DECEMBER 2006 REPORT OF THE 

ACADEMIC ADVISORY COMMITTEE TO 

VIRGINIA DEPARTMENT OF ENVIRONMENTAL QUALITY: 

FRESHWATER NUTRIENT CRITERIA FOR 

RIVERS AND STREAMS

VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY 

BLACKSBURG, VIRGINIA 

2007 
2

This special report is a publication of the Virginia Water Resources Research Center. 

Funds for the support of this project and publication are provided in part by the Virginia 

Department of Environmental Quality. The contents of this publication do not necessarily 

reflect the views or policies of the Virginia Department of Environmental Quality or the 

Virginia Water Resources Research Center. The mention of commercial products, trade 

names, or services does not constitute an endorsement or recommendation. 

This report is available online. Hard copies may be obtained from the 

Virginia Water Resources Research Center for a small fee. 

210 Cheatham Hall 

Blacksburg, VA 24061 

Phone: (540) 231-5624 

Fax: (540) 231-6673 

E-mail: water@vt.edu 

Web address: http://www.vwrrc.vt.edu

Stephen Schoenholtz, Director 

Virginia Tech does not discriminate against employees, students, or applicants on the basis of 

race, color, sex, sexual orientation, disability, age, veteran status, national origin, religion, or 

political affiliation. Anyone with questions that concern discrimination should contact the 

Equal Opportunity and Affirmative Action Office.
December 2006 Report of the 

Academic Advisory Committee to 

Virginia Department of Environmental Quality: 

Freshwater Nutrient Criteria for Rivers and Streams

Prepared by 

Carl E. Zipper, Department of Crop and soil Environmental Sciences, Virginia Tech 

Gene Yagow, Department of Biological Systems Engineering, Virginia Tech 

Jane L. Walker, Virginia Water Resources Research Center, Virginia Tech 

Eric. P. Smith, Department of Statistics, Virginia Tech 

Leonard A. Shabman, Resources for the Future 

Leonard A. Smock, Department of Biology, Virginia Commonwealth University 

Carl H. Hershner, Center for Coastal Resources Management, Virginia Institute for Marine 

Science 

Tamim Younos, Virginia Water Resources Research Center, Virginia Tech 

E. Fred Benfield, Department of Biological Sciences, Virginia Tech 

Paul A. Bukaveckas, Department of Biology, Virginia Commonwealth University 

Greg C. Garman, Center of Environmental Studies, Department of Biology, Virginia 

Commonwealth University 

Howard I. Kator, Department of Environmental & Aquatic Animal Health, Virginia Institute 

for Marine Science 

Wu-Seng Lung, Department of Civil Engineering, University of Virginia 

and 

Kurt Stephenson, Department of Agricultural and Applied Economics, Virginia Tech 

Virginia Water Resources Research Center 

210 Cheatham Hall 

Virginia Tech 

Blacksburg, VA 24061 

January 10, 2007 

VWRRC Special Report SR33-2007 

i

Acknowledgements 

This report was prepared by the Academic Advisory Committee to the Virginia Department 

of Environmental Quality (DEQ). This effort is partially funded through the Virginia DEQ. DEQ 

staff participated in the various meetings and discussions concerning the development of nutrient 

criteria in Virginia and provided data. Special thanks to Roger Stewart and Jason Hill at DEQ for 

assembling the databases used in the analysis in Section I.C. Thanks to Carl Hershner and Dan 

Schatt, Virginia Institute of Marine Sciences GIS lab, for conducting analysis of land use within 

monitoring station watershed areas. We also appreciate the contributions of the Virginia 

Department of Conservation and Recreation in providing Virginia Tributary Strategy cap-load 

data for 2010 used in Section II.A. 
ii
iii

Members of the Academic Advisory Committee 

Dr. Carl E. Zipper (Committee Chair) 

Associate Professor 

Department of Crop and Soil Environmental 

Sciences 

Virginia Tech 

330 Smyth Hall (0404) 

Blacksburg, VA 24061 

Phone: 540-231-9782 

Fax: 540-231-3431 

E-mail: czip@vt.edu

Dr. Tamim Younos (Project Leader) 

Associate Director 

Virginia Water Resources Research Center 

Virginia Tech 

210 Cheatham Hall (0444) 

Blacksburg, VA 24061 

Phone: 540-231-8039 

Fax: 540-231-6673 

E-mail: tyounos@vt.edu 

Dr. E. Fred Benfield 

Professor and Associate Dept. Head 

Department of Biology 

Virginia Tech 

2125 Derring Hall (0406) 

Blacksburg, VA 24061 

Phone: 540-231-5802 

Fax: 540-231-9307 

E-mail: benfield@vt.edu 

Dr. Paul Bukaveckas 

Associate Professor 

Department of Biology 

Center for Environmental Studies 

Virginia Commonwealth University 

P.O. Box 842012 

Richmond, VA 23284-2012 

Phone: 804-828-0168 

Fax: 804-828-0503 

E-mail: pabukaveckas@vcu.edu

Dr. Gregory C. Garman 

Associate Professor and Director 

Department of Biology 

Center for Environmental Studies 

Virginia Commonwealth University 

P.O. Box 842012 

Richmond, VA 23284-2012 

Phone: 804-828–7202 

Fax: 804-828-0503 

E-mail: gcgarman@vcu.edu 

Dr. Carl H. Hershner, Jr. 

Associate Professor and Director 

School of Marine Science 

Center for Coastal Resources Management 

Virginia Institute of Marine Science 

P.O. Box 1346 

Gloucester Point, VA 23062-1346 

Phone: 804-684-7387 

Fax: 804-684-7179 

E-mail: carl@vims.edu 

Dr. Howard I. Kator 

Associate Professor 

School of Marine Science 

Virginia Institute of Marine Science 

P.O. Box 1346 

Gloucester Point, VA 23062-1346 

Phone: 804-684-7341 

Fax: 804-684-7097 

E-mail: kator@vims.edu 

Dr. Wu-Seng Lung 

Professor 

Department of Civil Engineering 

University of Virginia 

P.O. Box 400742 

Charlottesville, VA 22904-4742 

Phone: 434-924-3722 

Fax: 434-982-2951 

Email: wl@virginia.edu 
iv

Dr. Leonard A. Shabman 

Resident Scholar 

Resources for the Future 

1616 P Street, NW 

Washington, DC 20036 

Phone: 202-328-5139 

E-mail: shabman@rff.org 

Dr. Eric P. Smith 

Professor and Interim Dept. Chair 

Department of Statistics 

Virginia Tech 

406-A Hutcheson Hall (0439) 

Blacksburg, VA 24061 

Phone: 540-231-7929 

Fax: 540-231-3863 

E-mail: epsmith@vt.edu 

Dr. Leonard A. Smock 

Professor and Department Chair 

Department of Biology 

Virginia Commonwealth University 

P.O. Box 842012 

Richmond, VA 23284-2012 

Phone: 804-828-1562 

Fax: 804-828-0503 

E-mail: lsmock@saturn.vcu.edu 

Dr. Kurt Stephenson 

Associate Professor 

Department of Agricultural and Applied 

Economics 

Virginia Tech 

208-B Hutcheson Hall (0401) 

Blacksburg, VA 24061 

Phone: 540-231-5381 

Fax: 540-231-7417 

E-mail: kurts@vt.edu 

Ms. Jane L. Walker 

Research Associate 

Virginia Water Resources Research Center 

Virginia Tech 

210 Cheatham Hall (0444) 

Blacksburg, VA 24061 

Phone: 540-231-4159 

Fax: 540-231-6673 

E-mail: janewalk@vt.edu 

Dr. Gene Yagow 

Research Scientist 

Department of Biological Systems 

Engineering 

Virginia Tech 

200 Seitz Hall (0303) 

Blacksburg, VA 24061 

Phone: 540-231-2538 

Fax: 540-231-3199 

E-mail: eyagow@vt.edu 
Acronyms and Abbreviations 

AAC: Academic Advisory Committee 

ALU: aquatic life use 

CEDS: Comprehensive Environmental Data System 

CFR: Code of Federal Regulations 

cfs: cubic feet per second 

cfs/sq.mi.: cubic feet per second per square mile 

Chl-a: chlorophyll-a

cm/yr: centimeters per year 

coseg: county segment 

CPMI: Coastal Plain Macroinvertebrate Index

DEQ: Virginia Department of Environmental Quality 

DT: detection limit 

EPA: Environmental Protection Agency 

FY: fiscal year 

GWLF: Generalized Watershed Loading Functions 

Ho: null hypothesis 

ha: hectare 

HBI: Hilsenhoff Biotic Index 

HSPF: Hydrological Simulation Program – FORTRAN 

HUP: Hydrologic Unit Program 

ID: identification code 

lbs/yr: pounds per year 

μg/L: micrograms per liter 

μS/cm: micro-Siemens per centimeter 

MGD: million gallons per day

mg/L: milligrams per liter 

mi2: square miles 

N: nitrogen 

NAWQA: USGS National Water-Quality Assessment 

NPS: non-point source 

NO2

- : nitrite

NO3

-

: nitrate 

P: phosphorus 

PO4

3-: phosphate 

PS: point source 

R2

: coefficient of determination 

SCI: Stream Condition Index 

SPARROW: Spatially Referenced Regression on Watersheds

TKN: total Kjeldahl nitrogen 

TMDL: total maximum daily load 

TN: total nitrogen 

TP: total phosphorous 

U.S.: United States 

USGS: United States Geological Survey 

vii

Contents 

Acknowledgements p. i 

Members of the Academic Advisory Committee p. iii 

Acronyms and Abbreviations p. v 

Contents p. vii 

Summary p. 1 

Section I. Localized Criteria for Wadeable Streams p. 3 

A. Review of Other Studies with Relevance to Development of 

Nutrient Criteria for Wadeable Streams in Virginia. p. 3 

B. Analysis of Nutrient Concentrations in Approved TMDLs in 

Virginia p. 9 

C. Analysis of DEQ Monitoring Data to Develop Nutrient Screening 

Values p. 11 

D. Ambient Monitoring Nutrient Distributions p. 27 

Section II. Downstream Loading p. 29 

A. Pilot Application of Load-Duration Approach to Rappahannock 

River Basin p. 29 

B. Downstream-loading Component of Nutrient Criteria for 

Freshwater Rivers and Streams: Recommended Approach p. 49 

References p. 53 

1

Summary

This report reviews activities conducted by the Academic Advisory Committee to the 

Virginia Department of Environmental Quality (DEQ) between July and December 2006. 

Activities were conducted for the purpose of developing recommendations for DEQ regarding 

nutrient criteria for freshwater rivers and streams. 

In its June 2006 report to DEQ, the AAC recommended that DEQ establish nutrient criteria 

for rivers and streams by addressing independently the two effects described by the 

Environmental Protection Agency (EPA): localized and downstream-loading effects. The current 

report and activities address nutrient criteria development within that framework. 

Section I of this report addresses the development of screening values for wadeable streams. 

Reference (i.e., characteristics of relatively undisturbed or least disturbed streams) and effectthreshold concentrations for in-stream nutrients that have been suggested by other studies are 

reviewed. Reference values for total nitrogen (TN) and total phosphorus (TP) tend to vary by 

U.S. EPA’s Nutrient Ecoregions and are generally higher in the eastern portions of the state than 

in the west. Effect-threshold values derived from other studies tend to vary more widely in part 

due to the variety of effect endpoints that have been employed in developing these thresholdconcentration estimates. A review of TMDL studies that have been completed for nutrientimpaired streams revealed few usable reference concentrations because they are generally loadbased studies (and do not report the streamflow information that could be used to calculate 

nutrient concentrations). An analysis of DEQ monitoring data found that Stream Condition Index 

(SCI, an indicator of benthic macroinvertebrate community status) values tend to vary negatively 

with nutrient concentrations. When using a statewide data set, these data allow the development 

of statistically significant regression models of the in-stream nutrient-SCI relationships. 

Application of these models yields “critical values” (i.e., nutrient values corresponding with the 

SCI = 60 impairment threshold) for TN (0.8 mg/L), total Kjeldahl nitrogen (TKN) (0.3 mg/L), 

and TP (0.05 mg/L). However, even after a data-selection process intended to focus analysis on 

sites where no influence by non-nutrient stressors is evident, the variability of SCI response to 

nutrients is large; thus, the analysis did not allow direct derivation of appropriate screening 

values. 

The committee is also conducting analyses to address development of localized criteria in 

wadeable streams; those analyses are not described in this report. 

Section II of the report addresses the downstream-loading component of nutrient criteria. A 

pilot-scale analysis using data from the Rappahannock River Basin illustrates a potential 

approach to developing numeric nutrient criteria to address downstream-loading effects. The 

committee recommends that the downstream-loading component be developed as narrative 

criteria within an effective water-quality management framework. This framework includes 

localized criteria with numeric components, clearly defined impairment-designation processes, 

and water-quality management processes that are effective in addressing and mitigating 

impairments. 

The report is presented as a draft, in anticipation of interactions among AAC members, 

interactions between the AAC and DEQ, and further analyses to be conducted in the coming 

months. A final report for the current fiscal year is due to DEQ in the summer of 2007. 
2
3

I.A. Review of Other Studies with Relevance to Development of Nutrient 

Criteria for Wadeable Streams in Virginia 

C. E. Zipper and J. L. Walker 

This section reviews studies conducted by others with relevance to the development of 

nutrient criteria for wadeable streams in Virginia. Numeric values suggested by other studies that 

pertain to wadeable streams in Virginia, its neighboring states, and the mid-Atlantic region are 

provided in Table 1. Because the AAC has completed an extensive review of scientific literature 

on nutrient criteria development (Walker et al. 2006), this review will be brief. Several of the 

studies summarized below have been reviewed with greater detail in Walker et al. (2006). 

Reference Values Representing “Relatively Undisturbed” or “Least Disturbed” Conditions 

U.S. EPA calculated 25th-percentile values for water-borne nutrients in rivers and streams 

from all available data by aggregate nutrient ecoregions for the conterminous U.S. (for Virginia’s 

waters, see U.S. EPA 2000a-c). EPA designated these 25th percentiles as “reference values,” 

noting that the 25th percentiles of “all water bodies” tend to correspond with the 75th percentiles 

of reference water bodies (i.e., relatively undisturbed or least disturbed water bodies of a given 

type). The AAC (2006) calculated comparable values for Virginia using Virginia DEQ ambient 

(10/99 – 9/05) and probabilistic (2001 – 2004) monitoring data for the ecoregions in Virginia 

(Because only a few streams in Aggregate Ecoregion 14 were sampled by the probabilistic 

monitoring program, the 25th percentile was not calculated from this data set). The AAC 

suggested that subregion 63 of Aggregate Ecoregion 14 was a more appropriate reference for 

Virginia because of its development status. 

Smith et al. (2003) used modeling procedures (Spatially Referenced Regression on 

Watersheds, SPARROW) to estimate by EPA Aggregate Nutrient Ecoregion the natural 

background nutrient concentrations in rivers and streams. SPARROW is a recognized waterquality monitoring procedure that was developed over an extended time period by the U.S. 

Geological Survey (USGS). SPARROW utilizes USGS in-stream water-quality databases for 

calibration. Smith et al. (2003) note that the EPA assumption that the 25th percentiles of “all 

water bodies” tends to correspond with the 75th percentiles of reference water bodies is supported 

by very few studies, and that such findings may be influenced by the fact that most reference 

water bodies occur in relatively small streams. They expressed their results as the 75th percentile 

of predicted natural-background concentrations by ecoregion to allow comparisons to EPA’s 

calculated 25th-percentile “reference” values. These comparisons were made with and without 

consideration of recent atmospheric nitrogen deposition. 

In another study, the researchers followed the U.S. EPA reference approach in proposing 

nutrient criteria values but developed alternative nutrient regions, called environmental nutrient 

zones. The ecoregion subdivisions were based on differences in many factors (e.g., soils, climate, 

vegetation, geology, and land use). The nutrient zones were determined from the relations 

between median TN or median TP and the most statistically significant environmental 

characteristics (excluding land use). The zones that included data from the New and Big Sandy 

rivers in Virginia (from the Kanawha-New River Basins NAWQA study) are Environmental 

Nitrogen Zone-4 (ENZ-4) and Environmental Phosphorus Zone-2 (EPZ-2). Criteria were 
4

proposed using the 75th percentile of data from sites within each zone that represent “reference” 

conditions, i.e., sites in watersheds with less than 25 percent agricultural land use. Proposed 

criteria values for the zones that included data from the New River and Big Sandy River were 

0.67 mg/L for TN and 0.05 mg/L for TP (Robertson et al. 2001). Criteria were also suggested 

based on the 25th percentile of all the data within a given zone. From the 25th percentile of all the 

data for ENZ-4, a TN criterion of 0.51 mg/L could be expected. The 25th percentile for EPZ-2 

would yield a TP criterion of 0.02 mg/L (Robertson et al. 2001). 

Clark et al. (2000) obtained data from 85 sites across the United States and used those data to 

estimate concentrations and yields of selected nutrients in streams draining relatively 

undeveloped basins. The median values in Table 1 are from sites in Virginia, its neighboring 

states, and other states within EPA Region 3. Nitrate-N data were available for 16 sites of this 

type, whereas other nutrient parameters were only available from 7 sites. The data represent 

mean annual flow-weighted concentrations for the 1990 – 1995 time periods. 

The AAC has recommended to Virginia DEQ that nutrient concentrations in “relatively 

undisturbed” or least-disturbed streams should not be considered as an appropriate basis for 

establishing nutrient criteria in the rivers and streams of Virginia. Instead, the numeric criteria 

should be based on a method that adheres closely to the Code of Federal Regulations (CFR) by 

protecting the designated uses of the water body. In fact, the CFR defines the term criteria as 

“elements of State water quality standards, expressed as constituent concentrations, levels, or 

narrative statements, representing a quality of water that supports a particular use. When criteria 

are met, water quality will generally protect the designated use” [40 CFR 131.3(b)]. 

Other Suggested Nutrient Thresholds 

A nutrient threshold can be defined as the concentration at which an effect, such as 

eutrophication or biological impairment, begins to occur. Potential threshold values derived from 

studies conducted within Virginia and the region are included here. Some of the published 

nitrogen and phosphorus values discussed in this section, however, were not presented in the 

literature as “threshold values.” For example, Hornberger et al. (1977) recommend not using the 

values from their study as strict thresholds but rather general indications of the trophic condition. 

Hornberger et al. (1977) used a subjective ranking of the eutrophic state of six river sites, 

five in Virginia and one in New Hampshire, based on their best professional judgment, 

knowledge of the land use, and measurements of nitrates, phosphates, and chlorophyll-a. They 

also determined the productivity at each site from continuous measurements of dissolved 

oxygen, temperature, and solar radiation and then compared their proposed eutrophic state with 

the results from the productivity study. They determined that productivity measurements can be 

used to classify the eutrophic state of rivers. Based on the production and respiration 

measurements, Hornberger et al. (1977) classified Baker River in New Hampshire as “clean” 

(oligotrophic). This river had NO3+NO2(N) concentrations of 0.1 – 0.2 mg/L and PO4(P) 

concentrations of <0.003 mg/L. From the productivity measurements in the Rappahannock 

River, the water quality was listed as possibly eutrophic even though the river seemed 

“unpolluted” from the qualitative impressions of the authors. The range of nutrient 

concentrations for the Rapphannock River overlapped with those of the high eutrophic rivers 

(NO3+NO2[N] = 0.3 – 0.7 mg/L; PO4[P] = 0.003 – 0.05 mg/L). The nutrient concentrations for 

the four rivers rated eutrophic from the productivity measurements (Mechums, South Fork 
5

Rivanna, Rivanna, and South) ranged from 0.1 to 1.0 mg/L for NO3+NO2(N) and <0.003 to 0.30 

mg/L for PO4(P) (Hornberger et al. 1977). 

Ponader et al. (2005) conducted a study of periphytic algae and diatom assemblages in 

Virginia streams. From the observed changes in the diatom assemblages, the authors suggested 

threshold limits of 0.5 mg/L for NO3-N and 0.05 mg/L for TP to protect against conditions they 

termed as nutrient impairment. NO3-N was used as an indicator of nitrogen status because other 

nitrogen-concentration variables (TKN, TN) were not measured successfully. The NO3-N 

threshold was selected because benthic chlorophyll-a levels above 100 mg/m2 occurred at NO3-N 

levels above 0.5 mg/L, although several sites with NO3-N levels above 0.5 mg/L did not exceed 

benthic chlorophyll-a levels of 100 mg/m2

. The TP threshold identification was based on the 

finding that several diatom species indices correctly assigned samples to the TP concentration 

categories 0.01 – 0.05 mg/L and 0.05 – 0.10 mg/L.

Stevenson et al. (2006) studied the correspondence of various algal biomass indicators with 

nutrient concentrations in 102 Michigan and northwestern Kentucky streams. High algal 

biomasses were rare in both areas if TP was < 0.03 mg/L and TN was <1 mg/L, and they 

recommended these levels as potential “targets to prevent a high probability of nuisance accrual 

of Cladophora.” They also recommended that, to protect streams with naturally low levels of 

productivity and algal biomass, “nutrient concentrations should probably be constrained” to 0.4 

mg/L TN and 0.01 mg/L TP. The lead author of this study is assisting the state of Kentucky in its 

development of nutrient criteria. 

Laboratory and field studies by Lemly (2000) and Lemly and King (2000) demonstrated a 

direct linkage between bacterial growth on benthic macroinvertebrates and macroinvertebrate 

mortality. Lemly and King (2000) studied two third-order, low flowing, cypress-gum wetland 

streams in the Cape Fear basin in North Carolina. The stream they classified as unenriched had 

macroinvertebrate orders that were free of bacterial growth, mean TN concentrations between 

0.715 – 1.97 mg/L, and mean TP concentrations below 0.200 mg/L (0.054 – 0.198 mg/L). The 

stream in the study that was classified as enriched, based primarily on land use (hog farms were 

present in the watershed), had macroinvertebrate orders that were colonized by bacteria, and 

generally had higher nutrient concentrations (mean TN = 1.93 – 3.89 mg/L; mean TP = 0.169 – 

0.620 mg/L) (Lemly and King 2000). 

A Technical Advisory Committee recommended to Virginia DEQ in 1987 that the range of 

0.1 – 0.2 mg/L for TP in flowing waters should be considered as appropriate for screening 

purposes (IEN 1987). The committee report provides little background on the reasoning applied 

by the group in recommending these limits, other than the likelihood that variations in natural 

background levels of TP in streams made a range of concentrations appropriate instead of 

designating a single threshold concentration. The group agreed that no standard for nitrogen in 

flowing waters was necessary.
6

Table 1. Reference values for nutrient concentrations and other potential threshold values in 

Virginia and regional waters reported in other studies. All values are expressed as total nitrogen 

and total phosphorous unless otherwise noted. 

Study N (mg/L) P (mg/L) Source 

 

Relatively Undisturbed / Least Disturbed 

Reference Values: 

 

Ecoregion 11 

25th percentile of Virginia ambient monitoring 

data 

0.38 ≤0.01 AAC 2006 

25th percentile of Virginia probabilistic 

monitoring data 

0.265 ≤0.01 AAC 2006 

25th percentile of available monitoring data 

(regional) 

0.31 0.01 U.S. EPA 

2000a 

75th percentile of SPARROW-modeled 

background 

0.29 

±0.09 

0.02 

±0.01 

Smith et al.

2003 

 

Ecoregion 9 

25th percentile of Virginia ambient monitoring 

data 

0.45 0.025 AAC 2006 

25th percentile of Virginia probabilistic 

monitoring data 

0.35 0.02 AAC 2006 

25th percentile of available monitoring data 

(regional) 

0.69 0.037 U.S. EPA 

2000b 

75th percentile of SPARROW-modeled 

background 

0.28 

±0.08 

0.05 

±0.02 

Smith et al.

2003 

 

Ecoregion 14 

25th percentile of Virginia ambient monitoring 

data 

0.92 0.054 AAC 2006 

25th percentile of available monitoring data 

(regional) 

0.71 0.031 U.S. EPA 

2000c 

25th percentile of available monitoring data 

(regional, subregion 63) 

0.87 0.0525 U.S. EPA 

2000c 

75th percentile of SPARROW-modeled 

background 

0.76 

±0.30 

0.02 

±0.005 

Smith et al.

2003 

 

Statewide and Regional 

75th percentile of environmental nutrient zones 

containing Virginia watersheds with less than 

25% agricultural land use 

0.67 0.05 Robertson et 

al. 2001 

25th percentile of all data in environmental 

nutrient zones containing Virginia streams 

0.51 0.02 Robertson et 

al. 2001 

 
7

Median values from “undeveloped, relatively 

undisturbed” stream basins in mid-Atlantic 

U.S. 

0.384 <0.03 Clark et al.

2000 

Medians, undeveloped (as oxidized N, 

phosphate P) 

0.176 <0.008 Clark et al.

2000 

 

Other Suggested Nutrient Thresholds 

Corresponding nutrient conditions for Baker 

River (NH) rated oligotrophic based on 

productivity measurements 

0.1 – 0.2 <0.003 Hornberger et 

al. 1977 

Corresponding nutrient conditions for 

Rappahannock River (VA) rated possibly 

eutrophic based on productivity 

measurements 

0.3 – 0.7 0.003 – 

0.05 

Hornberger et 

al. 1977 

Corresponding nutrient conditions for Virginia 

rivers rated eutrophic based on productivity 

measurements 

0.1 – 1.0 <0.003 – 

0.30 

Hornberger et 

al. 1977 

 

Virginia: Change in diatom assemblages 0.05 

Virginia: Periphytic algae >100 mg/m2 (as 

nitrate N) 

0.5 

Ponader et al.

2005 

 

Kentucky and Michigan: Prevent a high 

probability of nuisance Cladophora 

1.0 0.03 

Kentucky and Michigan: Protect stream 

communities where low levels of in-stream 

productivity are natural conditions 

0.4 0.01 

Stevenson et 

al. 2006 

 

North Carolina: “Unenriched” stream with 

benthic macroinvertebrates free of bacteria 

<1.97 <0.198 Lemly and 

King 2000 

North Carolina: “Enriched” stream with benthic 

macroinvertebrates colonized by bacteria 

>1.93 >0.169 Lemly and 

King 2000 

 

Current Virginia “screening values” n/a 0.1 – 0.2 IEN 1987 
8
9

I.B. Analysis of Nutrient Concentrations in Approved TMDLs in Virginia 

G. Yagow 

As of November 21, 2006, twelve TMDLs with a nutrient component had been approved in 

Virginia. Nine stream segments have approved TMDLs for phosphorus, and three segments have 

approved TMDLs for nitrates (DEQ 2006a). A summary of these TMDLs is given in Table 2. 

Table 2. Nutrient TMDLs approved in Virginia. 

Stream Contractor HUP Model Reference Watershed 

 

South Run Louis Berger A19R GWLF Popes Head Creek 

Cooks Creek Tetra Tech B25R, B26R GWLF Hays Creek? 

Mill Creek Tetra Tech B27R GWLF Hays Creek 

Pleasant Run Tetra Tech B29R GWLF Hays Creek 

Muddy Creek Tetra Tech B22R GWLF Hays Creek 

UT to 

Chickahominy 

GMU, Tetra Tech G05R Reckhow 

Spring Branch NR RC&D, MapTech K32R Eutromod 

NF Blackwater Tetra Tech L08R Big Chestnut Creek 

Muddy Creek UVA B42 HSPF 

Dry River B41, B43 HSPF 

North River not modeled 

Stream TMDL Annual 

Flow 

Average TP 

Endpoints 

TMDL In-stream N 

 (lbs P/yr) (cm/yr) (mg/L) (lbs N/yr) (mg/L) 

South Run 1,124 0.053 - 0.070 

Cooks Creek 9,367 

Mill Creek 6,001 

Pleasant Run 3,910 

Muddy Creek 6,088 

UT to 

Chickahominy 

432.69 

Spring Branch 427.10 0.0481* 

NF Blackwater 6,960 

Muddy Creek not given 10 

Dry River not given 10 

North River 

* Simulated by the EUTROMOD model in Bryant Pond as corresponding to the eutrophication threshold in Carlson's Trophic State 

Index. 

All of the nitrate TMDLs were developed for exceedences of the nitrate drinking water 

standard (10 mg/L NO3-N). These TMDLs used the nitrate standard as the TMDL target, and the 

TMDL was written in terms of concentrations rather than loads. 
10

All of the phosphorus TMDLs were based on stressor analyses for identified benthic 

impairments and used the reference watershed approach for setting the TMDL endpoints. All of 

the phosphorus TMDLs were defined in terms of annual loads. Only two of these TMDLs 

included information in their online reports related to in-stream concentration equivalents, as 

shown in Table 2. 

Although it would be possible to calculate mean annual concentrations if mean annual flows 

were available for the various TMDLs, this information was not presented in any of the reports 

except for South Run, and to some extent in the Spring Branch TMDL. In the South Run TMDL, 

mean annual flows and mean annual concentrations were presented for various allocation 

scenarios and compared with mean annual concentrations based on Tributary Strategy cap loads 

as shown in Table 3. 

Table 3. Comparison of South Run TMDL average annual total phosphorus concentrations for 

various allocation scenarios with Tributary Strategy mean annual target concentrations. 

Tributary Strategy* 

Nonpoint 

Source1 Point Source 

Average 

Annual 

Simulated TP Shenandoah River Rappahannock 

River Scenario 

(mg/L) (cfs) (mg/L) (MGD2

) (mg/L) Min Mean Max Min Mean Max 

1 0.053 9.58 1.59 0.072 0.070 

2 0.053 9.58 0.3 0.247 0.062 

3 0.053 9.58 - - 0.053 

0.054 0.126 0.219 0.055 0.122 0.27 

1

 Based on GWLF simulation results for South Run 

2 million gallons per day 

* Source: DEQ 2005 

The simulated annual average TP concentrations for South Run, the minimum mean annual 

concentrations for meeting Tributary Strategy cap loads, and the concentration in Spring Branch 

that corresponded with a Trophic Status Index of 60, all fall within a narrow band from 0.048 to 

0.070 mg/L. 
11

I.C. Analysis of DEQ Monitoring Data to Develop Nutrient Screening Values 

C. E. Zipper and E. P. Smith 

Summary 

This section describes an analysis of Virginia DEQ monitoring data conducted for the 

purpose of developing nutrient screening values. DEQ provided a data set that included both 

biological monitoring (benthic macroinvertebrates) and water-quality monitoring data for 

identical locations and similar times. Those data were screened for the purpose of identifying and 

removing observations for which the data record contains evidence of potential effects on the 

aquatic community by non-nutrient stressors. The data were then analyzed with the goal of 

defining relationships between in-stream nutrient concentrations and the Stream Condition Index 

(SCI), an indicator of the benthic community status. The analysis yielded statistically significant 

relationships between SCI and log-transformed nutrient variables (TKN, TN, and TP, expressed 

as mg/L concentrations). These relationships indicate that “critical values” (nutrient 

concentrations that correspond with the SCI = 60 impairment threshold) for TKN, TN, and TP 

are 0.3, 0.8, and 0.05 mg/L respectively. These levels are similar to the nutrient thresholds 

identified by prior studies (Section I.A. of this report) and TP impairment thresholds estimated 

by the few TMDL studies that cited concentrations (Section I.B.). However, SCI responses to 

nutrient concentrations are highly variable, as indicated by the wide prediction intervals 

occurring within the SCI-prediction models. Therefore, we consider this analysis to be 

inconclusive as a basis for recommending in-stream nutrient concentrations that may be used as 

screening values. 

Introduction 

The screening-value approach differs from other methods for defining water-quality criteria 

because nutrients differ from traditional stressors in a fundamental manner. Whereas traditional 

stressors generally exert toxic influences that directly degrade the system, low-level inputs of 

nutrients serve to increase the productivity of the system. At higher levels (over enrichment), 

nutrients may become a stress to the system. Furthermore, variations among physical 

characteristics of river-and-stream systems affect the responses of those systems to nutrient 

enrichment. As a result, biotic responses to nutrient enrichment at specific concentration levels 

are highly variable among river and stream systems. 

The screening value approach recommended to protect localized uses in wadeable streams in 

Virginia was described more fully in the June 2006 AAC report to DEQ. The process is 

summarized in Figure 1. The screening value approach is applied with the intention of limiting 

assessment errors despite the inherent variability of responses to nutrients by aquatic systems. 

Type I errors occur when water quality assessments list the stream as impaired, but the stream 

supports its designated uses. Type II errors occur when the water quality assessment fails to list 

the stream as impaired even though the stream does not support its designated uses (Figure 2). 
12

Is N or P concentration 

> screening value?

No

Not 

Impaired

Does visual assessment reveal 

algal growth sufficient to indicate 

potential nutrient impairment?

Is diurnal DO variation sufficient 

to indicate nutrient impairment?

Does macroinvertebrate

community indicate 

impairment?

Impaired. 

Do TMDL

No

Yes No

Yes

Yes

For exceptional resources (e.g., nutrient-sensitive T&E species): 

Anti-degradation approach.

Is N or P concentration 

> screening value?

No

Not 

Impaired

Does visual assessment reveal 

algal growth sufficient to indicate 

potential nutrient impairment?

Is diurnal DO variation sufficient 

to indicate nutrient impairment?

Does macroinvertebrate

community indicate 

impairment?

Impaired. 

Do TMDL

No

Yes No

Yes

Yes

For exceptional resources (e.g., nutrient-sensitive T&E species): 

Anti-degradation approach.

Figure 1. The screening process recommended by the Academic Advisory Committee to the 

Virginia Department of Environmental Quality for implementation as the localized component of 

nutrient criteria in wadeable streams (AAC 2006). 

Type I

Error

Type II

Error

Correct 

Decision

Correct 

Decision

Ho: Stream Supports 

Designated Use

TRUE FALSE

Waterquality 

assessment lists 

stream as 

impaired

NO

(Accept 

Ho)

YES

(Reject 

Ho)

Figure 2. A representation of the Type I and Type II error concepts as they apply to water 

quality assessments. 
13

A secondary goal of using the screening value approach is to efficiently utilize DEQ’s 

resources while meeting the goals of the Clean Water Act. A screening-value exceedance does 

not necessarily result in an impairment designation. Instead, the response to a screening-value 

exceedance is additional stream monitoring. The purpose of the additional monitoring is to 

provide a more definitive assessment. Therefore the screening value can be established 

conservatively so as to limit Type II errors despite the nutrient-response variability of stream 

communities. Type I errors (and the consequent resource expenditures for TMDL studies of 

streams where the designated use has not been impaired) can also be limited by this approach. 

Application of the screening-value approach, however, does require an evaluation of tradeoffs: Setting the screening value very conservatively, so as reduce the Type II error, probably 

increases the number of non-impaired sites caught by the screen. For each of these sites, DEQ 

must expend resources to make the correct assessment decision (limit Type I errors). Thus, the 

screening value approach embodies a trade-off between error limitation and water-monitoring 

resource expenditures. In this analysis, we evaluate potential screening values within the above 

conceptual framework. 

Methods 

A data set for use in this analysis was prepared by DEQ and provided to the AAC in late 

November 2006. As a first step in data preparation, DEQ personnel accessed the Ecological Data 

Application System (EDAS) database (1989 to present) to identify all records of benthic 

macroinvertebrate community assessment in Virginia streams that were conducted at a level 

sufficient to calculate a stream condition index (SCI) value (Burton and Gerritson 2003). All 

water-quality records associated with the biological monitoring location were accessed and used 

to create two data files: (1) all records within the three months preceding and one month 

following each biological monitoring observation, and (2) all water-quality monitoring records 

for locations where biological monitoring observations had been recorded since 1989. DEQ 

probabilistic, routine ambient, and special study biological monitoring observations were 

included. The biological monitoring records contained a calculated SCI value and various 

benthic macroinvertebrate and habitat metrics. The water-quality monitoring records contained 

site-measured parameters such as conductivity, pH, and dissolved oxygen. Laboratory measured 

nutrient parameters and other associated water-quality constituents such as non-filterable residue 

(total suspended solids) were also included. 

Nutrient Variables

Various analytical procedures have been used to estimate in-stream nutrients. The nutrient 

parameters in the data set, therefore, were analyzed and in some cases adjusted to produce a 

consistent water-quality data set suitable for subsequent analysis. 

When total nitrogen (TN) was not measured directly but constituent components were 

measured, TN was calculated as the sum of the constituents. Where nitrite-N was reported as 

≤0.05 mg/L, a proxy value for nitrite-N was estimated based on the corresponding nitrate-N 

value (the majority of nitrite-N values ≤0.05 mg/L were assigned as ≤0.01 mg/L). Where total 

Kjeldahl N (TKN) or oxidized N was not measured directly but could be calculated from 

measured values of TN and the corresponding TN component, those values were calculated and 
14

used in the analysis. The majority of TKN values were reported at 0.1 mg/L precision; those few 

values reported at 0.01 precision and >0.07 mg/L were rounded to 0.1 mg/L. When TKN was 

reported as ≤0.1 mg/L, that value was rounded to 0.05 mg/L for use in the analyses. 

Because TP was analyzed only to the 0.1 mg/L level of precision prior to July 1999, all data 

prior to July 1999 were deleted. Additionally, TP observations made after June 1999 that were 

reported as ≤0.1 mg/L (14 of 1286 TP observations) were deleted. The correspondence of 

measured ortho-P with measured TP values was evaluated to determine the feasibility of using 

measured ortho-P to estimate missing TP values; that possibility was rejected. The ortho-P 

variable was determined as unsuitable for use in subsequent analyses because of a high number 

of observations reported as ≤0.05 mg/L during the 2003 – 2004 period. 

Data Record Screening and Selection

Data were screened to identify and eliminate biological monitoring observations potentially 

influenced by non-nutrient stressors. Any monitoring location described or coded as being below 

a point-source discharge was removed from the database. DEQ “reference filters” (i.e., criteria 

used by DEQ biologists in locating biological monitoring references) were applied (Table 4, 

DEQ 2006b). Any location demonstrating a consistent pattern of failing to satisfy the reference 

filtering screens for reasons other than TN, TP, and dissolved oxygen (DO) were eliminated from 

subsequent analyses. The “% Urban” composition of watershed areas was determined for pre2005 probabilistic monitoring locations using land-use data compiled by the DEQ probabilistic 

monitoring group. Additionally, land-use data assembled by the Virginia Institute of Marine 

Sciences GIS lab in late 2005 was applied to DEQ monitoring stations to identify urban land-use 

percentages. For those monitoring locations represented in both DEQ and VIMS databases, the 

urban land-use percentages were in close agreement. 

Each biological monitoring observation was paired with a single water-quality observation. 

For most probabilistic samples, only one water-quality monitoring observation was available 

during the 3 months prior to or 1 month following the biological monitoring event. For locations 

where more than one water-quality measurement was available, criteria used to select the waterquality monitoring observation included (a) availability and completeness of water-quality 

nutrient measurements, (b) temporal proximity, and (c) measured total suspended solids 

concentrations that indicated a non-stormflow sampling event. 
15

Table 4. DEQ reference-filter standards for use in Mountain and Piedmont ecoregions. 

% Urban <5% 

Total Nitrogen* < 1.5 mg/L 

Total Phosphorus* < 0.05 mg/L 

Specific Conductance < 250 uS/cm 

Dissolved O2* > 6 mg/L 

pH < 6 or > 9 

Habitat Scores: 

Total >140** 

Channel Alteration >11 

Epifaunal Substrate/Cover >11 

Riparian Vegetative Zone >11 

Embeddedness*** >11 

* Not applied in current study 

** Interpreted as >10 per habitat element quantified. At most sites, fewer 

than 14 habitat variables had been evaluated. 

*** Mountains only 

Observations within the Coastal Plain physiographic region (EPA Level III ecoregions 63 

and 65 – see Figure 3) were scrutinized, recognizing that the SCI has been validated only for 

Virginia’s mountain and piedmont regions. Fifteen “Coastal Plain” observations were found to 

occur within the post-screening data. These observations were included because (a) recorded SCI 

values were highly correlated (R2 = 0.76) with the Coastal Plain Macroinvertebrate Index 

(CPMI) values used by DEQ for Coastal Plain assessments; (b) assessment decisions for 14 of 

the 15 sites would be identical using either the SCI or the CPMI value, and the 15th Coastal Plain 

site was located very close to the Piedmont boundary; and (c) inclusion of the Coastal Plain sites 

improved the statistical relationships between water-quality nutrients and SCI. 

Figure 3. EPA Nutrient Ecoregions (9, 11, and 14) and Level III Ecoregions in Virginia. 
16

Correspondence of SCI Values to Water-Quality Nutrient Levels 

Data analysis was initiated by evaluating the probability of detecting biological impairments 

at various water-quality nutrient levels using the scale in Table 5 (DEQ 2006b). Stressed and 

severely-stressed categories were considered to be impaired. 

Table 5: Virginia Stream Condition Index (SCI) scores and associated aquatic life use (ALU) 

tiers and assessment listings. 

SCI Score ALU ALU Tiers Assessment 

<42 Severe Stress Impaired 

>42 – <60 Stress Impaired 

>60 – <73 Good Not Impaired 

>73 Excellent Not Impaired 

A series of statistical models were constructed and analyzed in an effort to predict SCI scores 

based on in-stream nutrient and chlorophyll-a measurements. 

Results 

A total of 262 biological monitoring observations at 181 locations were selected as suitable 

for use in the subsequent analysis (Table 6). Distributions of nutrient and chlorophyll-a waterquality values were non-normally distributed (skewed) (Figure 4), as is commonly observed in 

water-quality studies. Therefore, log-transformed values were used in the development of the 

predictive models. 

Nutrient concentrations in the analyzed data set were highest in Ecoregion 14 and lowest in 

Ecoregion 11 (Table 7), as expected based on prior analyses (AAC 2006). TKN, TN, TP, and 

chlorophyll-a levels varied among the tiers of aquatic life use in Ecoregion 9, where the greatest 

number of SCI observations were located. In general, higher nutrient and chlorophyll-a values 

tended to occur in association with lower aquatic life use tiers in Ecoregion 9 (Table 7). This 

pattern was not present in Ecoregion 11. Instead, nutrient levels in Ecoregion 11 tended to 

decline with increasing benthic macroinvertebrate community stress (as indicated by declining 

SCI scores). Few benthic-impaired sites, however, were located in Ecoregion 11. Only 4 SCI 

observations, all indicating severely stressed communities, were located in Ecoregion 14. 

Histograms representing the percentages of benthic macroinvertebrate observations within 

various aquatic life use tiers for various nutrient-concentration ranges, statewide, are displayed in 

Figure 5. Stressed and severely stressed communities are assessed by DEQ as impaired. These 

histograms show generally increasing levels of stress with increasing nutrient and chlorophyll-a

values, although with varying consistency. This pattern is least evident for oxidized N (N as 

NO2

-

+NO3

-

) and is highly evident for TKN and TP. 
17

Table 6. Characteristics of biological and associated water-quality monitoring observations 

selected for analysis. Mean and median units: NO2+NO3, total Kjeldahl nitrogen (TKN), total 

nitrogen, and total phosphorus = mg/L; chlorophyll-a = μg/L. 

 - - - Ecoregion - - - State 

 11 9 14 

Observations 80 178 4 262 

Locations 55 122 4 181 

 

Count 

SCI 80 178 4 262 

NO2 + NO3 77 156 4 237 

TKN 70 147 4 221 

Total Nitrogen 73 162 4 239 

Total Phosphorus 79 171 4 254 

Chlorophyll-a 44 103 3 150 

 

Mean 

SCI 71.60 61.50 34.78 64.18 

NO2 + NO3 0.23 0.31 1.53 0.31 

TKN 0.12 0.36 0.68 0.29 

Total Nitrogen 0.34 0.65 2.11 0.58 

Total Phosphorus 0.017 0.047 0.110 0.04 

Chlorophyll-a 0.95 1.94 2.11 1.65 

 

Median 

SCI 74.30 64.18 33.43 67.78 

NO2 + NO3 0.12 0.17 1.44 0.16 

TKN 0.10 0.30 0.65 0.20 

Total Nitrogen 0.27 0.46 2.14 0.42 

Total Phosphorus 0.01 0.04 0.08 0.03 

Chlorophyll-a 0.50 1.14 1.99 0.89 
18

0.01

0.1

1

10

1 61 121 181

Observations ranked by increasing concentration

Concentration (mg/L)

Oxidized N

TKN

Total N

0.01

0.1

1

1 66 131 196

Observations ranked by increasing concentration

Concentration (mg/L)

Total P

0.1

1

10

100

1 39 77 115

Observations ranked by increasing concentration

Concentration (ug/L)

Cholorophyll a 

Figure 4. Distributions of selected variables within the data set used for analysis; because 

distributions are skewed, variables are plotted on log scales. 
19

Table 7. Mean nutrient and chlorophyll-a values (mg/L), stream condition index (SCI) values, 

and SCI observation counts by ecoregion and aquatic life use tier. 

 - - - - - - - - Aquatic Life Use Tier - - - - - - - - - 

 Excellent Good Stress Severe 

Stress

All

NO2+NO3 

Eco 11 0.17 0.33 0.16 0.05 0.23

Eco 9 0.31 0.30 0.32 0.36 0.31

Eco 14 1.53 1.53

All 0.23 0.31 0.30 0.56 0.31

 

TKN 

Eco 11 0.13 0.12 0.10 0.05 0.12

Eco 9 0.22 0.28 0.38 0.80 0.36

Eco 14 0.68 0.68

All 0.16 0.23 0.36 0.74 0.29

 

TN 

Eco 11 0.30 0.44 0.20 0.10 0.34

Eco 9 0.55 0.60 0.62 1.06 0.65

Eco 14 2.11 2.11

All 0.40 0.56 0.58 1.22 0.58

 

TP 

Eco 11 0.017 0.017 0.018 0.010 0.017

Eco 9 0.036 0.039 0.055 0.084 0.047

Eco 14 0.110 0.110

All 0.024 0.033 0.051 0.085 0.039

 

Chl-a 

Eco 11 1.01 0.92 0.88 0.50 0.95

Eco 9 1.08 1.26 3.06 2.59 1.94

Eco 14 2.11 2.11

All 1.04 1.16 2.75 2.39 1.65

 

SCI 

Eco 11 77.77 67.43 54.96 41.12 71.60

Eco 9 76.49 67.24 52.57 35.37 61.50

Eco 14 34.78 34.78

All 77.25 67.30 52.82 35.52 64.18

 

Count 

Eco 11 42 31 6 1 80

Eco 9 29 81 51 17 178

Eco 14 4 4

All 71 112 57 22 262
20

0%

25%

50%

75%

100%

.01 .02 .03 .04 .05 .06 .07-.08 .09-.12 >0.12

Total P (mg/L)

% of Observations

0%

25%

50%

75%

100%

<0.5 (DT) 0.51-0.79 '0.8-1.19 1.2-1.99 >2

Chlorophyll-a (ug/L)

% of Observations

0%

25%

50%

75%

100%

<0.5 (DT) 0.51-0.79 '0.8-1.19 1.2-1.99 >2

Chlorophyll-a (ug/L)

Excellent % of ObservationsGood

Severe Stress Stress

Figure 5. Histograms representing percentages of benthic macroinvertebrate observations within 

various aquatic life use tiers occurring within different nutrient categories. Stressed and severely 

stressed communities are defined as impaired. Histograms for nitrogen water-quality variables 

are displayed on the following page. 
21

0%

25%

50%

75%

100%

<0.1 >1.0 0.1-

0.19

0.2-

0.29

0.3-

0.39

0.4-

0.49

0.5-

0.59

0.6-

0.79

0.8-

0.99

NO2-NO3 N (mg/L)

% of Observations

0%

25%

50%

75%

100%

<=0.1 0.2 0.3 0.4 0.5 0.6-0.7 0.8 &

Above

TKN (mg/L 

% of Observations

0%

25%

50%

75%

100%

<0.2 0.2-0.29 0.3-0.39 0.4-0.49 0.5-0.59 0.6-0.79 0.8-0.99 >1.0

Total N (mg/L)

% of Observations
22

Several models were developed to test the dependence of SCI on in-stream nutrients using 

log-transformed nutrient concentrations as independent variables (Table 8). Of the nutrient 

variables determined as suitable for model development, TKN showed the strongest relationship 

with SCI (highest R2

) whereas oxidized N showed the weakest relationship. TP demonstrated a 

stronger monovariate relationship with SCI than did TN. A multivariate model combining TKN 

with TP demonstrated a slightly higher R2

 (0.24) than did a monovariate model using only TKN 

(R2

 = 0.23). Because the oxidized nitrogen showed such a weak relationship with SCI (R2

≤0.01), it was not considered further. 

Table 8. Stream Condition Index (SCI) prediction models evaluated. 

Independent 

Variable 

Functional Form Adjusted 

R2

Critical 

Value*

 

Oxidized N SCI = 61.4 - 1.38 * Ln(NO2+NO3) 0.008 

TKN SCI = 51.4 - 7.70 * Ln(TKN) 0.233 0.3 

TN SCI = 58.9 - 6.21 * Ln(TN) 0.127 0.8

TP SCI = 39.6 - 6.85* Ln(TP) 0.177 0.05

Chlorophyll-a SCI = 62.4 - 7.34 * Ln(Chl-a) 0.134 

TKN and TP SCI = 45.0 - 6.00 * Ln(TKN) -2.53 * Ln(TP) 0.241 n/a

* mg/L value where SCI = 60 

Using the monovariate models, “critical values” for TKN, TN, and TP were determined as 

the concentrations corresponding with SCI = 60. This SCI value was selected for defining critical 

nutrient values because it is used in stream assessments to differentiate impaired from nonimpaired stream systems. The resulting critical values identified for TN and TP (Table 8) were at 

levels higher than common reference values for ecoregions 9 and 11 (Table 1, Section I.A.), 

which comprise most of the state’s land area. The 60% confidence interval (which represents a 

20% risk of generating a Type II error, or a 20% probability that the screening value would fail 

to capture an impaired site) failed to encompass SCI = 60 for TKN and TP concentrations above 

common reference values (Table 1, Section I.A.; Figure 6). 

The bivariate prediction model (utilizing TKN and TP) was also unable to define higherthan-reference-value TKN and TP combinations that would be capable of predicting SCI > 60 

with acceptable precision (Figure 7). 
23

20

30

40

50

60

70

80

90

100

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

TP (mg/L)

Predicted SCI

predIBI

lower95

upper95

lower90

upper90

lower80

upper80

lower60

upper60

20

30

40

50

60

70

80

90

100

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

TKN (mg/L)

Predicted SCI

predIBI

lower95

upper95

lower90

upper90

lower80

upper80

lower60

upper60

Figure 6. Stream Condition Index (SCI) dependence on TP and TKN represented by 

monovariate prediction models (solid line), with 95%, 90%, 80%, and 60% prediction intervals 

(dashed lines). 
24

35

40

45

50

55

60

65

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

TKN (mg/L)

Lower 80% Confidence Interval -

Predicted SCI 

TP=.01

TP=.03

TP=.05

TP=.07

40

45

50

55

60

65

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

TKN (mg/L)

Lower 60% Confidence Interval - 

Predicted SCI 

TP=.01

TP=.02

TP=.03

TP=.05

TP=.07

Figure 7. Lower boundaries of the 80% (above) and 60% (below) Stream Condition Index 

(SCI)-prediction intervals for various (TKN, TP) combinations contained within the data set, as 

per the bivariate (TKN, TP) SCI prediction model. 
25

Discussion 

The indication that TKN is a better predictor of SCI than TN is consistent with the 

observation that TKN measurements from unfiltered samples (such as those which are utilized 

by DEQ monitoring) include planktonic algae biomass N. It appears reasonable to expect that 

planktonic algae levels would be higher in nutrient-impaired streams than in other streams. 

The nitrogen critical values of 0.8 mg/L for TN and 0.3 mg/L for TKN are consistent with 

the threshold value identified by Ponader et al. (2005) for periphytic algae > 100 mg/m2

 (0.5 

mg/L nitrate N). These critical values are comparable to nutrient thresholds identified in other 

studies (Section I.A.). The critical value for TP (0.05 mg/L) is identical to the TP value identified 

by Ponader et al. (2005) as a threshold for change in algal species composition in Virginia 

waters. The TP critical value is also comparable to in-stream P concentrations for the two 

nutrient TMDL studies that included TMDL restoration target concentrations (Section I.B.). 

The nutrient-SCI relationships embodied by the SCI prediction models are consistent with 

expectations and with the assumption that underlie the analysis: In all statewide relationships 

analyzed, high water-quality nutrient concentrations are related negatively to measured SCI 

values. These relationships, however, are highly variable, as evidenced by their low R2

 values 

and wide prediction intervals. Results indicate that the use of the critical values in Table 8 as 

screening values would be likely result in numerous Type II errors (waters with a SCI < 60 but 

not identified as possibly nutrient impaired by the TKN, TN, and TP screening values). Thus, if 

the screening values are to be defined with the goal of limiting Type II errors, defining screening 

values as nutrient concentrations lower than the critical values would be warranted. However, the 

data variance embodied by the models creates prediction intervals that are sufficiently wide to 

render them unusable as a basis for recommending statistically valid screening values. 

It is possible that the nutrient concentration-SCI variance may, in reality, be less than that 

indicated by the data set. Part of the variance could have occurred due to influences by nonnutrient stressors that were not successfully identified by the data screening procedure. Some low 

SCI scores (including SCI values < 60 that indicate impairment) were found to be present at very 

low nutrient levels. In fact, for both TN and TP, approximately 10% of the monitoring locations 

within the lowest concentration category (Figure 5) were associated with SCI < 60. We checked 

the presumption that such impairments may be non-nutrient related by searching the data set for 

all observations where low SCI values were associated with low TN and TP concentrations. Of 

the six observations found to be in the lowest quartile for TN, TP, and SCI, five also exhibited 

high Hilsenhoff Biotic Index (HBI) scores (highest quartile), indicating a community with a high 

level of tolerance to organic and nutrient pollution. Unfortunately, most of these observations 

were taken from probabilistic monitoring samples so we have no way to check whether the low 

nutrient values recorded by the single water-quality monitoring observation represented are 

characteristic of the nutrient concentrations that are generally experienced at these locations. 

What Are Appropriate Screening Values? 

It is possible that the data set used for the analysis reflects both nutrient and non-nutrient 

stressor effects. Although an effort was made to discard all SCI observations affected by nonnutrient stressors prior to data analysis, there is no guarantee that this goal was achieved. The 

possibility that the data set includes both nutrient and non-nutrient stressor effects must be 

considered given that analysis results include apparent benthic impairments (SCI values < 60) at 
26

locations with nutrient concentrations that are quite low relative to effect-based thresholds cited 

in Section I.A. 

Considering only the analysis that was described above, one interpretation could be that 

screening values could be defined as equivalent to reference values characteristic of “relatively 

undisturbed” or “least disturbed” waters because there appears to be some opportunity for Type 

II error regardless of the screening value selected. However, we would not argue for this 

interpretation, given the possibility that some of the low-nutrient concentration impairments (SCI 

values < 60) considered in the above analysis may have occurred in response to non-nutrient 

stressors. 

Therefore, we consider this analysis to be inconclusive as a basis for recommending instream nutrient concentrations that may be used as screening values. 
27

I.D. Ambient Monitoring Nutrient Distributions 

C. E. Zipper 

One task described by the AAC’s FY07 work program is to estimate the implications of a 

recommended screening hierarchy on additional monitoring that might be required as a result 

of implementing that screening hierarchy. Although we are not recommending a specific 

screening mechanism at this time, we believe it is useful to consider the potential DEQ 

monitoring-resource requirements as if screening values were to be implemented. As a 

general example, the resources required to conduct a visual assessment would be far less than 

those required to conduct a benthic macroinvertebrate assessment. 

The data included in Figure 8 are intended to serve as a first step in a consideration of the 

monitoring-resource requirements. These data are only a first step because the DEQ 

resources required by a screening-value exceedance will be influenced by how the screening 

process proceeds (See Figure 1, Section I.C.). These data were generated from analysis of the 

database assembled and analyzed in the course of completing the AAC FY06 work program. 

The database underlying this analysis was comprised of all DEQ ambient chemical 

monitoring observations collected over the October 1999 – September 2005 time periods, as 

provided by DEQ to the AAC. When total nitrogen (TN) values were not measured directly, 

they were calculated from measured values as described previously (Section I.C.). The few 

TP concentrations for samples collected after June 1999 that were recorded as less than or 

equal to 0.10 mg/L were removed from the database prior to analysis. All values recorded as 

being less than or equal to a detection limit have been analyzed as if they were equal to the 

detection limit value. An annual median was calculated for a monitoring site and included in 

Figure 8 only if 5 or more observations were recorded at that site during that year. 
28

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentile

TKN (mg/L)

Annual Median

Individual Observations

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentile

TN (mg/L)

Annual Median

Individual Observations

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Percentile

TP (mg/L)

Annual Median

Individual Observations

Figure 8. Percentile distributions of Virginia DEQ monitoring data: Individual observations 

(10/99 – 9/05), and annual medians (all monitoring stations with 5 or more monitoring 

observations per year), 2000 – 2004. 
29

II.A. Pilot Application of Load-Duration Approach to 

Rappahannock River Basin 

G. Yagow 

Task 

Develop a pilot application of the load-duration approach at four or five locations within a 

smaller basin, possibly the Rappahannock, to identify more specifically the issues that might be 

involved with flow estimation at DEQ sites without flow measurements and their translation into 

load thresholds for related 2010 cap-load allocations. 

Summary 

This section reports on an exploratory study to develop procedures for, create examples of, 

and identify other issues related to the use of load-duration curves as the basis for a possible 

flow-variable nutrient criteria. Along the way, a simpler approach, referred to as the cap-load 

method, evolved as a component of, and an alternative to, the more complex load-duration 

approach. Both approaches could be developed to set numeric nutrient criteria by major river 

basin and could be used in protecting the uses of downstream receiving waters. 

The cap-load approach is based on the point source (PS) and non-point source (NPS) cap 

loads assigned to county segments upstream of each monitoring station (determined from the 

Chesapeake Bay Tributary Strategy cap loads for the Rappahannock example). This approach is 

based on annual averages with numeric criteria developed from the annual cap loads and average 

daily flows estimated at the monitoring sites. 

The load-duration approach is similar to the cap-load approach but requires more in-depth 

analysis. This approach recognizes that PS contributions are not dependent on surface runoff and 

NPS load contributions may not increase linearly with increases in runoff and streamflow. This 

approach uses target cap loads and flow frequency curves (which represent the relationship 

between the daily streamflow and the percent days the given flow is exceeded) to produce loadduration curves. Loads estimated for each water sample (based on the nutrient concentration and 

daily flow) could be compared to the load-duration curve for the particular flow to determine if 

the criterion is exceeded. 

Introduction 

There are three types of data that must be measured or estimated as the basis for setting, and 

assessing compliance with a load-based nutrient criterion: (1) an allowable nutrient load to 

quantify the target load; (2) flow associated with the monitoring data, and (3) monitored nutrient 

concentrations. Each of these three parameters must be evaluated at the desired assessment 

points and aggregated over some time period. For this study, the following data sources were 

used for these three data types: Tributary Strategy cap loads, daily USGS flow data, and DEQ 

nutrient monitoring data. The boundary of the study area was the Chesapeake Bay model 
30

segment 5230, which is essentially hydrologic unit 02080103 and a small portion downstream, as 

shown in Figure 9. 

Figure 9. Chesapeake Bay Phase 4.3 Model Segment 5230 and related county segments. 

Cap-Load Data

Virginia Tributary Strategy cap-load data for 2010 were obtained as annual amounts from an 

Excel spreadsheet provided by the Virginia Department of Conservation and Recreation. Cap 

loads represent the maximum allowable average annual load needed to achieve water quality 

goals in the Chesapeake Bay (downstream loading criteria) and are a function of the upstream 

watershed area. Because the entire Chesapeake Bay portion of Virginia has been assigned N and 

P cap loads, estimates of maximum annual load were relatively easy to estimate at any point 

within this area. Cap loads have both nonpoint source (NPS) and point source (PS) components. 

Model segment 5230 contains part of 10 counties or cities, each part of which is known as a 

county segment or “coseg” under the Chesapeake Bay Tributary Strategies program in Virginia 

(see Figure 9). Within each coseg, separate allocations were made for nitrogen (N) and 

phosphorus (P) by NPS and by PS, as shown in Table 9. 
31

Table 9. Model 5230 coseg cap-load allocations for total nitrogen and total phosphorus. 

TN Cap Load (lbs/yr) TP Cap Load (lbs/yr) County Coseg ID PS NPS PS NPS 

Albemarle 230051003 0 5,009 0 710 

Culpeper 230051047 68,162 570,781 34,405 84,260 

Fauquier 230051061 40,177 444,563 14,203 68,279 

Fredericksburg 230051630 0 757 0 120 

Greene 230051079 0 81,024 0 13,465 

Madison 230051113 0 492,795 0 84,769 

Orange 230051137 35,526 264,254 13,839 38,982 

Rappahannock 230051157 0 326,545 0 54,454 

Spotsylvania 230051177 0 52,903 0 5,591 

Stafford 230051179 0 45,354 0 4,940 

Daily Flow Data

Daily flow data were obtained from selected USGS flow stations with at least 30-years of 

record, as shown in Table 10. A 30-year span was chosen as a reasonable basis for calculating 

long-term average daily flow since many statistics are based on 30-year weather normals. Daily 

streamflow data were downloaded for USGS stations from 

http://nwis.waterdata.usgs.gov/va/nwis/. 

Table 10. Selected USGS daily flow stations in Model Segment 5230. 

Station ID Stream Name

Drainage 

Area (mi2

)

1975-2004 

Unit-Area Flow 

(cfs/mi2)

Period of 

Record

01662000 Rappahannock River nr. Warrenton 194.98 0.99 1942 - 1986

01663500 Hazel River at Rixeyville 285.68 1.30 1942 - 2004*

01664000 Rappahannock River nr. Warrenton 619.58 1.20 1942 - 2004

01665500 Rapidan River nr. Ruckersville 114.65 1.40 1942 - 2004**

01666500 Robinson River nr. Locust Dale 179.00 1.35 1943 - 2004

01667500 Rapidan River nr. Culpeper 466.62 1.29 1930 - 2004

01668000 Rappahannock River nr. Fredericksburg 1,341.23 1.35 1907 - 2004

* Data missing between 10/92 and 10/02, and after 09/04.

** Data missing between 07/95 and 07/98.

To facilitate the calculation of average daily flow at the DEQ sites in this river basin, ArcGIS 

was used to generate watersheds that corresponded with the ambient monitoring sites within 

model segment 5230, as shown in Figure 10. Because flow is a function of drainage area, flow 

estimates for the DEQ sites were calculated by multiplying the unit-area flow (cfs/sq.mi.) at the 

nearest upstream or downstream USGS flow station (Table 10) with the drainage area of the 

watershed at the DEQ assessment point (Table 11). When two or more USGS flow stations were 

upstream on separate tributaries from the DEQ assessment point, an area-weighting was 

performed to calculate the estimated flow at the monitoring station. Calculations were not 

performed on the two DEQ sites without associated flow, as they were on smaller tributaries 

without clearly representative USGS flow stations. 
32

Figure 10. Watershed boundaries for Virginia Department of Environmental Quality (DEQ) 

ambient monitoring sites for Model Segment 5230. 

Also shows nearby U.S. Geological Survey flow stations. 

Table 11. DEQ ambient monitoring sites and associated USGS flow stations. 

DEQ Site ID Stream Name

Drainage 

Area (mi2)

Period of 

Record

No. of 

Samples

USGS 

Station

Calculated 

Average Daily 

Flow (cfs)

HAZ018.29 Hazel River 114.87 1987 - 2005 113 01663500 149.8

LDR000.70 Little Dark Run 109.58 1975 - 2004 214 01666500 148.4

RAP006.53 Rapidan River 674.28 1985 - 2005 193 01667500 868.2

RAP045.08 Rapidan River 237.89 1987 - 2005 173 01665500 334.0

ROB001.90 Robinson River 179.00 1975 - 2005 300 01666500 242.4

RPP113.47 Rappahannock River 1,341.23 1985 - 2004 574 01668000 1,811.1

RPP147.10 Rappahannock River 619.58 1975 - 2005 305 01664000 742.7

THO006.50 Thornton River 138.44 1985 - 2005 135 01663500 180.6

CAE000.25 Carter Run 54.95 2002 - 2005 26 01662000 54.5

GRT001.70 Great Run 25.02 1985 - 2005 114 01662000 24.8

RPP175.51 Rappahannock River 74.03 1987 - 2005 150 01662000 73.5

MTN000.59 Mountain Run 1990 - 2005 01665000

MTN022.45 Mountain Run 1973 - 2005 01665000

DPR001.70 Deep Run 1974 - 2005

MIR004.05 Mine Run 1974 - 2005

DEQ Sites with Associated Flow and Load-Duration Curves

DEQ Sites with Associated Flow Only

DEQ Sites with no Associated Flow
33

DEQ Nutrient Data

Monitored nutrient data were obtained for selected DEQ ambient monitoring sites from the 

following DEQ web site: http://gisweb.deq.virginia.gov/monapp/mon_query_form.cfm. 

Available nutrient data were downloaded for each site. Because nutrient analysis procedures and 

recorded parameters have changed over time, the following equations were made for the 

calculation of TN and TP. The numbers represent DEQ’s Comprehensive Environmental Data 

System (CEDS) parameter codes, as explained below: 

TN = ((((613 OR 615) AND (618 OR 620)) OR 630) AND 625) OR 600 

TP = 665 OR 70507 

where the numbers represent the following CEDS parameter codes: 

 613 = NO2-N Dissolved 

 615 = NO2-N Total 

 618 = NO3-N Dissolved 

 620 = NO3-N Total 

 630 = NO3-N + NO2-N Total 

 625 = Total Kjeldahl Nitrogen 

 600 = Total Nitrogen 

 665 = Total Phosphorus 

 70507 = Total Ortho-Phosphorus (only used when no value was recorded for 665). 

Cap-Load-Based Nutrient Criteria 

At the March 2006 AAC meeting, the Chesapeake Bay Tributary Strategy cap loads were 

discussed as a possible basis for setting variable nutrient criteria by major basin. This section 

illustrates the use of such a procedure at select DEQ sites in the Upper Rappahannock River 

Basin. 

PS cap loads were assigned to individual facilities and were manually assigned to appropriate 

watersheds based on facility location. NPS cap loads were distributed to each of the watersheds 

draining to DEQ ambient monitoring sites on an area-weighted basis as a fraction of each coseg 

(and their associated NPS cap-load allocation) contained within each watershed. A summary of 

PS and NPS loads summed for the watershed draining to each DEQ ambient site are shown in 

Table 12. 
34

Table 12. Cap-load allocations for point sources (PS) and non-point sources (NPS) calculated at 

DEQ ambient sites. 

PS NPS PS NPS

CAE000.25 14,206.39 40,177 85,863 14,203 13,187

DPR001.70 6,111.52 0 33,916 0 4,757

GRT001.70 6,465.11 0 39,075 0 6,001

HAZ018.29 29,746.81 0 158,334 0 25,827

LDR000.70 28,369.91 0 167,652 0 28,807

MTN019.75 9,858.88 68,162 56,692 34,405 8,369

RAP006.53 174,515.94 35,526 979,170 13,839 156,348

RAP045.08 61,542.93 35,526 337,754 13,839 55,486

ROB001.90 46,350.26 0 273,212 0 46,337

RPP113.47 412,735.99 143,865 2,283,228 62,447 355,451

RPP147.10 160,350.99 40,177 861,247 14,203 135,752

RPP175.51 19,143.26 0 100,682 0 16,176

THO006.50 35,823.36 0 169,471 0 28,261

Assessment Point Upstream 

Area (ha)

TN Cap Load (lbs/yr) TP Cap Load (lbs/yr)

The potential nutrient criteria derived from the cap-load approach were calculated for 

individual DEQ ambient monitoring sites from the annual cap loads in Table 12 and the average 

daily flows from Table 11. These potential concentration criteria (cap-load standards) are shown 

in Table 13. The nutrient data at each DEQ site were evaluated against these values for the most 

recent 5-years of data (2000 – 2004), representing a pseudo-assessment period. The number of 

samples used to calculate TN and TP at each site for this period, together with a calculated 

percent exceedences of the cap-load criteria are also shown in Table 13. Note that sites with PS 

components generally have larger allowable nutrient concentrations than sites without PS 

components. 

Table 13. Summary of average cap-load standard concentrations for total nitrogen (TN) and total 

phosphorus (TP) and percent exceedences for 2000 – 2004 data. 

TN (mg/L) TP (mg/L)

No. of TN 

Samples

% TN 

Exceedences

No. of TP 

Samples

% TP 

Exceedences

CAE000.25* 0.33 0.07 26 100.0% 26 100.0%

GRT001.70 0.10 0.02 114 22.8% 114 22.8%

HAZ018.29 0.54 0.09 5 0.0% 5 0.0%

LDR000.70 0.35 0.06 21 95.2% 21 47.6%

RAP006.53* 0.87 0.14 189 9.0% 189 1.6%

RAP045.08* 1.19 0.21 130 0.8% 130 0.8%

ROB001.90 0.57 0.10 29 37.9% 29 13.8%

RPP113.47* 0.68 0.12 113 68.1% 0 0.0%

RPP147.10* 0.62 0.10 27 29.6% 27 0.0%

RPP175.51 0.26 0.04 122 26.4% 149 26.4%

THO006.50 0.48 0.08 6 100.0% 6 16.7%

* Sites with PS Cap Load components.

Cap Load Standard 2000-2004

DEQ Site
35

Load-Duration-Based Nutrient Criteria 

The load-duration approach is essentially an extension of the cap-load approach but requires 

more in-depth analysis. The constant cap-load criteria, given in terms of concentrations, assume 

that nutrient loads increase linearly with increases in daily flow. However, we know that PS 

contributions are fairly constant over time and are independent of surface runoff, whereas NPS 

nutrient loads are more dependent on runoff and thus streamflow. Modeling data for one stream 

segment presented in last year’s AAC report showed increasing TP concentrations but decreasing 

TN concentrations with increasing flow. The load-duration approach permits variable allowable 

nutrient loads at different flows based on the combination of expected PS and NPS contributions 

in a watershed. Use of these variable target loads at different flows leads to criteria that are flowvariable concentrations. In watersheds with no PS contributions, or where a long-term average 

concentration criterion is preferred, there is no advantage to the load-duration approach. 

To illustrate the steps in the load-duration approach, the following example was created for 

the entire 5230 model segment. Daily streamflow was downloaded for USGS Station 01668000, 

loaded into a spreadsheet, and sorted by flow in descending order. A rank was then assigned to 

each daily flow, and the percentage of the total period was calculated for which the flow was 

exceeded (%DaysFlowExceeded). The flow frequency curve was then plotted as daily flow 

versus %DaysFlowExceeded, as shown in Figure 11. 

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

Figure 11. Flow Frequency Curve, USGS 01668000, 1975-2004. 
36

The average annual nutrient concentrations (cap-load criteria) were calculated by adding 

together the point source (PS) and nonpoint source (NPS) average annual load allocations for the 

entire model segment and dividing by the average annual daily flow (USGS station 01668000 

was roughly coincident with RPP113.47). The concentration criteria for this site were 0.68 mg/L 

for TN and 0.12 mg/L for TP (Table 13). Daily streamflow was multiplied by the TN 

concentration criterion to generate the TN load duration curve in Figure 12. 

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-constant

Figure 12. TN Load-Duration Curve, Model Segment 5230. 

Average daily flow on the collection date of each DEQ ambient sample was obtained from 

the USGS flow data and adjusted to the area of the watershed draining to the DEQ site. To 

calculate the sample-day loads, the average daily flow for the collection date was multiplied by 

the sample concentration. Sample-day loads were then plotted against the load-duration curve 

(Figure 13). Sample-day loads that lie above the TN load-duration curve are then assessed as 

exceeding the criteria. Percent exceedence was calculated both for the entire period of monitored 

nutrient data at this site and for the most recent 5-year (pseudo-assessment) period, 2000-2004, 

shown in parentheses in Figure 13. 
37

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-constant All Samples 2000-2004 Samples

TN Constant % Exceedence = 70.5% (68.1%)

Figure 13. TN Monitoring Data Compared with TN Load-Duration Curve. 

In the example above, the load curve is based on a constant concentration criterion derived 

from the average annual cap load and average annual daily flow. Two additional alternative 

methods were investigated to develop variable numeric nutrient criteria. Point sources typically 

have a permitted daily average load, which means that under varying flow conditions, there is a 

variable pollutant concentration that will achieve the permitted daily load. Theoretically, in order 

to achieve an average annual target pollutant load, as flow increases from some average baseline, 

the allowable pollutant concentration must decrease in order not to exceed a constant target load. 

Therefore, an option was considered to look at declining concentration criteria with increasing 

flow. 

NPS pollutant loads are primarily driven by runoff. Their pollutant contribution is typically 

minimal during baseflow and higher during larger flows. It should be anticipated, therefore, that 

pollutant concentrations from NPS loads, where they dominate, will be higher during runoff 

events. In order to meet a constant target pollutant load under these circumstances, lower 

allowable concentrations would be needed during baseflow. Therefore a second alternative 

option was considered to look at increasing concentration criteria with increasing flow. 

The following two sets of flow-variable curves were developed for nitrogen (TN) assuming 

constant PS inputs, and either an increasing (Variable) or decreasing (Declining) contribution 

from NPS as flow increased. Because phosphorus (TP) loads from NPS typically increase with 

increases in flow, only the increasing curve was developed for TP. 
38

• Variable: Increasing NPS load with increasing flow 

• Allowable Daily Load = PS + (NPS * (1- %Days Flow Exceeded))2 

• Declining: Decreasing NPS load with increasing flow 

Allowable Daily Load = PS + (NPS * 2 * %Days Flow Exceeded) 

The resulting variable daily loads were then divided by a daily flow that corresponded with a 

certain expected long-term return frequency to form two potential variable-flow nutrient 

concentration criteria. The Variable and Declining standard curves are shown along with the 

Constant standard curve for TN in Figure 14. Only the Variable and Constant criteria are shown 

for TP in Figure 15, as explained earlier. Sample-day loads were then compared against each 

potential criterion. The percent exceedence calculated for the period of record (Figure 14 and 

Figure 15) and the most recent 5-years of data (Figure 14, in parentheses) are included. 

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 50.1% (46.9%)

TN Constant % Exceedence = 70.5% (68.1%)

TN Declining % Exceedence = 60.2% (53.1%)

Figure 14. Three alternative TN Load-Duration Curves, RPP113.47.
39

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples

TP Variable % Exceedence = 37.9% (0%)

TP Constant % Exceedence = 37.9% (0%)

Figure 15. Two alternative TP Load-Duration Curves, RPP113.47.

The previous example illustrates the simplest case, where flow, cap load, and ambient 

nutrient monitoring data are all available at the same assessment point. Where flow and cap loads 

were evaluated at points not corresponding with the DEQ site, the assignment of values were 

similar to the approaches (e.g., area-weighting) used in developing the cap-load criteria. 

Following the procedures used in the previous example for DEQ site RPP113.47, load-duration 

curves for TN and TP were constructed for an additional seven DEQ ambient monitoring sites in 

this basin that contained sufficient data (Figures 16-29). 

Also located in the pilot study area were three DEQ sites associated with USGS Station 

01662000 and two DEQ sites that could have been associated with USGS Station 01665000 (an 

oversight in the indirect evolution of this analysis). Both of these USGS stations had the requisite 

30 years of data for constructing a flow frequency curve. However, since the gauges were 

discontinued in 1986 and 1999, respectively, there was no concurrent flow data for calculating 

sample-day loads from recent samples. Although these stations could not be evaluated with loadduration criteria, they did have sufficient data for evaluating cap-load criteria. 

Conclusions 

Both the cap-load and load-duration approaches could produce upstream nutrient 

concentration criteria where annual allowable loads are exceeded. Allowable cap loads within 

the Chesapeake Bay drainage can be redistributed fairly rationally to watersheds corresponding 

to any sub-area, such as watersheds corresponding to DEQ ambient monitoring sites. 
40

Unit-area flow can be calculated from long-term data at most USGS stations and applied to 

upstream areas of nearby DEQ sites. One situation where this strategy is not appropriate is where 

there are intervening tributaries of a certain order (to be determined) between the flow station 

and monitoring site. Similarly, monitored nutrient concentrations can be assumed to be 

representative of, and relatively constant along, a given stream reach on any given day unless 

there are intervening tributaries or major nutrient sources (such as PS discharges; faulty septic 

systems; livestock access; golf courses or other large, manicured green spaces; and non-buffered 

cropland) that would be expected to influence concentrations between the monitoring and 

assessment points. 

Distributed annual cap loads and long-term average daily flow can be used to calculate an 

average nutrient concentration that could be used as a basis for site-specific nutrient criteria. 

These criterion concentrations would vary from site-to-site, depending on the mixture of 

allowable PS and NPS loads within the upstream drainage. Criteria based on this methodology 

would also be much simpler than the load-duration approach because flow-frequency analyses 

and load-duration curves would not need to be constructed. Additionally, more USGS flow 

stations could be used for calculating long-term annual average unit-area flow because this 

method does not need current data for calculating sample-day loads. 

This pilot study, in hindsight, was not properly designed to analyze the potential for using 

load-duration curves to evaluate flow-variable nutrient criteria. Although six of the 13 DEQ sites 

in this study had PS components, only two sites had sizeable PS components (equal to 45% or 

more of the NPS cap load). Unfortunately, neither of the USGS stations associated with these 

two sites – CAE000.25 and MTN019.75 – had current flow data available. Thus, it was not 

possible to calculate sample-day loads for comparison with the load-duration curves, or for 

comparing the percent exceedences between data sets with dominant PS loads and those with 

dominant NPS loads. The original thinking was that at sites where PS loads dominated, an 

excessive number of low-flow exceedences may occur, even though total annual load cap loads 

would still be met. 

From the summary in Table 13, cap-load nutrient criteria tended to be larger or more 

permissive at sites with PS allocations than at the other sites. Because many of the cap-load TP 

criteria were at or below DEQ’s previous analytical minimum detection limit and samples below 

the detection limit were represented in this analysis as having the detection limit value (0.10 

mg/L prior to mid-1999), the number of TP exceedences was artificially inflated.
41

1

10

100

1,000

10,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 12.5% (0%)

TN Constant % Exceedence = 48.2% (0%)

TN Declining % Exceedence = 37.5% (40%)

Figure 16. TN Load-Duration Curve for HAZ018.29.

1

10

100

1,000

10,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples 2000-2004 Samples

TP Variable % Exceedence = 8.9% (0%)

TP Constant % Exceedence = 83.9% (0%)

Figure 17. TP Load-Duration Curve for HAZ018.29.
42

1

10

100

1,000

10,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 83.6% (85.7%)

TN Constant % Exceedence = 87.4% (95.2%)

TN Declining % Exceedence = 63.1% (81%)

Figure 18. TN Load-Duration Curve for LDR000.70.

1

10

100

1,000

10,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples 2000-2004 Samples

TP Variable % Exceedence = 29.4% (4.8%)

TP Constant % Exceedence = 80.8% (47.6%)

Figure 19. TP Load-Duration Curve for LDR000.70.

 
43

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 22.8% (5.8%)

TN Constant % Exceedence = 48.7% (9%)

TN Declining % Exceedence = 37.8% (6.9%)

Figure 20. TN Load-Duration Curve for RAP006.53.

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples 2000-2004 Samples

TP Variable % Exceedence = 5.7% (1.6%)

TP Constant % Exceedence = 5.7% (1.6%)

Figure 21. TP Load-Duration Curve for RAP006.53. 
44

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 9% (2.3%)

TN Constant % Exceedence = 9% (0.8%)

TN Declining % Exceedence = 27.6% (6.2%)

Figure 22. TN Load-Duration Curve for RAP045.08.

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples 2000-2004 Samples

TP Variable % Exceedence = 3.7% (1.5%)

TP Constant % Exceedence = 1.5% (0.8%)

Figure 23. TP Load-Duration Curve for RAP045.08.
45

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 22% (24.1%)

TN Constant % Exceedence = 52.8% (37.9%)

TN Declining % Exceedence = 36% (37.9%)

Figure 24. TN Load-Duration Curve for ROB001.90.

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples 2000-2004 Samples

TP Variable % Exceedence = 19.9% (20.7%)

TP Constant % Exceedence = 75.4% (13.8%)

Figure 25. TP Load-Duration Curve for ROB001.90. 
46

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 15.2% (11.1%)

TN Constant % Exceedence = 17.6% (29.6%)

TN Declining % Exceedence = 35.9% (29.6%)

Figure 26. TN Load-Duration Curve for RPP147.10.

1

10

100

1,000

10,000

100,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples 2000-2004 Samples

TP Variable % Exceedence = 8.7% (3.7%)

TP Constant % Exceedence = 3.1% (0%)

Figure 27. TP Load-Duration Curve for RPP147.10.
47

1

10

100

1,000

10,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Nitrogen Load (lbs/day)

Flow TN-variable TN-constant TN-declining All Samples 2000-2004 Samples

TN Variable % Exceedence = 27.6% (83.3%)

TN Constant % Exceedence = 67.1% (100%)

TN Declining % Exceedence = 44.7% (83.3%)

Figure 28. TN Load-Duration Curve for THO006.50.

1

10

100

1,000

10,000

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent of Days Flow Exceeded, 1975-2004

Daily Streamflow (cfs)

1

10

100

1,000

10,000

100,000

Daily Phosphorus Load (lbs/day)

Flow TP-variable TP-constant All Samples 2000-2004 Samples

TP Variable % Exceedence = 17.1% (0%)

TP Constant % Exceedence = 86.8% (16.7%)

Figure 29. TP Load-Duration Curve for THO006.50.
48
49

II.B. Downstream-Loading Component of Nutrient Criteria for Freshwater 

Rivers and Streams: Recommended Approach

Background 

The AAC has recommended that DEQ establish nutrient criteria for rivers and streams by 

addressing independently the two effects of nutrient over enrichment described by EPA: 

localized and downstream effects. Such criteria would be comprised of 

1) localized components intended to protect designated uses within any stream segment that 

is monitored and assessed, and 

2) downstream-loading components intended to be protective of designated uses in water 

bodies located downstream of any given stream segment. 

This section describes an approach to the downstream-loading component of nutrient criteria 

for streams and rivers in Virginia. 

Recommended Approach

The committee recommends that the downstream-loading component of Virginia’s nutrient 

criteria be developed as narrative criteria. The narrative criteria applied to each stream segment 

would be comprised of clear statements requiring that nutrient levels in any given stream 

segment be protective of designated uses in all downstream water bodies that receive waters 

carried by that stream segment. Should a water body located downstream from a given stream 

segment be found to suffer a nutrient impairment, a water-quality management process would be 

initiated for the impaired water body. That process would consider all nutrient sources, as well as 

other stressors, that contribute to the impaired water body and would take action to mitigate the 

impairment by addressing any sources found to contribute to the impairment. 

Rationale

There are several reasons for recommending this approach. First, assuming the water quality 

management processes referenced by the narrative criteria are effective, the narrative criteria 

would be capable of protecting the downstream receiving waters from impairment. 

Second, use of narrative criteria as the downstream-loading component of nutrient criteria 

would require fewer of DEQ’s resources than would numeric criteria. We make the preceding 

statement considering that nutrient criteria implementation requires regulatory development, and 

the following: 

1. Numeric-load limits for downstream receiving waters would require allocation of the 

water bodies’ total allowable loads to individual upstream segments, an activity that is 

well suited to a comprehensive watershed management process that may include TMDL 

development. If the numeric downstream-loading components of nutrient criteria were 

established, it would be necessary for DEQ to conduct this loading-allocation procedure 

for all of the state’s receiving water bodies regardless of impairment status. The process 

would be complicated because most stream segments contribute waters to numerous 

downstream water bodies. 
50

2. Numeric downstream-loading components of nutrient criteria applied to specific stream 

segments (to protect downstream waters from nutrient impairment) would need to be 

responsive to other water management activities. Thus, some of the numeric downstreamloading components established through an initial round of loading-allocation activities 

(as described in 1 above) would likely require subsequent revisions. For example: 

• For basins where allowable loadings have been assigned (e.g., Chesapeake Bay 

tributaries), future developments may cause those allowable loadings to change. For 

example, the current Tributary Strategy “cap loads” have been allocated and 

assigned based on assumptions that include application of specific non-point-source 

Best Management Practices; as time passes, those assumptions and the resulting 

allocations may change. 

• Implementation of “nutrient trading” in the Chesapeake Bay Watershed may also 

cause changes in how major Bay tributary cap loads are allocated among stream 

segments within tributary basins. 

• If a receiving water body is defined as nutrient impaired, the resultant water quality 

management plan may require an adjustment of its nutrient-loading limits as a 

means of restoring its capability to support its designated uses. Such an adjustment 

would also affect numeric downstream-loading components of contributing stream 

segments. 

3. The AAC has recommended that localized nutrient criteria for wadeable rivers and 

streams in Virginia be established as a process that includes screening values instead of 

strict numeric criteria. Although we have not recommended how localized criteria should 

be defined for non-wadeable streams, it is possible that we would recommend application 

of a similar logic. Because screening values are not intended for interpretation as 

thresholds for impairment, localized criteria established through a screening-value 

process would not provide a basis for defining numeric downstream-loading components 

for contributing stream segments. 

4. Regulatory development and revision processes for water quality uses and criteria in 

specific waters are resource intensive and can consume large amounts of DEQ’s 

analytical resources and staff time. These processes require the same types and levels of 

technical analysis required for developing and implementing water quality management 

plans. In contrast to the regulatory development required to define numeric downstreamloading components of nutrient criteria, the use and application of narrative criteria 

would require water-quality management plans only for stream systems that contain and 

contribute to impaired stream segments. 

We consider a process that achieves the goals of the Clean Water Act and better allocates 

DEQ’s resources as a superior process. Consumption of DEQ’s resources through extensive 

regulatory development can be expected to detract from the resources available for water-quality 

protection activities such as water-quality monitoring and assessment, and enforcement of 

standards. 
51

Conditions and Caveats

We make the recommendation for a narrative downstream-loading component of nutrient 

criteria with several conditions and caveats. As stated above, it is intended for application within 

a context that also includes: 

1. localized criteria that have numeric components and clearly defined impairmentdesignation processes, and 

2. water-quality management processes (including TMDLs) that are effective in addressing 

and mitigating impairments. 

We suggest that DEQ accept the recommendation only if DEQ is also willing to accept a 

logical consequence: implementation of the downstream-loading component as narrative criteria, 

as suggested above, would likely affect Virginia’s TMDL process. We make this statement 

considering that almost all nutrient problems occur due to nutrient contributions from both 

localized and upstream nutrient sources, and that the most nutrient-sensitive regions of many 

surface water systems are often the furthest downstream. 

An inherent assumption of this recommendation is that the water-quality management 

process referenced by the narrative criteria would be effective. The effects of elevated nutrient 

concentrations in individual water bodies are responsive to water-body characteristics and 

therefore highly variable among water bodies and difficult to predict. We believe that an 

effective way to enact narrative criteria would be to link them with a flexible and responsive 

water-quality management framework, such as the adaptive TMDL implementation framework 

that was recently endorsed by the U.S. EPA.1

 An adaptive implementation approach utilizes new 

information made available from monitoring activities conducted after initial TMDL 

implementation to refine further water-quality management actions so as to achieve waterquality management goals. We cite adaptive implementation as an example of the flexible and 

responsive water-quality management process that we believe would complement a decision by 

DEQ to rely on a narrative mechanism to define the downstream-loading component of nutrient 

criteria. Furthermore, we believe that adaptive implementation would enhance the potential for a 

narrative criteria mechanism to be effective. 

 1. See: Memorandum, EPA Assessment and Watershed Protection Division to Regions I-X Water Division Directors. 

“Clarification Regarding ‘Phased’ Total Maximum Daily Loads.” 2 August 2006. 
52
53

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