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

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

Extreme sea levels may cause damage and the disruption of activities in coastal areas. Thus, predicting extreme sea levels is essential for coastal management. Statistical inference of robust return level estimates critically depends on the length and quality of the observed time series. Here, we compare two different methods for extending a very short (inline-formula∼ 10-year) time series of tide gauge measurements using a longer time series from a neighbouring tide gauge: linear regression and random forest machine learning. Both methods are applied to stations located in the Kattegat Basin between Denmark and Sweden. Reasonable results are obtained using both techniques, with the machine learning method providing a better reconstruction of the observed extremes. By generating a set of stochastic time series reflecting uncertainty estimates from the machine learning model and subsequently estimating the corresponding return levels using extreme value theory, the spread in the return levels is found to agree with results derived by more physically based methods.

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Dubois, Kévin / Dahl Larsen, Morten Andreas / Drews, Martin / et al: Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data. 2024. Copernicus Publications.

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Rechteinhaber: Kévin Dubois et al.

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