Articles | Volume 20, issue 1
Technical note
12 Jan 2024
Technical note |  | 12 Jan 2024

Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data

Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson

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Revised manuscript under review for NHESS
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Cited articles

Andersson, M.: Climate Adaptation by Managed Realignment. Future mean and extreme sea levels, SMHI, Report number: 2021/912/9.5, 16–17, 2021. 
Andrée, E., Su, J., Dahl Larsen, M. A., Drews, M., Stendel, M., and Skovgaard Madsen, K.: The role of preconditioning for extreme storm surges in the western Baltic Sea, Nat. Hazards Earth Syst. Sci., 23, 1817–1834,, 2023. 
Bellinghausen, K., Hünicke, B., and Zorita, E.: Short-term prediction of extreme sea-level at the Baltic Sea coast by Random Forests, Nat. Hazards Earth Syst. Sci. Discuss. [preprint],, 2023. 
Bernier, N. B., Thompson, K. R., Ou, J., and Ritchie, H.: Mapping the return periods of extreme sea levels: Allowing for short sea level records, seasonality, and climate change, Glob. Planet. Change, 57, 139–150,, 2007. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,, 2001. 
Short summary
Coastal floods occur due to extreme sea levels (ESLs) which are difficult to predict because of their rarity. Long records of accurate sea levels at the local scale increase ESL predictability. Here, we apply a machine learning technique to extend sea level observation data in the past based on a neighbouring tide gauge. We compared the results with a linear model. We conclude that both models give reasonable results with a better accuracy towards the extremes for the machine learning model.