NiuHePengEtAl2021

Référence

Niu, Z., He, H., Peng, S., Ren, X., Zhang, L., Gu, F., Zhu, G., Peng, C., Li, P., Wang, J., Ge, R., Zeng, N., Zhu, X., Lv, Y., Chang, Q., Xu, Q., Zhang, M., Liu, W. (2021) A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services. Journal of Advances in Modeling Earth Systems, 13(6). (Scopus )

Résumé

Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process-based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process-based ES model (CEVSA-ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA-ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon-water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA-ES model with nine flux sites comprising 39 site-years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site-years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA-ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial-temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins. © 2021. The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.

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@ARTICLE { NiuHePengEtAl2021,
    AUTHOR = { Niu, Z. and He, H. and Peng, S. and Ren, X. and Zhang, L. and Gu, F. and Zhu, G. and Peng, C. and Li, P. and Wang, J. and Ge, R. and Zeng, N. and Zhu, X. and Lv, Y. and Chang, Q. and Xu, Q. and Zhang, M. and Liu, W. },
    JOURNAL = { Journal of Advances in Modeling Earth Systems },
    TITLE = { A Process-Based Model Integrating Remote Sensing Data for Evaluating Ecosystem Services },
    YEAR = { 2021 },
    NOTE = { cited By 0 },
    NUMBER = { 6 },
    VOLUME = { 13 },
    ABSTRACT = { Terrestrial ecosystems provide multiple services interacting in complex ways. However, most ecosystem services (ESs) models (e.g., InVEST and ARIES) ignored the relationships among ESs. Process-based models can overcome this limitation, and the integration of ecological models with remote sensing data could greatly facilitate the investigation of the complex ecological processes. Therefore, based on the Carbon and Exchange between Vegetation, Soil, and Atmosphere (CEVSA) models, we developed a process-based ES model (CEVSA-ES) integrating remotely sensed leaf area index to evaluate four important ESs (i.e., productivity provision, carbon sequestration, water retention, and soil retention) at annual timescale in China. Compared to the traditional terrestrial biosphere models, the main innovation of CEVSA-ES model was the consideration of soil erosion processes and its impact on carbon cycling. The new version also improved the carbon-water cycle algorithms. Then, the Sobol and DEMC methods that integrated the CEVSA-ES model with nine flux sites comprising 39 site-years were used to identify and optimize parameters. Finally, the model using the optimized parameters was validated at 26 field sites comprising 135 site-years. Simulation results showed good fits with ecosystem processes, explaining 95%, 92%, 76%, and 65% interannual variabilities of gross primary productivity, ecosystem respiration, net ecosystem productivity, and evapotranspiration, respectively. The CEVSA-ES model performed well for productivity provision and carbon sequestration, which explained 96% and 81% of the spatial-temporal variations of the observed annual productivity provision and carbon sequestration, respectively. The model also captured the interannual trends of water retention and soil erosion for most sites or basins. © 2021. The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union. },
    AFFILIATION = { Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; National Ecosystem Science Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China; Key Laboratory of Dryland Agriculture, Ministry of Agriculture, Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China; Key Laboratory of Western China’s Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, China; Center for Ecological Forecasting and Global Change, College of Forestry, Northwest Agriculture and Forest University, Yangling, China; Department of Biology Science, Institute of Environment Sciences, University of Quebec at Montreal, Montreal, Canada; Institute of Surface-Earth System Science, Tianjin University, Tianjin, China; School of Environment and Resources, Zhejiang A & F University, Hangzhou, China; Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, Chongqing University of Posts and Telecommunications, Chongqing, China },
    ART_NUMBER = { e2020MS002451 },
    AUTHOR_KEYWORDS = { CEVSA-ES model; China; ecosystem services; model-data fusion; remote sensing },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1029/2020MS002451 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108545136&doi=10.1029%2f2020MS002451&partnerID=40&md5=e574d00a55c9fe5e533a1797c0d78fba },
}

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