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@PHDTHESIS{Klosterhalfen:862712,
      author       = {Klosterhalfen, Anne},
      title        = {{M}odel-based {S}ource {P}artitioning of {E}ddy
                      {C}ovariance {F}lux {M}easurements},
      volume       = {461},
      school       = {Universität Bonn},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2019-02963},
      isbn         = {978-3-95806-401-0},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {XVI, 132},
      year         = {2019},
      note         = {Dissertation, Universität Bonn, 2019},
      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},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / TERENO - Terrestrial Environmental
                      Observatories (TERENO-2008) / IDAS-GHG - Instrumental and
                      Data-driven Approaches to Source-Partitioning of Greenhouse
                      Gas Fluxes: Comparison, Combination, Advancement
                      (BMBF-01LN1313A)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-HGF)TERENO-2008 /
                      G:(DE-Juel1)BMBF-01LN1313A},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2019073108},
      url          = {https://juser.fz-juelich.de/record/862712},
}