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

作者：Zhaosheng Wang, Renqiang Li, Qingchun Guo, Zhaojun Wang, Mei Huang, Changjun Cai, Bin Chen · 发表于：Heliyon · 年份：2023 · DOI：10.1016/j.heliyon.2023.e17243 · 被引用次数：8 · 研究领域：Forest Management and Policy、Land Use and Ecosystem Services、Forest ecology and management

China's forests play a vital role in the global carbon cycle through the absorption of atmospheric CO 2 to mitigate climate change caused by the increase of anthropogenic CO 2 . 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 d e x ) 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 CO 2 , which is approximately 52.03% of the cumulative increased CO 2 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 CO 2 . Compared with the carbon stock in th...