Large-scale Process-based Urban Hydrological Modeling Framework
Urban flooding presents a significant global challenge, exacerbated by increasingly frequent extreme climate events and rapid urbanization. Mitigation efforts typically involve implementing various strategies to enhance stormwater storage and infiltration. However, the complexity of these strategies in large metropolitan areas, coupled with the often inadequate representation of below-ground urban stormwater networks (BUSNs) in current hydrologic models, highlights the necessity of a comprehensive and scalable framework for planning and assessing potential vulnerabilities. This dissertation addresses these challenges by introducing a novel, physically-based urban modeling framework designed to simulate urban runoff generation and routing across natural and urban surface areas and through BUSNs. Our framework's innovative design ensures a balanced representation of natural and urban components and effectively overcomes data scarcity and computational limitations common in large-scale urban modeling. It utilizes graph theory and various land datasets to derive BUSNs, providing a practical solution to the BUSN data scarcity issue. The algorithm demonstrated its effectiveness in four U.S. metropolitan areas with partially available BUSN data. Applicable at both local, e.g., watershed, and larger, e.g., Conterminous United States (CONUS), scales, first, we evaluated our framework at nine representative urban watersheds in Houston, Texas. We gained crucial insights into the capacity and limitations of BUSNs during flood events, underlining the importance of diverse flood mitigation strategies. Particularly, we demonstrated the nonlinear relationship between the reduction impact of peak flows and the magnitude of the peak, stressing the role of increased storage capacity for impervious areas in flood mitigation. Second, at the CONUS scale, we integrated urban water management practices and lake routing, presenting a comprehensive tool for assessing mitigation strategies. Moreover, we devised a multiscale approach for efficient derivation of BUSNs at CONUS scale, by drawing on concepts from graph theory and hydrologic conditioning of elevation data. An in-depth evaluation revealed complex interactions between natural and anthropogenic factors in influencing hydrological responses, highlighting the varied impacts of BUSNs and reservoir operations on flooding. Our proposed modeling framework is an effective tool for simulating urban hydrological responses under different hydroclimate conditions, assisting in urban planning, water management strategies, and mitigating urban flooding risks. It addresses existing models' limitations related to modeling BUSNs, providing a comprehensive and scalable solution to climate change-induced urban flooding.