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Statements

Subject Item
n2:RIV%2F00216208%3A11310%2F14%3A10281685%21RIV15-MSM-11310___
rdf:type
n10:Vysledek skos:Concept
rdfs:seeAlso
http://geography.cz/sbornik/wp-content/uploads/downloads/2014/10/g14-3_s199-217_kucerova_jenicek.pdf
dcterms:description
The knowledge of the water volume stored in the snowpack, including its spatial distribution, is vital for many hydrological applications. Such information is useful for hydrological forecasts and it is often used for the calibration of snowmelt runoff models. Data from four field measurements of the snow water equivalent (SWE) carried out in two winter seasons were assessed by ten interpolation methods. Meas- urements from both snow accumulation and snowmelt periods were evaluated. The ability of methods to predict SWE at unmeasured locations was assessed by the means of cross validation. The best prediction accuracy of SWE was achieved by means of multiple a simple linear regressions, residual kriging and cokriging methods. The accuracy was enhanced by the use of elevation, aspect, slope and vegetation as variables in the calculation of the SWE. Elevation and vegetation show a significant correlation with the SWE in the study area. The multiple regression gave best results for snow accumulation period. However, the spatial variability of SWE was not successfully explained for snowmelt periods. The knowledge of the water volume stored in the snowpack, including its spatial distribution, is vital for many hydrological applications. Such information is useful for hydrological forecasts and it is often used for the calibration of snowmelt runoff models. Data from four field measurements of the snow water equivalent (SWE) carried out in two winter seasons were assessed by ten interpolation methods. Meas- urements from both snow accumulation and snowmelt periods were evaluated. The ability of methods to predict SWE at unmeasured locations was assessed by the means of cross validation. The best prediction accuracy of SWE was achieved by means of multiple a simple linear regressions, residual kriging and cokriging methods. The accuracy was enhanced by the use of elevation, aspect, slope and vegetation as variables in the calculation of the SWE. Elevation and vegetation show a significant correlation with the SWE in the study area. The multiple regression gave best results for snow accumulation period. However, the spatial variability of SWE was not successfully explained for snowmelt periods.
dcterms:title
Comparison of selected methods used for the calculation of the snowpack spatial distribution, Bystřice River basin, Czechia Comparison of selected methods used for the calculation of the snowpack spatial distribution, Bystřice River basin, Czechia
skos:prefLabel
Comparison of selected methods used for the calculation of the snowpack spatial distribution, Bystřice River basin, Czechia Comparison of selected methods used for the calculation of the snowpack spatial distribution, Bystřice River basin, Czechia
skos:notation
RIV/00216208:11310/14:10281685!RIV15-MSM-11310___
n3:aktivita
n6:I n6:S n6:P
n3:aktivity
I, P(GA13-32133S), P(GAP209/12/0997), S
n3:cisloPeriodika
3
n3:dodaniDat
n15:2015
n3:domaciTvurceVysledku
n12:6544312 n12:3464164
n3:druhVysledku
n4:J
n3:duvernostUdaju
n18:S
n3:entitaPredkladatele
n17:predkladatel
n3:idSjednocenehoVysledku
8187
n3:idVysledku
RIV/00216208:11310/14:10281685
n3:jazykVysledku
n14:eng
n3:klicovaSlova
interpolation methods; cross validation; snow
n3:klicoveSlovo
n8:interpolation%20methods n8:cross%20validation n8:snow
n3:kodStatuVydavatele
CZ - Česká republika
n3:kontrolniKodProRIV
[8847C9B0D277]
n3:nazevZdroje
Geografie. Sborník české geografické společnosti (Geography Journal of Czech Geographic Society)
n3:obor
n11:DA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n7:GA13-32133S n7:GAP209%2F12%2F0997
n3:rokUplatneniVysledku
n15:2014
n3:svazekPeriodika
119
n3:tvurceVysledku
Jeníček, Michal Kučerová, Dana
n3:wos
000342652600001
s:issn
1212-0014
s:numberOfPages
19
n19:organizacniJednotka
11310