Machine learning-based assessment of storm surge in the New York metropolitan area.

Storm surge generated from low-probability high-consequence tropical cyclones is a major flood hazard to the New York metropolitan area and its assessment requires a large number of storm scenarios. High-fidelity hydrodynamic numerical simulations can predict surge levels from storm scenarios. However, an accurate prediction requires a relatively fine computational grid, which is computationally expensive, especially when including wave effects.

Climate change exacerbates hurricane flood hazards along US Atlantic and Gulf Coasts in spatially varying patterns.

One of the most destructive natural hazards, tropical cyclone (TC)-induced coastal flooding, will worsen under climate change. Here we conduct climatology-hydrodynamic modeling to quantify the effects of sea level rise (SLR) and TC climatology change (under RCP 8.5) on late 21st century flood hazards at the county level along the US Atlantic and Gulf Coasts.