Accurate quantifying of methane (CH4) emissions is a critical aspect of current research on regional carbon budgets. However, due to limitations in observational data, research methodologies, and an incomplete understanding of process mechanisms, significant uncertainties persist in the assessment of wetland CH4 fluxes in China. In this study, we developed a machine learning model by integrating measured CH4 fluxes with related environmental data to produce a high-resolution (1 km) dataset of CH4 fluxes from China's wetlands for the period 2000-2020. Our results estimate that the wetland CH4 flux in China is approximately 1.54±0.03 mg CH4 m-2 h-1, with total annual emissions of 10.85±0.26 Tg CH4 yr-1. Yangtze River Basin (6.01 Tg CH4 yr-1), Northeastern China (1.65 Tg CH4 yr-1), and the Qinghai-Tibetan Plateau (1.34 Tg CH4 yr⁻1) were identified as the primary contributing regions. Notably, total CH4 emissions from China's wetlands exhibited a significant declining trend from 2000 to 2020, primarily driven by a substantial decrease in emissions from the Yangtze River Basin and Southern China, where paddy field wetlands are predominant. In contrast, an increasing trend was observed in Northeastern China and the Tibetan Plateau, characterized by natural wetlands. Further analysis revealed that the spatial and temporal dynamics of CH4 emissions from China's wetlands are closely linked to vegetation activity. This study highlights the spatial and temporal patterns of wetland CH4 fluxes in China and investigates their potential driving mechanisms, offering valuable data support and a theoretical foundation for national CH4 emission reduction strategies and wetland management programs.
Keywords: China; climate change; methane; temperature; vegetation activity; wetlands.
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