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@ARTICLE{Baatz:838844,
      author       = {Baatz, D. and Kurtz, W. and Hendricks Franssen, H. J. and
                      Vereecken, H. and Kollet, S. J.},
      title        = {{C}atchment tomography - {A}n approach for spatial
                      parameter estimation},
      journal      = {Advances in water resources},
      volume       = {107},
      issn         = {0309-1708},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2017-07354},
      pages        = {147 - 159},
      year         = {2017},
      abstract     = {The use of distributed-physically based hydrological models
                      is often hampered by the lack of information on key
                      parameters and their spatial distribution and temporal
                      dynamics. Typically, the estimation of parameter values is
                      impeded by the lack of sufficient observations leading to
                      mathematically underdetermined estimation problems and thus
                      non-uniqueness. Catchment tomography (CT) presents a method
                      to estimate spatially distributed model parameters by
                      resolving the integrated signal of stream runoff in response
                      to precipitation. Basically CT exploits the information
                      content generated by a distributed precipitation signal both
                      in time and space. In a moving transmitter-receiver concept,
                      high resolution, radar based precipitation data are applied
                      with a distributed surface runoff model. Synthetic stream
                      water level observations, serving as receivers, are
                      assimilated with an Ensemble Kalman Filter. With a joint
                      state-parameter update the spatially distributed Manning's
                      roughness coefficient, n, is estimated using the coupled
                      Terrestrial Systems Modelling Platform and the Parallel Data
                      Assimilation Framework (TerrSysMP-PDAF). The sequential data
                      assimilation in combination with the distributed
                      precipitation continuously integrates new information into
                      the model, thus, increasingly constraining the parameter
                      space. With this large amount of data included for the
                      parameter estimation, CT reduces the problem of
                      underdetermined model parameters. The initially biased
                      Manning's coefficients spatially distributed in two and four
                      fixed parameter zones are estimated with errors of less than
                      $3\%$ and $17\%,$ respectively, with only 64 model
                      realizations. It is shown that the distributed precipitation
                      is of major importance for this approach.},
      cin          = {IBG-3},
      ddc          = {550},
      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:000410674200013},
      doi          = {10.1016/j.advwatres.2017.06.006},
      url          = {https://juser.fz-juelich.de/record/838844},
}