Vegetation Type Dominates the Spatial Variability in CH4 Emissions Across Multiple Arctic Tundra Landscapes

Abstract

Methane (CH 4 ) emissions from Arctic tundra are an important feedback to global climate. Currently, modelling and predicting CH 4 fluxes at broader scales are limited by the challenge of upscaling plot-scale measurements in spatially heterogeneous landscapes, and by uncertainties regarding key controls of CH 4 emissions. In this study, CH 4 and CO 2 fluxes were measured together with a range of environmental variables and detailed vegetation analysis at four sites spanning 300 km latitude from Barrow to Ivotuk (Alaska). We used multiple regression modelling to identify drivers of CH 4 flux, and to examine relationships between gross primary productivity (GPP), dissolved organic carbon (DOC) and CH 4 fluxes. We found that a highly simplified vegetation classification consisting of just three vegetation types (wet sedge, tussock sedge and other) explained 54% of the variation in CH 4 fluxes across the entire transect, performing almost as well as a more complex model including water table, sedge height and soil moisture (explaining 58% of the variation in CH 4 fluxes). Substantial CH 4 emissions were recorded from tussock sedges in locations even when the water table was lower than 40 cm below the surface, demonstrating the importance of plant-mediated transport. We also found no relationship between instantaneous GPP and CH 4 fluxes, suggesting that models should be cautious in assuming a direct relationship between primary production and CH 4 emissions. Our findings demonstrate the importance of vegetation as an integrator of processes controlling CH 4 emissions in Arctic ecosystems, and provide a simplified framework for upscaling plot scale CH 4 flux measurements from Arctic ecosystems.

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

“…Furthermore, classifications based on landscape-level PFTs may result in higher classification errors at the tower footprint scale (~300-500 m) relative to classifications based on vegetation community type [28]. The vegetation community, chosen as the basis for our classification scheme, also better matches upscaling schemes used in local and regional carbon assessments, where land cover maps are used in conjunction with small area (e.g., 1 m chamber) flux measurements to obtain landscape level estimates of carbon exchange and greenhouse gas emissions [15]. Although the classifications resulting from this study do not directly reflect PFT types, our vegetation communities can be re-classified to PFT types for input in carbon flux models (in Section 3.2, a comparison of vegetation communities and PFTs is provided).…”

“…Although the distribution of Arctic tundra vegetation is relatively well established at coarser scales (e.g., 1:7,500,000) and documented in databases such as the Circumpolar Arctic Vegetation Map ((CAVM team, 2003; [23]) and others [24,25], knowledge of local tundra community distributions remains ambiguous at many locations. Understanding the fine-scale spatial distribution of tundra communities, combined with improved high resolution vegetation mapping, is needed to understand relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g.,~1 m chamber flux measurements; [15]) and those encompassing multiple vegetation communities (e.g.,~100 m eddy covariance measurements; [26]). …”

“…For example, an increasing growing season length can enhance carbon uptake through photosynthesis and thereby reduce atmospheric carbon dioxide (CO 2 ) concentrations [13]. Conversely, increasing temperatures may heighten methane (CH 4 ) and CO 2 release from thawing permafrost, enhancing plant-mediated transport of CH 4 [14,15] and contributing to rising levels of greenhouse gases in the atmosphere.…”

Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems

“…Furthermore, due to CH 4 global warming potential [1,6], 2 of 21 an increase in emissions could result in additional atmospheric warming [7,8]. Current and future arctic CH 4 emissions are affected by spatially heterogeneous vegetation communities, environmental and climatic parameters and microtopographic characteristics [9][10][11]. Different vegetation communities emit CH 4 at different rates [12], therefore, plant community distributions could be used to upscale CH 4 fluxes to the landscape scale and improve emission estimates in carbon cycle [13,14] and CH 4 budget models [15].…”

“…Eddy covariance (EC) and chambers are the most common methods to measure CH 4 fluxes in the field, and both give useful information [5,9,10]), but can also result in inaccurate emission predictions [29,30]. EC towers measure trace gas fluxes with a high temporal resolution across a footprint with radii of roughly 100-300 m for a tower 1-3 m tall [31].…”