% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Klosterhalfen:836078,
      author       = {Klosterhalfen, Anne and Herbst, Michael and Weihermüller,
                      Lutz and Graf, Alexander and Schmidt, Marius and Stadler,
                      Anja and Schneider, Karl and Subke, Jens-Arne and Huisman,
                      Johan Alexander and Vereecken, Harry},
      title        = {{M}ulti-site {C}alibration and {V}alidation of a {N}et
                      {E}cosystem {C}arbon {E}xchange {M}odel for {C}roplands},
      journal      = {Ecological modelling},
      volume       = {363},
      issn         = {0304-3800},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2017-05202},
      pages        = {137-156},
      year         = {2017},
      abstract     = {Croplands play an important role in the carbon budget of
                      many regions. However, the estimation of their carbon
                      balance remains difficult due to diversity and complexity of
                      the processes involved. We report the coupling of a
                      one-dimensional soil water, heat, and CO2 flux model
                      (SOILCO2), a pool concept of soil carbon turnover (RothC),
                      and a crop growth module (SUCROS) to predict the net
                      ecosystem exchange (NEE) of carbon. The coupled model,
                      further referred to as AgroC, was extended with routines for
                      managed grassland as well as for root exudation and root
                      decay. In a first step, the coupled model was applied to two
                      winter wheat sites and one upland grassland site in Germany.
                      The model was calibrated based on soil water content, soil
                      temperature, biometric, and soil respiration measurements
                      for each site, and validated in terms of hourly NEE measured
                      with the eddy covariance technique. The overall model
                      performance of AgroC was sufficient with a model efficiency
                      above 0.78 and a correlation coefficient above 0.91 for NEE.
                      In a second step, AgroC was optimized with eddy covariance
                      NEE measurements to examine the effect of different
                      objective functions, constraints, and data-transformations
                      on estimated NEE. It was found that NEE showed a distinct
                      sensitivity to the choice of objective function and the
                      inclusion of soil respiration data in the optimization
                      process. In particular, both positive and negative day and
                      nighttime fluxes were found to be sensitive to the selected
                      optimization strategy. Additional consideration of soil
                      respiration measurements improved the simulation of small
                      positive fluxes remarkably. Even though the model
                      performance of the selected optimization strategies did not
                      diverge substantially, the resulting cumulative NEE over
                      simulation time period differed substantially. 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 eddy covariance measurements.},
      cin          = {IBG-3},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / IDAS-GHG - Instrumental and Data-driven
                      Approaches to Source-Partitioning of Greenhouse Gas Fluxes:
                      Comparison, Combination, Advancement (BMBF-01LN1313A) /
                      MACSUR - Modelling European Agriculture with Climate Change
                      for Food Security (2812-ERA-158)},
      pid          = {G:(DE-HGF)POF3-255 / G:(DE-Juel1)BMBF-01LN1313A /
                      G:(DE-BLE)2812-ERA-158},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000413609300012},
      doi          = {10.1016/j.ecolmodel.2017.07.028},
      url          = {https://juser.fz-juelich.de/record/836078},
}