Upscaling Methane Flux From Plot Level to Eddy Covariance Tower Domains in Five Alaskan Tundra Ecosystems

• Published in: Frontiers in Environmental Science
• Main Authors: Wang, Yihui, Yuan, Fengming, Arndt, Kyle A., Liu, Jianzhao, He, Liyuan, Zuo, Yunjiang, Zona, Donatella, Lipson, David A., Oechel, Walter C., Ricciuto, Daniel M., Wullschleger, Stan D., Thornton, Peter E., Xu, Xiaofeng
• Language: unknown
• Published: 2022
• Subjects: 54 ENVIRONMENTAL SCIENCES; Arctic; Alaska North Slope; north slope; Tundra; Alaska
• Online Access: http://www.osti.gov/servlets/purl/1876265; https://www.osti.gov/biblio/1876265; https://doi.org/10.3389/fenvs.2022.939238

Spatial heterogeneity in methane (CH 4 ) flux requires a reliable upscaling approach to reach accurate regional CH 4 budgets in the Arctic tundra. In this study, we combined the CLM-Microbe model with three footprint algorithms to scale up CH 4 flux from a plot level to eddy covariance (EC) tower domains (200 m × 200 m) in the Alaska North Slope, for three sites in Utqiagvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo), for a period of 2013–2015. Three footprint algorithms were the homogenous footprint (HF) that assumes even contribution of all grid cells, the gradient footprint (GF) that assumes gradually declining contribution from center grid cells to edges, and the dynamic footprint (DF) that considers the impacts of wind and heterogeneity of land surface. Simulated annual CH 4 flux was highly consistent with the EC measurements at US-Beo and US-Bes. In contrast, flux was overestimated at US-Brw, US-Atq, and US-Ivo due to the higher simulated CH 4 flux in early growing seasons. The simulated monthly CH 4 flux was consistent with EC measurements but with different accuracies among footprint algorithms. At US-Bes in September 2013, RMSE and NNSE were 0.002 μmol m -2 s -1 and 0.782 using the DF algorithm, but 0.007 μmol m -2 s -1 and 0.758 using HF and 0.007 μmol m -2 s -1 and 0.765 using GF, respectively. DF algorithm performed better than the HF and GF algorithms in capturing the temporal variation in daily CH 4 flux each month, while the model accuracy was similar among the three algorithms due to flat landscapes. Temporal variations in CH 4 flux during 2013–2015 were predominately explained by air temperature (67–74%), followed by precipitation (22–36%). Spatial heterogeneities in vegetation fraction and elevation dominated the spatial variations in CH 4 flux for all five tower domains despite relatively weak differences in simulated CH 4 flux among three footprint algorithms. The CLM-Microbe model can simulate CH 4 flux at both plot and landscape scales at a high ...