Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0) : Model Development, Network Assessment, and Budget Comparison

• Published in: AGU Advances
• Main Authors: McNicol, Gavin, Fluet-Chouinard, Etienne, Ouyang, Zutao, Knox, Sara, Zhang, Zhen, Aalto, Tuula, Bansal, Sheel, Chang, Kuang-Yu, Chen, Min, Delwiche, Kyle, Feron, Sarah, Goeckede, Mathias, Liu, Jinxun, Malhotra, Avni, Melton, Joe R., Riley, William, Vargas, Rodrigo, Yuan, Kunxiaojia, Ying, Qing, Zhu, Qing, Alekseychik, Pavel, Aurela, Mika, Billesbach, David P., Campbell, David I., Chen, Jiquan, Chu, Housen, Desai, Ankur R., Euskirchen, Eugenie, Goodrich, Jordan, Griffis, Timothy, Helbig, Manuel, Hirano, Takashi, Iwata, Hiroki, Jurasinski, Gerald, King, John, Koebsch, Franziska, Kolka, Randall, Krauss, Ken, Lohila, Annalea, Mammarella, Ivan, Nilson, Mats, Noormets, Asko, Oechel, Walter, Peichl, Matthias, Sachs, Torsten, Sakabe, Ayaka, Schulze, Christopher, Sonnentag, Oliver, Sullivan, Ryan C., Tuittila, Eeva-Stiina, Ueyama, Masahito, Vesala, Timo, Ward, Eric, Wille, Christian, Wong, Guan Xhuan, Zona, Donatella, Windham-Myers, Lisamarie, Poulter, Benjamin, Jackson, Robert B.
• Other Authors: Soils and climate change, Institute for Atmospheric and Earth System Research (INAR), Department of Physics, Micrometeorology and biogeochemical cycles, Viikki Plant Science Centre (ViPS), Ecosystem processes (INAR Forest Sciences)
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley Periodicals, Inc. 2023
• Subjects: Eddy covariance; Flux; Global; Methane; Random forest; Wetland; 4112 Forestry; 1172 Environmental sciences; Nash; Sutcliffe; Tundra
• Online Access: http://hdl.handle.net/10138/566183

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 similar to 0.52-0.63 and 0.53). UpCH(4) 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 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for ...