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...
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ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/2640567 2023-05-15T18:33:05+02:00 Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness Tuovinen, Juha Pekka Aurela, Mika Hatakka, Juha Räsänen, Tuomas Aleksi Virtanen, Tarmo Mikola, Juha Ivakhov, Viktor Kondratyev, Vladimir Laurila, Tuomas 2019 http://hdl.handle.net/11250/2640567 https://doi.org/10.5194/bg-16-255-2019 eng eng European Geosciences Union Biogeosciences. 2019, 16 (2), 255-274. urn:issn:1726-4170 http://hdl.handle.net/11250/2640567 https://doi.org/10.5194/bg-16-255-2019 cristin:1727134 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no CC-BY 255-274 16 Biogeosciences 2 Journal article Peer reviewed 2019 ftntnutrondheimi https://doi.org/10.5194/bg-16-255-2019 2020-02-12T23:32:27Z 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. publishedVersion © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Article in Journal/Newspaper Tiksi Tundra Siberia NTNU Open Archive (Norwegian University of Science and Technology) Tiksi ENVELOPE(128.867,128.867,71.633,71.633) Biogeosciences 16 2 255 274 |
institution |
Open Polar |
collection |
NTNU Open Archive (Norwegian University of Science and Technology) |
op_collection_id |
ftntnutrondheimi |
language |
English |
description |
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. publishedVersion © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. |
format |
Article in Journal/Newspaper |
author |
Tuovinen, Juha Pekka Aurela, Mika Hatakka, Juha Räsänen, Tuomas Aleksi Virtanen, Tarmo Mikola, Juha Ivakhov, Viktor Kondratyev, Vladimir Laurila, Tuomas |
spellingShingle |
Tuovinen, Juha Pekka Aurela, Mika Hatakka, Juha Räsänen, Tuomas Aleksi Virtanen, Tarmo Mikola, Juha Ivakhov, Viktor Kondratyev, Vladimir Laurila, Tuomas Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness |
author_facet |
Tuovinen, Juha Pekka Aurela, Mika Hatakka, Juha Räsänen, Tuomas Aleksi Virtanen, Tarmo Mikola, Juha Ivakhov, Viktor Kondratyev, Vladimir Laurila, Tuomas |
author_sort |
Tuovinen, Juha Pekka |
title |
Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness |
title_short |
Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness |
title_full |
Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness |
title_fullStr |
Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness |
title_full_unstemmed |
Interpreting eddy covariance data from heterogeneous Siberian tundra: Land-cover-specific methane fluxes and spatial representativeness |
title_sort |
interpreting eddy covariance data from heterogeneous siberian tundra: land-cover-specific methane fluxes and spatial representativeness |
publisher |
European Geosciences Union |
publishDate |
2019 |
url |
http://hdl.handle.net/11250/2640567 https://doi.org/10.5194/bg-16-255-2019 |
long_lat |
ENVELOPE(128.867,128.867,71.633,71.633) |
geographic |
Tiksi |
geographic_facet |
Tiksi |
genre |
Tiksi Tundra Siberia |
genre_facet |
Tiksi Tundra Siberia |
op_source |
255-274 16 Biogeosciences 2 |
op_relation |
Biogeosciences. 2019, 16 (2), 255-274. urn:issn:1726-4170 http://hdl.handle.net/11250/2640567 https://doi.org/10.5194/bg-16-255-2019 cristin:1727134 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/bg-16-255-2019 |
container_title |
Biogeosciences |
container_volume |
16 |
container_issue |
2 |
container_start_page |
255 |
op_container_end_page |
274 |
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1766217413027692544 |