Projecting Compound Flood Risk to the Coastal Cities

Abstract Submission: ​​Climate variability and climate change pose major challenges for coastal cities globally, disproportionately affecting socioeconomically vulnerable communities.​ The vulnerable communities often lack access to critical resources such as emergency shelter, safe housing, reliable transportation, and financial means for post-disaster recovery. ​​In the Southeast US, over 19 million people face high flood risk coupled with significant social vulnerability. For instance, the recent tropical storm Debby caused extensive flooding in Savannah, Georgia, where residents navigated waist-deep water, and low-income neighborhoods suffered severe damage. Understanding the likelihood of simultaneous extreme precipitation and storm surges, along with the inter-connected processes between them, is crucial for developing effective flood management strategies. This study highlights the potential for co-occurrence of these complex interactions in southeast US (Gulf of Mexico), utilizing an integrated, multi-disciplinary research framework. The framework includes climate models, remote sensing, physically based hydrologic-hydrodynamic modeling, and machine learning techniques. Our findings indicate that minority populations in low-lying coastal cities are projected to face a significantly greater risk of compound flooding. This research informs targeted strategies to protect vulnerable coastal communities and critical infrastructures from the escalating flood risks associated with climate and anthropogenic disturbances.

Learning Objectives/Expected Outcome (Optional) : 1. Analyze the relationship between socioeconomic vulnerability and increased flood risk in coastal communities, with a focus on the Southeast US.
2. Evaluate the importance of understanding compound flooding events (simultaneous extreme precipitation and storm surges) for effective flood management strategies.
3. Examine how interdisciplinary research methods, including climate models, remote sensing, hydrologic-hydrodynamic modeling, and machine learning, can be used to assess and predict flood risks in coastal areas.