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Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness
Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness
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.
- Norwegian University of Science and Technology Norway
- University of Helsinki Finland
- Finnish Meteorological Institute Finland
- Norwegian University of Science and Technology (NTNU) Norway
- Voeikov Main Geophysical Observatory Russian Federation
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AARI: Archive of Tiksi standard meteorological observations (1932-2016), Russian Federal Service for Hydrometeorology and Environmental Monitoring, St Petersburg, Russia, available at: http://www.aari.ru/resources/d0024/archive/description_ e.html, last access: 13 September 2018.
Amiro, B. D.: Footprint climatologies for evapotranspiration in a boreal catchment, Agr. Forest Meteorol., 90, 195-201, 1998.
Arriga, N., Rannik, Ü, Aubinet, M., Carrara, A., Vesala, T., and Papale, D.: Experimental validation of footprint models for eddy covariance CO2 flux measurements above grassland by means of natural and artificial tracers, Agr. Forest Meteorol., 242, 75-84, 2017. [OpenAIRE]
Asner, G. P., Scurlock, J. M. O., and Hicke, J. A.: Global synthesis of leaf area observations: implications for ecological and remote sensing studies, Global Ecol. Biogeogr., 12, 191-205, 2003.
Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy Covariance: A Practical Guide to Measurement and Data, Springer ScienceCBusiness Media B.V., Dordrecht, 2012.
Aurela, M., Lohila, A., Tuovinen, J.-P., Hatakka, J., Riutta, T., and Laurila, T.: Carbon dioxide exchange on a northern boreal fen, Boreal Environ. Res., 14, 699-710, 2009. [OpenAIRE]
Aurela, M., Laurila, T., Tuovinen, J.-P., Hatakka, J., Linkosalmi, M., Virtanen, T., Mikola, J., Asmi, E., Ivakhov, V., Kondratyev, V., Uttal, T., and Makshtas, A.: CH4 and CO2 exchange on permafrost tundra in Tiksi, Siberia, in: Proceedings of the 1st Pan-Eurasian Experiment (PEEX) Conference and the 5th PEEX Meeting, edited by: Kulmala, M., Zilitinkevich, S., Lappalainen, H. K., Kyrö, E.-M., and Kontkanen, J., Report Series in Aerosol Science, 163, 60-62, 2015.
Böhner, J. and Selige, T.: Spatial prediction of soil attributes using terrain analysis and climate regionalisation, in: SAGA - Analysis and Modelling Applications, edited by: Böhner, J., McCloy, K. R., and Strobl, J., Göttinger Geographische Abhandlungen, 115, University of Göttingen, Germany, 13-28, 2006.
Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang, Q.: Methane emissions from wetlands: biogeochemical, microbial, and modeling perspectives from local to global scales, Glob. Change Biol., 19, 1325-1346, 2013.
Budishchev, A., Mi, Y., van Huissteden, J., Belelli-Marchesini, L., Schaepman-Strub, G., Parmentier, F. J. W., Fratini, G., Gallagher, A., Maximov, T. C., and Dolman, A. J.: Evaluation of a plotscale methane emission model using eddy covariance observations and footprint modelling, Biogeosciences, 11, 4651-4664, https://doi.org/10.5194/bg-11-4651-2014, 2014. [OpenAIRE]
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Citations provided by BIP!Popularity provided by BIP!- Norwegian University of Science and Technology Norway
- University of Helsinki Finland
- Finnish Meteorological Institute Finland
- Norwegian University of Science and Technology (NTNU) Norway
- Voeikov Main Geophysical Observatory Russian Federation
- Department of Geography Norwegian University of Science and Technology (NTNU) Norway
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.