4.6 Article

A fundamental theorem for eco-environmental surface modelling and its applications

期刊

SCIENCE CHINA-EARTH SCIENCES
卷 63, 期 8, 页码 1092-1112

出版社

SCIENCE PRESS
DOI: 10.1007/s11430-019-9594-3

关键词

HASM; FTEEM; Spatial upscaling; Spatial downscaling; Spatial interpolation; Data fusion; Model-data assimilation; Model coupling

资金

  1. National Natural Science Foundation of China [41930647, 41590844, 41421001, 41971358]
  2. Strategic Priority Research Program (A) of the Chinese Academy of Sciences [XDA20030203]
  3. Innovation Project of LREIS [O88RA600YA]
  4. Biodiversity Investigation, Observation and Assessment Program (2019-2023) of the Ministry of Ecology and Environment of China

向作者/读者索取更多资源

We propose a fundamental theorem for eco-environmental surface modelling (FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth's surface system modeling (FTESM). The Beijing-Tianjin-Hebei (BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling (HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-mean-square error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

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