Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness

Abstract

Abstract. The non-uniform spatial integration, an inherent feature of the eddy covariance (EC) method, creates a challenge for flux data interpretation in a heterogeneous environment, where the contribution of different land cover types varies with flow conditions, potentially resulting in biased estimates in comparison to the areally averaged fluxes and land cover attributes. We modelled flux footprints and characterized the spatial scale of our EC measurements in Tiksi, a tundra site in northern Siberia. We used leaf area index (LAI) and land cover class (LCC) data, derived from very-high-spatial-resolution satellite imagery and field surveys, and quantified the sensor location bias. We found that methane (CH4) fluxes varied strongly with wind direction (−0.09 to 0.59 µgCH4m-2s-1 on average) during summer 2014, reflecting the distribution of different LCCs. Other environmental factors had only a minor effect on short-term flux variations but influenced the seasonal trend. Using footprint weights of grouped LCCs as explanatory variables for the measured CH4 flux, we developed a multiple regression model to estimate LCC group-specific fluxes. This model showed that wet fen and graminoid tundra patches in locations with topography-enhanced wetness acted as strong sources (1.0 µgCH4m-2s-1 during the peak emission period), while mineral soils were significant sinks (−0.13 µgCH4m-2s-1). To assess the representativeness of measurements, we upscaled the LCC group-specific fluxes to different spatial scales. Despite the landscape heterogeneity and rather poor representativeness of EC data with respect to the areally averaged LAI and coverage of some LCCs, the mean flux was close to the CH4 balance upscaled to an area of 6.3 km2, with a location bias of 14 %. We recommend that EC site descriptions in a heterogeneous environment should be complemented with footprint-weighted high-resolution data on vegetation and other site characteristics.

FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and Future Directions

“…Furthermore, there is evidence that traditionally unmeasured surfaces (i.e., tree stems) are important sources of CH 4 to the atmosphere and could explain spatial heterogeneity within ecosystems (Barba et al 2019). Accurately representing spatial heterogeneity and the relative fraction of uplands and wetlands is imperative for interpreting and predicting CH 4 emissions within many ecosystems, and for upscaling flux measurements regionally and globally as wetlands are hot spots for carbon cycling (Treat et al 2018a;Tuovinen et al 2019;Rößger et al 2019). Flux footprint analysis characterizing the fractional coverage of the dominant surface types, particularly the fraction of open water and aerenchymatous plants, is important for interpreting EC CH 4 flux measurements and quantifying annual CH 4 budgets at spatially heterogeneous sites (Franz et al 2016;Helbig et al 2017a;Jammet et al 2017) (Fig.…”

Response to Referee 1

“…Data availability. The micrometeorological data used in this study can be accessed via the Zenodo data repository (Tuovinen et al, 2018).…”

Quantifying the impact of emission outbursts and non-stationary flow on eddy-covariance CH<sub>4</sub> flux measurements using wavelet techniques

“…The eddy-covariance (EC) technique, a well-established method for the direct quantification of turbulent surfaceatmosphere exchange processes (Aubinet et al, 2012), can provide valuable information on current CH 4 flux rates between various types of ecosystems and the atmosphere (e.g., Taylor et al, 2018;Rößger et al, 2019;Tuovinen et al, 2019), including insights into processes and controls (e.g., Pirk et al, 2016;Kittler et al, 2017b;Neumann et al, 2019) that can be used to improve future projections. However, the data quality of EC measurements depends strongly on the adherence to several theoretical assumptions, e.g., steady-state conditions and horizontal homogeneity , which frequently limits data availability.…”

“…Spatial heterogeneity in the emission patterns of methane surrounding the flux tower (e.g., Rey-Sanchez et al, 2019) may also lead to pronounced variability in the observed CH 4 flux time series (Tuovinen et al, 2019). Particularly for wetland ecosystems, ecosystem characteristics such as inundation level or vegetation composition may vary at the finest spatial scales (Muster et al, 2012;McEwing et al, 2015), creating microsite variability with strong gradients in methane emissions.…”

“…In contrast, for CH 4 fluxes, no consensus on a gap-filling method has yet emerged within the EC community. Several studies succeeded in establishing data-driven links between CH 4 fluxes and environmental conditions such as peat or soil temperature, friction velocity, or the water table (e.g., Wille et al, 2008;Zona et al, 2009;Jackowicz-Korczynski et al, 2010) using both linear and nonlinear functional relationships. Other approaches include gap interpolation (e.g., Rinne et al, 2007;Tagesson et al, 2012), process-based modeling (Forbrich et al, 2011) or artificial neural networks (e.g., Dengel et al, 2013).…”