Integrated Modeling and Optimization of Stormwater Control Measures for Sustainable Watershed Management Under Urbanization and Changing Rainfall Patterns

Abstract

Rapid urbanization and climate change (CC) are altering the natural hydrological cycle, contributing to more frequent and severe flooding. These changes are particularly pronounced in mixed urban–rural watersheds, where diverse land uses complicate hydrologic modeling and stormwater planning. To improve resilience in these complex systems, coupled models are essential. While SWAT and SWMM are widely used for runoff simulation, neither can independently represent both urban and rural hydrology. Moreover, most studies assess either CC or land use/land cover (LULC) change in isolation, overlooking their combined effects. This represents an important gap in mixed land use watersheds, where different areas respond differently to these changes. Additionally, watershed resilience also requires a tool that accurately represents hydrological processes across land uses, evaluates Stormwater Control Measures (SCMs), including Low Impact Development (LID) controls and Best Management Practices (BMPs), and optimizes their selection, sizing, and placement to reduce runoff and minimize costs. This study introduces an integrated modeling and optimization tool to enhance the understanding of hydrological processes and improve SCM management in mixed land use watersheds under the combined effects of urbanization and climate variability. First, a Python-based automated calibration tool was developed to streamline SWMM model calibration, reducing manual effort while improving efficiency and predictive accuracy. Second, a novel coupling of SWAT+ and SWMM was implemented, using SWAT+ for rural areas and SWMM for urban zones. This resulted in more accurate streamflow simulations and improved representation of both high- and low-flow conditions. Third, the study assessed the combined impacts of climate and LULC changes, revealing spatial variability in streamflow responses across the urban and rural portions of the watershed. Finally, an integrated simulation–optimization tool was developed to identify optimal LID and BMP strategies that reduce peak runoff, decrease total runoff volume, and minimize implementation and maintenance costs. The tools and methods developed in this dissertation offer practical solutions for stakeholders, planners, and SCM designers. They support more accurate streamflow modeling, better assessment of climate and land use impacts, and cost-effective SCM design. Ultimately enhancing the resilience of mixed urban–rural watersheds in a changing environment.