Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta

Methane fluxes on an active flood plain situated in the Siberian Lena River Delta were studied applying the eddy covariance method. During the growing season, the observed fluxes exhibited a great deal of temporal variability, which was largely the result of the pronounced spatial variability of soi...

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Published in:Agricultural and Forest Meteorology
Main Authors: Rößger , N., Wille, C., Veh, G., Boike, J., Kutzbach, L.
Format: Article in Journal/Newspaper
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/21.11116/0000-0003-E3A2-5
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spelling ftpubman:oai:pure.mpg.de:item_3039040 2023-08-20T04:07:51+02:00 Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta Rößger , N. Wille, C. Veh, G. Boike, J. Kutzbach, L. 2019-03 http://hdl.handle.net/21.11116/0000-0003-E3A2-5 eng eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.agrformet.2018.06.026 http://hdl.handle.net/21.11116/0000-0003-E3A2-5 Agricultural and Forest Meteorology info:eu-repo/semantics/article 2019 ftpubman https://doi.org/10.1016/j.agrformet.2018.06.026 2023-08-01T23:56:05Z Methane fluxes on an active flood plain situated in the Siberian Lena River Delta were studied applying the eddy covariance method. During the growing season, the observed fluxes exhibited a great deal of temporal variability, which was largely the result of the pronounced spatial variability of soil and vegetation characteristics within the footprint. Explaining this variability was based on three data-driven modelling approaches: the automatically operating algorithms stepwise regression as well as neural network, and a mechanistic model, which utilised exponential relationships between the methane flux and both flux drivers soil temperature and friction velocity. A substantial improvement in model performance was achieved by applying footprint information in the form of relative contributions of three vegetation classes to the flux signal. This aspect indicates that the vegetation served as an integrated proxy for flux drivers, whose characteristics permanently varied according to the shifting source area. The neural network performed best in explaining the variability of the observed methane fluxes. However, validating the models’ generalisability revealed that the mechanistic model provided the most predictive power suggesting that this model best captured the causality between the methane flux and its drivers. After integrating the gap-filled time series, all models yielded footprint budgets that were similar in magnitude. These budgets, however, lacked representativity due to the sensor location bias, i.e. their strong dependence on tower location, measurement height and wind field conditions. Thus, an unbiased budget of the total area of the flood plain was estimated utilising the mechanistic model. Initially, a downscaling procedure partitioned the observed flux with a seasonal mean of 0.012 μmol m -2 s -1 into three individual vegetation class fluxes accounting for shrubs (0.0004 μmol m -2 s -1 ), sedges (0.052 μmol m -2 s -1 ) and intermediate vegetation (0.018 μmol m -2 s -1 ). These decomposed fluxes ... Article in Journal/Newspaper lena river Tundra Max Planck Society: MPG.PuRe Agricultural and Forest Meteorology 266-267 243 255
institution Open Polar
collection Max Planck Society: MPG.PuRe
op_collection_id ftpubman
language English
description Methane fluxes on an active flood plain situated in the Siberian Lena River Delta were studied applying the eddy covariance method. During the growing season, the observed fluxes exhibited a great deal of temporal variability, which was largely the result of the pronounced spatial variability of soil and vegetation characteristics within the footprint. Explaining this variability was based on three data-driven modelling approaches: the automatically operating algorithms stepwise regression as well as neural network, and a mechanistic model, which utilised exponential relationships between the methane flux and both flux drivers soil temperature and friction velocity. A substantial improvement in model performance was achieved by applying footprint information in the form of relative contributions of three vegetation classes to the flux signal. This aspect indicates that the vegetation served as an integrated proxy for flux drivers, whose characteristics permanently varied according to the shifting source area. The neural network performed best in explaining the variability of the observed methane fluxes. However, validating the models’ generalisability revealed that the mechanistic model provided the most predictive power suggesting that this model best captured the causality between the methane flux and its drivers. After integrating the gap-filled time series, all models yielded footprint budgets that were similar in magnitude. These budgets, however, lacked representativity due to the sensor location bias, i.e. their strong dependence on tower location, measurement height and wind field conditions. Thus, an unbiased budget of the total area of the flood plain was estimated utilising the mechanistic model. Initially, a downscaling procedure partitioned the observed flux with a seasonal mean of 0.012 μmol m -2 s -1 into three individual vegetation class fluxes accounting for shrubs (0.0004 μmol m -2 s -1 ), sedges (0.052 μmol m -2 s -1 ) and intermediate vegetation (0.018 μmol m -2 s -1 ). These decomposed fluxes ...
format Article in Journal/Newspaper
author Rößger , N.
Wille, C.
Veh, G.
Boike, J.
Kutzbach, L.
spellingShingle Rößger , N.
Wille, C.
Veh, G.
Boike, J.
Kutzbach, L.
Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta
author_facet Rößger , N.
Wille, C.
Veh, G.
Boike, J.
Kutzbach, L.
author_sort Rößger , N.
title Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta
title_short Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta
title_full Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta
title_fullStr Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta
title_full_unstemmed Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta
title_sort scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the lena river delta
publishDate 2019
url http://hdl.handle.net/21.11116/0000-0003-E3A2-5
genre lena river
Tundra
genre_facet lena river
Tundra
op_source Agricultural and Forest Meteorology
op_relation info:eu-repo/semantics/altIdentifier/doi/10.1016/j.agrformet.2018.06.026
http://hdl.handle.net/21.11116/0000-0003-E3A2-5
op_doi https://doi.org/10.1016/j.agrformet.2018.06.026
container_title Agricultural and Forest Meteorology
container_volume 266-267
container_start_page 243
op_container_end_page 255
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