Projecting U.S. coastal storm surge risks and impacts with deep learning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912565258354688 |
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| author | Rice, Julian R. Balaguru, Karthik Rollano, Fadia Ticona Wilson, John Daniel, Brent Judi, David Sun, Ning Leung, L. Ruby |
| author_facet | Rice, Julian R. Balaguru, Karthik Rollano, Fadia Ticona Wilson, John Daniel, Brent Judi, David Sun, Ning Leung, L. Ruby |
| contents | Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100-year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Projecting U.S. coastal storm surge risks and impacts with deep learning Rice, Julian R. Balaguru, Karthik Rollano, Fadia Ticona Wilson, John Daniel, Brent Judi, David Sun, Ning Leung, L. Ruby Atmospheric and Oceanic Physics Machine Learning Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100-year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina. |
| title | Projecting U.S. coastal storm surge risks and impacts with deep learning |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2506.13963 |