Upscaling Wetland Methane Emissions From the FLUXNETCH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison
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AbstractWetlands are responsible for 20%31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Datadriven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottomup estimates of wetland CH4 emissions. Here, we develop a sixpredictor random forest upscaling model (UpCH4), trained on 119 siteyears of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNETCH4 Community Product. Network patterns in sitelevel annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (NashSutcliffe Efficiency 0.520.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 14643 TgCH4y1 for 20012018 which agrees closely with current bottomup land surface models (102181 TgCH4y1) and overlaps with topdown atmospheric inversion models (155200 TgCH4y1). 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 extratropical 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 humidtoarid 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).
