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@ARTICLE{Gebler:867886,
      author       = {Gebler, S. and Kurtz, W. and Pauwels, V. R. N. and Kollet,
                      S. J. and Vereecken, H. and Hendricks Franssen, H.‐J.},
      title        = {{A}ssimilation of {H}igh‐{R}esolution {S}oil {M}oisture
                      {D}ata {I}nto an {I}ntegrated {T}errestrial {M}odel for a
                      {S}mall‐{S}cale {H}ead‐{W}ater {C}atchment},
      journal      = {Water resources research},
      volume       = {55},
      number       = {12},
      issn         = {1944-7973},
      address      = {[New York]},
      publisher    = {Wiley},
      reportid     = {FZJ-2019-06488},
      pages        = {10358-10385},
      year         = {2019},
      abstract     = {Land surface‐subsurface modeling combined with data
                      assimilation was applied on the Rollesbroich hillslope
                      (Germany). Dense information from a soil moisture sensor
                      network was assimilated with the ensemble Kalman filter
                      applying different scenarios including the update of model
                      states with or without updating of saturated soil hydraulic
                      conductivity on an ensemble size of 128 (or 256)
                      realizations with 3‐D heterogeneous fields of Mualem‐van
                      Genuchten parameters. Simulations were also carried out with
                      a synthetic test case mimicking the Rollesbroich site, to
                      get more insight in the role of model structural errors. The
                      combination of joint updating of model states and hydraulic
                      conductivity was more efficient in updating the soil water
                      content than state updating alone for the real‐world case.
                      On average, the root‐mean‐square error at the sensor
                      locations was reduced by $14\%$ if states and parameters
                      were updated jointly, but discharge estimation was not
                      improved significantly. Synthetic simulations showed much
                      better results with an overall root‐mean‐square error
                      reduction by $55\%$ at independent verification locations in
                      case of daily soil water content data assimilation including
                      parameter estimation. Individual synthetic data assimilation
                      scenarios with parameter estimation showed an increase of
                      the Nash‐Sutcliffe‐Efficiency for discharge from −0.04
                      for the open loop run to 0.61. This shows that data
                      assimilation in combination with high‐resolution
                      physically based models can strongly improve soil moisture
                      and discharge estimation at the hillslope scale. Large
                      performance differences between synthetic and real‐world
                      experiments indicated the limits of such an approach
                      associated with model structural errors like errors in the
                      prior geostatistical parameters.},
      cin          = {IBG-3 / JARA-HPC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118 / $I:(DE-82)080012_20140620$},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / High-resolution regional reanalysis with
                      TerrSysMP $(jibg36_20181101)$},
      pid          = {G:(DE-HGF)POF3-255 / $G:(DE-Juel1)jibg36_20181101$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000501839000001},
      doi          = {10.1029/2018WR024658},
      url          = {https://juser.fz-juelich.de/record/867886},
}