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 ...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-meansquare 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.展开更多
On 21 December of 2019,four lower respiratory tract samples were collected from patients with pneumonia of unknown cause in Wuhan,China[1].This disease was later diagnosed as a coronavirus disease.The World Health Org...On 21 December of 2019,four lower respiratory tract samples were collected from patients with pneumonia of unknown cause in Wuhan,China[1].This disease was later diagnosed as a coronavirus disease.The World Health Organization(WHO)named the coronavirus disease as COVID-19 on 11 February and declared a pandemic on 11 March 2020.The COVID-19 is a previously unknown disease with no tools available for pharmaceutical management at the early stage of its outbreak.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 41930647, 41590844, 41421001 & 41971358)the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (Grant No. XDA20030203)+1 种基金the Innovation Project of LREIS (Grant No. O88RA600YA)the Biodiversity Investigation, Observation and Assessment Program (2019–2023) of the Ministry of Ecology and Environment of China。
文摘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-meansquare 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.
基金supported by the National Natural Science Foundation of China(41930647,41421001,41590844)Strategic Priority Research Program(A)of Chinese Academy of Sciences(XDA20030203)+1 种基金Innovation Project of State Key Laboratory of Resources and Environment Information System(O88RA600YA)Biodiversity Investigation,Observation and Assessment Program(2019-2023)of the Ministry of Ecology and Environment of China。
文摘On 21 December of 2019,four lower respiratory tract samples were collected from patients with pneumonia of unknown cause in Wuhan,China[1].This disease was later diagnosed as a coronavirus disease.The World Health Organization(WHO)named the coronavirus disease as COVID-19 on 11 February and declared a pandemic on 11 March 2020.The COVID-19 is a previously unknown disease with no tools available for pharmaceutical management at the early stage of its outbreak.
