Multi-scale Modeling of Arctic Methane Cycling Using the CLM-Microbe Model

• Main Author: Wang, Yihui
• Other Authors: Xu, Xiaofeng
• Format: Other/Unknown Material
• Language: English
• Published: eScholarship, University of California 2022
• Subjects: Ecology; Arctic; arctic methane; Tundra; Alaska
• Online Access: https://escholarship.org/uc/item/6f7932vq

Methane (CH4) emissions from Arctic polygonal tundra are spatially heterogeneous due to the complex soil hydrology. This spatial heterogeneity in CH4 emissions requires a reliable upscaling approach to reach accurate regional CH4 budgets in the Arctic tundra. Additionally, Arctic regions have been warming two to four times faster than the global average in recent decades. CH4 emission from the Arctic is increasing under climate warming. However, interactions among temperature, soil water table and vegetation complicate a full understanding of emission rates and their magnitude in a changing climate. In this dissertation, I applied the CLM‐Microbe model to examine microtopographic impacts on CH4 and CO2 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. 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). Substrate availability for methanogenesis was identified as the most important factor determining CH4 emission. Upscaled CH4 emissions at the eddy covariance (EC) domain using an area‐weighted approach were underestimated by 20% and 25% at daily and hourly time steps. Combined with three footprint algorithms, I upscaled CH4 fluxes from a plot level to EC domains (200 m × 200 m) for three sites in Utqiaġvik (US-Beo, US-Bes, and US-Brw), one in Atqasuk (US-Atq) and one in Ivotuk (US-Ivo). Three footprint algorithms are 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. DF performed better than HF and GF algorithms in capturing the temporal variation in daily CH4 flux in each ...