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@ARTICLE{Baatz:834511,
      author       = {Baatz, Roland and Hendricks-Franssen, Harrie-Jan and Han,
                      Xujun and Hoar, Tim and Bogena, Heye and Vereecken, Harry},
      title        = {{E}valuation of a cosmic-ray neutron sensor network for
                      improved land surface model predictio},
      journal      = {Hydrology and earth system sciences},
      volume       = {21},
      number       = {5},
      issn         = {1607-7938},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2017-04442},
      pages        = {2509 - 2530},
      year         = {2017},
      abstract     = {In-situ soil moisture sensors provide highly accurate but
                      very local soil moisture measurements while remotely sensed
                      soil moisture is strongly affected by vegetation and surface
                      roughness. In contrast, Cosmic-Ray Neutron Sensors (CRNS)
                      allow highly accurate soil moisture estimation at the field
                      scale which could be valuable to improve land surface model
                      predictions. In this study, the potential of a network of
                      CRNS installed in the 2354 km2 Rur catchment (Germany) for
                      estimating soil hydraulic parameters and improving soil
                      moisture states was tested. Data measured by the CRNS were
                      assimilated with the local ensemble transform Kalman filter
                      in the Community Land Model v. 4.5. Data of four, eight and
                      nine CRNS were assimilated for the years 2011 and 2012 (with
                      and without soil hydraulic parameter estimation), followed
                      by a verification year 2013 without data assimilation. This
                      was done using (i) a regional high resolution soil map, (ii)
                      the FAO soil map and (iii) an erroneous, biased soil map as
                      input information for the simulations. For the regional soil
                      map, soil moisture characterization was only improved in the
                      assimilation period but not in the verification period. For
                      the FAO soil map and the biased soil map soil moisture
                      predictions improved strongly to a root mean square error of
                      0.03 cm3/cm3 for the assimilation period and 0.05 cm3/cm3
                      for the evaluation period. Improvements were limited by the
                      measurement error of CRNS (0.03 cm3/cm3). The positive
                      results obtained with data assimilation of nine CRNS were
                      confirmed by the jackknife experiments with four and eight
                      CRNS used for assimilation. The results demonstrate that
                      assimilated data of a CRNS network can improve the
                      characterization of soil moisture content at the catchment
                      scale by updating spatially distributed soil hydraulic
                      parameters of a land surface model.},
      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:000401436400001},
      doi          = {10.5194/hess-21-2509-2017},
      url          = {https://juser.fz-juelich.de/record/834511},
}