U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with:

Virginia Department of Conservation and Recreation

Use of the Hydrological Simulation Program–

FORTRAN and Bacterial Source Tracking for 

Development of the Fecal Coliform Total Maximum 

Daily Load (TMDL) for Christians Creek, Augusta 

County, Virginia

Water-Resources Investigations Report 03-4162
Cover image of Christians Creek watershed from U.S. Department of Agriculture-Farm Service Agency-Aerial Photography 

Field Office, Digital Orthophoto Quadrangle, MrSID mosaic
Use of the Hydrological Simulation Program–

FORTRAN and Bacterial Source Tracking for 

Development of the Fecal Coliform Total Maximum 

Daily Load (TMDL) for Christians Creek, Augusta 

County, Virginia

By Douglas L. Moyer and Kenneth E. Hyer 

Water-Resources Investigations Report 03-4162

Prepared in cooperation with:

Virginia Department of Conservation and Recreation

Richmond, Virginia

2003

U.S. Department of the Interior

U.S. Geological Survey
U.S. DEPARTMENT OF THE INTERIOR

GALE A. NORTON, Secretary

U.S. GEOLOGICAL SURVEY

Charles G. Groat, Director

The use of trade or product names in this report is for identification purposes 

only and does not constitute endorsement by the U.S. Government.

For additional information write to: Copies of this report can be purchased from:

District Chief U.S. Geological Survey

U.S. Geological Survey Branch of Information Services

1730 East Parham Road Box 25286, Federal Center

Richmond, VA 23228 Denver, CO 80225-0286

dc_va@usgs.gov

Information about water resources in Virginia is available on the World Wide Web at http://va.water.usgs.gov
Contents III

Abstract ....................................................................................................................................................................................................... 1

Introduction ................................................................................................................................................................................................ 2

Background ..................................................................................................................................................................................... 2

Purpose and scope ........................................................................................................................................................................ 4

Christians Creek watershed characteristics ............................................................................................................................. 4

Modeling approach ........................................................................................................................................................................ 5

Acknowledgments ......................................................................................................................................................................... 5

Description of models ............................................................................................................................................................................... 5

Streamflow model .......................................................................................................................................................................... 5

Pervious and impervious land segments ........................................................................................................................ 6

Stream channels ................................................................................................................................................................. 9

Subwatershed delineation ................................................................................................................................................ 9

Land use ............................................................................................................................................................................... 9

Channel network ................................................................................................................................................................. 11

Meteorological and streamflow data .............................................................................................................................. 11

Calibration approach ......................................................................................................................................................... 13

Fecal coliform model ..................................................................................................................................................................... 14

Pervious and impervious land segments ........................................................................................................................ 14

Stream channels ................................................................................................................................................................. 16

Limitations of the fecal coliform model ........................................................................................................................... 17

Point and nonpoint source representation ..................................................................................................................... 17

General quantification of fecal coliform bacteria ......................................................................................................... 17

Source-specific quantification of fecal coliform bacteria ........................................................................................... 19

Pervious land segments ......................................................................................................................................... 19

Impervious land segments ..................................................................................................................................... 26

Fecal coliform bacteria in the subsurface ...................................................................................................................... 26

Water-quality data .............................................................................................................................................................. 28

Bacterial source tracking .................................................................................................................................................. 36

Calibration approach........................................................................................................................................................... 37

Data limitations .................................................................................................................................................................... 39

Requirements for the fecal coliform TMDL ........................................................................................................................................... 39

Designation of endpoint ................................................................................................................................................................ 39

Margin of safety ............................................................................................................................................................................. 40

Scenario development .................................................................................................................................................................. 40

Reductions from point and nonpoint sources ............................................................................................................................ 40

Results from the streamflow and fecal coliform models .................................................................................................................... 41

Streamflow model calibration results ......................................................................................................................................... 41

Input-source error .......................................................................................................................................................................... 47

Streamflow model verification results ........................................................................................................................................ 52

Final streamflow model parameters ........................................................................................................................................... 56

Fecal coliform model calibration results .................................................................................................................................... 61

Final fecal coliform model parameters ....................................................................................................................................... 68

Fecal coliform TMDL ................................................................................................................................................................................. 70

Present conditions ......................................................................................................................................................................... 70

Scenarios for fecal coliform load reductions ............................................................................................................................ 70

Directions for future research ................................................................................................................................................................. 76

Summary ..................................................................................................................................................................................................... 76

References cited ....................................................................................................................................................................................... 77

CONTENTS
IV Contents

Figures

1. Map showing land use, streams, stream-gaging station, and water-quality sampling 

stations in the Christians Creek watershed, Augusta County, Virginia .............................................................................. 3

2. Rainfall-routing processes, associated with pervious land segments, represented by 

the Hydrological Simulation Program-FORTRAN for the simulation of streamflow in 

Christians Creek, Augusta County, Virginia ............................................................................................................................. 8

3. Map showing hydrologic subwatersheds, land use, and reaches as represented in the 

streamflow and fecal coliform models for Christians Creek, Augusta County, Virginia .................................................. 10

4-31. Graphs showing:

4. Routing processes represented by the Hydrological Simulation Program-FORTRAN 

for the simulation of fecal coliform bacteria transport ................................................................................................. 16

5. Observed fecal coliform bacteria concentrations for Christians Creek at Route 831, 1991-98 .............................. 30

6. Observed fecal coliform bacteria concentrations for Christians Creek at Route 794, 1991-98 .............................. 30

7. Relation between observed fecal coliform bacteria concentrations for Christians Creek at 

Route 831 and Route 794, 1991-97 ..................................................................................................................................... 31

8. Monthly distribution of observed fecal coliform bacteria concentrations for Christians Creek 

at Route 831, 1991-97 ........................................................................................................................................................... 32

9. Monthly distribution of observed fecal coliform bacteria concentrations for Christians Creek 

at Route 794, 1991-97 ........................................................................................................................................................... 33

10. Observed fecal coliform bacteria concentrations from stream-water samples for Christians 

Creek at Route 794, during low-flow periods .................................................................................................................. 34

11. Observed fecal coliform bacteria concentrations from stream-water samples for Christians 

Creek at Route 794, during low-flow periods .................................................................................................................. 35

12. Distribution of the top ten contributors of fecal coliform bacteria identified by bacterial 

source tracking .................................................................................................................................................................... 37

13. Daily rainfall and observed and simulated daily mean streamflows for water years 1994 (A), 

1995 (B), 1996 (C), and 1997 (D), ......................................................................................................................................... 43

14. Simulated daily streamflow in relation to observed daily streamflow, water years 1994-97 ................................. 45

15. Residuals for simulated minus observed daily streamflow, water years 1994-97 .................................................... 46

16. Flow-duration curves for observed and simulated daily mean streamflow, water years 1994-97 ........................ 46

17. Observed and simulated daily streamflow (Winter, January-March; Spring, April-June; 

Summer, July-September; Fall, October-December), water years 1994-97 .............................................................. 48

18. Seasonal flow-duration curves for observed and simulated daily mean streamflow, 

Winter, January-March (A), Spring, April-June (B), Summer, July-September (C), and 

Fall, October-December (D), water years 1994-97 ......................................................................................................... 49

19. Hourly rainfall and observed and simulated daily mean streamflow, February 23-24, 1994 (A) 

and September 6-7, 1996 (B) .............................................................................................................................................. 51

20. Daily rainfall and observed and simulated daily mean streamflow for water years 1992 (A), 1993 (B) ................ 54

21. Simulated daily streamflow in relation to observed daily streamflow, water years 1992-93 ................................. 55

22. Residuals for simulated minus observed daily streamflow, water years 1992-93 .................................................... 55

23. Flow-duration curves for observed and simulated daily mean streamflow, water years 1992-93 ........................ 56

24. Observed and simulated daily streamflow (Winter, January-March; Spring, April-June; 

Summer, July-September; Fall, October-December), water years 1992-93 .............................................................. 57

25. Seasonal flow-duration curves for observed and simulated daily mean streamflow, 

Winter, January-March (A), Spring, April-June (B), Summer, July-September (C), and 

Fall, October-December (D), water years 1992-93 ......................................................................................................... 58

26. Hourly rainfall and observed and simulated daily mean streamflow, April 21-22, 1992 (A) and 

March 17-19, 1996 (B) ......................................................................................................................................................... 60

27. Simulated daily minimum and maximum concentrations, and observed instantaneous 

concentrations of fecal coliform bacteria at Route 831, water years 1992 (A), 1993 (B), 

1994 (C), 1995 (D), 1996 (E), and 1997 (F) ........................................................................................................................... 62

28. Simulated daily minimum and maximum concentrations, and observed instantaneous 

concentrations of fecal coliform bacteria at Route 794, water years 1992 (A), 1993 (B), 

1994 (C), 1995 (D), 1996 (E), and 1997 (F) ........................................................................................................................... 65
Contents V

29. Observed and simulated percent contribution from the simulated sources in the watershed 

to the total instream fecal coliform bacteria load at Route 794, initial calibration (A), and 

final calibration (B) .............................................................................................................................................................. 69

30. Simulated 30-day geometric mean fecal coliform concentrations before (A) and after (B) 

incorporation of the Total Maximum Daily Load (TMDL) allocation scenario at Route 831 

and Route 794, water years 1992-97 ................................................................................................................................. 73

31. Simulated 30-day geometric mean fecal coliform concentrations before (A) and after (B) 

incorporation of the Total Maximum Daily Load (TMDL) allocation scenario at Route 831 

and Route 794 at the end of storm reach 6, water years 1992-97 ................................................................................ 75

Tables

1. Hydrologic parameters used in the simulation of streamflow in Christians Creek, 

Augusta County, Virginia ............................................................................................................................................................. 7

2. Aggregated hydrologic response units used to develop the watershed model ............................................................... 11

3. Meteorological and streamflow data used in the streamflow model ................................................................................. 12

4. Initial streamflow model parameters and percent imperviousness in six subwatersheds 

represented in the streamflow model ...................................................................................................................................... 13

5. Parameters used in the simulation of the transport and storage of fecal coliform bacteria ......................................... 15

6. Permitted point-source dischargers of fecal coliform bacteria, 1992-97 .......................................................................... 18

7. Private permitted point-source dischargers of fecal coliform bacteria ............................................................................ 18

8. Initial values of the total amount of feces produced daily and fecal coliform bacteria per gram 

of feces generated by the human population in the residential hydrologic response unit 

represented in the fecal coliform model ................................................................................................................................. 21

9. Initial values of the total amount of feces produced daily and fecal coliform bacteria per gram of 

feces generated by the dog and cat populations in the urban and residential 

hydrologic response units represented in the fecal coliform model .................................................................................. 21

10. Initial values of the total amount of feces produced daily and fecal coliform per gram of feces 

generated by dairy cattle, beef cattle, and heifers represented in the fecal coliform model ........................................ 22

11. Initial values of the total hours per day dairy cattle spend in a given month in the pasture, 

in confinement, and with access to a stream ......................................................................................................................... 23

12. Percentage of the total stored liquid dairy cattle waste applied to cropland ................................................................... 23

13. Total hours per day beef cattle and heifers spend in a given month in the pasture, 

with access to a stream, and in confinement ......................................................................................................................... 24

14. Percentage of stored beef cattle and heifer manure and poultry litter applied to cropland .......................................... 24

15. Initial population values of wildlife sources of fecal coliform bacteria in the fecal coliform model ............................ 27

16. Initial values of the total amount of feces produced daily and fecal coliform bacteria per gram of 

feces generated by deer, goose, duck, raccoon, muskrat, and beaver represented 

in the fecal coliform model ........................................................................................................................................................ 27

17. Initial values of the total amount of feces produced daily and fecal coliform bacteria per gram of 

feces generated by the dog population in the urban impervious hydrologic response unit 

represented in the fecal coliform model ................................................................................................................................. 28

18. Fecal coliform bacteria concentrations for water-quality samples collected by the Virginia 

Department of Environmental Quality at two water-quality monitoring stations ............................................................. 29

19. Fecal coliform bacteria concentrations for water-quality samples collected by the 

U. S. Geological Survey during low-flow and stormflow conditions at Route 794 (01624800) 

and at five other sites ................................................................................................................................................................. 36

20. Initial values of WSQOP used for the various land-use types represented in the fecal coliform model ..................... 38

21. Current (post-2001) permitted point-source dischargers of fecal coliform bacteria ....................................................... 40

22. Observed and simulated runoff values for Route 794, water years 1994-97 ...................................................................... 41

23. Observed and simulated annual runoff, water years 1994-97 .............................................................................................. 42

24. Simulated total annual and seasonal runoff, interflow and baseflow for calibration period, 

water years 1994-97 .................................................................................................................................................................... 42

25. Observed and simulated runoff values for Route 794, water years 1992-93 ...................................................................... 52
VI Contents 

26. Observed and simulated annual runoff, water years 1992-93 .............................................................................................. 53

27. Simulated total annual and seasonal runoff, surface runoff, interflow and base flow 

for verification period, water years 1992-93 ........................................................................................................................... 53

28. Final parameters and percent imperviousness used in six subwatersheds represented in 

the streamflow model ................................................................................................................................................................. 61

29. Final values of WSQOP used for the land-use types represented in the fecal coliform model ..................................... 68

30. Final values of the total amount of feces produced daily and fecal coliform per gram of feces 

generated by the human, dog and cat populations in the residential hydrologic response unit 

represented in the fecal coliform model ................................................................................................................................. 71

31. Final values of the total amount of feces produced daily and fecal coliform per gram of feces 

generated by cattle, poultry, horses, and sheep represented in the fecal coliform model ............................................ 72

32. Final values for population, total amount of feces produced daily and fecal coliform 

bacteria per gram of feces for deer, goose, duck, raccoon, muskrat, and beaver represented 

in the watershed model .............................................................................................................................................................. 72

33. Total annual load of fecal coliform bacteria delivered from the various land-use types, 

direct deposition by cattle and humans and permitted discharges for present conditions .......................................... 73

34. Scenarios for reducing fecal coliform bacteria loads and associated percent reductions from 

nonpoint and point sources represented in the fecal coliform model ............................................................................... 74

35. Total annual loads of fecal coliform bacteria delivered from the land-use types, point sources, 

and permitted discharges for present conditions and after incorporation of total maximum daily 

load (TMDL) allocation ............................................................................................................................................................... 75
 Contents VII 

Horizontal coordinate information is referenced to the North American Datum of 1927 (NAD27).

Temperature: Temperature is reported in degrees Celsius (°C), which can be converted to degrees Fahrenheit (°F) as follows: °F = 1.8 (°C) + 32°

Abbreviated water-quality units: Bacterial concentrations are reported in units of colonies per 100 milliliters (col/100 mL).

CONVERSION FACTORS, DATUM, AND ABBREVIATED WATER-QUALITY UNITS

Multiply By To obtain

Length

inch (in.) 2.54 centimeter

foot (ft) 0.3048 meter

mile (mi) 1.609 kilometer

Area

acre 4,047 square meter

acre 0.4047 hectare

square mile (mi2) 259.0 hectare

square mile (mi2) 2.590 square kilometer

Volume

gallon (gal) 3.785 liter

gallon (gal) 0.003785 cubic meter

million gallons (Mgal) 3,785 cubic meter

cubic foot (ft3) 0.028317 cubic meter

acre-foot (acre-ft) 1,233 cubic meter

Flow

cubic foot per second (ft3/s) 0.02832 cubic meter per second

million gallons per day (Mgal/d) 0.04381 cubic meter per second

inch per hour 0.0254 meter per hour

inch per year 2.54 centimeter per year

Mass

ounce, avoirdupois (oz) 28.35 gram (g)

pound, avoirdupois (lb) 0.4536 kilogram

pound per acre (lb/acre) 1.121 kilogram per hectare
IV Contents
Abstract 1

ABSTRACT

Impairment of surface waters by fecal coliform 

bacteria is a water-quality issue of national scope 

and importance. Section 303(d) of the Clean Water 

Act requires that each State identify surface waters 

that do not meet applicable water-quality 

standards. In Virginia, more than 175 stream 

segments are on the 1998 Section 303(d) list of 

impaired waters because of violations of the 

water-quality standard for fecal coliform bacteria. 

A total maximum daily load (TMDL) will need to 

be developed by 2006 for each of these impaired 

streams and rivers by the Virginia Departments of 

Environmental Quality and Conservation and 

Recreation. A TMDL is a quantitative 

representation of the maximum load of a given 

water-quality constituent, from all point and 

nonpoint sources, that a stream can assimilate 

without violating the designated water-quality 

standard. Christians Creek, in Augusta County, 

Virginia, is one of the stream segments listed by 

the State of Virginia as impaired by fecal coliform 

bacteria. Watershed modeling and bacterial source 

tracking were used to develop the technical 

components of the fecal coliform bac- teria TMDL 

for Christians Creek. The Hydrological Simulation 

Program–FORTRAN (HSPF) was used to 

simulate streamflow, fecal coliform 

concentrations, and source-specific fecal coliform 

loading in Christians Creek. Ribotyping, a 

bacterial source tracking technique, was used to 

identify the dominant sources of fecal coliform 

bacteria in the Christians Creek watershed. 

Ribotyping also was used to determine the relative 

contributions of specific sources to the observed 

fecal coliform load in Christians Creek. Data from 

the ribotyping analysis were incorporated into the 

calibration of the fecal coliform model.

Study results provide information regarding the 

calibration of the streamflow and fecal coliform 

bacteria models and also identify the reductions in 

fecal coliform loads required to meet the TMDL 

for Christians Creek. The calibrated streamflow 

model simulated observed streamflow 

characteristics with respect to total annual runoff, 

seasonal runoff, average daily streamflow, and 

hourly stormflow. The calibrated fecal coliform 

model simulated the patterns and range of 

observed fecal coliform bacteria concentrations. 

Observed fecal coliform bacteria concentrations 

during low-flow periods ranged from 40 to 2,000 

colonies per 100 milliliters, and peak 

concentrations during stormflow periods ranged 

from 23,000 to 730,000 colonies per 100 

milliliters. Additionally, fecal coliform bacteria 

concentrations were generally higher upstream 

and lower downstream. Simulated source-specific 

contributions of fecal coliform bacteria to instream 

load were matched to the observed contributions 

from the dominant sources, which were beaver, 

cats, cattle, deer, dogs, ducks, geese, horses, 

humans, muskrats, poultry, raccoons, and sheep. 

According to model results, a 96-percent reduction 

in the current fecal coliform load delivered from 

the watershed to Christians Creek would result in 

compliance with the designated water-quality 

goals and associated TMDL.

Use of the Hydrological Simulation Program–FORTRAN and 

Bacterial Source Tracking for Development of the Fecal 

Coliform Total Maximum Daily Load (TMDL) for Christians 

Creek, Augusta County, Virginia

By Douglas L. Moyer and Kenneth E. Hyer
2 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

INTRODUCTION

Background

Surface-water impairment by fecal coliform 

bacteria is a water-quality issue of national scope and 

importance. Section 303(d) of the Clean Water Act 

requires that each State identify surface waters that do 

not meet applicable water-quality standards. In 

Virginia, more than 175 stream segments are on the 

1998 Section 303(d) list of impaired waters because of 

violations of the fecal coliform bacteria standard (an 

instantaneous water-quality standard of 1,000 

col/100 mL, or a geometric mean water-quality 

standard of 200 col/100 mL). Christians Creek, in 

Augusta County, Virginia (fig. 1), is one of these 

impaired streams. Fecal coliform bacteria 

concentrations that are elevated above the State 

water-quality standard indicate an increased risk to 

human health when these waters are contacted through 

swimming or other recreational activities.

In Virginia, total maximum daily load (TMDL) 

plans will need to be developed by 2006 for impaired 

waterbodies on the State 1998 Section 303(d) list. 

TMDLs are a quantitative representation of all the contaminant contributions to a stream and are defined as

where ∑ WLAs (waste-load allocations) represents the 

sum of all the point-source loadings, ∑ LAs (load allocations) represents the sum of all the nonpoint-source 

loadings, and MOS represents a margin of safety. The 

sum of these loading terms and assigned margin of 

safety constitute the TMDL and represent the loading 

of a particular constituent that the surface waterbody 

can assimilate without violating the State water-quality 

standard. The TMDL must meet eight conditions in 

order to be approved by the U.S. Environmental Protection Agency (USEPA). These conditions ensure that the 

TMDL (1) is designed to implement applicable 

water-quality standards; (2) includes a total allowable 

load as well as individual waste-load allocations and 

load allocations; (3) considers the effect of background 

contaminant contributions; (4) considers critical environmental conditions (periods when water quality is 

most affected); (5) considers seasonal variations; (6) 

includes a margin of safety; (7) has been subject to 

public participation; and (8) can be met with reasonable assurance. Once a TMDL is established, 

source-load contributions then can be reduced through 

implementation of source-control management practices until the target TMDL is achieved. 

In Virginia, the primary tool for developing TMDLs 

in impaired watersheds has been the Hydrological Simulation Program–FORTRAN (HSPF) watershed model. 

HSPF is a continuous simulation watershed model 

designed to simulate the transport and storage of water 

and associated water-quality constituents by linking 

surface, soil, and instream processes (Donigian and 

others, 1995). HSPF recently has been demonstrated to 

be an effective tool for the simulation of fecal coliform 

bacteria for TMDL development (U.S. Environmental 

Protection Agency, 2000). HSPF has been used extensively to simulate watershed hydrology (Ng and 

Marsalek, 1989; Donigian and others, 1995; Berris, 

1996; Dinicola, 1997; Srinivasan and others, 1998; 

Zarriello, 1999) and water-quality constituents such as 

nutrients in agricultural runoff (Bicknell and others, 

1985; Donigian, 1986; Moore and others, 1988; Linker 

and others, 1996), sediment (Sams and Witt, 1995; 

Fontaine and Jacomino, 1997), atrazine (Laroche and 

others, 1996), and water temperature (Chen and others, 

1998). 

One of the major difficulties in developing TMDLs 

for waters contaminated by fecal coliform bacteria is 

that the potential sources of bacteria are numerous and 

the magnitude of their contributions commonly is 

unknown. Potential sources of fecal coliform bacteria 

include all warm-blooded animals (humans, pets, 

domesticated livestock, birds, and wildlife). The lack of 

information on the bacteria sources hinders the development of accurate load allocations and the identification of appropriate source-load reduction measures. 

Information about the major fecal coliform sources that 

impair surface-water quality would improve the ability 

to develop effective watershed models and may lead to 

more scientifically defensible TMDLs.

Bacterial source tracking (BST) is a recently developed tool for identifying the sources of fecal coliform 

bacteria found in surface waters (Hyer and Moyer, 

2003). This technology identifies specific differences 

among fecal coliform bacteria present in the feces of 

different animal species. Time, diet, environment, and 

many other factors may have contributed to produce 

these evolutionary distinctions; BST uses these species-specific distinctions to identify the animal source 

TMDL = ΣWLAs + + ΣLAs MOS (1)
Introduction 3

Figure 1. Land use, streams, stream-gaging station, and water-quality sampling stations in the Christians Creek watershed, Augusta County, Virginia.

79 ̊ 10'

79 ̊ 05'

79 ̊

78 ̊ 55'

38 ̊ 10'

38 ̊ 05'

0 2 4 MILES

0 2 4 KILOMETERS

64

64

81

AUGUSTA CO

Greenville

Christians

Creek

Christians

Creek

Christians Creek

Watershed

COASTAL PLAIN

PIEDMONT

BLUE RIDGE

APPALACHIAN

PLATEAU

VALLEY

AND

RIDGE

EXPLANATION

Urban

Residential

Forest

Pasture

Cropland

Hayland

LAND USE

1BCST012.32

1BCST021.76

01624800

01624615 01624615

01624615

Impaired reach—Classified as impaired with respect to fecal 

coliform bacteria by Virginia Department of Environmental Quality

Nonimpaired reach

01624660 01624660

01624700 01624700

01624800 01624800

01624900 01624900

01624620 01624620

Middle River

1BCST021.76 1BCST021.76

1BCST012.32 1BCST012.32

County boundaries from 1:100,000, TIGER files, 1994

Land use from Virginia Department of Conservation and Recreation, Carterra imagery 1996, 1997, and 1998,

 5-meter resolution panchromatic Indian Remote Sensing fused with 1997 Landsat, 30-meter resolution

Hydrography digitized from 1:24,000, 7.5-minute quadrangles

Universal Transverse Mercator 17 projection, NAD 27, central meridian 81

o

00'W

Base from USGS Digital Elevation Model 7.5-minute quadrangles, 30-meter resolution

Mills

Folly

Meadow

Run

Creek

Goose

Barterbrook Branch

U.S. Geological Survey stream gage (currently inactive) and 

station number and Virginia Department of Environmental 

Quality ambient water-quality sampling station and 

station number (Route 794)

U.S. Geological Survey continuum sampling site 

and station number

Virginia Department of Environmental 

Quality ambient water-quality 

sampling station and station 

number (Route 831)
4 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

of an unknown fecal coliform that has been isolated 

from a waterbody. The BST method chosen to identify 

the dominant sources of fecal coliform bacteria in the 

Christians Creek watershed is ribotyping (Hyer and 

Moyer, 2003), which involves an analysis of the specific DNA (deoxyribonucleic acid) sequence that codes 

for the production of ribosomal RNA (ribonucleic 

acid). Ribotyping identifies bacteria sources with a 

degree of precision that makes it well suited for use in 

the development of a fecal coliform TMDL.

In 1999, the U.S. Geological Survey (USGS), in 

cooperation with the Virginia Department of Conservation and Recreation (DCR), began a 3-year study to 

develop a fecal coliform bacteria TMDL for the Christians Creek watershed. The primary objective was to 

develop a HSPF model to simulate streamflow and the 

transport of fecal coliform bacteria within the watershed. Specific project objectives were to (1) produce 

calibrated models of watershed streamflow and fecal 

coliform bacteria transport, (2) incorporate BST information into the fecal coliform model calibration process, (3) estimate fecal coliform source-load reductions 

required to meet State water-quality standards, and (4) 

define the TMDL for fecal coliform bacteria for Christians Creek. These objectives ensure that the Christians 

Creek TMDL would (1) include a total allowable load 

as well as individual waste load and load allocations; 

(2) consider the effect of background contaminant contributions; (3) consider critical environmental conditions; (4) consider seasonal variations; and (5) include 

a margin of safety. The primary objectives for DCR 

were to ensure that the Christians Creek TMDL was 

designed to implement applicable water-quality standards; was developed with public participation; and can 

be met with reasonable assurance.

Purpose and Scope

This report describes the development and calibration of the HSPF model for streamflow and fecal 

coliform bacteria as part of determining the TMDL for 

the Christians Creek watershed. The model simulation 

period is from October 1991 to September 1997. This 

report also documents the methodology for incorporating BST data into the calibration of the fecal coliform 

model and demonstrates how these data enhance 

TMDL development. Current source-specific fecal 

coliform bacteria loads in Christians Creek are presented as well as the load reductions needed to meet the 

designated TMDL and associated State water-quality 

standard. 

Christians Creek Watershed 

Characteristics

Christians Creek, located in Augusta County, Va., 

originates northwest of Greenville, Va., and extends to 

the confluence with the Middle River. The entire 

31.5-mi-long reach is classified as impaired with 

respect to fecal coliform bacteria (Virginia Department 

of Environmental Quality, 1998). The basin has a 

drainage area of 107 mi2, and an estimated population 

of 12,000 (1990 Census). A recently deactivated USGS 

stream gage (station number 01624800; fig. 1), still 

operational for instantaneous stage determinations, is at 

Route 794 (Sangers Lane), and has a period of record 

from 1967 to 1997. DEQ has performed monthly sampling of fecal coliform bacteria at Route 794 and also at 

Route 831 (Old White Hill Road) (DEQ station number 

1BCST021.76; fig. 1) since 1991. Route 794 was the 

primary sampling site for this study.

The Christians Creek watershed lies in the Valley 

and Ridge physiographic province. Underlying the 

watershed are 10 geologic formations dominated by 

limestone and dolomite; information about each formation is summarized from the work of Rader (1967). The 

Martinsburg Formation (calcareous shale and sandstone) is the dominant formation within the basin. 

Other formations in the watershed include the Edinburg 

Formation (argillaceous limestone and shale), Lincolnshire Formation (cherty limestone), New Market Limestone (limestone with dolomite beds near the base), 

Beekmantown Formation (dolomite and limestone), 

Chepultepec Formation (limestone and dolomite), 

Conococheague Formation (limestone, dolomite, and 

sandstone), and Elbrook Formation (limestone and 

dolomite). Karst features, such as sinkholes and caves, 

are evident in portions of the watershed. Alluvial material (composed of sand and clay) is present in portions 

of the floodplain adjacent to Christians Creek. Small 

amounts of fault breccia (large blocks of dolomite and 

limestone with crush conglomerate) also occur in the 

basin. 

The soils of the Christians Creek watershed have 

been described thoroughly (Hockman and others, 

1979) and are best classified as a product of their parent 

material. Much of the soil in the watershed has formed 

from the residuum of interbedded limestone, dolomite, 
Description of Models 5

and calcareous shale. Three soil assemblages have been 

identified in this category. The Frederick-Christian-Rock outcrop assemblage consists of deep, 

well-drained, silt loam or fine sandy loam soils with 

limestone outcrop areas. The Frederick-Bookwood-Christian assemblage consists of deep to moderately deep, well-drained, silt loam or fine sandy loam 

soils; scattered sinkholes or rock outcrops also may 

occur. The Chilhowie-Edom assemblage consists of 

deep to moderately deep, well drained, silt loam or silty 

clay loam soils with occasional bedrock outcrops. Considerable soil has also formed from the residuum of 

shale and thin interbedded sandstone and limestone. 

These soils are part of the Berks-Weikert-Sequoia 

assemblage, which consists of shallow to deep, 

well-drained, silt loam or shaly silt loam soils. On 

flood plains and terraces, soils have formed in the alluvial or colluvial material. Although not extensive 

within the watershed, these soils are part of the Buchanan-Wheeling-Buckton assemblage, which consists of 

deep, somewhat poorly drained to well-drained soils. 

These are generally silt loam, loam, or fine sandy loam 

soils, although some areas are gravelly or cobbly.

Land use in the watershed is dominated by agricultural practices that are potential sources of fecal 

coliform bacteria in the watershed. Major components 

of the animal husbandry in this watershed include the 

production of beef cattle, dairy cattle, heifers, broilers, 

and turkeys. Other potential sources of fecal coliform 

bacteria within the watershed are human-related (failing septic systems, leaking or overflowing sewer lines, 

cross-pipes, and straight pipes), domestic animals 

(dogs and cats), waterfowl (geese, ducks, and sea 

gulls), and other wildlife (such as deer, raccoons, 

opossum, rabbits, muskrats, ground hogs, foxes, and 

beaver).

Modeling Approach

Streamflow and bacterial transport in the Christians 

Creek watershed were simulated by means of the 

Hydrological Simulation Program–FORTRAN (HSPF) 

version 11 (Bicknell and others, 1997). HSPF is a continuous simulation and lumped parameter watershed 

model that is used to simulate the transport and storage 

of water and associated water-quality constituents by 

linking surface, soil, and instream processes (Donigian 

and others, 1995). HSPF represents these mechanisms 

of transport and storage for three unique land segments 

or model elements: pervious land segments 

(PERLND), impervious land segments (IMPLND), and 

stream channels (RCHRES). Natural variability in 

these hydrologic transport mechanisms occurs because 

of spatial changes in watershed characteristics such as 

topography, land use, and soil properties; HSPF 

accounts for this variability by simulating runoff from 

smaller, more homogeneous portions of the watershed. 

Thus, for modeling purposes, the watershed is disaggregated into subwatersheds with similar land-use and 

topographical features. Each subwatershed is refined 

further into hydrologic response units (HRU) that represent areas within each land segment with similar 

watershed characteristics such as land use (Leavesley 

and others, 1983). HSPF links the movement of water 

and constituents from each HRU to generate an overall 

watershed response. 

Acknowledgments

We thank DCR for their cooperation, input, and 

assistance with this study. The Virginia Department of 

Environmental Quality (DEQ) is acknowledged for 

their technical support and assistance. The authors also 

would like to thank Tom Jobes (Aqua Terra Consultants) for his help with the development of the model. 

Michael J. Gearheart, Trisha L. Baldwin, and Robert R. 

Lotspeich provided outstanding technical support on 

this project. Allison A Shipp and Michael E. Lewis are 

acknowledged for providing technical reviews of this 

report. Lastly, we are indebted to the residents of and 

stakeholders in the Christians Creek watershed for their 

enthusiastic participation in this study. Without their 

cooperation, this study would not have been possible.

DESCRIPTION OF MODELS

The following sections describe the streamflow and 

fecal coliform bacteria models used in this study for 

development of the fecal coliform TMDL for the Christians Creek watershed.

Streamflow Model

The first step in generating a watershed-scale bacterial transport model is the simulation of streamflow. 

The mechanisms by which precipitation is routed from 

the land surface, through the various soil layers, and to 
6 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

the stream channel must be represented accurately in 

order to build a bacterial transport model. The following sections summarize the transport mechanisms associated with the PERLND, IMPLND, and RCHRES 

modules. A detailed description of the hydrologic portion of HSPF is in Bicknell and others (1997).

Pervious and Impervious Land Segments

The dominant feature of the pervious land segment 

(PERLND) module is the component for calculating 

the hydrologic water budget (PWATER). PWATER 

includes parameters that represent storage (vegetative, 

surface, shallow subsurface, and deep subsurface) and 

transport (evaporation, transpiration, inflow, and outflow) components of the hydrologic cycle (table 1). 

PWATER simulates the storage and transport of precipitation along three flow paths: overland flow, interflow 

(shallow subsurface flow), and base flow (active 

ground-water discharge). Storage and transport parameters are refined to simulate the hydrologic routing 

through each HRU, generating a simulated watershed 

response between and during precipitation events.

The simulated hydrologic cycle indicates how these 

storage and transport parameters govern the overall 

stream response within the watershed (fig. 2). Precipitation falling on the watershed is first intercepted 

(CEPSC) and stored by the vegetation. Most of the precipitation then is routed to the land surface because the 

surface area of the intercepting vegetation is small relative to the total volume of precipitation. The volume of 

water that remains on the vegetation is lost to the atmosphere through evaporation. 

Water that falls on the land surface is captured and 

stored temporarily (SURS) before being transported 

along three potential pathways: (1) Stored water begins 

to infiltrate the subsurface (INFILT). The infiltrating 

water is distributed among the upper-zone storage 

(UZSN), lower-zone storage (LZSN), active 

ground-water storage (AGWS), and inactive 

ground-water storage. (2) Water also is routed to interflow storage (IFWS) just beneath the land surface. This 

pathway is active when the deeper subsurface storages 

are full and the rate of precipitation approaches the rate 

of infiltration. Water held in interflow storage is 

released as interflow to the stream. The residence time 

for the stored water is governed by the interflow recession constant (IRC). (3) The stored water is routed 

directly to the stream through overland flow. This pathway is active when all subsurface storages are full 

and/or the precipitation rate exceeds the infiltration 

capacity of the soils. Overland flow is governed by the 

length (LSUR), slope (SLSUR), and roughness 

(NSUR) of the overland flow path. 

Water in upper-zone storage (UZSN) ultimately is 

lost to the atmosphere (through evapotranspiration) and 

the deeper subsurface (through delayed infiltration). 

Water that infiltrates to the deeper subsurface will be 

divided among lower-zone storage (LZSN), inactive 

ground-water storage, and active ground-water storage 

(AGWS). Water stored in the lower zone can be lost to 

the atmosphere through evapotranspiration (LZETP). 

Water that is transported to inactive ground-water storage is lost from the simulated basin and is never transported to the simulated stream reach. The portion of 

infiltrating water that is allocated to inactive 

ground-water storage is governed by DEEPFR. Water 

that enters AGWS either through delayed infiltration 

from UZSN or through direct infiltration from surface 

storage is either lost to the atmosphere through evapotranspiration (AGWETP) or transported to the simulated stream reach through base flow. The residence 

time for water in AGWS storage is controlled by 

AGWETP and the active ground-water recession constant (AGWRC). Finally, a portion of the base flow is 

removed through evapotranspiration (BASETP) prior 

to entering the stream channel. 

The component under the impervious land segment 

(IMPLND) module that calculates the hydrologic water 

budget is IWATER. Simulation of the flux and storage 

of precipitation falling on impervious land segments is 

less complex than for pervious land segments because 

there are no infiltration and subsurface processes. Similar to PWATER, IWATER contains parameters that represent the storage (rooftop and surface) and transport 

(evaporation and runoff) components of the hydrologic 

cycle. These parameters are unique to each impervious 

HRU so that precipitation runoff may be simulated 

accurately.

The routing of precipitation in IWATER is similar 

to the surface runoff routing in the PERLND module. 

Precipitation that falls on the watershed is first intercepted by impervious surfaces (building tops, urban 

vegetation, and asphalt wetting) that extend above the 

land surface (impervious retention storage – RETS). 

Most of the precipitation is passed to the land surface 

because the storage capacity of the intercepting surfaces is relatively small compared to the volume of 

incoming precipitation. The water that remains in 
Description of Models 7

Parameter Definition Unit

AGWETP

Active ground-water ET. Represents the fraction of stored ground water that is subject to direct evaporation 

and transpiration by plants whose roots extend below the active ground-water table. Accounts for the 

fraction of available PET that can be met from active ground-water storage.

none

AGWRC Active ground-water recession rate. Represents the ratio of current ground-water discharge to that from 24 

hours earlier. 1 per day

BASETP Base flow ET. ET by riparian vegetation from active ground water entering the stream channel. Represents 

the fraction of PET that is fulfilled only as ground-water discharge is present. none

CEPSC Interception storage capacity of vegetation. inches

DEEPFR Fraction of infiltrating water that is lost to deep aquifers. Represents the fraction of ground water that 

becomes inactive ground water and does not discharge to the modeled stream channel. none

INFEXP Infiltration equation exponent. none

INFILD Ratio of maximum and mean soil-infiltration capacities. none

INFILT Index to mean soil infiltration rate. INFILT governs the overall division of available moisture between 

surface and subsurface flow paths. High values of INFILT divert more water to the subsurface flow paths. inches per hour

INTFW Interflow coefficient that governs the amount of water that enters the ground from surface detention storage. none

IRC Interflow retention coefficient. Rate at which interflow is discharged from the upper-zone storage. 1 per day

KVARY Ground-water recession flow parameter. Describes nonlinear ground-water recession rate. 1 per inch

LSUR Length of the overland flow plane. feet

LZETP Lower-zone evapotranspiration ET. Percentage of moisture in lower-zone storage that is subject to ET. none

LZSN Lower-zone nominal storage. Defines the storage capacity of the lower-unsaturated zone. inches

NSUR Surface roughness (Manning’s n) of the overland flow plane. none

RETS Retention-storage capacity of impervious surfaces. inches

SLSUR Average slope of the overland flow path. none

UZSN Upper-zone normal storage. Defines the storage capacity of the upper-unsaturated zone. inches

Table 1. Hydrologic parameters used in the simulation of streamflow in Christians Creek, Augusta County, Virginia

[ET, evapotranspiration; PET, potential evapotranspiration]
8 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek Figure 2. Rainfall-routing processes, associated with pervious land segments, represented by the Hydrological Simulation Program-FORTRAN for the simulation of str

Augusta County, Virginia. (See table 1 for definition of hydrologic parameters.) Actual evapotranspiration Interception storage (CEPSC) Precipitation Surface storage (SURS) Overla

Flow Interflo

(IFWS

Baseflo

Active ground- water storage (AGWS) Upper-zone storage (UZSN) Lower-zone storage (LZSN) Inactive ground-water storage LZETP AGWETP BASETP DEEPFR INFILT INTFW 
Description of Models 9

RETS is lost to the atmosphere through evaporation. 

Water that is routed to the land surface is captured and 

momentarily stored in surface-detention storage 

(SURS). This stored water then is transported to the 

simulated stream reach as surface runoff. Overland 

flow is governed by the length (LSUR), slope 

(SLSUR), and roughness (NSUR) of the overland flow 

path.

The urban and pasture (primarily loafing lots or 

heavily grazed areas adjacent to the stream channel) 

land segments represented in the model contain both 

pervious and impervious features. The main objective 

associated with the calibration of the impervious area 

represented in the model is to determine the fraction of 

impervious area within the urban and pasture land 

types. This impervious fraction can be broken into two 

categories, “hydrologically effective” or “hydrologically ineffective” (Zarriello, 1999). Hydrologically 

effective areas drain directly to stream channels and are 

represented by the IMPLND module. Hydrologically 

ineffective areas drain onto pervious land types, such as 

grassland or forest, and are better represented by the 

PERLND module. For example, rain that falls on a 

rooftop, and then is transported to a grassy lawn, would 

be considered hydrologically ineffective. Initial estimates were that urban land use contains between 18- 

and 50-percent and pasture contains 1-percent effective 

impervious (Northern Virginia Planning District Commission, 1980). This initial estimate was refined during 

model calibration of stormflow timing and magnitude. 

For instance, overestimating the impervious area will 

cause a greater volume of water to be routed directly to 

the stream through surface runoff (in contrast to the 

delayed response associated with pervious land segments) during a storm event; thus, the simulated storm 

response will be earlier and of greater magnitude than 

the observed storm response.

Stream Channels

The RCHRES module in HSPF is used to simulate 

the routing of water and associated water-quality constituents through a stream channel network that consists of a series of connected stream reaches. For this 

study, only one reach was simulated within each subwatershed. Water is supplied to a reach from PERLND 

(overland flow, interflow, and base flow), IMPLND 

(overland flow), point sources (sewage-treatment plants 

or STPs), and upstream segments. These inflows are 

assumed to enter the reach at a single upstream point 

and the water is transported downstream in a unidirectional manner. Actual channel properties (width, depth, 

cross-sectional area, slope, and roughness) are measured in order to develop the relation among stage 

(water depth), surface area, volume, and discharge 

(streamflow). Stage, surface area, volume, and discharge information are specified in a function table 

(FTABLE) and are used to govern stream discharge for 

a given inflow. Water transported down a reach is 

assumed to follow the kinematic wave function (Martin 

and McCutcheon, 1999).

Subwatershed Delineation

A critical step in the simulation of streamflow and 

bacterial transport within a watershed is characterization of the watershed morphology. The morphology 

consists of watershed characteristics such as topography (slope, aspect, and elevation), soil types, and land 

use. Within the watershed boundary, each of these 

characteristics typically is highly variable. For example, the southern portion of the Christians Creek watershed has a higher elevation and steeper slopes than the 

northern portion. To account for these topographical 

variations within HSPF, the watershed is broken into 

smaller, more homogeneous subwatersheds. There also 

may be variations in land use within each subwatershed; land uses with similar hydrologic responses are 

grouped into a single HRU. For example, high-intensity 

residential and high-intensity commercial are assumed 

to have similar hydrologic responses and were grouped 

to form an urban HRU. The following section documents the methods used to delineate subwatersheds, 

aggregate land uses, and establish the stream channel 

network for the Christians Creek watershed.

Six subwatersheds were identified within the Christians Creek watershed on the basis of variations in 

land-surface elevation and slope (fig. 3). The area of 

each subwatershed was determined by delineating 

along the natural drainage boundary. These drainage 

boundaries were identified using the USGS Digital Elevation Model (DEM) from the Greenville, Stuarts 

Draft, Staunton, Waynesboro West, and Fort Defiance 

7.5-minute quadrangles. The DEM coverage has a cell 

size of 30 meters.
10 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 3. Hydrologic subwatersheds, land use, and reaches as represented in the streamflow and fecal coliform models for Christians Creek, 

Augusta County, Virginia.

1

3

2

4

6

5

Reach 1

Reach 3

Reach 4

Reach 6

Reach 5

Reach 2

79 ̊ 10'

79 ̊ 05'

79 ̊

78 ̊ 55'

38 ̊ 10'

38 ̊ 05'

Urban

Residential

Pasture

Cropland

Forest

Hayland

Subwatershed boundary

EXPLANATION

Subwatershed

Land use, in percent

1 10 3 2 8 45 31

2

3

4

5

6

1

5

12

5

2

2

3

4

3

7

7

7

9

8

4

8

17

2

6

9

46

45

43

38

64

36

22

30

41

15

Urban Residential Pasture Cropland Hayland Forest

LAND USE

Subwatershed boundaries from USGS Digital Elevation 

Model 7.5-minute quadrangles, 30-meter resolution

Universal Transverse Mercator 17 projection, NAD 27, 

 central meridian 81

o

00'W

Land use from Virginia Department of Conservation and 

 Recreation, Carterra imagery 1996, 1997, and 1998, 5-meter 

 resolution panchromatic Indian Remote Sensing fused with 

 1997 Landsat, 30-meter resolution

Folly Mills

Meadow Run

Christians Creek

Christians Creek

Totals may not add to 100 percent because of rounding or other categories 

not listed.
Description of Models 11

Land Use

DCR provided land-use data in the form of a Geographic Information System (GIS) coverage for the 

Christians Creek Watershed. The GIS coverage provides land-use/land-cover information. The land-use 

coverage identifies 19 possible land-use types, which 

were combined into 6 general types based on hydrological routing similarities: urban, residential, cropland, 

hayland, pasture, and forest (table 2). Each of these 

general land-use types represents the HRUs for each 

subwatershed.

Channel Network

A single stream channel (reach) is represented in 

each of the six subwatersheds simulated in HSPF. The 

routing of runoff from one reach to a connected downstream reach is governed by the stage, cross-sectional 

area, storage, and discharge information contained in 

the FTABLE. An FTABLE was created for each stream 

reach by first collecting data on stream channel morphology. Stream-channel surveys (transects) were performed by USGS at both the upstream and downstream 

ends of each reach based on techniques described in 

Davidian (1984). At each transect, coordinate data 

(depth at a given position along the transect) were 

recorded. Estimates of channel roughness (Manning’s 

n) were made on the basis of channel median grain 

size, irregularity (width to depth ratios), alignment 

(abrupt changes in channel width), obstructions 

(debris), vegetation (instream and bank vegetation), 

and meandering (Barnes, 1967; Arcement and 

Schneider, 1989; Coon, 1998). Channel slope was estimated by dividing the change in elevation from the 

upstream and downstream transects by the reach 

length. Transect coordinate data were loaded into the 

Channel Geometry Analysis Program (CGAP) to identify the area, width, wetted perimeter, and hydraulic 

radius of cross sections at successive water-surface elevations (Regan and Schaffranek, 1985). These data 

from CGAP along with channel roughness and channel 

slope were loaded into the program Generate FTABLE 

(GENFTBL, provided with CGAP). GENFTBL creates 

an FTABLE for each stream reach as required by 

HSPF. The stage and discharge information (rating 

table) from the stream gage at Route 794 (USGS station 01624800) was incorporated into the FTABLE for 

reach segment 4. 

Six subwatersheds (1–6) represent the morphological features of the Christians Creek watershed (fig. 3). 

Within each subwatershed there are 8 HRUs, including 

6 pervious (urban, residential, cropland, hayland, pasture, and forest) and 2 impervious areas (urban and pasture). Each subwatershed has a single reach that is 

governed by an FTABLE. Reaches 1, 3, 4, and 6 represent Christians Creek. Reaches 2 and 5 represent the 

Folly Mills and Meadow Run tributaries, respectively.

Meteorological and Streamflow Data

Rainfall data were obtained from the National Climatic Data Center. These data are collected hourly at 

the Staunton Sewage Treatment Plant (SSTP) rain gage 

that is approximately 6 mi west of the USGS stream 

gage on Christians Creek. This rain gage has been 

operational since August 1, 1948. Average annual rainfall measured between 1991 and 1997 was 40.2 in., 

with a maximum annual rainfall amount of 52.0 in. in 

1996 and a minimum annual rainfall amount of 35.1 in. 

in 1991. The 30-year average rainfall at the SSTP gage 

is 41.1 in. (Climatological Data Annual Summary for 

Virginia, 1999). Missing data in the hourly rainfall 

record were supplemented with data from the Sherando, Spottswood, Middlebrook, and Stoney Creek

Hydrologic Response Unit

Area

Acres

Percent

of 

watershed

Urban1 3,208 4.7

Residential2 2,357 3.4

Cropland 5,379 8.3

Hayland3 32,523 47.3

Pasture4 5,066 7.4

Forested5 19,896 28.9

Table 2. Aggregated hydrologic response units used to develop the 

watershed model for Christians Creek, Augusta County, Virginia

[Land-use data from Virginia Department of Conservation and 

Recreation]

1 Includes urban impervious, medium-density residential, high-density residential, 

commercial and services, industrial, transportation, mixed urban or built up, open 

urban land, and barren.

2 Includes residential impervious, low-density residential, mobile home park, 

wooded residential, poultry operations, and farmstead.

3 Includes improved pasture and permanent hay.

4 Includes pasture impervious, unimproved pasture and grazed woodland.

5 Includes harvested forest land.
12 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

rain gages in and around the Christians Creek watershed (table 3). These gages are part of the National 

Weather Service’s Automated Flood Warning System 

for Augusta County, Va. Data gaps were filled primarily with data from the Middlebrook rain gage, which is 

nearest to the SSTP. For the 1991–94 time period, data 

from Spottswood were used when rainfall data from 

both SSTP and Middlebrook were missing. For 1995–

97, average rainfall data from Spottswood, Sherando, 

and Stoney Creek (activated in 1995) rain gages generally were used when data from both SSTP and Middlebrook were missing. 

Daily minimum temperature, daily maximum temperature, percent cloud cover, dew-point temperature, 

and wind-speed data were collected for the purpose of 

calculating potential evapotranspiration (PET) for the 

Christians Creek watershed (table 3). Daily minimum 

and maximum temperature data were collected from 

SSTP. Missing temperature data were supplemented 

with temperature data collected at the Dale Enterprise 

weather station. Dew-point temperature and percent 

cloud-cover data were collected from the Lynchburg 

Regional Airport. Collection of percent cloud-cover 

data at the airport ended June 1996, so percent 

cloud-cover data for the period July 1996–December 

1997 were obtained from Quantico Marine Corp Air 

Station. Wind-speed data required for calculating PET 

were collected from Elkins–Randolph Airport, Elkins, 

W.Va. Daily PET values were calculated using the 

Hamon equation (Hamon, 1961), which is part of the 

USEPA software package WDMUtil (U.S. Environmental Protection Agency, 2001). The average of the 

annual PET values was compared and calibrated to 

average annual evaporation from a Class A Pan (Kohler 

and others, 1959). A Class A Pan coefficient of 76 percent was applied, in the model, to the calculated PET 

values because values of evaporation from a Class A 

Pan generally are higher than actual evapotranspiration 

Type of data Location of data collection Latitude

Longitude Source Recordingfrequency Period of record

Rainfall (in.) Staunton Sewage Treatment Plant 38°10′52′′

79°05′25′′ NCDC hourly

daily

1/1/73–12/31/99

8/1/48–12/31/99

Rainfall (in.) Sherando 37°59′45′′

78°59′30′′ NWS hourly 4/1/91–12/31/99

Rainfall (in.) Spottswood 37°57′42′′

79°12′44′′ NWS hourly 4/1/91–12/31/99

Rainfall (in.) Middlebrook 38°02′54′′

79°13′45′′ NWS hourly 4/1/91– 12/31/99

Rainfall (in.) Stoney Creek 37°59′24′′

79°07′22′′ NWS hourly 10/1/93–12/31/99

Minimum air temperature (°F) Staunton Sewage Treatment Plant 38°10′52′′

79°05′25′′ NCDC daily 8/1/48–12/31/99

Maximum air temperature (°F) Staunton Sewage Treatment Plant 38°10′52′′

79°05′25′′ NCDC daily 8/1/48 – 12/31/99

Minimum air temperature (°F) Dale Enterprise Weather Station 38°27′19′′

78°56′07′′ NCDC daily 8/1/48– 2/31/99

Maximum air temperature (°F) Dale Enterprise Weather Station 38°27′19′′

78°56′07′′ NCDC daily 8/1/48– 2/31/99

Cloud cover (percent) Lynchburg Regional Airport 37°20′15′′

79°12′24′′ NCDC hourly 8/1/48–6/30/96

Cloud cover (percent) Quantico Marine Corp Air Station 

(MCAS)

38°30′00′′

77°18′00′′ NCDC hourly 4/1/45–5/31/98

Dew point temperature (°F) Lynchburg Regional Airport 37°20′15′′

79°12′24′′ NCDC hourly 1/1/48–6/30/96

Wind speed (360° and knots) Elkins-Randolph Airport, 

Elkins, W.Va.

38°53′07′′

79°51′10′′ NCDC hourly 1/1/64–12/31/99

Streamflow (ft3/sec) Christians Creek at Fishersville 

(Route 794)

38°07′42′′

78°59′41′′ USGS hourly

daily

10/1/90–9/30/97

10/1/67–9/30/97

Table 3. Meteorological and streamflow data used in the streamflow model for Christians Creek, Augusta County, Virginia

[in., inches; °F, degrees Fahrenheit; NCDC, National Climatic Data Center; NWS, National Weather Service; ft3/sec, cubic feet per second]
Description of Models 13

(Kohler and others, 1959). Daily values of PET were 

disaggregated to hourly values using WDMUtil. 

Streamflow data for Christians Creek, for the 

period October 1, 1990–September 30, 1999, were collected by the USGS every 15 minutes at the Christians 

Creek at Fishersville stream gage (USGS station number 01624800) (fig. 1; table 3). Hourly streamflow values were used for the streamflow simulation. Average 

annual streamflow for the period October 1, 1990–

September 30, 1997 (water years 1991-97), was 

75.4 ft3/s with a maximum average annual streamflow 

of 113.2 ft3/s during water year 1996 and a minimum 

average annual streamflow of 41.5 ft3/s during water 

year 1995.

All model input (meteorological, streamflow, and 

water-quality) time-series datasets were loaded into the 

Watershed Data Management format (WDM) using the 

computer program WDMUtil. WDMUtil provides the 

functionality of summarizing, listing, and graphing 

datasets in the WDM format. Input datasets can be 

retrieved in HSPF from and output datasets (simulated 

streamflow and fecal coliform bacteria) written to the 

WDM file.

Calibration Approach 

The objective of the streamflow modeling effort 

was to simulate the observed water budget and hydrologic response in the Christians Creek watershed. The 

6-year simulation period extended from April 1, 1991, 

to September 30, 1997, and included a 4-year calibration and a 2-year verification period. Key steps in the 

development of the calibrated model of streamflow for 

the Christians Creek watershed included collection of 

historical meteorological and streamflow data, determination of the effective impervious area, calibration of 

hydraulic parameters, and evaluation of the model 

results.

A suite of physically based hydraulic parameters 

governs the streamflow simulation in HSPF. These 

hydraulic parameters are categorized as fixed and 

adjusted parameters. Fixed hydraulic parameters can be 

measured or are well documented in the literature and 

can be used with a high degree of confidence, such as 

the length, slope, width, depth, and roughness of a 

stream channel. Fixed hydraulic parameters are held 

constant in HSPF during model calibration. Adjusted 

hydraulic parameters are highly variable in the environment or are immeasurable, such as the infiltration rate 

and the extent of the lower zone storage area. These 

adjusted hydraulic parameters represent the hydrologic 

transport and storage components in HSPF; each 

HRU Imperviousness

(percent) AGWETP AGWRC(1 per day) BASETP DEEPFR

INFILT

(inches 

per hour)

INTFW

IRC

(1 per 

day)

KVARY

(1 per 

inch)

LZETP LZSN

(inches)

UZSN

(inches)

U – 0.00 0.985 0.00 0.50 0.02 0.40 0.60 0.00 0.20 7.00 0.50

R – .00 .985 .00 .50 .02 .40 .60 .00 .20 7.00 .50

P – .00 .985 .00 .50 .02 1.00 .60 .00 .20 10.00 .80

H – .00 .985 .00 .50 .03 .80 .60 .00 .20 8.50 .60

C – .00 .985 .00 .50 .03 .80 .60 .00 .20 8.50 .60

F – .00 .985 .00 .50 .09 1.00 .65 .00 .20 9.50 .70

UI 38 – – – – – – –– – – –

PI 1 – – – – – – –– – – –

Table 4. Initial streamflow model parameters and percent imperviousness in six subwatersheds represented in the streamflow model for 

Christians Creek, Augusta County, Virginia

[HRU, Hydrologic Response Unit; see table 1 for definitions of parameters; U, Urban; R, Residential; P, Pasture; H, Hayland; C, Cropland; 

F, Forest; UI, Urban impervious; PI, Pastureland impervious; –, not applicable]
14 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

parameter is adjusted/calibrated until simulated streamflow closely represents observed streamflow. Eleven 

parameters were adjusted to obtain a calibrated model 

of streamflow for the Christians Creek watershed 

(table 4). 

Results from the streamflow model were evaluated 

for both the calibration and verification periods. The 

period from October 1, 1993, to September 30, 1997 

(water years 1994-97), was selected for calibration 

because of the observed variability in average annual 

streamflow. The largest (113.2 ft3/s) and smallest 

(41.5 ft3/s) amount of average annual streamflow 

occurred during this period. Calibration over this 

period ensures that the streamflow model will account 

for this increased hydrologic variability. Results from 

the model calibration were evaluated on the basis of 

comparisons between simulated and observed streamflow with respect to water budget (total runoff volume), 

high-flow and low-flow distribution (comparison of 

low-flow and high-flow periods), stormflow (comparison of stormflow volume, peak, and recession), and 

season (seasonal runoff volume). These comparisons 

were performed using Expert System for the Calibration of the Hydrological Simulation Program–

FORTRAN (HSPEXP) (Lumb and others, 1994). 

Seven calibration criteria, expressed as a percent difference, were established in HSPEXP to aid in the evaluation of simulated and observed runoff:

Finally, graphs were used to compare simulated and 

observed streamflow with respect to daily and hourly 

streamflow, flow-duration curves, and residuals. 

The calibrated streamflow model was verified by 

simulating streamflow during the period from April 4, 

1991, to September 30, 1993, using the adjusted hydrologic parameters obtained during model calibration. 

Model verification was performed once and was not 

used in the iterative calibration process. Results from 

model verification were evaluated following the same 

protocol as described for evaluation of the calibrated 

model results.

Fecal Coliform Model

After the streamflow model is calibrated, the next 

step in generating a watershed-scale bacterial transport 

model is to simulate the transport of bacteria from the 

land surface, to the stream channel, and through the 

stream network. In HSPF, this is accomplished by linking the fecal coliform simulation to the streamflow 

simulation. The following sections summarize the simulation of fecal coliform bacteria in the PERLND, 

IMPLND, and RCHRES modules. Additional information regarding the simulation of fecal coliform bacteria 

using HSPF can be found in Bicknell and others 

(1997).

Pervious and Impervious Land Segments

The PQUAL module is used to simulate the transport of fecal coliform bacteria from pervious land segments. Similar to the PWATER module, PQUAL 

simulates storages and fluxes of bacteria along three 

flow paths: overland flow, interflow, and base flow. 

There are 11 model parameters used to simulate fecal 

coliform bacteria (table 5). Collectively, these parameters govern the total fecal coliform loading from each 

HRU to a given stream reach.

The processes by which the transport of fecal 

coliform bacteria is simulated can be split into two categories: surface and subsurface (interflow and base 

flow) (fig. 4). The surface processes begin with deposition of feces containing fecal coliform bacteria onto the 

land surface by numerous sources in the watershed 

(people, pets, livestock, and wildlife). Fecal coliform 

deposition is established by the accumulation rate 

(ACCUM). These bacteria are stored on the surface 

(SQO) and are allowed to accumulate until the storage 

limit (SQOLIM) is reached. Bacteria are removed from 

surface storage by either die-off or washoff. The 

removal rate (REMQOP) of the stored bacteria through 

die-off is defined by the ratio of the accumulation rate 

(ACCUM) and the storage limit (SQOLIM). Bacteria 

remaining in storage are removed through washoff by 

overland flow. The amount of bacteria removed from 

surface storage (SOQUAL) during a given storm event 

is controlled by both the amount of overland flow generated (SURO) and the susceptibility of the bacteria to 

washoff by overland flow (WSFAC). SURO is identified for each HRU during the hydrologic calibration. 

WSFAC is a function of the rate of runoff that results in 

90 percent washoff of stored fecal coliform bacteria in 

a given hour (WSQOP). Below are the governing equaCalibration criterion Percent difference

Total annual runoff 10

Highest 10-percent flows 10

Lowest 50-percent flows 15

Winter runoff 15

Spring runoff 15

Summer runoff 15

Fall runoff 15
Description of Models 15

tions for the release of fecal coliforms from storage on 

the land surface to the receiving stream channel:

where SOQUAL is the amount of fecal coliform 

bacteria washed off the land surface 

(number of colonies/acre/interval), 

SQO is surface storage of fecal coliform bacteria 

(number of colonies/acre),

SURO is the total amount of surface runoff 

(in/interval),

WSFAC is susceptibility of fecal coliform bacteria 

to washoff (per inch), and

WSQOP is the rate of surface runoff that results in 

90 percent washoff of fecal coliform bacteria in 

1 hour (in/hr).

In the simulation of the transport of fecal coliform 

bacteria through the subsurface, PQUAL allows for the 

storage and release of bacteria from interflow (IQO) 

and active ground-water (AQO) storages. The subsurface transport processes represented are simplified considerably compared to those used to represent surface 

transport. A concentration of fecal coliform bacteria is 

assigned to both IQO and AQO and is held constant 

during the simulation. These bacteria are transported to 

the stream channel with interflow and base flow. The 

total volume of interflow and base flow that discharges 

to the stream channel is established during the streamflow model calibration.

IQUAL is used to simulate the transport of fecal 

coliform bacteria from impervious land segments. The 

IQUAL module only simulates surface washoff of fecal 

coliform bacteria because impervious land segments do 

not have a subsurface component. The transport processes and governing equations (2, 3) used in IQUAL 

are identical to those used in the surface washoff component of PQUAL. Generally, bacteria stored on an 

impervious land segment are more susceptible to 

washoff than those stored on pervious land segments; 

thus, WSFAC for impervious land segments is greater 

than WSFAC for pervious land segments.

SOQUAL SQO∗ 1 e( ) –SURO∗WSFAC = ( ) –

WSFAC 2.30

WSQOP = ---------------------

Parameter Definition Unit

ACCUM Accumulation rate of fecal coliform bacteria on the land surface. number of colonies per acre per day

AOQUAL Transport of fecal coliform bacteria through base flow (ground-water discharge). number of colonies per day

AQO Storage of fecal coliform bacteria in active ground water. number of colonies per ft3

IOQUAL Transport of fecal coliform bacteria through interflow. number of colonies per day

IQO Storage of fecal coliform bacteria in interflow. number of colonies per feet

REMQOP Removal rate (die-off) for fecal coliform bacteria stored on the land surface. 

Removal rate is based on the ratio of ACCUM/SQOLIM. 1 per day

SOQUAL Transport of fecal coliform bacteria through overland flow. number of colonies per acre per day

SQO Storage of fecal coliform bacteria on the land surface. number of colonies per acre

SQOLIM Asymptotic limit for the storage of fecal coliform bacteria on the land surface if no washoff occurs. number of colonies per acre

WSFAC Susceptibility of fecal coliform bacteria to washoff. Susceptibility is defined by 2.30/WSQOP. per inch

WSQOP Rate of surface runoff that results in 90-percent washoff of the stored fecal coliform bacteria in one 

hour. inches per hour

Table 5. Parameters used in the simulation of the transport and storage of fecal coliform bacteria in Christians Creek, Augusta County, Virginia

[ft3, cubic feet]

(3)

(2)
16 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Stream Channels

GQUAL is the component in the RCHRES module 

used to simulate the transport of fecal coliform bacteria 

through the channel network. Bacteria are routed to the 

simulated stream channels from the various PERLND 

and IMPLND HRUs, point source inputs (sewage-treatment plants and instream animals), and 

upstream stream segments. These bacteria enter the 

simulated stream segment at a single upstream point 

and are either transported to the next downstream 

stream segment or are removed through die-off. The 

portion of bacteria removed from the simulated stream 

channel through die-off is based on a first-order decay 

rate of 1.1 day–1 (U.S. Environmental Protection 

Agency, 1985) and is determined by the following 

equations:

where DDQALT is the number of bacteria removed 

through die-off (number of colonies/interval),

DQAL is the concentration of bacteria for the time 

interval (number of colonies/100 mL),

KGEN is the generalized first-order decay rate corrected for temperature (number of 

colonies/interval), and

DDQALT DQAL∗ 1 e( ) –KGEN = ( ) – ∗VOL

KGEN KGEND = ( )( ) THGEN ( ) TW20

Figure 4. Routing processes represented by the Hydrological Simulation Program-FORTRAN for the simulation of fecal coliform bacteria 

transport in Christians Creek, Augusta County, Virginia. (See table 5 for definition of fecal coliform bacteria transport and storage parameters.)

Fecal coliform

accumulation

Surface storage

(SQO) Die-off

Interflow storage

(IQO)

Active groundwater storage

(AQO)

To stream

(RCHRES)

Total fecal coliform

transport to stream

channel

Fecal coliform transport

with base flow

AOQUAL

IOQUAL

SOQUAL

Fecal coliform transport

with interflow

Fecal coliform transport

with overland flow

Removal rate

(REMQOP)

Rate of surface washoff

(WSQOP)

Accumulation rate (ACCUM)

Storage limit (SQOLIM)

(4)

(5)
Description of Models 17

VOL is the volume of water in the reach (ft3).

KGEND is the base first-order decay rate 

(number of colonies/interval),

THGEN is the temperature correction parameter, 

dimensionless, and

TW20 is the temperature of the water ({C) for interval minus 20.

Limitations of the Fecal Coliform Model

The most critical limitation associated with the 

fecal coliform model is that fecal coliform bacteria are 

simulated as a dissolved constituent. Fecal coliform 

bacteria, however, are particulate constituents and are 

deposited and resuspended once delivered to the active 

stream channel. The transport mechanisms associated 

with deposition and resuspension are not simulated 

explicitly. However, mechanisms that mimic deposition 

and resuspension are simulated through interflow and 

base-flow pathways (see Fecal Coliform Bacteria in the 

Subsurface).

Point and Nonpoint Source Representation

A key step in simulating the transport of fecal 

coliform bacteria is to determine the total amount of 

bacteria deposited on the land surface (representing 

nonpoint sources) or deposited directly in the stream 

channel (representing point sources). For this study, the 

total amount of bacteria deposited by each of the dominant sources of fecal coliform bacteria was estimated. 

This information was the primary input dataset for the 

fecal coliform model; the fecal coliform deposition 

information is analogous to rainfall data used in the 

runoff model. The following sections explain how the 

fecal coliform deposition rate was established for the 

various point sources (for example, STPs) and nonpoint 

sources (people, pets, livestock, and wildlife) within 

the Christians Creek watershed.

There are six permitted point source dischargers of 

fecal coliform bacteria in the Christians Creek watershed (table 6). Three are STPs, each of which is permitted by DEQ to release treated wastewater to Christians 

Creek or associated tributaries. According to the permit, this wastewater may not contain fecal coliform 

bacteria concentrations that exceed 200 col/100 mL. 

The maximum permitted discharge rate and fecal 

coliform concentration were used to represent each of 

these STPs in the Christians Creek watershed model. 

These STPs were represented as a continuous and 

direct supply of water and bacteria to the respective 

simulated stream channel. The combined total annual 

load from the STPs is 2.33 x 1012col/year. There also 

are 12 private permitted dischargers, including 9 residences and 3 small businesses, in the watershed 

(table 7). Combined, these private dischargers contribute 3.32 x 1010col/year to Christians Creek.

Most of the fecal coliform bacteria in Christians 

Creek are derived from and represented as nonpoint 

sources. These bacteria are deposited on the land surface by many different sources (people, pets, livestock, 

and wildlife) and subsequently are transported to the 

stream network with rainfall runoff. Two critical pieces 

of information must be obtained to simulate the transport of fecal coliform bacteria derived from nonpoint 

sources using HSPF. First, the dominant sources of 

fecal coliform bacteria in the watershed must be identified. A survey was conducted of fecal coliform sources 

in the Christians Creek watershed, and 13 sources were 

identified as potentially dominant and represented in 

the model. These 13 sources are beavers, cats, cattle, 

deer, dogs, ducks, geese, horses, humans, muskrats, 

poultry, raccoons, and sheep. Second, the total daily 

amount of fecal coliform bacteria deposited on the land 

surface or directly in streams (straight pipes, cattle in 

streams, and beaver) by each of the identified sources 

must be determined for both pervious and impervious 

land segments. 

General Quantification of Fecal Coliform Bacteria

The amount of fecal coliform bacteria deposited on 

the land surface daily is represented by ACCUM in 

HSPF. Every source represented in the model has a 

specific fecal coliform accumulation rate. The following equation is used to calculate ACCUM for each 

fecal coliform source:

where ACCUM is the fecal coliform bacteria accumulation rate (number of colonies/acre/day),

Fprod is the feces produced per day (g/day),

ACCUM ( ) Fprod∗FCden POPN

HAB = ----------------------------------------------------------- (6)
18 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Permit 

number Owner Facility

Discharge rate 

(million gallons 

per day)

Fecal coliform

limit (number of 

colonies per 100 

milliliters)

Annual fecal coliform 

load (number of 

colonies per year)

VA0025291 Augusta County Service 

Authority

Fishersville Sewage 

Treatment Plant 0.7 200 1.94 x 1012

VA0022306 Augusta County Service 

Authority

Staunton Plaza Sewage 

Treatment Plant .09 200 2.49 x 1011

VA0022292 Augusta County Service 

Authority Brookwood Interchange .03 200 8.32 x 1010

VA0020427 Augusta County School 

Board Rivershead High School .014 200 3.88 x 1010

VA0089061 Woodlawn Village L.L. 

Corp.

Woodlawn Village Mobile 

Home Park .007 200 1.94 x 1010

VA0086738 Southern States Coop, Inc. Southern States Coop 0 200 0

Total 2.33 x 1012

Table 6. Permitted point-source dischargers of fecal coliform bacteria in Christians Creek, Augusta County, Virginia, 1992-97

Permit

number

Discharge rate 

(gallons per day)

Fecal coliform

limit (number of colonies 

per 100 milliliters)

Annual fecal coliform 

load (number of 

colonies per year)

VAG401655 1,000 200 2.76 x 109

VAG401967 1,000 200 2.76 x 109

VAG401968 1,000 200 2.76 x 109

VAG401082 1,000 200 2.76 x 109

VAG401138 1,000 200 2.76 x 109

VAG401159 1,000 200 2.76 x 109

VAG401195 1,000 200 2.76 x 109

VAG401203 1,000 200 2.76 x 109

VAG401443 1,000 200 2.76 x 109

VAG401449 1,000 200 2.76 x 109

VAG401896 1,000 200 2.76 x 109

VAG401969 1,000 200 2.76 x 109

Total 3.32 x 1010

Table 7. Private permitted point-source dischargers of fecal coliform bacteria in Christians 

Creek, Augusta County, Virginia 
Description of Models 19

FCden is the number of fecal coliform bacteria per 

gram of feces produced (number/g),

POPN is the population size, dimensionless, and

HAB is the habitat area (acres).

The calculation of ACCUM is based on values of 

Fprod, FCden, HAB, and POPN that are source specific, and selection of these values is challenging. 

Information on Fprod and HAB generally is well documented for individual species. Therefore, single values 

of Fprod and HAB are used and held constant throughout the entire modeling effort. Values of FCden and 

POPN, however, generally are more variable and 

poorly documented compared to values of Fprod and 

HAB. For example, dog, cat, and human feces have 

measured FCden ranges of 4.1 x 106 col/g to 4.3 x 

109 col/g; 8.9 x 104 col/g to 2.6 x 109 col/g; and 1.3 x 

105 col/g to 9.0 x 109 col/g, respectively (Mara and 

Oragui, 1981). This wide range in measured values of 

FCden is typical of most of the sources represented in 

the model; therefore, considerable uncertainty is associated with choosing a single value of FCden to represent a given species. Additionally, exact population 

numbers commonly are unknown for the human, pet, 

and wildlife populations, and the proportion of the population that contributes to the instream fecal coliform 

load also is unknown. Because of the uncertainty associated with values of FCden and POPN, two decision 

rules were established that limit the number of parameters adjusted while refining ACCUM for each source:

(1) When the population size for a given source is 

well documented, then that value will be used 

and held constant. 

 (2) When the population size for a given source is 

unknown, POPN will be treated as an adjusted 

parameter and potentially modified during the 

model-calibration process while FCden is held 

constant. 

Under the first decision rule, FCden will be treated 

as an adjusted variable and potentially modified during 

the model-calibration process. Adjustments to FCden 

account for the uncertainty associated with fixed values 

of Fprod, POPN, and HAB. Under the second decision 

rule, adjustments to POPN account for the uncertainty 

associated with the fixed values of Fprod, FCden, and 

HAB. The resulting POPN value, following calibration, 

will be identified as an “effective” value that accounts 

for the uncertainty associated with the fixed values of 

Fprod, FCden, and HAB.

In HSPF, the total accumulation rate of fecal 

coliform bacteria on the land surface is bounded by a 

storage limit (SQOLIM). This storage limit enables the 

model to account for the natural die-off of bacteria 

stored on the land surface. For this study, the storage 

limit was set to 9 times the accumulation rate, which 

represents a decay rate of 0.1 day-1 (U.S. Environmental Protection Agency, 1985). 

Source-Specific Quantification of Fecal Coliform 

Bacteria

The quantification of fecal coliform bacteria generated by the various sources within the Christians Creek 

watershed is documented in the following section. The 

sources described in this section are humans, dogs, 

cats, beef cattle, dairy cattle, heifers, broilers, turkeys, 

horses, sheep, deer, geese, ducks, raccoons, muskrats, 

and beavers. These sources are described with respect 

to their contribution to the pervious and impervious 

land segments within the basin.

Pervious Land Segments

The Christians Creek watershed has a human 

population of approximately 12,000 (1990 Census). 

Within the watershed, many pathways can allow 

human-derived fecal coliform bacteria to enter 

Christians Creek. These pathways include failing septic 

systems, overflowing sewer lines, leaking sewer lines, 

and straight pipes (direct discharge of untreated sewage 

from private residences), the cumulative effect of 

which was represented by a land application of human 

waste. The fecal coliform bacteria accumulation rate 

for the land-applied bacteria was calculated using 

equation 6. The values used to calculate the initial 

accumulation rate are in table 8. On average, one 

person generates approximately 150 g of feces per day 

(Geldreich and others, 1962) and an estimated 4.66 x 

108 col/g of human feces (Mara and Oragui, 1981). The 

initial population value (POPN) used was based on an 

estimated septic-system failure rate of 15 percent, 
20 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

which is consistent with failure rates determined for 

nearby communities (Virginia Polytechnic Institute and 

State University, 2000). In the Christians Creek 

watershed, 2,950 houses have septic systems. The 

average household occupancy rate for Augusta County 

is 2.69 people (1990 Census). POPN is the most 

uncertain value in equation 6 and, therefore, is adjusted 

during the model calibration process. These bacteria 

then are distributed over the residential land type 

(HAB) (table 8).

Straight pipes were represented as point sources in 

HSPF. Three factors were used to estimate the number 

of straight pipes in each subwatershed: the number of 

houses in each subwatershed, the age of each house, 

and the proximity of each house to the nearest stream. 

The number of houses was identified by using the 

emergency-911 database for Augusta County, Va., 

which was provided as a GIS coverage by the county. 

These houses were placed in three possible age 

categories (pre-1964, 1964-84, and post-1984). The 

selection of the age categories was based on two 

versions of USGS 7.5-minute topographic maps. The 

first series of topographic maps were derived from 

1964 aerial photography and were photo-revised in 

1984. Therefore, houses represented on the 1984 maps 

would have been built between 1964 and 1984. The 

proximity of each house to the nearest stream was 

identified using the emergency-911 coverage. The total 

number of houses within 150 ft of a stream was 

identified. Ten percent and 2 percent of the houses 

identified to be within 150 ft of a stream, in the 

pre-1964 and 1964-84 age categories, respectively, 

were assumed to have a straight pipe (Virginia 

Polytechnic Institute and State University, 2000). 

Based on the outlined methodology, there are an 

estimated number of four straight pipes within the 

Christians Creek watershed (table 8). The estimated 

number of fecal coliform bacteria discharged from each 

straight pipe is based on the occupancy for a 

single-family residence of 2.69 people and the daily per 

capita fecal coliform production rate of 6.99 x 

1010 col/day (table 8).

Fecal coliform bacteria derived from dogs were 

represented as a land application to both urban and residential land types. The accumulation rate for these 

bacteria was calculated using equation 6. Initial values 

used to calculate ACCUM are listed in table 9. On 

average, one dog generates 450 g of feces per day 

(Weiskel and others, 1996), and an estimated 4.11 x 

106 col/g of feces (Mara and Oragui, 1981). The initial 

value for the total number of dogs in the watershed was 

based on the estimate of one dog per three people. This 

estimate was refined further to account for the approximately 20 percent of dog waste that is picked up and 

disposed of. Additionally, 10 percent of the dog waste 

was assumed to be deposited on impervious surfaces 

such as parking lots and roads. The POPN value in 

table 9 represents the initial estimated number of dogs 

whose feces are deposited outdoors and are picked up 

and disposed of. Because the actual number of dogs in 

the watershed is unknown, POPN is treated as a fitted 

value during the model-calibration process. 

Fecal coliform bacteria derived from cats were represented as a land application to both urban and residential land types. The accumulation rate for these 

bacteria was calculated using equation 6. Initial values 

used to calculate ACCUM are listed in table 9. On 

average, one cat generates 20 g of feces per day (Jutta 

Schneider, Virginia Department of Conservation and 

Recreation, written commun., 2000), and an estimated 

1.49 x 107 col/g of feces (Mara and Oragui, 1981). The 

initial value for the total number of cats in the watershed was based on an estimate of two cats per three 

people. It was assumed that 70 percent of the estimated 

number of cats deposits their feces outdoors. The 

POPN value in table 9 represents the initial estimated 

number of cats that deposit their feces outdoors. 

Because the actual number of cats that deposit their 

feces outdoors is unknown, POPN is treated as a fitted 

value during the model-calibration process. 

There are approximately 10,000 beef cattle, 1,650 

dairy cattle, and 2,100 heifers in the Christians Creek 

Watershed. Each of these cattle types has different estimated daily fecal production rates (American Society 

of Agricultural Engineers, 1998) and associated fecal 

coliform densities (Mara and Oragui, 1981) (table 10). 

The fecal coliform bacteria derived from cattle feces 

can be transported to Christians Creek along three possible pathways: (1) Feces generated while cattle are 

confined are stored and later distributed over the various croplands in the watershed, and then transported to 

the stream network with surface runoff. (2) Feces are 

deposited directly on the pastureland by grazing cattle, 

and then transported to the stream network through surface runoff. (3) Feces are deposited directly in Christians Creek and associated tributaries by cattle standing 

in these streams. Each of these three pathways is represented in HSPF.
Description of Models 21

Subwatershed1 Fprod

(grams) FCden POPN(number of humans)

HAB

(acres)

Human–land applied

1 150 4.66 x 108 209 289

2 150 4.66 x 108 135 206

3 150 4.66 x 108 260 309

4 150 4.66 x 108 172 485

5 150 4.66 x 108 253 808

6 150 4.66 x 108 161 261

Human–straight pipes

1 150 4.66 x 108 3 –

2 150 4.66 x 108 3 –

3 – – – –

4 150 4.66 x 108 3 –

5 – – – –

6 150 4.66 x 108 3 –

Table 8. Initial values of the total amount of feces produced daily and fecal coliform bacteria per gram of feces generated by 

the human population in the residential hydrologic response unit represented in the fecal coliform model, Christians Creek, 

Augusta County, Virginia 

[Fprod, feces produced per day; FCden, fecal coliform bacteria per gram of feces; POPN, population size; HAB, habitat area; 

-, not applicable]

1See figure 3 for location of subwatersheds.

Subwatershed1 Fprod

(grams) FCden

POPN

(number)

HAB

(acres)

Residential Urban Residential Urban

Dogs

1 450 4.11 x 106 368 199 289 414

2 450 4.11 x 106 238 129 206 153

3 450 4.11 x 106 458 248 309 495

4 450 4.11 x 106 303 164 485 1,244

5 450 4.11 x 106 446 242 808 196

6 450 4.11 x 106 283 153 261 429

Cats

1 20 1.49 x 107 654 393 289 414

2 20 1.49 x 107 423 254 206 153

3 20 1.49 x 107 814 490 309 495

4 20 1.49 x 107 537 324 485 1,244

5 20 1.49 x 107 792 477 808 196

6 20 1.49 x 107 502 302 261 429

Table 9. Initial values of the total amount of feces produced daily and fecal coliform bacteria per gram of feces generated by the dog

and cat populations in the urban and residential hydrologic response units represented in the fecal coliform model, Christians Creek, 

Augusta County, Virginia

[Fprod, feces produced per day; FCden, fecal coliform bacteria per gram of feces; POPN, population size; HAB, habitat area]

1See figure 3 for location of subwatersheds.
22 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Dairy cattle are confined in or around milking parlors between 7.2 and 18.0 hr/day during the summer 

and winter months, respectively (table 11). The feces 

generated by confined dairy cattle are collected and 

stored in anaerobic lagoons. Fecal coliform bacteria 

stored in anaerobic lagoons are subject to die-off; the 

die-off rate was simulated using the equation

where Ct is the fecal coliform bacteria load at 

time t,

Co is the initial fecal coliform bacteria load, 

t is the time in days, and

K is the first order decay rate (day-1).

Co was set to the number of fecal coliform bacteria 

produced annually by dairy cattle in confinement. The 

time (t) these bacteria were stored was set to 100 days, 

which represents the average storage capacity of dairy 

lagoons in the Christians Creek watershed. A decay 

rate (K) of 0.375 day-1 was used to represent the decay 

rate observed in an anaerobic lagoon (Crane and 

Moore, 1986). The amount of fecal coliform bacteria 

remaining after the 100 days of storage (Ct), which 

then is available for manual application to croplands, is 

incorporated into equation 6 to determine the accumulation rate per acre of cropland. Because the number of 

dairy cattle in the watershed is known, FCden is 

adjusted during the model-calibration process. The percentage of stored dairy waste applied to cropland varies 

from month to month (table 12); the monthly field 

application rate and the number of cattle in confinement were represented by means of monthly ACCUM 

values. The fecal coliform bacteria from the dairy 

waste applied to cropland are treated as a nonpoint 

source in the model simulation.

Beef cattle and heifers spend an average of 9.6 

hours per day in confinement during the months of 

December, January, and February (table 13). During 

these months, the cattle are confined in a small area but 

are housed in barns where they deposit feces and associated fecal coliform bacteria. The accumulating 

manure is removed routinely and stored until it can be 

applied to cropland. Fecal coliform bacteria are subject 

to die-off during the manure storage phase. Equation 7 

was used to determine the total amount of bacteria 

removed from the stored manure through die-off. Co

was set to the total number of fecal coliform bacteria 

produced yearly by beef cattle and heifers in confinement. The total time (t) these bacteria were stored was 

set to 30 days. A decay rate (K) of 0.066 day-1 was 

used to represent the decay rate in an uncovered 

manure pile (Crane and Moore, 1986). The amount of 

fecal coliform bacteria remaining after the 30 days of 

storage (Ct), which then is available for manual application to croplands is incorporated into equation 6 to 

determine the accumulation rate per acre of cropland. 

Because the number of beef cattle and heifers in the 

watershed is known, FCden is treated as an adjusted 

value during the model-calibration process. The percentage of stored manure applied to cropland varies 

from month to month (table 14); the monthly field 

application rate and number of cattle in confinement 

were represented in the model by means of monthly 

ACCUM values. The bacteria from the manure applied 

to cropland are treated as a nonpoint source in the 

model simulation.

Dairy cattle spend between 5.5 and 15.8 hours per 

day in the pasture (table 11) while beef cattle and heifers spend between 13.9 and 23.0 hours per day in the 

pasture (table 13). In the model, pasture is represented 

by both pasture and hayland, with 80 percent of the cattle distributed on pasture and 20 percent distributed on 

hayland. These cattle deposit feces with associated 

fecal coliform bacteria directly onto the pasture. The 

daily total number of bacteria deposited on the pasture 

is determined by the time cattle spend in the pasture 

and the daily fecal coliform production rate. Monthly 

values of ACCUM were used to represent the monthly

Source Average daily

Fprod (grams per day) FCden

Dairy cattle 54,545 8.18 x 105

Beef cattle 20,909 1.87 x 106

Heifers 39,091 6.40 x 104

Table 10. Initial values of the total amount of feces produced daily and 

fecal coliform per gram of feces generated by dairy cattle, beef cattle, 

and heifers represented in the fecal coliform model, Christians Creek, 

Augusta County, Virginia

[Fprod, Feces produced per day; FCden, Fecal coliform bacteria per gram 

of feces]

Ct Coe–Kt = (7)
Description of Models 23

Month

Time (hours)

Pasture Access to stream Confinement

January 5.5 0.5 18.0

February 5.5 .5 18.0

March 13.4 1.0 9.6

April 15.8 1.0 7.2

May 15.8 1.0 7.2

June 13.8 3.0 7.2

July 13.8 3.0 7.2

August 13.8 3.0 7.2

September 15.8 1.0 7.2

October 15.8 1.0 7.2

November 13.4 1.0 9.6

December 5.5 .0 18.0

Table 11. Initial values of the total hours per day dairy cattle spend in a given 

month in the pasture, in confinement, and with access to a stream in the Christians 

Creek watershed, Augusta County, Virginia

Month Application amount1

(percent)

January 0.0

February 5.0

March 25.0

April 20.0

May 5.0

June 7.5

July 2.5

August 5.0

September 12.5

October 7.5

November 10.0

December .0

Table 12. Percentage of the total 

stored liquid dairy cattle waste 

applied to cropland in the Christians 

Creek watershed, Augusta County, 

Virginia

1From Virginia Department of 

Conservation and Recreation
24 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Month

Time (hours per day)

Pasture Access to stream Confinement

January 13.9 0.5 9.6

February 13.9 .5 9.6

March 23.0 1.0 .0

April 23.0 1.0 .0

May 22.5 1.5 .0

June 20.5 3.5 .0

July 20.5 3.5 .0

August 20.5 3.5 .0

September 22.5 1.5 .0

October 23.0 1.0 .0

November 23.0 1.0 .0

December 13.9 .5 9.6

Table 13. Total hours per day beef cattle and heifers spend in a given month 

in the pasture, with access to a stream, and in confinement in the Christians 

Creek watershed, Augusta County, Virginia

Month Application amount1 

(percent)

January 0.0

February 5.0

March 25.0

April 20.0

May 5.0

June 5.0

July 5.0

August 5.0

September 10.0

October 10.0

November 10.0

December .0

Table 14. Percentage of stored beef 

cattle and heifer manure and poultry 

litter applied to cropland in the 

Christians Creek watershed, 

Augusta County, Virginia

1From Virginia Department of Conservation 

and Recreation
Description of Models 25

varying number of cattle in the pastures, and the bacteria from the feces deposited directly to the pasture are 

treated as a nonpoint source in the model simulation.

When stream access is provided, cattle spend an 

average of 0.5 hr/day during cold months to 3.5 hr/day 

during warm months (table 13) in and near streams. In 

order to determine and simulate the total amount of 

feces that is deposited directly to the stream, the number of cattle with direct access to a stream must be 

identified. This number is estimated by first identifying 

the total number of pasture and hayland land segments 

(pastures) that are bordered by Christians Creek or its 

major tributaries, Folly Mills Creek, Barterbrook 

Branch, Goose Creek, and Meadow Run (fig. 1). GIS 

coverages for land use and stream networks in the 

Christians Creek watershed revealed that 35 percent of 

all pastures are bordered by a major stream. The number of cattle in the major streams is determined by the 

equation

where CattleInstream is the number of dairy cattle, 

beef cattle, or heifers in the stream,

CattleTotal is the total number of dairy cattle, beef 

cattle, or heifers in the pastures, and

TAccess is the estimated time spent in the stream.

In the model, 30 percent of the fecal coliform bacteria generated by CattleInstream are represented as deposited directly in the stream whereas the remaining 70 

percent is allocated to pastures. This 70 percent represents the feces that are deposited near but not directly 

in the stream channel. The 30 percent that is directly 

deposited into the stream is represented using monthly 

values to account for the varying time cattle spend in 

the stream each month. This direct deposition is represented in the model as a point source.

There are 10,000 broilers and 172,000 turkeys in 

the Christians Creek watershed. In addition, 3,000 tons 

of poultry litter are imported annually. The imported 

litter and the resident broiler and turkey population was 

represented in the model as combined poultry. A fecal 

production rate for turkey of 231 g/day (American 

Society of Agricultural Engineers, 1998) and an estimated fecal coliform density of 1.82 x 109 col/g (Mara 

and Oragui, 1981) were used to determine the total 

number of fecal coliform bacteria produced per day. 

Because the entire poultry population is confined to 

poultry houses, the generated poultry litter is stored and 

later applied to cropland. The extent of fecal coliform 

bacteria die-off during poultry litter storage was determined using equation 7. Co was set to the total number 

of fecal coliform bacteria produced yearly by poultry. 

The time (t) these bacteria were stored was set to 90 

days, which is the average poultry litter storage time. A 

decay rate (K) of 0.08 day-1 was used to represent the 

decay rate observed for poultry litter applied to the soil 

surface (Giddens and others, 1973). The amount of 

fecal coliform bacteria remaining (Ct) after the 90 days 

of storage is incorporated into equation 6 to determine 

ACCUM. The percentage of stored poultry litter 

applied to cropland varies from month to month 

(table 14); the monthly field application rate was represented in the model by means of monthly ACCUM values. Because the number of poultry in the watershed is 

known, FCden is adjusted during the model-calibration 

process. The fecal coliform bacteria from the poultry 

litter applied to cropland are treated as a nonpoint 

source in the model simulation. 

There are 600 horses and 1,100 sheep in the Christians Creek watershed. The average fecal production 

rate for horses and sheep is 23,182 g/day and 

1,091 g/day, respectively (American Society of Agricultural Engineers, 1998). The fecal coliform density 

assumed for horse feces is 1.81 x 105 col/g (American 

Society of Agricultural Engineers, 1998) and 1.80 x 

105 col/g for sheep (Mara and Oragui, 1981). ACCUM 

values for horses and sheep are adjusted during the calibration process, as needed, through the FCden parameter. Horses and sheep deposit their waste directly 

onto pasture. The bacteria applied to pasture are treated 

as a nonpoint source in the model simulation.

DCR provided information on the numbers and 

housing of livestock, as well as application rates of liquid dairy waste and manure to cropland and pasture in 

the Christians Creek watershed (Jutta Schneider, Virginia Department of Conservation and Recreation, 

written commun., 2000).

The wildlife sources represented in the model are 

deer, geese, ducks, raccoons, muskrats, and beavers. 

These sources were selected on the basis of information from the Virginia Department of Game and Inland 

Fisheries (VDGIF) and watershed surveys performed 

by the USGS as part of this study. The population of 

CattleInstream CattleTotal ( ) ( ) 0.35

TAccess

24 = ------------------ (8)
26 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

each of these wildlife species was estimated on the 

basis of habitat area, species density within the habitat 

area, and seasonal migration (table 15). GIS coverages 

for animal habitat and land use were used to determine 

the size of each animal’s habitat. For example, Canada 

geese prefer to be within 300 ft of streams on all land 

segments except forested; therefore, the total acres of 

Canada geese habitat is equal to the sum of the acres of 

all land segments within 300 ft of a stream, except forested, in the habitat area. The population density for 

geese and ducks increases during the winter months 

(December, January, and February) because of migration. The amount of fecal coliform bacteria produced 

daily by each wildlife species (table 16) is used in 

equation 6 to identify ACCUM for each wildlife species represented in the model. POPN for all wildlife 

species except deer, and FCden for deer, are adjusted 

during the model-calibration process. Monthly values 

of ACCUM are adjusted for geese and ducks in order to 

account for migration. The feces of all wildlife species 

except beaver are deposited directly to the land segments in their habitat; therefore, these sources of fecal 

coliform bacteria are represented in the model as nonpoint sources. Beaver feces are deposited directly in 

streams and, therefore, are represented as a point 

source.

Impervious Land Segments

Dogs are the only source in the model that is 

assumed to deposit feces on impervious surfaces 

(table 17). Ten percent of the total waste generated by 

dogs is assumed to fall directly on the impervious portions of the urban land-use type. The bacteria from the 

feces directly deposited on impervious surfaces are 

modeled as a nonpoint source. The fecal coliform accumulation rate is calculated using equation 6 and is 

based on fecal production from 10 percent of the dog 

population.

Fecal Coliform Bacteria in the Subsurface

The decision to represent fecal coliform bacteria in 

the subsurface was based primarily on results from 

intensive monitoring of fecal coliform bacteria during 

stormflow and base-flow conditions in Christians 

Creek (Hyer and Moyer, 2003). Data collected by Hyer 

and Moyer (2003) support two hypotheses regarding 

the transport of fecal coliform bacteria. First, in addition to the surface runoff, fecal coliform bacteria may 

be transported along subsurface pathways. Other studies have found that bacteria can infiltrate and move 

through the shallow subsurface (Rahe and others, 1978; 

Wright, 1990; Miller and others, 1991; Pasquarell and 

Boyer, 1995; Howell and others, 1995; Felton, 1996; 

McMurry and others, 1998). Second, fecal coliform 

bacteria may be transported by other mechanisms that 

mimic subsurface pathways, such as resuspension of 

fecal coliforms from streambed sediments by animals 

walking in the stream, sloughing of fecal coliforms 

from the surface of streambed sediments, or advective 

transport of fecal coliforms from the streambed sediment by ground-water recharge (Goyal and others, 

1977; LaLiberte and Grimes, 1982; Burton and others, 

1987; Sherer and others, 1988; Marino and Gannon, 

1991). These bacteria transport mechanisms were simulated by incorporating the subsurface modules for 

interflow and base flow. 

Interflow represents water that is transported 

through the shallow subsurface (soil water). The travel 

time for soil water to reach the stream is greater than 

water transported as surface runoff; thus, soil water 

affects the stream hydrograph by decreasing the rate of 

recession following a storm event. Similarly, fecal 

coliform bacteria transported with interflow will extend 

the period of elevated fecal coliform bacteria concentrations following a storm event. Hyer and Moyer 

(2003) observed elevated fecal coliform concentrations 

for up to 2 days following storm events in Christians 

Creek. Fecal coliform bacteria associated with instream 

suspended sediment may contribute to post-storm elevated fecal coliform concentrations and are represented 

by simulation of the interflow component. Hyer and 

Moyer (2003) observed similar post-storm responses 

for streamflow, suspended sediment, and fecal coliform 

bacteria. In HSPF, the post-storm response for fecal 

coliform bacteria concentration was represented by 

assigning a concentration of 1,500 col/100 mL 

(424,800 col/ft3) to interflow. These bacteria were 

linked to the top four fecal coliform bacteria sources 

identified by Hyer and Moyer (2003). These sources 

are cattle, dogs, humans, and poultry.

Base flow, which represents the portion of ground 

water that enters the stream, is the dominant component of the stream hydrograph during periods of 

extended dry weather. Fecal coliform bacteria observed 

during these base flow periods typically are transported 

through diffuse ground-water input or pathways that 

mimic this diffuse input, such as resuspension of fecal 

coliforms from streambed sediments by animals walking in the stream, sloughing of fecal coliforms from the 

surface of streambed sediments, and advective trans-
Description of Models 27

Wildlife

source

Land-use

type Habitat1 Population density2

(number per acre)

POPN

(number)

Deer F, P Entire watershed 0.040 975

Goose–Summer U, R, P, H, C Within 300 feet of streams and ponds .078 373

Goose–Winter U, R, P, H, C Within 300 feet of streams and ponds .11 526

Duck–Summer U, R, P, H, C Within 300 feet of streams and ponds .047 225

Duck–Summer F Within 300 feet of streams and ponds .016 35

Duck–Winter U, R, P, H, C Within 300 feet of streams and ponds .063 301

Duck–Winter F Within 300 feet of streams and ponds .031 67

Raccoon F Within 2,640 feet of streams and ponds .055 1,083

Raccoon R, P, H, C Within 2,640 feet of streams and ponds .023 1,042

Muskrat U, R, P, H, C, F Within 60 feet of streams and ponds .500 479

Beaver F Within 60 feet of streams and ponds .016 5

Beaver U, R, P, H, C Within 60 feet of streams and ponds .008 5

Table 15. Initial population values of wildlife sources of fecal coliform bacteria in the fecal coliform model, Christians 

Creek, Augusta County, Virginia

[POPN, population size; F, Forest; P, Pasture; U, Urban; R, Residential; H, Hayland; C, Cropland]

1Paul Bugas, Virginia Department of Game and Inland Fisheries, oral commun., 1999, and U.S. Department of Agriculture, Forest Service, 

Rocky Mount Research Station, Fire Sciences Laboratory, Fire Effects Information System (January, 2000). 2Paul Bugas, Virginia Department of Game and Inland Fisheries, oral commun., 1999.

 

Wildlife

source

Fprod

(grams) FCden

Deer 772 3.30 x 106

Goose 225 3.55 x 106

Duck 150 4.90 x 107

Raccoon 450 1.11 x 107

Muskrat 100 2.50 x 105

Beaver 200 1.00 x 103

Table 16. Initial values of the total amount of feces produced daily and 

fecal coliform bacteria per gram of feces generated by deer, goose, duck, 

raccoon, muskrat, and beaver represented in the fecal coliform model, 

Christians Creek, Augusta County, Virginia

[Fprod, feces produced per day; FCden, fecal coliform bacteria per gram of 

feces]
28 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

port of fecal coliforms from the streambed sediment by ground-water inputs. Results from Hyer and 

Moyer (2003) indicate that bacteria linked to poultry, 

pet, and other nonpoint sources were present in 

base-flow samples from Christians Creek. Although 

the transport mechanism is unknown, nonpoint source 

signatures in base flow are represented through the 

ground-water module. In HSPF, a fecal coliform bacteria concentration of 100 col/100 mL (28,320 col/ft3) 

was assigned to base flow. These bacteria also were 

linked to cattle, dogs, humans, and poultry identified by 

Hyer and Moyer (2003).

Water-Quality Data

DEQ monitors water quality in streams and rivers 

across the State. One constituent monitored is fecal 

coliform bacteria, which are derived from the intestinal 

tract of warm-blooded animals. These bacteria are used 

as an indicator organism for identifying the presence of 

fecal contamination and associated pathogens such as 

Salmonella and Shigella. The predominant form of 

fecal coliform bacteria is Escherichia coli (E. coli). 

DEQ collects and analyzes water samples to determine 

if a particular stream or river is in compliance with the 

State water-quality standard for fecal coliform bacteria, 

which is an instantaneous concentration of 

1,000 col/100 mL. Sites with fecal coliform bacteria 

concentrations greater than 1,000 col/100 mL pose a 

risk to individuals who are in direct contact with the 

contaminated water because of the increased likelihood 

of encountering a pathogen (U.S. Environmental Protection Agency, 1986). DEQ has established a lower 

detection limit of 100 col/100 mL and an upper detection limit of 8,000 col/100 mL for enumeration of fecal 

coliform bacteria. Therefore, fecal coliform bacteria 

concentrations reported by DEQ of 100 and 8,000 

col/100 mL have an actual concentration of 0–

100 col/100 mL or greater than or equal to 

8,000 col/100 mL, respectively. DEQ generally collects 

water-quality samples monthly under low-flow or post 

stormflow conditions; peak stormflow water-quality 

samples are not collected routinely. 

DEQ collects monthly water-quality samples at two 

long-term monitoring stations on Christians Creek 

(fig. 1; table 18). Samples are analyzed for fecal 

coliform bacteria using the membrane filtration technique. Results of monitoring during 1991-97 show that 

fecal coliform bacteria concentrations were higher than 

the State instantaneous water-quality standard in 64.4 

percent of the samples taken at the upstream site 

(Route 831) (fig. 5) and in 33.8 percent of the samples 

taken at the downstream site (Route 794) (fig. 6). Comparison of the fecal coliform bacteria data from the 

Route 831 and Route 794 monitoring stations (fig. 7), 

by means of a two-sided Wilcoxon rank sum test, indicates significantly higher concentrations at the Route 

831 station relative to those at the Route 794 station (p 

= 0.0004). Seasonal patterns also were identified in the 

data (figs. 8-9). Generally, fecal coliform concentrations are higher during the warmer months (April–

October) and lower during the cooler months (November–March). This seasonal pattern was more pronounced at the Route 831 station than the Route 794 

station and is consistent with the animal practices in the

Subwatershed1 Fprod

(grams) FCden POPN(number)

HAB

(acres)

1 450 4.11 x 106 22 26

2 450 4.11 x 106 14 10

3 450 4.11 x 106 28 32

4 450 4.11 x 106 18 170

5 450 4.11 x 106 27 13

6 450 4.11 x 106 17 27

Table 17. Initial values of the total amount of feces produced daily and fecal coliform bacteria per 

gram of feces generated by the dog population in the urban impervious hydrologic response unit 

represented in the fecal coliform model, Christians Creek, Augusta County, Virginia

[Fprod, feces produced per day; FCden, fecal coliform bacteria per gram of feces; 

POPN, population size; HAB, habitat area]

1See figure 3 for location of watersheds.
Description of Models 29

watershed (increased animal density and activity 

around the streams during the hot summer months) and 

possible seasonal differences in bacteria survivorship. 

Similar seasonal patterns have been observed in other 

studies of fecal coliform concentrations and loads 

(Christensen and others, 2001; Baxter-Potter and Gilliland, 1988).

The USGS collected water-quality data for this 

study at six sites in Christians Creek from March 1999 

to October 2000 (Hyer and Moyer, 2003). All 

stream-water samples were analyzed for the enumeration of fecal coliform bacteria following standard 

USGS methods for the membrane filtration technique 

(Myers and Sylvester, 1997). Stream-water samples 

were collected over the complete range of hydrologic 

conditions (table 19). 

Low-flow samples were collected every 6 weeks at 

Route 794. Some of these low-flow sampling events 

were on the recession limbs of storm events. Typically, 

between four and eight depth-integrated samples were 

collected during each low-flow sampling event. Consecutive samples were collected at three locations 

across the stream width (the center of the channel and 

approximately halfway to each stream bank). The 

depth-integrated samples were collected at 5-minute 

intervals, providing a degree of time-integration during 

each sampling event. Results of the water-quality samples collected under low-flow and recession-flow conditions indicate that 50 percent of the low-flow samples 

exceeded the State fecal coliform bacteria standard 

(fig. 10). Recession-flow samples generally had fecal 

coliform concentrations that were elevated relative to 

the low-flow samples. The fecal coliform data also 

exhibited a strong seasonal pattern; higher concentrations were observed during the warmer months (June–

September) than during the cooler months (October–

May). This seasonal pattern for concentrations of fecal 

coliform bacteria is consistent with the pattern identified in the historical data.

Stormflow samples were collected during five 

storm events (September 9, 1999; November 11, 1999; 

March 20, 2000; April 25, 2000; and June 28, 2000) at 

Route 794. At least 10 water samples were collected 

across the storm hydrograph (rising limb, plateau, and 

falling limb) during each storm event. The fecal 

coliform concentrations observed during these storm 

events are elevated considerably relative to the State 

water-quality standard (fig. 11) and the low-flow concentrations. A large range of concentrations was 

observed during each storm because sampling was 

done over the entire hydrograph. Peak fecal coliform 

concentrations observed during these storms ranged 

from 23,000 to 730,000 col/100 mL. Elevated fecal 

coliform concentrations during storm events have been 

observed in previous studies (Christensen and others, 

2001; Bolstad and Swank, 1997). In general, these elevated stormflow concentrations are interpreted as 

resulting from a combination of a flushing response 

(whereby fecal coliform bacteria that have been deposited near the stream are washed off the land surface and 

into the stream) and a resuspension of streambed sediments containing fecal coliform bacteria (Hunter and 

others, 1992; McDonald and Kay, 1981).

Five continuum sampling sites in addition to Route 

794 were established along Christians Creek (fig. 1; 

table 19). These six sites were sampled three times 

(March 25, 1999; July 27, 1999; and August 1, 1999) to 

examine how well the intensive sampling at Route 794 

represented the entire watershed. These samples were 

collected as a single, depth-integrated sample from the 

approximate center of the stream channel. Additionally, 

data from these continuum sites provided information 

on the spatial variability observed in concentrations of 

fecal coliform bacteria (table 19). These data are con

Station

number1 Station name Latitude

Longitude Period of record

Fecal coliform bacteria concentration, in colonies per 100 

milliliters

Minimum Maximum Mean Median

1BCST021.76 Route 831 38°03′22′′

79°04′18′′ 1991-2001 100 8,000 2,526 1,500

1BCST012.32 Route 794 38°07′43′′

78°59′41′′ 1979-2001 100 8,000 1,205 600

Table 18. Fecal coliform bacteria concentrations for water-quality samples collected by the Virginia Department of Environmental Quality at 

two water-quality monitoring stations on Christians Creek, Augusta County, Virginia

1See figure 1 for location of stations.
30 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 5. Observed fecal coliform bacteria concentrations for Christians Creek at Route 831, Augusta County, Virginia, from 1991-98. 

(Data from Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)

1991 1992 1993 1994 1995 1996 1997 1998 0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

Virginia's water-quality standard (instantaneous measurement)

Upper detection limit (Virginia Department of Environmental Quality)

FECAL COLIFORM BACTERIA CONCENTRATION, 

IN COLONIES PER 100 MILLILITERS

Figure 6. Observed fecal coliform bacteria concentrations for Christians Creek at Route 794, Augusta County, Virginia, from 1991-98. (Data from 

Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)

1991 1992 1993 1994 1995 1996 1997 1998 0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000 Upper detection limit (Virginia Department of Environmental Quality)

Virginia's water-quality standard (instantaneous measurement)

FECAL COLIFORM BACTERIA CONCENTRATION, 

IN COLONIES PER 100 MILLILITERS
Description of Models 31

Figure 7. Relation between observed fecal coliform bacteria concentrations for Christians Creek at Route 831 and Route 794, Augusta County, 

Virginia, 1991-97. (Observed data from Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

(79)

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

(69)

Route 794 Route 831

Far outlier

EXPLANATION

Mean

75th

25th

50th

Sample size (69)

Outlier

90th

10th

Percentile—Percentage of values equal 

to or less than indicated values
32 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 8. Monthly distribution of observed fecal coliform bacteria concentrations for Christians Creek at Route 831, Augusta County, 

Virginia,1991-97. (Data from Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)

MONTH

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

(3)

(6)

(7)

(7)

(7)

(5)

(5)

(5)

(6)

(6)

(7)

(5)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

Far outlier

EXPLANATION

Mean

75th

25th

50th

Sample size (5)

Outlier

90th

10th

Percentile—Percentage of values equal 

to or less than indicated values
Description of Models 33

Figure 9. Monthly distribution of observed fecal coliform bacteria concentrations for Christians Creek at Route 794, Augusta County, 

Virginia1991-97. (Data from Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)

(7)

(8)

(7)

(7)

(7) (6)

(6)

(7) (5)

(7) (7)

(5)

MONTH

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

Far outlier

EXPLANATION

Mean

75th

25th

50th

Sample size (5)

Outlier

90th

10th

Percentile—Percentage of values equal 

to or less than indicated values
34 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 10. Observed fecal coliform bacteria concentrations from stream-water samples for Christians Creek at Route 794 during 

low-flow periods, Augusta County, Virginia.

10

100

1,000

10,000

100,000

(8)

(3)

(3)

(6)

(4)

(7)

(7) (8)

(8)

(8)

(9)

(8)

(8)

(1)

(4)

8

9/27/00

10/5/00

/28/00

8/3/00

3/30/00

5/10/00

6/20/00

3/1/00

1/18/00

12/6/99

10/25/99

7/2

9/13/99

8/99

6/16/99

5/5/99

SAMPLING DATE

Virginia's water-quality standard (instantaneous measurement)

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

Storm-impacted sample (rain

within last 48 hours, receding flow)

EXPLANATION

Mean

75th

25th

50th

Sample size (7)

Outlier

90th

10th

Percentile—Percentage of values equal 

to or less than indicated values
Description of Models 35

Figure 11. Observed fecal coliform bacteria concentrations from stream-water samples for Christians Creek at Route 794 during low-flow periods, 

Augusta County, Virginia.

100

1,000

10,000

100,000

1,000,000 (10)

(14)

(12)

(13)

(10)

9/5/99 4/25/00 11/1/99 3/20/00 6/28/00

SAMPLING DATE

Virginia's water-quality standard 

(instantaneous measurement)

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

Far outlier

EXPLANATION

Mean

75th

25th

50th

Sample size (12)

Outlier

90th

10th

Percentile—Percentage of values equal 

to or less than indicated values
36 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

sistent with patterns observed in the fecal coliform bacteria collected by DEQ at Route 831 and Route 794; 

fecal coliform concentrations generally are higher 

upstream and lower downstream.

Bacterial Source Tracking

BST is a rapidly growing technology with various 

analytical techniques; the technique used depends on 

the study goals. In general, these techniques rely on 

molecular, genetics-based approaches (also known as 

“genetic fingerprinting”) or phenotypic (relating to the 

physical characteristics of an organism) distinctions 

among the bacteria of different sources. There are three 

primary genetic techniques for bacterial source tracking. Ribotyping characterizes a small, specific portion 

of the bacteria’s DNA sequence (Samadpour and 

Chechowitz, 1995). Pulsed-field gel electrophoresis 

(PFGE) is similar to ribotyping but typically is performed on the entire genome of the bacteria (Simmons 

and others, 1995). Polymerase chain reaction (PCR) 

amplifies selected DNA sequences in the bacteria’s 

genome (Makino and others, 1999). Phenotypic techniques generally involve an antibiotic resistance analysis, in which resistance patterns for a suite of different 

concentrations and types of antibiotics are developed 

(Wiggins, 1996; Hagedorn, and others, 1999).

Although all the techniques described above are 

promising for identifying bacteria sources, the ribotyping technique was used to identify the sources of fecal 

coliform bacteria impairing Christians Creek (Hyer and 

Moyer, 2003). Ribotyping involves an analysis of the 

specific DNA sequence that codes for the production of 

ribosomal RNA (ribonucleic acid). Ribotyping has 

been demonstrated to be an effective technique for distinguishing bacteria from the feces of multiple animal 

species (Carson and others, 2001). This technique has 

been performed successfully and used to identify bacteria sources in both freshwater (Samadpour and 

Chechowitz, 1995) and estuarine systems (Ongerth and 

Samadpour, 1994). Furthermore, the technique has 

been used to identify the species-specific sources of 

bacteria contributing to impairments in both urban 

(Herrera Environmental Consultants, Inc., 1993) and 

wilderness systems (Farag and others, 2001). The 

Station

number1

Station

name

Latitude

Longitude

Number of 

samples

Fecal coliform bacteria concentration, in colonies per 100 milliliters

Minimum Maximum Mean Median

Low-flow samples

01624800 Route 794 38°07′42′′

78°59′41′′ 104 29 43,000 3,190 1,010

Stormflow samples

01624800 Route 794 38°07′42′′

78°59′41′′ 66 130 730,000 78,216 7,611

Continuum samples

01624615 Route 693 38°02′08′′

79°11′56′′ 3 5 71 28 7

01624620 Route 604 38°01′08′′

79°10′05′′ 3 87 1,500 629 300

01624660 Route 340 38°02′38′′

79°05′17′′ 3 230 3,800 2,010 2,000

01624700 Route 635 38°05′35′′

79°01′54′′ 3 23 6,400 2,774 1,900

01624800 Route 794 38°07′42′′

78°59′41′′ 3 15 1,800 868 790

01624900 Route 612 38°11′35′′

78°56′07′′ 3 9 830 326 140

Table 19. Fecal coliform bacteria concentrations for water-quality samples collected by the U. S. Geological Survey during low-flow and

stormflow conditions at Route 794 (01624800) and at five other sites along the continuum of Christians Creek, Augusta County, Virginia

1See figure 1 for location of stations
Description of Models 37

broad applicability of ribotyping makes it well suited 

for use in this study.

The Microbial Source Tracking Laboratory at the 

University of Washington (UWMSTL) performed the 

bacterial source tracking for all samples in this study. 

Refer to Hyer and Moyer (2003) for specific details 

regarding the ribotyping technique used in Christians 

Creek.

The results from the BST study indicate that a 

diverse collection of organisms contribute to the 

impairment of Christians Creek (Hyer and Moyer, 

2003). Hyer and Moyer (2003) identified 22 different 

sources of fecal coliform bacteria; the top 10 contributors identified by the ribotyping analysis are poultry, 

cattle, human, dog, horse, and deer, with cat, duck, 

goose, and raccoon considered minor sources, making 

up less than 5 percent of the total contributors (fig. 12).

The poultry category (fig. 12) was adjusted to 

improve the data interpretation. The poultry category 

represents a combination of chicken, turkey, and other 

poultry sources. The ribotyping technique sometimes 

can be used to distinguish chickens from turkeys (and, 

in these cases, the two are identified separately), 

whereas, in other cases, an unknown isolate can be 

identified only as either a chicken or a turkey isolate (in 

this case, the isolate is labeled as poultry). Additionally, a general avian category was identified by the 

ribotyping analysis. The avian category represents 

strains of fecal coliform bacteria that can occur in multiple bird species. Whereas the poultry category was 

specific to chickens and turkeys, the avian category is 

more extensive, encompassing all birds, including 

chickens and turkeys. For data interpretation and watershed modeling purposes, this avian category was distributed among all the observed bird species.

Quantitatively, the avian category was assumed to 

be distributed proportionally, according to the occurrence of each individual bird species. For example, the 

poultry contribution to Christians Creek is 71 percent 

of all the bird species that are identified uniquely; 

therefore, 71 percent of the avian category was attributed to poultry. A detailed description of the manipulation of the avian category is in Hyer and Moyer (2003).

Calibration Approach

The calibrated fecal coliform model can be used to 

accurately simulate the range of observed fecal 

coliform concentration data as well as observed BST 

data from the Christians Creek Watershed. The simulations cover approximately a 7-year period from April 1, 

1991, to September 30, 1997. 

A suite of water-quality transport and storage 

parameters governs the simulation of fecal coliform 

bacteria in HSPF. As with the streamflow simulation, 

these parameters are categorized as fixed and adjusted. 

Fixed parameters can be measured or are well documented in the literature, and can be used with a high 

degree of confidence. The fecal coliform model parameters that were fixed (held constant) during the calibration process were the bacteria die-off rates associated with bacteria on the land surface (REMQOP) and 

instream (KGEN). Adjusted parameters exhibit a high 

degree of variability and uncertainty in the environment. Four parameters representing fecal coliform bacteria transport and storage components were adjusted 

to obtain a calibrated fecal coliform model for the 

Christians Creek watershed: fecal coliform accumulation rate (ACCUM); susceptibility of bacteria to surface runoff (WSFAC); storage of fecal coliform 

bacteria in interflow (IQO); and storage of fecal 

coliform bacteria in active ground water (AQO). The 

fecal coliform model was calibrated to (1) low-flow 

fecal coliform concentrations collected by DEQ from 

1991 through 1997, (2) the range of stormflow fecal 

coliform concentrations collected by USGS from 1999 

through 2000, and (3) BST data collected by Hyer and 

Moyer (2003). 

Figure 12. Distribution of the top ten contributors of fecal coliform 

bacteria identified by bacterial source tracking in the Christians Creek 

watershed, Augusta County, Virginia.

0

5

10

15

20

25

30

PERCENT CONTRIBUTION

Cattle

Poultry

Human

Dog

Horse

Deer

Cat

Raccoon

Goose

Duck

IDENTIFIED BACTERIA SOURCE
38 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

The fecal coliform model first was calibrated to the 

data collected 1991 through 1997 by DEQ during 

low-flow periods. The primary sources represented in 

the model that contribute fecal coliform bacteria during 

low-flow periods are direct deposition by instream cattle, permitted point source dischargers, nonpermitted 

point source dischargers (straight pipes), and active 

ground-water discharge (AQO). The low-flow periods 

represented in the model were calibrated by adjusting 

the inputs from instream cattle and active ground-water 

discharge. 

Next, the fecal coliform model was calibrated to 

data collected 1999 through 2000 by the USGS during 

stormflow and recession-flow periods. This step, which 

focused on the range of fecal coliform bacteria concentrations during peak stormflow and stormflow recession, was achieved by adjusting ACCUM and WSFAC. 

WSFAC was adjusted by revising the rate of surface 

runoff required to remove 90 percent of the surfacestored bacteria (WSQOP). The initial values of 

WSQOP ranged from 0.3 to 0.7 in/hr (table 20). Lower 

values of WSQOP result in more bacteria being washed 

off the land surface per unit rate of surface runoff than 

do higher values. Thus, decreasing WSQOP will generate increased fecal coliform concentrations during individual storm events. However, when changes to 

WSQOP did not produce sufficient adjustments to 

resulting peak fecal coliform concentrations, then 

ACCUM was adjusted. The post-storm fecal coliform 

recession rate was calibrated by adjusting the fecal 

coliform concentration in interflow storage (IQO). 

Increasing the amount of bacteria in IQO decreases the 

fecal coliform bacteria recession rate. The initial value 

of IQO was set to 1,500 col/100 mL.

Finally, the model was calibrated to BST data collected 1999 through 2000 (Hyer and Moyer, 2003). 

These data provide information on the sources of fecal 

coliform bacteria to Christians Creek and also are 

treated as being representative of the percent contribution by each source to the total instream fecal coliform 

load. Not all bacteria sources identified by means of 

BST were included explicitly in the model because the 

fecal coliform model was developed before the results 

of the BST study (Hyer and Moyer, 2003) were available. The minor sources identified by Hyer and Moyer 

(2003) not included in the model contributed a total of 

2.6 percent of the E. coli isolates identified. However, 

98.4 percent of the E. coli isolates identified by means 

of BST (including poultry, cattle, humans, dogs, 

horses, deer, cats, ducks, geese, raccoons, beaver, 

sheep, and muskrats) were represented in the model. 

Source-specific instream fecal coliform loads are determined by simulating each source independently. Each 

source-specific instream fecal coliform load is a product of bacteria transported through surface runoff, 

interflow, base flow, and various point sources. The 

sum of the source-specific fecal coliform contributions 

is equal to the total fecal coliform contribution used to 

calibrate the model to observed concentration data. The 

fecal coliform accumulation rate (ACCUM) is adjusted 

for each source represented in the model in order to 

calibrate the simulated source-specific instream load to 

observed BST data. This calibration step helps to 

reduce the inherent error in the calculated ACCUM 

value for each source. As a result, the dominant contributing sources in the watershed identified by means 

of BST are represented in the model.

The calibration of the fecal coliform model was 

evaluated through graphical comparisons and comparison of the observed historical geometric mean concentrations to the simulated geometric mean 

concentrations. Plots were compared of (1) simulated 

daily minimum and maximum fecal coliform concentrations and observed fecal coliform concentrations, 

and (2) simulated and observed percent contributions to 

instream fecal coliform load. The geometric mean is a 

measure of central tendency that is unbiased by 

extreme high and low values and is defined as 

Land-use

type

WSQOP

(inch per hour)

Urban 0.5

Residential .5

Cropland .6

Hayland .6

Pasture .6

Forest .7

Urban impervious .3

Table 20. Initial values of WSQOP used for 

the various land-use types represented in 

the fecal coliform model for Christians 

Creek, Augusta County, Virginia

[WSQOP, Rate of surface runoff required to 

remove 90 percent of the surface-stored 

fecal coliform bacteria]
Requirements for the Fecal Coliform TMDL 39

where GM is the geometric mean,

 is nth root of the product of 

the n quantities, a1, . . . , an. 

The geometric mean of the simulated daily fecal 

coliform concentrations was compared to the geometric 

mean of the monthly samples collected by DEQ. The 

comparison of the simulated and observed geometric 

mean concentrations was done after model calibration 

and was not a part of the iterative calibration process. 

Data Limitations

Model calibration was hindered by limitations associated with the historical fecal coliform bacteria data 

from DEQ. These limitations include (1) censoring of 

the data by upper and lower detection limits, and (2) 

lack of data during peak stormflow periods. DEQ collects these data to determine if a particular stream is in 

compliance with the State water-quality standard, not 

to determine the actual fecal coliform bacteria concentration. Quantitative data, however, are preferred for 

use during model calibration. In addition, DEQ collects 

these data primarily under low-flow and recession-flow 

conditions. The lack of data during stormflow periods 

limits model calibration of simulated stormflow 

responses. Therefore, data collected by the USGS for 

this study were incorporated into the model calibration 

process to provide information on the response of fecal 

coliform bacteria concentrations during stormflow 

periods.

The model-construction and -calibration process 

also was limited by the uncertainty associated with the 

fecal coliform accumulation rate (ACCUM) for each 

source. This uncertainty is linked to the four parameters used to calculate ACCUM: feces produced per day 

(Fprod), number of fecal coliform bacteria per gram of 

feces produced (FCden), population size (POPN), and 

habitat area (HAB). Most of this uncertainty is associated with FCden and POPN. The range of observed 

FCden values in previous studies (Hussong and others, 

1979; Smith, 1961; Wheater and others, 1979) commonly extends over 2–5 orders of magnitude. For 

example, Mara and Oragui (1981) found FCden for 

dogs, cats, and humans ranges from 4.1 x 106 col/g to 

4.3 x 109 col/g; 8.9 x 104 col/g to 2.6 x 109 col/g; and 

1.3 x 105 col/g to 9.0 x 109 col/g, respectively (Mara 

and Oragui, 1981). Values of POPN commonly are 

unknown for the human, pet, and wildlife populations, 

and the proportion of the population that contributes to 

the instream fecal coliform load also is unknown. This 

uncertainty for each animal type is of major concern 

because ACCUM is the primary input parameter for the 

simulation of fecal coliform bacteria; ACCUM values 

are analogous to precipitation data in the streamflow 

model. As a result of the uncertainty associated with 

ACCUM, BST data collected by the USGS (Hyer and 

Moyer, 2003) were incorporated into the fecal coliform 

model-calibration process. By using BST data, the simulated contributions to instream fecal coliform bacteria 

load from each represented source were matched to the 

observed contributions.

REQUIREMENTS FOR THE FECAL 

COLIFORM TMDL

After the fecal coliform model was calibrated, the 

TMDL for Christians Creek was determined. The 

TMDL is defined as the sum of all waste-load allocations (WLAs) from point sources and load allocations 

(LAs) from nonpoint sources and natural background 

(equation 1). The TMDL includes a margin of safety 

(MOS) that explicitly accounts for uncertainties incorporated into the TMDL development process. In addition, the TMDL is set at a level that ensures that the 

fecal coliform loads from the point sources and nonpoint sources can be assimilated without exceeding the 

State water-quality standard. 

Designation of Endpoint

Prior to identifying the TMDL for Christians Creek, 

a numeric endpoint was established by DEQ; this value 

is used to evaluate the attainment of acceptable water 

quality and represents the water-quality goal that will 

be targeted through load reduction strategies designated in the TMDL plan. The numeric endpoint for the 

Christians Creek TMDL was determined by DEQ and 

DCR on the basis of the State water-quality standards, 

which specify a maximum fecal coliform concentration 

of 1,000 col/100 mL at any time, or a geometric mean 

criterion of 200 col/100 mL for two or more samples 

GM a1 ( )... an [ ] ( ) 1 n⁄ =

[ ] () () n a an

1

1 . . .

(9)
40 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

over a 30-day period. The geometric mean criterion 

was used as the TMDL endpoint because continuous 

simulation modeling generates more data points than 

the minimum number of samples required for the calculation of the geometric mean. 

Margin of Safety

An explicit 5-percent MOS, as required by DEQ 

and DCR, was incorporated into the TMDL for Christians Creek. Thus, the numeric endpoint was decreased 

from a 30-day geometric mean of 200 col/100 mL to 

190 col/100 mL. 

Scenario Development

The objective of load-reduction scenario development was to generate a series of scenarios that, if 

implemented, would generate water-quality conditions 

that meet the State standard, including the designated 

MOS, thus establishing the TMDL for Christians 

Creek. Each load-reduction scenario was simulated 

over the time period used for model calibration (1991–

97). During scenario development, the fecal coliform 

load from a given source(s) was reduced iteratively 

until the target water-quality conditions were met. 

These load reduction scenarios then were provided to 

the State and local watershed managers, who then 

selected a scenario and designated it as the TMDL for 

Christians Creek. 

Reductions from Point and Nonpoint 

Sources

Representation of permitted point source discharges 

in the Christians Creek model was modified during the 

fecal coliform load allocation assessment to reflect the 

current (post-2001) or proposed discharge rates for the 

various facilities in the watershed (table 21). Major 

modifications to the permitted point-source discharges 

used for model calibration were (1) discharge rate proposed for Fishersville sewage treatment plant (STP) 

(VA0025291) was increased from 0.7 mgd to 4.0 mgd, 

and (2) the Greenville STP (VA0090417) was added. 

The increased discharge rate from the Fishersville STP 

is a result of service-area expansion to include areas 

once serviced by the Staunton Plaza STP (VA0022306) 

and Brookwood Interchange STP (VA0022292), which 

were removed from service in July and August 2001. 

The Greenville STP was scheduled to begin discharging to Christians Creek by the end of 2002. Fecal 

coliform load reductions from permitted and general 

point sources are not required by DEQ as part of the 

TMDL development for Christians Creek because the 

dischargers are required to operate at or below the geometric mean water-quality standard of 200 col/100 mL.

Fecal coliform loads were reduced from nonpoint 

sources, including direct instream deposition and land 

surface runoff, which impact water quality during 

low-flow and stormflow periods. Direct instream deposition was reduced through source-specific reductions 

from instream cattle and straight pipes. The fecal 

coliform load associated with surface runoff was 

Permit 

number Owner Facility

Discharge rate 

(million gallons 

per day)

Fecal coliform

permit limit (number of 

colonies per 100 

milliliters)

Annual fecal coliform 

load (number of 

colonies per year)

VA0025291 Augusta County Service 

Authority

Fishersville SewageTreatment Plant 4.0 200 1.10 x 1013

VA0090417 Augusta County Service 

Authority

Greenville SewageTreatment Plant 0.25 200 6.90 x 1011

VA0020427 Augusta County School 

Board

Rivershead High 

School 0.016 200 4.41 x 1010

VA0089061 Woodlawn Village L.L. Corp. Woodlawn Village 

Mobile Home Park 0.015 200 4.16 x 1010

VA0086738 Southern States Coop, Inc. Southern States Coop 0 200 0

Total 1.18 x 1013

Table 21. Current (post-2001) permitted point-source dischargers of fecal coliform bacteria in Christians Creek, Augusta County, Virginia
Results from the Streamflow and Fecal Coliform Models 41

reduced through source-specific reductions from the 13 

sources represented in the model. As represented in the 

HSPF model, any source-specific fecal coliform load 

reduction on the land surface has a comparable reduction in both interflow and base flow. For example, a 

75-percent reduction of dog-derived fecal coliform 

bacteria on the land surface will result in a 75-percent 

reduction of these bacteria in both interflow and base 

flow. 

RESULTS FROM THE STREAMFLOW AND 

FECAL COLIFORM MODELS

Streamflow Model Calibration Results

The calibrated streamflow model was assessed initially by comparing simulated and observed streamflow 

at Route 794 against predefined criteria (table 22). 

Observed and simulated total annual runoff for water 

years 1994–97 was 57.87 and 58.25 in., respectively. 

The percent difference of 0.66 percent is within the 

designated 10-percent criterion and indicates that the 

simulated water budget closely approximates the 

observed water budget. The range of observed and simulated flows during the calibration period was evaluated by comparing the total of the highest 10-percent 

flows and the lowest 50-percent flows. The highest 

10-percent flows category is representative of major 

storm events, whereas the lowest 50-percent is representative of base-flow conditions. The percent difference between the total of the highest 10-percent and 

lowest 50 percent simulated and observed flows was 

within the designated criteria of 10- and 15-percent difference. Additionally, the seasonality inherent in the 

observed and simulated seasonal flows was compared. 

Simulated total winter (January, February, and March) 

and spring (April, May, and June) runoff was –7.99 

percent and –5.63 percent less, respectively, than the 

observed seasonal runoff. Simulated total summer 

(July, August, and September) and fall (October, 

November, and December) runoff were 1.61 (15.30 

percent) and 1.47 in. (15.30 percent) greater than the 

observed summer and fall runoff, respectively.

The observed and simulated annual runoff for the 

calibration period ranged from 7.72 to 21.37 and 9.77 

to 20.88 in., respectively (table 23). The percent difference between the simulated and observed annual runoff 

ranged from –15.82 to 26.55 percent. The long-term 

average annual runoff for Christians Creek for water 

years 1968–97 is 14.07 in. (White and others, 1998). 

Based on this long-term average, the streamflow model 

simulated runoff over a range of hydrologic extremes 

from dry (1995) to wet (1996). 

Runoff 

category

Observed

(inches)

Simulated

(inches)

Difference

(percent)1

Criterion

(percent)

Total annual runoff 57.87 58.25 0.66 10

Highest 10-percent flow2 24.90 24.26 -2.55 10

Lowest 50-percent flow3 10.08 10.94 8.48 15

Winter runoff 25.16 23.15 -7.99 15

Spring runoff 12.60 11.89 -5.63 15

Summer runoff 10.52 12.13 15.30 15

Fall runoff 9.61 11.08 15.30 15

Table 22. Observed and simulated runoff values for Route 794 for Christians Creek, Augusta County, Virginia, water years 1994-97

1Value calculated as simulated minus observed divided by observed times 100. 2The sum of all streamflow values with a 10-percent chance or less of being equaled or exceeded, and converted to runoff values 

(indicative of stormflow conditions).

3The sum of all streamflow values with a 50-percent chance or greater of being equaled or exceeded, and converted to runoff values 

(indicative of base-flow conditions).
42 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Similar to total amount of runoff simulated, the 

pathways by which the streamflow model routes 

incoming precipitation is important. Total simulated 

runoff was derived from surface runoff, interflow, and 

base flow (table 24). Between 50.19 and 73.16 percent 

of the annual runoff for water years 1994–97 was 

derived from base flow (ground-water inputs). Rutledge and Mesko (1996) calculated a base-flow index 

of 72.2 percent for an adjacent watershed from streamflow data at South River near Dooms, Va. (station number 01626850), for the period 1981–90. Base-flow 

contribution to streamflow in Christians Creek varies 

seasonally from 72.92 percent in the spring to 50.95 

percent in the summer and contributions from surface 

runoff during spring and summer range from 12.53 to 

31.90 percent, respectively (table 24).

Various graphical comparisons provided information on the quality of the calibrated streamflow model. 

The hydrographs for water years 1994–97 show the 

simulated and observed streamflow response to individual precipitation events (fig. 13). These hydrographs 

show generally good agreement between simulated and 

observed daily mean streamflow values. A strong correlation was observed between simulated and observed 

streamflow where 89 percent of the variability in 

observed streamflow is explained by simulated streamflow (fig. 14). Residual plots display the measured difference between simulated and observed streamflow; 

no difference will generate a residual equal to zero. 

Residuals between simulated and observed streamflow 

in Christians Creek for water years 1994-97 are distributed uniformly around zero, indicating no bias in the 

model simulation (fig. 15). Flow-duration curves show 

the percentage of time a particular streamflow is 

equaled or exceeded and represent the combined 

effects of watershed characteristics such as climate, 

topography, and hydrogeologic conditions on the distribution of flow magnitude through time (Searcy, 1959). 

Flow-duration curves for simulated and observed daily 

flows in Christians Creek are similar over the majority 

of flow conditions except for the extreme low (less than 

20 ft3/s) flows (fig. 16).

Water 

year

Observed 

(inches)

Simulated 

(inches)

Difference

(percent)1

1994 14.76 15.79 6.98

1995 7.72 9.77 26.55

1996 21.37 20.88 -2.29

1997 14.03 11.81 -15.82

Total 57.87 58.25 0.66

Table 23. Observed and simulated annual runoff, Christians Creek, 

Augusta County, Virginia, water years 1994-97

1Value calculated as simulated minus observed divided by observed times 100.

Water year Annual runoff 

(inches)

Surface runoff 

(inches)

Interflow

 (inches)

Base flow

 (inches)

Base-flow index 

(percent)

1994 15.79 2.58 3.60 9.35 59.21

1995 9.77 1.51 1.44 6.57 67.25

1996 20.88 6.15 3.95 10.48 50.19

1997 11.81 1.08 1.83 8.64 73.16

Total1 58.25 11.32 10.82 35.04 60.15

Water years 

1994-1997

Total runoff 

(inches)

Surface runoff 

(inches)

Interflow

 (inches)

Base flow 

(inches)

Base-flow index 

(percent)

Winter 23.15 4.49 5.64 12.75 55.08

Spring 11.89 1.49 1.47 8.67 72.92

Summer 12.13 3.87 1.80 6.18 50.95

Fall 11.08 1.47 1.91 7.44 67.15

Total1 58.25 11.32 10.82 35.04 60.15

Table 24. Simulated total annual and seasonal runoff, interflow and base flow for calibration period, 

Christians Creek, Augusta County, Virginia, water years 1994-97

1May not add to indicated value because of rounding.
Results from the Streamflow and Fecal Coliform Models 43

Figure 13. Daily rainfall and observed and simulated daily mean streamflows for water years 1994 (A), 1995 (B), 1996 (C), and 1997 (D), 

Christians Creek, Augusta County, Virginia.

0

200

400

600

800

1,000

1,200

1,400

1,600

0

2

4

STREAMFLOW, IN CUBIC FEET PER SECOND

(A)

RAINFALL, IN INCHES

(B)

STREAMFLOW, IN CUBIC FEET PER SECOND

RAINFALL, IN INCHES

200

0

400

600

800

1,000

1,200

1,400

1,600

0

2

4

1993 1994

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

1994 1995

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Observed

Simulated

Rainfall

Observed

Simulated

Rainfall
44 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 13. Daily rainfall and observed and simulated daily mean streamflows for water years 1994 (A), 1995 (B), 1996 (C), and 1997 (D), 

Christians Creek, Augusta County, Virginia—Continued.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

0

2

4

6

8

Maximum simulated (5,520)

Maximum precipitation (7.4)

Maximum observed (4,630)

STREAMFLOW, IN CUBIC FEET PER SECOND

(C)

RAINFALL, IN INCHES

1995 1996

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Observed

Simulated

Rainfall
Results from the Streamflow and Fecal Coliform Models 45

Figure 13. Daily rainfall and observed and simulated daily mean streamflows for water years 1994 (A), 1995 (B), 1996 (C), and 1997 (D), 

Christians Creek, Augusta County, Virginia—Continued.

0

200

400

600

800

1,000

1,200

1,400

1,600

0

2

4

(D)

STREAMFLOW, IN CUBIC FEET PER SECOND

RAINFALL, IN INCHES

Observed

Simulated

Rainfall

1996 1997

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Figure 14. Simulated daily streamflow in relation to observed daily streamflow, at Christians Creek, Augusta County, Virginia, 

water years 1994-97.

5

10

50

100

500

1,000

5,000

8,000

OBSERVED STREAMFLOW, IN CUBIC FEET PER SECOND

SIMULATED STREAMFLOW, IN CUBIC FEET PER SECOND

5 10 50 100 500 1,000 5,000 8,000

R2=0.89

1:1 line
46 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 15. Residuals for simulated minus observed daily streamflow, Christians Creek, Augusta County, Virginia, 

water years 1994-97.

1994 1995 1996 1997

-1,400

-1,200

-1,000

800

600

400

200

0

200

400

600

800

1,000

1,200

1,400

SIMULATED MINUS OBSERVED STREAMFLOW, 

IN CUBIC FEET PER SECOND

O D F A J A O D F A J A O D F A J A O D F A J A

Figure 16. Flow-duration curves for observed and simulated daily mean streamflow, Christians Creek, Augusta County, Virginia, water years 1994-97

5

10

50

100

500

1,000

5,000

8,000

Observed

Simulated

STREAMFLOW, IN CUBIC FEET PER SECOND

PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99 99.5 99.9
Results from the Streamflow and Fecal Coliform Models 47

Graphical comparisons also were used to further 

evaluate the observed and simulated seasonal hydrologic response in Christians Creek. The distribution of 

simulated and observed daily flows during the winter, 

spring, summer, and fall months shows that simulated 

and observed flows for each season have similar 

means, medians, and variability (fig. 17). In addition, 

simulated flow-duration curves for winter, spring, and 

summer closely approximate the observed seasonal 

flow-duration curves (fig. 18). The simulated and 

observed fall flow-duration curves are similar over the 

majority of the flow conditions and variability 

increases only during the low-flow periods.

The streamflow model calibration also was evaluated using hourly simulated and observed streamflow 

data. This shortened time step allows for detailed evaluation of stormflow characteristics such as timing, 

peak flows, volume, and flow recession. For a storm 

event during February 23–24, 1994, simulated and 

observed stormflow characteristics are similar except 

for stormflow timing (fig. 19A). The simulated stormflow response occurs approximately 2 hours before the 

observed response. This time lag is present primarily 

because the Staunton sewage-treatment plant (SSTP) 

rainfall gage is approximately 6 mi. west of the streamflow gage on Christians Creek. Storm movement in the 

Shenandoah Valley generally is from the southwest to 

the northeast; therefore, rain falls at the rain gage 

before falling over the rest of the watershed. The simulated streamflow peak on February 24th is a result of 

measured rainfall occurring at the SSTP rain gage and 

not over the rest of the watershed. 

An example of a storm event for which the stormflow response was not well simulated resulted during 

September 6–7, 1996 (fig. 19B). On September 6th, 

approximately 7.40 in. of rain fell in association with 

Hurricane Fran. The discrepancies in the simulated and 

observed stormflow characteristics can be linked to 

rainfall volume and intensity data and/or model calibration. Measured rainfall at the SSTP rain gage during 

Hurricane Fran was 7.40 in. while 2.28, 6.84, and 7.86 

in. of rainfall was measured at the nearby Todd Lake, 

Dale Enterprise, and Sherando rain gages (operated by 

the National Oceanic and Atmospheric Administration), respectively. The simulated storm hydrograph is 

more jagged and undersimulates runoff compared to 

the observed hydrograph, indicating that the rainfall 

volume and intensity measured at SSTP is not representative of what fell in the watershed. Another explanation is that the model is not well calibrated to 

simulate a storm event of this magnitude. 

Input-Source Error

Three factors account for many of the differences 

between simulated and observed streamflow. The primary factor is the quality and representativeness of the 

input (rainfall) data. Other factors are the occurrence of 

snow in the watershed and model error that results 

because extreme events cannot be simulated in the 

model.

The most important input dataset to the streamflow 

model is rainfall. Because of the spatial and temporal 

variability associated with rainfall, however, data collected at a rain gage may not always be representative 

of the rainfall in the surrounding areas/watershed. 

Additionally, 33 percent of the rainfall data at the SSTP 

rain gage were missing for the period 1990–97. Rainfall data from nearby gages were used to fill these missing data. In some instances during the calibration 

period, in addition to the examples discussed previously, rainfall data were not representative of the actual 

rainfall distribution over the entire watershed. For 

example, on August 17, 1994, the measured daily rainfall at SSTP was 2.10 in. (fig. 13A). The simulated 

daily mean streamflow on August 17th was 387 ft3/s, 

whereas the observed daily mean streamflow was 

169 ft3/s (fig. 13A). The amount of rainfall recorded at 

SSTP on this date was compared with rainfall measurements of 1.24, 1.90, 2.72, 3.68, and 4.72 in. at nearby 

Craigsville, Dale Enterprise, Middlebrook, and 

Spottswood rain gages (operated by the National Oceanic and Atmospheric Administration), respectively. 

Because the data recorded at SSTP fell within the range 

of rainfall data from surrounding gages, the data value 

from SSTP was used during the simulation. However, 

the observed streamflow data indicate that less than 

2.10 in. of rain fell within the Christians Creek watershed. This result is one example of model error that 

occurred because of input rainfall data.

Snowfall on the watershed also caused differences 

between simulated and observed streamflow. Snow 

accumulation and melt were not included in the streamflow model for Christians Creek because winter is not a 

critical water-quality season with respect to fecal 

coliform bacteria exceedances, and snowmelt is not a 

dominant feature of annual runoff in the watershed. 

During a snowfall event, the volume of water in the
48 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 17. Observed and simulated daily streamflow (Winter, January-March; Spring, April-June; Summer, July-September; Fall, October-December), 

Christians Creek, Augusta County, Virginia, water years 1994-97.

5

10

50

100

500

1,000

5,000

8,000

(361) (361)

(364) (364)

(368) (368)

(368) (368)

Winter Spring Summer Fall

STREAMFLOW, IN CUBIC FEET PER SECOND

Observed Simulated Observed Simulated Observed Simulated Observed Simulated

Streamflow

Far outlier

EXPLANATION

Mean

75th

25th

50th

Sample size (364)

Outlier

90th

10th

Percentile—Percentage of values equal 

to or less than indicated values
Results from the Streamflow and Fecal Coliform Models 49

Figure 18. Seasonal flow-duration curves for observed and simulated daily mean streamflow, Winter, January-March (A), Spring, April-June (B), 

Summer, July-September (C), and Fall, October-December (D), in Christians Creek, Augusta County, Virginia, water years 1994-97.

5

10

50

100

500

1,000

5,000

8,000

STREAMFLOW, IN CUBIC FEET PER SECOND

PERCENT CHANCE EXCEEDED

(A)

Observed

Simulated

Observed

Simulated

10

50

100

500

1,000

5,000

8,000

STREAMFLOW, IN CUBIC FEET PER SECOND

PERCENT CHANCE EXCEEDED

(B)

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99 99.5

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99 99.5
50 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 18. Seasonal flow-duration curves for observed and simulated daily mean streamflow, Winter, January-March (A), Spring, April-June (B), 

Summer, July-September (C), and Fall, October-December (D), in Christians Creek, Augusta County, Virginia, water years 1994-97—Continued.

5

10

50

100

500

1,000

5,000

8,000

STREAMFLOW, IN CUBIC FEET PER SECOND

(D)

5

10

50

100

500

1,000

5,000

8,000

STREAMFLOW, IN CUBIC FEET PER SECOND

(C)

Observed

Simulated

Observed

Simulated

PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99 99.5

PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99 99.5
Results from the Streamflow and Fecal Coliform Models 51

Figure 19. Hourly rainfall and observed and simulated daily mean streamflow, February 23-24, 1994 (A) and September 6-7, 1996 (B), 

Christians Creek, Augusta County, Virginia.

February 23, 1994 February 24, 1994

0

400

800

1,200

1,600

2,000

2,400 0

0. 1

0. 2

(A)

RAINFALL, IN INCHES

Observed

Simulated

Rainfall

Observed

Simulated

Rainfall

HOURLY STREAMFLOW, IN CUBIC FEET PER SECOND

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

0

1

2

HOURLY STREAMFLOW, IN CUBIC FEET PER SECOND

RAINFALL, IN INCHES

(B)

0H 4H 8H 12H 16H 20H 4H 8H 12H 16H 20H

September 6, 1996 September 7, 1996

0H 4H 8H 12H 16H 20H 4H 8H 12H 16H 20H
52 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

snow typically is recorded at the rainfall gage. This 

recorded volume is treated as a volume of rain and used 

in the streamflow model. The resulting simulated 

streamflow response is an initial oversimulated peak 

followed by an extended period of undersimulated 

storms. The initial oversimulation is caused by the 

recorded volume of snow being treated like rainfall 

instead of the snow accumulating on the land surface. 

The extended period of undersimulated storms occurs 

because of the additional volume of water stored in the 

snow on the ground that is not accounted for by the 

model. Therefore, greater amounts of runoff per volume of incoming rain are observed than are simulated. 

These discrepancies resulted during the following time 

periods: January 14–28, 1994; March 2–15, 1994; and 

December 5–20, 1996 (fig. 13).

Streamflow Model Verification Results

The verification process tests the capacity of the 

calibrated streamflow model to simulate streamflow 

during a time period that was not used for model calibration and, thus, is the best test of model reliability.

Hydrologic model verification results were first 

assessed by comparing simulated and observed streamflow from the Route 794 stream gage for water years 

1992-93 (table 25). Observed and simulated total 

annual runoff for water years 1992-93 was 25.96 and 

25.53 in., respectively. The –1.66 percent difference is 

within the designated 10-percent criterion and indicates 

that the simulated water budget closely approximates 

the observed water budget. The percent difference 

between the total of the highest 10-percent flows was 

–8.24 percent. The total of the lowest 50-percent flows 

was 4.67 and 4.72 in. for observed and simulated flows, 

respectively. Simulated and observed winter (January, 

February, and March), spring (April, May, and June), 

and summer (July, August, and September) runoff were 

well within the designated criterion. Simulated total 

fall (October, November, and December) runoff was 

0.68 in. (17.96 percent) greater than the observed fall 

runoff.

The observed and simulated annual runoff for water 

years 1992-93 were 10.36 and 15.60, and 10.35 and 

15.18 in., respectively (table 26) which yielded –0.10 

and –2.69 percent differences. The long-term average 

annual runoff for Christians Creek for water years 

1968-97 is 14.07 in. (White and others, 1998). Based 

on this long-term average, the verification of the calibrated streamflow model included a dry (1992) and an 

average (1993) year. Total simulated runoff was 

derived from surface runoff, interflow, and base flow 

(table 27). A total of 63.18 percent of the total annual 

runoff for water years 1992-93 was derived from base 

flow (ground-water inputs), which is consistent with 

the findings from Rutledge and Mesko (1996) for an 

adjacent watershed to Christians Creek, calculated 

from streamflow data at South River near Dooms, Va. 

(station number 01626850), where the base-flow index 

for the period 1981–90 was 72.2 percent. Base-flow 

contribution to streamflow in Christians Creek varied

Runoff 

category

Observed

(inches)

Simulated

(inches)

Difference

(percent)1

Criterion

(percent)

Total annual runoff 25.96 25.53 -1.66 10

Highest 10-percent flow2 11.17 10.25 -8.24 10

Lowest 50-percent flow3 4.67 4.72 1.07 15

Winter runoff 11.33 10.76 -5.03 15

Spring runoff 9.14 8.56 -6.35 15

Summer runoff 2.24 2.38 6.25 15

Fall runoff 3.23 3.81 17.96 15

Table 25. Observed and simulated runoff values for Route 794, Christians Creek, Augusta County, Virginia, 

water years 1992-93

1Value calculated as simulated minus observed divided by observed times 100. 2The sum of all streamflow values with a 10-percent chance or less of being equaled or exceeded, and converted to runoff values 

(indicative of stormflow conditions).

3The sum of all streamflow values with a 50-percent chance or greater of being equaled or exceeded, and converted to runoff values 

(indicative of base-flow conditions).
Results from the Streamflow and Fecal Coliform Models 53

seasonally from 86.75 percent in the summer to 50.65 

percent in the winter, whereas contributions from surface runoff ranged from 4.62 percent during the summer to 21.38 percent during the winter (table 27).

Various graphical comparisons also were used to 

evaluate the results of the streamflow model verification. Hydrographs for the verification period generally 

show good agreement between simulated and observed 

daily mean values for streamflow during individual 

rainfall events (fig. 20). A strong correlation was 

observed between simulated and observed streamflow 

where 87 percent of the variability in observed streamflow is explained by simulated streamflow (fig. 21). 

This plot also shows an area where the model is underpredicting streamflow during low-flow periods, which 

primarily occurred in the first two months of the verification period. The residuals between simulated and 

observed streamflow in Christians Creek for water 

years 1992-1993 vary normally around zero, indicating 

a lack of bias in the model simulation (fig. 22). Flow 

duration curves for simulated and observed daily flows 

are similar for flows greater than 20 cfs (80 percent 

chance exceeded) (fig. 23). Increased separation 

between simulated and observed results for flows less 

than 20 ft3/s. This separation can be explained by the 

undersimulation during the October–November 1992 

time period.

Additional graphical comparisons were made to 

further evaluate the observed and simulated seasonal 

hydrologic response in Christians Creek. The distribution of simulated and observed daily flows during the 

winter, spring, summer, and fall months shows that 

simulated and observed flows for each season have 

similar means and medians (fig. 24). Simulated and 

observed streamflows for the winter and spring months 

exhibit nearly identical variation while simulated summer and fall streamflows had greater variability than 

the observed flows. Simulated and observed fall 

streamflows show increased variability for low flows 

that can be linked to the October–November 1992 time 

period. Flow-duration curves also illustrate how closely 

the model simulates the observed seasonal hydrologic 

response (fig. 25). Simulated flow-duration curves for 

winter and spring closely approximate the observed 

flow-duration curves. The simulated and observed 

flow-duration curves for summer and fall exhibit the 

greatest separation for flows less than 20 ft3/s.

Water 

year

Observed 

(inches)

Simulated 

(inches)

Difference

(percent)1

1992 10.36 10.35 -0.10

1993 15.60 15.18 -2.69

Total 25.96 25.53 -1.66

Table 26. Observed and simulated annual runoff, Christians Creek, 

Augusta County, Virginia, water years 1992-93

1Value calculated as simulated minus observed divided by observed times 100.

Water year Annual runoff 

(inches)

Surface runoff 

(inches)

Interflow

 (inches)

Base flow

 (inches)

Base-flow index 

(percent)

1992 10.35 1.62 1.64 6.83 65.99

1993 15.18 2.43 3.19 9.30 61.26

Total1 25.53 4.05 4.83 16.13 63.18

Water years 

1992-1993

Total runoff 

(inches)

Surface runoff 

(inches)

Interflow

 (inches)

Base flow 

(inches)

Base-flow index 

(percent)

Winter 10.76 2.30 2.88 5.45 50.65

Spring 8.56 1.24 1.35 5.83 68.15

Summer 2.38 .11 .09 2.06 86.75

Fall 3.81 .40 .50 2.78 72.99

Total1 25.51 4.05 4.83 16.12 63.19

Table 27. Simulated total annual and seasonal runoff, surface runoff, interflow and base flow for 

verification period, Christians Creek, Augusta County, Virginia, water years 1992-93

1May not add to indicated value because of rounding.
54 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 20. Daily rainfall and observed and simulated daily mean streamflow for water years 1992 (A), 1993 (B) Christians Creek, 

Augusta County, Virginia.

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

2,200

0

2

4

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

2,200

0

2

4

STREAMFLOW, IN CUBIC FEET PER SECOND

(B)

STREAMFLOW, IN CUBIC FEET PER SECOND

RAINFALL, IN INCHES RAINFALL, IN INCHES

(A)

Observed

Simulated

Rainfall

Observed

Simulated

Rainfall

1991 1992

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

1992 1993

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep
Results from the Streamflow and Fecal Coliform Models 55

Figure 21. Simulated daily streamflow in relation to observed daily streamflow, at Christians Creek, Augusta County, Virginia, 

water years 1992-93.

1

5

10

50

100

500

1,000

3,000

OBSERVED STREAMFLOW, IN CUBIC FEET PER SECOND

SIMULATED STREAMFLOW, IN CUBIC FEET PER SECOND

1 5 10 50 100 500 1,000 3,000

R2=0.87

1:1 line

Figure 22. Residuals for simulated minus observed daily streamflow, Christians Creek, Augusta County, Virginia, water years 1992-93.

-1,400

-1,200

-1,000

-800

-600

-400

-200

0

200

400

600

800

1,000

1,200

1,400

SIMULATED MINUS OBSERVED STREAMFLOW, 

IN CUBIC FEET PER SECOND

1992 1993

O N D J F M A M J J A S O N D J F M A M J J A S
56 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

The streamflow model verification also was evaluated on an hourly time step. The simulated and 

observed stormflow responses for the April 21-22, 

1992, storm event are similar with respect to storm timing, peaks, volume, and recession (fig. 26A). The simulated and observed stormflow responses did not match 

closely for the March 17-19, 1996, event (fig. 26B). On 

March 17, 1.64 in. of rain fell on top of a pre-existing 

18 in. of snow and the streamflow model accounted 

only for the volume of rainwater, not the 18 in. of snow 

already on the ground. Consequently, the simulated and 

observed stormflow responses are different with 

respect to stormflow peaks and volume. The storm 

peak that occurred on March 18th is a result of 0.88 in. 

of rainfall at SSTP while a lesser amount fell over the 

Christians Creek watershed. 

Final Streamflow Model Parameters

The results of the streamflow model calibration 

demonstrate its effectiveness for simulating the streamflow response in Christians Creek. Final values for the 

11 hydraulic parameters used to calibrate the streamflow model and the urban effective impervious area are 

used in the fecal coliform model simulation (table 28). 

Figure 23. Flow-duration curves for observed and simulated daily mean streamflow, water years 1992-1993, Christians Creek, 

Augusta County, Virginia.

1

5

10

50

100

500

1,000

3,000

Observed

Simulated

STREAMFLOW, IN CUBIC FEET PER SECOND

PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99 99.5 99.86
Results from the Streamflow and Fecal Coliform Models 57

Figure 24. Observed and simulated daily streamflow (Winter, January-March; Spring, April-June; Summer, July-September; Fall, 

October-December), Christians Creek, Augusta County, Virginia, water years 1992-93.

1

5

10

50

100

500

1,000

3,000 (181) (181)

(182) (182)

(184)

(184)

(184)

(184)

STREAMFLOW, IN CUBIC FEET PER SECOND

Winter Spring Summer Fall

Observed Simulated Observed Simulated Observed Simulated Observed Simulated

Streamflow

Far outlier

EXPLANATION

Mean

75th

25th

50th

Sample size (182)

Outlier

90th

10th

Percentile—Percentage of values equal 

to or less than indicated values
58 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 25. Seasonal flow-duration curves for observed and simulated daily mean streamflow, Winter, January-March (A), Spring, April-June (B), 

Summer, July-September (C), and Fall, October-December (D), in Christians Creek, Augusta County, Virginia, water years 1992-93.

1

5

10

50

100

500

1,000

3,000

STREAMFLOW, IN CUBIC FEET PER SECOND

(A)

5

10

50

100

500

1,000

3,000

STREAMFLOW, IN CUBIC FEET PER SECOND

(B)

Observed

Simulated

Observed

Simulated

PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99

PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99
Results from the Streamflow and Fecal Coliform Models 59

Figure 25. Seasonal flow-duration curves for observed and simulated daily mean streamflow, Winter, January-March (A), Spring, April-June (B), 

Summer, July-September (C), and Fall, October-December (D), in Christians Creek, Augusta County, Virginia, water years 1992-93—Continued.

1

5

10

50

100

500

1,000

3,000

STREAMFLOW, IN CUBIC FEET PER SECOND

(C)

Observed

Simulated

Observed

Simulated

1

5

10

50

100

500

1,000

3,000

STREAMFLOW, IN CUBIC FEET PER SECOND

(D) PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99

PERCENT CHANCE EXCEEDED

0.05 0.1 0.5 1 2 5 10 15 20 30 40 50 60 70 80 85 90 95 98 99
60 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 26. Hourly rainfall and observed and simulated daily mean streamflow, April 21-22, 1992 (A) and March 17-19, 1996 (B), Christians Creek, 

Augusta County, Virginia.

0

250

500

750

1,000

1,250

1,500 0

0.1

0.2

(B)

RAINFALL, IN INCHES

HOURLY STREAMFLOW, IN CUBIC FEET PER SECOND

0

500

1,000

1,500

2,000

2,500

3,000

3,500 0

0.2

0.4

0.6

HOURLY STREAMFLOW, IN CUBIC FEET PER SECOND

RAINFALL, IN INCHES

(A)

Observed

Simulated

Rainfall

Observed

Simulated

Rainfall

April 21, 1992 April 22, 1992

0H 4H 8H 12H 16H 20H 4H 8H 12H 16H 20H

March 17, 1996 March 18, 1996 March 19, 1996

0H 6H 12H 18H 6H 12H 18H 6H 12H 18H
Results from the Streamflow and Fecal Coliform Models 61

Fecal Coliform Model Calibration Results

The fecal coliform model is the primary tool for 

quantifying loads, simulating transport mechanisms, 

and identifying load-reduction strategies for fecal 

coliform bacteria in the Christians Creek watershed. 

Direct comparisons are made between simulated and 

observed fecal coliform bacteria concentrations and 

percent contribution from each source to instream fecal 

coliform bacteria load; these comparisons evaluate the 

effectiveness of the calibrated fecal coliform model in 

simulating the fate and transport of fecal coliform bacteria in the watershed. 

Data from the two DEQ long-term water-quality 

monitoring stations, Route 831 and Route 794, were 

used to calibrate portions of the fecal coliform model. 

These DEQ data were used primarily for the calibration 

of fecal coliform bacteria concentrations less than 

16,000 col/100 mL (mostly base-flow and recessionflow periods) because these observed data are affected 

by the DEQ upper detection limit for concentrations 

higher than 16,000 col/100 mL. USGS data collected 

1999 through 2000 were used to calibrate fecal 

coliform bacteria concentrations greater than 16,000 

colonies per 100 mL by using the range of concentrations observed during stormflow periods. The calibration results were evaluated initially by comparing 

graphs of simulated and observed fecal coliform concentrations. However, observed instream fecal coliform 

concentrations are representative only of instream conditions at the time of sample collection, whereas the 

fecal coliform model simulates 24 concentrations 

within a 1-day period. Therefore, simulated daily maximum and minimum concentrations were plotted 

against the observed data from Route 831 (fig. 27) and 

Route 794 (fig. 28). Spikes in simulated fecal coliform 

concentrations are the result of rainfall events where 

bacteria are washed off the land surface. Increases in 

simulated fecal coliform concentrations when spikes do 

not occur are the result of point source (instream cattle, 

straight pipes, and permitted discharges) and diffuse 

ground-water inputs. The capacity of the model to simulate fecal coliform concentrations during low-flow, 

stormflow and post-stormflow conditions was evaluated (figs. 27 and 28). In general, these conditions were 

simulated well in the model. The fecal coliform model 

had a tendency to undersimulate fecal coliform concentrations at Route 831 and oversimulate fecal coliform 

concentrations at Route 794. Attempts to correct Route 

794 caused greater discrepancies at Route 831 and vice 

versa. Therefore, the resulting calibrated fecal coliform 

model is an average of upstream and downstream concentrations. Simulated maximum fecal coliform concentrations during storm events ranged from 1,500 to 

800,000 col/100 mL. Observed maximum fecal 

coliform concentrations in water samples collected by 

the USGS at Route 794 during 1999–2000 storm events 

ranged from 23,000 to 730,000 col/100 mL (Hyer and 

Moyer, 2003). These data indicate that observed fecal

HRU Imperviousness

(percent) AGWETP AGWRC(1 per day) BASETP DEEPFR

INFILT

(inches 

per hour)

INTFW

IRC

(1 per 

day)

KVARY

(1 per 

inch)

LZETP LZSN

(inches)

UZSN

(inches)

U – 0.00 0.965 0.00 0.11 0.03 1.00 0.60 0.00 vm 7.00 0.70

R – .00 .965 .00 .11 .05 1.00 .60 .00 vm 7.00 .70

P – .00 .965 .00 .11 .06 1.00 .60 .00 vm 8.00 .70

H – .00 .965 .00 .11 .06 1.00 .60 .00 vm 8.00 vm

C – .00 .965 .00 .11 .06 1.00 .60 .00 vm 8.00 vm

F – .00 .965 .00 .11 .08 3.00 .60 .00 vm 9.00 .70

UI 6 – – – – – – – – – – –

PI 0 – – – – – – – – – – –

Table 28. Final parameters and percent imperviousness used in six subwatersheds represented in the streamflow model for Christians Creek, 

Augusta County, Virginia

[HRU, Hydrologic Response Unit; see table 1 for definition of parameters; U, Urban; R, Residential; P, Pasture; H, Hayland; C, Cropland; 

F, Forest; UI, Urban impervious; PI, Pasture impervious; –, not applicable; vm, varies monthly]
62 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 27. Simulated daily minimum and maximum concentrations, and observed instantaneous concentrations of fecal coliform bacteria at 

Route 831, water years 1992 (A), 1993 (B), 1994 (C), 1995 (D), 1996 (E), and 1997 (F), Christians Creek, Augusta County, Virginia. (Data from 

Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

900,000

(A)

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

900,000

(B)

1991 1992

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

1992 1993

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Simulated maximum

Simulated minimum

Observed

Simulated maximum

Simulated minimum

Observed

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS
Results from the Streamflow and Fecal Coliform Models 63

Figure 27. Simulated daily minimum and maximum concentrations, and observed instantaneous concentrations of fecal coliform bacteria at 

Route 831, water years 1992 (A), 1993 (B), 1994 (C), 1995 (D), 1996 (E), and 1997 (F), Christians Creek, Augusta County, Virginia. (Data from 

Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)—Continued

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

900,000

(C)

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

900,000 (D)

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

1994

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

1995

1993

1994

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Simulated maximum

Simulated minimum

Observed

Simulated maximum

Simulated minimum

Observed

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS
64 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 27. Simulated daily minimum and maximum concentrations, and observed instantaneous concentrations of fecal coliform bacteria at 

Route 831, water years 1992 (A), 1993 (B), 1994 (C), 1995 (D), 1996 (E), and 1997 (F), Christians Creek, Augusta County, Virginia. (Data from 

Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)—Continued

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

900,000

(E)

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

900,000

(F)

Simulated maximum

Simulated minimum

Observed

Simulated maximum

Simulated minimum

Observed

1996

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

1997

1995

1996

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS
Results from the Streamflow and Fecal Coliform Models 65

Figure 28. Simulated daily minimum and maximum concentrations, and observed instantaneous concentrations of fecal coliform bacteria at 

Route 794, water years 1992 (A), 1993 (B), 1994 (C), 1995 (D), 1996 (E), and 1997 (F), Christians Creek, Augusta County, Virginia. (Data from 

Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)

1992

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

1993

1991

1992

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

800,000

(A)

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

800,000

(B)

Simulated maximum

Simulated minimum

Observed

Simulated maximum

Simulated minimum

Observed

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS
66 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Figure 28. Simulated daily minimum and maximum concentrations, and observed instantaneous concentrations of fecal coliform bacteria at 

Route 794, water years 1992 (A), 1993 (B), 1994 (C), 1995 (D), 1996 (E), and 1997 (F), Christians Creek, Augusta County, Virginia. (Data from 

Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)—Continued

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

800,000

(C)

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

800,000

(D)

1994

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

1995

1993

1994

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Simulated maximum

Simulated minimum

Observed

Simulated maximum

Simulated minimum

Observed

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS
Results from the Streamflow and Fecal Coliform Models 67

Figure 28. Simulated daily minimum and maximum concentrations, and observed instantaneous concentrations of fecal coliform bacteria at 

Route 794, water years 1992 (A), 1993 (B), 1994 (C), 1995 (D), 1996 (E), and 1997 (F), Christians Creek, Augusta County, Virginia. (Data from 

Roderick V. Bodkin, Virginia Department of Environmental Quality, written commun., 1999.)—Continued

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

800,000

(E)

50

100

500

1,000

5,000

10,000

50,000

100,000

500,000

800,000

(F)

1996

Oct Nov Dec

1997

1995

1996

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Jan Feb Mar Apr May Jun Jul Aug Sep

Simulated maximum

Simulated minimum

Observed

Simulated maximum

Simulated minimum

Observed

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS
68 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

coliform concentrations during stormflow periods are 

similar to simulated concentrations. The simulated 

recession of fecal coliform concentrations following a 

storm event ranged from 1 to 7 days (figs. 27 and 28). 

This range is consistent with the findings from Hyer 

and Moyer (2003) that elevated fecal coliform concentrations are maintained for 1 to 5 days following a 

storm event.

The calibrated fecal coliform model also was evaluated by comparing simulated with observed BST data 

collected at Route 794. These data describe the percent 

contribution of fecal coliform bacteria from various 

sources to Christians Creek during an 18-month time 

period. The mean annual percent contribution to the 

total instream fecal coliform load from each represented source was simulated using the fecal coliform 

model. The initial comparison following model calibration between the simulated and observed BST data to 

observed concentration data revealed that simulated 

contributions from cattle (68 percent), duck (9 percent), 

and sheep (9 percent) are overestimated, whereas the 

simulated contributions from the remaining sources 

were underestimated (fig. 29A). This initial comparison of simulated and observed BST data revealed that 

the input sources to the model were not represented 

accurately. Adjustments were made to the ACCUM 

values for each source until the simulated BST signature closely approximated the observed BST signature 

(fig. 29B). 

The calibrated fecal coliform model also was evaluated through comparison of the 30-day geometric mean 

for the simulated fecal coliform bacteria concentrations 

with the geometric mean of observed (period of record) 

concentrations. This comparison was a final check on 

the calibrated fecal coliform model but was not part of 

the iterative calibration process. The geometric means 

of the observed fecal coliform data at Route 831 and 

Route 794 for the period 1991-97 are 1,429 and 558 

col/100 mL, respectively. Fecal coliform concentrations generally are higher at the upstream Route 831 

site than the Route 794 site. The geometric means of 

the simulated fecal coliform concentrations at Route 

831 and Route 794 are 1,619 and 1,057 col/100 mL, 

respectively. The pattern in the simulated geometric 

mean fecal coliform concentrations is similar to the 

observed concentrations in that the fecal coliform 

model simulates higher fecal coliform concentrations 

at the Route 831 site than the Route 794 site. 

The simulated geometric mean concentrations at 

Route 831 and Route 794 are higher than the observed 

concentrations, primarily because different data sets 

are used to calculate the simulated and observed geometric means. The simulated geometric mean concentration is calculated using daily mean concentrations of 

fecal coliform bacteria; thus, elevated concentrations 

generated during stormflow periods are represented, 

increasing the geometric mean. The observed geometric mean concentration is calculated using instantaneous monthly concentrations, so that not all of the 

elevated fecal coliform bacteria concentrations generated during stormflow periods are represented, and the 

resulting geometric mean is lower. Nonetheless, the 

comparison between simulated and observed geometric 

mean concentrations provides additional data on the 

accuracy of the fecal coliform model for simulating the 

fate and transport of fecal coliform bacteria in the 

Christians Creek watershed. 

Final Fecal Coliform Model Parameters

WSQOP (rate of surface runoff that results in 

90-percent washoff of fecal coliform bacteria in 1 hour) 

was the only non-source-specific fecal coliform model 

parameter adjusted during the calibration process. 

WSQOP was used to adjust the washoff response of the 

fecal coliform bacteria to rainfall events. Also, 

WSQOP was used during the calibration of simulated 

storm peaks. The final calibrated values of WSQOP for 

each land-use type represented in the model range from 

0.2 to 0.6 in. per hour (table 29).

Land-use

types

WSQOP

(inch per hour)

Urban 0.3

Residential .3

Cropland .5

Hayland .5

Pasture .5

Forest .6

Urban impervious .2

Table 29. Final values of WSQOP used for 

the land-use types represented in the fecal 

coliform model for Christians Creek, 

Augusta County, Virginia

[WSQOP, Rate of surface runoff required to 

remove 90 percent of the surface-stored 

fecal coliform bacteria]
Results from the Streamflow and Fecal Coliform Models 69

Figure 29. Observed and simulated percent contribution from the simulated sources in the watershed to the total instream fecal coliform 

bacteria load at Route 794, initial calibration (A), and final calibration (B), Christians Creek, Augusta County, Virginia.

0

10

20

30

40

50

60

70

Observed

Simulated

PERCENT CONTRIBUTION

(A)

0

5

10

15

20

25

30

PERCENT CONTRIBUTION

Observed

Simulated

(B)

Poultry Cattle Human Dog Horse Deer Cat Duck Goose Raccoon Beaver Sheep Muskrat

Poultry Cattle Human Dog Horse Deer Cat Duck Goose Raccoon Beaver Sheep Muskrat

SOURCE

SOURCE
70 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

The two source-specific model parameters adjusted 

during the calibration process were the fecal coliform 

accumulation rate on the land surface (ACCUM) and 

the limit of storage of fecal coliform bacteria on the 

land surface (SQOLIM). ACCUM for each source was 

manipulated during calibration; SQOLIM was maintained at 9 times ACCUM. The total fecal coliform 

contributions from humans, dogs, and cats were calibrated by adjusting their initial estimated population 

(POPN) values (table 30). The percentage of dogs 

depositing their feces on impervious areas was 

decreased from 10 percent to 1 percent. ACCUM values for cattle, poultry, horses, and sheep were calibrated by adjusting FCden (number of bacteria per 

gram of feces produced) (table 31). ACCUM for deer 

was calibrated by adjusting the FCden, whereas 

ACCUM values for geese, ducks, raccoons, muskrats, 

and beavers were calibrated through adjustments to 

POPN (table 32). POPN values for humans, dogs, cats, 

geese, ducks, raccoons, muskrats, and beavers are a 

result of model calibration and represent the populations to account for the uncertainty associated with the 

fixed values of Fprod, FCden, and habitat area (HAB); 

POPN values do not represent the actual populations in 

the watershed.

FECAL COLIFORM TMDL

Present Conditions

The simulated fecal coliform bacteria concentrations in Christians Creek, water years 1991-97, were 

converted to 30-day geometric mean concentrations. 

The 30-day geometric mean concentrations indicate 

that predicted fecal coliform concentrations at both 

Route 831 and Route 794 exceed the State geometric 

mean water-quality standard of 200 col/100 mL 

(fig. 30A). Based on the peak fecal coliform 30-day 

geometric mean concentrations of 3,448 and 

6,160 col/100 mL at Route 794 and Route 831, respectively, an approximately from 94- to 97-percent reduction of the current instream fecal coliform load is 

needed to meet the designated water-quality standard. 

Most of the fecal coliform load (99.6 percent) 

entering Christians Creek is from nonpoint sources in 

the watershed (table 33). Thus, most of the fecal 

coliform bacteria are transported during stormflow 

periods. However, the incorporation of a geometric 

mean calculation and the need for compliance with the 

geometric mean water-quality standard places a greater 

emphasis on base-flow conditions that are dominated 

by point source contributions. The geometric mean calculation is used to identify an unbiased average in the 

presence of outliers, such as elevated concentrations of 

fecal coliform bacteria associated with stormflow 

events. In order to meet the State water-quality standard, reductions are needed in fecal coliform loads 

from both nonpoint sources and sources depositing 

directly in the streams. 

Scenarios for Fecal Coliform Load 

Reductions

Total instream fecal coliform load reductions of 

approximately 94–97 percent will reduce the observed 

fecal coliform concentrations below the State 

water-quality standard and designated 5-percent MOS 

(30-day geometric mean of 190 col/100 mL). Three 

source-load reduction scenarios for meeting the 

water-quality goals for Christians Creek were developed through discussions including DCR, DEQ, USGS 

(in a technical advisory role) and local stakeholders 

(table 34). These scenarios feature source-specific 

reductions in fecal coliform loads from nonpoint 

sources and point sources, including direct deposition 

from cattle in streams and straight pipes. Scenario 1 

requires a 100-percent reduction in the present fecal 

coliform loading from cattle, poultry, sheep, horses, 

humans, dogs and cats (nonpoint sources), a 90-percent 

reduction in fecal coliform loading from parking lots 

and roads, and a 100-percent reduction in the fecal 

coliform loading from cattle in streams and straight 

pipes (point sources) in order to ensure that the State 

water-quality standard is not exceeded. Scenarios 2 and 

3 require greater reductions in fecal coliform loading 

from wildlife sources (50 and 94 percent, respectively) 

and parking lots and roads (100 and 99 percent, respectively), whereas lesser reductions are needed from the 

livestock and pet sources in order to ensure that the 

State water-quality standard is not exceeded. These 

three scenarios were discussed and evaluated in a public review process led by DCR and DEQ, and scenario 

3 was chosen for the Christians Creek watershed.

After the source-load reduction strategies in scenario 3 were incorporated into the watershed model, 

simulated fecal coliform concentrations at both Route 

831 and Route 794 met the water-quality goals 

(fig. 30B). Simulated fecal coliform concentrations at 

the mouth of Christians Creek (reach 6, fig. 31) also 
Fecal Coliform TMDL 71

Subwatershed1 Fprod

(grams) FCden

POPN

(number)

HAB

(acres)

Residential Urban Residential Urban

Human–Land Applied

1 150 4.66 x 108 272 – 289 –

2 150 4.66 x 108 176 – 206 –

3 150 4.66 x 108 339 – 309 –

4 150 4.66 x 108 224 – 485 –

5 150 4.66 x 108 330 – 808 –

6 150 4.66 x 108 209 – 261 –

Human–Straight Pipes

1 150 4.66 x 108 3 – – –

2 150 4.66 x 108 3 – – –

3 na – – – – –

4 150 4.66 x 108 3 – – –

5 na – – – – –

6 150 4.66 x 108 3 – – –

Dog

1 450 4.11 x 106 3,121 1,860 289 414

2 450 4.11 x 106 2,019 1,202 206 153

3 450 4.11 x 106 3,887 2,316 309 495

4 450 4.11 x 106 2,567 1,530 485 1,244

5 450 4.11 x 106 3,784 2,255 808 196

6 450 4.11 x 106 2,398 1,429 261 429

Dog Impervious

1 450 4.11 x 106 – 19 – 26

2 450 4.11 x 106 – 12 – 10

3 450 4.11 x 106 – 23 – 32

4 450 4.11 x 106 – 15 – 170

5 450 4.11 x 106 – 23 – 13

6 450 4.11 x 106 – 14 – 27

Cat

1 20 1.49 x 107 8,291 4,990 289 414

2 20 1.49 x 107 5,362 3,228 206 153

3 20 1.49 x 107 10,324 6,215 309 495

4 20 1.49 x 107 6,818 4,105 485 1,244

5 20 1.49 x 107 10,051 6,051 808 196

6 20 1.49 x 107 6,370 3,835 261 429

Table 30. Final values of the total amount of feces produced daily and fecal coliform per gram of feces generated by the human, dog and

cat populations in the residential hydrologic response unit represented in the fecal coliform model, Christians Creek, Augusta 

County, Virginia 

[Fprod, feces produced per day; FCden, fecal coliform bacteria per gram of feces; POPN, population size; HAB, habitat area; -, not 

applicable

1See figure 3 for location of subwatersheds.
72 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

Source Average daily

Fprod (grams) FCden

Dairy cattle 54,545 1.98 x 105

Beef cattle 20,909 5.60 x 105

Heifers 39,091 1.41 x 106

Poultry 231 1.83 x 109

Horse 23,182 4.44 x 106

Sheep 1,091 1.80 x 106

Table 31. Final values of the total amount of feces produced daily and fecal coliform 

bacteria per gram of feces generated by cattle, poultry, horses, and sheep 

represented in the fecal coliform model, Christians Creek, 

Augusta County, Virginia

[Fprod, feces produced per day; FCden, fecal coliform bacteria per gram of feces]

Wildlife

source

Land-use

type

Population density

(number per acre)

POPN 

(number)

Fprod

(grams) FCden

Deer F, P 0.040 975 772 2.24 x 108

Goose–Summer U, R, P, H, C 4.87 23,293 225 3.55 x 106

Goose–Winter U, R, P, H, C 6.23 30,359 225 3.55 x 106

Duck–Summer U, R, P, H, C .71 3,395 150 4.90 x 107

Duck–Summer F .19 413 150 4.90 x 107

Duck–Winter U, R, P, H, C 1.50 7,175 150 4.90 x 107

Duck–Winter F .31 674 150 4.90 x 107

Raccoon F .22 4308 450 1.11 x 107

Raccoon R, P, H, C .09 3821 450 1.11 x 107

Muskrat U, R, P, H, C, F .500 479 100 2.50 x 105

Beaver F .016 5 200 1.00 x 103

Beaver U, R, P, H, C .008 5 200 1.00 x 103

Table 32. Final values for population, total amount of feces produced daily and fecal coliform bacteria per gram of feces for 

deer, goose, duck, raccoon, muskrat, and beaver represented in the watershed model, Christians Creek, Augusta County, Virginia

[Fprod, feces produced per day; FCden, fecal coliform bacteria per gram of feces; POPN, population size; F, Forest; P, Pasture;

U, Urban; R, Residential; H, Hayland; C, Cropland]
Fecal Coliform TMDL 73

will meet the water-quality goals following implementation of scenario 3, thus bringing Christians Creek into 

compliance with the State water-quality standard from 

the headwaters to the mouth. Changes to the present 

fecal coliform load allocation following the incorporation of the source-specific load reductions in scenario 3 

are in table 35. Average annual fecal coliform loading 

pre- and post-TMDL allocations are 3.69 x 1016 and 

1.39 x 1015 col/year, respectively. The percent reductions in the fecal coliform load delivered from the various land types ranged from 94 to 99 percent as a result 

of the reduction scenario. The needed percent reduction 

in the fecal coliform load delivered from cattle in 

Figure 30. Simulated 30-day geometric mean fecal coliform concentrations before (A) and after (B) incorporation of the Total Maximum Daily 

Load (TMDL) allocation scenario at Route 831 and Route 794 for Christians Creek, Augusta County, Virginia, water years 1992-97.

1992 1993 1994 1995 1996 1997 5

10

50

100

500

1,000

5,000

8,000

30-day geometric mean water-quality standard and margin of safety (190 colonies per 100 milliliters)

Route 794

Route 831

Route 794

Route 831

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

(A)

(B)

A

Land-use types

Total annual load of fecal 

coliform bacteria for present 

conditions (colonies per year)

Contribution

(percent)

Urban 2.54 x 1015 6.88

Residential 1.01 x 1016 27.34

Pasture 1.15 x 1016 31.14

Cropland 7.05 x 1015 19.09

Hayland 4.17 x 1015 11.29

Forest 1.38 x 1015 3.74

Urban Impervious 4.92 x 1013 .13

Point Sources

Instream deposition from cattle and humans 1.44 x 1014 .39

Permitted discharges 2.36 x 1012 .01

Total 3.69 x 1016 100

Table 33. Total annual load of fecal coliform bacteria delivered from the various land-use types, direct 

deposition by cattle and humans and permitted discharges for present conditions in Christians Creek, 

Augusta County, Virginia
74 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek Percent reduction from present fecal coliform load 

Scenario number Nonpoint sources Point sourc

Cattle Poultry Sheep Horse Human Dog Cat Goose Duck Deer Raccoon Muskrat Beaver Parking lots and roads Cattle in streams Straight pipes 

1 100 100 100 100 100 100 100 0 0 0 0 0 0 90 100 100 2 100 100 100 100 100 100 100 50 50 50 0 0 0 100 98 100 3 94 94 94 94 100 94 94 94 94 94 94 0 0 99 99 100 

Table 34. Scenarios for reducing fecal coliform bacteria loads and associated percent reductions from nonpoint and point sources represented in the fecal coliform mo
Fecal Coliform TMDL 75

Figure 31. Simulated 30-day geometric mean fecal coliform concentrations before (A) and after (B) incorporation of the Total Maximum Daily 

Load (TMDL) allocation scenario at Route 831 and Route 794 at the end of storm reach 6 for Christians Creek, Augusta County, Virginia, water 

years 1992-97.

1992 1993 1994 1995 1997 1996 5

10

50

100

500

1,000

5,000

8,000

30-day geometric mean water-quality standard and margin of safety (190 colonies per 100 milliliters)

End of reach 6 (TMDL allocation scenario)

End of reach 6 (existing conditions)

FECAL COLIFORM BACTERIA CONCENTRATION,

IN COLONIES PER 100 MILLILITERS

(A)

(B)

Land-use types

Total annual load of fecal 

coliform bacteria for present 

conditions (colonies per year)

Total annual load after 

incorporation of TMDL

(colonies per year)

Reduction

(percent)

Urban 2.54 x 1015 1.01 x 1014 96.01

Residential 1.01 x 1016 1.23 x 1014 98.78

Pasture 1.15 x 1016 4.96 x 1014 95.69

Cropland 7.05 x 1015 4.28 x 1014 93.94

Hayland 4.17 x 1015 1.66 x 1014 96.01

Forest 1.38 x 1015 5.81 x 1013 95.79

Urban impervious 4.92 x 1013 3.21 x 1011 99.35

Point Sources

Instream deposition from cattle and humans 1.44 x 1014 1.12 x 1012 99.22

Permitted discharges 2.36 x 1012 1.18 x 1013 NC

Total 3.69 x 1016 1.39 x 1015 96.23

Table 35. Total annual loads of fecal coliform bacteria delivered from the land-use types, point sources, and permitted 

discharges for present conditions and after incorporation of total maximum daily load (TMDL) allocation in Christians Creek, 

Augusta County, Virginia 

[NC, no change]
76 Use of the Hydrological Simulation Program–FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform TMDL for Accotink Creek

streams and straight pipes equaled 99 percent while no 

reduction in fecal coliform loading from permitted dischargers (sewage treatment plants) was required. The 

resulting TMDL equation (see eq. 1) that meets the 

fecal coliform bacteria water-quality goals for Christians Creek is 

1.46 x 1015 col/yr (TMDL) = 1.18 x 1013 col/yr 

(∑WLAs) + 1.38 x 1015 col/yr (∑LAs) + 

6.96 x 1013 col/yr (MOS).

Attaining the designated water-quality goals for 

Christians Creek is a three-step process:

(1) Determination of the fecal coliform bacteria 

TMDL for Christians Creek.

(2) Development of a plan for reducing the current 

fecal coliform loading to Christians Creek.

(3) Implementation of the source-load reduction 

strategies and follow-up monitoring to ensure 

that the TMDL plan and implementation result 

in achievement of the water-quality goals for 

Christians Creek.

DIRECTIONS FOR FUTURE RESEARCH

This study demonstrated the utility of incorporating 

both HSPF and BST data into the process of developing a TMDL for fecal coliform bacteria. This process 

would be enhanced by continued refinement of BST 

techniques and research in the following areas:

• The range of fecal coliform densities for various 

warm-blooded species and how this range varies 

temporally and spatially.

• The effect of sediment on the transport and storage 

of fecal coliform bacteria.

• The fate and transport of fecal coliform bacteria in 

the shallow subsurface (both the unsaturated zone 

and the shallow aquifer system) and potential contributions to the instream fecal coliform load. 

SUMMARY

The U.S. Geological Survey (USGS), in cooperation with the Virginia Department of Conservation and 

Recreation (DCR), began a 3-year study in 1999 to 

develop a total maximum daily load (TMDL) for fecal 

coliform bacteria in the Christians Creek watershed. 

The Virginia Department of Environmental Quality 

(DEQ) determined that Christians Creek is impaired by 

fecal coliform bacteria because of violations of the 

State water-quality standard (1,000 colonies/100 mL). 

This study demonstrates the utility of incorporating 

both watershed modeling using Hydrological Simulation Program–FORTRAN (HSPF) and bacterial source 

tracking (BST) as tools in the development of a fecal 

coliform bacteria TMDL. Attaining the designated 

water-quality goals for Christians Creek involves a 

three-step process, determined by DCR and DEQ, 

which is (1) determination of the fecal coliform 

TMDL, (2) development of a plan for reducing the current fecal coliform loading, and (3) implementation of 

the source-load reduction strategies and follow-up 

water-quality monitoring. Specific objectives of this 

study were to (1) produce calibrated models of watershed streamflow and fecal coliform bacteria transport, 

(2) incorporate BST information into the fecal coliform 

model calibration process, (3) estimate fecal coliform 

source-load reductions required to meet the State 

water-quality standard, and (4) define the TMDL for 

fecal coliform bacteria for Christians Creek. The major 

findings and conclusions of the study are:

• The calibrated streamflow model simulated 

observed streamflow characteristics with respect to 

total annual runoff, seasonal runoff, average daily 

streamflow, and hourly stormflow.

• BST identified that the major contributors of fecal 

coliform bacteria in Christians Creek are poultry, 

cattle, humans, dogs, horses, and deer.

• The calibrated fecal coliform model simulated the 

patterns and range of fecal coliform bacteria concentrations observed by DEQ (1991-97) and USGS 

(1999-2000).

• The calibrated fecal coliform model simulated 

source-specific instream fecal coliform loads comparable to the source-specific percent contribution 

identified in Christians Creek by BST.

• Incorporation of BST data reduces the uncertainty 

associated with determining source-specific fecal 

coliform loading in the watershed. 

• A 96-percent reduction in the current fecal 

coliform load delivered to Christians Creek is 

required to meet the designated water-quality goals 

and associated TMDL. 
References Cited 77

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