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@ARTICLE{Pauwels:878390,
      author       = {Pauwels, Valentijn R. N. and Hendricks-Franssen, Harrie-Jan
                      and De Lannoy, Gabriëlle J. M.},
      title        = {{E}valuation of {S}tate and {B}ias {E}stimates for
                      {A}ssimilation of {SMOS} {R}etrievals {I}nto {C}onceptual
                      {R}ainfall-{R}unoff {M}odels},
      journal      = {Frontiers in water},
      volume       = {2},
      issn         = {2624-9375},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2020-02826},
      pages        = {4},
      year         = {2020},
      abstract     = {For an accurate estimation of land surface state variables
                      through remote sensing data assimilation, it is important to
                      estimate the forecast and observation biases as well. This
                      study focuses on the evaluation of a methodology to estimate
                      land surface state variables, together with model forecast
                      and observation biases. Two conceptual rainfall-runoff
                      models (HBV and GRKAL) are used for this purpose. Soil
                      moisture data, retrieved by the Soil Moisture Ocean Salinity
                      (SMOS) mission, are assimilated into these models for 59
                      unregulated sub-basins of the Murray-Darling basin in
                      Australia. When both models simulate similar soil moisture
                      values, the methodology results in similar forecast and
                      observation bias estimates for both models. The same
                      behavior is obtained when the temporal evolution of the soil
                      moisture simulations is different, but with a similar
                      long-term mean climatology. However, when the long-term mean
                      climatology of both models is different, but with a similar
                      temporal evolution, the bias estimates from both models have
                      a different climatology as well, but with a high temporal
                      correlation. The overall conclusion from this paper is that
                      observation bias estimation is of key importance when
                      updating internal state variables in a conceptual
                      rainfall-runoff system that is calibrated to produce
                      realistic discharge output for possibly biased internal
                      state variables, and that the relative partitioning of bias
                      into forecast and observation bias remains a model-dependent
                      challenge.},
      cin          = {IBG-3},
      ddc          = {333.7},
      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:000659406000001},
      doi          = {10.3389/frwa.2020.00004},
      url          = {https://juser.fz-juelich.de/record/878390},
}