Ocean Science
Ocean Science
OS
Articles | Volume 20, issue 1
Articles | Volume 20, issue 1
https://doi.org/10.5194/os-20-21-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-20-21-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
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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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.
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