Formation of homogeneous regions for regional frequency analysis of extreme significant wave heights

Abstract

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.

The use of historical information for regional frequency analysis of extreme skew surge

“…The method to form homogeneous regions proposed by Weiss et al (2014a) is used to get physically and statistically homogeneous regions from the database shown above. This methodology is based on a storm propagation criterion identifying the most typical storms footprints.…”

The concept of credible duration for the Regional Frequency Analysis including historical data: application of the FAB method to a skew storm surge database

“…The method to form homogeneous regions proposed by Weiss et al (2014a) is used to get physically and 30 statistically homogeneous regions for the database shown above. This methodology is based on a storm propagation criterion identifying the most typical storms footprints.…”

Adaptation of the L-Moments Method for the Regionalization for Maximum Annual Temperatures in Colombia

“…The approach of RFA is to use the attainable data from gauged sites within a homogeneous region, to compute quantiles of several return periods in ungauged sites [1]- [3]. A regional approach to frequency analysis in poorly gauged areas makes possible a significant reduction of uncertainty associated with the assessment of hydrological events with a high return period [2], [4]- [8].…”

“…Whenever the grouped stations fulfill the assumption of statistical homogeneity, then RFA will take advantage of all the available information and will make reliable assessments of extreme quantiles using the fitted regional pdf [7], [8].…”