Abstract
Abstract. Many hydrological studies are devoted to the identification of events that are expected to occur on average within a certain time span. While this topic is well established in the univariate case, recent advances focus on a multivariate characterization of events based on copulas. Following a previous study, we show how the definition of the survival Kendall return period fits into the set of multivariate return periods.Moreover, we preliminary investigate the ability of the multivariate return period definitions to select maximal events from a time series. Starting from a rich simulated data set, we show how similar the selection of events from a data set is. It can be deduced from the study and theoretically underpinned that the strength of correlation in the sample influences the differences between the selection of maximal events.
Highlights
Studying extremes in hydrological multivariate time series often aims at getting an estimate of the size of events to be expected in a period of 10, 50 or 100 years
In a previous study (Gräler et al, 2013), the practical impact of different bivariate multivariate return period definitions has been studied based on a simulated data set
The Kendall return period (KRP) introduced by Salvadori et al (2011) is an approach that shares a unique property with the univariate return periods: the critical layer separating safe from dangerous events is unique for every design return period
Summary
Studying extremes in hydrological multivariate time series often aims at getting an estimate of the size of events to be expected in a period of 10, 50 or 100 years This information is relevant for the construction of many hydrological structures such as dams and dykes. Multivariate maxima are often selected based on a single driving variable (e.g. peak discharge) and the associated variables (e.g. volume and duration) are studied in a multivariate setting This does not a priori reflect the joint extreme characteristic that is the actual focus of such a study. Different notions of maximality can be defined following the above return period definitions These allow to calculate the empirical joint extremeness and to select the maxima of multivariate time series.
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