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| Hauptseite > Publikationsdatenbank > Source partitioning of H2O and CO2 fluxes based on high-frequency eddy covariance data: a comparison between study sites > print |
| Hauptseite > Publikationsdatenbank > Source partitioning of H2O and CO2 fluxes based on high-frequency eddy covariance data: a comparison between study sites > print |
| 001 | 861629 | ||
| 005 | 20220930130208.0 | ||
| 024 | 7 | _ | |a 10.5194/bg-16-1111-2019 |2 doi |
| 024 | 7 | _ | |a 1726-4170 |2 ISSN |
| 024 | 7 | _ | |a 1726-4189 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2019-02075 |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Klosterhalfen, Anne |0 P:(DE-Juel1)159313 |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Source partitioning of H2O and CO2 fluxes based on high-frequency eddy covariance data: a comparison between study sites |
| 260 | _ | _ | |a Katlenburg-Lindau [u.a.] |c 2019 |b Copernicus |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1554299489_3488 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 520 | _ | _ | |a For an assessment of the roles of soil and vegetation in the climate system, a further understanding of the fluxcomponents of H2O and CO2 (e.g., transpiration, soil respiration) and their interaction with physical conditions andphysiological functioning of plants and ecosystems is necessary. To obtain magnitudes of these flux components, we appliedsource partitioning approaches after Scanlon and Kustas (2010; SK10) and after Thomas et al. (2008; TH08) tohigh-frequency eddy covariance measurements of 12 study sites covering different ecosystems (croplands, grasslands,and forests) in different climatic regions. Both partitioning methods are based on higher-order statistics of the H2O andCO2 fluctuations, but proceed differently to estimate transpiration, evaporation, net primary production, and soil respiration.We compared and evaluated the partitioning results obtained with SK10 and TH08, including slight modificationsof both approaches. Further, we analyzed the interrelations among the performance of the partitioning methods, turbulencecharacteristics, and site characteristics (such as plant cover type, canopy height, canopy density, and measurement height).We were able to identify characteristics of a data set that are prerequisites for adequate performance of the partitioningmethods.\\SK10 had the tendency to overestimate and TH08 to underestimate soil flux components. For both methods, the partitioningof CO2 fluxes was less robust than for H2O fluxes. Results derived with SK10 showed relatively large dependencieson estimated water use efficiency (WUE) at the leaf level, which is a required input. Measurements of outgoinglongwave radiation used for the estimation of foliage temperature (used in WUE) could slightly increase the qualityof the partitioning results. A modification of the TH08 approach, by applying a cluster analysis for the conditionalsampling of respiration–evaporation events, performed satisfactorily, but did not result in significant advantages comparedto the original method versions developed by Thomas et al. (2008). The performance of each partitioning approachwas dependent on meteorological conditions, plant development, canopy height, canopy density, and measurementheight. Foremost, the performance of SK10 correlated negatively with the ratio between measurement height and canopyheight. The performance of TH08 was more dependent on canopy height and leaf area index. In general, all site characteristicsthat increase dissimilarities between scalars appeared to enhance partitioning performance for SK10 and TH08. |
| 536 | _ | _ | |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) |0 G:(DE-HGF)POF3-255 |c POF3-255 |f POF III |x 0 |
| 536 | _ | _ | |a IDAS-GHG - Instrumental and Data-driven Approaches to Source-Partitioning of Greenhouse Gas Fluxes: Comparison, Combination, Advancement (BMBF-01LN1313A) |0 G:(DE-Juel1)BMBF-01LN1313A |c BMBF-01LN1313A |f Nachwuchsgruppen Globaler Wandel 4+1 |x 1 |
| 536 | _ | _ | |a TERENO - Terrestrial Environmental Observatories (TERENO-2008) |0 G:(DE-HGF)TERENO-2008 |c TERENO-2008 |f TERENO-2008 |x 2 |
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| 700 | 1 | _ | |a Drüe, Clemens |0 0000-0002-8240-9031 |b 3 |
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| 700 | 1 | _ | |a González-Dugo, María P. |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Heinemann, Günther |0 0000-0002-4831-9016 |b 6 |
| 700 | 1 | _ | |a Jacobs, Cor M. J. |0 0000-0003-4079-9067 |b 7 |
| 700 | 1 | _ | |a Mauder, Matthias |0 0000-0002-8789-163X |b 8 |
| 700 | 1 | _ | |a Moene, Arnold F. |0 0000-0003-3614-8544 |b 9 |
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| 700 | 1 | _ | |a Ramos Rodríguez, Mario |0 P:(DE-HGF)0 |b 13 |
| 700 | 1 | _ | |a Scanlon, Todd M. |0 0000-0001-8869-1675 |b 14 |
| 700 | 1 | _ | |a Schmidt, Marius |0 P:(DE-Juel1)144420 |b 15 |u fzj |
| 700 | 1 | _ | |a Steinbrecher, Rainer |0 0000-0002-5931-4210 |b 16 |
| 700 | 1 | _ | |a Thomas, Christoph K. |0 0000-0003-2277-6013 |b 17 |
| 700 | 1 | _ | |a Valler, Veronika |0 P:(DE-Juel1)165760 |b 18 |
| 700 | 1 | _ | |a Zeeman, Matthias J. |0 0000-0001-9186-2519 |b 19 |
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| 773 | _ | _ | |a 10.5194/bg-16-1111-2019 |g Vol. 16, no. 6, p. 1111 - 1132 |0 PERI:(DE-600)2158181-2 |n 6 |p 1111 - 1132 |t Biogeosciences |v 16 |y 2019 |x 1726-4189 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/861629/files/invoice_Helmholtz-PUC-2019-15.pdf |
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