Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions

作者：Qing Zhu, Daniel J. Jacob, Kunxiaojia Yuan, Fa Li, Benjamin R. K. Runkle, Min Chen, A. Anthony Bloom, Benjamin Poulter, James D. East, W. J. Riley, Gavin McNicol, John R. Worden, Christian Frankenberg, Meghan Halabisky · 发表于：Environmental Research Letters · 年份：2025 · DOI：10.1088/1748-9326/adad02 · 被引用次数：12 · 研究领域：Atmospheric and Environmental Gas Dynamics、Peatlands and Wetlands Ecology、Climate variability and models

Abstract Wetlands are the single largest natural source of atmospheric methane (CH 4 ), contributing approximately 30% of total surface CH 4 emissions, and they have been identified as the largest source of uncertainty in the global CH 4 budget based on the most recent Global Carbon Project CH 4 report. High uncertainties in the bottom–up estimates of wetland CH 4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH 4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH 4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH 4 emissions, as well as how they are used in top–down inversions. These bottom–up estimates, using (1) empirical biogeochemical modeling (e.g. WetCHARTs: 125–208 TgCH 4 yr −1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH 4 yr −1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH 4 yr −1 ). Bottom–up estimates are subject to significant uncertainties (∼80 Tg CH 4 yr −1 ), and the ranges of different estimates do not overlap, further amplifying the overall uncertainty when combining multiple data products. These substantial uncertainties highlight gaps in our understanding of wetland CH 4 biogeochemistry an...