Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 13 11 2009 10.5194/hess-13-2203-2009 http://www.hydrol-earth-syst-sci.net/13/2203/2009/ http://www.hydrol-earth-syst-sci.net/13/2203/2009/hess-13-2203-2009.html http://www.hydrol-earth-syst-sci.net/13/2203/2009/hess-13-2203-2009.pdf 2203 2219 2009-11-20 Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic L. Gaál ladislav.gaal@gmail.com J. Kyselý Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Boční II 1401, 141 31 Prague 4, Czech Republic Department of Land and Water Resources Management, Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, 813 68 Bratislava, Slovakia In this paper, we implement the region-of-influence (ROI) approach for modelling probabilities of heavy 1-day and 5-day precipitation amounts in the Czech Republic. The pooling groups are constructed according to (i) the regional homogeneity criterion (assessed by a built-in regional homogeneity test), which requires that in a pooling group the distributions of extremes are identical after scaling by the at-site mean; and (ii) the 5<i>T</i> rule, which sets the minimum number of stations to be included in a pooling group for estimation of a quantile corresponding to return period <i>T</i>. The similarity of sites is evaluated in terms of climatological and geographical site characteristics. We carry out a series of sensitivity analyses by means of Monte Carlo simulations in order to explore the importance of the individual site attributes, including hybrid pooling schemes that combine both types of the site attributes with different relative weights. <br><br> We conclude that in a dense network of precipitation stations in the Czech Republic (on average 1 station in a square of about 20&times;20 km), the actual distance between the sites plays the most important role in determining the similarity of probability distributions of heavy precipitation. There are, however, differences between the optimum pooling schemes depending on the duration of the precipitation events. While in the case of 1-day precipitation amounts the pooling scheme based on the geographical proximity of sites outperforms all hybrid schemes, for multi-day amounts the inclusion of climatological site characteristics (although with much lower weights compared to the geographical distance) enhances the performance of the pooling schemes. 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