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@ARTICLE{Tang:859189,
      author       = {Tang, Qi and Schilling, Oliver S. and Kurtz, Wolfgang and
                      Brunner, Philip and Vereecken, Harry and Hendricks Franssen,
                      Harrie-Jan},
      title        = {{S}imulating {F}lood-{I}nduced {R}iverbed {T}ransience
                      {U}sing {U}nmanned {A}erial {V}ehicles, {P}hysically {B}ased
                      {H}ydrological {M}odeling, and the {E}nsemble {K}alman
                      {F}ilter},
      journal      = {Water resources research},
      volume       = {54},
      number       = {11},
      issn         = {0043-1397},
      address      = {[New York]},
      publisher    = {Wiley},
      reportid     = {FZJ-2019-00079},
      pages        = {9342 - 9363},
      year         = {2018},
      abstract     = {Flood events can change the riverbed topography as well as
                      the riverbed texture and structure, which in turn can
                      influence the riverbed hydraulic conductivity (Krb) and
                      river‐aquifer exchange fluxes. A major flood event
                      occurred in the Emme River in Switzerland in 2014, with
                      major implications for the riverbed structure. The event was
                      simulated with the fully integrated hydrological model
                      HydroGeoSphere. The aim was to investigate the effect of the
                      spatial and temporal variability of riverbed topography and
                      Krb on predictions of hydraulic states and fluxes and to
                      test whether data assimilation (DA) based on the ensemble
                      Kalman filter (EnKF) can better reproduce flood‐induced
                      changes to hydraulic states and parameters with the help of
                      riverbed topography changes recorded with an unmanned aerial
                      vehicle (UAV) and through‐water photogrammetry. The
                      performance of DA was assessed by evaluating the
                      reproduction of the hydraulic states for the year 2015.
                      While the prediction of surface water discharge was not
                      affected much by the changes in riverbed topography and in
                      Krb, using the UAV‐derived postflood instead of the
                      preflood riverbed topography reduced the
                      root‐mean‐square error of predicted heads (RMSE [h]) by
                      $24\%.$ If, in addition to using the postflood riverbed
                      topography, also Krb and aquifer hydraulic conductivity
                      (Kaq) were updated through DA after the flood, the RMSE (h)
                      was reduced by $55\%.$ We demonstrate how updating of Krb
                      and Kaq based on EnKF and UAV‐based observations of
                      riverbed topography transience after a major flood event
                      strongly improve predictions of postflood hydraulic states.},
      cin          = {IBG-3 / JARA-HPC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118 / $I:(DE-82)080012_20140620$},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255) / Better predictions with environmental
                      simulation models: optimally integrating new data sources
                      $(jicg41_20100501)$},
      pid          = {G:(DE-HGF)POF3-255 / $G:(DE-Juel1)jicg41_20100501$},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000453369400043},
      doi          = {10.1029/2018WR023067},
      url          = {https://juser.fz-juelich.de/record/859189},
}