 

reservoirs such as Claytor Lake do not develop the internal nutrient dynamics at work in larger 

reservoirs such as Smith Mountain Lake. 
67

ACKNOWLEDGEMENTS 

The following organizations have supported the trophic monitoring programs on Smith Mountain 

Lake and Claytor Lake: 

American Electric Power 

Friends of Claytor Lake 

Friends of the Lake (Smith Mountain Lake) 

Ferrum College 

Franklin County 

Bedford County 

Pittsylvania County 

Pulaski County 

Smith Mountain Lake Association 

Virginia Department of Environmental Quality 

Virginia Department of Game and Inland Fisheries 

Virginia Environmental Endowment 

REFERENCES 

American Public Health Association. 1995. Standard Methods for Examination of Water and 

Wastewater, 16th edition. Washington, DC. 

Carlson, R.E. 1977. A trophic state index for lakes. Limnol. Oceanog. 22(2):361-369. 

Johnson, David M., Carolyn L. Thomas, and Carol Love. 2000, 2002, & 2004. Ferrum College Water 

Quality Lab Procedures Manual. Published by Ferrum College, Ferrum, VA. 

Thomas, Carolyn L. and David M. Johnson. 1988, 1992, & 2002. Smith Mountain Lake/Claytor Lake 

Volunteer Monitoring Manual. Published by Ferrum College, Ferrum, VA. 

William Walker. 1999. Simplified Procedures for Eutrophication Assessment and Prediction:

User Manual. US Army Corps of Engineers Instruction Report W-96-2, September 1996 

(Updated April 1999).
68

APPENDIX C 

Responses to DEQ Questions Concerning 

Dissolved Oxygen Criteria for Lakes 

Dr. John C. Little, Vickie Singleton, Lee Bryant 

1. Should the current dissolved oxygen criteria (5 ppm daily average and 4 ppm 

minimum) apply at all depths of a lake or only to the epilimnion of a stratified lake or 

reservoir or to a depth of 1 m (or 2x Secchi depth) during non-stratified conditions? 

To the authors’ knowledge, the United States Environmental Protection Agency (EPA) has 

not issued specific guidance on how states should apply the existing dissolved oxygen (DO) 

criteria to lakes and reservoirs. Therefore, states can interpret and apply the DO criteria for 

stratified water bodies as appropriate. In the absence of direction from EPA, states such as 

Maryland (MDE, 2004), Minnesota (MPCA, 2003), Oregon (ODEQ, 2003), and West Virginia 

(WV EQB, 2004) do not currently address the effects of stratification on DO concentrations in 

their water quality regulations. Alternatively, Colorado (CDPHE, 2005), Iowa (IDNR, 2004), 

and Pennsylvania (PDEP, 2000) only apply DO criteria to the epilimnion of stratified water 

bodies. Other states have vague references to stratification effects in their DO criteria. North 

Carolina’s regulations state that ambient DO can be lower in lake bottom waters “if caused by 

natural conditions” (NC DENR, 2004). Some states specify DO concentrations for arbitrary 

depths in the water column. For protection of warm water aquatic life in Kentucky, DO must be 

measured at “mid-depth in waters 10 ft or less and at representative depth in other waters.” For 

cold water lakes and reservoirs that support trout, the DO concentration “in waters below the 

epilimnion shall be kept consistent with natural water quality” (KDW, 2004). Tennessee 

regulations require that DO “be measured at mid-depth in waters 10 ft or less and at a depth of 5 

ft for waters greater than 10 ft in depth” (TDEC, 2004). 

The existing water quality standards for Virginia recognize the effects of stratification on 

hypolimnetic DO concentrations as referenced in Section 9 VAC 25-260-55 (VDEQ, 2004b). 

However, the State Water Control Board may have difficulty establishing site-specific DO 

criteria “that reflect the natural quality of that water body or segment,” in accordance with Part 

E, because no natural reference conditions exist for constructed impoundments (refer to 

additional discussion in Response 4). 

Currently, the Virginia Department of Environmental Quality (DEQ) applies existing DO 

criteria to the entire water column of lakes and reservoirs during stratified and non-stratified 

conditions (Younos, 2004). This has resulted in a number of impoundments being classified as 

Category 4 (does not require a TMDL) or 5 (requires a TMDL) impaired because of DO criteria 

violations (Table 1) (VDEQ, 2004d). Category 4 and 5 waters are those that were determined to 

be impaired due to natural and anthropogenic sources, respectively. DEQ applied a multi-step 

procedure to establish whether anthropogenic pollutants were causing hypolimnetic DO 

violations. The general approach involves assessment of water quality data and evaluation of 

anecdotal information in the watershed. Trophic State Indices (TSIs) were calculated for each 

impaired water body to determine if excessive nutrients are contributing to low DO 

concentrations in the hypolimnion. 
69

The current methodology used by DEQ to apply existing DO criteria to constructed 

impoundments is sound and scientifically defensible. Until revised DO criteria that more 

specifically address stratification and designated uses in lakes and reservoirs are established, the 

current approach should be adequate. After development of revised DO criteria, reservoirs that 

were previously classified as Category 5 impaired may be reclassified as waters supporting one 

or more designated uses. Therefore, effort should be focused on determining reservoir-specific 

DO criteria before proceeding with TMDL development for Category 5 DO-impaired waters. 

2. Should dissolved oxygen criteria be developed specifically for lakes? 

We recommend that DO criteria be established specifically for lakes and reservoirs. For 

development of nutrient criteria for Virginia water bodies, the DEQ plans to classify state surface 

waters by type (estuaries, lakes and reservoirs, and rivers and streams) (VDEQ, 2004c). 

Additionally, the Academic Advisory Committee recommended that nutrient criteria 

development be based on water body types (Virginia Water Resources Research Center, 2004). 

Therefore, it is likely that nutrient criteria will be proposed and referenced by water type. 

Developing DO criteria specifically for lakes and reservoirs will provide consistency between 

water quality regulations. Additionally, lakes and reservoirs respond differently to nutrient 

inputs than estuaries and rivers and streams, which is why guidance documents for state nutrient 

criteria development were published by water body type (US EPA, 1998). Differing responses 

among surface water types will likely translate to differing DO characteristics because DO is a 

secondary response variable to nutrient loading (Virginia Water Resources Research Center, 

2004). Typically, the primary source of oxygen into a water body is atmospheric diffusion. 

Diffusion of oxygen into and within water is relatively slow, so mixing is required for DO to be 

in equilibrium with the atmosphere. Consequently, small, turbulent streams and rivers are often 

near saturation with respect to DO throughout their depths. This is in contrast to the distribution 

of oxygen in density-stratified lakes and impoundments, which varies with depth and is 

controlled by hydrodynamics, photosynthetic inputs, and losses to chemical and biotic oxidations 

(Wetzel, 2001). 

We also recommend that DO criteria development for lakes and reservoirs be based on 

designated uses of the water bodies. Basing DO criteria on designated uses is similar to the 

approach used by EPA for development of ambient DO criteria for the Chesapeake Bay and its 

tidal tributaries (US EPA, 2003). It is also consistent with previous recommendations of the 

Academic Advisory Committee (Virginia Water Resources Research Center, 2004). Basing 

water quality criteria on designated uses has been applied successfully by British Columbia for 

developing phosphorus criteria and is also used by the Canadian Federal government in 

specifying a number of water quality parameters (US EPA, 2000). 

3. If the answer to no. 2 is yes, should dissolved oxygen criteria be developed that apply to 

the entire water column or to the upper layer only or should there be different criteria 

for different depths within a lake? 

To address the effects of stratification on DO concentrations throughout the water column, 

we recommend that separate criteria be developed for the epilimnion and hypolimnion and that 

criteria development be based on designated uses of the water bodies. Application of a single 

DO criterion for all depths within a given lake or reservoir may be unnecessarily stringent and 
70

not required to fully support the water body’s designated uses during stratification. When the 

water column is completely mixed, a single DO criterion that supports the water body’s 

designated uses should be applied to all depths. If the primary cause of anoxic conditions in the 

lower depths of stratified impoundments is lack of reaeration by the atmosphere, then, 

theoretically, oxic conditions should exist when the lake is completely mixed. 

Dissolved oxygen criteria for stratified water bodies should ensure that at least one layer 

exists in the reservoir where temperature, DO, and pH requirements are being met to support 

designated uses. A similar approach has been proposed for thermally stratified reservoirs in 

Oregon, although specific DO criteria for the hypolimnion have not been developed (ODEQ, 

2004). As an example, if DO criteria are developed for protection of warm water aquatic life in a 

particular reservoir, specifying DO criteria for the hypolimnion may not be necessary if water 

quality conditions in the epilimnion can support the target species throughout the stratification 

period. With regard to protection of water supply use, hypolimnetic DO criteria may not be 

required for a given impoundment if water utility(ies) can only withdraw raw water for treatment 

from the epilimnion. 

Specifying different DO criteria for different water column depths or regions has recently 

been applied by EPA to the Chesapeake Bay and its tidal tributaries (US EPA, 2003). Dissolved 

oxygen criteria were derived to protect estuarine species living in different habitats, also referred 

to as tidal water designated uses, which are influenced by natural processes in the Bay. The 

criteria reflect ambient oxygen dynamics, as evidenced by seasonal application of deep-water 

and deep-channel DO criteria that account for the effects of water column stratification. Both 

deep-water and deep-channel regions are below the pycnocline during periods of Bay 

stratification (late spring to early fall). Deep-water criteria were set at levels to protect shellfish 

and juvenile and adult fish, and to foster recruitment success of the bay anchovy. Deep-channel 

criteria were set to provide seasonal refuge and to protect the survival of bottom sedimentdwelling worms and clams. During periods of complete water column mixing, the higher DO 

criteria associated with open-water fish and shellfish use applies to deep-water and deep-channel 

designated uses (US EPA, 2003). 

4. Should dissolved oxygen criteria be established by lake use (water supply, fishing, or 

recreation)? 

Because the vast majority of lentic systems in Virginia are constructed impoundments, 

establishing DO criteria based on water body designated use is a reasonable methodology. This 

approach is a logical step considering reservoirs are artificial water bodies created for specific 

uses and functions. Impoundments are built and managed for various purposes including flood 

control, navigation, municipal or agricultural water supply, hydroelectric generation, and game 

fish production. Management practices often affect physical, biological, and chemical 

characteristics of the reservoir (US EPA, 2000). Developing DO criteria based on designated 

impoundment uses is recommended over specifying criteria based on a reference condition 

approach because the reference or undisturbed state for a reservoir is usually a lotic ecosystem. 

Therefore, the reference condition method is not at all applicable to constructed impoundments. 

Basing DO criteria on reservoir uses will avoid unnecessarily stringent criteria being applied to 

some water bodies while still protecting designated and existing uses. For instance, it is likely 

that the minimum DO criteria for protection of recreation use or water supply use is lower than 
71

that required for protection of aquatic life. For aquatic life use, minimum DO needs can vary 

depending on the target species (cold water or warm water). 

Designated uses have already been determined for Virginia water bodies for biennial 

preparation of the 305(b)/303(d) integrated water quality assessment. The six existing 

designations are aquatic life use, fish consumption use, shellfish consumption use, swimming 

use, public water supply use, and wildlife use (Virginia DEQ, 2004d). Of these designated uses, 

only aquatic life and public water supply are directly affected by low DO concentrations in the 

water column of lakes reservoirs, and recreation may be considered to be indirectly affected. 

Compliance with fish consumption use is determined by comparison of fish tissue data with state 

screening values for toxic pollutants. Shellfish consumption use is not impaired if harvesting 

restrictions are not issued by the Virginia Department of Health. Criteria for support of wildlife 

use involve toxics known to be harmful to aquatic life in the water column. Currently, support of 

swimming use for a water body is demonstrated by compliance with bacteriological criteria such 

as fecal coliform and E. coli (Virginia DEQ, 2004b). 

Ambient freshwater DO criteria for the protection of aquatic life, both cold and warm water 

species, have been determined previously by EPA (US EPA, 1986). In preparation of DO 

criteria specific to the Chesapeake Bay, EPA conducted a preliminary survey of the literature 

since the 1986 freshwater document was published and found effects data that confirmed that the 

DO criteria remained protective. Therefore, EPA believes that the existing freshwater criteria 

accurately account for the anticipated effects of low DO on freshwater aquatic species (US EPA, 

2003). 

To our knowledge, EPA has not developed ambient DO criteria for the support of public 

water supply use in lakes and reservoirs, and neither have most states. Alaska specifies that DO 

concentrations must be at least 4 mg L-1 in waters designated for drinking water supply. 

However, this standard does not apply to lakes or reservoirs where water is withdrawn from 

below the thermocline (ADEC, 2003). Colorado requires minimum DO concentrations of 3 mg 

L-1 for waters designated for domestic water supply, but the standard is intended to apply to only 

the epilimnion and metalimnion of stratified lakes and reservoirs (CDPHE, 2005). Florida and 

West Virginia have specified that surface waters used for potable water supply have DO 

concentrations of at least 5 mg L-1 (FDEP, 2002 and WV EQB, 2004). 

 Hypolimnetic oxygen depletion in stratified water bodies may lead to increases in hydrogen 

sulfide, ammonia, and phosphorus, and the release of reduced iron and manganese from the 

sediments. If entrained into the productive surface zone, phosphorus may stimulate algal growth, 

which exacerbates the problem because decaying algae ultimately fuel additional oxygen 

demand. Hydrogen sulfide and reduced iron and manganese are undesirable in drinking water 

and usually require additional treatment (Cooke and Carlson, 1989). The extra oxidant may react 

with natural organic matter increasing the formation of disinfection by-products. 

The effects of hypolimnetic anoxia on chemical and biological parameters of concern to 

drinking water treatment are well documented. However, a cursory review of the scientific 

literature revealed little information on suggested DO criteria for protection of raw water 

supplies. The published studies that are most relevant to the effects of low DO concentrations on 

water treatment processes involve hypolimnetic aeration or oxygenation. These techniques are 

commonly used to add dissolved oxygen to water bodies while preserving stratification. Studies 

documenting the effects of hypolimnetic aeration and oxygenation have been reviewed by Fast 

and Lorenzen (1976), Pastorok et al. (1982), McQueen and Lean (1986), and Beutel and Horne 

(1999). McQueen and Lean (1986) found that for generally all installations, hypolimnetic 
72

oxygen levels increased; iron, manganese, and hydrogen sulfide levels decreased; and 

chlorophyll levels were not altered. The effects of hypolimnetic aeration on phosphorus were 

more variable. McQueen et al. (1986) attribute this to pH levels and iron availability for 

phosphorus sedimentation. The effects on nitrogen were not consistent either; ammonium and 

total nitrogen decreased in some studies but increased in others. In their review, Beutel and 

Horne (1999) reported that average hypolimnetic DO concentrations were maintained at greater 

than 4 mg L-1 in all cases and oxygenation decreased hypolimnetic concentrations of dissolved 

phosphorus, ammonia, manganese, and hydrogen sulfide by 50-100 percent. 

A number of hypolimnetic oxygenation systems have been installed in potable water supply 

lakes or reservoirs. The City of Norfolk installed hypolimnetic aerators in Lakes Prince and 

Western Branch, Virginia, two water supply reservoirs. Because of the aeration system, the City 

has discontinued prechlorination of raw water at the treatment plant, and noticeable 

improvements have been observed in reservoir aesthetics (Cumbie et al., 1994). St. Mary Lake, 

British Columbia is a multi-use water body that supports potable water supply, a trout fishery, 

and recreation. An aeration system installed in 1985 has generally maintained DO at 5 mg L-1 in 

the hypolimnion and has decreased phosphorus concentrations (Nordin et al., 1995). 

Hypolimnetic oxygenation in Upper San Leandro Reservoir, California decreased ozone 

requirements by 35 percent and chlorine requirements by 14 percent at the treatment facility. 

Also, manganese concentrations in the raw water were decreased, resulting in decreased chlorine 

dosing. Consequently, the concentration of trihalomethanes in the finished water, which are 

regulated disinfection by-products, decreased by over 50 percent. Overall, the oxidant savings 

was greater than twice the cost of oxygen required to operate the hypolimnetic oxygenation 

system (Jung et al., 2003). 

To provide some insight into the potential economic impact of remediating low-DO 

conditions in Virginia reservoirs, capital costs for select aeration and oxygenation systems are 

shown in Table 2. The primary types of devices currently in use include full-lift aerators, Speece 

Cones, and bubble plume diffusers. Full-lift aerators operate by injecting compressed air near 

the bottom of the hypolimnion. The air-water mixture travels up a vertical pipe to the lake 

surface where gasses are vented to the atmosphere. The aerated water is then returned through 

another pipe downward to the hypolimnion. In Speece Cones, oxygen is injected into an 

enclosed chamber that is typically located in the hypolimnion, and water is either pumped or 

entrained into the device (Beutel and Horne, 1999). Oxygen transfer occurs within the chamber, 

and oxygenated water is discharged to the hypolimnion. Pure oxygen or compressed air can also 

be introduced into the hypolimnion through the use of diffusers to form a rising, unconfined 

bubble plume. This oxygenation method is most suitable for deep lakes where the bulk of the 

bubbles dissolve in the hypolimnion and the momentum produced by the plume is low enough to 

prevent intrusion into the thermocline (Wüest et al., 1992). 

It should be noted that maintenance of oxic conditions in the hypolimnion does not always 

result in a reduction of productivity and algal growth in lakes. Based on more than 10 years of 

data on hypolimnetic oxygenation and artificial mixing in two eutrophic lakes, Gächter and 

Wehrli (1998) found that internal cycling of phosphorus was not affected by increased 

hypolimnetic DO concentrations. Their research indicated that the sediment-water interface 

remained anoxic even in the presence of an oxic hypolimnion. The authors concluded that 

excessive organic matter loading and phosphorus precipitation exhausted the hypolimnetic DO 

supply and exceeded the phosphorus retention capacity of the sediments after diagenesis. 
73

In summary, the information currently available regarding appropriate DO criteria for lakes 

and reservoirs used for drinking water supply is limited to non-existent. EPA has not developed 

ambient DO criteria for the support of public water supplies, and the vast majority of states do 

not have DO criteria specifically for this designated use. The effects of hypolimnetic anoxia on 

water quality parameters related to drinking water treatment are well documented, and 

hypolimnetic oxygenation is a proven mitigation technique. However, because of insufficient 

information available at this time, we can recommend only preliminary DO criteria for protection 

of water supply designated uses. It is suggested that the existing freshwater DO criteria for nontrout waters (5 mg L-1 daily average, 4 mg L-1 minimum) be applied to all strata used for potable 

water supply within a given reservoir. This is comparable to the approximate, rule-of-thumb DO 

value of 5 mg L-1 typically desired in influent raw water by treatment plant managers. It should 

be noted that maintaining DO at this level is commonly thought to decrease soluble iron and 

manganese concentrations and control the formation of hydrogen sulfide, but this has not been 

well established. Therefore, DO criteria for protection of water supply designated uses may need 

to be revised after further study by EPA or the scientific and engineering community. 

Regarding primary and secondary contact recreation, we are not aware of DO criteria 

development by EPA for the protection these designated uses. Also, the vast majority of states 

have not developed DO standards for recreational uses or the aesthetic quality of lakes and 

reservoirs. Where such state criteria exist, they are typically part of an all-encompassing limit to 

be applied to the most sensitive designated water use. One exception is Alaska, which specifies 

that DO concentrations must be at least 4 mg L-1 in waters designated for primary or secondary 

contact recreation (ADEC, 2003). Also, Colorado requires minimum DO concentrations of 3 mg 

L-1 for primary and secondary contact recreational waters. However, the standard is intended to 

apply to only the epilimnion and metalimnion of stratified lakes and reservoirs (CDPHE, 2005). 

South Dakota specifies minimum DO levels of 5 mg L-1 for immersion recreation and limited 

contact recreation waters (SD DENR, 1997). In Virginia, if all reservoirs are designated for 

aquatic life and/or water supply use, the DO criteria to support these uses would more than likely 

be adequate to support swimming and other recreational uses. Therefore, separate DO criteria 

specifically for recreation and aesthetics are probably not necessary for Virginia. A similar 

conclusion was drawn for application of DO standards in waters of British Columbia (BC 

MELP, 1997). 

5. Should dissolved oxygen criteria differ for natural lakes and constructed 

impoundments? 

We recommend that separate DO criteria be developed for natural lakes and constructed 

impoundments. While studies of reservoir ecosystems have found functional similarities 

between artificial and natural lakes, natural lake ecosystems have many characteristics that are 

significantly different than reservoirs. The ratio of drainage basin area to water body surface 

area is frequently higher for reservoirs than natural lakes (Cooke and Carlson, 1989). Because 

reservoirs are usually formed in river valleys, their morphometry is typically dendritic, narrow, 

and elongated. This is in contrast to the predominantly circular or elliptical shape of natural 

lakes (Wetzel, 2001). Most reservoirs have asymmetrical depth distributions in the longitudinal 

direction, with the maximum depths occurring adjacent to the dam. Near the vertical dam wall, 

unusual chemical and temperature stratifications can occur, which differ dramatically from those 

typically present in natural lakes (Cole, 1994). Reservoirs often have higher flushing rates and 
74

lower hydraulic residence times than natural lakes. Additionally, discharges from reservoirs are 

not always from the surface and are frequently from deeper waters. Because reservoirs are 

constructed for various uses, surface levels in these water bodies typically fluctuate more than in 

natural lakes as water is stored and released (Cole, 1994). 

Because the watershed area in relation to surface area for reservoirs is much larger than for 

natural lakes, inflows to reservoirs have more energy for erosion, higher sediment-load carrying 

capabilities, and cause increased dispersion of dissolved and particulate concentrations into the 

receiving water body. Runoff influent to reservoirs is usually greater and influenced more 

significantly by precipitation events. These characteristics induce higher but more irregular 

nutrient and sediment loading rates in reservoirs compared to natural lakes, which affects 

biological processes (Wetzel, 2001). In turn, differences in light attenuation and nutrient 

availability between natural and artificial lakes can result in different productivity rates and, 

subsequently, differing hypolimnetic dissolved oxygen concentrations. Dissolved oxygen is a 

secondary response variable to nutrient inputs (Virginia Water Resources Research Center, 

2004). 

We recommend that site-specific dissolved oxygen criteria be developed for the two natural 

lakes in the state, Mountain Lake and Lake Drummond. These water bodies are located in 

distinctly different ecological regions, and hence, each is a unique natural resource. Mountain 

Lake is the only notable natural lake in the unglaciated region of the southern Appalachian 

Highlands (Cawley et al., 2001). Lake Drummond is a blackwater lake located in the Great 

Dismal Swamp, which is considered to be the most northern “southern” type swamp on the east 

coast of the United States (Johannesson et al., 2004). In addition to dissolved oxygen data 

currently collected on Lake Drummond by the Virginia Department of Environmental Quality 

(Younos, 2004), numerous studies have been published on Mountain Lake (Simmons and Neff, 

1974; Obeng-Asamoa, 1976; Parson, 1988; Beaty and Parker, 1993; Beaty and Parker, 1995; 

Cawley et al., 1999) and Lake Drummond (Duke et al., 1969; Anderson et al., 1977; Phillips and 

Marshall, 1993; Merten and Weiland, 2000). This information can facilitate development of sitespecific dissolved oxygen criteria for each natural water body. 

These recommendations are consistent with related recommendations of the Academic 

Advisory Committee regarding freshwater nutrient criteria (Virginia Water Resources Research 

Center, 2004). 

6. Should dissolved oxygen criteria be developed specifically for the hypolimnion? 

Expanding on the response to question 3, we believe that dissolved oxygen criteria should 

be developed specifically for the hypolimnion of constructed impoundments to address the 

effects of stratification. As stated previously, hypolimnetic DO criteria should take into account 

designated uses of the water body and what conditions will be required in the hypolimnion to 

achieve these uses during stratification. This is consistent with the recommendations of the 

Academic Advisory Committee regarding development of Virginia freshwater nutrient criteria 

(Virginia Water Resources Research Center, 2004). Additionally, hypolimnetic DO criteria 

should also consider the potential downstream effects of reduced oxygen concentrations in 

waters released from the lower depths of constructed impoundments. Per 9 VAC 25-260-10 of 

the Virginia Water Quality Standards, “in designating uses of a water body and the appropriate 

criteria for those uses, the board...shall ensure that its water quality standards provide for the 

attainment and maintenance of the water quality standards of downstream waters”. Virginia 
75

streams and rivers downstream of reservoirs are currently affected by releases of hypolimnetic 

waters. For example, almost 6 miles of the Roanoke River have been classified as Category 5 

impaired waters for DO because of hypolimnetic water discharge upstream from Lake Gaston 

(VDEQ, 2004d). Also, nearly 6 miles of the Meherrin River are designated Category 5 impaired 

for DO due to hypolimnetic releases from an upstream impoundment (VDEQ, 2004d). 

The release of hypoxic or anoxic waters from stratified impoundments is currently regulated 

for licensure of existing and new hydropower projects. The United States Federal Energy 

Regulatory Commission (FERC) is increasingly specifying minimum DO concentrations in 

discharge waters from hydropower reservoirs (Mobley, 1997). 

7. What type of Use Attainability Analysis (UAA) would be needed to demonstrate 

appropriate dissolved oxygen criteria for lakes? 

In order to demonstrate appropriate DO criteria for lakes and reservoirs, a multi-phase Use 

Attainability Analysis (UAA) is recommended. In accordance with applicable UAA 

methodology (US EPA, 1994; OWR, 2001; VDEQ, 2004a), we feel that a comprehensive multiphase UAA approach should be based on: 

1. A review of supporting literature and historical data 

2. Routine, on-site surveys performed to analyze parameters relating to DO levels (e.g., 

sediment loading and organic matter (OM) deposition rates, nutrient loading (especially 

phosphorous (P)), hydraulic input and withdrawal locations within a limnological system, 

stratification depths, and specific chemical analyses such as DO (via Hydrolab and 

modified Winkler measurements), total-P (TP), BOD, and COD) 

3. Correlation of TP TSI approach with epilimnetic and hypolimnetic DO measurements to 

help establish overall UAA (see further discussion in Response 9) 

A multi-phase UAA approach would best characterize the combined influence of the various 

processes impacting DO levels (in both the epilimnion and the hypolimnion) and subsequent 

attainable use on a site-specific basis. Epilimnetic oxygen levels are primarily controlled by 

photosynthesis, microbial respiration, resupply from the atmosphere, and water column demand. 

Hypolimnetic oxygen levels are typically governed by sediment oxygen demand (SOD). 

Organic or nutrient loading of thermally stratified lakes and reservoirs may lead to significant 

depletion of DO in the lower hypolimnetic water. Hypolimnetic oxygen depletion often results 

in the release of Fe, Mn, and P from sediment oxide precipitates, thereby decreasing water 

quality and increasing drinking-water treatment costs. Release of P can promote excessive algal 

growth, which stimulates eutrophication and can have detrimental effects on the health and 

diversity of the plant, fish, and benthic populations. The abundance of algae is directly 

influenced by the ratios of supplied nutrients. Even small differences in the nutrient ratios (e.g., 

N to P) can have significant effects on competing algal species (Gächter and Muller, 2003; 

Lewandowski et al. 2003). Additional oxygen is consumed as these algal blooms die, settle into 

the hypolimnion, and are degraded by aerobic sediment microbes. Sediment loading may also 

impact oxygen demand by introducing additional Fe, Mn, and P into the system and by partially 

controlling oxygen diffusion rates into the sediment (Muller et al., 2002). Thus, when evaluating 

use attainability, it is important to consider all influences on DO levels: water column demand, 

respiratory demand via microorganisms, SOD, and oxygen resupply. Optimal water quality and 

corresponding use may be established and maintained by controlling P loading or by adding 
76

oxygen to lake and reservoir systems. Hypolimnetic oxygenation systems, which preserve 

stratification, are increasingly used to replenish DO (refer to Response 4). 

Due to the fact that complex interactions between oxygen availability and P cycling have 

such control on water quality and subsequent use attainability, we recommends using DO and TP 

as key parameters in use attainability analyses. It has been shown that DO levels may not 

directly correlate with soluble P levels due to benthic microbial activity and the formation of 

ferrous (reduced Fe) phosphate precipitates, thus supporting the need to separately quantify both 

DO and TP levels (Gächter and Muller, 2003). Because of the various nutrient and oxygen 

requirements specific to each designated use (e.g., cold-water fishery vs. drinking water supply), 

it seems that a UAA evaluating both DO and TP levels should be performed to address the 

particular attainable use criteria for a site. 

8. Can historical DO/temperature depth profile data such as the 1983 EPA Clean Lakes 

Program funded 8 month sampling of 32 lakes in VA be used to demonstrate expected 

dissolved oxygen levels in undisturbed or forested watersheds? 

Historical DO and temperature depth profile data may be extremely valuable resources for 

establishing DO reference levels and anticipated stratified zones. Historical data, such as that 

obtained during the 1983 EPA Clean Lakes VA sampling program, can be used to establish 

expected, base-line DO levels for watersheds with conditions similar to those sampled during this 

EPA study. However, this data should be used predominantly for general guideline purposes, as 

information obtained during the 1983 EPA Clean Lakes study was collected primarily to 

establish base-line data in preparation for subsequent Clean Lakes Program projects (US EPA, 

1982). Base-line DO estimates should be verified by current DO measurements and modified in 

order to accurately characterize existing, reservoir-specific conditions. DO availability and 

depletion rates are very site-specific and transient as they may be significantly influenced by 

variables including sedimentation rates, nutrient loading, OM deposition, local sediment mineral 

(e.g., Fe, Mn, Ca, P) composition, and lake morphometry. Unfortunately, inadequate tributary 

data were obtained during the EPA sampling program due to drought conditions at the time of 

testing (US EPA, 1982). Thus, the transient and possibly considerable influence of nutrient and 

sediment loading was not included in the subsequent trophic state evaluations. 

The concentration and decay of OM present at the sediment surface are typically considered 

to govern oxygen demand (particularly hypolimnetic) in lakes and reservoirs, with high 

concentrations of OM resulting in an increased oxygen demand (Kalin and Hantush 2003). 

However, evidence shows that organic degradation rates may not directly correlate with OM 

concentrations, raising the possibility that different levels of oxygen availability and differing 

rates of OM delivery via sediment focusing may govern SOD (Meckler et al. 2004). Thus, 

variations in oxygen availability, nutrient loading, and OM concentrations (all of which are 

highly site-specific parameters) may have significant impact on nutrient cycling, SOD, and 

hypolimnetic DO levels on a reservoir-specific basis. 

 In addition to establishing base-line DO estimates, historical data may also be very useful 

for determining trends in DO and temperature over time as a function of variations in 

anthropogenic and natural influences (Evans et al., 1996; Nishri et al., 1998; Little and Smol, 

2001). It is likely that these influences have changed significantly since the early 1980’s in 

many VA regions, resulting in altered DO and temperature conditions from those documented 

during the 1983 EPA Clean Lakes VA sampling program. Research has shown that temporal 
77

and spatial variations in lacustrine processes may have considerable control over subsequent 

SOD, DO, and TP levels (Hanson et al., 2003; House, 2003; Kalin and Hantush, 2003; Dittrich et 

al., 2004; Meckler et al., 2004). Transient lacustrine processes (e.g., sediment loading following 

storm events and intermittent accumulation of OM) can have a substantial impact on SOD in the 

zone-of-influence downstream of the discharge point in many systems, subsequently impacting 

water-column DO levels. Nishri et al. (1998) found significant variations in limnological 

parameters over time with epilimnetic DO concentrations increasing by ~20%, hypolimnetic H2S 

concentrations increasing ~75%, and a long-term decrease in zooplankton biomass (~ 50%) from 

1970 to 1991 as a result of reduced allochthonous OM loading and enhanced OM burial in the 

hypolimnetic sediments of Lake Kinneret (Israel). Significant variations in sediment loading and 

OM deposition may have occurred in numerous VA lakes and reservoirs during the last two 

decades. Because OM deposition and accumulation over time have been shown to have strong 

influence on sediment composition and trends in lake metabolism and DO levels, existing 

conditions may deviate considerably from DO and temperature data obtained during the 1983 

EPA study (Hanson et al. 2003). 

Each reservoir is impacted differently by both external (e.g., anthropogenic nutrient loading, 

hydraulic inputs (river, streams), local soil/mineral composition, allocthonous OM loading) and 

internal (bank erosion, water-withdrawal locations, autochthonous OM loading, lake 

morphometry) processes that have strong influence on DO levels. Using historical data from 32 

of the 100+ constructed reservoirs in VA for current estimates of existing reservoir DO in 

undisturbed regions may therefore inadequately represent specific reservoir conditions. 

Nevertheless, historical DO and temperature profiles can be invaluable for establishing 

background information and general estimates of DO levels in undisturbed, forested areas, 

especially when paired with current DO measurements and site-specific data.

9. Could the TMDL program TP/DO TSI approach be used as a template for UAA 

demonstrations? 

The Trophic State Index (TSI) total phosphorus (TP) approach is established as a predictor 

of algal biomass as a function of soluble TP (Carlson, 1977). A TSI value of 60 or greater for 

any one of the 3 indices (chlorophyll-a (CA), Secchi disk (SD), and TP) indicates that nutrient 

loading is negatively impacting designated uses of a particular lake or reservoir. A TSI value of 

60 corresponds to a CA concentration of 20 μg/l, a SD measurement of 1 meter, and a TP 

concentration of 48 μg/l. TSI ratings are based on the following equations, as defined by 

(Carlson, 1977): 

TSI(SD) = 10(6 - (ln SD / ln 2)) 

TSI(CA) = 10(6 - ((2.04 - 0.68 ln CA ) / (ln 2))) 

TSI(TP) = 10(6 - ((ln 48 / TP) / (ln 2))) 

TP is a significant parameter for characterizing limnological trophic states and the TP TSI 

approach may yield a satisfactory approximation of oxygen availability/depletion with respect to 

certain attainable use determinations. However, while strongly indicative of potential 

eutrophication problems, TP analyses alone may not comprehensively indicate corresponding 

DO levels. Admittedly, this may or may not be problematic depending on the intended use of 

the lake or reservoir of concern. 
78

Because of the complex interactions between oxygen levels and P cycling (as defined in 

Response 7) and the resulting impacts on water quality, it is important to evaluate both P and DO 

levels when estimating potential DO demand and subsequently establishing DO criteria. A 

combined TP/DO TSI approach may be an appropriate method for establishing UAA 

demonstrations as long as both TP and DO levels are quantified and correlated. While TP can be 

a strong indicator of DO levels and trophic states, particularly in regions of high photosynthetic 

activity and productivity, other biogeochemical processes may strongly impact DO in 

hypolimnetic regions. Conventional wisdom suggests that oxic sediments retain Fe, Mn, and P, 

thereby promoting improved water quality, while anoxic conditions exacerbate water quality as 

these chemicals and associated compounds are released into the hypolimnion. However, recent 

studies have suggested that benthic microbial activity and the formation of ferrous phosphate 

precipitates (e.g., vivianite) may have a significant influence on sediment/water cycling of 

chemicals and biomineral formation (Gächter and Muller, 2003), indicating that the conventional 

wisdom needs to be re-examined. Thus, elevated hypolimnetic DO concentrations may not 

necessarily result in increased P retention in the benthic sediments or reduced TP levels from the 

water column. Conversely, low TP concentrations in the water column may not always be 

indicative of relatively high levels of hypolimnetic DO, as it is possible that considerable P 

remains complexed in ferrous precipitates under low DO conditions. 

Thus, depending on water use, TSI TP data alone may or may not be directly representative 

of water quality and corresponding DO criteria (Carlson, 1977). It seems that it would be highly 

beneficial to pair TP TSI data with supporting DO measurements. A strong UAA approach 

could be established by incorporating TP TSI methodology with routine DO measurements 

(particularly during stratification) and site-specific data to determine potential drains on oxygen 

demand via natural (sediment deposition, introduced Fe- and Mn-minerals, retention time) and 

anthropogenic (nutrient loading, hydraulic inputs and withdrawals) sources. This approach 

would use soluble TP and DO measurements to identify potential eutrophication problems that 

may exacerbate DO depletion and subsequently decrease water quality. 

Conclusions and Recommendations 

In summary, we recommend that DO criteria be established specifically for Virginia lakes 

and reservoirs and that separate criteria be developed for natural lakes and constructed 

impoundments. Site-specific criteria should be developed for the two natural lakes in the state, 

Mountain Lake and Lake Drummond. To address the effects of stratification on DO 

concentrations throughout the water column, separate criteria for the epilimnion and 

hypolimnion are recommended, and criteria development should be based on designated uses of 

the water bodies. Application of a single DO criterion for all depths within a given lake or 

reservoir may be unnecessarily stringent and not required to fully support the water body’s 

designated uses during stratification. Dissolved oxygen criteria for stratified water bodies should 

ensure that at least one layer exists where temperature, DO, and pH conditions can support 

designated uses. 

Hypolimnetic DO criteria should account for the potential downstream effects of reduced 

oxygen concentrations in waters released from the lower depths of constructed impoundments. 

Currently, almost 6 miles of the Roanoke River are classified as Category 5 impaired waters for 

DO because of hypolimnetic water discharge upstream from Lake Gaston (VDEQ, 2004d). 
79

Also, nearly 6 miles of the Meherrin River are designated Category 5 impaired for DO due to 

hypolimnetic releases from an upstream impoundment (VDEQ, 2004d). 

Because the vast majority of lentic systems in Virginia are constructed impoundments, 

establishing DO criteria based on water body designated use is a reasonable methodology. 

Designated uses have already been determined for Virginia water bodies for biennial preparation 

of the 305(b)/303(d) water quality assessment reports (VDEQ, 2004d). Of the six existing 

designated uses, only aquatic life and public water supply are directly affected by low DO 

concentrations in lakes reservoirs, and recreation may be considered to be indirectly affected. 

Ambient freshwater DO criteria for the protection of aquatic life, both cold and warm water 

species, have been determined previously by EPA (US EPA, 1986). 

EPA has not developed ambient DO criteria for the support of public water supplies, and the 

vast majority of states do not have DO criteria specifically for this designated use. The effects of 

hypolimnetic anoxia on water quality parameters related to drinking water treatment are well 

documented. However, because of insufficient information available, we can recommend only 

preliminary DO criteria for protection of water supply uses. It is suggested that the existing 

freshwater DO criteria for non-trout waters (5 mg L-1 daily average, 4 mg L-1 minimum) be 

applied to all strata used for potable water supply within a given reservoir. This is comparable to 

the approximate, rule-of-thumb DO value of 5 mg L-1 typically desired in influent raw water by 

treatment plant managers. Because the direct treatment benefits of this particular DO 

concentration in lakes and reservoirs have not been well established, DO criteria for protection of 

water supply designated uses may need to be revised after further study by EPA or the scientific 

and engineering community. 

Separate DO criteria specifically for protection of recreational uses is not recommended at 

this time for Virginia. If all reservoirs are designated for aquatic life and/or water supply use, 

then the DO criteria to support these uses would more than likely be adequate to support primary 

and secondary recreational uses. 

Compliance with DO criteria in lakes and reservoirs will likely be determined through field 

data collection. Measurements are typically obtained at appropriate intervals through the water 

column on each sampling date. Dissolved oxygen concentrations are measured with a sensing 

probe or using a modified Winkler technique. The minimum frequency for characterizing 

mixing and the oxic status of a water body is dependent on the oxygen depletion rate. In some 

locations, the minimum required frequency may be monthly; in others, it may be as high as daily 

(US EPA, 2000). Temperature profiles will also be required to determine the onset of 

stratification and to delineate the density strata within water bodies. Dissolved oxygen data from 

most, if not all, of Virginia’s significant reservoirs has been or is currently being collected, as 

evidenced by the biennial 305(b)/303(d) water quality assessment reports (VDEQ, 2004d). To 

ensure that representative DO data are being obtained to accurately characterize each reservoir’s 

oxic status, existing sampling procedures should be reviewed. As referenced in the Nutrient 

Criteria Technical Guidance Manual–Lakes and Reservoirs (US EPA, 2000), there are a number 

of publications that provide further information on sampling designs for lakes and reservoirs 

(Carlson and Simpson, 1996; Gaugush, 1987; Gaugush, 1986; Reckhow, 1979; Reckhow and 

Chapra, 1983). 

With respect to the type of Use Attainability Analysis (UAA) needed to demonstrate 

appropriate DO criteria for lakes and reservoirs, a multi-phase UAA is recommended, based on 

1) a review of supporting literature and historical data; 2) routine, on-site surveys performed to 

analyze parameters relating to DO levels; and 3) correlation of TP TSI approach with epilimnetic 
80

and hypolimnetic DO measurements to help establish overall UAA. We feel that a multi-phase 

UAA approach would best characterize the combined influence of the various processes 

impacting DO levels (in both the epilimnion and the hypolimnion) and subsequent attainable use 

on a site-specific basis. Due to the fact that complex interactions between oxygen availability 

and P cycling have such control on water quality and subsequent use attainability, we 

recommend using DO and TP as key parameters in use attainability analyses. 

Historical data, such as that obtained during the 1983 EPA Clean Lakes VA sampling 

program, may be very useful for establishing expected, base-line DO levels for watersheds with 

conditions similar to those sampled during the 1983 sampling program. Additionally, historical 

data may also be valuable for determining trends in DO and temperature over time as a function 

of variations in anthropogenic and natural influences. Regarding the use of historical data for 

estimates of current DO conditions, this data should be used predominantly for general guideline 

purposes, as information obtained during the 1983 EPA Clean Lakes study was collected 

primarily to establish base-line data in preparation for subsequent Clean Lakes Program projects 

(US EPA 1982). Base-line DO estimates should be verified by current DO measurements and 

modified with respect to existing, reservoir-specific conditions. DO availability and depletion 

rates are very site-specific and transient as they may be significantly influenced by variables 

including sedimentation rates, nutrient loading, OM deposition, local sediment mineral (e.g., Fe, 

Mn, Ca, P) composition, and lake morphometry. Thus, variations in oxygen availability, nutrient 

loading, and OM concentrations (all of which are highly site-specific parameters) may have 

significant impact on nutrient cycling, SOD, and hypolimnetic DO levels, emphasizing the need 

for current DO measurements on a reservoir-specific basis. 

 We feel that a combined TP/DO TSI approach may be an appropriate method for 

establishing UAA demonstrations as long as both TP and DO levels are quantified and 

correlated. Because of the complex interactions between oxygen levels and P cycling and the 

resulting impacts on water quality, it is important to evaluate both P and DO levels when 

estimating potential DO demand and subsequently establishing DO criteria. While TP may be a 

strong indicator of DO levels and trophic states, particularly in regions of high photosynthetic 

activity and productivity, other biogeochemical processes may strongly impact DO in 

hypolimnetic regions. A strong UAA approach could be established by incorporating TP TSI 

methodology with routine DO measurements and site-specific data to determine potential drains 

on oxygen demand via natural and anthropogenic sources. This approach would use soluble TP 

and DO measurements to identify potential eutrophication problems that may exacerbate DO 

depletion and subsequently decrease water quality. 
81

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Committee to Virginia Department of Environmental Quality–Freshwater Nutrient 

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86

Table 1. Significant Reservoirs by Region as of August 2004 (VDEQ, 2004d) 

Reservoir Location 

Surface 

Area 

(acres) 

Public 

Water 

Supply? 

2004 DO 

Impairment 

Category 

Northern Regional Office – 13 Lakes 

Able Lake Stafford County 185 Yes 

Lake Anna Louisa County 9,600 

Aquia Reservoir (Smith 

Lake) 

Stafford County 219 Yes 

Beaverdam Reservoir Loudoun County 350 Yes 

Burke Lake Fairfax County, VDGIF 218 

Goose Creek Reservoir Loudoun County 140 Yes 

Lake Manassas Prince William County 741 Yes 

Motts Run Reservoir Spotsylvania County 160 Yes 

Mountain Run Lake Culpepper County 75 Yes 5C 

Ni Reservoir Spotsylvania County 400 Yes 

Northeast Creek 

Reservoir 

Louisa County 49 Yes 

Occoquan Reservoir Fairfax County 1,700 Yes 5C 

Pelham Lake Culpepper County 253 Yes 5C 

Piedmont Regional Office – 11 Lakes 

Airfield Pond Sussex County, VDGIF 105 

Amelia Lake Amelia County, VDGIF 110 

Brunswick Lake Brunswick County, 

VDGIF 

150 

Lake Chesdin Chesterfield County 3,196 Yes 5A 

Chickahominy Lake Charles City County 1,500 Yes 5A 

Diascund Reservoir New Kent County 1,700 Yes 4C 

Emporia Lake Greensville County 210 Yes 4C 

Falling Creek Reservoir Chesterfield County 110 

Great Creek Reservoir 

(Bannister Lake) 

Lawrenceville 305 4C 

Swift Creek Lake Chesterfield County 156 

Swift Creek Reservoir Chesterfield County 1,800 Yes 4C 

South Central Regional Office – 22 Lakes 

Briery Creek Lake Prince Edward County, 

VDGIF 

850 

Brookneal Reservoir Campbell County 25 Yes 

Cherrystone Lake Pittsylvania County 105 Yes 4C 

Georges Creek 

Reservoir 

Pittsylvania County 1 Yes 

Gordon Lake Mecklenburg County, 

VDGIF 

157 
87

Reservoir Location 

Surface 

Area 

(acres) 

Public 

Water 

Supply? 

2004 DO 

Impairment 

Category 

Graham Creek 

Reservoir 

Amherst County 50 Yes 4C 

Halifax Reservoir Halifax County 410 Yes 

Holiday Lake Appomattox County 145 

Kerr Reservoir (VA 

portion) 

Halifax County, VDGIF 35,251 Yes 5A 

Keysville Lake Charlotte County 42 Yes 

Lake Conner Halifax County, VDGIF 111 

Lake Gaston (VA 

portion) 

Brunswick County 5,529 Yes 5A 

Lunenberg Beach Lake Town of Victoria 13 Yes 

Modest Creek Reservoir Town of Victoria 29 Yes 

Nottoway Falls Lake Lunenburg County 60 Yes 5A 

Nottoway Lake Nottoway County 188 

Nottoway Pond Nottoway County 65 Yes 

Pedlar Lake Amherst County 75 Yes 5C 

Roaring Creek Pittsylvania County 19 Yes 

Stonehouse Creek 

Reservoir 

Amherst County 125 

Thrashers Creek 

Reservoir 

Amherst County 110 

Troublesome Creek 

Reservoir (SCS 

Impoundment No. 2) 

Buckingham County 85 Yes 

South West Regional Office – 9 Lakes 

Appalachia Reservoir Wise County 17 Yes 

Big Cherry Lake Wise County 76 Yes 5A 

Byllsby Reservoir Carroll County 335 

J. W. Flannigan 

Reservoir 

Dickenson County, 

ACOE 

1,143 Yes 5A 

Hungry Mother Lake Smyth County 108 Yes 5A 

Lake Keokee Lee County, VDGIF 100 5A 

Laurel Bed Lake Russell County, VDGIF 300 

North Fork Pound 

Reservoir 

Wise County, ACOE 154 Yes 5A 

South Holston Reservoir Washington County, 

TVA 

7,580 Yes 5A 

Tidewater Regional Office – 20 Lakes 

Lake Cahoon Suffolk City 508 Yes 

Lake Burnt Mills Isle of Wight County 711 Yes 

Harwood Mill Pond York County 300 Yes 5A 
88

Reservoir Location 

Surface 

Area 

(acres) 

Public 

Water 

Supply? 

2004 DO 

Impairment 

Category 

Lake Kilby Suffolk City 226 Yes 

Lee Hall Reservoir Newport News 230 Yes 5A 

Little Creek Reservoir Norfolk City 193 Yes 

Little Creek Reservoir James City County 860 Yes 

Lake Lawson Norfolk City 77 Yes 

Lone Star Lake F Suffolk City 20 Yes 

Lone Star Lake G Suffolk City 50 Yes 

Lone Star Lake I Suffolk City 39 Yes 

Lake Meade Suffolk City 511 Yes 

Lake Prince Suffolk City 946 Yes 

Lake Smith Norfolk City 193 Yes 5A 

Speights Run Lake Suffolk City 94 Yes 

Stumpy Lake Virginia Beach 210 Yes 

Waller Mill Reservoir York County 315 Yes 

Lake Whitehurst Norfolk City 480 Yes 

Lake Wright Norfolk City 49 Yes 

Western Branch 

Reservoir 

Norfolk City 1,265 Yes 

Valley Regional Office – 12 Lakes 

Beaver Creek Reservoir Albemarle County 104 Yes 

Mount Jackson 

Reservoir 

Shenandoah County 0.7 Yes 

Coles Run Reservoir Augusta County, USFS 9 Yes 

Elkhorn Lake Augusta County, USFS 55 Yes 4C 

Lake Frederick Frederick County, 

VDGIF 

120 4C 

Ragged Mountain 

Reservoir 

Albemarle County 54 Yes 4C 

Rivanna Reservoir Albemarle County 390 Yes 

Staunton Dam Lake Augusta County 30 Yes 

Strasburg Reservoir Shenandoah County 5.3 Yes 

Switzer Lake Rockingham County, 

USFS 

110 

Sugar Hollow Reservoir Albemarle County 47 Yes 4C 

Totier Creek Reservoir Albemarle County 66 Yes 5A 

West Central Regional Office – 15 Lakes 

Beaverdam Creek 

Reservoir 

Bedford County 123 Yes 

Bedford Reservoir Bedford County 28 Yes 

Carvins Cove Reservoir Botetourt County 630 Yes 4C 

Claytor Lake Pulaski County 4,483 Yes 4C 
89

Reservoir Location 

Surface 

Area 

(acres) 

Public 

Water 

Supply? 

2004 DO 

Impairment 

Category 

Clifton Forge Reservoir Alleghany County, USFS 16 Yes 

Fairystone Lake Henry County 168 

Gatewood Reservoir Pulaski County 162 

Hogan Lake Pulaski County 40 Yes 

Leesville Reservoir Bedford County 3,400 Yes 4C 

Little River Reservoir Montgomery County 113 

Martinsville Reservoir Henry County 220 Yes 

Lake Moomaw Bath County, USFS 2,430 4C 

Philpott Reservoir Franklin, Henry, and 

Patrick Counties; ACOE 

2,879 4C 

Smith Mountain Lake Bedford, Franklin, and 

Pittsylvania Counties 

19,992 Yes 4C, 5A 

Talbott Reservoir Patrick County 165 

Total 102 Lakes Statewide 
90

Table 2. Capital costs of representative hypolimnetic aeration and oxygenation systems. 

Waterbody 

Maximum 

Depth 

(m) 

Volume 

(106 m3) 

Aerator or 

Oxygenator 

Type 

Application Year Installed Oxygen Addition (kg d-1) Capital Cost (2005 $) 

Richard B. Russell Reservoir, Georgia 

47 1,270 bubble plume 

diffuser 

hydropower 1985 200,000 $1.6M 

Lakes Prince and Western Branch, Virginia 

11 38 full-lift 

aerator 

water 

supply 

1991 10,700 $2.8M 

Camanche Reservoir, California 

41 545 Speece Cone hydropower 1993 9,000 $1.8M 

Spring Hollow Reservoir, Virginia 

55 7.2 bubble plume 

diffuser 

water 

supply 

1998 250 $120K 

Upper San Leandro Reservoir, California 

 51 bubble plume 

diffuser 

water 

supply 

2002 9,000 $450K 
91

Appendix D. Maryland-USGS Proposal to EPA 

A REGIONAL APPROACH 

TO THE DEVELOPMENT OF 

A RESPONSE-BASED NUTRIENT CRITERION 

FOR WADEABLE STREAMS 

IN NUTRIENT ECOREGIONS IX, XI, & XIV 

a proposal 

Submitted to the 

U.S. Environmental Protection Agency 

by 

Joseph R. Beaman, Ecotoxicologist 

Section Head, Toxics and Standards 

Maryland Department of the Environment 

Technical & Regulatory Services Administration 

Baltimore, Maryland 

Phone: (410) 537-3633 

e-mail: jbeaman@mde.state.md.us 

June 17, 2003 

Revised: September 10, 2003 

Revised: September 25, 2003 
92

I. AGENCY OVERVIEW

Maryland Department of the Environment (MDE) 

Maryland Department of the Environment (MDE) is the State government agency in 

charge of restoring and maintaining the quality of ground and surface waters. This 

requirement is accomplished through the enforcement of water quality standards and 

controls on point and non-point sources of pollution. To ensure that water quality 

standards are met, the Department monitors its waters on a rotating watershed basin 

system. The Department has accomplished much of its monitoring efforts through the 

management of grant monies awarded by EPA. Some specific monitoring initiatives 

include tidal and nontidal water quality assessment, monitoring in support of TMDL 

development and the fish and shellfish tissue-monitoring program. 

United States Geological Survey 

The U.S. Geological Survey (USGS), Pennsylvania District, has a long history of 

investigations into nutrients in streams and their relations to water quality. Staff from the 

Pennsylvania district have conducted long-term investigations of nutrients in the 

Conestoga River Basin (Lietman, 1997) as part of the Rural Clean Waters Program 

(RCWP). Most recently, we have completed a study of the relations of nutrients to 

biological indicators for lakes and streams in Pennsylvania and West Virginia (Brightbill 

and Koerkle, 2003). Two other studies, both long-term and involving evaluations of 

BMPs are nearing completion (Schreffler and others, in preparation: Galeone and 

Brightbill, in preparation). Capabilities within the District include macroinvertebrate 

identification and analysis, use of continuous water-quality monitors, field water-quality 

assessments, and data management and analysis. Other capabilities within the USGS 

include state-of-the-art laboratory analyses, macroinvertebrate identifications, 

development of statistical techniques for trend analysis, development of regional waterquality models, and collection and analysis of regional and nationwide environmental 

data. 

II. BACKGROUND 

Nutrients have been cited as one of the leading causes of impairment for streams in the 

United States. The number of waters adversely affected by nutrients is documented in 

the various 305(b) reports prepared by the states and submitted to the U.S. Environmental 

Protection Agency (EPA). Impairment from nutrients may reach as high as 50 percent of 

all waters surveyed in a state. 

To address these problems, the EPA is in the process of helping states establish nutrient 

criteria. The EPA has published nutrient criteria technical guidance manuals for three 

aquatic environments (Lakes and Reservoirs, Rivers and Streams, and Estuarine and 

Coastal Marine Waters), with a fourth document (Wetlands) scheduled for publication in 
93

December 2003. Also, EPA has published suggested nutrient criteria for all 14 nutrient 

ecoregions across the United States. 

These suggested criteria were established based on an examination of reference 

conditions and levels of nutrients that lead to impairment. This approach has gained 

praise and acceptance from many in the scientific and regulatory community for its 

scientific validity. Yet, others have called for more of an effects-based approach where 

response variables and water uses are factored into the nutrient criteria. 

The research suggested in this proposal will (1) use an effects-based approach to 

calculate nutrient criteria and (2) assess periphyton communities in EPA Region III 

streams. An effects-based approach means we look at the effects caused by nutrients to 

calculate a nutrient criterion level. The nutrient criteria can be used to support to EPA’s 

reference condition approach or can be used as an alternative to the EPA approach. The 

periphyton community assessment can be used to evaluate the ecological health of the 

periphyton community for the streams studied and will provide initial data for future 

development of a periphyton index of biotic integrity (P-IBI). 

EPA’s Request for Proposals asked for a compilation and evaluation of existing data to 

address the proposed research. Available data are not summarized here because (1) the 

database contains an inadequate number of chlorophyll a measurements for streams and 

(2) the dissolved oxygen measurements residing in the current database are collected 

primarily during daylight hours. Critical dissolved oxygen concentrations occur at night. 

Further, the existing data for much of EPA Region III have recently been analyzed 

(Brightbill and Koerkle, 2003). 

III. QUALITY ASSURANCE PROCEDURES

Quality assurance procedures will include using a written, standardized protocol for field 

work, training for all personnel who collect field data, and standardized field datacollection sheets for all field work. Duplicates, blanks, and reference materials will be 

used to assure the quality of laboratory work. Laboratory QC will include lab 

participation in the U.S. Geological Survey’s Inter-Laboratory Evaluation Program 

(Woodworth and Connor, 2003). 

Quality control for the data will include standardized field data-collection sheets, periodic 

reviews of the data sheets, spot checks of the electronic data, and both tabular and 

graphical displays of the data to check for anomalies. 

Periphyton identifications will be done using widely accepted keys. Verification of 

periphyton identifications will be done by an outside lab for 5 percent of the samples. 
94

IV. DESCRIPTION OF THE PROJECT

Purpose and Scope: 

Two overall objectives are proposed: 

1. Use response variables (dissolved oxygen and periphyton chlorophyll a) to 

suggest nutrient criteria for streams in EPA’s Region 3. This is the primary 

objective. Data from the second objective will be used as input information to 

reach this primary objective. 

2. Examine periphyton communities for streams in EPA’s Region 3 to see if 

eutrophic conditions are associated with specific community characteristics. 

Study sites would include several 2nd, 3rd, and 4th order streams in Nutrient Ecoregions 

IX, XI, and XIV, all in EPA’s Region 3. These Ecoregions comprise most of the land 

area of the states in EPA’s Region 3 and extend beyond Region 3 to include most of the 

southeastern U.S. The proposed work would involve field data collection during the 

critical summer months of 2004. Data evaluation and report preparation would follow 

through the fall and winter of 2004 and into 2005 with a final report due on September 

30, 2005. 

The final report for the project (page 9) will specifically address the use of periphyton 

community structure and chlorophyll a for predicting over-enrichment in streams. 

Project Overview: 

This proposal defines a cooperative approach to (1) defining nutrient criteria for streams 

in EPA Region 3 and (2) examining periphyton community structure for wadeable 

streams (Strahlher Order 2-4). The success of this project is based on the cooperative and 

collaborative efforts of EPA Region III (funding source), and National (USGS) and State 

agencies (MDE, WV DEP, VA DEP PA DEP) responsible for management of 

environmental quality. A cooperative approach is required for a region-wide effort and to 

pool resources for funding and personnel. 

Development of Nutrient Criteria: 

Other researchers have attempted to define the relations between nutrients and 

periphyton. These efforts have been only partly successful. One reason is that the 

researchers pooled all available data for the analysis, then looked for relations. But, 

nutrients are transient. The concentrations observed in a stream on any day will likely be 

different the next day or the next week. Therefore, the proposed work will examine 

nutrient-periphyton relationships for a short term only -- during the critical summer 

period of July and August. Oxygen consumption is largest at night. Thus, nighttime 

measurements collected during the hottest, driest period of the year are critical for 
95

establishing nutrient criteria. Measurements at other times of the year are superfluous, 

and even confusing. 

Our approach will be to select a subset of the streams used for the periphyton community 

work to examine nutrient concentrations, periphyton biomass, and dissolved oxygen 

concentrations. For this subset of streams, we will install continuous recording dissolved 

oxygen monitors for a brief period (48 hours). It is important to measure the variability 

of dissolved oxygen for a diel cycle because the lowest dissolved oxygen concentrations 

will occur at night when photosynthesis is shut down, but respiration continues. (Another 

reason for the failure of previous nutrient studies is that routine monitoring is usually a 

daytime activity.) The dissolved oxygen measured during routine daytime monitoring 

fails to capture the lowest dissolved oxygen concentrations. 

Assessment of Stream Periphyton Communities: 

Examination of periphyton communities is an effects-based approach that will attempt to 

link nutrient conditions within a stream system to the growth of periphyton communities 

within that stream. Periphyton are important to stream ecology because they are the main 

primary producers. This means that they are one of the first links in the aquatic food 

chain and can ultimately affect the populations of other species as well. Periphyton are 

also useful indicators of stream environmental conditions because they quickly respond 

to changes and are sensitive to a number of disturbances, including nutrient overenrichment. Nutrients, especially limiting nutrients, can vary in time and space in the 

water column, and they are transient. As a resident biotic component in streams, 

periphyton can quickly register and incorporate transient or episodic changes in nutrient 

conditions, or act as nutrient "sinks." Thus, having an understanding of the periphyton 

community in a stream can reveal a lot about the ecological health and the nutrient status 

of that stream. 

In addition to nutrients, periphyton growth is controlled by available sunlight, time 

available to grow since the last high-flow event, streambed stability, soil type, substrate 

availability, water speed, and grazing by invertebrates. Finally, upstream land use (e.g., 

acreage and proximity of agricultural uses or publicly owned treatment works (POTWs)) 

and channel modification (e.g., width of the wooded riparian buffer strip) play an 

important role in determining nutrient additions to streams. Healthy streams typically 

have little obvious periphyton, because growth is cropped by invertebrate grazers and 

turned into invertebrate biomass. Nuisance blooms are usually a symptom of a system 

stressed by things like over-supply of nutrients and high temperatures (that increase algal 

growth rates but stress some invertebrate grazers). Analysis of periphyton coverage, 

biomass, and speciation (e.g. abundances of eutrophic species), as well as the 

aforementioned factors will be used to determine relations between nutrients and 

periphyton communities in Region III streams. 
96

Approach: 

Field work for the project will be done by the cooperating states (Maryland, Virginia, 

West Virginia, and Pennsylvania), using common protocols (mutually agreed upon) 

documented and distributed by the USGS. Training for all field personnel will assure 

uniform data collection across the Region. Data management and analysis will be 

handled jointly by MDE and USGS. Report preparation will primarily be the 

responsibility of the USGS with assistance from MDE. 

Development of Nutrient Criteria: 

Several streams (our goal is 12 streams for this work) will be selected for the nutrient 

criteria work. The streams selected will represent a range of nutrient conditions and will 

come from at least three Nutrient Ecoregions. At each stream, all the measurements from 

the periphyton component of the research (including chlorophyll a, nutrients, standard 

field measurements, and total organic carbon) described in the next section of this 

proposal will be recorded. In addition, continuous dissolved oxygen (DO) monitors will 

be deployed for at least two 24-hour periods during the hottest part of the summer. 

Minimum DO values are critical for aquatic life and this plan will capture those minimum 

DO values, or values that are near minimum for the year. Data from the continuous 

monitors will be downloaded in the field to a notebook computer. These data will then be 

transferred to computers housed at the USGS in New Cumberland, Pennsylvania for 

analysis. Nutrient concentrations associated with DO concentrations lower than the 

applicable water-quality standard (5 mg/L) will be considered to be excessive. 

Assessment of Stream Periphyton Communities: 

Stream locations will be selected to represent major natural and human factors that are 

thought to significantly influence the quality of water. The overall approach will consist 

of a goal-oriented, stratified random sampling design. The advantage of this approach is 

that fewer resources are expended since only “targeted” streams within each stratum will 

be sampled. Also, selection of a range of nutrient conditions will facilitate establishment 

of nutrient-algal (periphyton) relationships that will enable the eventual development of 

an index of biotic integrity based on those relationships. The goal will be to include 

streams that represent a wide spectrum of biotic conditions, from reference conditions to 

eutrophic conditions. 

Because the main study objective is the development of ecoregion-specific nutrient 

criteria, the initial stratification will be by nutrient ecoregion. The Level III Aggregate 

Nutrient Ecoregions of interest in EPA Region 3 are Ecoregion IX (Southeastern 

Temperate Forested Hills and Plains), Ecoregion XI (Central and Eastern Forested 

Uplands), and Ecoregion XIV (Eastern Coastal Plain). Other Ecoregions within the EPA 

Region 3 boundaries represent relatively small areas and will not be sampled in the 

proposed research. An equal number of samples will be allocated to each ecoregion. 
97

Next, Strahler stream order will be used to further segregate potential sampling reaches 

within each ecoregion. One or two Strahler orders will be selected based on availability 

of information in each ecoregion. Finally, a review of the existing data (EPA Nutrient 

database 1990-1998) will enable stratification by nutrient condition. Streams will be 

separated into high, moderate, and low nutrient conditions/potential based on the 

assessment of available in-stream nutrient data, as well as land use information. 

At the completion of this stratification process, each ecoregion should have a list of 

potential sampling sites based on stream order and nutrient condition. Stations (sampling 

reaches) will be selected randomly from the entire population of streams within each 

stratum. The sampling reach, described by Meador and others (1993), represents the 

sampling unit for ecological assessments used by NAWQA, and the Maryland Biological 

Stream Survey (MBSS) network (Reference, date). A randomization program will be 

used to select “x” number of samples from the pool of available streams within each 

strata. Our initial goal is to sample 15 streams within each of the three Nutrient 

Ecoregions. The procedure will be repeated for each stratum. 

A modified version of the periphyton sampling techniques and methods used in the 

USGS NAWQA (Moulton and others, 2002) and EPA’s EMAP studies will be used. A 

single qualitative multi-habitat sample and duplicate quantitative single habitat samples 

(one for taxonomic identification, and one for biomass measurements), and a waterquality sample will be collected from each sampling reach. Riffle habitat will be used for 

quantitative sampling. In the Coastal Plain (Ecoregion XIV), riffle habitat may not be 

commonly available in all sampling reaches due to ecoregional geology. In this case, 

quantitative artificial substrate samplers (i.e., periphytometers) will be used in all 

sampling reaches in Ecoregion XIV. 

Qualitative periphyton samples are intended to provide a list of species (taxa richness) 

present in the sampling reach. Samples of algae are collected from each periphyton 

microhabitat present in the sampling reach and composited into one sample. A subsample of the qualitative periphyton sample also can be used to assist with the 

identification or verification of diatom species and varieties in quantitative periphyton 

samples from the same location. Water quality can be assessed by interpreting 

autecological information, the taxon-specific physiological requirements or tolerance for 

defined ranges of water-quality conditions. Quantitative periphyton samples are 

collected to measure the relative abundance and density (algal cells per square 

centimeter) of each taxon present in each selected stream habitat type (riffle) in a 

sampling reach. Quantitative samples are collected using a variety of sampling devices 

(i.e., NAWQA periphyton sampling device SG-92 or the whole-rock approach); the 

appropriate choice of sampling equipment is dictated by the character of the dominant 

periphyton growth forms and microhabitats in the sampling reach. Samples are 

processed, stored, and transported using protocols established by EMAP studies. Habitat 

assessments patterned after EPA’s Rapid Bioassessment Protocols (including photo 

documentation and geo-referencing) will complete the process for each site. 
98

Samples for taxonomic identification will be shipped to a laboratory chosen through an 

open bidding process. All genera in the samples will be identified to species level to 

enable the development of autecological indices for the three ecoregions. A photo library 

will be developed for the agency for future reference purposes. A sub-sample of the 

quantitative sample, as well as samples for biomass determination will be retained at 

MDE for processing. MDE field staff are competent at periphyton identification to the 

genus level. This sub-sample will be used to build taxonomic expertise to species level, 

thus enabling the agency to develop in-house capacity for future assessment efforts. 

Periphyton data will be related to corresponding physical, chemical, and biological data 

at each basic fixed site to evaluate taxon-specific responses to differences or changes in 

water chemistry, to assess the effects of algal communities on water quality, and to 

integrate physical, chemical, and biological characteristics into regional assessments of 

water quality. 

Analytical Requirements: 

Field water-quality samples will be sent to a contract laboratory for analysis. Analyses 

will be needed for a broad range of nutrients including dissolved nitrite+nitrate, dissolved 

ammonia nitrogen, organic nitrogen, dissolved ortho-phosphate, and total phosphorus. 

Total organic carbon will also be measured to provide insight into the role of organic 

matter in oxygen depletion in the streams. Chlorophyll a will be measured as a surrogate 

for algal biomass. 

Data Management: 

Initially, data from the project will be housed in electronic files at the USGS 

Pennsylvania District, New Cumberland, Pennsylvania. A back-up duplicate copy of all 

data files will be stored at MDE offices, Baltimore, Maryland. Near the end of the 

project, data will be placed into EPA’s national database, STORET. A commercially 

available database management system such as Access will be used for data storage and 

management. Commercially available software will be used for graphical and statistical 

analyses. 

Evaluation Plan: 

The periphyton community data will be analyzed by looking for indicator species and by 

comparing several different community metrics. The final selected eutrophic indicators 

will offer the best representation of the health of the system, with attention given to 

simplicity. The idea is to develop a tool that is reflective of stream conditions, yet can be 

routinely implemented by most field biologists employed by state resource agencies. 

Data from the project will be examined using a variety of traditional and multivariate 

approaches. Non-parametric techniques are likely to be used as biological data 

frequently are not normally distributed. Data transformations may be needed to deal with 

non-linear relationships. Graphical presentation of the data will be emphasized. 
99

For the development of nutrient criteria, graphing measured nutrient concentrations for 

several streams against minimum dissolved oxygen concentrations are expected to 

produce a relationship generalized in Figure 1. The nutrient concentration that results in 

meeting the water-quality standard (dissolved oxygen concentration of 5.0 mg/L) will 

provide the basis for the proposed nutrient criterion. 

Figure 1. Expected relation between measured nutrients and minimum dissolved oxygen 

in Region III streams (generalized). 

Data gathered to populate the graph in Figure 1 are not all expected to fall on the line 

depicted in Figure 1. Instead, some data points will fall above the line. However, no data 

points should fall below the line. Data would be expected to fall above the line when 

some factor other than the nutrient being considered is limiting. For example, Figure 2 

shows what the data might look like for several streams, some having light as the limiting 

factor, and some having nitrogen as the limiting factor. 

Minimum Dissolved Oxygen Concentration 

Measured 

Nutrient 

Concentration 

5 mg/L 

Proposed nutrient 

criterion 
100

Figure 2. Hypothetical data distribution for Region III streams. 

For those streams where nitrogen is limiting, the data point falls on the line. If light (or 

any other variable) is limiting, periphyton will not grow to their maximum potential. 

Minimum dissolved oxygen concentrations will be larger than the case where nitrogen is 

limiting. The data point will fall above the line. For those data points that fall on (or 

near) the line, a least squares regression, based on a General Linear Model, will be used 

to quantify the relation between nutrients and dissolved oxygen. This relation will be 

used to quantify the nutrient criterion. 

We will measure dissolved organic carbon to account for oxygen-demanding organic 

matter in the stream. The goal is to measure relations between nutrients and minimum 

dissolved oxygen concentrations that are not influenced by variables other than nutrients. 

 

This approach has a weakness that minimum dissolved oxygen concentrations are 

influenced by productivity of the attached algae as well as the standing crop of attached 

algae. We do not propose to measure algal productivity. Intuitively, algal productivity 

and algal standing crop are likely related, so measurement of standing crop by 

chlorophyll a concentrations should suffice. 

Minimum Dissolved Oxygen Concentration 

Measured 

Nitrogen 

Concentration 

5 mg/L 
101

Products: 

1. Semi-annual progress reports will document progress on the project. 

2. Project personnel will present a paper from the work at one scientific meeting to 

encourage exchange of ideas among the scientific community. 

3. Project personnel will provide a seminar for one Region 3 Regional Technical 

Assistance Group (RTAG)1

 meeting to help disseminate the results of the work. 

4. A final, published report will be provided to EPA approximately one year 

following completion of laboratory analyses. This report will specifically address 

the feasibility of using periphyton community structure and chlorophyll a

measurements to define nutrient over-enrichment. 

1

The RTAG is a group of scientists and environmental managers that meets periodically 

to exchange information about nutrient criteria development and implementation and 

serves as an advisory body for each EPA Region on nutrient issues. 

Schedule: 

The following table depicts the time line to be followed for the project. 

Federal fiscal year 2004 

(Months) 

Federal fiscal year 2005 

(Months)

Task

O N D J F M A M J J A S O N D J F M A M J J A S 

Project planning. 

Site selection. 

Meetings with partners. 

 

Order supplies and 

equipment. 

 

Prepare sampling 

protocols 

 

Write and submit QA 

plan, safety plan. 

 

Field data collection 

Diurnal DO work 

Data management and 

analysis. 

 

Report preparation, 

review, and publication 

 

Public presentation. 
102

Benefits: 

Responding to the Clean Water Action Plan, the U.S. Environmental Protection Agency 

has been tasked with responding to the problem of nutrient enrichment in U.S. 

waterways. One of the ways the EPA is addressing this task is to provide guidance for 

the states to establish water-quality standards for nutrients. The results of this study will 

provide support for a response-based approach to establishing these nutrient standards for 

streams. 

The results of this study and further investigations support the mission of the USGS by 

providing a regional pattern of water quality in the mid-Atlantic states. This knowledge 

and other pertinent information relating to nutrients will assist states faced with 

establishing water-quality standards for nutrients. 

REFERENCES CITED

Brightbill, Robin A., and Koerkle, Edward H., 2003, Relations of biological indicators to nutrient 

data for lakes and streams in Pennsylvania and West Virginia, 1990-98: U.S. Geological Survey 

Water-Resources Investigations Report 03-4033, 67 p. 

Galeone, D.L., and Brightbill, R.A. In preparation, Effects of streambank fencing on water 

quality, Lancaster County, Pennsylvania: U.S. Geological Survey Water-Resources Investigations 

Report. 

Lietman, P.L., 1997, Evaluation of agricultural best-management practices in the Conestoga 

River headwaters, Pennsylvania: A summary report 1982-1990: U.S. Geological Survey WaterSupply Paper 2493. 

Meador, M.R., Hupp, C.R., Cuffney, T.F., and Gurtz, M.E., 1993, Methods for characterizing 

stream habitat as part of the national Water-quality Assessment Program: U.S. Geological Survey 

Open-File Report 93-408, 48 p. 

Moulton II, S.R., Kennon, J.G., Goldstein, R.M., and Hambrook, J.A., 2002, Revised protocols 

for sampling algal, invertebrate, and fish communities as part of the National Water-Quality 

Assessment Program: U.S. Geological Survey Open-File Report 02-150, 75 p. 

Schreffler, C.L., Galeone, D.G., Veneziale, J., Olsen, L., and O’Brien, D.L., In preparation, 

Effects of spray irrigated treated effluent on water quantity and quality, and the fate and transport 

of nitrogen in a small watershed, New Garden Township, Chester County, Pennsylvania: U.S. 

Geological Survey Water-Resources Investigations Report. 

Woodworth, M.T., and Connor, B.F., 2003, Results of the U.S. Geological Survey’s Analytical 

Evaluation Program for Standard Reference Water Samples Distributed in September, 2002: U.S. 

Geological Survey Open-File Report 02-481, 108 p. 

Roth, N.E., and others, 2001, Maryland biological stream survey 2000-2004 Volume I: 

Ecological Assessment of Watersheds Sampled in 2000: Maryland Department of Natural 

Resources, variously paged. 
103

Appendix E. Pennsylvania-USGS Proposal to EPA 

A RESPONSE-BASED APPROACH 

FOR DEVELOPMENT OF NUTRIENT CRITERIA 

IN THE MID-ATLANTIC STATES 

a proposal 

Submitted to the 

U.S. Environmental Protection Agency 

by 

Will Brown Jr. 

Pennsylvania Department of Environmental Protection 

Bureau of Water Supply and Waste Water Management 

Rachael Carson State Office Building 

P.O. Box 8467 

Harrisburg, Pennsylvania 17105-8467 

Phone: (717) 783-2951 

e-mail: willbrown@state.pa.us 

June 22, 2004 
104

I. ABSTRACT

An expansion of a nutrient criteria development project currently underway in Region 3 is 

proposed. Periphyton biomass and community structure will be evaluated in relation to nutrient 

concentrations. Nutrient criteria will be suggested based on the response variables, chlorophyll a

and nighttime dissolved oxygen. Most previous studies have neglected to include nighttime 

dissolved oxygen. Collaboration among Region 3 states Pennsylvania, Maryland, Virginia, and 

West Virginia and Delaware will allow sampling in each of these states and the District of 

Columbia. A cooperative agreement with the USGS will add technical expertise to the project. 

II. ORGANIZATIONAL OVERVIEW

Pennsylvania Department of Environmental Protection (PADEP): 

The Pennsylvania Department of Environmental Protection (PADEP) is in charge of maintaining 

and restoring the quality of ground and surface waters. This requirement is accomplished through 

the enforcement of water quality standards established by the PADEP and by controls on point 

and non-point sources of pollution. To ensure that water quality standards are met, the 

Department regularly monitors the waters of Pennsylvania through the Water Quality Network 

which includes 123 standard stations and 26 reference stations. Monitoring and modeling in 

support of TMDL development is also a function of the PADEP. Most significantly for this 

proposal., the PADEP is charged with establishing water-quality standards, including nutrient 

standards, for the State. 

United States Geological Survey: 

The U.S. Geological Survey (USGS), Pennsylvania District, has a long history of investigations 

into nutrients in streams and their relations to water quality. Staff from the Pennsylvania district 

have conducted long-term investigations of nutrients in the Conestoga River Basin (Lietman, 

1997) as part of the Rural Clean Waters Program (RCWP). Most recently, we have completed a 

study of the relations of nutrients to biological indicators for lakes and streams in Pennsylvania 

and West Virginia (Brightbill and Koerkle, 2003). Two other studies, both long-term and 

involving evaluations of BMPs are nearing completion (Schreffler and others, in preparation: 

Galeone and Brightbill, in preparation). Capabilities within the District include 

macroinvertebrate identification and analysis, use of continuous water-quality monitors, field 

water-quality assessments, and data management and analysis. Other capabilities within the 

USGS include state-of-the-art laboratory analyses, macroinvertebrate identifications, 

development of statistical techniques for trend analysis, development of regional water-quality 

models, and collection and analysis of regional and nationwide environmental data. 

III. BACKGROUND 

Nutrients have been cited as one of the leading causes of impairment for streams in the United 

States. The number of waters adversely affected by nutrients is documented in the various 305(b) 

reports prepared by the states and submitted to the U.S. Environmental Protection Agency 
105

(EPA). Impairment from nutrients may reach as high as 50 percent of all waters surveyed in a 

state. 

To address these problems, the EPA is in the process of helping states establish nutrient criteria. 

The EPA has published nutrient criteria technical guidance manuals for three aquatic 

environments (Lakes and Reservoirs, Rivers and Streams, and Estuarine and Coastal Marine 

Waters). A fourth document (Wetlands) was scheduled for publication in December 2003, but 

that publication has been delayed and is expected in December 2004. Also, EPA has published 

suggested nutrient criteria for all 14 nutrient ecoregions across the United States. 

These suggested criteria were established based on an examination of reference conditions and 

levels of nutrients that lead to impairment. This approach has gained praise and acceptance from 

many in the scientific and regulatory community for its scientific validity. Yet, others have called 

for more of an effects-based approach where response variables and water uses are factored into 

the nutrient criteria. 

The research suggested in this proposal will (1) use an effects-based approach to calculate 

nutrient criteria and (2) assess periphyton communities in EPA Region III streams. An effectsbased approach means we will look at the effects caused by nutrients to calculate a nutrient 

criterion level. The nutrient criteria can be used to support to EPA’s reference condition 

approach or can be used as an alternative to the EPA approach. The periphyton community 

assessment can be used to evaluate the ecological health of the periphyton community for the 

streams studied and will provide initial data for future development of a periphyton index of 

biotic integrity (P-IBI). 

Existing data include an inadequate number of chlorophyll a measurements for streams. Further, 

the dissolved oxygen measurements residing in the current database are collected primarily 

during daylight hours. Critical dissolved oxygen concentrations occur at night. Further, the 

existing data for much of EPA Region III have recently been analyzed (Brightbill and Koerkle, 

2003). 

The work proposed would simply extend ongoing work which is being supported by EPA under 

grant number X798398201-0 awarded to the Maryland Department of the Environment. 

IV. PROJECT DESCRIPTION

A. Purpose and Scope 

Two overall objectives are proposed: 

1. Use response variables (dissolved oxygen and periphyton chlorophyll a) to suggest 

nutrient criteria for streams in EPA’s Region 3. This is the primary objective. Data from 

the second objective will be used as input information to reach this primary objective. 

2. Examine periphyton communities for streams in EPA’s Region 3 to see if eutrophic 

conditions are associated with specific community characteristics. 
106

Study sites would include several 3rd and 4th order streams in Nutrient Ecoregions IX, XI, and 

XIV, all in EPA’s Region 3. These Ecoregions comprise most of the land area of the states in 

EPA’s Region 3 and extend beyond Region 3 to include most of the southeastern U.S. The 

proposed work would involve field data collection during the critical summer months of 2005. 

Data evaluation and report preparation would follow through the fall and winter of 2005 and into 

2006 with a final report due on September 30, 2006. 

B. Project Overview 

This proposal defines a cooperative approach for (1) defining nutrient criteria for streams in EPA 

Region 3 and (2) examining periphyton community structure for wadeable streams (Strahlher 

Order 3 and 4). The success of this project is based on the cooperative and collaborative efforts 

of EPA Region III (funding source), and National (USGS) and State environmental agencies in 

Region 3. A cooperative approach is required for a region-wide effort, and to pool resources for 

funding and personnel. The work proposed is an expansion of work already in progress in 

Region 3 under a grant from EPA to the Maryland Department of the Environment. This 

proposal extends the ongoing work to additional sites. The Pennsylvania Department of 

Environmental Protection (PaDEP) will be the lead agency for the research. The U.S. Geological 

Survey will support the PaDEP in field data collection and will assume the lead role in data 

management, data analysis, and report preparation. 

Development of Nutrient Criteria: 

Other researchers have attempted to define the relations between nutrients and periphyton. These 

efforts have been only partly successful. One reason is that the researchers pooled all available 

data for the analysis, then looked for relations. But, nutrients are transient. The concentrations 

observed in a stream on any day will likely be different the next day or the next week. Therefore, 

the proposed work will examine nutrient-periphyton relationships for a short term only -- during 

the critical summer period of July and August. Oxygen consumption is largest at night. Thus, 

nighttime measurements collected during the hottest, driest period of the year are critical for 

establishing nutrient criteria. Oxygen measurements at other times of the year or at other times of 

the day are superfluous, and perhaps even confusing. 

Our approach will be to select streams across the five Region 3 states and the District of 

Columbia to examine nutrient concentrations, periphyton biomass, and nighttime dissolved 

oxygen concentrations. For these streams, we will install continuous recording dissolved oxygen 

monitors for a 48-hour period. It is important to measure the variability of dissolved oxygen for a 

diel cycle because the lowest dissolved oxygen concentrations will occur at night when 

photosynthesis is shut down, but respiration continues. (Another reason for the failure of 

previous nutrient studies is that routine monitoring is usually a daytime activity.) Dissolved 

oxygen measured during routine daytime monitoring fails to capture the lowest dissolved oxygen 

concentrations. 

Assessment of Stream Periphyton Communities: 

Examination of periphyton communities is an effects-based approach that will attempt to link 

nutrient conditions within a stream system to the growth of periphyton communities within that 
107

stream. Periphyton are important to stream ecology because they are the main primary 

producers. This means that they are one of the first links in the aquatic food chain and can 

ultimately affect the populations of other species as well. Periphyton are also useful indicators of 

stream environmental conditions because they quickly respond to changes and are sensitive to a 

number of disturbances, including nutrient over-enrichment. As a resident biotic component in 

streams, periphyton can quickly register and incorporate transient or episodic changes in nutrient 

conditions, or act as nutrient "sinks". Thus, having an understanding of the periphyton 

community in a stream can reveal a lot about the ecological health and the nutrient status of that 

stream. 

C. Technical Approach 

Field Data Collection: 

Field work for the project will be led by the Pennsylvania DEP and its cooperator, the U.S. 

Geological Survey. Cooperating states (Maryland, Virginia, West Virginia, and Delaware) have 

agreed to support the effort. We will use common protocols (mutually agreed upon), which are 

fully documented and distributed by the USGS. These protocols will be the same as adopted 

under the previous nutrient criterion grant awarded in 2004 to the Maryland Department of the 

Environment. Training for all field personnel will assure uniform data collection across the 

Region. 

Several streams (our goal is 24 streams for this work which would be added to 24 streams 

assessed under the previous grant for a total of 48 streams) will be selected for the nutrient 

criteria work. The streams selected will represent a range of nutrient conditions and will come 

from three Nutrient Ecoregions. At each stream, periphyton biomass (chlorophyll a and ash free 

dry mass), nutrients, standard field measurements, and total organic carbon) will be measured. In 

addition, continuous dissolved oxygen (DO) monitors will be deployed for at least two 24-hour 

periods during the hottest part of the summer. Minimum DO values are critical for aquatic life 

and this plan will capture those minimum DO values, or values that are near minimum for the 

year. Data from the continuous monitors will be downloaded in the field to a notebook computer. 

These data will then be transferred to computers housed at the USGS in New Cumberland, 

Pennsylvania for analysis. Nutrient concentrations that are associated with DO concentrations 

lower than the applicable water-quality standard (5 mg/L) will be considered excessive. 

Analytical Requirements: 

Field water-quality samples will be sent to the Pennsylvania DEP laboratory for analysis. 

Analyses will be needed for a broad range of nutrients including dissolved nitrite+nitrate, 

dissolved ammonia nitrogen, organic nitrogen, dissolved ortho-phosphate, and total phosphorus. 

Total organic carbon will also be measured to provide insight into the role of organic matter in 

oxygen depletion in the streams. Chlorophyll a and AFDM will be measured as surrogates for 

algal biomass. 
108

Data Management: 

Initially, data from the project will be housed in electronic files at the USGS Pennsylvania 

District, New Cumberland, Pennsylvania. A back-up duplicate copy of all data files will be 

stored at PADEP offices, Harrisburg, Pennsylvania. Near the end of the project, data will be 

placed into EPA’s national database, STORET. A commercially available database management 

system such as Access will be used for data storage and management. Commercially available 

software will be used for graphical and statistical analyses. All data from the project will be 

shared with Region 3 States and the EPA. 

Assessment of Stream Periphyton Communities: 

To the extent possible, stream locations will be selected to represent major natural and human 

factors that are thought to significantly influence the quality of water. Ideally, these the selected 

streams will already be monitored under the existing statewide water-quality monitoring network 

for each state. Also, our plan is to include streams that represent a wide spectrum of biotic 

conditions, from reference conditions to eutrophic conditions. Selection of a range of nutrient 

conditions will facilitate establishment of nutrient-algal (periphyton) relationships that will 

enable the eventual development of an index of biotic integrity based on those relationships. 

Because the main study objective is the development of ecoregion-specific nutrient criteria, the 

initial stratification will be by nutrient ecoregion. The Level III Aggregate Nutrient Ecoregions 

of interest in EPA Region 3 are Ecoregion IX (Southeastern Temperate Forested Hills and 

Plains), Ecoregion XI (Central and Eastern Forested Uplands), and Ecoregion XIV (Eastern 

Coastal Plain). Other Ecoregions within the EPA Region 3 boundaries represent relatively small 

areas and will not be sampled in the proposed research. 

A modified version of the periphyton sampling techniques and methods used in the USGS 

NAWQA (Moulton and others, 2002) and EPA’s EMAP studies will be used. A quantitative 

sample from riffle habitat and a water-quality sample will be collected from each sampling site. 

Part of the periphyton sample will be used for taxonomic identification, and part for biomass 

measurements. In the Coastal Plain (Ecoregion XIV), riffle habitat may not be commonly 

available in all sampling reaches. In this case, quantitative artificial substrate samplers will be 

used. 

Community structure of periphyton communities are intended to provide a list of species (taxa 

richness) present in the sampling reach. Water quality can be assessed by interpreting 

autecological information, the taxon-specific physiological requirements or tolerance for defined 

ranges of water-quality conditions. Samples for taxonomic identification will be shipped to a 

laboratory chosen through an open bidding process. All genera in the samples will be identified 

to species level to enable the development of autecological indices for the three ecoregions. A 

photo library will be developed for future reference purposes. 

Quantitative periphyton samples are collected to measure the relative abundance and density 

(algal cells per square centimeter) of each taxon present in each stream. Quantitative samples are 

collected using a variety of sampling devices (i.e., NAWQA periphyton sampling device SG-92 

or the whole-rock approach); the appropriate choice of sampling equipment is dictated by the 
109

character of the dominant periphyton growth forms and microhabitats in the sampling reach. For 

this study, an attempt will be made to collect all samples using the NAWQA SG-92. This will 

allow for consistency among samples. Samples are processed, stored, and transported using 

protocols established by EMAP studies. 

Development of Nutrient Criteria:

The periphyton community data will be analyzed by looking for indicator species and by 

comparing several different community metrics (Stevenson and others, in press). The final 

selected eutrophic indicators will offer the best representation of the health of the system, with 

attention given to simplicity. The idea is to develop a tool that is reflective of stream conditions, 

yet can be routinely implemented by most field biologists employed by state resource agencies. 

Data from the project will be examined using a variety of traditional and multivariate 

approaches. Non-parametric techniques are likely to be used as biological data frequently are not 

normally distributed. Data transformations may be needed to deal with non-linear relationships. 

Graphical presentation of the data will be emphasized. 

For the development of nutrient criteria, graphing measured nutrient concentrations for several 

streams against minimum dissolved oxygen concentrations is expected to produce a relationship 

generalized in Figure 1. The nutrient concentration that results in meeting the water-quality 

standard (dissolved oxygen concentration of 5.0 mg/L) will provide the basis for the proposed 

nutrient criterion. 

 

Figure 1. Expected relation between measured nutrients and minimum dissolved oxygen in 

Region III streams (generalized). 

Data gathered to populate the graph in Figure 1 are not all expected to fall on the line depicted in 

Figure 1. Instead, some data points will fall above the line. However, no data points should fall 

below the line. Data would be expected to fall above the line when some factor other than the 

nutrient being considered is limiting. For example, Figure 2 shows what the data might look like 

Minimum Dissolved Oxygen Concentration 

Measured 

Nutrient 

Concentration 

Proposed nutrient criterion 

5 mg/L 
110

for several streams, some having light as the limiting factor, and some having nitrogen as the 

limiting factor.

Figure 2. Hypothetical data distribution for Region III streams. 

For those streams where nitrogen is limiting, the data point falls on the line. If light (or any other 

variable) is limiting, periphyton will not grow to their maximum potential. Minimum dissolved 

oxygen concentrations will be larger than in the case where nitrogen is limiting. The data point 

will fall above the line. For those data points that fall on (or near) the line, a least squares 

regression, based on a General Linear Model, will be used to quantify the relation between 

nutrients and dissolved oxygen. This relation will be used to quantify the nutrient criterion. 

Figure 2 demonstrates the importance of using the work proposed in this proposal to extend the 

work from the proposal funded in 2003. Under the previous proposal, only 15 data points would 

be available for plotting in Figure 2. If this proposal is funded, our goal will be to populate figure 

2 with as many as 48 data points, each data point representing a different Region 3 stream. 

This approach has a weakness that minimum dissolved oxygen concentrations are influenced by 

productivity of the attached algae as well as the standing crop of attached algae. We do not 

propose to measure algal productivity. Intuitively, algal productivity and algal standing crop are 

likely related, so measurement of standing crop by chlorophyll a concentrations should suffice. 

In reality, algal standing crop is not always a good predictor of algal productivity. 

Products: 

This proposal will extend work funded previously (EPA grant X798398201-0). Products listed 

below would include data from both parts of the work, but would not be duplicated for the two 

phases of work. 

Minimum Dissolved Oxygen Concentration 

Measured 

Nitrogen 

Concentration 

5 mg/L 
111

1. Semi-annual progress reports will document progress on the project. 

2. Project personnel will present a paper from the work at one scientific meeting to 

encourage exchange of ideas among the scientific community. 

3. Project personnel will provide a seminar for one Region 3 Regional Technical Assistance 

Group (RTAG)1

 meeting to help disseminate the results of the work. 

4. A final, published report will be provided to EPA approximately one year following 

completion of laboratory analyses. This report will specifically address the feasibility of 

using periphyton community structure and chlorophyll a measurements to define nutrient 

over-enrichment. 

1

The RTAG is a group of scientists and environmental managers that meets periodically to 

exchange information about nutrient criteria development and implementation and serves as an 

advisory body for each EPA Region on nutrient issues. 

V. QUALITY-ASSRUANCE PROCEDURES

Quality assurance procedures will include using a written, standardized protocol for field work, 

training for all personnel who collect field data, and standardized field data-collection sheets for 

all field work. Duplicates, blanks, and reference materials will be used to assure the quality of 

laboratory work. Laboratory QC will include lab participation in the U.S. Geological Survey’s 

Inter-Laboratory Evaluation Program (Woodworth and Connor, 2003). 

Quality control for the data will include standardized field data-collection sheets, periodic 

reviews of the data sheets, spot checks of the electronic data, and both tabular and graphical 

displays of the data to check for anomalies. 

Field instruments will be calibrated on site and log books will be kept for each field instrument. 

Separate field instruments will be used as checks for the continuous monitoring sondes and these 

checks will be performed at the beginning and end of the deployment period. 

Periphyton identifications will be done using widely accepted keys. Duplicate periphyton 

identifications will be done by an outside lab for 5 percent of the samples. 

VI. TRANSFERABILITY

Responding to the Clean Water Action Plan, the U.S. Environmental Protection Agency has been 

tasked with addressing to the problem of nutrient enrichment in U.S. waterways. One of the ways 

the EPA is accomplishing this task is to provide guidance for the states to establish water-quality 

standards for nutrients. The results of this study will provide support for a response-based 

approach to establishing these nutrient standards for streams. 
112

Information from the proposed work is directly transferable to other locations within the same 

Nutrient Ecoregion. Techniques developed could be used for any Nutrient Ecoregion. 

The results of this study and further investigations support the mission of the USGS by providing 

a regional pattern of water quality in the Mid-Atlantic States. This knowledge and other pertinent 

information relating to nutrients will assist states faced with establishing water-quality standards 

for nutrients. 

VII. PROJECT SCHEDULE

The following table shows the time line to be followed for the project. 

Federal fiscal year 2005 

(Months) 

Federal fiscal year 2006 

(Months)

Task

O N D J F M A M J J A S O N D J F M A M J J A S 

Project planning. 

Site selection. 

Meetings with partners. 

 

Order supplies and 

equipment. 

 

Write and submit QA 

plan, safety plan. 

 

Field data collection 

Diurnal DO work 

Data management and 

analysis. 

 

Report preparation, 

review, and publication 

 

Public presentation. 

VIII. REFERENCES CITED

Brightbill, Robin A., and Koerkle, Edward H., 2003, Relations of biological indicators to nutrient 

data for lakes and streams in Pennsylvania and West Virginia, 1990-98: U.S. Geological Survey 

Water-Resources Investigations Report 03-4033, 67 p. 

Galeone, D.L., and Brightbill, R.A. In preparation, Effects of streambank fencing on water 

quality, Lancaster County, Pennsylvania: U.S. Geological Survey Water-Resources 

Investigations Report. 
113

Lietman, P.L., 1997, Evaluation of agricultural best-management practices in the Conestoga 

River headwaters, Pennsylvania: A summary report 1982-1990: U.S. Geological Survey WaterSupply Paper 2493. 

Meador, M.R., Hupp, C.R., Cuffney, T.F., and Gurtz, M.E., 1993, Methods for characterizing 

stream habitat as part of the national Water-quality Assessment Program: U.S. Geological 

Survey Open-File Report 93-408, 48 p. 

Moulton II, S.R., Kennon, J.G., Goldstein, R.M., and Hambrook, J.A., 2002, Revised protocols 

for sampling algal, invertebrate, and fish communities as part of the National Water-Quality 

Assessment Program: U.S. Geological Survey Open-File Report 02-150, 75 p. 

Schreffler, C.L., Galeone, D.G., Veneziale, J., Olsen, L., and O’Brien, D.L., In preparation, 

Effects of spray irrigated treated effluent on water quantity and quality, and the fate and transport 

of nitrogen in a small watershed, New Garden Township, Chester County, Pennsylvania: U.S. 

Geological Survey Water-Resources Investigations Report. 

Stevenson, R.J., Hill, b., and Herlihy, A.T., in press, A comparison of approaches for 

establishing nutrient criteria based on algal attributes in mid-Atlantic streams: 

Woodworth, M.T., and Connor, B.F., 2003, Results of the U.S. Geological Survey’s Analytical 

Evaluation Program for Standard Reference Water Samples Distributed in September, 2002: U.S. 

Geological Survey Open-File Report 02-481, 108 p. 

Roth, N.E., and others, 2001, Maryland biological stream survey 2000-2004 Volume I: 

Ecological Assessment of Watersheds Sampled in 2000: Maryland Department of Natural 

Resources, variously paged. 
114

Appendix F. 

The Academy of Natural Sciences – Virginia DEQ Proposal to EPA 

The Development of an Algae-based 

Water Quality Monitoring Tool for Virginia Streams 

A Proposal Submitted To: 

Dr. Lawrence D. Willis 

Regional Monitoring Coordinator 

West Central Regional Office 

Virginia Department of Environmental Quality 

3019 Peters Creek Road 

Roanoke, VA 24019 

Submitted By: 

The Academy of Natural Sciences 

 Patrick Center for Environmental Research 

1900 Benjamin Franklin Parkway 

Philadelphia, PA 19103-1195 

May 5, 2004
115 

The Development of an Algae-based 

Water Quality Monitoring Tool for Virginia Streams 

Background 

Nutrient enrichment is the cause of approximately half of the reported impairments in 

National waters. Within rivers and streams, chronic symptoms of over-enrichment include low 

dissolved oxygen, fish kills, increased sediment accumulation, and species and abundance shifts 

of flora and fauna. An often immediate effect of excess nutrients in streams is the development 

of nuisance levels of algae (Blum 1956, Francoeur 2001). In addition to causing water quality 

problems and altering ecosystem structure and function, nuisance algae growth can harm the 

designated use of streams and rivers in a variety of ways (Dodds and Welch 2000, U.S. EPA 

2000a, U.S. EPA 2000b, ENSR 2001) and have been associated with human health problems 

(Oliver and Schindler 1980, Ward et al. 1996). Concerns over nutrient enrichment in the nation’s 

water systems have led the US EPA to: a) develop water quality standards designed to protect the 

legally “designated uses” of the nation’s waters, b) restore and maintain the chemical, physical, 

and biological integrity, and c) propose standards for nitrogen and phosphorus. Because chemical 

monitoring alone can underestimate degradation in living systems, a number of biological 

measurements have been developed to provide a direct assessment of resource condition. If the 

biota is not present at the level expected, researchers have direct confirmation of physical or 

chemical degradation in the stream. 

Until recently, biological monitoring programs have almost exclusively focused on fish 

and macroinvertebrate taxa and largely excluded periphyton (benthic algae) communities. This is 

because the high diversity and regional differences of taxa make the initial development of an 

algae-based biomonitoring program a sizable undertaking. However, periphyton communities, 

especially those dominated by diatoms, are excellent ecological indicators of water quality. 

Because periphyton are directly affected by physical and chemical environmental changes, and 

usually have brief life cycles and rapid rates of reproduction, they are valuable indicators of 

short-term impacts. Periphyton assemblages are also sensitive to some pollutants which may not 

visibly affect other taxa or only show affects at higher concentrations. Perhaps most importantly, 

periphyton has specific ecological requirements that correlate strongly with environmental 

conditions (Pan et al. 1996, Stevenson and Pan 1999, Pan et al. 2000, Potopova and Charles 

2002). 

As the discussion concerning nutrient enrichment of surface waters and the establishment 

of nutrient limits and thresholds grows, state (Bahls 1993, Kentucky Division of Water 1993, 

Oklahoma Conservation Commission 1993) and national (Barbour et al. 1999) programs are 

being developed to examine the relationship between periphyton communities and nutrient 

concentrations. Six states (Florida, Idaho, Kentucky, Massachusetts, Montana, and Wyoming) 

currently use information from periphyton communities as a water quality management tool 

although several states evaluate biomass in terms of chlorophyll a (chl a) as part of their surface 

water monitoring program (Kroeger et al. 1999). Other states (Maryland and New Jersey) are 

developing relational models for using algal indices of biotic integrity and establishing nutrient 

limits (Ponader and Charles 2003, Beaman 2003). 

Virginia currently has water quality monitoring programs in place but has not developed 

algal indices for freshwater bioassessment. The Virginia Department of Environmental Quality’s 
116 

(VA DEQ) Freshwater Probabilistic Monitoring (ProbMon) Network (VA DEQ 2003) consists 

of a set of randomly chosen water quality monitoring stations (currently ~ 60/year distributed 

across the state) used to make statistically-based assessments of Virginia’s streams. The goals of 

the project are to examine and assess water quality state-wide, identify and determine the extent 

of impairments in water quality, and identify potential threats to water quality and the types of 

streams that are most at risk. In addressing these objectives, the ProbMon Network will identify 

the water quality issues that policy makers need to address, assess the effectiveness of current 

management strategies at protecting resources, examine how effort can be redirected to better 

protect the most threatened resources, and identify where more stream protection is needed (VA 

DEQ 2003). 

The ProbMon Network conducts macroinvertebrate surveys, records a variety of physical 

habitat parameters, and collects water and sediment samples for chemical analyses in the spring 

and fall, and uses these data to assess stream health. Although these data provide important 

information regarding water quality, they do not provide sufficient information to assess nutrient 

enrichment in Virginia streams. The development of an algal index, based on periphyton 

community structure as it relates to environmental conditions, will provide much-needed nutrient 

enrichment and water quality information on which policy-makers can base environmental 

decisions and will be an initial step in developing a biomonitoring tool for assessing nutrient 

impairment. 

Virginia DEQ has secured funding from the US Environmental Protection Agency (EPA) 

to collect algal taxonomy and biomass samples from the ProbMon sites for the development of 

an algal-based tool for stream assessment. The U.S. EPA technical guidance manual for rivers 

and streams (U.S. EPA 2000a) recommends three approaches for development of nutrient and 

algal criteria: (1) the use of reference streams, (2) applying predictive relationships to select 

nutrient concentrations that will result in appropriate levels of algal biomass, and (3) developing 

criteria from thresholds established in the literature. In addition, the Ambient Water Quality 

recommendations for U.S. EPA Rivers and Streams Aggregate Nutrient Ecoregion IX (U.S. EPA 

2000b) include establishing nutrient reference conditions in rivers and streams based on the 

upper 25th percentile (75th percentile) of all nutrient data from all reaches sampled, or 

determining the lower 25th percentile of the population of all streams within a region. A review 

of this approach for the New England Interstate Water Pollution Control Commission revealed 

that the ranges of predicted biomass production responses to nutrients, as tested for the 

subecoregions 59 and 84, would be below consensus threshold values (ENSR 2001). 

Nevertheless, the establishment of reference conditions based on percentiles will set different 

threshold values in different regions, depending on the range of overall water quality in the rivers 

of each particular region. These thresholds will be too high in ecoregions with rivers having 

predominantly high nutrient concentrations as compared to ecoregions with mainly low nutrient 

rivers, and vice versa. The applicability of this method to Virginia needs further review. In the 

proposed study, the VA DEQ will apply the proposed U.S. EPA percentile method to data 

collected from Virginia streams to: 

1. explore the relationships between algal biomass as well as algal species composition and 

nutrients in Virginia streams, 

2. develop and test algal indicators of nutrients and water quality applicable to Virginia 

streams and rivers, 
117 

3. develop algal indices that can be used in ongoing biomonitoring programs in Virginia, 

and 

4. assess which indicators may be best for monitoring nutrient impairment in Virginia 

streams and rivers. These indicators will be based on an understanding of algal dynamics 

in Virginia streams, and be able to distinguish between situations where nutrient 

concentrations are high due to natural environmental conditions and those that result 

from anthropogenic influences. 

The role of the Academy of Natural Sciences (ANS) in this project is to train VA DEQ 

biologists on algal collection methods, provide additional in-field direction and assist with algal 

sampling at a select subset of sites, analyze community composition of soft algae and diatom 

samples, and provide community composition data and basic community metrics (taxa richness, 

percent dominant taxa, percent taxa dominance, and siltation index). Virginia DEQ biologists 

will use these data to develop algal indicators of stream and river health based on predictive 

relationships that can be applied in a regulatory context as criteria for identifying nutrient 

impairment. 

Study Approach 

 Virginia DEQ, with the assistance of an ANS biologist at some study sites (10-20), will 

sample periphyton for biomass and community composition in conjunction with the VA DEQ’s 

ProbMon Network (40-70 sites). Periphyton sampling will be conducted during the fall (AugustOctober) 2004 and will focus on algal biomass (chlorophyll a (chl a) and ash-free dry mass 

(AFDM)) and species composition. Field collections of algae will be conducted by ANS-trained 

VA DEQ biologists. Algal samples will be collected from natural rock substrates, where 

available, using techniques consistent with those used in ANS algal assessment protocols 

(Ponader and Charles 2003) and the EPA Rapid Bioassessment protocols for periphyton 

(Barbour et al. 1999). At each site, three sample types will be collected in triplicate: 3 composite 

diatom samples, 3 composite soft algae samples, and 3 quantitative composite biomass samples 

for chl a and AFDM measurement. For all samples, algae will be scraped from 4-5 randomly 

selected rocks, placed into containers, and preserved with formalin (soft algae and diatom 

samples) or by keeping samples on ice until sent to the laboratory for processing (biomass 

sample). The surface area from which algae was sampled will be determined for biomass 

samples (Ennis and Albright 1982, Moulton et al. 2002). Virginia DEQ will ship diatom and soft 

algae samples to ANS accompanied by sample chain of custody forms. 

Chlorophyll a and AFDM analyses will be conducted by the VA DEQ laboratories 

following Standard Methods (APHA 1992) to determine algal biomass at each site. Upon 

receiving the taxonomy samples, an ANS biologist will compare them to the chain of custody 

forms and report any discrepancies to the VA DEQ. Samples will be prepared for analysis by an 

ANS biologist. Community composition will be determined by identifying diatom and common 

soft algae taxa to the lowest practical taxonomic level and counted using ANS Phycology 

Section protocols, common taxonomic references, and type material from the ANS Diatom 

Herbarium. Species identification, relative abundance, and basic community metrics (taxa 

richness, percent dominant taxa, percent taxa dominance, and siltation index) will be presented to 
118 

the VA DEQ in the form of a letter report. Data will be provided as Access Database or Excel 

files, as preferred. Slides analyzed for this study will be archived in the ANS diatom herbarium. 

Project Schedule 

Project tasks will begin in July 2004 and continue through August 2005. A specific 

timeline for project tasks is outlined in Table 1. 

Table 1. Timeline for specific ANS tasks in the development of an algae-based water 

quality monitoring tool for Virginia streams. 

Project Task 2004 2005 

J A S O N D J F M A M J J A

Development of a QA/QC and Field 

Safety Plan

 

Training of VA DEQ staff 

In-field assistance with collection of algal 

samples

 

Sample cataloguing and processing 

Preparation of taxonomy samples by 

ANS staff

 

Identification and enumeration of algal 

taxa 

 

Data entry and QA/QC 

Writing and distribution of final project 

report and data 

 
119 

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