PNNL

Abstract

Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk in a changing ocean-atmosphere system is difficult due to the phenomenon's rarity and physical complexity. Recent advances in the applications of machine learning and artificial intelligence to natural hazards 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 hurricane events, accounting for projected future atmospheric conditions. This study marks the most extensive number of storms ever modeled in such an assessment. 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 an approximately 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.