A Framework for Flexible Peak Storm Surge Prediction
- URL: http://arxiv.org/abs/2204.13168v2
- Date: Fri, 31 Mar 2023 18:36:49 GMT
- Title: A Framework for Flexible Peak Storm Surge Prediction
- Authors: Benjamin Pachev, Prateek Arora, Carlos del-Castillo-Negrete, Eirik
Valseth, Clint Dawson
- Abstract summary: Storm surge is a major natural hazard in coastal regions, responsible for significant property damage and loss of life.
We develop a novel surrogate model for peak storm surge prediction based on a multi-stage approach.
We demonstrate our modeling framework on two study areas: the Texas coast and the northern portion of the Alaskan coast.
- Score: 2.362412515574206
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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. 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 . Additionally, we
propose a new formulation of the surrogate problem in which storm surge is
predicted independently for each point. This allows for predictions to be made
directly for locations not present in the training data, and significantly
reduces the number of 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 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. For both datasets, the surrogate model achieves similar
performance to ADCIRC on real events when compared to observational data. In
both cases, the surrogate models are many orders of magnitude faster than
ADCIRC.
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