Regional analysis was used to improve estimates of the probabilities of extreme precipitation events in the Czech Republic. The delimitation of 4 homogeneous regions was based on statistical procedures (cluster analysis of site characteristics and subsequent tests for regional homogeneity). The regions also reflect climatological differences in precipitation regimes and synoptic patterns that cause heavy precipitation. The Generalized Extreme Value (GEV) distribution was identified as the most suitable distribution for modelling maximum annual 1 to 7 d precipitation amounts according to the L-moment ratio diagram and goodness-of-fit tests. Only in the northeast region (which is most prone to the occurrence of extremely high precipitation totals) was the Generalized Logistic (GLO) distribution preferred. The regional approach considerably lessened the between-site variation of estimates of the shape parameter of the GEV/GLO distribution compared to at-site procedures, and the estimates of high quantiles (e.g. 50 yr return values) were more reliable and climatologically consistent in individual regions. Different growth curve shapes are characteristic of the 4 regions, the betweenregion variability being larger for multi-day than 1 d events. Particularly noteworthy is the heavy tail of distributions of 5 and 7 d annual maxima in the northeast region, reflected also in the inapplicability of the general 4-parameter kappa distribution in regional homogeneity tests. A nonparametric statistical test on the value of the tail index supports a hypothesis that data in this flood-prone area may be drawn from a distribution with a very heavy tail for which L-moments do not exist.
KEY WORDS: Extreme precipitation event · Regional frequency analysis · L-moments · Design values · Central Europe · Czech RepublicResale or republication not permitted without written consent of the publisher Clim Res 33: 243-255, 2007
Several approaches to estimating distributions of precipitation extremes are compared by means of simulation experiments, and their applications into observed data in the Czech Republic are evaluated. Regional frequency models, which take into account data in fixed or flexible 'regions' when fitting a distribution at any site, lead to estimates with much smaller errors compared to a single-site analysis, and efficiently reduce random and climatologically irrelevant variations in the estimates of the model parameters and high quantiles. The region-of-influence (ROI) methodology with a built-in regional homogeneity test is recognized as a useful approach, with the model based on proximity of sites outperforming the Hosking-Wallis regional frequency analysis. Comparison of estimates of the return period of a heavy precipitation event on 24 June 2009, which triggered a disastrous local flash flood, illustrates that the at-site analysis leads to unrealistic and extremely uncertain estimates that strongly depend on whether or not a single outlying observation is involved in the sample, while all regional methods yield return periods in the same order of several hundreds of years, notwithstanding whether the 2009 data are included in the sample. The ROI method may be found useful for modelling probabilities of other meteorological variables, extremes of which are strongly influenced by sampling variability, and may also represent an efficient tool for 'smoothing' random variations in the estimates of model parameters and high quantiles of precipitation in high-resolution regional climate model simulations.
Extreme high precipitation amounts are among environmental events with the most disastrous consequences for human society. This paper deals with the identification of 'homogeneous regions' according to statistical characteristics of precipitation extremes in the Czech Republic, i.e. the basic and most important step toward the regional frequency analysis. Precipitation totals measured at 78 stations over 1961−2000 are used as an input dataset. Preliminary candidate regions are formed by the cluster analysis of site characteristics, using the average-linkage clustering and Ward's method. Several statistical tests for regional homogeneity are utilized, based on the 10-yr event and the variation of L-moment statistics. In compliance with results of the tests, the area of the Czech Republic has been divided into four homogeneous regions. The findings are supported by simulation experiments proposed to evaluate stability of the test results. Since the regions formed reflect also climatological differences in precipitation regimes and synoptic patterns causing high precipitation amounts, their future application may not be limited to the frequency analysis of extremes.
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