Biological Systems Engineering, Department of



Gavin McNicol, Stanford University
Etienne Fluet-Chouinard, Stanford University
Zutao Ouyang, Stanford University
Sara Knox, The University of British Columbia
Zhen Zhang, University of Maryland, College Park
Tuula Aalto, Finnish Meteorological Institute
Sheel Bansal, United States Geological Survey
Kuang Yu Chang, Lawrence Berkeley National Laboratory
Min Chen, University of Wisconsin-Madison
Kyle Delwiche, Department of Environmental Science, Policy, and Management
Sarah Feron, Rijksuniversiteit Groningen
Mathias Goeckede, Max Planck Institute for Biogeochemistry
Jinxun Liu, United States Geological Survey Western Region
Avni Malhotra, Universität Zürich
Joe R. Melton, Environment and Climate Change Canada
William Riley, Lawrence Berkeley National Laboratory
Rodrigo Vargas, University of Delaware
Kunxiaojia Yuan, Lawrence Berkeley National Laboratory
Qing Ying, University of Maryland, College Park
Qing Zhu, Lawrence Berkeley National Laboratory
Pavel Alekseychik, Natural Resources Institute Finland (Luke)
Mika Aurela, Finnish Meteorological Institute
David Billesbach, University of Nebraska-LincolnFollow
David I. Campbell, The University of Waikato
Jiquan Chen, Michigan State University
Housen Chu, Lawrence Berkeley National Laboratory
Ankur R. Desai, University of Wisconsin-Madison
Eugenie Euskirchen, University of Alaska Fairbanks
Jordan Goodrich, The University of Waikato
Timothy Griffis, University of Minnesota Twin Cities
Manuel Helbig, Dalhousie University
Takashi Hirano, Hokkaido University
Hiroki Iwata, Shinshu University
Gerald Jurasinski, Universität Rostock
John King, NC State University
Franziska Koebsch, Georg-August-Universität Göttingen
Randall Kolka, USDA Forest Service
Ken Krauss, United States Geological Survey
Annalea Lohila, Finnish Meteorological Institute
Ivan Mammarella, Helsingin Yliopisto

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Date of this Version



McNicol, G., Fluet-Chouinard, E., Ouyang, Z., Knox, S., Zhang, Z., Aalto, T., et al. (2023). Upscaling wetland methane emissions from the FLUXNET-CH4 eddy covariance network (UpCH4 v1.0): Model development, network assessment, and budget comparison. AGU Advances, 4, e2023AV000956. https://doi. org/10.1029/2023AV000956


© 2023. The Authors. This is an open access article under the terms of the Creative Commons Attribution License,


Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (