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 surge hazard is critical for 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 with Response Surfaces (JPM-RS) for probabilistic hazard characterization, 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 high-fidelity 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, arising from surge-SLR interaction, and the sea-level change from 1992 to the target year, where nonlinearities are based on high-fidelity storm surge simulations and subsequent analysis by USACE. In this study, this framework was applied to the Chesapeake Bay region of the U.S. and used to estimate the SLR-adjusted probabilistic tropical cyclone flood hazard 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 hazard assessment that accounts for future sea-level rise.
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Physical Drivers of Changes in Probabilistic Surge Hazard Under Sea Level Rise
Surge hazards induced by tropical cyclones have caused substantial economic losses and casualties for coastal communities worldwide. Under projected sea level rise (SLR), it is not well understood how the probabilistic surge hazard may change. Coastal planning and engineering usually adopt the “bathtub” method to evaluate the surge‐SLR response, where surge with SLR is considered to be the exact summation of the two. This method has been shown by previous studies to be unreliable. Studies on surge‐SLR response either use a low‐fidelity model setup or rely on individual storm's surge to represent the probabilistic surge due to the high computational burden. Herein, we use high‐fidelity numerical models in the Tampa region, West Florida, consider 188 synthetic storms and four SLR scenarios, and investigate the surge‐SLR response and its physical drivers. Compared to the direct summation of present‐day surge and SLR amount, results show that the probabilistic surge with SLR can be 1.0 m larger, while different individual storm's surge with the same magnitude can be 1.5 m larger or 0.1 m smaller, indicating the importance of not relying on a limited number of surge events to assess the probabilistic surge response to SLR. Investigation of the physical drivers shows that distinct topographic features of the study area and storm forward speed notably affect the surge‐SLR response. When considering 1.3 m or larger SLR in the study area, complex topography, and large surge events, the effects of SLR on the probabilistic surge are hard to predict and should be investigated more carefully.
more » « less- NSF-PAR ID:
- 10455609
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth's Future
- Volume:
- 7
- Issue:
- 7
- ISSN:
- 2328-4277
- Page Range / eLocation ID:
- p. 819-832
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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