Wang Zhaosheng, Li Renqaing, Guo Qingchun, Wang Zhaojun, Huang Mei, Cai Changjun, Chen Bin
Key Laboratory of Ecosystem Network Observation and Modeling, National Data Center for Ecological Sciences, Institute of Geographic Sciences and Natural Resources Research, CAS, China.
School of Geography and Environment, Liaocheng University, Liaocheng 252000, China.
Heliyon. 2023 Jun 17;9(6):e17243. doi: 10.1016/j.heliyon.2023.e17243. eCollection 2023 Jun.
China's forests play a vital role in the global carbon cycle through the absorption of atmospheric CO to mitigate climate change caused by the increase of anthropogenic CO. 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 () 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, which is approximately 52.03% of the cumulative increased CO 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. Compared with the carbon stock in the historical period, the cumulative absorption of CO 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 CO respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83-72.22 Bt CO, 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.
中国的森林通过吸收大气中的二氧化碳在全球碳循环中发挥着至关重要的作用，以缓解人为二氧化碳增加所导致的气候变化。评估中国森林生态系统的碳汇潜力至关重要。结合归一化植被指数（NDVI）、实地调查数据以及基于过程模型模拟的植被和土壤碳密度数据，我们开发了基于过程模型的先进学习集成模型——多模型随机森林集成（MMRFE）模型，以评估中国森林生态系统在历史时期（1982 - 2021年）和未来时期（2022 - 2081年，无NDVI驱动数据）的碳储量。同时，我们提出了一种新的碳汇指数，以科学准确地评估碳汇状况并识别碳汇强度区域，降低随机误判为碳汇的概率。新的MMRFE模型在中国森林植被和土壤碳密度模拟方面显示出良好的结果（与观测值显著正相关，r = 0.94，P < 0.001）。模拟结果表明，森林生态系统历史碳储量累计增加1.33Pg C相当于48.62亿吨二氧化碳，约占中国1959年至2018年累计增加二氧化碳排放量的52.03%。在未来60年里，中国森林生态系统每年将吸收1.69（RCP4.5情景）至1.85（RCP8.5情景）亿吨二氧化碳。与历史时期的碳储量相比，中国森林生态系统在2032 - 2036年、2062 - 2066年和2077 - 2081年对二氧化碳的累计吸收量分别约为11.25 - 39.68亿吨、110.66 - 121.49亿吨和101.31 - 111.11亿吨二氧化碳。在历史和未来时期，历史碳汇指数所确定的中强碳汇强度区域覆盖了森林总面积的65%，累计吸收量分别约为31.60亿吨以及65.83 - 72.22亿吨二氧化碳。未来，中国森林生态系统具有较大的碳汇潜力，呈非连续增长趋势。然而，碳汇潜力不应被低估。值得注意的是，中等碳汇强度区域应是自然碳固存行动的优先区域。本研究不仅为准确估算森林生态系统未来碳汇潜力提供了重要的方法依据，也为未来森林生态系统碳固存行动提供了重要的决策支持。
