Development of a Multi-Objective Decision Support Tool for Selecting Cost-Effective Stormwater Management Strategies at a Watershed Scale across Florida

Urban stormwater management is becoming an increasingly important issue in many urban areas due to urbanization and climate change. Low-impact development (LID), also called best management practice (BMP), or green infrastructure (GI) are semi-engineered stormwater management approaches that have been widely studied and applied around the world for nutrient reduction, but often get intertwined with environmental and socioeconomic constraints. Both planning and optimization can result in more systematic and strategic solutions to address these constraints. The main objective of this study is to develop a multi-objective decision support tool to assess the most cost-effective BMP strategies for nutrient reduction for selected watersheds in different ecoregions across Florida. Firstly, data on ground elevation, land use, soil, and precipitation were collected from publicly available sources. The data were then processed using various spatial analysis tools in ArcGIS and later were applied to develop a cost equation based on the life cycle cost and performance of the BMPs. The developed cost equation was then combined with a well-known multi-objective optimization algorithm, the non-dominated sorting genetic algorithm II (NSGAII), for various single and series-used BMP applications (bioretention, green roof etc.). The outcome of the study will indicate the most cost-effective BMP strategies for nutrient reduction in the selected watersheds across Florida. The methodology adopted in this study is applicable to large-scale watershed areas and is simply transferrable to any region of the United States. This tool will aid decision-makers and planners in developing a scientifically thorough plan to achieve the watershed's cost-effective BMP goals.