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@ARTICLE{Tajiki:877401,
      author       = {Tajiki, M. and Schoups, G. and Hendricks Franssen, H. J.
                      and Najafinejad, A. and Bahremand, A.},
      title        = {{R}ecursive {B}ayesian {E}stimation of {C}onceptual
                      {R}ainfall‐{R}unoff {M}odel {E}rrors in {R}eal‐{T}ime
                      {P}rediction of {S}treamflow},
      journal      = {Water resources research},
      volume       = {56},
      number       = {2},
      issn         = {1944-7973},
      address      = {[New York]},
      publisher    = {Wiley},
      reportid     = {FZJ-2020-02171},
      pages        = {e2019WR025237},
      year         = {2020},
      abstract     = {Conceptual rainfall‐runoff models account for the spatial
                      dynamics of hydrological processes in a basin using simple
                      spatially lumped storage‐flow relations. Such rough
                      approximations introduce model errors that are often
                      difficult to characterize. Here, we develop and apply a
                      methodology that recursively estimates and accounts for
                      model errors in real‐time streamflow prediction settings
                      by adding time‐dependent random noise to the internal
                      states (storages) of the hydrological model. Magnitude of
                      the added noise depends on a precision (inverse variance)
                      parameter that is estimated from rainfall‐runoff data. A
                      recursive Bayesian technique is used for estimation:
                      posteriors of hydrological parameters and states are updated
                      through time with an ensemble Kalman filter, whereas the
                      posterior of the precision parameter is updated recursively
                      using a novel gamma density approximation technique.
                      Applying this algorithm to different model error scenarios
                      allows identification of the main source of model errors.
                      The methodology is applied to short‐term streamflow
                      prediction with the Hymod rainfall‐runoff model in a
                      semi‐cold, semi‐humid basin in Iran. Results show that
                      (i) streamflow prediction in this snow‐dominated basin is
                      more affected by model errors in the slow flow than the
                      quick flow component of the model, (ii) accounting for model
                      errors in the slow flow component improves both low and high
                      flow predictions, and (iii) predictive performance further
                      improves by accounting for Hymod parameter uncertainty in
                      addition to model errors. Overall, accounting for model
                      errors increased Nash‐Sutcliffe efficiency (by $1–5\%),$
                      reduced mean absolute error (by $2–43\%),$ and improved
                      probabilistic predictive performance (by $50–80\%).$},
      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:000535672800052},
      doi          = {10.1029/2019WR025237},
      url          = {https://juser.fz-juelich.de/record/877401},
}