Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2507-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2507-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 Jun 2025
Data description paper |
| 12 Jun 2025
| 12 Jun 2025
WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022
Qing Ying
CORRESPONDING AUTHOR
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Benjamin Poulter
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Jennifer D. Watts
Woodwell Climate Research Center, Falmouth, MA, USA
Kyle A. Arndt
Woodwell Climate Research Center, Falmouth, MA, USA
Anna-Maria Virkkala
Woodwell Climate Research Center, Falmouth, MA, USA
Lori Bruhwiler
NOAA ESRL, 325 Broadway, Boulder, CO, USA
Youmi Oh
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
NOAA ESRL, 325 Broadway, Boulder, CO, USA
Brendan M. Rogers
Woodwell Climate Research Center, Falmouth, MA, USA
Susan M. Natali
Woodwell Climate Research Center, Falmouth, MA, USA
Hilary Sullivan
Woodwell Climate Research Center, Falmouth, MA, USA
Amanda Armstrong
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Eric J. Ward
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Luke D. Schiferl
Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, USA
Clayton D. Elder
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Ames Research Center, Earth Science Division, Moffett Field, CA, USA
Olli Peltola
Natural Resources Institute Finland (Luke), Latokartanonkaari 9, Helsinki, Finland
Annett Bartsch
b.geos, Korneuburg, Austria
Ankur R. Desai
Department of Atmospheric and Oceanic Sciences, University of Wisconsin, Madison, WI, USA
Eugénie Euskirchen
Institute of Arctic Biology, University of Alaska, Fairbanks, AK, USA
Mathias Göckede
Max Planck Institute for Biogeochemistry, Jena, Germany
Bernhard Lehner
Department of Geography, McGill University, Montréal, QC, Canada
Mats B. Nilsson
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
Matthias Peichl
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
Oliver Sonnentag
Département de géographie, Université de Montréal, Montréal, QC, Canada
Eeva-Stiina Tuittila
School of Forest Sciences, University of Eastern Finland, Joensuu, Finland
Torsten Sachs
GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
Institute of Geoecology, Technische Universität Braunschweig, Braunschweig, Germany
Aram Kalhori
GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
Masahito Ueyama
Graduate School of Agriculture, Osaka Metropolitan University, Osaka, Japan
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
National Tibetan Plateau Data Center (TPDC), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resource (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
Data sets
WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016-2022 Qing Ying et al. https://doi.org/10.5281/zenodo.10802153
Model code and software
WetCH4: An Ml-based Modeling and Upscaling Framework for Wetland Methane Fluxes Qing Ying https://doi.org/10.5281/zenodo.10882613
Short summary
We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022...
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