Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2507-2025
https://doi.org/10.5194/essd-17-2507-2025
Data description paper
 | 
12 Jun 2025
Data description paper |  | 12 Jun 2025

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

Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang

Viewed

Total article views: 2,539 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,805 464 270 2,539 112 74 91
  • HTML: 1,805
  • PDF: 464
  • XML: 270
  • Total: 2,539
  • Supplement: 112
  • BibTeX: 74
  • EndNote: 91
Views and downloads (calculated since 03 Apr 2024)
Cumulative views and downloads (calculated since 03 Apr 2024)

Viewed (geographical distribution)

Total article views: 2,539 (including HTML, PDF, and XML) Thereof 2,493 with geography defined and 46 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Jul 2025
Download
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.
Share
Altmetrics
Final-revised paper
Preprint