WetCH4: 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. Howev...

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Bibliographic Details
Main Authors: Ying, Qing, Poulter, Benjamin, Watts, Jennifer D., Arndt, Kyle A., Virkkala, Anna-Maria, Bruhwiler, Lori, Oh, Youmi, Rogers, Brendan M., Natali, Susan M., Sullivan, Hilary, Schiferl, Luke D., Elder, Clayton, Peltola, Olli, Bartsch, Annett, Armstrong, Amanda, Desai, Ankur R., Euskirchen, Eugénie, Göckede, Mathias, Lehner, Bernhard, Nilsson, Mats B., Peichl, Matthias, Sonnentag, Oliver, Tuittila, Eeva-Stiina, Sachs, Torsten, Kalhori, Aram, Ueyama, Masahito, Zhang, Zhen
Format: Text
Language:English
Published: 2024
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Online Access:https://doi.org/10.5194/essd-2024-84
https://essd.copernicus.org/preprints/essd-2024-84/
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Summary: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 applies the most complete database of eddy covariance (EC) observations available to date, and 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 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 0.46 and 0.62 and a mean absolute error (MAE) of 23 nmol m -2 s -1 and 21 nmol m -2 s -1 for daily and monthly fluxes, respectively. Based on the model results, we estimated an annual average of 20.8 ±2.1 Tg CH 4 yr -1 for the northern wetland region (2016–2022) and total budgets ranged from 13.7–44.1 Tg CH 4 yr -1 , depending on wetland map extents. Although 86 % of the estimated CH 4 budget occurred during the May–October period, a considerable amount (1.4 ±0.2 Tg CH 4 ) occurred during winter. Regionally, the West Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH 4 emissions. Significant issues with data coverage remain, with only 23 % of the sites observing year-round and most of the data from 11 wetland sites in Alaska and 10 bog/fen sites in Canada and Fennoscandia, and in general, Western Siberian Lowlands are underrepresented by EC CH 4 sites. Our results provide high spatiotemporal information on the wetland emissions in the high-latitude carbon ...