Jackson State University, Mississippi, United States
Abstract Submission: This study presents a comprehensive analysis of compound flooding events in the coastal regions of the Contiguous United States (CONUS). We utilize hourly storm surge data from long-term tide gauges and corresponding precipitation records from multiple datasets to investigate the joint occurrence and dependencies of extreme phenomena. Our analysis employs advanced statistical techniques, including Kendall’s rank correlation coefficient, copula theory and Bayesian Networks, to capture the joint probability distributions and tail dependencies of extreme events. This approach allows us to understand future maximum precipitation and storm surge events for various return periods, considering observed trends or/and potential climate change projections. We also investigate temporal variations in these dependencies using multi-year moving windows, revealing long-term trends that may signal climate velocity. Additionally, we explore the influence of tropical and extratropical cyclones on compound flooding through examination of synoptic weather patterns, providing insights into potential future risks. For Galveston, TX, we also combine hydrological modeling-based insights, using a well-calibrated GSSHA, that can simulate coastal dynamics and inland hydrology. Through high-resolution simulation of various hurricane scenarios, we incorporate projected changes in storm intensity and frequency. Ultimately, this detailed understanding of potential inundation scenarios can inform concerned authorities and help mitigate the impacts of compound flooding in a changing climate.
Learning Objectives/Expected Outcome (Optional) : After studying this research, learners will be able to: 1. Understand the concept of compound flooding in coastal regions of CONUS. 2. Apply advanced statistical techniques like Kendall's rank correlation, copula theory, and Bayesian Networks to analyze extreme weather events. 3. Interpret joint probability distributions and tail dependencies of extreme precipitation and storm surge events. 4. Analyze temporal variations in weather dependencies using multi-year moving windows. 5. Evaluate the impact of tropical and extratropical cyclones on compound flooding. 6. Comprehend the integration of hydrological modeling (GSSHA) with statistical analysis for flood risk assessment. 7. Assess potential future inundation scenarios considering climate change projections.
