Preprints
https://doi.org/10.5194/egusphere-2024-3144
https://doi.org/10.5194/egusphere-2024-3144
14 Oct 2024
 | 14 Oct 2024
Status: this preprint is open for discussion.

Development of a wind-based storm surge model for the German Bight

Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke

Abstract. Storm surges pose significant threats to coastal regions, including the German Bight, where strong winds from north-westerly directions drive extreme water levels. In this study, we present a simple, effective storm surge model for the German Bight, utilizing a multiple linear regression approach based solely on 10 m effective wind as the predictor variable. We train and evaluate the model using historical skew surge data from 1959 to 2022, incorporating regularization techniques to improve prediction accuracy while maintaining simplicity. The model consists of only five terms, namely the effective wind at various locations with different time lags within the North Sea region, and an intercept. It demonstrates high predictive skill, achieving a correlation of 0.882. This indicates that, despite its extreme simplicity, the model performs just as well as more complex models. The storm surge model provides robust predictions for both moderate and extreme storm surge events. Moreover, due to its simplicity, the model can be effectively used in climate simulations in future studies, making it a valuable tool for assessing future storm surge risks under changing climate conditions, independent of the ongoing and continuous sea level rise.

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Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke

Status: open (until 25 Nov 2024)

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Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke
Laura Schaffer, Andreas Boesch, Johanna Baehr, and Tim Kruschke

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Short summary
We developed a simple yet effective model to predict storm surges in the German Bight, using wind data and a multiple linear regression approach. Trained on historical data from 1959 to 2022, our storm surge model demonstrates high predictive skill and performs as well as more complex models, despite its simplicity. It can predict both moderate and extreme storm surges, making it a valuable tool for future climate change studies.