A framework for flexible peak storm surge prediction

Storm surge is a major natural hazard in coastal regions, responsible both for significant property damage and loss of life. Accurate, efficient models of storm surge are needed both to assess long-term risk and to guide emergency management decisions. While high-fidelity regional-and global-ocean circulation models such as the ADvanced CIRCulation (ADCIRC) model can accurately predict storm surge, they are very computationally expensive. Consequently, there have been a number of efforts in recent years to develop data-driven surrogate models for storm surge.Here we develop a novel surrogate model for peak storm surge prediction based on a multi-stage approach. In the first stage, points are classified as inundated or not. In the second, the level of inundation is predicted for each point. Additionally, we propose a new formulation of the surrogate problem in which storm surge is predicted independently for each point. This new formulation has the potential to allow for predictions to be made directly for locations not present in the training data, and significantly reduces the number of required model parameters.We demonstrate our modeling framework on two study areas: the Texas coast and the northern portion of the Alaskan coast. For Texas, the model is trained with a database of 446 synthetic hurricanes. The model is able to accurately match ADCIRC predictions on a test set of synthetic storms. We further present a test of the model on Hurricanes Ike (2008) and Harvey (2017). For both storms, we find that the model predictions have comparable accuracy to ADCIRC hindcasts when compared to actual observational data. For Alaska, the model is trained on a dataset of 109 historical surge events. We test the surrogate model on actual surge events including the recent Typhoon Merbok (2022) that take place after the events in the training data. As with the Texas dataset, the surrogate model achieves decent performance against observational data. In both cases, the surrogate models are many orders of magnitude faster than ADCIRC.

Automated summary

Storm surge is a significant natural hazard in coastal regions, and accurate models are needed for predicting its impact. Traditional ocean circulation models are computationally expensive, leading to the development of data-driven surrogate models. This study presents a novel surrogate model for predicting peak storm surge, which shows comparable accuracy to traditional models but with much faster computational speed. The model is tested in Texas and Alaska, yielding promising results.