United States
Environmental Protection
Agency
Office of Water
(4503F)
Washington, DC 20460
EPA841-B-97-006
May 1997
c/EPA
Compendium of Tools for
Watershed Assessment
and TMDL Development
Internet Address (URL) • http://www.epa.gov
Recycled/Recyclable •Printed with Vegetable Oil Based Inks on Recycled Paper (20% Postconsumer)
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Compendium of Tools for Watershed
Assessment and TMDL Development
L, Shoemaker, M. Lahlou, M. Bryer
D. Kumar, K. Kratt
Tetra Tech, Inc.
Fairfax, Virginia
Contract No. 68-C3-0303
Project Managers
Donald Brady
Chris Laabs
Anne Weinberg
Watershed Branch
Assessment and Watershed Protection Division
Office of Wetlands, Oceans, and Watershed
United States Environmental Protection Agency
401 M Street, SW
Washington, DC 20460
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Disclaimer
The information contained in this compendium is based on publications and literature provided
by model developers. No verification or testing of model accuracy or function is implied by this
review. The U.S. Environmental Protection Agency does not provide support for any model
unless explicitly mentioned. Mention of trade names or commercial products does not consti-
tute endorsement or recommendation for use.
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Foreword
This document represents an update to and expansion of a previous EPA publication,
Compendium of Watershed-scale Models for TMDL Development, EPA 841-R-92-002
(USEPA, Office of Water, 1992). The revised manual, renamed Compendium of Tools
for Watershed Assessment and TMDL Development, broadens the review of models and
techniques from solely watershed loading models to include receiving water models
and ecological assessment techniques and models.
As EPA has recognized with the promotion of the Watershed Protection Approach,
water quality managers today face complex water resource problems that require
integrated solutions across traditional program areas. Compendium of Tools for
Watershed Assessment and TMDL Development supports the Watershed Protection
Approach by summarizing available techniques and models that assess and predict
physical, chemical, and biological conditions in waterbodies. It is intended to provide
watershed managers and other users with information helpful for selecting models
appropriate to their needs and resources. Specifically, this document includes infor-
mation regarding:
• A wide range of watershed-scale loading models. (This section has been
updated from the previous Compendium to include new models and refer-
ences).
• Field-scale loading models.
• Receiving water models, including eutrophication/water quality models, toxics
models, and hydrodynamic models.
• Integrated modeling systems that, for example, link watershed-scale loading
with receiving water processes.
• Ecological techniques and models that can be used to assess and/or predict the
status of habitat, single species, or biological community.
Comments and suggestions from the user community help us in improving our
publications, and we invite the user community to send their comments and sugges-
tions to:
Donald J. Brady, Chief
Watershed Branch, OWOW
USEPA (4503F)
401 M Street, SW
Washington, DC 20460
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Contents
Acknowledgments. fx
Acronyms • xf
1. Introduction and Purpose 1
1.1 Background 2
1.2 Models and analytical tools for watershed assessment and
TMDL development 4
2. Review of Selected Loading and
Receiving Water Models 7
2.1 Introduction 7
2.2 Watershed loading and receiving water model development
and distribution 8
2.2.1 USEPA 8
2.2.2 USDA 8
2.2.3 USCOE 9
2.2.4 Other Federal Agencies , 9
2.2.5 Universities 9
2.2.6 Other 10
2.3 Watershed-scale loading models 10
2.3.1 Simple methods 15
2.3.2 Mid-range models 17
2.3.3 Detailed models 19
2.4 Field-scale loading models 21
2.5 Receiving water models 23
2.5.1 Hydrodynamic models 27
2.5.2 Steady-state water quality models 29
2.5.3 Dynamic water quality models 32
2.5.4 Mixing zone models 34
2.6 Integrated modeling systems 35
3. Ecological Assessment Tecfuiiques and Models 41
3.1 Introduction 41
3.2 Approaches to ecological assessments 42
3.2.1 Comparative analyses 42
3.2.2 Index/classification methods 44
3.2.3 Ecological models 45
3.3 Habitat assessment techniques 46
3.4 Species/biological community assessment techniques 54
4. Model Selection 61
4.1 Preliminary model selection considerations 62
4.2 Model calibration and verification 63
4.3 Watershed loading models 64
4.4 Receiving water models 71
4.5 Ecological assessment techniques and models 77
References 87
Glossary 103
Appendix A: Loading models fact sheets A.I
Appendix B: Receiving water models fact sheets B.I
Appendix C: Ecological assessment techniques
and models fact sheets C.I
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List of Tables
Table 1. Available EPA Guidance and Other References Helpful
for Watershed Assessment and TMDL Development 3
Table 2. Evaluation of Model Capabilities - Simple Models 11
Table 3. Evaluation of Model Capabilities - Mid-Range Models 12
Table 4. Evaluation of Model Capabilities - Detailed Models 14
Table 5. Evaluation of Capabilities - Hydrodynamic Models 26
Table 6. Evaluation of Capabilities - Steady-State Water Quality Models 28
Table 7. Evaluation of Capabilities - Dynamic Water Quality Models 29
Table 8. Ways in Which Ecological Assessments Can Support
the Five Steps in the Water Quality-Based Approach 41
Table 9. Evaluation of Model/Technique Capabilities—
Habitat Assessment Techniques 43
Table 10. Evaluation of Model/Technique Capabilities—
Species/Biological Community Assessment Techniques 44
Table 11. Five Tiers of the Rapid Bioassessment Protocols 57
Table 12. A Descriptive List of Model Components—Simple Methods 65
Table 13. A Descriptive List of Model Components—Mid-Range Models 66
Table 14. A Descriptive List of Model Components—Detailed Models 66
Table 15. Input and Output Data—Simple Methods 67
Table 16. Input and Output Data—Mid-Range Models 68
Table 17. Input and Output Data—Detailed Models 69
Table 18. Input Data Needs for Watershed Models 70
Table 19. Range of Application of Watershed Models—
Simple Methods 70
Table 20. Range of Application of Watershed Models—
Mid-Range Models 71
Table 21. Range of Application of Watershed Models—
Detailed Models 71
Table 22. A Descriptive List of Model Components—
Hydrodynamic Models 72
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Table 23. A Descriptive List of Model Components—
Steady-State Water Quality Models 73
Table 24. A Descriptive List of Model Components—
Dynamic Water Quality Models 74
Table 25. Input and Output Data—Hydrodynamic Models 76
Table 26. Input and Output Data—Steady-State Water Quality Models 77
Table 27. Input and Output Data—Dynamic Water Quality Models 78
Table 28. Range of Application—Hydrodynamic Models 78
Table 29. Range of Application—Steady-State Water Quality Models 79
Table 30. Range of Application—Dynamic Water Quality Models 79
Table 31. A Descriptive List of Model/Technique Components—
Habitat Assessment Techniques 80
Table 32. A Descriptive List of Components—Species/Biological
Community Assessment Techniques 81
Table 33. Input and Output Data—Habitat Assessment Techniques 82
Table 34. Input and Output Data—Species/Biological Community
Assessment Techniques 84
Table 35. Range of Application—Habitat Assessment Techniques
and Models 86
Table 36. Range of Application—Species/Biological Community
Assessment Techniques and Models 86
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Acknowledgments
The Compendium of Took for Watershed Assessment and TMDL Development
was developed under the direction of Don Brady, Christopher Laabs, and
Anne Weinberg in EPA's Watershed Branch by Tetra Tech, Inc., Fairfax,
Virginia, under EPA Contract Number 68-C3-0303. Technical editing was
performed by Marti Martin and graphic design was provided by the Tetra
Tech Production Department.
The authors would like to especially thank the following persons for their
thoughtful review of this document: Thomas Barnwell, D. King Boynton,
Mimi Dannel, Jerry LaVeck, Peter Nolan, Margaret Thielke, Mark
Voorhees, and Bruce Zander. The authors would also like to thank the
many persons in the Watershed Branch and the TMDL Regional Coordina-
tors for their support and assistance throughout the development of the
Compendium. In addition, we would like to thank the many model develop-
ers and practitioners throughout the country who provided information
and continue to build our experience and understanding of how models
can better support watershed management and TMDL development.
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Acronyms
Acronyms
AGNPS: Agricultural Nonpoint Source Pollution Model
ANSWERS: Areal Nonpoint Source Watershed Environment Response Simulation
ARS: Agricultural Research Services
AUTO-QI: Automated Q-ILLUDAS
BMP: Best management practice
BOD: Biochemical oxygen demand
CE-QUAL-ICM: Three-dimensional, time-variable, integrated-compartment
eutrophication model
CE-QUAL-RIV: Hydrodynamic and water quality model for streams
CE-QUAL-W2: Two-dimensional, laterally averaged hydrodynamic and water
quality model
CEAM: Center for Exposure Assessment Modeling
CNE: Curve Number Equation
COD: Chemical oxygen demand
COE: Corps of Engineers
CORMIX: Cornell Mixing Zone Expert System
CREAMS: Chemicals, Runoff and Erosion from Agricultural Management Systems
CSO: Combined sewer overflows
CSTR: Continuously Stirred Tank Reactor
CU: Catalog Unit
CWA: Clean Water Act
DECAL: Deposition Calculation for organic accumulation near marine outfalls
DEM: Digital Elevation Model
DO: Dissolved oxygen
DR3M or DR3M-QUAL: Distributed Routing Rainfall Runoff Model
DYNHYD5: Link-node tidal hydrodynamic model
DYNTOX: Dynamics Toxics model
EFDC: Environmental Fluid Dynamics Computer Code
EMC: Event mean concentrations
EPA/OST: Environmental Protection Agency, Office of Science and Technology
EPA/OWOW: Environmental Protection Agency, Office of Wetlands, Oceans, and
Watersheds
EPA: Environmental Protection Agency
EPT: Ephemeroptera, Plecoptera, and Trichoptera
EUTROMOD: Eutrophication Model
EXAMSII: Exposure Analysis Modeling System
FGETS: Food and Gill Exchange of Toxic Substances
FHWA: Federal Highway Administration
GIS: Geographic information system
GLEAMS: Groundwater Loading Effects of Agricultural Management Systems
GWLF: Generalized Watershed Loading Function
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Compendium of Tbols for Watershed Assessment and TMDL Development
HEC: Hydraulic Engineering Center (U.S. Army Corps of Engineers)
HEP: Habitat Evaluation Procedure
HES: Habitat Evaluation System
HGM: Hydrogeomorphic Assessment
HMEM: Habitat Management Evaluation Method System
HQI: Habitat Quality Index
HSI: Habitat Suitability Index
HSPF: Hydrologic Simulation Program-FORTRAN
HU: Habitat Unit
HUG: Hydrologic Unit Code
HUMUS: Hydrologic Unit Model for the United States
HUSLE: Hydrogeomorphic Universal Soil Loss Equation
HUV: Habitat Unit Values
IBI: Index of Biotic Integrity
ICI: Invertebrate Community Index
IFIM: Instream Flow Incremental Methodology
IWB: Index of Well-Being
LA: Load Allocation
LC: Load Capacity
NALMS: North American Lake Management Society
NOAA: National Oceanic and Atmospheric Administration
NPDES: National Pollutant Discharge Elimination System
NFS: Nonpointsource
NPSMAP: Nonpoint Pollution Source Model for Analysis and Planning
NRCS: U.S. Department of Agriculture Natural Resources Conservation Service
NURP: National Urban Runoff Program -
P8-UCM: PS-Urban Catchment Model
PHABSIM: Physical Habitat Simulation System
PVA: Population Viability Analysis
QHEI: Qualitative Habitat Evaluation Index
QUAL2E: Enhanced Stream Water Quality Model
RBP: Rapid Bioassessment Protocols
SCS: Soil Conservation Service (USDA) CNowNRCS)
SITEMAP: Stormwater Intercept and Treatment Evaluation Model for Analysis and
Planning
SLAMM: Source Loading and Management Model
SLOSS-PHOSPH: Sediment and Phosphorous Prediction
SMPTOX4: Simplified Method Program - Variable complexity stream toxics model
SNTEMP: Stream Network Temperature Model
SOD: Sediment Oxygen Demand
SSTEMP: Stream Segment Temperature Model
STORM: Storage, Treatment, Overflow, Runoff Model
SWAT: Soil and Water Assessment Tool
SWM: Stanford Watershed Model
SWMM: Storm Water Management Model
SWRRB-WQ: Simulation for Water Resources in Rural Basins - Water Quality
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Acronyms
TMDL: Total maximum daily load
TPM: Tidal Prism Model
TSLIB: Time-Series Library
TSS: Total suspended solids
USDA-ARS: Um'ted States Department of Agriculture, Agricultural Research Service
USDA: United States Department of Agriculture
USEPA: United States Environmental Protection Agency
USFWS: United States Fish and Wildlife Service
USGS: United States Geological Survey
USLE: Universal Soil Loss Equation
VIMS: Virginia Institute of Marine Sciences
VirGIS: Virginia Geographic Information System
WASPS: Water Quality Analysis Simulation Program
WEPP: Water Erosion Prediction Project
WES: U.S. Army Corps of Engineers Waterways Experiment Station
WETH: Wetland Evaluation Technique (version 2.0)
WLA: Wasteload allocations
WMM: Watershed Management Model
WQS: Water Quality Standards
WSM: Watershed Screening Model
WUA: Weighted Usable Area
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Compendium of Tools for Watershed Assessment and TMDL Development
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Chapter 1. Introduction and Purpose
1
Introduction and Purpose
Simulation models are used extensively in water quality planning and pollution
control. Models are applied to answer a variety of questions, support watershed
planning and analysis, and develop total maximum daily loads (TMDLs). Some
models are highly specialized to simulate environmental phenomena and components
of pollution problems; others incorporate more comprehensive assessment tech-
niques. For example, there are specialized models to predict localized pollutant
transport, at the field scale, and comprehensive watershed-scale models that simulate
pollutant loading, transport and transformation. Historically, models have been used
to support the analysis of wastewater discharges by modeling biochemical oxygen
demand (BOD) and resulting in-stream dissolved oxygen (DO) concentrations.
As the Environmental Protection Agency's (EPA's) water programs and their counter-
parts in state pollution control agencies have increasingly emphasized watershed-
based assessment and integrated analysis of point and nonpoint sources, modeling
has been used to evaluate a wider range of pollutant transport and environmental
response issues. Most recently, attention has been focused on assessing "ecosystems,"
resulting in a more holistic assessment of watershed systems. This emphasis on
ecosystems offers new challenges for the use of models, indices, and classification
systems to assess and manage watershed systems.
This document discusses three major categories of models—watershed loading,
receiving water, and ecological. Watershed loading models simulate the generation
and movement of pollutants from the point of origin (source) to discharge into
receiving waters. Receiving water models simulate the movement and transformation
of pollutants through lakes, streams, rivers, estuaries, or nearshore ocean areas. Some
receiving water models also include eutrophication processes such as algal and
macrophyte life cycles. Ecological assessment techniques can include habitat and
species classification and index systems, as well as ecological and lexicological models
that explicitly simulate biological communities and their response to stressors such as
toxics and habitat modification.
The models differ in how capabilities, detail, and accuracy are incorporated into
specific processes. The selection of the appropriate model depends on the application
needs. The definition of modeling objectives is an essential first step in the develop-
ment of a modeling approach. In some cases, objectives will be best met by using a
combination of models. In other cases, very simplified assessment techniques might
be sufficient to support decision-making needs. The selection of the model can be
based on criteria such as value of resource considered, data needs, application cost,
accuracy required, type of pollutants/stressors considered, management consider-
ations, and user experience. Selection and application of a watershed model or
analysis tool is often part of the consensus-building process in development of a
watershed plan. Stakeholder involvement in the model selection process can help in
the acceptance of model results, and in making ensuing decisions based on those
results.
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Compendium oflbohfor Watershed Assessment and TMDL Development
This document summarizes the available models and tools that can be used to support
watershed assessment and TMDL development. The document includes a wide range
of tools and offers selection criteria to assist the user in choosing the model(s) appro-
priate for a particular application. Many of the models reviewed were developed or
sponsored by federal or state agencies; however, a few models included here were
developed by universities or private companies. Available models and assessment
techniques were identified based on user experience, case studies, and literature
searches. Key distributors were contacted for information on current model distribu-
tion and availability. The models that were acquired and reviewed are available
through the public domain or at minimal cost and are generally recognized in the
literature. Each model was reviewed with respect to its theoretical basis, range of
applicability, and input requirements. Recent references on model application, testing,
and support were also compiled. The review materials were used as the basis for
generating model summaries, tables, and fact sheets.
By providing information on technical tools for developing and implementing water-
shed projects and TMDLs within a broader water quality-based management strategy,
this document supports state and federal agencies in establishing ecologically based
controls on a watershed basis. Although this document focuses on the available tools
and selection criteria, model selection and application are only a portion of the
framework for developing a successful watershed management program or TMDL.
This document focuses on the availability of models, their characteristics and selection
of candidate models for watershed assessment and TMDL development. The scope of
this document does not include broader features of model use including monitoring,
calibration, validation and modeling design/application. More information is avail-
able in other publications and in the respective user's guides and documentation of
the various models. Additional information on watershed planning and TMDL devel-
opment can be found in the references cited in Table 1.
1.1 The Watershed Protection Approach (WPA) is a strategy for effectively protecting and
Background! restoring aquatic ecosystems and protecting human health (USEPA, 1995a, 1995b).
The WPA has four major features: targeting priority problems, a high level of stake-
holder involvement, integrated solutions that make use of the expertise and authority
of multiple agencies, and measuring success through monitoring and other data
gathering. The WPA brings a vision of water quality protection programs that feature
watersheds as the fundamental management unit. Management is targeted to priority
watersheds. WPA projects are designed to be consistent with state regulatory pro-
grams such as total maximum daily loads (TMDLs). The WPA provides an opportunity
for states to share resources and expertise across multiple agencies to protect water
quality and public health.
The EPA document Watershed Protection: A Statewide Focus describes the elements of a
successful watershed project as building a project team and public support, defining
the problem, setting goals and identifying solutions, implementing controls, and
measuring success and making adjustments. The elements of the WPA process are
interconnected, and each is important to the process, although the elements are not
necessarily addressed sequentially.
Modeling and analysis can be instrumental to the development of successful water-
shed projects. Models can be used to assist in targeting watersheds, developing goals
and objectives, defining solutions, developing plans for management implementation,
and tracking progress toward achieving goals. Actions taken in a watershed or basin
should draw on the full range of methods and tools available, integrating them into a
coordinated, multiorganization process focusing on the problems. Selection of appro-
priate models will be guided by the needs of the specific watershed project. Within a
watershed project context, models might be needed to address multiple stressors and
interrelation
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Chapter 1. Introduction and Purpose
Table 1. Available EPA Guidance and Other References Helpful for Water-
shed Assessment and TMDL Development
Guidance for Water Quality-based Decisions: The TMDL Process (EPA 440/4-91 -001)
This document defines and clarifies the requirements under section 303(d) of the Clean Water Act.
Its purpose is to help state water quality program managers understand the application of total
maximum daily loads within the water quality-based approach to establish pollution control limits for
waters not meeting water quality standards.
Inventory of EPA Headquarters Ecosystem Tools (EPA 230-S-95-001)
This document contains summaries of tools developed at EPA Headquarters relevant to the scientific/
technical, economic, planning/management, and socio-political analyses of ecosystems.
Watershed Protection: A Statewide Approach (EPA 841 -R-95-004)
This document discusses the process of establishing a statewide watershed approach that focuses on
organizing and managing by basins. In a basin approach, activities such as water quality monitor-
ing, planning, and permitting are coordinated on a set schedule within large watersheds or basins.
Involvement of other natural resource agencies is actively sought to achieve water quality and
ecosystem goals. The document presents examples from many states that have adoptecl or begun
the transition to watershed management.
Watershed Protection: A Project Focus (EPA-841 -R-95-003)
This document is a companion to the preceding report and focuses on developing watershed-
specific programs or projects. It illustrates how the broader aspects of watershed management can
be brought to bear on water quality and ecological concerns. The guide provides a blueprint for
designing and implementing watershed projects, and it includes references and case studies for
specific elements of the process.
A Quick Reference Guide: Developing Nonpoint Source Load Allocations for TMDLs (EPA 841 -B-92-001)
This document directs TMDL developers to existing technical guidance from other programs while
more detailed TMDL technical guidance is developed.
TMDL Case Study Series
This series of case studies published by EPA illustrates real-world TMDL applications that the user can
consult when appropriate.
The following documents provide more detailed technical guidance and are available from the Office of
Science and Technology (4305) or Office of Wetlands, Oceans and Watersheds (4503F), USEPA, 401 M
Street, SW, Washington, DC 20460. Some documents are also available from the National Technical
Information Service, phone (703) 487-4650, fax (703) 321-8547; and the National Center for Environ-
mental Publications and Information, phone (513) 489-8190, fax (513) 569-7186.
Technical Guidance Manual for Performing Waste Load Allocations - Book II Streams and Rivers - Chapter
1, Biochemical Oxygen Demand/Dissolved Oxygen (EPA 440/4-84-020)
Technical Guidance Manual for Performing Waste Load Allocations - Book II Streams and Rivers - Chapter
2, Nutrient/Eutrophication Impacts (EPA 440/4-84-021)
Technical Guidance Manual for Performing Waste Load Allocations - Book II Streams and Rivers - Chapter
3, Toxic Substances (EPA 440/4-84-022)
Technical Guidance Manual for Performing Waste Load Allocations - Simplified Analytical Method for
Determining NPDES Effluent Limitations for POTWs Discharging into Low-Flow Streams
Technical Guidance Manual for Performing Waste Load Allocations - Book IV Lakes and Impoundments -
Chapter 2, Nutrient/Eutrophication Impacts (EPA 440/4-84-019)
Technical Guidance Manual for Performing Waste Load Allocations - Book IV Lakes, Reservoirs and
Impoundments—Chapter 3, Toxic Substances Impact (EPA 440/4-87-002)
Technical Guidance Manual for Performing Waste Load Allocations - Book VI Design Conditions - Chapter
I Stream Design Flow for Steady-State Modeling (EPA 440/4-87-004)
Technical Guidance Manual for performing Waste Load Allocations - Book VII: Permit Averaging (EPA 440/
Handbook - Stream Sampling for Waste Load Allocation Applications (EPA 625/6-86/013)
Technical Guidance Manual for Performing Waste Load Allocations - Book III Estuaries - Part 1 - Estuaries
and Waste Load Allocation Models (EPA 823/R-92-002)
Technical Guidance Manual for Performing Waste Load Allocations Book III Estuaries - Part 2 - Application
ofEstuarine Waste Load Allocation Models (EPA 823-R-92-003)
Technical Guidance Manual for Performing Waste Load Allocations - Book III: Estuaries -Part 3- Use of
Mixing Zone Models in Estuarine Waste Load Allocations (EPA 823-R-92-004)
Technical Guidance Manual for Performing Waste Load Allocations - Book III - Estuaries - Part 4 - Critical
Review of Coastal Embayment and Estuarine Waste Load Allocation Modeling (EPA 823-R-92-005)
Technical Support Document for Water Quality-based Toxics Control (EPA 505/2-90-001)
Processes, Coefficients and Models for Simulating Toxic Organics and Heavy Metals in Surface Water (EPA/
vQQ/3~8//Q 15)
Rates, Constants, and Kinetics Formulations in Surface Water Quality Modeling (Bowie et al., 1985 EPA/
600/3-85/040)
Water Quality Assessment: A Screening Procedure for Toxic and Conventional Pollutants in Surface and
Groundwater, Parts I and II (Mills et al., 1985, EPA 600/6-85/002a and EPA 600/6-85/002b)
Watershed Tools Directory: A Collection of Watershed Tools (EPA/841-B-95-005)
Modeling of Nonpoint Source Water Quality in Urban and Non-urban Areas (Doniqian and Huber, 1991,
EPA/600/3-91/039)
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Compendium o/Tbok/br Watershed Assessment and TMDL Development
ships, evaluate management practice effectiveness, or assess receiving water response
to changes in loadings. In some cases, modeling tools might not be available or
practical to assess the range of stressors and complex systems present within the
watershed system.
For those water quality-limited waters where existing or proposed controls do not or
are not expected to result in attainment and/or maintenance of the applicable water
quality standards (VVQSs), section 303 (d) of the Clean Water Act (CWA) requires
states to develop TMDLs (USEPA, 1991b). The water quality-based approach consists
of five steps, the first three of which constitute the TMDL process: (1) identification of
water quality-limited waters that require TMDLs and the pollutants causing impair-
ment, (2) priority ranking and targeting of identified waters,
(3) TMDL development, (4) implementation of pollution control actions, and
(5) monitoring and assessment of control effectiveness.
In complex situations or where nonpoint source reductions are part of the TMDL, a
"phased approach" may be used. Under this approach, the best available data for
water quality conditions and control actions are used to develop TMDLs that consider
point and nonpoint sources of pollution, with the stipulation that additional monitor-
ing and evaluation will be performed to assess and revise, if necessary, the initial
TMDL allocations. In fact, the final step of the water quality-based approach provides
for continuous evaluation and improvement of a TMDL and associated control actions.
Models and data analysis techniques can be used in the implementation of each phase
of the water quality-based approach, including the initial evaluation, ranking and
targeting, TMDL development, evaluation of controls, and program tracking.
1.2
Models and
analytical tools for
watershed
assessment and
TMDL development
One challenge faced by water quality managers is the lack of integrated, scientifically
sound approaches to identify problems in watersheds and to predict the results of
potential control actions on receiving water quality and aquatic habitat. In setting
priorities and gathering information for the development of a TMDL, it might be
necessary to use several techniques, models, or analytical tools in assessing different
components of the complex watershed system. Because of the limitations on applica-
bility and predictive capabilities, care must be taken when selecting a model or
analytical tool for watershed assessment and TMDL development.
A review of selected watershed loading models and receiving water models is pre-
sented in Chapter 2. The review provides an evaluation of each models features and
capabilities. Models are compared based on complexity, capabilities, and interface
characteristics. Chapter 2 also includes a brief overview of field-scale loading models
(which can be useful for assessing nonpoint source loading changes corresponding to
alternative land-use strategies) and integrated modeling systems (which link different
model types and data sources to provide more comprehensive watershed assessment
tools). Refer to Figure 1 for an overview of the models described in Chapter 2.
To address the CWA's challenge to restore and maintain the biological quality of the
Nation's waters, a variety of ecological assessment techniques and models have been
included in Chapter 3 of this document. These approaches are different from the
loading and receiving water models described in Chapter 2 because they focus on
evaluating a waterbody by directly examining or predicting the status of a habitat,
biological population, or biological community. Biological resources like benthic
macroinvertebrates and fish have the ability to integrate the effects of different
stressors over space and time, thereby providing an overall measure of the impacts!
from these stressors. Many of the techniques reviewed compare the results of assess-
ments to empirically defined reference conditions in a similar ecological region; others
attempt to predict the effects of changes in hydrology or water chemistry on a habitat
or species. Ecological assessment techniques and models can support comprehensive
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Chapter 1. Introduction and Purpose
watershed assessments and implementation of controls, and thereby provide valuable
insights during the TMDL process.
Chapter 4 provides a more detailed discussion of the selection of models for water-
shed assessment and TMDL development. Decision criteria and factors to be consid-
ered for the various components included in the watershed loading, receiving water,
and ecological assessment techniques are reviewed. The information provided in
Chapter 4 can be used to assess the suitability of the models for a specific situation.
Appendices A, B, and C include detailed fact sheets for each of the watershed, receiv-
ing water, and ecological technique or models reviewed, respectively. The fact sheets
identify contact points, key features of the model, and recent references. A list of
acronyms and a glossary of terminology used for discussing modeling and model
features are also included.
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Compendium oflbokfor Watershed Assessment and TMDL Development
Figure 1. Overview of Models Described in Chapter 2.
Watershed-scale loading Models
(Section 2.3}
Simple Methods
• EPA Screening
• Simple Method
• Regression Method
• SLOSS-PHOSPH
• Watershed
• Federal Highway
Aministration Model
• Watershed Management Model
Mid-Range Models
• SITEMAP
• GWLF
• Urban Catchment Model
• Automated Q-ILLUDAS
• AGNPS
• SLAMM
Detailed Models
• STORM
• ANSWERS
• DR3M-QUAL
• SWRRBWQ
• SWMM
• HSPF
Field-Scale Loading Models
(Section 2.4)
• CREAM/GLEAMS
• Opus
• WEPP
Integrated Modeling Systems
(Section 2.6}
• PC-VIRGIS
• WSTT
• LWMM
• GISPLM
• BASINS
Receiving Water Models
(Section 2.5)
I ^
Hydrodynamic
Models
• RIVMOD-H
• DYNHYD5
• EFDC
• CH3D-WES
Steady-State
Models
EPA Screening
Procedures
EUTROMOD
PHOSMOD
BATHTUB
QUAL2E
EXAMS II
TOXMOD
SMPTOX3
Tidal Prism Model
DECAL
I
Dynamic
Water-Quality
Models
• DYNTOX
• WASPS
• CE-QUAL-RIV1
• CE-QUAL-W2
• CE-QUAL-ICM
• HSPF
I
Mixing Zone
Models
•CORMIX
•PLUME
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Chapter 2. Review of Selected Loading and Receiving Water Models
2
Review of Selected Loading
and Receiving Water Models
2.1
Introduction
Empirical formulations:
Simple mathematical
relationship based upon
observed data rather than
theoretical relationships.
Deterministic models:
Mathematical models
designed to produce system
responses or outputs (e.g.,
runoff) to temporal or
spatial inputs (e.g., precipi-
tation).
Steady-state model:
Mathematical model of fate
and transport that uses
constant values of input
variables to predict constant
values (e.g., receiving water
quality concentrations).
Dynamic model:
A mathematical formulation
describing the physical
behavior of a system or
process and its temporal
variability.
Hydrodynamic Model:
Mathematical formulation
used in describing circula-
tion, transport, and deposi-
tion processes in receiving
water.
During the last 20 years, numerous loading and receiving water quality models have
been developed. Loading models include techniques primarily designed to predict
pollutant movement from the land surface to waterbodies. Loading models can
include simple loading rate assessments in which loads are a function of land use
type. Loading models can also be complex simulation techniques that more explicitly
describe the processes of rainfall, runoff, sediment detachment, and transport to
receiving waters. Some loading models operate on a watershed scale, integrating all
loads within a watershed. Some watershed models allow for the subdivision of the
watershed into contributing subbasins. For the purposes of this document, primary
emphasis is given to loading models that analyze systems on a watershed scale.
Field-scale models are loading models that are designed to operate on a smaller,
more localized scale. Field-scale models have traditionally included models that special-
ize in agricultural systems. Field-scale models have also been used to answer local
management questions or to support the selection of best management practices. A brief
discussion of field-scale models is included in Section 2.3.
Receiving water models emphasize the response of a waterbody to pollutant load-
ings, flows, and ambient conditions. Again, a range of complexity is encompassed
from simple empirical formulations to deterministic models. Receiving water
assessments can include examination of flow (hydrodynamics), as well as chemical and
biological processes. The emphasis of the receiving water models discussed here is
on "far-field" models, or models that assess impacts after initial mixing across larger
areas. The three general categories of receiving water models discussed include hydrody-
namic models, steady-state water quality models, and dynamic water quality
models. More localized impact analysis, assessed by "near-field" models, is addressed in
a brief section on mixing zone models.
In some cases models that internally link the loading and receiving water response
have been developed. Often watershed management planning requires that both the
loading and receiving water response be assessed. For example, in the development
of a TMDL for a lake, a receiving water model can be used to determine the phos-
phorus loading capacity that will protect the lake from accelerated eutrophication. A
loading model can be used to determine the sources of the phosphorus loads, the
magnitude of the loads, and the potential reductions under a variety of management
scenarios. Ultimately, the loading and receiving water models can be used to deter-
mine the optimum combination of loads for for the protection of water quality.
Integrated modeling systems link the models, data, and user interface within a single
system. New developments in modeling systems have increasingly relied on geo-
graphic information systems (GISs) and database management systems to support
modeling and analysis. Section 2.5 describes some of the currently available model-
ing systems and the trends in new modeling system development.
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Compendium of Tools for Watershed Assessment and TMDL Development
2.2
Watershed loading
and receiving water
model development
and distribution
Some of the models developed over the past two decades have been (and still are) used
successfully by watershed and receiving water managers. Other models have had limited
use or have been incorporated into larger and more comprehensive modeling systems.
Some models have been used solely for highly specialized research and development
purposes. The emphasis of this review is on those models which are typically available
and are being used for the assessment and management of watersheds and receiving
waters. In addition, watershed and receiving water models are constantly being updated,
revised, and modified to meet current needs. Many of the models reviewed were devel-
oped or sponsored by federal or state agencies; however, a few models were developed
by universities or private companies.
2.2.1 USEPA The U.S. EPA, through the Center for Exposure Assessment Modeling (CEAM),
currently distributes and provides limited support for several commonly applied
watershed loading and receiving water models. The Hydrologic Simulation Program
- FORTRAN (HSPF) is a highly versatile model capable of simulating mixed-land-use
watersheds (urban and rural). The Storm Water Management Model (SWMM)
provides detailed simulation capabilities for assessing primarily urban watersheds.
The versatility of HSPF and SWMM for simulating a wide range of land uses and
their continual upgrading make these models two of the most detailed and widely
applied to watershed studies. Receiving water models distributed by CEAM include
QUAL2E, WASPS, and SMPTOX4. QUAL2E is widely applied for assessment of BOD and
nutrient loading to rivers and streams under steady-state conditions. WASPS provides
more detailed and comprehensive analysis of receiving waters under steady-state or time-
variable conditions. SMPTOX4 provides simplified steady-state assessment of toxics
concentrations in rivers and streams. WASPS and QUAL2E are widely applied to address
watershed-based receiving water issues and development of TMDLs. Other models
distributed by CEAM include CORMIX, a mixing zone model; EXAMSII, a fate and
exposure model for assessing toxics in receiving waters; and PLUMES, a model interface
for preparing and running both new-field and far-field plume models.
EPA's Office of Science and Technology (OST) has also sponsored the development of
user interfaces and integrated modeling systems to facilitate the use of the SWMM,
SWRRBWQ, HSPF, and QUAL2E models. Windows versions of the SWMM,
SWRRBWQ and QUAL2E models are distributed by OST and are available on the OST
web site at www.epa.gov/ost/tools. An integrated modeling system, Better Assessment
Science Integrating Point and Nonpoint Sources (BASINS), is distributed by OST. OST
also distributes the Dynamic Toxics (DYNTOX) model, which can assess instream
toxicity based on a range of effluent discharge levels. EPA's Office of Wetlands, Oceans
and Watersheds (OWOW) distributes the Watershed Screening and Targeting Tool
(WSTT), an integrated modeling system that includes the Watershed Screening Model
(WSM).
The U.S. Department of Agriculture's Agricultural Research Service (ARS) has developed
2.2.2 USDA several well-documented models that can be used for watershed assessment and develop-
ment of TMDLs in predominantly agricultural areas. Watershed loading models devel-
oped by the USDA include the Agricultural Non-Point Source Pollution Model (AGNPS)
and Simulator for Water Resources in Rural Basin-Water Quality (SWRRBWQ). Due to
its distributed design, in which the simulation area is divided into cells, the AGNPS
model is well suited to linkage with geographic information systems (GISs). Although
currently still limited to design storm simulations, AGNPS has been widely applied to
watershed-based assessment of agricultural nonpoint sources. The SWRRBWQ model
provides watershed-scale assessment using continuous simulation. The SWRRBWQ
model has been incorporated into the Soil and Water Assessment Tool (SWAT) under
development by the USDA Agricultural Research Services in Temple, Texas. SWAT is
currently being applied to a national-scale modeling project, Hydrologic Unit Model for
the United States (HUMUS), scheduled for completion in 1997 (Srinivasan et al., 1995).
-------
Chapter 2. Review of Selected Loading and Receiving Water Models
The SWAT model allows for simulation of larger and more complex watersheds with
numerous subbasins. Some of the relevant field-scale models supported by the USDA
include Chemicals, Runoff, Erosion from Agricultural Management Systems
(CREAMS); Groundwater Loading Effects of Agricultural Management Systems
(GLEAMS); Opus; Water Erosion Prediction Project (WEPP); Environmental Policy
Integrated Climate (EPIC); and the Nitrate Leaching and Economic Analysis Program
(NLEAP). Field-scale models can provide site-specific analysis of management
practice alternatives and effectiveness. In some cases field-scale models can be used
to support broader watershed management planning needs.
2.2.3 I/SCOE The U.S. Army Corps of Engineers distributes models through the Hydraulic Engi-
neering Center (HEC) in Davis, CA and the Waterways Experiment Station (WES) in
Vicksburg, Mississippi. The HEC developed a continuous urban simulation model
including dry-weather sewer flows, in the Storage, Treatment, Overflow, Runoff Model
(STORM). With the support of HEC, STORM has been used extensively for planning
purposes and for evaluating control strategies for combined sewer overflows. The U.S.
Army Engineer WES distributes the BATHTUB, CE-QUAL-RIV1, CE-QUAL-W2, CE-QUAL-
ICM, and CH3-HEM models. BATHTUB applies a series of empirical eutrophication
models to morphologically complex lakes and reservoirs. The CE-QUAL-RIV1 model
simulates one-dimensional dynamic transport and water quality in rivers and
streams. The CE-QUAL-W2 simulates two-dimensional, laterally averaged hydrody-
namics and transport in rivers, lakes, and reservoirs. The CE-QUAL-ICM model was
developed for three-dimensional applications, such as the Chesapeake Bay, and is
typically linked with the CH3-HEM hydrodynamic model.
2.2.4 Other The U.S. Geological Survey (USGS) has developed and applied the Distributed
Federal Agencies Routmg Rainfall Runoff Model (DR3M-QUAL), which calculates runoff and pollutant
loads in urban watersheds. The USGS has also developed a statistical method for
estimating pollutant loads. The U.S. Federal Highway Administration (FHWA)
developed and uses a statistically based approach for assessing stormwater runoff
from highways and developing preliminary pollution control options.
2.2.5 Universities A number of models have also been developed at universities or other research
institutions. The Areal Nonpoint Source Watershed Environment Response Simula-
tion (ANSWERS) model was developed at the University of Georgia to predict the
movement of sediment through relatively large agricultural watersheds. Its cell-
based design, similar to AGNPS, has resulted in research developing linked applica-
tions with GIS. Researchers at Virginia Polytechnic Institute have continued develop-
ment and testing of the ANSWERS model. The Source Loading and Management
Model (SLAMM) was developed at the University of Alabama for the purpose of
evaluating urban management practices for sediment, nutrients, and other urban
pollutants, including toxics and water-demanding substances. The Generalized
Watershed Loading Functions (GWLF) model was developed at Cornell University. The
GWLF model considers runoff from urban and agricultural land uses and integrated
pollution from both point and nonpoint sources. WATERSHED is a simple nonpoint
source model developed at the University of Wisconsin to assess the cost-effectiveness
of stormwater control practices.
Universities have also been active in the development, testing, and application of
receiving water models. For example, the Virginia Institute of Marine Sciences
(VIMS) supports the development of the Environmental Fluid Dynamics Computer
Code (EFDC) and related receiving water modeling tools. The EFDC is a three-
dimensional hydrodynamic and salinity numerical model that has been linked with
various water quality models for receiving water simulations. Most recently, re-
searchers at VIMS have developed a fully linked water quality model, HEM-3D,
which incorporates EFDC (Park et al., 1995).
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Compendium of Tools for Watershed Assessment and TMDL Development
2.2.6 Other Several state and local agencies have participated in the development of nonpoint
source models. The Illinois State Water Survey developed a watershed loading model,
Auto-QI, for continuous simulation of pollutant loading from urban areas. The Wash-
ington Metropolitan Council of Governments developed a simplified approach, "the
Simple Method," for estimating pollutant loads from urban land uses (Schueler, 1987).
This approach relies on data from the National Urban Runoff Program (NURP) for
default values. The Watershed Management Model (WMM) was developed for the
Florida Department of Environmental Regulation to evaluate nonpoint source pollu-
tion loads and control strategies from mixed-land-use watersheds. The Urban
Catchment Model (P8-UCM) was developed for the Narragansett Bay Project. The P8-
UCM model predicts pollutant loading and transport of stormwater runoff from urban
watersheds.
The North American Lake Management Society (NALMS) distributes models specially
designed for the assessment of eutrophication and toxics in lakes. Models available from
NALMS include EUTROMOD, PHOSMOD, and TOXMOD. EUTROMOD includes routines
to estimate nutrient loadings and in-lake response. PHOSMOD evaluates the effects of
seasonal and long-term phosphorus loads on lake condition.
Other models developed and supported by private companies include the Stormwater
Intercept and Treatment Evaluation Model for Analysis and Planning (SITEMAP).
SITEMAP is a spreadsheet-based model designed to simulate stream segment load
capacities, point source wasteload allocations, and nonpoint source load allocations.
2.3
Watershed-scale
loading models
Simple methods can be
used to support an assess-
ment of the relative
significance of different
sources, guide decisions for
management plans, and
focus continuing monitor-
ing efforts.
Thoroughly assessing a watershed or developing a TMDL often requires the use of
watershed loading models to evaluate the effects of land uses and practices on
pollutant loading to waterbodies. For discussion purposes, loading models are
divided into categories based on complexity, operation, time step, and simulation
technique. Watershed-scale loading models can be grouped into three categories—
simple methods, mid-range models, and detailed models. In addition to the different
classes of models, the level of application of a given model can vary depending on
the objectives of the analysis. The three categories of models and types of available
models in each category are discussed below.
Simple methods. The major advantage of simple methods is that they can provide
a rapid means of identifying critical areas with minimal effort and data requirements.
Simple methods are typically derived from empirical relationships between physi-
ographic characteristics of the watershed and pollutant export. They can often be
applied using a spreadsheet program or hand-held calculator. Simple methods are often
used when data limitations and budget and time constraints preclude the use of complex
models. They are used to diagnose nonpoint source pollution problems where relatively
limited information is available. They can be used to support an assessment of the
relative significance of different sources, guide decisions for management plans, and
focus continuing monitoring efforts.
Typically, simple methods rely on large-scale aggregation and neglect important
features of small patches of land. They rely on generalized sources of information
and therefore have low to medium requirements for site-specific data. Default values
provided for these methods are derived from empirical relationships that are evalu-
ated based on regional or site-specific data. The estimations are usually expressed as
mean annual values.
Simple methods provide only rough estimates of sediment and pollutant loadings and
have very limited predictive capability. The empiricism contained in the models limits
their transferability to other regions. Because they often neglect temporal variability,
simple methods might not be adequate to model water quality problems for which
-------
Chapter 2. Review of Selected Loading and Receiving Water Models
loadings of shorter duration are important. They might be sufficient for problems such
as nutrient loadings to and eutrophication of long-residence-time waterbodies (e.g.,
lakes, reservoirs).
As shown in Table 2, these methods use large simulation time steps to provide long-term
averages or annual estimates. Although they can easily be adapted to estimate seasonal
or storm event loadings, their accuracy decreases because they cannot capture the large
fluctuations of pollutant loading or concentration usually observed at smaller time steps.
Pollutant loads are determined from export coefficients (e.g., the Watershed model) or as
a function of the sediment yield (e.g., EPA screening procedures, SLOSS-PHOSPH). The
Simple Method, the USGS regression method, and the FHWA model are statistically
based approaches developed from past monitoring information. Their application is
limited to the areas for which they were developed and to watersheds with similar land
uses or activities.
Mid-range models. The advantage of mid-range watershed-scale models is that
they evaluate pollution sources and impacts over broad geographic scales and
therefore can assist in defining target areas for pollution mitigation programs on a
watershed basis. Several mid-range models are designed to interface with geo-
graphic information systems (GISs), which greatly facilitate parameter estimation.
Greater reliance on site-specific data gives mid-range models a relatively broad range of
regional applicability. However, the use of simplifying assumptions can limit the accu-
racy of their predictions to within about an order of magnitude (Dillaha, 1992) and can
restrict their analysis to relative comparisons.
Table 2. Evaluation of Mode! Capabilities—Simple Models
Criteria
Land
Uses
Time
Scale
Hydrology
Pollutant
Loading
Pollutant
Routing
Model
Output
Input
Data
BMPs
Urban
Rural
Point Sources
Annual
Single Event
Continuous
Runoff
Baseflow
Sediment
Nutrients
Others
Transport
Transformation
Statistics
Graphics
Format Options
Requirements
Calibration
Default Data
User Interface
Evaluation
Design Criteria
Documentation
EPA
Screening1
O
0
-
•
0
.
_ 4
.
0
0
O
.
-
-
-
-
O
-
•
.
O
-
•
Simple
Method1
0
-
.
•
0
.
0
-
0
0
0
-
-
-
-
-
O
-
•
-
O
-
•
Regression
Method1
0
O
.
•
0
-
-
-
0
0
0
-
-
-
-
-
O
-
0
.
-
-
•
SLOSS-
PHOSPH 2
-
0
-
•
.
-
-
-
0
0
-
-
-
-
-
-
0
O
0
.
O
-
•
Watershed
0
0
O
•
-
.
-
-
0
0
0
-
.
0
0
0
O
0
O
0
0
-
•
FHWA
O3
O
-
•
0
.
0
-
-
0
0
-
-
O
-
-
O
-
0
O
0
-
•
WMM
•
•
0
•
-
.
0
O
.
0
0
-
O
O
O
O
O
0
0
0
0
-
0
i Not a computer program.
2 Coupled with GIS.
3 Highway drainage basins.
4 Extended versions recommend
use of SCS-curve num ber
method for runoff estimation.
High
Medium O Low - Not incorporated
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Compendium of Tools for Watershed Assessment and TMDL Development
Mid-range models
attempt a compromise
between the empiricism of
the simple methods and the
complexity of detailed
mechanistic models.
This class of model attempts a compromise between the empiricism of the simple
methods and the complexity of detailed mechanistic models. Mid-range models use a
management-level approach to assess pollutant sources and transport in watersheds
by incorporating simplified relationships for the generation and transport of pollut-
ants. Mid-range models, however, still retain responsiveness to management objec-
tives and actions appropriate to watershed management planning (Clark et al.,
1979). They are relatively simple and are intended to be used to identify problem
areas within large drainage basins or to make preliminary, qualitative evaluations of
BMP alternatives (Dillaha, 1992).
Unlike the simple methods, which are restricted to predictions of annual or storm loads,
mid-range tools can be used to assess the seasonal or interannual variability of nonpoint
source pollutant loadings and to assess long-term water quality trends. Also, they can be
used to address land use patterns and landscape configurations in actual watersheds.
Table 3. Evaluation of Model Capabilities—Mid-Range Models
Criteria
Land Uses
Time Scale
Hydrology
Pollutant
Loading
Pollutant
Routing
Model Output
Input Data
BMPs
Urban
Rural
Point Sources
Annual
Single Event
Continuous
Runoff
Baseflow
Sediment
Nutrients
Others
Transport
Transformation
Statistics
Graphics
Format Options
Requirements
Calibration
Default Data
Jser Interface
Evaluation
Design Criteria
Documentation
SITEMAP
•
•
O
-
0
•
•
o
-
•
-
o
-
Q
6
•
O
o
•
•
o
-
•
GWLF
•
•
O
-
-
-
o
-
o
9
•
0
0
•
•
o
-
•
P8-UCM
•
-
•
-
•
•
•
O
•
•
•
0
-
-
•
•
0
o
o
•
•
•
•
Auto-QI
•
-
-
-
-
•
•
0
•
•
•
Q
-
-
-
0
o
Q
O
O
Q
V
Q
AGNPS
-
•
•
-
•
.
•
-
•
•
-
•
-
-
•
•
Q
O
' Q
Q
*
O
•
SLAMM
•
.
•
-
-
•
•
O
•
•
•
«
-
o
o
•
0
Q
Q
•
Q
0
«
High W Medium O Low - Not Incorporated
-------
Chapter 2. Review of Selected Loading and Receiving Water Models
If properly applied and
calibrated, detailed models
can provide relatively
accurate predictions of
variable flows and water
quality at any point in a
watershed.
They are based primarily on empirical relationships and default values. In addition,
they typically require some site-specific data and calibration.
Mid-range models are designed to estimate the importance of pollutant contributions
from multiple land uses and many individual source areas in a watershed. Thus, they
can be used to target important areas of pollution generation and identify areas best
suited for controls on a watershed basis. Moreover, the continuous simulation
furnished by some of these models provides an analysis of the relative importance of
sources for a range of storm events or conditions. In an effort to reduce complexity
and data requirements, these models are often developed for specific applications.
For instance, mid-range models can be designed for application to agricultural,
urban, or mixed watersheds. Some mid-range models simplify the description of
transport processes while emphasizing possible reductions available with controls;
others simplify the description of control options and emphasize changes in concentra-
tions as pollutants move through the watershed.
Table 3 describes the key features of mid-range models. Because mid-range models
attempt to use smaller time steps in order to represent temporal variability, they require
additional meteorologic data (e.g., daily weather data for the GWLF, hourly rainfall for
SITEMAP). They also attempt to relate pollutant loadings to hydrologic (e.g., runoff)
and erosion (e.g., sediment yield) processes. These models usually include detailed
input-output features (e.g., AGNPS, GWLF), making applications easier to process.
Several of these models (SITEMA? Auto-QI) were developed in existing computing
environments (e.g., Lotus 1-2-3) to make use of their built-in graphical and statistical
capabilities. It should be noted from Tables 2 and 3 that neither the simple nor the mid-
range models consider degradation and transformation processes, and few incorporate
detailed representation of pollutant transport within and from the watershed. Although
their applications might be limited to relative comparisons, they can often provide water
quality managers with useful information for watershed-level planning decisions.
Detailed models. Detailed models best represent the current understanding of
watershed processes affecting pollution generation. Detailed models are best able to
identify causes of problems rather than simply describing overall conditions. If
properly applied and calibrated, detailed models can provide relatively accurate
predictions of variable flows and water quality at any point in a watershed. The
additional precision they provide, however, comes at the expense of considerable
time and resource expenditure for data collection and model application.
Detailed models use storm event or continuous simulation to predict flow and
pollutant concentrations for a range of flow conditions. The models are complex and
were not designed with emphasis on their potential use by the typical state or local
planner. Many of these models were developed for research into the fundamental land
surface and instream processes that influence runoff and pollutant generation rather than
to communicate information to decision makers faced with planning watershed manage-
ment.
Detailed models incorporate the manner in which watershed processes change over
time in a continuous fashion rather than relying on simplified terms for rates of
change (Addiscott and Wagenet, 1985). They tend to require rate parameters for
flow velocities and pollutant accumulation, settling, and decay instead of capacity
terms. The length of time steps is variable and depends on the stability of numerical
solutions as well as the response time for the system (Nix, 1991). Algorithms in
detailed models more closely simulate the physical processes of infiltration, runoff,
pollutant accumulation, instream effects, and groundwater/surface water interaction.
The input and output of detailed models also have greater spatial and temporal
resolution. Moreover, the manner in which physical characteristics and processes
differ over space is incorporated within the governing equations (Nix, 1991). Link-
-------
ComjwniMum of Tools for Watershed Assessment and TMDL Development
age to biological modeling is possible because of the comprehensive nature of
continuous simulation models. In addition, detailed hydrologic simulations can be used
to design potential control actions.
Table 4 shows that these models use small time steps to allow for continuous and
storm event simulations. However, input data file preparation and calibration require
professional training and adequate resources. Some of these models (e.g., STORM,
SWMM, ANSWERS) were developed not only to support planning-level evaluations
but also to provide design criteria for pollution control practices. If appropriately
applied, state-of-the-art models such as HSPF and SWMM can provide accurate
estimations of pollutant loads and the expected impacts on water quality. New
interfaces developed for HSPF and SWMM, and links with GISs, can facilitate the use
of complex models for environmental decision making. However, their added accu-
racy might not always justify the amount of effort and resources they require.
Application of such detailed models is more cost-effective when used to address
complex situations or objectives.
A qualitative description of each model is presented in the following section to supple-
ment the information reported in Tables 2,3, and 4. For a more technical description of
each of the watershed loading models, refer to Appendix A.
Table 4. Evaluation of Model Capabilities—Detailed Models
Criteria
Land Uses
Time Scale
Hydrology
Pollutant
Loading
Pollutant
Routing
Model
Output
Input
Data
BMPs
Urban
Rural
Point Sources
Annual
Single Event
Continuous
Runoff
Baseflow
Sediment
Nutrients
Others
Transport
Transformation
Statistics
Graphics
Format Options
Requirements
Calibration
Default Data
User Interface
Evaluation
Design Criteria
Documentation
STORM
•
-
•
-
O
•
•
O
•
•
•
-
-
O
-
•
O
O
Q
•
9
Q
•
ANSWERS
-
•
-
-
•
-
•
-
•
•
-
e
-
.
-
•
•
0
o
.
«
Q
W
DR3M-QUAL
•
-
•
-
O
•
•
0
•
•
-
•
-
•
e
•
•
9
•
Q
•
O
0
SWRRBWQ/
SWAT
0
•
•
-
o
o
o
o
•
•
0
e
-
e
«
e
Q
O
•
«
«
.
•
SWMM
•
O
•
-
0
o
•
o
•
•
•
«
•
•
•
•
HSPF
•
«
•
-
•
•
•
•
•
•
•
•
•
•
o
•
•
•
o
_
•
•
•
• High
Medium
O Low - Not Incorporated
-------
Chapter 2. Review of Selected Loading and Receiving Water Models
2.3.1 EPA Screening Procedures (fact sheet, page A.9). The EPA Screening Proce-
Simole methods dures, developed by the EPA Environmental Research Laboratory in Athens, Georgia,
(McElroy et al., 1976; Mills et al., 1985) include methodologies to calculate pollutant
loads from point and nonpoint sources, including atmospheric deposition, for prelimi-
nary assessment of water quality. The procedures consist of loading functions and simple
empirical expressions relating nonpoint pollutant loads to other readily available
parameters. Data required generally include information on land use/land cover,
management practices, soils, and topography. Although these procedures are not coded
into a computer program, several computer-based models have adapted the loading
function concept to predict pollutant loadings. An advantage of this approach is the
possibility of using readily available data as default values when site-specific informa-
tion is lacking. Application of these procedures requires minimum personnel training
and practically no calibration. However, application to large, complex watersheds
should be limited to pre-planning activities. Many of the techniques included in the
manual were incorporated into current models such as GWLF.
The Simple Method (fact sheet, page A.21). The Simple Method is an empirical
approach developed for estimating pollutant export from urban development sites in
the Washington, DC, area (Schueler, 1987). It is used at the site-planning level to
predict pollutant loadings under a variety of development scenarios. Its application
is limited to small drainage areas of less than one square mile. Pollutant concentrations
of phosphorus, nitrogen, chemical oxygen demand, biochemical oxygen demand (BOD),
and metals are calculated from flow-weighted concentration values for new suburban
areas, older urban areas, central business districts, hardwood forests, and urban high-
ways. The method relies on the National Urban Runoff Program (NURP) data for default
values (USEPA, 1983). A graphical relationship is used to determine the event mean
sediment concentration based on readily available information. This method is not
coded into a computer program but can be easily implemented with a hand-held
calculator.
USGS Regression Approach (fact sheet, page A.33). The regression approach
developed by USGS researchers is based on a statistical description of historic
records of storm runoff responses on a watershed level (Tasker and Driver, 1988).
This method may be used for rough preliminary calculations of annual pollutant
loads when data and time are limited. Simple regression equations were developed
using available monitoring data for pollutant discharges at 76 gaging stations in 20
states. Separate equations are given for 10 pollutants, including dissolved and total
nutrients, chemical oxygen demand, and metals. Input data include drainage area,
percent imperviousness, mean annual rainfall, general land use pattern, and mean
minimum monthly temperature. Application of this method provides storm-mean
pollutant loads and corresponding confidence intervals. The use of this method as a
planning tool at a regional or watershed level might require preliminary calibration
and verification with additional, more recent monitoring data.
Simplified Pollutant Yield Approach (SLOSS-PHOSPH) (fact sheet, page
A. 19). This method uses two simplified loading algorithms to evaluate soil erosion,
sedimentation, and phosphorus transport from distributed watershed areas. The
SLOSS algorithm provides estimates of sediment yield, whereas the PHOSPH algo-
rithm uses a loading function to evaluate the amount of sediment-bound phospho-
rus. Application to watershed and subwatershed levels was developed by Tim et al.
(1991) based on an integrated approach coupling these algorithms with the Virginia
Geographical Information System (VirGIS). The approach was applied to the Nomini
Creek watershed, Westmoreland County, Virginia, to target critical areas of nonpoint
source pollution at the subwatershed level (USEPA, 1992c). In this application,
analysis was limited to phosphorus loading; however, other pollutants for which
input data or default values are available can be modeled in a similar fashion. The
approach requires full-scale GIS capability and trained personnel.
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Compendium of Tbols for Watershed Assessment and TMDL Development
Watershed (fact sheet, page A.35). Watershed is a spreadsheet model developed at
the University of Wisconsin to calculate phosphorus loading from point sources, com-
bined sewer overflows (CSOs), septic tanks, rural croplands, and other urban and rural
sources. It can be used to evaluate the trade-offs between control of point and nonpoint
sources (Walker et al., 1989). It uses an annual time step to calculate total pollution
loads and to evaluate the cost-effectiveness of pollution control practices in terms of cost
per unit load reduction. The program uses a series of worksheets to summarize water-
shed characteristics and to estimate pollutant loadings for uncontrolled and controlled
conditions. Because of the simple formulation describing the various pollutant loading
processes, the model can be applied using available default values with minimum
calibration effort. Watershed was applied to study the trade-offs between controlling
point and nonpoint sources in the Delavan Lake watershed in Wisconsin.
The Federal Highway Administration (FHWA) Model (fact sheet, page
A.11). The FHWA's Office of Engineering and Highway Operations has developed a
simple statistical spreadsheet procedure to estimate pollutant loading and impacts to
streams and lakes that receive highway stormwater runoff (Federal Highway Admin-
istration, 1990). The procedure uses several worksheets to tabulate site characteris-
tics and other input parameters, as well as to calculate runoff volumes, pollutant
loads, and the magnitude and frequency of occurrence of instream pollutant concen-
trations. The FHWA model uses a set of default values for pollutant event-mean
concentrations that depend on traffic volume and the rural or urban setting of the
highway's pathway. The Federal Highway Administration uses this method to identify
and quantify the constituents of highway runoff and their potential effects on receiv-
ing waters and to identify areas that might require controls.
Watershed Management Model (WMM) (fact sheet, page A.37). The Water-
shed Management Model was developed for the Florida Department of Environmental
Regulation for watershed management planning and estimation of watershed pollutant
loads (Camp, Dresser, and McKee, 1992). Pollutants simulated include nitrogen, phos-
phorus, lead, and zinc from point and nonpoint sources. The model is implemented in
the Lotus 1-2-3 spreadsheet environment and will thus calculate standard statistics and
produce plots and bar charts of results. Although it was developed to predict annual
loadings, this model can be adapted to predict seasonal loads provided that seasonal
event mean concentration data are available. In the absence of site-specific information,
the event concentrations derived from the NURP surveys maybe used as default values.
The model includes computational components for stream and lake water quality
analysis using simple transport and transformation formulations based on travel time.
The WMM has been applied to several watersheds including the development of a master
plan for Jacksonville, Florida, and the Part II estimation of watershed loadings for the
NPDES stormwater permitting process. It has also been applied in Norfolk County,
Virginia; to a Watershed Management Plan for North Carolina; to a wasteload alloca-
tion study for Lake Tohopekaliga, near Orlando, Florida; and for water quality planning
in Austin, Texas (Pantalion et al., 1995).
2.3.2 Stormwater Intercept and Treatment Evaluation Model for Analysis and
Mid-range models ^ani^nS (SITEMAP) (fact sheet, page A.23). SITEMAP, previously distributed
under the name NPSMAP, is a dynamic simulation program that computes, tabulates,
and displays daily runoff, pollutant loadings, infiltration, soil moisture, irrigation water
demand, evapotranspiration, drainage to groundwater, and daily outflows, water and
residual pollutant levels in retention basins or wetland systems (Omicron Associates,
1990). The model can be used to evaluate user-specified alternative control strategies,
and it simulates stream segment load capacities (LCs) in an attempt to develop point
source wasteload allocations (WLAs) and nonpoint source load allocations (LAs).
Probability distributions for runoff and nutrient loadings can be calculated by the model
based on either single-event or continuous simulations. The model can be applied in
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Chapter 2, Review of Selected Loading and Receiving Water Models
urban, agricultural, or complex watershed simulations. SITEMAP operates within the
Lotus 1-2-3 programming environment and is capable of producing graphic output.
Although this model requires a minimum calibration effort, it requires moderate effort to
prepare input data files. The current version of the program considers only nutrient
loading; sediment and other pollutants are not yet incorporated into the program. The
model is easily interfaced with CIS (ARC/INFO) to facilitate preparation of land use
files. SITEMAP has been applied as a component of a full watershed model to the
Tualatin River basin for the Oregon Department of Environmental Quality, and to the
Fairview Creek watershed for the Metropolitan Service District in Portland, Oregon.
Generalized Watershed Loading Functions (GWLF) Model (fact sheet,
page A. 13). The GWLF model was developed at Cornell University to assess the point
and nonpoint loadings of nitrogen and phosphorus from urban and agricultural water-
sheds, including septic systems, and to evaluate the effectiveness of certain land use
management practices (Haith et al., 1992). One advantage of this model is that it was
written with the express purpose of requiring no calibration, making extensive use of
default parameters. The GWLF model includes rainfall/runoff and erosion and sediment
generation components, as well as total and dissolved nitrogen and phosphorus load-
ings. The current version of this model does not account for loadings of toxics and
metals. The GWLF model uses daily time steps and allows analysis of annual and
seasonal time series. The model also uses simple transport routing, based on the delivery
ratio concept. In addition, simulation results can be used to identify and rank pollution
sources and evaluate basinwide management programs and land use changes. The most
recent update of the model incorporates a septic (on-site wastewater disposal) system
component. The model also includes several reporting and graphical representations of
simulation output to aid in interpretation of the results. This model was successfully
tested on a medium-sized watershed in New York (Haith and Shoemaker, 1987). A
version of the model with an enhanced user interface and linkages to national databases,
WSM (Watershed Screening Model), has recently become available and is distributed
with the EPA Office of Wetlands, Oceans and Watersheds' (OWOWs) computer program
Watershed Screening and Targeting Tool (WSTT).
Urban Catchment Model (P8-UCM) (fact sheet, page A.17). The P8-UCM
program was developed for the Narragansett Bay Project to simulate the generation
and transport of stormwater runoff pollutants in small urban catchments and to
assess impacts of development on water quality, with minimum site-specific data. It
includes several routines for evaluating the expected removal efficiency for particu-
lar site plans, selecting or siting best management practices (BMPs) necessary to
achieve a specified level of pollutant removal, and comparing the relative changes in
pollutant loads as a watershed develops (Palmstrom and Walker, 1990). Default
input parameters can be derived from NURP data and are available as a function of
land use, land cover, and soil properties. However, without calibration, the use of
model results should be limited to relative comparisons. Spreadsheet-like menus and
on-line help documentation make extensive user interface possible. On-screen
graphical representations of output are developed for a better interpretation of
simulation results. The model also includes components for performing monthly or
cumulative frequency distributions for flows and pollutant loadings.
Automated Q-ILLUBAS (AUTO-QI) (fact sheet, page A.5). AUTO-QI is a
watershed model developed by the Illinois State Water Survey to perform continuous
simulations of stormwater runoff from pervious and impervious urban lands (Terstriep et
al., 1990). It also allows the examination of storm events or storm sequence impacts on
receiving water. Critical events are also identified by the model. However, hourly
weather input data are required. Several pollutants, including nutrients, chemical
oxygen demand, metals,_and bacteria, can be analyzed simultaneously. This model also
includes a component to evaluate the relative effectiveness of best management prac-
tices. An updated version of AUTO-QI, with an improved user interface and linkage to a
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Compendium of Toob for Watershed Assessment and TMDL Development
geographic information system (ARC/INFO on PRIME computer), has been completed by
the Illinois State Water Survey. This interface is provided to generate the necessary input
files related to land use, soils, and control measures. AUTO-QI was verified on the
Boneyard Creek in Champaign, Illinois, and applied to the Calumet and Little Rivers to
determine annual pollutant loadings.
Agricultural Nonpoint Source Pollution Model (AGNPS) (fact sheet,
page A. 1). Developed by the USDA Agricultural Research Service, AGNPS addresses
concerns related to the potential impacts of point and nonpoint source pollution on
water quality (Young et al., 1989). It was designed to quantitatively estimate pollution
loads from agricultural watersheds and to assess the relative effects of alternative
management programs. The model simulates surface water runoff along with nutrient
and sediment constituents associated with agricultural nonpoint sources, as well as point
sources such as feedlots, wastewater treatment plants, and stream bank or gully erosion.
The available version of AGNPS is event-based; however, a continuous version is under
active development CNeedham and Young, 1993). The structure of the model consists of
a square grid cell system to represent the spatial distribution of watershed properties.
This grid system allows the model to be connected to other software such as GIS and
digital elevation models (DEMs). This connectivity can facilitate the development of a
number of the model's input parameters. Two new terrain-enhanced versions of the
model— AGNPS-C, a contour-based version, and AGNPS-G, a grid-based version—have
been developed to automatically generate the grid network and the required topographic
parameters (Panuska et al., 1991). Vieux and Needham (1993) describe a CIS-based
analysis of the sensitivity of AGNPS predictions to grid-cell size. Engel et al. (1993)
present GRASS-based tools to assist with the preparation of model inputs and
visualization and analysis of model results. Tim and Jolly (1994) used AGNPS with
ARC/INFO to evaluate the effectiveness of several alternative management strategies
in reducing sediment pollution in a 417 hectare watershed in southern Iowa. The model
also includes enhanced graphical representations of input and output information.
Source Loading and Management Model (SLAMM) (fact sheet, page A.25).
The SLAMM model (Pitt, 1993) can identify pollutant sources and evaluate the effects of
a number of different stormwater control practices on runoff. The model performs
continuous mass balances for particulate and dissolved pollutants and runoff volumes.
Runoff is calculated by a method developed by Pitt (1987) for small-storm hydrology.
Runoff is based on rainfall minus initial abstraction and infiltration and is calculated for
both pervious and impervious areas. Triangular hydrographs, parameterized by a
statistical approach, are used to simulate flow. Exponential buildup and rain wash-off
and wind removal functions are used for pollutant loadings. Water and sediment from
various source areas are tracked by source area as they are routed through various
treatment devices. The program considers how particulates filter or settle out in control
devices. Particulate removal is calculated based on the design characteristics of the basin
or other removal device. Storage and overflow of devices are also considered. At the
outfall locations, the characteristics of the source areas are used to determine pollutant
loads in solid and dissolved phases. Loads from various source areas are summed.
SLAMM has been used in conjunction with a receiving water quality model (HSPF) to
examine the ultimate effects on urban runoff from Toronto for the Ontario Ministry of
the Environment. SLAMM was also used to evaluate control options for controlling urban
runoff in Madison, Wisconsin, using GIS information (Thum et al., 1990). The State of
Wisconsin uses SLAMM as part of its Priority Watershed Program. It was used in Port-
land, Oregon, for a study evaluating CSOs.
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Chapter 2. Review of Selected Loading and Receiving Water Models
2.3.3 Storage, Treatment, Overflow Runoff Model (STORM) (fact sheet, page
Detailed models A-27)- STORM is a U.S. Army Corps of Engineers (COE) model developed for continu-
ous simulation of runoff quantity and quality, including sediments and several conserva-
tive pollutants. It also simulates combined sewer systems (Hydrologic Engineering
Center, 1977). STORM has been widely used for planning and evaluation of the trade-off
between treatment and storage control options for CSOs. Long-term simulations of
runoff quantity and quality can be used for the construction of duration-frequency
diagrams. These diagrams are useful in developing urban planning alternatives and
designing structural control practices. STORM was primarily designed for modeling
stormwater runoff from urban areas. It requires relatively moderate to high calibration
and input data. STORM was initially developed for mainframe computer usage; how-
ever, several versions have been adapted by various individual consultants for use on
microcomputers. The model has been applied recently to water quality planning in the
City of Austin, Texas (Pantalion et al., 1995).
Area! Nonpoint Source Watershed Environment Response Simulation
Model (ANSWERS) (fact sheet, page A.3). ANSWERS is a comprehensive model
developed to evaluate the effects of land use, management schemes, and conservation
practices or structures on the quantity and quality of water from both agricultural and
nonagricultural watersheds (Beasley, 1986). The distributed structure of this model
allows for a better analysis of the spatial as well as temporal variability of pollution
sources and loads. It was initially developed on a storm event basis to enhance the
physical description of erosion and sediment transport processes. Data file preparation
for the ANSWERS program is rather complex and requires mainframe capabilities,
especially when dealing with large watersheds. The output routines are quite flexible;
results maybe obtained in several tabular and graphical forms. The program has been
used to evaluate management practices for agricultural watersheds and construction
sites in Indiana. It has been combined with extensive monitoring programs to evaluate
the relative importance of point and nonpoint source contributions to Saginaw Bay. This
application involved the computation of unit area loadings under different land use
scenarios for evaluation of the trade-offs between load allocations (LAs) and wasteload
allocations (WLAs). Recent model revisions include improvements to the nutrient
transport and transformation subroutines (Dillaha et al., 1988). Bouraoui et al. (1993)
describe the development of a continuous version of the model.
Multi-event urban runoff quality model (DR3M-QUAL) (fact sheet, page
A.7). DR3M is a watershed model for routing storm runoff through a branched system of
pipes and/or natural channels using rainfall as input. The model provides detailed
simulation of storm-runoff periods selected by the user and a daily soil-moisture account-
ing between storms. Kinematic wave theory is used for routing flows over contributing
overland-flow areas and through the channel network. Storm hydrographs may be saved
for input to DR3M-QUAL, which simulates the quality of surface runoff from urban
watersheds. The model simulates impervious areas, pervious area, and precipitation
contributions to runoff quality, as well as the effects of street sweeping and/or detention
storage. Variations of runoff quality are simulated for user-specified storm-runoff
periods. Between these storms, a daily accounting of the accumulation and wash-off of
water-quality constituents on effective impervious areas is maintained. Input to the
model includes the storm hydrographs, usually from DR3M. The program has been
extensively reviewed within the USGS and applied to several urban modeling studies
(Brabets, 1986; Guay, 1990; Lindner-Lunsford and Ellis, 1987).
Simulation for Water Resources in Rural Basins - Water Quality
(SWRRBWQ) (fact sheet, page A.31). The SWRRBWQ model was adapted from
the field-scale CREAMS modelbyUSDAto simulate hydrologic, sedimentation, nutrient,
and pesticide movement in large, complex rural watersheds (Arnold et al., 1989).
SWRRBWQ uses a daily time step to evaluate the effect of management decisions on
water, sediment yields, and pollutant loadings. The processes simulated within this
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Compendium of Tools for Watershed Assessment and TMDL Development
model include surface runoff, percolation, irrigation return flow, evapotranspiration,
transmission losses, pond and reservoir storage, sedimentation, and crop growth. The
model is useful for estimation of the order of magnitude of pollutant loadings from
relatively small watersheds or watersheds with fairly uniform properties. Input require-
ments are relatively high, and experienced personnel are required for successful simula-
tions. SWRRBWQ was used by the National Oceanic and Atmospheric Administration
CNOAA) to evaluate pollutant loadings to coastal estuaries and embayments as part of its
national Coastal Pollution Discharge Inventory. The model has been run for all major
estuaries on the east coast, west coast, and Gulf coast for a wide range of pollutants
(Donigian and Huber, 1991). Although SWRRBWQ is no longer under active develop-
ment, the technology is being incorporated into the Soil and Water Assessment Tool
(SWAT) as part of the Hydrologic Unit Model for the United States (HUMUS) project at
Temple, Texas (Arnold et al., 1993; Srinivasan and Arnold, 1994). EPA's Office of
Science and Technology (OST) has recently developed a Microsoft Windows-based
interface for SWRRBWQ to allow convenient access to temperature, precipitation, and
soil data files.
Storm Water Management Model (SWMM) (fact sheet, page A.29). SWMM is
a comprehensive watershed-scale model developed by EPA (Huber and Dickinson, 1988).
It was initially developed to address urban stormwater and assist in storm-event analysis
and derivation of design criteria for structural control of urban stormwater pollution,
but it was later upgraded to allow continuous simulation and application to complex
watersheds and land uses. SWMM can be used to model several types of pollutants
provided that input data are available. Recent versions of the model can be used for
either continuous or storm event simulation with user-specified variable time steps. The
model is relatively data-intensive and requires special effort for validation and calibra-
tion. Its application in detailed studies of complex watersheds might require a team
effort and highly trained personnel. SWMM has been applied to address various urban
water quantity and quality problems in many locations in the United States and other
countries (Donigian and Huber, 1991; Huber, 1992). In addition to developing compre-
hensive watershed-scale planning, typical uses of SWMM include predicting CSOs,
assessing the effectiveness of BMPs, providing input to short-time-increment dynamic
receiving water quality models, and interpreting receiving water quality monitoring data
(Donigian and Huber, 1991). Warwick and Tadepalli (1991) describe calibration and
verification of SWMM on a 10-square-mile urbanized watershed in Dallas, Texas.
Tsihrintzis et al. (1995) describe SWMM applications to four watersheds in South
Florida representing high- and low-density residential, commercial, and highway
land uses. Ovbiebo and She (1995) describe another application of SWMM in a
subbasin of the Duwamish River, Washington. EPA's Office of Science and Technology
distributes a Microsoft Windows interface for SWMM that makes the model more
accessible. A postprocessor allows tabular and graphical display of model results and
has a special section to help in model calibration.
The Hydrological Simulation Program - FORTRAN (HSPF) (fact sheet, page
A. 15). HSPF is a comprehensive package developed by EPA for simulating water
quantity and quality for a wide range of organic and inorganic pollutants from complex
watersheds (Bicknell et al., 1993). The model uses continuous simulations of water
balance and pollutant generation, transformation, and transport. Time series of the
runoff flow rate, sediment yield, and user-specified pollutant concentrations can be
generated at any point in the watershed. The model also includes instream quality
components for nutrient fate and transport, biochemical oxygen demand (BOD), dis-
solved oxygen (DO), pH, phytoplankton, zooplankton, and benthic algae. Statistical
features are incorporated into the model to allow for frequency-duration analysis of
specific output parameters. Data requirements for HSPF are extensive, and calibration
and verification are recommended. The program is maintained on IBM microcomputers
and DEC/VAX systems. Because of its comprehensive nature, the HSPF model requires
highly trained personnel. It is recommended that its application to real case studies be
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Chapter 2. Review of Selected Loading and Receiving Water Models
2.4
Field-scale
loading models
Field-scale models can be
useful in assessing manage-
ment practices on a
micro-scale as part of design-
ing plans to achieve nonpoint
source load reductions for
watershed studies and
TMDLs.
carried out as a team effort. The model has been extensively used for both screening-
level and detailed analyses. HSPF is being used by the Chesapeake Bay Program to
model total watershed contributions of flow, sediment, nutrients, and associated constitu-
ents to the tidal region of the Bay (Donigian et al., 1990; Donigian and Patwardhan,
1992). Moore et al. (1992) describe an application to model BMP effects on a Tennessee
watershed. Scheckenberger and Kennedy (1994) discuss how HSPF can be used in
subwatershed planning. Ball et al. (1993) describe an application of HSPF in Australia.
Lumb et al. (1990) describe an interactive program for data management and analysis
that can be effectively used with HSPF. Lumb and Kittle (1993) present an expert system
that can be used for calibration and application of HSPF. Donigian et al. (1996)
describe the use of HSPF to identify and quantify the relative pollutant contributions
from both point and nonpoint sources and to evaluate agricultural BMPs for the LeSueur
basin of southern Minnesota.
While watershed-scale loading models consider relatively large areas at an abbrevi-
ated level of detail, field-scale loading models represent smaller, homogenous areas
in more depth. Field-scale models can be used to support watershed projects and
TMDL development, particularly in the areas of management practices assessment
and testing. In some cases, field-scale modeling can be used as a basis for the selection
of recommended practices forbasinwide implementation. For example, the CREAMS
model was applied to representative fields in the Chesapeake Bay watershed to assess the
benefits of various BMPs (Shirmohammadi et al., 1992). Field-scale models can be used
as part of a "nested" modeling analysis, where results from the field-scale analysis are
incorporated into larger basin- or watershed-scale modeling efforts. Field-scale models
can therefore be useful in assessing management practices on a microscale as part of
designing plans to achieve nonpoint source load reductions for watershed studies and
TMDLs.
Field-scale loading models address many of the interactive processes that occur in a
small catchment or field. They are generally continuous models that can be used to
study the effects of alternative management scenarios on the movement of water and
pollutants within and from a small catchment system. Four public-domain field-scale
models developed by the U.S. Department of Agriculture's Agricultural Research
Service (USDA-ARS) are presented here. Chemicals, Runoff, and Erosion from
Agricultural Management Systems (CREAMS) and Groundwater Loading Effects of
Agricultural Management Systems (GLEAMS) are well-documented models that have
gained wide acceptance among users. Opus (not an acronym) is a state-of-the-art
field-scale model that comprehensively represents both physical and chemical
processes occurring in an agricultural field. The Water Erosion Prediction Project
(WEPP) model was developed to provide predictions of water erosion based on
fundamental hydrologic and erosion mechanics science.
Brief descriptions of each of the field-scale models are included below. Since field-
scale models are of only limited applicability to watershed planning, no fact sheets are
included for the models.
CREAMS/GLEAMS. CREAMS is a lumped-parameter, continuous simulation model that
uses separate hydrology, erosion, and chemistry submodels connected by pass files. The
hydrology component has two options, depending on availability of rainfall data. Option
one uses daily rainfall with runoff estimated using the Soil Conservation Service (SCS)
curve number, while option two requires hourly (breakpoint) rainfall with runoff
estimated using the Green-Ampt equation. The erosion component uses the Universal Soil
Loss Equation (USLE) parameters but considers the basic processes of soil detachment,
transport, and deposition. The basic concepts for modeling treat nutrient transport as
proceeding separately in adsorbed and dissolved phases. Soil nitrogen is modified by
nitrification-denitrification processes and by plant uptake. Pesticides in runoff are
partitioned between the solution and sediment phases using a simplified isotherm model.
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Compendium of Tools for Watershed Assessment and TMDL Development
CREAMS allows the simulation of user-defined agricultural BMPs including soil incorpo-
ration of pesticides and various conservation tillage practices. CREAMS is no longer
under active development and has been replaced by the GLEAMS model.
The hydrology and erosion components in CREAMS and GLEAMS are very similar.
However, option two in the hydrology component of CREAMS is not available in
GLEAMS, and several erosion parameters that were user-specified in CREAMS are
internally driven in GLEAMS. The nutrient component in GLEAMS contains signifi-
cant enhancements over the CREAMS nutrient component, using detailed consider-
ation of various nitrogen and phosphorus transformation processes. Animal waste
applications can be explicitly modeled, with estimates of decomposition and disposi-
tion. GLEAMS also has a more comprehensive component for subsurface routing and
mass-balance of pesticides and nutrients. Alternative land-use management options
are typically specified by changes in the SCS curve number and Universal Soil Loss
Equation (USLE) parameters.
Cooper et al. (1992) evaluated the ability of the CREAMS model to predict loadings
of runoff, sediment, and nutrients from a New Zealand grazed pasture. They con-
cluded that although CREAMS has limitations in representing the dynamics of
grazed pastures, it shows potential as a water quality management tool in pastoral
watersheds. Reyes et al. (1993) modified GLEAMS to account for shallow water table
fluctuations and improve its ability to simulate nonpoint loadings in alluvial shallow
water table soils. Yoon et al. (1994) applied GLEAMS to predict nutrient losses from
land application of poultry litter.
The GLEAMS model can be obtained by writing to David C. Moffitt at the South
National Technical Center, EO. Box 6567, Fort Worth, TX 76115 or contacting him at
(817) 334-5232 extension 3650.
Opus. Opus (not an acronym) is a lumped-parameter, continuous simulation model
for studying the potential pollution from various agricultural management practices.
The model simulates the effect of weather, soil type, crop, topography, and manage-
ment action on nonpoint source pollutant losses. Processes modeled include hydrol-
ogy, erosion, crop growth, agricultural management, nutrient cycling and transport,
and pesticide fate and transport. The model allows detailed simulation using
breakpoint data on the time-intensity pattern of rainfall or a more lumped approaich
using either recorded daily rainfall or stochastically generated rainfall. The field size
in Opus is limited to catchments with a single rain gage record and a single soil
profile. The simulation time step in Opus varies by process and conditions from fractions
of a second in some hydrologic components to years in annual management cycles, with
many processes proceeding on a daily time step.
Management options in Opus are specified as part of the field description. These include
the type and direction of tillage, and the use of terracing, impoundments, and grass
buffer strips. Management is assumed to occur on a multiyear rotation basis. The user
can choose crops to grow, tillage procedures to use, and pesticide and nutrient (includ-
ing animal waste) applications. Zacharias and Heatwole (1993) evaluated the ability of
Opus to predict differences in pesticide losses from two plots under alternative tillage
practices.
The Opus model may be obtained by contacting Roger E. Smith at USDA-ARS, Water
Management Research Unit, AERC CSU, Fort Collins, CO 80523 or contacting him at
(970) 491-8511 or roger@lily.aerc.colostate.edu.
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Chapter 2. Review of Selected Loading and Receiving Water Models
2.5
Receiving water
models
Water Erosion Prediction Project (WEPP). The WEPP model is a distributed-
parameter, continuous-simulation model developed to provide a new generation of water
erosion prediction technology. The model requires inputs for rainfall amounts and
intensity; soil textural qualities; plant growth parameters; residue decomposition
parameters; effects of tillage implements on soil properties and residue amounts;
slope shape, steepness, and orientation; and soil credibility parameters. Parameters
used for predicting erosion, including soil roughness, surface residue cover, canopy
height, canopy cover, and soil moisture are updated on a daily basis. The basic
output from WEPP consists of runoff and erosion summary information, which can
be produced on a storm-by-storm, monthly, annual, or average annual basis. The
time-integrated estimates of runoff, erosion, sediment delivery, and sediment enrich-
ment are contained in this output, as well as the spatial distribution of erosion.
Tillage impacts on various soil properties and model parameters are simulated
within the soils component of the WEPP model. Tillage activity during a simulation
results in a decrease in soil bulk density, increase in soil porosity, changes in soil
roughness and ridge height, rill destruction, increased infiltration, and changes in
credibility parameters. WEPP simulates consolidation, including its impacts on soil
parameters due to time and rainfall after tillage. The plant growth component in
WEPP predicts potential growth based on daily heat accumulation, with actual
growth decreased depending on moisture and temperature stress, and actual nitro-
gen uptake. Several types of residue management may be represented in WEPP
including residue removal, shredding, burning, and contact herbicide application.
The most recent release of WEPP (version 95.7) allows watershed-scale simulation.
The watershed simulation component combines results from each field (or hillslope)
and routes sediment and runoff through channels and impoundments in the water-
shed. Additional inputs required for watershed applications include channel soils,
topography, and hydraulic characteristics, and specification of impoundments if
present.
Tiscareno-Lopez et al. (1993, 1994) present the results of a sensitivity analysis of
WEPP for rangeland applications. Elliot et al. (1995) used WEPP to simulate erosion
losses from timber harvest areas. They concluded that the model showed consider-
able promise as a tool to help forest managers predict the onsite erosion and offeite
sedimentation due to timber harvest.
The WEPP model can be obtained by writing to Dennis Flanagan, USDA-ARS-NSERL,
1196 Building SOIL, Purdue University, West Lafayette, IN 47907-1196 or contacting
him at (317) 494-8673 or Flanagan@soils.ecn.purdue.edu.
The use of models to predict a receiving waterbody's response to various pollutant
loading scenarios is often an important aspect of watershed assessment and TMDL
development. Receiving water models are used to examine the interactions between
loadings and response, evaluate loading capacities (LCs), and test various loading
scenarios. As with watershed loading models, receiving water models vary widely in
complexity. For traditional point source abatement, where biodegradable pollutant
discharges are the major concern, simple, steady-state models of the dissolved
oxygen (DO) balance are commonly used by planners and pollution control authori-
ties. For assessment of eutrophication and toxics, more comprehensive models have
evolved to incorporate a wider range of processes. Other recent reviews of receiving
water models include Ambrose et al. (1995). For additional sources of information
related to receiving water models, refer to the list of references in Table 1.
A fundamental concept for the analysis of receiving waterbody response to point and
nonpoint source inputs is the principle of mass balance (or continuity). Receiving
water models typically develop a mass balance for one or more interacting constitu-
ents, taking into account three factors: transport through the system, reactions
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Compendium of Tools for Watershed Assessment and TMDL Development
Receiving water models
are used to examine the
interactions between
loadings and response,
evaluate loading capacities
(LCs), and test various
loading scenarios.
within the system, and inputs into the system. The first factor describes the hydro-
logic and hydrodynamic regime of the water system; the second, the biological,
chemical, and physical reactions that affect constituents; and the third, the inputs to
or withdrawals from the system because of anthropogenic activities and natural
phenomena (O'Conner et al., 1975). The complexity of a receiving water model
depends on the way in which these three factors are incorporated. The simplest
models use a steady-state, one-dimensional framework with steady inputs. The more
complex models typically use hydrodynamic relationships, consider interactions
between constituents, allow distributed nonpoint inputs, and are capable of provid-
ing dynamic, multidimensional simulations.
The various physical, chemical, and biological processes considered by a receiving
water model are represented mathematically by mechanistic and/or empirical
relationships between forcing functions and state variables (Jorgensen, 1989).
Forcing functions are variables or functions of an external nature that are regarded
in the model formulation as directly influencing the state of the receiving waterbody.
Point and nonpoint source loadings to the waterbody are examples of forcing func-
tions; other examples are temperature and solar radiation. State variables, such as
DO and chlorophyll a concentrations, define the state of the receiving waterbody.
When the predicted values of state variables change because of changes to forcing
functions, the state variables are regarded as model outputs. In the context of TMDL
development, the typical situation would involve manipulating forcing functions that
are controllable (e.g., point source loadings and, to an extent, nonpoint source
loadings) and observing the effect on state variables of interest.
Receiving water models are typically described in terms of their representation of
space (spatial domain), time (temporal domain), flow simulation (hydrodynamics),
transport processes, inputs (forcing functions), and state variables. Other factors
considered in the review of receiving water models include user interface and
inherent application complexity. For discussion purposes, receiving water models are
grouped into three classes—hydrodynamic models, steady-state water quality
models, and dynamic water quality models. The features and evaluation criteria for
each class are discussed below. Brief descriptions of each of the models included in
each type of model follow this section. Near-field models, or mixing zone models,
are briefly discussed in a separate section. Summary tables are not included for these
models, although fact sheets are provided in Appendix B.
Hydrodynamic models. Surface water flow is fundamental to the simulation of
pollutant transport and transformation in waterbodies. Some of the key physical factors
affecting the health of a waterbody include the quantity and velocity of flow. Hydrody-
namic models simulate the "dynamic" or time-varying features of water transport. For
impoundments (lakes and reservoirs), the period of time the water is held within the
system (or retention time) affects eutrophication and toxic-related processes. For estua-
rine systems, mixing and flushing due to tidal influences and external freshwater inputs
are essential to understanding internal processes.
Hydrodynamic models can potentially represent the features of water movement in
rivers, streams, lakes, reservoirs, estuaries, near-coastal waters, and wetland systems.
Depending on the type of system and the model capabilities, spatial dimensions of the
simulation can include 1-D longitudinal, 2-D in the vertical, 2-D in the horizontal, or
fully 3-D formulations. Some 3-D models can be effectively "collapsed" to simulate
systems as 1-D or 2-D. Hydrodynamic models employ numerical solutions of the funda-
mental governing equations in order to predict water movement based on bottom
topography and shoreline geometry. Higher-order hydrodynamic models represent
systems as a cartesian grid or a curvilinear orthogonal grid (Mobley and Stewart, 1980;
Ryskin and Leal, 1983). Grid generation software can facilitate the interpolation of
spatially varying model input data, such as bottom topography, initial water depth, and
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Chapter 2. Review of Selected Loading and Receiving Water Models
Hydrodynamic models
simulate die "dynamic" or
time-varying features of
water transport.
bottom roughness, into cell inputs. Physical processes that maybe included in hydrody-
namic models include tidal, wind, and buoyancy or density forcing, and turbulent
momentum and mass transport. For shallow systems, such as some estuaries or wet-
lands, the ability of the model to represent wetting and drying is essential. The represen-
tation of vegetation resistance below and above the water surface can also be important
in shallow surface water systems. The representation of flow control by hydraulic
structures may need to be included for reservoir or managed river systems. Water
balance components such as rainfall, evaporation, infiltration, and groundwater interac-
tions can affect systems as well. For impounded waters, the ability of the model to
perform thermal simulation with surface heat exchange is also desirable.
Some hydrodynamic models (RIVMOD, DYNHYD5, EFDC, CH3D-WES) are distrib-
uted as stand-alone models and can be externally coupled with water quality models
such as WASPS and CE-QUAL-ICM. Other hydrodynamic models are internally
coupled, or connected, to the water quality and toxic simulation programs. The CE-
QUAL-RIV1 and CE-QUAL-W2 models are examples of internally coupled formula-
tion. Table 5 describes the key features of both the stand-alone hydrodynamic
models and the internally coupled models. Models that are limited to steady-state
(no variation in time) applications are included in the water quality modeling
section. A review of the table shows that capabilities vary widely in terms of dimen-
sion. For river modeling, a 1-D formulation is usually sufficient, although for certain
applications (e.g., sediment transport) 2-D horizontal models have been used.
Modeling of lakes is typically limited to 2-D vertical (x/z) models except in rare
cases, such as shallow, well-mixed lakes, where a 1-D representation is sufficient.
Estuaries are most frequently simulated using fully 3-D hydrodynamic grids to
account for the complex mixing and transport processes.
Water quality models. Water quality models can simulate the chemical and
biological processes that occur within a waterbody system, based on external and
internal inputs and reactions. For more detailed information on water quality model-
ing for nutrients and eutrophication, refer to USEPA, 1995. Eutrophication models
include those which simulate biological inputs, nutrients, and algal growth in rivers,
streams, lakes, reservoirs, and estuaries. Other receiving water models specialize in
the simulation of toxic constituents and their transformation and degradation in
waterbodies.
Water quality models can also be grouped by how they address changes over time. As
mentioned above, some models employ a steady-state formulation for simulation
purposes. Typical steady-state applications include use of design flow, or preselected
critical conditions, for the assessment of steady-state water quality impacts. Steady-
state formulations are the most commonly used and the easiest to implement.
However, steady-state applications are limited when addressing time-variable inputs
such as nonpoint source loads or examining waterbodies that experience short-term
violations of acute criteria (e.g., storm or CSO events).
For more detailed assessments of time-varying conditions in receiving waters, water
quality models can be linked with hydrodynamic models. As discussed earlier,
hydrodynamic models are either internally or externally coupled to water quality
models for dynamic simulations of receiving waters. The use of dynamic water
quality models allows for a more detailed evaluation of time-varying inputs, such as
nonpoint sources, and the examination of the short- and longer-term receiving water
response. Fully dynamic applications require a significant level of effort in order to
prepare data input files; set up, calibrate, and validate the model; and process output
data. Dynamic models can also be applied to steady-state conditions. In some cases,
because of their detailed algorithms and capabilities, dynamic models are used in
steady-state applications for testing and analysis of constituent interactions.
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Compendium of Tools for Watershed Assessment and TMDL Development
Table 5. Evaluation of Capabilities—Hydrodynamic models
Externally Coupled Models Internally Coupled Models
RIVMOD
DYNHYD5
EFDC
CH3D-WES
CE-QUAL-RIV1
CE-QUAL-W2
HSPF
Waterbody Type
Rivers/Streams
Lakes/Reservoirs
•
O
•
O
•
O
•
•
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Dimension O 9
1-D
2-D
3-D
•
-
-
•
-
-
•
-
-
•
•
O
.
Input Data Requirements
Requirements
Calibration
Grid generation/Interface
O
•
O
•
O
O
•
e
•
o
0
Output Data
Format options
Graphics
Hydrologic Structure
Simulation
Expertise Required
for Application
Documentation
•
O
•
O
•
•
9
O
O
•
•
O
•
•
•
O
O
•
•
•
•
O
•
O
•
•
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•
o
•
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®
«
i High
Medium
O Low - Not Incorporated
Water quality models
can simulate the chemical
and biological processes
that occur within a
waterbody system, based
on external and internal
inputs and reactions.
In addition to the physical/hydrologic essentials discussed above, the principal
differentiating factors for characterizing water quality models is how they address
the processes of advection, dispersion, and reaction. Advection is the primary
transport mechanism in a downstream and/or lateral direction. Advective transport
is often the dominant net transport mechanism, except in certain tidally mixed
systems. Dispersive transport represents mixing (lateral and longitudinal) caused by
local velocity gradients. Although dispersive transport is present to some extent in
all bodies of water, it is typically minimal in rivers, lakes, and reservoirs. Dispersive
transport can dominate, however, in tidally mixed systems. Reactions include the
processes and transformation of constituents within a waterbody. For eutrophication
models, temperature, oxygen, and nutrient cycling processes, and in some cases carbon
cycling, phytoplankton, periphyton, and aquatic plants, are considered. For assessment
of toxics, models can include transformation, speciation, and degradation of constitu-
ents. For both eutrophication and toxics, the interactions of constituents with the bottom
sediments are of concern. In some cases users can define fluxes from the bottom sedi-
ments. Other models use sophisticated simulations of sediment diagenesis. Modelers
continue to develop and link improved models of sediment diagenesis to water quality
models (e.g., CE-QUAL-ICM).
Tables 6 and 7 present a summary of the key features of water quality models. Steady-
state and dynamic models are grouped separately for review purposes. Short descriptions
of each of the models discussed are presented in the following sections under the sub-
headings of hydrodynamic models, steady-state water quality models, and dynamic
water quality models. Fact sheets for the specific models are provided in Appendix B.
River Hydrodynamics Model (RTVMOD-H) (fact sheet, page B.31). RIVMOD-
H is the hydrodynamic submodel of a dynamic sediment transport model (Hosseinipour
et al., 1994). The model provides predictions of gradually or rapidly varying flow in
water bodies which can be regarded as one-dimensional. RTVMOD-H is based on a
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Chapter 2. Review of Selected Loading and Receiving Water Models
model originally developed in the mid-1970s (Amein and Chu, 1975), which was
modified to accept time-varying lateral inflows by Brown and Hosseinipour (1991). The
governing flow equations are solved using a numerically efficient fully implicit scheme
that allows the use of longer computational time steps. RIVMOD-H has been soft-linked
to the WASPS model to provide hydrodynamic flow computations as part of the LWMM
modeling system (Dames and Moore, 1994). Warwick and Helm (1995) provide a
comparison of the RIVMOD-H and DYNHYD5 models.
2.5.1 Link-node tidal hydrodynamic model (DYNHYD5) (fact sheet, page B.13).
Models DYNHYD5 is a one-dimensional model that uses the relatively simple link-node
concept to represent a waterbody (Ambrose et al., 1987). The link-node representa-
tion is best applied to branching systems such as tidal rivers. The model solves the
one-dimensional equations of continuity and momentum describing the movement of
a long wave in a shallow water system. The model is distributed as a companion
model to WASPS and is typically applied externally to provide hydrodynamic flow
computations, which are then input to WASPS. Most applications of DYNHYD5 will
use a simulation time step on the order of 30 seconds to 5 minutes due to stability
requirements. However, the hydrodynamic output file created by DYNHYD5 may be
stored at any user-specified interval for use by a water quality simulation program.
This interval may range from 1 to 24 hours, depending on the type of water quality
simulation desired. If interest is focused on tide-induced transport, a 1- to 3-hour
interval should be used. On the other hand, with long-term simulations, a time
interval of 12 to 24 hours would be appropriate (Tetra Tech, 1995).
Environmental Fluid Dynamics Computer Code (EFDC) (fact sheet, page
B. 17). EFDC is a general-purpose three-dimensional hydrodynamic and salinity numeri-
cal model (Hamrick, 1992). The model maybe applied to a wide range of boundary-
layer-type environmental flows that can be regarded as vertically hydrostatic. The model
code uses a finite-difference scheme to solve the equations of motion and transport,
simulating density and topographically induced circulation, as well as tidal and wind-
driven flows, and spatial and temporal distributions of salinity, temperature, and
sediment concentration. In addition, the wetting and drying of shallow areas, hydraulic
control structures, vegetation resistance for wetlands, and Lagrangian particle tracking
may also be simulated by the model. EFDC has been integrated with a water quality
model to develop a three-dimensional hydrodynamic-eutrophication model, HEM-3D
(Park et al., 1995). The model was used to develop a three-dimensional hydrodynamic
and salinity numerical model of the Indian River Lagoon/Turkey Creek, with calibration
and validation for St. Johns river water management district, Palatka, Florida (Tetra
Tech, 1994). The EFDC model was linked to WASPS for application to the Norwalk
Harbor estuary in Norwalk, Connecticut, for the purposes of developing a TMDL
(Stoddardetal.,1995).
Curvilinear Hydrodynamics in Three-Dimensions-Waterways Experiment
Station (CH3D-WES) (fact sheet being prepared, page B.7). CH3D-WES was
developed as part of the Chesapeake Bay Model Package described by Cerco and Cole
(1993). The model is derived from the CH3D model (Sheng, 1986) and uses a general
curvilinear horizontal grid and a physical (Cartesian) vertical grid to provide computa-
tions of water surface, three-dimensional velocity field, salinity, and temperature. The
governing equations are solved using a numerically efficient finite-difference scheme
described by Johnson etal. (1991). The CH3D-WES includes consideration of the
physical processes of tides, wind, freshwater inflows, turbulence, density effects (salinity
and temperature), and the effect of the earth's rotation. The vertical turbulence algo-
rithms included in the model provide improved representation of stratification and
destratification in complex waterbodies such as the Chesapeake Bay. Johnson et al.
(1993) describe the validation of CH3D-WES in an application to six data sets from the
Chesapeake Bay.
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Compendium oflboJsfbr Watershed Assessment and TMDL Development
42
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Chapter 2. Review of Selected Loading and Receiving Water Models
2.5.2
Steady-state water
quality models
EPA Screening Procedures (fact sheet, page A.9). The EPA Screening Proce-
dures are a compilation of simplified methodologies that allow preliminary assess-
ment of conventional and toxic pollutants in rivers, impoundments, and estuaries
(Mills et al., 1985). Additionally, methods are included to calculate initial dilution
from a wastewater discharge. The compilation includes introductory material for
each of the methodologies to provide orientation toward relevant theory, and to state
limitations of the methodologies due to assumptions and simplifications. Conventional
pollutants considered in the screening procedures are BOD-DO, temperature, coliform
bacteria, nutrients, and sediment transport. The fate of toxics is assessed considering
volatilization, sorption, and first-order degradation. The EPA Screening Procedures
can be implemented using a hand-held calculator or spreadsheet program. Bowie et
al. (1985) provide a comprehensive source of information on rate constants and
coefficients that maybe used in applying the screening procedures.
Watershed and Lake Modeling Software (EUTROMOD) (fact sheet, page
B.19). EUTROMOD is a spreadsheet-based modeling procedure for eutrophication
management developed at Duke University and distributed by the North American
Lake Management Society (Reckhow, 1990). The steady-state modeling system allows
for internal calculations of both nonpoint source loading and lake response. The
system estimates nutrient loadings, various trophic state parameters, and
trihalomethane concentrations in lake water. The computation algorithms used in
EUTROMOD were developed based on statistical relationships and a continuously
stirred tank reactor model. Model results include the most likely predicted phosphorus
and nitrogen loading for the watershed and for each land use category. The model
also determines the lake response to various pollution loading rates. The spreadsheet
capabilities of the model allow graphical representations of the results and data
export to other spreadsheet systems for statistical analyses. The model was used in
conjunction with a GIS for establishing TMDLs to Wister Lake, Oklahoma (Hession et
al., 1995).
Table 7. Evaluation of Capabilities—Dynamic Water Quality Models
Water Body Type
Rivers/Streams
Lakes/Reservoirs
Estuaries
Coastal
Physical Processes
Advection
Dispersion
Heat Balance
Particle Fate
Eutrophication
Chemical Fate
Sediment-Water Interactions
External Loading-Dynamic
Internally Calculated NFS
Loading
User Interface
Documentation
DYNTOX
•
-
-
-
•
-
-
-
o
-
o
-
•
0
WASPS
•
0
•
e
•
•
-
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•
•
o
•
-
o
•
CE-QUAL-R1
•
-
-
-
•
•
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«
o
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o
-
-
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CE-QUAL-W2
•
•
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•
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•
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CE-QUAL-ICM
-
-
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HSPF
•
O
-
-
•
-
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•
• High
Medium
O Low
- Not Incorporated
-------
Compendium of Tbols for Watershed Assessment and TMDL Development
Seasonal and Long-term Trends of Total Phosphorus and Oxygen in
Stratified Lakes (PHOSMOD) (fact sheet, page B.25). PHOSMOD is a budget
model that can predict the long-term response of a lake to changes in phosphorus
loading (Chapra and Canale, 1991). In the model, the lake is treated as two layers: a
water layer and a surface sediment layer. A total phosphorus budget for the water
layer is developed with inputs from external loading and recycling from the sedi-
ments and considering losses due to flushing and settling. In the sediment layer
budget, total phosphorus is gained by settling and lost by recycling and burial. The
sediment-to-water recycling is dependent on the levels of sediment total phosphorus
and hypolimnetic oxygen, with the concentration of the latter estimated with a semi-
empirical model. Chapra and Canale (1991) present an application of the model and
an analysis to demonstrate how the model predictions replicate in-lake changes not
possible with simpler phosphorus budget models.
BATHTUB (fact sheet, page B.23). FLUX, PROFILE, and BATHTUB (Walker, 1986)
are a collection of programs designed to assist in the data reduction and model imple-
mentation phases of eutrophication studies in lakes and reservoirs. FLUX is a tool for
data reduction and preprocessing of tributary nutrient loadings from grab sampling and
flow records. The program can assist in error detection and sampling program design.
PROFILE provides displays of lake water quality data and assists in analysis of sampling
information. Data analysis procedures include hypolimnetic oxygen depletion rates,
spatial and temporal variability, and statistical summaries. BATHTUB allows the user to
segment the lake into a hydraulic network. Nutrient balance and eutrophication models
can be applied to the network to assess advection, dispersion, and nutrient sedimenta-
tion. Empirical relationships that have been calibrated and tested for reservoir applica-
tions are used to predict eutrophication-related water quality conditions. The segmented
structure of BATHTUB allows its application to single reservoirs, partial reservoirs,
networks of reservoirs, or collections of reservoirs, permitting regional comparative
assessments of reservoir conditions, controlling factors, and model performance. Inputs
and outputs can be expressed in probabilistic terms to account for limitations in input
data and intrinsic model errors. The programs and models have been applied to U.S.
Army Corps of Engineer reservoirs (Kennedy, 1995), as well as a number of other lakes
and reservoirs. BATHTUB was recently cited as an effective tool for lake and reservoir
water quality assessment and management, particularly where data are limited (Ernst et
al., 1994).
Enhanced Stream Water Quality Model (QUAL2E) (fact sheet, page B.29).
QUAL2E, originally developed in the early 1970s, is a one-dimensional water quality
model that assumes steady-state flow but allows simulation of diurnal variations in
temperature or algal photosynthesis and respiration (Brown and Barnwell, 1987).
QUAL2E represents the stream as a system of reaches of variable length, each of which is
subdivided into computational elements that have the same length in all reaches.
Withdrawals, branches, and tributaries can be incorporated into the prototype represen-
tation of the stream system. The basic equation used in QUAL2E is the one-dimensional
advection-dispersion mass transport equation. An implicit, backward difference scheme,
averaged over time and space, is employed to solve the equation. Water quality constitu-
ents simulated include conservative substances, temperature, bacteria, BOD, DO,
ammonia, nitrate and organic nitrogen, phosphate and organic phosphorus, and algae.
QUAL2E includes components that allow quick implementation of uncertainty analysis
using sensitivity analysis, first-order error analysis, or Monte Carlo simulation. The
model has been widely used for stream wasteload allocations and discharge permit
determinations in the United States and other countries. Paschal and Mueller (1991) used
QUAL2E to evaluate the effects of wastewater effluent on the South Platte River from
Chatfield reservoir through Denver, Colorado. Cubilo et al. (1992) applied QUAL2E to
the major rivers of the Comunldad de Madrid in Spain. Little and Williams (1992)
describe a nonlinear regression programming model for calibrating QUAL2E. Johnson
-------
Chapter 2. Review of Selected Loading and Receiving Water Models
and Mercer (1994) report a QUAL2E application to the Chicago waterway and Upper
Illinois River waterway to predict DO and other constituents in the DO cycle in response
to various water pollution controls. EPA's Office of Science and Technology (OST) has
recently developed a Microsoft Windows-based interface for QUAL2E that facilitates data
input and output evaluation.
Exposure Analysis Modeling System (EXAMSH) (fact sheet, page B.21).
EXAMSII (Burns, 1990) is an interactive modeling system that uses the principle of
mass balance and mathematical models of the kinetics and processes governing the
transport and transformation of chemicals to provide predictions of their probable
fate and persistence in aquatic ecosystems. EXAMSII is designed to evaluate the fate,
exposure, and persistence of toxic chemicals in water systems where the concentra-
tions of pollutants are at trace levels and the pollutant loading rates can be assumed
to be at steady state. The hydrologic transport processes considered are advection
and dispersion. The transformation processes included in the model are photolysis,
hydrolysis, biotransformation, oxidation, and sorption with sediments and biota.
Secondary daughter products and subsequent degradation of these products are also
considered. The interactive nature of EXAMSII and the ability of the modeling
system to store and easily modify previous inputs allows rapid and convenient
analysis of chemical fate and transport in aquatic ecosystems.
TOXMOD (fact sheet, page B.35). TOXMOD is based on an extension of a model-
ing framework presented by Chapra (1991) to assess the impact of toxic organic com-
pounds on lakes and impoundments. As in PHOSMOD, the receiving waterbody is
idealized as a lumped system consisting of a well-mixed reactor (water layer) underlaid
by a well-mixed sediment layer. A steady-state mass balance is developed for solids and
the toxic. The toxic is partitioned into dissolved and particulate forms, with the dis-
solved form for both water and sediment layers further subdivided into a component
associated with dissolved organic carbon. Particulates in the water layer are subdivided
into abiotic and biotic suspended solids. Burial and resuspension are considered for both
dissolved and particulate forms while diffusion acts selectively on the dissolved fraction.
Chapra (1991) has used the modeling framework on which TOXMOD is based to
develop a procedure for identifying priority pollutants that exhibit the weakest assimila-
tive capacity for a range of lakes.
Simplified Method Program - Variable Complexity Stream Toxics Model
(SMPTOX4) (fact sheet, page B.33). SMPTOX4 is a one-dimensional, steady-state
model based on an EPA-recommended technique (USEPA, 1980) for calculating water
column and streambed toxic substance concentrations caused by point source discharges
into streams and rivers. Three levels of complexity are available within the model. At the
simplest level, only total toxic pollutants can be predicted. The next level can be used to
predict toxic water column concentrations, but interactions with bed sediments are not
considered. The third level allows prediction of pollutant concentrations in dissolved and
particulate phases for the water column and bed sediments, as well as the total sus-
pended solids concentrations. Operating within a Windows environment, SMPTOX4
allows quick data input and easy access to routines for graphical output, sensitivity
analysis, and uncertainty analysis. SMPTOX4 also contains a data base of chemical
properties for many chemicals of concern.
i
Tidal Prism Model (TPM) (fact sheet, page B.37). TPM was originally developed
as a tool for water quality management of small coastal basins (Kuo and Neilson,
1988). Physical transport processes are simulated in terms of the concept of tidal
flushing. The numerical solution scheme implemented for solving the tidal flushing
equations is well suited to application in small coastal basins, including those with a
high degree of branching (Kuo and Park, 1994). The model allows consideration of
shallow embayments connected to the primary branches in the basin. The basic assump-
tions in the model are that the tide rises and falls simultaneously throughout the
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Compendium of Tools for Watershed Assessment and TMDL Development
2.5.3
Dynamic water quality
models
waterbody and that the system is in hydrodynamic equilibrium. Kinetic formulations in
TPM are similar to those in CE-QUAL-ICM (Cerco and Cole, 1993), and 23 state vari-
ables, including total active metal, fecal coliform bacteria, and temperature, can be
simulated. TPM includes a sediment submodel, also based on the sediment process
model in CE-QUAL-ICM, that considers the depositional flux of particulate organic
matter, its diagenesis, and the resulting sediment flux. TPM has been applied to a
number of tidal creeks and coastal embayments in Virginia (Kuo and Neilson, 1988).
Simplified Deposition Calculation for Organic Accumulation Near
Marine Outfalls (DECAL) (fact sheet, page B.ll). DECAL is a steady-state
analytical model of sediment deposition for coastal areas impacted by outfalls (Farley,
1990). The model predicts metal and trace organic chemical accumulations in sediments
near municipal ocean outfalls. DECAL considers coastal transport, particle dynamics,
and organic carbon cycles. The model simulates the effects of coagulation and settling of
effluent particles and natural organic material; however, bioturbation and sediment
diagenesis of organic carbon are not included. Sediment-water exchange can be consid-
ered using a coefficient. When the coefficient is specified in units of reciprocal time,
DECAL computes the flux of organic matter and a trace constituent to sediments. If the
coefficient is specified in units of length per time, the accumulation of organic matter
and trace constituent in bed sediments is computed. Short-term current speeds and
directions and the components of the tidal ellipse, as well as long-term net advection
currents and directions, have to be specified by the user. An application of DECAL to
outfalls in Orange and Los Angeles counties in California showed model predictions
agreed quite well with field observations (Farley, 1990).
Dynamic Toxics wasteload allocation model (DYNTOX) (fact sheet, page
B.15). DYNTOX was developed for use in wasteload allocation of toxic substances
(Limno-Tech, 1994). The fundamental analytical solution used in DYNTOX assumes a
steady-state condition over the course of one day. The model provides a probabilistic
framework for assessing toxic discharge impacts over a range of historical and future
conditions. Three probabilistic simulation techniques can be used to calculate the
frequency and severity of instream toxicity at different effluent discharge levels. In the
continuous simulation approach, the model is run for a specified period of recorded
history and the results are analyzed for frequency and duration. In the Monte Carlo
method, inputs are described by probability distributions. Random input sets are then
used to execute the model repeatedly and describe the model output in terms of a
probability distribution. Both the continuous simulation and Monte Carlo methods
produce probability distributions of calculated daily downstream concentrations from.
which the recurrence interval of any concentration of interest can be obtained. Probabil-
ity distributions of running-averaged concentrations for anytime period of interest can
also be obtained. The lognormal analysis requires that all inputs be described by
lognormal distributions, which allows computation of exceedance probabilities for the
toxic concentration at the point of mixing through numerical integration.
Water Quality Analysis Simulation Program (WASPS) (fact sheet, page
B.39). WASPS is a general-purpose modeling system for assessing the fate and transport
of conventional and toxic pollutants in surface waterbodies (Ambrose, 1987). WASPS
has a modular structure and allows the incorporation of specialized user-written routines
into its computational structure. The model can be applied in one, two, or three dimen-
sions and is designed for linkage with the link-node hydrodynamic model DYNHYD5 for
dynamic simulation purposes. WASPS has also been successfully linked with other
hydrodynamic programs such as RIVMOD (Dames and Moore, 1994) and EFDC
(Stoddard et al., 1995). WASPS includes two submodels for water-quality/eutrophication
and toxics, referred to as EUTRO5 and TOXI5, respectively. In EUTRO5, the transport
and transformation of up to eight state variables in the water column and sediment bed
can be simulated. These state variables include dissolved oxygen, carbonaceous BOD,,
phytoplankton carbon and chlorophyll a, ammonia, nitrate, organic nitrogen, organic
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Chapter 2. Review of Selected Loading and Receiving Water Models
phosphorus, and orthophosphate. In TOXI5 the transport and transformation of one to
three chemicals and one to three types of particulate material can be simulated. A
significant advantage of the WASPS system is that the EUTRO5 and TOXI5 submodels
can be used at variable levels of complexity by considering different processes, variables,
and computations. WASPS requires the user to input information on geometry, advective
and dispersive flows (from hydrodynamic model or user), settling and resuspension
rates, boundary conditions, external loadings (point and nonpoint source), and initial
conditions. The waterbody is divided into a series of segments for simulation purposes.
The WASP modeling system has been used in a wide range of regulatory and water
quality management applications for rivers, lakes, and estuaries. Lang and Fontaine
(1990) describe an application to predict the transport and fate of organic contami-
nants in Lake St. Glair, Michigan. Cheng et al. (1994) describe the development and
application of a CIS-based modeling framework using a watershed loading model
and WASE Lu et al. (1994) used the model to simulate the transport and fate of DO,
BOD and organic nitrogen in untreated wastewater discharges in Weeks Bay, Ala-
bama. Lung and Larson (1995) used EUTRO5 to evaluate phosphorus loading
reduction scenarios for the Upper Mississippi River and Lake Pepin. Cockrum and
Warwick (1994) used WASP to characterize the impact of agricultural activities on
instream water quality in a periphyton dominated stream. Stoddard et al. (1995)
describe a fully three-dimensional application of EUTRO5 in conjunction with the
EFDC hydrodynamic model to assess the effectiveness of total nitrogen removal
options from a wastewater treatment plant.
Hydrodynamic and Water Quality Model for Streams (CE-QUAL-RIV1)
(fact sheet, page B.3). CE-QUAL-RIV1 is a dynamic, one-dimensional (longitudinal)
water quality model for unsteady flows in rivers and streams (Zimmerman and Dortch,
1989). The model has two submodels for hydrodynamics (RIV1H) and water quality
(RIV1Q). Output from the hydrodynamic solution is used to drive the water quality
model Water quality constituents include temperature, dissolved oxygen, carbonaceous
BOD, organic nitrogen, ammonia nitrogen, nitrate nitrogen, orthophosphate phospho-
rus, coliform bacteria, dissolved iron, and dissolved manganese. The effects of algae and
macrophytes can also be included as external forcing functions specified by the user. CE-
QUAL-RJV1 allows simulation of branched river systems with multiple hydraulic control
structures such as run-of-the-river dams, waterway locks and dams, and reregulation
dams. The model was developed to simulate the transient water quality conditions
associated with unsteady flows that can occur on highly regulated rivers. Zimmerman
and Dortch (1989) applied the model to provide examples of potential water quality
impacts associated with operation alternatives for a regulation dam proposed for
construction downstream from Buford Dam on the Chattahoochee River near Atlanta,
Georgia. The RIV1Q component of CE-QUAL-RIV1 was used to develop statistical
relationships to allow prediction of downstream water temperatures associated with
different operational scenarios (Nestler et al., 1993a).
Two-dimensional, Laterally Averaged, Hydrodynamic and Water Quality
Model (CE-QUAL-W2) (fact sheet, page B.5). CE-QUAL-W2 is a two-dimensional,
laterally averaged hydrodynamic and water quality model (Cole and Buchak, 1994). CE-
QUAL-W2 is best applied to stratified waterbodies like reservoirs and narrow estuaries
where large variations in lateral velocities and constituents do not occur. The water
quality and hydrodynamic routines are directly coupled; however, the water quality
routines can be updated less frequently than the hydrodynamic time step, which can
reduce the computation burden for complex systems. The model simulates the interac-
tion of physical factors (such as flow and temperature regimes), chemical factors (such
as nutrients), and algal interactions. The constituents are arranged in four levels of
complexity, permitting flexibility in model application. The first level includes materials
that are conservative and noninteractive, or do not affect other materials in the first
level. The second level allows the user to simulate the interactive dynamics of oxygen-
phytoplankton-nutrients. The third level allows simulation of pH and carbonate species,
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Compendium of Tools for Watershed Assessment and TMDL Development
and the fourth level allows simulation of total iron. The model has been applied to
rivers, lakes, reservoirs, and estuaries (Hall, 1987; Martin, 1988). Barnese and
Bohannon (1994) report initial efforts to apply CE-QUAL-W2 to Taylorsville Lake in
Kentucky.
Three-dimensional, Time-variable, Integrated-compartment Eutrophica-
tion Model (CE-QUAL-ICM) (fact sheet, page B.I). CE-QUAL-ICM was developed
as the integrated-compartment eutrophication model component of the Chesapeake Bay
model package (Cerco and Cole, 1993), which also includes a three-dimensional
hydrodynamic component and a sediment-diagensis model. The model incorporates
detailed algorithms for water quality kinetics. Interactions among the state variables are
described in 80 partial differential equations that employ over 140 parameters (Cerco
and Cole, 1993). The state variables can be categorized into a group and five cycles—
the physical group and the carbon, nitrogen, phosphorus, silica, and dissolved oxygen
(DO) cycles. An improved finite-difference formulation is used to solve the mass conser-
vation equation for each grid cell and for each state variable. CE-QUAL-ICM was
coupled with the three-dimensional hydrodynamic and benthic-sediment model compo-
nents of the Chesapeake Bay model package to develop a state-of the-art 3-D model of
the Chesapeake Bay (Cerco and Cole, 1993). The model was employed to simulate long-
term trends in Chesapeake Bay eutrophication (Cerco, 1995). Mark et al. (1992) used
CE-QUAL-ICM to assess the water quality impacts of a confined disposal facility in Green
Bay, Wisconsin.
The Hydrological Simulation Program - FORTRAN (HSPF) (fact sheet,
page A.15). HSPF is a comprehensive modeling system for simulation of watershed
hydrology, point and nonpoint loadings, and receiving water quality for both conven-
tional pollutants and toxicants (Bicknell et al., 1993). The receiving water compo-
nent allows dynamic simulation of one-dimensional stream channels with several
hydrodynamic routing options available. The eutrophication/water quality routines
simulate BOD-DO interactions, temperature, and phytoplankton dynamics as affected
by nutrients and organic material. The toxics routines combine organic chemical
process kinetics with sediment balance algorithms to predict dissolved and sorbed
chemical concentrations in the upper sediment bed and overlying water column. A
data preprocessing and expert system have been developed to support model input
file and meteorologic data file preparation (Lumb et al., 1990; Lumb and Kittle,
1993). Chen et al. (1995) described the development of a updated heat balance compo-
nent for HSPF and initial model application for water balance and stream temperature
simulation in Oregon. HSPF is being used by the Chesapeake Bay Program to model
total watershed contributions of flow, sediment, nutrients, and associated constituents to
the tidal region of the Bay (Donigian et al., 1990; Donigian and Patwardhan, 1992).
Ball et al. (1993) describe an application of HSPF in Australia.
2.5.4 Mixing zone models are often described as "near-field" models; they assess limited areas
Mixing zone models °^ conteminant mixing in the vicinity of a wastewater discharger. Mixing zone models
can be used in the development of discharger permits, and as part of this process can be
applied during TMDL development. Although some of the more detailed and sophisti-
cated water quality models can be configured to assess near-field impacts, several
models that specialize in evaluating local impacts have been developed. Short descrip-
tions of near field-models developed for coastal areas, rivers, and streams are provided
below.
Cornell Mixing Zone Expert System (CORMIX) (fact sheet, page B.9).
CORMIX is a series of models, embedded in an expert system shell, for the analysis,
prediction, and design of aqueous toxic or conventional pollutant discharges into
diverse waterbodies, with emphasis placed on the geometry and dilution characteris-
tics of the initial mixing (near-field) zone. The model can be used to evaluate
discharge compliance with regulatory constraints (Jones and Jirka, 1991). The
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Chapter 2. Review of Selected Loading and Receiving Water Models
Mixing zone models
assess limited areas of
contaminant mixing in the
vicinity of a wastewater
discharger.
2.6
Integrated Modeling
Systems
model can consider nonconservative pollutants with first-order decay and wind
effects on plume mixing. Submodels within the CORMIX system allow analysis of
submerged single-point discharges, submerged multiport diffuser discharges, and
buoyant surface discharges. CORMIX conveys information to the user through
qualitative descriptions and detailed quantitative numerical predictions.
PLUMES (fact sheet, page B.27). PLUMES is a model interface and manager for
preparing common input and running two initial dilution (near-field) plume models
(Baumgartner et al., 1994). Two far-field algorithms are automatically initiated
beyond the zone of initial dilution. The near-field models are relatively sophisticated
mathematical models for analyzing and predicting the initial dilution behavior of
aquatic plumes, while the far-field algorithms are relatively simple implementations of
far-field dispersion equations. PLUMES is applicable for discharges in marine and fresh
water, with multiple outfalls types, configurations, and buoyant and dense plumes.
This section discusses some of the trends in watershed and receiving water model
development and introduces a new generation of modeling systems that are just becom-
ing available to watershed managers. Current trends include four types of system
enhancements, which result in tools that are:
• Easier to use (often by the addition of Windows-based pre- and post-processors).
• Capable of linking models to each other (e.g., a loading model and a receiving
water model).
• Capable of linking models to databases (e.g., GISs);
• Built from modules that allow the user the flexibility to choose a specialized
analysis.
Although enhancements to model algorithms and methods continue and new versions of
traditional models (e.g., HSPF, SWMM, QUAL2E) continue to be released, much of the
recent research and development activity in watershed and receiving water modeling is
centered on the way the modeler interacts with the system. Interfaces under development
for models take advantage of new graphical user interfaces (GUI) and software to ease
data input, output analysis, and calibration/validation procedures. New interface shells
have been developed or are under development for some of the most widely used models,
such as SWMM, QUAL2E, and HSPF by EPA, other federal agencies, private consultants,
and universities. Most interface development efforts so far have focused on building
shells, without modifying the original model code. Future development is likely to
include newly coded models, taking full advantage of object-oriented coding procedures
and other more recent software development trends.
The advent of GIS has already profoundly affected the modeling community. CIS pro-
vides excellent capabilities for data preparation for watershed and receiving water
modeling applications. More recently, models are being tightly linked with GIS, allowing
users to modify data and analyze resulting model output within the GIS. Cell-based
models such as AGNPS and ANSWERS are well suited for linkage with GIS. Recent
research includes the development of fully integrated, grid-based, distributed models.
Limitations of the trend toward distributed models are the spatial variability and
potentially high computational requirements (Zhang et al., 1995). Examples of distrib-
uted hydrologic models include Catchment Hydrology Distributed Model (CHDM)
(Lopes, 1995), and r.hydro.CASC2D (Ogden and Saghafian, 1995). The newest hydro-
logic models, such as CASC2D, are fully linked with GIS, using a cell-based representa-
tion of the watershed system. Brigham Young University, in cooperation with the WES
Hydraulics Laboratory, has developed a hydrologic modeling preprocessor, the Water-
shed Modeling System (WMS) (Nelson etal., 1995). WMS automatically delineates the
dominant flow paths and calculates contributing areas (Nelson et al., 1995). Most fully
distributed models currently include rainfall-runoff estimation and flow routing. In the
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Compendium of Toob for Watershed Assessment and TMDL Development
future, such models might incorporate water quality simulation and pollutant trans-
port as well. GIS is also used for data preparation and output display for receiving
water models. For example, the University of Buffalo is currently developing a WASP/
GIS linkage and interface. WES and Brigham Young University are developing
FastTABS for two-dimensional analysis flow and sediment transport in rivers, streams,
and estuaries (Holland, 1993).
Similar to GIS linkages, modeling systems are being developed that manage data
systems and multiple models. Summarized below are examples of two such systems,
the Watershed Screening and Targeting Tool (WSTT), and Better Assessment Science
Integrating Point and Nonpoint Sources (BASINS). The objective of such systems is to
provide the user with a fully integrated data, analysis, and modeling framework.
Such systems allow managers to perform watershed assessment and TMDL-related
characterizations and assessments, identify data needs, and prioritize and target
resources.
The remainder of this section presents a sampling of efforts in these areas; many
more integrated systems exist throughout the country at the local and state levels.
Fact sheets have not been drafted for these models, and relevant contact information
is included with each description.
Virginia Geographic Information System (PC-VirGIS). PC-VirGIS is a fully
functional personal computer GIS, data analysis, and modeling system developed at
the Information Support Systems Laboratory (ISSL), Biological Systems Engineering
Department, Virginia Tech. The VirGIS database has approximately 20 layers of base
and derived data covering 18 million acres in Virginia. Raster files are at a 33.3-
meter cell size in the database, although cell size can be selected in PC-VirGIS. Vector
files use a standard DLG-3 optional data structure. Models in the PC-VirGIS package
can be accessed either from a menu interface or through a response file (command
line information retrieved from a database file), and some models that can be
accessed only through a response file. These models use the spatial information
contained in the VirGIS database and apply hydrologic/water quality modeling
procedures for:
• Total annual soil loss (from USLE) - cell loss rate determination
• Total annual stream nitrogen load determination
• Total annual stream phosphorus load determination
• Total annual stream sediment load (from USLE with delivery ratio) determina-
tion
• Cell sediment delivery ratio determination
• Spatial analysis models to rank stream pollutant loads
Modeling procedures for VirGIS under development and/or being field-tested include:
• Watershed Management System:
- Nonpoint source simulation models for sediment and nutrients (total
nitrogen and phosphorus) from chemical fertilizers and animal waste
delivered to stream
- Nonpoint-source-to stream entry point conversion
- Instream routing
- Simulation of stream biological status
- Calculation of Critical Site Index based on water quality goals for the
basin
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Chapter 2. Review of Selected Loading and Receiving Water Models
- Evaluation of alternative best management practice (BMP) strategies to meet
water quality goals
• Groundwater vulnerability to pesticide contamination
• Groundwater recharge modeling
• Hydrologic/water quality models: Kinetic Runoff and Erosion Model (KINEROS),
Finite Element Storm Hydrograph Model (FESHM), Penn State Runoff Quality
Model (PSRM-QUAL), and Agricultural Nonpoint Source Pollution Model (AGNPS)
Results from PC-VirGIS can be displayed in map and tabular formats. This modeling
system, although developed for Virginia, can be modified and applied elsewhere.
For more information, contact Vernon Shanholtz; MapTech, Inc.; Virginia Tech Corpo-
rate Research Center; 1872 Pratt Drive, Suite 1300-A; Blacksburg, VA 24060-6363, Ph
(540) 231-8512, FAX (540) 231-3327.
GISPLM. GISPLM is a phosphorus loading model that was developed to address
management issues in the Lake Champlain watershed (Artuso and Walker, 1997). Flows
and phosphorus loads are evaluated using dimatological data, watershed features that
are accessed via a GIS, and other local data. BMPs are defined for up to 12 land use
categories and estimates of load reduction efficiency, capital cost, and annual operating
cost (based on literature values) are available for each BMP The user can specify a
target load reduction as a percentage of the load predicted with no controls and GISPLM
will search for the spatial allocation of controls which achieves the reduction at mini-
mum cost. Estimates of capital cost and operating costs are also generated, and indi-
vidual control measures can be specifically included or excluded from the allocation
process.
Surface runoff from pervious areas in GISPLM is predicted by HYDRO, a compiled
Fortran program. Calculations are driven by daily precipitation and air temperature
data, and algorithm and parameter estimates are taken from the GWLF model (see
Section 2.3.2). LOADS, another compiled Fortran program, calculates flows and
phosphorus loads based on runoff concentrations specified as a function of land use
categories. LOADS produces an output file containing the total area, flow, load,
impervious area, curve number, and surface runoff for each subwatershed in the study
area.
The remaining calculations are performed within the GISPLM workbook (Quattro Pro
version 7.0). Flows and loads from each source category (runoff, animal units, point
sources) are totaled by model segment. Loads are adjusted to account for BMPs and
loads and flows are totaled by segment and routed downstream to the mouth of the
watershed. Empirical models (Vbllenweider, 1976; Walker, 1987) are used to estimate
the retention of phosphorus in lakes or impoundments optionally located at the down-
stream ends of segments.
Several graphical and tabular output formats that can be modified to suit project needs
are provided in GISPLM. Model results can be displayed visually using ArcView 3.0
software. Although GISPLM is configured specifically for application to the LaPlatte
River watershed in Vermont and was developed generally for Northeastern watersheds,
guidance for developing applications to other areas is provided. (Note: Peer review of
the GISPLM model at the time of publication was not yet complete.)
For more information on the GISPLM model, contact Rick Hopkins, Vermont Department
of Environmental Conservation, Water Quality, 103 South Main St, Waterbury, VT 05671-
0408, Ph (802) 241-3770, FAX (802) 241-3287.
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Compendium of Tools for Watershed Assessment and TMDL Development
Watershed Screening/Targeting Tool (WSTT). WSTT, developed by EPA's
Office of Wetlands, Oceans and Watersheds and EPA Region 4, is a screening and
targeting tool intended to help watershed managers, EPA Regions, and state agencies
evaluate and target watersheds based on specific environmental indicators. WSTT
provides an interactive, user-friendly, two-step evaluation and targeting process. The
first step allows for preliminary screening based on multiple criteria. Each criterion
can be compared with a default or user-defined reference value. Data from EPA
mainframe databases allow the user to compare reference values with land use and
water quality observations from watersheds under consideration. The second level of
targeting, comparative analysis, allows for a more detailed examination of water-
sheds using multiple objectives and criteria. This analysis also permits the user to
include subjective weights and additional data in the targeting procedure.
An additional component of WSTT is the linkage to the Watershed Screening Model
(WSM), which allows for estimation of point and nonpoint pollutant loads from the
watershed. WSM predicts runoff, streamfiow, erosion, se,diment load, and nutrient
loads for each cataloging unit modeled and presents results as graphs and tables to
show seasonality and annual variability. WSTT can prepare watershed-specific (user-
selected) input data files for use in the WSM simulation. WSTT also provides for
direct access to WSM, where users can create or modify input files using a series of
input screens. The addition of WSM to WSTT allows users to compare estimated
loads as another option in the screening and targeting process.
WSTT is made up of 5 key components (1) databases; (2) watershed selection using
maps or tables; (3) report generation (tables or graphics to screen, file, or printer);
(4) targeting options (two types); and (5) data preparation for the WSM. Databases
currently included are an accounting unit (AU)/catalog unit (CU) summary table;
land use (National Resource Inventory (NRI) summary of acres per land use cat-
egory); water quality (summarized by CU for 45 parameters); water quality station
locations; water supplies (number, flow, location, type); point sources (number, flow,
location, type); waterbodies (number, size); and WSM output.
System requirements for WSTT include an IBM-compatible PC, with a 386 or better
processor, DOS version 3.3 or higher, a SVa-inch floppy drive, an EGA/VGA/SVGA
monitor and adapter, and a hard disk with at least 7 MB of free space for program
installation.
For additional information, contact the Watershed Branch (4503F), EPA Office of
Wetlands, Oceans and Watersheds, 401M Street, SW, Washington 20460, Ph (202)
260-7074, FAX (202) 260-1977 orlaabs.chris@epamaU.epa.gov.
Linked Watershed/Waterbody Model (LWWM). The LWWM, developed by
Dames and Moore, Inc. and AScI Corporation for the Southwest Florida Water
Management District (SWFWMD), is a linked model that can be used to rapidly
evaluate and prioritize the effects of both point and nonpoint source loads on
receiving waters. The LWWM analytically obtains GIS information from ARC/INFO
coded output that is used to generate land use and soil type data by subbasin for the
RUNOFF Block of EPA's Storm Water Management Model (SWMM). This part of the
LWWM simulates storm events to predict runoff contaminant loads and water
quantity for nonpoint sources. The time series of pollutant loads and the water
quantity from SWMM are subsequently used as input for the River Hydrodynamics
and Sediment Transport Model (RIVMOD), which calculates the longitudinal
distributions of flows in a one-dimensional waterbody through time. Finally, EPA's
Water Quality Analysis Simulation Program (WASPS) incorporates loads, flow
distributions, and water quality data to simulate the movement and interaction of
pollutants in water.
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Chapter 2. Review of Selected Loading and Receiving Water Models
The information generated by the LWWM is accessible through interactive graphs and
other interfaces. System requirements for LWWM include a high-speed personal
computer (486/33 or higher), at least 40 MB of free disk space, and at least 4 MB of
total random access memory (RAM).
LWWM is a public domain model; for additional information, contact Mike Holtcamp
or Ray Kurz, SWFWMD, 7601 US Highway 301N, Tampa, FL 33637, (813) 985-7481
or michael-h%9217@etic66.dep.state.fl.us or download from the website: http://
www.det.state.fl.us/swfwmd/.
Better Assessment Science Integrating Point and Nonpoint Sources
(BASINS). BASINS is a multipurpose environmental analysis system developed by
EPA's Office of Water to help regional, state, and local agencies perform watershed-
and water quality-based studies. BASINS integrates data on water quality and quan-
tity, land uses, and point and nonpoint source loadings, with supporting nonpoint and
water quality models, providing the ability to perform comprehensive assessments of
any watershed (at the cataloging unit level) in the continental United States. The
system is distributed on CD-ROM and requires ArcView-2.1 software. BASINS has
three major modules—screening and targeting, nonpoint source modeling to estimate
loadings to receiving waters, and point-nonpoint integration.
The screening and targeting module helps the user characterize a watershed by
examining river monitoring and status data that includes: drinking water supply sites,
water quality monitoring station summaries, bacteria monitoring station summaries,
USGS gaging stations, and Permit Compliance System (PCS) sites and computed
loadings. The nonpoint source module helps the user estimate nonpoint source
loadings of nutrients, sediment, bacteria, and toxics at a cataloging unit (USGS 8-
digit) level anywhere in the country using data provided by the system. The model
predicts loadings in mixed-land-use watersheds, including agricultural, forested, and
urban areas. At the cataloging unit level, all data required for modeling are provided
by the system.
The properties of the Nonpoint Source Model (NPSM) used in BASINS are: (1) Time
step - variable or user-defined; (2) Spatial - initially, single watershed; future,
subwatersheds; (3) Pollutants - nutrient species, sediment, bacteria, and toxics; (4)
Urban - dust and dirt accumulation on impervious areas; (5) Rural - water balance
using evapotranspiration and infiltration calculation; (6) Baseflow - baseflow reces-
sion curve, optional two-stage upper and lower zone; and, (7) Output - user-defined
location and time step. The NPSM combines a Windows-based interface with EPA's
Hydrologic Simulation Program-FORTRAN model, and is linked to ArcView.
Integration of nonpoint and point source loadings in BASINS is done by TOXI-ROUTE,
a screening-level stream routing model that performs simple dilution calculations
under mean and low flow conditions for entire watersheds. The model integrates the
nonpoint source loadings described above with point source loadings, obtained from
permit data derived from the PCS. For situations that require a modeling approach
that is more detailed than the simple dilution used by TOXI-ROUTE, BASINS can use
the nonpoint and point data with EPA's QUAL2E water quality model.
BASINS was released in September 1996 and EPA is planning on annually updating
the system by adding new data, new databases, expanded state coverage, and en-
hanced modeling capabilities. For more information, contact Marjorie Coombs
Wellman or Jerry LaVeck, EPA Office of Science and Technology (4305), Standards and
Applied Science Division, 401 M Street, SW, Washington, DC 20460, Ph (202) 260-
9821, FAX (202) 260-9830 orwellman.marjorie@epamail.epa.gov. (Jerry LaVeck: Ph
(202) 260-7771 or laveck.jerry@epamail.epa.gov.)
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Compendium oflbolsfor Watershed Assessment and TMDL Development
Visit the BASINS website for information on new updates, answers to frequently asked
questions, and additional documentation at http://www.epa.gov/ostwater/BASINS/
A limited number of BASINS version 1 CD-ROMS will be distributed free of charge
upon request through the National Center for Environmental Publications and Infor-
mation (NCEPI), EO. Box 42419, Cincinnati, OH 45242. Tel: (513) 489-8190. Fax:
(513) 891-6685. Web Site: http://www.epa.gov/ncepihom/index.ntmL The package
includes:
•User's Manual: Better Assessment Science Integrating Point and Nonpoint
Sources. BASINS Version 1.0, May 1996 (EPA Document No.: EPA-823-R-
96-001).
•A compact disk specific to one of 10 regions of interest within the contermi-
nous US. The EPA regions are listed below with the corresponding docu-
ment number for each cd.
1. EPA Region 1 (CT, ME, MA, NH, RI, VT);
2. EPA Region 2 (NJ, NY);
3. EPA Region 3 (DE, DC, MD, PA, VA, WV);
4. EPA Region 4 (AL, FL, GA, KY, MS, NC, SC, TN);
5. EPA Region 5 (IL, IN, MI, MN, OH, WI);
6. EPA Region 6 (AR, LA, NM, OK, TX);
7. EPA Region 7 (IA, KS, MO, NE);
8. EPA Region 8 (CO, MT, ND, SD, UT, WY);
9. EPA Region 9 (AZ, CA, NV);
10. EPA Region 10 (ID, OR, WA);
EPA-823-C-96-001
EPA-823-C-96-002
EPA-823-C-96-003
EPA-823-C-96-004
EPA-823-C-96-005
EPA-823-C-96-006
EPA-823-C-96-007
EPA-823-C-96-008
EPA-823-C-96-009
EPA-823-C-96-010
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Chapter 3. Ecological Assessment Techniques and Models
Ecological Assessment Techniques
and Models
3.1
Introduction
Ecological assessments
are studies that examine or
predict the status of a
habitat, a biological
population, or a biological
community to provide an
interpretation of a
waterbody's ecological
health.
This chapter presents a wide variety of ecological assessment techniques and models
to help watershed managers address the Clean Water Act's challenge to restore and
maintain the physical and biological quality of the Nation's waters. Ecological assess-
ments are studies that examine or predict the status of a habitat, a biological popula-
tion, or a biological community to provide an interpretation of a waterbody's ecologi-
cal health. Ecological assessments can provide additional information and interpreta-
tion of watershed and waterbody conditions that can be helpful for developing TMDLs
and other feasible and comprehensive watershed management solutions (Table 8).
Numerous techniques have been developed by many agencies and organizations to
perform environmental and ecological assessment studies (Atkinson, 1985;
Schuytema, 1982). This chapter focuses on those techniques which have potential
applicability to watershed management and the TMDL process. To facilitate selection
Table 8. Ways in Which Ecological Assessments Can Support the Five
Steps in the Water Quality-Based Approach
1. Identification of Water Quality-Limited Waters That Require TMDLs
• Determine waters that are not meeting designated uses, or are threatened, stressed, or
impaired, by assessing numbers and diversity of aquatic biota.
• Enable states to meet reporting requirements for listing waters that need TMDLs.
2. Priority Ranking and Targeting Listed Waters
• Interpret ecological assessment data to determine the relative vulnerability of waterbodies to
specific stressors.
• Assist in characterizing the magnitude and significance of impairments.
• Combine with water quality evaluations to assist in determining certain explicative cause-effect
relationships needed for restoration alternatives.
3. TMDL Development
• Provide the data necessary for selection of a TMDL endpoint, and aid in developing TMDLs for
nonchemical stressors that have been identified through ecological assessments.
• Indicate the type and geographic extent of stressors that should be controlled to improve
habitat and overall ecological integrity.
4. Implementation of Control Actions
• Provide data for selecting and siting required controls, including habitat restoration.
5. Assessment of Water Quality-Based Control Actions
• Act as a component of an integrated monitoring approach to measure system response to
control of stressors following implementation of management actions.
• Provide information over time about the ecological integrity of a waterbody and indicate
whether decisions are achieving the biological endpoints specified by a TMDL.
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Compendium of "tools for Watershed Assessment and TMDL Development
of an appropriate technique, the chapter first discusses different ways of conducting
assessments and then describes each ecological assessment technique in one of two
categories: habitat assessments and species/community assessments. This distinction
has been made to underscore the importance of considering both living resources and
the physical,, biological, and chemical surroundings on which they depend. Assessing
the habitat and the species or community, and their relationships, provides additional
information to watershed managers useful in characterizing problems and determin-
ing restoration solutions.
Summaries of the capabilities of the techniques and models reviewed in the chapter
are presented in Tables 9 and 10. Fact sheets that outline each technique are also
provided in Appendix C.
3.2
Approaches to
ecological
assessments
Three general approaches exist for performing ecological assessments: comparative
analyses, index/classification procedures, and ecological modeling techniques. As
discussed in this chapter, these techniques are not mutually exclusive, and frequently
overlap or can be used in combination to provide a comprehensive assessment meth-
odology. Several ecological techniques are usually needed to address the stressors on a
watershed.
3.2.1
Comparative analyses
Comparative analyses are a broad-based category of assessments that rely on field
measurements, monitoring data, and statistical analysis as a basis for determining the
status of a habitat or species/community. The main objectives of comparative analyses
are to (1) identify the type and location of impairments, (2) characterize both the
absolute and relative magnitude of impairments, (3) generate criteria for prioritizing
and ranking waterbodies, and (4) track and evaluate the benefit of a control action.
When performing a comparative analysis, data are collected for waterbodies on a
specific spatial and temporal scale and are then compared to one of the following:
(1) similar, unaffected sites (i.e., paired site analysis);
(2) composited reference (i.e., unimpaired or minimally impaired) site condi-
tions; or
(3) historical data from the same site characterizing the "before impairment" or
"control implementation" condition. For screening-level techniques (e.g.,
reconnaissance bioassessment), best professional judgment can also be used
in place of a comparative site to assess the collected data.
Paired-site approaches involve the use of control and treatment sites for the detection
of changes in biological condition. They are useful for the detection of ecological
effects from changes in water quality and quantity, habitat quality, or land use fea-
tures. A key element of the approach, as the name implies, is the simultaneous
monitoring of (1) sites that are not affected by the changes for which the monitoring
is being conducted (control sites) and (2) separate sites that are impaired or affected
by a "treatment" (treatment sites), for example, implementation of best management
practices (BMPs). Many of the techniques described in this chapter can be applied
using the paired approach.
Composited reference site assessment techniques form an approach in which data are
compared to "reference" biological communities or habitats (reference conditions),
which represent biological communities and habitats in unimpaired or minimally
impaired waterbodies in the ecological region (or subregion) of interest. A reference
condition is derived from numerous reference sites within an ecoregion during an
index period (Gibson et al., 1994). EPA's Biological Criteria: Technical Guidance for
-------
Chapter 3. Ecological Assessment Techniques and Models
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-------
Compendium ofTbolsfar Watershed Assessment and TMDL Development
Streams and Small Rivers (1994) also describes the process for classifying and select-
ing reference sites.
Because of the difficulties in reducing ecological data into a single meaningful num-
ber, many comparative analysis techniques rely on aggregating ecological/ monitoring
data into metrics and deriving a representative index, based on which comparisons
can be easily made.
3.2.2
Index/classification
methods
Index and classification methods are techniques based on comparative analyses, but
they go a step further by analyzing and aggregating data into a numerical index (or
indices) that describes the overall integrity of a habitat or community. This index (or
indices) can then be compared to reference sites or can be used in a "before and after"
comparison. For example, the Index of Biological Integrity (IBI) is a technique that
uses 12 metrics (describing species composition, trophic composition, and fish abun-
dance and conditions) to assess attributes that are assumed to correlate with the
ecological health of a waterbody. Individually, each metric provides information about
a specific attribute of the sampling site, and consequently on the type and magnitude
of impairment. When examined together, metrics characterize the underlying biologi-
cal integrity of a site.
Table 10. Evaluation of Model/Technique Capabilities—Species/Biological
Community Assessment Techniques
Criteria
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assessment
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assessment
Assessment
technique
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Chapter 3. Ecological Assessment Techniques and Models
Classification techniques, such as the Rosgen method, reduce several measurable
indicators into various categories. Analysis of these categories provides an indication
of the presence of an impairment and also assists in defining the need for and selec-
tion of restoration programs.
3.2.3 In cases where biotic data are lacking, where obtaining such data is cost-prohibitive,
mnd&l« or where predictions of future conditions are needed, ecological models can provide a
moaeis means to characterize existing conditions, predict potential impacts from a proposed
action, and identify potential sources of impairments.
Ecological modeling is a wide-ranging, relatively new scientific field that focuses on
quantifying the relationships between the biotic and abiotic components of an ecosys-
tem. It can include, for example, simulations of population and community dynamics,
oxygen balance estimation, fate and transport of toxics and their impact on a biologi-
cal community (e.g., ecotoxicological models), and eutrophication modeling
(Jorgensen, 1995). Because Section 2.5 of this document (receiving water models)
discusses approaches for simulating biochemical interactions (e.g., dissolved oxygen)
and algal growth (e.g., eutrophication), further use of the term "ecological model" in
this document is limited to those methods which focus explicitly on how species and
biological communities are affected by exposure to stressors (both from direct contact
and through habitat modification).
Abiotic and biotic relationships in ecological models are typically simulated using
mathematical algorithms describing either statistical relationships or mechanistic
processes. Statistical models, such as regression or principal components analysis,
derive generalizations about ecological conditions using experimental and/or observa-
tional data (Suter, 1993). Mechanistic models, on the other hand, attempt to quantita-
tively describe a phenomenon by its underlying causal mechanisms, often by integrat-
ing complex sets of spatial and temporal data and reproducing the principal compo-
nent and relationship in the model (Suter, 1993).
Because of the complexity inherent in ecosystem processes that affect aquatic species
and communities, a few predictive (and generally statistically based) models exist that
have demonstrated applicability to TMDL development and watershed management.
On the other hand, most of the mechanistic modeling efforts that incorporate inter-
specific, intraspecific, and abiotic (e.g., chemical and physical) interactions, while
considering temporal and spatial heterogeneity in these factors, have been pursued at
the research level and require large amounts of data and novel analytical techniques
(e.g., STAC, 1993; Turner et al., 1995). Nevertheless, continuing advancements in
knowledge about ecosystems, the need for more quantitative data to better manage
natural resources, and increased accessibility to sophisticated computing and program-
ming equipment will continue to lead to improvements in developing mechanistic
models that more easily and realistically represent ecosystems, and that can eventu-
ally be applied at a practical level.
One area of model development currently being explored is in large-scale, holistic
ecosystem modeling. The approach used in the Chesapeake Bay, for example, links a
variety of submodels that describe important processes and interactions in the bay
(STAC, 1993). Included in the modeling system are:
• Ecosystem process models that determine the flow of nutrients and organic
materials.
• Water quality models that consider both loading and receiving water processes.
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Compendium oflbokjbr Watershed Assessment and TMDL Development
Habitat assessments
are used to define existing
conditions and/or to
examine the impact of a
given stress or environmen-
tal change on terrestial and
aquatic communities.
3.3
Habitat assessment
techniques
• Spatially explicit fish bioenergetic models that identify habitats with the highest
potential for growth.
• Individual-based fishery dynamics models that describe population dynamic*:.
• Ecosystem regression models that identify strong relationships is systems.
• Ecosystem network analysis models that allow examination of indirect connec-
tions between species in an ecosystem.
• Landscape spatial models that incorporate space as well as time to quantita-
tively predict and describe landscape phenomena through the use of GISs.
Advances in ecological risk assessment methods also offer promise for TMDL develop-
ment and watershed management (e.g., Barnthouse, 1992; USEPA, 1994e). Ecological
risk assessments (i.e., assessments that use data to estimate the probability that some
undesired ecological event will occur) have typically involved extrapolating results of
laboratory toxicily tests to estimate the effects on aquatic ecosystems (Bartell et al.,
1992). Existing models, such as the EPA-supported Comparative Toxicology Models,
EXAMS, and FGETS, examine either the fate (movement and transformation) or
effects (direct effect on biota) of toxics through aquatic ecosystems. New modeling
approaches in ecological risk assessment are focusing on:
• Integrating fate and effects models, where physical and chemical processes that
influence the exposure concentration of the toxic chemicals are explicitly
included with simulations of the effects of stress on biota (Bartell et al., 1992).
AQUATOX, being jointly developed by EPA's Office of Science and Technology
and the Office of Pollution Prevention and Toxics, is one such model.
• Incorporating spatial data via GISs into ecological risk assessment frameworks
(e.g., Clifford et al., 1995).
• Applying the ecological risk assessment methodology to cases that consider
stressors other than toxics. For example, Brody et al. (1993) use the methodol-
ogy to assess the probability of ecological risk as it relates to changes in hydrol-
ogy and subsequent changes in wildlife habitat to a watershed in Louisiana.
Habitat quality is a critical determinant of ecological integrity (Plafkin et al., 1989),
and the condition of physical habitat has a direct effect on the condition of biological
communities. Habitat assessments for aquatic ecosystems typically evaluate habitat
structure, which influences the overall health of the water resource. Physical param-
eters, such as the substrate, channel morphology, water quality, bank structure, and
riparian vegetation, are often used to assess or predict the condition of the waterbody.
Other factors, such as structural heterogeneity of microhabitats, temporal persistence,
and an energy base consistent with the water resource type, size, and region, are also
used to assess a waterbody's integrity.
Habitat assessment techniques are used to define existing conditions and/or to
examine the impact of a given stress or environmental change on terrestrial and
aquatic communities. The existing status of a community can be determined through
evaluation of variables such as habitat type; species abundance and distribution; level
of disturbance; connectedness to corridors, confluences, or greenways; and percent of
surrounding development or exposure to pollutants. Once the habitat has been
assessed, changes to it can be measured or modeled, and subsequently evaluated.
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Chapter 3. Ecological Assessment Techniques and Models
Habitat Evaluation Procedure/Habitat Suitability Indices (fact sheet,
page C.3). The Habitat Evaluation Procedure (HEP) is a species-based index method
designed by the U.S. Fish and Wildlife Service and used to document the quality and
quantity of available habitat for selected aquatic and terrestrial wildlife species
(USFWS, 1980; Wakely and O'Neil, 1988). HEP provides information for two general
types of habitat comparisons: (1) the relative value of different areas at the same
point in time and (2) the relative value of the same area at future points in time. By
combining the two types of comparisons, the impact of proposed or anticipated land
or water use changes on habitat can be quantified.
HEP analysis begins with three basic steps:
(1) Defining the study area
(2) Delineating cover types
(3) Selecting evaluation species
The study area should include sites where direct or indirect biological changes are
expected to occur as a result of a proposed action. The concept of cover types used in
HEP is analogous to habitat types, which include deciduous forest, coniferous forest,
grassland, residential woodland, and medium-sized warmwater stream. Evaluation
species (i.e., indicator species) are used in HEP to quantify habitat units (HUs), and a
typical HEP study incorporates four to six species. The analysis is structured around
the calculation of HUs for each evaluation species in the study area before and after a
proposed action. The number of HUs is defined as the product of the Habitat Suitabil-
ity Index (HSI, a measure of habitat quality) and the total area of available habitat
(habitat quantity).
For stream assessments, HEP provides a method that correlates physical habitat
characteristics to fishery resources. The technique is a useful fisheries management
tool because it identifies the physical habitat features that reduce the biological
integrity of the waterbody. The habitat features typically evaluated include tempera-
ture, turbidity, velocity, depth, cover, pool and riffle sizes, riparian vegetation, bank
stability, and siltation. The habitat parameters are correlated to fish species based on
an evaluation of their importance to the life cycle of the species.
Habitat Suitability Indices (HSIs) are modeled components of HEPs developed to
provide an understanding of habitat requirements for species by identifying the key
habitat variables and the range and optimum for each variable. Using mathematical
models, HSIs provide an index between 0 and 1 indicating habitat quality (0 - unsuit-
able, 1 - optimal). HSIs characterize species-habitat relationships and are helpful in
identifying the physical habitat conditions that are vital to a given species, as well as
identifying the ranges and optimal conditions necessary for species survival and
propagation. For fish, habitat requirements are evaluated for four life stages: spawn-
ing/embryo, larvae/fry, juvenile, and adult. The applicability of each species-specific
HSI is designated according to season, minimum habitat area, and verification level
(i.e., expert review and evaluation, and whether model design is based on literature
or field tests).
Three software programs have been developed by the U.S. Fish and Wildlife Service to
assist with application of the HEP (Mangus, 1990). The HEP Accounting Program
computes the values needed to use the HEP procedures (USFWS, 1980) and can
evaluate up to 25 species, 15 planning alternatives, and 15 management plans. Inputs
include the areas of usable habitat for each species and HSIs for each species over
time for each management alternative. The Habitat Management Evaluation Method
System (HMEM) software allows a user to investigate and compare the cost-effective-
ness of different management alternatives to achieve desired HUs for a selected
-------
Compendium oflbokfor Watershed Assessment and TMDL Development
species. HSI modeling system software can be used to compute an HSI value for
selected species from field measurements of habitat variables. The software allows a
user to examine intermediate values for each species model and evaluate model
response to specific habitat variables with a response surface analysis; it can also
perform sensitivity analysis. The software has a library of over 200 models for aquatic
and terrestrial species. HSI software also transfers habitat models to HMEM, where
the user specifies the constraints for each management activity. Following compilation
of species arid management models, strategies are ranked according to their cost-
effectiveness (i.e., lowest management cost and highest HUs).
Habitat Evaluation System (fact sheet, page C.5). The Habitat Evaluation
System (HES) is a community-based index evaluation technique originally developed
by the U.S. Army Corps of Engineers to evaluate water resource projects in the lower
Mississippi Valley area (U.S. Army Corps of Engineers, 1976). A modified version
(U.S. Army Corps of Engineers, 1980) is commonly used today and can be applied to
assess the impacts of development projects for two aquatic habitats (streams and
lakes) and four terrestrial habitats (wooded swamps, upland forests, bottomland
hardwood forests, and open lands). HES can also be used to estimate the terrestrial
wildlife value of aquatic habitats.
HES assumes that presence, abundance, and diversity of animal populations in a
habitat are determined by biotic and abiotic factors that can be readily quantified.
HES determines the quality of a particular habitat type through the use of functional
curves that relate habitat quality and carrying capacity to these factors. HES uses
general habitat characteristics that indicate quality for aquatic and terrestrial wildlife
communities as a whole.
Six steps are involved in an HES:
(1) Obtaining habitat type and land use acreage.
(2) Deriving Habitat Quality Index (HQI) scores.
(3) Deriving Habitat Unit Values (HUVs) (4) Projecting HUVs for future with- and
without-project conditions.
(5) Using HUVs to assess impacts of project alternatives.
(6) Determining mitigation requirements, if any.
The first step in the HES is delineating the acreage of each habitat type in the project
area for existing conditions, future without-project conditions, and future with-project
conditions. The second step consists of deriving HQI scores for each land use category
or habitat type. Data are obtained on several key variables (e.g., species associations,
benthic diversity, sinuosity index, total dissolved solids, land use type) for each habitat
type from field measurements, literature, and historical information. Each variable
measured is then converted to an HQI score (between 0 and 1) using functional
curves developed for that variable and habitat. The third step combines the habitat
type or land use size data (acreage) and the associated HQI scores to compute an HUV
for the habitat. Next, HUVs over the life of the project are projected based on esti-
mated changes in land use or habitat size. Estimated changes can be developed using
engineering and related planning studies.
Step 5 consists of calculating total and/or annualized HUVs for each habitat type for
the with- and without-project scenarios. The impacts from each alternative can be
estimated by subtracting the with-project HUV from the without-project HUV Total
impacts from a project can then be determined by summing the impacts for all
affected habitats, allowing trade-off analyses and comparisons between plans. For
complex projects with several habitat types, computer software is available for HES
-------
Chapter 3. Ecological Assessment Techniques and Models
steps 1 through 5 (U.S. Army Corps of Engineers, 1980). Inputs to this software are
the data for land use or habitat size and HQI scores. Finally, HES can be used to
determine the amount and type of mitigation necessary to compensate any possible
adverse impacts from a project.
Wetland Evaluation Technique (fact sheet, page C.25). The Wetland Evalua-
tion Technique version 2.0 (WET II) is a community-based index evaluation approach
that can provide a broad overview of potential project impacts on several wetland
habitat functions (Adamus et al., 1987). WET II evaluates functions and values in
terms of social significance, effectiveness, and opportunity. A project team implements
WET II by identifying the physical, chemical, and biological characteristics of a
wetland through the use of predictor species or characteristics within a habitat
representative of the study area. A series of questions are asked for each predictor to
more precisely define its relationship to the habitat and determine the social signifi-
cance of the wetland area. The predictors are then evaluated for each function's
effectiveness and opportunity based on interpretation keys that define the relationship
between predictor and wetland function or value; the evaluation ratings are high,
moderate, or low. Similar ratings are also used for significance. The ratings are then
combined to give a final rating of functional significance.
This method was designed primarily for conducting an initial, rapid evaluation of
wetland functions and values. However, WET II can be applied in a variety of other
situations or circumstances including (1) comparison of different wetlands in terms of
their functions and values; (2) selection of priorities for wetland acquisition or more
detailed, site-specific research; (3) selection of priority wetlands for advanced identifi-
cation; (4) identification of options for conditioning of permits; (5) determination of
the effects of preproject and post-project activities on wetland functions and values;
and (6) comparison of created or restored wetlands with reference, or preimpact,
wetlands during mitigation.
Hydrogeomorphic Assessment (HGM) (fact sheet, page C.7). HGM is a
hydrogeomorphic classification and assessment methodology for determining the
integrity of physical, chemical, and biological functions of wetlands as they compare
to reference conditions (Brinson, 1993). Absent in the methodology is the use of
predictor species, which significantly reduces the time and effort required to conduct
an assessment. Instead, the method focuses on identifying wetland groups that exhibit
a relatively narrow range of variation in the properties that fundamentally influence
how wetlands function. The HGM method relies on the use of reference wetlands,
which represent a collection of sites of a specific wetland class that can be used for
developing the upper and lower boundaries of functioning within the class. The steps
in the assessment approach are:
(1) Classify wetlands according to HGM properties.
(2) Make connections between the properties of each wetland class and the
ecological functions that they perform based on logic and research results.
(3) Develop functional profiles for each wetland class.
(4) Choose reference wetlands that represent the range of both natural and
human-imposed stresses and disturbances.
(5) Design the assessment method using indicators calibrated to reference
wetlands.
The HGM classification uses principles of hydrogeomorphology to separate wetlands
into functional classes at a gross level, and it serves as the organizing principle for the
development of an assessment method. Because the classification is hierarchical and
modular, it can be easily modified for different geographic regions or scales. To
-------
Compendium oflbolsfar Watershed Assessment and TMDL Development
establish the relationship between fundamental properties and functions of a wetland
(step 2), extensive data sets are not needed. With the establishment of reference
wetlands (steps 3 and 4), in which functions have already been evaluated, the site
being evaluated is compared to the reference group of the same class. This avoids the
need to establish an arbitrary scale for ranking; the scale is defined by the variation
within the reference population itself.
The connections established between hydrogeomorphic properties and functions can
then be summarized in "functional profiles" for wetlands that have been assessed. A
functional profile is a body of descriptive information that characterizes a functional
wetland class or a single wetland (the reference wetland). At a minimum, one must
develop a profile on a small reference population as a basis for the scaling of functions
within a class (step 4), but the profile must also provide the basis for comparison
between the reference population and a new site undergoing assessment. Step 4,
determining where a wetland falls along the scale of function, requires a method for
estimating or quantifying the properties of the wetland that determine how it func-
tions. This step is still in the development process.
The final step in the HGM is the development of the assessment method. The assess-
ment tasks include, but are not limited to, (1) acquiring maps (topographic, National
Wetland Inventory, land use, etc.), soil surveys, aerial photographs, hydrologic data
(discharge, water levels), water quality data, and land use of the watershed; (2)
becoming acquainted with the site by walking the boundary and several traverses; (3)
filling out field sheets related to developing a profile of the site (water source, hydro-
dynamics, vegetation cover, soil type); (4) assessing whether indicators of functioning
are present; and (5) developing narrative that describes the rank of the wetland
relative to the reference wetland population.
Visual-based Habitat Assessment (fact sheet, page C.23). The habitat
assessment procedure, an index-based methodology originally developed for the
Rapid Bioassessment Protocols (RBPs) (Plafkin et al., 1989), were based on Stream.
Classification Guidelines for Wisconsin (Ball, 1983) and Methods of Evaluating Stream,
Riparian, andBiotic Conditions (Platts et al., 1983). The habitat assessment param-
eters were later modified to include additional assessment parameters for high-
gradient streams, as well as new, more appropriate parameters for low-gradient
streams (Barbour and Stribling, 1991). Additional modifications have since been
made based on evaluations of observer bias and include an increase in parameter
objectivity, the use of different parameters for different targeted biological assem-
blages, and a nonweighted point-scoring framework (Barbour et al., 1995).
The habitat assessment procedures use 10 parameters to characterize the integrity of
habitat conditions. The parameters characterize substrate, instream cover, channel
morphology, and riparian and bank structure and stability on a site-specific basis. Each
parameter is assigned a numerical score within a gradient of optimal (20) to poor (0),
based on visual inspection or a minimal amount of measurement. The approach
incorporates the assumptions that there is a continuum of conditions for each param-
eter within each stream type, and that the continuum is easily recognized by experi-
enced biologists. The continuum for each parameter is divided into four parts that
represent optimal, suboptimal, marginal, and poor habitat quality. The scoring range
within each part allows for a judgment of differential conditions (e.g., high, middle,
low) and for better resolution among varying conditions. The final score for the site is
calculated by summing the scores for each parameter. Although significant variability
exists between streams, some generalizations among stream types can be made based
on gradient. Higher-gradient streams of the montane and piedmont regions are
assessed using the "riffle/run prevalence" parameters, and the "glide/pool prevalence"
parameters are used for the valley/plains and coastal plains streams.
-------
Chapter 3. Ecological Assessment Techniques and Models
This final habitat assessment score is compared to the score established for regionally
expected reference conditions. The judgment criteria for the site are optimal, subopti-
mal, marginal, and poor. The judgment criteria are defined as follows: optimal—meets
natural expectations; suboptimal—less than desirable, but satisfies expectations in
most areas; marginal—moderate level of degradation, severe degradation at intermit-
tent areas; poor—characteristics of parameters substantially altered, severe degrada-
tion.
Qualitative Habitat Evaluation Index (fact sheet, page G.23). The Qualita-
tive Habitat Evaluation Index (QHEI) provides an empirical, quantified evaluation of
the general lotic macrohabitat characteristics important to fish communities (Rankins,
1991). The index is a composite of the quantitative values for six physical habitat
characteristics obtained from visual estimates. Ohio EPA relates these characteristics to
tiered aquatic life uses assigned to warmwater streams in Ohio.
The QHEI is based on a composite of six habitat variables: substrate, instream cover,
riparian characteristics, channel characteristics, pool and riffle quality, and gradient
and drainage area. Visual estimates of several components for each habitat variable
are assigned scores based on observed or predicted relationships with fish species
diversity and/or measures of community integrity. The characteristics of each habitat
variable are related to tiered aquatic life uses for warmwater streams (i.e., exceptional
warmwater habitat, warmwater habitat, modified warmwater habitat, and limited
resource water). To accommodate widespread application, the index considers
covariate habitat quality factors at the ecoregion, reach, and subbasin levels. On a
200- to 500-meter stream segment, the QHEI can be completed in less than 1 hour.
In Ohio, the QHEI was significantly correlated to the Index of Biotic Integrity (IBI)
(Rankins, 1991), demonstrating the strong influence of habitat quality on fish commu-
nities. The correlation varied with differences in stream size and ecoregion, suggesting
the influence of factors other than site-specific habitat quality. Fish communities in
streams with relatively intact habitat throughout the drainage can compensate for
short reaches of poor habitat; however, stream basins with extensively degraded
habitat will not support sensitive fish species and fish community structure will be
drastically altered.
Rosgen's Stream Classification (fact sheet, page C.19). The Rosgen ap-
proach for stream classification and restoration uses morphological stream characteris-
tics to organize streams into relatively homogenous stream types (Rosgen, 1994). This
classification method was developed for use as a tool to predict a stream's behavior
based on its geomorphologic condition, to extrapolate data from one stream for use
on another with similar characteristics, and to provide a consistent frame of reference
when comparing one stream to another (Rosgen and Fittante, 1986). The criteria used
to organize streams into types represent measured variables that govern channel
morphology and determine the stream's dominant features.
There are four hierarchical levels of classification based on the desired levels of
resolution and project objectives (Rosgen, 1994). Level I is used to provide a broad
morphological characterization by integrating landform and fluvial features of valley
morphology with channel relief, pattern, shape, and dimension (Rosgen, 1994). The
influences of climate, depositional history, and life zones or ecotones (desert shrub,
alpine, etc.) on channel morphology are also considered at Level I.
Level II delineates streams into major, broad categories (A through G) that provide a
more detailed level of interpretation and extrapolation than Level I. Stream types are
separated based on discrete channel patterns, entrenchment ratios, width/depth
ratios, sinuosity, dominant channel-material particle sizes, and slope ranges, which
results in a total of 42 major stream types.
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Compendium oflbolsfor Watershed Assessment and TMDL Development
Level III provides a very detailed description of the existing stream conditions, as well
as specific information for predicting responses to outside influences. This is accom-
plished by integrating information on riparian vegetation, depositional patterns,
meander patterns, confinement features, fish habitat indices, flow regime, river size
category, debris occurrence, channel stability index, and bank credibility.
Level IV provides reach-specific information on channel processes and involves direct
measurement/observation of sediment transport, bank erosion rates, aggradation/
degradation processes, and stream geometry. The Level IV classification also uses
biological data such as fish biomass, aquatic macroinvertebrates, and riparian vegeta-
tion evaluations.
Applications for the classification system include the ability to evaluate sensitivity to
disturbance and to predict stream behavior as a result of changes in the watershed;
the assessment of impacts; the ability to design stable, self-maintaining channels in
restoration work; the ability to determine flow resistance; and the selection of appro-
priate fish habitat improvement structures. At the highest classification level (Level
IV), the Rosgen system can be used to provide sediment, hydraulic, and biological
information related to specific stream types. It can also evaluate the effectiveness of
mitigation and impact assessments by stream type.
Instream Flow Incremental Methodology (IFIM) (fact sheet, page C.11).
IFIM is a conceptual framework that consists of a collection of analytical procedures,
indices, and computer models used to assess riverine habitats (Bovee, 1982, 1986;
Gordon et al., 1992). Developed by the U.S. Fish and Wildlife Service, National
Ecology Research Center, Aquatic Systems Branch, IFIM attempts to determine the
effects of any of a number of hydraulic modifications on aquatic habitat through a
complete process consisting of the application of seven steps (Gordon et al., 1992):
(1) Describe the state of the river system in key variables.
(2) Develop functions that describe the habitat preferences of identified species.
(3) Develop functions that integrate the macro- and microhabitat availability of
the system.
(4) Incrementally change one or more variables (e.g., discharge or channel
morphology) to reflect a management option, and determine the available
habitat for this new system.
(5) Determine alternatives or other actions to avoid or correct adverse impacts
from the previous step.
(6) Repeat steps 3 and 4 to develop a range of management options.
(7) Evaluate alternatives and perform selection.
IFIM considers changes to both microhabitat (the distribution of structural and
hydraulic features that form the living space for an organism) and macrohabitat
(channel characteristics, temperature, and water quality) (Gordon et al., 1992).
Included as components of IFIM are the Physical Habitat Simulation System and the
Time Series Library, both of which are used to develop habitat preference and avail-
ability functions.
Physical Habitat Simulation System. (PHABSIM). PHABSIM is a collection of computer
programs that form the key microhabitat simulation component of IFIM (used in steps
2, 3, and 4). PHABSIM relies on the assumption that aquatic species will react to
hydraulic changes in a stream by selecting the most favorable conditions (Gordon et
al., 1992). To measure this, PHABSIM produces habitat-discharge relationships that
estimate how suitable habitats for aquatic species change with discharge by describing
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Chapter 3. Ecological Assessment Techniques and Models
local physical niches using depth, velocity, and stream channel characteristics.
PHABSIM has two components: hydraulic simulation (in which the user selects from
three types of calculations to calculate water-surface elevations and velocities) and
habitat simulation (in which the user selects from three types of models to compute
the amount of physical habitat available for a particular species).
The final habitat-discharge relationships produced by PHABSIM show the change in
Weighted Usable Area (WUA) with discharge (Gordon et al., 1992). The WUA is an
indicator of physical habitat suitability for a certain life stage of a certain species for a
given stream reach. Physical habitat (depth, velocity, cover, and substrate) is assessed
for a stream reach for a given discharge and then combined with habitat suitability
curves to determine the WUA for that discharge. By calculating WUAs for numerous
discharges, the method describes the relative habitat suitability of a stream under
different flow conditions. Since changes in habitat resulting from changes in
streamflow can be quantified, PHABSIM can provide answers to "what if" water
management questions.
PHABSIM uses a combination of standard, one-dimensional, steady-flow, open-
channel hydraulic models and habitat models to describe WUAs under a variety of
channel configurations and flow management conditions. Use of simulation models
allows physical habitats to be described for unmeasured discharges. This approach
allows cost savings in collecting field data because each flow does not have to be
measured, and it allows practitioners to describe flow conditions that would be too
dangerous to measure.
Time Series Library (TSLIB). TSLIB uses a set of programs to create monthly or daily
habitat time series and habitat-duration curves using the habitat-discharge relation-
ships produced by PHABSIM (Gordon et al., 1992). The programs can calculate basic
statistics for monthly data, generate flow-duration habitat curves for designated
months, and create monthly or annual habitat time series for four to seven life stages
of selected species.
MNSTREM Stream Temperature Model (fact sheet, page C.13). MNSTREM
is a dynamic stream water temperature simulation model developed for the simula-
tion of water temperatures in the experimental streams of the U.S. EPA/Monticello
Ecological Research Station (Gulliver, 1977; Stefan et al., 1980). It has been applied
to assess the impacts of instream flow requirements upon water temperature in the
Central Platte River, Nebraska (Sinokrot et al., 1996) and other streams ranging in
size from the Mississippi River to a 50 cfs stream. MSTREM solves the one-dimen-
sional heat advection-dispersion equation and incorporates heat exchange with the
atmosphere. MSTREM has been found to predict hourly stream temperatures with
standard errors of only 0.2 and 0.3°C when accurate weather paramaters and stream
morphology data are available. MNSTREM was extended to include streambed heat
flux in the heat budget, side stream inflow, and groundwater inflow by Sinokrot and
Stefan (1994).
Data requirements for MNSTREM include location, weather data, and stream data.
Location data consist of latitude and altidude. Weather data include air temperature,
relative humidity, solar radiation, wind velocity, cloud cover, and air pressure. Stream
data are total length of river reach, cross-sectional area and surface width as a func-
tion of discharge, upstream water temperature as a boundary condition, observed
water temperature (hourly) for calibration, daily stream flow rate, groundwate
inflow/outflow, and streambed data (temperature profile in the sediment data (tem-
perature profile in the sediment).
Stream Network/Segment Temperature Model (SNTEMP/SSTEMP) (fact
sheet, page C.21). SNTEMP and SSTEMP are computer models that estimate how
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Compendium of Tools far Watershed Assessment and TMDL Development
3.4
Species/biological
community
assessment
techniques
Biological communities
integrate the effects of
different pollutant stressors,
such as excess nutrients,
toxic chemicals, increased
temperature, and excessive
sediment loading, and thus
provide an overall measure
of the aggregate impact of
the stressors.
the temperature of a stream changes with altered conditions of flow, riparian shade,
and meteorological conditions (Theurer et al., 1984). SNTEMP is a more complicated
program that can simulate a stream network with multiple tributaries for multiple
time periods. SSTEMP is a simplified version of SNTEMP that can assess only a single
stream for a single time period.
Both programs require input parameters that describe the stream geometry, hydrology,
and meteorology to simulate minimum, mean, and maximum daily water tempera-
ture. SNTEMP and SSTEMP assume that water in the system is instantaneously and
thoroughly mixed at all times, that all stream geometry (e.g., slope, shade, friction
coefficient) is characterized by mean conditions, that distribution of lateral inflow is
uniformly apportioned throughout the segment length, and that solar radiation and
other meteorological and hydrological parameters are 24-hour means. The programs
also handle the special case of a dam with steady-state release at the upstream end of
the segment. The companion programs SHADE and SOLAR can be used in tandem
with SNTEMP/SSTEMP to calculate percent shade, solar radiation, and day length,
PHABSIM can also be used to calculate the width-flow function. Incorporation of
macrohabitat temperature suitability as described in the Instream Flow Incremental
Methodology (see Bovee, 1982) is a logical next step for factoring temperature
consequences of altered streamflow into management decisions. SNTEMP and
SSTEMP are typically used in deciding whether regulatory requirements are being met
for fisheries in rivers and streams.
Riverine Community Habitat Assessment and Restoration Concept
(RCHARC). RCHARC is a simulation model developed recently at the U.S. Army
Corps of Engineers Waterways Experiment Station that relates aquatic habitat quality
to hydraulic diversity based on a "comparison standard" reach approach (Nestler et
al., 1993a,b). The comparison standard river system (GSRS) used represents the ideal
or target habitat for an aquatic community as defined by channel morphology and
flow frequency (Peters et al., 1995). RCHARC assumes that for a given discharge, a
distribution of flow depths and velocities exists that represents habitat of varying
quality; changes in the frequency and distribution of these depths and velocities will
therefore change the composition of the aquatic community (Peters et al., 1995).
RCHARC integrates field observations, survey data, and the U.S. Army Corps of
Engineers' HEC-2 computer model (which calculates water surface elevations and
velocities) to generate three-dimensional bivariate plots of velocity, depth, and percent
occurrence of species for each stream segment. Habitat similarity between comparison
reaches is determined by assessing the velocity-depth distributions for a range of
discharges. Such comparisons offer great promise in planning restoration activities.
The RCHARC model is still being validated; a Beta test for RCHARC was recently
completed in Rapid Creek in South Dakota (Peters et al., 1995). No fact sheet or
model evaluation has been included because of its developmental stage.
This section includes techniques for evaluating the status of a species, population, or
biological community in a waterbody, or examining or predicting the effects of
changing water quality conditions on a species, population, or biological community.
The central purpose of assessing biological condition is to determine how well a
waterbody supports aquatic life. Biological communities integrate the effects of
different pollutant stressors, such as excess nutrients, toxic chemicals, increased
temperature, and excessive sediment loading, and thus provide an overall measure of
the aggregate impact of the stressors. Although biological communities respond to
changes in water quality more slowly than water quality actually changes, they
respond to stresses of various degrees over time.
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Chapter 3. Ecological Assessment Techniques and Models
Tools that use biological surveys and other direct measurements of biota in surface
waters often compare them to reference conditions to evaluate the overall health of
an aquatic species or community. These assessments commonly use benthic macroin-
vertebrates and fish, as well as assemblages of plankton, macrophytes, and periphy-
ton, as indicators of the condition of biological communities. Comparisons of macroin-
vertebrates or fish communities can be made between those characterizing the
reference condition and those found at monitored sites to determine how closely they
resemble one another. It is important to note that many of these techniques can be
modified to accommodate local situations, and frequently states or local governments
adapt the techniques to establish regional or statewide biological assessment pro-
grams.
Screening-level or Reconnaissance Bioassessment (fact sheet, page
C.I7). The simplest bioassessment approach that can be used to obtain useful infor-
mation about the status of an aquatic community and condition of a site is a screen-
ing-level, or reconnaissance, bioassessment (Plafkin et al., 1989; USEPA, 1994c). This
type of survey can be done inexpensively and with few resources. If the screening-
level bioassessment is conducted by a trained and experienced biologist with a
knowledge of aquatic ecology, taxonomy, and field sampling techniques, the results
will have the greatest validity. Since a screening-level bioassessment is done without
the benefit of comparison to unimpaired sites, a judgment of biological condition is
made based solely on the presence or absence of indicator taxa, dominance of nui-
sance or sensitive taxa in the sampled habitats, or evenness of taxonomic distribution.
A trained biologist will be able to determine whether the biota at a site are moder-
ately or severely impaired using this approach, but subsequent sampling is often
necessary to confirm any findings. The most useful application of this approach is for
problem identification or screening and for setting pollution abatement priorities.
Examples of reconnaissance techniques are the Rapid Bioassessment Protocols (RBPs)
I and IV (Plafkin et al. 1989; USEPA, 1994c). A summary of all five RBPs is also
provided in Table 11. RBP-type methods for fish and invertebrates have been adapted
for use by many states and federal agencies and are in use across the country
(Southerland and Stribling, 1995).
EBP I. RBP I is a screening assessment involving the systematic documentation of
visual observations by a trained professional (Plafkin et al., 1989). The first element
of RBP I is a habitat assessment that consists of inspection of the instream habitat for
the amount of embeddedness; type of bottom substrate; depth; flow velocity; presence
of scoured areas or areas of sediment deposition; relative abundance of different
habitat types (pools, riffles, runs); presence of woody debris, aquatic vegetation,
riparian vegetation, and bank erosion; and proximity of altered land uses. Biological
sampling for this type of bioassessment involves macroinvertebrate collection, from
which calculations of relative abundance and number of orders/families represented
are made. Calculations of basic community structure can also be made if specimen
identifications are sufficiently detailed to allow determination of the functional
feeding group the organisms occupy.
RBP IV The purpose of RBP IV is to serve as a screening tool by maximizing existing
knowledge of fish communities through the use of a questionnaire and general habitat
and water quality data (Plafkin et al., 1989). The questionnaire surveys local, state,
and university fish biologists to obtain information such as historical trends, and
incidents of tainting and fish tissue contamination. This technique provides a quick
and inexpensive assessment of a large number of waterbodies. Development of a
questionnaire is flexible, but the questionnaire should provide information including
the integrity of the fish community, frequency of occurrence of limiting factors and
causes, frequency of occurrence of particular fish community conditions throughout
time and space, effects of waterbody type and size on these conditions, likelihood of .
improvement/degradation, and the major limiting factor (Plafkin et al., 1989).
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Compendium oflbokfor Watershed Assessment and TMDL Development
Questionnaires are often disseminated more easily and receive a better response if
sent in electronic form.
Multimetric Approaches for Biological Assessment (fact sheets, pages
C.9 and C. 17). Accurate assessment of biological condition requires a method that
integrates biotic responses through an examination of patterns and processes from the
organism level to the ecosystem level (Karr et al., 1986). Multimetric approaches
define an array of measures, or metrics, that individually provide information on
community structure, taxonomic composition, individual condition, and biological
processes. Numerous biological metrics have been tested in various regions of the
country, primarily for fish and benthos. Summaries of those used have recently been
presented (Harbour et al., 1995; Gibson et al., 1994). Those presented here are some
of the more common approaches and include the Rapid Bioassessment Protocols
(RBPs) II, III, and V (also known as the Index of Biotic Integrity) (Karr, 1981; Plafkin
et al., 1989); the Invertebrate Community Index or ICI (DeShon, 1995); and the
Index of Well-Being (Gammon, 1980; Hughes and Gammon, 1987).
The raw data collected during these biological surveys consist entirely of taxonomic:
identifications and numbers of individuals within each taxon. The level of identifica-
tion—whether to family, genus, or species—depends on the method being used. For
instance, RBPII involves identification to the family level, whereas RBP III involves
identification to the lowest practical level, generally genus or species. These data are
used to calculate or enumerate a variety of values, or metrics. Each reflects a different
characteristic of community structure and has a different range of sensitivity to
pollution stress (Plafkin et al., 1989). Appropriately developed metrics can be used to
draw conclusions about different aspects of the biological condition at a site, and
measurements of multiple metrics in a biological assessment will yield a more accu-
rate representation of the overall biological condition at a site. Gray (1989) stated
that the three best-documented biological responses to environmental stressors are a
reduction in species richness, a change in species composition to dominance by
opportunistic species, and a reduction in the mean body size of organisms. Though the
last type of biological response (change in mean body size) might be well-docu-
mented, it is rarely used in the more common bioassessment protocols because the
level of effort for an accurate interpretation can be prohibitive.
RBP n. RPBII provides a more detailed methodology than RBP I for characterizing
benthic macroinvertebrate communities (Plafkin et al., 1989). RBP II characterizes the
severity of an impairment into one of three categories (none, moderate, severe), gives
a generic indication of impairment cause, and ranks and prioritizes streams for further
assessment. This protocol uses systematic collection and analyses of benthic data to
detect sites of intermediate impairment and prioritize sites for more intensive assess-
ment. RBP II uses an integrated assessment of metrics that measure components of
family-level community structure.
In addition to the standard RBP habitat and water quality data collection (i.e., charac-
terizing and rating substrate/instream cover, channel morphology, and riparian/bank
structure; measuring conventional water quality parameters; and examining physical
characteristics), RBP II specifies examination of riffle/run community, sampling of
coarse paniculate organic matter, identification of a 100-organism subsample in the
field to family or order level, and analysis of coarse particulate organic matter and
functional feeding group of riffle/run in the field. The metrics that are developed with
the collected data are taxa richness, Family Biotic Index, ratio of scrapers/filtering
collectors, ratio of EPT (Ephemeroptera, Plecoptera, and Trichoptera) and chironomid
abundances, percent contribution of dominant family, EPT index, community similar-
ity index, and ratio of shredders/total. Plafkin et al. (1989) describe collection proce-
dures and the computation of each metric in further detail. Each metric is given a
score when compared to that of a reference condition, and all metrics are summed to
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Chapter 3. Ecological Assessment Techniques and Models
determine the overall biological condition. In cases where final scores border on
established ranges, additional data such as those from water quality, habitat, and
physical assessments can aid in the final evaluation of biological condition.
RBP III. RBP III is the most rigorous bioassessment technique for characterizing the
health of a benthic invertebrate community (Plafkin et al., 1989). This technique
involves systematic field collection of data similar to that of RBP II, but also includes
subsequent laboratory analysis to detect more subtle degrees of waterbody impair-
ment. Use of RBP III allows determination of the severity of an impairment into one of
four categories (no, slight, moderate, severe); it gives a generic indication of its cause;
establishes a basis for trend monitoring; and prioritizes streams for further assess-
ment.
In addition to the standard RBP habitat and water quality data collection (see RBP II),
RBP III focuses on the sampling of benthic macroinvertebrates plus cursory field
observation of periphyton, macrophyton, slime, and fish communities. The metrics
developed from data collection include taxa richness, Hilsenhoff Biotic Index, ratio of
scrapers/filtering collectors, ratio of EPT and chironomid abundances, percent contri-
bution of dominant taxon, EPT index, community similarity index, and ratio of
shredders/total. Similar to RBP II, each metric is given a score when compared to that
of a reference condition, and all metrics are summed to determine the overall biologi-
cal condition. In cases where final scores border on established ranges, additional data
such as those from water quality, habitat, and physical assessments can aid in the final
evaluation of biological condition.
Invertebrate Community Index (ICQ. The ICI was developed by the Ohio Environmen-
tal Protection Agency as a principal measure of overall macroinvertebrate community
health (DeShon, 1995). The ICI is a single value calculated by summing 10 structural
and compositional community metrics, each of which is attributed a score of 0, 2,4,
or 6 points based on watershed area and comparisons with scores developed from
ecoregional reference sites. The 10 metrics collected in the development of the ICI are
total number of taxa, number of mayfly taxa, number of caddisfly taxa, number of
dipteran taxa, percent mayfly composition, percent caddisfly composition, percent
tribe tanytarsini midge composition, percent other dipteran and noninsect composi-
tion, percent tolerant organisms, and number of qualitative EPT taxa.
Table 11. Five Tiers of the Rapid Bioassessment Protocols
Level or
Tier
I
II
III
IV
V
Organism
Group
benthic
invertebrates
benthic
invertebrates
benthic
invertebrates
fish
fish
Relative Level
of Effort
low; 1-2 hr per site (no
standardized sampling)
intermediate; 1 .5-2.5 hr
per site (all taxonomy
performed in field)
most rigorous; 3-5 hr
per site (2-3 hr of total
are for lab taxonomy)
low; 1-3 hr per site (no
field work involved)
most rigorous; 2-7 hr
per site (1-2 hr per site
are for data analysis)
Level of Taxonomy/
Where Performed
order, family/field
family/field
genus or species/
laboratory
not applicable
species/ field
Level of Expertise
Required
one highly trained
biologist
one highly trained
biologist and one
technician
one highly trained
biologist and one
technician
one highly trained
biologist
one highly trained
biologist and 1-2
technicians
Source: Plafkin etal., 1989.
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Compendium oflbolsfar Watershed Assessment and TMDL Development
Structural and compositional metrics, rather than functional metrics, were chosen
because of their historic use and ease of derivation and interpretation (DeShon,
1995). The metrics chosen do, however, incorporate into the scoring scheme function-
ally based differences between macroinvertebrates over a range of stream conditions.
As with other multimetric approaches, the strength of the ICI is its ability to compare
the biological integrity of a stream with reference conditions (DeShon, 1995). With
changes to collection methodologies, metric selection, and reference conditions to
account for geographic setting and ecoregions other than those in Ohio, the ICI
approach can be used successfully to assess the condition of macroinvertebrate
communities throughout the country.
HBP V/Index ofBiotic Integrity (IBI). RBP V, which is also known as the IBI, is a broadly
based index that is firmly grounded in fisheries community ecology and is used to
measure the biological integrity of a waterbody. When tied to ecological systems, the
term "biological integrity" has been defined as the ability to support and maintain a.
balanced, integrated, adaptive community of organisms having a species composition,
diversity, and functional organization comparable to that of the natural habitat of the
region (Karr and Dudley, 1981). Systems with biotic integrity can withstand or rapidly
recover from most perturbations imposed by natural environmental processes and can
survive many of the major disruptions induced by humans. Biotic integrity is pos-
sessed by aquatic ecosystems in which composition, structure, and function have not
been adversely impaired by human activities.
The IBI was designed to include a range of attributes of fish assemblages. Its 12
metrics fall into 3 broad categories: species composition, trophic composition, and fish
abundance and conditions (Karr, 1981). These metrics assess attributes that are
assumed to correlate with biotic integrity. Individually, each metric provides informa-
tion about a specific attribute of the sampling site. Together, they characterize the
underlying biotic integrity of that site. The values of the 12 metrics, however, are
functions of the underlying biotic integrity; biotic integrity is not a function of the
metrics. The metrics developed by Karr et al. (1986) applied to warmwater fish
assemblages most commonly found in midwestern streams. They are not suitable for
the fish assemblages that inhabit coldwater and montane streams. Chandler and
Maret (1991) developed 20 metrics for coldwater, salmonid-dominated streams such
as those found in the western and northwestern United States. Applying Principal
Component Analysis and Multiple Discriminant Analysis to field data collected from
Idaho streams, Robinson and Minshall (1992) narrowed the list down to six important
metrics.
At a given site, data are obtained for each of these metrics and evaluated in light of
what might be expected at an unimpacted or relatively unimpacted site located in a
similar geographical region on a stream of comparable size. A numerical rating is then
assigned to each metric based on whether its evaluation deviates strongly from,
deviates somewhat from, or approximates expectations. The sum of the 12 ratings, in
turn, yields an overall site score. The strength of the IBI is its ability to integrate
information from individual, population, community, zoogeographic, and ecosystem
levels into a single ecologically based index of the quality of a water resource. A
recent review further discusses application of the IBI (Simon and Lyons, 1995).
Index of Weil-Being. The Index of WeU-being (Gammon, 1980; Hughes and Gammon,
1987) incorporates measures of species abundance and diversity estimates in approxi-
mately equal fashion, thereby representing the quality of fish assemblages more
realistically than a single measure of abundance or diversity.
The measures of abundance include the number and biomass of individuals, and the
Shannon-Weaver diversity index is calculated for number of individuals and biomass.
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Chapter 3. Ecological Assessment Techniques and Models
The sensitivity of the index can be increased for degraded environments by a minor
modification, resulting in a modified IWB. The computational formula remains the
same; however, any of 13 highly tolerant species, exotics, and hybrids are deleted
from the number and biomass components of the IWB. The tolerant and exotic species
are included in the two calculations for the Shannon-Weaver diversity index. The
modification is designed to eliminate the undesired effect caused by the high abun-
dance of tolerant species, while retaining the desired influence of the diversity indices.
The IWB is most frequently used in concert with the IBI and the Invertebrate Commu-
nity Index (ICI) to identify impact type (e.g., complex toxic) based on biological
response signatures (Ybder, 1991). This combination of ecological measures of com-
munity structure and function has been used for assigning causes of and sources to
aquatic life use impairments in Ohio streams and rivers.
Population Viability Analysis (fact sheet, page C.15). Population viability
analyses (PVAs) model the effects of demographic, genetic, or environmental variabil-
ity on population stability to examine how expected time to extinction changes with
the environment, population structure, or behavior. An important innovation of this
risk assessment method is the consideration of uncertainty due to unknown or unpre-
dictable events. Uncertainty is incorporated by modeling variation in population
parameters and estimating probabilities of extinction over specified periods of time
instead of using a single estimate for an unspecified time. PVAs have been used mostly
in a generalized sense to determine how a population will respond to environmental
changes, rather than specifically to assess risk from alternative management scenarios.
However, the method is potentially applicable to specific cases involving land develop-
ment.
The accurate projection of population growth requires a knowledge of the age struc-
ture of the population and the survival and fecundity of individuals of each age. This
is often achieved using a life table (or matrix) approach in which the demographic
parameters include annual rates of survival, growth or change among defined life
history stages, and fecundity. Life tables set out the fecundities and probabilities of
survival for each age class of individuals in a population and use an "accounting"
formulation to calculate future population size on the basis of current size and rates of
growth, death, and birth.
Food and Gill Exchange of Toxic Substances (FGETS) (fact sheet, page
C.I). The Food and Gill Exchange of Toxic Substances (FGETS) program is a FOR-
TRAN simulation model that predicts temporal dynamics of a fish's whole-body
concentration (ig chemical/(g live weight fish)) of nonionic, nonmetabolized organic
chemicals that are bioaccumulated from water and food (Barber et al., 1988,1991).
FGETS also calculates the time to reach the chemical's lethal activity by assuming that
the chemical elicits its pharmacological response through a narcotic mode of action.
FGETS can be used to analyze the bioaccumulation of organic chemicals under
laboratory or field conditions, and its predictions have been shown to agree well with
both types of data. For laboratory applications, FGETS can be used to model either
constant flow or static exposures. For field assessments, FGETS can be used to simu-
late the chemical bioaccumulation in multiple fish species that are exposed to either
constant or time-varying water concentrations and that feed on either single or
multiple food resources. For such assessments, FGETS can be configured to predict the
dietary accumulation of chemicals in fish that feed on multiple fish species, plankton/
drift organisms, and benthos. The relative contributions of these food items can be
specified as a function of either the fish's age or size.
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Compendium of "took for Watershed Assessment and TMDl Development
FGETS considers both the biological attributes of the fish and the physicochemical
properties of the chemical that determine diffusive exchange across gill membranes
and intestinal mucosa. Important biological characteristics addressed by the model are
the fish's gill and intestinal morphometry, the body weight of the fish, and the frac-
tional aqueous, lipid, and structural organic composition. Relevant physicochemical
properties are the chemical's aqueous diffusivity, the molar volume, and the n-
octanol/water partition coefficient, which is used as a surrogate to quantify chemical
partitioning to the fish's lipid and structural organic fractions. FGETS is parameterized
for a particular fish species by means of morphological, physiological, and trophic
databases that delineate the fish's gill morphometry, feeding and metabolic demands,
and body composition. Presently, joint water and food exposure is parameterized for
salmonids, centrarchids, cyprinids, percids, and ictalurids.
AQUATOX. AQUATOX is an ecosystem fate and effects model authored by Dr. Rich-
ard A. Parks that is being developed by EPA's Office of Science and Technology. Upon
completion, the model will predict the ecological effects of chemical (nutrient and
toxic) loadings from their point of entry to the top of the aquatic food chain by
estimating the amount of toxicant per unit biomass over time. AQUATOX, which will
run in a Microsoft Windows-95 format, accounts for many ecological processes,
including nutrient effects (e.g., growth, algae biomass, and nutrient recycling), acute
toxicity and subsequent effects on trophic structure, feeding and predation rates,
bioaccumulation, and chemical conversions (e.g., nitrification, volatilization, and
hydrolysis). Potential applications of AQUATOX are the evaluation of different man-
agement scenarios, testing relative risks of several stressors, and factoring biological
componenets into water quality modeling. Because AQUATOX is currently undergoing
testing and verification (and is not available for distribution), no fact sheet or analysis
of capabilities has been included in this compendium.
For more information, contact Marjorie Coombs Wellman, EPA Office of Science and
Technology (4305), Standards and Applied Science Division, 401 M Street, SW,
Washington, DC 20460, Ph (202) 260-9821, FAX (202) 260-9830 or
weUman.marjorie@epamail.epa.gov.
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'Chapter 4. Model Selection
4
Model Selection
AAodel Selection Criteria (as
adapted from Nix, 1990)
1. Hardware Availability. With
the evolution of the ever more
powerful personal computer, this
factor becomes less constraining
but still must be considered
given today's technology.
2. Availability of trained
personnel. Water resource
models are becoming more user
friendly and are thus easier to
use by the lay-person. However,
the expertise of an experienced
water resources or environmen-
tal engineer is invaluable for
developing model parameters
and critically evaluating model
results.
3. Long-term commitment to
the model. If a number of
future projects will require the
use of a particular model, it may
be advantageous to use this
particular model for a current
project even when the model is
less than optimal for the current
application. Sometimes it may be
more beneficial to invest heavily
in one model than to switch
models from project to project.
4. In-house model experience.
Experience with a particular
model is often available in-
house. In this case, the fact that
no "warm-up period" is necessary
in learning a new model may out
weigh the costs of using a less
than optimal model.
5. Acceptance and support of
the model. If a model is not
widely used, it becomes more
difficult to establish credibility
and to interpret its results.
6. Commitment to modeling
as a tool. The various interest
groups involved in a project
study must be willing to accept
model results if the model is to
be useful in implementing policy
decisions.
Although mathematical simulation models and assessment techniques are
becoming more integrated at the various levels of watershed and water
quality analysis, selecting the model or set of models that best matches
project objectives is still a complex task since more models are becoming
available to users. Models and assessment techniques reviewed in the
previous chapters cover a wide range of functions and can be applied, either
directly or with minimum modification, to support a majority of decisions
associated with watershed planning and management issues.
As the federal government and state and local agencies progress in resolving
the programmatic issues associated with watershed characterization and
management, there is an increasing need for analytical tools to support the
decision-making process. This increased need is further amplified by the
adoption of a holistic and watershed-based approach to resource manage-
ment. This broader, ecologically based approach involves integrated analy-
ses of multiple stressors by incorporating the physical, biological, and
chemical components of a watershed system. The success of the watershed
approach resides in the ability to consider multiple spatial scales, from
upland terrestrial habitats to downstream receiving waterbodies, and to
consider the time-varying and dynamic loading conditions. Watershed
management decisions require the consideration of existing conditions, as
well as the projection of anticipated future changes in various components
of a watershed. The most challenging tasks of understanding the cause-
effect relationships within a watershed include selecting the most appropri-
ate mix of assessment tools, developing the most cost-effective procedures to
use these tools, and generating the needed information to support the
decisions made using the tools.
Selection of a watershed or water quality model or a combination of models
is an important decision, not only because of the time and resources a
modeling effort involves, but also because of the technical expertise required
to maintain a model. Before selecting a model or set of models, watershed
managers should determine both the need for modeling and the commit-
ment of their program in using mathematical models to support manage-
ment decisions. The success of adopting a model or set of models usually
requires a firm commitment to provide the human and financial resources
necessary to apply and contribute to further enhancement and development
of the model(s). Nix (1990) compares the selection and use of a mal-
adapted model to using no model at all. Such maladapted models can
produce misleading results and lead to further complications and controver-
sial decisions. Nix (1990) also advises that it is desirable to select a model
that meets the most application requirements and has demonstrated applica-
tions and continuous support from the developer and user communities.
Even if the model is not ideal, Nix (1990) recommends that the user allow
for the development of in-house expertise, rather than switching models
from application to application.
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Compendium oflbolsjbr Watershed Assessment and TMDL Development
4.1
Preliminary
Model Selection
Considerations
Model Selection
and Project Steps:
TMDL Example
Project Step Level of Detail
Identify
impairments
No to low level
Priority ranking Low to
medium
TMDL Medium to
development high
Implementation High (design)
Assess
effectiveness
Medium to
high
The first section of this chapter provides a brief description of considerations that
outline the preliminary steps in selecting a model. Such considerations will assist
users in characterizing the intended use of the model(s). Well-defined objectives can
help users select the appropriate model, and design the model application. The
remaining sections of this chapters provide comparative tables useful in selecting
watershed loading models, receiving water response models, and ecological assess-
ment techniques.
Following is a list of considerations that can support the development of a framework
for reviewing and selecting the appropriate model or assessment technique to meet
project needs:
A single model might not be enough. Previous chapters of this document
presented an extensive review of modeling and technical tools available to
environmental managers to address multiple and often conflicting objectives and
associated technical and economic constraints. Because most of these tools were
developed in isolation to address a specific objective, they do not offer the
completeness necessary to cover all management decisions. Individually, they do
not explain the complex interrelationships governing all watershed stressors and
the economic and environmental implications. One of the major dilemmas
facing today's watershed manager is not only "which model to use," but also
"how many models or techniques should be used" to allow for an integrated
analysis and to support the conceptual design of an "integrated watershed protec-
tion approach." As the number and sources of stressors increase, a single model
might not be able to represent all the watershed components and pathways of
interest. A combination of tools working together within a structured framework
to capture the spatial and temporal variability of each stressor and the interaction
among these stressors and the resulting impact may be needed.
A single model can be applied at various levels of detail. Throughout this
document, model and assessment techniques have been grouped from simple to
complex classes. Nevertheless, more sophisticated models (e.g., SWMM, HSPF) can
be applied at various levels of detail. In many cases, it is advantageous to adopt a
more detailed model to address various scientific and engineering applications than to
continuously switch models from one phase of a project to another or from one
project to another (Nix, 1990). As indicated in the previous chapters, no one model is
ideal, and although most simple and mid-range models can provide valuable informa-
tion for screening- and planning- level decisions, they are of a little use for advanced
phases in the development of TMDLs or siting and designing of management plans.
Models/techniques should be matched with the project phase. Most
watershed or water quality management studies are performed in several phases,
ranging from the screening and planning level to more detailed analysis and design of
management measures. For example, the TMDL process in some cases employs a
phased approach. In the preliminary phases of a project, screening-level tools are
usually sufficient to support management decisions associated with prioritization and
ranking. During this phase, the model results are typically used for relative compari-
sons. As projects move to advanced phases dealing with TMDL development or the
design of management measures to meet certain water quality or ecological goals, a
higher degree of accuracy is required of the model prediction results. During these
advanced phases, model selection and configuration need to be defensible, additional
data and monitoring is required, and the results must be verified.
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Chapter 4. Model Selection
Characterize Man-
agement Decisions
Types of anticipated use
• Compliance and permitting
• Continuous management
of a resource(s)
• Watershed development
considering management
of point and nonpoint
sources
• "One-shot" modeling
effort
Impact and significance of
decisions
• Ecological impact
• Human health impact
• Economic impact
Level of defensibility
• Level of accuracy of model
results
• Calibration and verification
needed
Characterize Ecological
Components to be
Addressed by a Model
Prepare check lists
• Known impacted
environments
• Suspected stressors
Identify potential
interrelationships
• Stressor vs. type of impact
Identify impact pathways
Define a list of pertinent
processes to model
Define the spatial and
temporal resolution needed to
model selected processes
4.2
Model Calibration
and Validation
Characterize management decisions. One major element that should be
considered in selecting a model(s) is the intended use of that model and the type and
importance of the decisions to be made based on model results. In many cases a
detailed analysis of implications of the project decisions and their required level of
defensibility will dictate not only the selection of a given model(s), but most impor-
tantly the way the model should be configured and applied.
When adopting the watershed approach and accepting the use of mathematical
models and assessment techniques to support management decisions, it is the water-
shed manager's responsibility to ensure that the selected model and the way it is
applied meet the minimum validity and accuracy required for a successful application.
A cost-effective approach can be taken by committing to use one or a limited set of
models, to provide sufficient human and financial resources, and to contribute to the
scientific and practical growth of the model through active participation within the
developer's and users' group supporting the model.
Characterize ecological components to be addressed by a model. Math-
ematical models are developed based on a set of algorithms representing environmen-
tal processes and pathways. The most detailed model available might not include all
the required processes needed to simulate a given multiple-stressor problem; the idea
is to find a model that best fits the problem at hand and provides the flexibility for
further enhancement and development. A simple way of generating selection criteria
to assist in finding a model that best fits the short- and long-term decision-making
needs is to develop a series of checklists based on available problem statements
characterization studies and current understanding of watershed stressors. Such a
checklist will include an inventory of known impacted environments and suspected
stressors. This approach allows for determining interrelationships between the
stressors, impacts, and corresponding pathways, and also permits identifying and
ranking impact processes (e.g., agricultural nonpoint source loading) that should be
represented in a selected model. Furthermore, the analysis of such processes and
review of available literature and past studies will allow the model user to define the
minimum spatial and temporal resolution necessary to represent each process within
the desirable accuracy.
Define anticipated types of management alternatives to be modeled/
assessed. The objectives of most watershed and water quality projects consist of
developing potential management and restoration alternatives. The strength of the
selected model or assessment technique provides the ability to evaluate such manage-
ment and restoration plans and therefore display the environmental and economic
trade-off between plans. One set of selection criteria that should be considered in
evaluating models is the ability to simulate the anticipated management practices for
the project.
The results of loadings, receiving water, and ecological simulations are more meaning-
ful when they are accompanied by some sort of confirmatory analysis. The capability
of any model to accurately depict water quality conditions is directly related to the
accuracy of input data and the level of expertise required to operate the model. It is
also largely dependent on the amount of data available. Detailed models lacking the
required verification calibration and validation are limited in accuracy.
Verification involves checking the governing equations of a model to determine if they
have been accurately entered. Calibration involves minimization of deviation be-
tween measured field conditions and model output by adjusting parameters of the
model (Jewell et al., 1978). Data required for this step are a set of known input
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Comptndlum oflbolsjbr Watershed Assessment and TMDL Development
Verification: Checking
the equations within a
model to ensure they have
been accurately entered.
Calibration: Testing and
tuning of a model to a set of
field data not used in the
model validation step or in
the development of the
model; also includes
minimization of deviations
between measured filed
conditions and output of a
model by selecting appropri-
ate model coefficients.
Validation: Subsequent
testing of a pre-calibrated
model to additional field
data, usually under different
external conditions, to
further examine the model's
ability to predict future
conditions.
Modeling Management
Afternatives
Define the type of anticipated
management practices
• Types of stressors (pollutant,
runoff, temperature,
imperviousness, etc.)
• Nonstructural practices
• Structural practices
Define the level of simulation
needed
• Compare management plan
• Generate design criteria to
meet specific goals
4.3
Watershed
Loading Models
values along with corresponding field observation results. The results of the
sensitivity analysis provide information as to which parameters have the greatest
effect on output. For the best results, CSO models should be calibrated during
storm events as opposed to dry flow periods (Water Pollution Control Federation,
1989).
Validation involves the use of a second set of independent information to check the
model calibration. The data used for validation should consist of field measure-
ments of the same type as the data output from the model. Specific features such
as mean values, variability, extreme values, or all predicted values may be of
interest to the modeler and require testing (Reckhow and Chapra, 1983). Models
are tested based on the levels of their predictions, whether descriptive or predictive.
More accuracy is required of a model designed for absolute versus relative predic-
tions. If the model is calibrated properly, the model predictions will be acceptably
close to the field observations.
In many cases, observed data for model calibration and validation might be insuffi-
cient or unavailable. Model selection must be based on an assessment of the
available data. Screening-level applications might be possible with limited input
data. As noted by Donigian and Rao (1988), most models are more accurate when
applied in a relative rather than an absolute manner. Model output data concerning
the relative contribution of a watershed to overall pollutant loads is more reliable
than an absolute prediction of the impacts of one control alternative viewed alone.
When examining model output from watershed-pollution sources, it is important to
note three factors that can influence the model output and produce unreasonable
data. First, suspect data can result from calibration or validation data that are
insufficient or inappropriately applied. Second, any given model, including detailed
models, might not represent enough detail to adequately describe existing condi-
tions and generate reliable output. Finally, modelers should remember that all
models have limitations and the selected model might not be capable of simulating
desired conditions. Model results must therefore be interpreted within the limita-
tions of their testing and their range of application. Inadequate model calibration
and validation can result in spurious model results, particularly when used for
absolute predictions. Data limitations might require that model results be used only
for relative comparisons.
Based on a review of project needs and objectives, and the considerations discussed
above, the user can select the appropriate tools for watershed assessment or TMDL
development. In the following sections, each category of modeling is discussed, and
some of the considerations for selection of a specific model within each category are
reviewed.
Most watershed loading models include three components: a hydrology component,
which estimates the quantity of runoff and streamflow generated from the water-
shed or subwatersheds; an erosion and sediment component, which drives the
amount of sediment delivered to a receiving waterbody; and a quality component,
which computes the pollutant loadings. The basic simulation functions used in each
model to generate pollutant loadings are presented in Tables 12,13, and 14. These
tables also present the type of pollutant handled by each model and the correspond-
ing computation time steps.
As shown in Tables 12 through 14, most models are based on similar mathematical
formulations. The curve number equation (CNE) developed by the USDA-SCS is
widely used for simulating runoff and stream flows (e.g., SITEMAP, GWLF, P8-UCM,
AGNPS, STORM, SWRRBQ), and the Universal Soil Loss Equation (USLE) is com-
monly used for determining erosion and sediment yield from rural areas or water-
sheds (e.g., EPA screening procedures, Water Screen, Watershed, SLOSS-PHOSPH,
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Chapter 4. Model Selection
Table 12. A Descriptive List of Model Components - Simple Methods
Model
EPA
Screening
Procedures
The Simple
Method
RGQrssston
Method
SLOSS/PHOSP
Watershed
RHWA
WMM
Man
Land Use
Mixed
watershed
Urban
Urban
Rural
Mixed
watershed
Highways
Mixed
watershed
Hydrology
N/A
Runoff
coefficient
WA
N/A
N/A
Runoff
coefficient,
observed
data
Runoff
coefficient
Erosion/
Sediment
USLE-MUSLE
N/A
N/A
USLE
USl£
N/A
N/A
Pollutant
Load
Loading functions,
potency factors
Mean
concentration
Regression
equations
Loading functions
Unit area loadings
Median
concentration
Event mean
concentration
Pollutants
Wide range1
NURPdata:
TSS.P,
metals, O&G
TSS,N,P,
COD, metals
P
Wide range
TSS, N, P,
organic,
metals
N, P, lead,
zinc
Time Scale
Mean annual
Variable
(annual,
monthly,
event)
Storm event
Annual
Annual
Storm event
Annual
'Depends on available pollutant parameters and default data.
N = nitrogen O&G = oil and gas P = phosphorus TSS =
total suspended solids COO = chemical oxygen demand
Storm event simula-
tion: The use of a model to
simulate the response to a
single storm event.
Continuous simulation:
The use of a model to
simulate the response of a
catchment to a series of
storm events and the
hydrological processes that
occur between them.
GWLF, AGNPS, STORM, SWRRBQ). Pollutant loadings from rural areas are often
calculated based on loading functions or potency factors (e.g., EPA screening proce-
dures, Water Screen, AGNPS, SWRRBQ, HSPF). For urban areas, unit area loading
rates (e.g., GWLF) or buildup and wash-off functions (e.g., STORM, SWMM) are
widely used. The advantage of the CNE- and USLE-based models is that detailed
default parameters are available for a wide variety of soil conditions and agricultural
management techniques. The differences among models using similar simulation
functions reside in the degree of spatial discretization they use, the number of pro-
cesses for which they account, and the computational time steps they use.
Many of the simple methods do not take hydrologic processes into account when
simulating pollutant loads. When dealing with urbanized areas, simple methods
usually generate runoff based on empirical or statistical relationships between runoff
coefficients and the degree of imperviousness (e.g., the Simple Method, FHWA, and
WMM). It is, however, difficult to extrapolate such relationships to rural and agricul-
tural areas.
Detailed models use more complex formulations for simulating runoff and sediment
yield. The hydrology component generally involves a set of deterministic equations to
represent the elements of the water balance equation (e.g., infiltration, evapotranspi-
ration, groundwater recharge and/or seepage, depression storage). These models also
use a physical description of the erosion and sediment yield mechanisms (e.g., soil
detachment, transport, and deposition). Predictions of pollutant wash-off are usually
made based on exponential decay functions (e.g., SWMM) with hourly time steps.
Default values for parameters are pollutant- and site-specific and therefore might not
be readily available, making calibration difficult and time-consuming. In most cases,
additional laboratory testing and field measurement might be required.
-------
Compendium oflbokjbr Watershed Assessment and TMDL Development
Table 13. A Descriptive List of Model Components - Mid-Range Models
Model
SITEMAP
GWLF
P8-UCM
Auto-QI
AGNPS
SIAMM
Main
Land Use
Mixed
watershed
Mixed
watershed
Urban
Urban
Agriculture
Urban
watershed
Hydrology
SCS curve
number
SCS curve
number
SCS curve
number
(modified),
TR20
Water balance
SCS curve
number
Small
storm-based
coefficient
Erosion/
Sediment
N/A
Modified
USLE
N/A
N/A
Modified
USLE
WA
Pollutant
Load
Runoff
concentration
Unit loading
rates
Nonlinear
accumulation
Accumulation
and wash-off
Potency factors
Nonlinear
accumulation
and wash-off
Pollutants
N,P
N,P
TSS, N, P,
metals
Wide Range
N,P
N, P, COD
bacteria, metals
Time Scale
Storm event,
Continuous
Storm event.
Continuous
Storm event.
Continuous
Storm event.
Continuous
Storm event
Storm event,
Continuous
'Depends on available pollutant parameters and default values.
N = nitrogen P = phosphorus TSS = total suspended solids
COD = chemical oxygen demand
Table 14. A Descriptive List of Model Components - Detailed Models
Mode)
STORM
ANSWERS
DR3M-QUAL
SWRRBWCy
SWAT
SWMM
HSPF
Main
Land Use
Urban
Agriculture
Urban
Agriculture
Urban
Mixed
watershed
Hydrology
Runoff
coefficient -
SCS curve
numbers- Unit
hydrograph
Distributed
storage model
Surface storage
balance
kinematic wave
method
SCS curve
number
Nonlinear
reservoir
Water balance
of land surface
and soil
processes
Erosion/
Sediment
USLE
Detachment
transport
equations
Related to
runoff volume
and peak
Modified
USLE
Modified
USLE
Detachment/
wash-off
equations
Pollutant
Load
Buildupfwash-
off functions
Potency
factors
(correlation
with sediment)
Buildup/wash-
off functions
Loading
functions
Buildup/wash-
off functions
Loading/wash-
off functions
and sub-
surface
concentrations
Pollutants
P, N, COD,
metals
N/A
TSS, N, P,
organics,
metals
N, P, COD,
metals,
bacteria
Wide range'
Wide range1
Time Scale
Continuous
Storm event
Continuous
Continuous
Storm event
continuous
Storm event
continuous
'Depends on available pollutant parameters and default values.
N - nitrogen P = phosphorus TSS = total suspended solids COD = chemical oxygen demand
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Chapter 4. Model Selection
The type and amount of input data required for operation, calibration, and verifica-
tion of the model and the output results should be considered in the model selection
process. Depending on the type of formulations the model uses, input data can range
from simple watershed characteristics to hourly meteorological parameters, pollutant
transformation kinetic coefficients, and field onitoring data. Tables 15, 16, and 17
present a brief summary of input and output information for each of the models
reviewed.
Novotny and Chesters (1981) have developed three sets of input parameters that
might be required for a typical modeling application (Table 18). Interpretation of the
type and amount of data required, along with information contained in the preceding
tables, can be used to evaluate the time and resources required to apply a given model
for a given situation or project. For a detailed listing of input requirements, refer
directly to the model documentation.
Watershed loading models are usually developed to target a specific setting, character-
ized primarily by land use or land activity. Few models are developed to evaluate
watersheds with mixed land uses. Among the detailed models, HSPF appears to be
the most versatile for watersheds with complex land use/land cover. SWMM, STORM,
and DR3M-QUAL are designed primarily for urban areas, while ANSWERS and
SWRRB are primarily agricultural models. Among the mid-range models, SITEMAP
and GWLF are the two models that account for both rural and urban watersheds. The
GWLF model offers the possibility of generating long-term time series of pollutant
loadings at various time steps, allowing analysis of seasonal and interannual variabili-
ties. GWLF also allows evaluation of watershed response to changes in land use
patterns and point and nonpoint source loadings. Urban models such as P8-UCM and
Table 15. Input and Output Data - Simple Methods
Models
EPA
Screening
Procedures
The Simple
Method
Regression
SLQSS/
PHOSPH
Watershed
FHWA
WMM
Main Input Data
Watershed and land use data
Loading factors (default values)
Annual rainfall data
Land use and impetviousness data
Pollutant mean concentration
BMP removal efficiencies
Mean annual rainfall
Mean minimum January temperature
Drainage areas and land use
Percent imperviousness
Rainfall erosh/rty factor
Soil, crop, topography, and land use data
Rainfall erosn/Hy factor
Land use and soil parameters
Unit loading rates
BMP cost information
Site and receiving water data
Row and storm event concentrations
Land use and soil data
Annual precipitation and evaporation
Inputs from baseflow and precipitation
Event mean concentrations in runoff
Reservoir, lake, or stream hydraulic
characteristics
Removal efficiencies of proposed BMPs
Output Information
Mean annual sediment and pollutant loads
Runoff volume and pollutant
concentration/load, storm or annual
Mean annual storm event toad and confidence
interval
Mean annual loads of sediment and
phosphorus
Mean annual pollutant toads;
BMP cost-effectiveness
Statistics on storm runoff and concentrations;
impacts on receiving water
Annual urban and rural pollutant loads from
point and nonpoint sources, including septic
tanks; toad reductions from combined effects
of multiple BMPs; in-lake nutrient
concentrations as related to trophic state;
concentrations of metals
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Compendium oflbokfor Watershed Assessment and TMDL Development
Table 16. Input and Output - Mid-Range Models
Models
SITEMAP
GWLF
P8-UCM
Auto-QI
AGNPS
SLAMM
Main Input Data
Meteorologic and hydrologic data, hourly
or daily (maximum one year)
Watershed and channel parameters
Point sources and pollutant parameters
(e.g., decay)
Meteorologic and hydrologic data, daily
Land use and soil data parameters Nutrient
loading rates
Meteorologic and hydrologic data, hourly
storm or storm sequence
Land use and soil parameters
BMP information
Hourly/daily rainfall
Watershed and land use data
BMP removal rates
Watershed, land use, management, and
soil data
Rainfall data, topography
BMP removal data
Hourly rainfall data
Pollution source characteristics, areas, soil
type, imperviousness, and traffic Structure
characteristics
Output Information
Runoff and nutrient loadings
Pollution load allocations
Monthly and annual time series of runoff,
sediment, and nutrients
Daily runoff and pollutant loads
BMP removal efficiencies
Continuous or storm event simulation of
runoff and selected pollutants
Storm runoff volume and peak flow Sediment,
nutrient, and COD concentrations
Pollutant load by source area
BMP evaluation and cost estimates
SLAMM were mainly designed for evaluating management practices to control urban
stormwater runoff. Simple methods use generic empirical relationships that can be
used in both rural and urban settings provided site-specific or default values are
available.
Model applications may be classified as screening, intermediate, or detailed depend-
ing on the focus and objectives of the application. Simple methods are most fre-
quently used for screening applications; however, mid-range and detailed models
allow for a wider range of applications. Screening applications are generally per-
formed at the preplanning level, with specific objectives such as comparisons of the
relative contribution of point and nonpoint sources using a relatively limited set of
available information. Screening analyses can consider a broad range of land use
types and sources and can be performed at various stages of project development
(e.g., planning, evaluation of alternatives, preliminary design). At the planning level,
screening applications can be directed toward scoping the project objective and
identifying general areas where controls or additional sampling might be required.
Intermediate applications provide a more detailed description of the geographic
variables that contribute to nonpoint pollution, in addition to consideration of mul-
tiple point sources. Intermediate applications can assist in the identification of
specific point and nonpoint source activities and in preliminary selection of pollution
control options incorporating a higher degree of spatial variation within land uses.
As it becomes necessary to accurately distinguish differences in pollutant characteris-
tics from multiple-source areas, pollutant behavior is considered in more detail and a
more mechanistic description of pollutant generation, transformation, and removal by
various control practices is required. Detailed applications are, therefore, necessary to
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Chapter 4. Model Selection
Table 17. Input and Output Data - Detailed Models
Models
Main Input Data
Output Information
STORM
Hourly rainfall data
Buildup and wash-off parameters
Runoff coefficient and soil type
Event-based runoff and pollutant loads
Storage and treatment utilization and
number of overflows
Hourly hydrographs and pollutographs
ANSWERS
Hourly rainfall data
Watershed, land use, and soil data
BMP design data
Predicts storm runoff (volume and peak
flow)
Sediment detachment and transport Analysis
of relative effectiveness of
agricultural BMPs
DR3M-QUAL
Meteorologic and hydrologic data Watershed
characteristics related to runoff
Channel dimensions and kinematic wave
parameters
Characteristics of storage basins
Buildup and wash-off coefficients
Continuous series of runoff and pollutant
yield at any location in the drainage
system
Summaries for storm events
Hydrographs and pollutographs
SWRRBWQ/
SWAT
Meteorologic and hydrologic data Watershed
and receiving waterbody
parameters
Land use and soil data
Pond and reservoir data
Continuous water and sediment yield
Peak discharge
Water quality concentrations and loads
SWMM
Meteorologic and hydrologic data
Land use distribution and characteristics
Accumulation and wash-off parameters
Decay coefficients
Continuous and event-based runoff and
pollutant loads
Transport through streams and reservoirs
Analysis of control strategies
HSPF
Meteorologic and hydrologic data
Land use distribution and characteristics
Loading factors and wash-off parameters
Receiving water characteristics
Decay coefficients
Time series for runoff and pollutant
loadings
Analysis of impacts on receiving water
Analysis of controls
provide either storm-based or continuous simulation of water and water
quality processes and to assist in developing design criteria for achieving project
objectives.
The potential range of applications of watershed models in planning, evaluation of
management measures, and analysis of impacts on the quality of receiving waters is
illustrated in Tables 19, 20, and 21. The tables show that the majority of the models
can be used for screening-level applications. The simple methods, in particular,
provide only an order-of-magnitude estimate on an annual basis and therefore are
limited to screening applications at the planning level. Some of the mid-range models
(e.g., GWLF, SITEMAR and AGNPS) incorporate point and nonpoint source pollution
routines and are also good candidates for screening activities. SLAMM, P8-UCM, and
SIMPTM are primarily urban runoff models, and their application to evaluation of
urban stormwater control practices and strategies might be useful at an intermediate
level. SWMM, HSPF, DR3M, STORM, and SWRRB stand out from the others as
models capable of providing a detailed indication of the contribution of pollutants
from various point and nonpoint sources. Their simulation capabilities allow for
evaluation of control strategies and development of design criteria.
Application of detailed models such as HSPF and SWMM for screening purposes, using
estimated default values for a number of parameters, can reduce time and input
requirements. However, representative default values for many of the detailed models
-------
Compendium of "fools for Watershed Assessment and TMDL Development
Table 18. Input Data Needs for Watershed Models
1. System Parameters
Watershed size
Subdivision of the watershed into homogenous subareas
Imperviousness of each subarea
Slopes
Fraction of impervious areas directly connected to a channel
Maximum surface storage (depression plus interception storage)
Soil characteristics including texture, permeability, credibility, and composition
Crop and vegetative cover
Curb density or street gutter length
Sewer system or natural drainage characteristics
2. State Variables
Ambient temperature
Reaction rate coefficients
Adsorption/desorption coefficients
Growth stage of crops
Daily accumulation rates of litter
Traffic density and speed
Potency factors for pollutants (pollutant strength on sediment)
Solar radiation (for some models)
3. Input Variables
Precipitation
Atmospheric fallout
Evaporation rates
Source: After Novotny and Chester, 1981.
Table 19. Range of Application of Watershed Models—Simple Methods.
Simple
Methods
EPA Screening
The Simple Method
Regression
SLOSS/PHOSPH
Watershed
FWHA
WMM
Watershed Analysis
Screening
•
•
•
0
•
•
•
Intermediate
—
-
-
-
-
-
o
Detailed
—
-
-
-
-
-
-
Control Analysis
Planning
—
O
-
-
O
o
*
Design
—
-
-•
-
-
-
-
Receiving
Water
Quality
O
-
-
-
-
0
e
High
Medium
O Low
- Not Available
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Chapter 4. Model Selection
Table 20. Range of Application of Watershed Models—Mid-Range Models
Mid-Range
Methods
SITEMAP
GWLF
P8-UCM
Auto-QI
AGNPS
SLAMM
Watershed Analysis
Screening
•
•
•
•
•
•
Intermediate
O
0
Q
•
•
e
Detailed
O
O
«
0
O
Q
Control Analysis
Planning
O
-
0
«
•
•
Design
-
-
•
O
O
Q
Receiving
Water
Quality
O
-
-
O
O
O
High
Medium
. Not Incorporated
Table 21. Range of Application of Watershed Models—Detailed Models
Detailed
Methods
STORM
ANSWERS
DR3M-QVAL
SWRRBQ/SWAT
SWMM
HSPF
Watershed Analysis
Screening
•
•
Q
O
«
«
Intermediate
•
•
•
•
•
•
Detailed
O
9
•
•
•
•
Control Analysis
Planning
•
•
•
•
•
•
Design
O
O
Q
O
O
Q
Receiving
Water
Quality
O
O
O
0
-
•
High
Medium
Low
Not Incorporated
4.4
Receiving Water
Models
are difficult to obtain. In addition, their accuracy as screening tools might be jeopar-
dized by replacing mechanistic equations with their simplified forms and including
inappropriate default values. Urban stormwater runoff models, such as SWMM, HSPF,
SLAMM, P8-UCM, and DR3M-QUAL, are capable of providing design criteria for a
number of structural practices. Models with such capabilities, however, are data-
intensive and require trained profession-als to operate the model, select appropriate
default values, and interpret the results.
The major considerations in the selection of one or more models to simulate a receiv-
ing waterbody's response to various pollutant loading scenarios are (1) the waterbody
type; (2) whether flow rates are to be represented as steady or unsteady; (3) the
various hydrodynamic, water quality, toxics, and sediment processes that need to be
modeled; and (4) data available for model parameterization, calibration, and verifica-
tion. The key components of hydrodynamic, steady-state water quality, and dynamic
water quality models are shown in Tables 22, 23, and 24, respectively.
Unsteady flow rates can be simulated by a separate hydrodynamic model (RTVMOD-
H, DYNHYD5, EFDC, CH3D-WES) and input to a water quality model in a external
linkage. Some models such as CE-QUAL-RIV1 and CE-QUAL-W2 allow for internal
hydrodynamic simulation. Selection of hydrodynamic models depends on the water-
body types and circulation processes that affect water quality conditions. For rivers
-------
Compendium oflbokjbr Watershed Assessment and TMDL Development
Table 22. A Descriptive List of Model Components • Hydrodynamic
Models
Model
Dimension
Horizontal
Coordinate
System
Vertical
Coordinate System
Vertical
Mixing
Solution
Technique
Externally Coupled
R1VMOD-H
DYNHYD5
EFDC
CH3D-WES
1-D
1-D
1-D, 2-D
(x/y.x/z).
3-D
1-D, 2-D
(x/y, */z),
3-D
N/A
Link Node
Cartesian,
orthogonal
boundary fitted,
laterally averaged
Cartesian,
orthogonal
boundary fitted,
laterally averaged
N/A
N/A
Staircase Cartesian,
sigma
transformation to
local bathymetry
Staircase Cartesian
N/A
N/A
Turbulence
closure
Turbulence
closure
Implicit
Explicit
Runge-Kutta
Implicit
Implicit
Internally Coupled
CE-QUAL-
R1V1
CE-QUAL-
W2
HSPF
1-D
1-D, 2-D
(tfz)
1-D
N/A
Cartesian, laterally
averaged
N/A
N/A
Staircase Cartesian
N/A
N/A
Wind shear
N/A
Implicit (RIV1H)
Implicit
Implicit
that are laterally and vertically well-mixed, a one-dimensional representation is
generally sufficient. The one-dimensional formulation captures the longitudinal
transport processes that dominate in most river systems. Both RTVMOD-H and CE-
QUAL-RIV1 share the same one-dimensional formulation. DYNHYD5 is also applied to
river systems, although the model experiences numerical stability problems in high-
gradient streams. In lakes, where vertical stratification and mixing dominate, a two-
dimensional formulation is normally preferred. Mixing is often influenced by tem-
perature and most hydrodynamic models applied to lakes consider heat balance. CE-
QUAL-W2 is an example of a model that specializes in modeling vertically stratified
systems (x/z). Three-dimensional models such as EFDC can also be collapsed for a
two-dimensional representation. Full three-dimensional simulations are typically
reserved for estuarine and near coastal systems. Estuaries experience complex
circulation patterns due to tidal influences, freshwater inflows, wind-induced mixing,
temperature and salinity gradients, and physical geometry. Three-dimensional
models, such as EFDC and CH3D-WES, simulate many of these key features. In some
cases estuaries are simulated as one- (DYNHYD5/WASP5, TPM), or two-dimensional
systems in order to simplify the analysis process.
Some of the simple and easy-to-use models employ empirically based solution tech-
niques to assess eutrophication processes. Models such as EUTROMOD, PHOSMOD,,
EPA Screening Methods, and BATHTUB evaluate loading and lake/reservoir response
based on these empirically based statistical relationships. These models do not
explicitly describe each process (e.g., algal growth), resulting in low input data
requirements and limited calibration requirements. Drawbacks of such models
include limitations in application areas and accuracy. For example, EUTROMOD was
developed for lakes in North Carolina and has limitations for application in other
regions. PHOSMOD enhances the simplified empirical framework with consideration
-------
Chapter 4. Model Selection
Table 23. A Descriptive List of Model Components - Steady-State Water
Quality Model
Model
EPA
Screening
Methods
EUTROMOD
PHOSMOD
BATHTUB
QUAL2E
EXAMSII
TOXMOD
SYMPTOX4
TPM
DECAL
Waterfaody
Type
River, lake/
reservoir,
estuary,
coastal
Lake/reservoir
Lake/reservoir
Lake/reservoir
Rivers, (well-
mixed/
shallow lakes
or estuaries)
Rivers
Lake/reservoir
River/reservoir
Estuaries
Coastal
Parameters Simulated
Waterbody nitrogen,
phosphorus, chlorophyll , or
chemical concentrations
DO, phosphorus
DO, nitrogen, phosphorus,
chlorophyll
DO, CBOD, temperature, organic
N, ammonia, nitrite, nitrate,
organic P, dissolved
phosphorous, phytoplankton,
fecal coliform, arbitrary
nonconservative substances,
three conservative substances
Conservative and
nonconservative substances
Conservative and
nonconservative substances
Conservative and
nonconservative substances
DO, CBOD, NBOD, temperature,
ammonium, nitrate, nitrite,
organic nitrogen, total
phosphate, organic phosphorus,
salinity, inorganic suspended
solids, dissolved labile, and
refractory participate organic
carbon, dissolved silica,
partial late biogenic silica, fecal
coliform, total active metal
Sediment, conservative and
nonconservative substances
Processes Simulated
Physical
Dilution,
advection,
dispersion
Dilution
Dilution
Dilution
Dilution,
advection,
dispersion,
heat balance
Dilution,
advection,
dispersion
Dilution,
advection,
dispersion
Dilution,
advection,
dispersion
Dilution,
advection,
dispersion, heat
balance,
particle fate
Dilution,
advection,
dispersion,
particle fate
Chemical/Biological
First-order decay
empirical relationships
between nutrient
loading and
eutrophication indices
Empirical relationships
between nutrient
loading and
eutrophication indices
Empirical relationships
between phosphorus
loading and
eutrophication indices
Empirical relationships
between nutrient
loading and
eutrophication indices
First-order decay,
DO-BOD cycle,
nutrient-algal cycle
First-order decay,
process kinetics,
daughter products,
exposure assessment
First-order decay,
sediment burial and
release
First-order decay,
sediment exchange
First-order decay,
DO-BOD cycle,
nutrient-algal cycle,
carbon cycle, silica cycle,
benthic algae, sediment
digenesis
First-order decay
of the benthic flux component for long-term assessments of lake/reservoir concentra-
tions.
Water quality processes considered in a model help to define the ability of that model
to simulate the fate and transport of pollutants and the eutrophication process.
Models typically consider dissolved oxygen (DO) and biochemical oxygen demand
(BOD) relationships, and in some cases CBOD. Temperature, salinity, and bacteria can
also be modeled explicitly. Eutrophication models consider the nitrogen and phospho-
-------
Compendium of Tbolsjbr Watershed Assessment and TMDL Development
Table 24. A Descriptive List of Model Components - Dynamic Water
Quality Models
Mode!
DYNTOX
WASPS
CE-QUAL-
RIV1
CE-QUAL-
W2
CE-QUAL-
ICM
HSPF
Waterbody
Type
River
Estuary, river,
(well mixed/
shallow lake)
Rivers
Lakes
Estuaries,
rivers, lakes,
coastal
River, (well-
mixed/shallow
lakes)
Parameters Simulated
Conservative and
nonconservative substances
DO, CBOD, NBOD,
ammonium, nitrate, nitrite,
organic nitrogen, total
phosphate, organic
phosphorus, inorganic
suspended solids, fecal
coliform, conservative
and nonconservative
substances
DO, CBOD, temperature,
ammonia, nitrate, algae,
coliform, phosphate, organic
nitrogen
DO, CBOD, NBOD,
temperature, ammonium,
nitrate, nitrite, organic
nitrogen, total phosphate,
organic phosphorus, salinity,
inorganic suspended solids,
dissolved, labile, and refrac-
tory participate organic
carbon, dissolved silica, par-
ticulate biogenic silica, fecal
coliform, total active metal
DO, CBOD, NBOD,
temperature, ammonium,
nitrate, nitrite, organic
nitrogen, total phosphate,
organic phosphorus, salinity,
inorganic suspended solids,
dissolved, labile, and
refractory participate organic
carbon, dissolved silica, par-
ticulate biogenic silica, fecal
coliform, total active metal
DO, BOD, nutrients,
pesticide, sediment, organic
chemicals, and temperature
Processes Simulated
Physical
Dilution,
advection
Dilution,
advection,
dispersion,
reaeration
Dilution,
advection,
dispersion, heat
balance
Dilution,
advection,
dispersion, heat
balance
Dilution,
advection,
dispersion, heat
balance, particle
fate, sediment
digenesis
Dilution,
advection, heat
balance, particle
fate, cohesive/
noncohesive
sediment
transport
Chemical/Biological
First-order decay
First-order decay,
process kinetics,
daughter products,
hydrolysis, oxidation,
volatilization,
photolysis, equilibrium
adsorption. Settling,
DO-CBOD, nutrient-
algal cycle
First-order decay,
DO-CBOD, nutrient-
algal cycle
First-order decay,
DO-CBOD, nutrient-
algal cycle, carbon
cycle
First-order decay,
DO-BOD, nutrient-algal
cycle, carbon cycle,
silica cycle, zoo-
plankton, sediment
diagenesis
First-order decay,
process kinetics,
daughter products,
hydrolysis, oxidation,
volatilization,
photolysis, benthic
demand, respiration,
nutrient-algal cycle
rus cyd.es to model phytoplankton. Zooplankton and benthic algae are also modeled
in some cases. Some of the most comprehensive models include the silica cycle and
carbon cycle. A key consideration for model selection is the capability of the model to
simulate sediment oxygen demand (SOD) and fluxes of nutrients from the bottom
sediments (e.g., CE-QUAL-ICM, TPM). Some allow the user to specify constant flux
rates, while others include explicit simulation of sediment diagenesis (e.g., CE-QUAL-
ICM, TPM).
Among the steady-state models that specialize in eutrophication, only QUAL2E and
TPM provide detailed simulation of water quality processes. QUAL2E considers DO-
-------
Chapter 4. Model Selection
BOD and algal growth cycles, with limited consideration of sediment functions.
Although TPM is considered a steady-state model for estuarine assessment, it incorpo-
rates the same sophisticated water quality processes as CE-QUAL-ICM, such as carbon
and silica cycles and sediment diagenesis.
When the receiving waterbody's response over time is of interest, the CE-QUAL series
of dynamic models developed by the Corps of Engineers represent the most compre-
hensive water quality models available. CE-QUAL-RIV1 provides a more simplified
assessment of eutrophication processes and is best suited to application in well-mixed
streams and rivers where a one-dimensional representation is appropriate. CE-QUAL-
W2 can be applied to waterbodies where a two-dimensional representation is re-
quired, such as stratified lakes. It also includes simulation of algal, carbon, and silica
cydes. CE-QUAL-ICM represents the state of the art in public domain water quality
models. It includes comprehensive assessment of DO, algal cycles (three species),
carbon and silica cydes, and sediment diagenesis.
WASPS has been widely applied to estuarine and river assessment. It includes both
water quality and toxics modeling, offering a wide range of flexibility. The model
considers comprehensive DO and algal processes, but does not indude the carbon and
silica cydes or a full sediment diagenesis model. A pore water flux approach is
induded for sediment analysis; however, flux rates are more typically defined by the
user. WASPS can be used in full three-dimensional simulations by linking with an
appropriate hydrodynamic model such as EFDC or CH3D-WES. For many applica-
tions, the forcing functions and state variables induded in WASPS are more than
sufficient, although a comprehensive monitoring data set is still required for full
calibration and validation. Although CE-QUAL-ICM has expanded functionality, the
expertise and data requirements for it are considerably higher than those of WASPS
for a complete application.
Chemical and sediment processes considered define a model's capability to simulate
the fate and transport of toxics. Most models consider first-order degradation; some
consider process kinetics, in which degradation rates are predicted from various
environmental functions. Some models have the ability to track daughter products
resulting from degradation. Equilibrium linear sorption, generally characterized by a
partition coefficient, is considered by most models that spedalize in toxics. Models
that simulate sediment processes generally employ a mass balance approach with
deposition, resuspension, and burial rates input by the user. However, some models
can simulate deposition and resuspension rates for noncohesive and, less frequently,
cohesive sediment. HSPF is one of the few models that consider cohesive and
noncohesive sediment transport. For most models, such as WASPS, sediment trans-
port fluxes are input by the user.
SMPTOX4 and EXAMSII represent rivers as simplified systems, using steady-state
transport processes and first-order decay for modeling toxics. User interfaces result in
models that are more easily used but less predictive. DYNTOX uses a probabilistic
framework to assess the impact of toxic discharges over a range of historical and
future conditions, thereby allowing an analysis of the frequency and duration of
exposure above specified limits. Although technically a steady-state solution of first-
order decay and mixing, DYNTOX offers both continuous simulation and Monte Carlo
options. More detailed dynamic- and process-based toxics evaluation is offered by
WASPS's TOXIWASP module and HSPF.
Tables 25, 26, and 27 review the input and output data requirements and tables 28,
29, and 30 review the range of applicability for the receiving water models discussed.
Clearly, the three-dimensional formulations require the most rigorous data collection
efforts. Data input file preparation can be laborious for three-dimensional grid
systems. Some packages, such as EFDC, indude grid generation software to fadlitate
-------
Compendium oftookfor Watershed Assessment and TMDL Development
Table 25. Input and Output Data - Hydrodynamic Models
Model
Main Input Data
Output Information
Externally Coupled
RIVMOD-H
DYNHYD5
EFDC
CH3D-WES
River geometry and boundary
conditions, inflows, withdrawals,
meteorologic data
Waterbody geometry and
boundary conditions, inflows,
withdrawals, meteorologic data
River geometry, bathymetry,
geometric data, grid system, and
boundary conditions, inflows,
withdrawals, meteorologic data
River geometry, bathymetry,
geometric data, grid system, and
boundary conditions, inflows,
withdrawals, meteorologic data
Water surface elevations, velocities, and
temperatures
Water surface elevations, velocities
Water surface elevations, velocity
magnitude and orientation,
temperature, salinity, and conservative
tracer
Water surface elevations, velocity
magnitude and orientation,
temperature
Internally Coupled
CE-QUAL-RIV1
CE-QUAL-W2
HSPF
River geometry and boundary
conditions, inflows, withdrawals,
meteorologic data
Waterbody geometry, bathymetry,
and boundary conditions, inflows,
withdrawals, meteorologic data
River geometry and boundary
conditions, inflows, withdrawals,
meteorologic data
Water surface elevations, velocities, and
temperatures
Water surface elevations, velocities
longitudinal and vertical, and
temperature
Water surface elevations, velocities, and
temperatures
input file preparation. Water quality data gathering and input file creation can range
from the minimal (EUTROMOD, PHOSMOD) to extreme (CE-QUAL-ICM) for full
application. In some cases, extensive data-gathering efforts are needed to compile
data for calibration and validation of detailed modeling efforts (e.g., WASPS, CE-
QUAL-W2, CE-QUAL-ICM, HSPF).
The input data requirements range widely depending on the type and level of applica-
tion for the receiving water model. Simple empirically based models are generally
limited to screening or mid-range applications. QUAL2E incorporates more flexibility
and can be applied in a more rigorous fashion with full calibration and validation.
Even more detailed water quality models can initially be applied in a minimal fashion,
using only a subset of the available state variables. This allows the model to be set up
with available data and enhanced and broadened as information becomes available,,
WASPS and CE-QUAL-W2 models are examples of techniques that can be used at
various levels of detail. Some models, such as HSPF and CE-QUAL-RIV1, can be used
to examine river operations or structural controls. Similarly, CE-QUAL-W2 can be
used to evaluate reservoir operations and their effect on water quality.
Models that specialize in toxics and point source discharge assessments have been
used successfully in the development of permit limits and TMDLs. Although simple,
models such as DYNTOX and SYMTOX4 can be appropriate when the waterbody
under consideration meets the underlying assumptions of the model. DECAL is a
specialized model used in coastal areas for assessing the impacts of proposed ocean
outfalls.
-------
Chapter 4. Model Selection
Table 26. Input and Output Data - Steady-State Water Quality Model
Model
EPA
Screening
Methods
EUTROMOD
PHOSMOD
BATHTUB
QUAL2E
EXAMSII
TOXMOD
SYMPTOX4
TPM
DECAL
Main input Data
Gimate, waterbody morphometry, external
loadings
Qimate, lake morphometry, watershed
characteristics (land use)
Climate, lake morphometry, external
loadings, benthic flux
Qimate, lake morphometry, external
loadings
Climate, river geometry, stream network,
flow, boundary conditions, 26 physical,
chemical, and biological properties for
each reach, inflows/ withdrawals
Stream geometry, flow, chemical loadings,
total pollutant and suspended solids
concentrations, physical/chemical
coefficients
Lake morphometry, initial conditions,
external loadings, benthic flux
Stream geometry, flow, total pollutant and
suspended solids concentrations,
physical/chemical coefficients and rates
Qimate, geometric data, boundary
conditions, up to 1 40 parameters for full
simulation of water quality kinetics
Coastal geometry, tidal oscillations,
loadings, initial and boundary conditions
Output Information
Waterbody nitrogen, phosphorus,
chlorophylla, or chemical concentrations
Lake DO, nitrogen, phosphorus, and
chlorophylla concentrations
Lake DO, phosphorus, and chlorophyll
concentrations
Lake DO, nitrogen, phosphorus, and
chlorophylla concentrations
DO, CBOD, nitrogen, phosphorus, .
conservative and nonconservative
constituent concentrations
Chemical exposure, fate and persistence
Conservative and nonconservative
substance concentrations
Conservative and nonconservative
substance concentrations in total,
dissolved and particulate forms, in the
water column and bed sediments.
Suspended solids concentration in water
column.
DO, CBOD, NBOD, temperature,
ammonium, nitrate, nitrite, organic
nitrogen, total phosphate, organic
phosphorus, salinity, inorganic suspended
solids, dissolved, labile, and refractory
particulate organic carbon, dissolved silica,
particulate biogenic silica, fecal coliform,
total active metal
Contour plots of suspended particle
concentrations in lower water layers. Daily
averaged deposition rates of organic
material.
4.5
Ecological
Assessment
Techniques and
Models
As with loading and receiving water model selection, the selection of an ecological
assessment technique (or set of techniques) is driven by a number of factors:
• Goals (e.g., an assessment of existing conditions, the prioritization of stream
restoration projects, or the prediction of future conditions following land use
change).
• Objectives (e.g., determine/predict the type of habitat, its quality and/or
quantity, and the integrity of resident species/community).
• Level of detail necessary to accomplish the goals (e.g., screening-level, interme-
diate, or detailed).
• Availability of data (including reference conditions).
-------
Compendium of Tbolsfor Watershed Assessment and TMDL Development
Table 27. Input and Output Data - Dynamic Water Quality Model
Model
DYNTOX
WASPS
CE-QUAL-
RIV1
CE-QUAL-
W2
CE-QUAL-
ICM
HSPF
Main Input Data
River geometry, flow (continuous records
or statistical summaries), external
loadings, boundary conditions
Waterbody geometry, climate, waterbody
segmentation, flow (or input from
hydrodynamic model), boundary
conditions, initial conditions, benthic flux,
external loadings, spatially variable and
time-variable functions, rate constants
River geometry, climate, river
segmentation, upstream boundary
conditions, initial conditions, external
loadings, benthic flux, spatially variable
and time-variable functions, rate
constants
Lake geometry, climate, waterbody
segmentation, boundary conditions,
initial conditions, external loadings or
withdrawals, benthic flux, spatially
variable and time-variable functions, rate
constants
Waterbody geometry, climate, grid, flow
(or input from hydrodynamic model),
boundary conditions, initial conditions,
external loadings, spatially variable and
time-variable functions, rate constants
River, well-mixed/shallow lakes
Output Information
Conservative and nonconservative
substance concentrations, plots of return
period for water quality violations below
each discharge
DO, CBOD, NBOD, ammonium, nitrate,
nitrite, organic nitrogen, total phosphate,
organic phosphorus, inorganic suspended
solids, fecal coliform, conservative and
nonconservative substance concentrations
for each segment and user-defined time
interval
DO, CBOD, temperature, ammonia,
nitrate, algae, coliform, phosphate,
organic nitrogen concentrations for each
segment and user-defined time interval
DO, CBOD, NBOD, temperature,
ammonium, nitrate, nitrite, organic
nitrogen, total phosphate, organic
phosphorus, salinity, inorganic suspended
solids, dissolved, labile, and refractory
participate organic carbon, dissolved silica,
particulate biogenic silica, fecal coliform,
total active metal concentrations for each
segment and user-defined time interval
DO, CBOD, NBOD, temperature,
ammonium, nitrate, nitrite, organic
nitrogen, total phosphate, organic
phosphorus, salinity, inorganic suspended
solids, dissolved, labile, and refractory
particulate organic carbon, dissolved silica,
particulate biogenic silica, fecal coliform,
total active metal concentrations for each
segment and user-defined time interval
DO, CBOD, nutrients, pesticide, sediment,
and organic chemical concentrations for
each segment and user-defined time
interval
Table 28. Range of Application—Hydrodynamic Models.
Hydrodynamic Analysis
Screening
Externally Coupled
RIVMOD-H
DYNHYD5
EFDC
CH3D-WES
•
•
0
o
Intermediate
e
•
Q
e
Detailed
O
0
• •
•
Water Supply-Control Analysis
Operations/Management
Planning
•
O
•
•
Design
Q
-
9
0
Internally Coupled
CE-QUAL-RIV1
CE-QUAL-W2
HSPF
•
o
e
e
•
•
o
•
Q
w
•
Q
O
•
0
High
Medium
Low
- Not Incorporated
-------
Chapter 4. Model Selection
Table 29. Range of Application—Steady-State Water Quality Models.
Model
EPA Screening Methods
EUTROMOD
PHOSMOD
BATHTUB
QUAL2E
EXAMSII
TOXMOD
SMPTOX3
TPM
DECAL
Screening
Intermediate
O
Q
O
e
•
•
Q
e
•
•
Detailed
-
-
-
-
w
-
-
e
o
9
Management Planning
and Analysis
O
e
e
e
•
0
o
•
•
•
High
Medium
O Low
- Not Incorporated
Table 30. Range of Application—Dynamic Water Quality Models
Model
DYNTOX
WASPS
CE-QUAL-RIV1
CE-QUAL-W2
CE-QUAL-ICM
HSPF
Water Quality Analysis
Screening
•
O
•
O
O
Int.
O
•
•
•
*
e
Detailed
0
•
•
•
•
Management
Planning
and Analysis
O
•
•
•
•
•
High
Medium
O
- Not Incorporated
• Applicability of use with other ecological assessments, as well as loadings and
receiving water models.
• Level of expertise required (many techniques require professional biologists to
collect and analyze data).
• Cost.
This section addresses ecological assessment technique selection for those techniques
discussed in Chapter 3. Data in the tables included in this section can assist with the
evaluation and selection of appropriate techniques for watershed assessment and
TMDL development. Tables 31 and 32 provide a descriptive list of technique compo-
nents, including biota/habitat type assessed and methodology. Tables 33 and 34
present a brief summary of input and output information for each of the techniques
reviewed. The potential range of applications of ecological assessment techniques and
models is illustrated in Tables 35 and 36.
Because of the inherent connection between a species or community and its habitat,
the techniques presented are often best used in combination with each other, as well
as with loading and receiving water models, to provide a holistic depiction of an
aquatic ecosystem. Frequently, habitat assessment techniques are combined with
-------
Compendium oflbolsjbr Watershed Assessment and TMDL Development
Table 31. A Descriptive List of Model/Technique Components - Habitat
Assessment Techniques
Technique/
Model
HEP/HSI
HES
WET II
HGM
Visual-based
Habitat
Assessment
QHEI
Rosgen's
Stream
Classificatio
IFIM
(PHABSIM/
TSLIB)
SNTEMP/
SSTEMP
Habitat Type
Assessed
Terrestrial/
aquatic
Terrestrial/
aquatic
Wetland
Wetland
Aquatic
Aquatic
Aquatic
Aquatic
Aquatic
Habitat
Parameter
Quantity and
quality
Quantity and
quality
Quality
Quality
Quality
Quality
Quantity and
quality
Quantity and
quality
Quality
Habitat Level
Assessed
Single or
multiple
species
Community
Single or
multiple
species
Community
Community
Community
N/A
Single or
multiple
species
N/A
Methodology
Modeling of habitat quantity and
quality using key parameters
collected from field; can simulate
effects of future
development/conditions
Modeling of habitat quantity and
quality using abiotic and biotic
field-collected data; can simulate
effects of future
development/conditions
Collection and analysis of physical,
chemical, and biological predictors to
assess wetland functions
Data collection and classification;
development and comparison to
reference conditions
Multimetric collection and analysis;
comparison to reference conditions
Multimetric collection and analysis;
comparison to reference conditions
Collection and analysis of
morphological stream data;
classification to predict stream
behavior
Modeling of aquatic habitat quantity
and quality using key parameters
collected from field; can simulate
effects of future
development/conditions
Modeling of stream temperature
using stream geometric, hydrologic,
and meteorologic data
species or community assessments to provide additional data or analyses necessary for
decision making. Also, different tools of the same assessment type can be applied
through time. For example, a watershed manager might initially conduct a screening-
level assessment for benthic invertebrate communities (e.g., RBP I) and then, based
on results showing an impairment, perform a more detailed assessment (e.g., RBP III)
to characterize that impairment and establish monitoring trends. Rosgen's stream
classification might also be used concurrently to explore opportunities for stream
restoration.
Generalized assessment of existing habitat condition can be achieved using a variety
of techniques. Screening-level techniques, such as visual-based habitat assessment,
QHEI, and the habitat assessment components of RBPs, are approaches based on
minimal field data collection and analysis to determine the status of aquatic habitat
integrity. These techniques are most helpful for watershed managers who want to
know whether impairments exist in a waterbody and how to prioritize watersheds for
more detailed assessments in the future.
Consideration of the effects of a proposed project or other future conditions on
general aquatic habitat can best be achieved using the HEP, HES, and IFIM procedures.
-------
Chapter 4. Model Selection
Table 32. A Descriptive List of Model/Technique Components - Speciesl
Biological Community Assessment Techniques
Technique/
Model
RBPI
RBPII
RBP III
RBPIV
RBP V (IBI)
ICI
IWB
PVA
FGETS
Biota Assessed
Benthic
macroinvertebrates
Benthic
macroinvertebrates
Benthic
macroinvertebrates
Fish
Fish
Benthic
macroinvertebrates
Fish
Any
Any
Data Source
Field
Field
Field
Questionnaire
Field
Field
Field
Field/literature
Field/literature
Methodology
Visual only
Analysis of eight metrics in the field;
comparison to reference conditions
Analysis of eight metrics in the field and
laboratory; comparison to reference conditions
Analysis of questionnaire data
Analysis of 12 metrics in the field; comparison
to reference conditions
Analysis of 10 metrics in the field; comparison
to reference conditions
Analysis of species abundance and diversity in
the field; comparison to reference conditions
Modeling of wildlife population stability using
data describing birth, death, and growth rates
Modeling of fish bioaccumulation of chemicals
based on biological attributes and
physicochemical properties
These techniques can be applied to a variety of situations, and require significant data,
calibration, and analysis. Although of little value to TMDL development, the HES and
HEP techniques are also capable of assessing changes in the quantity and quality of
terrestrial habitat.
Rosgen's stream classification and SNTEMP/SSTEMP are methodologies that provide
information about specific components of aquatic habitats. Rosgen's stream classifica-
tion can be used to predict changes in stream morphology from watershed changes,
and to design stable channels as part of restoration efforts. The SNTEMP/SSTEMP
models can estimate stream temperature changes (that can be linked to fish health)
following changes in climate, stream hydrology, and riparian conditions.
WET II and HGM are highly specialized techniques that focus on wetland assessment
and do not have relevance beyond that habitat type. Both methods use physical,
chemical, and biological data to understand wetland functions; WET II can be used to
compare wetlands or assess the effects of a proposed project; HGM focuses on deter-
mining the functional integrity of a wetland as it compares to other comparable
(reference) wetlands.
Species/biological community assessment techniques generally assess aquatic resource
integrity by focusing on either benthic invertebrate or fish communities. Again, a
combination of these methods can provide a more detailed understanding of the
waterbody.
RBP I (benthic invertebrates) and RBP IV (fish) are screening-level protocols that use
easilycollected data to establish whether a waterbody is impaired, to give a general
indication of the impairment, and to identify whether further assessment is needed.
RBP II and ICI (both for benthic invertebrates) and IWB (for fish) are intermediate-
level techniques that use field-collected data to perform multimetric analysis and
compare results with reference conditions for that ecological region. Using more
-------
Compendium oflbokjbr Watershed Assessment and TMDL Development
Table 33. Input and Output - Habitat Assessment Techniques
Technique/
Model
HEP/HSI
HES
WET II
HGM
Visual-based
Habitat
Assessment
Output Information
A quantitative
assessment of the
quality and quantity of
available habitat for
selected wildlife
species in terms of
proposed or
anticipated land use
changes, and the
cost-effectiveness of
different management
alternatives to achieve
desired HUs for a
selected species.
A quantitative
assessment of the
quality and quantity of
available habitat for
entire wildlife
communities in terms
of proposed or
anticipated land use
changes.
A "broad-brush,"
quantitative
assessment of
potential project
impacts on several
wetland habitat
functions.
A quantitative
assessment of the
functioning of
wetlands that uses the
concepts of
hydrogeomorphic
classification,
functional capacity,
reference domain, and
reference Wetlands.
A quantitative
assessment, based on
qualitative
information, of
aquatic habitat quality
in wadable streams
and rivers.
Main Input Data
Data to be collected include delineation of cover types (e.g.,
deciduous forest, coniferous forest, grassland, residential
woodland) within the project area; size (acreage) of existing
habitat for each evaluation species; selection of evaluation
species; Habitat Suitability Index (HSI) reflecting current
habitat conditions for each evaluation species; future habitat
conditions for each evaluation species.
HSI data collection includes (1) species-specific habitat use
information such as general information (e.g., geographic
distribution); age, growth, and food requirements; water
quality, depth, and flow; species-specific habitat
requirements; reproductive information; (2) species-specific
life history information for each life stage (spawning/ embryo,
fry, juvenile, and adult); (3) suitability indices for each habitat
variable.
Baseline data on habitat types and land uses in the project
area. Size (acreage) of each habitat type and land use for
existing and future conditions. Measurements of key variables
(e.g., percent understory, number of large trees, number of
mast trees, species associations, number of snags) identified
for each habitat and land use type for existing conditions.
Projected measurements of same key variables for future
conditions.
Baseline data (e.g., water source, hydrodynamics, surface
roughness, vegetation cover, soil type) characterizing the
following wetland functions and values: groundwater
discharge, groundwater recharge, sediment stabilization,
flood flow alteration, sediment retention, toxicant retention,
nutrient transformation, production export, wildlife diversity,
aquatic diversity, recreation, uniqueness/heritage.
Baseline data to develop a reference set of wetlands
representing the range of conditions that exist in a wetland
ecosystem and its landscape in a reference domain. Baseline
data on the condition of assessment wetland variables (e.g.,
surface and subsurface water storage, nutrient cycling,
retention of particulates, organic matter export, spatial
structure of habitat, distribution and abundance of
invertebrates and vertebrates, plant community
characteristics, etc.) measured directly or indirectly using
indicators to develop a relationship between variable
conditions in the assessment wetland and functional capacity
of the reference set.
Data to be collected include instream cover (fish)(riffle/run
only), bottom substrate/available cover (glide/pool only),
epifaunal substrate (riffle/run only), pool substrate
chacterization (glide/pool only), embeddedness (riffle/run
only), pool variability (glide/pool only), channel alteration,
sediment deposition, frequency of riffles (riffle/run only),
channel sinuosity (glide/pool only), channel flow status, bank
vegetative protection, bank stability, riparian vegetative zone
width.
-------
Chapter 4. Model Selection
Table 33. Input and Output - Habitat Assessment Techniques
(continued)
Technique/
Model
Output Information
Main Input Data
QHEI
A quantitative assessment
based on qualitative
information. Developed
to help distinguish the
influence of habitat
effects on fish
communities in
midwestern streams.
Data to be collected include substrate (type, origin, and
quality), instream cover (type and amount), channel
morphology (sinuosity, development, channelization,
stability, modifications/other), riparian zone and bank
erosion (riparian width, floodplain quality, and bank
erosion), glide/pool and riffle/run quality (max. depth,
morphology, current velocity, riffle/run depth, riffle/run
substrate, and riffle/run embeddedness), gradient,
drainage area, percent pool, percent glide, percent riffle,
percent run.
Rosgen's
Stream
Classification
A quantified classification
system that can be used
to predict stream
behavior and to apply
interpretive information.
Interpretations can be
used to evaluate a
stream's sensitivity to
disturbance, recovery
potential, sediment
supply, vegetation
controlling influence, and
streambank erosion
potential.
Data to be collected depend on the level of classification:
Level 1: landform, lithology, soils, climate, depositional
history, basin relief, valley morphology, river, profile
morphology, general river pattern.
Level 2: channel pattern, sinuosity (usually expressed as
Schumm's ratio), gradient or slope, entrenchment or
entrenchment ratio (width of floodplain: the bankfull
width of channel surface), channel bed material,
width/depth ratio.
Level 3: riparian vegetation, depositional patterns,
meander patterns, confinement features, fish habitat
indices, flow regime, river size category, debris
occurrence, channel stability index, bank credibility.
IFIM
(PHABSIM/
TSLIB)
A quantitative assessment
(usually in graphical form)
of the changes in a given
species' habitat with
changes in hydrologic
regime.
Detailed data collection is required for both physical
(e.g., depth, velocity, stream channel characteristics,
riparian cover) and biological (e.g., life history and
habitat preference information for the species of
concern) characteristics of the stream.
SNTEMP/
SSTEMP
Minimum, mean, and
maximum daily water
temperature for a stream
segment.
20 input parameters are required that describe the
stream geometry (e.g., segment length, elevation,
roughness, shading), hydrology (e.g., segment inflow
and outflow, dam locations), and meteorology (e.g., air
temperature, relative humidity, solar radiation).
detailed data and analysis, these intermediate techniques provide impairment identifi-
cation, the ability to rank sites for control action, and the ability to prioritize sites for
further assessment. RBP III (benthic invertebrates) and RBP V/IBI (fish) are detailed
methodologies that require significant field (and laboratory for RBP III) analysis to
develop multiple metrics and compare results with reference conditions. Detailed
techniques provide impairment identification, bases for trend monitoring, the ability
to rank sites for control action, and the ability to prioritize sites for further assessment
based on more detailed data and analysis.
Although their applicability to TMDLs might be peripheral, PVA and FGETS can
provide interesting analyses that model the response of biota to changes in environ-
mental conditions. PVA is used to assess population stability with changes in demo-
graphic., genetic, and environmental variability. FGETS models the bioaccumulation of
chemicals in fish and could be used in tandem with receiving water analyses to
determine the risk of chemical presence to aquatic biota. Both techniques are rela-
tively sophisticated and require significant data collection and analysis.
-------
Compendium of "tools for Watershed Assessment and TMDL Development
Table 34. Input and Output - Species/Biological Community Assessment
Techniques
Technique/Model
Screening-
level
approaches
Multimetric
approaches
RBPI
RBPIV
RBPII
RBP III
ICI
Output Information
Based on a macroinvertebrate
community assessment, RBP 1
determines whether an
impairment exists in a stream
(or whether further investi-
gation is needed) and gives a
generic indication of impair-
ment cause (e.g., habitat,
organic enrichment toxicity).
Based on a fish community
assessment, RBP IV determines
whether an impairment exists in
a stream (or whether further
investigation is needed) and
gives a generic indication of
impairment cause.
Based on benthic macro-
invertebrate collection and
analysis, RBP II characterizes the
severity of an impairment into
one of three categories, gives a
generic indication of its cause,
and ranks and prioritizes
streams of further assessment.
Based on benthic macro-
invertebrate collection and
analysis, RBP III characterizes
the severity of an impairment
into one of four categories,
gives a generic indication of its
cause, establishes a basis for
trend monitoring, and
prioritizes streams for further
assessment.
ICI provides a quantitative
measure of overall macro-
invertebrate community
condition.
Main Input Data
Characterize and rate substrate/instream
cover, channel morphology, and
riparian/bank structure; measure
conventional water quality parameters;
examine physical characteristics; determine
relative abundance of benthic
macroinvertebrates.
Characterize and rate substrate/instream
cover, channel morphology, and
riparian/bank structure; measure
conventional water quality parameters;
examine physical characteristics;
questionnaire survey regarding fish
communities; survey ecoregional reference
reaches and randomly selected streams.
Characterize and rate substrate/instream
cover, channel morphology, and
riparian/bank structure; measure
conventional water quality parameters;
examine physical characteristics; examine
riffle/run community and sample coarse
participate organic matter; 1 00-organi;;m
subsample identified in field to family or
order level; functional feeding group
analysis of riffle/run and coarse particulate
organic matter in the field. Data
describing reference conditions are also
necessary.
Characterize and rate substrate/instream
cover, channel morphology, and
riparian/bank structure; measure
conventional water quality parameters;
examine physical characteristics; examine
riffle/run community and sample coarse
particulate organic matter; collect riffle/run
benthos, collect coarse particulate organic
matter sample; determine shredder
abundance; perform riffle/run analysis in
laboratory, identify 100-organism
subsample to species level and perform
functional feeding group analysis. Data
describing reference conditions are also
necessary.
Data necessary for development of the ICI
include total number of taxa, number of
mayfly taxa, number of caddisfly taxa,
number of dipteran taxa, percent mayfly
composition, percent caddisfly
composition, percent tribe tanytarsini
midge composition, percent other dipteran
and noninsect composition, percent
tolerant organisms, and number of
qualitative EPT taxa. Data for reference;
conditions are also necessary.
-------
Chapter 4. Model Selection
Table 34. Input and Output - Species/Biological Community Assessment
Techniques (continued)
Technique/Model
Output Information
Main Input Data
Multimetric
approaches
(continued)
RBPV
(IBI)
Based on fish collection and
analysis, RBP V computes a
quantitative index that
incorporates individual,
population, community,
zoogeographic, and ecosystem-
level information to evaluate
biological integrity as one of five
classes; it also gives a generic
indication of impairment cause,
establishes a basis for trend
monitoring, and ranks and
prioritizes streams for further
assessment.
Data to be collected include
substrate/instream cover, channel
morphology, and riparian/bank structure;
conventional water quality parameters;
physical characteristics; major habitats and
cover types; total number of native fish
species; number and identity of darter
species; number and identity of sunfish
species; number and identity of sucker
species; number and identity of intolerant
species; proportion of individuals as
tolerant species; proportion of individuals
as omnivores; proportion of individuals as
insectivorous cyprinids; proportion of
individuals as piscivores (top carnivores);
number of individuals in sample;
proportion of individuals as hybrids;
proportion of individuals with disease,
tumors, fin damage, and skeletal
anomalies. Data describing reference
conditions are also necessary.
IWB
The IWB provides a quantitative
measure of the quality of a fish
assemblage.
Data to be collected include number of
individuals/kilometer; biomass of
individuals/kilometer; Shannon-Weaver
diversity index (number of individuals in
sample and number of individuals of
species in the sample). Data describing
reference conditions are also necessary.
Population Viability
Analysis (PVA)
PVAs supply a quantified
analysis of the stability of a
specified population following a
change in environment,
population structure, or
behavior.
Data required include the age structure of
the population being studied, and the
survival and fecundity of each age.
FGETS
FG ETS predicts the temporal
dynamics of a fish's whole-body
concentration of nonioonic,
nonmetabolized, organic
chemicals that are bioaccumu-
lated from water and food.
Data required include morphological,
physiological, and trophic parameters that
describe the gill morphometry, feeding
and metabolic demands, and body
composition for the species in questions;
and relevant physicochemical parameters
that describe partitioning to the fish's lipid
and structural organic fractions for a
specific chemical.
-------
Compendium of "tools for Watershed Assessment and TMDL Development
Table 35. Range of Application—Habitat Assesment Techniques and Models.
Technique/
Model
HEP/HSI
HES
WET II
HGM
Visual-based Habitat Assessment
QHEI
Rosgen's Stream Classification
IFIM (PHABSIM/TSLIB)
SNTEMR/SSTEMP
Terrestrial
•
•
-
-
-
-
-
-
-
Habitat Assessment
Aquatic
Q
•
-
-
O
O
O
•
9
Wetland
-
-
•
•
-
-
-
-
-
Level of complexity addressed: £ High
Medium
O Low
— Not Applicable
Table 36. Range of Application—Species/Biological Community Assessment
Techniques and Models.
Technique/
Model
RBPI
RBPII
RBP III
RBPIV
RBP V (IBI)
ICI
IWB
PVA
FGETS
Assessment Type
Benthic community
O
e
•
-
-
e
-
-
-
Fish
community
-
-
-
O
•
-
O
-
-
Single-species
(Bioaccumulation and
population modeling)
-
-
-
-
-
-
-
•
•
Level of complexity addressed:
High
Medium
Q Low
- Not Applicable
-------
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Glossary
Glossary
Acute toxicity: A chemical stimulus severe enough to rapidly induce an effect; in
aquatic toxicity tests, an effect observed within 96 hours or less is considered acute.
When referring to aquatic toxicology or human health, an acute effect is not always
measured in terms of lethality.
Adsorption-desorption: Adsorption is the process by which nutrients such as
inorganic phosphorus adhere to particles via a loose chemical bond with the surface
of clay particles. Desorption is the process by which inorganic nutrients are released
from the surface of particles back into solution.
Advection: Bulk transport of the mass of discrete chemical or biological constitu-
ents by fluid flow within a receiving water. Advection describes the mass transport
due to the velocity, or flow, of the waterbody.
Aerobic: Environmental conditions characterized by the presence of dissolved
oxygen; used to describe biological or chemical processes that occur in the presence
of oxygen.
Algae: Any organisms of a group of chiefly aquatic microscopic nonvascular plants;
most algae have chlorophyll as the primary pigment for carbon fixation. As primary
producers, algae serve as the base of the aquatic food web, providing food for zoop-
lankton and fish resources. An overabundance of algae in natural waters is known as
eutrophication.
Algal bloom: Rapidly occurring growth and accumulation of algae within a body of
water, which usually results from excessive nutrient loading and/or a sluggish circula-
tion regime with a long residence time. Persistent and frequent blooms can result in
low oxygen conditions.
Algal growth: Algal growth is related to temperature, available light, and the
available abundance of inorganic nutrients (N, P, Si). Algal species groups (e.g.,
diatoms, greens, etc.) are typically characterized by different maximum growth rates.
Algal respiration: Process of endogenous respiration of algae in which organic
carbon biomass is oxidized to carbon dioxide.
Algal settling: Phytoplankton cells (algae) are lost from the water column by
physical sedimentation of the cell particles. Algal biomass lost from the water column
is then incorporated as sediment organic matter and undergoes bacterial and bio-
chemical reactions releasing nutrients and consuming dissolved oxygen.
Ambient water quality: Natural concentration of water quality constituents prior
to mixing of either point or nonpoint source load of contaminants. Reference ambi-
ent concentration is used to indicate the concentration of a chemical that will not
cause adverse impact to human health.
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Compendium oflbolsjbr Watershed Assessment and TMDL Development
Ammonia: Inorganic form of nitrogen; product of hydrolysis of organic nitrogen
and denitrification. Ammonia is preferentially used by phytoplankton over nitrate for
uptake of inorganic nitrogen.
Ammonia toxicity: Under specific conditions of temperature and pH, the un-
ionized component of ammonia can be toxic to aquatic life. The un-ionized compo-
nent of ammonia increases with pH and temperature.
Anaerobic: Environmental condition characterized by zero oxygen levels. Describes
biological and chemical processes that occur in the absence of oxygen.
Analytical model: Exact mathematical solution of the differential equation formu-
lation of the transport, diffusion, and reactive terms of a water quality model. Ana-
lytical solutions of models are often used to check the magnitude of the system
response computed using numerical model approximations.
Anoxic: Aquatic environmental conditions containing zero or little dissolved oxygen.
See also anaerobic.
Anthropogenic: Relating to or resulting from the influence of human activities on
nature.
Aquatic ecosystem: Complex of biotic and abiotic components of natural waters.
The aquatic ecosystem is an ecological unit that includes the physical characteristics
(such as flow or velocity and depth), the biological community of the water column
and benthos, and the chemical characteristics such as dissolved solids, dissolved
oxygen, and nutrients. Both living and nonliving components of the aquatic ecosys-
tem interact and influence the properties and status of each component.
Assimilative capacity: The amount of contaminant load (expressed as mass per
unit time) that can be discharged to a specific stream or river without exceeding water
quality standards or criteria. Assimilative capacity is used to define the ability of a
water body to naturally absorb and use waste matter and organic materials without
impairing water quality or harming aquatic life.
Attached algae: Photosynthetic organisms that remain in a stationary location by
attachment to hard rocky substrate. Attached algae, usually present in shallow hard-
bottom environments, can significantly influence nutrient uptake and diurnal oxygen
variability.
Autotroph: An organism that derives cell carbon from carbon dioxide. The conver-
sion of carbon dioxide to organic cell tissue is a reductive process that requires a net
input of energy. The energy needed for cell synthesis is provided by either light or
chemical oxidation. Autotrophs that use light, phototrophs, include photosynthetic
algae and bacteria. Autotrophs that use chemical energy, chemotrophs, include
nitrifying bacteria.
Background levels: The chemical, physical, and biological conditions that would
result from natural geomorphological processes such as weathering and dissolution.
Bacterial decomposition: Breakdown by oxidation, or decay, of organic matter by
heterotrophic bacteria. Bacteria use the organic carbon in organic matter as the
energy source for cell synthesis.
Baseflow: That part of the runoff contribution that originates from springs or wells.
-------
Glossary
Benthic: Relating to or occurring at the bottom of an aquatic ecosystem.
Benthic ammonia flux: The decay of organic matter within the sediments of a
natural water results in the release of ammonia nitrogen from the interstitial water of
sediments to the overlying water column. Benthic release, or regeneration, of ammo-
nia is an essential component of the nitrogen cycle.
Benthic denitrification: Under anaerobic, or low-oxygen, conditions denitrifying
bacteria synthesize cellular material by reducing nitrate to ammonia and nitrogen gas.
Denitrification is a component of the overall nitrogen cycle and has been shown to
account for a significant portion of the "new" nitrogen loading to freshwater and
estuarine ecosystems.
Benthic nitrification: Under aerobic conditions, nitrifying bacteria synthesize
cellular material by oxidizing ammonia to nitrite and nitrate. Benthic nitrification is a
component of the overall nitrogen cycle and has been shown to account for a signifi-
cant portion of the nitrogen budget of shallow freshwater and estuarine ecosystems.
Benthic organisms: Organisms living in, or on, bottom substrates in aquatic
ecosystems.
Benthic photosynthesis: Synthesis of cellular carbon by algae attached to the
bottom of a natural water system. Benthic photosynthesis typically is limited to
shallow waters because of the availability of light at the bottom.
Best management practices (BMPs): Methods, measures, or practices that are
determined to be reasonable and cost-effective means for a landowner to meet
certain, generally nonpoint source, pollution control needs. BMPs include structural
and nonstructural controls and operation and maintenance procedures.
Biochemical oxygen demand (BOD): The amount of oxygen per unit volume of
water required to bacterially or chemically oxidize (stabilize) the oxidizable matter in
water. Biochemical oxygen demand measurements are usually conducted over specific
time intervals (5,10, 20, 30 days). The term BOD generally refers to the standard 5-
day BOD test.
Biological nutrient removal (BNR): A waste treatment method that employs
natural biological processes to reduce the quantity of nitrogen and phosphorus
discharged to natural waters. Treatment processes employ the movement of primary
effluent through aerobic, anoxic/anaerobic zones to facilitate bacterially mediated
processes of nitrification and denitrification.
Biomass: The amount, or weight, of a species, or group of biological organisms,
within a specific volume or area of an ecosystem.
Boundary conditions: Values or functions representing the state of a system at its
boundary limits.
Calibration: Testing and tuning of a model to a set of field data not used in the
development of the model; also includes minimization of deviations between mea-
sured field conditions and output of a model by selecting appropriate model coeffi-
cients.
Carbonaceous: Pertaining to or containing carbon derived from plant and animal
residues
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Compendium oflbobjbr Watershed Assessment and TMDL Development
Catchment: The area producing the runoff passing a particular channel or stream
location.
Channel: A natural stream that conveys water; a ditch or channel excavated for the
flow of water.
Channel improvement: The improvement of the flow characteristics of a channel
by clearing, excavation, realignment, lining, or other means in order to increase its
capacity. Sometimes used to connote channel stabilization.
Channel stabilization: Erosion prevention and stabilization of velocity distribu-
tion in a channel using jetties, drops, revetments, vegetation, and other measures.
Chloride: An atom of chlorine in solution, bearing a single negative charge.
Chlorophyll: A group of green photosynthetic pigments that occur primarily in the
chloroplast of plant cells. The amount of chlorophyll a, a specific pigment, is fre-
quently used as a measure of algal biomass in natural waters.
Chronic toxicity: Toxicity impact that lingers or continues for a relatively long
period of time, often one-tenth of the life span or more. Chronic effects could include
mortality, reduced growth, or reduced reproduction.
Coliform bacteria: A group of bacteria that normally live within the intestines of
mammals, including humans. Coliform bacteria are used as an indicator of the
presence of sewage in natural waters.
Combined sewer overflows (CSOs): A combined sewer carries both wastewater
and stormwater runoff. CSOs discharged to receiving water can result in contamina-
tion problems that may prevent the attainment of water quality standards.
Complete mixing: No significant difference in concentration of a pollutant exists
across the transect of the waterbody.
Concentration: Amount of a substance or material in a given unit volume of
solution. Usually measured in milligrams per liter (mg/L) or parts per million (ppm).
Conservative substance: Substance that does not undergo any chemical or
biological transformation or degradation in a given ecosystem.
Contamination: Act of polluting or making impure; any indication of chemical,
sediment, or biological impurities.
Continuous simulation: The use of a model to simulate the response of a water-
shed to a series of storm events and the hydrological processes that occur between
them.
Conventional pollutants: As specified under the Clean Water Act, conventional
contaminants include suspended solids, coliform bacteria, biochemical oxygen de-
mand, pH, and oil and grease.
Cross-sectional area: Wet area of a waterbody normal to the longitudinal compo-
nent of the flow.
Decay: Gradual decrease in the amount of a given substance in a given system due
to various sink processes including chemical and biological transformation, dissipation
to other environmental media, or deposition into storage areas.
-------
Glossary
Decomposition: Metabolic breakdown of organic materials; the by-product forma-
tion releases energy and simple organics and inorganic compounds, (see also Respira-
tion)
Denitrification: The decomposition of ammonia compounds, nitrites, and nitrates
(by bacteria) that results in the eventual release of nitrogen gas into the atmosphere.
Design stream flow: The stream flow used to conduct water quality modeling.
Designated use: Uses specified in water quality standards for each waterbody or
segment regardless of actual attainment.
Detritus: Any loose material produced directly from disintegration processes.
Organic detritus consists of material resulting from the decomposition of dead organic
remains.
Diagenesis: Production of sediment fluxes as a result of the flux of paniculate
organic carbon in the sediment and its decomposition. The diagenesis reaction can be
thought of as producing oxygen equivalents released by various reduced species.
Dilution: Addition of less concentrated liquid (water) that results in a decrease in
the original concentration.
Discharge Monitoring Report (DMR): Report of effluent characteristics submit-
ted by a municipal or industrial facility that has been granted an NPDES discharge
permit.
Discharge permit (NPDES): A permit issued by the U.S. EPA or a state regulatory
agency that sets specific limits on the type and amount of pollutants that a municipal-
ity or industry can discharge to a receiving water; it also includes a compliance
schedule for achieving those limits. The permit process was established under the
National Pollutant Discharge Elimination System (NPDES), under provisions of the
Federal Clean Water Act.
Dispersion: The spreading of chemical or biological constituents, including pollut-
ants, in various directions from a point source, at varying velocities depending on the
differential in-stream flow characteristics.
Dissolved oxygen (DO): The amount of oxygen that is dissolved in water. It also
refers to a measure of the amount of oxygen available for biochemical activity in a
waterbody, and as indicator of the quality of that water.
Dissolved oxygen sag: Longitudinal variation of dissolved oxygen representing
the oxygen depletion and recovery following a waste load discharge into a receiving
water.
Distributed model: A model in which the physical heterogeneities of a watershed
are included.
Diurnal: Actions or processes that have a period or a cycle of approximately one
tidal-day or are completed within a 24-hour period and which recur every 24 hours.
Domestic wastewater: Wastewater discharged from residences and from commer-
cial, institutional, and similar facilities; also called sanitary wastewater.
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Compendium oflbokfor Watershed Assessment and TMDL Development
Drainage basin: A part of the land area enclosed by a topographic divide from
which direct surface runoff from precipitation normally drains by gravity into a
receiving water. Also referred to as a watershed, river basin, or hydrologic unit.
Dye study: Use of conservative substances to assess the physical behavior of a
natural system in response to a given stimulus.
Dynamic model: A mathematical formulation describing the physical behavior of a
system or a process and its temporal variability.
Dynamic simulation: Modeling of the behavior of physical, chemical, and/or
biological phenomena and their variation over time.
Ecosystem: An interactive system that includes the organisms of a natural commu-
nity association together with their abiotic physical, chemical, and geochemical
environment.
Effluent: Municipal sewage or industrial liquid waste (untreated, partially treated,
or completely treated) that flows out of a treatment plant, septic system, pipe, or
other conduit.
Effluent plume: Delineates the extent of contamination in a given medium as a
result of effluent discharges (or spills). Usually shows the concentration gradient
within the delineated areas or plume.
Epiphyte: A plant growing on another plant; more generally, any organism growing
attached to a plant.
Estuary: Brackish-water area influenced by the tides where the mouth of the river
meets the sea.
Estuarine number: Nondimensional parameter accounting for decay, tidal disper-
sion, and advection velocity. Used for classification of tidal rivers and estuarine
systems.
Eutrophication: Enrichment of an aquatic ecosystem with nutrients (nitrates,
phosphates) that accelerate biological productivity (growth of algae and weeds) and
an undesirable accumulation of algal biomass.
Eutrophication model: Mathematical formulation that describes the advection,
dispersion, and biological, chemical, and geochemical reactions that influence the
growth and accumulation of algae in aquatic ecosystems. Models of eutrophication
typically include one or more species groups of algae, inorganic and organic nutrients
(N, P), organic carbon, and dissolved oxygen.
Extinction coefficient: Measure for the reduction (absorption) of light intensity
within a water column.
Factor of safety: Coefficient used to account for uncertainties in representing,
simulating, or designing a system.
Fate of pollutants: Physical, chemical, and biological transformation in the nature
and changes of the amount of a pollutant in an environmental system. Transforma-
tion processes are pollutant-specific. However, they have comparable kinetics so that
different formulations for each pollutant are not required.
-------
Glossary
Fecal coliform bacteria: Bacteria that are present in the intestines or feces of
warm-blooded animals. They are often used as indicators of the sanitary quality of
water. See Coliform bacteria.
Field-scale: Taking place at the sub-basin or smaller level. Field scale modeling
usually refers to geographic areas composed of one land use (e.g., a Cornfield).
First-order kinetics: Describes a reaction in which the rate of transformation of a
pollutant is proportional to the amount of that pollutant in the environmental system.
Flocculation: The process by which suspended colloidal or very fine particles are
assembled into larger masses or flocules that eventually settle out of suspension.
Flux: Movement and transport of mass of any water quality constituent over a given
period of time. Units of mass flux are mass per unit time.
Forcing functions: External empirical formulation used to provide input describing
a number of processes. Typical forcing functions include parameters such as tempera-
ture, point and tributary sources, solar radiation, and waste loads and flow.
Geochemical: Refers to chemical reactions related to earth materials such as soil,
rocks, and water.
Geographic information system (GIS): Computer programs that link features
commonly seen on maps (such as roads, town boundaries, waterbodies) with related
information not usually presented on maps, such as the type of road surface, popula-
tion, type of vegetation, land use, or water quality information. A GIS is a unique
information system in which individual observations can be spatially referenced to
each other.
Gradient: The rate of decrease (or increase) of one quantity with respect to an-
other; for example, the rate of decrease of temperature with depth in a lake.
Groundwater: Phreatic water or subsurface water in the zone of saturation.
Groundwater inflow describes the rate and amount of movement of water from a
saturated formation.
Half-saturation constant: Nutrient concentration at which the growth rate is half
die maximum rate. Half-saturation constants define the nutrient uptake characteris-
tics of different phytoplankton species. Low half-saturation constants indicate the
ability of the algal group to thrive under nutrient-depleted conditions.
Heterotroph: An organism that uses organic carbon for the formation of cell tissue.
Bacteria are examples of heterotrophs.
Hydraulics: The physical science and technology of the static and dynamic behavior
of fluids.
Hydrodynamic model: Mathematical formulation used in describing circulation,
transport, and deposition processes in receiving water.
Hydrograph: A graph showing variation in stage (depth) or discharge of water in a
stream over a period of time.
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Compendium oflbols far Watershed Assessment and TMDL Development
Hydrologic cycle: The circuit of water movement from the atmosphere to the
earth and return to the atmosphere through various stages or processes, such as
precipitation, interception, runoff, infiltration, storage, evaporation, and transpiration.
Hydrology: The science dealing with the properties, distribution, and circulation of
water.
Hydrolysis: Reactions that occur between chemicals and water molecules resulting
in the cleaving of a molecular bond and the formation of new bonds with components
of the water molecule.
In situ: In place; in situ measurements consist of measurements of components or
processes in a full-scale system or a field rather than in a laboratory.
Initial conditions: The state of a system prior to the introduction of an induced
stimulus. Conditions at the start-up of system simulations.
Initial mixing zone: Region immediately downstream of an outfall where effluent
dilution processes occur. Because of the combined effects of the effluent buoyancy,
ambient stratification, and current, the prediction of initial dilution can be compli-
cated.
<
Interstitial water: Water contained in the interstices, which are the pore spaces or
voids in soils and rocks.
Kinetic processes: Description of the rate and mode of change in the transforma-
tion or degradation of a substance in an ecosystem.
Light saturation: Optimal light level for algae and macrophyte growth and photo-
synthesis.
Loading, load, loading rate: The total amount of material (pollutants) entering
the system from one source or multiple sources; measured as a rate in weight per unit
time.
Load allocation (LA): The portion of a receiving water's total maximum daily load
that is attributed either to one of its existing or future nonpoint sources of pollution or
to natural background sources.
Long stream: A receiving water in which nutrients are in excess of growth-limiting
conditions, and where the travel time allows growth and physical accumulation of
algal biomass.
Longitudinal dispersion: The spreading of chemical or biological constituents,
including pollutants, downstream from a point source at varying velocities due to the
differential in-stream flow characteristics.
Low-flow (7Q10): The 7-day average low flow occurring once in 10 years. This
probability-based statistic is used in determining stream design flow conditions and
evaluating the water quality impact of effluent discharge limits.
Lumped model: A model in which the physical characteristics of a watershed are
assumed to be homogeneous.
Macrophyte: Large, vascular, rooted aquatic plant.
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Glossary
Margin of safety (MOS): A required component of the TMDL that accounts for
the uncertainly about the relationship between the pollutant load and the quality of
the receiving waterbody. This uncertainly can be caused by insufficient or poor-
quality data or a lack of knowledge about the water resource and pollution effects.
Mass balance: An equation that accounts for the flux of mass going into a defined
area and the flux of mass leaving the defined area. The flux in must equal the flux
out.
Mathematical model: A system of mathematical expressions that describe the
spatial and temporal distribution of water quality constituents resulting from fluid
transport and the one, or more, individual processes and interactions within some
prototype aquatic ecosystem. A mathematical water quality model is used as the basis
for TMDL evaluations.
Mechanistic model: A model that attempts to quantitatively describe a phenom-
enon by its underlying casual mechanisms.
Mineralization: The transformation of organic matter into a mineral or an inor-
ganic compound.
Mixing characteristics: Refers to the tendency for natural waters to blend; i.e.,
for dissolved and paniculate substances to disperse into adjacent waters.
Monte Carlo simulation: A stochastic modeling technique that involves the
random selection of sets of input data for use in repetitive model runs. Probability
distributions of receiving water quality concentrations are generated as the output of a
Monte Carlo simulation.
N/P ratio: The ratio of nitrogen to phosphorus in an aquatic system. The ratio is
used as an indicator of the nutrient limiting conditions for algal growth; also used as
an indicator for the analysis of trophic levels of receiving waters.
Natural waters: Flowing water within a physical system that has developed
without human intervention, in which natural processes continue to take place.
Nitrate (NO3) and nitrite (NO2): Oxidized nitrogen species. Nitrate is the form
of nitrogen preferred by aquatic plants.
Nitrification: The oxidation of ammonium salts to nitrites (via Nitrosomonas
bacteria) and the further oxidation of nitrite to nitrate (via Nitrobacter bacteria).
Nitrogenous biochemical oxygen demand (NBOD): The oxygen demand
associated with the oxidation of nitrate.
Nonconservative substance: Substance that undergoes chemical or biological
transformation in a given environment.
i
Nonpoint source pollution: Pollution that is not released through pipes but
rather originates from multiple sources over a relatively large area. Nonpoint sources
can be divided into source activities related to either land or water use including
failing septic tanks, improper animal-keeping practices, agricultural and forestry
practices, and urban and rural runoff.
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Compendium oflbolsfor Watershed Assessment and TMDL Development
Numerical model: Model that approximates a solution of governing partial
differential equations which describe a natural process. The approximation uses a
numerical discretization of the space and time components of the system or process.
Nutrient: A primary element necessary for the growth of living organisms. Carbon
dioxide, nitrogen, and phosphorus, for example, are nutrients required for phy-
toplankton growth.
Nutrient limitation: Deficit of nutrient (e.g., nitrogen and phosphorus) required
by microorganisms in order to metabolize organic substrates.
One-dimensional (1-D) model: A mathematical model defined along one spatial
coordinate of a natural water system. Typically, 1-D models are used to describe the
longitudinal variation of water quality constituents along the downstream direction of
a stream or river. In writing the model, it is assumed that the cross-channel (lateral)
and vertical variability is relatively homogenous and can, therefore, be averaged over
those spatial coordinates.
Organic matter: The organic fraction that includes plant and animal residue at
various stages of decomposition, cells and tissues of soil organisms, and substances
synthesized by the soil population. Commonly determined as the amount of organic
material contained in a soil or water sample.
Organic nitrogen: Form of nitrogen bound to an organic compound.
Orthophosphate (O_PO4_P): Form of phosphate available for biological metabo-
lism without further breakdown.
Outfall: Point where water flows from a conduit, stream, or drain.
Oxidation: The chemical union of oxygen with metals or organic compounds
accompanied by a removal of hydrogen or another atom. It is an important factor for
soil formation and permits the release of energy from cellular fuels.
Oxygen demand: Measure of the dissolved oxygen used by a system (microorgan-
isms) in the oxidation of organic matter. See also Biochemical oxygen demand.
Oxygen depletion: Deficit of dissolved oxygen in a water system due to oxidation
of organic matter.
Oxygen saturation: Natural or artificial reaeration or oxygenation of a water
system (water sample) to bring the level of dissolved oxygen to saturation. Oxygen
saturation is greatly influenced by temperature and other water characteristics.
Partition coefficients: Chemicals in solution are partitioned into dissolved and
particulate adsorbed phases based on their corresponding sediment-to-water partition-
ing coefficient.
Peak runoff: The highest value of the stage or discharge attained by a flood or
storm event; also referred to as flood peak or peak discharge.
Periphyton: Attached benthic algae.
Photoperiod: Time period of the seasonal response by organisms to change in the
length of the daylight period; for example, flowering, germination of seeds, reproduc-
tion, migration, and diapause are frequently under photoperiod control.
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Glossary
Photosynthesis: The biochemical synthesis of carbohydrate-based organic com-
pounds from water and carbon dioxide using light energy in the presence of chloro-
phyll. Photosynthesis occurs in all plants, including aquatic organisms such as algae
and macrophytes.
Phyla: Species groups of the same family of organisms. Phyla of phytoplankton
include diatoms, blue-green algae, dinoflagellates, and green algae.
Phytoplankton: A group of generally unicellular microscopic plants characterized
by passive drifting within the water column. See Algae.
Plankton: A group of generally microscopic plants and animals passively floating,
drifting, or swimming weakly. Plankton include phytoplankton (plants) and zooplank-
ton (animals).
Point source: Pollutant loads discharged at a specific location from pipes, outfalls,
and conveyance channels from either municipal wastewater treatment plants or
industrial waste treatment facilities. Point sources can also include pollutant loads
contributed by tributaries to the main receiving water stream or river.
Pollutant: A contaminant in a concentration or amount that adversely alters the
physical, chemical, or biological properties of a natural environment. The term
includes pathogens, toxic metals, carcinogens, oxygen-demanding substances, or other
harmful substances. Examples of pollutant sources include dredged spoil, solid waste,
incinerator residue, sewage, garbage, sewage sludge, munitions, chemical waste,
biological material, radioactive materials, heat, wrecked or discharged equipment,
sediment, cellar dirt, hydrocarbons, oil, and municipal, industrial, and agricultural
waste discharged into surface water or groundwater.
Postaudit: A subsequent examination and verification of model predictive perfor-
mance following implementation of an environmental control program.
Pretreatment: The treatment of wastewater or runoff to remove or reduce con-
taminants prior to discharge into another treatment system or a receiving water.
Primary productivity: A measure of the rate at which new organic matter is
formed and accumulated through photosynthesis and chemosynthesis activity of
producer organisms (chiefly, green plants). The rate of primary production is esti-
mated by measuring the amount of oxygen released (oxygen method) or the amount
of carbon assimilated by the plant (carbon method).
Primary treatment plant: Wastewater treatment process where solids are re-
moved from raw sewage primarily by physical settling. The process typically removes
about 25-35 percent of solids and related organic matter (BODS).
Priority pollutant: Substance listed by the U.S. EPA under the Federal Clean
Water Act as a harmful substance that has priority for regulatory controls. The list
includes metals (13), inorganic compounds (2), and a broad range of naturally
occurring or artificial organic compounds '(111)-
Publicly owned treatment works (POTW): Municipal wastewater treatment
plant owned and operated by a public governmental entity such as a town or city.
Raw sewage: Untreated municipal sewage.
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Compendium oflbolsjbr Watershed Assessment and TMDL Development
Reaction rate coefficient: Coefficient describing the rate of transformation of a
substance in an environmental medium characterized by a set of physical, chemical,
and biological conditions such as temperature and dissolved oxygen level.
Reaeration: The net flux of oxygen occurring from the atmosphere to a body of
water with a free surface.
Receiving waters: Creeks, streams, rivers, lakes, estuaries, groundwater forma-
tions, or other bodies of water into which surface water and/or treated or untreated
waste is discharged, either naturally or in constructed systems.
Refractory organics: A broad lumping of anthropogenic organic chemicals that
resist chemical or bacterial decomposition, including many pesticides, herbicides,
household and industrial cleaners and solvents, photofinishing chemicals, and dry-
cleaning fluids.
Reserve capacity: Pollutant loading rate set aside in determining stream waste
load and load allocations accounting for uncertainty and future growth.
Residence time: Length of time that a pollutant remains within a section of a
stream or river. The residence time is determined by the streamflow and the volume
of the river reach or the average stream velocity and the length of the river reach.
Respiration: Biochemical process by means of which cellular fuels are oxidized
with the aid of oxygen to permit the release of the energy required to sustain life;
during respiration oxygen is consumed and carbon dioxide is released.
Roughness coefficient: A factor in velocity and discharge formulas representing
the effects of channel roughness on energy losses in flowing water. Manning's "n" is a
commonly used roughness coefficient.
Scour: To abrade and wear away. Used to describe the weathering away of a terrace
or diversion channel or streambed. The clearing and digging action of flowing water,
especially the downward erosion by stream water in sweeping away mud and silt on
the outside of a meander or during flood events.
Secchi depth: A measure of the light penetration into a water column. Light
penetration is influenced by turbidity.
Secondary treatment: A waste treatment process in which oxygen-demanding
organic materials (BOD) are removed by bacterial oxidation of the waste to carbon
dioxide and water. Bacterial synthesis of wastewater is enhanced by injection of
oxygen.
Sediment: Particulate organic and inorganic matter that accumulates in a loose,
unconsolidated form on the bottom of natural waters.
Sediment oxygen demand (SOD): The oxygen demand required for the aerobic
and anaerobic decomposition of organic bottom solids. The oxygen consumed in
aerobic decomposition represents another dissolved oxygen sink for the waterbody.
Sedimentation: Process of deposition of waterborne or windborne sediment or
other material; also refers to the infilling of bottom substrate in a waterbody by
sediment (siltation).
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Glossary
Short stream: A receiving water where nutrients are in excess of growth-limiting
conditions and where the time of travel within the stream reach is not sufficient to
allow growth and physical accumulation of algal biomass.
Simulation: The use of mathematical models to approximate the observed behavior
of a natural water system in response to a specific known set of input and forcing
conditions. Models that have been validated, or verified, are then used to predict the
response of a natural water system to changes in the input or forcing conditions.
Sorption: The adherence of ions or molecules in a gas or liquid to the surface of a
solid particle with which they are in contact.
Spatial segmentation: A numerical discretization of the spatial component of a
system into one or more dimensions; forms the basis for application of numerical
simulation models.
Steady-state model: Mathematical model of fate and transport that uses constant
values of input variables to predict constant values of receiving water quality concen-
trations.
Stoichiometric ratio: Mass-balance-based ratio for nutrients, organic carbon, and
algae (e.g., nitrogen-to-carbon ratio).
STORET: U.S. Environmental Protection Agency (EPA) national mainframe data
base for storage and retrieval (STORET) of water quality data. STORET includes
physical, chemical, and biological data measured in waterbodies throughout the
United States.
Storm runoff: Rainfall that does not evaporate or infiltrate the ground because of
impervious land surfaces or a soil infiltration rate lower than rainfall intensity, but
instead flows onto adjacent land or waterbodies or is routed into a drain or sewer
system.
Stratification (of waterbody): Formation of water layers, each with specific
physical, chemical, and biological characteristics. As the density of water decreases
due to surface heating, a stable situation develops with lighter water overlying heavier
and denser water.
Streamflow: Discharge that occurs in a natural channel. Although the term "dis-
charge" can be applied to the flow of a canal, the word "streamflow" uniquely de-
scribes the discharge in a surface stream course. The term "streamflow" is more
general than "runoff" because streamflow can be applied to discharge regardless of
whether it is affected by diversion or regulation.
Substrate: Bottom sediment material in a natural water system.
Surface waters: Water that is present above the substrate or soil surface. Usually
refers to natural waterbodies such as rivers, lakes and impoundments, and estuaries.
Suspended solids or load: Organic and inorganic particles (sediment) suspended
in and carried by a fluid (water). The suspension is governed by the upward compo-
nents of turbulence, currents, or colloidal suspension.
Temperature coefficient: Rate of increase in an activity or process over a 10 °C
increase in temperature. Also referred to as the Q10.
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Compendium oflbokfbr Watershed Assessment and TMDL Development
Tertiary treatment: Waste treatment processes designed to remove or alter the
forms of nitrogen or phosphorus compounds contained in domestic sewage.
Three-dimensional (3-D) model: Mathematical model defined along three
spatial coordinates (length, width, and depth) where the water quality constituents
are considered to vary over all three spatial coordinates.
Total Kjeldahl nitrogen (TKN): The total of organic and ammonia nitrogen in a
sample, determined by the Kjeldahl method.
Total maximum daily load (TMDL): The sum of the individual wasteload
allocations, load allocations, and a margin of safety (MOS) required to achieve water
quality standards. The MOS accounts for scientific uncertainty about whether the
TMDL reflects the actual loading capacity of the waterbody.
Total coliform bacteria: A particular group of bacteria that are used as indicators
of possible sewage pollution. They are characterized as aerobic or facultative anaero-
bic, gram-negative, nonspore-forming, rod-shaped bacteria that ferment lactose with
gas formation within 48 hours at 35 °C. See also Fecal coliform bacteria.
Toxic substances: Those chemical substances, such as pesticides, plastics, heavy
metals, detergents, solvents, or any other materials, which are poisonous, carcino-
genic, or otherwise directly harmful to human health and the environment.
Toxicant: A poisonous agent that kills or injures animal or plant life.
Transit time: In nutrient cycles, average time that a substance remains in a particu-
lar form; ratio of biomass to productivity.
Transport of pollutants (in water): Transport of pollutants in water involves
two main processes: (1) advection, resulting from the flow of water, and (2) diffusion,
or transport due to turbulence in the water.
Travel time: Time period required by a particle to cross a transport route such as a
watershed, river system, or stream reach.
Tributary: A lower order stream compared to a receiving waterbody. "Tributary to"
indicates the largest stream into which the reported stream or tributary flows.
Trickling filter: A wastewater treatment process consisting of a bed of highly
permeable medium to which microorganisms are attached and through which waste-
water is percolated or trickled.
Turbidity: Measure of the amount of suspended material in water.
Turbulent flow: A flow characterized by irregular, random-velocity fluctuations.
Turbulence: A type of flow in which any particle may move in any direction with
respect to any other particle and in a regular or fixed path.. Turbulent water is agi-
tated by cross current and eddies. Turbulent velocity is that velocity above which
turbulent flow will always exist and below which the flow may be either turbulent or
laminar.
Two-dimensional (2-D) model: Mathematical model defined along two spatial
coordinates where the water quality constituents are considered averaged over the
third remaining spatial coordinate. Examples of 2-D models include descriptions of
the variability of water quality properties along (a) the length and width of a river
-------
Glossary
that incorporates vertical averaging or (b) the length and depth of a river that incor-
porates lateral averaging across the width of the waterbody.
Ultimate biochemical oxygen demand (UBOD or BODu): Long-term oxygen
demand required to completely stabilize organic carbon in wastewater or natural
waters.
Uncertainty factors: Factors used in the adjustment of toxicity data to account for
unknown variations. Where toxicity is measured on only one test species, other
species may exhibit more sensitivity to that effluent. An uncertainty factor would
adjust measured toxicity upward and downward to cover the sensitivity range of
other, potentially more or less sensitive species.
Unstratified: Indicates a vertically uniform or well-mixed condition in a water
body. See also Stratification.
Validation (of a model): Subsequent testing of a precalibrated model to addi-
tional field data, usually under different external conditions, to further examine the
model's ability to predict future conditions. Same as verification.
Verification (of a model): Subsequent testing of a precalibrated model to addi-
tional field data, usually under different external conditions, to further examine the
model's ability to predict future conditions. Same as validation.
Volatilization: Process by which chemical compounds are vaporized (evaporated)
at given temperature and pressure conditions by gas transfer reactions. Volatile
compounds have a tendency to partition into the gas phase.
Waste load allocation (WLA): The portion of a receiving water's total maximum
daily load that is allocated to one of its existing or future point sources of pollution.
Wastewater: Usually refers to effluent from a sewage treatment plant. See also
Domestic wastewater.
Wastewater treatment: Chemical, biological, and mechanical procedures applied
to an industrial or municipal discharge or to any other sources of contaminated water
to remove, reduce, or neutralize contaminants.
Water quality: The biological, chemical, and physical conditions of a waterbody; a
measure of the ability of a waterbody to support beneficial uses.
Water quality criteria (WQC): Water quality criteria comprise numeric and
narrative criteria. Numeric criteria are scientifically derived ambient concentrations
developed by EPA or states for various pollutants of concern to protect human health
and aquatic life. Narrative criteria are statements that describe the desired water
quality goal.
Water quality standard (WQS): A law or regulation that consists of the benefi-
cial designated use or uses of a waterbody, the numeric and narrative water quality
criteria that are necessary to protect the use or uses of that particular waterbody, and
an antidegradation statement.
Watershed: The area of land from which rainfall (and/or snowmelt) drains into a
stream or other waterbody. Watersheds are also sometimes referred to as drainage
basins. Ridges of higher ground generally form the boundaries between watersheds.
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Compendium oflbolsjbr Watershed Assessment and TMDL Development
At these boundaries, rain falling on one side flows toward the low point of one
watershed, while rain falling on the other side of the boundary flows toward the low
point of a different watershed.
Watershed-scale: Taking place at the watershed level, as opposed to modeling at
smaller geographic areas (such as fields or sub-basins).
Wind mixing: A physical process that occurs when wind over a free water surface
influences the atmospheric reaeration rate.
Zero-order kinetics: The rate of transformation or degradation of a substance; the
reaction rate of change isindependent of the concentrations in solution.
Zooplankton: Minute animals (protozoans, crustaceans, fish embryos, insect
larvae) that live in a waterbody and are moved aimlessly by water currents and wave
action.
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Appendix A:
Watershed-Scale Loading
Models—Fact Sheets
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Compendium of Tbols for Watershed Assessment and TMDL Development
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
AGNPS: Agricultural Nonpoint Source
Pollution Model
1. Distributor:
The source code and appropriate documenta-
tion of the latest version (5.0) is available to
the general public at the following Internet
anonymous FTP site: ftp.mrsars.usda.gov.
For information about the content of this FTP
site and instructions on downloading,
execute the following commands at the FTP
prompt: "open ftp.mrsars.usda.gov" "anonymous" "your E-mail
address" , "cdpub/ars/agnps"
, "bin" , "Getabout.txt"
. Please see notes in updating
version below.
2. Type of Modeling:
• Simulation of pollutant loads from
agricultural watersheds
• Storm-event simulation
• Single, continuous, multiple, and
diffuse source/release
• Distributed modeling using a grid
system with square elements
• Screening, intermediate, and detailed
applications
• Evaluation of best management
practices (BMPs).
3. Model Components:
• Rainfall/runoff assessment
• Water quality analysis (emphasis on
nutrients and sediments)
• Point source inputs available (feed-
lots, springs, wastewater treatment
plant discharge, stream bank, and
gully erosion)
• Source accounting, which allows
pollutants to be tracked as they move
through the watershed.
• Linkage to CIS possible
4. Method/Techniques:
AGNPS can be used to evaluate nonpoint
source pollution from agricultural watersheds.
The model allows comparison of the effects of
implementing various conservation alterna-
tives within the watershed. Cropping systems,
fertilizer application rates, point source loads,
nutrient contributions from feedlots, and the
effect of terraced fields can be modeled. Any
24-hour duration precipitation amount can be
simulated using NRCS rainfall types I, la, II,
or III, with peak discharges determined using
NRCS TR-55 methodology. The Universal Soil
Loss Equation (USLE), adjusted for slope
shape, predicts local sediment yield within the
originating cell. An estimate of gully erosion
occurring in a cell can be input by the user.
Sediment and runoff routing through
impoundment terrace systems can also be
simulated. Some versions are linked to CIS
with automatic generation of terrain
parameters (Panuska et al., 1991).
5. Applications:
• Erosion, sediment, and chemical
transport
• Surface water flow routing
6. Number of Pollutants:
Sediment, nutrients, pesticides, and
chemical oxygen demand (COD)
7. Limitations:
• Only a single event version is
currently available, although a
continuous simulation version that
includes snowmelt and frozen soil
components should be released soon.
• Lacks nutrient transformation and
instream processes.
• Needs further field testing for
pollutant transport component.
• No simulation of subsurface soil
processes.
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Compendium of Tools for Watershed Assessment and TMDL Development
• Rainfall intensity is not considered in
the runoff analysis.
8. Experience:
The AGNPS model is widely applied to rural
watersheds, commonly using a GIS frame-
work. Prato et al. (1989) describe an
application to provide an economic assess-
ment of soil erosion and water quality in
Idaho. Panuska et al. (1991) integrated two
terrain-enhancing programs into AGNPS to
automate data input. Vieux and Needham
(1993) describe a GIS-based analysis of the
sensitivity of AGNPS predictions to grid-cell
size. Engel et al. (1993) present GRASS-
based tools to assist with the preparation of
model inputs and visualization and analysis
of model results. Needham and Young
(1993) describe the development of a
continuous version of AGNPS. Tim and Jolly
(1994) used AGNPS with ARC/INFO to
evaluate the effectiveness of several alterna-
tive management strategies in reducing
sediment pollution in a 417-ha watershed in
southern Iowa.
9. Updating Version:
Ann AGNPS 1.0 (a continuous simulation
version of AGNPS; under the leadership of
the USDA-ARS National Sedimentation Lab in
Oxford, Mississippi.)
For more information, contact Fred Theuer
(301) 504-8642. The model should be
available for public release in June of 1997.
10. Input Data Requirements:
• Topography and soil characteristics
• Meteorologic data
• Land use data (cropping history and
nutrient applications)
• Point source data
• Global geomorphic parameter input
capability is permitted for hydraulic
channel geometry and/or stream
length
11. Simulation Output:
• Hydrology output: storm runoff
volume and peak rate
• Sediment output: sediment yield,
concentration, particle size distribu-
tion, upland erosion, amount of
deposition
• Chemical output: pollutant concentra-
tion and load
12. References Available:
Engel, B.A., R. Srinivasan, and C. Rewerts.
1993. Modeling erosion and surface water
quality. In Geographic Information Systems:
Proceedings of the Seventh Annual GRASS
Users Conference, Lakewood, CO, March 16-
19, 1992. National Park Service Technical
Report NPS/NRGISD/NRTR-93/13.
Needham, S.E., and R.A. Young. 1993. ANN-
AGNPS: A continuous simulation watershed
model. In Proceedings of the Federal Inter-
agency Workshop on Hydrologic Modeling
Demands for the 90's, Fort Collins, CO, June
6-9, 1993. U.S. Geological Survey Water
Resources Investigation Report 93-4018.
Panuska, J.C., I.D. Moore, and L.A. Kramer.
1991. Terrain analysis: Integration into the
agricultural nonpoint source (AGNPS)
pollution model. Journal of Soil and Water
Conservation 46(l):59-64.
Prato, T, H. Shi, R. Rhew, and M. Brusven.
1989. Soil erosion and nonpoint-source
pollution control in an Idaho watershed.
Journal of Soil and Water Conservation
44(4):323-328.
Tim, U.S., and R. Jolly. 1994. Evaluating
agricultural nonpoint-source pollution using
integrated geographic information systems
and hydrologic/water quality model. Journal
of Environmental Quality 23:25-35.
Vieux, B.E., and S. Needham. 1993. Non-
point-pollution model sensitivity to grid-cell
size. JournaZ of Water Resources Planning and
Management 119(2):141-157.
Young, R.A., C.A. Onstad, D.D. Bosch, and
WE Anderson. 1986. Agricultural Nonpoint
Source Pollution Model: A watershed analysis
tool Agriculture Research Service, U.S.
Department of Agriculture, Morris, MN.
Young, R.A., C.A. Onstad, D.D. Bosch, and WE
Anderson. 1989. AGNPS: A nonpoint-source
pollution model for evaluating agriculture
watersheds. Journal of Soil and Water
Conservation 44:168-173.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
ANSWERS;: Areal Nonpoint Source Water-
shed Environment Response Simulation
1. Distributor:
Dr. David Beasley
Department of Agricultural Engineering
North Carolina State University
Raleigh, NC
(919) 515-2694
2. Type of Modeling:
• Simulation of agricultural watersheds
with emphasis on erosion and
sediment yield
• Distributed simulation using a grid
system
• Storm event simulation
• Single and diffuse source/release
• Screening and intermediate applica-
tions
• Evaluation of BMPs
3. Model Components:
• Rainfall/runoff assessment
• Overland flow and channel flow
• Loading of nutrients and pesticides
• Erosion, sediment transport, and
deposition
4. Method/Techniques:
This model simulates the effects of land use,
management, and conservation practices on
the quality and quantity of water in a
watershed. The hydrology component is
based on surface and subsurface water
movement relationships using a modified
form of the Holton infiltration model.
Erosion processes are predicted by an event-
based particle detachment and transport
model. The quality component was added
to the model to compute pollutant loadings
based on correlation relationships between
concentration, sediment yield, and runoff
volume. Improvements to the pollutant
loading and transformation routines have
been incorporated by Dillaha et al. (1988). A
continuous version of the model is under
development (Bouraoui et al., 1993).
5. Applications:
• Hydrologic and erosion response of
agriculture land and construction
sites
• Movement of water in overland,
subsurface, and channel flow phases
• Identification of critical areas for
erosion and sedimentation control
• Siting and evaluation of BMPs
6. Number of Pollutants:
Sediment and nutrients (phosphorus and
nitrogen)
7. Limitations:
• Mainframe computer required for
large watershed simulation.
• Complexity of input data file.
• Snowmelt processes and pesticide
modeling are not included.
• No chemical transformation of
nitrogen and phosphorus.
• Small time steps are necessary for
finite difference algorithms and
restrict the simulation to a single
event.
• Requires small element grid; assumes
homogeneous condition within each
element.
8. Experience:
Applied successfully in Indiana on agricul-
tural watersheds and construction sites for
best management practice (BMP) evalua-
tion. Evaluated the relative importance of
point and nonpoint source contributions to
Saginaw Bay. De Roo et al. (1992) report a
Monte Carlo simulation based procedure to
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Compendium of Tools for Watershed Assessment and TMDL Development
evaluate the effects of spatial variations in
the values of the infiltration parameter on
the results of the ANSWERS model.
9. Updating Version:
N/A
10. Input Data Requirements:
Detailed description of the watershed
topography, drainage network, soils, and
land use (available from USDA-SCS soil
surveys, land use, and cropping surveys)
11. Simulation Output:
• Alternative erosion control manage-
ment practices on an element basis or
entire watersheds (flow and sedi-
ment)
• Limited graphical representation of
output results
12. References Available:
Beasley, D.B. 1986. Distributed parameter
hydrologic and water quality modeling. In
Agricultural Nonpoint Source Pollution: Model
Selection andApplication, ed. A. Giorgini and
F. Zingales, pp. 345-362.
Beasley, D.B., and L.F. Huggins. 1981.
ANSWERS User's Manual EPA905/ 9-82-001.
U.S. Environmental Protection Agency,
Region 5, Chicago, IL.
Bouraoui, F., T.A. Dillaha, and S.
Mostaghimi. 1993. ANSWERS 2000: Water-
shed model for sediment and nutrient transport.
ASAE Paper No. 93-2079. American Society
of Agricultural Engineers, St. Joseph, MI.
De Roo, A. P. J., L. Hazelhoff, and G. B. M.
Heuvelink 1992. Estimating the effects of
spatial variability of infiltration of a distrib-
uted runoff and soil erosion model using
Monte Carlo methods. Hydrological Processes
6:127-143.
Dillaha, T.A., C.D. Heatwole, M.R. Bennett, S.
Mostaghimi, V.O. Shanholtz, and B.B. Ross.
1988. Water quality modeling for nonpoint
source pollution control planning: Nutrient
transport. Report No. SW-88-02. Virginia
Polytechnic Institute and State University,
Dept. of Agricultural Engineering.
Freedmann, P.L., and D.W. Dilks. 1991.
Model capabilities - A user focus. In EPA
Workshop on the Water Quality-based Approach
for Point Source and Nonpoint Source Controls,
June 1991, pp. 26-28. EPA 503/9-92-001.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
Automated Q-ILLUAS (AUTO-QI)
1. Distributor:
Robert A. Sinclair
Illinois State Water Survey
2204 Griffith Drive
Champaign, IL 61820-7495
Cost: $50
(217) 333-4952
2. Type of Modeling:
• Urban stormwater processes
• Storm event simulation of runoff and
continuous soil moisture simulation
• Nonpoint source pollutant loading
and event mean concentration
simulation (EMC)
• Screening and intermediate applica-
tions
• Evaluation of BMPs
3. Model Components:
• Rainfall/runoff assessment from
pervious and impervious areas
• Pollutant loadings and EMC analysis
• Simulation of BMPs, separate or
overlapping
• Linkage to geographic information
system (GIS)
4. Method/Techniques:
AUTO-QI is based on continuous simulation
of soil moisture. Runoff volumes are
adjusted for soil moisture, pervious and
impervious depression storage, interception,
and infiltration based on Horton infiltration
curves. Exponential pollutant accumulation
and wash-off functions are used to deter-
mine the pollutant loads. The impacts of a
series of pollutant reduction practices are
simulated based on user-supplied removal
efficiencies. The model handles nine
different kinds of land use-soil combina-
tions.
5. Applications:
• Simulation of runoff volumes,
pollutant loads, and event mean
concentrations for a watershed with
different land use types
• Comparison of pollutant levels with
and without BMPs and with various
fertilizer application rates
6. Number of Pollutants:
Several pollutants including nitrogen,
phosphorus, chemical oxygen demand
(COD), metals, and bacteria (at least four at
once)
7. Limitations:
• Does not include any kind of hydrau-
lic or hydrologic routing.
• Does not calculate pollutant removal
efficiencies; removal efficiencies must
be supplied by the user.
• Lacks nutrient transformation and
instream processes.
• Tested in the State of Illinois only
• No simulation of subsurface soil
processes.
8. Experience:
• Simulation of urban pollutant loads
for suspended solids, phosphorus, and
lead from the greater Lake Calumet
area after calibration on Boneyard
Creek in Champaign, Illinois
(Terstriep et al., 1990).
• Preliminary water quality simulation
for Waukegan City for fecal coliforms,
nitrogen, phosphorus, BOD, sus-
pended solids, lead, chromium, and
zinc. Based on past calibration and
literature values (Cardona et al.,
1995).
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Compendium of Tools for Watershed Assessment and TMDL Development
9.UpdatingVendon:
October 1990
10. Input Data Requirements:
• Daily and hourly rainfall data
• Monthly evaporation and evapotrans-
piration values
• BMP removal efficiencies
• Soil infiltration parameters
• Land use parameters and soil types
for each subcatchment
• Buildup and wash-off characteristics
of each pollutant
• For CIS interface: land use, soil, basin
and subbasin coverages
11. Simulation Output:
• A summary for the watershed by
event is created for rainfall, total
runoff, total runoff duration, maxi-
mum rainfall and runoff events, and
maximum event duration
• Average event mean concentrations
and loadings for each pollutant
constituent
• Output ASCII files are created
containing detailed information of
runoff per land use for each storm
event
• Output ASCII files are created
containing detailed information about
wash-off data for each constituent
and each storm event.
12. References Available:
Cardona, M.E., J. Stillman, and R.A.
Sinclair. 1975. Waukegan Stormwater
Quality Simulation: Application ofAUTO-QI
modeling. Prepared for the U.S. Environmen-
tal Protection Agency by the Illinois State
Water Survey.
Terstriep, M.L., M.T. Lee, E.R Mills, A.V
Greene, and M.R. Rahman. 1990. Simula-
tion of urban runoff and pollutant loading
from the Greater Lake Calumet area. Pre-
pared for the U.S. Environmental Protection
Agency, Region 5, Water Division, Watershed
Management Unit, Chicago, IL, by the
Illinois State Water Survey.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
DR3M-QUAL: Multi-Event Urban Runoff
Quality Model
1. Distributor:
Kathleen M. Flynn
415 National Center
Mail stop 437
U.S. Geological Survey
Reston, VA 20192
(703) 648-5313
E-mail: h20.softeusgs.gov
http://watetusgs.gov/software/
2. Type of Modeling:
• Urban stormwater pollutant loads
• Continuous simulation
• Continuous, intermittent, and diffuse
source/release
• Intermediate and detailed applica-
tions
3. Model Components:
• Rainfall/runoff assessment
• Water quality analysis
4. Method/Techniques:
DR3M is a watershed model for routing
storm runoff through a branched system of
pipes and/or natural channels using rainfall
as input. The model provides detailed
simulation of storm runoff periods selected
by the user and a daily soil moisture
accounting between storms. Kinematic wave
theory is used for routing flows over
contributing overland-flow areas and
through the channel network. Storm
hydrographs may be saved for input to
DR3M-QUAL.
DR3M-QUAL is a model for simulating the
quality of surface runoff from urban
watersheds. The model simulates impervious
areas, pervious area, and precipitation
contributions to runoff quality as well as the
effects of street sweeping and/or detention
storage. Variations of runoff quality are
simulated for user-specified storm runoff
periods. Between these storms, a daily
accounting of the accumulation and wash-off
of water quality constituents on effective
impervious areas is maintained. Input to the
model includes the storm hydrographs,
usually from DR3M.
Empirical equations use relationships
between sediment yield and runoff volume
and peak to simulate erosion. The erosion
parameters are selected based on the USLE.
The transport process is modeled assuming
plug flow and using a Lagrangian scheme.
Calibration is required for accurate quality
predictions. However, default values may be
used for screening-level analysis.
5. Applications:
• Rainfall/runoff assessment
• Surface water quality analysis
6. Number of Pollutants:
• Sediment, nitrogen, and phosphorus,
metals, and organics
7. Limitations:
• No interaction among quality
parameters
• Weak sediment transport simulation
8. Experience:
The program has been extensively reviewed
within the USGS and applied to several
urban modeling studies (Brabets, 1986;
Guay, 1990; Lindner-Lunsford and Ellis,
1987) .
9. Updating Version and System
requirements:
Version II (1982). DR3M has been success-
fully installed and run on a number of
different computer platforms. An update to
DR3M uses a watershed data management
file for the input and output time series.
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Compendium of Tools for Watershed Assessment and TMDL Development
DR3M-QUAL may require some modifica-
tions to be PC-compatible.
10. Input Data Requirement;:
• Daily rainfall, daily evaporation, and
storm event rainfall at a constant
time step
• Subcatchment data: area, impervious-
ness, length, slope, roughness, and
infiltration parameters
• Trapezoidal or circular channel
dimensions and kinematic wave
parameters
• Stage-area-discharge relationships for
storage basins
• Water quality parameters, including
buildup and wash-off coefficients
13. Simulation Output:
• Time series of runoff hydrographs
and quality pollutographs (concentra-
tion or load vs. time) at any location
in the drainage system
• Summaries for storm events
• Graphical output of water quality and
quantity analysis
14. References Available:
Alley, W.M., and P.E. Smith. 1982. Distributed
Routing Rainfall-Runoff Model - Version U.
Open File Report 82-344. U.S. Geological
Survey, Reston, VA.
Alley, W.M., and P.E. Smith. 1982. Multi-
event Urban Runoff Quality Model Open File
Report 82-764, U.S. Geological Survey.
Reston, VA.
Brabets, T. P. 1986. Quantity and quality of
urban runoff from the Chester CreekBasin,
Anchorage, Alaska. Water Resources Investi-
gations Report 86-4312. U.S. Geological
Survey, Denver, CO.
Guay. J. R. 1990. Simulation of urban runoff
and river water quality in the San Joaquin
River near Fresno, California. In Symposium
Proceedings on Urban Hydrology, American
Water Resources Association, Denver, CO,
November 4-8,1990, pp. 177-182.
Lindner-Lunsford, J.B., and S.R. Ellis. 1987.
Comparison of conceptually based and regression
rainfall-runoff models, Denver Metropolitan
Area, Colorado, andpotential applications in
urban areas. Water Resources Investigations
Report 87-4104. U.S. Geological Survey,
Denver, CO.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
EPA Screening Procedures
1. Distributor:
OTIS PB 86122496
National Technical Information Services
5285 Port Royal Road
Springfield, VA 22161
(703) 487-4650
Please refer to document number
PB86122496
(EPA/600/6-85/002a).
Complete title is 'Water Quality Assessment:
A Screening Procedure for Toxic and Conven-
tional Pollutants in Surface and Groundwater
- Part 1."
2. Type of Modeling:
• Not a computer program, consists of a
series of equations/techniques
• Screening application
• Assessment of point and nonpoint
source loadings, including salt loads
in irrigation return flows and dry/wet
atmospheric deposition loads
• Impact of point and nonpoint sources
for conventional and toxic pollutants
in rivers, impoundments, and
estuaries
3. Model Components:
• Prediction of sediment, nutrient, and
pesticide losses.
• Receiving water impacts consider
BOD-DO interactions, temperature,
coliform bacteria, nutrients, sediment
transport. Toxicant processes consid-
ered are volatilization, sorption, and
first-order degradation.
4. Method/Techniques:
Agricultural nonpoint loads are based on the
Universal Soil Loss Equation (USLE), SCS
runoff curve number procedure, and loading
functions using enrichment ratios. Urban
nonpoint loads are estimated using the
buildup-wash-off concept. Receiving water
analyses are carried out using simplified
water quality kinetics, sediment-toxicant
interactions, and a mass balance approach
that assumes steady-state conditions.
5. Applications:
Loading functions have been incorporated
into several hydrologic models to estimate
pollutant loadings. Several of the simplified
procedures for receiving water impacts have
also been incorporated into water quality/
eutrophication and toxicant models.
6. Number of Pollutants:
Nitrogen, phosphorus, sediment, heavy
metals, pesticides, organics, salinity, BOD-
DO interactions, coliform bacteria
7. Limitations:
• Accuracy is limited when default
parameters are substituted for site-
specific data.
• Neglects seasonal variation in
predicting annual loadings.
• Considers only steady-state conditions
for receiving water analyses and
greatly simplifies water quality
kinetics and toxicant processes.
8. Experience:
EPA Screening Procedures have been
applied (Donigian and Huber, 1991) to the
Sandusky River in northern Ohio and the
Patuxent, Ware, Chester, and Occoquan
basins in the Chesapeake Bay region (Davis
et al., 1981; Dean et al., 1981). Bowie et al.
(1985) provide a comprehensive source of
information on rate constants and coeffi-
cients that may be used in applying the
screening procedures.
9. Updating Version:
N/A
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Compendium of Tools for Watershed Assessment and TMDL Development
10. Input Data Requirements:
• USLE parameters, SCS runoff curve
number, enrichment ratios
• Geometric/morphometric data to
define receiving waterbody, and rate
constants/coefficients for various
water quality and toxicant processes
11. Simulation Output:
• Annual pollutant loads (maybe
adopted for computation of seasonal
or storm event loadings)
• Prediction of steady-state response of
receiving waterbody to point and
nonpoint source loadings for conven-
tional and toxic pollutants
12. References Available:
Bowie, G.L., WB. Mills, D.B. Porcella, C.L.
Campbell, R.R. Pagenkopf, G.L. Rupp, K.M.
Johnson, EW.H. Chan, and S.A. Gherini.
1985. Rates, constants, and kinetic formula-
tions in surface water quality modeling. 2nd
ed. Prepared for the U.S. Environmental
Protection Agency.
Davis, M.J., M.K. Snydei; and J.W. Nebgen.
1981. River basin validation of the water
quality assessment methodology for screening
nondesignated 208 areas - Volume I: Nonpoint
source load estimation. U.S. Environmental
Protection Agency, Athens, GA.
Dean, J.D., B. Hudson, and W.B. Mills.
1981. River basin validation of the MRI
nonpoint calculator and Terra Tech's
nondesignated 208 screening methodologies,
Vol n. Chesapeake-Sandusky nondesignated
208 screening methodology demonstration.
U.S. Environmental Protection Agency,
Athens, Georgia.
Donigian, A. S., and W. C. Huber. 1991.
Modeling of nonpoint source water quality in
urban and non-urban areas. EPA/600/3-91/
039. U.S. Environmental Protection Agency.
Mills W.B., B.B. Borcella, M.J. Ungs, S.A.
Gherini, K.V Summers, Mok Lingsung, G.L.
Rupp, G.L. Bowie, and D.A. Haith. 1985.
Water quality assessment: A screening
procedure for toxic and conventional pollut-
ants in surface and ground water, Parts 1 and
2. ERA/600/6-85/002a,b. U.S. Environmen-
tal Protection Agency, Environmental
Research Laboratory, Athens, GA.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
FHWA: The Federal Highway Administration
Model
1. Distributor:
Office of Engineering and Highway
Operations R&D
Federal Highway Administration
6300 Georgetown Pike
McLean, VA 22101
2. Type of Modeling:
• Statistical
• Screening application
3. Model Components:
• Computation of water quality impact
from site data for either lakes or
streams
• Simple evaluation of controls
4. Method/Techniques:
Pollutant loadings and the variability of
loadings are estimated from runoff volume
distributions and event mean concentrations
for the median runoff event at a site.
Rainfall is converted to runoff using a runoff
coefficient calculated from the percent
imperviousness. Runoff velocity is esti-
mated from runoff intensity. Mean runoff
concentrations are calculated from site
median pollutant concentrations, coefficient
of variation for event mean concentrations
(EMCs), and the mean EMC as:
MCR = TCR • J(1+CVCR2)
where:
MCR = mean EMC for site (mg/L).
TCR = site median pollutant concentration
(mg/L)
CVCR = coefficient of variation of EMCs
Mean event mass loading is computed as:
M(Mass) = MCR- MVR- (62.45.10~6)
where:
M(Mass) = mean pollutant mass loading
(pounds per event)
MCR = mean runoff concentration
(mg/L)
MVR = mean storm event runoff
volume (cf)
Annual loads are calculated by multiplying
by the number of storms per year. Pollutant
buildup is based on traffic volumes and
surrounding area characteristics.
5. Applications:
• Evaluation of lake and stream impacts
of highway stormwater discharges
• Uncertainty analysis of runoff and
pollutant concentrations, or loads
• Highway stormwater runoff manage-
ment
6. Number of Pollutants:
Heavy metals (copper, lead, and zinc),
nitrogen, and phosphorus.
7. Limitations:
• Assesses seasonal variability in a
limited manner as expressed in the
probability distributions of the
output.
• Limited in its evaluation of controls.
• Does not consider the soluble fraction
of pollutants or the precipitation and
settling of phosphorus in lakes.
8. Experience:
The FHWA model has been used by the
Federal Highway Administration to evaluate
the impacts of stormwater runoff from
highways and their surrounding drainage
areas.
9. Updating Version:
N/A
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Compendium of Tools for Watershed Assessment and TMDL Development
10. Input Date Requirements:
• Hourly rainfall data to be trans-
formed into mean and coefficient of
variation
• Drainage and paved areas, average
rainfall volumes, intensities, and
durations
• Coefficients of variation are required
for all average rainfall characteristics
• Traffic volumes for the surrounding
area are required
• Runoff concentrations (average and
coefficient of variations) for each
pollutant
11. Simulation Output;
• Mean and variance of in-stream or
lake concentrations
• Mean and variance of pollutant
loadings and concentrations in runoff
12. References Available:
Driscoll, E.D., P.E. Shelley, and E.W. Strecker.
1990. Pollutantloadingsandimpactsfrom
highway stormwater runoff, Volume I: Design
procedure. Prepared for the Office of Engi-
neering and Highway Operations R&D,
Federal Highway Administration.
Driscoll, E.D., P.E. Shelley and E.W. Strecker.
1990.PoZZutant loadings and impacts from
highway stormwater runoff, Volume II: Users
guide for interactive computer implementation of
design procedure. Prepared for the Office of
Engineering and Highway Operations R&D,
Federal Highway Administration.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
GWLF: Generalized Watershed Loading
Functions
Department of Agricultural and Biological
Engineering
Cornell University
Ithaca, NY 14853
(607) 255-2802
2. Type of Modeling:
• Pollutant loads from urban and
agricultural watersheds, including
septic systems
• Continuous simulation using daily
time step
• Point and nonpoint sources
• Screening to intermediate application
• Evaluation of effects of land use
changes
3. Model Components:
• Rainfall/runoff assessment
• Surface water/groundwater quality
analysis
4. Method/Techniques:
This model is based on simple runoff,
sediment, and groundwater relationships
combined with empirical chemical param-
eters. It evaluates streamflow, nutrients,
soil erosion, and sediment yield values from
complex watersheds. Runoff is calculated by
means of the SCS curve number equation.
The Universal Soil Loss Equation (USLE) is
applied to simulate erosion. Urban nutrient
loads are computed by exponential accumu-
lation and wash-off functions. Nutrient loads
from septic systems are calculated by
estimating the per capita daily load from
each type of septic system considered and
the number of people in the watershed
served by each type.
Groundwater runoff and discharge are
obtained from a lumped-parameter water-
shed water balance for both shallow
saturated and unsaturated zones. Daily
water balances are calculated for unsatur-
ated and shallow saturated zones.
The model does not require water quality
data for calibration.
5. Applications:
Relatively large watersheds with multiple
land uses and point sources
6. Number of Pollutants:
Total and dissolved nutrients (nitrogen and
phosphorus) and sediment
7. Limitations:
• Simulation of peak nutrient fluxes is
weak.
• Stormwater storage and treatment
are not considered.
8. Experience:
GWLF was validated for an 85,000-hectare
watershed from the West Branch Delaware
River Basin in New York using a 3-year
period of record
9. Updating Version and System
Requirements:
Version 2.00 (1992). PC-compatible.
10. Input Data Requirements:
• Daily precipitation and temperature
data and runoff source areas
• Transport parameters: runoff curve
numbers, soil loss factor, evapotrans-
piration cover coefficient, erosion
product, groundwater recession and
seepage coefficients, and sediment
delivery ratio
Chemical parameters: urban nutrient
accumulation rates, dissolved
nutrient concentrations in runoff, and
solid-phase nutrient concentrations in
sediment
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Compendium of Tools for Watershed Assessment and TMDL Development
• For septic systems, estimates of the
per capita nutrient load in septic
system effluent and per capita
nutrient losses due to plant uptake,
as well the number of people served
by each type of septic system consid-
ered
• Point sources
11. Simulation Output:
• Monthly precipitation, evapotranspi-
ration, groundwater discharge to
streamflow, watershed runoff,
streamflow, watershed erosion and
sediment yield, and total nitrogen
and phosphorus loads in streamflow.
• Annual erosion from each land use,
nitrogen and phosphorus loads from
each land use and in streamflow, and
annual loads from septic systems.
12. References Available:
Delwiche, L.L.D., and D.A. Haith. 1983.
Loading functions for predicting nutrient
losses from complex watersheds. Water
Resources Bulletin 19(6):951-959.
Haith, D.A. 1985. An event-based procedure
for estimating monthly sediment yields.
Transactions of the American Society of
Agricultural Engineers 28(6):1916-1920.
Haith, D.A., and L.L. Shoemaker. 1987.
Generalized watershed loading functions for
stream flow nutrients. Water Resources
Bulletin 23(3):471-478.
Haith, D.A. 1990. Mathematical models of
nonpoint-source pollution. Cornell Quarterly
25(1):26.
Haith, D.A., R. Mandel, and R. S. Wu. 1992.
GWLF - Generalized Watershed Loading
Functions, Version 2.0 - User's manual
Department of Agricultural Engineering,
Cornell University, Ithaca, NY.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
HSPF: Hydrological Simulation Program -
FORTRAN
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment Modeling
(CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
Models are available for FTP from:
ftp://fcp.epa.gov/epa_ceam/wwwhtml/
software.htm
2. Type of Modeling:
• Pollutant load and water quality in
complex watersheds
• Continuous and storm event simula-
tion
• Single, continuous, intermittent,
multiple, and diffuse source/release
• Screening, intermediate, and detailed
applications
• BMP evaluation and design criteria
3. Model Components:
• Watershed hydrology assessment
• Surface water quality analysis
(conventional and toxic organic
pollutants)
• Soil/groundwater contaminant runoff
processes with instream hydraulic
and sediment-chemical interactions
(saturated and unsaturated zones)
• Pollutant decay and transformation
4. Method/Techniques:
This model calculates surface and subsurface
pollutant transport from complex watersheds
to receiving waters. Hydrolysis, oxidation,
photolysis, biodegradation, volatilization, and
sorption are used to describe the transfer and
reaction processes. First-order kinetic
processes are employed to model sorption.
Water quality is simulated by a lumped-
parameter model. Three sediment types
(sand, silt, and day) and a single organic
chemical, as well as transformation products
of that chemical, can be simulated. Currently,
potency factors are used for all pervious
areas, but enhancements are under way to
use detailed agrichemical modules to better
represent the impacts of agricultural BMPs.
Calibration is required for model application.
Because of the modular approach, detail of
application can be varied depending on data
availability and modeling needs.
5. Applications:
• Surface and subsurface pollutant
transport to receiving water with
subsequent simulation of instream
transport and transformations
• Watershed hydrology and water
quality for both conventional and
toxic organic pollutants
• Evaluation of BMPs and development
of design criteria
6. Number of Pollutants:
Seven pollutants: three sediment compo-
nents (sand, silt, and clay), one pesticide or
other toxic pollutant (user-specified), BOD,
ammonia or nitrate, and orthophosphate
7. Limitations:
• The techniques used in the Stanford
Watershed Model (SWM) are as-
sumed to be appropriate for the area
being modeled.
• Limited to well-mixed rivers and
reservoirs.
• Extensive water quality sampling data
required for calibration or verifica-
tion.
• Highly trained staff required for
model application.
8. Experience:
HSPF is being used by the Chesapeake Bay
Program to model total watershed contribu-
tions of flow, sediment, nutrients, and
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Compendfum of "fools for Watershed Assessment and TMDL Development
associated constituents to the tidal region of
the Bay (Donigian et al., 1990; Donigian and
Patwardhan, 1992). Moore et al. (1992)
describe an application to model BMP effects
on a Tennessee watershed. Scheckenberger
and Kennedy (1994) discuss how HSPF may
be used in subwatershed planning. Ball et al.
(1993) describe an application of HSPF in
Australia. Lumb et al. (1990) describe an
interactive program for data management
and analysis that can be effectively used with
HSPF. Lumb and Kittle (1993) have pre-
sented an expert system that can be used for
calibration and application of HSPF.
9. Updating Version:
Version 10,11 (1995)
10. Input Data Requirements:
• Continuous rainfall records
• Continuous records of evapotranspi-
ration, temperature, and solar
intensity
• A large number of parameters need
to be specified (some default values
are available)
11. Simulation Output:
• Time series of the runoff flow rate,
sediment load, and nutrient and
pesticide concentrations
• Time series of water quantity and
quality at any point in a watershed
• Frequency and duration analysis
routine
12. References Available:
Ball, J. E., M. J. White, G. de R. Innes, and L.
Chen. 1993. Application of HSPF on the
Upper Nepean Catchment. In Proceedings of
Hydrology and Water Resources Symposium,
Newcastle, New South Wales, Australia, June
30-July2,1993, pp. 343-348.
Bicknell, B. R., J. C. Imhoff, J. L. Kittle, A. S.
Donigian, and R. C. Johanson. 1993. Hydro-
logical Simulation Program - FORTRAN
(HSPF): User's manualfar release 10.0. EPA
600/3-84-066. Environmental Research
Laboratory, U.S. Environmental Protection
Agency, Athens, GA.
Donigian, A.S., Jr., B.R. Bicknell, L.C. Linker, J.
Hannawald, C. Chang, and R. Reynolds.
1990. Chesapeake Bay Program Watershed Model
application to calculate bay nutrient loadings:
Preliminary Phase I findings and recommenda-
tions. Prepared for the U. S. Chesapeake Bay
Program, Annapolis, MD, by AQUA TERRA
Consultants.
Donigian, A.S., Jr., and A.S. Patwardhan.
1992. Modeling nutrient loadings from
croplands in the Chesapeake Bay Watershed.
In Proceedings of Water Resources sessions at
Water Forum '92, Baltimore, MD, August 2-6,
1992, pp. 817-822.
Donigian, A.S., Jr., B.R. Bicknell, and J.C.
Imhoff. 1994. Hydrological Simulation
Program - FORTRAN (HSPF). Chapter 12 in
Computer models ofwatershed hydrology, ed.
V.P. Singh. Water Resources Publications,
Littleton, CO.
Lumb, A.M., J.L. Kittle, and K.M. Flynn.
1990. Users manual for ANNIE, A computer
program for interactive hydrologic analyses and
management. Water Resources Investigations
Report 89-4080. U. S. Geological Survey,
Reston, VA.
Lumb, A.M., and J.L. Kittle. 1993. Expert
System for calibration and application of
watershed models. In Proceedings of the
Federal Interagency Workshop on Hydrologic
Modeling Demands for the 90's, Fort Collins,
CO, June 6-9, 1993. U.S. Geological Survey
Water Resources Investigation Report 93-
4018.
Moore, L.W., C.Y. Chew, R.H. Smith, and S.
Sahoo. 1992. Modeling of Best Management
Practices on North Reelfoot Creek, Tennes-
see. Water Environment Research 64(3) :241-
247.
Scheckenberger, R.B., and A.S. Kennedy.
1994. The use of HSPF in subwatershed
planning. In Current practices in modelling the
management of stormwater impacts, ed. W.
James, pp. 175-187. Lewis Publishers, Boca
Raton, FL.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
P8-UCM: Urban Catchment Model
1. Distributor:
Richard Ribb
Narragansett Bay Project
291 Promenade Street
Providence, RI02908-5767
(401) 277-4700, ext 7271
2. Type of Modeling:
• Urban watersheds
• Storm event/sequence simulation
• Surface water quality analysis
• Evaluation of BMPs and development
of design criteria
• Single, continuous, and diffuse
source/release
• Screening application
3. Model Components:
• Stormwater runoff assessment
• Surface water quality analysis
• Routing through structural controls
4. Method/Techniques;:
The P8 program predicts the generation and
transport of stormwater runoff pollutants in
small urban catchments. It consists mainly of
methods derived from other tested urban
runoff models (i.e., SWMM, HSPF, D3RM,
TR-20). Runoff from impervious areas is
calculated directly from rainfall once
depression storage is exceeded. Particle
build-up and wash-off processes are
obtained using equations derived primarily
from the SWMM program. The SCS curve
number equation is used to predict runoff
from pervious areas, and water balance used
to calculate percolation. Baseflow is simu-
lated by a linear reservoir. Without calibra-
tion, use of model results should be limited
to relative comparisons.
5. Applications:
• Development/Comparison of
stormwater management plans
• Watershed-scale land use planning
• Site planning and evaluation for
compliance
• Effectiveness of sedimentation ponds
and constructed wetlands
• Selecting and sizing BMPs
6. Number of Pollutants:
Total suspended solids (TSS), total phospho-
rus, total Kjeldahl nitrogen, lead, copper,
zinc, and hydrocarbons
7. Limitations:
• No snowfall, snowmelt, or erosion is
calculated.
• Effects of variations in vegetation
type/cover on evapotranspiration are
not considered.
• Watershed lag is not simulated.
8. Experience:
• Computation of total suspended
solids (TSS) removal efficiency of
various BMPs for compliance with
state NFS plans for Rhode Island
• Evaluation of stormwater strategies
for New York city's wastewater
treatment facilities.
• To meet requirements of NPDES
municipal stormwater permit for the
city of St. Paul, Minnesota
• Watershed planning for New York
City's water supply system
9. Updating Version:
Version 1.1 (1990)
-------
Compendium of Thais for Watershed Assessment and TMDL Development
10. Input Data Requirements:
• Device (hydraulic) parameters for
pond, basin, buffer, pipe, splitter, and
aquifer
• Watershed parameters: areas,
impervious fraction and depression
storage, street-sweeping frequency,
SCS runoff curve number for
pervious portion
• Particle parameters: accumulation/
wash-off parameters, runoff concen-
trations, street-sweeper efficiencies,
settling velocities, decay rates,
filtration efficiencies
• Water quality component parameters:
pollutant concentrations
• Air temperatures required for stream
baseflow computations
11. Simulation Output:
• Water and mass balances, removal
efficiencies, mean inflow/outflow
concentrations, and statistical summa-
ries by device and component
• Comparison of flow, loads, and
concentration across devices
• Peak elevation and outflow ranges for
each device
• Sediment accumulation rates by
device
• Violation frequencies for event mean
concentrations
12. References Available:
Palmstrom, N., and W.W. Walker, Jr. 1990.
P8 Urban Catchment Model: User's guide,
program documentation, and evaluation of
existing models, design concepts and Hunt-
Potowomut data inventory. The Narraganselt
Bay Project Report No. NBP-90-50.
Walker, W.W., 1990. Urban Catchment Model
Program Documentation, Version J.I. Prepared
for IEP, Inc., Northborough, MA and
Narragansett Bay Project, Providence, RI.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
Sediment and Phosphorus Prediction
(SLOSS, PHOSPH)
1. Name of Distributor:
N/A (see references below)
2. lype of Modeling:
• Sediment yield and phosphorus
loading from a watershed
• Annual prediction (may be adapted to
storm events)
• The prediction equations have been
incorporated into the PC-VirGIS
system(Yagow et al., 1992).
• Screening application
3. Model Components:
• Two simple models for sediment and
phosphorus
4. Method/Techniques:
SLOSS uses the Universal Soil Loss Equa-
tion (USLE) to predict erosion and a
delivery ratio employed to predict watershed
sediment yield:
A=
where:
As =
K; =
LS, =
Cj =
P. =
n =
Lf =
t = exp[-(blSflI/l)]
soil loss per unit of watershed
soil credibility factor
topographic factor
land use/land cover management
factor
support practice factor
the maximum number of cells
delivery ratio
land cover factor
slope function; and
length of the flow path between
cell i and the channel outlet
Phosphorus loading is calculated as the
product of the average phosphorus content
of the surface soil and a phosphorus enrich-
ment ratio.
where
Tps = total sediment-associated phospho-
rus delivered to the stream outlet
PC. = average phosphorus content of the
surface soil layer for soil in cell i
Ls = sediment yield for each cell
ERp = phosphorus enrichment ratio
5. Applications:
• Identify critical areas of pollutant
production in watersheds
• Predict annual soil loss and phospho-
rus yields
6. Number of Pollutants:
• SLOSS predicts erosion and sediment
yield
• PHOSPH predicts phosphorus loading
7. Limitations:
• Does not address seasonal variation.
• Considers sediment and phosphorus
only.
• Most suited to application in a GIS
framework.
8. Experience:
Applied to Nomini Creek watershed in
Westmoreland County, Virginia (USEPA,
1992).
9. Updating Version:
N/A
10. Input Data Requirements:
• Parameters for the USLE (soil
credibility, cropping and management
factors, topography, and rainfall
erosivity factor) and channel param-
eters
-------
Compendium of Tools for Watershed Assessment and TMDL Development
• Phosphorus concentration in soil,
phosphorus enrichment ratio
11. Simulation Output:
• Mean annual loads of sediment and
phosphorus
12. References Available:
Shanholtz, V O., C. J. Desai, N. Zhang, J. W.
Kleene, and C. D. Metz. 1990. Hydrologic/
water quality modeling in a GIS environment.
Paper No. 90-3033. ASAE Summer Meeting,
Columbus, OH. American Society of Agricul-
tural Engineers, St. Joseph, MI.
USEPA, 1992. TMDL Case Study #4: Nomini
Creek Watershed. TMDL Case Study Series.
EPA841-F-93-004. U.S. Environmental
Protection Agency, Office of Water, Washing-
ton, DC.
Yagow, E.R., VO. Shanholtz, and J.M. Flagg.
1992. Agricultural NFS model applications
with a PC-based GIS. Paper No. 92-2013.
ASAE Summer Meeting, Charlotte, NC.
American Society of Agricultural Engineers,
St. Joseph, MI.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
The Simple Method
1. Distributor:
Metropolitan Washington Council of
Governments (MW-COG)
777 North Capitol Street, Suite 300
Washington, DC 20002
(202) 962-3200
2. Type of Modeling:
• Not a computer program
• Pollutant concentration from urban
drainage areas
• Diffuse source
• Storm-based computations
• Screening application
3. Model Components:
• Pollutant export in storm runoff
• Sediment event mean concentration
estimates
• Threshold exceedance frequencies
4. Method/Techniques:
The Simple Method uses the following
expression as its governing equation:
where:
Lj = pollutant loading (Ib/year)
P = average annual rainfall (inches)
Pj = unidess correction factor to
account for storms that produce no runoff
R,, = runoff coefficient (dimensionless)
C = flow-weighted mean pollutant
concentration (mg/L)
A = area of development (acres)
Runoff is estimated using runoff coefficients
for the fraction of rainfall converted to
runoff. The portion of storms that do not
produce runoff are accounted for by a
correction factor determined based on
analysis of site-specific or regional precipita-
tion pattern (p = 0.9 for Washington, DC,
area). Runoff coefficients are determined
based on the following equation:
where:
PI =
= 0.05+0.009 -PI
percent imperviousness
Pollutant concentrations in runoff depend
on the land use/land activity and can be
obtained from sampling programs such as
the NURP program. Sediment event mean
concentrations are calculated as a function
of the surface area of the drainage basin. It
is assumed that the channels in urban
watersheds are a major source of sediment
and thus larger watersheds will have higher
event mean concentrations. Factors such as
the channel stability, storage, and stream
velocity are taken into account in the event
mean concentration determination.
5. Applications:
• Estimate increased pollutant loading
from an uncontrolled development
site
• Estimate expected extreme concentra-
tion that occurs over a specified
interval of time
6. Number of Pollutants:
Phosphorus, nitrogen, COD, BOD, metals,
including zinc, copper, and lead
7. Limitations:
• Limited to watersheds where data are
available or must assume national
NURP values.
• Intended for recently stabilized
suburban watersheds.
• Limited to small watersheds (less than
1 square mile).
-------
Compendium of Tbols for Watershed Assessment and TMDL Development
• Application limited to relative
comparisons.
8. Experience:
The Simple Method is used to evaluate
development plans in the metropolitan
Washington, DC, area. The Simple Method
has also been used by municipalities in
preparation of NPDES stormwater permits.
9. Updating Version:
N/A
10. Input Data Requirements:
• Characteristics of pollution sources
• Flow and concentrations of point
sources
• Areas served by urban land uses such
as storm sewers, combined sewers,
and unsewered areas along with their
corresponding unit area loads for the
pollutant of concern
• Areas and unit area loads for grass
and woodland areas
• Parameters for the USLE for croplands
• Pollutant delivery ratios and pollutant
reduction efficiency ratio
11. Simulation Output:
• Total annual loads and load reduc-
tions achieved by controls for the site
or watershed
• Program costs and cost per unit load
removed
12. References Available:
Northern Virginia Planning District Commis-
sion. 1981. Comparison of nonpoint pollution
loadings from suburban and downtown
central business districts. Northern Virginia
Planning District Commission, Annandale,
VA.
Northern Virginia Planning District Commis-
sion. 1990. Analysis of the recommended
guidance calculation procedure for the
Chesapeake Bay Preservation Act. Draft
report, Northern Virginia Planning District
Commission, Annandale, VA.
Schueler, T.R. 1987. Controlling urban
runoff: A practical manual for planning and
designing urban BMPs. Document No. 87703,
Metropolitan Washington Council of
Governments, Washington, DC.
USGS Regression Method
Treatment schemes and associated
costs
-------
Appendix A: Watershed-Scale Loading Models—Fact Sheets
SITEMAP: Stormwater Intercept and Treat-
ment Evaluation Model for Analysis and
Planning
1. Distributor:
Dr. Jack Douglas Smith
Omicron Associates
12364 NW Barnes Road, Suite 180
Portland, OR 97229
(503) 644-5526
2. Type of Modeling:
• Nonpoint source runoff and pollutant
loadings analysis
• Continuous simulation
• Small watershed or drainage area
• Management practices analysis
• Control strategy screening
3. Model Components:
• Runoff and pollutant loadings
• SCS runoff hydrology
• Diversions through wet detention and
wetland system controls
• Soil moisture
• Irrigation and drainage
• Snowfall and snowmelt
• Operates in Lotus 1-2-3 spreadsheet
4. Method/Techniques:
SITEMAP is a spreadsheet-based program
that operates within the Lotus 1-2-3
graphical interface programming environ-
ment. SITEMAP is a dynamic simulation
program that computes, tabulates, and
displays daily runoff, pollutant loadings,
infiltration, soil moisture, irrigation water
demand, evapotranspiration, drainage to
groundwater, daily outflows, and water and
residual pollutant levels in retention basins
or wetland systems (natural or engineered).
Nitrogen and phosphorus are typically
modeled pollutants.
5. Applications:
• Nonpoint source runoff and pollutant
loadings, including performance of
nonpoint source control systems
• Assessment of land use changes and
land management practices
• Irrigation and groundwater recharge
6. Number of Pollutants:
Any two during a single simulation
7. Limitations:
Operates in Lotus 1-2-3 version 2.01, with
IMPRESS add-in, or version 2.2, with
WYSIWYG graphical interface; is not
supported for versions 3.0 or later.
8. Experience:
Applied as a component of the full water-
shed model NPSMAP in the Tualatin River
basin for Oregon Department of Environ-
mental Quality and in the Fairview Creek
watershed for the Metropolitan Service
District (Metro) in Portland, Oregon.
9. Updating Version:
Version 1.1 (1993)
1O. Input Data Requirements:
• Land use category
• Hydrologic soil group (SCS classifica-
tion)
• SCS runoff curve number
• Soil moisture parameters
• Pollutant suspensions, washoff
parameters
• Wetland system or retention basin
dimensions
• Weather records (daily or event)
including rainfall, snowfall, tempera-
ture, and evapotranspiration
-------
Compendium o/ Tbols for Watershed Assessment and TMDL Development
11. Simulation Output:
• Daily record pf all computation results
in spreadsheet format
• User-specified graphic displays
• User-specified graphic displays
• User-specified statistical summaries
• Complete Lotus graphics display,
printing, file management
12. References Available:
Omicron Associates. 1990. Nonpoint
Pollution Source Model for Analysis and
Planning (NPSMAP) - Users manual Oregon
Department of Environmental Quality,
Portland, OR. (See Technical Reference
Sections 10.1 @NPSCOM? 10.2 @SOILM,
10.4 ©WETLAND.)
-------
Appendix A: Watershed-Scale Loading Models—Fact Sheets
SLAMM: Source Loading and Management
Model
1. Distributor:
Through workshops taught by:
Dr. Robert Pitt
Department of Civil and Environmental
Engineering
The University of Alabama at Birmingham
1150 Tenth Avenue South, Room 257
Birmingham, AL 35204-4401
(205) 934-8430
SLAMM is distributed as part of graduate
stormwater management classes at the
University of Alabama at Birmingham and as
part of stormwater workshops that have
been conducted in many locations. Attend-
ees receive program training and a copy of
the computer-executable code. For informa-
tion concerning additional stormwater
workshops featuring SLAMM, contact:
Mr. David Eckhoff
Division of Special Studies
University of Alabama at Birmingham
917 llth Street South
Birmingham, AL 35294-4480
(205) 934-3870
Mr. Pat Eagan
Engineering Professional Development
University of Wisconsin - Madison
432 North Lake Street
Madison, WI
2. Type of Modeling:
• Continuous and diffuse source/
release
• Continuous series of storm events (up
to 350)
• Screening application
• Evaluation of controls and pollutant •
sources.
3. Model Components:
• Rainfall/runoff assessment
• Water quality analysis
4. Method/Techniques:
This program can identify pollutant sources
and evaluate the effects of a number of
different stormwater control practices on
runoff. SLAMM performs continuous mass
balances for paniculate and dissolved
pollutants and runoff volumes. Runoff is
calculated by a method developed by Pitt
(1987) for small storm hydrology. Runoff is
based on rainfall minus initial abstraction
and infiltration and is calculated for both
pervious and impervious areas. Triangular
hydrographs, parameterized by a statistical
approach are used to simulate flow. Expo-
nential buildup and rain wash-off and wind
removal functions are used for pollutant
loadings. Water and sediment from various
source areas is tracked by source area as it is
routed through various treatment devices.
The program considers how particulates
filter or settle out in control devices.
Paniculate removal is calculated based on
the design characteristics of the basin or
other removal device. Storage and overflow
of devices is also considered. At the outfall
locations, the characteristics of the source
areas are used to determine pollutant loads
in solid and dissolved phases. Loads from
various source areas are summed.
5. Applications:
• Evaluates multiple control strategies
such as wet detention basins, porous
pavement, infiltration devices, street
cleaning, catchment cleaning, grass
swales, roof runoff disconnections,
and paved parking lot disconnections,
individually or in combination
• Planning tool for urban runoff quality
and quantity assessments
• Applicable to the study of stormwater
pollutant control from a wide variety
of rainfall regions
6. Number of Pollutants:
Paniculate and dissolved pollutants (depend-
ing on the calibration information), such as
paniculate and filterable forms of residue,
-------
Compendium of Tools for Watershed Assessment and TMDL Development
phosphorus, phosphate, total Kjeldahl
nitrogen, chemical oxygen demand (COD),
fecal coliform bacteria, aluminum, copper,
lead, and zinc
7. Limitations:
• Does not evaluate snowmelt and
baseflow conditions.
• Evaluates runoff characteristics at the
source area within the watershed and
at the discharge outfall but does not
consider instream processes that
remove or transform pollutants.
• Does not develop or evaluate specific
hydraulic designs, except for grass
swales and detention ponds.
• Does not model erosion from pervious
areas or construction sites.
8. Experience:
SLAMM has been used in conjunction with
receiving water quality models (HSPF) to
examine the ultimate effects on urban
runoff from Toronto for the Ontario Ministry
of the Environment. SLAMM was also used
to evaluate control options for controlling
urban runoff in Madison, Wisconsin, using
CIS information. The State of Wisconsin
uses SLAMM as part of its Priority Water-
shed Program. It was used in Portland,
Oregon, for a study evaluating CSOs.
9. Updating Version:
Current version is 6.3
10. Input Data Requirements:
• Rainfall start and end dates (and
times) and rainfall depths
• Areas of each source type and effective
SCS soil type
• Building and traffic density
• Pavement texture, roof pitch, and
presence of alleys
• Land use
• Pond shape, size, and type of outlet
structures of wet detention basins or
percolation ponds
• Soil infiltration rates for infiltration
devices
11. Simulation Output:
• Source area and outfall flow volume
estimates for each rainfall event and
land use
• Source area and outfall paniculate
residue mass discharge and concen-
tration estimates for each rainfall
event and land use
• Relative source area runoff volume
and paniculate residue mass contri-
bution estimates for each rainfall
event
• Mass discharge, concentration, and
relative contribution estimates for
each pollutant selected
• Cost estimates of stormwater control
practices, graphical summaries,
baseflow predictions, and snowmelt
predictions are under development.
12. References
Pitt, R. 1986. The incorporation of urban
runoff controls in Wisconsin's Priority
Watershed Program. In Advanced Topics in
Urban Runoff Research. American Society of
Civil Engineers.
Pitt, R. 1987. Small storm urban flow and
paniculate washoff contributions to outfall
discharges. Ph.D. dissertation, Civil and
Environmental Engineering Department,
University of Wisconsin, Madison, WI.
Pitt, R. 1993. Source loading and manage-
ment model (SLAMM). Presented at the
National Conference on Urban Runoff
Management, March 30-April 2, Chicago, IL.
Thum, EG., S.R. Pickett, B.J. Niemann, Jr.,
and S.J. Ventura. 1990. LIS/GIS: Integrating
nonpoint pollutant assessment with land
development planning. Wisconsin land
Information Newsletter 5(2):1-12.
Ventura, S.J., and K.H. Kim. 1993. Modeling
urban nonpoint source pollution with a
geographical information system. Water
Resources Bulletin 29(2):189-198.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
STORM: Storage, Treatment, Overflow,
Runoff Model
1. Distributor:
Mainframe version:
U.S. Army Corps of Engineers
Hydrologic Engineering Center (HEC)
609 Second Street
Davis, CA 95616
Enhanced PC version (ProStorm) with pre-
and post-processors:
Dodson & Associates, Inc.
5629 FM 1960 West, Suite 314
Houston, TX 77069-4216
(281) 440-3787
2. Type of Modeling:
• Urban runoff processes
• Continuous simulation (hourly time
steps)
• Continuous and diffuse source/
release
• Screening application
3. Model Components:
• Rainfall/runoff assessment
• Water quality analysis
• Statistical and sensitivity analysis
4. Method/Techniques:
This is a quasidynamic program. A modified
rational formula is used for hydrology
simulation. Rainfall/runoff depth and
volumes are computed by means of an area-
weighted runoff coefficient and the SCS
curve number equation, respectively. The
Universal Soil Loss Equation (USLE) is
applied to simulate erosion. Water quality is
simulated by linear buildup and first-order
exponential wash-off coefficients. Calibra-
tion is advisable, but relative comparisons
can be evaluated without calibration.
5. Applications:
• Storm and combined sewer overflows
including dry-weather flow
• Surface water quantity and quality
routing with storage/ treatment
option
• Urban areas assessments
6. Number of Pollutants:
Six prespecified pollutants: suspended
solids, settleable solids, BOD, total
coliforms, ortho-phosphate, and total
nitrogen
7. Limitations:
• Little flexibility in parameters to
calibrate to observed hydrographs.
• Lacks microcomputer version.
• Requires a large amount of input
data.
8. Experience:
STORM was extensively used in the late
1970s and early 1980s. The model was
applied to the San Francisco master drain-
age plan for abatement of combined sewer
overflows. STORM continues to be used to
assess runoff and management practices in
urban areas.
9. Updating Version and System
requirements:
Version 1.0 (1977) for mainframe systems.
PC version (ProStorm) also available.
10. Input Data Requirements:
• SCS curve number, buildup and wash-
off parameters
• Runoff coefficient and soil type
-------
Compendium of Tools for Watershed Assessment and TMDL Development
11. Simulation Output:
• Storm event summaries (runoff
volume, concentrations, and loads)
• Summaries of storage and treatment,
utilization, total overflow loads and
concentrations
Hourly hydrographs and
pollutographs (concentration vs. time)
• Statistical summaries on annual and
total simulation period basis (percent-
age of runoff passing through storage
and the number of overflows)
12. References Available:
Abbott, J. 1977. Guidelines for calibration and
application of STORM. Training Document
No. 8. U.S. Army Corps of Engineers,
Hydrologic Engineering Center. Davis, CA.
Abbott, J. 1978. Testing of several runoff models
on an urban watershed. Technical Memoran-
dum No. 34. ASCE Urban Water Resources
Research Program, ASCE, New York, NY.
Donigian, A.S., Jr., and W.C. Huber. 1991.
Modeling of nonpoint source water quality in
urban and non-urban areas. EPA/600/3-91/
039. U.S. Environmental Protection Agency,
Environmental Research Laboratory,
Athens, Georgia.
Hydrologic Engineering Center. 1977.
Storage, Treatment, Overflow, Runoff Model,
STORM, User's manual. Generalized Com-
puter Program 723-S8-L7520. U.S. Army
Corps of Engineers, Davis, CA.
Najarian, T.O., T.T. Griffin, and V.K.
Gunawardana. 1986. Development impacts
on water quality: A case study. Journal of
Water Resources Planning and Management,
ASCE, 112(l):20-35.
Pantalion, J., A. Scharlach, and G. Oswald.
1995. Water quality master planning in an
urban watershed. In Watershed Management:
Planning for the 21st Century, proceedings of
the ASCE's First International Conference of
Water Resources Engineering, San Antonio,
TX, August 14-16,1995, pp. 330-339.
Shubinski, R.P., A.J. Knepp, and C.R. Bristol.
1977. Computer program documentation/or the
continuous storm runoff model SEM-STORM.
Report to the Southeast Michigan Council of
Governments, Detroit, MI.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
SWMM: Storm Water Management Model
1. Name of Distributor:
Model Distribution Coordinator
Center for Exposure Assessment Modeling
(CEAM), USEPA
960 College Station Road
Athens, GA 30605-2700
(706)355-8400
Models are available for FTP from:
ftp://ftp.epa.gov/epa_ceam/wwwhtml/
software.htm
Windows SWMM is available: Includes
windows menus for SWMM input and some
post processing. Contact:
Gerald D. LaVeck
Environmental Protection Agency
Office of Science & Technology (4305)
401 M Street, SW
Washington, DC 20460
(202)260-7771
or go to:
http://www.epa.gov/ost/Tools
SWMM manuals and programs may also be
obtained from:
Dr. Wayne C. Huber
Dept. of Civil Engineering
Oregon State University
202 Apperson Hall
Corvallis, OR97331-2302
Phone (541) 737-4934
Fax: (541) 737-3052
Dr. William James
CHI, 36 Stuart St.
Guelph, Ontario N1E 4S5
Phone: (519) 767-0197
Fax: (519) 767-2770
Web: www.chi.on.ca/
[Contact for prices.]
The executable program, Fortran code and
documentation files are also available on the
internet via anonymous FTP at OSU at:
engr.orst.edu, path: /pub/swmm/pc and at
the Web site: www.orst.edu/dept/ccee/
swmm.htm
2. Type of Modeling:
• Urban stormwater processes
• Continuous and storm event simula-
tion with variable and user-specified
time steps (wet and dry weather
periods)
• Single, continuous, intermittent,
multiple, and diffuse source/release
• Screening, intermediate, and detailed
planning applications
• Evaluation of BMPs and development
of design criteria
3. Model Components:
• Rainfall/runoff assessment
• Water quality analysis
• Point source inputs available
4> Method/Techniques:
This model simulates overland water
quantity and quality produced by storms in
urban watersheds. Several modules or
blocks are included to model a wide range of
quality and quantity watershed processes. A
distributed parameter sub-model (RUNOFF)
describes runoff based on the concept of
surface storage balance. The rainfall/runoff
simulation is accomplished by the nonlinear
reservoir approach. The lumped storage
scheme is applied for sofl/groundwater
modeling. For impervious areas, a linear
formulation is used to compute daily/hourly
increases in particle accumulation. For
pervious areas, a modified Universal Soil
Loss Equation (USLE) determines sediment
load. The concept of potency factors is
applied to simulate pollutants other than
sediment.
5. Applications:
• Urban stormwater and combined
systems
• Surface water routing
• Urban watershed analysis, including
baseflow contributions
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Compendium of Tools for Watershed. Assessment and TMDL Development
6. Number of Pollutants:
Limited to 10 pollutants, including sediment
7. Limitations:
• Lack of subsurface quality routing
• No interaction of quality processes
(apart from adsorption)
• Weak scour-deposition routines
8. Experience:
Applied to urban hydrologic quantity/
quality problems in scores of U.S. cities as
well as extensively in Canada, Europe, and
Australia. The model has been used for very
complex hydraulic analysis for combined
sewer overflow mitigation, as well as for
many stormwater management planning
studies and pollution abatement projects,
and there are many instances of successful
calibration and verification (Huber, 1992).
Warwick and Tadepalli (1991) describe
calibration and verification of SWMM on a
10-square-mile urbanized watershed in
Dallas, Texas. Tsihrintzis etal.(1995) describe
SWMM applications to four watersheds in
South Florida representing high- and low-
density residential, commercial, and high-
way land uses. Ovbiebo and She (1995)
describe an application of SWMM in a
subbasin of the Duwamish River, Washing-
ton.
9. Updating Version, System Requirements:
Version 4.30 (1994)
10. Input Data Requirements:
• Rainfall hyetographs, antecedent
conditions, land use, and topography
• Dry-weather flow and soil characteris-
tics
• Gutters/pipes - hydraulic inputs
• Pollutant accumulation and wash-off
parameters
• Hydraulics and kinetic parameters
11. Simulation Output;
• Time series of flow, stage, and
constituent concentration at any point
in watershed
• Seasonal and annual summaries
12. References Available:
Donigian, A.S., Jr., and W.C. Huber. 1991.
Modeling of nonpoint source water quality in
urban and non-urban areas. EPA/600/3-91/
039. U.S. Environmental Protection Agency,
Environmental Research Laboratory,
Athens, GA.
Huber, W.C., and R.E. Dickinson. 1988. Storm
Water Management Model Version 4, User's
manual. EPA 600/ 3-88/ OOla (NTIS PB88-
236641/ AS). U.S. Environmental Protection
Agency, Athens, GA.
Huber, W. C. 1992. Experience with the US.
EPA SWMM Model for analysis and solution
of urban drainage problems. Proceedings,
Inundaciones YRedes De Drenaje Urbano, ed. J.
Dolz, M. Gomez, and J. P. Martin, eds.,
Colegio de Ingenieros de Caminos, Canales
Y Puertos, Universitat Politecnica de
Catalunya, Barcelona, Spain, pp. 199-220.
Ovbiebo, T., and N. She. 1995. Urban runoff
quality and quantity modeling in a subbasin
of the Duwamish River using XP-SWMM.
Watershed Management: Planning for the 21st
Century, American Society of Civil Engi-
neers, San Antonio, TX, August 14-16,1995,
pp.320-329.
Tshihrintzis, V. A., R. Hamid, and H. R.
Fuentes. 1995. Calibration and verification of
watershed quality model SWMM in sub-
tropical urban areas. In Proceedings of the
First International Conference -Water Re-
sources Engineering. American Society of
Civil Engineers, San Antonio, TX, August 14-
16,1995, pp 373-377.
Warwick, J. J., and P. Tadepalli. 1991. Efficacy
of SWMM application. Journal of Water
Resources Planning and Management
117(3):352-366.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
SWRRBWQ: Simulator for Water
Resources in Rural Basins - Water Quality
1. Name of Distributor:
Jeff Arnold or Nancy Sammons
USDA-ARS
808 E. Blackland Rd.
Temple, XX 76502
(817) 770-6502 or (817) 770-1308
arnold@brcsunO .tamn. edu
samm.ons@brcsunO.tamn.edu
The SWRRBWQ Windows interface is
available on the Internet at:
http://www.epa.gov/ost/tools
For more information contact:
Jerry LaVeck
USEPA (4305)
401M Street, SW
Washington, DC. 20460
(202)260-7771
laveck.jerry@epamail.epa.gov
2. Type of Modeling:
• Hydrologic and related processes in
large, complex rural basins
• Diffuse source/release
• Screening, intermediate, and detailed
applications
3. Model Components:
• Rainfall/runoff assessment
• Surface water quality analysis
• Below-root-zone leaching losses
4. Method/Techniques:
SWRRBWQ is a comprehensive, continuous
simulation model covering aspects of the
hydrologic cycle, pond and reservoir
storage, sedimentation, crop growth,
nutrient yield, and pesticide fate. A basin can
be divided into a maximum of 10 subbasins
to account for differences in soils, land use,
crops, topography, vegetation, or weather.
The model partitions nitrate loss between
runoff, lateral subsurface flow, percolation,
and crop uptake. Runoff volume is estimated
using a modification of the SCS curve
number method for continuous models, and
peak runoff rate predictions are based on a
modification of the rational formula.
Sediment yield is calculated using several
procedures including the Hydrogeomorphic
Universal Soil Loss Equation (HUSLE).
Nutrient, pesticide, and sediment yields at
the basin outlet are determined after
accounting for channel transmission losses
and deposition. SWRRBWQ allows for
simultaneous computation on each subbasin
and routes the water, sediment, and
chemicals from the subbasin outlets to the
basin outlet. It also has a lake water quality
component that tracks the fate of pesticides
and phosphorus from their initial application
on the land to their final deposition in a lake.
Calibration is not specifically required but is
desirable.
5. Applications:
• Usefulness of ponds or reservoirs to
trap sediment at the subbasin or the
watershed outlet can be determined.
• Effects of farm-level management
systems, such as crop rotations,
tillage, planting date, irrigation
scheduling, and fertilizer and pesticide
application rates and timing.
6. Number of Pollutants:
Sediment, nitrogen, phosphorus, and
pesticides
7. Limitations:
• A maximum of 10 subareas per
analysis is allowed.
• Organic waste applications cannot be
modeled.
• As nutrients and pesticides flow from
each subbasin to the basin outlet, no
degradation occurs.
• Hydraulic residence time is not
considered by the lake water quality
module.
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Compendium of Tools for Watershed Assessment and TMDL Development
8. Experience:
The model was tested on 11 large water-
sheds. The testing results showed that
SWRRBWQ can simulate water and sediment
yield under a wide range of soils, climate,
land use, topography, and management
systems.
9. Updating Version:
SWRRBWQ is no longer under active
development; however, the technology is
being incorporated in the Soil and Water
Assessment Tool (SWAT) as part of the
Hydrologic Unit Model for the United States
(HUMUS) project at Temple, Texas (Arnold
etal., 1993; Srinivasan and Arnold, 1994).
10. Input Data Requirements:
• Meteorological data (daily precipita- ,
tion and solar radiation)
• Soils, land use, and fertilizer and
pesticide application
11. Simulation Output:
• Daily runoff volume and peak rate,
sediment yield, evapotranspiration,
percolation, return flow, and pesticide
concentration in both runoff and
sediment
• Nutrient concentrations/loads
12. References Available:
Arnold, J.G., B.A. Engel, and R. Srinivasan.
1993. A continuous time, grid cell watershed
model. In Proceedings of Application of
Advanced Information Technologies for the
Management of Natural Resources, sponsored
by ASAE, June 17-19,1993, Spokane, WA.
Arnold, J.G., J.R. Williams, A.D. Nicks, and
N.B. Sammons. 1989. SWRRB, a basin scale
simulation model for soil and water resources
management. Texas A&M Press.
Arnold, J.G., and J.R. Williams. 1987.
Validation of SWRRB - simulator for water
resources in rural basins. Journal of Water
Resources Planning and Management
113(2):243-256.
Srinivasan, R., and J.G. Arnold. 1994.
Integration of a basin-scale water quality
model with GIS. Water Resources Bulletin
30(3):453^162.
Williams, J.R., A.D. Nicks, and J.G. Arnold.
1985. Simulator for water resources in rural
basins. Journal of Hydraulic Engineering, ASCE
lll(6):970-986.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
USGS Regression Method
1. Name of Distributor:
Gary D. T&sker
U.S. Geological Survey
430 National Center
Reston, VA 22092
(703) 648-5892
2. Type of Modeling:
• Not a computer program
• Pollutant concentration from urban-
ized watersheds
• Statistical approach
• Annual, seasonal, or storm event
mean pollutant loads
• Screening applications
3. Model Components:
• Regression equations for mean storm
event pollutant load estimation
• Confidence interval around the mean
4. Method/Techniques:
Regression equations were developed from
historical records of storm loads for 10
pollutants at 76 gaging stations in 20 states.
Ten explanatory parameters were used to
reflect possible site variability associated
with pollutant processes. The
nonuniformity of the variance required a
generalized least squares analysis. The
general form of the regression model is as
follows:
--- _ . ..[a+b-JDA + cIA + dMAR + eMJT + #2] „
where:
W =
DA =
IA =
MAR =
MJT =
X, =
the mean load, in pounds, associ-
ated with a runoff event
drainage area in square miles
impervious area, in percent of DA
mean annual rainfall, inches
mean minimum January tempera-
ture, in degrees Fahrenheit
land-use indicator variable
BCF = bias correction factor
The regression coefficients (a, b, c, d, e, and
f) for different pollutants may be obtained
from Gary and Tasker (1988). The mean
annual pollutant load can be calculated by
multiplying W by the mean annual number
of storm events.
5. Applications:
• Estimation of average mean annual
storm event loads when data are
severely limited
• Comparing different locations
6. Number of Pollutants:
Chemical oxygen demand, suspended solids,
dissolved solids, total nitrogen, total
ammonia-nitrogen (NH3-N), total phospho-
rus, dissolved phosphorus, total copper, total
lead, and total zinc.
7. Limitations:
• Valid only for areas for which
regression coefficients are provided,
i.e., regional transferability is
limited.
• Valid only within the range of
observed values of pollutant loads
and explanatory variables.
• Tends to underestimate the contribu-
tions of snowmelt or extreme events.
• Does not address causation.
• Applies only to small watersheds.
8. Experience:
Used by cities and counties to estimate
pollutant loadings from storm-sewer outfalls
as part of the NPDES permit application
process.
9. Updating Version:
N/A
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Compendium of Tools for Watershed Assessment and TMDL Development
10. Input Data Requirements:
• Drainage areas
• Percent imperviousness
• Mean annual rainfall
• Land use indicator
• Mean minimum January temperature
• Mean annual number of storm events
11. Simulation Output:
• Average annual storm event load and
confidence interval
12. References Available:
Tasker, G.D., and N.E. Driver. 1988. Nation-
wide regression models for predicting urban
runoff water quality at unmonitored sites.
Water Resources Bulletin 24(5): 1091-1101.
Tasker, G.D., E.J. Gilroy, and M.E. Jennings.
1990. Estimation of mean urban stormwater
loads at unmonitored sites by regression. In
Symposium Proceedings on Urban Hydrology,
American Water Resources Association,
Denver, CO, November 4-8, 1990, pp. 127-
138.
Sediment and Phosphorus Prediction
(SLOSS, PHOSPH)
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
Watershed
1. Distributor:
John F.Walker
U.S. Geological Survey
6417 Normandy Lane
Madison, WI 53719-1133
(608) 274-3535
2. Type of Modeling:
• Various multiple point sources plus
continuous and diffuse source/release
• Screening application
3. Model Components:
• Program is divided into seven
worksheets. The first summarizes
basic watershed characteristics. The
next three worksheets estimate
pollutant loads from point sources
and cropland and noncropland
agricultural land uses for controlled
and uncontrolled conditions. Sources
are totaled for controlled and uncon-
trolled conditions by worksheet 5.
• Program costs and cost-effectiveness
per unit load reduction are also
calculated.
4. Method/Techniques:
Separate methods are used to calculate
urban, rural non-cropland, and rural
cropland loads. Urban loads are calculated
from point estimates of flow and concentra-
tion, rural non-cropland loads are estimated
on a unit area basis, and rural cropland
loads are based on the Universal Soil Loss
Equation (USLE). The rainfall factor (R) in ,
the USLE is unspecified for use as a calibra-
tion parameter. Delivery ratios and trapping
efficiencies for tributary wetlands are used
to convert eroded sediment to sediment
delivered. These values are also calibrated.
The model uses the sorting features of the
EXCEL spreadsheet program for the
Macintosh computer to rank the most cost-
effective alternatives.
5. Applications:
• Phosphorus loading from point
sources, CSOs, septic tanks, rural
cropland, and non-cropland rural
sources was estimated for Delavan
Lake watershed in Wisconsin.
•• Evaluation of the trade-offs between
control of point and nonpoint
sources.
6. Number of Pollutants:
Used for only one at a time, e.g., phosphorus
7. Limitations:
• Cannot assess seasonal variability.
• Can assess only a limited number of
land management control practices.
• Requires calibration to determine the
rainfall factor and the sediment
delivery ratio.
• Can assess only contaminants
associated with soils and sediments.
8. Experience:
Watershed was applied to the study of point
and nonpoint sources in the Delavan Lake
watershed in Wisconsin. It was determined
that runoff controls would be insufficient to
meet water quality standards. Instead of
focusing controls for phosphorus on non-
point sources, the study recommended
several in-lake controls.
9. Updating Version:
N/A
10. Input Data Requirements:
• Sources of pollution along with their
respective position and point of entry
to the basin
• Flows and concentrations of point
sources
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Compendium of Tools for Watershed Assessment and TMDL Development
Areas served by urban land uses such
as storm sewers, combined sewers,
and unsewered areas along with their
corresponding unit area loads for the
pollutant of concern
Areas and unit area loads for grass
and woodland areas
Parameters for the USLE for croplands
Pollutant delivery ratios and pollutant
reduction efficiency ratio
Treatment schemes and associated
costs
11. Simulation Output:
• Total annual loads and load reduc-
tions achieved by controls for the site
or watershed
• Program costs and cost per unit load
removed
12. References Available:
Monteith, T.J., R.A. Sullivan, T.M. Heidtke,
and W.C. Sonzogni. 1981. Watershed
handbook: A management technique for
choosing among point and nonpoint control
strategies. Prepared for the U.S. Environ-
mental Protection Agency, Region 5,
Chicago, IL.
Walker, J.F., S.A. Pickard, and W.C.
Sonzogni. 1989. Spreadsheet watershed
modeling for nonpoint-source pollution
management in a Wisconsin basin. Water
Resources Bulletin 25(1): 139-147.
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Appendix A: Watershed-Scale Loading Models—Fact Sheets
WMM: Watershed Management Model
1. Distributor:
Prepared by Camp, Dresser & McRee Inc.
for:
Stormwater and Nonpoint Source Section
Florida Department of Environmental
Regulation
Twin Towers Office Building
2600 Blair Stone Road
Tallahassee, Florida 32301-8241
(904)488-0782
2. 'Type of Modeling:
• Watershed stormwater pollutant
loads
• Multiple diffuse source release
• Annual time steps
• Screening application
3. Model Components:
• Computation of annual nutrient and
metal loads to reservoirs
• Computation of in-lake or in-stream
water quality from pollutant loads
• Load reduction estimates for site or
regional BMP implementation
• Uptake and removal in stream
courses
• Estimates of annual pollutant loads
from basefiow
• Comparison with point sources
• Failing septic tank loads
• Chlorophyll a and nutrient concentra-
tions in downstream lakes and
reservoirs
4v Method/Techniques:
Runoff coefficients are used for rural areas;
for urban areas runoff is based on a linear
function of the percent imperviousness.
Loading of nutrients and metals is based on
event mean concentrations measured locally
or from NURP data. Basefiow is estimated
from flow records and concentrations. There
is a choice of three lake water quality routines
that output mean annual concentrations of
chlorophyll a. (The model can be adapted to
predict seasonal loads or chlorophyll a
concentrations provided that seasonal event
mean concentration data are available.)
Simple calculations are included for in-stream
transport and transformation based on travel
time. The program can assess the relative
contributions of point and nonpoint sources.
Resultant water quality is predicted with a
version of the Vbllenweider eutrophication
model, adapted to lakes in the southeastern
United States. Removal of metals associated
with sediments in reservoirs is estimated from
the sediment-trapping efficiency of the
reservoir.
5. Applications:
Estimates the annual nonpoint source loads,
including basefiow and precipitation inputs,
for management planning.
6. Number of Pollutants:
Total phosphorus, total nitrogen, lead, and
zinc
7. limitations:
• Accuracy is limited when default
parameters are substituted for site-
specific data.
• Neglects seasonal variation.
• Does not predict sediment yields.
• Does not evaluate control practices
except through assumption of a
constant removal fraction.
• Does not consider loadings associated
with snowmelt events.
• Can assess only relative impacts of
land use categories or controls.
& Experience:
The model has been applied to between 10
and 15 watersheds. It has been used as part
-------
Compendium of Tools for Watershed Assessment and TMDL Development
of a wasteload allocation study for Lake
Tohopekaliga and for Jacksonville, Florida,
watershed's Master Plan. It has been applied
in Norfolk County, Virginia, and to a
Watershed Management Plan for North
Carolina.
9. Updating Version:
Under development
10. Input Data Reqtdr
its:
• Land use and soil types
• Average annual precipitation,
evaporation, and evapotranspiration
• Nutrient concentrations in precipita-
tion
• Annual baseflow and baseflow
pollutant concentrations
• Event mean concentrations in runoff
• Reservoir, lake, or stream hydraulic
characteristics
• Removal efficiencies of proposed
BMPs
11. Simulation Output:
• Annual pollutant loads from point
and nonpoint sources, including both
agricultural and urban land use
• Relative magnitude of inputs from
point sources and septic tanks
• Load reductions from combined
effects of multiple BMPs
• In-lake nutrient concentrations as
related to trophic state; also, concen-
trations of metals are evaluated for
the reservoir
• Standard statistics and bar graphs of
results
12. References Available:
Camp, Dresser and McKee (COM). 1992.
Watershed Management Model user's manual,
Version 2.0. Prepared for the Florida
Department of Environmental Regulation,
Tallahassee, FL.
Pantalion, J., A. Scharlach, and G. Oswald.
1995. Water quality master planning in an
urban watershed. In Watershed Management:
Planning for the 21st Century. Proceedings of
the ASCE's First International Conference of
Water Resources Engineering, San Antonio,
TX, August 14-16,1995, pp. 330-339.
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Appendix B:
Receiving Water
Models—Fact Sheets
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Compendium of "Dials for Watershed Assessment and TMDL Development
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Appendix B: Receiving Water Models—Fact Sheets
CE-QUAL-ICM: A Three-Dimensional Time-
Variable Integrated-Comparf ment
Eutrophication Model
1. Distributor:
Water Quality and Contaminant
Modeling Branch
Environmental Laboratory
U.S. Army Engineer Waterways
Experiment Station
3909 Halls Ferry Road
Vicksburg, MS 39180
(601) 634-3785
2. Type of Modeling/Application:
• May be applied to most waterbodies in
1-, 2-, or 3-D
• Unsteady flow
• Predicts time-varying concentrations of
water quality constituents
• Advective and dispersive transport
• Considers sediment diagenesis benthic
exchange
• Finite difference
3. Model Processes:
• Temperature
• Salinity
• DO-carbon balance
• Nitrogen cycle
• Phosphorus cycle
• Silicon cycle
• Phytoplankton (up to 3 species)
• Zooplankton
• Bacteria
• First-order decay
• Sediment process rates may be input
or simulated using the diagenesis sub-
model
4. Method/Techniques:
CE-QUAL-ICM incorporates detailed algo-
rithms for water quality kinetics. Interactions
among state variables are described in 80
partial-differential equations that employ over
140 parameters (Cerco and Cole, 1993). An
improved finite-difference method is used to
solve the mass conservation equation for each
cell in the computational grid and for each
state variable. The state variables can be
categorized into six groups and cycles—the
physical group, and the carbon, nitrogen,
phosphorus, silica, and dissolved oxygen (DO)
cycles.
5. Limitations:
Although the model has full capabilities to
simulate state-of-the-art water quality
kinetics, it is potentially limited by available
data for calibration and verification. In
addition, the model may require significant
technical expertise in aquatic biology and
chemistry to be used appropriately.
6. Experience:
Used in conjunction with a hydrodynamic
model and a benthic-sediment model to
develop a state-of-the-art 3-D model of the
Chesapeake Bay. The model was employed to
simulate long-term trends in Chesapeake Bay
eutrophication (Cerco, 1995). Marketal.
(1992) used CE-QUAL-ICM to assess the
water quality impacts of a confined disposal
facility in Green Bay, Wisconsin.
7. Updating Version and System
Requirements:
Model is currently under active development,
and the capability to simulate toxicants is
planned. PC-compatible. The model is
computationally intensive for large
waterbodies particularly when all processes
are simulated.
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Compendium of Took for Watershed Assessment and TMDL Development
8. Input Data Requirements:
Geometric data to define the finite difference
representation of the waterbody have to be
defined, and approximately 140 are param-
eters needed to specify kinetic interactions.
Initial and boundary conditions have to be
specified.
9. Outputs:
Temperature, salinity, inorganic suspended
solids, diatoms, blue-green algae and other
phytoplankton, dissolved, labile, and refrac-
tory components of particulate organic
carbon, organic nitrogen, and organic
phosphorus ammonium, nitrite and nitrate,
total phosphate, dissolved oxygen, chemical
oxygen demand, dissolved silica, particulate
biogenic silica.
10. References Available:
Cerco, C.F., and T. Cole. 1995. User's Guide to
the CE-QUAL-ICM. Release Version 1.0.
Technical Report EL-95-1, U.S. Army
Engineer Waterways Experiment Station,
Vicksburg, MS.
Cerco, C.E, and T. Cole. 1993. Three-
dimensional eutrophication model of the
Chesapeake Bay. Journal of Environmental
Engineering 119(6):1006-1025.
Cerco, C.E 1995. Simulation of long-term
trends in Chesapeake Bay Eutrophication.
Journal of Environmental Engineering 121(4):
298-310.
Mark, D. J., B.W. Bunch, and N.W. Scheffner.
1992. Combined Hydrodynamic and water
quality modeling of Lower Green Bay. In Water
Quality '92: Proceedings of the 9th Seminar.
U.S. Army Engineers Waterways Experiment
Station, San Antonio, TX, March 16-20,1992
p. 226-233.
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Appendix B: Receiving Water Models—Fact Sheets
CE-QUAL-RIV1: Hydrodynamic and Water
Quality Model for Streams
1. Distributor:
Water Quality and Contaminant
Modeling Branch
Environmental Laboratory
U.S. Army Engineer Waterways
Experiment Station
3909 Halls Ferry Road
Vicksburg, MS 39180
(601) 634-3670
2. Type of Modeling/Application:
• Rivers and estuaries
• Far-field
• One-dimensional, branching
• Unsteady flow
• Predicts time-varying concentrations of
water quality constituents
• Advective and dispersive transport
• Finite difference
3. Model Processes:
• Temperature
• Salinity
• DO-BOD
• Nitrogen cycle
• Phosphorus cycle
• Phytoplankton in water column
• Benthic algae and macrophytes
• Bacteria
• First-order decay
4. Method/Techniques:
CE-QUAL-RIV1 was developed to simulate
transient water quality conditions associated
with highly unsteady flows that can occur in
regulated rivers. The model consists of two
codes: RIV1H, a stand-alone hydraulic routing
code, and RTV1Q, a water quality code that
uses output from RIV1H to provide dynamic
water quality simulation.
An implicit, finite-difference method is used
to solve the continuity and momentum
equations in RIV1H, with cross-sectional area
and discharge as dependent variables. RIV1H
allows the simulation of dynamically coupled,
branched river systems with multiple control
structures. In RIV1Q, an explicit, finite-
difference method is used to solve the
constituent advective transport and reaction
equations and calculate dynamic changes in
the concentrations of water quality variables.
5. Limitations:
• Only applicable to situations where
flow is predominantly one-dimen-
sional.
• The program may exhibit numerical
instability under certain conditions.
6. Experience:
Applied to provide examples of potential
water quality impacts associated with
operations alternatives for a regulation dam
proposed for construction downstream from
Buford Dam on the Chattahoochee River near
Atlanta, Georgia (Zimmerman and Dortch,
1989).
The RIV1Q component of CE-QUAL-RIV1 was
used to develop statistical relationships to
allow prediction of downstream water
temperatures associated with different
operational scenarios (Nestier et al., 1993).
7. Updating Version and System
Requirements:
Last updated in 1990. PC-compatible.
8. Input Data Requirements:
RIV1H requires river geometry and boundary
conditions to perform hydraulic calculations.
Geometric data include locations of control
structures, streambed elevations, river cross
sections, and distances between nodes.
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Compendium of Tbols for Watershed Assessment and TMDL Development
Manning's coefficients are used to describe
channel roughness. Boundary conditions
include initial flow rates and stages, lateral
inflows or withdrawals, and boundary
conditions defined by discharge, stage, or a
stage-discharge rating curve.
RIV1Q requires initial instream and inflow
boundary water quality concentrations,
meteorologic data for temperature computa-
tions, and rate coefficients.
9. Outputs:
Dissolved oxygen, carbonaceous biochemical
oxygen demand, temperature, organic
nitrogen, ammonia nitrogen, nitrate nitrogen,
orthophosphate, dissolved iron, dissolved
manganese, coliform bacteria.
10. References Available:
Environmental Laboratory. 1990. CE-QUAL-
RTV1: A dynamic, one-dimensional (longitudi-
nal) water quality model for streams: User's
manual instruction report. U.S. Army
Engineer Waterways Experiment Station,
Vicksburg, Miss.
Nestler, J.M., L.T. Schneider, and B.R. Hall.
1993. Development of a simplified approach for
assessing the effects of water release tempera-
tures on tailwater habitat downstream of Fort
Peck, Garrison, and Fort Randall Dams.
Technical Report EL-93-23. U.S. Army
Engineer Waterways Experiment Station,
Vicksburg, MS.
Zimmerman, M.J., and M.S. Dortch. 1989.
Modeling water quality of a reregulated
stream below a peaking hydropower dam.
Regulated Rivers: Research and Management
4:235-247.
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Appendix B: Receiving Water Models—Fact Sheets
CE-QUAL-W2: A Two-Dimensional, Laterally
Averaged, Hydrodynamic and Water Quality
Model
1. Distributor:
Water Quality and Contaminant
Modeling Branch
Environmental Laboratory
U.S. Army Engineer Waterways
Experiment Station
3909 Halls Ferry Road
Vicksburg, MS 39180
(601) 634-3785
2. Type of Modeling/Application:
•. May be applied to most water bodies in
1-D or laterally averaged 2-D (X/Z)
• Unsteady flow
• Predicts time-varying concentrations of
water quality constituents
• Advective and dispersive transport
• Finite difference
3. Model Processes:
• Temperature
• Salinity
• DO-carbon balance
• Nitrogen cycle
• Phosphorus cycle
• Silicon cycle
• Phytoplankton
• Bacteria
• First-order decay
4. Method/Techniques:
CE-QUAL-W2 is a two-dimensional, longitudi-
nal/vertical, hydrodynamic and water quality
model. The hydrodynamic and water quality
routines are directly coupled; however, the
water quality routines can be updated less
frequently than the hydrodynamic time step,
which can reduce the computational burden
for complex systems.
The water quality routines incorporate 21
constituents in addition to temperature and
include constituent interactions during anoxic
conditions. The constituents are arranged in
four levels of complexity, permitting flexibility
in model application. The water quality
component is modular, allowing constituents
to be easily added as additional subroutines.
5. Limitations:
• Because the model assumes lateral
homogeneity, it is best suited for
relatively long and narrow waterbodies
exhibiting strong longitudinal and
vertical water quality gradients; it may
be inappropriate for large waterbodies.
• Only one algal compartment is
included, and algal succession cannot
be modeled.
• No zooplankton or macrophytes are
modeled.
• Simplified sediment oxygen demand
component based on first-order decay.
6. Experience:
The model has been applied to rivers, lakes,
reservoirs, and estuaries (Adams et al., 1993;
Hall, 1987; Martin, 1988). Barnese and
Bohannon (1994) report initial efforts to
apply CE-QUAL-W2 to Taylorsville Lake in
Kentucky.
7. Updating Version and System
Requirements:
Version 2.0 (1994). PC-compatible.
8. Input Data Requirements:
Geometric data are required to define the
finite difference representation of the
waterbody. Initial and boundary conditions
have to be specified. Required hydraulic
parameters include horizontal and vertical
dispersion coefficients for momentum and
temperature/constituents and the Chezy
coefficient, used to calculate boundary
friction. Simulation of water quality kinetics
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Compendium of Tools for Watershed Assessment and TMDL Development
requires the specification of approximately 60
coefficients. Finally, data are required to
provide boundary conditions and assess
model performance during calibration.
9. Outputs:
The hydrodynamic component of the model
predicts water surface elevations, velocities,
and temperatures. Water quality constituents
that maybe modeled include a conservative
tracer, inorganic suspended solids, coliform
bacteria, total dissolved solids, labile and
refractory dissolved organic matter, algae,
dissolved oxygen, ammonia-nitrogen, nitrate-
nitrogen, phosphorus, total inorganic carbon,
pH, carbonate, and total iron.
10. References Available:
Adams, R.W., E.L. Thackston, R.E. Speece,
D.J. Wilson, and R. Cardozo. 1993. Effect of
Nashville's combined sewer overflows on the
water quality of Cumberland River. Technical
Report No. 42. Environmental and Water
Resources Engineering, Vanderbilt University,
Nashville, TN.
Barnese, L.E., and J.A. Bohannon. 1994. The
distribution of nutrients and phytoplankton in
Taylorsville Lake -
A model study. In Symposium Proceedings on
Responses to Changing Multiple-Use Demands:
New Directions for Water Resources Planning
and Management. American Water Resources
Association, Nashville, TN, April 17-20,1994,
pp. 33-35.
Cole, R.W., and E.M. Buchak. 1995. CE-QUAL-
W2: A two-dimensional, laterally averaged,
hydrodynamic and water quality model.
Version 2.0. Instructional Report EL-95-1,
U.S. Army Engineer Waterways Experiment
Station, Vicksburg, MS.
Hall, R.W. 1987. Application of CE-QUAL-W2
to the Savannah River Estuary. Technical
Report EL-87-4. U.S. Army Engineer Water-
ways Experiment Station, Vicksburg, MS.
Martin, J.L. 1988. Application of two-
dimensional water quality model. Journal of
Environmental Engineering 114(2):317-336.
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Appendix B: Receiving Water Models—Fact Sheets
CH3D-WES: Curvilinear Hydrodynamics in
Three-Dimensions - Waterways Experiment
Station
1. Distributor
Water Quality and Contaminant Modeling
Branch
Environmental Laboratory
U.S. Army Engineer Waterways Experiment
Station
3909 Halls Ferry Road
Vicksburg, MS 39180-6199
(601) 634-3785
2. Type of Modeling/Application:
• Hydrodynamic model developed for
the Chesapeake Bay Program.
• Physical processes impacting circula-
tion and vertical mixing that can be
modeled include tides, wind, density
effects (salinity and temperature),
freshwater inflows, turbulence, and
the effect of the Earth's rotation.
3. Model Processes:
CH3D-WES makes hydrodynamic computa-
tions on a curvilinear or boundary-fitted
planfbrm grid. Deep navigation channels and
irregular shorelines can be modeled because
of the boundary-fitted coordinates feature of
the model. Vertical turbulence is predicted by
the model and is crucial to a successful
simulation of stratification, destratification,
and anoxia. A second-order model based
upon the assumption of local equilibrium of
turbulence is employed.
4. Method/Techniques:
Capabilities of CH3D are illustrated by its
application to the Chesapeake Bay. The
numerical grid employed in the Chesapeake
Bay model has 734 active horizontal cells and
a maximum of 15 vertical layers, resulting in
3,992 computational cells. Grid resolution is
1.52 m vertical and approximately 10 km
longitudinal and 3 km lateral. The x, y
coordinates of the grid are transformed into
the ,c-curvilinear coordinates to allow for
better handling of the complex horizontal
geometries. Velocity is also transformed so
that its components are perpendicular to the
,c-coordinate lines, thus allowing boundary
conditions to be prescribed on a boundary-
fitted grid in the same manner as a Cartesian
grid. Major tributaries are modeled three-
dimensionally in the lower reach of the bay
and two-dimensionally with constant width in
the upper reach.
5. Limitations:
• Considerable technical expertise in
hydrodynamics is required to use the
model effectively.
6. Experience:
Johnson et al. (1993) validated the model by
applying it to six data sets. The first three
data sets contained approximately one
month's worth of data each and represented a
dry summer condition, a spring runoff, and a
fall wind-mixing event. The last three
applications were yearlong simulations for
1984 (a wet year), 1985 (a dry year), and
1986 (an average year). Results demonstrate
that the model is a good representation of the
hydrodynamics of the Chesapeake Bay and its
major tributaries.
Cerco et al. (1993) used CH3D-WES in
conjunction with CE-QUAL-ICM to predict
water column and sediment processes that
affect water quality in the Chesapeake Bay.
Data from 1984-1986 were used and the
linked modeling approach was successful in
predicting the spring algal bloom, onset and
breakup of summer anoxia, and coupling of
organic particle deposition with sediment-
water nutrient and oxygen fluxes.
7. Updating Version and System
Requirements:
• Model requires a Unix Workstation or
Super Computer.
8. Input Data Requirements:
Basic inputs required are time-varying water-
surface elevations, salinity, and temperature
conditions at the ocean entrance and at
freshwater inflows at the head of all tributar-
ies. Time-varying wind and surface heat
exchange data must also be prescribed at one
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Compendium oflbolsfor Watershed Assessment and TMDL Development
or more locations. All input data, including
initial conditions, bathymetry boundary, and
computational control data are input from
fixed files.
9. Outputs:
The CH3D-WES model can be used to predict
system response to water levels, flow
velocities, salinities, temperatures, and the
three-dimensional velocity field. Predictions
can be made for each cell of the grid at a
specified time interval.
10. References available:
Cerco, C.E and T. Cole. 1993. Three-
Dimensional Eutrophication Model of
Chesapeake Bay. Journal of Environmental
Engineering. 119(6): 1006-1025.
Johnson, B.H., K. W. Kim, R.E. Heath, B.B.
Hsieh, and H.L. Butler. 1993. Validation of
Three-Dimensional Hydrodynamic Model of
Chesapeake Bay. Journal of Hydraulic
Engineering. 119(1):2-20.
Johnson, B.H., R.E. Heath, B.B. Hsieh, K.W.
Kim, H.L. Butler. 1991. User's Guide for a
Three-Dimensional Numerical Hydrodynamic,
Salinity, and Temperature Model of Chesapeake
Bay. Department of the Army, Waterways
Experiment Station, Corps of Engineers,
Vicksburg, MS.
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Appendix B: Receiving Water Models—Fact Sheets
CORMIX: Cornell Mixing Zone Expert
System
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment Modeling
(CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 546-8400
Models are available for FTP from:
ftp://ftp.epa.gov/epa_ceam/wwwhtml/
ceamhome.htm
2. Type of Modeling/Application:
• May be applied to most waterbodies.
• Near-field and far-field hydrodynamic
mixing processes
• Single Port, Multiport, and Surface
Discharges
• Includes effects of plume boundary
interaction
• Can be applied to tidal environments
3. Model Processes:
• Computation of physical parameters
and length scales to allow hydrody-
namic classification of the given
discharge/ambient situation into one of
many possible generic flow configura-
tions.
• Detailed numerical prediction of
effluent plume characteristics.
4. Method/Techniques:
CORMIX predicts plume geometry and
dilution characteristics within a receiving
water's initial mixing zone and allows an
analysis of toxic or conventional pollutant
discharges into diverse waterbodies. The
model is able to consider nonconservative
pollutants with first-order decay and wind
effects on thermal plume mixing.
Submodels within the CORMIX system can be
used to predict the geometry and dilution
characteristics of effluent flow from different
discharging systems. The first submodel
considers a submerged single-port diffuser of
arbitrary density discharging into a waterbody
that may have ambient stratification of
different types. The second submodel applies
to commonly used types of submerged
multiport diffuser discharges under the same
general effluent and ambient conditions as
the first submodel. The third submodel
considers buoyant surface discharges that
result when an effluent enters a larger
waterbody laterally through a canal, channel,
or near-surface pipe.
As the name implies, CORMIX is embedded in
an expert system shell that greatly facilitates
data input, provides range checking for
inputs, and allows convenient output analysis.
5. Limitations:
• All CORMIX submodels assume steady-
state ambient and discharge condi-
tions.
• CORMIX gives limited quasi-steady
state predictions in unsteady tidal
environments
6. Experience:
The CORMIX system has been extensively
verified by the developers and independent
users through comparison of simulation
results to available field and laboratory data
on mixing processes, and has undergone
extensive peer review. The system has been
used for a wide range of applications, ranging
from a single submerged pipe discharging into
a small stream with rapid cross-sectional
mixing to complicated multiport diffuser
installations in deep, stratified coastal waters.
7. Updating Version and System
Requirements:
Version 3.2 (1996). PC MS-DOS compatible.
8. Input Data Requirements:
All inputs are entered interactively and
include complete specification of the site or
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Compendium of Tools for Watershed Assessment and TMDL Development
case, ambient conditions, discharge character-
istics, level of output detail, and regulatory
definitions.
9. Outputs:
The output consists of qualitative descriptions
and detailed quantitative numerical predic-
tions. Qualitative information includes
physical information and insight into the
reasoning employed by the system and flow
class descriptions. The post-processor
CORGRAPH is included to give 2-D sketch
graphics of resulting plumes. TheFFLOCATR
post-processor can be used to compare field
dye test data to plume predictions when
detailed ambient cross-section data is
available. Quantitative output provides
details on the effluent plume trajectory and
mixing and regulatory compliance.
10. References available:
Akar, EJ. and G.H. Jirka. 1991. CORMK2:An
Expert System, for Mixing Zone Analysis of
Conventional and Toxic Multipart Diffuser
Discharges. EPA/600/3-91/073. U.S.
Environmental Protection Agency, Center for
Exposure Assessment Modeling, Athens, GA.
Doneker, R.L. and G.H. Jirka. 1990.
CORMK1: An Expert System for Mixing Zone
Analysis of Conventional and Toxic Single Port
Aquatic Discharges. EPA/600/3-90/012. U.S.
Environmental Protection Agency, Center for
Exposure Assessment Modeling, Athens, GA.
Jirka, G.H. and RJ. Akar. 1991. Hydrodynamic
classification of submerged multiport diffuser
discharges. Journal of Hydraulic Engineering
Jirka, G.H., and R.L. Doneker. 1991. Hydrody-
namic classification of submerged single port
discharges. Journal of Hydraulic Engineering
117(9):1095-1112.
Jirka, G.H., R.L. Doneker, and S.W. Hinton.
1996. User's Manual for CORMDf: A Hydrody-
namic Mixing Zone Model and Decision Support
System for Pollutant Discharges Into Surface
Waters. To be published by USEPA, Office of
Water, Office of Science and Technology,
1996. Available at http://ese.ogi.edti.
Jones, G.R. and G.H. Jirka. 1991. CORMDC3:
An expert system for the analysis and prediction
of buoyant surf ace discharges. Technical
report. DeFrees Hydraulics Laboratory, School
of Civil and Environmental Engineering,
Cornell University, Ithaca, NY.
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Appendix B: Receiving Water Models—Fact Sheets
A Simplified Deposition Calculation
for Organic Accumulation Near Marine
Outfalls
1. Distributor:
Marine Pollution Control Branch
Oceans and Coastal Protection Division
Office of Coastal Protection
USEPA
401 M Street, SW
Washington, DC 20460
(202) 260-8448
2. Type of Modeling/Application:
• Coastal waterbodies
• Two-dimensional, depth-averaged
• Steady, point source loadings
• Steady, tidally driven flow
• Analytical, steady-state predictions
• Advective and dispersive transport
3. Model Processes:
• Particle deposition and accumulation of
organic material in sediments employ-
ing a second-order rate law
• Metal and trace organic chemical
accumulations in sediments
• Carbon fixation by phytoplankton
• First-order decay of organic material in
water column and sediment
4. Method/Techniques:
In DECAL, the removal of organic material
from the water column is assumed to occur
primarily within the time scale of one to
several days. Transport, particle dynamics, ,
and organic carbon cycling are described by
averaging over a daily period to account for
tidal oscillations. The user can specify long-
term net drift.
The water column consists of a well-mixed
surface and lower layer, separated by a
pycnocline region. The daily-averaged
discharge of effluent is distributed over an
extended spatial domain by tidal oscillations.
Particle deposition rates are determined from
coagulation and settling kinetics and are
described by a second-order dependency on
mass concentration. Carbon fixation by
phytoplankton is expressed by measured
productivity rates. Decomposition of organic
material in the water column and sediments is
described by first-order decay.
5. Limitations:
• Plume entrainment, tidal oscillations,
and mixing in the surface and lower
waters are assumed to be instanta-
neous.
• Diffusion through the pycnocline and
horizontal dispersion are not consid-
ered significant over travel distances
larger than about 15 miles.
• The distribution of daily-averaged
sewage discharge is assumed to be
uniformly distributed over the tidal-
excursion ellipse.
6. Experience:
Applied to Orange County and Los Angeles
County outfalls using calibrated modeling
coefficients (Farley, 1990).
7. Updating Version and System
Requirements:
Last updated in 1987. PC-compatible.
8. Input Data Requirements:
Wasteflow characteristics (flow rates and
effluent solids concentrations), outfall diffuser
location and geometry, background oceano-
graphic information (total water column
depth, height of the lower layer, and rate of
phytoplankton primary production), short-
term tidal oscillations, and long-term nontidal
flows.
9. Output
Output from DECAL is given as sets of
contour plots for suspended particle concen-
trations in the lower water layer, for the daily-
averaged deposition rates of organic material,
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Compendium of Tbok far Watershed Assessment and TMDL Development
or for organic accumulation of particles in
sediments.
10. References Available:
Farley, K.J. 1990. Predicting organic accumula-
tion in sediments near marine outfalls.
Journal of Environmental Engineering 116(1):
144-165.
Tetra Tech. 1987. A simplified
deposition calculation (DECAL) for organic
accumulation near marine outfalls. Final
report. Prepared for Marine Operations
Division, Office of Marine and Estuarine
Protection, USEPA, Washington DC, by Tetra
Tech, Inc.
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Appendix B: Receiving Water Models—Fact Sheets
DYNHYD5: Link-Node Tidal Hydrodynamic
Model
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment
Modeling (CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
2. Type of Modeling/Application:
• Well-mixed unstratified rivers and
estuaries (one-dimensional)
• Variable tidal cycles, wind, and
unsteady inflows
3. Model Processes:
DYNHYD5 solves the one-dimensional
equations of continuity and momentum for a
branching or channel-junction (link-node)
computational network. Most applications of
DYNHYD5 will use a simulation time step on
the order of 30 seconds to 5 minutes due to
stability requirements. However, the hydrody-
namic output file created by DYNHYD5 may
be stored at any user-specified interval for
use by a water quality simulation program.
This interval may range from 1 to 24 hours,
depending on the type of water quality
simulation desired. If interest focuses on tide-
induced transport, a 1- to 3-hour interval
should be used. On the other hand, with long-
term simulations, a time interval of 12 to 24
hours would be appropriate.
4. Method/Techniques:
DYNHYD5 solves one-dimensional equations
describing the propagation of a long wave
through a shallow water, using an explicit
two-step Runge-Kutta procedure. The
continuity equation, based on the conserva-
tion of volume, is used to predict water
heights (heads) and volumes. The equation of
motion, based on the conservation of momen-
tum, predicts water velocities and flows, and
includes terms accounting for local inertia,
convective inertia, gravitational acceleration,
frictional resistance, and wind stress along the
channel axis.
5. Limitations:
• Applicable only to situations where
flow is predominantly well-mixed
vertically and laterally (one-dimen-
sional).
• Assumes channels can be adequately
represented by a constant top width
with a variable hydraulic depth.
• Assumes wave length is significantly
greater than the depth, and bottom
slopes are moderate.
• Difficult to apply to small rivers or
streams with steep hydraulic grades.
6. Experience:
The model is distributed as part of the
comprehensive WASPS modeling system and
is typically applied externally to provide
hydrodynamic flow computations, which are
then input to the WASPS water quality
model. Various versions of DYNHYD have
been applied to several rivers and estuaries as
part of wasteload allocation and eutrophica-
tion studies. There are many examples of
successful calibration and validation. Warwick
and Heim (1995) provide a comparison of
the performance of DYNHYD and RIVMOD
models.
7. Updating Version and System
Requirements:
Released with WASP Version 5.10 (1993).
PC-compatible. Pre- and post- processors are
supplied with the model.
8. Input Data Requirements:
Data requirements include a description of
the physical geometry and the forcing
functions. For junction elements, initial
surface elevation, surface area, and bottom
elevation must be given. For channel ele-
ments, length, width, hydraulic radius,
channel orientation, initial velocity, and
Manning's roughness coefficient are required.
Seaward boundary elevations can be de-
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Compendium of Tools for Watershed Assessment and TMDL Development
scribed by an average repetitive tidal function
or by specifying the high and low tidal heights
versus time for multiple tidal cycles.
9. Outputs:
The model reports time-variable channel
flows and velocities, as well as junction
volumes and depths throughout the computa-
tional network at user-specified print
intervals.
10. References Available:
Ambrose, R.B., T.A. Wool, and J.L. Martin.
1993. The water quality analysis simulation
program, WASPS version 5.10. Part A: Model
Documentation. U.S. Environmental Protec-
tion Agency, Office of Research and Develop-
ment, Environmental Research Laboratory,
Athens, GA.
Warwick, J.J., and K.J. Heim. 1995. Hydrody-
namic modeling of the Carson River and
Lahontan Reservoir, Nevada. Water Resources
Bulletin 31(l):67-77.
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Appendix B: Receiving Water Models—fact Sheets
DYNTOX: Dynamic Toxics Model
1. Distributor:
Ibrahima B. Goodwin
Office of Water
Office of Science and Technology
USEPA
401 M Street, SW
Washington, DC 20460
(202) 260-1308
2. Type of Modeling/Application:
• One-dimensional rivers and streams
• Steady-state predictions
• Explicitly accounts for duration and
frequency of loadings using a probabi-
listic framework
3. Model Processes:
• Effluent mixing with upstream flow,
including consideration of incomplete
lateral mixing at discharge point
• First-order decay
4. Method/Techniques:
The fundamental analytical solution in
DYNTOX assumes a steady-state condition
over the course of a day. The model allows the
use of three probabilistic simulation tech-
niques to calculate the frequency and severity
of instream toxicity at different effluent
discharge levels. The three procedures
considered are continuous simulation, Monte
Carlo simulation, and lognormal analysis.
In the continuous simulation approach, the
model is run for a specified period of re-
corded history and the results are analyzed
for frequency and duration.
In the Monte Carlo method, inputs are
described by probability distributions.
Random input sets are then used to repeat-
edly execute the model and describe the
model output in terms of a probability
distribution. Both the continuous simulation
and Monte Carlo methods produce probability
distributions of calculated daily downstream
concentrations from which the recurrence
interval of any concentration of interest can
be obtained. Probability distributions of
running averaged concentrations for any time
period of interest can also be obtained.
The lognormal analysis requires all inputs to
be described by lognormal distributions, which
allows computation of exceedance probabili-
ties for the toxic concentration at the point of
mixing through numerical integration.
5. Limitations:
• Simulates only steady-state conditions
• Dispersion is assumed to be negligible
in the longitudinal direction
• Does not consider daughter products
or processes.
• Kinetic reactions are restricted to first-
order loss of total pollutant
• The lognormal analysis is limited to one
effluent discharge without decay
6. Experience:
The framework on which the DYNTOX model
is based was applied to modeling stream
toxics in the Flint River, Michigan (USEPA,
1984).
7. Updating Version and System
Requirements:
Version 2.0 (1994). PC-compatible.
8. Input Data Requirements:
Upstream boundary data describing flow and
concentration in the river upstream of the
discharges, water quality standards, time of
travel between outfalls, and effluent data.
Drainage area ratios can be specified for each
reach of the system under study to account
for nonpoint sources of water entering the
stream. Depending on the simulation method
used, additional model parameters upstream
and effluent data specific to the method are
required. The continuous simulation and
Monte Carlo methods require a first-order
decay rate.
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Compendium of Tbols for Watershed Assessment and TMDL Development
9. Outputs:
DYNTOX provides tabular and graphic
outputs indicating the return period (in
years) of water quality standard violations
below each discharge and the percent of time
that predicted receiving water quality falls in
different concentration ranges, as well as the
return period for selected concentrations.
10. References Available:
Limno-Tech, Inc. 1994. Dynamic Toxics
Wasteload Allocation Model (DYNTOX). Version
2.0. Users manual Limno-Tech, Inc., Ann
Arbor, MI.
USEPA. 1984. Technical guidance manual for
performing waste load allocations - Book II,
Streams and rivers, Chapters, Tbxic substances.
U.S. Environmental Protection Agency, Office
of Water Regulations and Standards, Monitor-
ing and Data Support Division, Washington,
DC.
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Appendix B: Receiving Water Models—Fact Sheets
EFDC: Environmental Fluid Dynamics
Computer Code
1. Distributor
JohnM.Hamrick
Tetra Tech, Inc.
10306 Eaton Place, Suite 340
Fairfax, VA 22030
703-385-6000
ham@visi.net
or
Virginia Institute of Marine Science
School of Marine Science
The College of William and Mary
Gloucester Point, VA 23052
(804) 642-7000
2. Type of Modeling/Application:
• General purpose three-dimensional
hydrodynamic and transport model
applicable to rivers, lakes, reservoirs,
estuaries, wetlands, and coastal
regions.
• Model simulates tidal, density, and
wind driven flow; salinity; tempera-
ture; and sediment transport.
• Two built in, fully coupled water
quality/eutrophication submodels are
included in the code, as well as a
toxicant transport and fate model.
3. Model Processes:
The EFDC model solves the vertically hydro-
static, free-surface, variable-density turbu-
lent-averaged equations of motion and
transport equations for turbulence intensity
and length scale, salinity, and temperature in
a stretched, vertical coordinate system, and
horizontal coordinate systems that maybe
Cartesian or curvilinear-orthogonal. Equa-
tions describing the transport of suspended
sediment, toxic contaminants, and water
quality state variables are also solved.
Multiple size classes of cohesive and non-
cohesive sediments and associated deposition
and resuspension processes and bed
geomechanics are simulated. Toxics are
transported in both the water and sediment
phases in the water column and bed. The
built in 20 state variable water quality model
is based on the CE-QUAL-ICM reaction
kinetic. The 10 state variable reduced water
quality model is functionally equivalent to
WASPS. Other model features include:
drying and wetting, hydraulic structure
representation, vegetation resistance, and
Lagrangian particle tracking. The model also
accepts radiation stress fields from wave
refraction-diffraction models, which allows
simulation of longshore currents and sedi-
ment transport.
4. Method/Techniques:
EFDC uses a finite difference scheme with
three time levels and an internal-external
mode splitting procedure to achieve separa-
tion of the internal shear or barodinic mode
from the external free-surface gravity wave or
barotropic mode. An implicit external mode
solution is used with simultaneous computa-
tion of a two-dimensional surface elevation
field by a multicolor successive overrelaxation
procedure. The external solution is completed
by calculation of the depth-integrated
barotropic velocities using the new surface
elevation field. Various options can be used for
advective transport in EFDC. These include
the "centered in time and space" scheme, and
the "forward in time and upwind in space"
scheme.
5. Limitations:
• Considerable technical expertise in
hydrodynamics is required to use the
model effectively.
• Expertise in eutrophication processes
is required to use the water quality
component.
6. Experience:
The EFDC model has been used for modeling
studies in the estuaries of the Chesapeake
Bay System, the Indian River Lagoon and
Lake Okeechobee in Florida, the Peconic Bay
System in New York, Stephens Passage in
Alaska, and Nan Wan Bay, Taiwan. The model
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Compendium of Idols for Watershed Assessment and TMDL Development
has also been used to simulate large scale
wetlands flow and transport in the Ever-
glades.
7. Updating Version and System
Requirements:
The universal FORTRAN source code is
maintained compatible with DEC, IBM, HP,
SGI and SUN Unix workstations and Cray
Supercomputers as well as PC-compatibles
and Power Macintoshs. Microsoft, Lahey and
Absoft compilier are supported on PC, with
Language Systems and Absoft compilier
supported on Macintoshes.
8. Input Data Requirements:
Input data to drive the model include open
boundary water surface elevation, wind and
atmospheric thermodynamic conditions, open
boundary salinity and temperature, volumet-
ric inflows and inflowing concentrations of
sediment and water quality state variables.
Input file templates are included with the
source code and the user's manual to aid in
input data preparation.
9. Outputs:
The model outputs include water surface
elevation, horizontal velocities, salinity,
temperature, sediment concentration, and
toxicant concentration. Water quality
concentrations can also be predicted in a
variety of formats suitable for time series
analysis and plotting, horizontal and vertical
plane vector and contour plotting, and three-
dimensional slice and volumetric visualization.
Post processing software is available to
convert generic output files for use by a
numbers of scientific visualization applica-
tions.
10. References available:
Hamrick, J.M. 1992. A three-dimensional
environmental fluid dynamics computer code:
theoretical and computational aspects.
SRAMSOE #317, The College of William and
Mary, Gloucester Point, VA.
Hamrick,J.M. 1992. Estuarineenvironmen-
tal impact assessment using a three-dimen-
sional circulation and transport model. In
Estuarine and Coastal Modeling, Proceedings of
the 2nd International Conference, ed. M. L.
Spaulding et al., pp. 292-303. American
Society of Civil Engineers, New York.
Hamrick, J. M. 1996. A User's Manual for the
Environmental Fluid Dynamics Computer Code
(EFDC). The College of William and Mary,
Virginia Institute of Marine Science, Special
Report 331, 234 pp.
Hamrick, J. M., and T. S. Wu. 1996. Compu-
tational design and optimization of the
EFDC/HEM3D surface water hydrodynamic
and eutrophication models. In Computational
Methods for Next Generation Environmental
Models, ed. G. Delich, Society of Industrial and
Applied Mathematics, Philadelphia. In press.
Park, K., A. Y. Kuo, J. Shen, and J. M. Hamrick.
1995. A three-dimensional hydrodynamic-
eutrophication model (HEM3D): description of
water quality and sediment processes submodels.
The College of William and Mary, Virginia
Institute of Marine Science, Gloucester Point,
VA.. Special Report 327,113 pp.
Tetra Tech. 1994. User's guide for the three-
dimensional EFDC hydrodynamic and salinity
model of Indian River Lagoon and Turkey
Creek. Final report. Tetra Tech, Inc., Fairfax,
VA.
-------
Appendix B: Receiving Water Models—Fact Sheets
EUTROMOD: Watershed and Lake Modeling
Procedure
1. Distributor
North American Lake Management Society
(NALMS)
PO Box 5443
Madison, WI 53705
(608) 233-2836
2. Type of Modeling/Application:
• Provides guidance and information for
managing eutrophication in lakes and
reservoirs
• Collection of spreadsheet-based
nutrient loading and lake response
models
• Predicts lakewide, growing season
average conditions as a function of
annual nutrient loadings
• Allows for uncertainty analysis by
providing estimates of model error and
hydrologic variability.
3. Model Processes:
• Annual watershed point and nonpoint
source loadings
• Nonlinear regression equations from
multi-lake regional data sets in the
United States used to predict lake
response
4. Method/Techniques:
EUTROMOD is a spreadsheet-based water-
shed and lake modeling procedure for
eutrophication management, with an
emphasis on uncertainty analysis. The model
estimates nutrient loading, various trophic
state parameters, and trihalomethane
concentration in the lake using data pertain-
ing to land use, pollutant concentrations, and
lake characteristics. EUTROMOD uses several
algorithms based on statistical relationships
and a continuously stirred tank reactor
(CSTR) model. The model was developed
using empirical data from the USEPA's
national eutrophication survey, with trophic
state models used to relate phosphorus and
nitrogen loading to f n-lake nutrient concentra-
tions. The phosphorus and nitrogen concen-
trations were then related to maximum
chlorophyll level, Secchi disk depth, dominant
algal species, hypolimnetic dissolved oxygen
status, and trihalomethane concentration.
EUTROMOD allows for uncertainty analysis
by considering the error in regression
equations employed, and using an annual
mean precipitation and coefficient of variation
to account for hydrologic variability.
5. Limitations:
• Specific to watersheds in the south-
eastern United States.
• Provides only predictions of growing
season average conditions.
6. Experience:
Used in conjunction with a CIS for establish-
ing total maximum daily loads to Wister Lake,
Oklahoma (Hession et al., 1995).
7. Updating Version and System
Requirements:
Last updated in 1990. PC-compatible.
8. Input Data Requirements:
Data required for simulating basin loadings •
and lake response include information about
climate, watershed characteristics, and lake
morphometry. Climate parameters include
precipitation and lake evaporation estimates.
Several parameters are needed to describe
the watershed in terms of land use, soils, and
topography. Lake morphometry is described
using surface area and mean depth.
9. Outputs:
The output from EUTROMOD consists of
predicted phosphorus and nitrogen loads by
category, and lake responses. The lake
responses include total phosphorus and
nitrogen concentrations in the lake influent
averaged for all inputs and land uses, total P
and N concentrations in the lake, chlorophyll
a concentration, Secchi disk depth, the
probability that the blue-green algae dominate
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Compendium of Tbois for Watershed Assessment and TMDL Development
the algae population and that the hypolim-
nion remains oxic, trihalomethane concentra-
tions.
10. References Available:
Hession, W.C., D.E. Storm, S.L. Burks, M.D.
Smolen, R. Lakshminarayanan, and C.T. Haan.
1995. Using EUTROMOD with a CIS for
establishing total maximum daily loads to
WisterLake, Oklahoma. In Impact of animal
waste on the land-water interface, 53-60.
Lewis Publishers. In press.
Reckhow, K.H. 1990. EUTROMOD spreadsheet
program - a regional modeling scheme for
nutrient runoff and lake trophic state modeling.
School of Forestry and Environmental Studies,
Duke University, Durham, NC.
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Appendix B: Receiving Water Models—Fact Sheets
EXAMS II: Exposure Analysis Modeling
System
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment Modeling
(CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
Models are available for FTP from:
ftp://ftp.epa.gov/ epa_ceam/wwwhtml/
ceamhome.htm
2. Type of Modeling/Application:
• Streams/Rivers and lakes/reservoirs
in one, two, or three dimensions
• Steady flow
• Steady-state/Quasidynamic predic-
tions
• Advective and dispersive transport
• Considers benthic exchange
• Inputs may be steady or variable
3. Model Processes:
• First-order decay, daughter prod-
ucts
• Process kinetics
• Equilibrium sorption
• Pore water advection
• Local sediment mixing
4. Method/Techniques:
EXAMSII is an interactive modeling system
that uses the principle of mass balance and
mathematical models of the kinetics and
processes governing the transport and
transformation of chemicals to provide
predictions of their probable fate and
persistence in aquatic ecosystems. The
hydrologic transport processes considered are
advection and dispersion. The transformation
processes included in the model are photoly-
sis, hydrolysis, biotransformation, oxidation,
and sorption with sediments and biota.
Secondary daughter products and subsequent
degradation of these products are also
considered.
EXAMSII includes separate mathematical
models of the kinetics of the physical,
chemical, and biological processes governing
transport and transformations of chemicals.
An advantage in using the model is its ability
to accept standard water quality parameters,
chemical data, and system characteristics for
which information is readily available.
5. Limitations:
• Designed to evaluate consequences of
long-term, primarily time-averaged
chemical loadings, thus transient
effects cannot be analyzed.
• Chemicals are assumed not to radically
change the environmental variables
that drive their transformations.
• Sorption isotherms are assumed to be
linear, and biotransformation kinetics
are assumed to be second-order rather
than Michaelis-Menton-Monod.
6. Experience:
EXAMSII has been used in a wide range of
regulatory applications for the USEPA. The
model has been validated and reviewed by
independent experts (Mulkey et al., 1986;
Schnoor et al., 1987).
7. Updating Version and Systems
Requirements:
Version 2.941 (1995). PC-compatible. The
model includes pre- and post- processing
systems.
8. Input Data Requirements:
Basic inputs include system geometry and
hydrology specification, a set of chemical
loadings on each sector of the ecosystem, and
parameters that define the strength of the
advective and dispersive transport pathways.
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Compendium of Tools for Watershed Assessment and TMDL Development
Although EXAMSII allows for the entry of
extensive environmental data, the program
can be run with a much-reduced data set
when the chemistry of a compound of interest
precludes some of the transformation
processes. Chemical parameters include
molecular weight, solubility, and ionization
constants of the compound. Sediment-
sorption/biosorption, volatilization, photolysis,
hydrolysis, oxidation, andbiotransformation
processes may also be specified.
9. Outputs:
The output includes summary tables of input
data and predictions of chemical exposure,
fate, and persistence. The exposure summary
includes expected (long-term chronic, 96-
hour acute, 21-day chronic) environmental
concentrations due to a specified pattern of
chemical loadings. The fate summary gives
the distribution of chemicals in the system
and the relative dominance of each transport
and transformation process. Plots of longitudi-
nal and vertical concentration profiles can be
obtained.
10. References Available:
Burns, LA. 1990. Exposure analysis modeling
system: User's guide for EXAMSII Version 2.94,
EPA/600/3-89/084. U.S. Environmental
Protection Agency, Athens, GA.
Mulkey, L.A., R.B. Ambrose, and T.O. Barnwell.
1986. Aquatic fate and transport modeling
techniques for predicting environmental
exposure to organic pesticides and other
toxicants: A comparative study. In Urban
runoff pollution. Springer-Verlag, New York,
NY.
Schnoor, J.L., C. Sato, D. McKetchnie, and D.
Sahoo. 1987. Processes, coefficients, and models
for simulating toxic organics and heavy metals
in surface waters. EPA/600/3-87/015. U.S.
Environmental Protection Agency, Athens, GA.
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Appendix B: Receiving Water Models—Fact Sheets
FLUX, PROFILE, and BATHTUB: Methods for
the Description and Prediction of
Eutrophication-Related Processes in
Lakes and Reservoirs
1. Distributor:
Dr. Robert Kennedy
Ecosystem Processes and Effects Branch
Environmental Laboratory
U.S. Army Engineer Waterways Experiment
Station
3909 Halls Ferry Road
Vicksburg, MS 39180
(601) 634-3659
Software and user documentation available
through the Internet. For notices of availabil-
ity contact:
http://limnos.wes.army.mil
For additional information, contact:
kennedy@limnos.wes.army.mil
2. Type of Modeling/Application:
• Lakes and reservoirs
• Mass loading computation
• In-lake data description/assessment
• Nutrient and water balance computa-
tion
• Models of eutrophication-related
responses
• Steady-state, empirical models
• Assessment and evaluation of selected
management alternatives
3. Model Processes:
• Nutrient and water balances in a
segmented hydraulic network
• Nutrient sedimentation
• Algal (chlorophyll) response to
flushing, light, and nutrient concentra-
tion
• Hypolimnetic oxygen depletion
4. Method/Techniques:
FLUX - Provides an estimation of tributary
mass discharges (loadings) from sample
concentration data and continuous (e.g.,
daily) flow records. Five estimation methods
are available and potential errors in estimates
are quantified.
PROFILE - Facilitates analysis and reduction of
in-lake water quality data. Algorithms are
included for calculation of hypolimnetic
oxygen depletion rates and estimation of
area-weighted, surface-layer mean concentra-
tions of nutrients and other eutrophication
response variables.
BATHTUB - Applies a series of empirical
eutrophication models to morphologically
complex lakes and reservoirs. The program
performs steady-state water and nutrient
balance calculations in a spatially segmented
hydraulic network that accounts for advective
and diffusive transport, and nutrient
sedimentation. Eutrophication-related water
quality conditions (total phosphorus, total
nitrogen, chlorophyll a, transparency, and
hypolimnetic oxygen depletion) are predicted
using empirical relationships derived from
assessment of reservoir data (Walker, 1985,
1986).
5. Limitations:
Applications of BATHTUB are limited to
steady-state evaluation of relationships
between nutrient loading, transparency and
hydrology, and eutrophication responses.
Short-term responses and effects related to
structural modifications or responses to
variables other than nutrients cannot be
explicitly evaluated.
6. Experience:
The programs and models have been applied
to U.S. Army Corps of Engineer reservoirs
(Kennedy, 1995), as well as a number of other
lakes and reservoirs. BATHTUB was recently
cited as an effective tool for lake and reservoir
water quality assessment and management,
particularly where data are limited (Ernst et
al., 1994).
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Compendium of Tools for Watershed Assessment and TMDL Development
7. Updating Version and System
Requirements:
The current version is being updated (see
Section 2.2). PC-compatible.
8. Input Data Requirements:
BATHTUB requires information describing
watershed characteristics, water and nutrient
loads, lake or reservoir morphology, and lake
or reservoir water quality.
9. Outputs:
FLUX - Graphic and tabular displays allow
users to evaluate input data and calculate
results.
PROFILE - Graphic and tabular displays allow
users to evaluate and summarize lake or
reservoir water quality data.
BATHTUB - Model outputs include tabular
and/or graphic displays of segment hydrau-
lics, water and nutrient balances, predictions
of nutrient concentrations, transparency,
chlorophyll a concentrations, and oxygen
depletion. Statistics relating observed and
predicted values are provided.
10. References Available:
Ernst, M.R., W. Frossard, and J.L. Mancini.
1994. Two eutrophication models make the
grade. Water Environment and Technology,
November, 15-16.
Kennedy, R.H., 1995. Application of the
BATHTUB Model to selected south eastern
reservoirs. Technical Report EL-95-14, U.S.
Army Engineer Waterways Experiment
Station, Vicksburg, MS.
Walker, W.W., 1985. Empirical methods for
predicting eutrophication in impoundments;
Report 3, Phase III: Model Refinements.
Technical Report E-81-9, U. S. Army Engineer
Waterways Experiment Station, Vicksburg,
MS.
Walker, W. W., 1986. Empirical methods for
predicting eutrophication in Impoundments;
Report 4, Phase E: Applications Manual.
Technical Report E-81-9. U.S. Army Engineer
Waterways Experiment Station, Vicksburg,
MS.
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Appendix B: Receiving Water Models—Fact Sheets
PHOSMOD: Seasonal and Long-Term Trends
of Total Phosphorus and Oxygen in
Stratified Hakes
1. Distributor:
North American Lake Management Society
(NALMS)
PO Box 5443
Madison, WI53705
(608) 233-2836
2. Type of Modeling/Application:
• Modeling framework designed to
assess the impact of phosphorus
loading on stratified lakes
• Allows rapid generation and analysis of
phosphorus loading scenarios
3. Model Processes:
• Lake stratification into two segments:
the water layer and a surface sediment
layer
• Computes total phosphorus and
hypolimnetic oxygen concentrations,
taking sediment-water interactions into
account
4. Method/Techniques:
PHOSMOD uses a modeling framework
described by Chapra and Canale (1991) for
assessing the impact of phosphorus loading
on stratified lakes. A total phosphorus budget
for the water layer is developed with inputs
from external loading and recycling from the
sediments and considering losses due to
flushing and settling. In the sediment layer,
total phosphorus is gained by settling and lost
by recycling and burial. The sediment to water
recycling is dependent on the levels of
sediment total phosphorus and hypolimnetic
oxygen, with the concentration of the latter
estimated with a semi-empirical model.
5. Limitations:
• Steady-state analyses.
• Developed to assess long-term trends
only
6. Experience:
Chapra and Canale (1991) present an
application of the model to Shagawa Lake in
Michigan and demonstrate how its predictions
replicate in-lake changes not possible with
simpler phosphorus budget models.
7. Updating Version and System
Requirements:
Version 1.0 (1991). PC-compatible. Pre- and
post-processor provided.
8. Input Data Requirements:
Lake stratification periods and morphometry;
initial lake total phosphorus, sediment
parameters, initial hypolimnetic DO concen-
trations; settling and burial rates for sedi-
ments; time series of flow and inflow phos-
phorus concentrations; print and calculation
times. Observed data, if available, may also be
input for display with outputs.
9. Outputs:
Tabular and graphical output of lake total
phosphorus; percentage of total phosphorus
in sediment; hypolimnetic DO concentrations;
days of anoxia at specified print intervals.
10. References Available:
Chapra, S., and R.E Canale. 1991. Long-term
phenomenological model of phosphorus and
oxygen for stratified lakes. Water Research
25(6):707-715.
Chapra, S. 1991. PHOSMOD 1.0 - Software to
model seasonal and long-term trends of total
phosphorus and oxygen in stratified lakes.
CADWES Working Paper No. 14, The
University of Colorado, Boulder, CO.
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Compendium of Tools for Watershed Assessment and TMDL Development
-------
Appendix B: Receiving Water Models—Fact Sheets
PLUMES: Dilution Models for Effluent
Discharges
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment
Modeling (CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
.(706) 355-8400
Models are available for FTP from:
ftp://ftp.epa.gov/epa_ceam/wwwhtml/
ceamhome.htm
2. Type of Modeling/Application:
• May be applied to most deep
waterbodies.
• Near-field hydrodynamic mixing
processes
• Point source buoyant or submerged
discharges
• Single or multiple inputs
3. Model Processes:
• Consists of two initial dilution models
(RSB and UM) with two far-field
algorithms automatically initiated
beyond the initial dilution zone.
• Incorporates the flow classification
scheme of the CORMIX modeling
system and provides recommendations
for model usage under a range of
mixing conditions.
4. Method/Techniques:
PLUMES incorporates two relatively sophisti-
cated initial dilution models (RSB and UM)
and two relatively simple far-field algorithms.
RSB is based on experimental studies on
multiport diffusers in stratified currents. UM
is the latest in a series of models first devel-
oped for atmospheric and freshwater
applications and later for marine applications.
Outstanding UM features are the Lagrangian
formulation and the projected area entrain-
ment (PAE) hypothesis, which is a statement
of forced entrainment—the rate at which
mass is incorporated into the plume in the
presence of current. The Lagrangian
formulation offers comparative simplicity that
is useful in developing PAEs.
The far-field algorithms are relatively simple
implementations of dispersion equations
applied to nearshore coastal waters, and
confined channels.
5. Limitations:
• RSB is a an empirical model developed
from experimental studies under
stable ambient stratification, and it may
have limited application in situations
where ambient layers are unstratified
or unstable.
• The PAE hypothesis, which was
developed for plumes discharged to
open, unbounded environments, free
from interference, is assumed to be
valid in UM.
• The farfield algorithms in PLUMES are
relatively simplistic compared to the
initial dilution models.
6. Experience:
The PLUMES modeling system is recom-
mended for use in designing outfall diffusers.
7. Updating Version and System
Requirements:
Version 3.0 (1994). PC-compatible.
8. Input Data Requirements:
Port geometry, spacing, and total flow. Plume
diameter and depth, effluent salinity and
temperature. Ambient conditions in receiving
water and far-field distance.
9. Outputs:
CORMIX flow classification, pollutant
concentration and dilution ratios at various
points in the plume.
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Compendfum of Tools for Watershed Assessment and TMDL Development
10. References Available:
Baumgartner, D. J., W.E. Frick, and EJ.W.
Roberts. 1994. Dilution models for effluent
discharges. 3rd ed. ERA/600/R-93/139. U.S.
Environmental Protection Agency, Newport,
OR.
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Appendix B: Receiving Water Models—Fact Sheets
QUAL2E: The Enhanced Stream Water
Quality Model
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment Modeling
(CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
Models are available for FTP from:
ftp://ftp.epa.gov/epa_ceam/wwwhtml/
software.htm
Windows QUAL2E is also available. Contact
Gerald D. LaVeck
Environmental Protection Agency
Office of Science and Technology (4305)
401 M Street, SW
Washington, DC 20460
(202) 260-7771
or go to http://www.epa.gov/ost/tools
Documentation for QUAL2E is also available at
http://www.epa.gov/ord/webpubs/qual2e/.
2. lype of Modeling/Application:
• Water quality/Eutrophication
• Far-field
• Stream/River
• 1-D, branching
• Steady flow
• Steady-state/Quasidynamic (diurnal
variations in meteorological inputs)
• Advective/Dispersive transport
• Finite difference
3. Model Processes:
• Temperature
• Salinity
• BOD-DO
• Nitrogen cycle
• Phosphorus cycle
• Chlorophyll a (is modeled as the
indicator of planktonic algae biomass;
benthic algae is not considered)
• Conservative constituent
• Nonconservative constituent
• First-order kinetics of constituents
• Uncertainty analysis
4. Method/Techniques:
The QUAL2E model permits simulation of
several water quality constituents in a
branching stream system using an implicit
backward-difference, finite-difference
solution to the one-dimensional advective-
dispersive equation. The stream is conceptu-
ally represented as a system of reaches of
variable length, each of which is subdivided
into computational elements that have the
same length in all reaches. A mass and heat
balance is applied for every element. Mass
may be gained or lost from elements by
transport processes, external sources and
sinks, or internal sources and sinks. The
UNCAS component allows quick implementa-
tion of uncertainty analysis using sensitivity
analysis, first-order error analysis, or Monte
Carlo simulation.
5. limitations:
• Considers only steady flow.
• Only time-varying forcing functions are
the dimatologic variables that primarily
affect diurnal temperature and
dissolved oxygen.
6. Experience:
The QUAL series of models has a two-decade
history in water quality management and
wasteload allocation studies. Paschal and
Mueller (1991) used QUAL2E to evaluate the
effects of wastewater effluent on the South
Platte River from Chatfield reservoir through
Denver, Colorado. Cubilo et al. (1992) applied
QUAL2E to the major rivers of the
Comunidad de Madrid in Spain. Little and
Williams (1992) describe a nonlinear
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Compendium of Tbok for Watershed Assessment and TMDL Development
regression programming model for calibrating
QUAL2E. Johnson and Mercer (1994) report
a QUAL2E application to the Chicago water-
way and Upper Illinois River waterway to
predict DO and other constituents in the DO
cycle in response to various water pollution
controls.
7. Updating Version and System
Requirements:
Version 3.21 (1995). PC-compatible. A
Windows-based pre- and post-processor is
available from EPA's Office of Science and
Technology.
8. Input Data Requirements:
The stream is represented by a network of
headwaters, reaches, and junctions. Twenty-
six physical, chemical, and biological proper-
ties have to be specified for a reach.
9. Outputs:
Dissolved oxygen, biochemical oxygen
demand, temperature, chlorophyll a, ammo-
nia-N, nitrite-N, nitrate-N, organic N, organic
P, dissolved P, coliforms, arbitrary
nonconservative constituents, three conserva-
tive constituents.
10. References Available:
and QUAL2E-UNCAS: Documentation and user
manual. EPA-600/3-87/007. U.S. Environ-
mental Protection Agency, Athens, GA.
Cubilo, E, B. Rodriguez, and T.O. Barnwell, Jr.
1992. A system for control of river water
quality for the community of Madrid using
QUAL2E. Water Science and Technology 26(7/
8): 1867-1873.
Johnson, C.R., and G. Mercer. 1994. Modeling
the water quality processes of the Chicago
waterway. In Proceedings of the National
Symposium on Water Quality, American Water
Resources Association, Chicago, IL, November
6-10, 1994, p. 315.
Little, K.W., and R.E. Williams. 1992. Least-
squares calibration of QUAL2E. Water
Environment Research 64(2): 179-185.
Paschal, J. E., Jr., and D. K. Mueller. 1991.
Simulation of water quality and the effects of
wastewater effluent on the South Platte River
from Chatfleld Reservoir through Denver,
Colorado. Water-Resources Investigations
Report 91-4016. U.S. Geological Survey,
Denver, CO.
Brown, L.C., and T.O. Barnwell. 1987. The
enhanced stream water quality model QUAL2E
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Appendix B: Receiving Water Models—Fact Sheets
RIVMOD-H: River Hydrodynamics Model
1. Distributor:
RIVMOD-H can be requested with the WASPS
modeling package from:
Model Distribution Coordinator
Center for Exposure Assessment Modeling
(CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
Ftp from: Ftp://ftp.epa.gov/epa_ceam/
wwwhtml/ceamhorne.htm
2. Type of Modeling/Application:
• Applicable to rivers, streams, tidal
estuaries, reservoirs, and other
waterbodies where the one-dimen-
sional assumption is appropriate
• Considers time-varying lateral inflows
3. Model Processes:
RIVMOD-H solves the one-dimensional
equations of unsteady flow using a fully
implicit finite difference method. The model
can be used for flow routing only or can be
linked with a water quality modeling package.
4. Method/Techniques:
RIVMOD-H solves the governing flow
equations using a numerically efficient fully
implicit scheme that overcomes the restriction
of the Courant gravity wave criterion,
permitting the use of longer time steps (in
comparison with explicit schemes). The
numerical solution scheme is very flexible and
allows the specification of a weighting factor
for fully explicit, fully implicit, or any other
combination of implicit-explicit solutions. The ,
model has the capability of handling flow or
head as boundary conditions. The specifica-
tion of head as a boundary condition allows
the use of the model where an open boundary
is required (e.g., an estuary or a river flowing
into a lake). The model has been soft-linked to
the WASPS and SWMM models as part of the
LWWM modeling system (Dames and Moore,
1994).
5. Limitations:
• May be inappropriate in situations
where large lateral or vertical gradi-
ents exist.
• Neglects the effect of eddy diffusivity.
• Assumes hydrostatic pressure
distribution is valid at every point in
the channel, and that the water
surface slope is small.
6. Experience:
The model has been applied on several rivers
in the United States and abroad
(Hosseinipour et al., 1994). Warwick and
Heim (1995) provide a comparison of the
performance of DYNHYD and RIVMOD-H.
7. Updating Version and System
Requirements:
Released with the LWWM modeling system
(Dames and Moore, 1994). PC-compatible.
8. Input Data Requirements:
Data requirements for RIVMOD-H include
channel morphometry, bed elevations, and
initial and boundary conditions. If cross-
sectional topography data are available,
separate software can be used to generate
exponential rating functions for cross-
sectional area and wetted perimeter as a
function of depth. The model then uses these
relationships to automatically calculate the
area and wetted perimeter as the water
depth changes. This feature allows the model
to use natural cross sections, and therefore
simulation results should be closer to the
natural behavior of the stream.
9. Outputs:
Time-variable water surface elevations or
stages and discharges for unsteady flows at
specified cross-sections and time intervals.
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Compendium o/7bo!s/or Watershed Assessment and TMDL Development
10. References Available:
Dames and Moore. 1994. User's Manual -
Linked Watershed/Waterbody Model Prepared
for the Southwest Florida Water Management
District. Dames and Moore, Tampa, FL.
Hosseinipour, E.Z., R.B. Ambrose, Jr., J.L.
Martin, and T. Wu. 1994. RIVMOD-H - A One-
Dimensional hydrodynamic model - Model
Theory and User's Manual. In User's manual -
Linked Watershed/Waterbody Model Dames
and Moore, Tampa, FL.
Warwick, J.J., and K.J. Heim. 1995. Hydrody-
namic modeling of the Carson River and
Lahontan Reservoir, Nevada. Water Resources
Bulletin 31(l):67-77.
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Appendix B: Receiving Water Models—Fact Sheets
SMPTOX4: Simplified Method Program -
Variable-Complexity Stream Toxics Model
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment
Modeling (CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
Models are available for FTP from:
ftp://ftp.epa.gov/epa_ceam/wwwhtml/
software.htm
2. Type of Modeling/Application:
• Streams/Rivers in one dimension
• Steady flow
• Steady-state predictions
• Advective and dispersive transport
• Considers benthic exchange
• Capability to simultaneously model
multiple chemicals
3. Model Processes:
• First-order decay
• Equilibrium sorption
• Sediment processes may be input
4. Method/Techniques:
SMPTOX4 is a steady-state, one-dimensional
analytical model for predicting suspended
solids, and dissolved and paniculate toxicant
concentrations in the water column and
streambed resulting from point source
discharges into streams and rivers, based on
an EPA-recommended technique (USEPA,
1980). Three levels of complexity are
available within the model. At the simplest
level, only total toxic pollutants can be
predicted. The next level can be used to
predict toxic water column concentrations but
interactions with bed sediments are not
considered. The third level allows prediction
of pollutant concentrations in dissolved and
paniculate phases for the water column and
bed sediments, as well as the total suspended
solids concentrations. Operating within a
Windows environment, SMPTOX4 allows
quick data input and easy access to graphical
output, sensitivity analysis, and uncertainty
analysis. SMPTOX4 also contains a database
of chemical properties for many chemicals of
concern.
5. limitations:
• Steady-state predictions only.
• Nonpoint source loadings cannot be
simulated.
• Does not consider daughter products
or process.
• Process kinetics are not simulated.
6. Experience:
The users manual presents an example
application using data from investigations on
the Flint River, Michigan, in EPA's guidance
manual for stream toxics modeling (USEPA,
1984).
7. Updating Version and System
Requirements:
Version 2.01(1993). PC-compatible.
8. Input Data Requirements:
Flow, total pollutant and suspended solids
concentrations, geomorphic parameters,
physical/chemical coefficients and rates.
Observed pollutant concentrations may be
input for use during model calibration.
9. Outputs:
Model calculations for total, dissolved, and
paniculate concentrations for the toxicant in
the water column and bed sediments, and
suspended solids concentration in the water
column at incremental river miles throughout
the length of the stream.
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Compendium of "tools for Watershed Assessment and TMDL Development
10. References Available:
Limno-Tech, Inc. 1993. Simplified method
program - Variable complexity
stream toxics model (SMPTOX3) Version 2.01.
Users manual Limno-Tech, Inc., Ann Arbor,
MI.
USEPA. 1980. Simplified analytical method for
determining NPDES effluent limitations for
POTWs discharging into low-flow streams. U.S.
Environmental Protection Agency, Office of
Water Regulations and Standards, Monitoring
and Data Support Division, Washington, DC.
USEPA. 1984. Technical guidance manual for
performing waste load allocations - Book II,
Streams and rivers, Chapters, Toxic substances.
U.S. Environmental Protection Agency, Office
of Water Regulations and Standards, Monitor-
ing and Data Support Division, Washington,
DC.
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Appendix B: Receiving Water Models—Fact Sheets
TOXMOD: Long-Term Trends of Toxic
Organics in Lakes
1. Distributor:
North American Lake Management Society
(NALMS)
EO. Box 5443
Madison, WI53705
(608) 233-2836
2. Type of Modeling/Application:
• Modeling framework designed to
assess the impact of toxic organic
compounds on lakes and impound-
ments
6. Experience:
Chapra (1991) has used the modeling
framework on which TOXMOD is based to
develop a procedure for identifying priority
pollutants that exhibit the weakest assimila-
tive capacity for a range of lakes.
7. Updating Version and System
Requirements:
Version 1.0 (1991). PC-compatible. Pre- and
post-processor provided.
Allows rapid generation and analysis of 8- InPut Data Requirements:
scenarios
3. Model Processes:
• Lake idealized as a well-mixed reactor
(water layer) underlain by a weE-
mixed sediment layer
• Computes sediment and water
concentration of toxicant
4. Method/Techniques:
TOXMOD is based on an extension of a
modeling framework presented by Chapra
(1991) to assess the impact of toxic organic
compounds on lakes and impoundments. A
steady-state mass balance is developed for
solids and toxics. Toxics are partitioned into
dissolved and paniculate forms, with the
dissolved form for both water and sediment
layers further subdivided into a component
associated with dissolved organic carbon.
Participates in the water layer are subdivided
into abiotic and biotic suspended solids.
Burial and resuspension are considered for
both dissolved and participate forms while
diffusion acts selectively on the dissolved
fraction.
5. Limitations:
• Steady-state analyses.
• Developed to assess long-term trends
only.
Lake depth and surface area; sediment
thickness and area; solids mass balance data,
including settling and burial rates for
sediments; dissolved organic carbon concen-
trations; sorptionand volatilization coeffi-
cients and decay rates of toxicant; initial
toxicant concentration; time series of flow and
inflow toxicant concentrations; print and
calculation intervals. Observed data, if
available, can also be input for display with
outputs.
9. Outputs:
Tabular and graphical output of sediment and
water toxicant concentration at specified print
intervals.
10. References Available:
Chapra, S. 1991. Toxicant-loading concept for
organic contaminants in Lakes. Journal of
Environmental Engineering 117(5):656-677.
Chapra, S. 1991. TOXMOD 1.0 - Software to
model long-term trends of toxic organics in
lakes. CADWES Working Paper No. 13, The
University of Colorado, Boulder, CO.
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Compendium of Tools for Watershed Assessment and TMDL Development
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Appendix B: Receiving Water Models—Fact Sheets
TPM: Tidal Prism Model
1. Distributor:
Albert Y.Kuo
Virginia Institute of Marine Science
School of Marine Science
The College of William and Mary
Gloucester Point, VA 23062
(804) 642-7212
2. Type of Modeling/Application:
• Primarily applicable to small coastal
basins and tidal creeks
• May be applied to marinas where tidal
forces are predominant with oscillating
flow (e.g., an estuary or a tidal river)
• Steady-state model capable of simulat-
ing up to 23 water quality variables
3. Model Processes:
• Simulates physical transport processes
in terms of the concept of tidal flushing
• Relatively detailed kinetic model that
allows a more complete description of
the eutrophication process
• Includes a sediment process model
that considers the depositional flux of
paniculate organic matter, its diagen-
esis, and the resulting sediment flux
4. Method/Techniques:
TPM predicts the longitudinal distribution of
conservative and nonconservative substances
at slack-before-ebb (highslackwater). The
model is best applied to an elongated
embayment or tidal creek, where the creek is
branched and/or freshwater discharge is
negligibly small. The basic assumptions in the
model are that die tide rises and falls
simultaneously throughout the waterbody
and that the system is in hydrodynamic
equilibrium. Kinetic processes included in
TPM are based on the formulations used in
CE-QUAL-ICM (Cerco and Cole, 1994).
Twenty-three state variables are considered
including total active metal, fecal coliform
bacteria, and temperature. The sediment
process model in TPM has 16 water-quality-
related model state variables and fluxes.
Benthic sediments are represented as two
layers in the sediment model. The lower layer
is permanently anoxic, while the upper layer
maybe oxic or anoxic depending on dissolved
oxygen concentration in the overlying water.
5. Limitations:
• The waterbody being simulated must
be in hydrodynamic equilibrium.
• Only applicable to waterbodies where
tidal forces are predominant with
oscillating flow; the model therefore is
not applicable to marinas located on a
sound or an open sea.
6. Experience:
The model has been applied to a number of
tidal creeks and coastal embayments in
Virginia (Kuo and Neilson, 1988).
7. Updating Version and System
Requirements:
Latest version released in September 1994.
PC-compatible.
8. Input Data Requirements:
Two basic types of input data are required—
geometric and physical. Geometric data define
die system being simulated, including die
returning ratio, initial concentration, and
boundary conditions. Physical data include
water temperature, reaction rates, point and
nonpoint sources, and initial and boundary
conditions for water quality parameters
modeled.
9. Outputs:
Temperature, salinity, inorganic suspended
solids, diatoms, blue-green algae and other
phytoplankton, dissolved, labile, and refrac-
tory paniculate organic carbon, organic
nitrogen, and organic phosphorus ammo-
nium, nitrite and nitrate, total phosphate,
dissolved oxygen, chemical oxygen demand,
dissolved silica, particulate biogenic silica, total
active metal, and fecal coliform bacteria.
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Compendium of Tools for Watershed Assessment and TMDL Development
10. References available:
Cerco, C.F., and T. Cole. 1993. Three-
dimensional eutrophication model of the
Chesapeake Bay. Journal of Environmental
Engineering 119(6):1006-1025.
Kuo, A.Y., and B.J. Neilson. 1988. A modified
tidal prism model for water quality in small
coastal embayments. Water Science Technology
20(6/7):133-142.
Kuo, A.Y., and K. Park. 1994. A PC-based tidal
Prism water quality model for small coastal
basins and tidal creeks. SRAMSOE No. 324.
The College of William and Mary, Gloucester
Point, VA.
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Appendix B: Receiving Water Models—Fact Sheets
WASPS: Water Quality Analysis Simulation
Program
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment
Modeling (CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
Models are available for FTP from:
ftp://ftp.epa.gov/epa_ceam/wwwhtml/
ceamhome.htm
2. Type of Modeling/Application:
• May be applied to most waterbodies in
one, two, or three dimensions
• Can be linked with simulated
hydrodynamics
• Predicts time-varying concentrations of
water quality constituents
• Advective and dispersive transport
• Considers benthic exchange
• Finite difference
3. Model Processes:
• Temperature
• Salinity
• Bacteria
• DO-BOD
• Nitrogen cycle
• Phosphorus cycle
• Phytoplankton
• First-order decay, daughter products
• Process kinetics
• Equilibrium sorption
• Net resuspension/deposition
4. Method/Techniques:
WASPS is a general-purpose modeling system
for assessing the fate and transport of
conventional and toxic pollutants in surface
waterbodies.The model simulates time-
varying processes of advection and disper-
sion, considering point and diffuse mass
loading, and boundary exchange.
WASPS includes two submodels for water
quality/eutrophication and toxics, referred to
as EUTRO5 and TOXI5, respectively. In
EUTRO5, the transport and transformation of
up to eight state variables in the water column
and sediment bed may be simulated. In
TOXI5, the transport and transformation of
one to three chemicals and one to three types
of particulate material can be simulated.
5. Limitations:
• There is a potential for instability or
numerical dispersion in the user-
specified computational network.
• If chemical concentrations in the
waterbody are much higher than tra>
level, the assumptions of linear
partitioning and transformation in
TOXI5 begin to break down.
• Zooplankton dynamics are not
simulated in EUTRO5 although their
effect may be described by user-
specified forcing functions that vary in
space and time.
• Intermediate-level method for
computation of sediment oxygen
demand and benthic nutrient fluxes.
6. Experience:
Used in a wide range of regulatory and water
quality management applications for rivers,
lakes, and estuaries. Lang and Fontaine
(1990) describe an application to predict the
transport and fate of organic contaminants in
Lake St. Glair, Michigan. Cheng et al. (1994)
describe the development and application of a
GIS-based modeling framework using a
watershed loading model and WASE Lu et al.
(1994) used the model to simulate the
transport and fate of DO, BOD, and organic
nitrogen in untreated wastewater discharges
in Weeks Bay, Alabama. Lung and Larson
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Compendfum of Tbols for Watershed Assessment and TMDL Development
(1995) used EUTRO5 to evaluate phosphorus
loading reduction scenarios for the Upper
Mississippi River and Lake Pepin. Cockrum
and Warwick (1995) used WASP to character-
ize the impact of agricultural activities on
instream water quality in a periphyton-
dominated stream. Tetra Tech (1995)
describes a full three-dimensional application
of EUTRO5 in conjunction with the EFDC
hydrodynamic model to assess the effective-
ness of options for total nitrogen removal
from a wastewater treatment plant.
7. Updating Version and System
Requirements:
Version 5.10 (1993). PC-compatible. Pre-
and post-processors are available from the
distributor.
8. Input Data Requirements:
The body of water to be simulated must be
divided into a series of completely mixed
computational segments. Loads, boundary
concentrations, and initial concentrations
must be specified for each state variable.
Forcing functions must be specified for time
and spatially variable parameters.
InTOXIS, up to 12 spatially variable environ-
mental variables, such as pH and light
extinction, maybe specified as needed. In
addition, up to 17 time-variable functions may
be used to study diurnal or seasonal effects on
pollutant behavior. In EUTRO5, up to 16
spatially variable environmental parameters,
60 rate constants, and 14 time-variable
functions can be specified.
9. Outputs:
TOXI5 provides time-variable chemical
concentrations for every segment at the
specified output time interval. Chemical
concentrations are reported for the dissolved
and sorbed phases, and as neutral and ionic
concentrations.
EUTRO5 reports a set of state variable
concentrations, forcing functions, and process
rates for every segment at the specified
output time interval. Variable concentrations
include dissolved oxygen, carbonaceous
biochemical oxygen demand (BOD), ultimate
BOD, phytoplankton carbon and chlorophyll a,
total nitrogen, ammonia, nitrate, organic
nitrogen, total inorganic nitrogen, organic
phosphorus, and inorganic phosphorus.
10. References Available:
Ambrose, R.B., T.A. Wool, and J.L Martin.
1993. The water quality analysis simulation
program, WASPS version 5.10. Part A: Model
documentation. U.S. Environmental
Protection Agency, Office of Research and
Development, Environmental Research
Laboratory, Athens, GA.
Cheng, C., J.F. Atkinson, and J.V DePinto.
1994. A coupled CIS-water quality modeling
study. In Proceedings of the 1994 Hydraulic
Engineering Conference, American Society of
Civil Engineers, Buffalo, NY, 1994, pp. 247-
251.
Cockrum, D.K., and J.J. Warwick. 1994.
Assessing the impact of agricultural activi-
ties on water quality in a periphyton-
dominated stream using the Water Quality
Analysis Program (WASP). In Proceedings of
the Symposium on the Effects of Human-
Induced Changes on Hydrologic Systems,
American Water Resources Association,
Jackson Hole, WY, June 26-29, 1994, p.
1157.
Lang, G.A., and T.D. Fontaine. 1990.
Modeling the fate and transport of organic
contaminants in Lake St. Glair. Journal of
Great Lakes Research 16(2):216-232
Lu, Z., G.C. April, D.C. Raney, and W.W.
Schroeder. 1994. DO, BOD, and organic
nitrogen transport in Weeks Bay, Alabama.
In Proceedings of the National Symposium on
Water Quality, American Water Resources
Association, Chicago, IL, November 6-10,
1994, pp. 191-200.
Lung, W., and C.E. Larson. 1995. Water
quality modeling of the upper Mississippi
River and Lake Pepin. Journal of Environ-
mental Engineering 121(10):691-699.
Tetra Tech. 1995. Hydrodynamic and water
quality mathematical modeling study of
Norwalk Harbor, Connecticut: Final report.
Tetra Tech, Inc., Fairfax, VA.
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Appendix C:
Ecological Assessment
Techniques and Models—
Fact Sheets
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Compendium of Tools for Watershed Assessment and TMDL Development
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
FGETS: Food and Gill Exchange of Toxic
Substances
1. Distributor:
Model Distribution Coordinator
Center for Exposure Assessment Modeling
(CEAM)
USEPA
960 College Station Road
Athens, GA 30605-2700
(706) 355-8400
2. Type of Modeling/Technique:
Fish bioaccumulation simulation modeling for
laboratory conditions (constant flow or static
exposures) or for field assessments (for
multiple fish species that are exposed to
constant or time-varying water concentrations
and that feed on either single or multiple food
resources).
3. Methods:
FGETS considers both the biological attributes
of the fish and the physicochemical properties
of the chemical that determine diffusive
exchange across gill membranes and intestinal
mucosa. The model is based on a set of
diffusion and forced convection partial
differential equations, coupled to a process-
based fish growth formulation. Chemical
exchange rates are estimated using funda-
mental principles of passive diffusion and
thermodynamics rather than phenomenologi-
cal toxicokinetic data.
4. Applications:
FGETS provides regulators and practitioners
with an objective, process-based assessment
of residue-based, lexicological responses and
dietary exposures for fish assemblages.
5. Experience:
Used extensively for ecotoxicology studies.
6. Updating Version and System
Requirements:
Version 3.0.18 was released in September
1994. FGETS operates on IBM PCs and
compatibles in DOS.
7. Input Data Requirements:
Morphological, physiological, and trophic
parameters that describe the gill morphom-
etry, feeding and metabolic demands, and
body composition for the species in question;
and relevant physicochemical parameters that
describe partitioning to the fish's lipid and
structural organic fractions for a specific
chemical.
8. Outputs:
• Temporal dynamics of a fish's whole-
body concentration (ig chemical/(g live
weight fish)) of nonionic,
nonmetabolized organic chemicals that
are bioaccumulated from water and
food.
• Calculation of the time to reach the
chemical's lethal activity by assuming
that the chemical elicits its pharmaco-
logical response through a narcotic
mode of action.
9. References Available:
Barber, M.C., LA. Suarez, and R.R. Lassiter.
1988. Modeling bioconcentration of nonpolar
organic pollutants by fish. Environmental
Toxicology and Chemistry 7: 545-558.
Barber, M.C., L.A. Suarez, and R.R. Lassiter.
1991. Modeling bioaccumulation organic
pollutants in fish with an application to PCBs
in the Great Lakes salmonids. Canadian
Journal of Fisheries and Aquatic Sciences
48:318-337.
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Compendium of Toots for Watershed Assessment and TMDL Development
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Appendix C: Ecological Assessment Techniques and Modeb—Fact Sheets
HEP/HSI: Habitat Evaluation Procedures/
Habitat Suitability Indices
1. Distributor:
U.S. Fish and Wildlife Service
National Ecology Research Center
2627 Redwing Road
Fort Collins, CO 80526
(303) 226-9421
BBS: (303) 226-9365 (N/8/1)
Internet: http://www.fws.gov
2. Type of Modeling/Application:
A species-based evaluation method that
determines the quality and quantity of
available habitat for selected aquatic and
terrestrial wildlife species, and measures the
impact of proposed or anticipated land or
water use changes on that habitat.
3. Model Components:
Three software programs have been devel-
oped to assist with the HEP:
• HEP Accounting Program computes
the values needed to use the HEP
procedures.
• Habitat Management Evaluation
Method System (HMEM) software
allows a user to investigate and
, compare the cost-effectiveness of
different management alternatives to
achieve desired HUs for a selected
species.
• HSI modeling system software is used
to compute an HSI value for selected
species from field measurements of
habitat variables.
4. Method/Techniques:
HEP analysis begins with three basic steps:
(1) defining the study area, (2) Delineating
cover types, and (3) selecting evaluation
species.
Evaluation species (i.e., indicator species) are
used in HEP to quantify habitat units (HUs); a
typical HEP study incorporates four to six
species. The analysis is structured around the
calculation of HUs for each evaluation species
in the study area. The number of HUs is
defined as the product of the Habitat
Suitability Index (HSI, a measure of habitat
quality) and the total area of available habitat
(habitat quantity).
HUs are then used to make comparisons of
(1) the relative value of different areas at
the same point in time and/or (2) the
relative value of the same area at future
points in time.
5. Applications:
• Quantitative assessment of habitat
conditions for wildlife species
Comparison of the impacts of project
alternatives on wildlife resources
6. Experience:
Used extensively by the U.S. Fish and
Wildlife Service, the U.S. Army Corps of
Engineers, and the U.S. Bureau of Reclama-
tion.
7. System Requirements:
All three software programs operate on IBM
PCs and compatibles in DOS.
8. Input Data Requirements:
Data to be collected include delineation of
cover types (e.g., deciduous forest, coniferous
forest, grassland, residential woodland)
within the project area; size (acreage) of
existing habitat for each evaluation species;
selection of evaluation species; Habitat
Suitability Index (HSI) reflecting current
habitat conditions for each evaluation species;
future habitat conditions for each evaluation
species.
HSI data collection includes
(1) species-specific habitat use information
such as general information (e.g., geographic
distribution); age, growth, and food require-
ments; water quality, depth, and flow;
species-specific habitat requirements;
reproductive information; (2) species-specific
life history information for each life stage,
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Compendium of TboZj for Watershed Assessment and TMDL Development
(i.e., spawning/embryo, fry, juvenile, and
adult); (3) suitability indices for each habitat
variable.
9. Outputs:
• A quantitative assessment of the
quality and quantity of available
habitat for selected wildlife species in
terms of proposed or anticipated land
use changes
• The cost-effectiveness of different
management alternatives to achieve
desired HUs for a selected species
10. References Available:
USFWS. 1980. Habitat Evaluation Procedures
(HEP). ESM102. U.S. Department of the
Interior, U.S. Fish and Wildlife Service,
Division of Ecological Services, Washington,
DC.
USFWS. 1981. Standards for the Development
of Habitat Suitability Index Models. ESM103.
U.S. Department of the Interior, U.S. Fish and
Wildlife Service, Division of Ecological
Services, Washington, DC.
Wakely, J.S., and L.J. O'Neil. 1988. Tech-
niques to increase efficiency and reduce effort
in applications of the Habitat Evaluation
Procedures (HEP). Technical Report EL-88-
13. U.S. Army Engineers Waterways
Experiment Station, Vicksburg, MS.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
HES: Habitat Evaluation System
1. Distributor:
U.S. Army Engineer Waterways
Experiment Station
3909 Halls Ferry Road
Vicksburg, MS 39180
(601) 634-5276
2. Type of Modeling/Application:
A community-based evaluation technique
used to assess the impacts of development
projects for two aquatic habitats (streams and
lakes) and five terrestrial habitat evaluations
(wooded swamps, upland forests, bottomland
hardwood forests, open lands, and terrestrial
wildlife value of aquatic habitats).
3. Method/Techniques:
HES assumes that presence, abundance, and
diversity of animal populations in a habitat
are determined by biotic and abiotic factors
that can be readily quantified. HES deter-
mines the quality of a particular habitat type
through the use of functional curves that
relate habitat quality and carrying capacity to
these factors. HES uses general habitat
characteristics that indicate quality for aquatic
and terrestrial wildlife communities as a
whole.
Six steps are involved in an HES:
(1) obtaining habitat type and land use
acreage; (2) deriving Habitat Quality Index
(HQI) scores; (3) deriving Habitat Unit Values
(HUVs); (4) projecting HUVs for future with-
and without-project conditions; (5) using
HUVs to assess impacts of project alternatives;
(6) determining mitigation requirements, if
any
For complex projects with several habitat
types, computer software is available for
making HES computations for steps 1-5.
Inputs to this software are the data for land
use or habitat size and HQI scores.
4. Applications:
• Evaluating the effects of projects on
the quantity and quality of wildlife
habitats in the Lower Mississippi Valley
Region of the United States.
• Aiding in the selection between project
alternatives.
5. Experience:
HES has been used in major ecosystems in the
Lower Mississippi Valley Region. With
revisions to curves, weights, and other
variables, it can be applied to many other
areas of the United States.
6. Updating Version and System
Requirements:
N/A
7. Input Data Requirements:
• Baseline data on habitat types and land
uses in the project area
• Size (acreage) of each habitat type and
land use for existing and future
conditions
• Measurements of key variables (e.g.,
percent understory, number of large
trees, number of mast trees, species
associations, number of snags)
identified for each habitat and land use
type for existing conditions
• Projected measurements of same key
variables for future conditions
9. Outputs:
A quantitative assessment of the quality and
quantity of available habitat for entire wildlife
communities in terms of proposed or antici-
pated land use changes
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Compendium of Took for Watershed Assessment and TMDL Development
10. References Available:
U.S. Army Corps of Engineers. 1976. A
tentative Habitat Evaluation System (HES) for
water resources planning. Lower Mississippi
Valley Division, Vicksburg, MS.
U.S. Army Corps of Engineers. 1980. A
Habitat Evaluation System for water resources
planning. Lower Mississippi Valley Division,
Vicksburg, MS.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
HGM: Hyd rogeomorphic Assessment
1. Contact:
Daniel Smith
U.S. Army Engineer Waterways
Experiment Station
3909 Halls Ferry Road
Vicksburg, MS 39180
(601) 634-2718
2. l^pe of Modeling/Application:
HGM (currently under development) is a
hydrogeomorphic classification and assess-
ment methodology for determining the
integrity of physical, chemical, and biological
functions of wetlands as they compare to
reference conditions.
3. Method/Techniques:
HGM focuses on identifying wetland groups
that exhibit a relatively narrow range of
variation in the properties that fundamentally
influence how wetlands function. The HGM
method relies on the use of reference
wetlands, which represent a collection of sites
of a specific wetland class that can be used for
developing the upper and lower boundaries
of functioning within the class. The steps in
the assessment approach are (1) classify
wetlands according to HGM properties, (2)
make connections between the properties of
each wetland class and the ecological func-
tions that they perform based on logic and
research results, (3) develop functional
profiles for each wetland class, (4) choose
reference wetlands that represent the range
of both natural and human-imposed stresses
and disturbances, and (5) design the
assessment method using indicators cali-
brated to reference wetlands.
4. Applications:
Once completed, HGM will be able to assess
the degree to which a wetland performs
expected physical, chemical, and biological
functions.
5. Experience:
Once completed, HGM will be used by the U.S.
Army Corps of Engineers and other agencies
to evaluate the quality of wetlands within a
context of reference conditions.
6. Updating Version and System
Requirements:
N/A
7. Input Data Requirements:
• Baseline data to develop a reference
set of wetlands representing the range
of conditions that exist in a wetland
ecosystem and its landscape in a
reference domain
• Baseline data on the condition of
assessment wetland variables (e.g.,
surface and subsurface water storage,
nutrient cycling, retention of particu-
lates, organic matter export, spatial
structure of habitat, distribution and
abundance of invertebrates and
vertebrates, plant community charac-
teristics, etc.) measured directly or
indirectly using indicators to develop a
relationship between variable condi-
tions in the assessment wetland and
functional capacity of the reference set
8. Outputs:
A quantitative assessment of the functioning
of wetlands that uses the concepts of
hydrogeomorphic classification, functional
capacity, reference domain, and reference
wetlands.
9. References Available:
Brinson, M.M. 1993. A hydrogeomorphic
classification for wetlands. Wetlands Research
Program Technical Report WRP-DE-4. U.S.
Army Engineers Waterways Experiment
Station, Vicksburg, MS.
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Appendix G: Ecological Assessment Techniques and. Models—Fact Sfteecs
ICI and IWB: Invertebrate Community Index
and Index of Well-Being
1. Distributor:
N/A - see references
2. Type of Modeling/Technique:
Two biological indices that are usually used in
tandem with the RBP V (IBI) (see fact sheet,
p. C.17) to provide a measure of the integrity
of aquatic invertebrate communities (ICI) and
fish communities (IWB) based on field-
collected data.
3. Method/Techniques:
The ICI is a single value calculated by
summing 10 structural and compositional
community metrics describing invertebrate
communities. Each metric is attributed a
score of 0, 2,4, or 6 points based on water-
shed area and comparisons with scores
developed from ecoregional reference sites.
Each metric also incorporates into the scoring
scheme functionally based differences
between macroinvertebrates over a range of
stream conditions. The sum of all 10 metric
provides an overall ranking for the waterbody.
IWB incorporates measures of fish species
abundance and diversity estimates in the
computational formula as follows:
IWB = 0.5 InN + 0.5 InB + H'N + H'B
where:
N = number of individuals caught per
kilometer
B = biomass of individuals caught per
kilometer
H* = Shannon-Weaver diversity index
N
N
where:
N = number of individuals in the sample
= biomass of sample
n( = number of individuals of species i in
the sample
= biomass of species i in the sample
4. Applications:
By assessing the biological condition of a
waterbody, ICI and IWB can be used to
determine whether a waterbody is impaired,
to provide information for ranking sites and
prioritization for further assessment, and to
establish a basis for trend monitoring.
5. Experience:
The ICI and IWB have been used extensively
in the state of Ohio (where they were
developed) for assigning causes of and
sources to aquatic life use impairments in
Ohio streams and rivers. With changes to
collection methodologies, metric selection, and
reference conditions to account for geographic
setting and ecoregions other than those in
Ohio, the ICI and IWB approaches can be
used successfully to assess the condition of
macroinvertebrate communities throughout
the country.
6. Updating Version and System
Requirements:
N/A
7. Input Data Requirements:
Data necessary for development of the ICI
include total number of taxa, number of
mayfly taxa, number of caddisfiy taxa,
number of dipteran taxa, percent mayfly
composition, percent caddisfly composition,
percent tribe tanytarsini midge composition,
percent other dipteran and noninsect
composition, percent tolerant organisms, and
number of qualitative EPT taxa. Data for
reference conditions are also necessary.
Data to be collected for the IWB include
number of individuals/kilometer; biomass of
individuals/kilometer; Shannon-Weaver
diversity index (number of individuals in
sample and number of individuals of species i
in the sample). Data describing reference
conditions are also necessary.
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Compendium of Tools for Watershed Assessment and TMDL Development
8. Outputs:
• ICI provides a quantitative measure of
overall macroinvertebrate community
condition.
• IWB provides a quantitative measure
of the quality of a fish assemblage.
9. References Available:
DeShon, J.E. 1995. Development and
application of the Invertebrate Community
Index, InBioZogzcaZ assessment and criteria:
Took far water resource planning and decision
making, ed. W.S. Davis and T.P. Simon, pp.
217-229. Lewis Publishers, Boca Raton, FL.
Gammon, J.R. 1980. The use of community
parameters derived from electrofishing
catches of river fish as indicators of environ-
mental quality. In Seminar on water quality
management tradeoffs. EPA-905/9-80-009.
U.S. Environmental Protection Agency,
Washington, DC.
Hughes, R.M., and J.R. Gammon. 1987.
Longitudinal changes in fish assemblages and
water quality in the Willamette River, Oregon.
Transactions of the American Fisheries Society,
116(2): 196-209.
Ohio EPA. 1987. Biological criteria for the
protection of aquatic life. VolI-III. Ohio
Environmental Protection Agency, Division of
Water Quality Monitoring and Assessment,
Surface Water Section, Columbus, OH.
Yoder, C.0.1991. The integrated biosurvey as
a tool for evaluation of aquatic life use
attainment and impairment in Ohio surface
waters. In USEPA, Biological criteria: Research
and regulation. Proceedings of a symposium.
EPA-440/5-91-005. U.S. Environmental
Protection Agency, Office of Water, Washing-
ton, DC.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
IFIM: The Instream Flow Incremental
Methodology
1. Distributor:
Riverine and Wetlands Ecosystem
Branch
National Biological Service
4512 McMurray Avenue
Fort Collins, CO 80525-3400
(303) 226-9337
2. lype of Modeling/Technique:
A conceptual framework that consists of a
collection of analytical procedures and
computer models used to assess riverine
habitats.
3. Model Components: .
• Physical Habitat Simulation System
(PHABSIM)
• Time-Series Library (TSLIB)
4. Methods:
IFIM attempts to determine the effects of any
of a number of hydraulic modifications on
aquatic habitat through a complete process
that steps through the description of the river
system and available habitat and incremen-
tally changes one or more variables describing
the system to reflect a management option,
and determining the available habitat for this
new system. Each option is then evaluated
and a management strategy is selected.
IFIM considers changes to both microhabitat
(the distribution of structural and hydraulic
features that form the living space for an
organism) and macrohabitat (channel
characteristics, temperature, and water
quality).
PHABSIM is a collection of computer pro-
grams that form the key microhabitat
simulation component of IFIM. Relying on the
assumption that aquatic species will react to
hydraulic changes in a stream by selecting the
most favorable conditions, PHABSIM uses a
combination of standard, one-dimensional,
steady-flow, open-channel hydraulic models
and habitat models to describe the Weighted
Usable Area (a measure of habitat) under a
variety of channel configurations and flow
management conditions.
TSLIB uses a set of computer programs to
create monthly or daily habitat time-series
and habitat-duration curves using the habitat-
discharge relationships produced by
PHABSIM. It can calculate basic statistics for
monthly data, generate flow-duration habitat
curves for designated months, and create
monthly or annual habitat time series for four
to seven life stages of selected species.
5. Applications:
IFIM, and its components, can be applied as
guidelines to solve problems regarding the
hydraulic disturbance of a riverine ecosystem.
6. Experience:
Used extensively by the U.S. Fish and Wildlife
Service and state fisheries management
agencies.
7. System Requirements:
PHABSIM and TSLIB operate on IBM PCs and
compatibles in DOS and are written in
FORTRAN.
8. Input Data Requirements:
Detailed data are required for both physical
characteristics (e.g., depth, velocity, stream
channel characteristics, riparian cover) and
biological characteristics (e.g., life history and
habitat preference information for the species
of concern) of the stream.
9. Outputs:
Quantitative assessment (usually in graphical
form) of the changes in a given species'
habitat with changes in hydrologic regime
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Compendium of Tools for Watershed Assessment and TMDL Development
10. References Available:
Bovee, K. D. 1982. A guide to stream habitat
analysis using the Instream now Incremental
Methodology. Instream Flow Information
Paper 12. FWS/OBS-82/26. U.S. Department
of the Interior, U.S. Fish and Wildlife Service,
Office of Biological Services.
Bovee, K.D. 1986. Development and evaluation
of habitat suitability criteria for use in the
Instream now Incremental Methodology.
Instream Flow Information Paper 21. U.S.
Fish and Wildlife Service Biological Report 86.
Milhous, R.T., M.A. Updike, and D.M.
Schneider. 1989. Physical Habitat Simulation
System reference manual—Version II.
Instream Flow Information, Paper No. 26.
Biological Report 89(16). U.S. Fish and
Wildlife Service, National Ecology Research
Center, Fort Collins, CO.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
MNSTREM: Minnesota Stream
Temperature Model
1. Distributor:
St. Anthony Falls Laboratory
University of Minnesota
Mississippi River at Third Avenue, SE
Minneapolis, MN 55414
2. Type of Modeling/Application:
MNSTREM is a computer model that simu-
lates dynamic stream temperatures averaged
over one to six hours. Water temperature is
assumed to be laterally uniform, but they can
be highly unsteady, with strong longitudinal
gradients.
3. Method/Techniques:
MNSTREM solves the unsteady, one-dimen-
sional advection-dispersion equation with
non-linear source terms for water tempera-
ture. The control volume technique is used,
with source/sink inputs across the water
surface from evaporation, conduction, long
wave radiation, and solar radiation; and
sediment-water heat flux from conduction.
Groundwater flow and tributaries can also be
incorporated as inputs to the system. The
primary coefficients that can be calibrated are
in the wind function (relating wind velocity to
surface shear), a shading coefficient (the
fraction of the water surface that is shaded),
and Manning's n value (factional resistance of
the river bottom).
4. Applications:
MNSTREM can be used any time that
dynamic water temperatures are to be
simulated for a stream. It has been developed
for maximum accuracy with minimum
calibration, and therefore requires substantial
input data. Its best use is for situations where
maximum and minimum temperatures are
important to the simulation. It has been used
to predict hourly temperatures in the U.S.
EPA Monticello Experimental Streams
(standard errors of between 0.2 and 0.3
degrees C without calibration), numerous
streams in the upper Midwest USA (standard
errors of approximately 1 degree C with
calibration), and in the central Platte River
(standard errors of approximately 1.5
degrees C) where diel variations were as high
as 18 degrees C.
5. Experience:
MNSTREM is used primarily when models
that predict average daily temperature are
not suitable. All applications, to date, have
been performed by individuals at the St.
Anthony Falls Laboratory or by alumni of the
Laboratory.
6. System Requirements:
MNSTREM is written in FORTRAN and runs
on PCS.
7. Input Data Requirements:
Initial conditions: water temperature,
Manning's n values, cross-sectional area and
surface width at various locations along the
reach to be simulated. Groundwater tem-
perature and discharge, and tributary
discharge, if relatively constant, can also be
input with initial conditions.
Boundary conditions over time: upstream
temperature, discharge, solar radiation,
relative humidity, wind velocity, air tempera-
ture, and cloud cover.
Other data: calendar day, latitude, altitude,
and for calibration, water temperature.
8. Outputs:
Stream water temperature averaged over one
to six hour time periods, the standard error
for any comparison with measured data, and
heat inputs from solar radiation, long wave
radiation evaporation and conduction.
9. References available:
Gulliver, J.S. 1977. Analysis of Surface Heat
Exchange and Longitudinal Dispersion in a
Narrow Open Field Channel with Application to
Water Temperature Prediction. M.S. Thesis,
University of Minnesota, Minneapolis, MN.
187 p.
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Compendium of Tools for Watershed Assessment and TMDL Development
Sinokrot, B.A. and H.G. Stefan. 1992.
Deterministic Modeling of Stream Water
Temperatures: Development and Applications to
Climate Change Effects on Fish Habitat. Project
Report 337. St. Anthony Falls Laboratory.
University of Minnesota, Minneapolis, MN.
Sinokrot, B.A. and H.G. Stefan. 1993.
Stream Temperature Dynamics: Measure-
ments and modeling. Water Resources
Research. 29(7): 2299-2312.
Sinokrot, B.A. and H.G. Stefan. 1994.
Stream water-temperature sensitivity to
weather and bed parameters. J. of Hydraulics
Engineering. 120(6): 722-736.
Sinokrot, B.A, R. Gu, and J.S. Gulliver. 1996.
Impacts ofln-Stream Flow Requirements Upon
Water Temperature in the Central Platte River.
Project Report 381. Prepared for U.S.
Environmental Protection Agency, Region VIII.
University of Minnesota. St. Anthony Falls
Laboratory.
Stefan, H.G., J. Gulliver, M.G. Hahn, and A.Y.
Fu. 1980. Water Temperature Dynamics in
Experimental Field Channels: Analysis and
Modeling. Project Report 193. St. Anthony
Falls Laboratory. University of Minnesota,
Minneapolis, MN.
Sinokrot, B., and H.G. Stefan. 1994. Stream
water-temperature sensitivity to weather and
bed parameters. J. Hydraulic Engineering.
120(6): 722-736.
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Appendix C: Ecological Assessment Techniques and Models—fact Sheets
PVA: Population Viability Analyses
1. Distributor:
A commercially available form of a PVA is
RAMAS, available from:
Applied Biomathematics, Inc.
100 North Country Road
Setauket, NY 11733-1345
(800) 735-4350
fax (516) 751-3435
2. Type of Modeling/Technique:
Population dynamics modeling for aquatic or
terrestrial populations that examines how
expected time to extinction changes with the
effects of demographic, genetic, or environ-
mental variability on population stability.
3. Methods:
The accurate projection of population growth
requires a knowledge of the age structure of
the population and the survival and fecundity
of individuals of each age. This is often
achieved using a life table (or matrix)
approach in which the demographic param-
eters include annual rates of survival, growth
or change among defined life history stages,
and fecundity. Life tables set out the
fecundities and probabilities of survival for
each age class of individuals in a population
and use an "accounting" formulation to
calculate future population size on the basis of
current size and rates of growth, death, and
birth. PVAs also incorporate uncertainty due
to unknown or unpredictable events by
modeling variation in population parameters
and estimating probabilities of extinction over
specified periods of time, instead of using a
single estimate for an unspecified time.
4. Applications:
PVAs can provide risk assessors and other
scientists with simulations of the impact of a
stressor (that has been translated into
demographic parameters) to examine how
expected time to extinction changes with the
environment, population structure, or
behavior. PVAs have been used mostly in a
generalized sense to determine how a
population will respond to environmental
changes, rather than specifically to assess risk
from alternative management scenarios.
5. Experience:
Used extensively for ecological risk analysis
and wildlife population research.
6. System Requirements:
RAMAS operates on IBM PC- and compatibles
in DOS.
7. Input Data Requirements:
Age structure of the population being studied;
survival and fecundity of each age or life
stage.
8. Outputs:
PVAs supply a quantified analysis of the
stability of a specified population following a
change in environment, population structure,
or behavior.
9. References Available:
Begon, M., and M. Mortimer. 1986. Population
ecology: A unified study of animals andplants.
Blackwell Scientific Publications, London.
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Appendix C: Ecological Assessment Techniques and Models—fact Sheets
RBPs: Rapid Bioassessmenf Protocols
1. Distributor:
N/A - see references
2. Type of Modeling/Application:
A set of five protocols that offer techniques of
varying complexity to characterize the
biological integrity of streams and rivers.
3. Model Components:
RBJLL. A screening-level protocol involving
the systematic documentation of visual
observations by a trained professional
focusing on benthic macroinvertebrate
communities.
RBPII: A mid-level protocol involving
integrated assessment of metrics that
measure components of family-level commu-
nity structure in the field for benthic
macroinvertebrate communities.
RBP III: A detailed protocol involving
systematic field collection of data for
macroinvertebrate communities and metric
computation similar to that of RBP II, but also
includes subsequent laboratory analysis to
permit detection of more subtle degrees of
waterbody impairment.
RBP IV: A screening-level protocol
involving the use of a questionnaire to
maximize existing knowledge offish commu-
nities.
RBP V (also known as the Index of Biotic
Integrity or IBI): A detailed protocol involving
the collection of data to compute 12 metrics
describing the biological integrity of fish
communities.
4. Methods/Techniques:
All five RBPs use the collection and analysis of
biological, physical, and chemical data to
assess the biological integrity of streams or
rivers.
For RBPs I and W, a screening approach is
used to obtain information about the status of
an aquatic community and condition of a site.
These protocols are done without the benefit
of comparison to unimpaired sites; therefore,
the judgment of biological condition is made
by a professional based solely on the presence
or absence of indicator taxa, dominance of
nuisance or sensitive taxa in the sampled
habitats, or evenness of taxonomic distribu-
tion.
For RBPs HI, IV and V (IBI), multimetric
approaches are used that define an array of
measures, or metrics, that individually
provide information on community structure,
taxonomic composition, individual condition,
and biological processes. Each metric is given
a score based on the collected data and that of
reference conditions (unimpaired or mini-
mally impaired conditions). All metrics are
summed and compared to reference condi-
tions to determine the overall biological
condition.
5. Applications:
RBPs can be used to determine whether
biological impairments exist in a stream or
river, to provide information for ranking sites
and prioritization for further assessment, and
to establish a basis for trend monitoring.
6. Experience:
RBPs, and modifications to them by local,
state, and regional organizations, have been
used successfully in a variety of watershed
management applications.
7. Updating Version and System
Requirements:
N/A
8. Input Data Requirements:
For all five protocols, habitat assessment and
water quality data are necessary to character-
ize and rate substrate/instream cover,
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Compendium of Tbols for Watershed Assessment and TMDL Development
channel morphology, and riparian/bank
structure; measure conventional water quality
parameters; and examine physical character-
istics. For biological assessment:
RBPI: Determine relative abundance
of benthic macroinvertebrates.
RBP IT: Examine riffle/run community
and sample coarse paniculate organic matter;
identify 100-organism subsample identified in
field to family or order level; perform
functional feeding group analysis of riffle/run
and coarse participate organic matter in the
field. Data describing reference conditions
are also necessary.
RBP III: Examine riffle/run community and
sample coarse paniculate organic matter;
collect riffle/run benthos, collect coarse
paniculate organic matter sample; determine
shredder abundance; perform riffle/run
analysis in laboratory, identify 100-organism
subsample to species level and perform
functional feeding group analysis. Data
describing reference conditions are also
necessary.
RBP IV: Questionnaire survey regarding
fish communities; survey ecoregional
reference reaches and randomly selected
streams.
RBPV: Major habitats and cover types; total
number of native fish species; number and
identity of darter species; number and
identity of sunfish species; number and
identity of sucker species; number and
identity of intolerant species; proportion of
individuals as tolerant species; proportion of
individuals as omnivores; proportion of
individuals as insectivorous cyprinids;
proportion of individuals as piscivores (top
carnivores); number of individuals in sample;
proportion of individuals as hybrids; propor-
tion of individuals with disease, tumors, fin
damage, and skeletal anomalies. Data
describing reference conditions are also
necessary.
9. Outputs:
RBPI: Determination of whether impairment
exists; indication of generic cause (habitat,
organic enrichment, toxicity).
RBP II: Characterization of biological
conditions as impairment (none, moderate,
severe); indication of generic cause.
RBP III: Evaluation of site impairment (none,
slight, moderate, severe); indication of
generic cause.
RBP IV: Determination of whether impair-
ment exists; indication of generic cause.
RBPVflBI): Evaluation of biological integrity
as excellent, good, fair, poor, very poor;
indication of generic cause of impairment.
10. References Available:
Karr, J.R. 1981. Assessment of biotic
integrity using fish communities. Fisheries
6(6):21-27.
Karr, J.R., K.D. Fausch, EL. Angermeier, ER.
Yant, and I.J. Schlosser. 1986. Assessing
biological integrity in running waters a method
and its rationale. Illinois Natural History
Survey Special Publication 5.
Plafkin, J.L., M.T. Barbour, K.D. Porter, S.K.
Gross, and R.M. Hughes. 1989. Rapid
bioassessment protocols for use in streams and
rivers: Benthic macroinvertebrates and fish.
EPA 440/4-89/001. U.S. Environmental
Protection Agency, Office of Water, Washing-
ton, DC.
Simon, T.E, and J. Lyons. 1995. Application of
the Index of Biotic Integrity to evaluate water
resource integrity in freshwater ecosystems.
InBiological assessment and criteria: Tools for
water resource planning and decision making,
ed., W.S. Davis and T.E Simon, pp. 245-262.
Lewis Publishers, Boca Raton, FL.
Southerland, M., and J.B. Stribling. 1995.
Status of biological criteria development and
implementation. Chapter 7 in Biological
criteria tools for water resources planning and
decision making, ed. W.D. Davis and T. Simon,
pp. 79-94. Lewis Publishers, Boca Raton, FL.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
Rosgen's Stream Classification
1. Distributor:
N/A - see references
2. Type of Modeling/Technique:
A classification method that uses morphologi-
cal stream characteristics to organize streams
into relatively homogenous stream types to
predict a stream's behavior based on its
appearance, to extrapolate data from one
stream for use on another with similar
characteristics, and to provide a consistent
frame of reference when comparing one
stream to another.
3. Method/Techniques:
There are three levels of classification based
on the desired level of resolution and project
objectives. Level 1 is used to provide a broad
morphological characterization by integrating
landform and fluvial features of valley
morphology with channel relief, pattern,
shape, and dimension. Level 2 delineates
streams into major, broad categories (A
through G) that provide a more detailed level
of interpretation and extrapolation than Level
1. Stream types are separated based on
discreet channel patterns, entrenchment
ratios, width/depth ratios, sinuosity, dominant
channel-material particle sizes, and slope
ranges, which results in a total of 42 major
stream types. Level 3 provides a very
detailed description of the existing stream
conditions, as well as specific information for
predicting responses to outside influences.
This is accomplished by integrating informa-
tion on riparian vegetation, depositional
patterns, meander patterns, confinement
features, fish habitat indices, flow regime,
river size category, debris occurrence, channel
stability index, and bank credibility.
4. Application:
• Evaluate sensitivity to disturbance and
to predict stream behavior as a result
of changes in the watershed
• Assess impacts to stream morphology
• Design stable, self-maintaining
channels in restoration work
• Determine flow resistance
• Selection of appropriate fish habitat
improvement structures
5. Experience:
This classification system (and modified
versions of it) have been applied successfully
to various streams throughout the United
States.
6. Updating Version and System
Requirements:
N/A
7. Input Data Requirements:
Data to be collected depend on the level of
classification:
Level 1: landform, lithology, soils, climate,
depositional history, basin relief, valley
morphology, river, profile morphology, general
river pattern.
Level 2: channel pattern, sinuosity (usually
expressed as Schumm's ratio), gradient or
slope, entrenchment or entrenchment ratio
(width of flood plain: the bankfull width of
channel surface), channel bed material,
width/depth ratio.
Level 3: riparian vegetation, depositional
patterns, meander patterns, confinement
features, fish habitat indices, flow regime,
river size category, debris occurrence, channel
stability index, bank credibility.
8. Outputs:
A quantified classification system that can be
used to predict stream behavior and to apply
interpretive information. Interpretations can
be used to evaluate a stream's sensitivity to
disturbance, recovery potential, sediment
supply, vegetation controlling influence, and
streambank erosion potential.
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Compendium of TboZs for Watershed Assessment and TMDL Development
9. References Available:
Rosgen, D.L. 1994. A classification of natural
rivers. Catena 22:169-199.
Rosgen, D.L., and B.L. Fittante. 1986. Fish
habitat structures: A selection guide using
stream classification. In 5th 7h3ut Stream
Habitat Improvement Workshop, Lock Haven
University, Lock Haven, PA. Penn. Fish Comm.
Publics., Harrisburg, PA.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
SNTEMP/SSTEMP: Stream Network/Stream
Segment Temperature Models
1. Distributor:
Riverine and Wetlands Ecosystem Branch
National Biological Service
4512 McMurray Avenue
Fort Collins, CO 80525-3400
(303) 226-9319
2. Type of Modeling/Application:
Computer models that simulate mean daily
water temperature for a stream segment for a
single time period (SSTEMP) or for a stream
network with multiple tributaries for multiple
time periods (SNTEMP). Minimum and
maximum water temperatures can also be
estimated from equations utilizing the stream
characteristics.
3. Method/Techniques:
SNTEMP and SSTEMP are computer models
that estimate how the temperature of a
stream changes with altered conditions of
flow, riparian shade, and meteorologic
conditions. They calculate the heat flux
components for the stream segment and then
transport that heat downstream. Both models
assume that (1) water in the system is
instantaneously and thoroughly mixed at all
times; (2) all stream geometry (e.g., slope,
shade, friction coefficient) is characterized by
mean conditions; (3) distribution of lateral
inflow is uniformly apportioned throughout
the segment length; and (4) solar radiation
and the other meteorological and hydrological
parameters are 24-hour means.
The programs also handle the special case of a
dam with steady-state release at the up-
stream end of the segment. The companion '
programs SHADE and SOLAR can be used in
tandem with SNTEMP/SSTEMP to calculate
percent shade, solar radiation, and day
length.
4. Applications:
SNTEMP and SSTEMP are typically used in
deciding whether regulatory requirements
are being met for fisheries in rivers and
streams. IFIM (see page C.ll) is a logical
next step for factoring temperature conse-
quences of altered streamflow into manage-
ment decisions.
5. Experience:
Used extensively by the U.S. Fish and Wildlife
Service and state fisheries management
agencies.
6. System Requirements:
SNTEMP and SSTEMP operate on IBM PCs
and compatibles in DOS, and are written in
FORTRAN.
7. Input Data Requirements:
Twenty input parameters are required that
describe trie stream geometry (e.g., segment
length, elevation, roughness, shading),
hydrology (e.g., segment inflow and outflow,
dam locations) and meteorology (e.g., air
temperature, relative humidity, solar radia-
tion).
8. Outputs:
• Minimum, mean, and maximum daily
water temperature for a single stream
segment and time period (SSTEMP) or
for a stream network with multiple
tributaries for multiple time periods
(SNTEMP).
• Other outputs include the intermedi-
ate parameters average width, average
depth and slope, and heat flux
components (atmospheric, convection,
conduction, evaporation, friction, solar
radiation, vegetative radiation, and
water's back radiation).
9. References Available:
Theurer, F.D., and K.A. Voos. 1982. IFG's
instream water temperature model validation.
In Conference on Water and Energy: Technical
and policy issues, ASCE proceedings of the
Hydraulics Conference, Pittsburgh, PA, and
Fort Collins, CO, May 23-26 and June 23-27,
1982, pp. 315-318.
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Compendium of Tbofa for Watershed Assessment and TMDL Development
Theurer, F.D., K.A. Voos, and W.J. Miller. 1984.
Instream Water Temperature Model. Instream
Flow Information paper 16. Cooperative
Instream Flow and Aquatic System Group,
U.S. Fish and Wildlife Service, Fort Collins,
CO.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
Visual-Based Habitat Assessments
1. Distributor:
N/A - see references
2. Type of Modeling/Application:
A variety of data collection procedures (e.g.,
the Qualitative Habitat Evaluation Index) that
characterize the integrity of aquatic habitats.
3. Method/Techniques:
These techniques are based on field-collected
data that characterize aquatic habitat through
parameters such as substrate, instream cover,
riparian characteristics, channel characteris-
tics, pool and riffle quality, and gradient to
and drainage area.
Each parameter is assigned a numerical score
within a gradient of optimal to poor, based on
visual inspection or a minimal amount of
measurement. The scoring range within each
part allows for a judgment of differential
conditions (e.g., high, middle, low) and for
better resolution among varying conditions.
The final score for the site is calculated by
summing the scores for each parameter. This
final habitat assessment score is compared to
the score established for regionally expected
reference conditions.
4. Applications:
• Quick and cost-effective estimation of
aquatic habitat quality that can be used
for determining whether impairments
exist and prioritizing streams for more
detailed assessment.
5. Experience:
Visual-based techniques are used by water-
shed managers throughout the United States.
6. Updating Version and System
Requirements:
7. Input Data Requirements:
Variable with technique, but generally include:
• Substrate (type, origin, and quality)
• Instream cover (type and amount)
• Channel morphology (sinuosity, flow
status, development, channelization,
stability, modifications/other)
• Riparian zone and bank erosion
(riparian width, floodplain quality, and
bank erosion)
• Glide/pool and riffle/run quality (max.
depth, morphology, current velocity,
riffle/run depth, riffle/run substrate,
and riffle/run embeddedness)
• Gradient
• Drainage area
• Percent pool/glide/riffle/run
8. Outputs:
A quantitative assessment, based on qualita-
tive information, of aquatic habitat quality
wadable streams and rivers.
9. References Available:
Ball, J. 1983. Stream classification guidelines
for Wisconsin. Wisconsin Dept. Nat. Res. Tech.
Bull. In Water quality standards handbook. U.S.
Environmental Protection Agency, Office of
Water Regulations and Standards, Washing-
ton, DC.
Barbour, M.T., and J.B. Stribling. 1991. Use of
habitat assessment in evaluating the biological
integrity of stream communities. EPA/440/5-
91-005. InBioZogicaZ criteria: Research and
regulation. Proceedings of a Symposium. U.S.
Environmental Protection Agency, Office of
Water, Washington, DC, pp. 25-38.
N/A
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Compendium of TboZs for Watershed Assessment and TMDL Development
Plafkin, J.L., M.T. Harbour, K.D. Porter, S.K.
Gross, and R.M. Hughes. 1989. Rapid
bioassessment protocols for use in streams and
rivers: Benthic macroinvertebrates and fish.
EPA 440/4-89/001. U.S. Environmental
Protection Agency, Office of Water, Washing-
ton, DC.
Platts, W.S., WE Megahan, and G.W Minshall.
1983. Methods for evaluating stream, riparian,
and biotic conditions. Gen. Tech. Rep. INT-138.
U.S. Department of Agriculture, U.S. Forest
Service, Ogden, UT.
Rankins, E.T. 1991. Use of the Qualitative
Habitat Evaluation Index for use attainability
studies in streams and rivers in Ohio. In:
Biological Criteria: Research and Regulation -
Proceedings of a Symposium. EPA-440/5-91-
005.
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Appendix C: Ecological Assessment Techniques and Models—Fact Sheets
WET II: Wetland Evaluation Technique,
version 2.0
1. Contact:
Daniel Smith
U.S. Army Engineer Waterways
Experiment Station
3909 Halls Ferry Road
Vicksburg, MS 39180
(601) 634-2718
WETWorks software
R.E Novitzki and Assoc., Inc.
4853 NW Bruno Place
Corvallis, OR 97330
(800) 758-0057
2. Type of Modeling/Application:
WET II is a community-based habitat evalua-
tion approach that can provide a broad
overview of potential project impacts on
several wetland habitat functions. Computer-
ized versions (WET from the U.S. Army
Corps and WETWorks, a commercial Windows
version of WET) are also available to apply the
evaluation techniques.
3. Method/Techniques:
WET II evaluates functions and values in
terms of social significance, effectiveness, and
opportunity. A project team implements WET
II by identifying the physical, chemical, and
biological characteristics of a wetland through
the use of predictor species or characteristics
within a habitat representative of the study
area. The predictors are evaluated for each
function's effectiveness and opportunity
based on interpretation keys that define the
relationship between predictor and wetland
function or value; the evaluation ratings are
high, moderate, or low. Ratings for each
predictor are combined to give a final rating of
functional significance.
4. Applications:
WET II was designed primarily for conducting
an initial, rapid evaluation of wetland
functions and values by the U.S. Army Corps
of Engineers. However, WET II can be applied
for other situations, such as prioritizing
wetlands for more detailed, site-specific
research, or determining the effects of pre-
project and post-project activities on wetland
functions and values.
5. Experience:
WET II has been used by the U.S. Army Corps
of Engineers and other agencies to evaluate
many of their water resources projects.
6. Updating Version and System
Requirements:
N/A
7. Input Data Requirements:
Baseline data (e.g., water source, hydrody-
namics, surface roughness, vegetation cover,
soil type) characterizing the following wetland
functions and values: groundwater discharge,
groundwater recharge, sediment stabilization,
flood flow alteration, sediment retention,
toxicant retention, nutrient transformation,
production export, wildlife diversity, aquatic
diversity, recreation, uniqueness/heritage
8. Outputs:
A "broad-brush," quantitative assessment of
potential project impacts on several wetland
habitat functions
9. References Available:
Adamus, ER., E. J. Clairain, Jr., R.D. Smith,
and R.E. Young. 1987. Wetland evaluation
technique. U.S. Army Engineers Waterways
Experiment Station, Vicksburg, MS.
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