Accounting for seasonality in trends of extreme precipitation

Abstract

For the estimation of daily precipitation extremes, the metastatistical extreme value distribution (MEVD) is known to perform superior to classical approaches like the generalized extreme value distribution, especially for small sample lengths. This is due to the fact that the MEVD incorporates all ordinary rainfall events within a block rather than only the extremes, which then emerge from repeated sampling of ordinary events. For daily rainfall extremes, the MEVD combines the Weibull distribution of ordinary daily rainfall events and the number of wet days per year as additional random variable. The MEVD provides yearly distributional parameters, which makes it already capable of analyzing temporal trends in daily precipitation extremes.But still, the MEVD in its current formulation does not take into account the seasonal, i.e. sub-yearly character of ordinary precipitation events. This problem becomes apparent when events originating from fundamentally different precipitation regimes show very similar MEVD parameters.In this contribution we therefore propose to explicitly model both the temporal trend and interannual seasonality of daily rainfall extremes and present an explicitly non-stationary MEVD formulation which is called temporal MEVD or TMEV. The TMEV is then used to derive historical trends of rainfall extremes in Austria. It is shown that the 50-year return level of daily rainfall in Austria has significantly increased over the last 30 years at the majority of Austrian observation sites. Furthermore the temporal change of the extreme value distribution is analyzed with respect to seasonality.