Significant advances have been made in the use of spatial and hydrologic models to quantify the impact of Best Management Practices (BMPs) and Low-Impact Development (LID) practices on water quality. Further advances are the goal of this work to add selection of BMP/LID and calculation of implementation costs, all integrated into a spatial decision support system (DSS). The Hydrologic Simulation Program in FORTRAN (HSPF), an unsteady flow model, was combined with links to desktop spatial data analysis tools, a spreadsheet listing BMP/LID and their implementation, operation, and maintenance cost data. Testing of the DSS, named Latis, allowed improvements in direct design of BMP, and a survey of landscape and engineering practitioners provided the impetus for a simplified version, Latis-LIDIA. 1. Introduction Commercial, industrial, and residential development is increasingly challenged to minimize disruption of the natural hydrologic regime to promote sustainability and comply with environmental regulations. In particular, site plans are being evaluated based on their water quality and quantity impacts on a watershed scale. Site development plans that maintain the hydrologic regime and sustain water quality downstream are consistent with the approach described as smart growth or low-impact development. Significant advances have been made in the use of spatial models, including geographical information systems (GISs) and sophisticated hydrologic models, to assess the impact of potential development. Similarly, experience with Best Management Practices (BMPs) provides good insight into how various management practices such as stormwater detention and vegetated areas contribute to improved water quality. The Tennessee Valley Authority (TVA), Environmental Protection Agency (EPA), Mississippi Department of Environmental Quality, and Mississippi State University encourage the use of low-impact/smart growth strategies and want to make their application rapid and easy. The work described here is intended to advance that goal. 1.1. Objectives This work was performed to develop Decision Support System (DSS) tools that will allow users to balance watershed protection with smart growth/low-impact site development strategies. Specifically, the objective is a DSS that will(i)predict time-varying runoff and water quality as a function of rainfall, site characteristics, and Best Management Practices (BMPs) for development sites within the Southeastern U.S;(ii)calculate BMP cost,(iii)allow various scenarios to be compared for effectiveness and cost,(iv)be GIS-based or
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