08 Jun 2023
 | 08 Jun 2023
Status: this preprint is open for discussion.

Technical note: Extending sea level time series for extremes analysis with machine learning and neighbouring station data

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

Abstract. Extreme sea levels may cause damage and 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 (~10 years) time series of tide gauge measurements using a longer time series from a neighbouring tide gauge: Linear Regression and Quantile Regression 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. 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 of the return levels is found to agree with results derived from more physically-based methods.

Kévin André Daniel Dubois et al.

Status: open (until 30 Sep 2023)

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  • RC1: 'Comment on egusphere-2023-1159', Anonymous Referee #1, 07 Jul 2023 reply
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Kévin André Daniel Dubois et al.

Kévin André Daniel Dubois et al.


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Short summary
Coastal floods are due to extreme sea levels which are difficult to predict because of their rarity. Long records of accurate sea levels at the local scale permit to increase their predictability. Here, we apply a machine learning technique to extend sea level observation data in the past based on a neighboring 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.