The duration of observation at a site of interest is generally too low to reliably estimate marine extremes. Regional frequency analysis (RFA), by exploiting the similarity between sites, can help to reduce uncertainties inherent to local analyses. Extreme observations in a homogeneous region are especially assumed to follow a common regional distribution, up to a local index. The regional pooling method, by gathering observations from different sites into a regional sample, can be employed to estimate the regional distribution. However, such a procedure may be highly affected by intersite dependence in the regional sample. This paper derives a theoretical model of intersite dependence, dedicated to the regional pooling method in a ''peaks over threshold'' framework. This model expresses the tendency of sites to display a similar behavior during a storm generating extreme observations, by describing both the storm propagation in the region and the storm intensity. The proposed model allows the assessment of (i) the regional effective duration of the regional sample and (ii) different regional hazards, e.g., return periods of storms. An application to the estimation of extreme significant wave heights from the numerical sea-state database ANEMOC-2 is provided, where different patterns of regional dependence are highlighted.
[1] Regional frequency analysis (RFA) is a valuable and well-known method which allows using all the information at the regional scale to improve the actual estimation of the probability of occurrence of extreme events at a given site. In the framework of the index flood method, a local index, representing the local specificities of a given site, is used to normalize at-site observations for the estimation of the regional distribution. It is an essential feature of this model, contrasting with common characteristics shared between the sites of the homogenous region. However, the specification of the local index can be a crucial point. In particular, the performance of the quantile estimator derived from a RFA can depend on the specification of the local index. Four regionalization models are proposed, where the local index is specified by different statistics in each model, and their performances are assessed through Monte Carlo simulations of several regional scenarios. Some guidelines are provided for the selection of the local index which is most adapted to the observed situation (including regional scenarios characterized by some degrees of asymmetry, homogeneity and inter-site correlation). A practical application on extreme skew storm surges is provided to illustrate the results.Citation: Weiss, J., and P. Bernardara (2013), Comparison of local indices for regional frequency analysis with an application to extreme skew surges, Water Resour.
Regional frequency analysis (RFA) can reduce uncertainties in the estimations of return levels, provided that homogeneous regions can be delineated. In the framework of extreme marine events, a physically based method to form homogeneous regions by identifying typical storms footprints is proposed. First, a spatiotemporal declustering procedure is employed to detect storms generating marine extremes. Second, the identification of the most typical storms footprints relies on a clustering algorithm based on a criterion of storm propagation. The resulting regions are readily explicable: sites from a given region are likely to be impacted by the same storms, and any storm impacting a region is likely to remain enclosed in this region. This procedure is fairly simple to implement, as the only information required is the time of occurrence of the observed extremes. An application to the estimation of extreme significant wave heights from the numerical sea-state database ANEMOC-2 is given. Six regions, both physically and statistically homogeneous, are delineated in the North-East part of the Atlantic Ocean. It is shown that the identification of storms footprints allows the increase of the overall statistical homogeneity. Combined with RFA, the proposed method highlights regional differences in the spatial extent and intensity of storms.
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