Abstract
The Arctic is warming at twice the rate of the global mean. This warming could further stimulate methane (CH4) emissions from northern wetlands and enhance the greenhouse impact of this region. Arctic wetlands are extremely heterogeneous in terms of geochemistry, vegetation, microtopography, and hydrology, and therefore CH4 fluxes can differ dramatically within the metre scale. Eddy covariance (EC) is one of the most useful methods for estimating CH4 fluxes in remote areas over long periods of time. However, when the areas sampled by these EC towers (i.e. tower footprints) are by definition very heterogeneous, due to encompassing a variety of environmental conditions and vegetation types, modelling environmental controls of CH4 emissions becomes even more challenging, confounding efforts to reduce uncertainty in baseline CH4 emissions from these landscapes. In this study, we evaluated the effect of footprint variability on CH4 fluxes from two EC towers located in wetlands on the North Slope of Alaska. The local domain of each of these sites contains well developed polygonal tundra as well as a drained thermokarst lake basin. We found that the spatiotemporal variability of the footprint, has a significant influence on the observed CH4 fluxes, contributing between 3% and 33% of the variance, depending on site, time period, and modelling method. Multiple indices were used to define spatial heterogeneity, and their explanatory power varied depending on site and season. Overall, the normalised difference water index had the most consistent explanatory power on CH4 fluxes, though generally only when used in concert with at least one other spatial index. The spatial bias (defined here as the difference between the mean for the 0.36 km2 domain around the tower and the footprint-weighted mean) was between ∣51∣% and ∣18∣% depending on the index. This study highlights the need for footprint modelling to infer the representativeness of the carbon fluxes measured by EC towers in these highly heterogeneous tundra ecosystems, and the need to evaluate spatial variability when upscaling EC site-level data to a larger domain.
Highlights
Methane (CH4) emissions from Arctic permafrost soils are a major source of uncertainty in the region’s future global warming potential (Schuur and Abbott 2011, IPCC 2013)
The footprint-weighted spatial indices (I) were compared with their mean value in the 0.36 km2 domain around the base of the tower. This domain was chosen because we aimed to examine whether tower footprints were representative of area from which remotely sensed (RS) data would typically be extracted, as a general assumption for eddy covariance (EC) towers is that the majority of flux occurs within a radius of 100 times the height of the tower (Burba and Anderson 2010).one would normally expect the footprint values and the local domain to be very similar
The overarching result from this study is to highlight the necessity of high-resolution footprint modelling when interpreting EC data from heterogeneous environments
Summary
Methane (CH4) emissions from Arctic permafrost soils are a major source of uncertainty in the region’s future global warming potential (Schuur and Abbott 2011, IPCC 2013). The Arctic is warming at twice the rate of the global mean (Blunden and Arndt 2019) and its frozen permafrost soils store 1300–1370 Pg of organic carbon (Hugelius et al 2014), twice the current atmospheric stock (IPCC 2013). One method of measuring trace gas fluxes central to understanding the current and future carbon budget is the eddy covariance (EC) technique, as it can bridge the gap between smaller plot scale chamber measurements and larger regional scale remotely sensed (RS) data from aircraft and satellite (Baldocchi 2003, Chen et al 2009).
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