Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison. McNicol, G., Fluet‐Chouinard, E., Ouyang, Z., Knox, S., Zhang, Z., Aalto, T., Bansal, S., Chang, K., Chen, M., Delwiche, K., Feron, S., Goeckede, M., Liu, J., Malhotra, A., Melton, J. R., Riley, W., Vargas, R., Yuan, K., Ying, Q., Zhu, Q., Alekseychik, P., Aurela, M., Billesbach, D. P., Campbell, D. I., Chen, J., Chu, H., Desai, A. R., Euskirchen, E., Goodrich, J., Griffis, T., Helbig, M., Hirano, T., Iwata, H., Jurasinski, G., King, J., Koebsch, F., Kolka, R., Krauss, K., Lohila, A., Mammarella, I., Nilson, M., Noormets, A., Oechel, W., Peichl, M., Sachs, T., Sakabe, A., Schulze, C., Sonnentag, O., Sullivan, R. C., Tuittila, E., Ueyama, M., Vesala, T., Ward, E., Wille, C., Wong, G. X., Zona, D., Windham‐Myers, L., Poulter, B., & Jackson, R. B. AGU Advances, 4(5):e2023AV000956, October, 2023.
Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison [link]Paper  doi  abstract   bibtex   
Abstract Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data‐driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH 4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH 4 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 CH 4 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 CH 4 emissions of 146 ± 43 TgCH 4  y −1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH 4  y −1 ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH 4  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 CH 4 fluxes has the potential to produce realistic extra‐tropical wetland CH 4 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 ( 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 (∼30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001–2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state‐of‐the‐art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground‐based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties. , Key Points Random forest models trained on FLUXNET‐CH4 methane fluxes reproduced spatiotemporal patterns in extra‐tropical wetlands ( R 2 : 0.59–0.64) Globally upscaled annual wetland methane emissions (146 TgCH 4  y −1 ) overlapped with land surface and inversion model ensemble estimates Humid/monsoon tropics dominate upscaled wetland methane emissions (∼68%) and uncertainties (∼78%) due to limited FLUXNET‐CH4 site coverage
@article{mcnicol_upscaling_2023,
	title = {Upscaling {Wetland} {Methane} {Emissions} {From} the {FLUXNET}‐{CH4} {Eddy} {Covariance} {Network} ({UpCH4} v1.0): {Model} {Development}, {Network} {Assessment}, and {Budget} {Comparison}},
	volume = {4},
	issn = {2576-604X, 2576-604X},
	shorttitle = {Upscaling {Wetland} {Methane} {Emissions} {From} the {FLUXNET}‐{CH4} {Eddy} {Covariance} {Network} ({UpCH4} v1.0)},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023AV000956},
	doi = {10.1029/2023AV000956},
	abstract = {Abstract
            
              Wetlands are responsible for 20\%–31\% of global methane (CH
              4
              ) emissions and account for a large source of uncertainty in the global CH
              4
              budget. Data‐driven upscaling of CH
              4
              fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH
              4
              emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH
              4
              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 CH
              4
              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 CH
              4
              emissions of 146 ± 43 TgCH
              4
               y
              −1
              for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH
              4
               y
              −1
              ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH
              4
               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 CH
              4
              fluxes has the potential to produce realistic extra‐tropical wetland CH
              4
              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 (
              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 (∼30\% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001–2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state‐of‐the‐art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground‐based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties.
          , 
            Key Points
            
              
                
                  
                    Random forest models trained on FLUXNET‐CH4 methane fluxes reproduced spatiotemporal patterns in extra‐tropical wetlands (
                    R
                    2
                    : 0.59–0.64)
                  
                
                
                  
                    Globally upscaled annual wetland methane emissions (146 TgCH
                    4
                     y
                    −1
                    ) overlapped with land surface and inversion model ensemble estimates
                  
                
                
                  Humid/monsoon tropics dominate upscaled wetland methane emissions (∼68\%) and uncertainties (∼78\%) due to limited FLUXNET‐CH4 site coverage},
	language = {en},
	number = {5},
	urldate = {2024-11-15},
	journal = {AGU Advances},
	author = {McNicol, Gavin and Fluet‐Chouinard, Etienne and Ouyang, Zutao and Knox, Sara and Zhang, Zhen and Aalto, Tuula and Bansal, Sheel and Chang, Kuang‐Yu and Chen, Min and Delwiche, Kyle and Feron, Sarah and Goeckede, Mathias and Liu, Jinxun and Malhotra, Avni and Melton, Joe R. and Riley, William and Vargas, Rodrigo and Yuan, Kunxiaojia and Ying, Qing and Zhu, Qing and Alekseychik, Pavel and Aurela, Mika and Billesbach, David P. and Campbell, David I. and Chen, Jiquan and Chu, Housen and Desai, Ankur R. and Euskirchen, Eugenie and Goodrich, Jordan and Griffis, Timothy and Helbig, Manuel and Hirano, Takashi and Iwata, Hiroki and Jurasinski, Gerald and King, John and Koebsch, Franziska and Kolka, Randall and Krauss, Ken and Lohila, Annalea and Mammarella, Ivan and Nilson, Mats and Noormets, Asko and Oechel, Walter and Peichl, Matthias and Sachs, Torsten and Sakabe, Ayaka and Schulze, Christopher and Sonnentag, Oliver and Sullivan, Ryan C. and Tuittila, Eeva‐Stiina and Ueyama, Masahito and Vesala, Timo and Ward, Eric and Wille, Christian and Wong, Guan Xhuan and Zona, Donatella and Windham‐Myers, Lisamarie and Poulter, Benjamin and Jackson, Robert B.},
	month = oct,
	year = {2023},
	pages = {e2023AV000956},
}
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