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@ARTICLE{Huisman:9706,
      author       = {Huisman, J. A. and Rings, J. and Vrugt, J.A. and Sorg, J.
                      and Vereecken, H.},
      title        = {{H}ydraulic properties of a model dike from coupled
                      {B}ayesian and multi-criteria hydrogeophysical inversion},
      journal      = {Journal of hydrology},
      volume       = {380},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {PreJuSER-9706},
      year         = {2010},
      note         = {We thank A. Scheuermann and A. Bieberstein at the IBF,
                      University of Karlsruhe and the BAW Karlsruhe for the
                      possibility to take measurements on the dike model. J.A.
                      Vrugt is supported by a J. Robert Oppenheimer Fellowship
                      from the Los Alamos National Laboratory postdoctoral
                      program. J.A. Huisman and J. Sorg are supported by Grant
                      HU1312/2-1 of the Deutsche Forschungsgemeinschaft.},
      abstract     = {Coupled hydrogeophysical inversion aims to improve the use
                      of geophysical data for hydrological model Parameterization.
                      Several numerical studies have illustrated the feasibility
                      and advantages of a coupled approach. However, there is
                      still a lack of studies that apply the coupled inversion
                      approach to actual field data. In this paper, we test the
                      feasibility of coupled hydrogeophysical inversion for
                      determining the hydraulic properties of a model dike using
                      measurements of electrical resistance tomography (ERT). Our
                      analysis uses a two-dimensional (2D) finite element
                      hydrological model (HYDRUS-2D) coupled to a 2.5D finite
                      element electrical resistivity code (CRMOD), and includes
                      explicit recognition of parameter uncertainty by using a
                      Bayesian and multiple criteria framework with the DREAM and
                      AMALGAM population based search algorithms. To benchmark our
                      inversion results, soil hydraulic properties determined from
                      ERT data are compared with those separately obtained from
                      detailed in situ soil water content measurements using Time
                      Domain Reflectometry (TDR). Our most important results are
                      as follows. (1) TDR and ERT data theoretically contain
                      sufficient information to resolve most of the soil hydraulic
                      properties, (2) the DREAM-derived posterior distributions of
                      the hydraulic parameters are quite similar when estimated
                      separately using TDR and ERT measurements for model
                      calibration, (3) among all parameters, the saturated
                      hydraulic conductivity of the dike material is best
                      constrained, (4) the saturation exponent of the
                      petrophysical model is well defined, and matches
                      independently measured values, (5) measured ERT data
                      sufficiently constrain model predictions of water table
                      dynamics within the model dike. This finding demonstrates an
                      innate ability of ERT data to provide accurate
                      hydrogeophysical parameterizations for flooding events,
                      which is of particular relevance to dike management, and (6)
                      the AMALGAM-derived Pareto front demonstrates trade-off in
                      the fitting of ERT and TDR measurements. Altogether, we
                      conclude that coupled hydrogeophysical inversion using a
                      Bayesian approach is especially powerful for hydrological
                      model calibration. The posterior probability density
                      functions of the model parameters and model output
                      predictions contain important information to determine if
                      geophysical measurements provide constraints on hydrological
                      predictions. (C) 2009 Elsevier B.V. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {ICG-4 / JARA-ENERGY},
      ddc          = {690},
      cid          = {I:(DE-Juel1)VDB793 / $I:(DE-82)080011_20140620$},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Engineering, Civil / Geosciences, Multidisciplinary / Water
                      Resources},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000274353000007},
      doi          = {10.1016/j.jhydrol.2009.10.023},
      url          = {https://juser.fz-juelich.de/record/9706},
}