Projecting U.S. coastal storm surge risks and impacts with deep learning

TL;DR

This paper uses deep learning to efficiently project future U.S. coastal storm surge risks considering climate change, revealing significant increases in population at risk and regional vulnerabilities.

Contribution

It introduces a deep learning storm surge model trained on synthetic events to assess future risks with climate change impacts.

Findings

100-year surge estimates match historical data

50% increase in population at risk by century's end

Heightened risk in Florida, Georgia, and South Carolina

Abstract

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.