Articles | Volume 25, issue 3
https://doi.org/10.5194/nhess-25-1139-2025
https://doi.org/10.5194/nhess-25-1139-2025
Research article
 | 
17 Mar 2025
Research article |  | 17 Mar 2025

Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast

Kai Bellinghausen, Birgit Hünicke, and Eduardo Zorita

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Cited articles

Andrée, E., Drews, M., Su, J., Larsen, M. A. D., Drønen, N., and Madsen, K. S.: Simulating wind-driven extreme sea levels: Sensitivity to wind speed and direction, Weather and Climate Extremes, 36, 100422, https://doi.org/10.1016/j.wace.2022.100422, 2022. a
Bellinghausen, K.: Storm Surge Model for the Baltic Sea, Zenodo [code], https://doi.org/10.5281/zenodo.7409633, 2022. a, b
Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., and Widmann, M.: Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change, Science Advances, 5, eaaw5531, https://doi.org/10.1126/sciadv.aaw5531, 2019. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Bruneau, N., Polton, J., Williams, J., and Holt, J.: Estimation of global coastal sea level extremes using neural networks, Environ. Res. Lett., 15, 074030, https://doi.org/10.1088/1748-9326/ab89d6, 2020. a, b, c, d, e, f
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Short summary
We designed a tool to predict the storm surges at the Baltic Sea coast with satisfactory predictability (80 % correct predictions), using lead times of a few days. The proportion of false warnings is typically as low as 10 % to 20 %. We were able to identify the relevant predictor regions and their patterns – such as low-pressure systems and strong winds. Due to its short computing time, the method can be used as a pre-warning system to trigger the application of more sophisticated algorithms.
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