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.
Highlights
Biosphere–atmosphere exchange of greenhouse gases (GHGs) is commonly measured using the micrometeorological eddy covariance (EC) method (Aubinet et al, 2012)
The variation of the footprint-weighted land cover class (LCC) contributions, calculated with Eq (5), as a function of wind direction demonstrates how the heterogeneity inherent in tundra landscape manifests itself in the EC measurement data (Fig. 3; see Fig. S3)
Turbulent mixing played a substantial role in the magnitude of relative LCC contributions, as the weighting of longer distances increased with increasing stability
Summary
Biosphere–atmosphere exchange of greenhouse gases (GHGs) is commonly measured using the micrometeorological eddy covariance (EC) method (Aubinet et al, 2012). This tower-based, non-intrusive technique provides spatially integrated flux data at the ecosystem scale with a typical integration domain of a few hectares. Heterogeneous landscapes consisting of a mosaic of differing vegetation and land cover patches, may entail issues on the interpretation of the spatial representativeness of measurements This stems from the fact that the EC integration process is equivalent to non-uniform weighting of the upwind surface elements that influence the measured flux, potentially resulting in an unequal and temporally varying contribution from different land cover types (Schmid, 2002). As the flux footprint is affected by other properties of the atmospheric flow, e.g. hydrostatic stability, directional averaging does not guarantee an unbiased flux estimate either
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