|SUSTAIN - A Framework for Placement of Best Management Practices in Urban Watersheds to Protect Water Quality (EPA/600/R-09/095) September 2009
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, decision-support 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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