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@ARTICLE{Vernieuwe:19607,
      author       = {Vernieuwe, H. and De Baets, B. and Minet, J. and Pauwels,
                      V.R.N. and Lambot, S. and Vanclooster, M. and Verhoest,
                      N.E.C.},
      title        = {{I}ntegrating coarse-scale uncertain soil moisture data
                      into a fine-scale hydrological modelling scenario},
      journal      = {Hydrology and earth system sciences},
      volume       = {8},
      issn         = {1027-5606},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {PreJuSER-19607},
      pages        = {3101 - 3114},
      year         = {2011},
      note         = {This work has been performed in the framework of the
                      STEREO-project SR/00/100, financed by the Belgian Science
                      Policy and project G.0837.10 granted by the Research
                      Foundation Flanders. Computational resources and services
                      used in this work were provided by Ghent University.},
      abstract     = {In a hydrological modelling scenario, often the modeller is
                      confronted with external data, such as remotely-sensed soil
                      moisture observations, that become available to update the
                      model output. However, the scale triplet ( spacing, extent
                      and support) of these data is often inconsistent with that
                      of the model. Furthermore, the external data can be cursed
                      with epistemic uncertainty. Hence, a method is needed that
                      not only integrates the external data into the model, but
                      that also takes into account the difference in scale and the
                      uncertainty of the observations. In this paper, a synthetic
                      hydrological modelling scenario is set up in which a
                      high-resolution distributed hydrological model is run over
                      an agricultural field. At regular time steps, coarse-scale
                      field-averaged soil moisture data, described by means of
                      possibility distributions ( epistemic uncertainty), are
                      retrieved by synthetic aperture radar and assimilated into
                      the model. A method is presented that allows to integrate
                      the coarse-scale possibility distribution of soil moisture
                      content data with the fine-scale model-based soil moisture
                      data. The method is subdivided in two steps. The first step,
                      the disaggregation step, employs a scaling relationship
                      between field-averaged soil moisture content data and its
                      corresponding standard deviation. In the second step, the
                      soil moisture content values are updated using two
                      alternative methods.},
      keywords     = {J (WoSType)},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Geosciences, Multidisciplinary / Water Resources},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000296745600007},
      doi          = {10.5194/hessd-8-6031-2011},
      url          = {https://juser.fz-juelich.de/record/19607},
}