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@ARTICLE{HendricksFranssen:280555,
      author       = {Hendricks-Franssen, Harrie-Jan and Neuweiler, Insa},
      title        = {{D}ata assimilation for improved predictions of integrated
                      terrestrial systems},
      journal      = {Advances in water resources},
      volume       = {86},
      issn         = {0309-1708},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2016-00323},
      pages        = {257 - 259},
      year         = {2015},
      abstract     = {Predicting states or fluxes in a terrestrial system, such
                      as, for example, a river discharge, groundwater recharge or
                      air temperature is done with terrestrial system models,
                      which describe the processes in an approximate way.
                      Terrestrial system model predictions are affected by
                      uncertainty. Important sources of uncertainty are related to
                      model forcings, initial conditions and boundary conditions,
                      model parameters and the model itself. The relative
                      importance of the different uncertainty sources varies
                      according to the specific terrestrial compartment for which
                      the model is built. For example, for weather prediction with
                      atmospheric models it is believed that a dominant source of
                      uncertainty is the initial model condition [12]. For
                      groundwater models on the other hand, a general assumption
                      is that parameter uncertainty dominates the total model
                      prediction uncertainty.Sequential data assimilation
                      techniques allow improving model predictions and reducing
                      their uncertainty by correcting the predictions with
                      measurement data. This can be done on-line with real-time
                      measurement data. It can also be done off-line by updating
                      model predictions with time series of historical data.
                      Off-line data assimilation is especially interesting for
                      estimating parameters in combination with model states, or
                      for a reanalysis of past states. The most applied sequential
                      data assimilation techniques for terrestrial system model
                      predictions are the Ensemble Kalman Filter (EnKF) [8] and
                      the Particle Filter (PF) [2]. EnKF provides an optimal
                      solution for Gaussian distributed parameters, states and
                      measurement data, whereas the PF is more flexible but
                      computationally more expensive and provides in theory an
                      optimal solution independent of the distribution type},
      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:000365623500001},
      doi          = {10.1016/j.advwatres.2015.10.010},
      url          = {https://juser.fz-juelich.de/record/280555},
}