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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 10
Hydrol. Earth Syst. Sci., 20, 4307-4322, 2016
https://doi.org/10.5194/hess-20-4307-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 4307-4322, 2016
https://doi.org/10.5194/hess-20-4307-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Oct 2016

Research article | 25 Oct 2016

The rainfall erosivity factor in the Czech Republic and its uncertainty

Martin Hanel1,2, Petr Máca1, Petr Bašta1, Radek Vlnas1, and Pavel Pech1 Martin Hanel et al.
  • 1Faculty of Environmental Sciences, Czech University of Life Sciences, Kamýcká 1176, Prague 6, Czech Republic
  • 2T. G. Masaryk Water Research Institute, Podbabská 30, Prague 6, Czech Republic

Abstract. In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10min data for 96 stations and corresponding R factor estimates, a number of spatial interpolation methods are applied and cross-validated. These methods include inverse distance weighting, standard, ordinary, and regression kriging with parameters estimated by the method of moments and restricted maximum likelihood, and a generalized least-squares (GLS) model. For the regression-based methods, various statistics of monthly precipitation as well as geographical indices are considered as covariates. In addition to the uncertainty originating from spatial interpolation, the uncertainty due to estimation of the rainfall kinetic energy (needed for calculation of the R factor) as well as the effect of record length and spatial coverage are also addressed. Finally, the contribution of each source of uncertainty is quantified. The average R factor for the area of the Czech Republic is 640MJha−1mmh−1, with values for the individual stations ranging between 320 and 1520MJha−1mmh−1. Among various spatial interpolation methods, the GLS model relating the R factor to the altitude, longitude, mean precipitation, and mean fraction of precipitation above the 95th percentile of monthly precipitation performed best. Application of the GLS model also reduced the uncertainty due to the record length, which is substantial when the R factor is estimated for individual sites. Our results revealed that reasonable estimates of the R factor can be obtained even from relatively short records (15–20 years), provided sufficient spatial coverage and covariates are available.

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The paper is focused on assessment of the contribution of various sources of uncertainty to the estimated rainfall erosivity factor. It is shown that the rainfall erosivity factor can be estimated with reasonable precision even from records shorter than recommended, provided good spatial coverage and reasonable explanatory variables are available. The research was done as an update of the R factor estimates for the Czech Republic, which were later used for climate change assessment.
The paper is focused on assessment of the contribution of various sources of uncertainty to the...
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