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

Abstract

<p><strong>Abstract.</strong> The non-uniform spatial integration inherent in the eddy covariance (EC) method provides an additional challenge for data interpretation when fluxes are measured in a heterogeneous environment, as the contribution of different surface types varies with flow conditions, potentially resulting in a bias as compared to the true areally averaged fluxes and surface attributes. We modelled flux footprints and characterized the spatial scale of our EC measurements at Tiksi, a tundra site in northern Siberia, including a comparison of different source area definitions. 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 (CH<sub>4</sub>) fluxes varied strongly with wind direction (from −0.09 to 0.59 μg m<sup>−2</sup> s<sup>−1</sup> on average), reflecting the distribution of different LCCs. Using footprint weights of grouped LCCs as explanatory variables for the measured CH<sub>4</sub> flux, we then developed a multiple regression model to estimate LCC-specific fluxes. This model showed that wet fen and graminoid tundra patches in locations with a high topography-based wetness index acted as strong CH4 sources (0.95 μg m<sup>−2</sup> s<sup>−1</sup>), while mineral soils were significant sinks (−0.13 μg m<sup>−2</sup> s<sup>−1</sup>). Finally, to assess the representativeness of CH<sub>4</sub> flux measurements, we upscaled the LCC-specific fluxes to different spatial scales. This assessment showed that, despite the surface heterogeneity and rather poor representativeness of EC data with respect to the areally averaged LAI and coverage of some LCCs, the mean CH<sub>4</sub> flux measured during summer 2014 was close to the corresponding balance upscaled to an area of 6.3 km<sup>2</sup>, 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 relevant site characteristics.</p>

Understanding spatial variability of methane fluxes in Arctic wetlands through footprint modelling

“…The results of this study support the importance of footprint modelling in heterogeneous environments, as found in earlier studies (Budishchev et al 2014, Tuovinen et al 2018. In previous work, Wang et al (2016) used a threshold for δ of 10%, i.e.…”

“…In another recent study, Treat et al (2018) found a 20%-65% underestimation of CH 4 emissions flux from a Siberian wetland site unless a high resolution wetland classification was used. While Tuovinen et al (2018), observed a somewhat contrasting result, in a Siberian shrub tundra site, where despite seeing a significant spatial bias (14%) and formally showing a 13% overestimation of methane flux for a 35.8 km 2 area, the results were not Figure 3. Variability in the spatial metrics sampled by the EC towers, due to temporal variation in the footprint data from 2013 to 2015, are shown by the grey bars.…”

“…A flux tower footprint defines the area being sampled by the EC tower Thurtell 1990, Horst andWeil 1994). Footprint modelling has been used to enable the interpretation of EC data in variable landscapes (Schmid and Lloyd 1999, Göckede et al 2006, Parmentier et al 2011, Tuovinen et al 2018, by assigning relative flux contribution to specific areas based on tower height, wind speed and direction, and turbulence (Leclerc and Thurtell 1990, Schuepp et al 1990, Burba and Anderson 2010. There are a few different types of footprint models including one-and two-dimensional analytic models, Lagrangian, large eddy simulations, and closure models (Vesala et al 2008).…”

“…Importantly the sites with the highest bias, ranging from −14% to 9%, were classifies as ombrotrophic bog (Chen et al 2011), a vegetation type similar to sites evaluated in this study. More recently, in Siberian tundra Tuovinen et al (2018) used footprint modelling on a half-hourly time scale to parse CH 4 sources and sinks by vegetation type, they also looked at the sites sensor location bias and found a 14% when looking at leaf area index regarding the wider 6.3 km 2 around the base of the tower.…”

Assessing the dynamics of vegetation productivity in circumpolar regions with different satellite indicators of greenness and photosynthesis

“…Continuous illumination during the polar day challenges temperature-respiration relationships obtained from night-time data (Runkle et al, 2013). Further, the heterogeneity of the landscape poses limits to the spatial representativeness of the relationships between the carbon fluxes and meteorological and soil conditions that have been identified in situ (Pirk et al, 2017;Tuovinen et al, 2018). Therefore, in spatial up-scaling exercises (Ueyama et al, 2013a;Marushchak et al, 2013;Huemmrich et al, 2013;Tramontana et al, 2016), strong extrapolations are necessary.…”