WetCH ₄ : a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022

Wetlands are the largest natural source of methane (CH 4 ) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH 4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH 4 emissions remain relatively uncertain. Here, we present estimates of daily CH 4 fluxes obtained using a new machine learning-based wetland CH 4 upscaling framework (WetCH 4 ) that combines the most complete database of eddy-covariance (EC) observations available to date with satellite remote-sensing-informed observations of environmental conditions at 10 km resolution. The most important predictor variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean R 2 of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m −2 s −1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 22.8±2.4 Tg CH 4 yr −1 for the northern wetland region (2016–2022), and total budgets ranged from 15.7 to 51.6 Tg CH 4 yr −1 , depending on wetland map extents. Although 88 % of the estimated CH 4 budget occurred during the May–October period, a considerable amount (2.6±0.3 Tg CH 4 ) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH 4 emissions. Overall, our results provide valuable new high-spatiotemporal-resolution information on the wetland emissions in the high-latitude carbon cycle. However, many key uncertainties remain, including those driven by wetland extent maps and soil moisture products and the incomplete spatial and temporal representativeness in the existing CH 4 flux database; e.g., only 23 % of the sites operate outside of summer months, and flux towers do not exist or are greatly limited in many wetland regions. These uncertainties will need to be addressed by the science community to remove the bottlenecks currently limiting progress in CH 4 detection and monitoring. The dataset can be found at https://doi.org/10.5281/zenodo.10802153 (Ying et al., 2024).

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