Articles | Volume 22, issue 16
https://doi.org/10.5194/bg-22-4061-2025
https://doi.org/10.5194/bg-22-4061-2025
Research article
 | 
25 Aug 2025
Research article |  | 25 Aug 2025

Assimilating multi-site eddy-covariance data to calibrate the wetland CH4 emission module in a terrestrial ecosystem model

Jalisha Theanutti Kallingal, Marko Scholze, Paul Anthony Miller, Johan Lindström, Janne Rinne, Mika Aurela, Patrik Vestin, and Per Weslien

Viewed

Total article views: 347 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
248 73 26 347 19 37
  • HTML: 248
  • PDF: 73
  • XML: 26
  • Total: 347
  • BibTeX: 19
  • EndNote: 37
Views and downloads (calculated since 26 Nov 2024)
Cumulative views and downloads (calculated since 26 Nov 2024)

Viewed (geographical distribution)

Total article views: 347 (including HTML, PDF, and XML) Thereof 345 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Aug 2025
Download
Short summary
We explored the possibilities of a Bayesian-based data assimilation algorithm to improve the wetland CH4 flux estimates by a dynamic vegetation model. By assimilating CH4 observations from 14 wetland sites, we calibrated model parameters and estimated large-scale annual emissions from northern wetlands. Our findings indicate that this approach leads to more reliable estimates of CH4 dynamics, which will improve our understanding of the climate change feedback from wetland CH4 emissions.
Share
Altmetrics
Final-revised paper
Preprint