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Extreme Sea Level Estimation Combining Systematic Observed Skew Surges and Historical Record Sea Levels

Abstract : The estimation of sea levels corresponding to high return periods is crucial for coastal planning and for the design of coastal defenses. This paper deals with the use of historical observations, that is, events that occurred before the beginning of the systematic tide gauge recordings, to improve the estimation of design sea levels. Most of the recent publications dealing with statistical analyses applied to sea levels suggest that astronomical high tide levels and skew surges should be analyzed and modeled separately. Historical samples generally consist of observed record sea levels. Some extreme historical skew surges can easily remain unnoticed if they occur at low or moderate astronomical high tides and do not generate extreme sea levels. The exhaustiveness of historical skew surge series, which is an essential criterion for an unbiased statistical inference, can therefore not be guaranteed. This study proposes a model combining, in a single Bayesian inference procedure, information of two different natures for the calibration of the statistical distribution of skew surges: measured skew surges for the systematic period and extreme sea levels for the historical period. A data-based comparison of the proposed model with previously published approaches is presented based on a large number of Monte Carlo simulations. The proposed model is applied to four locations on the French Atlantic and Channel coasts. Results indicate that the proposed model is more reliable and accurate than previously proposed methods that aim at the integration of historical records in coastal sea level or surge statistical analyses.
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Submitted on : Thursday, May 12, 2022 - 3:09:01 PM
Last modification on : Tuesday, November 15, 2022 - 10:58:34 AM


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Laurie Saint Criq, Eric Gaume, Yasser Hamdi, Taha Ouarda. Extreme Sea Level Estimation Combining Systematic Observed Skew Surges and Historical Record Sea Levels. Water Resources Research, 2022, 58 (3), ⟨10.1029/2021WR030873⟩. ⟨hal-03666527⟩



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