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±12.2TgCH4yr-1 (1Tg=1012g) from 1979 to 2022. Future emissions will reach 165.8±11.6, 185.6±15.0, and 193.6±17.2TgCH4yr-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.