Mechanistic Modeling of Microtopographic Impacts on CO2 and CH4 Fluxes in an Alaskan Tundra Ecosystem Using the CLM‐Microbe Model

• Main Authors: Yihui Wang, Fengming Yuan, Fenghui Yuan, Baohua Gu, Melanie S. Hahn, Margaret S. Torn, Daniel M. Ricciuto, Jitendra Kumar, Liyuan He, Donatella Zona, David A. Lipson, Robert Wagner, Walter C. Oechel, Stan D. Wullschleger, Peter E. Thornton, Xiaofeng Xu
• Format: Article
• Language: English
• Published: American Geophysical Union (AGU) 2019-12-01
• Series: Journal of Advances in Modeling Earth Systems
• Subjects: Arctic tundra; CH4 flux; microtopographic; sensitivity analysis; net carbon exchange
• Online Access: https://doi.org/10.1029/2019MS001771

Abstract Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM‐Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, 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 CO2 and CH4 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 CH4 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, CO2 + H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area‐weighted approach before validation against EC‐measured CH4 fluxes. The model underestimated the EC‐measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model‐data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.