The rainfall erosivity factor in the Czech Republic and its uncertainty

Other literature type, Article English OPEN
Hanel, Martin ; Máca, Petr ; Bašta, Petr ; Vlnas, Radek ; Pech, Pavel (2016)
  • Publisher: Copernicus Publications
  • Journal: (issn: 1607-7938, eissn: 1607-7938)
  • Related identifiers: doi: 10.5194/hess-20-4307-2016
  • Subject: T | G | GE1-350 | Geography. Anthropology. Recreation | Environmental technology. Sanitary engineering | Environmental sciences | Technology | TD1-1066

In the present paper, the rainfall erosivity factor (<i>R</i> factor) for the area of the Czech Republic is assessed. Based on 10 min data for 96 stations and corresponding <i>R</i> 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 <i>R</i> 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 <i>R</i> factor for the area of the Czech Republic is 640 MJ ha<sup>−1</sup> mm h<sup>−1</sup>, with values for the individual stations ranging between 320 and 1520 MJ ha<sup>−1</sup> mm h<sup>−1</sup>. Among various spatial interpolation methods, the GLS model relating the <i>R</i> 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 <i>R</i> factor is estimated for individual sites. Our results revealed that reasonable estimates of the <i>R</i> factor can be obtained even from relatively short records (15&ndash;20 years), provided sufficient spatial coverage and covariates are available.
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