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| Titel |
Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic |
| VerfasserIn |
L. Gaal, J. Kyselý |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 13, no. 11 ; Nr. 13, no. 11 (2009-11-20), S.2203-2219 |
| Datensatznummer |
250012058
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| Publikation (Nr.) |
 copernicus.org/hess-13-2203-2009.pdf |
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| Zusammenfassung |
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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