Preprints
https://doi.org/10.5194/egusphere-2024-912
https://doi.org/10.5194/egusphere-2024-912
27 May 2024
 | 27 May 2024
Status: this preprint is open for discussion.

An appraisal of the value of simulated weather data for quantifying coastal flood hazard in the Netherlands

Cees de Valk and Henk van den Brink

Abstract. With recently updated safety norms, assessment of flood safety in the Netherlands requires return values for coastal sea level and surface stress for a wide range of return periods up to 107 years. Estimates from measurements are highly uncertain. To reduce the uncertainty, a possible solution could be to replace measurements by simulated weather datasets much larger than a typical measurement record. However, systematic errors in simulations can easily outweigh any gains in precision. Combining insights from physics and extreme value theory with evidence from data, we argue that even as stress from present-day weather prediction models may be too high or too low, these data are suitable for estimating the shape of the upper tail of the distribution function of stress, and that this extends to simulated data of water level along the Dutch coast. As scale and location parameters can be estimated with sufficient precision from relatively short measurement records, we estimate return values from a combination of measurements (for scale/location of water level) and simulated data (for shape), assess their uncertainty, and discuss strengths and limitations of the approach and prospects for further exploiting simulated weather data to assess flood risk.

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Cees de Valk and Henk van den Brink

Status: open (until 08 Jul 2024)

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Cees de Valk and Henk van den Brink
Cees de Valk and Henk van den Brink

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Short summary
Recently updated flood safety norms in the Netherlands prescribe that sea dikes and other flood-protecting structures should withstand high sea levels reached very rarely, locally down to only once in 10 million years. We show that such levels can be estimated with reasonable accuracy by the cautious use of very large datasets of numerical simulations of weather and storm surge.