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@ARTICLE{Li:21350,
      author       = {Li, L. and Zhou, H.Y. and Gomez-Hernandez, J.J. and
                      Hendricks-Franssen, H.J.},
      title        = {{J}ointly mapping hydraulic conductivity and porosity by
                      assimilating concentration data via ensemble {K}alman
                      filter},
      journal      = {Journal of hydrology},
      volume       = {428-429},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {PreJuSER-21350},
      pages        = {152 - 169},
      year         = {2012},
      note         = {The authors gratefully acknowledge the financial support by
                      ENRESA (Project 0079000029) and the Spanish Ministry of
                      Science and Innovation (Project CGL2011-23295). Extra travel
                      Grants awarded to the first and second author by the
                      Ministry of Education (Spain) are also acknowledged. Dr.
                      Jichun Wu and an anonymous reviewer are grateful
                      acknowledged for their comments which helped improving the
                      final version of the manuscript.},
      abstract     = {Real-time data from on-line sensors offer the possibility
                      to update environmental simulation models in real-time.
                      Information from on-line sensors concerning contaminant
                      concentrations in groundwater allow for the real-time
                      characterization and control of a contaminant plume. In this
                      paper it is proposed to use the CPU-efficient Ensemble
                      Kalman Filter (EnKF) method, a data assimilation algorithm,
                      for jointly updating the flow and transport parameters
                      (hydraulic conductivity and porosity) and state variables
                      (piezometric head and concentration) of a groundwater flow
                      and contaminant transport problem. A synthetic experiment is
                      used to demonstrate the capability of the EnKF to estimate
                      hydraulic conductivity and porosity by assimilating dynamic
                      head and multiple concentration data in a transient flow and
                      transport model. In this work the worth of hydraulic
                      conductivity, porosity, piezometric head, and concentration
                      data is analyzed in the context of aquifer characterization
                      and prediction uncertainty reduction. The results indicate
                      that the characterization of the hydraulic conductivity and
                      porosity fields is continuously improved as more data are
                      assimilated. Also, groundwater flow and mass transport
                      predictions are improved as more and different types of data
                      are assimilated. The beneficial impact of accounting for
                      multiple concentration data is patent. (C) 2012 Elsevier
                      B.V. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Engineering, Civil / Geosciences, Multidisciplinary / Water
                      Resources},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000302503100012},
      doi          = {10.1016/j.jhydrol.2012.01.037},
      url          = {https://juser.fz-juelich.de/record/21350},
}