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@PHDTHESIS{Kurtz:150738,
      author       = {Kurtz, Wolfgang},
      title        = {{I}mproved characterization of river-aquifer interactions
                      through data assimilation with the {E}nsemble {K}alman
                      {F}ilter},
      volume       = {199},
      school       = {RWTH Aachen},
      type         = {Dr.},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2014-00784},
      isbn         = {978-3-89336-925-6},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {XXV, 125 S.},
      year         = {2013},
      note         = {RWTH Aachen, Diss., 2013},
      abstract     = {Exchange processes between rivers and groundwater are an
                      important driver for the hydrological,chemical and
                      ecological environment around streams and the cycling of
                      waterat the catchment scale. Management decisions for such
                      systems are very often derivedon the basis of model
                      predictions and it is therefore essential to properly
                      estimate therelevant model parameters that govern the
                      interaction between river and aquifer. Variouseld studies
                      indicate that hydraulic parameters in and around streams are
                      associatedwith a considerable uncertainty regarding their
                      temporal and spatial distribution. Theseuncertainties have
                      to be regarded in the estimation of hydraulic parameters and
                      dierentstochastic inversion methods are available for that
                      task. Among these methods, theEnsemble Kalman Filter (EnKF)
                      has been proven to work well for the characterizationof
                      subsurface parameters where its advantage over other
                      stochastic inversion techniquesis the calculation of a full
                      posterior probability density function without
                      linearizationaround an optimum, its computational eciency
                      and its ability to be used for real-timepredictions.In this
                      work, EnKF was applied to a 3D groundwater model of a well
                      eld within theLimmat aquifer in Zurich (Switzerland) which
                      is strongly inuenced by river-aquifer interactions.The
                      specic aim was to investigate dierent aspects of the
                      spatio-temporalcharacterization of river bed properties with
                      EnKF and to explore the worth of dierentconditioning data
                      for this site. In a rst study, the model was used in
                      syntheticexperiments where reference runs with temporally
                      varying river bed hydraulic conductivitieswere generated.
                      Then it was tested, to what extend state-parameter
                      updateswith EnKF are able to detect these changes in river
                      bed properties based on a limitedset of piezometric head
                      measurements from the reference simulations. In a second
                      study,it was investigated how the spatial representation of
                      heterogeneity inuences the updatingbehavior of EnKF. In this
                      case, synthetic references with spatially heterogeneouselds
                      of river bed permeabilities were generated and piezometric
                      head data from thesereferences were used to update four
                      dierent parameter ensembles that varied in thespatial
                      representation of heterogeneity (i.e., fully heterogeneous
                      versus zonated leakageparameters). ...},
      keywords     = {Dissertation (GND)},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {246 - Modelling and Monitoring Terrestrial Systems: Methods
                      and Technologies (POF2-246)},
      pid          = {G:(DE-HGF)POF2-246},
      typ          = {PUB:(DE-HGF)11},
      url          = {https://juser.fz-juelich.de/record/150738},
}