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

• Published in: Biogeosciences
• Main Authors: J.-P. Tuovinen, M. Aurela, J. Hatakka, A. Räsänen, T. Virtanen, J. Mikola, V. Ivakhov, V. Kondratyev, T. Laurila
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: Ecology; QH540-549.5; Life; QH501-531; Geology; QE1-996.5; Tiksi; Tundra; Siberia
• Online Access: https://doi.org/10.5194/bg-16-255-2019; https://doaj.org/article/aaa3406700f5473f820c912c33c53f48

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 ( CH 4 ) fluxes varied strongly with wind direction ( −0.09 to 0.59 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi mathvariant="normal">µ</mi><mi mathvariant="normal">g</mi><mspace width="0.125em" linebreak="nobreak"/><msub><mi mathvariant="normal">CH</mi><mn mathvariant="normal">4</mn></msub><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">s</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math> <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="74pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="0d2588859602ea064f5c650e369825fc"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-16-255-2019-ie00001.svg" width="74pt" height="16pt" src="bg-16-255-2019-ie00001.png"/></svg:svg> on average) during summer 2014, reflecting the distribution of different LCCs. Other environmental factors had ...