Mechanistic Modeling of Microtopographic Impacts on CO 2 and CH 4 Fluxes in an Alaskan Tundra Ecosystem Using the CLM-Microbe Model

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Wang, Yihui, Yuan, Fengming, Yuan, Fenghui, Gu, Baohua, Hahn, Melanie S., Torn, Margaret S., Ricciuto, Daniel M., Kumar, Jitendra, He, Liyuan, Zona, Donatella, Lipson, David A., Wagner, Robert, Oechel, Walter C., Wullschleger, Stan D., Thornton, Peter E., Xu, Xiaofeng
• Language: unknown
• Published: 2021
• Subjects: 54 ENVIRONMENTAL SCIENCES; Arctic; Tundra; Alaska
• Online Access: http://www.osti.gov/servlets/purl/1615822; https://www.osti.gov/biblio/1615822; https://doi.org/10.1029/2019MS001771

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO 2 and CH 4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM-Microbe, to examine the microtopographic impacts on CO 2 and CH 4 fluxes across seven landscape types in Utqia&#289vik, Alaska: trough, low-centered polygon (LCP) center, LCP transition, LCP rim, high-centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM-Microbe model against static-chamber measured CO 2 and CH 4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low-elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH 4 emissions rates with greater seasonal variations than high-elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO 2 + H 2 ) is the most important factor determining CH 4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH 4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area-weighted approach before validation against EC-measured CH 4 fluxes. The model underestimated the EC-measured CH 4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH 4 flux. The strong microtopographic impacts on CO 2 and CH 4 fluxes call for a model-data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.