Urban Watershed Hydrologic Characterization and Management through Data-Model Integration
Kun Zhang | Postdoctoral Research Associate
EC 3930 | October 20, 2022 | 2:30 PM – 3:30 PM
Urban watersheds, as highly nonlinear dynamical systems, are “stressed” by ongoing urbanization, deteriorating water infrastructure, and climate change. More frequent, damaging, and costly floods and degraded water quality increases the need for a new paradigm for urban stormwater management. More advanced monitoring and modeling strategies are needed to better characterize hydrologic processes and implement water infrastructure in urban watersheds. In this talk, first, some efforts in characterizing the partitioning of infiltrated stormwater and investigating its impact on water balances in urban watersheds will be introduced. In these works, a physically-based model was developed to quantify inflow and infiltration (I/I) into sanitary sewers, and hydrograph-based analysis was performed to quantify the impact of I/I on urban water balances and flow regimes. Second, some ongoing efforts in predicting hydrologic and water quality signals across watersheds will be introduced. In these works, sparse sensing technique was utilized to predict high-frequency hydrologic and water quality time-series and optimize the sampling strategies by leveraging the latent low-dimensional features in the signals.
Dr. Kun Zhang is a Postdoctoral research associate in the Department of Civil, Construction and Environmental Engineering at Marquette University. He obtained his Ph.D. from The University of Hong Kong in Civil Engineering in 2020. Dr. Zhang’s research at Marquette is mostly focused on studying the hydrologic impacts of climate and urbanization stressors and exploring solutions to increase the resilience of urban water infrastructure. He integrated physically-based and data-driven models with data to simulate surface and subsurface hydrologic processes in urban watersheds and optimize the implementation of green-grey stormwater infrastructure.