SUSTAIN - A Framework for Placement of Best Management

Practices in Urban Watersheds to Protect Water Quality

REPORT

Office of Research and Development

National Risk Management Research Laboratory - Water Supply and Water Resources Division

EPA/600/R-09/095 | September 2009 | www.epa.gov/nrmrl
SUSTAIN— 

A Framework for Placement of Best Management Practices in 

Urban Watersheds to Protect Water Quality 

by
 

Leslie Shoemaker, Ph.D. 

John Riverson Jr. 

Khalid Alvi 

Jenny X. Zhen, Ph.D., P.E.
 

Sabu Paul, Ph.D., P.E. 

Teresa Rafi 

Tetra Tech, Inc. 

10306 Eaton Place, Suite 340 

Fairfax, VA 22030 

In Support of
 

EPA Contract No. GS-10F-0268K 

Project Officer 

Dr. Fu-hsiung (Dennis) Lai 

Water Supply and Water Resources Division 

2890 Woodbridge Avenue (MS-104) 

Edison, NJ 08837 

National Risk Management Research Laboratory
 

Office of Research and Development 

U.S. Environmental Protection Agency
 

Cincinnati, OH 45268 
ii 

Disclaimer 

The U.S. Environmental Protection Agency, through its Office of Research and Development, funded and 

managed, and collaborated in the research described herein. It has been subjected to the Agency’s peer 

and administrative review and has been approved for publication. Any opinions expressed in this report 

are those of the author(s) and do not necessarily reflect the views of the Agency; therefore, no official 

endorsement should be inferred. Any mention of trade names or commercial products does not constitute 

endorsement or recommendation for use. 
 

 

iii 

Foreword 

The U.S. Environmental Protection Agency (EPA) is charged by Congress with protecting the nation’s 

land, air, and water resources. Under a mandate of national environmental laws, the Agency strives to 

formulate and implement actions leading to a compatible balance between human activities and the ability 

of natural systems to support and nurture life. To meet that mandate, EPA’s research program is 

providing data and technical support for solving environmental problems today and building a science 

knowledge base necessary to manage our ecological resources wisely, understand how pollutants affect 

our health, and prevent or reduce environmental risks in the future. 

The National Risk Management Research Laboratory (NRMRL) is the Agency’s center for investigation 

of technological and management approaches for preventing and reducing risks from pollution that 

threaten human health and the environment. The focus of the laboratory’s research program is on 

methods and their cost-effectiveness for prevention and control of pollution to air, land, water, and 

subsurface resources; protection of water quality in public water systems; remediation of contaminated 

sites, sediments and groundwater; prevention and control of indoor air pollution; and restoration of 

ecosystems. NRMRL collaborates with both public and private sector partners to foster technologies that 

reduce the cost of compliance and to anticipate emerging problems. NRMRL’s research provides 

solutions to environmental problems by developing and promoting technologies that protect and improve 

the environment; advancing scientific and engineering information to support regulatory and policy 

decisions; and providing the technical support and information transfer to ensure implementation of 

environmental regulations and strategies at the national, state, and community levels. 

This document has been produced as part of the laboratory’s strategic long-term research plan. EPA’s 

Office of Research and Development has made it available to help the user community and to link 

researchers with their clients. 

 Sally Gutierrez, Director

 National Risk Management Research Laboratory 
iv 

Abstract 

Watershed and stormwater managers need modeling tools to evaluate alternative plans for water quality 

management and flow abatement techniques in urban and developing areas. A watershed-scale, decisionsupport framework that is based on cost optimization is needed to support government and local 

watershed planning agencies as they coordinate watershed-scale investments to achieve needed 

improvements in water quality. 

The U.S. Environmental Protection Agency (EPA) has been working since 2003 to develop such a 

decision-support system. The resulting modeling framework is called the System for Urban Stormwater 

Treatment and Analysis INtegration (SUSTAIN). The development of SUSTAIN represents an intensive 

effort by EPA to create a tool for evaluating, selecting, and placing BMPs in an urban watershed on the 

basis of user-defined cost and effectiveness criteria. SUSTAIN provides a public domain tool capable of 

evaluating the optimal location, type, and cost of stormwater BMPs needed to meet water quality goals. It 

is a tool designed to provide critically needed support to watershed practitioners at all levels in developing 

stormwater management evaluations and cost optimizations to meet their existing program needs. Due to 

the complexity of the integrated framework for watershed analysis and planning, users are expected to 

have a practical understanding of watershed and BMP modeling processes, and calibration and validation 

techniques. 

SUSTAIN incorporates the best available research that could be practically applied to decision making, 

including the tested algorithms from SWMM, HSPF, and other BMP modeling techniques. Linking those 

methods into a seamless system provides a balance between computational complexity and practical 

problem solving. The modular approach used in SUSTAIN facilitates updates as new solutions become 

available. 

One major technical requirement for SUSTAIN is the ability to evaluate management practices at multiple 

scales, ranging from local to watershed applications. The local-scale evaluation involves simulations of 

individual BMPs and analyses of the impact of various combinations of practices and treatment trains on 

local water quantity and quality. The larger-scale evaluation could involve implementing hundreds or 

thousands of individual management practices to achieve a desired cumulative benefit. The required 

simulations and cost comparisons of such large-scale, distributed BMP options place significant 

challenges on the computational accuracy and simulation time for system modeling. SUSTAIN 

incorporates an innovative, tiered approach that allows for cost-effectiveness evaluation of both 

individual and multiple nested watersheds to address the needs of both local- and regional-scale 

applications. 

Previously available modeling tools are significantly limited with respect to simulation of sediment 

generation and its fate through natural runoff and treatment at a BMP. SUSTAIN partially resolves these 

sediment routing issues by considering three sediment fractions (i.e., sand, silt, and clay), but this 
approach remains a compromise because the state-of-the-art knowledge and the needed monitoring data 

are still limited. 

The SUSTAIN framework provides a comprehensive system with a modular structure that facilitates the 

incorporation of improved technologies in optimization, BMP simulation, and computational efficiency. 

A flexible integration and implementation of these improved methods and algorithms will be the focus of 

further enhancements to SUSTAIN. Expanding the SUSTAIN capabilities will allow users to choose the 

level of complexity and simulation detail that best suits project needs. EPA intends to support expansion 

of the capabilities and functionalities of the system to meet continuing water quality goals and the needs 

of the user community. 

This document describes the rationale for developing the framework and the uses of the framework; 

explains the system’s design, structure, and performance; details the underlying methods and algorithms 

that provide the framework’s predictive capabilities; and demonstrates the framework’s capabilities 

through two case studies. 

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Table of Contents 

Chapter 1 Introduction.........................................................................................................................1-1
 

1.1. Project Rationale .......................................................................................................................1-1
 

1.2. Overview of SUSTAIN ..............................................................................................................1-3
 

1.2.1. Structure of SUSTAIN......................................................................................................1-3
 

1.2.2. Multiple Scale Application Features................................................................................1-6
 

1.3. The Role of SUSTAIN in Watershed Applications....................................................................1-7
 

1.3.1. TMDL Development and Implementation.......................................................................1-8
 

1.3.2. Evaluation of GI Practices as Part of a CSO Control Program........................................1-9
 

1.4. SUSTAIN Application Process ................................................................................................1-10
 

1.5. About this Report ....................................................................................................................1-13
 

Chapter 2 SUSTAIN Design and Structure ........................................................................................2-14
 

2.1. Framework Manager ...............................................................................................................2-14
 

2.2. Simulation Modules ................................................................................................................2-16
 

2.3. Optimization Module ..............................................................................................................2-20
 

2.4. Post-Processor.........................................................................................................................2-22
 

2.5. Summary .................................................................................................................................2-24
 

Chapter 3 Simulation Methods and Algorithms ................................................................................3-25
 

3.1. Framework Manager ...............................................................................................................3-27
 

3.1.1. Data Management Component ......................................................................................3-27
 

3.1.2. BMP Site Selection........................................................................................................3-28
 

3.1.3. Routing Network............................................................................................................3-30
 

3.2. Land Module ...........................................................................................................................3-31
 

3.2.1. Weather Component ......................................................................................................3-33
 

3.2.2. Hydrology Component ..................................................................................................3-35
 

3.2.3. Water Quality Component.............................................................................................3-41
 

3.2.4. Important Considerations and Limitations: Land Module.............................................3-47
 

3.3. BMP Module ...........................................................................................................................3-49
 

3.3.1. BMP Simulation Component.........................................................................................3-52
 

3.3.2. Overland Flow Routing and Pollutant Interception .......................................................3-59
 

3.3.3. Aggregate BMP Component..........................................................................................3-63
 

3.3.4. BMP Cost Database Component ...................................................................................3-65
 

3.3.5. Summary of Management Practices and Treatment Processes in SUSTAIN .................3-72
 

3.3.6. Important Considerations and Limitations of the BMP Module....................................3-72
 

3.4. Conveyance Module................................................................................................................3-74
 

3.4.1. Methodology..................................................................................................................3-74
 

3.4.2. Important Considerations and Limitations of the Conveyance Module ........................3-82
 

3.5. Optimization Module ..............................................................................................................3-83
 

3.5.1. Problem Formulation .....................................................................................................3-83
 

3.5.2. Optimization Algorithms...............................................................................................3-86
 

3.5.3. Regional Application .....................................................................................................3-90
 

3.5.4. Important Considerations and Limitations: Optimization Module ...............................3-92
 

3.6. Post-Processor for Results Interpretation ................................................................................3-93
 

3.6.1. Storm Event Classification ............................................................................................3-93
 

3.6.2. Storm Event Viewer.......................................................................................................3-95
 

3.6.3. Storm Performance Summary........................................................................................3-97
 
3.6.4. Cost-effectiveness Curve .............................................................................................3-100
 

3.6.5. Important Considerations and Limitations: Post-Processor.........................................3-103
 

Chapter 4 Case Studies....................................................................................................................4-104
 

4.1. Upper North Branch Oak Creek Watershed..........................................................................4-104
 

4.1.1. Project Setting..............................................................................................................4-105
 

4.1.2. Data Collection and Analysis.......................................................................................4-108
 

4.1.3. Project Setup................................................................................................................4-108
 

4.1.4. Optimization and Results Analysis..............................................................................4-112
 

4.1.5. Summary......................................................................................................................4-120
 

4.2. Little Rocky Run Watershed .................................................................................................4-121
 

4.2.1. Project Setting..............................................................................................................4-121
 

4.2.2. Data Collection and Analysis.......................................................................................4-122
 

4.2.3. Project Setup................................................................................................................4-122
 

4.2.4. Optimization and Results Analysis..............................................................................4-128
 

4.2.5. Summary......................................................................................................................4-137
 

Chapter 5 References.......................................................................................................................5-138
 

Appendices 

Appendix A. Needs Analysis and Technical Requirements.................................................................A-143
 

Appendix B. Model Evaluation and Selection ..................................................................................... B-151
 

Appendix C. Summary of the Optimization Technical Panel Meeting ................................................ C-177
 

Appendix D. Appendix References ......................................................................................................D-185
 

vii 
viii 

List of Figures 

Figure 1-1. Overview of SUSTAIN components........................................................................................1-4
 

Figure 1-2. Watershed assessment points..................................................................................................1-5
 

Figure 1-3. Sample cost-effectiveness curve. ............................................................................................1-5
 

Figure 1-4. SUSTAIN’s multiple scales of application. .............................................................................1-6
 

Figure 1-5. Tiered application of SUSTAIN for developing cost-effectiveness curves. ............................1-7
 

Figure 1-6. Using SUSTAIN in the watershed planning process. ..............................................................1-8
 

Figure 1-7. SUSTAIN application process. ..............................................................................................1-10
 

Figure 2-1. SUSTAIN components and flow chart...................................................................................2-15
 

Figure 2-2. Comparison of runtime for various simulation units and periods.........................................2-19
 

Figure 2-3. Comparison of runtime for 1-yr simulation of various units. ...............................................2-19
 

Figure 2-4. Scatter Search evaluation scenario results. ...........................................................................2-21
 

Figure 2-5. NSGA-II evaluation scenario results. ...................................................................................2-21
 

Figure 2-6. Example storm event classification graph. ...........................................................................2-22
 

Figure 2-7. Example storm event viewer graph.......................................................................................2-23
 

Figure 2-8. Example performance summary report graph.......................................................................2-23
 

Figure 2-9. Example cost-effectiveness curve.........................................................................................2-24
 

Figure 3-1. The routing network showing the connections among the simulation components. ...........3-30
 

Figure 3-2. Schematic showing the land simulation processes. ..............................................................3-31
 

Figure 3-3. Conceptual view of surface runoff........................................................................................3-37
 

Figure 3-4. Two-zone groundwater model adapted from SWMM..........................................................3-39
 

Figure 3-5. Two-zone soil moisture storage under low water table condition. .......................................3-40
 

Figure 3-6. Two-zone soil moisture storage under high water table conditions......................................3-41
 

Figure 3-7. Schematic of sediment production and removal processes...................................................3-42
 

Figure 3-8. A schematic showing the BMP simulation processes modeled in SUSTAIN. ......................3-49
 

Figure 3-9. Wetland/lake/reservoir weir and orifice outflow. .................................................................3-52
 

Figure 3-10. Processes considered in an underdrain structure.................................................................3-56
 

Figure 3-11. Conceptual pond shapes simulated by Persson et al. (1999). .............................................3-58
 

Figure 3-12. Filter description for the sediment transport algorithm.......................................................3-61
 

Figure 3-13. Generic aggregate BMP schematic.....................................................................................3-64
 

Figure 3-14. Illustration of land use area assignment to aggregate BMP components............................3-64
 

Figure 3-15. Aggregate BMP testing configuration. ...............................................................................3-65
 

Figure 3-16. Schematic of sediment transport, deposition, and scour in conduits. .................................3-76
 

Figure 3-17. Conceptual overview of the optimization module. .............................................................3-84
 

Figure 3-18. Illustration of assessment points. ........................................................................................3-84
 

Figure 3-19. Comparison of Scatter Search and NSGA II optimization techniques. ..............................3-87
 

Figure 3-20. Tiered application of SUSTAIN for developing cost-effectiveness curves. ........................3-91
 

Figure 3-21. Construction of the tier-2 search domain using tier-1 results. ............................................3-91
 

Figure 3-22. Simulation process for each iteration run. ..........................................................................3-92
 

Figure 3-23. Number of storm events and total precipitation and peak intensity as a function of 

dry hours between storms....................................................................................................3-94
 

Figure 3-24. Precipitation events sorted by total precipitation volume and peak intensity.....................3-95
 

Figure 3-25. Storm event 1: August 9, 2001 6:00 p.m. to August 9, 2001 8:00 p.m. (0.66 in. to 

2 wet hours, peak: 0.48 in.).................................................................................................3-96
 

Figure 3-26. Storm event 2: May 14, 2001 11:00 a.m. to May 14, 2001 1:00 p.m. (0.95 in. to 4 

wet hours, peak: 0.38 in.)....................................................................................................3-96
 

Figure 3-27. Storm event 3: April 20, 2001 2:00 a.m. to April 21, 2001 5:00 a.m. (1.03 in. to 12 

wet hours, peak: 0.30 in.)....................................................................................................3-97
 
ix 

Figure 3-28. Storm event 4: February 8, 2001 12:00 a.m. to February 9, 2001 4:00 p.m. (2.34 

in. to 25 wet hours, peak: 0.61 in.)......................................................................................3-98
 

Figure 3-29. Sediment removal summary for 34 storm events arranged by baseline sediment 

load......................................................................................................................................3-98
 

Figure 3-30. Peak flow reduction summary for 34 storm events arranged by peak flows. ....................3-99
 

Figure 3-31. Peak flow comparison by storm event arranged by post-developed peak flows. ..............3-99
 

Figure 3-32. EMC of sediment for the corresponding storms. ..............................................................3-100
 

Figure 3-33. Example cost-effectiveness curve for sediment load reduction........................................3-100
 

Figure 3-34. BMP cost distribution by effectiveness for sediment load reduction on the costeffectiveness curve............................................................................................................3-101
 

Figure 3-35. Example cost-effectiveness and cost-distribution pair......................................................3-102
 

Figure 3-36. BMP storage distribution by cost-effectiveness interval. .................................................3-103
 

Figure 4-1. Oak Creek Watershed and Upper North Branch Oak Creek...............................................4-106
 

Figure 4-2. Upper North Branch Oak Creek Watershed case study road map. .....................................4-108
 

Figure 4-3. Average annual precipitation volume at Milwaukee Airport for water years 1988–
 

2002...................................................................................................................................4-109
 

Figure 4-4. Rainfall volume and intensity wet-interval distribution for water year 2001. ....................4-109
 

Figure 4-5. Comparison of daily flow at Model Outlet 58 with USGS 04087204 at South
 

Milwaukee.........................................................................................................................4-110
 

Figure 4-6. Comparison of monthly flow at Model outlet 58 with USGS 04087204 at South 

Milwaukee.........................................................................................................................4-110
 

Figure 4-7. Modeled vs. observed TSS (mg/L) at Oak Creek gage OC-05, water years 1995–
 

2001...................................................................................................................................4-111
 

Figure 4-8. Modeled vs. observed TSS (mg/L) at Oak Creek gage OC-05, water year 2001. ..............4-111
 

Figure 4-9. Land use distribution in the modeled subwatersheds..........................................................4-112
 

Figure 4-10. Subwatershed grouping for two-tiered optimization. .......................................................4-113
 

Figure 4-11. Aggregate BMP schematic. ..............................................................................................4-114
 

Figure 4-12. Aggregate BMP land use distribution...............................................................................4-114
 

Figure 4-13. Aggregate BMP arrangement in tier-1 subwatershed A. ..................................................4-116
 

Figure 4-14. Tier-1 cost-effectiveness curve for subwatershed A with the selected solutions..............4-117
 

Figure 4-15. Composition of best solutions on tier-1 cost-effectiveness curve for subwatershed
 

A........................................................................................................................................4-118
 

Figure 4-16. Schematic of tier-2 analysis network................................................................................4-119
 

Figure 4-17. Tier-2 cost-effectiveness curve. ........................................................................................4-119
 

Figure 4-18. Little Rocky Run watershed in Fairfax County, Virginia.................................................4-122
 

Figure 4-19. Selected study area in Little Rocky Run watershed..........................................................4-123
 

Figure 4-20. Little Rocky Run case study road map. ............................................................................4-123
 

Figure 4-21. SWMM calibration results at junction 3 of the Little Rocky Run watershed...................4-125
 

Figure 4-22. Representation of three subareas.......................................................................................4-126
 

Figure 4-23. SWMM-generated hydrograph at the outlet of the study area..........................................4-127
 

Figure 4-24. SWMM versus SUSTAIN-generated hydrograph comparison..........................................4-127
 

Figure 4-25. BMP placement schematic in the Little Rocky Run case study area................................4-131
 

Figure 4-26. Annual precipitation at the Washington Dulles International Airport. .............................4-134
 

Figure 4-27. Volume and intensity distribution of storm events in 1994. .............................................4-134
 

Figure 4-28. Results showing the benefits of BMPs. ............................................................................4-135
 

Figure 4-29. Domain of optimization searches and identified best solutions........................................4-136
 
List of Tables 

Table 1-1. Management Practices Supported by SUSTAIN.......................................................................1-4
 

Table 1-2. Typical Data Needs for SUSTAIN Application ......................................................................1-12
 

Table 2-1. Simulation of Sediment Transport in SUSTAIN.....................................................................2-17
 

Table 2-2. Transport Simulation of Other Pollutants in SUSTAIN..........................................................2-18
 

Table 3-1. Modules and Components in SUSTAIN .................................................................................3-26
 

Table 3-2. Summary of Inputs, Methods, and Outputs in FM.................................................................3-27
 

Table 3-3. GIS Data Requirement for BMP Suitability Analysis............................................................3-28
 

Table 3-4. Default Criteria for BMP Suitable Locations Used in SUSTAIN ...........................................3-29
 

Table 3-5. Structural BMP Options Available in the BMP Siting Tool..................................................3-30
 

Table 3-6. Inputs, Methods, and Outputs of the Land Module................................................................3-32
 

Table 3-7. Land Simulation Methods Used in SUSTAIN ........................................................................3-33
 

Table 3-8. Green-Ampt Parameters.........................................................................................................3-37
 

Table 3-9. List of Sediment Input Parameters for Pervious Land ...........................................................3-44
 

Table 3-10. Range of Values for Sediment Erosion Parameters for Pervious Land................................3-45
 

Table 3-11. Typical EMCs in Urban Runoff...........................................................................................3-47
 

Table 3-12. Summary of Inputs, Methods, and Outputs in the BMP Module.........................................3-50
 

Table 3-13. Available Optional Methods for BMP Simulation Processes ..............................................3-50
 

Table 3-14. Representative BMPs and Recommended Simulation Methods..........................................3-51
 

Table 3-15. Coefficient Cw (English units) for Rectangular Sharp-Crested Weirs.................................3-53
 

Table 3-16. Quality Ratings of Conceptual Pond Shapes Simulated by Persson et al. (1999)................3-58
 

Table 3-17. Recommended k' and C* Values..........................................................................................3-59
 

Table 3-18. Comparison of Aggregate vs. Distributed BMP Results......................................................3-66
 

Table 3-19. Simulation Run-Time Comparison: Aggregate vs. Distributed ...........................................3-66
 

Table 3-20. BMPs and Associated Construction Components................................................................3-67
 

Table 3-21. Components..........................................................................................................................3-70
 

Table 3-22. BMP Types...........................................................................................................................3-70
 

Table 3-23. BMP Components................................................................................................................3-70
 

Table 3-24. Component Costs .................................................................................................................3-70
 

Table 3-25. Unit Types............................................................................................................................3-70
 

Table 3-26. Reference Sources................................................................................................................3-71
 

Table 3-27. Structural BMPs and Major Treatment Processes................................................................3-73
 

Table 3-28. Summary of Inputs, Methods, and Outputs of the Conveyance Module .............................3-75
 

Table 3-29. Typical Manning’s Roughness Coefficient for Closed Pipes...............................................3-75
 

Table 3-30. Typical Manning’s Roughness Coefficient for Open Channels...........................................3-76
 

Table 3-31. List of Sediment Input Parameters for the Reach.................................................................3-82
 

Table 3-32. Summary of Inputs, Methods, and Outputs in the Optimization Module ............................3-83
 

Table 3-33. Example Control Targets for Typical Evaluation Factor Assessment in SUSTAIN.............3-85
 

Table 3-34. Post-processor Inputs, Methods, and Outputs......................................................................3-94
 

Table 4-1. Watershed Characteristics....................................................................................................4-106
 

Table 4-2. Upper North Branch Oak Creek Land Use Distribution ......................................................4-107
 

Table 4-3. Summary of Modeled Annual Average Outflow and TSS Load in Oak Creek 

Watershed..........................................................................................................................4-111
 

Table 4-4. BMP Parameters...................................................................................................................4-115
 

Table 4-5. BMP Cost Functions for the Case Study..............................................................................4-116
 

Table 4-6. Selected Tier-1 Solutions on the Cost-Effectiveness Curve.................................................4-117
 

Table 4-7. Selected Tier-2 Best Solutions.............................................................................................4-120
 

Table 4-8. List of Input Parameters for Both the Anderson and the USGS Methods............................4-124
 

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Table 4-9. Comparison of Predicted Peak Flows (ft3/s) in the Little Rocky Run Watershed................4-125
 

Table 4-10. Land Uses of the Case Study Area in the Little Rocky Run Watershed ............................4-126
 

Table 4-11. Major Input Parameters for Modeling of Three Subareas..................................................4-126
 

Table 4-12. Subbasin SWMM Parameters ............................................................................................4-129
 

Table 4-13. Hydrologic Soil Group Distribution in Study Area Subbasins ..........................................4-129
 

Table 4-14. TP Buildup and Washoff Parameter Values.......................................................................4-130
 

Table 4-15. BMP Parameters.................................................................................................................4-131
 

Table 4-16. Annualized BMP Cost Function ........................................................................................4-132
 

Table 4-17. Annualized Life Cycle Cost for a Bioretention Cell with a Surface Area of 900 ft2 .........4-132
 

Table 4-18. Annualized Life Cycle Cost for a Bioswale with a Surface Area of 900 ft2 ......................4-132
 

Table 4-19. 10-Yr Design Storm Peak Flows and TP Annual Load under Existing Conditions...........4-134
 

Table 4-20. Cost-Effective Solution Details..........................................................................................4-136
 
xii 

Acknowledgements 

The Tetra Tech project team would like to thank EPA’s project officer, Dr. Fu-hsiung (Dennis) Lai, for 

his active involvement in providing guidance and technical insight throughout the design and the system 

development process and for his detailed review of the interim and final reports. In particular, Dr. Lai 

was instrumental in incorporating in SUSTAIN the use of tier analysis for integrating multiple-scale 

watersheds and cost-effectiveness curves for presentation of solutions for decision planning. We 

appreciate the continuous support and recognition of the significance of this project by Sally Gutierrez, 

Director of EPA Office of Research and Development, National Risk Management Research Laboratory 

and Anthony N. Tafuri, Chief of Urban Watershed Management Branch. 

We appreciate the review and insight of the external reviewers Jim Carleton, EPA, Office of Water, 

Office of Science and Technology and Dr. Arthur McGarity, Swarthmore College, Pennsylvania. We are 

grateful to the team of seven optimization experts that provided additional review and excellent technical 

recommendations: Dr. James P. Heaney, University of Florida, Gainsville; Dr. Manuel Laguna, 

University of Colorado, Boulder; Dr. Arthur McGarity, Swarthmore College, Pennsylvania; Dr. S. Ranji 

Ranjithan, North Carolina State University; Dr. Christine Shoemaker, Cornell University; Dr. Richard 

Vogel, Tufts University, Boston; and Dr. Laura J. Harrell, Old Dominion University. In addition, we 

would like to thank Prince George’s County, Maryland, and Mow-Soung Cheng in particular, for making 

the county’s best management practice module and optimization routine available during the early stage 

of the SUSTAIN development. We would also like to thank the Fairfax County, Virginia, Department of 

Public Works and Environmental Services, Stormwater Planning Division for use of data related to the 

Little Rocky Run case study. Similarly, we would like to thank the Milwaukee Metropolitan Sewerage 

District and the Southeastern Wisconsin Regional Planning Commission for use of data related to the 

Upper North Branch Oak Creek case study. 

The project team also appreciates the feedback and suggestions from the nine beta testers: Jim Carleton, 

EPA, Office of Water, Office of Science and Technology; Dr. Abigail Hathway and Dr. Simon Doncaster, 

The University of Sheffield, U.K.; Dr. David Sample, Virginia Polytechnic Institute and State University; 

Dr. Nguyen Khoi, Naval Facilities, Norfolk, Virginia; Scott Job, Tetra Tech; Shohreh Karimipour, New 

York State Department of Environmental Conservation; Tsai You Jen, Parsons Brinckerhoff; and 

Valladolid Veronica, DuPage County, Illinois. Their feedback helped us enhance the program’s 

functionality. 

We appreciate the support of the user community and in particular the Environmental and Water 

Resources Institute of the American Society of Civil Engineers in the comments and discussion of best 

management practice modeling and technology over the past years. This system benefited from the 

discussion and presentations during the annual conferences. 

We would also like to acknowledge Dr. Guoshun Zhang and former employees of Tetra Tech, Dr. Ting 

Dai and Haihong Yang, for their contribution to the successful development of SUSTAIN.

Finally, we would like to extend our appreciation to two EPA retirees, Chi-Yuan (Evan) Fan and Daniel 

Sullivan, for their insight and support during the early phase of this project. Evan prepared the initial 

scope of work and was the Project Officer during the project procurement in 2002 and Dan was the 

Branch Chief of Urban Watershed Management Branch until 2003.
Acronyms and Abbreviations 

BASINS Better Assessment Science Integrating Point and Nonpoint Sources 

BOD Biochemical Oxygen Demand 

BMP Best Management Practice 

CALTRANS California Department of Transportation 

COD Chemical Oxygen Demand 

CSO Combined Sewer Overflow 

CSTR Continuously Stirred Tank Reactor 

DEM Digital Elevation Model 

DEQ Department of Environmental Quality 

EMC Event Mean Concentration 

EPA U.S. Environmental Protection Agency 

ET Evapotranspiration 

GA Genetic Algorithm 

GB Gigabyte 

GI Green Infrastructure 

GIS Geographic Information System 

HSPF Hydrologic Simulation Program—FORTRAN 

LID Low Impact Development 

LSPC Loading Simulation Program in C++ 

MS4 Municipal Separate Storm Sewer System 

NCDC National Climatic Data Center 

NHD National Hydrography Dataset 

NLCD National Land Cover Dataset 

NPDES National Pollutant Discharge Elimination System 

NRCS Natural Resources Conservation Service 

NSGA-II Non-dominated Sorting Genetic Algorithm II 

NWS National Weather Service 

O&M Operation and Maintenance 

PET Potential Evapotranspiration 

SUSTAIN System for Urban Stormwater Treatment and Analysis INtegration 

SWMM Stormwater Management Model 

TKN Total Kjeldahl Nitrogen 

TMDL Total Maximum Daily Load 

TN Total Nitrogen 

TP Total Phosphorus 

TSS Total Suspended Solids 

USGS U.S. Geological Survey 

USLE Universal Soil Loss Equation 

VFSMOD Vegetative Filter Strip Model 

WINSLAMM Source Loading and Management Model for Windows 

xiii 
Chapter 1 Introduction 

Surface water degradation resulting from the effects of urbanization on hydrology, water quality, and 

habitat is an issue of primary focus for multiple agencies at the federal, state, and local levels. A few 

examples of critical management issues facing planners and policy makers are ensuring the protection of 

source waters and the management of stormwater through peak flow mitigation, installation of sediment 

and erosion control devices, or implementation of best management practices (BMPs). Many 

management actions are needed throughout watersheds to achieve the desired effects on flow mitigation 

and pollutant reduction; however, no single standardized solution can be effective in all locations. 

Factors such as watershed size, scale, existing human activities, and natural characteristics can vary 

dramatically from one place to another. The major challenge faced by decision makers is how to select 

the best combination of practices to implement among the many options available that result in the most 

cost-effective, achievable, and practical management strategy possible for the location of interest. 

Realizing the need for improved tools to support that challenge and the opportunities presented by 

emerging science and technology, the U.S. Environmental Protection Agency (EPA) initiated a research 

project in 2003 to develop a fully integrated decision support framework for the selection and placement 

of stormwater BMPs at strategic locations in urban or developing watersheds. Development of a software 

system to meet that challenge has been conducted in a phased process. The resulting system, described in 

detail in this document, is called the System for Urban Stormwater Treatment and Analysis INtegration 

(SUSTAIN). This document and the SUSTAIN Version 1.0 system represent the culmination of work 

under Phase II of development. The software, companion user manual, and periodic updates will be 

available on the SUSTAIN Web site hosted by EPA, (http://www.epa.gov/ednnrmrl/models/sustain/). 

This document describes the rationale for developing the framework and the uses of the framework; 

explains the system’s design, structure, and performance; details the underlying methods and algorithms 

that provide the framework’s predictive capabilities; and demonstrates the framework’s capabilities 

through two case studies. The initial needs analysis and model review documentation developed under 

Phase I are also included in the appendices. This document, where appropriate, also examines the 

limitations of the current framework and recommendations for enhancing the framework to be addressed 

in future development phases. 

1.1. Project Rationale 

A wide range of programs exist in the United States to support the protection and restoration of 

waterbodies (i.e., rivers, lakes, estuaries). Most programs involve linking land-based actions to water 

quantity or quality goals with the ultimate goal of reducing the impacts on receiving waters. Models of 

varying scales and complexity have long been a part of developing mitigation plans, identifying 

management needs, and evaluating alternatives. Examples of situations where modeling can support 

decision making include source water protection plans, municipal separate storm sewer system (MS4) 

permits under the National Pollutant Discharge Elimination System (NPDES) Stormwater Program 

(Phase I and II), total maximum daily load (TMDL) implementation plans, and watershed-based master 

plans and restoration studies. 

In each case, water quality professionals need a framework to help address key stormwater management 

issues, e.g., to do the following: 

1-1
 
•	 Evaluate and select management options to achieve a loading target set by a TMDL 

•	 Develop cost-effective management options to implement a municipal stormwater program 

•	 Evaluate pollutant loadings and identify appropriately protective management practices for a 

source water protection study 

•	 Determine a cost-effective mix of green infrastructure (GI) measures to meet optimal flow 

reduction goals in a combined sewer overflow (CSO) control study 

Over the past decade, significant progress has been made in expanding our understanding, through 

detailed laboratory and field studies, of the wide array of available management techniques and their 

function and impact on urban hydrology and water quality processes. Today, managers increasingly 

incorporate a combination of on-site, GI technologies with more traditional structural practices as part of 

comprehensive watershed restoration plans. As a result, many municipalities implement various sitescale techniques (i.e., bioretention, rain barrels, swales, infiltration trenches) at different points throughout 

a drainage area to mitigate both the flow and associated pollutant impacts of urban drainage. Practitioners 

now need to evaluate both the localized site-scale benefits and the cumulative effects of implementing 

hundreds or even thousands of those practices across a broad watershed landscape. 

Concurrent with the evolution in management techniques, significant advances have been made in 

information technology over the past decade. Previous modeling of management alternatives was limited 

to highly simplified approaches for larger-scale regional studies. Next generation modeling systems now 

enable more detailed simulation techniques in combination with optimization tools, resulting in the ability 

to rapidly evaluate and compare multiple alternatives. Significantly faster computational speeds allow for 

interactive consideration of process-based simulations of flow and water quality with optimization 

searches. Software that facilitates spatial analysis, database management, and model execution is now 

readily available for practical application. Integration of simulation techniques with geographic 

information systems (GIS) has improved our ability to evaluate watershed management through multiple 

scales and at varying levels of complexity. Improved scientific understanding and advances in 

computational resources have now provided the opportunity to build more sophisticated and robust water 

resources modeling tools to support decision making. 

On the basis of an understanding of the needs of the user community, SUSTAIN was developed to address 

the following major design objectives: 

•	 It is intended for knowledgeable model users, including those at the local level, who are familiar 

with the technical aspects of watershed modeling 

•	 It provides users with the ability to evaluate the effects of multiple management practices and 

placement strategies to support decision making 

•	 It is specifically designed for and applicable to mixed land uses present in predominantly urban 

watersheds 

SUSTAIN includes hydrologic/hydraulic and water-quality modeling in watersheds and urban streams. It 

has the capability to search for optimal management solutions at multiple scales to achieve desired waterquality objectives based on cost-effectiveness. 

SUSTAIN was developed by combining publicly available modeling techniques, costs of management 

practices, and optimization tools in a geographically based framework to achieve the design objectives. 

SUSTAIN facilitates the objective analysis of multiple water quality management alternatives while 

1-2 
enabling consideration of interacting and competing factors such as location, scale, and cost. In 

developing SUSTAIN, the most applicable algorithms for simulating urban hydrology, pollutant loading, 

and treatment processes were packaged together from those in multiple, distinct models. The simulation 

processes incorporated into SUSTAIN have not been known to be previously bundled in a publicly 

accessible modeling framework. 

1.2. Overview of SUSTAIN 

SUSTAIN is a framework that facilitates a comprehensive stormwater management analysis of watersheds 

at multiple scales. SUSTAIN was carefully constructed to ensure a seamless package that provides a 

consistent level of technical rigor, employs the latest technology, and performs cost-effectiveness analysis 

to derive practical solutions to real-world problems. SUSTAIN includes algorithms for simulating urban 

hydrology, pollutant loading, and treatment processes packaged from multiple models that individually 

address such processes. To provide flexibility for future updates, the system uses linked modules that 

perform simulations on watershed land surfaces, in management practices, and through routing networks. 

SUSTAIN uses a graphical interface to allow users to visualize the study area, select locations for 

placement of management practices, and define the linkages among the various landscape features. The 

analytical framework lets users apply optimization tools to explore the wide range of possible costeffective solutions. 

Because many models are used to address watershed problems, and some regions have a long history of 

model development and testing, SUSTAIN was designed to interface with external models. Through the 

use of file exchanges (i.e., time series files), SUSTAIN can import externally generated watershed 

modeling information and can export time series results to receiving water models for additional detailed 

analysis. 

1.2.1. Structure of SUSTAIN 

SUSTAIN is built on a base platform interface using ArcGIS, which provides the user access to the 

framework components: a BMP siting tool; a watershed runoff and routing module; a BMP simulation 

module; a BMP cost database; a post-processor; and an optimization module. 

Figure 1-1 shows a generalized schematic of the overall framework. The ArcGIS-based Framework 

Manager (FM) is the overarching component that manages the data exchanges between the framework 

components. It provides linkages between external inputs, the land simulation, the BMP simulation, the 

conveyance simulation, the optimization module, and the post-processor. The FM checks for necessary 

data requirements before calling for simulation and optimization components. 

Each module in the framework serves a specific function and is typically applied in series. The 

application usually begins with the use of the BMP siting tool, which uses the ArcGIS platform and userguided rules to determine site suitability for various BMP options (Table 1-1). The land simulation 

module is used to generate runoff time series data to drive the BMP simulation. The conveyance module 

provides routing capabilities between land segments or BMPs or both. Users also have the option to 

import time series data from external watershed models (e.g., Hydrologic Simulation Program Fortran 

(HSPF) or Stormwater Management Model (SWMM)) instead of performing new land simulations in 

SUSTAIN. 

1-3 
 

Con

B

M

MP Siting 

Land Module 

BM

M

P

odule 

vey

Tool 

odu

ance

le 

Interpretation 

(Post Processor) 

Optim tion 

Figure 1-1. Overview of SUSTAIN components. 

The process-based BMP module provides simulation of 

management practices by using a combination of processes for 

storage retention, open-channel controls, filtration, biological 

purification, and mechanical structure facilitated separation. The 

cost database is organized according to BMP construction 

components (e.g., grading, backfilling, filter fabric) and populated 

with unit costs for each component. The optimization module uses 

results from other modules in the framework for evaluating and 

selecting a combination of BMP options that achieve a given 

pollutant-reduction goal at minimum cost. The optimization 

module is designed to efficiently search for this combination of 

BMPs. Finally, a post-processor presents the optimization results 

in a cost-effectiveness curve. 

From the GIS framework, the user first sets up a project 

representing a network of drainage areas, BMPs, and routing 

components. SUSTAIN then uses externally generated land useassociated flow and water quality time series data or internally 

generated data from BMP contributing areas and routes them 

through BMPs to predict flow and water quality time -series dat a at 

selected downstream locations . The user defines the assessment 

locations in the watershed whe re results are to be analyzed or 

compared (Figure 1-2). 

Table 1-1. Management 

Practices Supported by 

SUSTAIN 

Management Practice 

Bioretention 

Cistern 

Constructed Wetland 

Dry Pond 

Grassed Swale 

Green Roof 

Infiltration Basin 

Infiltration Trench 

Porous Pavement 

Rain Barrel 

Sand Filter (non-surface) 

Sand Filter (surface) 

Vegetated Filterstrip 

Wet Pond 

1-4 
Figure 1-2. Watershed assessment points. 

SUSTAIN’s optimization capability helps users identify desired economical BMP solutions that achieve 

u ser-de fi ned management target(s). Another benefit of the framework is its ability to reveal the BMP

cost- and pollutant-reduction effectiveness relationship, referred to as the cost-effectiveness curve. A 

sample cost-effectiveness curve is shown in Figure 1-3. Each point on the curve represents an optimal 

combination of BMPs that will collectively remove the targeted amount of pollutant load at the least cost. 

The BMP cost-effectiveness curve provides valuable information on the minimum costs at various 

reduction goals, the maximum achievable pollutant reductions, as well as the marginal costs. Load Reduction Load Reduction

Cost

Figure 1-3. Sample cost-effectiveness curve. 

1-5 
1.2.2. Multiple Scale Application Features 

Practitioners are confronted daily with the need to evaluate management practices at multiple scales, from 

an individual site to regional watershed studies. The site-scale evaluation might require a detailed 

assessment of individual BMPs or combinations of BMPs (i.e., treatment trains). Larger-scale watershed 

studies, typically over 100 square miles, could involve hundreds or thousands of individual management 

practices to achieve a desired cumulative benefit. Simulating and performing cost comparisons for each 

of these individual distributed BMP options would place a significant challenge on the accuracy and 

simulation time for modeling. Two approaches were developed in SUSTAIN to address the watershed 

scaling issue: aggregation and tiered or nested analysis methods. These methods facilitate the use of 

SUSTAIN at multiple scales as shown in Figure 1-4. 

 

 

 

 

Scale in Watershed Planning Uses of SUSTAIN 

Regional	 >100 

sq. mi. 

• Tiered watershed approach 

•	 Subdivide into relevant management 

zones 

•	 Pilot studies on selected watersheds 

Large watershed plan 

Midsize watershed plan 

Site scale 

10–100 

sq. mi. 

1-10 sq. 

mi. 

< 1 

sq. mi. 

•	 Tiered and aggregate watershed 

approach 

•	 Detailed modeling of selected high 

priority watersheds 

•	 Tiered and aggregate BMP 

approach used for initial planning 

•	 Detailed modeling on all or some 

watersheds 

• Detailed modeling and optimization 

of management practices 

Figure 1-4. SUSTAIN’s multiple scales of application. 

As an alternative to the explicit representation and routing of multiple distributed BMPs, the aggregate 

BMP approach creates a virtual BMP that represents all similarly functioning treatment devices in a 

watershed. This option can significantly reduce computational effort, especially when distributed BMPs 

are involved in the optimization process as decision variables. The aggregated approach uses four generic 

BMPs in sequence, each representing the function of many similar BMPs: on-site interception, on-site 

treatm nt, routing/attenuation, and regional storage e /treatment. 

For large watersheds that re quire detailed analyses, SUSTAIN provides a methodology for tiered or 

sequen c ed analysis. As illustrated in Figure 1-5, a relatively large watershed can be subdivided i nto 

several smaller subwatersheds on which detailed analysis is performed to derive a tier-1 costeffe cti veness curve. The tier-2 cost-effectiveness curve is derived from the three tier-1 curves by 

considering all feasible optimal combinations of BMPs that produce the target load reduction at the 

minimum cost. 

1-6 
The tiered approach can be applied to large watersheds that contain several subareas and to small 

watersheds that require the development of a detailed management plan, e.g., at a parcel or a street block 

level. 

Cost 

Reduc 

Load

tion 

Cost 

Re

Load

duction

SUSTAIN Tier 1 

SUSTAIN Tier 1 

Load

tion Reduc

SUSTAIN Tier 1 

Cost 

Target Load 

Reduction 

SUSTAIN Tier 2 

Cost 

Load 

Reduction 

Figure 1-5. Tiered application of SUSTAIN for developing cost-effectiveness curves. 

The tiered optimization in SUSTAIN not only provides an efficient and manageable means of analysis for 

large-scale applications, but also allows users flexibility in the placement of assessment points and in 

evaluating explicit expectations of load reductions at upstream locations. 

1.3. The Role of SUSTAIN in Watershed Applications 

Various practitioners, municipalities, and watershed groups at the regional and local levels can use the 

SUSTAIN framework to address a variety of management practice planning questions. Users might turn 

to SUSTAIN for the following: 

•	 Developing TMDL implementation plans 

•	 Identifying ma nagement practices to achieve pollutant reductions in an area under an MS4 

stormwater permit 

•	 Determining optimal GI strategies for reducing volume and peak flows to CSO systems 

•	 Evaluating the benefits of distributed GI implementation on water quantity and quality in urban 

streams 

1-7 
The SUSTAIN modeling framework can be employed at multiple phases in the wa tershed management 

process and at varying levels of detail (Figure 1-6). When applied early in the study phase, the analysis is 

typically at a low level of detail to explore the potential benefits of BMPs. After the initial assessment, 

SUSTAIN can be applied in greater detail as part of implementation planning to help identify the prefe rred 

management practices and develop the associated capital costs. Once a plan is implemented, SUSTAIN 

can be used to track and assess the performance of the installation using the monitoring data collected 

before and after installing BMPs. The monitored data can also be used to recalibrate and reverify 

SUSTAIN for extrapolating future benefits from additional BMPs. 

 

 

 

Watershed Planning Process Uses of SUSTAIN 

•	 Generalized assessment of 

management i mpacts and 

load reduction potential 

Identify Problem(s) & 

Set Goals 

Develop Plan 

Implement Plan 

Track Progress 

Achieve 

Management Goals 

Refine and adapt

plan if needed 

•	 Predict load reduction and cost 

for multiple management 

alternatives 

•	 Support selection of an optimal 

implementation plan 

•	 Evaluate project phases (cost 

and load reduction at each 

phase) 

•	 Recalibrate SUSTAIN based 

on newly collected data 

•	 Evaluate future benefits of 

implementation and/or 

adaptation of plan 

Figure 1-6. Using SUSTAIN in the watershed planning process. 

Within the context of each application, SUSTAIN provides the flexibility to support the application goals 

and address the localized issues of concern. Two examples of applications as described below include 

TMDL development and implementation, and the use of GI to help control CSOs. 

1.3.1. TMDL Development and Implementation 

TMDL projects are plans to reduce loadings to meet a defined water quality standards objective in 

impaired water (USEPA 1991). Each state defines impaired waters on its Clean Water Act (CWA) 

section 303(d) list. For lake TMDLs, the 303(d) listing might identify a nutrient impairment that results 

in periodic algal blooms and is limiting the ability of the lake to be used for recreation and as aquatic 

habitat. The TMDL development requires estimating nutrient loadings and evaluating the loading 

threshold that would keep the lake within acceptable water quality conditions and meeting applicable 

water quality standards. SUSTAIN uses the embedded SWMM rainfall-runoff routines to develop the 

loading characteristics from the land areas. The framework is ideally suited to evaluate the load reduction 

1-8 
potential as part of examining the reasonable assurance that the TMDL can be achieved at the prescribed 

load reductions. After the TMDL is developed, SUSTAIN can be used to develop the implementation plan 

that identifies the best combination of management practice type(s) and location(s), and the associated 

cost load reductions. 

In some areas, TMDL implementation is addressed by a municipal stormwater permit. For communities 

working to comply with wasteload allocations assigned in a TMDL, SUSTAIN provides a method to 

integrate stormwater permit activities with the requirements of the TMDL. Capitalizing on mapping and 

data collection activities typically undertaken as part of the MS4 implementation, SUSTAIN can be used 

to enumerate specific measures necessary for meeting TMDL reductions throughout the affected area. 

The framework can be used to pinpoint the best locations for optimizing pollutant reductions and to 

determine the mix of management practices that will achieve necessary load reductions for the least cost. 

Examples of the TMDL-related investigations that SUSTAIN supports include the following: 

•	 Optimizing the geographic focus of management activities (near the waterbody of concern or 

away from it) 

•	 Evaluating the benefits of installing rain barrels or rain gardens in a near-lake region 

•	 Enumerating specific management practices that must be implemented to satisfy the TMDL 

•	 Developing a funding request 

•	 Developing a projection of reduction potential for phased installation over time 

1.3.2. Evaluation of GI Practices as Part of a CSO Control Program 

Even with advances in sewer technology (e.g., sewer separation and deep sto rage), problems still remain 

with the operation of existing urban wastewater systems (NRDC 2006). Examples include impaired 

performance of wastewater treatment plants resulting from the influx of stormwater (infiltration and 

inflow), constraints on urban growth caused by an inadequate infrastructure, and aging combined sewer 

systems, which can require costly rehabilitation (USEPA 2004). CSO control programs typically focus 

on infiltrating or storing runoff to minimize peak flows to collection systems and reduce the frequency 

and size of overflow events. Programs are developed and implemented to comply with mandated CSO 

long-term control plans. Such challenges have led to the development of sustainable strategies for urban 

stormwater and wastewater management and new alternatives to the traditional centralized sewer systems, 

which comprise laterals, submains, and trunk lines all leading to a central treatment facility (Chocat et al. 

2007). Although large storage structures, tunnels, and sewer separation have been used successfully to 

significantly reduce CSOs in major cities, increased effectiveness and multiple benefits can be derived by 

adopting new GI approaches in combination with traditional approaches. 

As a means to redu ce volume entering a wastewater system and reduce its peak flows, GI can be applied 

throughsource controls and engineered BMP systems to infiltrate, evapotranspirate, or store stormwater 

runoff for beneficial uses (USEPA 2007). Those approaches are to keep stormwater run off from entering 

a combined sewer system and reduce o verflows (USEPA 2004). In addition, many GI approaches can be 

included in adaptive management strategies designed to be resilient to su ch system changing factors as 

population growth and climate change (USEPA 2008). 

SUS TA IN is designed to support linkage to other related models such as detailed sewer system models or 

receivi ng water models of affected rivers, lakes, and estuaries. Where existing sewer and watershed 

models are av ailable, SUSTAIN can be used to predict the most inexpensive GI practices that will result in 

reducedoverflow volumes and frequency. 

1-9 
1.4. SUSTAIN Application Process 

A typical SUSTAIN application scenario begins with the definition of study objectives, followed by data 

coll i ect on, project/model setup, formulation of the optimization problem, and analysis of results. Figure

1-7 is aflow dia gram illustrating the typical step-by-step process in SUSTAIN applications. 

 

 

 

Define

- What ques

ap b 

ti b 

plication o

ons need to 

jectives:

e answered?

Data Collection & Analysis 

• Study area review 

• GIS data: land use, stream, DEM, BMP sites, etc. 

• Watershed and BMP information/data 

• Compile monitoring data (calibration/validation) 

Project Setup 

• BMP representation: placement, configuration, and cost 

• Land/Watershed Representation 

• Routing network 

• Assessment point(s) 

• Test system application (externally calibrated model) 

• Calibrate/validate model (internal model) 

Put Optimization Processor to Work 

• Select decision variables (BMP dimensions) 

• Select assessment points (BMP/Outlet locations) 

• Select evaluation factors, control targets (end points) 

Results Analysis and Representation (Post-Processor) 

• Optimum BMP dimensions 

• Alternate solutions 

Figure 1-7. SUSTAIN application process. 

Fundamental to the setup and application of SUSTAIN is a clear definition of the study objective(s)— 

What is the question that is to be answered by the analysis? For example, the objective of the study 

might be to identify the set of management options (including both site- and regional-scale techniques) 

that achieve a required level of pollutant load reduction (i.e., annual load in lbs/yr). For a CSO study, the 

objective might be stated as, “to reduce frequency of overflow through extensive retrofit of the drainage 

area.” The reduction in overflow can be measured by the magnitude of peak flows in a collection system. 

The study objectives will define the scope and extent of the SUSTAIN application, which could include 

the areas to be modeled, runoff and pollutant factors to be simulated, additional data collection needs, the 

locations where the output will be evaluated (i.e., assessment points), and the determination of the 

optimization evaluation factors and control targets (i.e., endpoints). At each control target, SUSTAIN is 

capable of producing outputs in various time averaging periods and frequencies of occurrences that will 

facilitate the evaluation and comparison of management alternatives. The following lists the examples of 

output variations. 

• Average annual flow volume percent reduction based on an existing condition 

• Average annual flow volume 

• High-flow rate and allowed maximum duration (user specified) 

1-10 
•	 Peak-flow value and maximum exceedance frequency 

•	 Average annual sediment/pollutant load percent reduction with respect to an existing condition 

•	 Average annual sediment/pollutant load (load target) 

•	 Average sediment/pollutant concentration (themaximum average concentration allowed) 

•	 High sediment/pollutant concentration and duration (concentration thresh old value and allowed 

maxim um duration when concentration exceeds the threshold) 

•	 Long-term averagesediment/pollutant loa d (daily, monthly, annual, or any user specified time 

frame) 

•	 Exceedance frequency (the threshold value andmaximum exceedance frequency allowed) 

SUSTAIN has been design d with inheren e t flexibility inthe form ulation and setup of the application. The 

careful definition of the project objective, associated evaluation factors, and control targets will ensure the 

most appr opria te and useful application. 

The data collection process for a SUSTAIN application is similar to most modeling projects and in volves a 

thoroughcompilation and review of information a vaila ble for the study area. It generally includ es 

gathering applicable regional and site-scale GIS da ta la yers, digital elevation model (DEM) data, stream 

networks, locations of BMPs, land use data, critical so urce information, and monitoring data for 

calibration and validation. A summa ry of typical data needs is shown in Table 1-2. 

Setting up th e SUSTAIN pro ject involves using the datacollected to establish a representation of the land 

and pollutant so urces in thewatershed as well as the ro uting network, assessment points, and man agement 

practices to be evaluated. For site-scale analysis of ma nagement practices, locally derived higherresolution site scale data will likely be required. 

If the continuous time series data of flow and associa ted sedim ent/pollutants from a locally calibrated 

model study is available, the data can be imported into SUSTAIN without recreating them. Most models 

that operate on an hourly or shorter time step, such as HSPF, SWMM, Source Loading and Managem ent 

Model for Windows (WINSLAMM), are compatible w ith SUSTAIN. When importing information from 

an externally generated model, the SUSTAIN application builds on the documentation, testing, calibration, 

and validation of the external model. 

After project setup, the opti mization m odule synthesize s information from the BMP, land, and 

conveyan ce modules and generates solutions that are lo oped back for evaluation using the sam e modules 

again. Via this evolutionary search process, the optimi zer identifies the best or most cost-effective BMP 

solutions according to the user’s specific conditions and objectives. Finally, the post-processor analyzes 

optimization results using specific graphical and tabular reports that facilita te the classification of storm 

events for analysis, viewi ngthe time series of s pecific storm events, evaluating BMP performan ce by 

storm event, and developin g the cost-effectiveness c urves for treatment alternatives. 

1-11 
 

 

Table 1-2. Typical Data Needs for SUSTAIN Application 

Data 

Data 

Type Need Data Source 

Land use ESRI 

Grid 

Required for defining land 

use distribution 

National Land Cover Dataset (NLCD) 

(http://seamless.usgs.gov/website/seamless/vie 

wer.php) or locally derived 

Land use 

okup lo 

Dbf Table Required for assigning 

land use categories and 

groupings 

Standar d National Land Cover Dataset 

(NLCD) land cover code for NLCD land use 

(http://landcover.usgs.gov/classes.asp). 

or land cover mapping code for locally derived 

data 

Exte 

Mod

rnal 

el 

ASCII 

Text Files 

Required for external 

model linkage 

by 

land use 

Time series generated by calibrated model; 

Dig 

Elev 

Dat 

ital 

ation 

a (DEM) 

ESRI 

Grid 

Required for automatic 

delineation of drainage 

areas 

(http://seamless.usgs.gov/website/seamless/vie 

wer.php) or locally derived source 

Stre 

Net 

am 

work 

ESRI 

Shape 

File areas and for placing onRequired for automatic 

delineation of drainage 

stream management 

practices 

National Hydrography Dataset (NHD) from 

http://nhd.usgs.gov/data.html 

Precipitation ASCII 

Text File 

Required for internal land 

simulation and for 

estimating storm sizes for 

the post-processor 

National Climatic Data Center (NCDC). 

NCDC Summary of the Day (daily data) can 

also be obtained from (EarthInfo Inc., 

http://www.earthinfo.com). 

Other weather 

data 

ASCII 

Text File 

Required if snow melt is 

simulated for internal land 

simulation 

NCDC (temperature, evaporation, and wind 

speed) 

Pipes Data 

Entry 

Required if pipe/conduit is 

simulated 

Shape and dimensions (e.g., length, width, 

diameter) 

Stream 

Geometry 

Data 

Entry 

Required if stream routing 

is simulated 

Cross-sectional geometry (shape and related 

dimensions) 

Management 

Practices 

Data 

Entry 

Required Characteristics of installed and proposed 

management practices (e.g., size, shape, 

media, design specification); dependent on 

type of practice 

Flow ASCII 

Text File 

Required for calibration of 

internal modeling of 

runoff; recommended for 

system testing 

USGS real time data 

(http://waterdata.usgs.gov/nwis/rt) or local 

sampling 

Water Quality ASCII 

Text File 

Required for calibration of 

internal modeling of water 

quality; recommended for 

testing of water quality 

predictions 

USGS surface water data 

(http://waterdata.usgs.gov/nwis/sw) or 

EPA STORET data 

(http://www.epa.gov/storet/dw home html) or 

local sampling 

1-12 
1.5. About this Report 

This report provides the description and documentation to support the release of of SUSTAIN Version 1.0. 

As it is developed, EPA will release additional model information on the SUSTAIN Web site. The Web 

site also provides user guidance and responses to frequently asked questions regardin g the operation and 

use of the model. 

This SUSTAIN documentation report is organized as follows: 

Chapter 1 provides a general overview of the framework, its development, typical applications, and 

application process. 

Chapter 2 describes the structure of the framework, the roles and interactions of its major 

components, and its operational characteristics. 

Chapter 3 provides the detailed documentation of the analytical procedures and simulation processes, 

including equations and variables, which are adopted and incorporated into various parts of SUSTAIN. 

Chapter 4 presents two case studies to demonstrate how the framework is applied for selection and 

placement of BMPs. 

The appendices include a needs analysis f or developing a comprehensive placement framework, a 

review of land and BMP simulation models, and a summary of expert opinions on the current stateof-the-art in optimization concepts and methods to support development of the optimization 

component in SUSTAIN. It includes the rationale and supporting information used in formulating the 

framework design and selecting land and BMP simulation techniques that appear in SUSTAIN. 

1-13 
Chapter 2 SUSTAIN Design and Structure 

SUSTAIN is a comprehensive, multiscale watershed and water quality modeling application built on an 

ArcGIS platform linked to multiple simulation modules, an optimization module, and a post-processo r, 

which analyzes and helps interpret the results. The modular design of SUSTAIN has multiple a dvantages 

compared to previous modeling applications including the ability to incorporate simulation of new 

management practices as they are evolved, to operate independently for specific small watershed 

applications, and to provide flexibility to address multiple watershed scales. 

This chapter describes the system’s infrastructure, its major modules, and software platforms. It also 

explains how they are linked and interact. 

The SUSTAIN installation requires ESRI’s ArcGIS 9.3 and the Spatial Analyst extension. The application 

is compatible with Microsoft Windows 98, 2000, NT, XP, and Vista operating systems and requires at 

least 1GB of computer me mory and 5GB of free space on the hard disk. The sys tem also requires 

Microsoft Excel 2003, which is used as a post-processor for analyzing and interpreting results. 

SUSTAIN comprises the following modules: 

Fr amework Manager—to serve as the command module of SUSTAIN, manage data for system 

functions, provide linkages between the system modules, and create a simulation network to guide the 

mo deling and optimization activities 

Land module—to generate runoff and pollutant loads from the land through internal land simulatio n 

or importing precalibrated landsimulation time series 

BMP module—to perform process simulation of flow and water quality through BMPs 

Conveyance module—to perform routing of flow and water quality in a pipe or a channel 

Optimization module—to evaluate and identify cost-effective BMP placement and selection 

strategies for a preselected list of potential sites, applicable BMP types, and ranges of BMP size 

Po s t-Processor—to perform analysis and summarization of the simulation results for decision 

making 

2.1. Framework Manager 

The FM performs data management, spatial analysis, and network visualization. It integrates components 

from the GIS network, such as stre ams, conduits, and land uses, with relevant simulation modules, draws 

external time series data (e.g., rainfall, runoff) as required, and checks for necessary data requirement s 

before calling for simulation and optimization components. 

SUSTAIN is designed to interactively identify and manage the re quired databases, including geographic 

and tabular data sets. The primary function of data management is to define the paths where data are 

stored and to identify required data elements. SUSTAIN provides the option to store required geographic 

2-14
 
data on the hard disk or in a file-based geodatabase, which is a native data structure used by ArcGIS. The 

geodatabase is composed of tables and queries that allow data sharing and interchange among SUSTAIN’s

modules. 

The FM builds on the ESRI ArcGIS (version 9.3) platform to support the placement of BMPs, delineation 

of BMP tributary drainage areas and flow paths, and development of a schematized watershed simulation 

network that might include land parcels, management practices, and stream reaches. The GIS component 

also se rves as the user interface and includes the main application window with menus, buttons, and 

dialog boxes. The GIS interface allows a user to read and edit spatial and temporal data sets. 

All commonly used Microsoft Office applications can be easily linked to the platform. Microsoft Excel, a

popular and powerful application for displaying and manipulating simulation time series data and 

scientific graphics, was chosen as the post-processor for SUSTAIN. 

Figure 2-1 shows the framework design, including system components, relationships between 

components, and general flow of information. 

Figure 2-1. SUSTAIN components and flow chart. 

The SUSTAIN framework is designed to perform the following sequence of operations. 

• From the GIS view and database, the framework first develops a simulation network that defines 

the relationship between land-area units, BMPs, and stream systems on a watershed 

• The FM then identifies the modules (Land, BMP, Conduit, and Reach) to be used and prepares 

model input files 

• The FM routes the external inputs to appropriate modules and their outputs to the Output PostProcessor or other models 

• The FM sends outputs from Output Post-Processor to the Decision Optimization Engine 

2-15 

Decision Matrix 

Potential BMP types BMP configuration 

Location 

1 (0-1) A, B, C Depth Surface area GIS 

2 (0-1) A, B, C Depth Surface area 

Decision 

Sewershed Optimization 

Engine Land Modules 

Land 

(buffer strip) Land 

Watershed Network Land 

(Micro scale) Land BMP construction 

(Watershed) 

Land Conduit Network/Reach

Routing CSO Reach 

Framework 

Manager 

Output 

Post-Processor 

External Inputs
•	 The Optimization Engine evaluates the current option and selects the next preferred option from 

that contained in the Feasible Option Matrix on the basis of cost and defined flow and water

quality criteria. The preferred option can be a different combination of BMP locations and types. 

The Feasible Option Matrix contains types, configurations, locations, and costs of feasible BMP 

options 

•	 SUSTAIN performs numerous iterations of the sequence until the user-defined convergence 

criteria are met 

•	 The tool does not automatically select the best solution but is expected to be used as a tool to 

explore and test various approaches and eventually select optimal solutions on the basis of userdefined criteria and constraints 

2.2. Simulation Modules 

SUSTAIN’s modular simu lation core consists of three standalone simulation modules: the landscape 

simu lation module (Land), the standalone BMP module (BMP), and a conveyance simulation module 

(Conduit and Reach) as shown in the central box o f F igure 2-1. 

To provide a seamless and efficient operation, SUSTAIN selects and incorporates simulation routines from 

commonly used watershed and receiving water models (e.g., SWMM (Huber and Dickinson 1988), HSPF 

(Bicknell 2001), and Loading Simulation Program—C++ (LSPC) (Tetra Tech and USEPA 2002). The 

following is a summary list of simulation routines and the model where the routines were adopted: 

•	 Watershed/landscape models: SWMM’s atmospheric, land surface, and groundwat er 

compartments 

•	 Conveyance and pollutant routing: in HSPF/LSPC RCHRES and SWMM Transport compartment 

•	 BMP simulation m odels: Prince George’s County BMP module (Tetra Tech 2001 ) and selected 

buffer zone simulation techniques from the VFSMOD (Munoz-Carpena and Parsons 2003) 

The VFSMOD is programmed in FORTRAN computer lan guage and compiled as a standalone d ynamic 

link library, whereas other simulation modu les are coded in the visual C++ programming language. 

The standalone simulati on modules are packaged within SUSTAIN to perform the generation and 

transport of sediment and other pollutants at th e source (land use type), in the BMP, and in the 

convey ance system. Table 2-1 shows the inte raction betwee n the Land, BMP, and Conveyance m odules 

to handle transport of sediment in SUSTAI N. It shows the inputs, the methods used to simulate the 

sedimen t transport, and the resulting o utputs from these simu lation modules. Table 2-2 shows the 

interaction between the Land, BMP, and Co nveyance modu les to handle transport of other pollut ants in 

SUSTAIN. It also shows the inputs, the methods used to simu late the water quality processes, and the 

resulting outputs from the simulation mo dules. 

2-16 
 

↓ 

 

BMP 

Inpu ts

Inflowtime series . 

− Concentration time series for sand, silt, 

and clay particles 

− Settling velocity, critical shear stre ss, 

and density for sand, silt, and clay 

particles 

Simulation Methods 

− Simulation of transport, deposition, an d 

scouring of the sediment in BMP is 

computed using t he HSPF sediment 

transport algorithms for a lake 

− Exchange of cohesive sediments wi th 

the bed is calculated on the basis o f the 

bed shear stress and user-de fined 

critical shear stress for deposition and 

for scour 

− Bed shear stress is calculated as a 

function of an average flow velocity, 

aver age water depth, and mean particle 

size of the bed sedime nt in a BMP 

Out puts 

− Outflow time series 

− Concentration time series for sand, silt, 

and clay particles 

 ↓ ↓ 

Conveyance 

Inputs 

− In flow time s eries 

− Concentration time series for sand, silt, 

and clay particles 

− Se ttling velocity, critical shear stres s, 

an d density for sand, silt, and clay 

pa rticles 

Simul ation Methods 

− Si mulation of transport, deposition, a nd 

scourin g of the sediment in stream is 

compu ted using the HSPF sediment 

transport algorithms 

− Exchange of cohesive sediments wit h 

th e bed is calculated on the basis of the 

bed shear stress and user-defined 

critical shear stress for deposition and 

for scour 

− Bed shear stress is calculated as a 

function of the slope and hydraulic 

radius of the reach 

Outputs 

− Outflow time series 

− Concentration time series for sand, silt, 

and clay particles 

Table 2-1. Simulation of Sediment Transport in SUSTAIN 

Land 

Inputs 

− Clim series data 

− Coefficie etachment, washoff, and scour equations for the pervious 

land 

− Buildup an pervious surfaces 

ate time 

nts and exponents for soil d 

d washoff rates of solids on im 

Simulation 

− Productio g the 

HSPF sedi 

− Accumulat 

using the S lgorithms

 Methods 

n and removal of sediment on/from pervious land segment is computed usin 

ment algorithms 

ion and removal of sediment on/from the impervious land segment is computed 

WMM buildup and washoff a 

Outputs 

− Outflow tim 

− Concentrat 

e series 

ions time series of total sediment 

Split total sediment concentration into sand (0.05–2 .0 

mm diameter), silt (0.002–0.05 mm diameter), an d clay 

(< 0.002 mm diameter) concentr a tions. 

2-17 
 

 ↓ ↓
 

 

 

 

Table 2-2. Transport Simulation of Other Pollutants in SUSTAIN 

Land 

Inputs 

− Climate time series data 

− Buildup and washoff rates of the selected pollutant on the pervious and the 

impervious land surface 

Simulation Methods 

elected pollutant on pervious and impervious land segments is 

− Accumulation of the selected pollutant on pervious and impervious land 

segments is computed using the SWMM buildup functions. Power, 

Exponential, and Saturation functions are provided for computing the pollutant 

buildup loading on the land 

− Removal of the s 

computed using the SWMM washoff algorithms. Exponential, Rating Curve, 

and Even Mean Concentration methods are provided for computing the pollutant 

washoff loading from the land 

Outputs 

− Outflow time series 

− Concentrations time series for the selected pollutant 

BMP 

Inputs 

− Inflow time series 

− Concentration time series for the 

selected pollutant 

− 1st order decay rates for the selected 

pollutants 

− Background concent 

sele 

ration (C*) for the 

cted pollutants 

− Percent removal rate from the 

underdrain media for the selected 

pollutants 

Simulation Methods 

− Pollutant mixing is simulated with 

completely mixed tanks (CSTRs) in 

series in a BMP 

− Pollutant removal is calculated by first 

order decay with background 

concentration, i.e., k-C* method 

(Kadlec and Knight 1996) 

− Pollutant removal through the 

underdrain media is calculated on the 

basis of the user-defined percent 

reduction for the selected pollutants 

Outputs 

− Outflow time series 

− Concentration time series for the 

selected pollutant 

Conveyance 

Inputs 

− Inflow time series 

− Concentration time series for the 

selected pollutant 

− 1st order decay rates for the selected 

pollutants 

Simulation Methods 

− Pollutant routing is computed by 

assuming a single, continuously 

stirred tank reactor method in a 

reach 

− Pollutant removal is calculated by 

1st order decay rate in a reach (no 

C*) 

Outputs 

− Outflow time series 

− Concentration time series for the 

selected pollutant 

2-18 
When operated in simulation mode, computation time can be a concern for a large watershed-scale 

application because many BMP/conduits might be involved. To get realistic information about their run 

time, the following test runs of SUSTAIN were performed: 

• Different numbers of the same BMP unit and multiple simulation periods 

• Different numbers of the same conduit unit and multiple simulation periods 

• Different combinations of BMPs and conduits and multiple simulation periods 

The test examples were prepared for 1, 10, and 50 units (BMPs, conduits, or BMP/conduit combin ations) 

for simulation durations of 1, 5, and 10 years. The simulation time step of 5 minutes was used for a ll 

simulation runs. The computer configuration used for the tests was a 1.6 GHz CPU, 768 MB RAM, and 

Windows XP operating system. 

The results (Figure 2-2) show that the runtime increases almost linearly with the increase of the number 

of simulation units (i.e., BMP, conduit, or BMP/conduit combination). The runtime also increases 

linearly w ith the increase in simulation period. 

0 

200 

400 

600 

0 10 20 30 40 50 60 

Number of BMPs 

Ru 800 

1000 

1200 

1400 

1600 

me (sec) nti

BMP - 1 yr 

BMP - 5 yrs 

BMP - 10 yrs 

0 

200 

400 

600 

0 10 20 30 40 50 60 

Number of Conduits 

Runt 800 

1000 

1200 

1400 

1600 

me (sec) i

CONDUIT - 1 yr 

CONDUIT - 5 yrs 

CONDUIT - 10 yrs 

0 

200 

400 

600 

0 10 20 30 40 50 60 

Number of BMPs/Conduits 

Run 800 ime ( 1000 

1200 

1400 

1600 

sec) 

BMP/CONDUIT - 1 yr 

BMP/CONDUIT - 5 yrs 

BMP/CONDUIT - 10 yrs 

Figure 2-2. Comparison of runtime for various simulation units and periods. 

The results (Figure 2-3) also reveal that conduit simulation consumes much longer run times 

(approximately nine times longer) than BMP simulation, mainly because it requires solving the coupled 

continuity equation and Manning’s equation for conduit flow routing. 

160 

133 135 

100 

120 

140 

sec) 

BMP - 1 yr CONDUIT - 1 yr BMP/CONDUIT - 1 yr 

1 3 

16 

3 

25 

3 

27 

0 

20 

40 

1 10 

60 

80

Runtime ( 

50 

Number of BMPs/Conduits 

Figure 2-3. Comparison of runtime for 1-yr simulation of various units. 

To reduce the computational burden, it is desirable to simplify the routing simulation, particularly t hrough 

conduits, during optimization runs. Additional research is needed to develop credible methods that 

balance the computational efficiency and accuracy of hydraulic routing in conduits. 

2-19 
2.3. Optimization Module 

SUSTAIN’s optimization module uses evolutionary optimization techniques to identify the most costeffective BMP selection and placement alternatives that satisfy the user-defined decision criteria. The 

optimization module is interfaced with the BMP and land modules during the search process in an 

iterative and evolutionary fashion to pas s the performance data from simulation modules and cost 

information of a viable set of BMPs to the optimization module. The optimization module then 

systematically compares the cost and performance data and modifies the search path to generate a new set 

of viable BMP options and repeats the process until the set criteria to end the iteration are reached. 

Two optimization techniques are supported by SUSTAIN: the Scatter Search method and the Nondominated Sorting Genetic Algorithm II (NSGA-II) method. The Scatter Search method is a metaheuristic search technique that has been explored and used in optimizing complex systems (Glover et al. 

2000; Laguna and Marti 2002; Zhen etal. 2004). NSGA-II is an advanced genetic algorithm based on 

Pareto dominance, and uses non-domination and distribution instead of fitness value to score individuals 

(Deb et al. 2000). Section 3.5.2 has more expanded discussion of the Scatter Search and NSGA-II 

methods. 

To validate the performance of both search techniques, they were tested against a known solution. The 

objectives were twofold: (1) to evaluate the ability of Scatter Search to pick a known best solution for a 

single BMP given multiple pollutant performance functions and multiple pollutant load reduction 

objective criteria, and (2) to evaluate the ability of NSGA-II to generate a cost-effectiveness curve for a 

known, linear solution for a single BM P. 

For the known best solution, a hypothetical BMP was constructed that reduced both sediment and total 

nitrogen loads. The properties of this BMP were specified to yield a sediment removal effectiveness that 

was exactly double its nitrogen removal effectiveness. The cost-effectiveness curves were divided into 10 

equally spaced intervals. The objective functions for the optimization tests were tominimize the cost of 

achieving 20 percent, 40 percent, and 90 percent pollutant removal for both sediment and nitrogen (for a 

total of six hypothetical scenarios, labeled A through F). The scenario objective functions and results a re 

as follows: 

A. Minimize the cost to achieve a 20 per cent sediment and 10 per cent nitrogen removal. There is a 

minimum cost solution that achieves both of these criteria. 

B. Minimize the cost to achieve a 20 per cent sediment and 20 per cent nitrogen removal. Nitrogen is 

thelimiting pollutant that increases optimal cost from Scenario A. 

C. Minimize the cost to achieve a 40 per cent sediment and 20 per cent nitrogen removal. There is a 

minimum cost solution that achieves both of these criteria. 

D. Minimize the cost to achieve a 40 per cent sediment and 40 per cent nitrogen removal. Nitrogen is 

the limiting pollutant that increases optimal cost from Scenario C. 

E. Minimize the cost to achieve a 90 per cent sediment and 40 per cent nitrogen removal. There is a 

minimum cost solution that achieves both of these criteria. 

F. Minimize the cost to achieve a 90 per cent sediment and 90 per cent nitrogen removal. There is no 

possible solution that can achieve both of these criteria. 

The scenario results are pre sented in Figure 2-4, where the green circles represen t both the known 

solution and the solution selected by the optimizer. 

2-20 
2-21 

Nitrogen is Limiting Constraint 

D 

$800 $800 

$700 $700 

$600 $600 

$500 $500 

Sediment Nitrogen 

A 

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

Percent Reduction 

Sediment Nitrogen 

Nitrogen is Limiting Constraint 

B 

Cost	 Cost Cost Cost	 Cost Cost 

$400 

$300 

$400 

$300 

$200 $200 

$100 $100 

$0 $0 

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

Percent Reduction 

Sediment Nitrogen $800 $800 

$700 $700 

$600 $600 

$500 $500 

Sediment Nitrogen 

C $400 $400 

$300 $300 

$200 $200 

$100 $100 

$0 $0 

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

Percent Reduction Percent Reduction 

Sediment Nitrogen 

E 

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

Percent Reduction 

Sediment Nitrogen 

90% Nitrogen not Achievable 

F 

$800 $800 

$700 $700 

$600 $600 

$500 $500 

$400 $400 

$300 $300 

$200 $200 

$100 $100 

$0 $0 

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

Percent Reduction 

Figure 2-4. Scatter Search evaluation scenario results. 

The ability of NSGA-II to find and create the linear nitrogen and sediment removal cost-effectiveness 

curves was also evaluated. Figure 2-5 shows the NSGA-II solution plotted against the known linear 

solutions. Both the Scatter Search and NSGA-II optimization techniques were able to find a known linear 

solution with 100 percent efficiency (in terms of accuracy). In addition, both optimization techniques 

were able to select an optimum solution, given multiple objectives for controlling sediment and nitrogen 

simultaneously. Finally, the NSGA-II technique was able to predict a known linear cost-effectiveness 

curve. 

$0 

$100 

$200 

$300 

$400 

$500 

Cost 

$600 

$700 

$800 

Sediment (Known) Nitrogen (Known) 

Sediment (NSGAII) Nitrogen (NSGAII) 

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

Percent Reduction 

Figure 2-5. NSGA-II evaluation scenario results. 
 

Operationally, SUSTAIN’s optimization module incorporates a tiered approach that allows for costeffectiveness evaluation of both individual and multiple, nested watersheds to address the needs of both 

regional- and local-scale applications. 

2.4. Post-Processor 

The interpretation of time series results can be a daunting task, especially when multiple output locations, 

scenarios (e.g., without BMPs, with BMPs, and pre-developed conditions), and parameters of interest 

(e.g., inflows, outflows, pollutant loads and concentrations) must be considered. Natural precipitationdriven process simulation also produces a highly variable set of responses, ranging in magnitude, 

duration, intensity, treatment containment volume, attenuation, and pollutant removal effectiveness. This 

information is stored at hourly or sub-hourly intervals, and can span several years, depending on the 

length of simulation. 

The primary objective of SUSTAIN’s post-processor is to mine the modeling results to derive the most 

meaningful data to characterize the effectiveness of management strategies. The post-processor achieves 

this objective through the use of graphical and tabular reports of the model output. The post-processor 

uses Microsoft Excel 2003 to develop the following four analysis components of the model outputs. 

Storm Event Classification —This component evaluates the precipitation data that drive the sim ulation 

and categorizes them into a series of storm events on the basis of predefined criteria f or duration and 

antecedent moisture conditions. It produces a set of precipitation events over which BMP performance 

will be evaluated (Figure 2-6, for details regarding interpretation of the graph see Section 3.6). 

53% 

Sorted by Total Precipitation Volume 

82% 

Sorted by Peak Precipitation Intensity 

Selected Interval (Percentile) Total Rainfall Peak Intensity 

Figure 2-6. Example storm event classification graph. 

Storm Event Viewer—This component is used to visualize BMP performance hydrographs and 

pollutographs for specific storm events. It provides performance measurements at an assessment point for 

the specified storm events (Figure 2-7, for details regarding interpretation of the graph, see Section 3.6). 

2-22 
2-23 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4

Precipitation (in./hr) 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

Flow (cfs) 

Post-Developed 

The BMP Scenario 

Pre-Developed Condition 

20-Apr-0121-Apr-0122-A pr-01 

Figure 2-7. Example storm event viewer graph. 

Performance Summary Report—This component summarizes the performance of the modeled 

management strategy for all defined storm events. It paints a picture of the types of storms that are 

contained at the selected assessmen t point, as well as those that are bypassed and untreated. The report 

can be used to identify appropriate design storms that meet a specified treatment objective for modeling 

evaluation (Figure 2-8, for details regarding interpretation of the graph see Section 3.6). 

10% 

0% 

Relative Contribution Weighted Average Reduction Precipitation Event Reductions Pre-Developed Condition 

100% 50% 

90% 45% 0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%1%1%2%2%2%3%3%3%4%4%5%7%11% 12%

Sediment (Percent R ed uction)

80% 

70% 

60% 

50% 

40% 

30% 

20% 

40% 

35% 

30% 

25% 

20% 

15% 

10% 

5% 

0% 

Relative Contribution (Baselin e) 

0.010.010.010.080.090.110.180.320.541.760.040.660.220.110.590.280.550.530.660.740.831.521.591.082.511.412.150.871.712.632.224.412.982.34 

Precipitation Event Volume (in.) 

Figure 2-8. Example performance summary report graph. 

Cost-Effectiveness Report—This component generates the cost-effectiveness curve at a specified 

assessment point and plots other inferior solutions that were attempted during the simulation. It also 

characterizes several key indicators associated with the cost-effectiveness curve, such as BMP surface 

storage volume, surface area, and soil storage volume. The knowledge of how these indicators change at 

various points along the cost-effectiveness curve would help develop cost-effective strategies (Figure 2-9, 

for details regarding interpretation of the graph, see Section 3.6). 
Effec tivenes s (% Reduction) 

55% 

50% 

45% 

40% 

35% 

30% 

All Solutions 

Cost-Effectiveness Curve 

Selected Simulation 25% 

$0.0 $0 5 $1.0 $1 5 $2.0 $2 5 $3.0 $3 5 $4.0 $4 5 

Cost ( $ Million) 

Figure 2-9. Example cost-effectiveness curve. 

2.5. Summary 

This chapter provided an overview of the structure of SUSTAIN, system requirements, major simulation 

and optimization modules, input data processing and output results interpreta tion, and interoperational 

linkages amo ng the modules. The framewor k has been designed to maximiz e computational efficiency 

while preserving the flexibility to represent a wide range of watershed conditions and management 

practice configurations. 

Chapter 3 provides a detailed description of each mo dule and relevant algorithms, and the technical 

underpinnings and assumptions associated w ith the framework. 

. 

2-24 
Chapter 3 Simulation Methods and Algorithms 

This chapter describes the methods and algorithms that were built into SUSTAIN. SUSTAIN’s simulation 

capabilities are embed ded in three modules—land simulation, BMP simulation, and conveyance 

simulat ion—that are used in combination to support a range of watershed simulation needs and are tied 

together by an overarching framework manager that performs data management, BMP site selection, the 

routing network creatio n, and other functions. In addition to the simulation modules, SUSTAIN includes 

an optimization module to evaluate and identify cost-effective BMP placement strategies and a postprocessor to facilitate analysis and interpretation of mode l results. Table 3-1 summarizes an overview of 

the modules, components, and methods included i n SUSTAIN. 

The FM serves as the command module of SUSTAIN to manage data for system functions, provide 

linkages between the system modules, and create the necessary routing network required for simulation 

and optimization activities. 

In the land simulation module, surface runoff and water quality components are provided thro ugh an 

internal application of EPA’s SWMM (v ersion 5) (Huber and Dickinson 1988) or from an external 

linkage to a previously calibrated watershed model. The sediment erosion process is simulated using 

HSPF (Bicknell 2001); the particle size distribution for the eroded sediments is represented as fractional 

distribution of sand, silt, and clay. 

The BMP module uses a combination of process-based algorithms, including weir and orifice control 

structures, flow routing and pollutant transport, infiltration, evapotranspiration, and pollutant loss/decay 

simulation. A functional BMP module was inc orporated by adopting the Prince George’s County BMP 

Module (Tetra Tech 2001). The module was further enhanced by addi ng continuous stir tank reactors 

(CSTRs) in series and associa ted pollutant removal based on Kadlec and Knight’s (1996) k’–C* model, a 

Green-Ampt infiltration option, and dynamic simula tion of evapotranspiration. For stream buffer strip 

simulation, the process-based algorithm applied in the VFSMOD was adopted. 

The con eyance simulation module is used to vsimulate movement of water and pollutants among the 

physical parts of the watershed (land, BMP, conduit, reach). The module simulates flow and pollutant 

routing and employs the kinematic wave and CSTR approach used in the SWMM Transport 

compartment. A sediment transport component that employs the well-known algorithms from the HSPF 

and LSPC models is included. 

This chapter documents the function, design, inputs, and outputs of each SUSTAIN module and all 

associated components. 

3-25
 
 

 

Table 3 1. Modules and Comp - onents in SUSTAIN 

Module Component Methodology 

Framework Manager Data management component Path identification 

GIS Interfaces 

BMP site selection component ArcGIS 

Site suitability criteria 

Highlighted suitability areas 

Routing network component ArcGIS interfaces 

Land module Weather component Precipitation 

Evaporation 

Snowmelt 

H m ydrology co ponent Internal simulation 

Infiltration: Green-Ampt equation 

Overland flow 

Groundwater flow 

External simulation 

Unit area flows and loads 

File linkages 

Water quality component Erosion 

Pollutant buildup 

Pollutant washoff 

Particle size distribution 

BMP module Simulation component Storage routing method 

Infiltration/filtration methods 

Evapotranspiration method 

Underdrain method 

Pollutant routing and removal methods 

Buffer strip component Overland flow routing 

Pollutant interception 

Aggregate BMP component Interception 

Treatment 

Storage 

Cost database component Unit area cost estimates 

Construction components 

Conveyance module Routing component Flow routing 

Sediment 

Transport pollutant routing 

Optimization module Problem formulation 

NSGA-II 

Scatter Search 

Tiered analysis 

Post-processor Storm evaluation 

Storm viewer 

Performance summary report 

Cost-effectiveness report 

3-26 
Table 3-2. Summary of Inputs, Methods, and Outputs in FM 

Framework M er anag

Inputs 

− Geodat abase th spatial and tabular file wi data 

− Cost d atabase file 

− Defin e GIS la nd loo yers a kup ta bles 

− Define su itabil ity cr iteria fo r BM P locati ons ( optional) 

− Place B t the MPs asuitable locations on th ap e m 

− Defin e assess ment poi ) nt(s 

Methods 

− BMP suitable l ons m ocati ap is c reated us ing t ng too he BMP siti l (option al) 

− BM Ps are placed on the map using the BMP placement tool 

− BM P drainage s are de area ted using lineathe a uto/manual deline ation too ls. T he system allow s 

impo rting the existing drainage areas 

− A rout ing netw s creat ork i y connectin ed b g a drainage area to a BMP and ownstra d eam BMP 

throug h a reach or a conduit conveyance syste m 

− LAND, B H, an MP, REAC d CO NDUIT si mu lations are ca rried o ut by ca he dy lling t ic linam nk 

librari com piled i es n visu al C++ 

Outputs 

− BMPsuitable l tions m oca ap 

− Input text file forLAND si tion modul mula e 

− Inputtext file MP, R for B EACH, a NDU nd CO 

− Mod el simulati on resu lts 

IT simulat es ion modul 

− Display simulation results at the assessment point 

3.1. Framework Manager 

Table 3-2 provides an overview of the required i nputs, the methods used to manage and process the inputs 

and the resulting outputs from the FM module of SUSTAIN. Three major com ponents of the FM are 

described in Sections 3.1.1, 3.1.2, and 3.1.3. 

3.1.1. Data Management Component 

The data management component compiles and organizes the data required to run SUSTAIN, includin g 

geographic data in vector, raster, and/or tabular format. It also includes a cost database (BMPCosts.mdb) 

in Microsoft Access format. SUSTAIN supports the use of either a personal geodatabase or a file-based 

geodatabase as the primary repository for all geographic data sets. A file-based geodatabase is a 

collection of spati al and/or temporal data sets organized into a series of indexed folders and files. Each 

file-based geodatabase can store up to one terabyte of information. This option is recommended over the 

use of a personal geodatabase, which is limited in size to two gigabytes and does not support the storage 

of raster information. During various data processing steps, SUSTAIN creates a large number of 

intermediate data sets and stores them on the hard disk specified. 

The data management component is navigated through two levels using GIS interfaces. The first level of 

data management identifies the path to the cost database, the file geodatabase, and the temporary 

directory where all the intermediate geographic data are stored. The second level of data management 

identifies the required data layers. The required data set includes land use data in raster format, a land use 

lookup table, stream network, DEM (mandatory for the automatic watershed delineation option), and time 

series data (mandatory for the external land simulation option). 

3-27 
 

 

 

3.1.2. BMP Site Selection 

The BMP siting tool was dev eloped to ass ist users in selecting suitable locations for different types of low 

impact devel opment (LID) te chniques or conventional BMPs. The tool is implemented using ESRI’s 

ArcView 9.3 and the Spati al A nalyst ext ension. Site suitability is used as the dominant factor in 

identifying potential site locations (USEPA 1999a). Using GIS analysis and up to eight base data layers, 

the siting tool helps users identify suitable sites for placement of structural BMPs on the basis of 

suitability criteria including elevation, slope, soil type, urban land use, roads, water table depth, stream 

location, and d rainage area. Table 3-3 describes these eight GIS data layers that are used as the base input 

data for the tool. 

Table 3-3. GIS Data Requirement for BMP Suitability Analysis 

GIS Layer Format Desc ription 

DEM Raster file DEM is used to calculate the drainage slope and drainage area that are 

 identify the suitable locations for BMPs. 

The 

used to 

NLCD Land Use Raster file SGS Multi-Resolution Land Characteristics Consortium NLCD land 

d is used to eliminate the unsuitable areas for BMPs. 

The U 

use gri 

Percent 

Imperviousness 

Raster file pervious grid is used to identify the suitable locations for BMPs for 

ven suitability criteria. 

The im 

the gi 

Soil Shape file The soil data contain the soil properties such as hydrological soil group, 

which are used to identify suitable locations for BMPs. 

U arban L nd Use le Shape fi The urban land use data contain the boundaries for the buildings and the 

impervious areas needed to identify suitable locations for LIDs. 

Road Shape file The road layer is used to identify suitable locations for some BMPs that 

must be placed within a specific road buffer area. 

Stream Shape file The stream layer is used to define a buffer so that certain BMP types can be 

placed outside the buffer to minimize the impact on streams. 

Groundwater Table 

Depth 

Shape file The groundwater table depth layer is used to identify suitable locations for 

the infiltration BMPs; derived from monitoring data. 

Source: Lai et al. 2007 

The siting tool uses a site suitability criteria matrix and is populated with default criteria that the user can 

change to his or her preference or local knowledge. The default criteria in the tool as shown in Table 3-4 

are derived from two EPA reports (USEPA 2004a, 2004b). Users can modify these criteria through the 

interface. 

The output of the BMP siting tool analysis is a spatial map that highlights the areas that meet the selected 

default or user-specified site criteria for placement of the available BMPs. The system stores the data in 

the BMP suitability map that can be used as a backdrop during the placement of BMPs for simulation 

runs. Multiple spatial maps can be created for project areas on the basis of the various criteria selected by 

the user. Users can also import additional data sets or geographic coverages to further refine the utility of 

the spatial maps. For example, if BMPs can be placed only on publicly owned land, an ownership layer 

can be superimposed on the siting tool results to highlight potential BMP placement locations on such 

land. 

3-28 
 

 

Table 3-4. Default Criter ia for BMP Suitable Locations Used in SUSTAIN 

BMP 

Site Suitability Criteria 

Drainage 

Area 

(acre) 

Drainage 

Slope 

(%) 

Imperviousness 

(%) 

Hydrological 

Soil Group 

Water 

Table 

Depth 

(ft) 

Road 

Buffer 

(ft) 

Stream 

Buffer 

(ft) 

Bui 

Buffer 

(ft) 

lding 

Bioretention < 2 < 5 > 0 A–D > 2 < 100 > 100 -

Cistern -- -- -- -- -- -- -- < 30 

Constructed 

Wetland 

> 25 < 15 > 0 A–D > 4 -- > 100 --

Dry Pond > 10 < 15 > 0 A–D > 4 -- > 100 --

Grassed Swale < 5 < 4 > 0 A–D > 2 < 100 -- --

Green Roof -- -- -- -- -- -- -- --

Infiltration 

Basin 

< 10 < 15 > 0 A–B > 4 -- > 100 --

Infiltration 

rench T 

< 5 < 15 > 0 A–B > 4 -- > 100 --

Porous 

Pavement 

< 3 < 1 > 0 A–B > 2 -- -- --

Rain Barrel -- -­ -- -- -- -- -- < 30 

Sand Filter 

(non-surface) 

< 2 < 10 > 0 A–D > 2 -- > 100 --

Sand Filter 

(surface) 

< 10 < 10 > 0 A–D > 2 -- > 100 --

Vegetated 

Filterstrip 

-- < 10 > 0 A–D > 2 < 100 -- --

Wet Pond > 25 < 15 > 0 A–D > 4 -- > 100 --

To conceptualize the physical function of BMPs with regard to their associated landscape, four categories 

(or types) of BMPs are presented in the siting tool: (1) point LID, (2) point BMP, (3) linear BMP, and (4) 

area BMP. Point BMPs and LID include practices that capture upstream drainage at a specific location 

and can use a combination of detention, infiltration, evaporation, settling, and transformation to manage 

flow and remove pollutants. Linear BMPs are narrow linear shapes adjacent to stream channels that 

provide filtration of runoff; nutrient uptake; and ancillary benefits of stream shading, wildlife habitat, and 

aesthetic value. Area BMPs are land-based management practices that affect impervious area, land cover, 

and pollutant inputs (e.g., fertilizer, pet waste). Table 3-5 shows the structural BMP options included in 

BMP siting tool. 

3-29 
Table 3-5. Structural BMP Options Available in the BMP Siting Tool 

BMP Option BMP Type 

Bioretention Point LID 

Cistern Point LID 

Con structed Wetland Point BMP 

Dry Pond Point BMP 

Grassed Swale Linear BMP 

Green Roof rea BMP A 

Infiltration Basin oint BMP P 

Infiltration Trench Lin ear BMP 

Porous Pavement Area BMP 

Rain Barrel Point LID 

Sand Filter (non-surface) Linear BMP 

Sand Filter (surface) Point BMP 

Vegetated Filterstrip Linear BMP 

Wet Pond Point BMP 

3.1.3. Routing Network 

The system rou ting network as conceptualized in Figure 3-1 provides the connectivity among the various 

sim ulation components (land, BMP, conduit, reach) at the watershed level. After placing BMPs on the 

map and creating the drainage area for each BMP, the framework manager creates the routing network by 

connecting the land segments that drain to each BMP and connecting each BMP to t he downstream BMP 

through a reach or conduit segment. The connections are made automatically if the DEM is used to 

delineate the drainage areas. Alternatively, those connect ions can be made manually using the network 

tools in the framework manager interface. 

BMP 

REACH 

CONDUIT 

LAND 

REACH 

LAND LAND 

BMP BMP 

Routing Network 

Assessment Point 

Figure 3-1. The routing network showing the connections 

among the simulation components. 

3-30 
3.2. Land Module 

The land simulation module is used to derive runoff and pollutant loads from the land in one of two ways. 

By default, the land module computes the hydrograph and po llutograph using algorithms adapted from 

the SWMM (version 5) land surface compartment and sediment al gorithms adapted from the HSPF 

model. That is called the internal simulation op tion, whic h has been tested and verified to ensure accurate 

transformation of th e code from the o riginal models. The second option is to use ex ternally genera ted 

time series to represent hydrology and wa ter quality at the landscape level. The external option allows 

importatio n of the hydrograph and polluto graph for each land use category f rom a p re-calibrated ex ternal 

watershed m odel such as HSPF or LSPC. 

Figure 3-2 is a schematic of the land simulation proce ses that produce runoff from l and including tim e- s

varying rai n or snow accumulatio n and melting, evaporation from ponded surface, infiltration of rain or 

snowmelt into uns aturated soil, percolation of infiltrated water into groundwater, and nonlinear reservoir 

routing of overland flow. 

LAND Simulatio rocesses n P

Rain + Snowmelt 

Surface R f 

Evapotranspiration 

Infiltration 

unof 

Groundwater Recharge 

Groundwater 

Outflow 

Figure 3-2. Schematic showing the land simulation processes. 

Table 3-6 provides an overview of the required inputs, the methods used to process the inputs and 

simulate the hydrologic and water quality processes occurring on the landscape, and the resulting outputs 

of the land simulation module. 

3-31 
Table 3-6. Inputs, Methods, and Outputs of the Land Module 

Land Module 

Inputs 

Internal Option 

− Define pollutants 

− Define fraction of total sediments as sand, silt, and clay from each land use category 

− Reclassify/group land use categories (optional) 

− Define meteorological data (user-defined time step) 

− Define pollutant properties 

− Define land use properties 

− Define rain gauge properties 

− Define aquifer properties (optional) 

− Definesnowpack properties (optional)

− Definewatershed properties 

External Option 

− Hourly time step 

− Define pollutants 

− Define fraction of total se diments as sand, silt, and clay from each land use category 

− Reclassify/group la nd use categories (optional) 

− Assign pre-calibrated land output time series for each land use group 

Methods 

Internal Option 

− Weath er data is processed to convert precipitation values to snow or rain according to the 

te mperature 

− Sn owmelt is computed using the degree-day and National Weathe r Service equations 

− Ev apotranspiration is calc lated using a constant ET rate or tim u e series values supplied by the user 

− In filtration is computed using the Green-Ampt equ ation 

− Overland flow is computed using the Manning’sequation 

− Groundwat er outflow is computed as a function of groundwater and surface water heads 

− Productionand removal of sediments on pervious land is computed using the processed-based 

algorithms adopted from the HSPF model 

− Buildup and washoff of sediments on impervious land is computed using the algorithms adopted 

from the SWMM 

− Total sediment is divided into three sediment classes (sand, silt, and clay) according to a usersp ecified fraction for each class from each land use category 

− P llutants buildup and washoff rates are computed using othe functions adopted from the SWMM 

− Ou tflow, sediment, and pollutants are aggregated for all l and use categories 

ExternalOption 

− To tal sediment is divided into three sediment c lasses (sand, silt, and clay) according to a usersp eci fied fraction for each class from each land use category 

− W ithin each catchment area, unit-area outflow, sed iment, and pollutant loads for each land unit are 

mu ltiplied by actual land area to derive aggregate land contribution 

Outputs 

− Hourly outflow time series 

− Hourly sediment (sand, silt, an d clay) concen tration time series 

− Hourly pollutant concen ation time series tr 

3-32 
 

 

Three major components compose the land simulation module: weather, hydrology, and water quality. 

Multiple options are provided for representing various processes as are outlined in Table 3-7. 

Table 3-7. Land Simulation Methods Used in SUSTAIN 

Process Option 1 Option 2 Option 3 Reference 

Rainfall Weather data fil e -- -- Rossman 2005 

Snowme lt Degree-Day equat ion; 

NWS equation 

-- -- Rossman 2005 

Evaporation Constant value Monthly average 

value 

User-supplied 

time series 

Rossman 2005 

Infiltration Green-Ampt -- -- Rossman 2005 

Groundwater flow Modified two-zone 

groundwater model 

-- -- Rossman 2005 

Bicknell et al. 

2001 

O flow verland Non-linear reservoir -- -- Rossman 2005 

Pollutant buildup Power function Exponential 

function 

Saturation 

function 

Rossman 2005 

Pollutant washoff Exponential function Rating curve Event mean 

concentration 

Rossman 2005 

Street cleaning User-specified pollutant 

removal efficiency 

-- -- Rossman 2005 

S erosion 

an t 

ediment 

d transpor

Production and 

from the pervious land; 

Buildup and washoff from 

 removal 

the impervious land 

-- -- Bicknell et al. 

2001 

Rossman 2005 

Particle size 

distribution 

User defined (sand, silt, 

clay) 

-- -- Bicknell et al. 

2001 

The following paragraphs explain in greater detail the methods and algorithms implemented in the 

weather, hydrology, and water quality components of the land simulation module. 

3.2.1. Weather Component 

The weather component of the land module is adapted from the SWMM atmospheric compartment 

(Rossman 2005) that uses the daily air temperature, evaporation, and wind speed data from the userspecified climate file. The format for climate file is consistent with that used in the SWMM, where each 

line in the file contains a recording station name, year, month, day, maximum temperature, minimum 

temperature, and optionally, the evaporation rate and wind speed. The data must be in U.S. units: 

temperature in degrees F, evaporation in in./day, and wind speed in mi/hr, all separated by one or more 

spaces. 

An excerpt from the climate file format might look as follows: 

ST93738 2007 1 1 43 32 0.12 13.9 

ST93738 2007 1 2 45 23 0.04 5.84 

ST93738 2007 1 3 54 24 0.07 4.21 

The precipitation data is input in a separate file where each line of the file contains the station ID, year, 

month, day, hour, minute, and non-zero precipitation reading, all separated by one or more spaces. 

3-33 
 

An excerpt from the precipitation file format might look as follows: 

ST448903 2007 1 1 00 00 0.12 

ST448903 2007 1 1 01 00 0.04 

ST448903 2007 1 2 16 00 0.07 

Precipitation 

SUSTAIN’s land simulation module uses the precipitation data type in any one of these three formats: (1) 

intensity, where the value is anaverage rate (in./hr) over the recording interval; (2) interval volume, 

where the value is the volume of rain that fell in the recording interval (in.); or (3) cumulative volume, 

where the value represents the cumulative rainfall that has occurred since the start of the last series o f 

non-zero values (in.). The precipitation values are converted to snow amount according to the userspecified temperature below which precipitation falls as snow instead of rain (Rossman 2005). 

Snowmelt 

Snowmelt is computed at each s tep using a degree-day equation when it is dry and the National Weather 

Service (NWS) River Forecast System –Snow Accumulation and Ablation Model (Anderson 1973) 

during rainfall periods (Huber and Dickinson 1988). The two equ ations are presented below. 

Degree-Day Equation 

During periods of no rainfall, snowmelt iscomputed by the Degree-Day equation: 

Smelt = DHM ×(Ta −Tbase ) (3-1) 

where 

Smelt = snowmelt rate (water equivalent in./hr), 

DHM = melt coefficient (water equivalent in./hr-o

F), 

Ta = air temperature (o

F), and 

Tbase = snowmelt base temperature (o

F). 

NWS Equation 

During periods of rain, snowmelt is computed using Anderson’s (1973) NWS equation. Anderson 

combines the appropriate terms for each heat budget component into one equation for the melt rate: 

Smelt = (Ta − 32)× (0.00167 + Sγ×U adj + 0.007 × Prec)+ 8.5 ×Uadj × (EA − 0.18) (3-2) 

where 

Smelt = snowmelt rate (water equivalent in./hr), 

Ta = air temperature (o

F), 

Sγ = 7.5 γ (in.-Hg/o

F), 

γ = psychometric constant (in.-Hg/oF), 

Uadj = wind speed function (in./in.-Hg-hr), 

Prec = rainfall intensity (in./hr), and 

EA = saturation vapor pressure at air temperature (in.-Hg). 

The psych ometric constant γ is calculated as 

γ = 0.000359 × PA (3-3) 

where 

γ = psychometric constant (in.-Hg/ °F) and 

3-34 
PA = atmospheric pressure (in.-Hg). 

Average atmospheric pressure is calcu lated as a function of elevation, z: 

 ⎡ z ⎤ ⎡ z ⎤

2.4 

PA = (3-4) 29.9 - 1.02 0.0032 ⎢

⎣1000 ⎥⎦

+ ⎢

⎣1000 ⎥

⎦

 

where 

z = average elevation (ft). 

The wind speed function, Uadj, accounts for turbulent transport of sensible heat and water vapor. 

Anderson (1973) gives the following eq uation: 

Uadj =0.006 u (3-5) 

where 

Uadj = wind speed function (in./in.-Hg-hr) and 

u = average wind speed (mi/hr). 

The saturation vapor pres sure, EA, is given by the fol lowing exponen tial approximatio n: 

 6 ⎡ -7701.544 ⎤ EA = 8.1175 ×10 ×exp (3-6) ⎢ ⎥ ⎣(Ta + 405.0265)⎦

where 

EA = saturation vapor pressure at air te mperature (in.-Hg) and 

Ta = air temperature ( °

F). 

Evaporation 

Evaporation is calculated for standing water on land surfaces, subsurface water in groundwater aquifers, 

and water held in storage units. On the basis of the approach used in SWMM, evaporation is subtracted 

from the rainfall or water storage area prior to calculating infiltration. Evaporation rates can be stated as 

one of these three forms: a single constant value, a set of monthly average values, or a user-suppli ed time 

series input in the climate data file. If a climate file is used, the user-specified monthly pan coefficients 

are used to convert the pan evaporation data to free water-surf ace values (Rossman 2005). 

3.2.2. Hydrology Component 

The hydrology component simulates the rainfall runoff processes and provides the linkage between the 

meteorological information and movement of water into and across the land surface. The methods 

selected provide time variable response to meteorological inputs while using well-established metho ds for 

simulation. By building on methods that are established in the literature, users can rely on literature 

values and industry practice to develop input parameters for initial application and to use as a starting 

point for calibration. 

The hydrology compon ent of the land simulation module is adapted from the SWMM land surface and 

groundwater compartments (Rossman 2005). SUSTAIN uses the Green-Ampt method to compute the 

amount of infiltration of rainfall on the pervious land area into the unsaturated upper soil zone. The 

surface runoff is computed using Manning’s eq uation. 

3-35 
 

Infiltration Using Green-Ampt Equation 

The Green-Ampt infiltration method assumes that a sharp wetting front exists in the soil column which 

separates the unwetted zone of soil with some initial moisture content below and the wetted zone of soil 

above (Rossman 2005). The infiltration rate is calculated as a function of soil moisture, saturated 

hydraulic conductivity, and average wetting front suction head, and is based on Darcy’s law and the 

principle of mass conservation (Huber and Dickinson 1988). 

If I ≤ Ks, then f = I; 

If I > Ks, then f = I, until F = Fs =

(θs − θi ×ψ f

1 − I/Ks 

)

Following surface saturation, 

dF ⎡ (θs − θi )×ψ f ⎤ f = = K 1 + (3-7) s ⎢ ⎥ dt ⎣ F ⎦

 

 

 

For I > Ks, and f = I for I ≤ Ks 

where 

I = inflow rate (in./hr),

F = amount of infiltration (in.),

 Fs = amount of infiltration up to surface saturation (in.),

 F = infiltration rate (in./hr),

Ks = saturated hydraulic conductivity (in./hr), 

θ s = saturated moisture content, 

θ i = initial moisture content, and 

ψ f = average wetting front suction head (in. of water). 

This differential equation is solved iteratively to determine f at each time step by using Newton-Raphson 

method. The infiltration volume during the t ime interval is equal to the inflow volume if the surface does 

not saturate. If saturation occurs during the time interval, the infiltration volumes over each stage of the 

process within the time steps are calculated and summed. When there is no inflow, any water ponded on 

the surface is allowed to infiltrate and added to the cumulative infiltration volume. In using the GreenAmpt method, a complication occurs when the inflow rate starts at a value above, drops below, and the n 

rises above Ks again during the infiltration computation. In such a case, the moisture content needs to be 

redistributed as the assumption of saturation from the surface down to the wetting front does not hol d. A 

major advantage of the Green-Ampt method is that the input parameters (i.e., Ks,ψ f ,θ s ,θ i ) can be 

determined from physical measurements. As shown in Table 3-8, Rawls et al. (1983) provide typical 

values for the parameters. 

Overland Flow 

The conceptual view of the surface runoff calculation in SUSTAIN is illustrated in Figure 3-3, which is 

adapted from the SWMM5 user’s manual (Rossman 2005). The surface of each subwatershed is treated 

as a nonlinear reservoir. Inflow comes from precipitation and upstream subwatersheds. The outflows are 

infiltration, evaporation, and surface runoff to downstream areas. The maximum surface storage capacity 

is composed of ponding volume, surface wetting volume, and interception volume, normalized by surface 

area and is represented as depth. Surface runoff per unit area, Q, occurs only when the surface wate r 

3-36 
depth exceeds the maximum surface storage depth, dp, in which case the outflow is given by Manning’s 

equation: 

1.49 5/3 1/2 Q =W (d − d p ) S (3-8) 

n 

where 

Q = outflow rate (cfs), 

W = subwatershed width (ft), 

n = Manning’s roughness coefficient, 

d = water depth (ft), 

dp = depth of depression storage (ft), and 

S = subwatershed slope (ft/ft). 

Table 3-8. Green-Ampt Parameters 

Soil Texture 

Class 

Saturated 

Hydraulic 

Conductivity 

(in./hr) 

Suction Head 

(in.) 

Porosity 

(Fraction) 

Field 

Capacity 

(Fraction) 

Wilting Point 

(Fraction) 

Sand 4.74 1.93 0.437 0.062 0.024 

Loamy Sand 1.18 2.40 0.437 0.105 0.047 

Sandy Loam 0.43 4.33 0.453 0.190 0.085 

Loam 0.13 3.50 0.463 0.232 0.116 

Silt Loam 0.26 6.69 0.501 0.284 0.135 

Sandy Clay Loam 0.06 8.66 0.398 0.244 0.136 

Clay Loam 0.04 8.27 0.464 0.310 0.187 

Silty Clay Loam 0.04 10.63 0.471 0.342 0.210 

Sandy Clay 0.02 9.45 0.430 0.321 0.221 

Silty Clay 0.02 11.42 0.479 0.371 0.251 

Clay 0.01 12.60 0.475 0.378 0.265 

Source: Rawls et al. 1983 

Evaporation Rain/Snowmelt 

dp

d Q

p 

d 

InfiltrationInfiltration 

Source: Rossman 2005 

Figure 3-3. Conceptual view of surface runoff. 

Subwatershed width (W) can be estimated by dividing the subwatershed area by the length of the 

representative flow path. The depth of water over the subwatershed is continuously updated with time by 

solving a water balance Equation (3-9) for the subwatershed. 

3-37 
dd 1.49W 5/3 1/2 5/3 = i − (d − d ) S = i +WCON(d − d ) (3-9) e p e p dt A⋅ n 

1/2 1.49 ⋅W ⋅ S WCON = − (3-10) A⋅ n 

where 

 WCON = parameter for overland flow routing, 

d = water depth (ft), 

t = time (sec),

 W = subwatershed width (ft), 

A = surface area of subwatershed (ft2

), 

n = Manning’s roughness coefficient, 

ie = rainfall excess (ft/s), 

dp = depth of depression storage (ft), and 

S = subwatershed slope (ft/ft). 

Groundwater Flow 

Accounting for groundwater inputs is of greater significance with larger watersheds for the purpose of 

accounting for baseflow in streams. Groundwater simulation provides an important lin k between surface 

and sub su rface flow. In SUSTAIN, groundwater flow is simulated using the SWMM formulation 

(Rossman 2005). It also employs some modifications based on HSPF techniques for simulating the 

interaction between saturated soil water and unsaturated soil water when the water tab le approaches or 

rises above the ground (Bicknell et al. 2001). Those modifications were made to smo oth out the 

groundwater outflow response to account for the interaction of rising groundwater storage with the 

unsaturated zone s torages, cohesive water storage, and gravity water storage. Without those 

modifications, the groundwater level isa function of gravity storage only. The water in cohesive water 

storage is not available for groundwater outflow but is subject to evapotranspiration. It is assumed in 

SUSTAIN that there is no interaction between land groundwater and BMP deep percolation. Deep 

percolation water from BMPs is lost from the system. 

Two-zone Groundwater Model from SWMM 

In this formu lation, groundwater flow is a function of groundwater and surface water heads in the 

discharge channel, as shown in Equation (3-11). 

 Q 1 2

gw = A (H gw - E)B - A (Hsw - E)B + A3 H gw Hsw (3-11) 1 2 

where 

Qgw = groundwater flow (cfs), 

Hgw = elevation of groundwater table (ft), 

Hsw = elevation of surface water at receiving node (ft), 

E = elevation of node invert (ft), 

A1 = groundwater flow coefficient, 

B1 = groundwater flow exponent, 

A2 = surface water flow coefficient, 

B2 = surface water flow exponent, and 

A3 = surface-groundwater interaction coefficient. 

3-38 
The SWMM’s two-zone groundwater model is shown in Figure 3-4. The upper zone is unsaturated at 

varying moisture content, which is updated at each time step of infiltration and regeneration of infiltration 

capacity simulation. The lower zone is saturated, and hence its moisture content is fixed at the soil 

porosity. 

 

fI f f EUEL 

dU

fU

fG

dL

dTOT

dU 

fU 

fG 

dL 

dTOT 

Upper Zone 

Lower Zone 

fL 

Source: Rossman 2005 

Figure 3-4. Two-zone groundwater model adapted from SWMM. 

The fluxes shown in Figure 3-4 are expressed as volume per unit area per unit time and consist of the 

following: 

fI = infiltration from the surface; 

fEU = evapotranspiration from the upper zone, which is a fixed fraction of the unused surface 

evaporation; 

fU = percolation from the upper to lower zone, which depends on the upper zone moisture content θ

and depth dU; 

fEL = evapotranspiration from the lower zone, which is a function of the depth of the upper zone dU; 

fL = percolation from the lower zone to deep groundwater, which depends on the lower zone depth 

dL; and 

fG = lateral groundwater interflow to the conveyance network, which depends on the lower zone 

depth dL as well as depths in the receiving channel. 

Two-zone Groundwater Interaction from HSPF 

The effects of a rising water table differ under low water table and high water table conditions. A 

modified, two-zone groundwater representation in Figures 3-5 and 3-6 provides a higher-resolution option 

for characterizing subsurface conditions and improvements to runoff computation. Figure 3-5 shows the 

schematic of a two-zone soil moisture storage layer under low water table conditions, while Figure 3-6 

illustrates that under high water table conditions. 

The saturated and unsaturated zone interactions are a function of water transfer rates, existing saturation 

levels, and physical characteristics of the soils such as porosity. For modeling purposes, the total porosity 

is divided into two parts: porosity in micropores (ηmi, cohesion water) and porosity in macropores (ηma, 

gravitational water). Cohesion water is bonded in soil by capillary forces, and it is roughly equal to the 

difference between the wilting point and field capacity. Gravitational water drains from soils in the 

unsaturated zone by gravity forces. 

The groundw a ter level is the elevation of the saturated zone above an arbitrary datum such a s mean sea

level. The active groundwater storage is gravity water stored above the water elevation of a channel that 

3-39 
is within or adjacent to the land. A lower elevation is the maximum depth where soil moisture varies 

seasonally due to evapotranspiration. 

When the groundwater elevation is below the lower elevation (within the saturated Zone 1 as shown in 

Figure 3-5), there is no interaction between the saturated and the unsaturated zones. Groundwater 

elevation in this zone is computed as a function of the groundwater storage and the total porosity (the sum 

of macropores and micropores). 

ηmi ηma Land Surface 

Lower 

2 

Evapotranspiration 

ZONE 2

Depth 

ONE 1 

Hgw 

Het 

Hch Storage

ZO

Datum 

Active Groundwater 

Source: Bicknell et al. 2001 

Figure 3-5. Two-zone soil moisture storage under low water table condition. 

The groundwater elevation in this zone is calculated as: 

Sgw H gw = (3-12) (η + η ) mi ma 

where 

Hgw = groundwater elevation (ft), 

Sgw = total groundwater storage (ft), 

ηmi = soil porosity in micropores (large pores for cohesive water), and 

ηma = soil porosity in macropores (large pores for gravitational water). 

When the groundwater elevation reaches the higher elevation (within the unsaturated Zone 2 as shown in 

Figure 3-6), the groundwater storage starts interacting with the upper zone storages. Rising groundwater 

that occupies micropores is reassigned to the upper zone cohesive water storage. Groundwater storage 

shares macropores with the upper zone gravity water storage and is subject to evapotranspiration. 

Changes in groundwater storage are distributed between upper zone storages and groundwater storage, 

according to their relative saturation levels. Groundwater elevation in this zone is a function of upper 

zone water in macropores and groundwater storages and is calculated as: 

H + [S + (S − S )] et uz gw lz Hgw = (3-13)

ηma 

where 

Het = elevation at the maximum depth due to seasonal evapotranspiration (ft), 

Sgw = total groundwater storage (ft), including all water in the lower saturated zone and in 

macropores of the upper zone saturated soil, 

3-40 
 Suz = upper zone water storage in macropores of the unsaturated soil (ft), and 

Slz = groundwater storage below Het (ft), which is equal to Het × (ηmi + ηma). 

ηmi ηma Land Surface 

ZONE 2 

Hgw 

Het Active 

Lower 

Evapotranspiration 

Depth 

Groundwater 

1 

Storage 

ZONE 1

Hch 

Datum 

Source: Bicknell et al. 2001 

Figure 3-6. Two-zone soil moisture storage under high water table conditions. 

When the groundwater elevation is below Hch, the channel water elevation, there will be no outflow from 

the groundwater storage. When the groundwater elevation is above Hch, the groundwater outflow is 

computed as a function of the active groundwater storage, i.e., the gravity water storage above the channel 

water level. 

3.2.3. Water Quality Component 

The water quality component performs the transport of pollutants on the basis of total flow (runoff and/or 

infiltrated groundwater outflow) computed in the hydrology component. The simulation methods 

included for routing of sediments and pollutants are adapted from the SWMM land surface compartment 

(Rossman 2005) and from HSPF for sediment production and removal from pervious lands (Bicknell et 

al. 2001). SUSTAIN can simulate the generation and transport of any number of user-defined pollutants 

and divides them into two major groups: sediment and non-sediment pollutants. To facilitate sediment 

routing, the total sediment load is divided into three sediment classes—sand, silt, and clay—and the 

model allows users to define their distribution fractions. For pollutants that are associated with sediment, 

co-fractions are used to quantify the mass of pollutant as a direct proportion of sediment mass. The total 

sediment/non-sediment load is simulated for each defined land use category, and then the total is summed 

within each subwatershed or BMP drainage area for routing to a BMP or conduit component. 

Pervious Land Segment 

SUSTAIN computes the sediment load using the HSPF sediment algorithms (SEDMNT) and all nonsediment pollutant loadings using the SWMM buildup and washoff algorithms for pervious land segments 

(Lai et al. 2007). 

Production and Removal of Sediment 

HSPF simulates sediment production as a function of detachment/washoff or direct scour from a soil 

matrix. It assumes that the soil matrix contains an unlimited supply of sediment. User-specified, 

physically based model parameters are used to determine the specific rates and modes of how sediment is 

made available for transport with runoff. For example, the supporting management practice factor in the 

soil detachment by rainfall equation was based on the P factor in the Universal Soil Loss equation 

(USLE) (Wischmeier and Smith 1965). It is introduced to better evaluate agricultural conservation 

3-41 
practices on reducing erosion potential. Figure 3-7 represents the storages and fluxes used to simulate the 

detachment, attachment, and removal involved in the erosion processes on the pervious land surface. 

SEDwo

Washoff

Detache

of

d

S ntedime

S

t

EDdet

achment

 Rainfall

De

by

Caff

Sedime

Attachm

SED

Sediment

Removal

nt

ent

SEDscr

Scour of

Soil Matrix

By Water

Total S

Soil Matrix

(Unlimited)

Sdet

Detached

Storage

SEDwo 

Washoff 

Detache 

of 

d 

S ntedime

S 

t 

EDdet 

achment

 Rainfall 

De 

by 

Caff 

Sedime 

Attachm 

SED

Sediment 

Removal 

Total S nt 

ent 

Soil Matrix 

(Unlimited) SEDscr 

Scour of 

Soil Matrix 

By Water 

Sdet 

Detached 

Storage 

so 

oil & 

Source: Bicknell et al. 2001 

Figure 3-7. Schematic of sediment production and removal processes. 

Removal of sediment by water is simulated as washoff of detached sediment in storage (SEDwo) and scour 

of matrix soil (SEDscr). The washoff process involves two parts: the detachment/attachment of sediment 

from/to the soil matrix and the transport of this sediment. Detachment (SEDdet) occurs by rainfall. 

Attachment occurs only on days without rainfall; the rate of attachment is specified by parameter Caffix. 

Transport of detached sediment is by overland flow. The scouring of the matrix soil is simplified into one 

process by combining both pickup and transport by overland flow. 

Sediment Detachment by Rainfall 

Kinetic energy from rain falling on the sediment detaches particles which are then available to be 

transported by overland flow. The equation that simulates detachment is: 

⎛ Pcp ⎞

J r 

SED = ∆t × (1 −C )× P × K × ⎜

⎜ ⎟

⎟ (3-14) det r r ∆t ⎝ ⎠

where 

SEDdet = sediment detached from the soil matrix by rainfall (tons/acre/interval), 

∆t = number of hours/interval, 

Cr = fraction of the land covered by snow and vegetation, 

P = supporting management practice factor, 

Kr = coefficient for detachment of soil by rainfall, 

Pcp = rainfall (in./interval), and 

Jr = exponent for detachment of soil by rainfall. 

Sediment Removal by Overland Flow 

When simulating the washoff of detached sediment, the transport capacity of the overland flow is 

estimated and compared to the amount of detached sediment available. The transport capacity is 

calculated by the equation: 

3-42 
⎛ qs ⎞

Js 

(3-15) SED cap = ∆t × K s × ⎜ ⎟ ⎝ ∆t ⎠

where 

SEDcap = transport capacity of detached sedim ent in overland flow (tons/acre/interval), 

∆t = number of hours/int rval, e 

Ks = coefficien t for detached sediment by overland flow, 

qs = overland flow (in./interval), and 

Js = exponent for detached sediment by overland flow. 

When SEDcap is more than the amount of detached sediment in storage ( Sdet), the flow washes off all th e 

detached sediment st orage, and SEDwo becomes equal to Sdet. However, when SEDcap is less than Sdet, the 

situation is transpo rt limiting, so SEDwo is equal to SEDcap. 

Direct detachment and transport of the soil matrix by scouring (e.g., gullying) is sim ulated with t he 

equation: 

⎛ qs ⎞

J g 

SEDscr = ∆t × Kg 

× ⎜ ⎟ (3-16) 

⎝ ∆t ⎠

where 

SEDscr = scour of matrix soil (tons/acre/interval), 

∆t = number of hours/interval, 

Kg = coefficient for scour of the matrix soil, 

qs = surface flo w (in./interval), and

 Jg = exponent for scour of the matrix soil. 

The sum of the two fluxes, SEDwo and SEDscr, represents the total sediment outflow (SEDso) from the land 

segment. 

Re-attach ment of Deta ched Sediment 

Sedimen t attachme nt to the soil matrix is simulated by changes in SEDdet. Because the soil matrix is 

considered to be unlimi ted, no addition to the so il matrix is necessary when this occurs. Sdet is diminis hed 

at the start of each day following a da y without preci pitation. This decrease is cal culated by multiply ing

Sdet by (1.0 - C affix), where Caffix is the fraction by which detached sediment storage de creases each day a s a 

result of soil compaction. This fraction is a calibration p aramete r. 

Pervious Land Sediment Input Parameters 

Table 3-9 shows the recomm ended ranges of input parameters for simulating the pervious land segment. 

The minimum and ma ximum ranges given in Table 3-9 are for the numerical stability of the model. The 

actual values fall withi n those ranges and are typically defined by calibration and user experience. 

Additional HSPF parameterization guidance is available as part of the Better Assessment Scie nce 

Integrating Point and Nonpoint Sources (BASINS) technical note series at 

http://www.epa.gov/waterscience/basins/docs/tecnote8.pdf (USEPA 2006). 

3-43 
Table 3-9. List of Sediment Input Parameters for Pervious Land 

Parameters Default Value Min. Value Max. Value Units 

P 1.0 0.001 1.0 none 

Kr 0.0 0.0 none -

Jr none none none -

Caffix 0.0 0.0 1.0 per day 

Cr 0.0 0.0 1.0 none 

Ks 0.0 0.0 none -

Js none none none -

Kg 0.0 0.0 none -

Jg none none none -

Source: Bicknell et al. 2001 

Pervious Land Sediment Erosion Calibration 

The erosion process on pervious land areas is represented as the net result of detachment of soil particles 

by raindrop impact on the land surface and subsequent transport of the fine particles by overland flow. 

The primary sediment erosion calibration parameters are as follows: 

Kr = coefficient in soil detachment equation (pervious area) 

Ks = coefficient in sediment washoff equation (pervious area) 

Although a number of additional parameters are involved in sediment erosion calibration, such as those 

related to vegetation cover, agricultural practices, rainfall, and overland flow intensity, Kr and Ks are the 

primary parameters controlling sediment loading rates. Kr is usually estimated as equal to the erodibility 

factor, K, in the USLE (Wischmeier and Smith 1965) and then is adjusted in calibra tion. Ks is primarily 

evaluated through calibration and past experience. 

While Table 3-9 presents possible parameter ranges to e nsure model stability, Table 3-10 lists the 

sediment parameters along with typical and possible minimum an d maximum ranges of values based on 

application experience over the past 20 years. In addition, the HSPFParm database (USEPA 1999) 

provides calibrated parameter values for numerous watersheds across the United States. Additional 

guidance in sediment erosion calibration is provided in the HSPF Application Guide (Donigian et a l. 

1984). 

Pollutant Buildup 

In SUSTAIN, the amount of pollutant buildup over land is computed using one of the followin g functions 

available in SWMM, as a function of the number of preceding dry-weather days (R ossman 2005). 

Power Function 

Pollutant buildup, B, is accumulated proportional to time, t, raised to defined power C3 until a maximum 

limit is achieved: 

B = min (C1 ,C2 × ∆t

C3 ) (3-17) 

where 

3-44 

B = pollutant buildup (mass per unit area, e.g., lbs/acre), 

C1 = maximum buildup possible (mass per unit area, e.g., lbs/acre), 

C2 = buildup rate consta nt (mas s per unit area per unit time, e.g., lbs/acre/day), 

C3 = time exponent, and 
 

 

 ∆t = time, e.g., number of days. 

Table 3-10. Ra nge of Values for Sediment Erosion Parameters for Pervious Land 

Name onDefiniti Units 

Range of Values 

Function of... Comments 

Typical Possible 

Min xMa Min Max 

P Manag ement 

practice factor 

from USLE 

None0.0 1.0 0.0 1.0 Land use, 

agricultural 

practices 

Use the P factor 

from USLE 

Kr nt in 

nt 

Coefficie 

the soil 

detachme 

equation 

-- 0.15 0.45 0.05 0.75 Soils Estimate from the 

soil erodibility 

factor (K) in USLE 

Jr Exponent 

the soil 

 in 

detachment 

equation 

None 1.5 2.5 1.0 3.0 Soils, climate Usually start with 

value of 2.0 

Caffix Daily reduction 

in detached 

sediment 

Per 

day 

0.03 0.1 0.01 0.5 Soils, 

compaction, 

agricultural 

operations 

Reduces fine 

sediments 

following tillage 

Csv Fraction land 

surface 

protected from 

rainfall 

None 0.0 0.9 0.0 0.98 Vegetal cover, 

land use 

Seasonal/ monthly 

values are often 

used 

Ks Coefficient in 

the sediment 

washoff 

equation 

-- 0.5 5.0 0.1 10.0 Soils, surf ace

conditions 

Primary sediment 

calibration 

parameter 

Js Exponent in 

the sediment 

washoff 

equation 

None 1.5 2.5 1.0 3.0 Soils, surface 

conditions 

Usually use value 

of about 2.0 

Kg Coefficient in 

soil matrix 

scour equation 

-- 0.0 0.5 0.0 10.0 Soils, evidence 

of gullies 

evidence of gulli 

Calibration, used 

only if there is 

es 

Jg Exponent in 

soil matrix 

scour equation 

None 1.0 3.0 1.0 5.0 Soils, evidence 

of gullies 

Usually use value 

of about 2.5 

Source: Donigian and Love 2003 

Exponential Function 

Pollutant buildup, B, follows an exponential growth curve that approaches a maximum limit 

asymptotically: 

B = C1 × (1− e 2 ∆t ) −C × (3-18) 

where 

B = pollutant buildup (mass per unit area, e.g., lbs/acre), 

C1 = maximum buildup possible (mass per unit area, e.g., lbs/acre), 

C2 = buildup rate constant (per time, e.g., per day), and 

3-45 
C1t B = (3-19) C2 + t 

 ∆t = time, e.g., number of days. 

Saturation Function 

Pollutant buildup, B, begins at a linear rate then slows down over time until a saturation value is reached: 

where 

B = pollutant buildup (mass per unit area, e.g., lbs/acre), 

C1 = maximum buildup possible (mass per unit area, e.g., lbs/acre), 

C2 = half-saturation constant (days to reach half of the maximum buildup), and 

t = time, e.g., number of days. 

Pollutant Washoff 

The accumulated pollutants on a pervious land surface are washed off during runoff periods from a choice 

of available SWMM functions (Rossman 2005), which are all supported by SUSTAIN. These functions 

include the exponential washoff, rating curve washoff, and an event mean concentration (EMC). 

Exponential Washoff 

The washoff load, W, is proportional to the product of runoff raised to the defined power C2 and to the 

amount of pollutant buildup remaining at each simulation timestep: 

W = C1 × qs 

C2 × B (3-20) 

where 

W = pollutant washoff load (mass per unit area per time, e.g., lbs/acre/hr), 

C1 = washoff coefficient, 

C2 = washoff exponent, 

qs = runoff rate per unit area (e.g., in./hr), and 

B = pollutant buildup (mass per unit area, e.g., lbs/acre). 

Rating Curve Washoff 

The rate of pollutant washoff, W, is proportional to the runoff rate raised to the defined power C2: 

W = C1 × qs

C2 (3-21) 

where 

W = pollutant washoff load (mass per unit area per time, e.g., lbs/acre/hr), 

C1 = washoff coefficient, 

C2 = washoff exponent, and 

qs = runoff rate (user-specified flow units, e.g., in./hr). 

Event Mean Concentration (EMC) 

This is a special case of Rating Curve Washoff where the exponent is 1.0 and the coefficient C1 represents 

the washoff pollutant concentration in mass per volume. The typical EMCs for selected pollutants in 

urban runoff are shown in Table 3-11. 

3-46 
Table 3-11. Typical EMCs in Urban Runoff 

Pollutant EMC 

TSS (mg/L) 180–548 

BOD (mg/L) 12–19 

COD (mg/L) 82–178 

Total P (mg/L) 0.42–0.88 

TKN (mg/L) 1.90–4.18 

NO2/NO3-N (mg/L) 0.86–2.2 

Total Cu (μg/L) 43–118 

Total Pb (μg/L) 182–443 

Total Zn (μg/L) 202–633 

Source: USEPA 1983 

Impervious Land Segment 

SUSTAIN computes sediment and all other pollutant loadings from the impervious land segment using the 

pollutant buildup and washoff algorithms as defined in the previously described Pervious Land Segment 

section. For impervious land segments, SUSTAIN supports a street-sweeping algorithm adopted from 

SWMM (Rossman 2005). The user can specify days between sweeping, days since the last sweeping at 

the start of the simulation, the fraction of bu ildup of all sediment types (sand, silt, and clay) available for 

removal by sweeping, and the fraction of available buildup for each sediment type removed by sweeping . 

The parameters can differ by type of land use. 

3.2.4. Important Considerations and Limitations: Land Module 

Rainfall-runoff time series data are the drivers for BMP simulation and network routing in SUSTAIN. 

The relative modeled response from one land use to another is influenced by the physical characteristics 

of the land as defined by the model setup. In any modeling application, many important factors must be 

considered. A few of these considerations are highlighted for guidance on configuration and 

interpretation of results when applying the land module. They include model testing considerations and 

land segmentation. 

Model Testing: Calibration and Validation 

Calibration and validation is a process during which monitoring data are split into two independent 

periods: calibration and validation. Ideally, those are two typical periods (not extreme conditions) within 

a typical range of flow conditions. During the calibration period, key parameters are adjusted within 

reasonable ranges until the best fit with the observed data is determined. The performance of the 

calibrated model is then tested with data from a separate validation period. 

The SWMM-based method available in SUSTAIN, as well as similar rainfall-runoff models and methods 

used for externally generating time series, though physically based, are empirical in how they are applied. 

The models require calibration and validation of estimated model results with observed data. Observed 

data that are used for calibration are often collected at locations that drain multiple land use types. 

Because individual modeled land use time series are the fundamental units for runoff generation in 

3-47 
SUSTAIN, there is a need to ensure with reasonable confidence that modeled results are meaningful and 

applicable. 

An ideal modeling data set for calibration includes m onitoring several smaller watersheds, each with 

relatively homogeneous land uses (i.e., low-density urban, high-density urban, forest) for a range of storm 

conditions. For each monitored storm event, the recomm ended data includes high-resolution 

precipitation, flow hydrograph, and discrete or composite water qualit y sampling for pollutants of 

concern. Using the observed precipitation, flow, an d water quality data, the model is developed and 

tested for each land use type on the basis of physical ch aracteristics of the drainage area. Standard 

calibration techniques are typically applied to assess how well the observed and the modeled time series 

datamatch. 

A limitati on of any modeling effort is the inability to represent 100 percent of nature’s heterogeneity; the 

recognition of that fact influences the interpretation and application of calibrated model results. The setup 

of SUSTAIN is based on building representative land usetimes series data that allow users to make 

com parisons and extrapolate responses for current and potential fu ture land use and management 

conditions. Testing each land use and the combined behavior of mixed land use w atersheds against 

observed data is used to adjust model parameters for the best fit and to confirm model results. One 

com mon use of the calibrated data set is the extrapolation of re sults at one location to represent a response 

at other similar but ungaged locations. SUSTAIN includes land use reclassification to facilitate the 

application of models to various future or managed conditions and can be used to develop locally 

homogenized responses of similar land units. The primary ob jective of the model is to capture the unique 

essence of one type of land-use response relative to other types. When the available calibration data are 

limited, that objective becomes even more important for either extrapolatingtime series response from 

one calibrated area to another, or adopting loading estimates for various land uses from literature. 

Particle Size Distribution 

Most land-based erosion and sediment simulation techniques compu te total eroded sediment load. 

Because the simulation land segments are discret ized by the land use category, erosion is simulated 

uniquely and is characteristic of that land use category. For sediment produced by each land use, particle 

size distribution is represented as fractional distribution of the total sediment . That means that the total 

land-based sediment load, multiplie d by the corresponding size fraction, givesthe actual amount of 

sediment computed for each sediment si ze class. The user can allocate sediment into three classes ( sand, 

silt, and clay) . The three sediment classes are used to represent sed iment response behavior during 

transport in subsequent B MP or conduit m odules or both. 

Particle size distribution a nd associated pollut ant concentrations pro vide an important linka ge to BMP 

simulation algorith ms. For example, BMPs that remove pollutant t hrough trapping and settling of 

sediments will be especia lly sensitive to the user-specified particle size distributions. If residence time 

within the BMP i s short, o nly larger particles might be re moved effe ctively. For ext ernally generated 

time series data, SUSTAIN supports the o ption to specify each sediment class as anindependent time 

series rather than apply a size distribution to a bulk sediment m ass. This approach might be desirable in 

cases where p article size distribution changes dramatically during the course of a single storm event and 

detailed sediment mo nitoring data are available to justify the mode ling approach. 

Land Segmentation 

Finally, depending on th e size of the watershed being modeled, regional considerations can influence the 

robustness and utility of t he modeled land units. In fact, the way land units are class ifie d from the onset 

can have a strong bearing on how representative or portable the modeled land unit response will be. For 

example, in some places, it might be sufficient to model land units on land use alone; whereas, in others, 

the use of a hydrologic response units (HRUs) approach might provide a better representation. An HRU 

3-48 
is a combination of multiple physiographic characteristics, such as land use, soils, or slope. In flat regions 

where soils are relatively homogeneous, it might be sufficient to use a purely land-use-based 

classification approach. However, if soils or slopes are heterogeneous across multiple land units, the 

HRU approach might be a better way of organizing land units. 

3.3. BMP Module 

The BMP module is designed to provide a process-based simulation of flow and pollutant transport 

routing for a wide range of structural BMPs. It is designed so that new BMPs and alternative solution 

techniques can be added over time. The BMP module performs the following hydrologic processes to 

reduce land runoff volume and attenuate peak flows: evaporation of standing surface water, infiltration of 

ponded water into the soil media, deep percolation of infiltrated water into groundwater, and outflow 

through weir or orifice control structures. Figure 3-8 shows a schematic of the BMP simulation 

processes. 

BMP

Simulation 

Processes

Deep Percolation

Under drain outflow

Weir/Orifice Outflow

Evapotranspira ion

Infiltration

Inflo

 & Upstr

w

(Land eam)

BMP 

Simulation 

Processes 

Deep Percolation 

Under drain outflow 

Weir/Orifice Outflow 

Evapotranspira ion

Infiltration 

Inflo

 & U pstr

w

(Land eam) 

Figure 3-8. A schematic showing the BMP simulation processes modeled in SUSTAIN. 

Table 3-12 provides an overview of the required inputs, the methods used to manage and process the 

inputs, and the resulting outputs of the BMP module. 

Table 3-13 provides a summary of the key BMP simulation processes included in SUSTAIN. Option 1 is 

the default option. Option 2 provides a more data intensive alternative that simulates additional physical 

processes in the BMP. With regard to pollutant removal, Option 1 differs from Option 2 in that it does 

not include background concentration C*. Users can select processes from either option depending on the 

available data and level of detail required. 

Table 3-14 lists the BMP types and the associated applicable simulation methods. The BMP simulation 

techniques are chosen and implemented to provide a reasonable representation of the physical processes 

associated with detention, retention, and infiltration. 

3-49 
 

 

 

 

 

Table 3 12- . Summary of Inputs, Method s, and Outputs in the BMP Module 

BMP Module 

Inputs 

− Define BMP dimensions 

− Define substrate (soil an 

− Define sediment settling 

d underdrain media) properties 

 and transport parameters 

− Define cost for each functional component of a BMP 

− Hourly inflow time series 

− Hourly sediment (sand, silt, and clay) concentration time series 

ourly pollutant concentration time series 

− Define pollutant removal and routing parameters 

− H 

Methods 

− Eva (user-selected constant, monthly, or daily values; 

de ethod ) 

− In t or Holtan methods 

− Deep p user-specified background infiltration 

rain outflow is computed using orifice equation 

− Sediment (sand, silt, and clay) settling and routing is computed using the 

processed based rithms adopted from the HSPF model 

− Pollutant remova st 

− Pollutant routing 

method 

potranspiration is calculated 

rived from daily temperature using Hamon m 

filtration is computed using the Green-Amp 

ercolation is calculated according to 

rate 

− Surface outflow is computed using weir or orifice equations 

− Underd 

al 

l 

go 

is calculated using 1 order decay or k-C* method 

 is computed by using completely mixed or CSTR in series 

Outputs 

− Sub-hourly outflow time series 

− Sub-hourly sediment (sand, silt, and clay) concentration time series 

− Sub-hourly pollutant concentration time series 

Table 3-13. Available Optional Methods for BMP Simulation Processes 

Processes Option 1 Option 2 

Flow Routing Stage-outflow storage routing using weir or 

orifice equations 

For swale: kinematic routing by 

solving the coupled continuity 

equation and Manning’s equation 

Infiltration Green-Ampt method Holtan-Lopez equation 

Evapotranspiration Constant ET rate or monthly average value, 

or daily values 

Calculate potential ET using 

Hamon’s method 

Pollutant Routing Completely mixed, single CSTR CSTRs in series 

Pollutant Removal 1st order decay k’-C* method 

Buffer Strip (Sheet Flow) 

Flow Routing 

Kinematic wave overlan d flow routing --

Buffer Strip Sedimen 

ping 

t 

Trap 

Proce sed Uni Kentuc diment 

interc simul ethod plied 

in VFSMOD 

ss-ba v. of ky se 

eption ation m as ap --

Buffer Strip (Sheet F 

tant Removal 

low) 

Pollu 

1st or cayder de --

3-50 
 

 

 

Table 3-14. Representative BMPs and Recommended Simulation Methods 

BMP Recommended Simulation Methods 

Detention pond Constant ET rate or monthly average value, or daily values 

Calculate potential ET using Hamon’s method 

Stage-outflow storage routing using weir or orifice equations 

uting 

ort 

Completely mixed pollutant ro 

CSTR in series pollutant routing 

First order decay (k’-C* method) 

Sediment settling and transp 

Constructed wetland Green-Ampt method 

Holtan-Lopez equation 

Constant ET rate or monthly average value, or daily values 

Calculate potential ET using Hamon’s method 

Stage-outflow storage routing using weir or orifice equations 

Completely mixed pollutant routing 

CSTR in series pollutant routing 

First order decay (k’-C* method) 

Sediment settling and transport 

Bioretention Green-Ampt method 

Holtan-Lopez equation 

Constan rate or monthly average value, or daily values 

Calculate potential ET using Hamon’s method 

Stage-outflow storage routing using weir or orifice equations 

Completely mixed pollutant routing, single CSTR 

st 

er defined) 

t ET 

1 order decay, no C* 

Underdrain percent reduction (us 

Infiltration trench Green-Ampt method 

Holtan-Lopez equation 

Constant ET rate or monthly average value, or daily values 

Calculate potential ET using Hamon’s method 

low storage routing using weir or orifice equations 

Completely mixed pollutant routing, single CSTR 

1st order decay, no C* 

Stage-outf 

Hydrodyn amic sto e rag Stage-outflow storage routing using weir or orifice equations 

device llutant routing 

* 

Completely mixed po 

1st order decay, no C 

Sedimentation 

Grassed swale Kinematic flow routing by solving the coupled continuity equation and 

, single CSTR 

1st order decay, no C* 

Manning’s equation 

Completely mixed pollutant routing 

Sediment settling and transport using user defined settling velocity and 

critical shear stress 

Vegetated filterstrip Kinematic wave overland flow routing 

Process-based sediment interception simulation method (VFSMOD) 

st 1 ord er decay pollutant removal, no C* 

The following describes in more detail the methods and algorithms implemented in the BMP simulation 

module. Section 3.3.1 describes the BMP simulation component, Section 3.3.2 the buffer strip 

component, Section 3.3.3 the aggregate BMP component, and Section 3.3.4 the cost database component. 

3-51 
Q = C L h3/2 

w w w (3-23)

3.3.1. BMP Simulation Component 

BMPs in SUSTAIN are simulated using a combination of fundamental algorithms to represent the 

processes of storage, routing, infiltration, evapotranspiration, underdrain infiltration, and pollutant routing 

and removal. The fundamental algorithms associated with each method are shown below. 

Storage Routing Method 

Water balance storage routing is a commonly used method for flow routing in ponds and impoundments. 

∆V/∆t = I −O (3-22) 

where 

∆V = change in storage (volume), 

∆t = time interval (time), 

I = inflow (volume per unit time), and 

O = outflow (volume per unit time). 

Stage-outflow relationships are widely used for flow routing through an orifice or over a weir as shown in 

Figure 3-9. 

Reservoir level

h

Sharp crested weir

Orifice diameter

H

Reservoir level 

Qw

Qo

h 

Sharp crested weir 

Orifice diameter 

Qw 

H 

Qo 

Figure 3-9. Wetland/lake/reservoir weir and orifice outflow. 

Weir Outflow 

Three commonly used weir types (i.e., sharp-crested rectangular weir, sharp-crested triangular weir, and 

broad-crested rectangular weir) are supported in SUSTAIN. 

The equation for the rectangular, sharp-crested weir overflow is (Linsley et al. 1992): 

where 

Qw = outflow over sharp-crested weir (ft3

/s), 

Cw = coefficient of discharge, 

Lw = length of weir crest (ft), and 

h = depth of the water above weir crest (ft). 

Values of Cw (English units) for sharp-crested rectangular weirs are given in Table 3-15. 

3-52 
Table 3-15. Coefficient Cw (English units) for Rectangular Sharp-Crested Weirs 

Hd/h 

Head h on Weir, ft 

0.2 0.4 0.6 0.8 1.0 2.0 5.0 

0.5 4.18 4.13 4.12 4.11 4.11 4.10 4.10 

1.0 3.75 3.71 3.69 3.68 3.68 3.67 3.67 

2.0 3.53 3.49 3.48 3.47 3.46 3.46 3.45 

10 3.36 3.32 3.30 3.30 3.29 3.29 3.28 

∞ 3.32 3.28 3.26 3.26 3.25 3.25 3.24 

Source: Linsley et al. 1992 

Hd = Height of the weir 

The equation for the triangular (V-notch) sharp-crested weir overflow is (Linsley et al. 1992): 

Qw = C "
 w 

8
 

15
 

θ
 θ

⎜

⎛ tan ⎛

⎜ ⎞⎟ ⎞⎟

⎠
 

5/2 5/2 2g h 4.28C " h tan =
 w (3-24) ⎝ 2
 ⎠ ⎝
 2

where 

Qw = outflow over sharp-crested weir (ft3

/s), 

Cw

”

 = coefficient of discharge, default value is 0.58 for English units, 

h =depth of the water above weir crest (ft), 

θ = vertex angle of the V-notch, and 

g = acceleration of gravity (32.2 ft/s2

). 

True broad-crested weir flow occurs when the upstr eam head above the crest is between about 1/20 and 

1/2 the crest length in the direction of flow (USBR 2001). Equation (3-25) is applicable to broad-crested 

weirs, and it is recommended that weir coefficient Cw be determined by measuring the flow at various 

flow rates (Linsley et al. 1992). The value of the weir coefficient varies with h/Hd. One way of 

estimating Cw is to use the equation derived by Fox (University of British Columbia Department of 

Mechanics (No date) Fluid Dynamics Course Notes): 

0.65 2 C
 2g

⎛

= w 1/2 3
 (3-25) h ⎞ ⎜1+ ⎜

⎝
 

⎟

⎟ Hd ⎠

where 

h = depth of the water above the weir crest (ft), 

Hd = height of the weir (ft), and 

g = acceleration of gravity (32.2 ft/s2

). 

Orifice outflow 

The equation for the or ifice flow is: 

Qo = Co Ao 2gH (3-26)
 

where 

Qo = outflow through orifice (ft3

/s), 

Co = orifice coefficient of discharge, 

Ao = orifice cross sectional area (ft2

), 

3-53 
 g = acceleration due to gravity (ft/s2

), and 

H = depth of the water level above the orifice (ft). 

Infiltration/Filtration 

SUSTAIN supports two options for the simulation of infiltration in BMPs: (1) the Holtan-Lopez equation 

adopted from the Prince George’s County BMP module (Tetra Tech 2001) and (2) the Green-Ampt 

equation (for details, see Section 3.2.2) as is applied in the SWMM (Rossman 2005). 

Holtan-Lopez Empirical Model 

The Holtan-Lopez empirical model computes the infiltration rate as a function of the actual available soil 

water storage, Sa, of the surface soil layer, as shown below (Maidment 1993): 

f = GRI × A× S 1.4 + f a c (3-27)

where 

f = infiltration rate (in./hr), 

GRI = growth index of vegetation in percent maturity, varying from 0.1 to 1.0, 

A = infiltration capacity (an index representing surface-connected porosit y and density of plant 

roots), 

Sa = available storage in the surface layer (in.), and 

fc = constant final infiltration rate (in./hr). 

In Equation (3-27), A is the vegetative parameter that characterizes surface-connected porosity and the 

density of plant roots, which affect infiltration (a value of 0.8 is a typical number for sod or vegetation 

that would be found in a BMP). fc is the final constant infiltration rate (in./hr), which is a function of the 

hydrologic soil group. The value of fc ranges from 0.3 in./hr for group-A soils to between 0.0 and 0.05 

in./hr for group-D soils (Maidment 1993). In a continuous calculation, the available soil storage (Sa) and 

infiltration rate (f) are computed at each simulation time step. Available soil storage is updated each tim e 

increment and the infiltration is calculated. 

This method was developed using th e premise that soil moisture storage, surface-connected porosity, and 

the effect of root density of the control soil layer are the dominant factors influencing the infiltration 

process. 

A difficulty with using this method is estimating the control soil layer depth. For simulating the 

infiltration process, it is assumed that the soil column depth is the control depth because BMP devices 

normally have a confined soil/substrate layer. 

Green-Ampt Infiltration Equation 

This method is discussed in Section 3.2.2 in the hydrology component of land simulation module. The 

Green-Ampt equation can be applied to both surface runoff and BMP simulation. 

When performing BMP infiltration simulation, the impact of the underdrain layer, the impermeable 

bottom layer, or both, on the infiltration process needs to be considered in the simulation. Because the 

Green-Ampt method can be applied to a layered soil column, the underdrain layer can be represen ted as a 

separate layer under t he soil column. In cases where an impermeable layer is present at the bottom of the 

soil column, the infiltration rate ceases when the soil storage capacity is reached. 

A drawback of the Green-Ampt method is that it does not include a parameter to explicitly reflect the 

effect of the vegetation root zone on the infiltration rate. 

3-54 
 

Evapotranspiration 

Potential evapotranspiration (PET) time series can be estimated on the basis of the U.S. Weather Bureau 

Class A pan records with adjustment. For instance, the HSPF WDM utility estimates PET using the pan 

records, then the HSPF simulation model is used to adjust the time series for snow accumulation an d melt 

(Bicknell et al. 1997). When snow conditions are absent, only PET and precipitation are required. 

However, when snow conditions are considered, air temperature, rainfall, snow cover, water yield, and ice 

content of the snowpack are also re quired, and the evaporation data are adjusted. The input evaporation 

values are reduced to account for the fraction of the lan d segment covered by the snowpack. 

Several methods are avail a ble to estimate PET. The Penman-Mont eith method (Maidment 1993) requires 

values for solar radiation, air temperature, relative humidity, and wind speed. The Priestley-Taylor 

method (Maidment 1993) require s solar radiation, air temperature, and relative humidity. The third, 

Hargreaves method (Maidment 1993) requires air temperature only. 

SUSTAIN provides three options to estimate PET: (1) rely on the user-supplied monthly PET rate (2) 

calculate PET from the user-supplied pan evaporation time series input and mon thly pan coefficients, and 

(3) calculate the PET rate using Hamon’s method (1961). 

Hamon’s (1961) method generates daily PET using air temperature, a monthly variable coefficient, the 

number of hours of sunshine (computed from latitude), and absolute humidity (computed from air 

temperature). 

PET = C × D 2 × ρ (3-28) TS hr ws 

where 

PET = daily PET (in.), 

TS = monthly variable coefficient, and 

Dhr = possible hours of sunshine computed as a function of latitude and time of year. 

C

216.7 × p ρ = ws (3-29) ws Tav + 273.3 

where 

ρ ws = saturated water vapor density (absolute humidity) at daily mean air temperature (g/cm3

) 

and 

Tav = mean daily air temperature ( °

C). 

⎡17.26939 × Tav ⎤

pws = 6.108 × exp ⎢ ⎥ (3-30)

Tav + 273.3 ⎣ ⎦

where 

pws = saturated vapor pressure at the air temperature. 

Hamon (1961) suggests a constant value of 0.0055 for CTS. However, monthly values can be specified to 

avoid underestimating PET in some areas, especially for the winter months. 

3-55 
Calculate Actual Evapotranspiration 

Once PET is determined, the actual evapotranspiration (ET) is calculated as a function of PET and soil 

moistu re storages. While PET represents the maximum possible achievable ET on the basis of 

atmospheric c onditions alone,actual ET is determined using an accounting of the status of the various 

components of the hydrologic budget. The actual ET is equal to PET when the soil moisture is greater 

than or equal to the moisture at the field capacity and there is no actual ET if the moisture content is less 

than or equal to the moisture at the wilting point. 

Underdrain Method 

Underdrain Outflow 

The underdrain outflow in a BMP is modeled using a simple water balance concept. The available 

underdrain storage is represented as the total of void spaces beneath the upper soil layer. Inflow into 

underdrain storage is limited by the final infiltration rate of the upper soil layer. Because the primary 

function of the underdrain is to provide additional water storage and to delay outflow, the outflow pipe 

draining the underdrain layer is placed at the interface between the upper soil layer and the underdrain 

layer. Figure 3-10 illustrates the function of underdrain together with other substrate model components. 

Figure 3-10. Processes considered in an underdrain structure. 

Outflow from the underdrain layer is assumed to be unrestricted; therefore, no pipe outflow is required. 

Underdrain outflow is part of the modeled BMP effluent and occurs when all available underdrain stora ge 

is used up, when the water level meets or exceeds the underdrain level, or when both occur. Each 

infiltration management practice can be modeled with or without underdrain outflow. If underdrain 

outflow is enabled, the user must specify the thickness of the under drain storage layer, the media void 

fraction, and the background infiltration rate (Figure 3-10). Water and pollutants are removed from the 

system entirely through background infiltration. 

Underdrain Filtration of Pollutant 

Ifunderdrain is specified in the soil properties of a BMP, additional reduction in pollutant concentration 

from underdrain routing is simulated in the module using the underdrain percent removal, which is a 

user-supplied parameter. This option allows users the flexibility in estimating pollutant removals through 

the soil media of a BMP. 

3-56 
Pollutant Routing and Removal Methods 

The methods of pollutant routing to achieve pollutant reduction are described in this subsection for a 

completely mixed system and a multiple im poundments in series. The flow through a plug flow reactor 

(PFR), as a series of infinitely thin coherent plugs, each with a uniformcomposition , is perfectlymix ed in 

the cross direction but not in t he longi tudin al directio n (direction of flo w). Each plug of differential 

volume is consideredas a sepa rate ent ity, with an infinitesimally small volume and requires a very sm all 

time steps (in seconds). SUSTAIN uses one minuteto hour ly ti me step to simulate fl ow and pollutant 

routing and does not support plug flow option in the cur rent version. However, it can be seen that an 

infinite nu mber of small continuouslystirred tank reactors (CSTRs) operating in series would be 

equivalent to a PFR. 

First-Order Decay with Complete Mixing 

This method is commonly used and suitable for small pon ds when complete mixing is likely. 

d(VC) = I(t)CI (t) − O(t)C(t) − KC(t)V(t) (3-31)

dt 

where 

V = reservoir volume (ft3

), 

CI = influent pollutant concentration (mg/L), 

C = effluent and reservoir pollutant concentration (mg/L), 

3 I = inflow rate (ft /s), 

O = outflow rate (ft3

/s), 

t = time (sec), and 

K = decay co efficient (1/s).
 

Continuously Stirred Tank Reactors in Series and Kadlec and Knight’s Model 

CSTRs in series are used to represent a hydraulic condition intermediate between completely mixed and 

plug flow (Wong et al. 2001, 2002). That method is applied for simulating first-order pollutant removal 

processes (e.g., settlin g, decay) that occur in ponds, wetlands, and other similar BMPs. The calculation 

begins by estimating the number of reactors in series to be selected to represent the shape of the BMP, 

followed by applying first order kinetics with nonreactive background concentration (the k-C* model; 

Kadlec and Knigh t 1996). 

Step 1: Estimate N, the number of CST Rs in series. 

N, the number of CSTRs in series, can be appro ximated on the basis of BMP shape (Persson et al. 1999; 

Wong et al. 2001, 2002). Values of N fo r the various pond shapes, shown in Figure 3-11, are presented in 

Table 3-16. Highest N values are for ponds with a distributed inflow (pond E), baffles (pond G), and very 

elongated flow or high length to w idth ratio (pond J). 

3-57 
A B C D E 

G H I J 

KOPQ

Figure 3-11. Conceptual pond shapes simulated by Persson et al. (1999). 

Table 3-16. Quality Ratings of Conceptual Pond Shapes Simulated by Persson et al. (1999) 

Pond N≈1/(1-λ) Rating 

Qualitative 

J 10.0 Good 

G 4.2 

E 4.1 

P 2.6 Satisfactory 

Q 2.5 

I 1.7 Poor 

K 1.6 

A 1.4 

B 1.4 

O 1.3 

D 1.2 

H 1.1 

C 1.1 

Step 2: Apply first-order decay (k' - C* model) to each CSTR. 

After selecting the number of reactors, pollutants are modeled for each tank at each time step using the 

first-order kinetic model, described in Equation (3-32). 

* * −k' /q (3-32) (C −C )/(C −C ) = e out in 

where 

C* = background concentration (mg/L), 

Cin = input concentration (mg/L), 

Cout = output concentration (mg/L), 

q = hydraulic loading or overflow rate (m/yr), 

k' = k∙h = rate constant (m/yr), 

k = first order decay rate (1/yr), and 

h = pond depth (m). 

3-58 
This equation is computed separately for each time step at each CSTR. The main difference between thi s 

equation and ordinary first-order decay modeling for a CSTR is the inclusion of C*, the background 

concentration, below which the effluent cannot fall. Another advantage of this method is that using an 

areal rate constant (units of depth/time) instead of a volumetric one (units of inverse time) helps avo id 

having to specify an average depth or the volume for odd natural configurations; instead, only the pond 

surface area is required to compute the hy draulic loading rate q. 

Wong et al. (2002) recommend some k' and C* values as shown in Table 3-17, on the basis of limited 

model calibration for total suspended solids (TSS), total phosphorus (TP), and total nitrogen (TN) in 

urban areas near Melbourne. Those values should be used with caution. However, they could be used as 

a starting point in the absenceof local d a ta. 

Table 3-17. Recommended k' and C* Values 

T nt Measures reatme 

k' 

(m/yr) 

C* 

(mg/L) 

TSS TP TN TSS TP TN 

S ation Basins ediment 15,000 12,000 1,000 30 0.18 1.7 

Ponds 1,000 500 50 12 0.13 1.3 

Vegetated Swales 15,000 12,000 1,000 30 0.18 1.7 

Wetlands 5,000 2,800 500 6 0.09 1.3 

Localized calibration can be performed to customize the simulation technique for specific areas. The C* 

and k' values can be determined or calibrated using monitoring data (particle size distribution in 

particular) and treatment measure design specifications. 

3.3.2. Overland Flow Routing and Pollutant Interception 

The following algorithms for overland flow routing and pollutant interception simulation are employed in 

SUSTAIN for buffer strip simulation. 

Kinematic Wave Overland Flow Routing Method for Filter Strip Simulation 

Overland flow through filter strips can be simulated using a kinematic wave method and solving the 

coupled continuity and Manning’s equations. 

Mathematical Model 

Continuity equation 

∂h ∂q + = ie (t) (3-33)

∂t ∂x
 

Manning’s equation 

So 5/3 (3-34) q = q(h) = h 

n 

where 

h = overland flow depth (ft), 

3-59 
q = overland flow per unit width of the subcatchment (ft2

/s), 

n = Manning’s roughness coefficient, 

ie = rainfall excess depth (ft/s), 

t = time (s), and 

So = subcatchment slope. 

Initial conditi o n 

h = 0; 0 ≤ x ≤ L; t = 0 

Boundary condition 

h = h0 ; x = 0; t > 0 

where, h0 can be 0, a constant, or a time-dependent function, such as the incoming hydrograph from the 

adjacent subcatchment. The rainfall excess, ie, can be calculated from the hyetograph and Green-Ampt 

infiltration method at each time step. 

Numerical Solution 

The cou pled continuity equation and Manning’s equation are solved using Petrov-Galerkin (PG) 

formulation to compute the flow rate (q), velocity (v), and depth (h) throughout the plane for each time 

step. Kinematic shocks (oscillations in the solution) are introduced when a su dden change in conditions 

(e.g., slope, roughness, and inflow) occurs. For filter strip simulation, as the soil surface conditions are 

updated for each time step, the potential fo r kinematic shocks is further increased. The PG finite element 

method was found to reduce the amplitude and frequency of oscillations compared to a conventional 

Bubnov-Galerkin finite element solution, thus improving the model stability for situations that are subject 

to kinematic shocks (Muñoz-Carpena et al. 1993). 

VFSMOD Algorithms for Sediment Interception 

The sediment interception algorithm used in VFSMOD considers that when runoff reaches the upstr eam 

edge of the filter, the vegetation provides a sudden increase in hydraulic resistance, which slows the flow, 

lowers its transport capacity, and causes depos ition of the coarse material (particle diameter dp > 0.0037 

cm), which is carried mostly as bed-load transp ort. The sediment trapped in the first section of the filter 

forms a geometrical shape (the wedge zone), which is either triangular when Y (t) < H or trapezoidal after 

Y (t) = H, where Y (t) is the thickness of the deposited sediment layer and H is the effective height of th e 

vegetation. 

The wedge zone is characterized by three well-defined zones: O, A, and B (Figure 3-12). The sediment 

loads at points 1 and 2 (gs1 and gs2) are calculated using the flow values provided by the overland flow 

routingmodule at points 1 and 2, and Einstein’s sediment bed-load transport function. After solving the 

sediment transport equations for a time step, new values of roughness and/or slope are selected as nodal 

values for the finite element grid in zones A(t) and B(t); while values for the grids in zones C(t) and D(t) 

remain unchanged. Changes in surface saturated hydraulic conductivity values are assumed to be 

negligible. The new surface parameters are fed back into the hydrology model for the next time step. 

The changes in the surface condition (i.e., slope and roughness) due to sediment deposition are recorded 

and passed back to the flow module. 

3-60 
ENTRY
 

Flow needed at points 1 2 3 (see text)

Lt L(t)

X2(t) C(t)

D(t)

X1(t)

O(t)

A(t)

B(t)

Suspended Load Zone

EXIT

Wedge Zone

1

2

Sc 3

S qout, gsO

Field

qin, gsI

c

grass

gsi, qin

gsi, qin

gs1, q1df1

gs2, q2, df2

gs3, q3, df3

Y(t) = H

Sc

Wedge

X1(t) 

O(t) 

A(t) 

B(t) 

Suspended Load Zone 

EXIT 

Wedge Zone 

Flow needed at points 1 2 3 (see text) 

Lt L(t) 

X2(t) C(t) 

D(t) 

1 

2 

Sc 3

S qout, gsO 

Field 

qin, gsI 

c 

grass 

gsi, qin 

gsi, qin 

gs1, q1df1 

gs2, q2, df2 

gs3, q3, df3 

Y(t) = H Wedge 

Sc 

Figure 3-12. Filter description for the sediment transport algorithm. 

Einstein’s bed-load transport Equation (3-35) is solved using the method proposed by Barfield (MuñozCarpena 1993) to compute the sediment transport capacity. 

−0.28 ⎞

⎟

⎟

⎟

⎟

⎟

⎛

⎜

⎜

⎜

⎜

⎜

γ
 − γ
 d g (3-35) p sk = 1.08 s 

γ Rsk S k γ
 − γ 3 γ
 gd s 

s p γ ⎝ ⎠

where 

γ = water density (g/cm3), 

γ 3 s = sediment density (g/cm ), 

dp = particle diameter (cm), 

gsk = sediment load (g/cm-s) at point k (k = 1, 2), 

Sk = slope at point k, 

g = gravitational constant (cm/s2

), 

Rsk = spacing hydraulic radius at point k, defined as

Ss = grass spacing (cm), 

Ssd fk , 2d S fk s +

 

, and 2/3

qkn 

Rsk Sk 

dfk = modified flow depth (cm) at point k, defined as

q

2

k = unit width flow rate (cm /s) at point k. 

It was assumed that only sediment at the fine sand/silt threshold (diameter > 0.0037 cm) is considered in 

the wedge zone sediment routing and that fine sediment (diameter < 0.0037 cm) runs through to the 

suspended zone (Muñoz-Carpena 1993). Therefore, the user is required to input the percentage of coarse 

particles from incoming sediment that will be routed through the wedge. The calculated sediment 

transport capacity is compared with incoming sediment concentration. If the incoming sediment 

concentration is higher, deposition at the wedge will occur; if lower (meaning that there is enough energy 

3-61 
to transport sediment through the wedge and no deposition occurs), all sediment is transported to the 

suspended sediment zone (zones C(t) and D(t)). 

After the downside of the wedge, two zones, C and D, form the suspended load zone. It was assumed th at 

on zone C, sediment has covered the indentations of the surface so that bed-load transport and deposition 

occur, but the soil slope is not significantly changed. All bed-load transported sediment is captured 

before reaching zone D so only suspended sediment is transported and deposited in this zone until the 

flow reaches the end of the filter. Flow values at point 3 and the exit point are needed for the calculation 

and are provided by the flow module. The trapping capacity (Tr) Equation (3-36) developed by Tollner e t 

al. (1976) is used to simulate the sediment trapping for the suspended load zone: 

−0.91 

−3 ⎛ V3Rs3 ⎞ ⎛ Vf L ⎞ ⎡ 0.82 ⎤ ⎢−1.05×10 ⎜ ⎟ ⎜

⎜ ⎟

⎟ ⎥

gs2 − gso ⎢

⎣ ⎝ v ⎠ ⎝ V3h3 ⎠ ⎥

⎦ (3-36) Tr = = e 

gs2 

where 

gs2 = sediment load at point 2 (g/cm-s), 

gso = sediment load at the output point (g/cm-s), 

V3 = mean velocity at point 3 (cm/s), 

Vf = fall velocity (cm/s), 

v = kinematic viscosity of water (cm2

/s), and 

L = effective filter length (cm). 

Particle Deposition and Sediment Transport 

On the basis of the laboratory study by Deletic (2001) the particle deposition or trapping efficiency for 

sediment fraction s (particle with diameter of ds) can be estimated as a function of the particle fall 

number, Nf,s, which is calculated as: 

lVs N f,s = (3-37)

hV 

where l is the flow length, Vs the Stokes’ settling velocity of particle size ds, and V is the average mean 

flow velocity between grass blades. The sediment trapping efficiency Tr,s is expressed as: 

N 0.69 

T = f,s (3-38) r,s 0.69 N f,s + 4.95

The suspended sediment transport equation is expressed as: 

2 ∂(hqs,s /q) ∂qs,s ∂ (hqs,s /q) (3-39) + = Dis − λ qs s,s ∂t ∂x ∂x

2 

where qs,s is the sediment loading rate of fraction s per unit width, Dis is the dispersion coefficient, and λs 

is the trapping efficiency for fraction s per unit length calculated as λs=Tr,s/l. 

3-62 
Terrain Surface Level and Slope Changes 

The rise in the surface level, z, is modeled as the integral of trapped particles of all fractions of particle 

sizes and is expressed as: 

∂z(x,t) 1 1 = λsqs,sds (3-40) ∫ ∂ 1 − p ρs t s 

where p is the porosity of deposited sediment. 

The water quality for the pollutant (other than suspended sediment) is simulated by applying the mos t 

commonly used first order decay model. A first order decay is equivalent to an exponential decay, 

represented by the Equation (3-41). 

Ct =C0 e−kt (3-41) 

where 

Ct = concentration at time t (mass per volume), 

C0 = initial concentration at time zero (mass per volume), and 

k = reaction rate (per timestep). 

For the current phase of SUSTAIN development, the decision was mad e to include this complex, processbased simulation for filter strip simulations. This model is CPU intensive and, thus, is not used during the 

optimization process. Its purpose in SUSTA IN is for eval uation of filte r strip performance only (see 

Section 3.3.6). Future phases will include a s implified m ethod that ca n be incorporated into the

optimization module. 

3.3.3. Aggregate BMP Component 

The aggregate BMP component provides an optiona l method for assessing the comb ined impa ct of 

multiple BMPs on the watershed runoff and pollut ant load. The form ulation was developed to represent 

the aggregate characteristics of distri buted BMP, while reducing the user effort for m odel setup and 

computation time needed for sim ulation and optim ization. While th e BMP module in SUSTAIN performs 

explicit simulation of individual BMP pract ices for a defined manage ment ar ea, the aggregate BMP 

component evalua tes storage and infiltration characteristics for multiple BMPs simultaneously without 

explicit recognition of their spatial distribution and routing characteristics in the selected watershed. 

As illustrated in Figur e 3-13, an aggregate B MP consists of a series of process-based compone nts, 

including on-site inter ception, on-si te treatm ent, routing a ttenuation, a nd regional storage/treatment. For 

the aggregate BMPs, users are asked to input des ign drain age area and number of units for each aggregate 

BMP component. Each component can be used to repres ent one of a number of individual BM Ps. For 

example, on-site interception can be represented as a cistern, rain barrel, or green roof, while on-site 

treatment can be bioretention, porous pavement, or an infiltration trench. Each individual component can 

be enabled or disabled according to th e desired functionality and sized and parameterized using the BMP 

templates that are identical to that of the individual BMPs. 

3-63 
Figure 3-13. Generic aggregate BMP schematic. 

Aggregate BMPs can be applied to a user-defined drainage area. The land use distribution of the drainage 

area is automatically calculated on the basis of the land use map and populated into the land use 

distribution/assignment table as shown in Figure 3-14. Users then assign the percentage of each land use 

that contributes to each of the aggregate BMP components (Figure 3-14). Runoff and pollutant loads 

from the total drainage area of each component are lumped and routed through the respective component. 

The relative scales and sizes of individual BMPs are preserved in the aggregate representation. 

Figure 3-14. Illustration of land use area assignment to aggregate BMP components. 

To investigate the applicability of the aggregate BMP approach, a test case was conducted to compare the 

results of a SUSTAIN simulation using the aggregate approach to one using the distributed approach 

representing the same BMP scenarios. The distributed approach represents a fully articulated BMP and 

routing network, whereas the aggregate approach represents the component responses. Because of the 

lumped representation, the aggregate approach does not consider detailed routing between components. It 

is assumed that for small basins, the associated short time of concentration means that a fully articulated 

routing simulation is not necessary. Similarly, there is presumably some watershed size threshold, above 

which the lumped routing assumption might no longer be appropriate. 

That presumption was evaluated by developing five test simulation drainage areas of different sizes (1.3 

acres, 7.8 acres, 31.2 acres, 128 acres, and 256 acres). For each size, three simulation scenarios were 

applied: (1) an aggregate BMP representation, (2) a fully articulated network with conduit routing, and 

(3) a fully articulated network without conduit routing (conduit dimensions were set equal to zero). In 

this way, the test was designed to highlight the relative importance of the routing component to overall 

simulation results. Figure 3-15 illustrates the testing concept, showing the relative complexity of the 

distributed routing network scenarios as they increase in size vs. that of the aggregate representation. 

One-year simulations were conducted for each scenario with each drainage area size. Three factors were 

3-64 
computed and summarized for each run: total annual flow volume, peak flow rate, and total annual TSS 

load. 

Table 3-18 summarizes the results of the comparison. Among the three factors examined, peak flow rate 

is most sens itive to drainage area size. For example, at 128 acres, the peak flow rate values of the 

aggregate representation are significantly different from the distributed representations even though total 

flow volume and total TSS load values remain relatively constant. 

At the drainage area size of 256 acres, the total TSS load of the a ggregate representations were drastically 

different from th e distributed ones; however, the differences are much smaller for drainage areas les s than 

256 acres (less than 2 percent difference). Simulation run times f or va rious scenarios are listed in T able 

3-19, showing that on the basi s of this initial testing and as expected, t he distributed representation 

requires a m uch lon ger run time than the aggregate representation. Distri epre buted R sentation Aggr on egate Representati

Rooftop 

Pavement 

Agg Rain Barrel 

Agg. Bioretention 

1.3 acres 7.8 acres 31.2 acres 128 acres 256 acres 

Figure 3-15. Aggregate BMP testing con figuration. 

3.3.4. BMP Cost Database Component 

The BMP mod ule c o sting component provides the underlying cost dat abase used by the optimizatio n

component in evaluating BMP scenarios. The compilation of cost inform ation was predicated on 

obtaining cost data in a format that could be input into a uniform datab ase. This compilation was 

constrained by the non-uniformity with which available cost informati on for BMP cons truction is 

reported. As a result, it was determined that the best a pproach for building SUSTAIN’s cost database was 

to determine u nit co s ts (i.e., cost per square foot) for individual construction components of the overall 

BMP. Construction components include excavation, grading, filter fabric, and so forth. Basing the cost 

estimation routines on basic construction components rather than the whole BMP installation is aimed to 

minimize differences encountered because of site or locality factors. Users have the ability to override 

the data with their own locally derived information. 

3-65 
 

 

 

 

 

 

 

 

 

 

 

The cost database was developed by identifying individual construction components for each BMP 

technique simulated by SUSTAIN. Table 3-20 outlines the construction components for which data were 

compiled, the assumptions governing the general characteristics of the construction com ponent (e.g., 

hardwood mulch versus pine straw mulch ), and the associated BMP techniques. Unit costs for each 

component were compiled from retail sources and fromreference documents and programs involved in 

BMP implementation at the local, state, and federal levels. 

Table 3-18. Comparison of Aggregate vs. Distributed BMP Results 

Scenario 

Aggregate 

Representation 

Distributed 

Representation 

% Difference vs. Aggregate 

Representation 

w/o 

Routing Routing 

Distributed 

w/o Routing 

Distributed 

with Routing 

Total flow volume (ft3

/yr) 

1.3 acre with BMPs 32,845 33,401 33,924 1.69 3.29 

7.8 acre with BMPs 197,038 200,408 203,956 1.71 3.51 

31.2 acre with BMPs 787,793 808,312 822,903 2.60 4.46 

128 acre with BMPs 3,150,904 3,233,250 3,280,110 2.61 4.10 

256 acre with BMPs 6,301,805 6,466,500 6,563,871 2.61 4.16 

Peak Flow Rate (cfs) 

1.3 acre with BMPs 0.7 0.7 0.7 2.00 -0.59 

7.8 acre with BMPs 4.0 4.1 3.9 1.75 -1.75 

31.2 acre with BMPs 15.2 16.3 15.7 6.83 3.09 

128 acre with BMPs 58.3 65.1 60.4 11.58 3.52 

256 acre with BMPs 120.8 175.7 91.7 45.41 -24.09 

TSS load (lb/yr) 

1.3 acre with BMPs 280 282 281 0.57 0.20 

7.8 acre with BMPs 1,679 1,690 1,669 0.63 -0.60 

31.2 acre with BMPs 6,727 6,817 6,725 1.34 -0.03 

128 acre with BMPs 26,907 27,269 26,925 1.35 0.07 

256 acre with BMPs 53,814 68,794 67,685 27.84 25.78 

Table 3-19. Simulation Run-Time Comparison: Aggregate vs. Distributed 

Scenario Representation 

Aggregate 

w/o Routing 

Distributed 

w/ Routing 

Distributed 

Notes 

1.3 acre with BMPs < 0.3 sec 1 sec. 14 sec 19 BMPs, 5 conduits 

7.8 acre with BMPs < 0.3 sec 12 sec. 90 sec. 114 BMPs, 30 conduits 

31.2 acre with BMPs < 0.3 sec 60 sec 7 min 456 BMPs, 120 conduits 

128 acre with BMPs < 0.3 sec 7 min 30 min 1,824 BMPs, 480 conduits 

256 acre with BMPs < 0.3 sec 12 min 50 min 3,648 BMPs, 961 conduits 

3-66 
 

 

 

 

 

Table 3-20. BMPs and Associated Construction Components 

Construction 

Component Description Applicable BMP Techniques 

Excavation Using light equipment Bioretention Basin; Vegetated 

Swale; Porous Pavement; Wet 

ion Basin 

Pond; Dry Pond; Wetland; 

Infiltrat 

Grading/finishing Generally using light equipment or hand to 

minimize compaction except in the case of the 

ols to 

creation of detention ponds where compaction is 

necessary 

Swale; Porous P 

Bioretention Basin; Vegetated 

avement; Wet 

Pond; Dry Pond; Wetland; 

Infiltration Basin; Buffer Strip 

Backfilling Replacing excavated area with soil/planting media; 

required when using amended growing media 

Bioretention Basin; Wetland; 

Infiltration Basin 

Soil/Planting 

Media 

A typical mix is 50% sand, 30% planting soil (low 

clay content), and 20% shredded hardwood mulch 

Bioretention Basin; Wetland; 

Infiltration Basin; Green Roof 

Filter Fabric Often placed between gravel reservoir and 

underlying/overlying soil to reduce clogging of the 

reservoir void spaces 

Bioretention Basin; Porous 

Pavement; Infiltration Basin 

Gravel 1 Porous pavement filter course, smaller particle sizes Porous Pavement 

Gravel 2 Reservoirs; slightly larger particle sizes, no fines Bioretention Basin; Porous 

Pavement; Infiltration Basin 

Gravel 3 Erosion control (rocks/riprap) Wet Pond; Dry Pond; Wetland 

Underdrain Pipe 4-in. perforated PVC Bioretention Basin; Porous 

Pavement 

Mulch Shredded hardwood Bioretention Basin; Wetland; 

Infiltration Basin 

Rain Barrel Rain Barrel 

Green Roof 

System 

Green Roof 

Grass Framework assumes use of sod Bioretention Basin; Vegetated 

Swale; Wet Pond; Dry Pond; 

Infiltration Basin; Buffer Strip; 

Wetland 

Perennials Assumes planting density of 1 ft o.c. for 1-gal plants Bioretention Basin; Infiltration 

Basin; Wetland 

Small Trees Assumes planting densities of 15 ft o.c. Bioretention Basin 

Woody Shrubs Assumes planting densities of 3 ft o.c. Bioretention Basin 

Inlet Structure Wet Pond; Dry Pond; Wetland 

Outlet Structure Wet Pond; Dry Pond; Wetland 

Observation Well 4-in. PVC pipe Infiltration Basin 

Seal Bentonite (as opposed to geotextile) Wet Pond 

Porous Paving 

Material 

 Porous Pavement 

3-67 
Unit Cost Data Sources 

Costs for BMP constru ction com ponents were obtained from the following sources. 

Wholesale/Retail Bulk Material Pricing 

Several wholesale or retail companies were inventoried to p rovide bulk material pricing. Retailers were 

contacted to provide unit pricing for various raw materials such as mulch, sand, stone, and other 

commercial landscape mat erials. Retailers were also contacted to provide pricing for rain barrels. Unit 

cost data were compiled in 200 7. 

EPA Stormwater Technology Fact Sheets 

EPA’s Office of Water Municipal Technology Assessment Program sup ports innovative and alternative 

technology deve lopment through a number of efforts and partners. In 1999 the program produced a series 

of Stormwater Technology Fact Sheets (832-F-99-001 to 048), which are at 

http://www.epa.gov/owmitnet/mtb/ mtbfact.htm . The series provides information on advantages and 

disadvantages, design criteria, operations and maintenance, performance, and cost estimates re garding a 

range of managemen t technolog ies including biorete ntion, catch basins, flow diversion, infiltra tion 

trenches, modular sys tems, porous pavement, a nd others. 

California Department of Transportation (CALTRANS) 

In the late 1990s, CALTRANS began a stud y to evaluate structural BMPs for stormwater treatment. 

Among other things, the study ev aluated removal efficiencies, capital costs, and annual operation and 

maintenance (O&M) costs of individual applications. 

Fairfax County BMP Fact Sheets 

Fairfax County, Virgin ia, developed 25 fact sheets for inclusion in the county ’s Public Facilities Manual 

that present an overviewof the manag ement strategies and technologies for various current or p otential 

BMP/LID techniques used in the county. The fact sheets address seven different BMP categories 

including: (1) bioretent ion systems, (2) filtering technologies, (3) permeable pavements, (4) s it e design 

strategies, (5) soil amen dments, (6) vegetative sy stems, and (7) water conservation/reuse. The 

information in each fact sheet is consistent so that relative comparisons can be m ade on the critical 

design, construction, and maintenance issues. Information includes a general description of the BMP, 

water quality and quant ity controls, l ocation; design construction and materials, cost, maintenance, 

performance and inspec tion, and potential LEED (Leaders hip in Energy and Environmental De sign) 

credits. The fact sheets are at http://www.lowimpactdevelopment.org/fairfax.htm#ffx factshee t. 

Natural Resources Conservation Service (NRCS) Cost Share Data 

The U.S. Department of Agriculture’s (USDA’s) NRCS program Web site provides cost lists and tools 

developed by NRCS field office staff to support BMP cost-sharing programs under the department’s 

Environmental Quality In centives Program and other programs. Documents containing unit cost data for 

BMP com ponents are available for various years for 34 states. 

Michigan Department of Environmental Qua lity’s (DEQ’s) 319 BMP Cost Database 

The Michigan DEQ’s N onpoint Source Program administers the CWA section 319 grant prog ram fo r the 

state. It requires grantees to submit BMP cost share information for purposes of tracking the cost and 

location of BM Ps installed with DEQ Nonpoint Source grant funding and documenting expe nses for costshare practices. DEQ is required to produce annual reports for the EPA and the Michigan legislature to 

detail the use of state and federal grant funds. In addition, the data are also used to share information 

about specific practices with current and potential grant recipients. The SUSTAIN cost database adapted 

3-68 
 

cost data for certain practices such as green roofs and porous pavement because they were reported in the 

same units as the cost m odule database. Costs for other relevant BMPs are reported in the DEQ BMP 

Cost Share database; however, component costs are not broken down. As a result, these data were not 

used. 

Green Roofs 

EPA’s Heat Island Web site and the Great Lakes Water Institute’s Web site provide installation and 

maintenance costs per u nit for green roofs in urban a reas. The Web sites are 

http://www.epa.gov/hea tisland/mitigation/greenroofs.htm 

http://www.glwi.uwm.edu/research/genomics/ecoli/greenroof/roofinstall.php#costs. 

Minnesota Stormwater Manual (version 1.1) 

The Minnesota Stormwater Manual (Minnesota Pollution Control Agency 2005) providesunit costs for 

several BMPs. The Stormwater Manual discusses a variety of BMP approaches designed to lessen the 

impacts of urban development. The Manual explores an array of BMPs that can be implemen ted to 

control sediment and reduce runoff in a practical and flexible manner. 

Cost Database 

The cost database in theBMP module is a Microsoft Access database containing records related to unit 

costs of BMP construction components releva nt to the techniques simulated by SUSTAIN. In addition to 

the unit cost per compo nent, each record contains informatio n related to the source of the data and, to the 

extent possible, the year or general tim e period from which the cost data were re corded. In addition, 

O&M cost estimates were available for a limited number of records. All unit cost data were entered into 

the unit cost table using the unit in the original source; conversions to a consisten t unit of measure take 

place in the system using appropriate conversion factors. 

Using the Cost Component 

The Cost Component provides the flexibility of choosing the construction components and the associated 

costs for estimating the total cost of the BMP. 

User Data 

Users also have the option to enter their own cost data as well a s operations and maintenance costs. 

Selection of Data 

Cost estimates in the database are taken from sources across the country; however, not all states and 

regions are represented. Users can choose to base cost estimates on an average of all data in the database 

or they can choose to use only select sources of data if they deem specific sources to be adequately 

representative. In addition, the interface allows users the option of not including specific components 

when calculating the unit cost of a BMP. For example, in an area where it is not necessary to incl ude an 

impermeable seal on the bottom of a wet detention pond, the cost component can be turned off. O&M 

data were limited and, thus, are not included in the cost database. 

Table 3-21 through Table 3-26 provide additional information related to fields and tables that compo se 

the database. 

3-69 
 

 

 

Table 3-21. Components 

Column Details Type 

Components_ID Component ID number Numeric 

Components_txt Construction component Text 

Components_Desc General description of the component Text 

Table 3-22. BMP Types 

Column Details Type 

BMPType_ID BMP Type ID number Numeric 

BMPType_Code Lookup code (no spaces) (e.g., Bioretention Basin) Text 

BMPType_Desc Description of the BMP Type (e.g., Bioretention Basin) Text 

Table 3-23. BMP Components 

Column Details Type 

BMP_Component_ID Unique record identifier Numeric 

Component_ID Component ID number as in components table Numeric 

BMP Type_ID BMP Type ID number as in BMP Types table Numeric 

Table 3-24. Component Costs 

Column Details peTy 

Component_Cost_ID Unique record identifier Numeric 

Component_ID Construction Component Numeric 

Unit Unit on which cost estimate is based (e.g.,, ft, ft , ft , etc.) 2 3 Text 

Cos t Cost in dollars Numeric 

Year Year in which the construction cost estimate was developed for conversion 

and baseline comparisons 

Numeric 

Source_ID Code for the reference from which unit cost data weretaken Text 

Locale Geographic area for which cost estimate is applicable (e.g., national, state,

city specific) 

Text 

UseFlag Flag to differentiate useable records Numeric 

Note Notes regarding the data Text 

Orig_Unit Original unit on which cost estimate is based, before conversion to standard 

units 

Text 

O srig_Co t Original cost in dollars, before standardizing units Numeric 

Table 3-25. Unit Types 

Column Details Type 

UnitType_ID Unit Type ID number Numeric 

UnitType_Code Code for the unit of cost data used for specific BMP component costs (e.g., 

ft2

) 

Text 

UnitType_Desc Text description of unit of cost data for BMP component cost (e.g., square 

feet) 

Text 

3-70 
 

 

 

Table 3-26. Reference Sources 

Column Details Type 

Reference_Source_ID Unique record identifier Numeric 

Source_Type Assigned code for the reference/source from which unit cost data were 

taken 

Text 

Title Title of reference Text 

Year Year reference was published eric Num 

Author A gency under w se name the re rence was d eloped and distributed ho fe ev tTex 

Publication_Street Mailing street address, if listed Text 

Publication_City City lis r publishing ency/author OT city for whi ata are 

derived 

ted fo ag ; N ch d Text 

Publication_State State listed for publishin ency/author; NOT state for which data are 

derived 

g ag Text 

Publication_ZIP ZIP Co sted for publi ng agency/ r; NOT the ZIP Code for which 

data ar ved 

de li 

e deri 

shi autho eric Num 

Reference_Number Any co sted on the re title page by author/issu ency to 

identif official publications 

de li port used ing ag 

y and track 

Text 

Prepared_by Develop the referen f work wa pleted on beh ublic 

agency contractor) 

er of ce (i s com alf of p 

by 

Text 

Note Notes regarding the source and/or data Text 

Factors Affecting Developm ent of Cost Database and Implications for Use 

Format of available cost data 

All data used to populate the cost database are from nonproprietary sources. It is important to note that 

the nature of database construction requ ires that information be consistent and uniform. Unfortunately, a 

wide variety of formats are used in which cost data for BMP and construction components are reported. 

That presents a serious limitation to the amount of cost data available for the current phase of SUSTAIN 

development. To maximize the utility of the optimization functions of SUSTAIN, future development 

phases must include development of additional unit cost data. 

A limited number of estimates were available for O&M costs. If a range of costs was given in the 

original source, such as 5 through 7 percent of construction costs, the average was used in the database 

(e.g., 6 percent). 

NRCS data 

The NRCS unit cost data represents a significant portion of the cost records in the SUSTAIN cost 

database. The information available from the NRCS includes cost lists and various tools (such as 

spreadsheets) developed by NRCS field office staff to support BMP cost-sharing programs under the 

USDA’s Environmental Quality Incentives Program and other programs. In general, the BMPs and cost 

data represented in the NRCS data are focused on rural, agricultural applications; however, several 

projects have data available in counties with both urban and rural areas. Project and construction 

component costs are obviously reflective of local and regional economic factors. 

Note, too, that different states have adopted different approaches to developing this information; while 

some state NRCS field of fices responsible for developing this information employ economists, some do 

not. In Virginia, for example, costs are based on either real project numbers or on bids submitted by local 

3-71 
 

contractors for typical applications. Atypical (expensive, technically complicated, or unusual) projects 

are generally excluded from the universe of estimates used to generate average cost lists. 

The cost share data for 28 states were accessed and reviewed during development of the database, and 

appropriate records were selected for inclusion on the basis of consistency with the BMPs and 

components represented in SUSTAIN, as well as the applicability of units for which costs were reported. 

The potential exists that cost estimates derived from the NRCS data are low, relative to costs for proje cts 

in more urban areas, which might be expected to have higher unit costs because of higher operating, 

equipment rental, land, and other costs. Efforts were made to select data for inclusion in the database to 

address this potential for underestimation. For example, the Alabama cost estimator spreadsheet includes 

four records related to unit costs for the SUSTAIN component backfill. The analogous component in the 

Alabama Cost Estimator spreadsheet is called Earthfill. Cost estimates are available for < 3,000 yd3

; 

3,000–10,000 yd3; 10,000–30,000 yd3; and 30,000+ yd3. Unit costs for the smallest volume (< 3,000 yd3

) 

are highest; therefore, it was the estimate selected for inclusion in the SUSTAIN cost estimate database. 

Where the NRCS data provide multiple unit costs for a c omponent, the highest cost is used. 

Because the NRCS data represent such a significant proportion of the publicly available unit cost data 

related to implementation and construction of BMPs, they were included in the cost database for 

SUSTAIN. However, in recognition of the potential limitations associated with applying those data to 

urban areas, users may opt to exclude all NRCS data from cost calculations. Note that excluding NRCS 

data will result in a more restricted data set from which the m odel’s cost estimates will be based. 

3.3.5. Summary of Management Practices and Treatment Processes in SUSTAIN 

SUSTAIN provides multiple ways for simulating management practices that are widely used to treat 

runoff and mitigate flow volumes. Treatment processes may be grouped into the following categories: 

• Storage/detention or flow attenuation (i.e., those detaining and/or attenuating water) 

• Infiltration (i.e., those infiltrating water to the ground) 

• Filtration (i.e., those passing water through a porous medium) 

• Evapotranspiration (i.e., those losing water from surface and/or soil column) 

• Water quality (i.e., those performing pollutant removal) 

Table 3-27 lists the structural BMP types handled by SUSTAIN and the associated treatment processe s. 

3.3.6. Important Considerations and Limitations of the BMP Module 

Selecting an Appropriate Simulation Method 

The BMP module provides an array of simulation options with varying degrees of sophistication in t he 

required input and computational rigor. Such options provide flexibility for users to customize their 

problem formulations to suit the project needs. The user should carefully select the method or approach 

considering the overall problem formulation, availability of supporting data, and expected outco mes. For 

example, users can choose between the two infiltration options provided: the simple Holtan infiltration 

method or the iterative Green-Ampt infiltration method. To optimize BMP selection for a large study 

area having several infiltration BMPs, it might be advantageous to select the Holtan method over the 

Green-Ampt method because it might result in shorter computation time (e.g., reduced runtime for a 

single run by several seconds). Because it might be required to perform hundreds or thousands of BMP 

simulations during an optimization analysis, the accumulated run time savings of a few seconds per run 

3-72 
could be significant. On the other hand, users should also consider simulation accuracy and localized 

investm ent in model simulation in determining the appropriate technique. For example, if the GreenAmpt method has already been parameterized and successfully applied for land simulation (in SWMM), it 

would be wise to use the same method for sim ulating infiltration in the Land and BMP modules. 

Table 3-27. Structural BMPs and Major Treatm ent Processes 

Structural BMP Types Attenuation 

Storage/Detention 

or Flow 

Infiltration nFiltratio Evapotranspiration 

Water 

Quality 

Bio retention o + o o + 

Cistern + - - - -

Constructed Wetland + (o) - + + 

Dry Pond + (o) - + o 

Grassed Swale o + (o) - o 

Green Roof (o) - (o) + -

Infiltration Basin (o) + o - o 

Infiltration Trench (o) + o - o 

Porous Pavement - + (o) - o 

Rain Barrel + - - - -

San 

surf 

d Filter (nonace) (o) o + - + 

Sand Filter (surface) (o) o + - + 

Vegetated Filterstrip (o) o + o + 

Wet Pond + (o) - o o 

Note: ( ) optional function; + major function; o secondary function; – insign ificant function 

Nonstructural BMP Representation 

The previous discussions focused on the simulation methods for structural BMPs, but viable nonstructural 

BMPs are potentially effective. For example, street sweeping is a nonstructural BMP; however, it is 

handled as part of the land sim ulation module because it directly reduces pollutant sources upstream of 

structural BMPs. Other pollutant sourc e control practices, like fertilizer or pet waste management, are 

BMPs; but for a s imilar reason, they are accounted for through the land module. With the exception of 

street sweeping, SUSTAIN does not provide explicit representation for source control actions. Using 

standard mode ling practices, land use characteristics in the land module or in an external model can be 

modified to represent chan ges in land management. The user can create alternative land use categories 

that represent areas with and wi thout nonstructural BMPs. Note that SUSTAIN does not support the use 

of land uses as a decision variable in cost-optimization. 

Using VFSMOD in SUSTAIN 

The VFSMOD component for the sim u lation of veget ated filter strips has limitations in terms of how it is 

integrated within SUSTAIN. While VFSMOD has a detailed representation of sediment transport through 

a filter strip, it uses a simple first-order decay representation for water quality constituents and does not 

address sedim ent-associated pollutants. In addition, SUSTAIN acts as a pre-processor for generating an 

input filefor VFSMOD, and data flow to VFSMODis one-directional. For that reason, a filter strip 

modeled in VF SMOD cannot be integra ted with othe r BMPs or be included in an optimization 

formulation. Consequently, VFSMOD is primarily used as a BMP evaluation component for assessing 

filter strip performance. 

3-73 
Using the Aggregate BMP 

The behavior of an aggregated BMP typ e in a treatment train c onceptually represents the collected 

behavior of all B MPs of the same type inthe watershed. No attenuation of flow and pollutants, such as 

from routin g through a reach or a conduit, occurs fro m one upstream aggregated BMP to the one 

downstream. For small drainage areas, that assumption usuallyhas less impact because the routing effect 

is small and the travel time or time of co ncentration is short. As drainage area increases, so does the 

compounded impac t of routing through a large conveyance network. The aggregate BMP simulation 

results willbegin to diverge from the detailed distributed simulation. In summary, the aggregate BMP 

option tends to work best in watersheds t hat have a time of concentration similar to the simulation time 

step. Given a one-hour timestep simulation, a goodsize for watershed with a low- to moderate slope is 

between 50 and 150 acres. The aggregated BMP is probably best used for screening-level analysis to 

determine the tre atment potential of a watershed. Once treatment targets have been established, further 

analysis can be perform on a more full y articulated and detailed BMP network. 

Operation and Maintenance Assumpt ions and Costs 

The BMP cost database does not include O&M costs , and provisions are made to allow users to enter 

values in terms of a fraction o f construction costs or as an added fixed cost. Because of the limited and 

highly variable nature of th e O&M cost information, the cost is assumed to be evenly distributed over the 

entire life cycle of the BMP. In addition, the BMPs are assumed to be maintained to perform as designed 

and that performance does not degrade over time. 

3.4. Conveyance Module 

The conveyance m odule performs routing of flow and water quality through a conduit. In SUSTAIN, 

conduits are pipes or one-dimensional open channels that move water from one node to another in a 

watershedrouting network. The cross-sectional shapes of a conduit can be selected from a variet y of 

standard open and closed geometries. Irregular, natural, cross-section shapes are supported, as are userdefined, closed shapes. Flow and pollutant routing are simulated using algorithms from SWMM (ve rsion 

5) transport compartment (Rossman 2005). Sediment routing is simulated using reach sediment tran sport 

algorithms from HSPF (Bicknell et al. 2001). Table 3-28 provides an overview of the required inputs, the 

methods used to manage and process the inputs, and the resulting outputs of the conveyance module. 

3.4.1. Methodology 

This section discusses the methodologies applied by the conveyance module to handle flow routing, 

sediment settling and routing, pollutant removal, and pollutant routing. 

Flow Routing 

SUSTAIN uses the kinematic wave method to perform flow routing simulation. This method solves the 

continuity equation along with a simplified form of the momentum equation. Kinematic wave routing 

allows flow to vary both spatially and temporally in a conduit and results in attenuated and delayed 

outflow hydrographs as inflow is routed (Rossman 2005). The maximum flow that can be conveyed 

through a conduit is the full-flow Manning equation value. Any flow in excess of this conduit capa city is 

bypassed to t he downstream node as an untreated overflow. The typical Manning’s roughness 

coefficients for closed pipes and open channels are shown in Table 3-29and Table 3-30, respectively. 

3-74 
 

 

 

 

 

 

 

 

 

 

 

 

Table 3-28. Summary of Inputs, Methods, and Outputs of the Conveyance Module 

Inputs 

− Define conduit dimensions 

− Define co it initial condition parameters 

efine reach cross-section for irregular shape 

nsport parameters 

− Hourly sediment (sand, silt, and clay) concentration time series 

ndu 

− D 

− Define sediment settling and tra 

− Define pollutant removal and routing parameters 

− Hourly inflow time series 

− Hourly pollutant concentration time series 

Methods 

− Flow routing is computed using kinematic wave method 

diment (sand, silt, and clay) settling and routing is computed using the 

opted from the HSPF model 

using the 1st order decay method 

e CSTR method 

− Se 

process-based algorithms ad 

− Pollutant removal is calculated 

− Pollutant routing is computed using th 

Outputs 

− Sub-hourly outfl ow time series 

− Sub-hourly sediment (sand, silt, and clay) concentration time series 

− Sub-hourly pollutant concentration time series 

Table 3-29. Typical Manning’s Roughness Coefficient for Closed Pipes 

Conduit Material Manning’s Roughness 

Asbestos-cement pipe 0.011–0.015 

Brick 0.013–0.017 

Cast iron pipe 

 Cement-lined and seal coated 0.011–0.015 

Concrete (monolithic) 

 Smooth forms 

 Rough forms 

0.012–0.014 

0.015–0.017 

Concrete pipe 0.011–0.015 

Corrugated-metal pipe 

(1/2-in. x 2-2/3-in. corrugations)

 Plain 

 Paved invert 

 Spun asphalt lined 

0.022–0.026

0.018–0.022

0.011–0.015 

Plastic pipe (smooth) 0.011–0.015 

Vitrified clay 

 Pipes 

 Liner plates 

0.01 –0.015 

0.013–0.017 

Source: ASCE 1982 

3-75 
 

 

 

 

 

 

 

 

 

 

 

Table 3-30. Typical Manning’s Roughness Coefficient for Open Channels 

Conduit Material Manning’s Roughness 

Lined Channels 

Asphalt 0.013–0.017 

Brick 0.012–0.018

 Concrete 0.011–0.020 

Rubble or riprap 0.020–0.035 

Vegetal 0.030–0.40 

Excavated or dredged 

Earth, straight and uniform 0.020–0.030 

Earth, winding, fairly uniform 0.025–0.040 

Rock 0.030–0.045

 Unmaintained 0.050–0.140 

Natural channels (minor streams, top 

width at flood stage < 100 ft) 

Fairly regular section 0.030–0.070

 Irregular section with pools 0.040–0.100 

Source: ASCE 1982 

Sediment Transport 

This section describes the transport, deposition, and scour of inorganic sediment in free-flowing reaches 

using the HSPF algorithms (Bicknell et al. 2001). Figure 3-16 shows the principal state variables and 

fluxes involved in the sediment transport processes. 

 

 

 

 

 

 

SEDro_i SEDin_1Total

SandSediment

Outflow

SEDso
 Ssus_i Total Soil & SEDin_2Suspended SedimentSiltStorage Removal

_SEDds_i

SEDin_3Deposition

Clay+Scour

Sbed_ii=1for Sand

Bedi =2forSilt

Storagei = 3forClay

Figure 3-16. Schematic of sediment transport, deposition, and scour in conduits. 

Both the migration characteristics and the adsorptive capacities of sediment vary significantly with 

particle size. To facilitate analyses to account for the effects of particle sizes, SUSTAIN divides the 

3-76 
inorganic sediment load into three components (sand, silt, and clay), each with its own properties. Sand 

has a particle size ranging from 0.05 millimeter (mm) to 2.0 mm in diameter, silt from 0.002 mm to 0.05 

mm in diameter, and clay smaller than 0.002 mm. 

The system assumes that scour or deposition of inorganic sediment does not affect the hydraulic 

properties of the conduit. Furthermore, it is assumed that sand, silt, and clay deposit in different areas of 

the conduit bed so that the deposition or scour of one material is not linked to the changes of others. 

Longitudinal movement of bed sediments by flow shear stress is not modeled. 

First, the volume occupied by each component of bed sediment is calculated as shown in Equation (3-42). 

Sbed_i V = (3-42) bed_i
 ρ_i
 

where 

Vbed_i = volume occupied by component i of bed sediment (ft3

), 

Sbed_i = bed storage of component i of sediment (lb), and 

ρ_i = particle density of component i (lb/ft3). 

The volumes of the three components of bed sediment are summed, and the total bed volume is adjusted 

to account for voids in the sediment (i.e., the porosit y):

i=3 

∑Vbed_i (3-43) i=1 Vb = 1− η

where 

Vb = volume of bed (ft3

), 

Vbed_i = volume of sediment contained in the bed (sand, silt, and clay) (ft3

), and 

η = porosity of bed sediment (ratio of pore volume to total volume). 

Finally, the depth of bed sediment is calculated as: 

Vb d b = (3-44) Lr ×Wb 

where 

db = depth of bed (ft), 

Vb = volume of bed (ft3

), 

Lr = length of conduit (ft), and 

Wb = effective width of bed (ft). 

Cohesive sediments 

Two steps are used to model the deposition, scour, and transport processes of cohesive sediments (silt and 

clay). The first step computes the advective transport and the second step calculates the amount of 

deposition or scouring on the basis of the bed shear stress. 

3-77 
Advective Transport of Constituent 

This section computes the concentration of material in a conduit and the quantities of material that leave 

the conduit due to longitudinal advection. Two assumptions are made in the solution technique for 

normal advection: each constituent is uniformly dispersed throughout the waters of the conduit and is 

completely entrained by the flow—that is, the material moves at the same horizontal velocity as the w ater. 

The equation of continuity can be written as: 

SEDin − SEDro = (C ×V ) − (Cs ×Vs ) (3-45)

where 

SEDin = total inflow of sediment over the interval (lb), 

SEDro = total outflow of sediment over the interval (lb), 

C 3 s = sediment concentration at the start of the interval (lb/ft ), 

C = sediment concentration at the end of the interval (lb/ft3

), 

Vs = volume of water stored at the start of the interval (ft3

), and 

V = volume of water stored at the end of the interval (ft3

). 

The other basic equation states that the total outflow of material o ver the time interval is a weighted mean 

of two estimates; one based on conditions at the start of the interval, the other on ending conditions: 

SED ro = (C s ×Q s × js)+ (C ×Q ×cojs) (3-46)

where 

Qs = outflow rate at the start of the interval (ft3

/time interval), 

Q = outflow rate at the end of the interval (ft3

/time interval), 

js = weighting factor, and 

cojs = 1 – js. 

By combining Equations (3-45) and (3-46) we can solve fo r the concentration C: 

SED + C × (V − Q × js) in s s s C = (3-47)

V + Q × cojs 

The total amount of material leaving the conduit during the interval is calculated using Equation (3-46). 

If the conduit goes dry during the interval, the total amount of material leaving the conduit is the sum of 

the material coming in and the material leaving based on the concentration at the start of the interval: 

SEDro = SEDin + (Cs ×Qs × js) (3-48) 

Deposition and Scouring 

Exchange of cohesive sediments with the bed is dependent upon the shear stress exerted on the bed 

surface. When the shear stress (τ ) in the conduit is less than the user-supplied, critical, shear stress for 

deposition (τcd), sediment deposition occurs. On the other hand, when the shear stress is greater than the 

user-supplied, critical, shear stress for scour (τcs), scouring of cohesive bed sediments takes place. The 

rate of deposition for a particular fraction of cohesive sediment is bas ed on a simplification of Krone’s 

(1962) equation in the following form: 

3-78 
⎛ τ ⎞ D = ω×C × 1− (3-49) ⎜

⎜

τ ⎟

⎟

⎝ cd ⎠

where 

D = rate at which sediment settles out of suspension (lb/ft2

 interval), 

ω = settling velocity for cohesive sediment (ft/interval), 

C = concentration of suspended sediment (lb/ft3

), 

τ = shear stress (lb/ft2), and 

τcd = critical shear stress for deposition (lb/ft2). 

The rate of change of suspended sediment concentration in the conduit due to deposition can be expressed 

as: 

dC D = − (3-50) dt dav 

where 

dav = average depth of water in the conduit (ft). 

By substituting the expression for deposition rate (D) from Equation (3-49), and integrating and 

rearranging Equation (3-50), a solution can be obtained for the concentration of suspended sediment lo st 

to deposition during a simulation interval (Cdep): 

⎡ ⎧⎛ ω ⎞ ⎛ τ ⎞⎫⎤

Cdep = C × ⎢1− exp⎨⎜

⎜− ⎟⎟×⎜⎜1− ⎟⎟⎬⎥ (3-51) d τ ⎢⎣ ⎩⎝ av ⎠ ⎝ cd ⎠⎭⎦⎥

where 

C = concentration of suspended sediment at the start of interval (lb/ft3

), 

ω = settling velocity for sediment fraction (ft/interval), 

dav = average depth of water in conduit (ft), 

τ = shear stress (lb/ft2), and 

τcd = critical she ar stress for deposition (lb/ft2). 

The user must supp ly value s for settling velocity (ω) and critical sh ear stress for deposition (τcd) for silt 

and clayfractions in cohesive sedime nt. 

The amount of sed imen t in suspension (Ssus) is updatedby subtracting the amount settled. Likewise, the 

amount of sediment in bed (Sbed) is updated by adding the amount settled on it. 

The rate of resuspension, or scour, of cohesive sediments from the bed is derived from a modified form o f 

Partheniades’ (1962) equation: 

⎛ τ ⎞ S = μ ×⎜

⎜ −1⎟⎟ (3-52) 

⎝ τ cs ⎠

where 

S = rate at which sediment is scoured from the bed (lb/ft2

 interval), 

3-79 
 μ = erodibility coefficient for the sediment fraction (lb/ft2

 interval), and 

τcs = critical shear stress for scour (lb/ft2). 

The rate of change of suspended sediment fraction concentration in the conduit due to scour can be 

expressed as: 

dC S = − (3-53) dt dav 

By substituting the expression for scour rate (S) from Equation (3-52) and integrating and rearranging 

Equation (3-53), a solution can be obtained for the concentration of suspended sediment added to 

suspension by scour during a simulation interval (Cscr): 

μ ⎡ τ ⎤ C = × −1 (3-54) scr ⎢ ⎥ d τ av ⎣ cs ⎦

where 

μ = erodibility coefficient (lb/ft2 interval), and 

dav = average depth of water (ft). 

The user is required to supply values for the erodibility coefficient (μ) and critical shear stress for scour 

(τcs) for each fraction of cohesive sediment (silt and clay) that is modeled. 

The amount of sediment in suspension (Ssus) is updated by adding the scoured mass, as is the amoun t of 

sediment in bed (Sbed) by subtracting the scoured mass. 

If the amount of scoured sediment is greater than the original sediment in the bed, all sediment in the bed 

will be resuspended and the amount of sediment in the bed is set to zero. 

Non-cohesive Sediment 

Erosion and deposition of sand, or non-cohesive sediment, is affected by the amount of sediment tha t the 

flow is capable of carrying. If the amount of sand being transported is less than the flow can carry for the 

hydrodynamic conditions of the conduit, sand is scoured from the bed. This occurs until the actual sand 

transport rate becomes equal to the carrying capacity of the flow or until the available bed sand is all 

scoured. Conversely, deposition occurs if the sand transport rate exceeds the flow’s carrying capac ity. 

The sand transport capacity for a conduit is calculated by using an input power function of the velocity. 

The potential sand concentration (Cp) is determined by the following conversion: 

Cp = k ×υav 

j (3-55) 

where 

Cp = potential sand concentration (lb/ft3

), 

k = coefficient in the sandload suspension equation (input parameter), 

j = exponent in sandload suspension equation (input parameter), and 

υav = average velocity (ft/s). 

3-80 
 

 SED + SED + C × (V − Q × js) in ds s s s C = (3-58)

V + Q × cojs 

The potential outflow of sand (SEDpro) is calculated as: 

SED pro = (Cs ×Qs × js)+ (Cp ×Q ×cojs) (3-56) 

where Cs, Qs, js, Q, and cojs are as previously defined for Equations (3-45) and (3-46). 

The potential scour from, or deposition to, the bed storage is found using the continuity equation: 

SEDpds = (V ×Cp )− (Vs ×Cs )+ SEDpro − SEDin (3-57) 

where 

SEDpds = potential scour (+) or deposition (-) (lb), 

Cp = potential sand concentration at the end of the interval (lb/ft3

), 

Cs = sand concentration at the start of the interval (lb/ft3

), 

SEDpro = potential outflow of sand over the interval (lb), and 

SEDin = inflow of sand during the interval (lb). 

The potential scour is compared to the amount of available sand for resuspension. If scouring potential is 

less than the available sands, the demand is satisfied i n full and the bed storage is adjusted accordingly. If 

the potential scour cannot be satisfied by bed storage, all the available bed sand is suspended, and the bed 

storage is exhausted. The concentration of suspended sand (C) is calculated as: 

where 

C = concentration of sand at end of interval (lb/ft3

), 

Cs = concentration of sand at start of interval (lb/ft3

), 

SEDin = inflow of sand during the interval (lb), and 

SEDds = sand scoured from, or deposited to, the bottom (lb). 

The total amount of sand leaving the conduit during the interval is calculated using Equation (3-58). If a 

conduit goes dry during an interval, or if there is no outflow from the conduit, all the sand in suspension 

at the beginning of the interval is assumed to settle out, and the bed storage is correspondingly increased. 

Sediment Transport Input Parameters 

Parametric information required for silt and clay includes particle diameter (φ), particle settling velocity 

in still water (ω), particle density (ρ), critical shear stress for deposition (τcd), critical shear stress for scour 

(τcs), and erodibility coefficient (μ). Parameter values required for sand include median bed sediment 

diameter (φ50) and particle settling velocity (ω). Table 3-31 shows the range of input parameters that are 

recommended. 

Sediment Transport Calibration 

Sediment transport parameters are typically derived by calibration. The calibration process involves 

establishing initial parameter values and a subsequent adjustment process. The eroded material from each 

land use category is fractionated into sand, silt, and clay before entering a conduit using available soils 

information; typically, a single fractionation scheme is used for all conduits. The fraction should reflect 

the relative percent of the surface material (i.e., sand, silt, clay) available for input to the conduit. 

3-81 
Investigation of the bed material composition will also help provide insight into appropriate fractionation 

values. 

The initial sediment parameters—such as particle diameter, particle density, settling velocity, and bed 

depth and composition—and beginning calibration parameters can be evaluated from sources such as 

local/regional data, past experience, and handbooks or literature. The parameter values can then be 

adjusted on the basis of available site-specific data and calibration. 

Table 3-31. List of Sediment Input Parameters for the Reach 

Parameters Default Value Min. Value Max. Value Units 

φ 0.0 0.0 0.003 in. 

φ50 0.01 0.0001 100.0 in. 

ω none 0.02 500.0 in./s 

μ 0.0 0.0 none lb/ft2

/day 

ρ 2.65 1.0 4.0 lb/ft3 

τcd 1.0E10 1.0E-10 none lb/ft2 

τcs 1.0E10 1.0E-10 none lb/ft2 

k 0.0 0.0 none --

j 0.0 0.0 none --

Source: Bicknell et al. 2001 

Pollutant Transport 

The conveyance module simulates pollutant routing assuming that the conduit behaves as a CSTR. The 

algorithms are adopted from SWMM (version 5) transport compartment (Rossman 2005). The pollutant 

concentration exiting the conduit is determined by integrating the conservation of mass equation, using 

average values for quantities that might change over the time step such as flow rate and volume. 

SUSTAIN simulates sediment as a primary pollutant and assumes that all other pollutants follow the 1st 

order decay in a conduit as shown in Equation (3-41). 

3.4.2. Important Considerations and Limitations of the Conveyance Module 

Use of Kinematic Wave Routing Method 

Several issues are important to consider when applying the conveyance module to perform stream 

routing. The desire to maintain a high resolution of spatial detail must be balanced with the need to 

preserve time of concentration along the in-stream flow network in the watershed. In-stream travel time 

can have a significant influence on model stability and accuracy. SUSTAIN incorporates two types of 

routing algorithms: storage routing and kinematic wave. Those algorithms are most accurate when the 

flow time of the flood wave through individual reaches approximates the simulation time step. This is 

achieved during model configuration by either: (1) selecting a simulation time step that is as small as the 

travel time through the smallest transport segment in the network, or (2) sizing the transport segments in 

the network to have travel times that are at least greater than or equal to the simulation time step. 

In cases most likely encountered in the context of SUSTAIN applications, a kinematic wave routing 

method can usually maintain numerical stability with time steps on the order of 5 to 15 minutes. If those 

effects are not expected to be significant, kinematic wave routing can be an accurate and efficient method, 

especially for long-term simulations. Finally, it is important to recognize that kinematic wave routing 

does not account for backwater effects, entrance/exit losses, flow reversal, or pressurized flow and is 

restricted to dendritic network layouts. 

3-82 
Simulation Time and Number of Conveyance Elements 

Another aspect to consider in setting up a conveyance network for simulation is to determine the number 

of conveyance elements. The conveyance system is found to be the most computationally intensive 

component of t he network. It often needs an order of magnitude more time to run water through a 

conveyance than through a BMP. For this reason, the strategic selection and sizing of conve yances (i.e., 

using one representative pipe in place of several actual pi pes in a section of the net work) can have 

significant benefit in terms of computation time savings. If a conveyance network is a part of an 

optimization for mulation, a saving of several seconds per simulation run will translate into a significant 

reduction in com putational time because hundreds or eve n thousands of optimization runs are often 

required to arriveat optimal solutions. 

3.5. Optimization Module 

SUSTAIN includes an optimization module to develop cost-effective BMP placement and selection 

strategies on the basis of a pre-selected list of potential sites and applicable BMP types and size ranges. 

The module use s evolutionary optimiza tion techniques to perform the sear ches for optimal combinations 

of BMPs that meet the user-defined decision criteria. Table 3-32 summari zes the r equired inputs, 

methods used, and outputs and Figure 3-17 presents a conceptual overview of the mod ule. 

The optimization module works hand-in-hand with the BMP, land, and conveyanc e modules during the 

search process in an iterative and evolutionary fashion. The simulation mo dules evaluate the BMP 

performance, as d efined via evaluation factors, and cost data of a set of chosen BMP options and pass that 

information to t he optimization engine. The optimiza tion engine synthesizes the information, modifies 

the search path, a nd generates new solutions that are repe atedly evaluated using th e sim ulation modules. 

Through this e volutionary search process, the module will progressiv ely march t oward the identification 

of the best or most cost-effective BMP solutions th at meet the user’s specific conditions and objectives. 

Table 3-32. Summary of Inputs, Met hods, and Outputs in the Optimizatio n Module 

Inputs 

− Define decisi 

s ion factor(s) 

ana ) 

− Define BMP

on variables (the size ranges of potential BMPs ) 

− Define asse 

− Define m 

sment point(s) and evaluat 

gement targets (for the minimize cost option

 cost functions 

Methods 

− For 

− For the g

 the mini rmed using Scatte echn 

ene s perf ng technique 

mize cost option, optimization search is perfo r Search t ique 

rate cost-effectiveness curve option, optimization search i ormed usi NSGAII 

Outputs 

− For the minimize cost option, the optimization process outputs optimal solutions that meet the specified 

treatment targets 

− For the cost-effectiveness curve option, the optimization process outputs the optimal solutions along the costeffectiveness curve 

3.5.1. Problem Formulation 

The objective of the optimization module is to determine BMP locations, types, and design co nfigurations 

that minimize the total cost of management while satisfying water quality and quantity constraints. To 

formulate an optimization problem, SUSTAIN requires the user to specify three sets of information: 

decision variables, assessment points and evaluation factors, and management targets. 

3-83 
 

 

 

ArcGIS Map Interface 

Input File Loader 

BMP Performance 

Evaluation/Simulator 

BMP 

Calculator 

Changeable Variables 

(#, min, max, increment) 

Control Target 

Generate Initial 

BMP Input Values 

Generate BMP 

Model Input File 

Routing 

Cost 

Estimating 

Evaluation 

Factor 

SOLUTION 

Evaluation Factor & 

Cost Values 

Initial Run Results: 

New Changeable

Variable Values 

BMP Optimizer 

Feed new possible 

solution to 

evaluator/simulator 

based on the previous 

simulation result 

Figure 3-17. Conceptual overview of the optimization module. 

Decision Variables 

Placing BMPs at different spatial levels or locations (or both) affects the overall cost-effectiveness of the 

stormwater control system (Zhen 2004). Therefore, BMP location represents one important decision 

variable for optimization. The possible BMP locations are typically pre-selected on the basis of multi ple 

factors, including availability of space site characteristics (slope, soil infiltration rates, and water table 

elevation) and other logistical considerations. Another important decision variable involves BMP 

configuration. At a given feasible location of a BMP type, the configuration parameters can be treated a s 

decision variables with the specified minimum, maximum, and discreet search interval values. 

Assessment Point(s) and Evaluation Factor(s) 

An assessment point is a location where the water quality or quantity parameters or both are evaluated. 

Figure 3-18 shows an example of assessment points that can be at the watershed outlet, key tributary 

outlets, and the downstream node of a stream segment. 

Figure 3-18. Illustration of assessment points. 

3-84 
 

 

 

 

 

 

 

 

 

 

 

SUSTAIN provides the user an evaluation factor selection menu when defining the optimization problem. 

The framework allows for various averaging periods and frequencies consistent with that for typica l water 

quality criteria and TMDL related allocations. Table 3-33 lists the evaluation factor options in SUSTAIN. 

Table 3-33. Example Control Targets for Typical Evaluation Factor Assessment in SUSTAIN 

Control Target Target Value Note 

Flow 

Peak Discharge cubic feet per sec --

Parameters related 

to increased runoff 

from urbanization 

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Annual Average 

Volume 

cubic feet per year --

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Exceeding 

Frequency 

times per year of a given threshold flow rate (cfs) --

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Sediment 

Annual Average 

Load 

pounds per year value – 

biologically 

derived parameters 

to meet designated 

uses in waterbody 

Parameters to meet 

ality 

standards or 

of concern 

the water qu 

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Annual Average 

Concentration 

milligram per liter value --

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Maximum Days 

Average 

Concentration 

milligram per liter value of given days --

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Pollutants (TN, TP, or User Defined) 

Annual Average 

Load 

pounds per year value -­ Parameters to meet 

the pollutant 

or 

allocation) or other 

locally defined 

water-protection 

criteria (numeric 

concentration 

frequency of 

exceedance) or 

TMDL (load 

goals 

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Annual Average 

Concentration 

milligram per liter value --

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Maximum Days 

Average 

Concentration 

milligram per liter value of given days --

percent reduction of the existing condition 0–100 

fraction between existing and pre-developed conditions 0–1 

Management Targets 

Management targets can be related to either water quality or quantity. The user specifies the water quality 

or water quantity target value or range for each assessment point. 

3-85 
3.5.2. Optimization Algorithms 

Evolutionary search techniques have shown great promise in their ability to solve nonlinear, 

multiobjective, complex optimization problems such as the one above (Zhen and Yu 2004; Dorn and 

Ranjithan 2003; Harrell 2001; Perez-Pedini et al. 2005). Though such techniques demand extensive 

computing time, rapid advances in computing power and speed make the techniques more practical and 

applicable than ever before. 

Numerous evolutionary algorithm-based multiobjective optimiz ation procedures are available. Generally, 

the search techniques can be classified into two categories, i.e., (1) constraint method-based evolutionary 

algorithms, which u se single-objective evolutionary algorithms to solve multiobjective optimization 

problems by transfo rming the multiobjective problem to a single-objective problem via the constraint 

method, and then so lving it iteratively; (2) multiobjective evolutionary algorithms, which solve the 

problem in a single pass, where the population represents the set of nondominated solutions. In the 

SUSTAIN framework, two search techniques are implemented: Scatter Search and Nondominated Sorting 

Genetic Algorithm-II (NSGA-II)). Scatter Search is a single-ojective evolutionary algorithm, and NSGAII is a multiobjective e volutionary algorithm. 

Scatter Search is introduced by Glover (1977) as a heuristic for integer programming that expanded o n the 

concept of surrogate constraints. The Scatter Search method is an evolutionary search technique that has 

been explored and used in optimizing complex systems (Glover et al. 2000). Scatter Search shares some 

commonalties with the widely applied single-objective genetic algorithms (GAs) because both techniques 

are population-based approaches. However, Sca tter Search and GA have a number of distinct features of 

their own. GA approaches are predicated on the idea of choosing parents randomly to produce offspring 

and then introduce randomization to determine which components of the parents should be combined. By 

contrast, the Scatter Search approach does not emphasize randomization or being indifferent to choices 

among alternatives. Instead, it is designed to incorporate strat egic responses, both deterministic and 

probabilistic, that take account of evaluation history. Scatter Search focuses on generating relevant 

outcomes without losing the ability to produce diverse solutio ns (Laguna and Marti 2002). Because of 

that feature, it serves as a better optimization technique for identif ying the near-optimal solution with a 

specific target value. 

NSGA-II is one of the most efficient, multiobjective, evolutionary algorithms using the elitist approach 

(Deb et al. 2002). In NSGA-II, solutions are sorted on the basis of the degree of dominance within the 

population (i.e., if a given solution is not dominated by any other solution, that solution has the highest 

possible fitness). In addition, the algorithm seeks to preserve diversity along the first non-dominated 

front so that the entire Pareto-optimal region is found. NSGA-II has gained popularity in recent years and 

showed superiority over ot her multiobjective evolutionary algorithms, e.g. Pareto-Archived Evolution 

Strategy and Strength-Pareto EA, when applied to solve optimization problems associated with watershed 

management (Dorn and Ranjithan 2003). 

Comparison of Scatter Search and NSGA-II 

Both Scatter Search and NSGA-II are population-based evolutionary optimization techniques; however, 

they employ different search strategies. Scatter Search refines a scatter pattern around the targeted 

objectives by replacing members of a reference population, while NSGA-II defines a population a s 

individual solutions along a cost-effectiveness curve and refines the entire population with better 

solutions until the final solution approaches the true Pareto frontier. Pareto optimality is a concept 

commonly applied in economics and engineering. At any cost or reduction level, a solution is said to be 

Pareto optimal when no further improvements can be made. Figure 3-19 is a conceptual representation o f 

the two search routines. In Figure 3-19, the Pareto optimum frontier is represented in two-dimensional 

space as the solid cost-benefit arc in both graphs. A new dimension would be added for each additional 

3-86 
 

pollutant reduction target in the objective function. The concentric circles in the Scatter Search graph 

illustrate progressively better (narrower) reference populations, until the final set of best solutions are 

found clustered around the point along the Pareto frontier. The dashed lines in the NSGA-II graph 

illustrate progressively improving cost-benefit relationships with each new generation of solutions until 

the true Pareto frontier is realized in the last generation. Scatter Search clusters solutions around the 

defined objective on the Pareto frontier, while NSGA-II distributes the solutions along the entire trade-off 

frontier. Increasing the resolution for NSGA-II means increasing the number of individuals in the 

population, which increases the number of generations needed to find the Pareto optimal frontie r. 

Figure 3-19. Comparison of Scatter Search and NSGA II optimization techniques. 

For the minimize cost option, Scatter Search is more efficient (finding the best solutions with fewer runs 

of the simulation module) because the search is more focused around the target. For the cost-effectiveness 

curve option, NSGA -II (genetic algorithm) is more efficient because it applies the non-dominated sorting 

technique and the search proceeds in a manner of fronts. 

ScatterSearch 

The major operation steps of Scatter Search are described below. 

Generating a starting set of diverse points 

Generating a starting set of diverse points is accomplished by dividing the range of each variable into four 

sub-ranges of equal size. Next, a solution is constructed in two steps: a sub-range is first randomly 

selected and then a value is randomly chosen from the selected sub-range. The starting set of solution 

points also includes all variables at their lower bound, all variables at their upper bound, all variables at 

their midpoints, and other solution points suggested by the user. 

Choosing a subset of diverse points as the reference set 

The reference set (RefSet), is a collection of both high-quality solutions and diverse solutions that are 

used to generate new s olutions. Specifically, the RefSet consists of the union of two subsets, RefSet1 and 

RefSet2, of size b1 and b2, respectively. That is, |RefSet| = b = b1 + b2. The construction of the initial 

reference set starts with the selection of the best b1 solutions from the starting set of diverse po ints (P). 

The notion of best in this step is a measure given by the evaluation of the objective function. These 

3-87 
 

solutions are added to RefSet and deleted from P. For each improved solution in P – RefSet, the 

minimum of the Euclidean distances to the solutionsin RefSet is computed. Euclidean distance is the 

straight line distance between two points. For example, in a two-dimensional plane, the Euclidean 

distance is the straight line between point 1 at (x1, y1) and point 2 at (x2, y2) and is equal 

to (x - x )2 + (y - y )2 . 1 2 1 2 

Then, the solution with the maximum of these minimum distances is selected. This solution is added to 

RefSet and deleted from P and the minimum distances are updated. This process is repeated b2 times. 

The resulting reference set has b1 high-quality solutions and b2 diverse solutions. 

Starting the search for the optimal solution by using a linear combination method to construct new 

solution points from the reference solution points 

The linear combination is based on the three types of formulation, in which x′ and x′′ are reference 

solution points, and x1–3 is the newly generated solution points: 

x1 = x′ – d
 

x2 = x′′ – d
 

x3 = x′ + d
 

where and r is a random number in the range of (0, 1). 

Updating the RefSet 

In the course of searching for a global optimum, the RefSet is continuously updated. The solutions 

having better quality, or ones that can improve the diversity of the reference set, replace the old points in 

the set. 

Stop the search if the stopping criteria are met 

The stopping criteria can be defined, at the user’s option, either as the maximum number of itera tion runs, 

or the minimum improvement between updates of the reference set, or both, in which case, the search 

process will be stopped when either of the criteria is met. 

NSGA-II 

The major operation steps of NSGA-II are described below. 

Creation of first generation 

When applying the NSGA-II, a random parent population (P0) consisting of N solutions is first created. 

The population is then sorted by the non-dominant level. A solution x

(1) is non-dominant to another 

solution when x

(1) performs no worse than the other solution in all objectives, and x(1) performs better than 

the other solution in at least one objective. At the end of the sorting, each solution is assigned a fitnes s 

(or rank) equal to its non-dominant level, with a smaller value indicating that the solution is dominated by 

fewer other solutions. 

The processes of tournament selection, crossover, and mutation are used to create a child population (Q0), 

which has a same size of P0 with N solutions. 

x

" − x' 

d = r 

2 

3-88 
Main loop 

In the first step of the main loop, the parent population and the child population are combined (R0). The 

population of R0 will have 2N solutions. The 2N solutions in R0 are then sorted according to nondomination. Elitism is ensured in this step because both the parent and the child population are used in 

the sorting. The sorted 2N solutions will form various best non-dominated subsets (in which all the 

solutions are non-dominant to each other, but overall they dominate other subsets). The first N solutions 

from the ranked best non-dominant subsets, F1,..., Fi, are then selected to form a new parent population 

(P1). The new parent population is used to create a new child population (O1), and the process continues 

until the stopping criteria are met. 

The NSGA-II uses the crowding distance (the size of the largest cuboid enclosing solution x

(1) without 

including any other solution in the population) concept to maintain solution diversity. That is, in cases 

where two solutions have the same non-domination rank, the solution with larger crowding distance is 

always preferred. 

Stopping criteria 

The user can make the NSGA-II stop when the new parent population does not ch ange for two 

consecutive loops. The stopping criterion can also be that the fitness function does not improve after a 

certain numbe r of iterations. 

Optimization Options 

SUSTAIN provides two optimization options: (1) cost minimization, and (2) cost-effectiveness curve. In 

the cost-minimization option, the optimization search process identifies the near-optimal solutions that 

meet the user-specified management targets. With the cost-effectiveness curve option, the optimization 

process reveals all the cost-effective solutions within the user-specified management target range. 

Cost-Minimization Option 

With the objective of minimizing cost subject to desired water quality or water quantity objectives (or 

both) at a specified location (assessment point), the optimization problem formulation can be 

mathematically expressed as below. In the formulation, a group of BMPi (i = 1,..., n) forms the decision 

matrix, which defines the optimization engine’s search domain. For each potential location, the user 

defines the feasible range of BMP type and configura tion parameters. 

The objective is to: 

Minimize

n 

∑Cost(BMPi ) 

i=1 

 

 

subject to: 

Qj ≤ Qmaxj and 

Lk ≤ Lmaxk 

where 

BMPi = a set of BMP configuration decision variables assoc iated with location i, 

Qj = the computed amount of water quantity factor at the assessment poi nt j, 

Qmaxj =the maximum value of the water qua ntity factor targeted at the assessment point j, 

Lk = the computed amount of water quality loading factor at the assessment point k, and 

Lmaxk = the maximum value of the water quality loading targeted at the assessment point k. 

3-89 
Cost-Effectiveness Curve Option 

Under the cost-effectiveness curve option, the search aims at identifying the cost-effective solutions 

within the specified management target range. The multiobjective problem can be expressed as fol lows: 

Minimize 

Minimize EF 

n 

∑Cost(BMPi ) and 

i=1 

where 

BMPi = a set of BMP configuration decision variables associated with location i and 

EF = the management evaluation factor (EF) at one given assessment point, and the EF can be 

any of the options listed in Table 3-33. 

3.5.3. Regional Application 

SUSTAIN is able to evaluate management practices at multiple scales, ranging from local to watershed 

applications. Placement of BMPs at different spatial levels (i.e., on-site, subregional, and regional) 

affects the overall cost-effectiveness of the stormwater control system (Zhen and Yu 2004). Management 

plans often need to evaluate the cumulative benefit of manage ment practices at multiple- scale watersheds 

on downstream water quality in rivers, lakes, or estuaries. The site or local-scale evaluation involves 

simulation and analyses of individual BMPs and various combinations of practices and treatment train s to 

derive local runoff quantity and quality. For a larger-scale watershed, there could be hundreds or 

thousands of individual management practices that are implemented to achieve a desired cumulative 

benefit. SUSTAIN incorporates an innovative, tiered approach that allows for cost-effectiveness 

evaluation of both individual and multiple nested watersheds to address the needs of both regional an d 

local-scale applications (Figure 3-20). This section describes the procedures of the tiered optimization 

analysis approaches in SUSTAIN for sequentially identifying cost-effective BMPs on a regional scale. 

A relatively large watershed can usually be subdivided into several smaller subwatersheds as shown in 

Figure 3-20. Users need to select, with, say, the use of the siting tool in SUSTAIN, an appropriate suite o f 

feasible BMP options (types, configurations, and costs) at strategic locations for each subwatershed. 

SUSTAIN then generates the time series rainfall-runoff data from BMP drainage areas and routes them 

through BMPs, in parallel or in series, to produce the quantity and quality data at downstream assessment 

points. SUSTAIN uses the cost and effectiveness da ta to derive the cost-effectiveness curve that relates 

flow or pollutant-load reductions with costs. Each point on the cost-effectiveness curve represents an 

optimal combination of BMPs that will collectivelyremove the targeted amount of pollutant load at the 

least co st. 

The tiered optimization procedures implemented in SUSTAIN provides an efficient and manageable 

means for large-scale applications and allows users to evaluate and optimize on the basis of the 

hydrologic and water quality characteristics at the specified assessment points. Tier-1 performs the 

optimization search to develop cost-effectiveness curves for each tier-1 subwatershed. In a tier-2 

analysis, the tier-1 solutions are used to construct a new optimization search domain and run the transport 

module, if needed, with solutions from all the tier-1 subwatersheds to develop the combined costeffectiveness curve for the entire watershed. 

3-90 
Figure 3-20. Tiered application of SUSTAIN for developing cost-effectiveness curves. 

Figure 3-21 illustrates the tiered application process in more detail. At the first step (tier-1) of the tiered 

optimization analysis, the cost-effectiveness curve for each subwatershed is generated by performing 

continuous multiple optimization runs at incremental flow/pollutant reduction targets. In the second step 

(tier-2), the search domain is constructed using the tier-1 results. As shown, the search domain for tier-2 

contains the discrete solutions on the tier-1 cost-effectiveness curves at assessment points i and j. The 

third step is to perform the tier-2 optimization for the search domain constructed. The optimization 

engine strategically samples the discrete options in the search domain. The cost-effectiveness of each 

sample is measured, stored, and analyzed to guide the next search direction. 

Cost

Load Reduction

Cost

Load Re uction

Tier-1 Solutions for 

Assessment Point j

Tier1 Solutions for Assessment oint i

Tier 1 Cost Effectiveness Curve 

for Assessment Point 

Tier 1 Cost ffectiveness Curve 

for As essment Point i

Cost

Load Reduction Load Re uction

Cost

Load Reduction

Tier-1 Solutions for 

Assessment Point j

Tier1 Solutions for Assessment oint i

Tier 1 Cost Effectiveness Curve 

for Assessment Point 

Tier 1 Cost E fectiveness Curve 

for Assessment Point i

Cost

Load Reduction

Cost

Load Reduction

Tier-1 Cost Effectiveness Curve - Tier-1 Cost - -Effectiveness Curve -

interval interval

p

Tier-2 Search

Domain

j

interval interval

p

Tier-2 Search

Domain

j

interval

Cost 

Lo ad Re duc tion

interval 

Tier-1 Solutions for 

Assessment Point j 

Tier-1 So lu tion s fo rA ssessm en t point i

Tier-2 Search 

Domain 

for Assessment Point i for Assessment Point j

interval

Cost 

Lo ad Re duc tion

interval 

Figure 3-21. Construction of the tier-2 search domain using tier-1 results. 

3-91 
Figure 3-22 illustrates the simulation process used to generate the results for measuring the costeffectiveness of each iteration in the tier analysis. The simulated time series outputs for all discrete points 

on the tier-1 cost-effectiveness curve are stored and used when a point, hence the BMP options associated 

with it, is chosen in the tier-2 analysis. Similarly, the time series runoff data of the watershed area that is 

not part of the tributary areas of the tier-1 assessment points is generated and stored before the tiered 

analysis. This data, however, might also be generated during the tier-2 search process. The transport 

module is often required to perform routing of the time series data from the upstream tier-1 subwatershed 

to merge with that for the downstream tier-1 subwatershed. In such a manner, the tiered approach is 

applied to a large watershed which contains subwatersheds or to a small watershed that requires the 

development of a detailed management plan at a parcel- or a street-block-level. 

T

M

Channel

 r 

Tier-1 solution time series at assessment points

 are not covered 

ent points

Time series for the areas/catchment that

by tier-1 assessm

Transport 

Module: Time Series: 

Channel 

Conduit 

Simulation process for each iteration run: 

Tier-1 solution time series at assessment points

 are not covered 

ent points 

Time series for the areas/catchment that 

by tier-1 assessm 

Figure 3-22. Simulation process for each iteration run. 

3.5.4. Important Considerations and Limitations: Optimization Module 

Importance of Calibration 

The optimization engine performs iterative searches to identify cost-effective solutions. The search 

process is dependent on the cost and BMP treatment effectiveness values of each BMP or a combination 

of BMPs evaluated. Therefore, it is crucial to have calibrated watershed and BMP simulation modules, as 

well as good BMP cost data, to ensure meaningful results. The cost-effective solutions from evolutionary 

search techniques should be considered only near-optimal solutions, meaning that the solutions are not 

guaranteed to be the absolute best but are believed to be close to it. 

Minimizing Run-time and Computational Effort 

The approximate number of iterative runs to reach the near-optimal solutions is dependent on the number 

of decision variables, the number of discrete options of a decision variable, and the complexity of the 

problem. In general, the number of runs needed is reduced with a reduced number of decision variables, 

number of discrete options for a decision variable, and complexity of the problem. Experience gained 

from performing experiments is usually useful for estimating the number of runs needed for reaching 

near-optimal solutions. 

The optimization process based on long-term continuous simulation can require a large amount of 

computation time especially for a large watershed that has many feasible BMP sites and reaches/conduits. 

To achieve computational efficiency, it is advantageous to minimize the number of non-dummy conduits, 

decision variables, and search steps. 

3-92 
 

When using the cost-minimization option, the user is asked to set a treatment (e.g., pollutant reduction) 

target. To avoid setting an unreachable target, one can perform a simulation that maximizes treatment 

scenario by setting the BMP sizes to maximum for a site for estimating the maximum treatment 

effectiveness. 

Tiered Analysis Interface 

Although the SUSTAIN engine is capable of performing tiered optimization, the related interface is yet to 

be developed. Users must manually create all the input files to perform the tiered optimization analysis. 

A more robotic and automated interface will be developed to guide the users through the tiered analysis 

processes. 

3.6. Post-Processor for Results Interpretation 

The SUSTAIN post-processing module provides a centralized location for analyzing and interpreting 

simulation outputs at multiple loc ations, and for scenarios (e.g., existing development with and without 

BMPs, and pre-development conditions) and parameters of interest (e.g., inflows, outflows, and pollutant 

loads and concentrations). The framework allows users to evaluate simulation results that are highly 

variable in magnitude, duration, intensity, treatment containment volume, attenuation, and pollutantremoval effectiveness. The simulation outputs contain hourly or subhourly data, and can span several 

years, depending on the length of simulation. 

The primary objective of the post-processor is to mine the model results to derive meaningful information 

that best characterizes the effectiveness of the modeled management strategies. This is achieved by the 

use of specific graphical and tabular reports. Four components are in the post-processor: storm event 

classification, storm event viewer, storm performance summary, and cost-effectiveness curve. Table 3-3 4 

provides an overview of the required inputs, the methods used to manage and process the inputs, and the 

resulting outputs from SUSTAIN’s post-processor. 

3.6.1. Storm Event Classification 

Storms with identical hyetographs (i.e., precipitation pattern) can produce very different runoff respo nses 

depending on prior moisture conditions, seasonality, and geographic location. For example, if one storm 

follows a long, dry period, and another follows shortly afte r a relatively wet period, the runoff responses 

would be different. BMP performance is similarly affected by antecedent conditions. As a result, 

accurate interpretation of modeled BMP performance requires an understanding of prior precipitation and 

soil moisture. SUSTAIN’s post-processor facilitates this through its storm event classification method, 

whereby the time interval between storms (storm interval) is specified in order to define a storm event 

from a precipitation time series. 

The storm interval is the number of dry days between storms necessary to restore near-equilibrium soil 

moisture conditions. The interval varies by geographic region and can be selected using statistical 

evaluation of precipitation time series. For example, in regions such as Seattle, Washington, where 

rainfalls are frequent but of low intensity, a shorter storm event interval is more appropriate. Longer dry 

intervals, say, 3 days, are typically used in drier regions that rarely get rain, such as Southern California. 

Figure 3-23 shows how the derived set of storm events changes with the number of dry hours between 

storms. For each storm event, the graphs in Figure 3-23 show the total precipitation and the peak 

precipitation intensity during an interval, relative to other intervals. As expected, the number of events 

decreases as the dry interval (number of dry hours between storms) increases. 

3-93 
 

 

 

Table 3-34. Post-processor Inputs, Methods, and Outputs 

Post-Processor 

Inputs 

− Storm event classification 

− Rainfall time series file 

− Number of dry days between storm events 

− Evaluation period (start and end dates) 

− Time series files for storm viewer and performance summary 

− Development conditions: pre-developed, with, and without BMPs 

− Cost-effectiveness curve 

− Best solutions and all solutions 

− Associated model input file 

Methods 

− Storm event classification: divide time series into discrete storm events, separated by a user-defined 

minimum number of days for the entire evaluation period 

− Storm event viewer: browse and display modeled time series comparisons for three scenarios (with and 

without BMPs and pre-developed condition) for a user-selected storm event 

− Storm performance summary: display summary comparisons for scenarios (with BMPs and pre-developed 

cond 

− Cost-effectiveness curve: sup ns 

− Upon request, generate model results for a selected cost-effective solution 

ition, relative to the scenario without BMPs) for all storm events 

erimpose cost-effective solutions on all solutio 

Outputs 

− Storm 

− Storm 

− Storm performance summary EMC graphs for all storms 

 event classification: browsable and sortable list of storm events 

 event viewer: time series comparison graphs 

: load comparison graphs for all storm events and 

− Cost-effectiveness curve: cost distribution by BMP type and storage distribution by cost interval 

72 hours Æ 34 events 

48 hours Æ 55 events 

24 hours Æ 78 events 

Selected Interval Total Rainfall Peak Intensity 

Figure 3-23. Number of storm events and total precipitation and peak intensity 

as a function of dry hours between storms. 

3-94 
 

In the SUSTAIN post-processor, once the storm events are defined, the user has the option of sorting the 

events by total precipitation volume or by peak intensity, as shown in Figure 3-24. Note that the same 

event (August 9, 2001, 6:00 p.m.) has been highlighted in both Figure 3-23 and Figure 3-24. The event 

sorting provides additional insight into the details of the selected event relative to other events. For 

example, in terms of total rainfall volume, the selected event is in the 53rd percentile; however, in terms of 

peak intensity, it is in the 82nd percentile for the selected year. The derived storm event information is 

further explored by the storm event viewer and storm performance summary analysis as described in the 

next two sections. 

53% 

82% 

Sorted by Total Precipitation Volume 

Sorted by Peak Precipitation Intensity 

Selected Interval (Percentile) Total Rainfall Peak Intensity 

Figure 3-24. Precipitation events sorted by total precipitation volume and peak intensity. 

3.6.2. Storm Event Viewer 

Once the storm events are defined, the user can select a specific storm event to visualize its detailed time 

series responses. The storm event viewer plots the time series data for comparison under the following 

three conditions: (1) existing development without BMPs, (2) existing development with BMPs, and (3 ) 

before development. The viewer illustrates how BMP performance changes with increasing or decreasing 

storm size. Four example storms are shown below for illustration the use of the viewer. Those storm 

events were derived using 24 dry hours as the storm separation criterion. Storm event 1 (Figure 3-25) is 

the selected interval highlighted previously in the storm classification figures. 

In each of the storm viewer graphs, the blue shaded area represents the developed condition without 

BMPs (post-developed), the brown line represents the BMP scenario, and the green line represents t he 

pre-developed condition. Storm event 1 in Figure 3-25 shows excellent BMP performance because the 

outflow fromthe BMP scenario (brown line) is significantly reduced from the post-developed condition, 

indicating that the storm runoff was well controlled by the BMP installed. Figure 3-26 shows a slightly 

different response. Although the peak flow for this ev e nt is similar to that of the first one (slightly lower), 

the storm interval 2 generates significantly more outflow than storm event 1. This is because the pea k 

precipitation came after 2 hours of steady rainfall causing the ground to be saturated and thus more 

bypass flow through the BMPs. After the storm has ended, outflow persists for a number of hours 

because of the attenuation by BMPs in the drainage area. 

3-95 
14
 

12
 

10
 

8
 

6
 

4
 

2
 

0
 

Post-Developed 

The BMP Scenario 

Pre-Developed Condition 

9-Aug-01

10-Aug-01 

Flow (cfs) 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4

Precipitation (in./hr) 

Figure 3-25. Storm event 1: August 9, 2001 6:00 p.m. to August 9, 2001 8:00 p.m. 

(0.66 in. to 2 wet hours, peak: 0.48 in.). 

3-96 

14-May-0115-M ay-01 

0 

20 

40 

60 

80 

100 

120 

Flow (cfs) 

Post-Developed 

The BMP Scenario 

Pre-Developed Condition 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

Precipita ion (in./hr) 

Figure 3-26. Storm event 2: May 14, 2001 11:00 a.m. to May 14, 2001 1:00 p.m. 

(0.95 in. to 4 wet hours, peak: 0.38 in.). 

The dry-interval criterion is applied after the last precipitation. For a 24-hour dry interval criterion, a 

storm event begins with the first non-zero precipitation and ends at a minimum of 24 hours after the e nd 

of the last precipitation. The storm duration as is defined allows the runoff to be rightly associated with 

the storm that caused it. Storm event 3 in Figure 3-27 consists of two storms because the second storm 

began less than 24 hours after the end of the first storm. By definition, the second storm cannot be 

evaluated independently of the first storm because antecedent moisture conditions were not stabilized 

following the first storm. This approach of defining a storm event will reduce the noise associated with a 
time series response. Had those two storms been considered independently, the second storm in the 

interval—which has a slightly lower total precipitation, longer duration, and lower peak precipitation— 

would have appeared much worse than storm event 1 because of the antecedent conditions. 

It is also interesting to note that only the second storm generated measurable surface runoff for the predeveloped condition. This further illustrates the impact of antecedent moisture conditions on BMP 

performance. 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0 

1.2 

1.4 

Precipitation (in./hr) Flow (cfs) 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

Post-Developed 

Th e BMP Scenario 

Pre-Developed Condition 

20-Apr-0121-Apr-0122-Apr-01 

Figure 3-27. Storm event 3: April 20, 2001 2:00 a.m. to April 21, 2001 5:00 a.m. 

(1.03 in. to 12 wet hours, peak: 0.30 in.). 

Finally, storm event 4 in Figure 3-28 is the largest total precipitation volume of storms considered. It 

consists of a series of intermittent precipitation occurrences scattered over a 2-day period. By grouping 

the events into the same storm event, their effects are evaluated together because they are not independen t 

hydrologically. 

3.6.3. Storm Performance Summary 

The derived storm events are used to generate a BMP performance summary for the simulation period. 

The graph (as shown in Figure 3-29) displays summary comparisons of three scenarios (with and without 

BMPs and pre-development condition). The graph shows three series. The first series (circles) shows 

percent reduction by storm interval for the BMP scenario relative to the post-developed (with no BMPs) 

scenario. The second series (horizontal hashes) shows percent reduction for the pre-developed condition 

relative to the post-developed (with no BMPs). The series is plotted for reference purposes. If the BMP 

scenario (circle) is at or below the series (hash line), it indicates that BMP performance for that storm 

event is not performed at the level of pre-developed conditions. The final series is a bar graph, which 

follows the right axis. For each output constituent, the series computes the pollutant contribution (in 

percent) of each storm event with respect to the total rainfall series. Figure 3-29 shows a sediment 

removal summary for 34 storm events sorted by sediment load on the y-axis and storm event volume on 

the x-axis. 

3-97 
3-98 

8-Feb-019-Feb-01

10-Feb-01 

0.0 

0.2 

0.4 

0.6 

0.8 

1.0

Precipitation (in 

1.2 

1.4

./hr) 

0 

50 

100 

150 

200 

250 

300 

350 

Flow (cfs) 

Post-Developed 

The BMP Scenario 

Pre-Developed Condition 

Figure 3-28. Storm event 4: February 8, 2001 12:00 a.m. to February 9, 2001 4:00 p.m . 

(2.34 in. to 25 wet hours, peak: 0.61 in.). 

Precipitation Event Volume (in.) 

10% 

0% 0% 

5% 

Relative Contribution Weighted Average Reduction Precipitation Event Reductions Pre-Developed Condition 

Sediment (Perc ent Reduction) 

0.010.010.010.080.090.110.180.320.541.760.040.660.220.110.590.280.550.530.660.740.831.521.591.082.511.412.150.871.712.632.224.412.982.34 

100%

0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%1%1%2%2%2%3%3%3%4%4%5%7%9%11%12%31% 

50% 

90% 45% 

80% 40% 

70% 35% 

60% 30% 

50% 25% 

40% 20% 

30% 15% 

20% 10% 

Relative Contribution (B aseline) 

Peak Flow (cfs) 

Figure 3-29. Sediment removal summary for 34 storm events arranged 

by baseline sediment load. 

As shown in Figure 3-29, sediment load increases generally with increase in precipitation volume and 

smaller storms can be better managed by BMPs than the larger storms. In the example, the weigh ted 

average percent reduction is around 50 percent, which ill ustrates the influence of the larger loadproducing storms on the long-term average reduction. While the pre-development condition (horizontal 

hashes series) would contain close to 100 percent of the current baseline flow and load under the existing 

development condition, some large storms produce runoff and loads. For example, the largest load 

producing storm (2.34 in., which was also previously featured in Figure 3-28) shows the poorest BMP 

performance at 13 percent removal. This storm generated measurable sediment load even under the predeveloped condition. 
3-99 

Figure 3-30 shows the summary results of peak flow-reduction performance for this same storm series. 

The overall results are similar to that for sediment load reduction. For both sediment load and peak flow 

reductions, the 4.41-in. storm event performs among the highest because of its relatively low intensity. 

This storm event consists of four individual small storms with relatively high peak flow rates over an 11­

day period, and hence the runoff is largely contained by the BMPs. Peak Flow (Percent Reduction) 

Precipitation Event Volume (in.) 

Relative Contribution Weighted Average Reduction Precipitation Event Reductions Pre-Developed Condition 

100% 50% 

90% 45% 

0% 

10% 

0.01 0%

0%0%0%0%0%0%0%0%0%0%0%0%0%0%0%1%1%1%1%2%2%3%3%4%4%5%6%7%8%9%9%9%25% 

0.010.010.080.090.110.180.321.760.540.040.220.590.28

80% 40% 

70% 35% 

60% 30% 

50% 25% 

40% 20% 

30% 15% 

20% 10% 0.66 0.11 0.53 0.55 0.66 0.74 2.51 1.52 2.15 1.41 1.59 1.08 0.83 1.71 2.63 4.41 0.87 2.22 2.98 2.34 

0% 

5% 

Relative Contribution (Baseline) 

Figure 3-30. Peak flow reduction summary for 34 storm events arranged 

by peak flows. 

Additionally, the post-processor is designed to produce a different graph to show a summary comparison 

of peak flows or water quality constituents for the same three scenarios. For water quality constituents, it 

uses the EMC, which is computed as the total pollutant load divided by the total outflow volume of the 

storm. Figure 3-31 shows a peak flow comparison of three scenarios by storm event arranged by 

increasing peak flows for the existing development (baseline). Similarly, Figure 3-32 shows the EMCs of 

sediment for the corresponding storm events in Figure 3-30. It is interesting to note from Figure 3-32 that 

sediment EMC in pre-developed condition can be larger than the other two scenarios because of low 

runoff volumes when the watersheds are not yet developed. Depending on the study nature, attaining a 

specified EMC might be an important goal to achieve. 

300
 

250
 

200
 

150
 

100
 

50
 

0
 

350
 

Post-Developed 

The BMP Scenario 

Pre-Developed Condition 

Precipitation Event Volume (in.) 

0.010.010.010.080.090.110.180.321.760.540.040.220.590.280.660.110.53 0.550.660.742.511.522.151.411.591.080.831.712.634.410.872.222.982.34 

Figure 3-31. Peak flow comparison by storm event arranged 

by post-developed peak flows. 
25 

Post-Developed 

The BMP Scenario 

20 Pre-Developed Condition 

Sediment (mg/L) 15 

10 

5
 

0 

0.010.010.010.080.090.110.180.320.541.760.040.660.220.110.590.280.550.530.660.740.831.521.591.082.511.412.150.871.712.632.224.412.982.34 

Precipitation Event Volume (in.)

Figure 3-32. EMC of sediment for the corresponding storms. 

3.6.4. Cost-effectiveness Curve 

The final analysis component of the post-processor is creating a cost-effectiveness curve to facilitate 

decision making. The post-processor can generate and display this curve directly from the output only 

when the NSGA-II search method is used in the optimization module. It displays the curve one pollutant 

constituent at a time; however, the post-processor still allows a user to evaluate the resulting benefit to 

other constituents gained from optimizing performance for a single constituent. For example, one can 

evaluate how optimizing flow reductions impacts sediment reductions. Each optimization run generates 

two files from which the post-processor derives the cost-effectiveness curve: (1) AllSolutions.out, which 

contains cost-benefit summaries for each intermediate optimization run, and (2) BestSolutions.out, which 

contains cost-benefit summaries for the final population of points that constitutes the optimum frontier. 

Figure 3-33 shows an example of a cost-effectiveness curve for sediment load reduction. 

55% 

50% 

Effectiveness (% Reduction) 

45% 

40% 

35% 

25% 

30% 

$0.0 $0 5 $1.0 $1 5 $2.0 $2 5 $3.0 $3 5 $4.0 

Cost ($ Million) 

All Solutions 

Cost-Effectiveness Curve 

Selected Simulation

$4 5 

Figure 3-33. Example cost-effectiveness curve for sediment load reduction. 

3-100 
In Figure 3-33, cost (dollars in millions) is plotted on the x-axis, while effectiveness (% sediment load 

reduction) is plotted on the y-axis. All the intermediate solutions are plotted as smaller circles, while the 

optimum cost-effectiveness curve solutions that form the left- and upper-most boundaries of the search 

domain use more pronounced circle s. 

The data in a cost-effectiveness curve is related to the data generated in all the previously described pos tprocessor components described thus far. In Figure 3-33, as an example, the most cost-effective solution 

(at a cost of around $340,000) for 50% load reduction is highlighted. The cost, performance, and time 

series data associated with the BMPs that collectively provide 50% sediment load reduction can be 

retrieved from the information presented in Figure 3-29, in the rest of the storm performance graphs 

shown in Section 3.6.3, and the storm viewer graphs shown in Section 3.6.2. Using the post-processor, 

the user can navigate along points on the cost-effectiveness curve, generate individual runs for individual 

solutions on the fly, and toggle between the storm summary/individual storm viewers and the costeffectiveness curve to see BMP performance at multiple temporal levels of resolution. 

The post-processor can produce two additional types of information from the BestSolutions.out file: cost 

distribution by BMP type for a given point on the cost-effectiveness curve and BMP storage distribution 

by cost interval. Figure 3-34 shows the data of BMP cost distribution versus sediment reduction 

effectiveness on the cost-effectiveness curve and the 50% reduction point shown on Figure 3-32 is 

highlighted. For each effectiveness curve, the figure also shows the costs asso ciated with the selected 

BMP types. The inset pie chart shows the distribution of cost by BMP type associated with 50 percent 

effecti eness. v Note that the porous pavement option is not in the pie chart because it is not an optimal 

option to achieve the 50% reduction line tothe right of the selected point in Figure 3-33. That means that 

porous pavement option is likely feasible b ut not cost-effective for that level of load reduction. However, 

it wouldbe a cost-effective choice to achieve 52% or more reduction of sediment load as shown in Figure 

3-33. 

$0.0 

$0.5 

$1.0 

$1.5 

$2.0 

$2.5 

$3.0 

$3.5 

Cost ($ Million) 

POROUSPAVEMENT 

RAINBARREL 

BIORETENTION 

DRYPOND 

Selected Simulation 

4% 

86% 

0%10% 

33.1%34.9%37.7%39.1%41.8%43.7%44.8%45.5%46.2%46.9%47.6%48.1%48.9%49.5%50.0%50.8%51.2%51.6%51.7%51.8%51.8%51.9%51.9%52.0%52.0%52.0%52.0%52.0%52.1%52.1%52.1%52.2% 

Effectiveness (% Reduction) 

Figure 3-34. BMP cost distribution by effectiveness for se diment load reduction on the costeffectiveness curve. 

In the optimization process, the feasible BMP types are compared and screened based on simulation 

modeling, cost, and optimization input specification, to develop an order of cost-effective options for 

3-101 
maximizing their use. Figure 3-34 implies that to achieve sediment removal from runoff, dry ponds are 

the most cost-effective option, while porous pavement is the least. In some places, rain barrels are used in 

conjunction with bioretention, and the use of those two BMP types in the same space results in some 

variability that exhibits itself as noise in the graphs. In this example, there is no clear choice for 

maximizing one technique over the other because both rain barrels and bioretention were selected at a ll 

levels along the cost-effectiveness curve. Figure 3-35 provides detail about a point along the curve where 

BMP selection is fairly well distributed among the four broad categories of BMPs. However, the point 

represents a $2 million investment, which is well above the point of diminishing returns; similar 

performance can be achieved for less than $0.5 million. 

The cost distribution by BMP types as described represents the total cost for a given type in the drainage 

area that meets the goals set at the assessment point. While that cost distribution does not provide 

specific information about the spatial locations of actual BMPs, knowing the types of practices associated 

with each point along the cost-effectiveness curve provides insight into the reasoning and order of 

selecting individual practices. Cost ($ Mi llion) Effectiveness (% Red uction) 

55% 

50% 

45% 

40% 

35% 

$3.5 

$2.5 

$3.0 

25% 

30% 

$0.0 

POROUSPAVEMENT 

RAINBARREL 

BIORETENTION 

DRYPOND 

Selected Simulation 

$0 5 $1.0 $1 5 $2.0 $2 5 

Cost ($ Million) 

$3.0 $3 5 $4.0 

All Solutions 

Cost-Effectiveness Curve 

Selected Simulation 

$4 5 

$1.5 

$2.0 34% 

20% 

27% 

19% 

$1.0 

$0.5 

$0.0 

33.1%34.9%37.7%39.1%41.8%43.7%44.8% 45.5

%

46.2

%

46.9

%

47.6

%

48.1

%

48.9

%

49.5

%

50.0

%

50.8

%

51.2%51.6

%

51.7

%

51.8

%

51.8

%

51.9

%

51.9%52.0

%

52.0

%

52.0

%

52.0

%

52.0%52.1%52.1

%

52.1

%

52.2 % 

Effectiveness (% Reduction) 

Figure 3-35. Example cost-effectiveness and cost-distribution pair. 

3-102 
SUSTAIN provides another way to look at the preference of BMP types across a cost-effectiveness curve. 

Figure 3-36 shows BMP storage distribution by cost and effectiveness interval. The horizontal axis is 

cost, while the left vertical axis and line graph are effectiveness in terms of the percent of sediment 

reduction. The right vertical scale is the amount of storage (in acre-feet), associated with surface, soil, 

and underdrain storages. The graph shows which of the physical treatment processes is responsible for 

providing the effectiveness. For instance, the contribution from soil/underdrain storage is relatively small 

until around the $0.3 million interval (or 47% removal efficiency). At this point on the cost-effectiveness 

curve shown in Figure 3-33, the primary mode of pollutant removal is the use of dry ponds. Subsurface 

storage increases as bioretention plays a larger role and when porous pavement is used. 

55% 18 

Effectiveness (% Reduction) 

50% 

45% 

40% 

35% 

30% 

25% 

TotalSurfStorVol(acre-ft) 

TotalSoilStorVol(acre-ft) 

TotalUdrnStorVol(acre-ft) 

1_TSS_AAL_1 

16 

14
 

12
 

10
 

8
 

6
 

4
 

2
 

0
 

BMP Storage Component (acre-ft) 

$0.15$0.15$0.16$0.17$0.18$0.20$0.20$0.21$0.21$0.23$0.24$0.25$0.28$0.31$0.34$0.37$0.39$0.43$0.49$0.58$0.63$0.71$0.84$1.02$1.13$1.17$1.24$1.32$1.42$1.94$2.72$3.21 

Cost ($ Million) 

Figure 3-36. BMP storage distribution by cost-effectiveness interval. 

3.6.5. Important Considerations and Limitations: Post-Processor 

Microsoft Excel Macros Security Setting 

The post-processor uses Microsoft Excel 2003 Visual Basic Applications (VBA) to perform summary and 

analysis. VBA requires that the user’s Microsoft Excel security setting be set to at least medi um. The 

default setting is high, which will disable all macros without notifying the user. The medium setting will 

prompt the user when a spreadsheet is op ened that has macros and requires a response of enable or disable 

from the user to proceed. The low se tting always enab les m acros without prompting the user for a 

decision. The medium setting is reco mmended because the user has the option of disabling the macros if 

the spreadsheet i s not from a trusted source. 

Functional Limitations 

The post-processor is designed to perf orm analysis in conjunction with cost-effectiveness curves. The 

curves can be produced using only th e NSGA-II optim ization metho d. While it is possible to use the 

post-processor to visualize individual storms and time series data generated by the Scatter Search method, 

cost-effectiveness curve evaluation uses the full functi onality. Another limitatio n of the post-processor 

with regard to time series evaluation is that it shows o nly total inflow versus total outflow through a given 

node in the network. If an assessment point is also a BMP site, the post-processor does not have the 

ability to summarize th e complete history of surface a nd subsurface interactions (e.g., infiltration capacity 

exceedence, underdrain outflow, weir and orifice outflow ). In addition, the post-processor has the ability

to select and run only those solutions that appear along the cost-effectiveness curve. It cannot be used to 

directly select, run, and visualize information associat ed with a specific point that falls below the costeffectiveness curve. 

3-103 
 

Chapter 4 Case Studies 

To best demonstrate the functionality and help users visualize how they can apply SUSTAIN, two case 

studies were developed for metropolitan areas that represent typical settings for applying the framewo rk. 

Case studies provide an excellent opportunity to explore the capabilities of SUSTAIN as described in this 

document in the context of a real life scenario and demonstrate the application process beginning with 

data collection, problem setup, optimization, and results interpretation. The examples were specifically 

developed to highlight the core functions of BMP placeme nt and selection and the associated 

development of the best solution for the user-defined problem. The case studies also demonstrate how to 

apply k e y functions such as the multi-scale, tiered analys is, and the use of multiple control targets for 

opti mi zation. 

Ideal c se studies for testing a SUSTAIN should be consistent with the design requirements and the 

placem nt and selection of BMPs in urban are e as. For effective demonstration, the case studies also need 

to build on a history of monitoring data c ollection and analysis. The ideal case studies also have recent 

watershed-based studies that have resulted in calibrated and validated models that can be used for setup 

and comparison. The case studies were selected using the fo llowing criteria: 

• Dominantly urban land use 

• Water quality management needs 

• History of data collection 

• Calibrated/validated model application 

Two locations fit the criteria and were available for use—the Milwaukee Metropolitan Sewer District 

(MMSD) in Milwaukee, Wisconsin(the Oak Creek watershed), and Fairfax County, Virginia (the Little 

Rocky Run watershed). For each case study, locally derived data were used to develop the project setup 

and analysis. Next, specific problem objectives were identified that hig hlighted some important functions 

of SUSTAIN. For the Oak Creek watershed, the case study focuses on placement and selection of BMPs, 

using a single po llutant with a tiered approach, for effectiv eness evaluation at several targeted pollutant 

reduction goals. It also demonstrates how to integrate external model time series from an existing 

watershed model. For the Little Rocky Run watershed, the case study focused on placement and selection 

of BMPs on the basis of the evaluation of two concurrent control targets of peak flow and TSS reduction. 

It also demonstrates using the internal SWMM m odel for generating runoff and pollutant load time series. 

This chapter describes the pro ject setup, analysis process, and results interpretation for each case study. 

Through the demonstrations, the flexibi lity and potential of the framework is shown. Future 

demonstratio ns and applicatio ns throughout the user community will provide valuable experiences and 

insights on both the full potential of the existing framework and recommendations for the continued 

improvement of SUSTAIN. 

4.1. Upper North Branch Oak Creek Watershed 

Milwaukee is on the southwestern shore of Lake Michigan and is the largest city in Wisconsin and 2 3rd

largest (by population) in the nation. Milwaukee is the main cultural and economic center of the se vencounty Greater Milwaukee A rea, with an estimated population of 2,014,032 as of 2008. Four major river 

4-104
 
 

systems (Menomonee River, Kinnickinnic River, Oak Creek, and Root River) drain an area of more than 

1,100 s q uare miles that ultimately discharges through the Milwaukee River, in downtow n Milwaukee, to 

the harbor and Lake Michigan. Upstream areas are predominantly rural and agricultural including dairy 

farm s and crops such as corn, soybeans, and alfalfa. Nearer to the metropolitan area are suburban areas 

and commuter communities. The downtow n region is densely urbanized and drained by a combined 

sewer system. Over the past 20 years the city and the Milwaukee Metropolitan Sewerage District 

(MMSD) have taken significant steps to address CSOs by constructing a major storage tunnel. The 7.1­

m ile longNorthwest S ide Relief Sewer is adeep tunnel that can hold up to 89 million gallons of 

wastewater. The region has also led the way with various green programs such as the Greenseams 

program and been an advocate of innovative stormwater management techniques such as rain gardens, 

rain barrels, and downspout disconnections. Major environmental issues in the region include increas ed 

loadings of sediment, nutrients, and pathogens associated with urbanization and agriculture, and in the 

urban center CSOs. 

Regional water quality and environmental protection continue to be a priority in the area. Recognizing 

this need, the MMSD has led a long-range planning effort to identify improvements needed for its 

facilities to accommodate growth and protect water qu a lity through the year 2020. This effort is known 

as the MMSD 2020 Facility Plan. A related planning effort, known as the Regional Water Qual ity 

Management Plan Update (RWQMPU), was also conducted in coordination with the MMSD by the 

Southeastern Wisconsin Regional Planning Commission (SEWRPC) to update the regional water quality 

management plan for all the major watersheds draining through the greater Milwaukee area (SEWRPC 

2007). 

As part of the planning process, a comprehensive suite of models was applied to the four major tributarie s 

to assess current conditions and evaluate a range of management scenarios. Those models allowed 

planners to evaluate the potential water quality benefits of a range of implementation measures, includin g 

facility improvements and urban, suburban, and rural stormwater BMPs. The watershed modeling 

component was developed using LSPC (Tetra Tech and USEPA 2002). Hydrology and water quality 

models were developed and calibrated to provide a basis for modeling current conditions versus pot ential 

implementation scenarios. The modeling application was developed and tested using an extensive record 

of precipitation, flow, and water quality sampling. Calibration of the watershed models followed a 

sequential, hierarchical process that b egan with hydrology, followed by sediment erosion and transport, 

and, finally, calibration of chemical water quality. 

The Kinnickinnic River, Menomonee River, Milwaukee River, and Oak Creek watershed models were 

linked to a model of the Lake Michigan estuary so that the impact of upstream water quantity and quality 

could be simulated. During the RWQMPU study, several year 2020-projected management scenarios to 

reduce nutrient, bacteria, and sediment loading were evaluated (SEWRPC 2007) 

The history of environmental management, watershed planning, technical analyses, monitoring, and the 

availability of calibrated models made this location ideal for a case study application of SUSTAIN. A 

representative subwatershed, Oak Creek, was selected for examination for the case study, and TSS was 

selected as the pollutant for optimization and analysis. This case study examined the use of SUSTAIN for 

tiered analysis and BMP optimization for a single parameter. 

4.1.1. Project Setting 

The Oak Creek watershed covers parts of the cities of Milwaukee, South Milwaukee, Cudahy, Franklin, 

Greenfield, and Oak Creek and encompasses approximately 27 square miles. Table 4-1 provides a listing 

of the main characteristics of the watershed. 

4-105 
Table 4-1 . Watershed Characteristics 

W atershed drainage area (square miles) 26.2 

Miles of streams 21.2 

Milesof streams listed as outstanding or exceptional 

resource waters 

0 

Miles of streams on impaired waters list 13 

General threats to stream water quality Urban runoff 

Toxics 

Hydrological modification 

Stream bank erosion 

Number of named lakes 1 

Number of dams 1 

Threats to lake water quality Nutrient enrichment 

Sedimentation 

Figure 4-1. Oak Creek Watershed and Upper North Branch Oak Creek. 

4-106 
 

 

 

 

The watershed has three major streams—Mitchell Field Drainage Ditch, North Branch of Oak Creek, and 

Oak Creek. The longest stream of the three, Oak Creek, has a perennial length of approximately 13.1 

miles. North Branch of Oak Creek and Mitchell Field Drainage Ditch, which are tributaries to Oak 

Creek, have perennial lengths of approximately 5.8 and 2.4 miles, respectively. There is one reservoir in 

the watershed with a history of siltation and algal blooms. 

Water quality in Oak Creek is degraded in part because of elevated sediment, sediment-associated total 

phosphorus, and fecal coliform loads. Because sediment data were closely correlated with nutrients 

specifically and peak discharges in general, TSS was selected as the pollutant on which to focus the case 

study analysis. 

Pollution sources in the watershed include the following: 

• Stormwater runoff from impervious urban land 

• Runoff from agricultural lands 

• Eroding streambanks and sedimentation 

• Wildlife, pets, and residential lawns 

• Erosion from construction sites 

• Sanitary sewer overflows and industrial discharges 

• Leaking undergroun d storage tanks, landf ills, runoff from salvage yards 

The Upper North Branch O ak Creek (UNB ) area, shown in the upper-left corner of Figure 4-1, was 

selected for further evaluation in the case stud y. The UNB is a headwater area of concern because of poor 

habitat, elevated sediment load, a nd sediment-associated total phosphorus and fecal coliformloads. The 

UNB has a drainage area ofapproximately 4 .2 squ are miles with m ixed l and uses. The dominant 

residential and commercial land uses com pose 68 percent of the total area. Table 4-2 lists the land use 

distribution of thestudy area. The Oak Creekwatershed has discharges from 17 industrial facilities as 

well as sa nitary sewer overflows. 

Table 4-2. Uppe r North Bra nch Oak Creek Lan d Use Distributio n 

Land U pese Ty 

ea 

) 

Ar 

(acre 

Area Percentage 

(%) 

Water/wetland 41.0 2 

Forest 7323. 12 

Pasture/hay 88.5 3 

Crop 4168. 6 

Developed open space 263.4 10 

C commer ial 342.0 13 

High density residential 684.6 26 

Low density residential 766.3 29 

Total 2,677.7 100 

The UNB watershed provides an excellent setting for demonstrating how SUSTAIN can be applied in the 

implementation and tracking aspects of a watershed planning process. For this application, a single 

objective of TSS load reduction was selected. In terms of SUSTAIN functionality, this case study 

demonstrates the following: 

4-107 
 

 

 

   

     

   

     

   

       

   

   

       

   

• An external linkage to a previously modeled set of land use time series outputs 

• The use of aggregated BMPs 

• The tiered optimization approach 

4.1.2. Data Collection and Analysis 

The SUSTAIN case study application began with a review of watershed characteristics and compilation of 

related spatial and temporal model outputs. The next steps involved processing watershed model output 

data into the required input data format and conducting model setup. Model setup included land use 

reclassification and time series mapping, delineating potential BMP drainage areas, and selecting 

assessment points. The established model configuration was then used to evaluate different treatment 

scenarios to measure relative impacts, perform optimization analysis, and, finally, interpret and present 

results. Figure 4-2 is a roadmap of objectives and problem formulation for the case study. 

Results Analysis and Representation (Post-Processor) 

• Cost-effectiveness curve 

• Alternative solutions 

• Optimum BMP selection (spatial, by cost interval) 

Put Optimization Processor to Work! 

• Select decision variables (BMP dimensions) 

• Select assessment points (BMP/Outlet locations) 

• Select evaluation factors, control targets (end points) 

Project Setup 

• BMP representation: placement, configuration, and cost 

• LAND/WATERSHED Representation 

• Routing network 

• Assessment point(s) 

• Test system application (externally calibrated model) 

Data Collection & Analysis 

• Study area review 

• GIS data: land use, stream, DEM, BMP sites. 

• Watershed and BMP information/data 

• Compile monitoring data (calibration/validation) 

Case Study Objectives

Question to be

answered:

What are cost‐effective

strategies for reducing

nonpoint source loads

from a mid‐sized urban

watershed using

centralized BMPs,

distributed BMPs, or a

combination of both?

Control Target:

• TSS

Figure 4-2. Upper North Branch Oak Creek Watershed case study road map. 

4.1.3. Project Setup 

Available sources of information including land use maps, local pollutant source characterization reports, 

and water quality assessment reports for the larger Oak Creek watershed, were used to set up the 

4-108 
SUSTAIN project and identify potential locations for management practices. The setup for management 

analysis also required the selection of a typical precipitation year for use in comparing alternatives and 

assessing downstream impacts. From the analysis of precipitation data, the hydrologic year 2001 

(10/1/2000–9/30/2001) was determined to represent the average conditions in the watershed. Total 

rainfall depth for the year was close to the long-term average for the area. At the same time, both 

precipitation depth and intensity distribution during that year were relatively close to the long-term 

statistical average distribution. Figure 4-3 presents a graph of average annual precipitation at Milwaukee 

Airport for water years 1988–2002. 

4-109 

0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

) Annual Precipitation (in. 

Milwaukee Mitchell Airport (WI5479) 

198819891990199119921993199419951996199719981999200020012002 

Figure 4-3. Average annual precipitation volume at Milwaukee Airport for water years 1988–2002. 

Although the water year 2001 was not the closest year to the average annual value over the 15-yr 

evaluation period, it was the most recent average year among the set and had a typical rainfall magnitude 

and intensity distribution. Figure 4-4 shows rainfall volume and intensity distribution for wet intervals 

occurring in water year 2001. In the figure, the volume and intensity percentile ranges are based on the 

record of storms occurring over the 15-yr period. A year with a perfect typical distribution would have 

the same number of precipitation intervals in each bin. Number of Precipitation Intervals 

0 

2 

4 

6 

8 

10 

12 

14 

Rainfall Intensity 

Rainfall Volume 

Rainfall Intensity 3 3 2 3 5 4 2 5 6 3 

Rainfall Volume 3 4 2 3 1 4 6 2 5 6 

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

Figure 4-4. Rainfall volume and intensity wet-interval distribution for water year 2001. 
The previously developed model was successfully calibrated and validated for the Oak Creek watershed, 

inclusive of the land use types considered in this case study (SEWRPC 2007). Using SUSTAIN’s external 

modeling function, these previously developed land use time series were imported directly from the 

calibrated model. Figure 4-5 and Figure 4-6 show the modeled versus observed flow at the Oak Creek 

U.S. Geological Survey (USGS) gage for the selected water year 2001. Figure 4-7 and Figure 4-8 are 

examples of modeled versus observed TSS for water years 1995–2001 and water year 2001, respectively. 

Avg Monthly Rainfall (in.)
 

Avg Observed Flow (10/1/2000 to 9/30/2001 )
 

Avg Modeled Flow (Same Period)
 

0 

100 

200 

300 

400 

500 

600 

700 

800 

900 

Flow (cfs) 

0 

2 

4 

6 

8 

10 

12 

14 

Daily Rainfall (in.) 

Oct-00 Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01 

Date 

Figure 4-5. Comparison of daily flow at Model Outlet 58 with USGS 04087204 at South Milwaukee. 

Avg Flow (10/1/2000 to 9/30/2001) Avg Monthly Rainfall (in.)
 

Line of Equal Value
 Avg Observed Flow (10/1/2000 to 9/30/2001) 

Best-Fit Line Avg Modeled Flow (Same Period)
 

100
 100 

O N D J F M A M J J A S 

0 

1 

2 

3 

4 

5 

Monthly Rainfall (in.) 

Average Modeled Flow (cfs) 

y = 0.9228x + 3.4356 

R2

80 = 0.9148 

60 

40
 

80 

Flow (cfs)

60 

40 

20
 20 

0 0 6
 

0 	 20 40 60 80 100 10 11 12 1 2 3 4 5 6 7 8 9 
 

Average Observed Flow (cfs) Month
 

Figure 4-6. Comparison of monthly flow at Model outlet 58 with USGS 04087204 at South 

Milwaukee. 

The land use time series from the calibrated watershed model were exported as unit-area hydrographs and 

pollutographs for each modeled land use type. Table 4-3 is a summary of annual average outflow and 

TSS load by modeled land use in Oak Creek on a unit-are a basis. 

4-110 
Modeled (Reach 58) Obser ved (OAK Gage OC-05) 

0 

100 

200

T 

300 

400 

0 

600 

SS (mg/L) 

50 

O-94 M-95 D-95 J-96 F-97 S-97 A-98 N-98 J-9 9 J-00 A-00 M-01
 

Figure 4-7. Modeled vs. observed TSS (mg/L) at Oak Creek gage OC-05, water years 1995–2001. 

Modeled (Reach 58) Observed (OAK Gage OC-05) 

1000 

) 

100 

L /g (mSTS

10 

1
 

O-00 N-00 D-00 J-01 F-01 M-01 A-01 M-01 J-01 J-01 A-01 S-01
 

Figure 4-8. Modeled vs. observed TSS (mg/L) at Oak Creek gage OC-05, water year 2001. 

Table 4-3. Summary of Modeled Annual Average Outflow and TSS Load in Oak Creek Watershed
 

Land Use ID Land Use Name 

Area 

(acres) 

Flow 

(acre-in./yr) 

TSS 

(lb/acre/yr) 

1 GRAS S_B 1,183 11.8 117 

2 GRASS_C 7,782 11.2 120 

3 GRASS_D 231 11.1 156 

4 FOREST 1,087 11.4 105 

5 CROP_B 380 12.5 046 

6 CROP_C 1,395 11.7 1,253 

7 CROP_D 127 11.5 2,278 

8 PASTURE_B 156 13.7 30 

9 PASTURE_C 693 12.6 118 

10 PASTURE_D 110 12.3 263 

11 WETLAND 1,270 9.2 303 

12 ULTRA_LOW 58 22.3 527 

13 RESIDENTIAL 608 22.3 511 

14 COMMERCIAL 2,095 22.3 784 

15 INDUSTRIAL 403 22.3 913 

16 GOVT_INSTIT 106 22.3 529 

17 TRANS_FREE 229 22.3 949 

4-111 
4.1.4. Optimization and Results Analysis 

For the purposes of optimization, the study area was divided into 11 subwatersheds. Figure 4-9 shows the 

land use distribution in the study area, overlain with the modeled subwatersheds. 

Figure 4-9. Land use distribution in the modeled subwatersheds. 

4-112 
Figure 4-10 shows howthe s ubwatersheds were classified into six groups on the basis of prevailing land 

uses, suitable management practices, and location in the watershed. Groups 1 through 4 are labeled in the 

map as A, B, C, and D. For those areas, BMP placement was applied to urban land uses. Subwatershed 6 

was singled out for special consideration as the fifth group because it is not part of the drainage area of 

the other larger subwatersheds. Because of its small size, BMPs in subwatershed 6 were optimized 

directly during the tier-2 optimization. The remaining subwatersheds (2, 3, 5, 7, and 9) were mostly nonurban and were assumed to need no additional management and were not evaluated for BMP placement 

because the objective of this analysis was to target TSS loads from urban areas. 

A 

B 

C 

D 

Figure 4-10. Subwatershed grouping for two-tiered optimization. 

BMP Representation 

For this case study, both distributed and centralized BMP options were evaluated. Distributed BMP 

options include rain barrels, bioretention, and porous pavement. The centralized BMP option is an 

infiltration basin. 

To improve computational efficiency, the aggregate BMP option (as described in Section 3.3.3), was 

used. An aggregate BMP consists of a series of process-based optional components, including on-site 

interception, on-site treatment, routing attenuation, and regional storage/treatment. The aggregate BMP 

component evaluates storage and infiltration characteristics from multiple BMPs simultaneously without 

explicit recognition of their spatial distribution and routing characteristics within the selected watershed. 

For this case study example, the aggregate BMP had four component BMPs—rain barrels (on-site 

interception), bioretention (on-site treatment), porous pavement (on-site treatment), and dry infiltration 

4-113 
 

basin (regional treatment). Figure 4-11 is a schematic diagram of aggregate BMP components, drainage 

areas to BMPs, and BMP-to-BMP routing networks. Figure 4-12 lists the area percentage of each land 

use that contributes to each of the aggregate BMP components. 

Outlet 

Commercial 

Overflow 

Low-Density-Residential 

Rain Barrel 

Dry Infiltration Basin 

Bioretention 

Porous 

Pavement 

High-Density-Residential Open Space & Water 

Impervious 

Pervious 

Figure 4-11. Aggregate BMP schematic. 

Figure 4-12. Aggregate BMP land use distribution.

As shown in Figure 4-11, the rain barrel component collects runoff from rooftops (as part of the

impervious surfaces) in low- and high-density residential areas. Bypass from the rain barrel is routed to

bioretention, together with runoff from the non-rooftop impervious surfaces in low- and high-d ensity

residential areas and ieas and impervious areas that could be subjected to the porous pavement option in

commercial ammercial areas. It was assumed that the parking lot portions of the commercia l impervious area could

be converted to porous pavement as a treatma treatment optionent option. In additiIn addition to the distributed BMPs (i.e., rain

barrel, bioretention, and porous pavement), a centralized facility—dry infiltration basin—was also a

candidate for consideration. In addition to outflow from the bioretention comtion component, the centralized

facility receives outflow from part of the pervious areas in high-density residential and commercial land 

4-114 
 

uses. Outflow from the centralized facility is routed to the watershed outlet. The other areas that are n ot 

routed to any aggregate BMP components are assigned to drain directly to the watershed outlet. 

To run the optimization analysis, the user must define decision variables that will be used to explore th e 

various possible BMP configurations. For this analysis, the decision variables are the number of fixedsize units of the distributed BMP types and surface area for the centralized BMP type (dry infiltrat ion 

basin). Because the decision variable values range from zero to a maximum number depending o n the 

drainage area, it is possible for one component in the treatment train to never be selected. During the 

optimization scenario, if the BMP number or size value of zero is selected, that point w ill act as a transfer 

node in the network (i.e., inflow = outflow), and the associated cost that is a function of the number of 

BMPs or BMP surface area will not set to zero. 

The physical configuration parameters, infiltration, and water quality simulation parameters of each BMP 

components are listed in Table 4-4. 

Table 4-4. BMP Parameters 

Parameter 

Rain 

Barrel Bioretention 

Porous 

Pavement 

Dry Infiltration 

Basin 

Physical Configuration 

Unit size 28 ft3 60 ft2 0.1 acre Max: 3,000 ft2 

Design drainage area (acre) 0.02 0.1 0.1 N/A 

Substrate depth (ft) N/A 2.5 2 1 

Underdrain depth (ft) N/A 1 1 1 

Ponding depth (ft) 4 0.5 0.2 Orifice height: 0.5 

Weir height: 4 

Infiltration* 

Substrate layer porosity N/A 0.5 0.5 0.4 

Substrate layer field capacity N/A 0.3 0.2 0.3 

Substrate layer wilting point N/A 0.15 0.05 0.15 

Underdrain gravel layer porosity N/A 0.5 0.5 0.5 

Vegetative parameter, A N/A 0.6 1 0.6 

Underdrain background infiltration rate** 

(in./hr), fc 

N/A 0.5 0.5 0.5 

Media final constant infiltration rate (in./hr), fc N/A 3 3 1 

Water Quality*** 

TSS 1st order decay rate (1/day), k 0.2 0.2 0.2 0.2 

TSS filtration removal rate, Prem (%) N/A 85 60 85 

* Source: Tetra Tech 2001. 

** Soil map shows the majority background soil has hydrologic soil group of C; therefore, 0.5 in./hr background infiltration rate is
 

assumed. 

*** Based on calibration using University of Maryland monitoring data (Tetra Tech 2003). 

BMP Cost 

Cost estimation is a critical component because the optimization process needs the data to evaluate and 

compare cost-effectiveness of one scenario relative to the others. Table 4-5 presents the cost functions for 

the BMP types used in this case study (rain barrels, bioretention, porous pavement, and a dry infiltra tion 

basin). 

4-115 
Table 4-5. BMP Cost Functions for the Case Study 

BMP Type Cost Function Reference 

Rain Barrel $15/ft3 BMP Cost database 

Bioretention $6/ ft3 BMP Cost database 

Porous Pavement $12/ft2 BMP Cost database 

Dry Infiltration Basin Cost = 12.4 × V 0 760 

V is the volume of the basin in ft3 

CASQA Stormwater BMP 

Handbook (CASQA 2003) 

Optimization 

A two-tiered optimization approach was applied to this case study. As previously described, the same set 

of management actions (aggregate BMP) was applied to subwatersheds A, B, C, and D. Tier-1 

optimization analyses for these areas result in a unique, cost-effectiveness curve for each. A second 

round of optimization (tier-2) is then performed and assessed at the most downst ream outlet of the 

waters h ed. Tier-2 decision variables include discrete points (representing a com bined management 

option s ) along the tier-1 cost-effectiveness curves and BMP options for subwat ershed 6. For 

subwatershed 6, the optimization is allowed to select distributed or centralized BMPs, or a comb ination of 

both. The optimization objective is to maximize TSS load reduction at the watershed outlet and minimize 

the cost of implementation. 

Tier -1 Optimization for Subwatershed A 

This section describes the process for developing tier-1 cost-effectiveness curves using subwatershed A as 

an example. Figure 4-13 shows the BMP placement and routing network for tier-1 subwatershed A. The 

total area of subwatershed A is 515 acres. The subwatershed is further subdivided into six subareas, with 

sizes ranging from 36 to 138 acres. This subwatershed was intentionally subdivided into subwatersheds 

of about 100 acres in size because preliminary testing has shown that given an hourly time step, the 

aggregate BMP approach closely resembles a fully articulated network in areas of around 100 acres (see 

Section 3.3.6). The aggregate BMP treatment train previously described in the BMP Representation 

section is applied to each of the six subareas. The watershed outlet (J4 in Figure 4-13) is designated as 

the assessment point, and TSS annual load is used as the evaluation factor. 

 Aggregate BMP 

Junction 

 Assessment 

Figure 4-13. Aggregate BMP arrangement in tier-1 subwatershed A. 

4-116 
Tier-1 Optimization Results 

The full set of results of all tier-1 optimization runs are summarized in Figure 4-14 as small gray circles 

with near-optimal solutions in larger orange circles along the upper-left frontier. Each near-optimal 

solution represents one combination of decision variables, including the number of rain barrels, 

bioretention units, area of porous pavement, and size of infiltration basin for each subarea of 

subwatershed A. Figure 4-14 also highlights five solutions as green circles (numbered 1 to 5) selected to 

be the tier-2 search domain and their associated costs and TSS load reductions are summarized in Table 

4-6. These specific solutions were selected to account for the full range of achievable TSS reduction. Effectiveness (% Reduction) 

35% 

30% 

25% 

20% 

15% 

All Solutions
 

10%
 Cost-Effectiveness Curve 

Selected Solutions

5% 

$0.5 $1.0 $1.5 $2.0 $2 5 $3 0 $3 5 $4.0 $4.5 $5.0 

Cost ($ Million) 

Figure 4-14. Tier-1 cost-effectiveness curve for subwatershed A with the selected solutions. 

Table 4-6. Selected Tier-1 Solutions on the Cost-Effectiveness Curve 

Cost TSS Load Reduction 

Solution ID ($ million) (%) 

1 0.90 27.3 

2 1.37 29.9 

3 1.84 32.1 

4 2.83 32.7 

5 4.09 33.4 

Figure 4-15 shows the BMP cost distribution for all the near-optimal solutions on the cost-effectiveness 

curve with respect to the cost of four BMP options (rain barrel, bioretention, porous pavement, and dry 

infiltration basin). The graph also reveals the BMP selection preference with increase in total cost. The 

figure shows that the dry pond is the most cost-effective choice and, thus, is the first option to be fully 

used throughout the range. The next choices are bioretention and rain barrel. Porous pavement was the 

last to be considered suggesting that it is the least cost-effective BMP type among the four types 

considered in this case study for TSS load reduction. 

The same procedure was applied to develop the tier-1 cost-effectiveness curves for Subwatersheds B, C, 

and D. The four curves, together with that for subwatershed 6 and the remaining untreated areas, become 

the input decision variables for the tier-2 optimization analysis. 

4-117 
Cost Distribution ($ Million) 

$5.0 

$4.5 

$4.0 

$3.5 

$3.0 

$2.5 

$2.0 

$1.5 

$1.0 

$0.5 

$0.0 

POROUSPAVEMENT 

RAINBARREL 

BIORETENTION 

DRYPOND 

27.3%27.5%27.7%27.9%28.2%28.4%28.8%29.1%29.4%29.5%29.7%29.9%30.2%30.4%30.7%30.9%31.1%31.4%31.6%31.7%32.1%32.3%32.4%32.5%32.6%32.7%32.9%33.0%33.1%33.2%33.3%33.4% 

Effectiveness (% Reduction) 

Figure 4-15. Composition of best solutions on tier-1 cost-effectiveness curve for subwatershed A. 

Tier-2 Optimization 

The tier-2 optimation analysis is assessed at the outlet of the Oak Creek watershed. The decision 

variables include the selected points along the four tier-1 cost-effectiveness curves and BMP selection for 

subwatershed 6. The untreated areas are included as a fixed boundary condition of TSS load and not 

involved in the optimization runs. Since runoff from the untreated areas is expected to be relatively clean, 

the water would boost the assimilative capacity of streams in and downstream of the watershed. The 

inclusion of this relatively clean water will influence the optimization results. 

Figure 4-16 shows a schemati c of the tier-2 analysis network with the objective to develop the costeffectiveness curve that meents TSS reduction load goals at the outlet. The analysis involves stream 

routing. The four most downstream segments (J3–J4, J4–J10, J10–J13, and J6–J12) are simulated as 

trapezoidal channels, while shorter segments are assumed dummy conduits considering the travel time of 

those segments are likely less than an hour. This provides the benefit of reducing computation time. 

Tier-2 Optimization Results 

The optimization analysis resulted in the tier-2 cost-effectiveness curve shown in Figure 4-17. The costeffectiveness curve suggests that the maximum achievable TSS load reduction, given the objec tives and 

constraints associated with the study, is approximately 30 percent. To further examine the cost-effective 

solutions, three selected solutions are highlighted in Table 4-7. 

Table 4-7 shows that for the lowest tier-2 reduction target of 16 percent, only the most downstream 

subwatersheds C and D were treated. In addition, rain gardens were preferred in subwatershed 6. The 

most cost-effective solution that achieves a 16 percent load reduction at the outlet costs $1.85 million. At 

the next selected tier-2 target of 23 percent, the cost would increase to $2.75 million because 

subwatershed B is added to the list. Finally, for a 30 percent load reduction, all four tier-1 subwatersheds 

were selected for treatment. 

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A
 

B

C 

D 

35 

30%, $3.64 million 

30 

23%, $2.75 million 

25 

20 

16%, $1.85 million 

15 All Solutions 

Best Solutions 

Selected Solutions

10 

0.4 2.4 4.4 6.4 8.4 10.4 12.4 

Cost ($ million) 

TSS annual lo ad reducti on (%) 

Figure 4-16. Schematic of tier-2 analysis network. 

Figure 4-17. Tier-2 cost-effectiveness curve. 

4-119 
 

 

 

Table 4-7. Selected Tier-2 Best Solutions 

Tier-2 TSS Load % Reduction Target 16 23 30 

Total Cost ($million) 1.85 2.75 3.64 

Tier-1 Subwatersheds 

T SS Load % Reduction Allocation 

A 0 0 27.3 

B 0 27.3 27.3 

C 27.3 27.3 27.3 

D 27.3 27.3 27.3 

Subwatershed 6 Distributed Rain Barrels (#) 0 0 0 

Distributed Rain Gardens (ft2

) 2,100 2,100 2,100 

Distributed Porous Pavement (acre) 0 0 0 

Dry Infiltration basin (acre) 0 0 0 

A closer examination of the three tier-2 cost-effecti ve solutions suggests that it is more cost-effective to 

provide load reductions at lower subwatersheds t han at upper subw atersheds. This is because stream 

segments through natural processes will inherently provide some load reduction benefit from settling or 

transport routing. Hence, placing BMPs at upper reaches to reduce loads is not as cost-effective as at 

downstream reaches since the same investment in urban areas close to the ass essment point at the 

watershed outlet would yield a greater reduction. In other words, why spendmo ney to reduce a load 

when it can be reduced for free in stream tra nsport? It is a ssumed that the strea mbanks are stable and 

erosion does not take place. Had streambank erosion been factored in as a problem, the types of BMPs 

selected upstream to control peak discharge or pollutant load might have resulted in a completely 

different set of solutions. Note that SUSTAIN does not include a streambank erosion simulation 

mechanism. This case study example further highlights the importance of careful formulation of the 

problem and understanding of the associated implications and findings of the results. 

It is also interesting to observe that although higher levels of treatment options were available from 

among tier-1 subwatersheds (as shown in options 2 to 5 in Table 4-6), none of them were included in the 

the tier-2 optimal solutions due to unfavorably higher marginal costs. Furthermore, the load reduction in 

stream segments mitigated the need for additional treatment. 

It is impo rtant that readers examine the specific conclusions drawn from this exercise in the context of 

this specific problem formulation and the imposedassumptions and constraints. Had the same problem 

been formulated slightly differently, it might have resulted in a completely different set of solutions. 

When working with a relatively large watershed, hy drological an d wat er quality responses to various 

BMP treatment opt ions can be complex and result in several com petin g effects that could cloud intuitive 

interpretation. SUSTAIN provides a framework for quickly expl oring the response to multiple problem 

formulations and examining the im pact of th e inher ent cost and BMP performance as sumpti ons. Using 

the framework to examine the resp onses at several intermediate nodes in a complex network would help 

in selecting the best solutions. 

4.1.5. Summary 

This case study has demonstrated: (1) how SUSTAIN can be linked to an existing watershed model, (2) 

using aggregate BMPs, and (3) using a tiered watershed optimization approach for identifying the cost 

effectiveness of BMP solutions. In summary, the aggregate BMP approach is a simplified approach for 

preserving the physically based response of distributed BMP types that, preliminary tests have shown can 

4-120 
reduce computational effort without compromising accuracy when used appropriately. Also, the tiered 

approach is an efficient way of disaggregating an optimization problem for large watersheds into 

manageable units for decision making BMP placement. Finally, the results-interpretation process further 

highlights the fact that SUSTAIN is a tool, not an advisor. Users must interpret model results and weigh 

them in light of user-specified assumptions, problem formulation, and optimization goals. Its application 

must be preceded by an intimate understanding of the study area and the influential factors affecting 

decision making for stormwater management. 

4.2. Little Rocky Run Watershed 

Fairfax County is in Northern Virginia and is part of the Greater Washington, DC, metro area. The 

county has undergone significant growth over the years and is considered to be almost completely builtout according to the county’s comprehensive development plan (Fairfax County 2007). In many areas, 

stormwater management involves retrofitting existing facilities and incorporating new stormwater 

treatment for older areas in which treatment has not previously existed. To assist with planning efforts, 

the county is developing watershed management plans to assess watershed conditions, identify 

stormwater management needs, and prioritize future stormwater/BMP implementation efforts. The 

Fairfax County Department of Public Works and Environmental Services (DPWES) previously developed 

watershed management plans for 11 of the 30 watersheds in the county and began developing watershed 

management plans for the remaining 19 watersheds in 2006. The detailed plans incorporate an 

assessment of current and future watershed conditions and problem areas, identify the county’s structural 

and nonstructural needs, analyze stormwater management and BMP options, and prioritize recommended 

stormwater projects on the basis of measurable goals. County staff are using watershed plans and 

technical tools to address stormwater planning needs, MS4 and TMDL requirements, Chesapeake Bay 

nutrient- and sediment- reduction goals, and local stakeholder concerns. Hydrologic, hydraulic, and water 

quality models (SWMM, HEC-RAS, and other systems) were developed to assess watershed conditions 

and quantify the benefits of various stormwater management practices. The models provide the 

foundation for the watershed planning process. Little Rocky Run is included in the group of watershed 

management plans being developed by Fairfax County. 

SUSTAIN was used to evaluate the influence of various BMP scenarios for both a flood control target (10­

yr design storm peak flow rate) and a water quality target [total phosphorus (TP) reduction of 40 percent]. 

In terms of SUSTAIN functionality, this case study demonstrates the following: 

• The use of the internal SWMM land use time series generation option 

• Multiobjective optimization formulation for flood control and water quality objectives 

4.2.1. Project Setting 

In the western portion of the county (Figure 4-18), the Little Rocky Run watershed is approximately 7 

square miles in area, with an average imperviousness of 30 percent. Land use is primarily residential (61 

percent), followed by open space (19 percent), and areas occupied by institutional and public facilities (18 

percent). The watershed includes approximately 20 miles of stream channel and 13 regional ponds. The 

watershed was selected for development of a SUSTAIN case study because of several factors, including 

the availability of detailed watershed planning data and a recently completed and calibrated SWMM 

design storm model. Flood control in peak flow reduction and improvements in sediment and nutrient 

levels are two primary stormwater management goals for this recently developed watershed. 

4-121 
Figure 4-18. Little Rocky Run watershed in Fairfax County, Virginia. 

4.2.2. Data Collection and Analysis 

This SUSTAIN case study application began with a review of watershed characteristics followed by 

assembling information related to the existing watershed modeling activities for the area, including the 

simulated time series outputs and model parameters used in the existing watershed models. Available 

sources of information included land use maps, pollutant source information, existing BMP information, 

local BMP cost data, and the existing Little Rocky Run SWMM model. Figure 4-19 shows the selected 

watershed for the case study application—a mixed land use area in the headwaters of the Little Rocky 

Run watershed. 

4.2.3. Project Setup 

The next step was to transfer the compiled model parameters into the LAND module (based on SWMM5) 

in SUSTAIN for model setup. Model setup also involved land use reclassification, assigning flow and 

water quality time series to corresponding land use types, delineation of BMP tributary areas, and 

selection of assessment points. The established model configuration was then used to evaluate different 

treatment scenarios to measure relative impacts, perform optimization analysis, and finally, interpret and 

present results. Figure 4-20 shows a roadmap of this case study and its objectives. 

4-122 
 

 

 

 

    

     

     

   

     

       

       

   

   

Figure 4-19. Selected study area in Little Rocky Run watershed. 

Results Analysis and Representation (Post-Processor) 

• Alternative solutions 

• Optimum BMP selection (spatial, by cost interval) 

Put Optimization Processor to Work! 

• Select decision variables (BMP dimensions) 

• Select assessment points (BMP/Outlet locations) 

• Select evaluation factors, control targets (end points) 

Scenario Evaluation 

• Establish desired spatial scale for evaluation 

• Integrate existing BMP implementation plans 

• Assess unique land use conditions 

• Build routing network 

Data Collection & Processing 

• GIS data: land use, stream, DEM, BMP sites. 

• Existing SWMM model 

• Potential applicable BMP types and configurations 

• Compile monitoring data (cal bration/validation) 

Case Study Objectives

Question to be

answered:

What is a cost‐effective

management strategy

thatsimultaneously

provides flood control

and water quality benefit

for a small urban

watershed?

Control Targets:

• Peak flow reduction

• Total Phosphorus load

reduction

Figure 4-20. Little Rocky Run case study road map. 

4-123 
 

Existing SWMM Modeling 

A comprehensive SWMM (version 5) 2-, 10-, and 100-yr design storm model for the Little Rocky Run 

watershed was developed for watershed planning purposes (Tetra Tech 2009). The model calibration was 

performed at five locations following the county’s approved methodology (Tetra Tech 2008). These 

locations were selected from a consideration of land use, soils, slope, and major confluences along the 

mainstem. Calibration was performed beginning with the most upstream location and proceeding 

downstream to the watershed outlet. At each calibration location, SWMM model predictions were 

compared to the peak flows estimated by the Anderson and USGS methods (Anderson 1970; USGS 

2001), which are documented in the county’s SWMM calibration methods (Tetra Tech 2008). 

The Anderson approach was developed for estimating peak runoff rates from watersheds with varying 

degrees of development and design storms with recurrence intervals of 2.33 years to 100 years. The 

regression relationship in the approach was derived from analysis of flood data at eighty-one watersheds 

in and around Washington, DC with sizes ranging from 0.00034 mi2

 to 570 mi2. The imperviousness of 

these watersheds varies from less than 1 percent to 100 percent, and the watershed conditions from 

natural to fully developed (Anderson 1970). 

In the Anderson approach, the peak flow rate from a watershed is expressed as a function of five 

independent variables, including the watershed area, length, slope, percentage of imperviousness, and the 

type of drainage collector system. 

Alternatively, the USGS (2001) has developed regression equations, using urban runoff data from 199 

basins in 56 cities and 31 states, to relate urban peak flow rates with basin characteristics for various 

recurrence intervals at different regions. The regression equations were built into a computer program
 

called National Flood Frequency (NFF, Version 3.2) for peak flow predictions from recurrence interval 

storms ranging from 2 to 500 years, along with the corresponding confidence interval of the estimations. 

The SWMM calibration was performed using the 2-, 10-, and 100-yr 24-hour design storms. The 

watershed GIS data were used to generate the input data parameters, summarized in Table 4-8, for the 

Anderson approach and the USGS method. Model calibration locations are shown in Figure 4-19. 

Table 4-8. List of Input Parameters for Both the Anderson and the USGS Methods 

SWMM 

Junction 

Area 

(A) 

(mi2

) 

Index of 

Slope 

(S) 

(ft/mi) 

Longest 

Flow 

Path (L) 

(mile) 

Lag 

Time 

(T) 

(hr) 

% 

Impervious 

(I) 

Impervious 

Coefficient 

(K) 

Flood-Frequency Ratio 

(R) Development 

Factor 

(USGS 

2-yr 10-yr 100-yr Method) 

3 0.56 53.93 0.98 0.33 26.96 1.40 1.00 1.84 3.92 11.00 

13 2.19 49.72 2.35 0.52 22.40 1.34 1.00 1.89 4.12 10.00 

18 3.54 39.48 3.94 0.71 25.57 1.38 1.00 1.85 3.98 10.00 

28 6.19 42.71 6.47 0.90 28.89 1.43 1.00 1.82 3.84 11.00 

32 7.18 32.67 8.89 1.12 25.00 1.38 1.00 1.86 4.00 9.00 

Previous SWMM calibration studies of the Little Rocky Run watershed (Tsihrintzis and Hamid 1998; 

Zaghloul 1983) focused on the parameters of depression storage, infiltration parameters, and slope at each 

calibration location. The calibration target was based on the relationship between the Anderson approach 

peak flow estimation and the USGS method peak flow estimation range, as follows: 

•	 When the computed peak flows from the Anderson approach fall within the range USGS 

estimation range, the target flow for calibration is the USGS estimated range 

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•	 When the estimated flows from the Anderson approach is outside the flow range from the USGS 

method, the target flow for calibration is ±20 percent of the Anderson approach estimates 

The calibration process ends when a good match is reached between the SWMM predicted peak flow rate 

and the estimates from the Anderson/USGS methods. Table 4-9 compares the peak flows from the USGS 

method, the Anderson approach, and the calibrated SWMM. 

Table 4-9. Comparison of Predicted Peak Flows (ft3

/s) in the Little Rocky Run Watershed 

SWMM The USGS Method The Anderson Method SWMM 

Junction 2-yr 10-yr 100-yr 2-yr 10-yr 100-yr 2-yr 10-yr 100-yr 

3 124–510 271–1,113 400–2,300 341.9 629.0 1,338.8 289.7 580.1 1,021.4 

13 278–1,138 600–2,460 897–5,163 801.4 1,511.1 3,299.1 488.5 1,133.4 2,157.4 

18 387–1,585 819–3,361 1,222–7,038 1,056.0 1,957.2 4,197.9 612.5 1,457.3 2,839.7 

28 678–2,782 1,388– 

5,692 

2,051– 

11,809 

1,549.3 2,823.0 5,944.9 933.7 2,260.5 4,346.2 

32 564–2,316 1,188– 

4,872 

1,785– 

10,275 

1,506.7 2,801.2 6,027.0 945.3 2,308.0 4,498.2 

An example calibration plot is shown in Figure 4-21. It shows the predicted peak flows by three methods 

on the same graph. The figure shows that the calibrated SWMM model peak flow rates are within the 

USGS peak flow estimation range for 2-, 10-, and 100-yr 24-hour design storms. 

0 

500 

1000 

1500 

2000 

2500 

2-year 10-year 100-year 

Design storm frequency 

Peak flow rate (cfs) 

Estimated peak flow range by USGS method 

Estimated peak flow rate by Anderson method 

Calibrated SWMM prediction of peak flow rate 

Figure 4-21. SWMM calibration results at junction 3 of the Little Rocky Run watershed. 

Validation of Land Module in SUSTAIN 

Figure 4-19 shows the selected study area for the case study application—a mixed land use area in the 

headwaters of the Little Rocky Run watershed. The study area is approximately 240 acres, and the land 

use distribution of the area size and imperviousness are presented in Table 4-10. 

To ensure that SUSTAIN’s internal land simulation module would reproduce the results of the calibrated 

SWMM model, SUSTAIN was set up for the same area using the SWMM input parameters from the 

calibrated Little Rocky Run model (Table 4-11). A 10-yr, 24-hour design storm was used to compare the 

simulated surface runoffs using the internal land simulation option in SUSTAIN and standalone SWMM 

model. 

4-125 
 

Table 4-10. Land Uses of the Case Study Area in the Little Rocky Run Watershed 

Land Use Groups 

Area 

(acres) 

Percent of Total 

Area 

(%) 

Imperviousness 

(%) 

Transportation 65.4 27 52 

Residential and Other Urban 95.9 40 21 

Open Space 79.5 33 10 

Total 240.8 100 25 

The study area was subdivided into three subareas on the basis of the type of treatment received (Figure 

4-22). The areas that received quantity control onlt were grouped into subarea B1. The areas that use 

BMPs to provide water quality treatment were grouped into subarea C. Lastly, the areas not served by 

stormwater management facilities or received waivers because of the construction of downstream 

stormwater facilities were grouped into subarea D. Fairfax County (2007a and 2007b) include additional 

information on the subarea delineation and SWMM modeling approach. 

Table 4-11. Major Input Parameters for Modeling of Three Subareas 

Subarea C B1 D 

Area (acre) 4.82 36.63 167.64 

Width (ft) 21.37 162.43 743.38 

Slope (%) 1.31 1.31 1.31 

Imperviousness (%) 24.95 26.05 22.41 

Impervious Manning’s n 0.015 0.015 0.011 

Impervious Depression Storage (in.) 0.1 0.1 0.04 

Pervious Manning’s n 0.266 0.269 0.282 

Pervious Depression Storage (in.) 0.2 0.2 0.1 

Figure 4-22. Representation of three subareas. 

4-126 
Figure 4-23 shows the hydrograph generated at the outlet of the study area by the standalone SWMM 

model. The same model configuration was replicated in SUSTAIN using the internal land simulation 

module. Figure 4-24 shows a one-to-one comparison of the computed flows on the two separated 

hydrographs generated. The perfect duplication of peak flow computations is a good confirmation that 

the SWMM model codes were correctly incorporated into the Land Module of SUSTAIN. 

Figure 4-23. SWMM-generated hydrograph at the outlet of the study area. 

SWMM versus SUSTAIN 

Flow (cfs) Simulation in SW MM 70 

60 

50 

40 

30 

20 

10 

0 

y = x 

R2

 = 1 

0 10 20 30 40 50 60 70 
 

Flow (cfs) Simulation in SUSTAIN 

Figure 4-24. SWMM versus SUSTAIN-generated hydrograph comparison. 

4-127 
 

4.2.4. Optimization and Results Analysis 

The optimization problem can be mathematically expressed as: 

Objective: 

n 

Minimize ∑Cost(BMPi ) 

i=1 

Subject to: 

Q ≤ Qtarget 

LTP ≥ 40% 

where 

BMPi = BMP (i.e., bioswales, bioretention, and wet pond) configuration decision variables associated 

with location i, 

Q = the computed 10-yr design storm peak flow at the pond outlet, 

Qtarget = the target value of the 10-yr peak flow rate at the pond outlet, and 

LTP = the computed amount of TP load reduction percentage at the study area outlet. 

To facilitate the BMP evaluation, the study area was grouped into three subbasins, one for each of the 

three major land use types: transportation (27 percent), residential and other urban (40 percent), and open 

space (33 percent). The land use distribution of the area size and imperviousness are presented in Table 

4-10. This configuration was used to optimiz e the placement of BMPs to reduce peak flow rate and 

nutrient load. 

SWMM input parameters used to represent the physical characteristics of a subbasin for runoff 

computations include area, width, slope, percent imperviousness, Manning’s n for both pervious and 

im pervious surfaces, depression storage for both impervious and pervious surfaces, percentage of 

impervious surfaces with zero depression storage, subarea internal routing method and percentage, and 

the Horton infiltration parameters. Below is a summary of how e ach of the input parameters was 

generated. Table 4-12 summarizes the numerical values of the SWMM input parameters for the three 

subbasins. 

Area—thesurface area of a subbasin, calculated in a GIS environment using area summ ary f unctions. 

Width—the width of a subbasin, which, according to the SWMM User’s Manual, is calculated by 

dividing the subbasin area b y the longest flow path. The longest flow path is automatically generated 

using ArcHydro. 

Slope—slope for a subbasin is calculated as rise over run, in which the run represents the longest flow 

path, and rise represents the elevation difference between the starting and ending points of the longest 

flow path. 

Percent imperviousness—the percent imperviousness of a subbasin is calculated by dividing the total 

planimetric impervious area (i.e., building, roadway, parking lot, and sidewalk) by the total area of the 

subbasin. 

4-128 
 

Manning’s n—the Manning’s n values for both impervious and pervious surfaces are estimated on the 

basis of land surface characteristics. The area of each land use type in a subarea is used to calculate the 

area-weighted Manning’s n for the whole subarea. 

Depression storage—the depression storage is a volume that must be filled prior to the occurrence of 

runoff on both pervious and impervious areas. In the absence of users’ defined values, default values of 

0.2 in. for pervious surface and 0.1 in. for impervious surface are suggested. 

Percentage of impervious surface with zero depression storage—a default value of 25 percent is used 

in the initial model setup as suggested in the SWMM manual. 

Table 4-12. Subbasin SWMM Parameters 

Subbasin 1 2 3 

Land Use Groups Transportation 

Residential and 

Other Urban Open Space 

Area (acre) 65.4 95.9 79.5 

Width (ft) 145.22 404.77 237.19 

Slope (%) 1.31 1.31 1.31 

Imperviousness (%) 51.6 20.5 9.5 

Impervious Manning’s n 0.015 0.015 0.015 

Impervious Depression Storage (in.) 0.11 0.11 0.11 

Pervious Manning’s n 0.266 0.266 0.266 

Pervious Depression Storage 0.23 0.23 0.23 

Table 4-13 is a summary of the hydrologic soil group distribution in the case study area from countyprovided SSURGO data. Note that a major portion (90.6 percent) of subbasin 3 (open space) has D soils, 

while subbasins 1 (transportation) and 2 (residential and other urban) have a mixture of B and D soils. 

Table 4-13. Hydrologic Soil Group Distribution in Study Area Subbasins 

Soil Group B 

(%) 

Soil Group C 

(%) 

Soil Group D 

(%) 

Subbasin 1 56.35 6.14 37.51 

Subbasin 2 40.09 5.47 54.44 

Subbasin 3 8.64 0.81 90.55 

The Green-Ampt infiltration equation was used to simulate the infiltration loss. The equation requires 

that three parameters be specified. As a conservative assumption, the parameters for D soils are assumed 

for all three subbasins: 

• The average capillary suction (Su) = 3 in. 

• Hydraulic conductivity (Ks) = 0.5 in./hr 

• The initial moisture deficit (IMD) = 0.25 (fraction) 

4-129 
 

As described in Section 3.2.3, SUSTAIN uses the buildup and washoff processes for water quality 

simulation. The calibrated SWMM model for the Little Rock Run watershed focused only on flows and 

did not include water quality components. Pollutant loads from the watershed were estimated using a 

simplified spreadsheet tool, STEPL (provide a reference). Nevertheless, there are several SWMM 

modeling studies in Fairfax County in which the buildup and washoff parameters were determined 

(Behera et al. 2006). These studies were reviewd and representative buildup and washoff coefficients 

were derived as shown in Table 4-14 and used in SUSTAIN for estimati ng TP concentrations. 

Table 4-14. TP Buildup and Washoff Parameter Values 

Name 

Buildup 

B = Min(C1, C2t

C3 ) 

Washoff 

W = C1qC2B 

C1 C2 C3 C1 C2 

Residential Impervious 8.0 0.045 0.523 0.83 1.38 

Residential Pervious 6.3 0.031 0.42 0.70 1.49 

Transportation Impervious 7.4 0.04 0.429 0.76 1.403 

Transportation Pervious 6.3 0.031 0.42 0.70 1.49 

O pen Space Imperviou s 7.3 0.032 0.388 0.70 1.42 

Open Space Pervious 6.0 0.030 0.394 0.692 1.465 

BMP Representation 

Three BMP types considered in the analysis included bioswale, bioretention, and a regional wet pond.
 

Bioswales and bioretention facilities are distributed BMPs to treat runoff from local impervious areas 

with bioswales for highways and roads and bioretentions for residential and other urban areas. Regional 

wet ponds are centralized BMPs and typically are large facilities designed to treat large drainage areas. 

An existing regional wet pond was located at the study area outlet and, therefore, receives runoff from the 

entire study area. Figure 4-25 shows a flow chart diagram of the BMP simulation network. Table 4-1 5 

lists the BMP parameters that were used in the model. 

Bioswales 

Bioswales are modified vegetated swales that use bioretention media beneath the swale to improve water 

quality, reduce the runoff volume, and reduce the peak runoff rate. In addition to provide drainage 

conveyance function as traditional drainage swales, bioswales use vegetation, compost, and/or riprap to 

enhance infiltration, water retention, and removal of silt, nutrient and pollutants through a variety of 

physical, chemical, and biological processes. 

Bioretention 

Bioretention cells, also known as rain gardens, are small-scale, shallow vegetated depressions that provide 

water quality benefits by rapid filtering through soil media, biological and chemical reactions in the soil 

matrix and root zone, and infiltration into the underlying subsoil. Bioretentions can be designed as online or off-line facilities with respect to the stormwater conveyance. 

Regional Wet Pond 

As a traditional practice that is mainly used for flood control, regional ponds were constructed in rapidly 

urbanizing areas in Fairfax County. There is a regional pond at the outlet of the case study area. The 

pond was designed to attenuate the peak discharge for 2-, 10-, and 100-yr 24-hour design storms. 

4-130 
 

 

Regional Wet Pond 

Outlet 

Other Area 

Bioswale Bioretention 

Highway and Roads Residential and Other Urban 

Figure 4-25. BMP placement schematic in the Little Rocky Run case study area. 

Table 4-15. BMP Parameters 

Parameter Bioswale Bioretention Pond 

Regional Wet 

Physical Configuration 

Maximum size (acre) 0.36 0.33 2.24 

Drainage area (acre) 25.5 33.6 241 

Substrate depth (ft) 2 2.5 N/A 

Underdrain depth (ft) N/A 1 N/A 

Ponding depth (ft) 0.5 0.5 

Weir height: 4.75 

Orifice height: 1 

Infiltration* 

Substrate layer porosity 0.5 0.5 N/A 

Substrate layer field capacity 0.3 0.3 N/A 

Substrate layer wilting point 0.15 0.15 N/A 

Underdrain gravel layer porosity 0.5 0.5 N/A 

Vegetative parameter, A 0.6 0.6 N/A 

Underdrain background infiltration rate 

(in./hr), fc 

0.5 0.5 N/A 

Media fi nal constant infiltratio n rate (in./hr), 

fc 

3 3 N/A 

Water Quality** 

TP 1st o rder decay rate (1/day), k 0.2 0.2 0.2 

TP filtration removal rate, Prem (%) 65 65 N/A 

* Source: Tetra Tech 2001 

** Based on calibration using University of Maryland monitoring data (Tetra Tech 2003) 

BMP Cost 

For the case study, annualized life cycle cost functions summarized in Table 4-16 were used to estimate 

costs of BMPs considered. Annualized life cycle costs include the initial installation cost, as well as 

maintenance and replacement costs. 

4-131 
 

 

 

 

 

 

 

 

 

Table 4-16. Annualized BMP Cost Function 

peBMP Ty Annualized Cost Function Reference 

Bioretention $1.03/ft2

 per year Fairfax County BMP Factsheets (Fairfax 

County 2005) 

Bioswale $0.67/ft2

 per year Fairfax County BMP Factsheets (Fairfax 

County 2005) 

Wet pond Cost = 0.12 × 24.5 × V 0 705 ($ per year) 

V is the volume of the wet pond in ft3 

CASQA Stormwater BMP Handbook 

(CASQA 2003). 

Bioretention Cost 

Table 4-17 shows the breakdown of installation and annualized costs for a typical bioretention cell w ith a 

surface area of 900 ft 2

. A bioretention cell is assumed to have a life span of 25 years, at which point it 

will be removed and replaced. 

Table 4-17. Annualized Life Cycle Cost for a Bioretention Cell with a Surface Area of 900 ft2 

Item 

Required Cost per Year (2005 Dollars) 

0 1 2 3 4 5 6 7 8 9 10 ... 25 

Installation* 10,000 

Mulching and 

Debris Removal 

350 350 350 350 350 350 350 350 350 350 

Replace Vegetation 200 200 200 200 200 200 200 200 200 200 

Remove & Replace 10,000 

Total Co st 10,000 550 550 550 550 550 550 550 550 550 550 10,000 

Annualized Cost $925/year (includes replacement in year 25) 

Source: Fairfax County BMP Factsheets 

* The developer cost, which is not included in the annualized cost. 

Bioswale Cost 

Table 4-18 shows the installation cost and annualized costs for a bioswale with a surface area of 900 ft2

. 

A bioswale is also assumed to have a life span of 25 years, at which point it will be removed and 

replaced. 

Table 4-18. Annualized Life Cycle Cost for a Bioswale with a Surface Area of 900 ft2 

Item 

Required Cost per Year (2005 Dollars) 

0 1 2 3 4 5 6 7 8 9 10 ... 25 

Installation* 10,000 

Mowing 100 100 100 100 100 100 100 100 100 100 

Reseeding/Replanting 100 100 100 100 100 100 100 100 100 100 

Remove & Replace ,00010 

Total Co st 10,000 200 200 200 200 200 200 200 200 200 200 10,000 

Annualized Cost $600/year (includes replacement in year 25) 

Source: Fairfax County BMP Factsheets 

* The dev eloper cost, which is not included in the annualized cost. 

4-132 
Wet Pond Cost 

Wet Pond costs were estimated as described below. 

Construction Cost 

Cost = 24.5 × V 0.705 (see Table 4-16) (4-1) 

where 

Cost ($) = the cost for construction, design, and permitting, and 

V = volume of the wet pond in ft . 

3 

Converting the capital cost to annualized capital cost by assuming a 20-yr life span and 0.05 annual 

interest rate gives 

Annualized Capital Cost ($/yr) = 0.08 × 24.5 × V 0.705 (4-2) 

Maintenance Cost 

The mid-range of maintenance cost is approximated to be four percent of capital cost per year. 

0.705 Annualized Maintenance Cost ($/yr) = 0.04 × 24.5 × V (4-3) 

Annualized Life Cycle Cost (including capital and maintenanc e cost) 

Annualized Cost = 0.12 × 24.5 × V 0.705 (see Table 4-16) (4-4) 

where 

Annualized Cost ($) = the cost of construction, design, permitting, maintenance, and replacem ent, 

and 

V = the volume of thewet pond in ft3

. 

Simulation Period and Evaluation Factors 

As previously described, the objectives of this optimization case study are to provide flood control up to 

the 10-yr design storm and achieve a minimum of 40 percent reduction of TP load. Because of local 

water quality improvement needs and the ultimate downstream effect on the Chesapeake Bay, reduction 

in nutrient loading is an important objective for the stormwater ma nagement in Fairfax County. BMP 

effectiveness was measured at the designated assessment point at the study area outlet. 

The useof continuous simulation provides modelers with an opportunity to capture the dynamic 

responses of BMPs under various storm conditions. However, among the large potential sets of data , it is 

often possible to identify oneset of data that can reasonably represent either the long-term average 

treatment performance of BMPs or a critical condition that is most closely associated with the nature o f 

the problem being studied. From the analysis of precipitation data, the calendar year 1994 (January 1, 

1994–December 31, 1994) was determined to be representative of average hydrologic conditions in the 

watershed. The total rainfall depth for the year was close to the 17-yr average of 43.3 in. In addition, 

both pre cipitation depth and intensity distribution were relatively close to the long-term statis tical average 

distribution. Figure 4-26 shows the average annual precipitation at th e Washington Dulles International 

Airport for calendar years 1990–2006. 

As mentionedpreviously, calendar year 1994 had the most ty pical rainfall magnitude and intensity 

distribution from among the four highlighted years that were close to the 17-yr average. Figure 4-27 

shows the rainfall volume and intensity distribution for wet intervals occurring in calendar year 1994 at 

4-133 
 

 

Washington Dulles International Airport. In the figure, the volume and intensity percentile ranges 

correspond to storms occurring over the 17-yr period. A year with a perfect typica l distribution would 

have the same number of precipitation intervals in each bin. 

Washington Dulles Intl AP (448309) 

0 

10 

20 

30

recip 

40 

50

) 

60 

70 

Annual P itation (in. 

19901991199219931994199519961997199819992000200120022003200420052006 

Figure 4-26. Annual precipitation at the Washington Dulles International Airport. Number of Precipitation Inte rvals 

0 

1 

2 

3 

4 

5 

6

7 

8 

Rainfall Intensity 

Rainfall Volume 

Rainfall Intensity 4 5 3 3 6 6 4 5 0 4 

Rainfall Volume 4 4 4 5 7 3 4 1 5 4 

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

Figure 4-27. Volume and intensity distribution of storm events in 1994. 

Existing Condition with Regional Wet Pond 

The study area includes an existing regional w et pond that was designed to attenuate the peak discharge 

from 2-, 10-, and 100-yr storm events. Using SUSTAIN, the simulated 10-yr, 24-hour design storm p eak 

flows and TP annual loads are summarized in Table 4-19. Note that peak flow values are reasonably 

compar ed to the original SWMM model results assessed at the outlet of the case study area. 

Table 4-19. 10-Yr Design Storm Peak Flows and TP Annual Load under Existing Conditions 

Model 

10-yr, 24-hr Design Storm Peak Flow 

(cfs) 

TP Annual Load 

(lb/yr) 

Pond inflow Pond outflow Pond inflow Pond outflow 

SUSTAI N 108.3 53.4 78.2 59.8 

Fairfax County SWMM 107.7 50.4 N/A N/A 

4-134 
Plot of Evaluation Factors 

Plot of Evaluation Factors 

Figure 4-28. Results showing the benefits of BMPs. 

Optimization Setup 

Bioswales and bioretention cells were recognized as feasible BMPs for the case study area to attenuate 

peak flow and reduce pollutant load. The decision variables used in the study included the size of surface 

area of bioswales and bioretention cells, and the storage capacity of the wet pond. Although the size of an 

existing regional pond was treated as a decision variable, it is not to suggest that the existing pond size 

can be reduced. Instead, the benefit of adding supplemental storage capacity in the form of bioswales and 

bioretentions could demonstrate a net increase to the overall on-site storage capacity, thereby yielding 

additional flood control capacity for the wet pond. The difference between the required versus the 

optimiz ed size equals the net gain in flood control capacity for the wet pond. Two optimization 

objectives were defined in this exercise: (1) to maintain the 10-yr design storm peak flow at the pond 

outlet, and (2) to reduce the TP load by 40 percent. 

Optimization Results 

Using Scatter Search techniques, a near-optimal solution was identified after approximately 300 model 

runs. Figure 4-28 shows the near-optimal solution that meets both the 10-yr peak flow (top portion) and

TP load reduction (bottom portion) targets. This solution is presented alongside a scenario witho ut any 

BMPs (PostDev) and the existing condition with only the existing wet pond (Existing). This best solution 

carries an annualized life cycle cost of $64,400 per year. 

4-135 
 

 

 

 

Figure 4-29 presents all the solutions examined during the search process and the near-optimal solutions 

are highlighted in orange. The blue points indicate the cost and peak flow rate of all the solutions 

evaluated. The small number of blue points located to the left of the optimal solutions and below the flow 

rate target line are the solutions that meet the peak flow target, but not the water quality target. Table 

4-20 compares the best solution with the existing scenario and lists the selected BMP decision variable 

values for the best solution. It shows that, relative to existing condition life cycle cost (related to the 

existing wet pond), to achieve the 10-y r peak flow and the TP load reduction targets, an additional 

$35,000 per year would be needed. This additional investment for water quality improvement would 

require 0.36 acre (15,700 ft2

) of bioswales to treat runoff from roads and highways and 0.04 acre (1,740 

ft2

) of bioretention area to treat residential and other urban areas. The best solution also indicates that the 

new BMPs for water quality improvement yield a 17 percent net savings in terms of flood mitigation 

storage volume at the regional wet pond. 

0 

20 

80 

100 

120 

e (cfs) 

40 

50 

60 

40

10-yr P 

60

 Flow

30 

0 10000 20000 30000 40000 50000 60000 70000 80000 

eak Rat 

0 

10 

20 

Annualized Life Cycle Cost ($/yr) 

All solutions - 10yr peak flow 

Best solutions - 10yr peak flow 

10-yr peak flow target 

All solutions - TP load reduction 

Best solutions - TP load reduction 

TP reduction target 

TP Annua d R on l Loa eduti 

%( ) 

Figure 4-29. Domain of optimization searches and identified best solutions. 

Table 4-20. Cost-Effective Solution Details 

Existing Scenario Best Solution 

Annualized cost ($/yr ) 29,017 64,400 

10-yr peak flow (cfs) 53.4 53.0 

TP annual load reduction (%) 23.5 40.7 

BMP 

Regional pond area (acre) 2.24 1.86 

Bioswale area (acre) 0 0.36 

Bioretention area (acre) 0 0.04 

4-136 
4.2.5. Summary 

This case study has demonstrated: (1) a verification of the internal SWMM land module against the 

standalone SWMM 5 model and the use of the internal model to generate flow and pollutant loads from 

land, and (2) use of the Scatter Search optimization technique to find a near-optimal solution, given 

multiple control targets of peak flow and TP load reduction. Using optimization to guide decision making 

has been demonstrated to provide meaningful insights into the hydrologic response and benefits of BMPs 

for stormwater management. SUSTAIN is a powerful tool for decision-making. However, the outcomes 

from a SUSTAIN application would very much depend on the user-defined goals and assumptions. 

Hence, the results mus t be interpreted considering the use r-specified assumptions, problem formulation, 

and defined optimization constraints. As stated before, the SUSTAIN application must be accompanied by

an intimate understanding of the study area and the identification of influential factors that affect the 

decision making to achieve stormwater management goals. 

4-137 
 

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