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@ARTICLE{Hung:908929,
      author       = {Hung, Ching Pui and Schalge, Bernd and Baroni, Gabriele and
                      Vereecken, Harry and Hendricks Franssen, Harrie-Jan},
      title        = {{A}ssimilation of {G}roundwater {L}evel and {S}oil
                      {M}oisture {D}ata in an {I}ntegrated {L}and
                      {S}urface‐{S}ubsurface {M}odel for {S}outhwestern
                      {G}ermany},
      journal      = {Water resources research},
      volume       = {58},
      number       = {6},
      issn         = {0043-1397},
      address      = {[New York]},
      publisher    = {Wiley},
      reportid     = {FZJ-2022-02909},
      pages        = {e2021WR031549},
      year         = {2022},
      abstract     = {Integrated terrestrial system models predict the coupled
                      water, energy and biogeochemical cycles. Simulations with
                      these models are affected by uncertainties of model
                      parameters, initial and boundary conditions, atmospheric
                      forcings and the biophysical processes. Data assimilation
                      (DA) can quantify and reduce the uncertainty. This has been
                      tested intensively for single compartment models, but far
                      less for integrated models with multiple compartments. We
                      constructed a virtual reality (VR) with a coupled land
                      surface-subsurface model under the Terrestrial Systems
                      Modeling Platform, which mimics the Neckar catchment in
                      southern Germany. Soil moisture and groundwater level (GWL)
                      data extracted from the simulated VR are used as
                      measurements to be assimilated with
                      state-only/state-hydraulic parameter estimation. Soil
                      moisture DA improves soil moisture characterization in the
                      vertical profile and the neighboring grid cells, with a 40
                      ∼ $60\%$ reduction of root mean square error (RMSE) over
                      the observation points. In spite of a small ensemble size of
                      64 members, assimilating soil moisture data improved
                      saturated hydraulic conductivity estimation around the
                      measurement locations. The characterization of
                      evapotranspiration and river discharge only show limited
                      improvements $(1\%$ at observation points and less than
                      $0.1\%$ in RMSE at 3 selected gauge locations respectively).
                      GWL DA not only improves the GWL characterization (76 ∼
                      $88\%$ RMSE reduction at observation locations) but also
                      soil moisture for some cases. In addition, a clear
                      improvement in GWL characterization is observed up to 8 km
                      from the observations, and updating the model states of the
                      saturated zone only instead of the complete domain gives
                      better performance.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / DFG project 243358811 - FOR 2131:
                      Datenassimilation in terrestrischen Systemen},
      pid          = {G:(DE-HGF)POF4-2173 / G:(GEPRIS)243358811},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000810952400001},
      doi          = {10.1029/2021WR031549},
      url          = {https://juser.fz-juelich.de/record/908929},
}