Date of Award
Doctor of Philosophy
Changing climates, human population growth, and aging infrastructure threaten the availability and quality of one of life's most vital resources, water. Hydrological ecosystem services are goods and benefits derived from freshwater that include flood damage mitigation, water for agricultural and commercial use, swimmable and navigable waters, and healthy aquatic habitats. Using computer algorithms inspired by biological and ecological processes known as evolutionary algorithms and on-site stormwater management practices such structural best management practices (BMPs) and green stormwater infrastructure (GSI), this research aims to maximize hydrological ecosystem services at the watershed-scale in both agricultural and urban environments by integrating these algorithms with the watershed model Soil and Water Assessment Tool (SWAT), and the hydraulic model Storm Water Management Model (SWMM). This dissertation first develops an information theoretic approach to global sensitivity analysis for distributed models, demonstrated using SWAT, and later uses the sensitive model parameters in a multi-objective automatic calibration scheme using multi-objective particle swarm optimization (MOPSO). Multiple alternative watershed-scale BMP designs (parallel terraces, detention/infiltration ponds, field borders, and grade stabilization structures) that help minimize peak runoff and annual sediment yield were simultaneously identified using SWAT coupled with the species conserving genetic algorithm (SCGA). Finally, using recently developed economic estimates called triple bottom line (TBL) accounting, watershed-scale GSI designs are identified that reduce combined sewer overflow volumes in an urban setting while maximizing the net benefit across social, economic, and environmental categories. Overall, this dissertation research provides useful and relevant computer-based tools for water resources planners and managers interested in maximizing hydrological ecosystem services benefits.
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