Hu Qiwen, Li Jingxian, Xie Hanzhi, Huang Yao, Canadell Josep G, Yuan Wenping, Wang Jinyang, Zhang Wen, Yu Lijun, Li Shihua, Lu Xinqing, Li Tingting, Qin Zhangcai
School of Atmospheric Sciences, Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Neutrality, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China.
Key Laboratory of Atmospheric Environment and Extreme Meteorology, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
iScience. 2024 Oct 23;27(11):111237. doi: 10.1016/j.isci.2024.111237. eCollection 2024 Nov 15.
Rice cultivation constitutes a significant anthropogenic methane (CH) source and a crucial target for CH mitigation. However, global and regional emissions remain poorly constrained. In this study, we validated a global-process-based methane model for rice paddies (CH4MOD), analyzed the sensitivity of major emission drivers, and simulated management scenarios involving four water regimes and three organic matter amendments. CH4MOD simulations achieved a correlation coefficient of 0.76 across 986 CH flux observations globally, demonstrating its capability under different environmental conditions and management practices. The sensitivity analysis revealed water regime as the primary driver, followed by organic matter amendment and temperature. Under different crop management, CH emissions varied significantly from 8 to 78 Tg CH/yr. This wide range of emissions demonstrates the need to use and improve rice-specific emission models and spatiotemporal data on rice distribution, water, and residue management for accurately assessing local to global emissions and their climate mitigation potential.
水稻种植是重要的人为甲烷(CH)排放源,也是甲烷减排的关键目标。然而,全球和区域排放量仍难以精确确定。在本研究中,我们验证了基于全球过程的稻田甲烷模型(CH4MOD),分析了主要排放驱动因素的敏感性,并模拟了涉及四种水分管理方式和三种有机物料改良措施的管理情景。CH4MOD模拟在全球986个CH通量观测数据中实现了0.76的相关系数,表明其在不同环境条件和管理措施下的性能。敏感性分析表明,水分管理方式是主要驱动因素,其次是有机物料改良措施和温度。在不同的作物管理方式下,CH排放量从8 Tg CH/年到78 Tg CH/年显著变化。如此广泛的排放量表明,需要使用并改进特定于水稻的排放模型以及关于水稻分布、水分和秸秆管理的时空数据,以准确评估从地方到全球的排放量及其气候缓解潜力。