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The knowledge of extreme coastal water levels is useful for coastal floodingstudies or the design of coastal defences. While deriving such extremes withstandard analyses using tide-gauge measurements, one often needs to dealwith limited effective duration of observation which can result in largestatistical uncertainties. This is even truer when one faces the issue ofoutliers, those particularly extreme values distant from the others whichincrease the uncertainty on the results. In this study, we investigate howhistorical information, even partial, of past events reported in archivescan reduce statistical uncertainties and relativise such outlyingobservations. A Bayesian Markov chain Monte Carlo method is developed totackle this issue. We apply this method to the site of La Rochelle (France),where the storm Xynthia in 2010 generated a water level considered so far asan outlier. Based on 30 years of tide-gauge measurements and 8 historicalevents, the analysis shows that (1) integrating historical information inthe analysis greatly reduces statistical uncertainties on return levels (2)Xynthia's water level no longer appears as an outlier, (3) we could havereasonably predicted the annual exceedance probability of that levelbeforehand (predictive probability for 2010 based on data until the end of 2009of the same order of magnitude as the standard estimative probability usingdata until the end of 2010). Such results illustrate the usefulness of historicalinformation in extreme value analyses of coastal water levels, as well asthe relevance of the proposed method to integrate heterogeneous data in suchanalyses

natural hazards and earth system sciences

How historical information can improve estimation and prediction of extreme coastal water levels: application to the Xynthia event at La Rochelle (France)