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| 001 | 808763 | ||
| 005 | 20210129222854.0 | ||
| 037 | _ | _ | |a FZJ-2016-02381 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Klosterhalfen, Anne |0 P:(DE-Juel1)159313 |b 0 |u fzj |
| 111 | 2 | _ | |a 32nd Conference on Agricultural and Forest Meteorology, American Meteorology Society |g 32AF, AMS |c Salt Lake City |d 2016-06-20 - 2016-06-24 |w USA |
| 245 | _ | _ | |a Source Partitioning Based on High Frequency Eddy Covariance Data |
| 260 | _ | _ | |c 2016 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Source partitioning of eddy covariance measurements is routinely used for a better understanding of the exchange of greenhouse gases, especially between terrestrial ecosystems and the atmosphere. Quantifications of CO2 and H2O fluxes, their dynamics, variabilities and feedbacks with environmental drivers give a better insight of the biosphere’s sensitivity towards global change.The BMBF-funded project “Instrumental and Data-driven Approaches to Source-Partitioning of Greenhouse Gas Fluxes: Comparison, Combination, Advancement” (IDAS-GHG) aims at comparing and improving existing methods for partitioning of CO2 and H2O fluxes into their respective raw components. Data-driven approaches use existing (raw or processed) data of typical eddy covariance stations. Instrumental approaches of source partitioning require additional measurements at different parts of ecosystems and different methods, e.g. soil-flux chamber measurements, profile measurements or tracer measurements (isotopes).This present study is part of the data-driven approaches, using some results from additional profile measurements to gain further insights. SCANLON and SAHU (2008), and SCANLON and KUSTAS (2010) proposed an interesting method to estimate the contributions of photosynthesis, soil respiration (autotrophic and heterotrophic sources), transpiration and evaporation using measured high-frequency time series of CO2 and H2O fluxes - no extra instrumentation necessary. This method (SK10 in the following) is based on the dissimilarities of sources and sinks of CO2 and water vapor among the sub-canopy, canopy and atmosphere, which lead to unique “signals” in the eddy covariance measurements for air transported from differing locations. Thus, the flux-variance similarity theory is separately applied to the stomatal and non-stomatal components of the regarded fluxes.In this study we apply the SK10 analysis to observations of several agroecosystems, and compare the results to outcomes of other well-known source partitioning methods as well as to chamber and profile measurements. For example, state variables measured in the canopy air are used to test the profile assumption required by SK10 analysis.Scanlon, T.M., Kustas, W.P., 2010. Partitioning carbon dioxide and water vapor fluxes using correlation analysis. Agricultural and Forest Meteorology 150 (1), 89-99.Scanlon, T.M., Sahu, P., 2008. On the correlation structure of water vapor and carbon dioxide in the atmospheric surface layer: A basis for flux partitioning. Water Resources Research 44 (10), W10418, 15 pp. |
| 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 |
| 700 | 1 | _ | |a Graf, Alexander |0 P:(DE-Juel1)129461 |b 1 |u fzj |
| 700 | 1 | _ | |a Schmidt, Marius |0 P:(DE-Juel1)144420 |b 2 |u fzj |
| 700 | 1 | _ | |a Ney, Patrizia |0 P:(DE-Juel1)166467 |b 3 |u fzj |
| 700 | 1 | _ | |a Vereecken, Harry |0 P:(DE-Juel1)129549 |b 4 |u fzj |
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| 914 | 1 | _ | |y 2016 |
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