Title: WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016-2022
Citation: Ying, Q., Poulter, B., Watts, J. D., Arndt, K. A., Virkkala, A.-M., Bruhwiler, L., Oh, Y., Rogers, B. M., Natali, S. M., Armstrong, A., Ward, E. J., Sullivan, H., Schiferl, L. D., Elder, C., Peltola, O., Bartsch, A., Desai, A. R., Euskirchen, E., Göckede, M., … Zhang, Z. (2024). WetCH4: A Machine Learning-based Upscaling of Methane Fluxes of Northern Wetlands during 2016-2022 (Version 1.0.1) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.13893089
Study Site: Northern high latitudes (>45° N)
Purpose: Wetlands 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.
Abstract: This dataset (WetCH4) contains methane (CH4) emissions using three different wetland maps, their uncertainties, and underlying flux intensities from northern wetlands (>45° N). The dataset is a data-driven upscaling product using observations from northern eddy covariance CH4 flux sites and random forest machine learning. WetCH4 provides daily CH4 fluxes of northern wetlands at 10-km resolution from 2016 to 2022 and can be used to study regional CH4 budgets and wetland responses to climate change. The data products are provided in netCDF format files (.nc) with more details in the attributes of the files.
Supplemental Information Summary:
Responsible Parties:
Principal Investigator: Qing Ying
Collaborator: Benjamin Poulter
Collaborator: Jennifer D Watts
Collaborator: Kyle A Arndt
Collaborator: Anna-Maria Virkkala
Collaborator: Lori Bruhwiler
Collaborator: Youmi Oh
Collaborator: Brendan M Rogers
Collaborator: Susan M Natali
Collaborator: Amanda Armstrong
Collaborator: Eric J Ward
Collaborator: Hilary Sullivan
Collaborator: Luke D Schiferl
Collaborator: Clayton Elder
Collaborator: Olli Peltola
Collaborator: Annett Bartsch
Collaborator: Ankur R Desai
Collaborator: Eugénie Euskirchen
Collaborator: Mathias Göckede
Collaborator: Bernhard Lehner
Collaborator: Mats B Nilsson
Collaborator: Matthias Peichl
Collaborator: Oliver Sonnentag
Collaborator: Eeva-Stiina Tuittila
Collaborator: Torsten Sachs
Collaborator: Aram Kalhori
Collaborator: Masahito Ueyama
Collaborator: Zhen Zhang
Research: https://doi.org/10.5281/zenodo.13893089
Further Info: 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.
Status: Complete
Keywords:
methane,
mapping,
machine learning,
Geographical coordinates: North: 83.15, South: 45 East: -52.619 West: -141.010
Bounding Temporal Extent: Start Date: 2016-01-01, End
Date: 2022-12-31
