How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements?

doi:10.1007/s12145-021-00609-2

	How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements?
	Guo, Bing 2,3,4,5,6,7,8,9,10; Yang, Fei 4; Wu, Hongwei 3; Zhang, Rui 1; Zang, Wenqian 11; Wei, Cuixia 3; Jiang, Guangmao 3; Meng, Chao 6; Zhao, Huihui 11; Zhen, Xiaoyan 8
刊名	EARTH SCIENCE INFORMATICS
	2021-03-23
页码	12
关键词	Terrain Precipitation Spatial interpolation Optimal method Ordinary kriging method
ISSN号	1865-0473
DOI	10.1007/s12145-021-00609-2
通讯作者	Guo, Bing(guobingjl@163.com)
英文摘要	The spatial interpolation of meteorological data have important applications in ecological environment monitoring, such as soil erosion, ecological vulnerability evaluation. However, there are significant differences in the interpolation accuracy of climatic elements under different topographic and geomorphic conditions. Based on data of 810 meteorological stations across the country, five typical interpolation methods, namely ordinary Kriging method, inverse distance weight method, spline function method, natural neighborhood method and trend surface method, were utilized in this paper to analyze and compare the interpolation accuracy of five climate factors, namely temperature, precipitation, accumulated temperature(>10 degrees),wind speed and sunshine hours, under different topographic and geomorphological conditions. The results showed that: (1) Ordinary kriging method of temperature had better applicability in plain, hill and medium-large undulating mountain areas for temperature while the inverse distance weight method and spline function method had higher interpolation accuracy in the platform area and the small undulating mountain area, respectively. (2) The optimal interpolation of the precipitation in plain, platform and medium-large undulating mountain areas was ordinary kriging method while the inverse distance weight method and spline function method had better applicability in small undulating mountain area. (3) For accumulated temperature (>10 degrees C), the spline function method had higher interpolation accuracy in plain and platform areas, while ordinary kriging method had better applicability in hilly, small and medium-large undulating mountain area al (4) The optimal spatial interpolation of wind speed in plain and hilly areas was the inverse distance weight method. The natural neighborhood method and the spline function method had the best applicability in plateau, medium-large undulating mountainous areas and small undulating mountain areas, respectively. (5) For sunshine hours, the optimal spatial interpolations in plain and hilly areas were natural neighborhood method and spline function method, respectively, while the ordinary kriging method and the inverse distance weighting method had better applicability in platform, large undulating mountainous areas and small undulating mountainous areas, respectively .
资助项目	Open fund of Key Laboratory of National Geographic Census and Monitoring, MNR[2020NGCM02] ; Open Research Fund of the Key Laboratory of Digital Earth Science, Chinese Academy of Sciences[2019LDE006] ; Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources[KF-2020-05-001] ; Open fund of Key Laboratory of Land use, Ministry of Natural Resources[20201511835] ; Open Fund of Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology[DLLJ202002] ; Open foundation of MOE Key Laboratory of Western China's Environmental Systems, Lanzhou University[lzujbky-2020-kb01] ; fundamental Research funds for the Central Universities[lzujbky-2020-kb01] ; University-Industry Collaborative Education Program[201902208005] ; Open Fund of Key Laboratory of Meteorology and Ecological Environment of Hebei Province[Z202001H] ; Open Fund of Key Laboratory of Geomatics and Digital Technology of Shandong Province ; Open Fund of Key Laboratory of Geomatics Technology and Application Key Laboratory of Qinghai Province[QHDX-2019-04]
WOS研究方向	Computer Science ; Geology
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000631338600001
资助机构	Open fund of Key Laboratory of National Geographic Census and Monitoring, MNR ; Open Research Fund of the Key Laboratory of Digital Earth Science, Chinese Academy of Sciences ; Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources ; Open fund of Key Laboratory of Land use, Ministry of Natural Resources ; Open Fund of Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Open foundation of MOE Key Laboratory of Western China's Environmental Systems, Lanzhou University ; fundamental Research funds for the Central Universities ; University-Industry Collaborative Education Program ; Open Fund of Key Laboratory of Meteorology and Ecological Environment of Hebei Province ; Open Fund of Key Laboratory of Geomatics and Digital Technology of Shandong Province ; Open Fund of Key Laboratory of Geomatics Technology and Application Key Laboratory of Qinghai Province
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/160443]
专题	中国科学院地理科学与资源研究所
通讯作者	Guo, Bing
作者单位	1.Minist Nat Resources, Land Satellite Remote Sensing Applicat Ctr, Beijing 100048, Peoples R China 2.Minist Nat Resources, Key Lab UrbanLand Resources Monitoring & Simulat, Shenzhen 518000, Peoples R China 3.Shandong Univ Technol, Sch Civil Architectural Engn, Zibo 255000, Shandong, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Minist Nat Resources, Key Lab Natl Geog Census & Monitoring, Wuhan 430072, Peoples R China 6.China Land Survey & Planning Inst, Key Lab Land Use, MNR, Beijing 100035, Peoples R China 7.Geomat Technol & Applicat Key Lab Qinghai Prov, Xining 810001, Peoples R China 8.Key Lab Meteorol & Ecol Environm Hebei Prov, Shijiazhuang 050021, Hebei, Peoples R China 9.East China Univ Technol, Key Lab Digital Land & Resources Jiangxi Prov, Nanchang 330013, Jiangxi, Peoples R China 10.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Guo, Bing,Yang, Fei,Wu, Hongwei,et al. How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements?[J]. EARTH SCIENCE INFORMATICS,2021:12.
APA	Guo, Bing.,Yang, Fei.,Wu, Hongwei.,Zhang, Rui.,Zang, Wenqian.,...&Zhang, Hailing.(2021).How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements?.EARTH SCIENCE INFORMATICS,12.
MLA	Guo, Bing,et al."How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements?".EARTH SCIENCE INFORMATICS (2021):12.