## 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 Academ ic 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 fo r 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 Cr iteria Recommendations 29 Criteria Implementati on: 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 Pennsylva nia-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 Co mparison 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 114i 

### 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. Zipperii 

### Acknowledgements

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

• Academic Advisory Committee member s 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 Depar tment of Conservation and Recreation for providing data on the morphomet ric features of Virginia’s impoundments.

• Fisheries biologists with the Virg inia Department of Game and Inland Fisheries for contributing their assessment s to rate the quality of reservoir fisheries.

• Patricia Donovan, Virginia Te ch 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

μ g/L: micrograms per liter (1 μ g/L = 0.001 mg/L) mg/L : milligrams per liter (1 mg/L = 1,000 μ g/L) mg/m 2 : milligrams per square meter

n: number of observations in a sample (sample size) N: nitrogen NES: National Eutrophic Survey NO 2 - : nitrite NO 3 - : nitrate P: phosphorus

r: coefficient of correlation for a sample

r 2 : coefficient of determination for a sample R 2 : 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: volume1 

### Introduction

This interim report of t he 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 i ssues 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 Virgin ia (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 DE Q address nutrient criteria for the state’s two natural lakes (Mountain Lak e and Lake Drummond) separately.



### A. Preliminary Analyses

Ambient monitoring data from lakes and rese rvoirs 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 spec ified 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 fr om 1990 or later were cons idered in the analyses that follow. Total phosphorous (TP) values list ed as equal to or less than a 0.1 mg/L detection limit were discarded; all other obser vations 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 we re low compared to the bulk of the recorded observations and compared to the concentrations of concer n in the current analysis. Analyses that considered water-column nutrient concentrati ons were conducted using total nitrogen (TN) and TP. The TN variable was calculat ed 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 = NO 2 -N + NO 3 -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 DE Q’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 war m-weather season at individual lakes and recorded SD infrequently. SD measuremen ts have been routinely collected by DEQ (and precursor agency State Water Control Boar d) at lakes since the early 1970s but only in recent years have the data been elec tronically recorded and readily accessible. Of Chl-a observations prior to 1999, 21% ar e accompanied by SD measurements. In recent years, DEQ has increased the frequen cy and consistency of lake monitoring procedures, and the majority of locations m onitored are represented by data collected throughout the warm-weather season (Table 1). Current lakes’ monitoring procedures include 7 months of data colle ction at each lake monitored during a given year (April – October). Since and including 199 9, 76% of Chl-a observations are accompanied by SD measurements. Sixty nine perce nt of the total 1990-2003 obser vations in the database represent the 1999-2003 period (Fi gure 2). The fact that lake conditions in more recent years are heavily represented in the databas e 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. Wi thin this context, the uneven distribution of monitoring observations among lakes creates anal ytical difficulties. On one hand, it is desirable to conduct the analyses using the lar gest number of lakes possible so as to be able to derive conclusions based on water sa mpling data that are representative of conditions across the state and of temporal and regional vari ations. However, achieving such a sample size requires data from lake s with well documented conditions (i.e., with sufficient numbers of observations and a dist ribution 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 LakesObservations 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 developm ent. 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 obse rvation 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 26354 

### 0% 20% 40% 60% 80% 100% 120%

90 91 92 93 94 95 96 97 98 99 00 01 02 03Cumulative % 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 nutri ent inputs varies seasonally. Therefore, a preliminary analysis was conducted to charac terize 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 ava ilable 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 r epresented by 1 or more observations. For each variable, lakes were selected for inclus ion in the analysis if observations were present in at least 6 of the 7 sampling months (April – Octobe r). Using this subset of the DEQ monitoring data, a monthly median was ca lculated and plotted for each ecoregion (Figures 3 and 4).

Results demonstrate that nutrients and related parameters in Virginia impoundments vary month-to-mont h and seasonally, but not necessarily in the manner expected based on scientific st udies of natural lakes.
Both TN and TP appear to exhibit su mmer-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 pat tern but can state that exploratory application of other calculation methods generated similar patterns. Thus, the pattern does not appear to be an artifact5 of the analysis method. Chl-a tends to exhibi t 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 Virginia6 0 1 2 3 4 5 6 7

Apr May Jun Jul Aug Sep OctTSS (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 OctTP (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 OctTN (mg/L)

0 0.5

1 1.5

2 2.5

3

Apr May Jun Jul Aug Sep OctSD (m) 0 5 10 15 20 25 30

Apr May Jun Jul Aug Sep OctChl-a (ug/L) Eco 9
Eco 11
Eco 14

Figure 4. Seasonal variation of nutrient va riables 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); an d 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, info rmation from others sources including scientific literature, and pr ofessional 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 pub lished by the agency in July of 2002 (USEPA 2002). In conducting this analys is, we applied minor modifications to the EPA method to accommodate DEQ data characteristics.

The EPA method is to calculate the 25 th percentile of medians from all available lakes for Chl-a, TN, and TP, and the 75 th percentile of SD medians as surrogates for reference values. We applied this approach.

EPA calculated TN 25 th percentiles separately for measured and calculated TN values. We combined measured TN val ues and calculated TN values because measured values are sparse within DEQ’s da tabase. We then calculated a single 25 th percentile for TN from the combined data.
EPA recommends that the analysis be c onducted 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 t hat 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 m odified the concept of seasons to reflect data availability because the DEQ lake-moni toring 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, Augus t – September, Oc tober (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 – Augus t, 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 we re, in some cases, influenced by the season-definition method. This influence wa s 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 ca lculated a lake-median and the desired percentiles for each8 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 Banist er). The procedure applied to estimate residence times and its limitations are descr ibed below (Question 4). For comparison purposes, the EPA guidance criteria, which we re 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 63 a 2.10 1.20 0.46 0.020

a The portion of Virginia contained within Ecor egion 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 StateChl-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 StateSD (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 StateTN (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 StateTP (mg/L) Virginia
EPA Guidance Figure 5. Virginia reference values (25 th percentiles for Chl-a, TN, and TP; 75 th 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 reserv oirs nutrient criter ia guidance document (USEPA, 2000d), which considers each lake as a sampling unit. For each time period considered, the TP and Chl-a status of eac h 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 Inla nd 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 desirabl e 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 recomme nd that anglers avoid such lakes. 2 = fair: VDGIF biologists would recommend that anglers fishing such lakes not expect much in the wa y of fishing success. 3 = average: the lake s upports an adequate fishery. 4 = good: VDGIF would recommend such a lake for fishing. 5 = excellent: VDGIF would highly re commend such a lake for fishing.

Working with the entire data set, both parametric (Pearson) and non-parametric (Kendall’s) correlation analyses of fishery stat us with nutrient stat us were performed. Results are displayed in Table 3:

Table 3. Results of exploratory correlation anal ysis of nutrient variables vs. fishery status. Aggre- Seasonal Pearson: Kendall’s: Vari- 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 - Augu st 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 us eful results. Analysis of data plots confirmed that (a) well defined statistical rela tionships 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 variabl e-fishery status relationships is not subject to major influence by the time pe riod selected for calculation of nutrient variable medians. Fishery nut rient requirements are addressed through an alternative procedure described later in this document (See Section I-C).

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

and

Question 4: Are there detectable influences of fa ctors 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 la ke, 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 se lected 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 repr esented in either of the above data sets were generated in the Virginia Tech D epartment 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. Severa l water-yield prediction equations were generated and applied. O ne problem encountered in appl ication 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 R 2 ) 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 wate rshed 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 r ough estimates. In addition to the inaccuracies resulting from the crudeness of the water-yield estimation procedure, the12 accuracy of the physical parameter estima tes used in the calculation is also questionable. Where both DEQ and DCR provi ded estimates of physical parameters for lakes, these estimates were often in dis agreement. 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 lim itation “to eliminate small water bodies that, because of their size (and resulting hy drology) or uses (e.g., small agricultural impoundments), do not accurately represent typi cal 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 relati onships are represented in Figure 6.
Stepwise regressions were performed for t he purpose of determining the effect of other variables on the explanatory power of the TN and TP-bas ed Chl-a prediction models. The stepwise procedures were perfo rmed until all variable s 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), surfac e area (ac), retention time (days), median TSS (mg/L, calculated us ing the same procedure as TN and TP), estimated inflow (cfs), and the natural logs of the reservoir paramet ers (depth, retention time, surface area, volume). Complicati ons in the analysis occurred because the data set was poorly populated, i.e., variables were not consistently available for lakes. Therefore, as stepwise pr ocedures considered increas ing 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-squar es regression, such points tend to exert a disproportional influence on t he resultant functional form.
A number of efforts were employed to apply stepwise regression procedures as needed to overcome difficulties with the data se t, including selective removal of outliers from the data set. Those pr ocedures that yielded the most satisfactory results are summarized below (Table 4, Figure 6). As more outliers were removed, the R 2 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 bot h could be increased by adding additional variables through the stepwise regre ssion 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 prev ious studies, which may indicate that the residence time estimates are insufficiently accurate to aid the modeling procedure.13 To summarize the committee’s perspecti ve 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 t heir use in criteria developmen t. 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 TPLn 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

TNLn 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 R 2

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 < .0115 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 trophi c state condition to the public because it is an index. Total phosphorus, chlorophyll-a, and Secchi depth are all on a common, understand able 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 s ediments delivered to impoundments lead to levels of non-algal turbidity that interfer e with algal produ ction, especially in the upper channel, and thus distort the assumed correspondence between the TSI components. As documented in Appendix B for Smith M ountain 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 ext ent to which reservoirs vary in dissolved components that affect water clarity (such as tannins) is not known.
Virginia’s impoundments are highly variabl e in morphometric characteristics, watershed area, retention time, and other fact ors 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 impl emented by monitoring nutrient variables directly and not through use of TSI, which w ould add yet another source of variability to criteria implementation.16 

### C. Nutrient Requirements for Fish eries 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 i ndicator of the impoundments’ suitability for aquatic life. Giv en 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 recreat ional fisheries against those of other potential uses, including contact recreati on and public water supplies, in defining nutrient criteria fo r implementation.
This analysis is conducted in three steps: a review of scientif ic literature prepared by Dr. John Ney, graphic analysis of Virgin ia 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 biom ass of fish at or near the top of the trophic pyramid should be highly dependent on t he amount of primary production at the base (Lindemann 1942). Primary production in lakes is limit ed 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 Virg inia reservoirs is much greater.
However, the productivity of a fisher y 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 detrim ent of coldwater and coolwater fishes. In shallow lakes, nutrients can stimulate ex cessive macrophyte growth, reducing habitat for warmwater sportfish species (Wiley et al. 1984). The influence of nutrients and resulting primary production on fisheries producti vity in lakes and reservoirs should thus be parabolic, with low concentrations of nut rients constraining food supply and high concentrations limiting suitable habita t. The nutrient (phosphorus) or response (chlorophyll-a, Secchi disk water transpar ency) parameters that promote healthy fisheries will vary by waterbody type and t he species-specific requirements of the desired fishes.

What concentrations of nutrient or re sponse parameters will ensure the quality of Virginia’s reservoir fisheries? To address this question, we conducted a comprehensive search of relevant publish ed literature in library data bases and interviewed fisherieswater quality experts to identify further sour ces. The results are summarized below. This report proceeds from a general ov erview of the fisheries-water quality relationship to a consideration of the particular nature of that relationship in re servoirs (vs. natural lakes),17 followed by analysis of water quality requirem ents for Virginia’s three categories of reservoir fisheries: coldwater (t rout), 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 develop ed and published for regional and cosmopolitan sets of lakes. Correlations between primary producti on and fisheries product ivity are highly positive, the former explaining (r 2 ) 67-84% of the latter (Table 5). Correlations between total phosphorus (TP) concentration and fisher ies 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 (r 2 ) 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 ( μ g/L) as the independent variable and various measures of fish production in lakes and reservoirs.

Dependent Variable Data Set (n) % of Variation Explained (r 2 ) 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. I ndeed, most of the analyses of trophic state (e.g., Carlson’s TSI) are based on the relationships of phosphorus, chlorophyll-a, and water transpar ency (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 st rong correlation between transparency and phosphorus (r 2 =0.50), a weak relationship bet ween chlorophyll-a and phosphorus (r 2 = 0.10), and virtually no correlation betw een chlorophyll-a and transparency (r 2 < 0.01). In these impoundments, i norganic turbidity largely det ermined water transparency, and although the suspended sedime nt contained phosphorus, most of the phosphorus was not biologically availabl e. In contrast, the r 2 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 conc entrations. Interpreta tion of their scatter diagram indicates that to produce 10.0 mg/m 3 of chlorophyll-a (indicative of marginally19 eutrophic conditions) in the average natural lake would require 30 μ g/L total phosphorus, whereas the average re servoir would require 40 μ g/L total phosphorus.
High flushing rates (low retention times) also limit developm ent of phytoplankton biomass. In fact, the Technical Guidance Manual (USEPA 2000d) recommends that reservoirs with retention times < 14 days be exempted from nutri ent 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 val ue 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 irrelev ant 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 da mmed rivers, reservoirs lose nutrients through settling in a downstr eam direction. Thus a singl e reservoir may grade from eutrophic in its upper reaches to mesotrophic in its mid secti on to oligotrophic near the dam. Such systems can support good fisherie s 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 t he empiric relationship between phosphorus concentration and fisheries would be relative ly 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 pr edictors 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 ti me (4-438 d), and total fish standing stock (77-2,321 kg/ha) . Total phosphorus was easily the best predictor of fish standing stock (r 2 = 0.84), followed by Secch i disk depth (negative slope, r 2 = 0.42) and chlorophyll-a (r 2 = 0.31). Fish standi ng stock increased linearly over the range of total phosphorus (8-81 μ g/L) on a log-log scale, suggesting that maximum fish biomass would occur at higher phosphorus concentra tions (Ney 1996). Fish production will ultimately be limited by habitat loss, result ing 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 A ppalachian 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 μ g/L (r 2 = 0.51). Jones and Hoyer (1982) reported that annual s portfish (synonym here for “gamefish”) harvest increases linearly with total phosphorus over the range 15-90 μ g/L in 25 midwestern U.S. lakes (r 220 = 0.52) and with chlorophyll-a between 4 and 67 μ g/L (r 2 = 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 wit h total phosphorus concentration up to 500 μ g/L (r 2 = 0.84).

Fisheries in Virginia Impoundments

The fisheries of Virginia’s public reserv oirs 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 lar ge (> 500 acres) impoundments. Reservoirs managed solely for warmwater fisheries range from large systems (primarily in eastern Virginia) to ponds. Many of the smalle r impoundments are owned by the Virginia Department of Game and Inland Fisher ies (VDGIF) and managed primarily for sportfishing.
Within the overall sportfish complex, it has long been recognized that individual species respond differently to particu lar levels of lake fertility. The Technical Guidance Manual (USEPA 2000d) uses the work of Oglesby (1 977) to predict that as phosphorus in natural lakes increases, fisherie s will shift from coldwater (TP < 24 μ g/L) to coolwater (TP = 24-48 μ g/L) to warmwater (TP = 48-193 μ g/L); total fisheries yield (harvest) should progressively rise over this r ange 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 m any of the sportfish sp ecies 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 μ g/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 c onceptual 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 we ll 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 trou t are stocked alone or in combination. Because stocked put-and-take trout fis heries 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 ov er one or more years to reach harvestable size. Essentially, this requi res an oxygenated hypolimnion dur ing thermal stratification. The relevant water quality literature is spar se. In Minnesota, natural populations of lake trout ( Salvelinus namayacush) achieve peak abundance at TP = 6 μ g/L and chlorophyll-a = 1 μ g/L (Schupp and Wilson 1 993). 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 μ g/L and chlorophyll-a declined from 30 to 14 μ g/L (Elliott et al. 1996). A fertilization experiment in a small mountain la ke in British Columbia increased rainbow trout growth and interannual survival while raising TP from 4 to 9 μ g/L and chlorophyll-a from 1 to 6 μ g/L (Johnston et al. 1999).

On the basis of this liter ature and the concentrations r eported from elsewhere for successful trout fisheries, it appears that the following c oncentrations are adequate to sustain habitat and prom ote trout growth: TP ≤ 10 μ g/L and Chl-a ≤ 6 μ g/L.

Coolwater Fisheries: Virginia’s coolwater sportfish species are striped bass, hybrid striped bass (white bass x striped ba ss) 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 mainta ined by the stocking of hatchery-reared fingerlings on a put-grow-take basis; the si ngle exception is the striped bass population of Kerr reservoir, which is self-sustaining.
Walleye, striped bass, and hybrid striped ba ss prefer water temperatures in the range of 19-28 o C (Coutant 1985, Hokanson 1990, Ki lpatrick 2003). By late summer in Virginia reservoirs, this habitat is usually limited to the metalimnion/hypolimnion downlake region near the dam (Ney 1988, Kil patrick 2003). However, all three species can tolerate water temperatures of >28 o 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 wo rld. In Minnesota, walleye abundance peaks under mesotrophic conditions: TP of 15-25 μ g/L and Chlorophyll-a of 7-10 μ g/L (Schupp and Wilson 1993). Lake Erie’s walleye populations is thriving at chlorophyll-a of 5-15 μ g/L; it is actually projected to increase if phosphorus loading is doubled (Anderson et al. 2001). Walleye do we ll in lakes that experience occasional22 hypolimnetic anoxia, but poorly in lakes with Secchi disk transparency > 4 m (Schupp and Wilson 1993).
Striped bass also fare poorly under oli gotrophic conditions. When Lake Mead, Nevada, became oligotrophic (TP = 10 μ g/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 gradien t. The lower segment of Smith Mountain Lake has an oxygenated hypolimnion year-r ound, providing a summer thermal refuge for striped bass. However, striped bass congr egate 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 in dicates that coolwater fisheries can prosper in systems where TP >10 μg/L and where Chl-a ≤ 15 μg/L.

Warmwater Fisheries: Principal warmwater sportfi shes 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. Virg inia’s centrarchids include sunfishes (bluegill, redear, redbreast, and pumpkinseed), bla ck and white crappie, smallmout h bass, as well as the most-sought freshwater sportfish species, la rgemouth bass. Centrarchids are littoral and epilimnetic fishes that do not require an oxygenated hypolimnion as summer habitat. Nutrient-induced habitat limitations occur only in sha llow lakes that become choked with aquatic macrophytes. In such systems, the co ver provided by dense stands of “weeds” prevents largemouth bass from preying on sunfish; both largem outh bass and sunfish become stunted (Bennett 1962). Vi rginia 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, centar chid populations are food- limited rather than habitatlimited. Higher levels of nutri ents translate to more cent rarchid biomass. In fact, centrarchid lakes devoted primarily to fish ing are often fertilized at least annually. Auburn University, which pioneered resear ch on centrarchid management, recommends fertilization to achieve chlorophyll-a concentrations of 40-60 μ g/L (Maceina 2001). The VDGIF frequently fertilizes its small fishin g lakes to produce robust centrarchid populations for anglers. In these small (< 200 acres) lakes, chlorophyll-a in the 40-60
μ g/L range commonly results.
Obviously, larger reservoirs are not subj ect 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 μ g/L and chlorophyll-a ~20 μ g/L; white crappie do best under hypereutrophic conditions (TP ~100 μ g/L; chlorophyll-a ~60 μ g/L).23 In a study of 30 large Alabama reserv oirs, Maceina et al . (1996) found that growth of crappie and largem outh bass increased up to ~20 μ g/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 la kes. 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 μ g/L), as were populations of redear sunfish and black crappie.

This review indicates that warmwat er fisheries can thrive where TP ≤ 50 μ g/L and Chl-a is 20-40 μ g/L.

Virginia DEQ Data Interpretation

The above review summarizes scientific st udies from throughout the USA, while the analysis that follows is focused on Virgin ia conditions. The purpose of this analysis is to determine the maximum nutrient concent rations (TP and Chl-a levels) that sustain good-to-excellent recreational fisheries, by fishery type and by ecoregion.

The status of the recreat ional fishery in each impoundment was rated on a scale of 1 (poor) to 5 (excellent) by VDGIF biologi sts, in response to requests advanced by Dr. John Ney. The rating scale and process are described in Secti on I-B, Question 2. Each reservoir was classified as one of the following types, based on the professional knowledge of Dr. John Ney and consideri ng 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 ar e included within this category.

Trout Fisheries: Lakes managed for support of tr out, including those managed by VDGIF for this purpose. These lake s are generally small in size, and predominantly within the stat e’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 quit e small, and fish production is the primary use. All of lakes identified as fertilized fisherie s in the current analysis are owned and managed by VDGIF.

Warmwater Fisheries: The majority of the state’s impoundments; all impoundments not explicitly cl assified 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 lake s to be categorized as “other” include high levels of non-algal turbidity (including coloration of waters from watershed geology or tannins), low retention times, mechanica l aeration, the pres ence 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 i ndependently of TP.
The initial stage of this analysis was explor atory: statewide pl ots 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 va riety 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 pl ots were due to the number of lakes represented (because of varying number s and distributions of water quality observations among lakes, the number of lake s with sufficient observations for inclusion in each representation varied). This exercise led to the conclusion that development of nutrient indicators representing the entire Ap ril – October sampling period would be the preferred approach, as it would maximi ze 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, inst ead of aggregating values by season, we aggregated by m onth (April – October) so as to generate lake-median values that better represent DEQ’s lake m onitoring schedule. For each lake and nutrient variable, monthly medians were calculat ed 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 (A ppendix A, Table A- 2, Mo values). Lake median values for lakes that had been ra ted by VDGIF but were represented by fewer than 6 monthly medians for any nutrien t variable were calculated as the average of the S1, S2, and S3 medians (See Sectio n 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 medi ans (Figure 8). The plots are interpreted by identifying those Virginia impoundments that are consider ed to be representative of fishery types (i.e., the fishery in that la ke 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. Nu trient levels in these impoundments are not necessarily at the maximum level that woul d 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 (Y urk and Ney 1989, Ney 1996), the plots of fishery status vs. water column TP and Ch l-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 lakes25 are more subject to influence by non-nutrient fa ctors than fish populatio ns in large lakes, and relatively small lakes are heavily repr esented in the DEQ database. Non-nutrient factors capable of influencing fish populat ions include inorganic turbidity (suspended sediments) and lake physical feat ures 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
Other27 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
Other28 Table 7. Maximum TP and Chl-a lake medians (April – October, μg/L) corresponding with highquality recreational fisheries a (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. Howeve r, 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 defi ned by the USEPA. Input for ecoregion-level analysis was provided by VDGIF fisheries biologi sts’ 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 recommendati ons 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 ox ygenated hypolimnion during summer stratification. The hypolimnia of virtually al l 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 te mperature preferences are slightly lower than those of hybrid striped bass and walleye. However, healthy striped bass fisheries are depend ent 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 fo r striped bass. In ecoregion 9, good to excellent coolwater fisheries (predominantly striped bass) occur at Chl-a concentrations of 2-10 μ g/L and TP of 4-40 μ g/L. The premier inland striped bass fisheries in Virginia are Smith Mountain Lake and Kerr Reserv oir, with median Chl-a of 2.6 and 9.7 μ g/L, respectively, and median TP of 20 and 30 μ g/L. Of particular interest is Kerr Reservoir because it supports the only reproducing fr eshwater population of striped bass in Virginia. This exceptional resource prov ides 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 ther efore recommend candi date 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 reservoi rs are not limiting to warmwater fisheries. In ecoregion 9, highly-rated warmwate r fisheries occur at Chl-a up to 25 μ g/L (Diascund Reservoir) and TP to 40 μ g/L (Lake Chesdin). From a fi sheries perspective, a TP limit30 below 40 μ g/L would be counterproductive. Whether lower Chl-a levels would have the same result is less certain. Based on Al abama studies (Maceina et al. 1996, Maceina 2001), it appears that Chl-a of ~20 μ g/L may not be detrimental to centrarchid fisheries in larger reservoirs. For ecoregion 9 wa rmwater 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 la kes it owns to achieve Chl-a of 40-60
μ g/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 μ g/L and TP of 40 μ g/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 fisherie s by VDGIF in other Virginia ecoregions should also be managed based on the same rationale.

Ecoregion 11

Coolwater Fisheries:

In western Virginia, Claytor (fishery st atus 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 Holst on, has median Chl-a of 9.2 μ g/L and TP = 20 μ g/L. Flanagan’s coolwater fishery productivity is limited by low fertility (Chl-a = 2.8 μ g/L, TP = 10 μ g/L). Claytor is intermediate in Chl-a (6.2 μ g/L) and equivalent to South Holston in TP (20 μ g/L). As in ecoregion 9, a Chl-a limit of 10 μ g/L appears correct to support ecoregion 11 coolwater fisher ies. A TP limit of 20 μ g/L is also sound; higher TP in riverine Claytor Lake could endanger summe r habitat for striped bass (Kilpatrick 2003). For ecoregion 11, we recommend candidate cr iteria 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 Roberts on (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 fr om greater nutrient inputs (Steve Reeser, VDGIF, personal co mmunication). 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 sma ll (< 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 (anox ic hypolimnion) or have low fertility. An example of the former is S ugar Hollow Lake (rating of 2), where median Chl-a and TP are 3.8 and 15 μ g/L, respectively. The latter is repr esented by Switzer Lake (rating of 3), where chlorophyll-a is 1.5 and TP is < 8 μ g/L. The exception to this situation is Lake Moomaw (2,600 acres; rating 4), which sup ports a trophy trout fi shery, with stocked fingerlings growing to 5-12 lbs. over several years. Lake Moomaw has median chlorophyll-a and TP concentrations of 4 and 10 μ g/L, respectively. These values concur with literature reports for productive trout fisheries. Higher levels of either parameter could impact critical summer habitat. We th erefore recommend for ecoregion 11 (and all trout lakes in Virg inia) candidate criteria of 4 μg/L chlorophyll-a and 10 μg/L total phosphorus.

Ecoregion 14

Lakes in southeastern Virginia are prin cipally 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 Rese rvoir (5). The median TP in each of these reservoirs is 20 μ g/L; median Chl-a is11 μ g/L in Lake Prince and 9 μ g/L in Western Branch. Nutrient parameter concentrati ons greater than curr ently experienced are likely to limit habitat in this region, which experiences the longest periods of lake thermal stratification in Virginia. For coolwater fisheries in ecor egion 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 rat ed (4 or 5) lakes have Chl-a of 7-30 μ g/L and TP of 20-50 μ g/L, while ratings drop for more eutrophic lakes (Chl-a > 60 μ g/L; TP > 100
μ g/L). The nutrient concentrations to pr oduce 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 su stained 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 us ed in the above analysis (Table 9). Available data indicate that most of the state’s reserv oirs satisfy the candidate criteria. Fisheries in those reservoirs that have both water qualit y that satisfies the candidate criteria and low fishery status ratings can be presumed to be affected by factor s other than nutrient overenrichment; comments by VDGIF biolog ists, in many cases, document these effects (Appendix A, Table A-4). Several lake s with good (4) or excellent (5) fishery status ratings fail to satisfy the criteria, t hus demonstrating the reservoir responses to nutrient inputs vary due to differences in morphometri c and other factors. Lake Chickahominy, for example, supports an ex cellent fishery despite high phosphorous levels (median TP = 60 μg/L). Lake Chickahomi ny 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 ra tio (using lake median values, TN/TP = 12, vs. the data set’s mean value of 25). Lake C ohoon (4) and Lake Prince (5) also support high-quality fisheries despite marginal exceedance of candidate criteria.

Table 8. Candidate criteria to accommodate fi shery 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 ok33 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 ok34 Criteria Implementation: TP vs. Chl-a

A central issue in developing candidate criteria to protect aquatic life and recreational fisheries in impoundments is th e decision whether they are intended to identify sites experiencing nutri ent enrichment or the subset of these sites exhibiting symptoms of eutrophication (biol ogical impairment arising from excess nutrients). This is a key point because it informs subsequent dec isions 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 t he development of nutrient criteria for Virginia reservoirs. Based on this view point, we recommend that secondary metrics (specifically, chlorophyll concentrations) shoul d be the basis for establishing criteria. Our rationale is that reservoirs exhibit variable sensitivity to nutrient enrichment based on their flushing rate (residence time), critic al depth (ratio of optical to mixed depth), sediment influx, and other fact ors (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 mitigat ion targets. Because only a subset of reservoirs experiencing nutrient enrichment will experience inability to serve designated use, setting standards based on nutrient concentra tions would result in the classification of all nutrient-rich water bodies as impaired i rrespective 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 potent ial are continuous, there is no defensible basis for selecting an arbitrary threshold to distinguish sensitive vs. non-sensitive waterbodies. Adopting criteria based on biologic al attributes (chlor ophyll concentration) resolves this issue by identifying lakes wher e the effects of nutri ent enrichment have exceeded an algal-biomass thre shold and avoids mitigation efforts for lakes where nutrient concentrations may be high but al gal production is constrained by low autotrophic potential (fast flushi ng and low critical depth). Another important advantage of this approach is that chlorophyll concentra tions are directly related to a number of factors that have a direct effect on the wate r body’s capability to serve designated uses. These include: (1) taste/odor/toxi city 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 al gal blooms, and (3) effe cts 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 expectatio n that EPA may require that criteria be expressed as both as nutrient concentrations and as effect s-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 st udy. Three lakes with Chl-a medians > 50 μ g/L (outliers) are not represented.36 Nutrient Requirements for Fisheri es: 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 r equirements and timeline. Based on analyses described above, the Committee suggests candidate criteria that would be protective of fishery recreation and aquatic lif e in the state’s impoundment s (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 recreati on and public water supplies, in defining nutrient criteria for implem entation, and that DEQ’s implementation would include a systematic review and evaluat ion, 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 re servoir, 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 observati ons, 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 st ate’s reservoirs where fisheries are known to be impaired due to nutrient over enrichment, the available data do not allow for a precise definit ion of the criteria.

In the committee’s view, the candidate crit eria 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 im plementation of nut rient criteria should include a process that embodies system atic review, evaluation, and refinement as recommended by the our July 2004 r eport (AAC, 2004). We recommend that components of that review and evaluation process for lakes and reservoirs should include:37 • Maintenance of a consistent monito ring approach and development of a more complete data record with improved utilit y 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 categoriz e impoundments based on differences in response to nutrient inputs and to understand underlying reasons for those differences. Such a database would incl ude information on waterbody usage and morphometric/physiographic features (s uch as surface area, volume, mean depth, watershed areas, surface water infl ows, and retention time) that influence response to nutrient inputs.

• A process of responding to numeric criter ia violations that includes an evaluation of whether or not the wate r body in question is serving its designated use(s), combined with an associated process for ma king similar determinations in water bodies that are not in viol ation 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 documentati on of that rationale so as to enable the process of criteria review, evaluat ion, 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 Nu trient Criteria Developmen t for Wadeable Streams, by Leonard Smock

Introduction

As part of the process of developing nutri ent criteria for w adeable streams, the Virginia Department of Envir onmental Quality (DEQ) determi ned that it would be useful to identify existing or planned st udies from neighboring states that might assist Virginia in the development of its nutri ent criteria. Ident ifying data or studies focused on the relationships between nutrient concentration s and appropriate response variables from states that share nutrient ec oregions with Virginia could greatly assist and expedite DEQ’s nutrient criteria dev elopment process.
The Academic Advisory Committee was requested to review several ongoing studies, identified by DEQ, to determine t heir potential for aiding the development of Virginia’s nutrient criteria . These studies approach the problem with a focus on using periphyton as the primary res ponse variable affected by vary ing nutrient concentrations in streams. The studies are reviewed her e in terms of their proposed objectives, appropriateness of methodologie s, data to be produced, and pr ojected 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 Maryl and and Pennsylvania. The initial proposal was developed jointly by the Maryland De partment of the Environment and the Pennsylvania District of the US Geological Su rvey, titled “A Regional Approach to the Development of a Response-Based Nutrient Criterion for Wadeable St reams in Nutrient Ecoregions IX, XI, & XIV” (Appendix D). Th is proposal was funded, with field work having been completed during the summe r 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” (A ppendix E). That work also has been funded, with the in tent of effectively doubling the sampling effort of the39 Maryland-USGS project. Fiel d work for this project is scheduled for completion during the summer of 2005. Al though data from both pr ojects could be analyzed separately, it should be the intent of the agencies to ev entually pool their data to increase the statistical robustness of their analyses.

Objectives and Methodology

The overall objective of the two studi es is to use an effects-based approach, rather than a reference condi tion approach, to develop nutr ient criteria for wadeable streams in three nutrient ec oregions shared by Virginia, Maryland, Pennsylvania and West Virginia. The studies focus on t he linkage of nutrient concentrations with chlorophyll-a and minimum dissolved oxygen (DO) concentrations, as well as periphyton community attributes. The pr ojected outcome of the studies is the establishment of threshold nutrient concentra tions that result in a desired minimum DO concentration for streams in each of the th ree nutrient ecoregions. These thresholds then could be used either to support EPA’s suggested nutrient criteria based on reference conditions or as an alter native 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 bot h 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 st reams being sampled and thus something less than the projected 48 streams will be in cluded 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: C entral and eastern forested uplands; and XIV: Eastern coastal plain. Site selection is based on a stratified random design, with the three strata being ecor egion, stream order (2 nd through 4 th order), and nutrient environment (low, medium and high concentrati ons). All sampling is conducted during the summer months in order to maximize the likely respons e 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 st ream 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 ea ch stream are deriv ed using continuous recorders over a 48-hour peri od, allowing the determination of nighttime, minimum DO concentrations. Benthic chlorophyll-a c oncentrations are used as surrogates for periphyton biomass. Data also are collect ed at each stream on periphyton community metrics, including species compos ition of the diatom community.40 Analysis of projected outcomes of the studies

The three nutrient ecoregions included in th ese 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 assu mption must be made that there is sufficient homogeneity within each ecoregion such that the periphyton communities of streams across these geogr aphically 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 assumpti on, 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 pr ovide useful information that could assist in the development of nutrient criteria. One potential problem, however, concerns sampling replication. Only one sample per st ream 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 conc entrations, 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. T he 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 ar e pooled may there be sufficient replication of the test categorie s. The PA-USGS sampling effort for this coming year needs to sample as many streams as possible.
Data analysis focuses on determining the nu trient 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 atte mpt 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 str eams to be sampled probably have partial to full canopy cover, it is very likely that duri ng the summer, light will be the factor limiting periphyton growth in at least some, if not ma ny, of the streams. Unless a nutrient is the limiting factor, differences in periphyton growth and DO concentra tions 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 nut rient limited and that other points are fr om 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 ma thematically express the nutrient-DO relationship. Thus, only those points that are presumed to define the relationship are used to quantify the relationship. Unfort unately, there will be no data to allow a determination if any of the streams ar e nutrient limited and thus appropriate for41 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 dur ing the summer, it could be incorporated into the data analysis to remove its effects. Categorizing the ex tent of canopy cover (e.g., open, partial, and full) and using that information in a multiple regression may result in a more accurate determinat ion of the nutrient-DO relationship.
Another point to consider is that th e 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 peri phyton, both through respiration and their decay. Other sources of demand and reaeration are not incorporat ed into the study. The concentration of TOC in the water colu mn 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 am ong streams will not be taken into account; differences in stream geom orphology and hydrology can have a marked effect on reaeration and hence on the minimum DO concentration in a stream.
The above real and potentia l problems with the propos ed 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 crit eria, but the limitations of the studies must be fully recognized.
The data on periphyton community stru cture and species co mposition will be useful for the later development of a peri phyton IBI that could be correlated with the general nutrient environment and eutrophication st atus of the stream s. 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 thei r eutrophication status, which could lead to better establishment of a nutrient-periphyton relationship and thus nutrient criteria.

The Academy of Natural Sciences–Virgi nia DEQ Study on Periphyton-Nutrient Relationships

Overview

The Philadelphia Academy of Natura l Sciences and DEQ collaborated on a proposal (Appendix F) to EPA’s Environm ental 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 Novemb er 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 samp led 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 conditi ons in wadeable streams in Virginia. The desire is to determine algal indicators of nutrient conditi ons that are appropriate for streams in Virginia, that can be incorporated into biom onitoring programs, and that ultimately can distinguish between different states of eutrophication.
The study uses standard EPA sampling and laboratory protocol s. Some number less than the 40-70 sites that were proposed for sampling were actually sampled during the 2004 field season. Streams were c hosen from DEQ’s ongoing probabilistic biomonitoring sampling sites. Data will be av ailable for each stream on chlorophyll-a, algal biomass, and species co mposition 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 periphy ton 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 co mmunity to nutrient concentra tions in wadeable streams. The general approach should provide useful information that could be used in the development of nutrient criter ia, 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 ext ent 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-periphyt on relationship drawn from the study.
For the data to be useful in the developm ent of nutrient criteria, DEQ will have to be able to adequately characterize the nutrient conditions in the streams or at least categorize the eutrophication stat us 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 periphyt on growth in streams. Nutrient concentrations associated with an una cceptable level of stream eutrophication, as defined by periphyton biomass or chlor ophyll-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 ex ists 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-U SGS and PA-USGS studies.43 There is sufficient similarity in the met hodologies 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 t he existing data base.44 

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### Appendix A. Lakes and Reservoirs Data Summary

Table A-1. Correspondence of DEQ monitoring stat ions 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 Dias cund Reservoir
2-BRC001.55 Bear Creek Lake 2-DSC006.03 Diascund Reservoir
1ABEE000.40 Beaverdam Cr eek Reservoir 2- DSC006.46 Dias cund Reservoir
1ABEE001.40 Beaverdam Cr eek Reservoir 2- DSC006.65 Dias cund 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 (c oast) 2-WLN007.36 Douthat Lake 4AXKD003.34 Beaverdam Reservoir (Bed ford) 1BNTH045.36 Elkhorn Lake
4ASCB004.58 Bedford Reservoi r 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 Reserv oir 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 5A CDR000.30 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-PKC 016.91 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 Reservoi r 1AGOO003.82 Goose Creek Reservoir 4ACRR008.32 Cherrystone Lake 1A GOO004.89 Goose Creek Reservoir 2-CHK024.07 Chickahominy Lake 2-GRA000.40 Graham Reservoir 2-CHK025.15 Chickahominy Lake 5AGT C009.94 Great Creek Reservoir 2-CHK026.94 Chickahominy Lake 5AGT C011.35 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 Lake48 Station ID DEQ Lake Name Station ID DEQ Lake Name 6ACNR000.00 John W. Flannagan Re servoir 1ABRU017.58 Lake Manassas 6ACNR001.03 John W. Flannagan Re servoir 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-SFT0 06.10 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-LH R000.96 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 4A ROA140.66 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 4ABAU 005.34 Martinsville Reservoir49 Station ID DEQ Lake Name Station ID DEQ Lake Name

5AMDT004.94 Modest Creek Reservoi r 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 4A ROA180.21 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 Reserv oir 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 Lak e 1BSKD003.18 Switzer Lake
9-XBL001.02 Rural Retreat Lake 4ADAN196.09 Talbott Reservoir 2-MBN000.96 Sandy River Reservoi r 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 4ADA N187.94 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-NW B002.93 Western Branch Reservoir
4ABWR010.55 Smith Mountain Lake 2-NW B004.14 Western Branch Reservoir
4ABWR017.42 Smith Mountain Lake 2-NW B004.67 Western Branch Reservoir
4ACCK001.80 Smith Mountain Lake 2-NW B006.06 Western Branch Reservoir
4ACOA000.60 Smith Mountain La ke 6BXAR000.69 Wise Lake
4AGIL002.39 Smith Mountain Lake50 Table A-2. Lake median values derived by 4 methods a and ecoregion locations.

Lake Chl-aS1 Chl-aS2 Chl-aS3 Chl-aMo SDS1 SDS2 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 1.5051 Lake Chl-aS1 Chl-aS2 Chl-aS3 Chl-aMo SDS1 SDS2 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 TNS2 TNS3 TNMo TPS1 TPS2 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 0.03052 Lake TNS1 TNS2 TNS3 TNMo TPS1 TPS2 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 0.02053Lake TNS1 TNS2 TNS3 TNMo TPS1 TPS2 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 0.02554Lake TNS1 TNS2 TNS3 TNMo TPS1 TPS2 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 TSSS2 TSSS3 TSSMo 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 1455Lake TSSS1 TSSS2 TSSS3 TSSMo 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 3 357 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 2 258 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 Warmwate r 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 fish59 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 CuSO 4 treated

Lake Prince 5 Coolwater OK - Warmwater & Coolwater fish

Lake Robertson 4 Warmwater OK - Fe rtilized by VDGIF in the past

Lake Smith 3 Warmwater OK

Lee Hall Reservoir 3 Warmwater OK - CuSO 4 ; lots of vegetation Little Creek Reservoir (7LTR) 3 Warmwater OK - Treated with CuSO 4 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 Fluctuat ing Infertile - PWS/flood control

Waller Mill Reservoir 4 C oolwater OK - WW, CW

Western Branch Reservoir 5 Coolwater OK60 

### Appendix B

RESERVOIR DYNAMICS: A COMPARISO N 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, chlor ophyll-a, Secchi depth, nutrient dynamics

ABSTRACT

The dynamics and trophic states of Smith Mountai n 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 co llaborated with the Smith Mountain Lake Association (SMLA) since 1987 a nd with the Friends of Clayto r Lake (FOCL) since 1996 to monitor the trophic status of Smith Mountain Lake and Claytor Lake.

Both reservoirs are hydroelectri c facilities operated by American Electric Power (AEP), but are very different impoundments. Smith Mountain La ke has an average hyd raulic 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 Bl ackwater, that form the two main channels of the lake. The two channels are nearly perpendicu lar 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 configur ation, hydroelectric functions also produce differences in circulation patterns and wate r 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 stat e 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 qu ality with distance from the da m. The presentation concludes by considering how the differences might affect stra tegies for managing water quality in reservoirs.61 INTRODUCTION

With the assistance of citizen volunteers and stud ent interns, scientists from the Ferrum College Water Quality Lab have been monitoring the tr ophic 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 we re constructed for flood control, generation of hydroelectric power, and recreat ion. Claytor Lake, on the New Ri ver, dates back to the late 1930’s and is a “run-of-the-river” re servoir with an average hydrau lic 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 ch annels 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 w ith 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 de stabilizes the shoreline.

The trophic state of a lake depends on the de gree of nutrient enrich ment. As nutrients accumulate in the lake, algal production increases and, in turn, water clarity decreases. The biomass produced by algae settles in the lake, cau sing 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 seve re because increased algal populations produce more oxygen during the day and consume more oxygen at night.

Trophic status is evaluated by measuring the t ypical suite of trophic st ate 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 M ountain Lake Association (SMLA) and the Friends of Claytor Lake (FOCL). Samp les are collected at pe rmanently designated stations each two weeks during the period be tween Memorial Day and Labor Day. Trained volunteer monitors collect the samples and meas ure water clarity from a boat during a one-week sampling window, and student inte rns analyze the samples at th e 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 St ate Index (TSI) can be calculated from the seasonal average value of any one of the th ree trophic state para meters: total phosphorus concentration (TP) as the indicato r 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, th e three TSI values (TSI-TP, TSI-CHA, and TSISD), calculated from each of the three trophic st ate parameters, would be similar. The average of the three TSI values is the co mbined 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 insights62 into non-classical behavior could be gained by co mparing relative values of TP, CHA, and SD in the study set. The full trophic state data set from Smith M ountain Lake (1987-2004) and Claytor Lake (1996- 2004) has been summarized, compared with each ot her, 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 sa mple is collected with a rubber hose that has been conditioned in lake water, marked at one-meter intervals, and f itted with a rope and di ver’s weight. The water sample is mixed in a 4-L polyethylene bucket an d 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 La ke Volunteer Monitoring Manual (Thomas and Johnson, 2003).

Laboratory Procedures: Analytical methods are adapted from Standard Methods for the Ex amination of Water and
Wastewater (APHA, 1995). Total phosphorus is measured spectrophoto metrically 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 Clayt or 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), Clay tor 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 m iles 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 th e 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 Walke r’s study is larger than the range found in SML and CL.63 Table 1. Average values for trophic state para meters 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 Stud y 10 27 4 48 Walker Stud y 2.0 64.0 9.4 Walker Stud y 0.2 4.6 1.1 Secchi Depth (m) Total Phosphorus (ppb) Chlorophyll- a (ppb)

As part of the model development, Walker examined several relationships among the three trophic state parameters. Three of the relatio nships 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 value s for Smith Mountain Lake and Claytor Lake.

min max avg min max avg min max avg Smith Mtn Lake 0.2 7 0.52 0.42 Smith Mtn Lake 5. 6 14.0 8. 7 Smith Mtn Lake 0.0 9 0.17 0.1 3 Claytor Lake 0.40 1.00 0.60 Claytor Lake 1. 9 13. 6 9.2 Claytor Lake 0.0 3 0.2 5 0.2 Walker Stud y 0.1 3 5.2 0.61 Walker Stud y 1. 8 31.0 10.0 Walker Stud y 0.0 4 0.6 0.2

Low: < 0.4 ; allocthanous PM unimportant?

High: > 16; algal turbidity dominates expect high nutrient respons e algae limited by phosphorus low response to nutrients mean CHA/mean TP A lgal use of phosphorus suppl y Low: < 0.13; low phosphorus respons e

algae limited by N, light, or flushing rate High: > 0.40; high phosphorus respons e mean CHA * mean SD (mg/m 2 ) Light extinction; algal & non-algal turidit y 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/m 2 ) CHA/TP

Temporal Variation of trophic state parameters: The trophic state of Claytor Lake and Smith M ountain Lake has not changed significantly since the Ferrum Water Quality Lab began the monitori ng programs. Figure 1 di splays the combined trophic state index for Claytor Lake.

Figure 1. The combined trophic stat e index for Claytor Lake (1996 – 2004).

R 2 = 0.2388

30 40 50 60 70

1 9

96 19 98 20 00 20 02 20 04TSI-C64 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 fo r Smith Mountain Lake by zone (1987-2004).

R

2 = 0.0923

30 40 50 60 70

19 86 19 88 1990 1992 1994 1996 1998 2000 2002 2004TSI - Combined

R 2 = 0.1547

30 40 50 60 70

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004TSI - Combined

R 2

= 0.1538

30 40 50 60 70

1986 1988 199 0 1992 1994 199 6 1998 2000 20 02 2004TSI - Combined

R 2 = 0.0768

30 40 50 60 70

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004TSI - Combined

R 2

= 0.0768

30 40 50 60 70

1986 1988 199 0 1992 1994 199 6 1998 2000 20 02 2004TSI - Combined

R 2 = 0.0768

30 40 50 60 70

1986 1988 1990 1992 1994 1996 1998 2000 2002 2004TSI - 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 Tr ophic 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.

R 2 = 0.952

0 20 40 60 80

123456
ZoneTotal Phosphorus (ppb) 1987-2004

R 2 = 0.9448

0 20 40 60 80

123456

ZoneTotal Phosphorus (ppb) 2004

R 2 = 0.9902

0 20 40 60 80

123456 ZoneTotal 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

ZoneCHA (ppb)
R 2 = 0.9253

0 2 4 6 8 10

123456

ZoneCHA (ppb)

R 2 = 0.9953

0 2 4 6 8 10

123456

ZoneCHA (ppb)

Figure 5. Secchi depth by zone in Smith Mountain Lake.

R 2

= 0.9107

0 1 2 3 4

123456
ZoneSecchi Depth (m)

R 2 = 0.93

0 1 2 3 4

123456

ZoneSecchi Depth (m) 1987-2004

R 2 = 0.99

0 1 2 3 4

123456
ZoneSecchi Depth (m) 1987-2004 2004 2004 1987-2004 1987-200466 Figure 6 shows the variation of trophic state para meters 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 al gal 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 paramete r with distance from dam in Claytor Lake.

R 2

= 0.7722

25 35 45 55 65 75

0 5 10 15 20 25 Distance to Dam (mile)Total Phosphorus (ppb)

R 2 = 0.2041

0 5 10 15 20

0 5 10 15 20 25 Distance to Dam (mile)Chlorophyll- a (ppb)

R

2 = 0.7013

0 1 2 3

0 5 10 15 20 25 Distance to Dam (mile)Secchi Depth (m)

R 2 = 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 l ong narrow channels typical of reservoirs. As a result, the water is not as hom ogeneous 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 channe l becomes the lake and should meet state water quality criteria. AHRT of the reservoir should also be consid ered because “run-of-the-river” reservoirs such as Claytor Lake do not develop the intern al nutrient dynamics at work in larger reservoirs such as Smith Mountain Lake.67 ACKNOWLEDGEMENTS

The following organizations have supported th e 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 Instru ction 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 criteri a (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 s hould apply the existing dissolved oxygen (DO) criteria to lakes and reservoirs . Therefore, states can interp ret and apply the DO criteria for stratified water bodies as appropriate. In the absence of dire ction from EPA, states such as Maryland (MDE, 2004), Minnesota (MPCA, 2003), Oregon (ODEQ, 2003), and West Virginia (WV EQB, 2004) do not currently ad dress the effects of stratific ation on DO concentrations in their water quality regulations. Alterna tively, Colorado (CDPHE, 2005), Iowa (IDNR, 2004), and Pennsylvania (PDEP, 2000) only apply DO criter ia to the epilimnion of stratified water bodies. Other states have vague references to stratification eff ects 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 stat es specify DO concentrations for arbitrary depths in the water column. Fo r protection of warm water aqua tic life in Kentucky, DO must be measured at “mid-depth in waters 10 ft or less an d at representative depth in other waters.” For cold water lakes and reservoirs that support trout, the DO con centration “in waters below the epilimnion shall be kept consistent with na tural water quality” (K DW, 2004). Tennessee regulations require that DO “be meas ured 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 Virgin ia recognize the effect s 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 establ ishing site-specific DO criteria “that reflect the natural quality of th at 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 Envi ronmental Quality (DEQ) applies existing DO criteria to the entire water column of lakes and reservoi rs during stratifie d 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 whet her 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) we re calculated for each impaired water body to determine if excessi ve nutrients are co ntributing to low DO concentrations in the hypolimnion.69 The current methodology used by DEQ to appl y existing DO criteria to constructed impoundments is sound and scientifically defensib le. 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 deve lopment of revised DO crit eria, reservoirs that were previously classified as Ca tegory 5 impaired may be reclas sified as waters supporting one or more designated uses. Theref ore, effort should be focused on determining reservoir-specific DO criteria before proceeding with TMDL deve lopment for Category 5 DO-impaired waters.

2. Should dissolved oxygen criteria be developed specifically for lakes?

We recommend that DO criteria be establishe d specifically for lakes and reservoirs. For development of nutrient criteria for Virginia wa ter bodies, the DEQ plans to classify state surface waters by type (estuaries, lakes and reservoi rs, and rivers and streams) (VDEQ, 2004c). Additionally, the Academic Advisory Committ ee recommended that nutrient criteria development be based on water body types (Vir ginia Water Resources Research Center, 2004). Therefore, it is likely that nutri ent criteria will be proposed and referenced by water type. Developing DO criteria specifically for lakes and reservoirs will provide consistency between water quality regulations. Add itionally, lakes and reservoirs respond differently to nutrient inputs than estuaries and rivers and streams, wh ich 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 lik ely translate to differing DO characteristics because DO is a secondary response variable to nutrient loading (Virginia Wate r 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 rela tively slow, so mixing is required for DO to be in equilibrium with the atmosphere. Consequentl y, 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 impound ments, which varies with depth and is controlled by hydrodynamics, photosynthetic inputs, a nd losses to chemical and biotic oxidations (Wetzel, 2001). We also recommend that DO cr iteria 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 ambi ent DO criteria for the Chesapeake Bay and its tidal tributaries (US EPA, 2003). It is also c onsistent with previous recommendations of the Academic Advisory Committee (Virginia Wate r Resources Research Center, 2004). Basing water quality criteria on designated uses has been applied successfully by British Columbia for developing phosphorus criteria a nd is also used by the Canadian Federal government in specifying a number of water qual ity parameters (US EPA, 2000).

3. If the answer to no. 2 is yes, should dissolv ed 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 st ratification on DO concentrations throughout the water column, we recommend that separate cr iteria 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 and70 not required to fully support the water body’s designated uses dur ing stratification. When the water column is completely mixed, a singl e DO criterion that s upports 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 bo dies 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 pr oposed for thermally stratified reservoirs in Oregon, although specific DO criteri a for the hypolimnion have not been developed (ODEQ, 2004). As an example, if DO criteria are develope d for protection of warm water aquatic life in a particular reservoir, specifyi ng DO criteria for the hypolimnion may not be necessary if water quality conditions in th e epilimnion can support the target sp ecies 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 w ithdraw raw water for treatment from the epilimnion. Specifying different DO criteria for different wa ter column depths or regions has recently been applied by EPA to the Chesapeake Bay and its tidal tributaries (U S EPA, 2003). Dissolved oxygen criteria were derived to pr otect estuarine species living in different ha bitats, also referred to as tidal water designated uses, which are infl uenced by natural proces ses in the Bay. The criteria reflect ambient oxygen dynamics, as evid enced by seasonal appli cation of deep-water and deep-channel DO criteria that account for the e ffects 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 recr uitment success of the bay anchovy. Deep-channel criteria were set to provide s easonal refuge and to protect th e survival of bottom sedimentdwelling worms and clams. During periods of complete water column mixing, the higher DO criteria associated with open-water fish and shel lfish use applies to deep -water and deep-channel designated uses (US EPA, 2003).

4. Should dissolved oxygen criteria be establishe d by lake use (water supply, fishing, or recreation)?

Because the vast majority of lentic system s in Virginia are constructed impoundments, establishing DO criteria based on water body designated use is a reasonable methodology. This approach is a logical step cons idering reservoirs ar e 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, hydroelectri c generation, and game fish production. Management practices ofte n affect physical, biological, and chemical characteristics of the reservoir (US EPA, 2000). Developing DO crite ria based on designated impoundment uses is recommended over specif ying criteria based on a reference condition approach because the reference or undisturbed stat e 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 unn ecessarily stringent criteria being applied to some water bodies while still prot ecting designated and ex isting uses. For instance, it is likely that the minimum DO criteria for pr otection of recreation use or wa ter supply use is lower than71 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 determin ed for Virginia water bodies for biennial preparation of the 305(b)/303(d) integrated water quality a ssessment. The six existing designations are aquatic life use, fish consumption use, shel lfish 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 dir ectly 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. Shellfis h 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 co mpliance with bacteriological criteria such as fecal coliform and E. coli (Virginia DEQ, 2004b). Ambient freshwater DO criteria for the protectio n 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 c onducted a preliminary survey of the literature since the 1986 freshwater document was published a nd found effects data that confirmed that the DO criteria remained protective. Therefore, EPA believes that the existing freshwater criteria accurately account for the antici pated effects of low DO on freshw ater aquatic species (US EPA, 2003). To our knowledge, EPA has not developed ambi ent DO criteria for the support of public water supply use in lakes and reservoirs, and neithe r 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 lake s or reservoirs where water is withdrawn from below the thermocline (ADEC, 2003). Colorado requires minimum DO con centrations of 3 mg
L -1 for waters designated for domestic water suppl y, but the standard is intended to apply to only the epilimnion and metalimnion of stratified la kes and reservoirs (CDPHE, 2005). Florida and West Virginia have specified that surface wa ters 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 wate r bodies may lead to increases in hydrogen sulfide, ammonia, and phosphorus, and the rele ase of reduced iron and manganese from the sediments. If entrained into the productive surf ace zone, phosphorus may stimulate algal growth, which exacerbates the problem because decay ing 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 ex tra 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. Ho wever, a cursory review of the scientific literature revealed little info rmation on suggested DO criteria for protection of raw water supplies. The published studies th at are most relevant to the e ffects of low DO concentrations on water treatment processes invol ve hypolimnetic aeration or ox ygenation. These techniques are commonly used to add dissolved oxygen to water bodies while preserving stratification. Studies documenting the effects of hypolim netic aeration and oxygenation ha ve 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, hypolimnetic72 oxygen levels increased; iron, manganese, and hydrogen sulfide levels decreased; and chlorophyll levels were not altered. The e ffects of hypolimnetic aeration on phosphorus were more variable. McQueen et al. (1986) attribute this to pH le vels and iron availability for phosphorus sedimentation. The e ffects 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 averag e hypolimnetic DO concentrations were maintained at greater than 4 mg L -1 in all cases and oxygenation decreased hypo limnetic concentrati ons of dissolved phosphorus, ammonia, manganese, and hydr ogen 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 in stalled hypolimnetic aerators in Lakes Prince and Western Branch, Virginia, two wate r supply reservoirs. Because of the aeration system, the City has discontinued prechlorinati on of raw water at the treatment plant, and noticeable improvements have been observed in reservoir ae sthetics (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 Leandr o Reservoir, Califor nia decreased ozone requirements by 35 percent and chlo rine 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 trih alomethanes in the finished water, which are regulated disinfection by-products , decreased by over 50 percent. Overall, the oxidant savings was greater than twice the co st of oxygen required to opera te the hypolimnetic oxygenation system (Jung et al., 2003). To provide some insight into the potentia l economic impact of remediating low-DO conditions in Virginia reservoirs, capital cost s 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 aera tors operate by injecti ng compressed air near the bottom of the hypolimnion. The air-water mixt ure 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 hypolimn ion. Pure oxygen or compressed air can also be introduced into the hypolimnion through the us e of diffusers to form a rising, unconfined bubble plume. This oxygenation method is most su itable for deep lakes where the bulk of the bubbles dissolve in the hypolimnion and the mome ntum produced by the plume is low enough to prevent intrusion into the ther mocline (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 cap acity of the sediments after diagenesis.73 In summary, the information currently availabl e 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 wate r supplies, and the vast majority of states do not have DO criteria specifically for this desi gnated use. The effect s of hypolimnetic anoxia on water quality parameters related to drinki ng water treatment are well documented, and hypolimnetic oxygenation is a proven mitigation technique. However, because of insufficient information available at this time, we can r ecommend only preliminary DO criteria for protection of water supply designated uses. It is suggested that the existing freshw ater DO criteria for nontrout waters (5 mg L -1 daily average, 4 mg L -1 minimum) be applied to al l strata used for potable water supply within a given reservoir. This is comparable to the approx imate, rule-of-thumb DO value of 5 mg L -1 typically desired in influent raw wate r 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 forma tion of hydrogen sulfide, but this has not been well established. Therefore, DO criteria for prot ection of water supply designated uses may need to be revised after further study by EPA or th e scientific and engi neering community. Regarding primary and secondary contact re creation, we are not aware of DO criteria development by EPA for the protection these designate d 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 crite ria exist, they are typically part of an all-encompassing limit to be applied to the most sensitive designated wate r 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 minimu m DO concentrations of 3 mg
L -1 for primary and secondary contac t recreational waters. However, the standard is intended to apply to only the epilimnion and metalimnion of stratified lakes and re servoirs (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 Vi rginia, if all reservoirs are designated for aquatic life and/or water supply us e, the DO criteria to support thes e uses would more than likely be adequate to support swimming and other recrea tional 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 sta ndards 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 la ke ecosystems have many ch aracteristics that are significantly different than reservoirs. The ra tio of drainage basin area to water body surface area is frequently higher for reservoirs than natural lakes (Cooke a nd Carlson, 1989). Because reservoirs are usually formed in river valleys, their morphometry is typi cally dendritic, narrow, and elongated. This is in contra st to the predominantly circular or elliptical shape of natural lakes (Wetzel, 2001). Most reserv oirs have asymmetrical depth di stributions in the longitudinal direction, with the maximum depths occurring adj acent to the dam. Near the vertical dam wall, unusual chemical and temperature st ratifications can occur, which differ dramatically from those typically present in natural lakes (Cole, 1994). Reservoirs often have higher flushing rates and74 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 thes e water bodies typically fluctuate more than in natural lakes as water is stor ed and released (Cole, 1994). Because the watershed area in re lation to surface area for reservoi rs 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 partic ulate concentrations into the receiving water body. Runoff influe nt to reservoirs is usually greater and influenced more significantly by precipitation events. These char acteristics induce higher but more irregular nutrient and sediment loading rates in reservoi rs compared to natural lakes, which affects biological processes (Wetzel, 2001). In turn, differences in light at tenuation and nutrient availability between natural and artificial lakes can result in different productivity rates and, subsequently, differing hypolimnetic dissolved oxyge n concentrations. Dissolved oxygen is a secondary response variable to nutrient input s (Virginia Water Resources Research Center, 2004). We recommend that site-specifi c dissolved oxygen criteria be developed for the two natural lakes in the state, Mountain La ke and Lake Drummond. These water bodies are located in distinctly different ecological regions, and henc e, each is a unique natural resource. Mountain Lake is the only notable natural lake in the unglaciated re gion of the southern Appalachian Highlands (Cawley et al., 2001). Lake Drummond is a blackwate r 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 Virg inia Department of Environmental Quality (Younos, 2004), numerous studies have been publis hed on Mountain Lake (Simmons and Neff, 1974; Obeng-Asamoa, 1976; Parson, 1988; Beat y and Parker, 1993; B eaty 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 fres hwater nutrient crite ria (Virginia Water Resources Research Center, 2004).

6. Should dissolved oxygen criteria be develo ped specifically for the hypolimnion?

Expanding on the response to question 3, we be lieve 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 c onditions will be required in the hypolimnion to achieve these uses durin g stratification. This is consiste nt with the recommendations of the Academic Advisory Committee regarding developm ent of Virginia freshwater nutrient criteria (Virginia Water Resources Research Center, 2004). Additionally, hypolimnetic DO criteria should also consider the potenti al downstream effects of re duced oxygen concentrations in waters released from the lower depths of constructed impoundments. Per 9 VAC 25-260-10 of the Virginia Water Quality Sta ndards, “in designating uses of a water body and the appropriate criteria for those uses, the boar d...shall ensure that its water quality standards provide for the attainment and maintenance of the water quality standards of downstream waters”. Virginia75 streams and rivers downstream of reservoirs ar e currently affected by releases of hypolimnetic waters. For example, almost 6 miles of the Ro anoke 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 Me herrin 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 pr ojects. The United States Federal Energy Regulatory Commission (FERC) is increasingly specifying mini mum 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 o xygen criteria for lakes?

In order to demonstrate appropriate DO crite ria for lakes and reservoirs, a multi-phase Use Attainability Analysis (UAA) is recomme nded. 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 litera ture and historical data 2. Routine, on-site surveys perf ormed to analyze parameters relating to DO levels (e.g., sediment loading and organic matter (OM) depos ition rates, nutrient loading (especially phosphorous (P)), hydraulic input and withdrawal locations wi thin a limnological system, stratification depths, and speci fic chemical analyses such as DO (via Hydrolab and modified Winkler measurements), total-P (TP), BOD, and COD) 3. Correlation of TP TSI approach with epil imnetic and hypolimnetic DO measurements to help establish overall UAA (see fu rther discussion in Response 9)
A multi-phase UAA approach would best characteri ze the combined influence of the various processes impacting DO levels (in both the ep ilimnion and the hypolimnion) and subsequent attainable use on a site-specific basis. Epilim netic oxygen levels are primarily controlled by photosynthesis, microbial respira tion, 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 rese rvoirs 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, th ereby decreasing water quality and increasing drinking-wate r treatment costs. Release of P can promote excessive algal growth, which stimulates eutrophication and ca n have detrimental effects on the health and diversity of the plant, fish, and benthic popul ations. The abundance of algae is directly influenced by the ratios of supp lied nutrients. Even small differe nces in the nutrient ratios (e.g., N to P) can have significant effects on comp eting algal species (Gächter and Muller, 2003; Lewandowski et al. 2003). Additional oxygen is cons umed as these algal blooms die, settle into the hypolimnion, and are degraded by aerobic sedime nt 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 in to 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 mainta ined by controlling P loading or by adding76 oxygen to lake and reservoir systems. Hypo limnetic oxygenation systems, which preserve stratification, are increa singly used to replenish DO (refer to Response 4). Due to the fact that complex interactions between oxygen ava ilability and P cycling have such control on water quality and subsequent us e attainability, we r ecommends using DO and TP as key parameters in use attain ability 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 pr ecipitates, thus supporting the n eed 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 le vels should be perf ormed 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 da ta may be extremely valuable resources for establishing DO reference levels and anticipated stra tified zones. Historical data, such as that obtained during the 1983 EPA Clean Lakes VA sa mpling 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 pr edominantly for general guideline purposes, as information obtained during the 1983 EPA Clean Lakes study was collected primarily to establish base-line data in pr eparation 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 characteri ze existing, reservoir-specific conditions. DO availability and depletion rates are very site-spe cific and transient as they may be significantly influenced by variables including sedimentation rates, nutrient loadi ng, OM deposition, local sediment mineral (e.g., Fe, Mn, Ca, P) composition, and lake morpho metry. Unfortunately, inadequate tributary data were obtained during the EPA sampling pr ogram due to drought conditions at the time of testing (US EPA, 1982). Thus, the transient and possibly considerab le 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 hypolimne tic) in lakes and reservoirs, with high concentrations of OM resulting in an increa sed oxygen demand (Kalin and Hantush 2003). However, evidence shows that organic degradatio n rates may not directly correlate with OM concentrations, raising the possibility that di fferent levels of oxygen availability and differing rates of OM delivery via sedi ment 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 estimat es, 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 in fluences have changed significa ntly since the early 1980’s in many VA regions, resulting in altered DO and temperature c onditions from those documented during the 1983 EPA Clean Lakes VA sampling pr ogram. Research has shown that temporal77 and spatial variations in lacustrine processes may have considerable control over subsequent SOD, DO, and TP levels (Hanson et al., 2003; H ouse, 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 poi nt in many systems, subsequently impacting water-column DO levels. Nishri et al. (1998) found significant variations in limnological parameters over time with epilimnetic DO c oncentrations increasing by ~20%, hypolimnetic H 2 S 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 accumulati on 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 bot h external (e.g., anthr opogenic nutrient loading, hydraulic inputs (river, streams), local soil/min eral 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 cu rrent 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 w ith 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) tota l 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 (chlorophy ll-a (CA), Secchi disk (SD), and TP) indicates that nutrient loading is negatively impacting designated uses of a particular lake or re servoir. 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 ba sed 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 characteriz ing limnological trophic st ates and the TP TSI approach may yield a satisfactory approximation of oxygen availability/depletion with respect to certain attainable use determinations. Howe ver, 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 inter actions between oxygen levels and P cycling (as defined in Response 7) and the resulting impacts on water qual ity, 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 correlat ed. While TP can be a strong indicator of DO levels and trophic states, particularly in regions of high photosynthetic activity and productivity, other biogeochemi cal 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 re leased into the hypolimnion. However, recent studies have suggested that be nthic microbial activity and the formation of ferrous phosphate precipitates (e.g., vivianite) may have a signif icant 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, el evated 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 concentratio ns 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 s eems 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 TS I methodology with routine DO measurements (particularly during stratification) and site-specific data to dete rmine potential drains on oxygen demand via natural (sediment de position, introduced Fe- and Mn-min erals, retention time) and anthropogenic (nutrient loading, hydraulic inputs a nd withdrawals) sources. This approach would use soluble TP and DO measurements to identify potential eutrophi cation problems that may exacerbate DO depletion and subs equently 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 criter ia should be developed for the tw o natural lakes in the state, Mountain Lake and Lake Drummond. To a ddress the effects of stratification on DO concentrations throughout the water colum n, separate criteria for the epilimnion and hypolimnion are recommended, and criteria developm ent should be based on designated uses of the water bodies. Application of a single DO criterion for all de pths within a given lake or reservoir may be unnecessarily stringent and not required to fully support the water body’s designated uses during stratificati on. Dissolved oxygen criteria fo r 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 ar e classified as Category 5 impaired waters for DO because of hypolimnetic water discharge ups tream from Lake Gaston (VDEQ, 2004d).79 Also, nearly 6 miles of the Meherrin River ar e designated Category 5 impaired for DO due to hypolimnetic releases from an upstream impoundment (VDEQ, 2004d). Because the vast majority of lentic system s in Virginia are constructed impoundments, establishing DO criteria based on water body de signated use is a reasonable methodology. Designated uses have already been determined fo r 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 lif e 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 protectio n 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 re lated to drinking wate r treatment are well documented. However, because of insufficient information available, we can recommend only preliminary DO criteria for protection of water s upply 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 potab le water supply within a given rese rvoir. 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 reserv oirs have not been well establis hed, DO criteria for protection of water supply designated uses may need to be revi sed 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 de signated for aquatic life and/or water supply use, then the DO criteria to support these uses would mo re than likely be adequate to support primary and secondary recreational uses. Compliance with DO criteria in lakes and rese rvoirs will likely be determined through field data collection. Measurements ar e typically obtained at appropria te intervals th rough 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 de pendent on the oxygen depletion rate. In some locations, the minimum required fre quency may be monthly; in others , it may be as high as daily (US EPA, 2000). Temperature pr ofiles will also be required to determine the onset of stratification and to deli neate the density strata within wate r bodies. Dissolved oxygen data from most, if not all, of Virginia’s significant reservoi rs has been or is currently being collected, as evidenced by the biennial 305(b)/303(d) water qu ality assessment reports (VDEQ, 2004d). To ensure that representative DO da ta are being obtained to accurate ly characterize each reservoir’s oxic status, existing sampling pro cedures should be reviewed. As referenced in the Nutrient Criteria Technical Guidance Manua l–Lakes and Reservoirs (US EPA, 2000), there are a number of publications that provide fu rther information on sampling desi gns for lakes and reservoirs (Carlson and Simpson, 1996; Gaugush, 1987; Ga ugush, 1986; Reckhow, 1979; Reckhow and Chapra, 1983). With respect to the type of Use Attainabil ity Analysis (UAA) needed to demonstrate appropriate DO criteria for lake s and reservoirs, a multi-phase UAA is recommended, based on 1) a review of supporting literatur e and historical data; 2) routin e, on-site surveys performed to analyze parameters relating to DO levels; and 3) correlation of TP TSI approach with epilimnetic80 and hypolimnetic DO measurements to help establ ish overall UAA. We feel that a multi-phase UAA approach would best characterize the comb ined 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 be tween oxygen availability and P cycling have such control on water qu ality and subsequent use attainability, we recommend using DO and TP as key paramete rs 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 tre nds in DO and temperature over time as a function of variations in anthr opogenic and natural influences. Regard ing the use of historical data for estimates of current DO conditions, this data shou ld 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 prepara tion 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 existi ng, reservoir-specific conditions. DO availability and depletion rates are very site-specific and transient as th ey 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. T hus, variations in oxygen availability, nutrient loading, and OM concentrations (all of which are highly site-s pecific parameters) may have significant impact on nutrient cycling, SOD, an d 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 comp lex interactions between oxygen levels and P cycling and the resulting impacts on water qualit y, it is important to evaluate both P and DO levels when estimating potential DO demand and subsequently es tablishing DO criteria. While TP may be a strong indicator of DO levels and trophic states , particularly in regi ons of high photosynthetic activity and productivity, other biogeochemi cal processes may strongly impact DO in hypolimnetic regions. A strong UAA approach coul d 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 sour ces. This approach would use soluble TP and DO measurements to identify potential eu trophication problems that may exacerbate DO depletion and subsequen tly decrease water quality.81 References

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Phytopla nkton relationships to wa ter quality in Lake Drummond and two drainage ditches in th e Great Dismal Swamp, Virginia. Castanea 58(1): 18-33. Reckhow, K.H. 1979. Quantitative techniques for the assessment of lake quality. U.S. EPA Office of Water Planning a nd Standards. EPA-440/5-79-015. Reckhow, K.H., and S.C. Chapra. 1983. Engineeri ng approaches for lake management. Volume 1: Data Analysis and Empirical Modeling. Bu tterworth Publishers (Ann Arbor Science). Ann Arbor, MI. Simmons, G.M., Jr. and S.E. Neff. 1974. Observa tions on limnetic carbon assimilation rates in Mountain Lake, Virginia during its thermal st ratification periods. Va. J. Sci. 24(4): 206- 211. South Dakota Department of Environment and Natural Resources (SD DENR). 1997. Administrative Rules of South Dakota, Chapter 74:51:01–Surface Water Quality Standards. http://legis.state.sd.us /rules/rules/7451.htm#74:51:01. Tennessee Department of Environment and Conservation (TDEC). 2004. Tennessee Rules Chapter 1200-4-3: General Water Quality Criteria. http://www.state.tn.us/sos/rules /1200/1200-04/1200-04-03.pdf. United States Environmental Protection Agency (US EPA). 1982. Classi fication and Priority Listing of Virginia Lakes. EPA Gran t Number S-003219-01-0. State Water Control Board, Commonwealth of Virginia., Richmond, VA. January 1982. US EPA. 1986. Ambient Water Quality Crit eria for Dissolved Oxygen. EPA 440/5-86-003. http://www.epa.gov/cgi-bin/claritgw?op -NewSearch&template=clnovice. US EPA. 1994. Water Quality Standa rds Handbook. Second Edition. EPA-823-B-94-005. http://www.epa.gov/waterscience/s tandards/handbook/index.html. US EPA. 1998. National Strategy for the Develo pment of Regional Nutrient Criteria. EPA 822- R-98-002. http://www.epa.gov/watersci ence/standards/nutstra3.pdf. US EPA. 2000. Nutrient Crite ria Technical Guidance Manual– Lakes and Reservoirs. EPA-822- B00-001. http://www.epa.gov/waterscience/s tandards/nutrients/lakes/index.html. US EPA. 2003. Ambient Water Quality Criter ia for Dissolved Oxygen, Water Clarity, and Chlorophyll a , for the Chesapeake Bay and its Tidal Tributaries. EPA 903-R-03-002. http://www.epa.gov/region03/chesap eake/baycriteria.htm. Virginia Department of Environmental Qual ity (VDEQ). 2004a. Wate r Quality Assessment Guidance Manual. Virginia Department of Environmental Quality: 305(b)/303(d) Integrated Water Quality Report. http://www.deq.virginia.gov/wa terguidance/pdf/042006.pdf. VDEQ. 2004b. Virginia Administra tive Code 9 VAC 25-260–Virginia Water Quality Standards. http://www.deq.virginia.gov/ wqs/pdf/WQS04.pdf. VDEQ. 2004c. Nutrient Criteria Development Plan for the Commonwealth of Virginia, March 24, 2004 Resubmission. http://www.deq.vi rginia.gov/wqs/nutplan2.pdf. VDEQ. 2004d. Final 2004 305(b)/303(d) Water Quality Assessment Integrated Report. http://www.deq.state.va.us/wqa/ir2004.html.85 Virginia Water Resources Research Center. 2004. Draft Report of the Academic Advisory Committee to Virginia Department of E nvironmental Quality–Freshwater Nutrient Criteria. Virginia Water Resources Re search Center. Blacksburg, VA. 81 pp. West Virginia Environmental Quality Board (WV EQB). 2004. Legislative Rules, Title 46, Series 1–Requirements Governi ng Water Quality Standards. http://www.wveqb.org/2004fin alfiledruleforadobe.pdf. Wetzel, R.G. 2001. Limnology–Lak e and River Ecosystems, 3 rd ed. Academic Press. San Diego, CA. 1006 pp. Wüest, A., N.H. Brooks, and D.M. Imboden. 1992. Bubble plume modeling for lake restoration. Wat. Resour. Res. 28: 3235-3250. Younos, T. 2004. Personal communication. Virg inia Water Resources Research Center. Blacksburg, VA.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 Ches terfield 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 15787 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 To wn 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 5A88 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 Region al 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 4C89 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 Statewide90Table 2. Capital costs ofrepresentative hypolimnetic aeration and oxygenation systems.WaterbodyMaximumDepth(m)Volume(106m3)Aerator orOxygenatorTypeApplicationYearInstalledOxygenAddition(kg d-1)CapitalCost(2005 $)ReferencesRichard B.RussellReservoir,Georgia47 1,270 bubble plumediffuserhydropower 1985 200,000 $1.6M Mauldin et al.(1988), Beutel andHorne (1999), Little(2005)Lakes Prince andWestern Branch,Virginia11 38 full-liftaeratorwatersupply1991 10,700 $2.8M Burris and Little(1998), Burris et al.(2002), Little(2005)CamancheReservoir,California41 545 Speece Cone hydropower 1993 9,000 $1.8M Jung et al. (1999),Little (2005)Spring HollowReservoir,Virginia55 7.2 bubble plumediffuserwatersupply1998 250 $120K Little and McGinnis(2000), Little(2005)Upper SanLeandroReservoir,California51 bubble plumediffuserwatersupply2002 9,000 $450K EBMUD (2001),Jung et al. (2003),Little (2005)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, To xics 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, 200392 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 enfo rcement 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 a ssessment, monitoring in support of TMDL development and the fish and shellfish tissue-monitoring program.

United States Geological Survey

The U.S. Geological Survey (USGS), Penns ylvania District, ha s a long history of investigations into nutrients in streams and their relations to water quality. Staff from the Pennsylvania district have conducted long-te rm investigations of nutrients in the Conestoga River Basin (Lietman, 1997) as pa rt 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, bot h long-term and involving evaluations of BMPs are nearing completion (Schreffler a nd others, in preparation: Galeone and Brightbill, in preparation). Capabilities within the Distri ct include macroinvertebrate identification and analysis, use of continuous water-quality monitors, field water-quality assessments, and data management and anal ysis. Other capabilities within the USGS include state-of-the-art la boratory analyses, macroinve rtebrate identifications, development of statistical techniques for tr end 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 lead ing causes of impairment for streams in the United States. The number of waters advers ely affected by nutrients is documented in the various 305(b) reports prepared by the stat es 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 stat es establish nutrient criteria. The EPA has published nutrient cr iteria technical guidance manuals for three aquatic environments (Lakes and Reservoirs , Rivers and Streams, and Estuarine and Coastal Marine Waters), with a fourth doc ument (Wetlands) scheduled for publication in93 December 2003. Also, EPA has published suggest ed nutrient criteria for all 14 nutrient ecoregions across the United States.

These suggested criteria were establishe d based on an examination of reference conditions and levels of nutrien ts that lead to impairment. This approach has gained praise and acceptance from many in the scie ntific and regulatory community for its scientific validity. Yet, othe rs 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 wi ll (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 ca used by nutrients to calculate a nutrient criterion le vel. 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 inde x of biotic integrity (P-IBI).

EPA’s Request for Proposals asked for a comp ilation 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 residi ng in the current database are collected primarily during daylight hours. Critical disso lved oxygen concentrations occur at night. Further, the existing data for much of EP A Region III have recently been analyzed (Brightbill and Koerkle, 2003).

III. QUALITY ASSURANCE PROCEDURES

Quality assurance procedures will include usi ng a written, standardized protocol for field work, training for all personnel who collect field data, and standardized field datacollection sheets for all field wo rk. 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 Surv ey’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 electron ic data, and both tabular and graphical displays of the data to check for anomalies.

Periphyton identifications will be done usi ng widely accepted keys. Verification of periphyton identifications will be done by an outside lab for 5 percent of the samples.94 I V. 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 2 nd , 3 rd , and 4 th order streams in Nutrient Ecoregions IX, XI, and XIV, all in EPA’s Region 3. Th ese Ecoregions comprise most of the land area of the states in EPA’s Region 3 and ex tend beyond Region 3 to include most of the southeastern U.S. The proposed work woul d involve field data collection during the critical summer months of 2004. Data eval uation 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 periphy ton 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 (f unding source), and Nationa l (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 defi ne the relations between nutrients and periphyton. These efforts have been only partly successful. One reason is that the researchers pooled all available data for th e 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 consump tion is largest at ni ght. Thus, nighttime measurements collected during the hottest, dr iest period of the year are critical for95 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 st udies is that routine monitoring is usually a daytime activity.) The dissolved oxygen m easured during routine daytime monitoring fails to capture the lowest dissolved oxygen concentrations.

Assessment of Stream Periphyton Communities:

Examination of periphyton communities is an e ffects-based approach that will attempt to link nutrient conditions within a stream syst em 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 ar e 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 environmen tal conditions because they quickly respond to changes and are sensitive to a numbe r 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 co mponent 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 a nd the nutrient status of that stream.

In addition to nutrients, periphyton growth is controlled by available sunlight, time available to grow since the la st high-flow event, streambed stability, soil t ype, substrate availability, water speed, a nd 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 grow th is cropped by invertebrate grazers and turned into invertebrate biomass. Nuisan ce blooms are usually a symptom of a system stressed by things like over-supply of nutrien ts 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 re present 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 re search (includi ng chlorophyll a , nutrients, standard field measurements, and total organic carbon) described in the next section of this proposal will be recorded. In addition, c ontinuous 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 minimu m 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 concentr ations associated with DO c oncentrations 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 repres ent 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 de sign. The advantage of this approach is that fewer resources are expended since only “t argeted” streams within each stratum will be sampled. Also, selection of a range of nutrient conditions will facilitate establishment of nutrient-algal (periphyton) relationships th at 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 deve lopment of ecoregion-specific nutrient criteria, the initial stratifi cation will be by nutrient ecoregi on. The Level III Aggregate Nutrient Ecoregions of interest in EP A Region 3 are Ecoregion IX (Southeastern Temperate Forested Hills and Plains), Ecor egion XI (Central a nd Eastern Forested Uplands), and Ecoregion XIV (Eastern Coastal Plain). Other Ecoreg ions within the EPA Region 3 boundaries represent relatively small areas and will not be sampled in the proposed research. An equal number of sample s 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 or ders 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 stratifica tion by nutrient condition. Streams will be separated into high, moderate, and low nutrient conditions/potential based on the assessment of available in-stream nutrient da ta, as well as land use information.

At the completion of this stra tification process, each ecore gion should have a list of potential sampling sites based on stream orde r and nutrient condition. Stations (sampling reaches) will be selected randomly from th e 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 samp ling 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 dup licate quantitative single habitat samples (one for taxonomic identific ation, and one for biomass measurements), and a waterquality sample will be collected from each sa mpling reach. Riffle habi tat 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 pe riphyton sample also can be used to assist with the identification or verification of diatom species and varie ties in quantitative periphyton samples from the same location. Water quality can be assessed by interpreting autecological information, the taxon-specific physiological requirement s or tolerance for defined ranges of water-quality conditi ons. Quantitative periphyton samples are collected to measure the re lative abundance and density (algal cells per square centimeter) of each taxon presen t in each selected stream habitat type (riffle) in a sampling reach. Quantitative samples are co llected using a variet y 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 prot ocols 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 sh ipped to a laboratory chosen through an open bidding process. All genera in the sa mples will be identified to species level to enable the development of autecological indi ces 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 comp etent 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 fu ture assessment efforts.

Periphyton data will be relate d to corresponding physical, ch emical, and biological data at each basic fixed site to evaluate taxon-sp ecific 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 in to regional assessments of water quality.

Analytical Requirements:

Field water-quality samples will be sent to a contract labo ratory for analysis. Analyses will be needed for a broad range of nutrients including dissolved nitrite+nitrate, dissolved ammonia nitrogen, organic nitrogen, dissolv ed 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, Pennsyl vania. 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 avai lable software will be used for graphical and statistical analyses.

Evaluation Plan:

The periphyton community data will be anal yzed by looking for indi cator species and by comparing several different community metrics. The final selected eutrophic indicators will offer the best representation of the heal th of the system, with attention given to simplicity. The idea is to devel op a tool that is reflective of stream conditions, yet can be routinely implemented by most field biologi sts employed by state resource agencies. Data from the project will be examined using a variety of traditional and multivariate approaches. Non-parametric techniques ar e likely to be used as biological data frequently are not normally distributed. Data tr ansformations may be needed to deal with non-linear relationships. Graphical presen tation of the data will be emphasized.99 For the development of nutrient criteria, gr aphing measured nutrient concentrations for several streams against minimum dissolved oxygen concentrations are expected to produce a relationship generalized in Figure 1. The nutrient c oncentration that results in meeting the water-quality standard (dissolv ed oxygen concentrati on of 5.0 mg/L) will provide the basis for the pr oposed nutrient criterion.

Figure 1. Expected relation between measur ed 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 point s will fall above the line. However, no data points should fall below the line. Data woul d be expected to fall above the line when some factor other than the nut rient 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 criterion100 Figure 2. Hypothetical data dist ribution for Region III streams.

For those streams where nitrogen is limiting, th e data point falls on the line. If light (or any other variable) is limiting, periphyton w ill 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 li ne. 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 th e nutrient criterion.

We will measure dissolved organic car bon to account for oxygen-demanding organic matter in the stream. The goal is to meas ure relations between nutrients and minimum dissolved oxygen concentrations that are not influenced by vari ables other than nutrients.

This approach has a weakness that mini mum dissolved oxygen concentrations are influenced by productivity of the attached al gae as well as the sta nding crop of attached algae. We do not propose to measure algal productivity. Intuitively, algal productivity and algal standing crop are likely relate d, so measurement of standing crop by chlorophyll a concentrations should suffice. Minimum Dissolved Oxygen Concentration Measured Nitrogen Concentration

5 mg/L101 Products:

1. Semi-annual progress reports will do cument 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 semina r 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 provi ded 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 envir onmental managers that meets periodically to exchange information about nutrient cr iteria 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, th e U.S. Environmental Protection Agency has been tasked with responding to the pr oblem of nutrient enrichment in U.S. waterways. One of the ways the EPA is addr essing this task is to provide guidance for the states to establish water-q uality standards for nutrients. The results of this study will provide support for a response-ba sed approach to establishing these nutrient standards for

streams.

The results of this study and further inve stigations support the mission of the USGS by providing a regional pattern of water quality in the mid-Atla ntic 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 prepar ation, Effects of streambank fencing on water quality, Lancaster County, Pennsylvania: U.S. Ge ological Survey Water-Resources Investigations Report.

Lietman, P.L., 1997, Evaluation of agricultura l best-management practices in the Conestoga River headwaters, Pennsylvania: A summary re port 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-qua lity 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 co mmunities 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 wate r 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 Waters heds Sampled in 2000: Maryla nd Department of Natural Resources, variously paged.103 

### Appendix E. Pennsylvani a-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, 2004104 I. ABSTRACT

An expansion of a nutrient cr iteria development project curr ently underway in Region 3 is proposed. Periphyton biomass and community structur e will be evaluated in relation to nutrient concentrations. Nutrient criteria will be suggested based on th e response variables, chlorophyll a and nighttime dissolved oxygen. Most previous st udies have neglected to include nighttime dissolved oxygen. Collaboration among Region 3 st ates 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 Envi ronmental 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 es tablished by the PADEP and by controls on point and non-point sources of pollution. To ensure that water quality sta ndards are met, the Department regularly monitors the waters of Pennsylvania through the Water Quality Network which includes 123 standard stations and 26 re ference stations. Monitoring and modeling in support of TMDL development is also a functio n of the PADEP. Most significantly for this proposal., the PADEP is charged w ith 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 hist ory of investigations into nutrients in streams and thei r relations to water quality. Sta ff from the Pennsylvania district have conducted long-term investigations of nutri ents 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 ar e nearing completion (Schreffler and others, in preparation: Galeone and Brightbill, in pr eparation). Capabilities w ithin 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 an alysis of regional and nati onwide environmental data.

III. BACKGROUND

Nutrients have been cited as one of the leading causes of impa irment for streams in the United States. The number of waters a dversely affected by nutrients is documented in the various 305(b) reports prepared by the states and submitted to the U.S. Environmental Protection Agency105 (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 proce ss of helping states estab lish nutrient criteria. The EPA has published nutrient criteria tech nical guidance manuals for three aquatic environments (Lakes and Reservoirs, Rivers a nd Streams, and Estuarine and Coastal Marine Waters). A fourth document (Wetlands) was sc heduled 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 nutri ent ecoregions across the United States.

These suggested criteria were es tablished based on an examination of reference conditions and levels of nutrients that lead to impairment. Th is approach has gained praise and acceptance from many in the scientific and regulator y community for its scientific va lidity. Yet, others have called for more of an effects-based a pproach where response variables a nd 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 Re gion III streams. An effectsbased approach means we will look at the eff ects caused by nutrients to calculate a nutrient criterion level. The nutrient criteria can be used to suppor t 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 ecologi cal health of the peri phyton community for the streams studied and will provide initial data for future de velopment of a pe riphyton index of biotic integrity (P-IBI).

Existing data include an inad equate number of chlorophyll a measurements for streams. Further, the dissolved oxygen measurements residing in th e 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 re cently 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 Mary land 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 in formation to reach this primary objective.

2. Examine periphyton communities for streams in EPA’s Region 3 to see if eutrophic conditions are associated with sp ecific community characteristics.106 Study sites would include several 3 rd and 4 th 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 incl ude most of the southeastern U.S. The proposed work would involve field data collec tion during the critical summer months of 2005. Data evaluation and report prep aration would follow through the fa ll 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 effo rt, 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 Maryla nd Department of the Environment. This proposal extends the ongoing work to additiona l 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 coll ection 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 reas on is that the research ers pooled all available data for the analysis, then looked for relations. Bu t, nutrients are transien t. The concentrations observed in a stream on any day will likely be diff erent the next day or th e 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 criter ia. Oxygen measurements at other times of the year or at other times of the day are superfluous, a nd perhaps even confusing.

Our approach will be to select streams across th e five Region 3 states and the District of Columbia to examine nutrient concentrations, periphyton biomass, and nighttime dissolved oxygen concentrations. For these streams, we w ill install continuous r ecording 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 oxyge n concentrations will occur at night when photosynthesis is shut down, but respiration continues. (Anot her reason for the failure of previous nutrient studies is th at 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 eff ects-based approach that will attempt to link nutrient conditions within a stream system to th e growth of periphyton communities within that107 stream. Periphyton are important to stream ecolo gy because they are the main primary producers. This means that they are one of th e first links in the a quatic 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, includi ng nutrient over-enrichment. As a resident biotic component in streams, periphyton can quickly regi ster and incorporate transient or episodic changes in nutrient conditions, or act as nutrien t "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 stat es (Maryland, Virginia, West Virginia, and Delaware) have agreed to support the effort. We will use comm on protocols (mutually agreed upon), which are fully documented and distributed by the USGS. Th ese protocols will be the same as adopted under the previous nutrient crite rion grant awarded in 2004 to th e 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 th is 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 repres ent 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) monito rs 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 valu es, 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 tran sferred 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 standa rd (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 disso lved nitrite+nitrate, dissolved ammonia nitrogen, organic nitrogen, dissolved ortho-phosphate, and total phosphorus. Total organic carbon will also be measured to provi de 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, Pennsyl vania. 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 statisti cal 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 significan tly influence the quality of wate r. Ideally, these the selected streams will already be monitored under the exis ting statewide water-quality monitoring network for each state. Also, our plan is to include st reams that represent a wide spectrum of biotic conditions, from reference conditions to eutrophic conditions. Selection of a range of nutrient conditions will facilitate establis hment of nutrient-algal (periphyt on) relationships that will enable the eventual development of an index of biotic integrity based on those relationships.

Because the main study objective is the developmen t 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 Fo rested Uplands), and Ecoregion XIV (Eastern Coastal Plain). Other Ecoregions within the EP A Region 3 boundaries represent relatively small areas and will not be sampled in the proposed research.

A modified version of the periphyton sampli ng 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 sa mple will be collected from each sampling site. Part of the periphyton sample will be used fo r taxonomic identification, and part for biomass measurements. In the Coastal Plain (Ecoregi on XIV), riffle habitat may not be commonly available in all sampling reaches. In this case, qu antitative artificial substrate samplers will be used.

Community structure of periphyton communities ar e intended to provide a list of species (taxa richness) present in the sampling reach. Wate r quality can be asse ssed by interpreting autecological information, the ta xon-specific physiological requireme nts 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 pr esent in each stream. Quantitative samples are collected using a variet y of sampling devices (i.e., NAW QA periphyton sampling device SG-92 or the whole-rock approach); the appropriate choice of sampli ng equipment is dictated by the109 character of the dominant periphyt on growth forms and microhabita ts 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 anal yzed by looking for indi cator 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 deve lop a tool that is reflect ive of stream conditions, yet can be routinely implemented by most field biologists employed by stat e resource agencies. Data from the project will be examined using a variety of traditional and multivariate approaches. Non-parametric techniqu es are likely to be used as bi ological 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, graphi ng measured nutrient concentrations for several streams against minimum dissolved oxygen concentr ations is expected to produce a relationship generalized in Figure 1. The nutri ent 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 be tween 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 sh ows what the data might look like Minimum Dissolved Ox yg en Concentration Measured Nutrient Concentration

Proposed nutrient criterion

5 mg/L110 for several streams, some having light as the limiting factor, and some having nitrogen as the limiting factor.

Figure 2. Hypothetical data dist ribution for Region III streams.

For those streams where nitrogen is limiting, the da ta 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 nitr ogen is limiting. The data point will fall above the line. For those data points th at fall on (or near) the line, a least squares regression, based on a General Linear Model, w ill be used to quantif y the relation between nutrients and dissolved oxygen. This relation will be used to qua ntify 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 Figur e 2. If this proposal is funded, our goal will be to populate figure 2 with as many as 48 data points, each data poi nt representing a diffe rent Region 3 stream.

This approach has a weakness that minimum di ssolved oxygen concentrations are influenced by productivity of the attached alg ae as well as the standing crop of attached algae. We do not propose to measure algal productivit y. Intuitively, algal productivity and alga l 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 prev iously (EPA grant X798398201-0). Products listed below would include data from both parts of th e work, but would not be duplicated for the two phases of work. Minimum Dissolved Oxygen Concentration Measured Nitrogen Concentration

5 mg/L111 1. Semi-annual progress reports will do cument 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 T echnical 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 environm ental managers that meets periodically to exchange information about nutri ent criteria development and impl ementation 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 fi eld data-collection sheets for all field work. Duplicates, blanks, and reference ma terials will be used to assure the quality of laboratory work. Laboratory QC will include lab pa rticipation in the U.S. Geological Survey’s Inter-Laboratory Evaluation Progr am (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 ch ecks for the continuous monitoring sondes and these checks will be performed at the begi nning 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 nu trient standards for streams.112 Information from the proposed work is directly tr ansferable to other locations within the same Nutrient Ecoregion. Techniques developed co uld be used for any Nutrient Ecoregion.

The results of this study and fu rther investigations support the mission of the USGS by providing a regional pattern of water qual ity 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 prepar ation, Effects of streambank fencing on water quality, Lancaster County, Pe nnsylvania: U.S. Geological Survey Water-Resources Investigations Report.113 Lietman, P.L., 1997, Evaluation of agricultural be st-management practices in the Conestoga River headwaters, Pennsylvania: A summary re port 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 comm unities 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 treate d effluent on water quantity and qua lity, and the fate and transport of nitrogen in a small watershed, New Garden Township, Chester County, Pennsylvania: U.S. Geological Survey Water-Resour ces Investigations Report.

Stevenson, R.J., Hill, b., and Herlihy, A.T., in press, A comparison of approaches for establishing nutrient criteria based on alga l 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-F ile Report 02-481, 108 p.

Roth, N.E., and others, 2001, Maryland biolog ical stream survey 2000-2004 Volume I: Ecological Assessment of Watersheds Sample d 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 To ol 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, 2004115 

## The Development of an Algae-based Water Quality Monitoring To ol for Virginia Streams

Background Nutrient enrichment is the cause of approxi mately half of the reported impairments in National waters. Within rivers and streams, ch ronic 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 addi tion 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 va riety 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 nutrien t enrichment in the nation’s water systems have led the US EPA to: a) devel op 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 standa rds for nitrogen and phosphorus. Because chemical monitoring alone can underestimat e degradation in living system s, a number of biological measurements have been developed to provide a direct assessment of resource condition. If the biota is not present at the leve l expected, researchers have dire ct confirmation of physical or chemical degradation in the stream.

Until recently, biological monito ring programs have almost exclusively focused on fish and macroinvertebrate taxa and largely excluded periphyton (benthic algae) communities. This is because the high diversity and regi onal 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 exce llent ecological indicators of water quality. Because periphyton are directly affected by physi cal and chemical environmental changes, and usually have brief life cycles a nd rapid rates of reproduction, they are valuable indicators of short-term impacts. Periphyton assemblages are al so sensitive to some pollutants which may not visibly affect other taxa or onl y 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 enrichme nt of surface waters and the establishment of nutrient limits and thresholds grows, st ate (Bahls 1993, Kentucky Division of Water 1993, Oklahoma Conservation Commissi on 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, Ke ntucky, Massachusetts, Montana, and Wyoming) currently use information from periphyton comm unities 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 us ing algal indices of biotic in tegrity and establishing nutrient limits (Ponader and Charles 2003, Beaman 2003).

Virginia currently has water quality monito ring programs in place but has not developed algal indices for freshwater bioassessment. The Virginia Department of Environmental Quality’s116 (VA DEQ) Freshwater Probabilistic Monitoring (ProbMon) Network (VA DEQ 2003) consists of a set of randomly chosen water quality monito ring 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 qualit y state-wide, identify and determine the extent of impairments in water quality, and identify pot ential threats to water quality and the types of streams that are most at risk. In addressing th ese objectives, the ProbM on Network will identify the water quality issues that pol icy makers need to address, as sess the effectiveness of current management strategies at protec ting resources, examine how effort can be redirected to better protect the most threatened res ources, 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 str eam health. Although these data provide important information regarding water quality, they do not provide sufficient information to assess nutrient enrichment in Virginia streams. The devel opment of an algal index, based on periphyton community structure as it rela tes 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 Prot ection Agency (EPA) to collect algal taxonomy and biomass samples from the ProbMon sites for the development of an algal-based tool for stream assessment. Th e 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 us e of reference streams, (2) applying predictive relationships to select nutrient concentrations that will result in appropr iate levels of algal biomass, and (3) developing criteria from thresholds established in the l iterature. In addition, the Ambient Water Quality recommendations for U.S. EPA Rivers and Stream s Aggregate Nutrient Ec oregion IX (U.S. EPA 2000b) include establishing nutrient reference cond itions in rivers and streams based on the upper 25 th percentile (75 th percentile) of all nutrient data from all reaches sampled, or determining the lower 25 th percentile of the popul ation of all streams within a region. A review of this approach for the New England Intersta te Water Pollution Contro l Commission revealed that the ranges of predicted biomass producti on responses to nutrients, as tested for the subecoregions 59 and 84, would be below consensus threshold values (ENSR 2001). Nevertheless, the establishment of reference co nditions based on percentiles will set different threshold values in different re gions, depending on the range of ove rall water quality in the rivers of each particular region. These thresholds will be too high in ecoregions with rivers having predominantly high nutrient concentr ations as compared to ecoreg ions 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 betw een algal biomass as well as algal species composition and nutrients in Virginia streams,

2. develop and test algal indicat ors of nutrients and water qua lity 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 dis tinguish between situations where nutrient concentrations are high due to natural envi ronmental conditions a nd 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 met hods, provide additional in-field di rection 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 ta xa dominance, and siltation i ndex). Virginia DEQ biologists will use these data to develop algal indicators of stream and river health based on predictive relationships that can be app lied in a regulatory context as criteria for identifying nutrient impairment.

Study Approach Virginia DEQ, with the assistance of an ANS biologist at some st udy sites (10-20), will sample periphyton for biomass and community composition in conjunction with the VA DEQ’s ProbMon Network (40-70 sites). Periphyton samp ling 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 collecti ons 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 Ra pid Bioassessment protocols for periphyton (Barbour et al. 1999). At each site, three sample t ypes will be collected in triplicate: 3 composite diatom samples, 3 composite soft algae sample s, 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 pr eserved with formalin (soft algae and diatom samples) or by keeping samples on ice until se nt 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, M oulton et al. 2002). Virginia DE Q 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 de termine 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 lowe st practical taxonomic level a nd counted using ANS Phycology Section protocols, common taxonomic references , and type material from the ANS Diatom Herbarium. Species identific ation, relative abundance, and basic community metrics (taxa richness, percent dominant taxa, percent taxa domina nce, and siltation index) will be presented to118 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 st udy will be archived in th e 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.

2004 2005 Project Task 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 data119 Literature Cited

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