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Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China's forest ecosystem

文献类型：期刊论文

作者Wang, Zhaosheng; Li, Renqiang; Guo, Qingchun; Wang, Zhaojun; Huang, Mei; Cai, Changjun; Chen, Bin
刊名HELIYON
出版日期2023-06-01
卷号9期号:6页码:e17243
关键词Learning ensembles of process-based models Random forest model Forest ecosystem Carbon sink potential
ISSN号2405-8440
DOI10.1016/j.heliyon.2023.e17243
产权排序1
文献子类Article
英文摘要China's forests play a vital role in the global carbon cycle through the absorption of atmospheric CO2 to mitigate climate change caused by the increase of anthropogenic CO2. It is essential to evaluate the carbon sink potential (CSP) of China's forest ecosystem. Combining NDVI, field -investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi -model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China's forest ecosystem in historical (1982-2021) and future (2022-2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index (C Sin dex) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO2, which is approximately 52.03% of the cumulative increased CO2 emissions in China from 1959 to 2018. In the next 60 years, China's forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO2. Compared with the carbon stock in the historical period, the cumulative absorption of CO2 by China's forest ecosystem in 2032-2036, 2062-2066, and 2077-2081 are approximately 11.25-39.68, 110.66-121.49 and 101.31-111.11 Bt CO2, respec-tively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical C Sin dex covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83-72.22 Bt CO2, respectively. In the future, China's forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action.
WOS关键词TERRESTRIAL ECOSYSTEMS ; VEGETATION ; SEQUESTRATION
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001043504900001
出版者CELL PRESS
源URL[http://ir.igsnrr.ac.cn/handle/311030/194605]  
专题生态系统网络观测与模拟院重点实验室_外文论文
作者单位1.Institute of Geographic Sciences & Natural Resources Research, CAS
2.Institute of Zoology, CAS
3.Liaocheng University
4.Chinese Academy of Sciences
推荐引用方式
GB/T 7714		 Wang, Zhaosheng,Li, Renqiang,Guo, Qingchun,et al. Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China's forest ecosystem[J]. HELIYON,2023,9(6):e17243.
APA Wang, Zhaosheng.,Li, Renqiang.,Guo, Qingchun.,Wang, Zhaojun.,Huang, Mei.,...&Chen, Bin.(2023).Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China's forest ecosystem.HELIYON,9(6),e17243.
MLA Wang, Zhaosheng,et al."Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China's forest ecosystem".HELIYON 9.6(2023):e17243.

入库方式： OAI收割

来源：地理科学与资源研究所

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