% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Huber:17040,
      author       = {Huber, E. and Hendricks Franssen, H.J. and Kaiser, H.P. and
                      Stauffer, F.},
      title        = {{T}he role of prior model calibration on predictions with
                      {E}nsemble {K}alman {F}ilter},
      journal      = {Ground water},
      volume       = {49},
      issn         = {0017-467X},
      address      = {Oxford [u.a.]},
      publisher    = {Wiley-Blackwell},
      reportid     = {PreJuSER-17040},
      pages        = {845 - 858},
      year         = {2011},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {This paper, based on a real world case study (Limmat
                      aquifer, Switzerland), compares inverse groundwater flow
                      models calibrated with specified numbers of monitoring head
                      locations. These models are updated in real time with the
                      ensemble Kalman filter (EnKF) and the prediction improvement
                      is assessed in relation to the amount of monitoring
                      locations used for calibration and updating. The prediction
                      errors of the models calibrated in transient state are
                      smaller if the amount of monitoring locations used for the
                      calibration is larger. For highly dynamic groundwater flow
                      systems a transient calibration is recommended as a model
                      calibrated in steady state can lead to worse results than a
                      noncalibrated model with a well-chosen uniform conductivity.
                      The model predictions can be improved further with the
                      assimilation of new measurement data from on-line sensors
                      with the EnKF. Within all the studied models the reduction
                      of 1-day hydraulic head prediction error (in terms of mean
                      absolute error [MAE]) with EnKF lies between $31\%$
                      (assimilation of head data from 5 locations) and $72\%$
                      (assimilation of head data from 85 locations). The largest
                      prediction improvements are expected for models that were
                      calibrated with only a limited amount of historical
                      information. It is worthwhile to update the model even with
                      few monitoring locations as it seems that the error
                      reduction with EnKF decreases exponentially with the amount
                      of monitoring locations used. These results prove the
                      feasibility of data assimilation with EnKF also for a real
                      world case and show that improved predictions of groundwater
                      levels can be obtained.},
      keywords     = {Environmental Monitoring / Groundwater / Models,
                      Theoretical / 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},
      pubmed       = {pmid:21210793},
      UT           = {WOS:000297070200011},
      doi          = {10.1111/j.1745-6584.2010.00784.x},
      url          = {https://juser.fz-juelich.de/record/17040},
}