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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 |
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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. |
doi_str_mv | 10.1007/s11629-017-4564-z |
format | article |
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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 & 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. 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><subject>Accuracy</subject><subject>Climate models</subject><subject>Coefficients</subject><subject>Correlation analysis</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Depth</subject><subject>Distribution</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Ecological monitoring</subject><subject>Ecology</subject><subject>Environment</subject><subject>Geography</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>Kriging interpolation</subject><subject>Land surface temperature</subject><subject>Mountains</subject><subject>Predation</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Root-mean-square errors</subject><subject>Ruggedness</subject><subject>Snow</subject><subject>Snow depth</subject><subject>Spatial distribution</subject><subject>Spatial resolution</subject><subject>Statistical methods</subject><subject>Surface temperature</subject><subject>Variables</subject><issn>1672-6316</issn><issn>1993-0321</issn><issn>1008-2786</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kLtOxDAQRSMEEs8PoLNES8BjZ524hBUvCUQB1NYkcRLvBifYjlbLT_DLeFkKGgrLo5l772hOkpwCvQBK80sPIJhMKeRpNhNZ-rmTHICUPKWcwW6sRc5SwUHsJ4feLygVuSzgIPl6GTEY7EltfHCmnIIZLBka4u2wIrUeQ0dK9Lomsd3qoXU4dqbCvl-TlTZtF-LI6dZp7zfOpTOtsS0xloROk2tcoy2nHpex9OYnedN_NWh9h5Y8DZMNaKw_J_POWDxO9hrsvT75_Y-St9ub1_l9-vh89zC_ekyreENIG8hmBWosNCuzqkA-ozWdiSKHjBdVLnkpNM9EA7SsWC3iY5o3DCoNQtKC8aPkbJs7uuFj0j6oxTA5G1cqkEUW6Uguowq2qsoN3jvdqNGZd3RrBVRtuKstdxW5qw139Rk9bOvxUWtb7f4k_2v6Bn-viGs</recordid><startdate>2018</startdate><enddate>2018</enddate><creator>Liu, Yang</creator><creator>Li, Lan-hai</creator><creator>Chen, Xi</creator><creator>Yang, Jin-Ming</creator><creator>Hao, Jian-Sheng</creator><general>Science Press</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>M2P</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>SOI</scope><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></search><sort><creationdate>2018</creationdate><title>Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China</title><author>Liu, Yang ; Li, Lan-hai ; Chen, Xi ; Yang, Jin-Ming ; Hao, Jian-Sheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-f1458aea8e2b4c8a350d056871438c793b6e346f10bc2d6c2d2e3f21ce1690823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accuracy</topic><topic>Climate models</topic><topic>Coefficients</topic><topic>Correlation analysis</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Depth</topic><topic>Distribution</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Ecological monitoring</topic><topic>Ecology</topic><topic>Environment</topic><topic>Geography</topic><topic>Hydrologic models</topic><topic>Hydrology</topic><topic>Kriging interpolation</topic><topic>Land surface temperature</topic><topic>Mountains</topic><topic>Predation</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Root-mean-square errors</topic><topic>Ruggedness</topic><topic>Snow</topic><topic>Snow depth</topic><topic>Spatial distribution</topic><topic>Spatial resolution</topic><topic>Statistical methods</topic><topic>Surface temperature</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Yang</creatorcontrib><creatorcontrib>Li, Lan-hai</creatorcontrib><creatorcontrib>Chen, Xi</creatorcontrib><creatorcontrib>Yang, Jin-Ming</creatorcontrib><creatorcontrib>Hao, Jian-Sheng</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Science Journals</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Journal of mountain science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Yang</au><au>Li, Lan-hai</au><au>Chen, Xi</au><au>Yang, Jin-Ming</au><au>Hao, Jian-Sheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China</atitle><jtitle>Journal of mountain science</jtitle><stitle>J. 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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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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