Quantifying Global Wetland Methane Emissions With In Situ Methane Flux Data and Machine Learning Approaches

Abstract

Wetland methane (CH₄) emissions have a significant impact on the global climate system. However, the current estimation of wetland CH₄ emissions at the global scale still has large uncertainties. Here we developed six distinct bottom-up machine learning (ML) models using in situ CH₄ fluxes from both chamber measurements and the Fluxnet-CH₄ network. To reduce uncertainties, we adopted a multi-model ensemble (MME) approach to estimate CH₄ emissions. Precipitation, air temperature, soil properties, wetland types, and climate types are considered in developing the models. The MME is then extrapolated to the global scale to estimate CH₄ emissions from 1979 to 2099. We found that the annual wetland CH₄ emissions are 146.6 ± 12.2 Tg CH₄ yr⁻¹ (1 Tg = 10¹² g) from 1979 to 2022. Future emissions will reach 165.8 ± 11.6, 185.6 ± 15.0, and 193.6 ± 17.2 Tg CH₄ yr⁻¹ in the last two decades of the 21st century under SSP126, SSP370, and SSP585 scenarios, respectively. Northern Europe and near-equatorial areas are the current emission hotspots. To further constrain the quantification uncertainty, research priorities should be directed to comprehensive CH₄ measurements and better characterization of spatial dynamics of wetland areas. Our data-driven ML-based global wetland CH₄ emission products for both the contemporary and the 21st century shall facilitate future global CH₄ cycle studies.