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| Hauptseite > Publikationsdatenbank > Model-based Source Partitioning of Eddy Covariance Flux Measurements |
| Hauptseite > Publikationsdatenbank > Model-based Source Partitioning of Eddy Covariance Flux Measurements |
| Book/Dissertation / PhD Thesis | FZJ-2019-02963 |
2019
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
ISBN: 978-3-95806-401-0
Please use a persistent id in citations: http://hdl.handle.net/2128/22173 urn:nbn:de:0001-2019073108
Abstract: Terrestrial ecosystems constantly exchange momentum, energy, and mass (e.g., water vapor,CO$_{2}$) with the atmosphere above. This exchange is commonly measured with amicrometeorological technique, the eddy covariance (EC) method. Various components of the measured net fluxes, such as transpiration, evaporation, gross primary production, and soil respiration, cannot be depicted separately by the EC approach. Thus, so-called sourcepartitioning approaches have to be applied to CO$_{2}$ and water vapor EC data to gain a better understanding of the prevailing processes and their interrelations in terrestrial ecosystems. A large variety of partitioning procedures with diverse model approaches have been developed, including various driving variables, necessity of different input data and parameterizations. The most robust and commonly used source partitioning tools for CO$_{2}$ flux components, often primarily developed to fill gaps in EC measurements, are based on the notion that during night respiration fluxes prevail. They use non-linear regressed relationships of these nighttime observations and physical drivers (e.g., temperature in the approach after Reichstein et al.2005). Here, the challenge lies within extrapolating the nighttime relationship to daytime conditions, and analogous methods for water fluxes are lacking. In this thesis, next to the approach after Reichstein et al. (2005) various data-driven source partitioning approaches for H$_{2}$O and CO$_{2}$ fluxes were applied, compared, modified, and evaluated for multiple ecosystemsto get a better understanding of the methods’ functionality, dependencies, uncertainties, advantages, and shortcomings. We first describe the coupling and extension of the complex terrestrial ecosystem model AgroC. Further, we conducted a comprehensive model-data fusion study to clarify the CO$_{2}$ exchange in agroecosystems and estimate their annual carbon balance. For three test sites in Western Germany, AgroC was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated sufficiently in terms of hourly net ecosystem exchange (NEE) measured with the EC technique. Moreover, AgroC reproduced the flux dynamics very effectively after sudden changes in the grassland canopy due to mowing. In a second step, AgroC was optimized with the EC measurements to examine the effect of various objective functions, constraints, and data-transformations on the estimated carbon balance and to compare the results to the established gap-filling approach after Reichstein et al. (2005). It was found that modeled NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed extensively. Therefore, it is concluded that data-transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of EC measurements
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