Paper Number: 01-2066 An ASAE Meeting Presentation

TMDL Modeling of Fecal Coliform Bacteria with HSPF

Gene Yagow Research Scientist (eyagow@vt.edu) Theo Dillaha Professor (dillaha@vt.edu) Saied Mostaghimi Professor (smostagh@vt.edu) Kevin Brannan Research Associate (kbrannan@vt.edu) Conrad Heatwole Associate Professor (heatwole@vt.edu) Mary Leigh Wolfe Associate Professor (mlwolfe@vt.edu)

Biological Systems Engineering Department Virginia Tech, Blacksburg 24061-0303

Written for presentation at the 2001 ASAE Annual International Meeting

Sponsored by ASAE Sacramento Convention Center

Sacramento, California, USA July 30-August 1, 2001

Abstract. Fecal coliform TMDLs were developed for nine watersheds in Virginia using the HSPF model. The primary HSPF algorithms used to simulate FC loading and fate in the models are described in detail. Parameter values are summarized for all HSPF parameters related to FC simulation, as well as source data used external to the model for developing input loads from the various individual FC sources. Although there are many areas of uncertainty in modeling fecal coliform, a scientific approach was used in the evaluation of sources, the representation of the sources, and the evaluation of parameters used to simulate fecal coliform fate and transport with the HSPF model for nine sub-watersheds. The similarity of source reductions called for in each of the nine TMDLs support recommendations for the regional application of key results from TMDL studies to watersheds with similar sources and for the use of adaptive implementation as presented in a recent National Research Council report to Congress assessing the scientific basis of TMDLs (NRC, 2001).

Keywords. NPS pollution, uncertainty, parameterization, TMDLThe authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural Engineers (ASAE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASAE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASAE meeting paper. EXAMPLE: Author’s Last Name, Initials. Title of Presentation. ASAE Meeting Paper No. xx-xxxx. St. Joseph, Mich.: ASAE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASAE at hq@asae.org or 616.429.0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).

TMDL Modeling of Fecal Coliform Bacteria with HSPF Introduction The Total Maximum Daily Load (TMDL) development process, as recommended through guidance from EPA, centers around a modeling effort to define all contributing sources of a pollutant, quantify the sources, model existing conditions, and define what reductions will be needed to bring an impaired stream segment back into compliance with state standards (EPA, 1999). This paper focuses on TMDL development for impairments due to fecal coliform, and describes the various procedures used for modeling fecal coliform bacteria as a nonpoint source (NPS) pollutant. In Virginia alone, approximately 200 stream segments have been declared impaired due to violations of the state fecal coliform standard. Each of these stream segments must be addressed with a total maximum daily load (TMDL) plan, followed by implementation of best management practices (BMPs) to remediate the impairment. Large amounts of public and private funds are likely to be spent over the next 10-15 years for TMDL development and implementation. If the assumptions and “best professional judgment” used in model development are misguided, the desired goal of cleaner streams may once again not be achieved. Peer scrutiny is essential in evaluating the use and application of models to simulate fecal coliform and in identifying those issues towards which future research should be directed. During the past year, the authors have been involved in the development of nine fecal coliform TMDLs in Virginia using the Hydrological Simulation Program-FORTRAN (HSPF) model (Bicknell et al., 1996). These TMDLs were developed in three separate projects, as three of the impaired stream segments were linked on the North River (Mostaghimi et al., 2000a,b,c), five on the Big Otter River (Mostaghimi et al., 2000d), and a single impaired segment on Mountain Run (Yagow, 2001). Based on this experience, the objectives of this paper are to describe the methodology and procedures used to describe fecal coliform loading, to describe the evaluation of fecal coliform related parameters for the HSPF model, to summarize the significant results from these studies, and to identify weaknesses and areas of uncertainty in the modeling process. NPS Programs in Virginia The TMDL program is but the latest in programs whose goal is the reduction of NPS pollution. Until the advent of the TMDL program, NPS pollution control in Virginia was focused on sediment and nutrients (DCR, 1994). Indeed, since 1983, the Environmental Protection Agency’s Chesapeake Bay Program (EPA, 1983), which elevated the visibility of NPS pollution problems in the region along with increases in funding for reduction of these pollutants, has focused participating states’ efforts on reductions of nitrogen and phosphorus. The TMDL program required an abrupt broadening of traditional NPS programs into areas where the NPS community of scientists, program managers, and conservation technicians in Virginia had little experience. The TMDL program is driven by compliance of surface water quality with state standards (DEQ, 1998) and, up to the present, there are no state standards in Virginia surface waters for the traditional NPS pollutants of concern – sediment, nitrogen (except ammonia), or phosphorus (except for effluent standards). The first 303(d) list for TMDL priorities in Virginia revealed that 70% of the impaired stream segments were based on measurements of a totally different pollutant – fecal coliform bacteria (DEQ, 1994). The focus of this new program is currently

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outside of the realm of existing NPS programs. Existing programs will require a dramatic change in focus to accomodate the TMDL program. Additional pressure is added by the legally binding consent decrees issued to EPA in Virginia and many other states to implement neglected sections of the 1972 Clean Water Act on a negotiated timetable (DEQ, 2001). Slowly, a paradigm shift is occurring to incorporate the demands of the TMDL program within the focus of traditional NPS pollution control. Between now and 2010, an estimated 295 TMDLs will be required on 247 stream segments in Virginia’s non-shellfish waters. Of the 295 TMDLs to be developed, 164 are for fecal coliform (FC) bacteria (DEQ, 2000). Thirteen FC TMDLs are completed or in process, and another 16 are being contracted for completion by May 2002. Applicable Water Quality Standards Virginia has a two-part “no-exceedance” water quality standard for fecal coliform bacteria in non-shellfish waters: an instantaneous limit of 1,000 counts/100 mL sample, and a geometric mean of 200 counts/100 mL for two or more samples taken over a 30-day period (SWCB, 1997). Compliance with the Virginia standard is assessed by comparison with ambient water quality monitoring data, collected by the Virginia Department of Environmental Quality (DEQ). Since DEQ’s ambient water quality monitoring is conducted on a monthly or quarterly basis, sampling frequency is generally inadequate for comparison with the geometric mean standard (two or more samples over a 30-day period). Therefore, DEQ compares fecal coliform counts from individual samples with the instantaneous standard - 1,000 cfu/100 mL – to determine whether an individual sample violates or complies with the state water quality standard. Each of the nine stream segments addressed in this paper was listed on Virginia’s 303(d) TMDL priority list for having more than 10% of it’s samples over a 5-year assessment period in violation of the state instantaneous fecal coliform standard (DEQ, 1994). Watershed Characteristics TMDLs were developed for nine Virginia watersheds in three separate projects, whose locations are shown in Figure 1. The Mountain Run watershed spans the middle of Culpeper County in the Northern Piedmont physiographic region of the state. This 58,000-acre watershed is 61% agricultural, 25% forested, and contains the Town of Culpeper in the middle of the watershed. Two large water supply impoundments are upstream from the town. The North River project consists of three related watersheds (Pleasant Run, Dry River, and Mill Creek) located in Rockingham County in the Mountain & Valley physiographic region. Dry River is the largest of these three watersheds with approximately 57,000 acres, of which 11% is agricultural and 88% is forested. By contrast, the other two watersheds range in size from 5,300 to 9,600 acres, and contain a distinctively different land use distribution than Dry River, averaging 72% agricultural land and 56% forest. The Big Otter project consists of five related watersheds (Elk Creek, Big Otter River, Little Otter River, Machine Creek, and Sheep Creek) located in Bedford and Campbell counties in the Southern Piedmont physiographic region. These watersheds average 30,000 acres in size with 33% of the land in agriculture and 56% in forest. The Town of Bedford is located in the Little Otter River watershed. The distribution of land uses in each watershed is given in Table 1.

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NBEDFORD

ROCKINGHAM

CAMPBELL

CULPEPER

100000 0 100000 200000 Meters

BEDFORD

ROCKINGHAM

CAMPBELL

CULPEPER

North River

Mountain Run

Big Otter

Figure 1. Location of the Nine Virginia TMDL Watersheds

Table 1. Land Use Acreages in the Nine Virginia TMDL Watersheds

North River TMDLs Big Otter TMDLs Land Use

Mountain

Run Pleasant

Run Dry

River Mill

CreekElk

Creek Big

Otter Little Otter

Machine Creek

Sheep Creek

cropland 13,952 1,562 3,382 2,111 429 553 521 1,098 695pasture1 14,537 1,476 1,289 3,483 14,150 5,253 9,383 8,232 8,684pasture2 292 229 545 pasture3 465 536 273 urban/developing 4,369 114 60 506 858 829 3,128 366 347rural residential 2,205 486 831 1,045 5,574 829 1,825 1,098 1,737farmstead 145 453 205 loafing lot 198 49 144 9 forest 11,777 719 49,868 1,456 21,440 19,904 10,947 7,501 23,273comm/industrial 429 276 261 lakes 268 Total 47,306 5,308 56,792 9,633 42,880 27,645 26,065 18,294 34,736 The human and animal populations in each of the watersheds is shown in Table 2. The Mountain Run and Little Otter watersheds contained the largest human populations, both in numbers and in density, as might be expected with their included towns. Although the types of livestock varied from watershed to watershed, the livestock primarily consisted of beef and dairy in the Mountain Run watershed, poultry and dairy in the North River watersheds, and beef and horse in the Big Otter watersheds. The types of wildlife selected as significant also varied from watershed to watershed, but was fairly consistent within each project with multiple watersheds. Wildlife population was relatively minor in the North River watersheds, with deer, muskrat, and raccoon dominant in Mountain Run, and with deer and muskrat dominant in the Big Otter watersheds. The pet population was also much greater in the Big Otter watersheds than in the other two projects.

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Table 2. Human and Animal Populations in the Nine Virginia TMDL Watersheds

North River TMDLs Big Otter TMDLs Animal Type

Mountain

Run Pleasant

Run Dry

River Mill

Creek Elk

CreekBig

OtterLittle Otter

Machine Creek

Sheep Creek

human 17,500 1,067 2,278 3,047 6,158 2,458 10,910 2,303 2,283beef 3,192 760 400 2,011 3,410 1,210 1,697 1,464 1,500dairy cow 2,073 1,260 3,485 230 500 160 605 314dairyheifer 1,260 3,485 230 swine 45 horse 128 496 114 260 202 405broilers 99,000 1,153,000 228,000 layers 24,000 50,400 turkeys 35,000 369,300 112,000 deer 1,469 169 322 284 2,013 1,300 1,225 854 1,634woodduck 223 24 30 76 50 49 33 62goose 500 150 173 109 105 74 138muskrat 1,289 244 49 277 1,912 1,492 1,747 1,359 1,394raccoon 1,175 2 34 13 363 321 337 265 299beaver 192 156 173 135 147mallard 85 54 52 37 70wildturkey 212 202 110 75 235pets 409 450 1,101 2,463 983 4,364 921 913 Simulating Fecal Coliform Loading to Watersheds In the early days of agricultural NPS model development, fecal coliform was not one of the pollutants that received much attention, so none of the existing agricultural NPS models were designed to specifically model fecal coliform bacteria. The HSPF model, however, has the ability to model general quality constituents as dissolved substances, and this adaptation is recommended for development of fecal coliform TMDLs. There are many issues related to how well we are able to model fecal coliform bacteria. First of all, our knowledge of fecal coliform fate and transport processes in the environment is severely limited. Also, the HSPF model requires calibration with observed data. Since observed fecal coliform concentration data are limited and generally correspond with base-flow conditions, calibration, especially to stormflow, is somewhat imprecise. Most importantly, within HSPF, the simplest approach was chosen – that of modeling fecal coliform bacteria as a dissolved constituent – because even though sediment, nutrients, and competitive bacteria all affect fecal coliform fate and transport, the uncertainty associated with evaluating the additional parameters required for these constituents was assessed as being greater than the uncertainty associated with using the simplest approach. In order to minimize the uncertainty surrounding this pollutant, a scientific approach was used in evaluating and assigning fecal coliform-related parameter values based on the best available evidence, however minimal. The modeling process for fecal coliform bacteria consists of linking the model inputs (daily quantities or loads of fecal coliform from all sources deposited or applied within a watershed) with the model output (daily mean in-stream concentrations). The next steps in the TMDL

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process, therefore, consist of defining all contributing sources of fecal coliform and of quantifying the extent and distribution of these loads. The following types of sources were represented in the models created for TMDL development – point sources, nonpoint sources from impervious areas, direct nonpoint sources, and nonpoint sources from pervious areas. Each source type is discussed separately in the following section, along with the assessment, distribution, and model representation of sources specific to each of the nine watersheds. Point Sources Four permitted fecal coliform (FC) sources discharged into Mountain Run, two into Elk Creek, one into Machine Creek, and 4 into the Little Otter River. For existing conditions, flow and FC concentration were modeled using historical daily flow from the one discharger currently in operation on Mountain Run. For both existing and future conditions on the Big Otter watersheds, all permitted sources were modeled at permitted levels of monthly-averaged daily flow and the 30-day geometric mean FC standard concentration (200 cfu/100 mL). Nonpoint Sources from Impervious Areas Fecal coliform loadings to impervious areas were thought to derive from a variety of less-easily modeled or predictable sources, e.g. pigeons, rats, and pets. They were modeled using a buildup/washoff relationship as described in the HSPF Algorithms section with a constant rate calibrated for the Mountain Run watershed. Pervious land segments with impervious components were modeled at a loading rate half that of the impervious portions, with the impervious rate determined through calibration. All other watersheds applied a constant value from a previous TMDL study on a North River tributary watershed (Tetra Tech, 1999). Direct Nonpoint Sources This category consists of traditional nonpoint sources that under certain circumstances contribute FC loads directly to the stream. These direct nonpoint sources were defined as “straight-pipes” (homes contributing untreated waste directly to the stream), and directly deposited waste (from either livestock or wildlife). “Straight pipes” were defined as a percentage of older, unsewered homes located within a specified distance of streams. The number of older, unsewered homes was determined through GIS analyses of spatial data related to E-911 home locations, sewer locations, and known septic systems. Directly deposited waste from livestock was based on a monthly-variable amount of time spent in the stream by animals with access and the assumption that the percent waste deposited was proportional to the percent of time spent in the stream. Directly deposited waste from wildlife was estimated as a percent of daily load based on “best professional judgment”. Contributions from direct nonpoint sources were summed in each sub-watershed and input directly into their respective stream reaches.

Nonpoint Sources from Pervious Areas Loadings on pervious land surfaces originated from a number of human, livestock, and wildlife sources and were the most detailed values calculated for model input. The following procedure – including inventorying sources, characterizing source loads, and distributing loads to the various land uses in each sub-watershed – was used for calculating monthly loads from all sources for pervious areas. All major types of livestock and wildlife were first identified for the watershed, along with other potential sources of fecal coliform in the watershed, such as residential wastewater, milking parlor waste, and biosolids applications. Each source was then inventoried to quantify the population or areal extent of each source, and the load was distributed by sub-watershed. The literature and prior TMDL studies were reviewed for daily

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waste production rates and FC characteristics of the waste. These were combined with population numbers to calculate total daily FC loads for each sub-watershed. Each sub-watershed load was further distributed by the land uses and monthly variations in population associated with each source. These load values were arranged in a matrix of monthly FC load values for each unique sub-watershed/land use combination (known as a PERLND in the HSPF model) for each FC source, and also aggregated into a matrix that represented loads from all sources. A detailed description of each of these procedural components follows. Inventory Sources Livestock inventories generally began with numbers from the most recent Agricultural Census as distributed to individual state 14-digit hydrologic unit areas by Virginia’s lead NPS pollution control agency, the Department of Conservation and Recreation (DCR), and distributed amongst sub-watersheds based on farm locations and average herd size. Poultry populations were based on the number of poultry houses identified from FSA aerial photographs or USGS 7½-min topographic maps, and in E-911 databases, along with estimated house widths, space requirements/bird, and rotation schedules. Poultry populations were calculated as the number of individual birds averaged over all production cycles/year. Livestock and poultry populations were then refined in consultation with local agricultural agencies, producers, and producer organizations. Since a direct inventory of wildlife populations was neither practical nor feasible, an indirect approach was used to determine populations based on the available area of suitable habitat for each candidate species. First, suitable habitat areas were defined for individual wildlife species with respect to certain features such as streams or ponds, or within certain land uses, such as forest. For example, the following definitions were used for the Mountain Run watershed:

• deer: all forested areas and adjacent land parcels. • ducks: all forested areas within 400 meters (~¼ mile) of perennial streams. • geese: all areas within 100 meters of surface water impoundments and public parks,

excluding wooded and residential areas. • muskrat: all forested areas within 10 meters of perennial streams. • raccoon: all areas within 400 meters of perennial streams, excluding loafing lot and

pasture areas. A GIS was used to create these spatial buffers and to calculate the suitable habitat acreage available in each sub-watershed. Populations were calculated from the suitable habitat acreage for each species within each sub-watershed and literature values of typical species densities. Finally, these numbers were adjusted as necessary based on watershed reconnaissance and in consultation with local game and wildlife agency personnel and watershed residents. Human population was obtained from 1990 census data and updated to account for changes since 1990. Properly installed and maintained septic systems are designed to treat waste and were not considered to be a source of fecal coliform in the watersheds. However, improperly installed or maintained systems, and those rural residences without a septic treatment system, represent potential sources of human fecal coliform within the watershed. Improperly maintained septic systems were defined as a percent of unsewered homes by age category. House locations from local planning maps and E-911 data were matched with locations on USGS 7½-min topographic maps for various photo-revision dates to estimate the age of homes and their septic systems. Pet populations were estimated as 1 pet/household (North River and Big Otter TMDLs).

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Characterize Waste Production A daily fecal coliform production rate is needed in conjunction with daily population to calculate the total daily FC load from each source. The daily fecal coliform production rates/source were either obtained from a standard reference (ASAE, 1998), or calculated from published values of daily waste production/source and either published values or direct measurements of FC density in the waste. The values used to calculate loading rates in these TMDL studies are listed in Table 3. A more complete description of any derived values can be obtained from the individual TMDL study documents as referenced in the table.

Table 3. Daily Fecal Coliform (FC) Production Rates for Various Sources Values Data Sources

FC Daily Waste Daily FC Daily Waste Daily FC TMDL Source Production FC Density Produced Production FC Density Produced Study (gm/AU)a (106 cfu/gm) (106 cfu/AU)

Human-Related Human 1,950 d MR,NR,BOPets 450 h NR,BO

Livestock Beef 18,144 1.143 20,740 b g calculated MR Beef 27,240 0.947 25,800 f calculated c NR,BO Dairy 27,216 1.143 31,100 b g calculated MR Milk & dry cows 52,184 0.383 20,000 c calculated c NR,BO Heifers 18,481 0.498 9,200 b calculated c NR,BO Swine 20,412 3.300 67,360 b d d MR Horse 18,598 0.013 234 b d d MR Broilers 24 3.634 89 c calculated b NR,BO Layers 42 3.216 136 c calculated b NR Turkeys 128 0.727 93 c calculated b NR,BO

Wildlife Deer 772 0.450 347 g g calculated MR,NR,BODuck 299 16.230 4,853 g b calculated MR Duck 2,430 b NR,BO Goose 163 0.800 130 g g calculated MR Goose 799 h BO Muskrat 100 0.250 25 e g calculated MR,NR,BORaccoon 450 0.250 113 g g calculated MR,NR,BOa For livestock and poultry, 1 animal unit (AU) = 1,000 lbs of live weight; for wildlife, 1 AU = an individual animal.

b = ASAE, 1998 f = MWPS, 1993 BO = Big Otter TMDLs c = Mostaghimi et al., 2000a,b,c,d g = Yagow, 2001 MR = Mountain Run TMDL d = Geldreich, 1977 h = Weiskel et al., 1996 NR = North River TMDLs e = Kator and Rhodes, 1996

Distribute the Sources Over Space and Time Fecal coliform from each individual source was distributed to associated land uses within each sub-watershed on a monthly basis. Fecal coliform from livestock manure was distributed among the various land uses based on the following factors:

• % of time in confinement • % of time in loafing lot • differential stocking densities in overgrazed, unimproved, and improved pasture

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categories (all except Mountain Run) • seasonal changes in population (Mountain Run) • % of pastures with access to streams • % of cattle with access to streams • % of day with cattle in and around streams.

Fecal coliform from wildlife was evenly distributed spatially to all land within the defined “suitable habitat area” for each species, though the assessed “suitable habitat area” would vary from sub-watershed to sub-watershed based on the GIS analysis. FC loading rates on pervious urban areas were modeled as a constant over both space and time. FC loading rates from improperly maintained septic systems were based on the identified populations within each sub-watershed, but were constant over time. Simulate Collected and Stored Manure The fate of manure in storage was simulated based on the following information about the various types of livestock and poultry operations and their management, as applicable to each of the watersheds.

• type of manure storage (liquid, solid, poultry) • no. of days of available storage • no. or % of farms with each storage type • % of manure collected from various animal types • waste application schedule including amount or fraction of waste applied, month(s) of

application, type of land use receiving the waste, and application rates. Literature values for the first-order rate coefficients were applied to the different manure types, and composite fractions of the surviving FC were calculated for the various manure types and TMDL studies as reported in Table 4.

Table 4. Surviving Fraction of FC in Storage by Manure Type Applicable TMDL

North River TMDLs

Manure Type Mill Creek,

Pleasant Run

Dry River

MountainRun

TMDL

All Big Otter

TMDLs liquid 0.035 0.0624 0.0227 0.0078 solid 0.068 0.1621 -- --

poultry 0.099 0.1000 -- -- Pre-processing HSPF FC Load Inputs with a Spreadsheet A spreadsheet was used to create the fecal coliform loading table (MON-ACCUM) needed to simulate monthly loading to the land surface within HSPF. Use of the spreadsheet minimized redundant data input and allowed for flexibility in the creation of a loading table from various FC sources. This flexibility was desirable to allow representation of each FC source individually, all sources combined, or variable percentages of each source, as might be required to simulate load reductions in TMDL scenarios. The spreadsheet was organized so that all inputs were located on one worksheet, and all calculations and other data transformations were located on separate worksheets within the same spreadsheet. The spreadsheet inputs included information regarding animal inventories and FC production, distributions over space and time, and the combinations and percentages of FC sources to be included in the current modeling scenario. The load calculations for each FC source incorporated variations in population and monthly distributions of FC loads for each land use/sub-watershed combination. Where manure was subject to storage and/or land application after storage, reduction factors were used to

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account for die-off in storage and for different land application methods. Land application of this waste was also included in the spreadsheet distribution and calculation of FC loads. All calculations performed in the spreadsheet were referenced back to the original FC production, distribution, and reduction inputs. Changes made to any data inputs for a specific scenario, therefore, were automatically reflected in the calculations. The calculations were organized into FC load matrices by month and by PERLND (unique land use/sub-watershed combinations). All of the individual matrices were then summed into an aggregate matrix that represented the FC load from all sources. This matrix was then exported for input into HSPF as the MON-ACCUM table. TMDL Modeling with HSPF An Overview of the HSPF Model The Hydrological Simulation Program – Fortran (HSPF) is a spatially lumped, continuous simulation model that simulates hydrology and pollutant loading from the land surface, as well as in-stream processes affecting the fate and delivery of pollutants (Bicknell et al., 1996). The model can represent loading from both point and nonpoint sources, allows for many different pollutants to be represented in the model, and can represent the interaction of surface and subsurface flows. The model, however, is highly parameterized and requires calibration. HSPF represents watersheds as a series of interconnected sub-watersheds, each with three primary components – PERLND, IMPLND, and RCHRES. The first two components define the land surface, while the third component – RCHRES – represents stream reaches. PERLND stands for pervious land segment, and is a unique sub-watershed/land use combination. Each PERLND may have a corresponding impervious land component (IMPLND). Each sub-watershed connects to a specific RCHRES that is part of the stream network defined for the watershed. HSPF is a highly flexible modeling framework that allows the user to define the relationships that represent bacteria fate and transport. But, almost no information is available on appropriate values of parameters and coefficients needed to evaluate many of the mathematical algorithms. This uncertainty in parameter evaluation requires field measurements and calibration with observed in-stream concentrations to properly validate the relationships, which may not be readily available. This large degree of flexibility in constituent modeling, the ability to perform continuous simulation, and the ability to represent both point and non-point sources of pollution are all needed for modeling fecal coliform for TMDLs, and HSPF was originally the only model that was capable of meeting these criteria. A primary disadvantage of HSPF, however, is its limited ability to show spatial variability. Since NPS pollution is by definition spatially diverse, additional distribution of input loads was modeled using GIS and spreadsheets external to HSPF, in order to incorporate as much spatial diversity as possible. Fecal coliform was modeled as a dissolved substance in HSPF using separate buildup/washoff relationships for pervious and impervious areas, along with a first-order decay (disappearance) relationship within stream reaches. Three main parameters were used to define FC loading for both the PERLND and IMPLND areas:

ACQOP – daily loading onto the land surface, cfu/day SQOLIM – maximum daily buildup, a limit where daily buildup = daily die-off on the land

surface, cfu WSQOP – the amount of rainfall needed to remove 90% of the buildup, in/hr.

Two additional parameters were used in some of our models to represent FC concentrations in interflow (IOQC) and in groundwater (AOQC). Three parameters controlled the fate of FC within the stream reaches:

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FSTDEC – the first order decay rate coefficient, day-1

THFST – the temperature correction coefficient TWAT – mean monthly water temperature, °F. The ACQOP and SQOLIM parameters were evaluated as monthly values for each PERLND area, TWAT as monthly values, and all other parameters as single values for each watershed. The HSPF parameters and relationships are discussed in the following section, and the parameter values used in each of the nine Virginia TMDLs are listed in Table 5. Evaluating Fecal Coliform (FC) Parameters in HSPF The general approach taken for evaluation of the fecal coliform parameters was to use parameter measurements wherever possible; to research literature values, previous HSPF studies, and the BASINS Technical Note #6 (EPA, 2000) for recommended parameter values and reasonable ranges; and to calibrate those parameters with the least amount of physical measurement or other justification. For PERLND areas, the most confidence was placed in the ACQOP parameter, with any calibration adjustments made to the other two PERLND parameters for fecal coliform loading. For IMPLND areas, literature values from urban areas were used for the WSQOP parameter, and any calibration adjustments made to the ACQOP and SQOLIM parameters.

• ACQOP This parameter was evaluated as a monthly distribution of values for each PERLND based on contributions from the many sources of fecal coliform in the watershed. This matrix of values was calculated external to the model using a spreadsheet, as discussed previously. For IMPLND areas, several approaches were used. The Mountain Run and Little Otter watersheds both had sizeable areas with impervious components, but were modeled differently. Fecal coliform in runoff from the impervious areas in Mountain Run were calibrated with observed in-stream concentrations attributable specifically to an urban area, as stormflow from these areas was channeled directly into the stream. A daily loading rate (ACQOP) of 2.0 x 109 cfu/day was calibrated for this watershed. In the Little Otter watershed, runoff from impervious areas of the town primarily entered their combined storm and sewer system, which later contributed to combined sewer overflows (CSO) during large storm events. A rate of 1.0 x 107 cfu/day was used for all of the North River and Big Otter TMDLs to be consistent with a previously developed TMDL, upstream from one of the impaired stream segments on the North River (Tetra Tech, 1999).

• SQOLIM SQOLIM is used indirectly to simulate die-off on the land surface in between runoff (loading) events, and was evaluated by looking at its relationship with the first-order decay rate, k1, which is essentially ACQOP/SQOLIM. Since k1 is a constant, and SQOLIM is always greater than ACQOP, SQOLIM can be calculated as a multiple of ACQOP, such that SQOLIM = MF x ACQOP, where MF is the multiplication factor. For PERLND areas, an MF=9 (k1 = 0.111) was used, based on research by Thelin and Gifford (1983) and supported by limited field measurements in Mountain Run (Yagow, 2001). For IMPLND areas, a lower multiplication factor was used based on the following reasoning. Research conducted by Olivieri et al. (1977) reported that bacteria concentrations in runoff appeared to be independent of the days since the last rainfall event. This observation could be explained by a rapid pollutant buildup to its maximum accumulation, e.g. an accumulation limit not much greater than the daily loading amount, so that the accumulated load appeared to be constant. A calibrated MF = 2 was used for the Mountain Run watershed, and an MF = 3 for all of the other watersheds.

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• WSQOP

WSQOP is the amount of rainfall needed to remove 90% of accumulated pollutant on the land surface. Urban runoff models typically assume that 0.5 inch of rainfall in an hour will remove 90% of accumulated solids from impervious surfaces (Sartor et al., 1974; Novotny and Chesters, 1981), and this value was used for IMPLND areas in Mountain Run. For the North River and Big Otter TMDLs, the impervious component was insignificant, and no observable in-stream measurements were available for comparison. A starting value of 2.4 in/hr was used for these watersheds, with final calibrated values ranging from 1.0 – 2.4 in/hr, as shown in Table 5. A larger amount of rainfall would intuitively be needed to wash off a comparable percentage of accumulated pollutant from a pervious (PERLND) surface, because of infiltration and the various protective effects of cover on runoff velocity, detachment and transport. A value of 1.8 in/hr was calibrated for the Mountain Run watershed, while for the North River and Big Otter watersheds, the same values were generally used for both PERLND and IMPLND areas, unless modified for calibration purposes.

• IOQC and AOQC Values for FC concentrations in interflow (IOQC) and groundwater (AOQC) were assumed to be 0 for Mountain Run, while values used for all other watersheds were arrived at through consultation with EPA, and to be generally consistent with an upstream segment on which a previous TMDL had been completed (Tetra Tech, 1999).

• FSTDEC The first-order exponential decay coefficient was determined through calibration in the Mountain Run study as 1.00, while the other studies used a value of 1.15 for consistency with the previously developed upstream TMDL. This parameter simulates die-off and/or disappearance of a pollutant in the water column.

• THFST The EPA recommended default value was used in all of the studies.

• TWAT The Mountain Run study used the monthly form of this parameter to better represent seasonal fluctuations. Mean monthly air temperature was used as a surrogate for mean monthly water temperature. Temperature correction was not simulated as part of the decay function for the other watersheds. Calibration of Fecal Coliform Parameters Calibration was used to evaluate several of the parameters listed in Table 5. Ideally, calibration would be performed separately for each pollutant source, based on a set of monitored in-stream pollutant measurements corresponding to the area receiving loads from the single source. Rarely, however, are these kinds of data available for individual sources of fecal coliform. Occasionally, measurements are taken that can be related to, or isolate, the effects of pollutant loading from all sources on a given land use, such as urban, agricultural, or forested areas. When sources contributing to loading on the isolated land use can be narrowed down to one or two, the probability of a more reasonable calibration for these sources is greatly increased. Otherwise, calibration must be performed that adjusts loading from all sources equally, or only from the sources judged to be most dominant or critical by the modeler.

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Table 5. Fecal Coliform Parameter Values for TMDLs in Nine Virginia Watersheds5

North River TMDLs Big Otter TMDLs HSPF

FC Parameter

units

Mountain

Run TMDL

Pleasant

Run

Dry

River

Mill

Creek

Elk

Creek

Big Otter River

Little Otter River

Machine

Creek

SheepCreek

PERLND Module ACQOP1 cfu/day 0 -1011 109-1011 109-1011 109-1011 109-1011 109-1011 109-1011 109-1011 109-1011

SQOLIM2 cfu x 9 x 9 x 9 x 9 x 9 x 9 x 9 x 9 x 9 WSQOP in/hr 1.8 2.5 2.4 1.64 2.4 1.0 1.8 2.4 1.0

IOQC3 cfu/100mL 0 5 5 5 10 10 10 10 10 AOQC3 cfu/100mL 0 5 5 5 5 5 5 5 5

IMPLND Module ACQOP cfu/day 2 x 109 1 x 107 1 x 107 1 x 107 1 x 107 1 x 107 1 x 107 1 x 107 1 x 107

SQOLIM2 cfu x 2 x 3 x 3 x 3 x 3 x 3 x 3 x 3 x 3 WSQOP in/hr 0.5 2.4 2.4 2.4 2.4 1.0 1.0 2.4 1.8

RCHRES Module FSTDEC day-1 1.00 1.15 1.15 0.048 1.15 1.15 1.15 1.15 1.15

THFST 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 TWAT4 ºF 32 - 76 0 0 0 0 0 0 0 0

1 Range of values within the PERLND/monthly matrix. 2 Multiplication factor multiplied times ACQOP to simulate SQOLIM. 3 HSPF requires input in units of cfu/ft3. These translate as follows: 5 cfu/100 mL = 1,416 cfu/ft3; 10

cfu/100 mL = 2,832 cfu/ft3. 4 When a constant value of 0 is entered for TWAT, the temperature correction factor is ignored. 5 Values taken from actual HSPF input files.

The following is an example of a two-part fecal coliform calibration that was performed in the Mountain Run watershed. As mentioned previously, the Mountain Run watershed had a significant impervious area, and these urban-related areas were primarily in the upstream portion of the watershed. Additionally, an upstream monitoring station was available that essentially isolated a contributing area of primarily urban land use. The strategy, therefore, was first to calibrate the urban FC parameters at the upstream site, and then to calibrate the other sources based on available monitoring at the watershed outlet. As the calibration proceeded, it became obvious that the majority of the monitored samples were taken under base flow conditions. Since urban impervious areas contribute primarily during storm runoff events, and only one sample corresponded with identifiable storm runoff, literature values were used to approximate the upper bound of expected daily FC concentrations from urban runoff. Therefore, a combination of monitored data and expected maximum values were used to calibrate the urban buildup (ACQOP) and accumulated pollutant limit (SQOLIM) parameters for the Mountain Run watershed. Figure 2 compares the observed and simulated daily FC concentrations, after calibrating the urban buildup and washoff parameters. While a rationale for such a calibration can be given, the results presented in Figure 2 (with apparently mismatched simulated and observed values) is hardly likely to instill confidence when presented at a public TMDL meeting, even though it may be the best possible calibration.

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0

500

1000

1500

2000

2500

3000

3500

Aug-95 Nov-95 Feb-96 May-96 Aug-96 Dec-96 Mar-97 Jun-97 Sep-97

Feca

l Col

iform

, cfu

/100

mL

Observed 1000 cfu/100 mL Standard Simulated Figure 2. Urban Calibration: Mountain Run TMDL – Upstream Reach

Calibration was then performed at the watershed outlet with any needed adjustments to be taken only from non-urban FC loading parameters. The two dominant FC sources in the watershed were impervious area washoff and directly deposited waste by livestock. Since the impervious washoff parameters were already calibrated at the upstream reach, calibration at the outlet focused on adjustments to direct deposit loading from livestock and parameters related to in-stream decay (disappearance). The comparison of simulated vs. observed daily FC concentrations for the overall calibrated model is shown in Figure 3. Similar to the upstream station, most samples reflected ambient stream conditions.

0

1000

2000

3000

4000

5000

6000

7000

8000

Oct-95 Jan-96 Apr-96 Jul-96 Oct-96 Jan-97 Apr-97 Jul-97

Feca

l Col

iform

, cfu

/100

mL

1000 cfu/100 mL Standard Observed Simulated

Figure 3. Overall Calibration: Mountain Run TMDL – Watershed Outlet Figure 3 also raises another issue. The possibility exists that human error in sampling or analysis may affect some of the observed data. Two observed data points in the Mountain Run data could not be explained by any of the modeled inputs, and were taken on days without

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storm runoff. Although the concentration values were reasonable, they represented results at the maximum limit of the analytical techniques used by the laboratory and were 4 times greater than any other explainable observed data. In lieu of any other plausible explanation, these were attributed to human error. An alternative explanation is that comparison of simulated average daily FC counts with instantaneous sample measurements may be inappropriate, as the unknown daily variability in FC counts may well be greater than day-to-day variability. Calibration, therefore, becomes not just a matter of matching observed with simulated data, but in looking at the reasonableness of the available data and in understanding it’s limitations. The uncertainty involved with calibrated parameter values will increase, the further apart the observed data points are, as it increases the combination and range of different parameter values that might all produce equally acceptable calibration results, and the harder it becomes to justify any one given set of parameter values over another, even considering constraints based on literature and “best professional judgment”. Select Modeling Results As part of the TMDL reporting requirements, the following information was extracted from HSPF inputs and outputs. For analysis purposes, the loading information is most useful with a breakdown for individual sources and sub-watersheds.

• Loads Applied to the Land: These values were summarized from separate MON-ACCUM tables created for each FC source individually. A sample matrix from the Mountain Run watershed is shown in Table 6. Each column in Table 6 represents the annual load contributions from a single FC source, distributed across the watershed.

Table 6. Baseline Conditions in the Mountain Run Watershed: Annual Fecal Coliform Loads Applied to the Land (cfu * 1010/yr)

Sub-Watershed

Livestock

Wildlife

Septic

Urban Pervious

UrbanBuildup

Total

1 19,431 1,099 86 0 0 20,615 2 181,989 17,833 65 0 0 199,887 3 78,910 5,488 55 0 0 84,453 4 119,487 7,587 427 2,187 2,916 132,603 5 96,216 980 370 2,310 3,080 102,957 6 641,829 7,947 695 2,992 3,989 657,452 7 176,660 847 80 58 77 177,721 8 7 1,122 0 5,255 7,006 13,390 9 0 623 0 27,576 36,768 64,967

10 738,011 8,847 574 6,727 8,969 763,129 11 274,538 5,102 1,503 2,505 3,340 286,988 12 297,431 14,780 17 937 1,249 314,413 13 0 3,649 0 0 0 3,649 14 68,949 3,789 290 22,637 30,182 125,847

Total 2,693,457 79,692 4,161 73,183 97,577 2,948,070

• Edge-of-Stream (EOS) Delivered Loads: These loads represent the “Loads Applied to the Land” as reduced by fate and transport across the surface of the land to where it enters the stream. This load was assessed using the SOQUAL model output parameter from all pervious and impervious areas. A comparison between Loads Applied and the

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EOS Delivered Loads is shown in Figure 4. Note that only 2-4% of the applied loads from the first four source categories – those deposited on pervious areas – are delivered to the stream during overland flow, while the more directly-connected loads from impervious areas – urban washoff – have a higher 16% delivery rate. Not shown in the figure are those sources that contribute directly to the stream – direct nonpoint sources and point sources – which have 100% delivery to the streams, by definition.

LivestockWildlife

SepticUrbanPervious

UrbanWashoff

EOS Delivered Loads

Loads Applied to the Land

2,693,457

79,692

4,161

73,18397,577

76,769

1,548147 2,504 15,740

0

50,000

100,000

150,000

200,000

Feca

l Col

iform

Ann

ual L

oad,

cfu

* 1

010/y

r

Figure 4. Annual Load Comparison in Mountain Run Watershed: Loads Applied and Delivered to Edge-of-Stream (cfu * 1010/yr)

• 30-Day Geometric Mean FC Concentrations: Model simulations performed as part of

TMDL development for these watersheds were performed on a continuous basis and compliance was evaluated relative to the more restrictive 30-day geometric mean standard – 200 cfu/1000 mL – which then became the goal for each TMDL plan. A transformation of the simulated mean daily concentration (DQAL) was used for comparison with this standard. During the TMDL allocation phase, a 5% margin of safety was subtracted from the standard to represent the maximum allowable concentration, or TMDL target, resulting from both point and nonpoint source loading. Figure 5 illustrates the daily fluctuations in this mathematical construct for two different scenarios, each of which would meet the TMDL target for Mountain Run. Note that a table of annual or monthly means comparable to the loads in Table 6 would be meaningless, as the 30-day geometric mean, although calculated over a moving 30-day period, is calculated on a daily basis, and must meet the standard on a daily basis in the modeled TMDL scenario(s).

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TMDL Target

0

50

100

150

200

Jan-00 Apr-00 Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02 Jul-02 Oct-02 Jan-03 Apr-03 Jul-03 Oct-03

Feca

l Col

iform

Con

cent

ratio

n, c

fu/1

00 m TMDL Alt 1 L

TMDL Alt 2

Figure 5. 30-Day Geometric Mean FC Concentrations (Mountain Run TMDL)

Two results were especially significant and consistent among all nine TMDL watersheds:

• Livestock generally produced the vast majority of loading to the land surface, as in the illustration for Mountain Run watershed in Figure 4 and Table 6. This loading, however, was relatively insignificant as it related to the 30-day geometric mean FC concentration, even though occasional large instantaneous concentrations resulted from intense or prolonged rainfall events.

• Direct deposition by livestock and/or wildlife in streams were by far the dominant source(s) driving the 30-day geometric mean, as their influence was maximized during the prevalent low and normal flow conditions. Target TMDL concentrations could not be met without major reductions from these sources, as seen in Table 7.

Table 7. Recommended TMDL Reductions (% Reduction from Baseline Conditions)

Pervious Watershed Ag Wildlife Urban

Direct Livestock Deposits

Direct Wildlife Deposits

Urban Impervious Washoff

Improper Septic Systems

Straight Pipes

Milking Parlor Waste

CSO2

Mountain Run1

0 0 0 90-95 0-96 100 100

Pleasant Run

25 25 25 100 15 0 25 100

Dry River 0 0 0 84 0 0 0 100 Mill Creek 0 0 0 100 70 0 0 Sheep Creek

60 0 0 100 80 0 0 100

Elk Creek 60 0 0 97 70 0 0 100 Machine Creek

60 0 0 100 65 0 0 0

Little Otter River

60 60 60 100 70 0 60 100 100

Big Otter River

60 60 60 100 50 0 60 100

1 Reductions varied by sub-watershed. 2 Combined Sewer Overflow

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Additional significant results were specific to the components modeled in a specific watershed or set of related watersheds.

• The one watershed with significant impervious area – Mountain Run – illustrated that runoff from urban impervious areas was also a key contributor to in-stream 30-day geometric mean FC concentrations. Runoff, and thereby FC washoff, from impervious areas is produced more frequently and from lower intensity and shorter duration storms than from pervious areas. Any watershed with a significant urban area, therefore, will also likely require significant reductions from impervious washoff under typical FC loading scenarios, in order to meet the current 30-day geometric mean FC standard in Virginia.

• In all watersheds except Mountain Run and Dry River, the 30-day geometric mean FC standard could only be achieved if reductions were made in wildlife (background) contributions, after all other sources were controlled.

Areas of Uncertainty In watersheds where little or no data are available, where relationships between suspected causes and effects have not been quantified, and where resources are not available to gather additional data, much uncertainty exists in the allocation of loads and in the subsequent remediation of stream impairments. In the scientific analysis of these watersheds, researchers are required to apply the scientific method, to exercise their “best professional judgment” based on their experience and conceptualized understanding of the problem. During the application of HSPF to simulate FC in each of the nine TMDL watersheds, all of the above conditions applied. Confidence in the values of the following parameters could be significantly strengthened with additional data and research to reduce the uncertainty associated with the use of “best professional judgment”:

• FC contributions from groundwater (AOQC) and interflow (IOQC): Do well water samples provide an indication of FC concentrations in groundwater? Or do they only provide an indication of well site contamination? Do FC really exist in typical settings within either of these flows?

• FC contributions from direct stream deposits of waste by livestock: What is the relationship between the daily amount of time cows spend in the stream and the amount of daily waste deposited in the stream? What factors or mechanisms affect FC availability or release to the water column from an in-stream manure deposit?

• FC contributions from pasture: How is FC released into runoff from a “cow-pie” in a pasture area? How does crusting affect this availability?

• Distribution of wildlife FC on land and in-stream: Wildlife-typed DNA are present in-stream during low flow conditions (Yagow, 2001). Do wildlife defecate in-stream? Or are wildlife contributions to in-stream FC concentrations solely the result of runoff events?

• The role of sediment storage and bacterial regrowth: For these nine TMDLs, sediment was not simulated, although large concentrations of FC were measured in the channel sediment for several of these watersheds (Yagow, 2001; Mostaghimi et al., 2000). Because of the larger number of parameters that need to be quantified for sediment, and because of the lack of data to evaluate these parameters, FC contributions from sediment were compensated for during the calibration process through the collective adjustments to other FC parameters. Bacteria have been shown to be capable of re-growth in streams, though these events are thought to occur infrequently, and would require still additional sets of parameters to simulate nutrient dynamics and competitive

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organisms. What effect does this simplification have on simulated in-stream concentrations?

• Die-off in storage, on-the-land (SQOLIM), and in-stream (FSTDEC): Although time-series measurements of die-off are available from a variety of field and lab studies, most studies do not examine the specific mechanisms responsible for bacterial growth and die-off or disappearance. How important are these other mechanisms?

• Rate of FC buildup on impervious areas (ACQOP(IMPLND)): Most studies on urban areas measure buildup without investigation into the sources of the buildup, and instead relate buildup rates to surrounding land use (Olivieri et al., 1977; Mallard, 1980; Geldreich et al., 1968). Are there regional or site-specific factors that influence this rate as well?

• Pervious vs. impervious washoff rates (WSQOP): Most studies on buildup/washoff formulations have been performed on impervious areas. The TMDLs reported here used the assumption that pervious areas would require larger storms to remove the same amount as from an impervious area, and refined estimates of WSQOP through calibration. How does washoff really differ between pervious and impervious areas?

• FC calibration with limited data: Limited fecal coliform monitoring data appear to be fairly universal, at least for watersheds in Virginia. Since a large amount of “best professional judgment” was called for during the calibration process, different modelers could arrive at very different values for several parameters, each producing equally acceptable calibrations that could result in very different TMDL reduction scenarios. Are we missing some other significant parameter or process in the model, or is there a better way to represent fecal coliform fate and transport in a model?

Summary and Conclusions Fecal coliform TMDLs were developed for nine watersheds in Virginia using the HSPF model. The primary HSPF algorithms used to simulate FC loading and fate in the models are described in detail. Parameter values are summarized for all HSPF parameters related to FC simulation, as well as source data used external to the model for developing input loads from the various individual FC sources. Although there are many areas of uncertainty in modeling fecal coliform, a scientific approach was used in the evaluation of sources, the representation of the sources, and the evaluation of parameters used to simulate fecal coliform fate and transport with the HSPF model for nine sub-watersheds. In light of the similarity of source reductions called for in each of the corresponding TMDLs, these results support the following two recommendations in a recent National Research Council report to Congress assessing the scientific basis of TMDLs (NRC, 2001):

1. that key results be applied regionally to other impaired watersheds with similar sources, rather than going through the additional time and expense of developing individual TMDLs for each impaired watershed, and

2. that these applications be used in conjunction with a modified procedure called “adaptive implementation” – the process of taking implementation actions of a limited scope commensurate with available data and information, through the application of the scientific method to improve our understanding of the problem and its solution, while at the same time making progress toward attaining the water quality standard.

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References

ASAE. 1998. ASAE Standards, 45th edition. Standards, engineering practices and data developed and adopted by the American Society of Agricultural Engineers. Russell H. Hahn, editor. ASAE. St. Joseph, Michigan.

Bicknell, B. R., J.C. Imhoff, J. L. Kittle Jr., A. S. Donigian Jr., and R. C. Johanson. 1996. Hydrological Simulation Program – FORTRAN, User’s Manual for Release 11. U. S. Environmental Protection Agency, Environmental Research Laboratory. Atlanta, Georgia.

DCR. 1994. Virginia nonpoint source pollution watershed assessment report. Department of Conservation and Recreation, Division of Soil and Water Conservation. Richmond, Virginia.

DEQ. 2001. Total maximum daily load – Background. Available at http://www.deq.state.va.us/tmdl/backgr.html .

DEQ. 2000. Total maximum daily load program: A ten-year implementation plan. Department of Environmental Quality; Department of Conservation and Recreation; Department of Mine, Minerals and Energy; and Department of Health. Richmond, Virginia. Available at http://www.deq.state.va.us/tmdl/reports/hb30.html .

DEQ. 1998. Virginia 303(d) total maximum daily load priority list and report. Department of Environmental Quality and Department of Conservation and Recreation. Richmond, Virginia.

DEQ. 1994. Virginia water quality assessment for 1994. 305(b) report to EPA and Congress. Information Bulletin #597. Virginia Department of Environmental Quality. Richmond, Virginia.

EPA. 2000. BASINS Technical Note 6. Estimating hydrology and hydraulic parameters for HSPF. EPA-823-R-00-012. U. S. Environmental Protection Agency, Office of Water. Washington, D. C. Available at http://www.epa.gov/ost/basins/tecnote6.html .

EPA. 1999. Draft guidance for water quality-based decisions: The TMDL process (Second edition). EPA 841-D-99-001. U. S. Environmental Protection Agency, Office of Water. Washington, D. C. Available at http://www.epa.gov/owow/tmdl/tmdlguid.pdf .

EPA. 1983. Chesapeake Bay: A framework for action. U. S. Environmental Protection Agency, Region 3. Philadelphia, Pennsylvania.

Geldreich, E. E. 1977. Bacterial populations and indicator concepts in feces, sewage, stormwater and solid wastes. In: Berg, Gerald (ed). Indicators of viruses in water and food. Ann Arbor Science Publishers, Inc. Ann Arbor, Michigan.

Geldreich, E. E., L. C. Best, B. A. Kenner, and D. J. Van Donsel. 1968. The bacteriological aspects of stormwater pollution. J. Wat. Poll. Control Fed. 40: 1861-1872.

Kator, Howard and Martha Rhodes. 1996. Identification of pollutant sources contributing to degraded sanitary water quality in Taskinas Creek National Estuarine Research Reserve, Virginia. Special Report in Applied Marine Science and Ocean Engineering No. 336. The College of William and Mary. Williamsburg, Virginia.

Mallard, G. 1980. Microorganisms in stormwater—A summary of recent investigations. Open-File Report 80-1198. U. S. Geological Survey. Prepared in cooperation with the Long Island Regional Planning Board. Syosset, New York.

Mostaghimi, S. S. Shah, T.A. Dillaha, K.M. Brannan, M.L. Wolfe, C.D. Heatwole, M. Al-Smadi, J. Miller, G. Yagow, D. Cherry, and R. Currie. 2000a. Fecal Coliform TMDL for Dry River, Rockingham County, Virginia. Final Report Submitted to Virginia Departments of

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Environmental Quality and Conservation and Recreation, Richmond, Virginia. Available at http://www.deq.state.va.us/tmdl/tmdls/shenrvr/dryriver.pdf .

Mostaghimi, S. S. Shah, T.A. Dillaha, K.M. Brannan, M.L. Wolfe, C.D. Heatwole, M. Al-Smadi, J. Miller, G. Yagow, D. Cherry, and R. Currie. 2000b. Fecal Coliform TMDL for Pleasant Run, Rockingham County, Virginia. Final Report Submitted to Virginia Departments of Environmental Quality and Conservation and Recreation, Richmond, Virginia. Available at http://www.deq.state.va.us/tmdl/tmdls/shenrvr/millcr.pdf .

Mostaghimi, S. S. Shah, T.A. Dillaha, K.M. Brannan, M.L. Wolfe, C.D. Heatwole, M. Al-Smadi, J. Miller, G. Yagow, D. Cherry and R. Currie. 2000c. Fecal Coliform TMDL for Mill Creek, Rockingham County, Virginia. Final Report Submitted to Virginia Departments of Environmental Quality and Conservation and Recreation, Richmond, Virginia. Available at http://www.deq.state.va.us/tmdl/tmdls/shenrvr/pleasant.pdf .

Mostaghimi, S., T. A. Dillaha, K.M. Brannan, S. Shah, C. D. Heatwole, M. L. Wolfe, M. Al-Smadi, and J. Miller. 2000d. Fecal Coliform TMDL for Sheep Creek, Elk Creek, Machine Creek, Little Otter River, and Big Otter River, Bedford and Campbell Counties, Virginia. Final Report Submitted to Virginia Departments of Environmental Quality and Conservation and Recreation, Richmond, Virginia. Available at http://www.deq.state.va.us/tmdl/tmdls/roankrvr/bigotter.pdf .

MWPS. 1993. Livestock waste facilities handbook. 2nd Edition. MidWest Plan Service, Iowa State University. Ames, Iowa.

National Research Council. 2001. Assessing the TMDL approach to water quality management. National Academy Press. Washington, D. C. Pre-publication copy available for viewing at http://www.nap/edu/books/0309075793/html/ .

Novotny, V. and G. Chesters. 1981. Handbook of nonpoint pollution sources and management. Van Nostrand Reinhold Company. New York, New York.

Olivieri, V. P., C. W. Kruse, K. Kawata and J. E. Smith. 1977. Microorganisms in urban stormwater. EPA-600/2-77-087. U. S. Environmental Protection Agency. Cincinnati, Ohio.

Sartor, J. D. and G. B. Boyd. 1972. Water pollution aspects of street surface contaminants. EPA-R2-72-081. U. S. Environmental Protection Agency. Washington, D. C.

SWCB. 1997. 9 VAC 25-260-5 et seq. Water quality standards. Statutory authority: § 62.1-44.15(3a) of the Code of Virginia. Effective Date: December 10, 1997. State Water Control Board. Richmond, Virginia. Available at http://www.deq.state.va.us/wqs/ .

Tetra Tech. 1999. Fecal coliform TMDL development for Muddy Creek, Virginia. Prepared for the Muddy Creek TMDL Establishment Workgroup. Available at http://www.deq.state.va.us/tmdl/tmdls/shenrvr/muddyfe.pdf .

Thelin, R. and G. F. Gifford. 1985. Fecal coliform release patterns from fecal material of cattle. J. Environ. Qual. 12(1):57-63.

Weiskel, Peter A., Brian L. Howes and George R. Heufelder. 1996. Coliform contamination of a coastal embayment: sources and transport pathways. Environ. Sci. Technol. 30: 1872-1881.

Yagow, G. 2001. Fecal coliform TMDL, Mountain Run Watershed, Culpeper County, Virginia. Available at http://www.deq.state.va.us/tmdl/tmdls/rapprvr/mtrnfec.pdf .

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