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@ARTICLE{Koch:820922,
      author       = {Koch, Julian and Cornelissen, Thomas and Fang, Zhufeng and
                      Bogena, Heye and Diekkrüger, Bernd and Kollet, Stefan and
                      Stisen, Simon},
      title        = {{I}nter-comparison of three distributed hydrological models
                      with respect to seasonal variability of soil moisture
                      patterns at a small forested catchment},
      journal      = {Journal of hydrology},
      volume       = {533},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2016-06186},
      pages        = {234 - 249},
      year         = {2016},
      abstract     = {The objective of this study is to inter-compare three
                      spatially distributed hydrological models (HydroGeoSphere,
                      MIKE SHE and ParFlow-CLM) by means of their ability to
                      simulate soil moisture patterns. This study pools the
                      catchment modeling efforts which have been undertaken at the
                      Wüstebach catchment; one of TERENO’s hydrological
                      observatories. The catchment is densely instrumented with a
                      wireless sensor network (SoilNET) which allows continuous
                      measurements of the spatio-temporal soil moisture dynamics.
                      This unique dataset is ideal to benchmark hydrological
                      models as it poses distinct challenges like seasonality and
                      spatial heterogeneity. Two scenarios of soil parametrization
                      assess the modeling implications of moving from homogeneous
                      to heterogeneous porosity. The three given models perform
                      well in terms of discharge and accumulated water balance
                      components. However, their ability to predict soil moisture
                      is found to be more diverging. Interpretations are ambiguous
                      and depend on what performance metric and what level of
                      spatial aggregation is chosen. In comparison to the other
                      models, ParFlow-CLM performs more accurate at predicting the
                      temporal dynamics and the heterogeneity aggregated to
                      catchment scale. Nevertheless, at local scale HydroGeoSphere
                      and MIKE SHE provide more detailed soil moisture
                      predictions. Overall, a clear increase in performance can be
                      attested to the scenario that includes heterogeneous
                      porosity. Next to soil parametrization, topography is among
                      the main drivers of soil moisture variability which was
                      found to have an overemphasized feedback in ParFlow-CLM
                      compared to the other models. This study stresses that
                      further efforts toward spatially distributed input data need
                      to emerge alongside a more suitable soil parametrization
                      that can account for the observed heterogeneity and
                      seasonality of soil moisture.},
      cin          = {IBG-3},
      ddc          = {690},
      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)16},
      UT           = {WOS:000370086200020},
      doi          = {10.1016/j.jhydrol.2015.12.002},
      url          = {https://juser.fz-juelich.de/record/820922},
}