Probabilistic Tropical Cyclone Surge Hazard Under Future Sea-Level Rise Scenarios: A Case Study in The Chesapeake Bay Region, USA.

Storm surge flooding caused by tropical cyclones is a devastating threat to coastal regions, and this threat is growing due to sea-level rise (SLR). Therefore, accurate and rapid projection of the storm surges is critical for our coastal communities. This study focuses on developing a new framework that can rapidly predict storm surges under SLR scenarios for any random synthetic storms of interest and assign a probability to its likelihood. The framework leverages the joint probability method (JPM) for probability specification, a storm surge machine learning model, and a SLR model. The JPM probabilities are based on historical tropical cyclone track observations. The storm surge machine learning model was trained based on the storm surge simulations provided by the U.S. Army Corps of Engineers (USACE). The SLR was considered by adding the product of the normalized nonlinearity and the sea level change from 1992 to the target year, where nonlinearities are based on work by USACE. In this study, we applied this framework to the Chesapeake Bay region of the U.S. and estimated the SLR-adjusted 1% annual chance tropical cyclone flooding events in two areas: One is an urban Virginia site, and the other is a rural Maryland site. This new framework has the potential to aid in reducing future coastal storm risks in coastal communities by providing robust and rapid storm surge predictions that account for future sea-level rise.