% 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{Chaudhuri:860679,
      author       = {Chaudhuri, A. and Hendricks-Franssen, Harrie-Jan and
                      Sekhar, M.},
      title        = {{I}terative filter based estimation of fully 3{D}
                      heterogeneous fields of permeability and {M}ualem-van
                      {G}enuchten parameters},
      journal      = {Advances in water resources},
      volume       = {122},
      issn         = {0309-1708},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2019-01344},
      pages        = {340 - 354},
      year         = {2018},
      abstract     = {The accurate modeling of flow and transport in the vadose
                      zone for agricultural and environmental applications
                      requires knowledge about soil parameters. Soil parameters
                      vary in space depending on soil texture and structure. In
                      the present synthetic study we considered spatial variation
                      of permeability (k), inverse of capillary entry pressure
                      head (αVG) and exponent (n) of the Mualem-van Genuchten
                      model. The iterative Ensemble Kalman filter (IEnKF) can
                      estimate the spatially variable soil parameters if
                      measurements of water saturation at different locations and
                      times are available. We used as input daily precipitation
                      data from the Berambadi catchment (southern India). We first
                      considered that the parameters vary horizontally but are
                      constant in the vertical direction. In this case log (k)
                      and log (αVG) can be estimated satisfactorily with
                      $30\%–40\%$ reduction of RMSE (compared to open loop
                      runs), if the initial guess of the spatial correlation
                      lengths of the heterogeneous fields is equal to or larger
                      than the unknown, true values. The estimation of exponent n
                      is poorer as the reduction of RMSE is just $20\%.$ If
                      vertical heterogeneity of the parameters is considered the
                      estimation of log (k) and log (αVG) is only improved
                      for the upper 1.5 m and estimation of n is not improved.
                      We also demonstrate that the estimation problem can be
                      simplified when flow in the unsaturated zone is
                      predominantly vertical. If in this case soil hydraulic
                      parameters are estimated with IEnKF at measurement locations
                      and afterwards interpolated with kriging, results are
                      produced with a similar quality as with 3D-IEnKF.},
      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:000450094200027},
      doi          = {10.1016/j.advwatres.2018.10.023},
      url          = {https://juser.fz-juelich.de/record/860679},
}