2018
DOI: 10.5194/bg-2018-155
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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 ou… Show more

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Cited by 3 publications
(6 citation statements)
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References 49 publications
(85 reference statements)
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“…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.…”
Section: Discussionsupporting
confidence: 89%
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“…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.…”
Section: Discussionsupporting
confidence: 89%
“…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.…”
Section: Discussionmentioning
confidence: 90%
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“…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.…”
mentioning
confidence: 99%