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Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

Snow depth is a general input variable in many models of agriculture, hydrology, climate and ecology. This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging (GWRK) and regression kriging (RK)...

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Published in:Journal of mountain science 2018, Vol.15 (1), p.33-45
Main Authors: Liu, Yang, Li, Lan-hai, Chen, Xi, Yang, Jin-Ming, Hao, Jian-Sheng
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Language:English
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description Snow depth is a general input variable in many models of agriculture, hydrology, climate and ecology. This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging (GWRK) and regression kriging (RK) in a spatial interpolation of regional snow depth. The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor ( VIF ). Three variables, Height, topographic ruggedness index ( TRI ), and land surface temperature ( LST ), are used as explanatory variables to establish a regression model for snow depth. The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained. The results indicate that 1) the result of GWRK’s accuracy is slightly higher than that of RK ( R 2 = 0.55 vs. R 2 = 0.50, RMSE (root mean square error) = 0.102 m vs. RMSE = 0.077 m); 2) for the subareas, GWRK and RK exhibit similar estimation results of snow depth. Areas in the Bayanbulak Basin with a snow depth greater than 0.15 m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin. However, the GWRK resulted in more detailed information on snow depth distribution than the RK. The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.
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This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging (GWRK) and regression kriging (RK) in a spatial interpolation of regional snow depth. The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor ( VIF ). Three variables, Height, topographic ruggedness index ( TRI ), and land surface temperature ( LST ), are used as explanatory variables to establish a regression model for snow depth. The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained. The results indicate that 1) the result of GWRK’s accuracy is slightly higher than that of RK ( R 2 = 0.55 vs. R 2 = 0.50, RMSE (root mean square error) = 0.102 m vs. RMSE = 0.077 m); 2) for the subareas, GWRK and RK exhibit similar estimation results of snow depth. Areas in the Bayanbulak Basin with a snow depth greater than 0.15 m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin. However, the GWRK resulted in more detailed information on snow depth distribution than the RK. The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.</description><identifier>ISSN: 1672-6316</identifier><identifier>EISSN: 1993-0321</identifier><identifier>EISSN: 1008-2786</identifier><identifier>DOI: 10.1007/s11629-017-4564-z</identifier><language>eng</language><publisher>Heidelberg: Science Press</publisher><subject>Accuracy ; Climate models ; Coefficients ; Correlation analysis ; Correlation coefficient ; Correlation coefficients ; Depth ; Distribution ; Earth and Environmental Science ; Earth Sciences ; Ecological monitoring ; Ecology ; Environment ; Geography ; Hydrologic models ; Hydrology ; Kriging interpolation ; Land surface temperature ; Mountains ; Predation ; Regression analysis ; Regression models ; Root-mean-square errors ; Ruggedness ; Snow ; Snow depth ; Spatial distribution ; Spatial resolution ; Statistical methods ; Surface temperature ; Variables</subject><ispartof>Journal of mountain science, 2018, Vol.15 (1), p.33-45</ispartof><rights>Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Copyright Springer Science &amp; Business Media Jan 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-f1458aea8e2b4c8a350d056871438c793b6e346f10bc2d6c2d2e3f21ce1690823</citedby><cites>FETCH-LOGICAL-c316t-f1458aea8e2b4c8a350d056871438c793b6e346f10bc2d6c2d2e3f21ce1690823</cites><orcidid>0000-0001-5751-0982 ; 0000-0003-0432-0733 ; 0000-0002-1843-6908 ; 0000-0002-1914-0180 ; 0000-0003-3296-5838</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Liu, Yang</creatorcontrib><creatorcontrib>Li, Lan-hai</creatorcontrib><creatorcontrib>Chen, Xi</creatorcontrib><creatorcontrib>Yang, Jin-Ming</creatorcontrib><creatorcontrib>Hao, Jian-Sheng</creatorcontrib><title>Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China</title><title>Journal of mountain science</title><addtitle>J. Mt. Sci</addtitle><description>Snow depth is a general input variable in many models of agriculture, hydrology, climate and ecology. This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging (GWRK) and regression kriging (RK) in a spatial interpolation of regional snow depth. The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor ( VIF ). Three variables, Height, topographic ruggedness index ( TRI ), and land surface temperature ( LST ), are used as explanatory variables to establish a regression model for snow depth. The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained. 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Mt. Sci</stitle><date>2018</date><risdate>2018</risdate><volume>15</volume><issue>1</issue><spage>33</spage><epage>45</epage><pages>33-45</pages><issn>1672-6316</issn><eissn>1993-0321</eissn><eissn>1008-2786</eissn><abstract>Snow depth is a general input variable in many models of agriculture, hydrology, climate and ecology. This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging (GWRK) and regression kriging (RK) in a spatial interpolation of regional snow depth. The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor ( VIF ). Three variables, Height, topographic ruggedness index ( TRI ), and land surface temperature ( LST ), are used as explanatory variables to establish a regression model for snow depth. The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained. The results indicate that 1) the result of GWRK’s accuracy is slightly higher than that of RK ( R 2 = 0.55 vs. R 2 = 0.50, RMSE (root mean square error) = 0.102 m vs. RMSE = 0.077 m); 2) for the subareas, GWRK and RK exhibit similar estimation results of snow depth. Areas in the Bayanbulak Basin with a snow depth greater than 0.15 m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin. However, the GWRK resulted in more detailed information on snow depth distribution than the RK. The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.</abstract><cop>Heidelberg</cop><pub>Science Press</pub><doi>10.1007/s11629-017-4564-z</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-5751-0982</orcidid><orcidid>https://orcid.org/0000-0003-0432-0733</orcidid><orcidid>https://orcid.org/0000-0002-1843-6908</orcidid><orcidid>https://orcid.org/0000-0002-1914-0180</orcidid><orcidid>https://orcid.org/0000-0003-3296-5838</orcidid></addata></record>
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identifier ISSN: 1672-6316
ispartof Journal of mountain science, 2018, Vol.15 (1), p.33-45
issn 1672-6316
1993-0321
1008-2786
language eng
recordid cdi_proquest_journals_1984679939
source Springer Link
subjects Accuracy
Climate models
Coefficients
Correlation analysis
Correlation coefficient
Correlation coefficients
Depth
Distribution
Earth and Environmental Science
Earth Sciences
Ecological monitoring
Ecology
Environment
Geography
Hydrologic models
Hydrology
Kriging interpolation
Land surface temperature
Mountains
Predation
Regression analysis
Regression models
Root-mean-square errors
Ruggedness
Snow
Snow depth
Spatial distribution
Spatial resolution
Statistical methods
Surface temperature
Variables
title Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China
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