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@INPROCEEDINGS{Naz:845967,
      author       = {Naz, Bibi and Kurtz, Wolfgang and Springer, Anne and
                      Kollet, Stefan and Hendricks-Franssen, Harrie-Jan and
                      Montzka, Carsten and Sharples, Wendy and Görgen, Klaus and
                      Keune, Jessica},
      title        = {{A}ssimilation of remotely sensed soil moisture into the
                      {C}ommunity {L}and {M}odel for improving hydrologic
                      predictions over {E}urope},
      reportid     = {FZJ-2018-03145},
      year         = {2018},
      abstract     = {Accurate and reliable hydrologic simulations are important
                      for many applications, such as water resources management,
                      future water availability projections and predictions of
                      extreme events. However, the accuracy of waterbalance
                      estimates is limited by the lack of observations at large
                      scales and the uncertainties of model simulations due to
                      errors in model structure and inputs (e.g. hydrologic
                      parameters and atmospheric forcings). In this study, weused
                      a joint model parameter calibration and data assimilation
                      approach to improve continental-scale hydrologic estimates
                      of soil moisture, surface runoff, discharge and total water
                      storage. The assimilation experiment was conductedover a
                      time period from 2000 – 2014 with the Community Land
                      Model, version 3.5 (CLM3.5) integrated with the Parallel
                      Data Assimilation Framework (PDAF) in the Terrestrial System
                      Modeling Platform (TerrSysMPPDAF)at a spatial resolution of
                      approximately 3km over Europe. The model was forced with the
                      high-resolution reanalysis COSMO-REA6 from Hans-Ertel Centre
                      for Weather Research (HErZ). Using this modeling
                      framework,the coarse-resolution remotely sensed ESA CCI soil
                      moisture (SM) daily data were first downscaled to the model
                      resolution and then assimilated into TerrSysMP-PDAF. The
                      impact of remotely sensed soil moisture data on
                      improvingcontinental-scale hydrologic estimates was analyzed
                      through comparisons with independent observationsincluding
                      ESA CCI-SM, E-RUN runoff, GRDC river discharge and total
                      water storage from GRACE satellite.Cross-validation with
                      independent CCI-SM observations show that estimates of soil
                      moisture improved, particularlyin the summer and autumn
                      seasons. The assimilation experiment also showed overall
                      improvements in runoffparticularly during peak runoff. The
                      results demonstrate the potential of assimilating satellite
                      soil moisture observationsto improve high-resolution
                      hydrologic model simulations at the continental scale, which
                      is useful for waterresources assessment and monitoring.},
      month         = {Apr},
      date          = {2018-04-08},
      organization  = {EGU General Assembly 2018, Vienna
                       (Austria), 8 Apr 2018 - 13 Apr 2018},
      subtyp        = {After Call},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / EoCoE - Energy oriented Centre of Excellence
                      for computer applications (676629)},
      pid          = {G:(DE-HGF)POF3-255 / G:(EU-Grant)676629},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/845967},
}