Articles | Volume 13, issue 11
Hydrol. Earth Syst. Sci., 13, 2203–2219, 2009
https://doi.org/10.5194/hess-13-2203-2009
Hydrol. Earth Syst. Sci., 13, 2203–2219, 2009
https://doi.org/10.5194/hess-13-2203-2009

  20 Nov 2009

20 Nov 2009

Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic

L. Gaál1,2 and J. Kyselý1 L. Gaál and J. Kyselý
  • 1Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Boční II 1401, 141 31 Prague 4, Czech Republic
  • 2Department of Land and Water Resources Management, Faculty of Civil Engineering, Slovak University of Technology, Radlinského 11, 813 68 Bratislava, Slovakia

Abstract. 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 5T rule, which sets the minimum number of stations to be included in a pooling group for estimation of a quantile corresponding to return period T. 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.

We conclude that in a dense network of precipitation stations in the Czech Republic (on average 1 station in a square of about 20×20 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. This finding is in agreement with the climatological expectation since multi-day heavy precipitation events are more closely linked to some typical precipitation patterns over central Europe (related e.g. to the varied roles of Atlantic and Mediterranean influences) while the dependence of 1-day extremes on climatological characteristics such as mean annual precipitation is much weaker.

The findings of the paper show a promising perspective for an application of the ROI methodology in evaluating outputs of regional climate models with high resolution: the pooling schemes might serve for defining weighting functions, and the large spatial variability in the grid-box estimates of high quantiles of precipitation amounts may efficiently be reduced.

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