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

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Abstract

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°),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 °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 .

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Acknowledgments

This work was supported by the Open fund of Key Laboratory of National Geographic Census and Monitoring, MNR (grant no.2020NGCM02);Open Research Fund of the Key Laboratory of Digital Earth Science, Chinese Academy of Sciences (grant no. 2019LDE006); the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (grant no. KF-2020-05-001);Open fund of Key Laboratory of Land use, Ministry of Natural Resources (grant no.20201511835); Open Fund of Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology (grant no. DLLJ202002); Open foundation of MOE Key Laboratory of Western China’s Environmental Systems, Lanzhou University and the fundamental Research funds for the Central Universities (grant no. lzujbky-2020-kb01); University-Industry Collaborative Education Program (grant no.201902208005); Open Fund of Key Laboratory of Meteorology and Ecological Environment of Hebei Province (grant no.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 (grant no. QHDX-2019-04).

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Author notes

  1. Fei Yang and Hongwei Wu contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of UrbanLand Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, 518000, China

    Bing Guo

  2. School of Civil Architectural Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China

    Bing Guo, Hongwei Wu, Cuixia Wei, Guangmao Jiang, Dafu Zhang & Hailing Zhang

  3. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Beijing, 100101, China

    Bing Guo & Fei Yang

  4. Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources, Wuhan, 430072, China

    Bing Guo

  5. Key Laboratory of Land use, MNR, China Land Survey and Planning Institute, Beijing, 100035, China

    Bing Guo & Chao Meng

  6. Geomatics Technology and Application key Laboratory of Qinghai Province, Xining, 810001, China

    Bing Guo

  7. Key Laboratory of Meteorology and Ecological Environment of Hebei Province, Shijiazhuang, 050021, Hebei, China

    Bing Guo & Xiaoyan Zhen

  8. Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang, 330013, China

    Bing Guo

  9. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China

    Bing Guo

  10. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing, 100048, China

    Rui Zhang

  11. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China

    Wenqian Zang & Huihui Zhao

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  1. Bing Guo
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  2. Fei Yang
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Correspondence to Bing Guo.

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Communicated by: H. Babaie

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Guo, B., Yang, F., Wu, H. et al. How the variations of terrain factors affect the optimal interpolation methods for multiple types of climatic elements?. Earth Sci Inform 14, 1021–1032 (2021). https://doi.org/10.1007/s12145-021-00609-2

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  • DOI: https://doi.org/10.1007/s12145-021-00609-2

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