Abstract.A good knowledge of extreme storm surges is necessary to ensure protection against flood. In this paper we introduce a methodology to determine time series of skew surges in France as well as a statistical approach for estimating extreme storm surges. With the aim to cope with the outlier issue in surge series, a regional frequency analysis has been carried out for the surges along the Atlantic coast and the Channel coast. This methodology is not the current approach used to estimate extreme surges in France. First results showed that the extreme events identified as outliers in at-site analyses do not appear to be outliers any more in the regional empirical distribution. Indeed the regional distribution presents a curve to the top with these extreme events that a mixed exponential distribution seems to recreate. Thus, the regional approach appears to be more reliable for some sites than at-site analyses. A fast comparison at a given site showed surge estimates with the regional approach and a mixed exponential distribution are higher than surge estimates with an at-site fitting. In the case of Brest, the 1000-yr return surge is 167 cm in height with the regional approach instead of 126 cm with an at-site analysis.

Abstract. In France, nuclear facilities were designed around very low probabilities of failure. Nevertheless, some extreme climatic events have given rise to exceptional observed surges (outliers) much larger than other observations, and have clearly illustrated the potential to underestimate the extreme water levels calculated with the current statistical methods. The objective of the present work is to conduct a comparative study of three approaches to extreme value analysis, including the annual maxima (AM), the peaks-overthreshold (POT) and the r-largest order statistics (r-LOS). These methods are illustrated in a real analysis case study. All data sets were screened for outliers. Non-parametric tests for randomness, homogeneity and stationarity of time series were used. The shape and scale parameter stability plots, the mean excess residual life plot and the stability of the standard errors of return levels were used to select optimal thresholds and r values for the POT and r-LOS method, respectively. The comparison of methods was based on (i) the uncertainty degrees, (ii) the adequacy criteria and tests, and (iii) the visual inspection. It was found that the r-LOS and POT methods have reduced the uncertainty on the distribution parameters and return level estimates and have systematically shown values of the 100 and 500-year return levels smaller than those estimated with the AM method. Results have also shown that none of the compared methods has allowed a good fit at the right tail of the distribution in the presence of outliers. As a perspective, the use of historical information was proposed in order to increase the representativeness of outliers in data sets. Findings are of practical relevance, not only to nuclear energy operators in France, for applications in storm surge hazard analysis and flood management, but also for the optimal planning and design of facilities to withstand extreme environmental conditions, with an appropriate level of risk.

Abstract. Nuclear power plants located in the French Atlantic coast are designed to be protected against extreme environmental conditions. The French authorities remain cautious by adopting a strict policy of nuclear-plants flood prevention. Although coastal nuclear facilities in France are designed to very low probabilities of failure (e.g., 1000-year surge), exceptional surges (outliers induced by exceptional climatic events) have shown that the extreme sea levels estimated with the current statistical approaches could be underestimated. The estimation of extreme surges then requires the use of a statistical analysis approach having a more solid theoretical motivation. This paper deals with extreme-surge frequency estimation using historical information (HI) about events occurred before the systematic record period. It also contributes to addressing the problem of the presence of outliers in data sets. The frequency models presented in the present paper have been quite successful in the field of hydrometeorology and river flooding but they have not been applied to sea level data sets to prevent marine flooding.In this work, we suggest two methods of incorporating the HI: the peaks-over-threshold method with HI (POTH) and the block maxima method with HI (BMH). Two kinds of historical data can be used in the POTH method: classical historical maxima (HMax) data, and over-a-threshold supplementary (OTS) data. In both cases, the data are structured in historical periods and can be used only as complement to the main systematic data. On the other hand, in the BMH method, the basic hypothesis in statistical modeling of HI is that at least one threshold of perception exists for the whole period (historical and systematic) and that during a giving historical period preceding the period of tide gauging, only information about surges above this threshold have been recorded or archived. The two frequency models were applied to a case study from France, at the La Rochelle site where the storm Xynthia induced an outlier, to illustrate their potentials, to compare their performances and especially to analyze the impact of the use of HI on the extreme-surge frequency estimation.

Les outils de modélisation numérique, tel que le modèle hydraulique 1D Crue9 développé par la CNR, sont aujourd'hui couramment utilisés pour analyser les comportements hydrauliques et hydrologiques des cours d'eau. Ces outils reposent notamment sur un jeu de paramètres d'entrée, physiques ou numériques, qui comportent des incertitudes. L'outil Prométhée, développé par l'IRSN, permet de réaliser des propagations d'incertitudes et deux types d'analyses de sensibilité : une méthode déterministe (Morris), qui repose sur les méthodes dites de criblage, permet d'identifier les paramètres impactant la variation des sorties d'intérêts ; et une méthode probabiliste (FAST), qui repose sur l'analyse de la variance des sorties en fonction de la variance des facteurs d'entrée, et permet de hiérarchiser les paramètres d'entrée en fonction de la sensibilité des sorties. Les études de propagation d'incertitudes exigent la réalisation d'un grand nombre de calculs. Pour ce faire, le couplage Prométhée/Crue9 est utilisé : il instrumente les fichiers associés aux simulations hydrauliques, lance une multitude de calculs, et réalise un traitement des résultats en utilisant des outils statistiques. Cet outil couplé donne ainsi la possibilité d'accomplir des études de sensibilité, en particulier probabilistes, en paramétrant des modèles hydrauliques complexes faisant intervenir de nombreux paramètres.Numerical modeling tools, like Crue9 the 1D modeling software developed by CNR, are widely used to analyze hydraulic and hydrological behavior of rivers. Those tools are based on input parameters, with physical or numerical meaning; theses inputs are generally known with some uncertainties. The tool Prométhée, developed by IRSN, is able to realize uncertainties propagations, and two kinds of sensibility analysis: the first one, a determinist method (Morris) based on screening, is able to identify factors which influenced outputs variability; the second one, a probabilistic method (FAST) based on variance analysis of outputs regarding inputs variances, performs inputs ranking in function of outputs sensibilities. Uncertainties propagations studies require an important computational capacity; to do so; the Promethée/Crue9 coupling is used. The coupled tool is able to parameter Crue9 files for the hydraulic computations, to run lots of computation, and then to analyze results with statistic tools. This coupled tool gives the possibility to realize sensitivity studies by probabilistic method, to parameter realistic and complex model rivers, and to study the influence of several inputs variations.

Nuclear power plants in France are designed to withstand natural hazards. Nevertheless, some exceptional storm surges, considered as outliers, are not properly addressed by classical statistical models. Local frequency estimations can be obtained with acceptable uncertainties if the data set containing the extreme storm surge levels is sufficiently complete and not characterized by the presence of an outlier. Otherwise, additional information such as regional and historical storm surges may be used to mitigate the lack of data and the influence of the outlier by increasing its representativeness in the sample. The objective of the present work is to develop a regional frequency model (RFM) using historical storm surges. Here we propose a RFM using historical information (HI). The empirical spatial extremogram is used herein to form a homogenous region of interest centered on a target site. A related issue regards the reconstitution, at the target site and from its neighbors, of missed storm surges with a multiple linear regression (MLR). MLR analysis can be considered conclusive if available observations at neighboring sites are informative enough and the reconstitution results meet some criteria during the cross-validation process. A total of 35 harbors located on the French (Atlantic and English Channel) and British coasts are used as a whole region with the La Rochelle site as a target site. The Peaks-Over-Threshold frequency model is used. The results are compared to those of an existing model based on the index flood method. The use of HI in the developed RFM increases the representativeness of the outlier in the sample. Fitting results at the right tail of the distribution appear more adequate and the 100-year return level is about 30 cm higher. The 100-year return level in the initial fitting has a return period of about 30 years in the updated fitting.