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WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022

dc.contributor.authorYing, Qing
dc.contributor.authorPoulter, Benjamin
dc.contributor.authorWatts, Jennifer D
dc.contributor.authorArndt, Kyle A
dc.contributor.authorVirkkala, Anna-Maria
dc.contributor.authorBruhwiler, Lori
dc.contributor.authorOh, Youmi
dc.contributor.authorRogers, Brendan M
dc.contributor.authorNatali, Susan M
dc.contributor.authorSullivan, Hilary
dc.contributor.authorArmstrong, Amanda
dc.contributor.authorWard, Eric J
dc.contributor.authorSchiferl, Luke D
dc.contributor.authorElder, Clayton D
dc.contributor.authorPeltola, Olli
dc.contributor.authorBartsch, Annett
dc.contributor.authorDesai, Ankur R
dc.contributor.authorEuskirchen, Eugenie
dc.contributor.authorGoeckede, Mathias
dc.contributor.authorLehner, Bernhard
dc.contributor.authorNilsson, Mats B
dc.contributor.authorPeichl, Matthias
dc.contributor.authorSonnentag, Oliver
dc.contributor.authorTuittila, Eeva-Stiina
dc.contributor.authorSachs, Torsten
dc.contributor.authorKalhori, Aram
dc.contributor.authorUeyama, Masahito
dc.contributor.authorZhang, Zhen
dc.contributor.departmentid4100411710
dc.contributor.orcidhttps://orcid.org/0000-0002-1744-6290
dc.contributor.organizationLuonnonvarakeskus
dc.date.accessioned2025-07-28T12:32:54Z
dc.date.issued2025
dc.description.abstractWetlands are the largest natural source of methane (CH4) emissions globally. Northern wetlands (>45° N), accounting for 42 % of global wetland area, are increasingly vulnerable to carbon loss, especially as CH4 emissions may accelerate under intensified high-latitude warming. However, the magnitude and spatial patterns of high-latitude CH4 emissions remain relatively uncertain. Here, we present estimates of daily CH4 fluxes obtained using a new machine learning-based wetland CH4 upscaling framework (WetCH4) 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 R2 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 CH4 yr−1 for the northern wetland region (2016–2022), and total budgets ranged from 15.7 to 51.6 Tg CH4 yr−1, depending on wetland map extents. Although 88 % of the estimated CH4 budget occurred during the May–October period, a considerable amount (2.6±0.3 Tg CH4) occurred during winter. Regionally, the Western Siberian wetlands accounted for a majority (51 %) of the interannual variation in domain CH4 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 CH4 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 CH4 detection and monitoring. The dataset can be found at https://doi.org/10.5281/zenodo.10802153 (Ying et al., 2024).
dc.format.pagerange2507-2534
dc.identifier.citationHow to cite: Ying, Q., Poulter, B., Watts, J. D., Arndt, K. A., Virkkala, A.-M., Bruhwiler, L., Oh, Y., Rogers, B. M., Natali, S. M., Sullivan, H., Armstrong, A., Ward, E. J., Schiferl, L. D., Elder, C. D., Peltola, O., Bartsch, A., Desai, A. R., Euskirchen, E., Göckede, M., Lehner, B., Nilsson, M. B., Peichl, M., Sonnentag, O., Tuittila, E.-S., Sachs, T., Kalhori, A., Ueyama, M., and Zhang, Z.: WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022, Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, 2025.
dc.identifier.urihttps://jukuri.luke.fi/handle/11111/99767
dc.identifier.urlhttps://doi.org/10.5194/essd-17-2507-2025
dc.identifier.urnURN:NBN:fi-fe2025072879722
dc.language.isoen
dc.okm.avoinsaatavuuskytkin1 = Avoimesti saatavilla
dc.okm.corporatecopublicationei
dc.okm.discipline1172
dc.okm.internationalcopublicationon
dc.okm.julkaisukanavaoa1 = Kokonaan avoimessa julkaisukanavassa ilmestynyt julkaisu
dc.okm.selfarchivedon
dc.publisherCopernicus publications
dc.relation.doi10.5194/essd-17-2507-2025
dc.relation.ispartofseriesEarth system science data
dc.relation.issn1866-3508
dc.relation.issn1866-3516
dc.relation.numberinseries6
dc.relation.volume17
dc.rightsCC BY 4.0
dc.source.justusid123500
dc.subjectmethane fluxes
dc.subjectwetlands
dc.subjectmachine learning
dc.teh41007-00272001
dc.titleWetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022
dc.typepublication
dc.type.okmfi=A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä|sv=A1 Originalartikel i en vetenskaplig tidskrift|en=A1 Journal article (refereed), original research|
dc.type.versionfi=Publisher's version|sv=Publisher's version|en=Publisher's version|

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