Watershed and Receiving Water Modelingunix.eng.ua.edu/~rpitt/Publications/BooksandReports...Models are important tools for watershed and receiving water analyses because they enable

APPENDIX H

Watershed and Receiving Water Modeling

CONTENTS

Introduction ....................................................................................................................................843 Modeling Stormwater Effects and the Need for Local Data for Calibration and Verification ....845

Unit Area Loadings ..............................................................................................................845 Simple Models......................................................................................................................846 Complex Models ..................................................................................................................852 Receiving Water Models ......................................................................................................855 Geographical Information Systems (GIS) ...........................................................................857

Summary ........................................................................................................................................860 References ......................................................................................................................................866

INTRODUCTION

Models are important tools for watershed and receiving water analyses because they enable comprehensive evaluations of large systems and can predict future conditions. Models always have errors, but these can be reduced through good calibration and verification using locally obtained data, as described in this book.

For stormwater issues, most models can be separated into watershed models and receiving water models. Both are briefly addressed in this appendix. Many (and constantly increasing in numbers) public domain water quality models are available. Periodically, these are available on a CD-ROM from the EPA (Exposure Models Library and Integrated Model Evaluation System, EPA Office of Research and Development CD-ROM. EPA-600-C-92-002. Revised March 1996). Numerous specialized Internet sites also have download sites or links to the EPA download sites for acquiring these models and documentation. The main EPA source is through the EPA’s Athens, GA, Center for Exposure Assessment Modeling (CEAM), where much of the EPA’s water quality modeling support is available (downloads, short courses, etc.). One especially interesting reference available from Athens is Rates, Constants, and Kinetics Formulations in Surface Water Quality Modeling (second edition), EPA/600/3-85/040, prepared by Tetra Tech in 1985, but still highly useful. This report is available in PDF format from: http://www.epa.gov/ORD/WebPubs/surfaceH2O/surface.html. Not only does this report contain summaries of the model processes and lab and field data for the different fate processes, it also summarizes many field techniques that can be used to collect the needed local data.

The CEAM Internet site is at: http://www.epa.gov/CEAM/. The major models available at this web site are shown in Table H.1 (as of February 2000). These are all DOS-based, Fortran-coded programs. Very few Windows or Macintosh programs are available, but they will operate in the

843

844 STORMWATER EFFECTS HANDBOOK

Table H.1 DOS Programs Available to Download from the EPA’s Center for Exposure Assessment Modeling (CEAMS) Group

Version Release File Name/Size (MB) Description/Abstract/Release Notes Number Date

INSTALAN.EXE / 1.28 ANNIE-IDE tool kit 1.14 Sep 91 INSTALCI.EXE / .5 CEAM information system 3.21 May 95 INSTALCM.EXE / 1.63 CORMIX model / documentation 3.20 Dec 96 INSTALEX.EXE / 1.00 EXAMS model / documentation 2.97.5 Jun 97 INSTALFG.EXE / 1.07 FGETS model system 3.0.18 Sep 94 INSTALFW.EXE / 1.05 FEMWATER model / documentation 1.00 Jul 93 INSTALGC.EXE / 1.16 GCSOLAR model / documentation 1.20 Jul 99 INSTALHC.EXE / 8.44 HSCTM2D model / documentation 1.01 Nov 98 INSTALHS.EXE / 8.66 HSPF model / documentation 11.00 Apr 97 HSP11Y2K.EXE / .84 HSPF model / documentation / Year 2000 (Y2K) Patch 11.00 Dec 99 INSTALLC.EXE / .71 LC50 model / documentation 1.00 Jan 99 INSTALMT.EXE / 2.49 MINTEQ model / documentation 4.01 Dec 99 INSTALMS.EXE / 6.52 MMSOILS model / documentation 4.00 Jun 97 INSTALMM.EXE / 3.49 MULTIMED model / documentation 1.01 Dec 92 INSTALM2.EXE / 4.79 MULTIMED model / documentation 2.00 Beta Oct 96 INSTALDP.EXE / 3.34 MULTIMDP model / documentation 1.00 Oct 96 INSTALOF.EXE / .34 Sample ANNIE-IDE application 1.61 Sep 91 INSTALOX.EXE / .40 OXYREF data base / documentation 1.00 Dec 98 INSTALP2.EXE / 2.76 PRZM2 model / documentation 2.00 Oct 94 INSTALP3.EXE / 5.15 PRZM3 model / documentation 3.12 Beta Mar 98 INSTALPL.EXE / 1.44 PLUMES model / documentation 3.00 Dec 94 INSTALPT.EXE / 5.43 PATRIOT model / documentation 1.20 Nov 94 INSTALQ2.EXE / 2.21 QUAL2EU model system / Documentation 3.22 May 96 INSTALSW.EXE / 1.6 SWMM model system 4.30 May 94 INSTALSX.EXE / .39 SMPTOX3 model / documentation 2.01 Feb 93 INSTALWP.EXE / 3.14 WASP model / documentation 5.10 Oct 93

DOS shell of the Windows operating systems. Most of these programs were originally developed many years ago (with the processes reasonably well described in the Tetra Tech “rate” report of 1985, noted above).

There are numerous proprietary Windows “front-ends” for selected programs, along with proprietary versions that have substantial changes in the code. In addition, many private Internet sites also provide downloadable public domain water quality models, or “test” versions of commercial programs. Obviously, it is impossible to develop a complete list of available water quality models, and it is difficult for the user to select which model may be most appropriate for his or her specific use. Excellent model reviews are periodically prepared, such as Compendium of Tools for Watershed Assessment and TMDL Development, EPA-841-B-97-006, 1997. In addition, numerous listservers are available to provide excellent user support for specific models. A representative listing of list servers and other water quality modeling support is provided by Dr. Bill James at the University of Guelph at http://www.eos.uoguelph.ca/webfiles/james/homepage/Research/ListServers.html.

A major surface water quality modeling effort at EPA is directed toward supporting the Total Maximum Daily Load (TMDL) program. As part of this support, the BASINS model (Better Assessment Science Integrating Point and Nonpoint Sources), a Windows-based structure of several interconnected programs and a geographical information system (GIS), described later, has been developed. The main report is available as EPA-823, R-96001, May, 1996. Extensive Internet support, including downloads of the main program, and regional data, is available at http://www.epa.gov/OST/BASINS/. The structure of BASINS will allow additional models to be added to this framework. The extremely powerful aspect of BASINS is the GIS capabilities where local data can be easily integrated for model use. Individual CD-ROMS are available for each of the 10 EPA regions containing much local data. BASINS has six main components: nationally derived databases with Data Extraction Tools; assessment tools; utilities to facilitate organizing and evaluating the data, including land use

WATERSHED AND RECEIVING WATER MODELING 845

data; Watershed Characterization Reports; water quality models; and the Nonpoint Source Model. It currently uses portions of HSPF for the land-based modeling component (NPSM, the Nonpoint Source Model), and QUAL2E and TOXIROUTE for the stream water quality models. Even though many of model components are older Fortran-coded modules, the Windows and GIS interfaces makes the model relatively easy to use.

BASINS is a large-scale model and may be too complex for focusing on specific smaller areas, or when detailed evaluations are needed. Figure H.1 is an overview of the individual environmental models commonly used (and evaluated in Compendium of Tools for Watershed Assessment and TMDL Development). Obviously, BASINS, although extremely powerful and needed for some applications, currently does not offer the flexibility that the wide range of individual models can.

MODELING STORMWATER EFFECTS AND THE NEED FOR LOCAL DATA FOR CALIBRATION AND VERIFICATION

A typical use of stormwater monitoring data is to calibrate and verify a model that will be used to examine many questions. Common uses of models are to determine the major sources of pollutants and to design control programs to effectively reduce the problem discharges. There are three general classes of stormwater models:

• Unit area loadings • Simple models • Complex models

Unit Area Loadings

Table 2.5 included unit area loading estimates for stormwater, based on numerous observations from throughout North America (mostly from the EPA’s NURP projects, EPA 1983, and from other selected North American studies). Most of the available NURP data are from monitoring mediumdensity residential areas, but data from Wisconsin and Toronto included data from various land uses. These estimates are most useful when making preliminary assessments on a large scale, especially in preparing an experimental design for site-specific monitoring. As an example, these values can be used to identify the most significant land uses in a watershed and help direct the monitoring effort, as shown in Table 5.4 (repeated below as Table H.2) and Figure 5.7, a marginal benefit analysis. Obviously, the variations of unit area loadings can be very large, depending on specific conditions, but the basic rankings of land use related discharges are still useful for preliminary evaluations.

For most constituents, manufacturing industrial and commercial areas have the largest unit area loadings, while parks and low-density residential areas have the smallest unit area loadings. The importance of the areas in a watershed is obviously dependent on the size of the area. Mediumdensity residential areas comprise the majority of the land area for most cities, and therefore also for most large urban watersheds. These large areas increase the significance of this land use. However, relatively small amounts of industrial or commercial activity can overwhelm the residential contributions in small and moderate-sized drainages. Chapter 2 presented information showing the relative importance of industrial and residential areas in Toronto (Pitt and McLean 1986), based on a comprehensive monitoring program and measured unit area loadings for the major land uses.

The earlier Toronto discussion in Chapter 2 also showed how dry-weather flows and snowmelt contributions can be very important. That example stresses the need to consider all phases of flows that may be discharged from separate storm drainage systems. Few published unit area stormwater loading values include these other contributions that can have major effects on receiving water conditions.

Unit area loadings for a local area can be determined based on local monitoring data using one of the other modeling methods described below. Unit area loadings are a convenient method to summarize extensive monitoring data and to highlight potential problem areas, especially if integrated


Figure H.1 Environmental models commonly in use. (From EPA. Compendium of Tools for Watershed Assessment and TMDL Development. EPA-841-B-97-006. U.S. Environmental Protection Agency. 1997.)

with a GIS. GIS has been successfully used with nonpoint source modeling activities to display the unit area loadings predicted from monitoring and modeling programs for many alternatives. Otherwise, the massive amounts of data generated is difficult to summarize in an easily presentable manner.

Simple Models

Simplified stormwater models usually take the general form:

Unit Area Loading = (EMC) × (Rv) × (Rain)


Table H.2 Example Marginal Benefit Analysis

Critical Land Use Unit % Mass Straight

(ranked by % mass % of Area Relative per Accum. line Marginal per category) Area Loading Mass Category (% mass) Model Benefit

1 Older medium-density resid. 24 200 4800 22.8 22.8 6.25 16.5 2 High-density resid. 7 300 2100 10.0 32.7 12.5 20.2 3 Office 7 300 2100 10.0 42.7 18.8 24.0 4 Strip commercial 8 250 2000 9.5 52.2 25.0 27.2 5 Multiple family 8 200 1600 7.6 59.8 31.3 28.5 6 Manufacturing industrial 3 500 1500 7.1 66.9 37.5 29.4 7 Warehousing 5 300 1500 7.1 74.0 43.8 30.3 8 New medium-density resid. 5 250 1250 5.9 80.0 50.0 30.0 9 Light industrial 5 200 1000 4.7 84.7 56.3 28.4

10 Major roadways 5 200 1000 4.7 89.4 62.5 26.9 11 Civic/educational 10 100 1000 4.7 94.2 68.8 25.4 12 Shopping malls 3 250 750 3.6 97.7 75.0 22.7 13 Utilities 1 150 150 0.7 98.5 81.3 17.2 14 Low-density resid. with swales 5 25 125 0.6 99.1 87.5 11.6 15 Vacant 2 50 100 0.5 99.5 93.8 5.8 16 Park 2 50 100 0.5 100.0 100.0 0.0

Total 100 21075 100

Table H.3 Median EMCs and COVs for All Sites Monitored during NURP

Residential Mixed Commercial Open/Nonurban Pollutant Median COV Median COV Median COV Median COV

BOD5 mg/L 10.0 0.41 7.8 0.52 9.3 0.31 — — COD mg/L 73 0.55 65 0.58 57 0.39 40 0.78 TSS mg/L 101 0.96 67 1.14 69 0.85 70 2.92 Total lead µg/L 144 0.75 114 1.35 104 0.68 30 1.52 Total copper µg/L 33 0.99 27 1.32 29 0.81 — — Total zinc µg/L 135 0.84 154 0.78 226 1.07 195 0.66 Total Kjeldahl nitrogen µg/L 1900 0.73 1288 0.50 1179 0.43 965 1.00 NO2-N + NO3-N µg/L 736 0.83 558 0.67 572 0.48 543 0.91 Total P µg/L 383 0.69 263 0.75 201 0.67 121 1.66 Soluble P µg/L 143 0.46 56 0.75 80 0.71 26 2.1

From EPA. Results of the Nationwide Urban Runoff Program. Water Planning Division, PB 84-185552, Wash ington, D.C. December 1983.

where EMC is the event mean concentration, Rv is the volumetric runoff coefficient (or the effective impervious area, EIA), and Rain is the total rain depth for the period of concern (usually a year). With the appropriate conversions, this simple equation predicts the unit area loadings for the monitored area. This is the method used in the stormwater permit applications for the EPA’s NPDES (Nationwide Pollutant Discharge Elimination System) permit program.

The problems with this simplified model include: typically poor estimates of EMC, the Rv value varies for different rain depths, and the procedure cannot easily distinguish seasonal effects (unless EMC values are available for each season), and it cannot be used to evaluate the effectiveness of stormwater control practices.

The main problem with using this simplified model is obtaining an adequate estimate for the EMC. Table H.3 contains the basic concentration information from the EPA’s NURP studies (EPA 1983) that are generally used for these analyses. The coefficient of variation (COV) values for these median values are seen to vary from 0.5 to more than 1.0. Figure H.2, also from the EPA’s NURP studies (EPA 1983), illustrates the wide variations in observed concentrations for the common stormwater constituents. Wide concentration variations make it more difficult to distinguish between


LEGEND

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OF THE MEDIAN

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RESIDENTIAL MIXED COMMERCIAL OPEN RESIDENTIAL MIXED COMMERCIAL OPEN SITES SITES SITES SITES SITES SITES SITES SITES

(e) (g)

Figure H.2 Box plots of pollutant EMCs for different land uses. (From EPA. Results of the Nationwide Urban Runoff Program. Water Planning Division, PB 84-185552, Washington, D.C. December 1983.)

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SOLUBLE PHOSPHORUS

Figure H.2 (continued)

different land uses. As an example, Figure H.2 indicates that suspended solids, BOD, copper, zinc, and nitrite plus nitrate median values are not likely significantly different for any of the four land use categories shown. However, open site COD, phosphorus, and lead median concentrations are likely significantly less than for the other three land uses.

The stormwater permit program typically requires three events to be sampled to determine the EMC value. This small sampling effort likely results in inaccurate EMC estimates because of the relatively large variation in stormwater quality from the same sampling location. As seen in Figure H.3 (a duplicate of Figure 5.3), about 25 samples are required to estimate the EMC with an estimated error of 25% or less, if the COV is 0.5. Most of the time, the COV is even larger, requiring even more samples. The use of only three samples to determine the EMC value would likely result in errors of several hundred percent (using typical levels for confidence of 95% and power of 80%). Such large EMC errors would be reflected in similar errors in the calculated unit area loading values. This could result in incorrect conclusions concerning the relative pollutant sources and inappropriate expenditures of resources for stormwater control.

Errors also occur when selecting the volumetric runoff coefficient (Rv) value. For drainage design, the Rv value is assumed to be equal to the amount of directly connected impervious area. This is sometimes modified to be equal to the “effective” impervious area, as it is obvious that paved areas (and roofs) that drain to pervious areas contribute some runoff, but less than if the paved areas were directly connected to the drainage system. In addition, the Rv is different for different rain depths at the same area. Small rain depths are associated with relatively small Rv values, while larger rains produce larger Rv values, as shown in Figure H.4 (Pitt 1987). Table H.4 (Pitt 1987) illustrates how different urban surfaces contribute increasing fractions of rainfall as runoff. Therefore, if constant Rv values are used for all rains, large errors may occur for individual rains (overpredict for small rains and underpredict for large rains), although the annual average, or annual total, may be acceptable, assuming the monitored rains represent the complete set of annual

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rains. If only moderate to large rains are monitored (a typical goal), then the averaged Rv for the monitored rains would be larger than the true annual averaged Rv.

Typical estimation methods used for runoff volume were developed for large drainage design storms (several inches in depth) and are not appropriate for the smaller events that are most significant in water quality studies. Table H.4 (Pitt 1987) shows how these runoff coefficients (the fraction of rain that occurs as direct runoff) for impervious areas vary greatly for different rain depths. After several inches of rain (in the range for drainage design studies), the Rv values for all paved and roof areas are between 0.9 and 0.99, resulting in little error if a constant 0.9 value is used. However, at 0.1 to 0.4 in of rain (the rain range where the water pollutants are becoming important), the Rv values for the different paved and roof areas vary greatly (from 0.25 to 0.95).

Figure H.3 Sampling requirements for different error goals, alpha of 0.05 and beta of 0.20 (duplicate of Figure 5.3).

Figure H.4 Rainfall-runoff responses for pavement tests. (From Pitt, R. Small Storm Urban Flow and Particulate Washoff Contributions to Outfall Discharges. Ph.D. dissertation submitted to the Department of Civil and Environmental Engineering, University of Wisconsin, Madison. 1987. With permission.)

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Table H.4 Observed Directly Connected Runoff Coefficients for Impervious Areas

Depth When Coefficient Is

0.1 in 0.4 in 1.7 in about 0.9, in

Roads and other small impervious areas 0.4 0.6 0.8 3 Pitched roofs 0.7 0.9 0.98 0.25 Flat roofs 0.25 0.7 0.85 2 Large paved areas and freeways 0.95 0.97 0.99 0.05

From Pitt, R. Small Storm Urban Flow and Particulate Washoff Contributions to Outfall Discharges. Ph.D. dissertation submitted to the Department of Civil and Environmental Engineering, University of Wisconsin, Madison. 1987. With permission.

This would result in very large runoff prediction errors if a constant Rv value was assumed for all areas, especially when trying to predict where the runoff water originated.

Most of the annual rainfall is associated with many small individual events and not with the few rarer large rains. Figure H.5a shows measured rain and runoff distributions for Milwaukee during the 1983 NURP monitored rain year. Rains between 0.05 and 5 in were monitored during this period. Two large events (greater than 3 in) occurred during this monitoring period, which greatly bias these curves, compared to typical rain years. During this period:

• The median rain depth was about 0.3 in. • 66% of all Milwaukee rains were less than 0.5 in in depth. • For medium-density residential areas, 50% of runoff was associated with rains less than 0.75 in

for Milwaukee. • A 100-year, 24-hour rain of 5.6 in for Milwaukee could produce about 15% of the typical annual

runoff volume, but only contributes about 0.15% of the average annual runoff volume when amortized over 100 years.

• Typical 25-year drainage design storms (4.4 in in Milwaukee) produce about 12.5% of the typical annual runoff volume but only about 0.5% of the average runoff volume.

Figure H.5b shows measured Milwaukee pollutant discharges associated with different rain depths for a medium-density residential area. Suspended solids, COD, lead, and phosphates discharges are seen to closely follow the general shape of the runoff distribution shown in Figure H.5a.

100 100

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PO4

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Figure H.5 Accumulative distributions of Milwaukee rain, runoff, and pollutant loadings for medium-density residential areas monitored during 1981 to 1983 (duplicate of Figures 6.1 and 6.2).


Table H.5 Observed Disturbed Urban Soil Volumetric Runoff Coefficients (RV) for Different Rain Depths

Depth When RV Coefficient Is

0.1 in 0.4 in 1.7 in about 0.1

Clayey soils 0 0.15 0.25 0.2 Sandy soils 0 0 0.05 2.5

From Pitt, R. Small Storm Urban Flow and Particulate Washoff Contributions to Outfall Discharges. Ph.D. dissertation submitted to the Department of Civil and Environmental Engineering, University of Wisconsin, Madison. 1987. With permission.

Being able to accurately predict runoff volume is very important in order to reasonable predict runoff pollutant discharges. The shape of the runoff and pollutant runoff curves in Figure H.5 show three distinct regions (values given for Milwaukee):

• Common rains having relatively low pollutant discharges are associated with rains less than about 0.5 in in depth. These are key rains when runoff associated water quality violations, especially for bacteria, are of most concern.

• Rains between 0.5 and 1.5 in are responsible for about 75% of the runoff pollutant discharges and are key rains when addressing mass discharges.

• Rains greater than 1.5 in are associated with drainage design and are only responsible for relatively small portions of the annual pollutant discharges, even with the two unusually large rains that are included in these observations.

Similar relationships are observed for other regions in the country, but the specific rain depths associated with the three specific regions vary. In the southeast, the rain depths separating these three regions are about twice as large as observed for Milwaukee, for example.

Of course, the coefficients shown in Table H.4 can decrease substantially if the paved areas are not directly connected to the drainage system (especially important for roofs and parking areas), or if roadside grass swales are used. It should also be noted that disturbed urban soils contribute much more runoff for moderate rains than the typically expected values (Table H.5).

Complex Models

There are numerous models that fall in the mid-range and detailed model categories that are considered complex. These models all require the use of computers and varying amounts of input data, and they all require calibration and verification for local conditions. These models are constantly changing and new models are continually being developed. The selection of the most appropriate model for a specific situation is therefore important. A good source for model reviews that is periodically updated is the EPA’s Compendium of Watershed-Scale Models for TMDL Development (EPA 1997). This document was developed for watershed planners and regulators who are responsible for preparing “Total Maximum Daily Load” (TMDL) discharge limitations for receiving waters that are affected by many pollutant sources, including stormwater.

Tables H.6 and H.7 are model summaries from the TMDL report (EPA 1997), while Tables H.8 and H.9 list some of the attributes of many models (including data requirements and overall model complexity). The main distinctions between the mid-range models and the detailed models are that most of the mid-range models are considered “planning” models (for evaluations), while the detailed models are more oriented toward specific design (including greater time-resolution in predicted flows and concentrations). As an example, the mid-range models typically do not require nearly as many details pertaining to specific drainage system layouts as do the detailed models: the mid-range models can operate with more lumped parameters (larger-scale average conditions), while many of the detailed models require detailed drainage system layout information. More of the detailed models can also


Table H.6 Evaluation of Model Capabilities — Mid-Range Models

Criteria NPSMAP GWLF P8-UCM SIMPTM Auto-Ql AGNPS SLAMM

Land use Urban Rural Point sources

Time scale Annual Single event Continuous

Hydrology Runoff Baseflow

Pollutant loading Sediment Nutrients Others

Pollutant routing Transport Transformation

Model output Statistics Graphics Format options

Input data Requirements Calibration Default data User interface

BMPS Evaluation Design criteria

Documentation

H H H H H — H H H — — — H — M M H — — H H — — — — — — — L — H — — H — H N — — H — H H H H H H H H L H — L L — L — H — H H H H H H H H H H H — — H H H H H L L L L M — M — — — — — H — M L — L — — L M M H — — H L H H H H L H H M M M M M M M L L L L M L M H H M M L M M H H H M M M H L L H M M M M — — H L M M L H H H L M H M

From EPA. Compendium of Tools for Watershed Assessment and TMDL Development. EPA-841-B-97-006. U.S. Environmental Protection Agency. 1997.

include pollutant transformations and nonconservative pollutant behavior, especially for receiving water effects, than the mid-range models. Obviously, there are places where models of each type are needed. In some cases, it is useful to use a mid-range model to predict drainage area runoff conditions and a detailed model to evaluate specific issues pertaining to the drainage system and receiving waters. As an example, the Toronto Area Watershed Management Strategy program (TAWMS) used SLAMM to predict drainage area pollutant and flow discharges, SWMM to predict CSO discharges from the older sections of the city, and HSPF to evaluate receiving water conditions resulting from these discharges (OME 1986). In another example of multiple model use, engineers used SIMPTM in conjunction with SWMM to better predict Portland CSO overflow conditions (Roger Sutherland, personal communication, Columbia Slough Management Plan, prepared for the City of Portland’s Bureau of Environmental Services). In another example of multiple model use, several cities in Wisconsin have used SLAMM in conjunction with geographical information systems to better prepare the input files required by the program and to display the model results (Thum et al. 1990; Ventura and Kim 1993). The use of a GIS is an especially powerful tool to summarize massive amounts of information, especially when making presentations to the community and to politicians.

Most of the mid-range models were originally developed on personal computers and some have relatively easy-to-use interfaces. The use of “default” values is also common for these models, sometimes restricting the use of locally obtained calibration data. The mid-range models included on Table H.6 are:

• NPSMAP, the Nonpoint Source Model for Analysis and Planning model is a spreadsheet template developed by Omicron Assoc. that predicts nutrient loadings for urban and agricultural areas.

• GWLF, the Generalized Loading Functions model was developed at Cornell University to assess point and nonpoint loadings of nitrogen and phosphorus from relatively large agricultural and urban watersheds. It includes rainfall/runoff processes and erosion predictions. Most of the processes are controlled by default values.


• P8-UCM, the Urban Catchment Model was developed by John Walker for the Narragansett Bay Project to simulate stormwater pollutants in small urban catchments. Evaluations of various management practices are possible with P8, including help in their sizing for specific control objectives. It incorporates many default values from the EPA’s Nationwide Urban Runoff Program (EPA 1983).

• SIMPTM, the Simplified Particle Transport Model was developed by Roger Sutherland, of Pacific Water Resources, to simulate runoff, sediment, and yield of other pollutants from urban watersheds, including the evaluation of some control practices. Detailed particulate buildup and washoff processes are included, based on northwest regional data.

• Auto-QI, the Automated Qual-Illudas model was developed by Mike Trestriep at the Illinois State Water Survey to perform continuous simulations of runoff from impervious and pervious urban areas and to evaluate the effectiveness of selected control practices. It also includes components to examine receiving water impacts. A version of the model is linked to the ARC/INFO GIS program.

• AGNPS, the Agricultural Nonpoint Source pollution model was developed by the USDA Agricultural Research Service. It addresses potential impacts from point and nonpoint source pollution on surface and groundwater in agricultural watersheds. Alternative management programs are also evaluated. The spatial (grid) design of the model allows it to be interfaced to GIS and digital terrain models to simplify inputting the model parameters.

• SLAMM the Source Loading and Management Model was developed by Robert Pitt of the University of Alabama at Birmingham to evaluate the effects of urban development characteristics and source and outfall controls on pollutant discharges. It examines runoff from separate drainage areas that may include a wide variety of land uses and control practices. The outfall discharge estimates can then be evaluated in a separate model to evaluate receiving water impacts. Unique small storm hydrology and particulate washoff procedures, based on extensive field measurements, are incorporated in the model to more accurately predict the role of different source areas in generating stormwater pollutant discharges.

Detailed models were all originally developed on mainframe computers, but most have been ported to personal computers over the past several years. Most still have awkward user interfaces and require a group of skilled users to take advantage of most of their comprehensive capabilities, although proprietary Windows-based user interfaces and proprietary modifications of some of the more popular models (especially SWMM) are becoming common. The detailed models shown on Table H.7 include:

• STORM, the Storage, Treatment, Overflow Runoff Model was developed by the U.S. Army Corps of Engineers to continuously simulate urban runoff quantity, sediment, and several conservative pollutants. It has most commonly been used to evaluate the trade-offs between treatment and storage options for the control of CSOs.

• ANSWERS, the Areal Nonpoint Source Watershed Environment Response Simulation Model was developed by the University of Georgia to evaluate the effects of land use, management schemes, and conservation practices on the quantity and quality of watershed runoff. It stresses erosion and sediment transport processes.

• DR3M, the Distributed Routing Rainfall Runoff Model is supported by the U.S. Geological Survey and was developed to study conventional pollutants in predominantly urban areas. It produces detailed hydrographs and pollutant transport plots.

• SWRRBQ, the Simulation for Water Resources in Rural Basins model was developed by the USDA to simulate hydrologic, sedimentation, nutrient, and pesticide movement in large, complex, rural watersheds.

• SWMM the Storm Water Management Model was developed by the EPA to derive design criteria for structural stormwater controls. SWMM is likely the most commonly used detailed stormwater model, especially when evaluating sewerage issues and combined sewer overflows.

• HSPF, the Hydrological Simulation Program–FORTRAN was developed by the U.S. EPA to simulate water quantity and quality for a wide range of organic and inorganic pollutants from agricultural and urban watersheds. It is probably the most comprehensive model available, especially considering receiving water impacts. Chemical, biological, and physical processes are included to account for pollutant transport and transformations. However, much calibration information is required to effectively use all of HSPF’s capabilities.


Table H.7 Evaluation of Model Capabilities — Detailed Models

Criteria STORM ANSWERS DR3M SWRRBWQ SWMM HSPF

Land use Urban Rural Point sources

Time scale Annual Single event Continuous

Hydrology Runoff Baseflow

Pollutant loading Sediment Nutrients Others

Pollutant routing Transport Transformation

Model output Statistics Graphics Formal options

Input data Requirements Calibration Default data User interface

BMPs Evaluation Design criteria

Documentation

H — H L H H — H — H L M H — H H H H — — — — — — L H L L H H H — H H H H H H H H H H L — L H H H H H H H H H H H H H H H H — — H H H — M H H L H — — — — L H L — H H H H — H M M L H H H H H H M H H M H H L L M M H H M L H H M M — — M M — — M M H M H H M M M — H H H M M H H H

From EPA. Compendium of Tools for Watershed Assessment and TMDL Development. EPA-841-B-97-006. U.S. Environmental Protection Agency. 1997.

The mid-range models are probably the most commonly used because they are perceived as being easier to use and require less input information. That was certainly true when most of these detailed models were developed, but some of them, most notably SWMM, have a growing industry supporting their use, including the availability of much improved user interfaces. However, the cost of obtaining and using (entering and verifying) detailed information that may be required may not be justified by the intended use of the data generated by the model. The application of detailed models is more cost-effective when applied to address complex situations or objectives.

Dr. Bill James of the University of Guelph has long been an advocate of long-term continuous simulations. The cost of the required computer time has also decreased to the point where the use of long-term continuous simulations is no longer prohibitive, and is strongly recommended in order to obtain a much better understanding of watershed responses under a wide variety of conditions. The modeling of a few “design” storms may be satisfactory for simple drainage design considerations, where the only parameter of interest is peak flow rate. However, limiting simulations to only a few storms falls far short when a wide variety of water quality questions are important. The behavior of different stormwater quality control practices is also dependent on many different hydraulic parameters, not just peak flow rate. The use of several years of rainfall data in continuous simulations should therefore be considered the norm. If a model is not capable of continuous simulations, its usefulness is probably severely restricted to only the most rudimentary preliminary evaluations.

Receiving Water Models

Some of the models listed above include receiving water components (such as HSPF), but there are many additional models available that are specific for receiving waters and are in the public

L


Table H.8 Listing of Attributes of Commonly Used Urban Models

Attribute DR3M-QUAL HSFP Statisticala STORM SWMM

Sponsoring agency USGS EPA EPA HEC EPA Simulation typeb C,SE C,SE N/A C C,SE No pollutants 4 10 Any 6 10 Rainfall/runoff analysis Y Y Nc Y Y Sewer system flow routing Y Y N/A N Y Full, dynamic flow routing N N N/A N Yd

equations Surcharge Ye N N/A N Yd

Regulators, overflow structures, e.g., weirs, orifices, etc. N N N/A Y Y Special solids routine Y Y N N Y Storage analysis Y Y Yf Y Y Treatment analysis Y Y Yf Y Y Suitable for screening (S), design (D) S,D S,D S S S,D Available on microcomputer N Y Yg N Y Data and personnel requirementsh Medium High Medium Low High Overall model complexity i Medium High Medium Medium High

a EPA procedure. b C = continuous simulation, SE = single event simulation. c Runoff coefficient used to obtain runoff volumes. d Full dynamic equations and surcharge calculations only in Extran Block of SWMM. e Surcharge simulated by storing excess inflow at upstream end of pipe. Pressure flow not simulated. f Storage and treatment analyzed analytically. g FHWA study, Driscoll et al. (1989). h General requirements for model installation, familiarization, data requirement, etc. To be interpreted only very

generally. i Reflection of general size and overall model capabilities. Note that complex models may still be used to

simulate very simple systems with attendant minimal data requirements.

From EPA. Modeling of Nonpoint Source Water Quality in Urban and Non-urban Areas. Office of Research and Development. U.S. Environmental Protection Agency. Washington, D.C. EPA 600/3-91/039. 1991.

Table H.9 Listing of Commonly used Non-Urban Runoff Models

Attribute AGNPS ANSWERS CREAMS HSPF PRZM SWRRB UTM-TOX

Sponsoring agency USDA Purdue USDA EPA EPA USDA ORNL & EPA Simulation type C,SE SE C,SE C,SE C C C, SE Rainfall/runoff analysis Y Y Y Y Y Y Y Erosion modeling Y Y Y Y Y Y Y Pesticides Y N Y Y Y Y N Nutrients Y Y Y Y N Y N User-defined constituents N N N Y N N Y Soil processes

Pesticides N N Y Y Y Y N Nutrients N N Y Y N Y N

Multiple land type capability Y Y N Y N Y Y In-stream water quality simulation N N N Y N N Y Available on microcomputer Y Y Y Y Y Y N Data and personnel requirements M M/H H H M M H Overall model complexity M M H H M M/H H

Y = yes, N = no, M = Moderate, H = High, C = Continuous, SE = Storm Event.



domain and available through the EPA’s CEAM. Some included on the 1996 version of the Exposure Models Library and Integrated Model Evaluation System are listed below and in Figure H.6:

Surface Water Models

Selected for 1st and 2nd Level Reviews: Selected for 1st Level Review Only:

CEQUALRIV1 EXAMS:

CEQUALW2 FATE: fate of organics

CTAP: chemical transport & analysis program GCSOLAR

DYNTOX: dynamic toxicity model HEC-5Q & 6

EUTRO4 MICHRIV: transport in water & sediments

GEMS-EXAMS: geographical exposure modeling systems - EXAMs

PCPROUTE-PC: pollutant routing model

HSPF: hydrologic simulation program - fortran PLUMES:

QUAL2E: enhanced stream water quality model

RESTMP: water temperature model

REACHSCAN RIVMOD: sediment transport

SERATRA: in-stream sediment-contaminant transport

SEDDEP: settling of wastewater particulates

TOX 14 SMPTOX: stream toxic model

WQRRS: water quality (ecological cycling) in rivers and reservoirs

TERMS: thermal simulation of lakes

WASP5: water quality assessment program TWQM: downstream transformation of problem constituents

Figure H.6 Surface water models included on the CEAM CD-ROM.

• SWAT (contains the GLEANS pesticide fate model as a component) • PREWET (predicts fates of pollutants in wetlands) • GWLF (a simple transport model) • CREAMS (transport of soluble and sediment-attached chemicals) • WASP5 (especially the TOXI5 and EUTRO5 components) • MINTEQA2 (chemical equilibrium model) • TWQM (in-stream effects of reduced species that may be discharged from dams) • SMPTOX (toxicant interactions with stream bed sediments) • WATEQF (chemical equilibrium model) • VLEACH (chemical fate model)

Geographical Information Systems (GIS)

As indicated above, the use of GIS has become very important when modeling large areas. The main advantages of GIS include an ability to effectively display large amounts of information (relatively easy to incorporate with model output), and in some cases, to organize and automate the data input requirements for the models (requiring a much greater level of integration with a model). It has been especially important when working with nontechnical community groups and when summarizing modeling options. The visual presentation of the massive amounts of output results, or results from monitoring programs, is much more effective for communicating with diverse groups of people.


PUBLIC LAND RECORDS USED IN DIGITAL DATABASE

Data Item Custodian Document

Parcel Dane Co. Land Records & Regulation Section parcel maps Soils U.S.D.A. Soil Conservation Service Dane County Soil Survey Slope U.S.D.A. Soil Conservation Service Dane County Soil Survey Slope U.S. Geological Survey Quadrangle maps Wetlands Wis. Dept. of Natural Resources Wetlands inventory Hydrology U.S. Geological Survey Quadrangle maps Farm Tracts & Fields Dane County, A.S.C.S. NHAP aerial photo prints Woodlots Dane County, A.S.C.S. NHAP aerial photo prints Existing Land Uses Dane County, A.S.C.S. NHAP aerial photo prints Planned Land Uses Dane Co. Regional Planning Comm. Town of Burke Land Use Maps Planned Land Uses Dane Co. Regional Planning Comm. Hwy 151 Corridor Study Planned Land Uses City of Madison, Dept. of Planning Burke Heights Dev’t Plan Land Use Zoning Dane Co. Land Records & Regulation Section zoning maps Land Use Zoning City of Sun Prairie, Dept. of Planning City of Sun Pr. zoning maps Land Use Zoning City of Madison, Dept. of Planning City of Madison zoning maps Floodplain Federal Emergency Mgmt. Agency Flood boundary map Existing Parks Dane Co. Land Records & Regulation Section parcel maps Planned Parks Dane County Parks Division Cherokee Marsh Owner. Map Existing Sewers Madison Metro Sewerage District Sewer. Dist. Interseptor Map Existing Sewers City of Sun Prairie, Dept. of Engineer. City interseptor map Planned Sewers Madison Metro Sewerage District Collection System Design Rep. Urban Service Areas Dane Co. Land Records & Regulation Town of Burke Land Use Plan Traffic Counts Wis. Department of Transportation 1987 Highway Traffic maps Roads/Hwys U.S. Geological Survey Quadrangle maps Farm Tenure Dane County, A.S.C.S. Farm operator file Farm Tenure Dane Co. Land Records & Regulation Section Parcel maps Historic Buildings Wisconsin Historic Society Coded quadrangle maps Archeologic Sites Wisconsin Historic Society Coded quadrangle maps Watershed Boundary Dane Co. Regional Planning Comm. Watershed boundary map Watershed Boundary U.S. Geological Survey Quadrangle maps

Figure H.7 0Availability of data used in early GIS and stormwater modeling studies conducted in Dane County, WI. (From Pickett, S.R., O.G. Thum, and B.J. Hiemann. Using a land information system to integrate nonpoint source pollution modeling and land use development planning. Land Information and Computer Graphics Facility, The University of Wisconsin, Madison. 1989.)

GIS has been used for many years, but has recently become much more accessible with improvements in software and significant cost reductions in suitable computer equipment. Various communities in the State of Wisconsin, for example, have used GIS systems integrated with the Source Loading and Management Model (SLAMM) to graphically illustrate development and control options associated with urbanization (Haubner and Joeres 1996; Kim et al. 1993; Kim and Ventura 1993; Ventura and Kim 1993). Figure H.7 (Pickett et al. 1989) shows the availability of data used in some of the early studies conducted in Dane County, WI, while Figure H.8 (Kim and Ventura 1993) shows how the information is integrated with SLAMM to identify critical source areas. Figure H.9 (Pickett et al. 1989) is an example map showing expected changes in suspended solids discharges resulting from development options. SLAMM is currently available from www.winslamm.com.

As noted above, the current development and use of the BASINS model, especially for TMDL evaluations, relies heavily of a GIS framework (Lahlou et al. 1998). Tables H.10 through H.13, from the BASINS User’s Manual (Lahlou et al. 1998) describe the information contained on the CD-ROMS specific for each EPA region. This wealth of information is available to initial analyses for a specific area, but users are encouraged to incorporate high-resolution information and locally derived data sets for more accurate use. The cartographic data (Table H.10) includes hydrologic boundaries and major roadways, plus census areas and various political boundaries. The environmental data (Table H.11) are to support watershed characterization and environ-


Sewer network City street map Aerial photographCity limit

digitize digitize digitize

Land use polygons

digitize interpret

Sewershed boundaries

OVERLAY

land use, acreage, sewershed

"Collection unit" coverage

pollutant loadings

SLAMM Modeling

rain events soil conditions

Locate Critical Sewershed

Establish Control Practice

Figure H.80 Integration of information and modeling to identify critical source areas. (From Kim, K. and S. Ventura. Large-scale modeling of urban nonpoint source pollution using a geographical information system. Photogrammetric Eng. Remote Sensing, 59(10): 1539–1544. October 1993.)

mental analyses, and include data on soils, topography, land uses, and stream hydrography. This is the most important set of information for modeling local conditions. The environmental monitoring data (Table H.12) include statistical summaries of monitoring results, rainfall records, and limited biological conditions. The point source data (Table H.13) provide information on pollutant loadings from permitted facilities, plus locations of hazardous waste sites. The BASINS assessment tools allow users to make evaluations of water quality, while the available data management utilities delineate watershed boundaries, and can be used to modify the data, or to import new data into the system. The nonpoint source and stream models integrate these data to provide initial evaluations of watershed water quality conditions. BASINS can therefore be a very useful tool to focus specific monitoring efforts to investigate likely water quality problems and use impairments.


Burke Township, Dane County, Wisconsin

4

4 5

3

2

1 City of Sun Prairie

City of Madison

Percent Change in Nonpoint Source

Sediment Loadings

Plan-Buildout1vs.1

Modified Plan-Buildout1

5 0-10% Increase

4 0-10% Reduction

3 11-25% Reduction

2 26-50% Reduction

1 >50% Reduction

Outside Watershed

N Road N N Municipal Boundary

Scale in miles

0 .5 1 1.5

Figure H.90 Example of mapped results showing changes in suspended solids with different development options. (From Pickett, S.R., O.G. Thum, and B.J. Hiemann. Using a land information system to integrate nonpoint source pollution modeling and land use development planning. Land Information and Computer Graphics Facility, The University of Wisconsin, Madison. 1989.)

Table H.10 BASINS Base Cartographic Data

BASINS Data Product Source Description

Hydrologic unit boundaries U.S. Geological Survey Nationally consistent delineations of the hydrographic boundaries associated with major U.S. river basins

Major roads Federal Highway Administration Interstate and state highway network Populated place locations USGS Location and names of populated locations Urbanized areas Bureau of the Census Delineations of major urbanized areas used

in 1990 Census State and county boundaries USGS Administrative boundaries EPA regions USGS Administrative boundaries

From Lahlou, M., L. Shoemaker, S. Choudhury, R. Elmer, A. Hu, H. Manguerra, and A. Parker. BASINS, Better Assessment Science Integrating Point and Nonpoint Sources. Version 2.0. EPA-823-B-98-006. Exposure Assessment Branch, U.S. Environmental Protection Agency. Washington, D.C. November 1998.

SUMMARY

The amount of data required to use these models can be very large. Tables H.14 and H.15 list some of these data needs for watershed-scale models (EPA 1991). Much of the information can be obtained from locally available sources and data summaries, but much will have to be extracted from detailed maps or the basic data to obtain the information in the necessary formats, accuracies, or time scales. In addition, the models need to be calibrated for site-specific conditions (especially pollutant characteristics and rainfall runoff relationships) and verified (comparing monitored outfall quality and quantity with modeled values). Receiving water models also require much local information for efficient use. Besides the watershed-scale information listed in these tables, specific stream processes (such as described in the Rates,


Table H.11 BASINS Environmental Background Data


Ecoregions U.S. Environmental Ecoregions and associated delineations Protection Agency (USEPA)

National Water Quality Assessment USGS Delineations of study areas (NAWQA) study unit boundaries

1996 Clean Water needs survey USEPA Results of the wastewater control needs assessment by state

State soil and geographic U.S. Department of Soils information including soil (STATSGO) database Agriculture, Natural component data and soils

Resources Conservation Service (USDA-NRCS)

Managed area database University of California, Data layer including federal and Indian Santa Barbara lands

Reach file version 1 (RF1) USEPA Provides stream network for major rivers and supports development of stream routing for modeling purposes (1:500k)

Reach file version 3 (RF3) alpha USEPA Alpha version of Reach File 3; provides detailed stream network and supports development of stream routing for modeling purposes (1:100K)

Digital elevation model (DEM) USGS Topographic relief mapping; supports watershed delineations and modeling

Land use and land cover USGS Boundaries associated with land use classifications including Anderson Level 1 and Level 2

From Lahlou, M. et al., BASINS, Better Assessment Science Integrating Point and Nonpoint Sources. Version 2.0. EPA-823-B-98-006. Exposure Assessment Branch, U.S. Environmental Protection Agency. Washington, D.C. November 1998.

Table H.12 BASINS Environmental Monitoring Data


Water quality monitoring stations and data summaries

Bacteria monitoring stations and data summaries

Water quality stations and observation data

National sediment inventory (NSI) stations and database

Listing of fish and wildlife advisories

Gauge sites

Weather station sites

Drinking water supply (DWS) sites

Watershed data stations and database

Classified shellfish areas

USEPA Statistical summaries of water quality monitoring for physical and chemical-related parameters; parameterspecific statistics computed by station for 5-year intervals from 1970 to 1994 and 3-year interval from 1995 to 1997

USEPA Statistical summaries of bacteria monitoring; parameterspecific statistics computed by station for 5-year intervals form 1970 to 1994 and 3-year interval from 1995 to 1997

USEPA Observation-level water quality monitoring data for selected locations and parameters

USEPA Sediment chemistry, tissue residue, and benthic abundance monitoring data for fishing, including type of impairment

USEPA State reporting of locations with advisories for fishing, including 7Q10 low and monthly mean stream flow

USGS Inventory of surface water gaging station data including 7Q10 low and monthly mean stream

National Oceanic Location of selected first-order NOAA weather stations and Atmospheric Administration (NOAA)

USEPA Location of public water supplies, their intakes, and sources of surface water supply

NOAA Location of selected meteorologic stations and associated monitoring information used to support modeling

NOAA Location and extent of shellfish closure areas

From Lahlou, M. et al., BASINS, Better Assessment Science Integrating Point and Nonpoint Sources. Version 2.0. EPA-823-B-98-006. Exposure Assessment Branch, U.S. Environmental Protection Agency. Washington, D.C. November 1998.


Table H.13 BASINS Point Source/Loading Data


Permit compliance system (PCS) USEPA NPDES permit-holding facility information; sites and computed annual contains parameter-specific loadings to surface loadings waters computed using the EPA Effluent

Decision Support System (EDSS) for 1991–1996 Industrial facilities discharge (IFD) USPEA Facility information on industrial point source

discharges to surface waters Toxic release inventory (TRI) sites USEPA Facility information for 1987–1995 TRI public data; and pollutant releases data contains Y/N flags for each facility indicating

media-specific reported releases Superfund national priority list site USEPA Location of Superfund National Priority List sites

from CERCLIS (Comprehensive Environmental Response, Compensation and Liability Information System)

Resource conservation and USEPA Location of transfer, storage, and disposal recovery information system facilities for solid and hazardous waste (RCRIS) sites

Minerals availability U.S. Bureau of Mines Location and characteristics of mining sites systems/mineral industry location system (MAS/MILS)

From Lahlou, M. et al., BASINS, Better Assessment Science Integrating Point and Nonpoint Sources. Version2.0. EPA-823-B-98-006. Exposure Assessment Branch, U.S. Environmental Protection Agency. Washington, D.C.November 1998.

Table H.14 Typical Input Data Needs for Nonpoint Source Models

1. System parametersa. Watershed sizeb. Subdivision of the watershed into homogeneous subareasc. Imperviousness of each subaread. Slopese. Fraction of impervious areas directly connected to a channelf. Maximum surface storage (depression plus interception storage)g. Soil characteristics including texture, permeability, erodibility, and compositionh. Crop and vegetation coveri. Curb density or street gutter lengthj. Sewer system or natural drainage characteristicsk. Land use

2. State variablesa. Ambient temperatureb. Reaction rate coefficientsc. Adsorption/desorption coefficientsd. Growth stage of cropse. Daily accumulation rates of litterf. Traffic density and speedg. Potency factors for pollutants (pollutant strength on sediment)h. Solar radiation (for some models)

3. Input variablesa. Precipitationb. Atmospheric falloutc. Evaporation rates

Adapted from EPA. Modeling of Nonpoint Source Water Quality in Urban and Nonurban Areas. Office of Research and Development. U.S. Environmental ProtectionAgency. Washington, D.C. EPA 600/3-91/039. 1991.


Table H.15 Data Needs for Various Quality Prediction Methods

Method Data Potential Sourcea

Unit load Mass per time per unit tributary area Derive from constant concentration and runoff, literature values

Constant Runoff prediction mechanism (simple to complex) Existing model; runoff coefficient or simple concentration method

Constant concentration for each constituent NURP; local monitoring Spreadsheet Simple runoff prediction mechanism e.g., runoff coefficient, perhaps as function

of land use Constant concentration or concentration range NURP; local monitoring Removal fractions for controls NURP; Schueler (1987); local and state

publications Statistical Rainfall statistics NURP; Driscoll, et.al. (1989); Woodward

Clyde (1989); EPA SYNOP model Area, imperviousness. Pollutant median and CV NURP; Driscoll (1986); Driscoll, et al. (1989);

local monitoring Receiving water characteristics and statistics Local or generalized data

Regression Storm rainfall, area, imperviousness, land use Local data Rating curve Measured flow rates/volumes and quality NURP; local data

EMCs/loads Buildup Loading rates and rate constants Literature values Washoff Power relationship with runoff Literature values

a Must be calibrated and verified using local monitoring.


Constants, and Kinetics Formulations in Surface Water Quality Modeling report prepared by Tetra Tech 1985) require calibration and verification. Tables H.14 through H.17 (EPA 1991) list some of the water quality variables modeled and the processes simulated by representative receiving water models. The techniques presented in this book, supplemented by the noted references, will enable the user to effectively collect the needed local data for model calibration and verification. Few models attempt to address in-stream biological process (beyond photosynthesis/respiration for DO evaluations and bacteria die-off ). Biological beneficial uses are best compared to actual measurements and comparisons with reference streams. However, models are needed to predict likely future chemical and physical conditions that currently do not exist. The information in this book should enable reasonable evaluations of these predicted conditions for biological use impairments, at least by identifying potential areas of concern. The ability to model biological responses to chemical and physical changes (such as responses to habitat destruction and contaminated sediments that are likely the most serious issues in urban streams) is very uncertain. However, numerous site-specific investigations, especially in the Pacific Northwest and in Canada, are encouraging.

It is therefore important to consider the appropriate uses of models, especially in receiving water investigations. Models are important and critical tools in that they enable us to design experiments and monitoring activities effectively, and to look into the future and examine alternatives. However, there can be substantial error in their predictions, due to incorrectly described processes, lack of data and the natural variability of conditions that simply cannot be adequately explained. This error, coupled with our lack of understanding of cause and effect relationships between the more easily predicted physical/chemical parameters and biological conditions, warrants continued caution. With local experience associated with a commitment to long-term investigations in local waters, our understanding will improve along with our ability to make reasonable conclusions using modeling results.


Table H.16 Non-Toxic Constituents Included in Stream Models

CBOD or

total Organic Model Name DO BOD NBOD SOD Temp. Total P Organic P PO4 Total N N

WQAM X X X X X X X XDOSAG1 X X X X X**DOSAG3 X X X* X X** XSNSIM X X X X X**

QUAL-II X X X* X X XQUAL-IIe X X X* X X XRECEIV-II X X X* X X** X X X XWASP X X X* X X** X X XAESPO X X X* X X** X X XHSPF X X X* X X XHAR03 X X X**

FEDBAK03 X X X**

MIT-DNM X X X**

EXPLORE-1 X X X* X X** X X XWQRRS X X X* X X X

Model Name NH3 NO2 NO3 Carbon

Algae or

Chl-A Zooplankton pH Alkalinity TDS Coliform Bacteria

WQAM X XDOSAG1DOSAG3 X X X X X XSNSIMQUAL-II X X X X X XQUAL-IIe X X X X X XRECEIV-II X X X X X XWASP X X X X X X X XAESPO X X X X X X X X XHSPF X X X X X X X X XHAR03FEDBAK03MIT-DNMEXPLORE-1 X X X X X XWQRRS X X X X X X X X X X

X* NBOD simulated as nitrification of ammonia.

X** Temperature specified by model users.


WA

TE

RS

HE

D A

ND

RE

CE

IVIN

G W

AT

ER

MO

DE

LING

865

Table H.17 Conventional Pollutants Model Comparison as Used in Waste Load Allocations

Water Quality Hydraulic Variable Physical

Temporal Temporal Loading Types Spatial Water Quality Chemical/Biological Processes Model Variability Variability Rates of Loads Dimensions Water Body Parameters Modeled Processes Simulated Simulated

DOSAG-1 Steady-state Steady-state No Multiple point sources 1-D Stream DO, CBOD, NBOD, 1st-order decay of Dilution, network conservative NOBD,CBOD, advection,

coupled DO reaeration SNSIM Steady-state Steady-state No Multiple point sources 1-D Stream DO,CBOD, NBOD, 1ST-ORDER DECAY Dilution,

and nonpoint network CONSERVATIVE OF NBOD, CBOD, advection, sources coupled DO, benthic reaeration

demand(s), photosynthesis(s)

QUAL-II Steady-state Steady-state No Multiple point sources 1-D Stream DO, CBOD temperature, 1st-order decay of Dilution, or dynamic and nonpoint network ammonia, nitrate, nitrite, NBOD, CBOD, advection,

sources algae, phosphate, coupled DO, benthic reaeration, coliforms, nonconservative demand, CBOD heat balance substances, three setting, nutrient-algal conservative substances cycle

RECEIV-II Dynamic Dynamic Yes Multiple point sources 1-D or 2-D Stream DO, CBOD, ammonia, 1st-order decay of Dilution, network or nitrate, nitrite, total CBOD, coupled DO, advection, well-mixed nitrogen, phosphate, benthic demand, reaeration estuary coliforms, algae, salinity, CBOD settlings,

one metal ion nutrient-algal cycle

(s) = specified.

From EPA. Exposure Models Library and Integrated Model Evaluation System. EPA Office of Research and Development CD-ROM. EPA-600-C-92-002. Revised March 1996.


REFERENCES

EPA. Results of the Nationwide Urban Runoff Program. Water Planning Division, PB 84-185552, Washington, D.C. December 1983.

EPA. Modeling of Nonpoint Source Water Quality in Urban and Non-urban Areas. Office of Research andDevelopment. U.S. Environmental Protection Agency. Washington, D.C. EPA 600/3-91/039. 1991.

EPA. Exposure Models Library and Integrated Model Evaluation System. EPA Office of Research andDevelopment CD-ROM. EPA-600-C-92-002. Revised March 1996.

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Documents

Watershed and Receiving Water Modelingunix.eng.ua.edu/~rpitt/Publications/BooksandReports...Models are important tools for watershed and receiving water analyses because they enable