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@ARTICLE{Lievens:281469,
      author       = {Lievens, H. and Tomer, S. K. and Al Bitar, A. and De
                      Lannoy, G. J. M. and Drusch, M. and Dumedah, G. and
                      Hendricks-Franssen, Harrie-Jan and Kerr, Y. H. and Martens,
                      B. and Pan, M. and Roundy, J. K. and Vereecken, Harry and
                      Walker, J. P. and Wood, E. F. and Verhoest, N. E. C. and
                      Pauwels, V. R. N.},
      title        = {{SMOS} soil moisture assimilation for improved hydrologic
                      simulation in the {M}urray {D}arling {B}asin, {A}ustralia},
      journal      = {Remote sensing of environment},
      volume       = {168},
      issn         = {0034-4257},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2016-01162},
      pages        = {146 - 162},
      year         = {2015},
      abstract     = {This study explores the benefits of assimilating SMOS soil
                      moisture retrievals for hydrologic modeling, with a focus on
                      soil moisture and streamflow simulations in the Murray
                      Darling Basin, Australia. In this basin, floods occur
                      relatively frequently and initial catchment storage is known
                      to be key to runoff generation. The land surface model is
                      the Variable Infiltration Capacity (VIC) model. The model is
                      calibrated using the available streamflow records of 169
                      gauge stations across the Murray Darling Basin. The VIC soil
                      moisture forecast is sequentially updated with observations
                      from the SMOS Level 3 CATDS (Centre Aval de Traitement des
                      Données SMOS) soil moisture product using the Ensemble
                      Kalman filter. The assimilation algorithm accounts for the
                      spatial mismatch between the model (0.125°) and the SMOS
                      observation (25 km) grids. Three widely-used methods for
                      removing bias between model simulations and satellite
                      observations of soil moisture are evaluated. These methods
                      match the first, second and higher order moments of the soil
                      moisture distributions, respectively. In this study, the
                      first order bias correction, i.e. the rescaling of the long
                      term mean, is the recommended method. Preserving the
                      observational variability of the SMOS soil moisture data
                      leads to improved soil moisture updates, particularly for
                      dry and wet conditions, and enhances initial conditions for
                      runoff generation. Second or higher order bias correction,
                      which includes a rescaling of the variance, decreases the
                      temporal variability of the assimilation results. In
                      comparison with in situ measurements of OzNet, the
                      assimilation with mean bias correction reduces the root mean
                      square error (RMSE) of the modeled soil moisture from 0.058
                      m3/m3 to 0.046 m3/m3 and increases the correlation from
                      0.564 to 0.714. These improvements in antecedent wetness
                      conditions further translate into improved predictions of
                      associated water fluxes, particularly runoff peaks. In
                      conclusion, the results of this study clearly demonstrate
                      the merit of SMOS data assimilation for soil moisture and
                      streamflow predictions at the large scale.},
      cin          = {IBG-3},
      ddc          = {050},
      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:000361405500013},
      doi          = {10.1016/j.rse.2015.06.025},
      url          = {https://juser.fz-juelich.de/record/281469},
}