Establishing flood thresholds for sea level rise impact communication

This paper proposes a high tide flood (HTF) thresholding system using machine learning (ML) to estimate sea level rise (SLR) and HTF thresholds at a 10 km spatial resolution along US coastlines. The system complements conventional methods and estimates these values for ungauged coastal areas. Trained and validated against NOAA gauge data, the system shows promising results (average Kling-Gupta Efficiency of 0.77). The results aim to raise community awareness of SLR impacts and inform adaptation planning.

Sadaf Mahmoudi, Hamed Moftakhari, David F. Muñoz, William Sweet, Hamid Moradkhani