Providing actionable compound flood hazard and risk information is crucial for supporting emergency planning, management, and response, especially in low-lying areas where approximately 190 million people are vulnerable to flooding. However, conducting hazard and risk assessments in inland, transition, and coastal zones is challenging due to (i) nonlinear interactions among pluvial, fluvial, and coastal flood drivers, (ii) statistical dependences emerging from those drivers, and (iii) the underlying uncertainty that propagates and/or cascades in the modeling chain. Characterizing such interactions, dependences, and uncertainty is a complex task that requires hybrid modeling approaches, including robust statistical methods, process-based models, and state-of-the-art machine learning architectures. Hybrid approaches have the ability to link spatiotemporal patterns derived from multiple remote sensing and in-situ observations with local and large-scale model simulations. In this seminar, I will present examples of hybrid modeling approaches applied to compound flood and extreme water level predictions associated with tropical and extratropical cyclones along with advantages, limitations, and future research work.

David F. Muñoz is an Assistant Professor in the Civil and Environmental Engineering Department at Virginia Tech and P.I. of the Compound flood hazard and Risk Assessment in Low-lying areas (CoRAL) Lab. His research team works at the interface of data-driven and process-based modeling with the aim to understand compound extreme dynamics, their evolution in the context of climate change, and broad impacts on coastal communities and ecosystems vulnerable to flooding. Dr. Muñoz is an active member of the American Geophysical Union (AGU), European Geosciences Union (EGU), American Water Resources Association (AWRA), and the American Society of Civil Engineers (ASCE).

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