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DOI10.1088/1748-9326/ab94e8
Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations
Song X.; Li F.; Harrison S.P.; Luo T.; Arneth A.; Forrest M.; Hantson S.; Lasslop G.; Mangeon S.; Ni J.; Yue C.; Hickler T.; Luo Y.; Sitch S.; Xu X.; Zhu Z.
发表日期2020
ISSN17489318
卷号15期号:9
英文摘要Vegetation biomass is a key and active component of the carbon cycle. Though China's vegetation biomass in recent decades has been widely investigated, only two studies have quantitatively assessed its century-scale changes so far and reported totally opposite trends. This study provided the first multi-model estimates of China's vegetation biomass change for the 20th century and its responses to historical changes in environmental and anthropogenic factors, based on simulations evaluated with the field observations from 3757 inventory plots in China and bias-corrected using machine learning (Gaussian process regression). A significant decline in vegetation biomass over the 20th century was shown by bias-corrected simulations from the six Dynamic Global Vegetation models (DGVMs) with trends ranging from -32.48 to -11.10 Tg C yr-1 and a mean trend of -17.74 Tg C yr-1. Land use and land cover change (LULCC) was primarily responsible for the simulated downward trend (-50.71 to -24.28 Tg C yr-1), while increasing atmospheric CO2 concentration lead to increased vegetation biomass (+9.27 to + 13.37 Tg C yr-1). Climate change had limited impacts on the long-term trend (-3.75 to + 5.06 Tg C yr-1). This study highlights the importance of LULCC for historical reconstruction and future projection of vegetation biomass over China. It also suggests that the incorrect change in China's forest area for 1980-2000 in the LULCC dataset used as model input data of many existing and ongoing model intercomparison projects (MIPs) has likely led to inaccurate estimations of historical vegetation biomass changes in China. © 2020 The Author(s). Published by IOP Publishing Ltd.
语种英语
scopus关键词Biomass; Climate change; Land use; Anthropogenic factors; Field observations; Gaussian process regression; Historical changes; Historical reconstruction; Land use and land cover change; Model inter comparisons; Multi-Model Simulations; Vegetation; assessment method; ecological modeling; field method; observational method; phytomass; simulation; twentieth century; vegetation dynamics; China
来源期刊Environmental Research Letters
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/153831
作者单位International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; School of Archaeology, Geography and Environmental Science, Reading University, Reading, United Kingdom; Department of Earth System Science, Tsinghua University, Beijing, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China; Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany; Senckenberg Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage, Germany; Geospatial Data Solutions Center, University of California, Irvine, CA, United States; Department of Physics, Imperial College London, London, United Kingdom; Now at CSIRO, Data61, Brisbane, QLD, Australia; Zhejiang Normal University, Jinhua, Zhejiang, China; State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest AandF University, Yangling, Sh...
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Song X.,Li F.,Harrison S.P.,et al. Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations[J],2020,15(9).
APA Song X..,Li F..,Harrison S.P..,Luo T..,Arneth A..,...&Zhu Z..(2020).Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations.Environmental Research Letters,15(9).
MLA Song X.,et al."Vegetation biomass change in China in the 20th century: An assessment based on a combination of multi-model simulations and field observations".Environmental Research Letters 15.9(2020).
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