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@PHDTHESIS{Post:826716,
      author       = {Post, Hanna},
      title        = {{O}n model and measurement uncertainty in predicting land
                      surface carbon fluxes},
      volume       = {347},
      school       = {RWTH Aachen},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2017-00934},
      isbn         = {978-3-95806-190-3},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {xviii, 135 S.},
      year         = {2016},
      note         = {RWTH Aachen, Diss., 2016},
      abstract     = {The Net Ecosystem Exchange (NEE) of CO$_{2}$ between the
                      land surface and the atmosphere refers to the difference of
                      photosynthetic CO$_{2}$ uptake and CO$_{2}$ release via
                      ecosystem respiration. NEE is an important indicator for the
                      net carbon source or sink function of an ecosystem and a
                      crucial variable for understanding and predicting feedback
                      mechanisms between climate and ecosystem change. NEE is
                      typically measured by micrometeorological methods like eddy
                      covariance (EC). At continental or global scales, land
                      surface models (LSMs) such as the Community Land Model (CLM)
                      are commonly used to predict NEE and other fluxes by
                      simulating the coupled carbon, nitrogen, water and energy
                      cycle of the land surface. In order to support future
                      decision making in climate politics and environmental
                      planning, it is important to improve LSM carbon flux
                      predictions at regional scales. A central goal of this PhD
                      work was therefore to combine measured EC data and CLM to
                      estimate NEE for the Rur catchment area. For the last
                      decade, model-data fusion approaches like parameter
                      estimation have increasingly been applied to reduce the
                      uncertainty of carbon flux estimates, because both EC
                      measurements and LSM predictions are uncertain. In order to
                      use EC data in meaningful model-data fusion or LSM
                      evaluation approaches, an estimate of the measurement
                      uncertainty is required. Thus, in the first part of the
                      thesis, the NEE measurement uncertainty was studied for one
                      grassland site in Germany located in the Rur catchment. At
                      present, many uncertainty estimation approaches exist, but
                      none are generally accepted and applied. The classical
                      two-tower approach, which is based on the standard
                      deviations of the fluxes measured simultaneously at two
                      nearby EC towers, is one of the most well-known approaches.
                      It provides linear regression functions between the flux
                      magnitude and the random error, which are commonly adopted
                      by scientists for a fast estimation of the random error. In
                      previous studies, the (classical) two-tower approach has
                      yielded robust uncertainty estimates, but care must be taken
                      to meet the often competing requirements of statistical
                      independence (non-overlapping footprints) and ecosystem
                      homogeneity when choosing an appropriate tower distance.
                      Thus, an extension of the classical two-tower approach is
                      proposed here that corrects systematic differences of the
                      NEE fluxes measured synchronous at the two EC tower
                      stations. The role of the tower distance was investigated
                      with help of a roving station separated between 8 m and 34
                      km from a permanent EC grassland station. For evaluation,
                      uncertainty estimates obtained from a different, raw-data
                      based method were used as reference. The herein introduced
                      correction for systematic flux differences applied to
                      weather-filtered data substantially reduced the
                      overestimation of the two-tower based NEE measurement
                      uncertainty for all distances (except 8 m) by 79\% (34 km
                      distance) to 100\% (95 m distance). Results indicated that
                      the sensitivity of the two-tower approach to the tower
                      distance was reduced, which enhances the applicability of
                      the extended two-tower approach. [...]},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      url          = {https://juser.fz-juelich.de/record/826716},
}