The rainfall erosivity factor in the Czech Republic and its uncertainty
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F16%3A71191" target="_blank" >RIV/60460709:41330/16:71191 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.5194/hess-20-4307-2016" target="_blank" >http://dx.doi.org/10.5194/hess-20-4307-2016</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5194/hess-20-4307-2016" target="_blank" >10.5194/hess-20-4307-2016</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
The rainfall erosivity factor in the Czech Republic and its uncertainty
Popis výsledku v původním jazyce
In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10?min 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.
Název v anglickém jazyce
The rainfall erosivity factor in the Czech Republic and its uncertainty
Popis výsledku anglicky
In the present paper, the rainfall erosivity factor (R factor) for the area of the Czech Republic is assessed. Based on 10?min 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.
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
DA - Hydrologie a limnologie
OECD FORD obor
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Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2016
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Hydrology and Earth System Sciences
ISSN
1027-5606
e-ISSN
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Svazek periodika
20
Číslo periodika v rámci svazku
10
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
16
Strana od-do
4307-4322
Kód UT WoS článku
000387064900001
EID výsledku v databázi Scopus
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