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@ARTICLE{Kurtz:829605,
      author       = {Kurtz, Wolfgang and Lapin, Andrei and Schilling, Oliver S.
                      and Tang, Qi and Schiller, Eryk and Braun, Torsten and
                      Hunkeler, Daniel and Vereecken, Harry and Sudicky, Edward
                      and Kropf, Peter and Hendricks-Franssen, Harrie-Jan and
                      Brunner, Philip},
      title        = {{I}ntegrating hydrological modelling, data assimilation and
                      cloud computing for real-time management of water resources},
      journal      = {Environmental modelling $\&$ software},
      volume       = {93},
      issn         = {1364-8152},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2017-03285},
      pages        = {418 - 435},
      year         = {2017},
      abstract     = {Online data acquisition, data assimilation and integrated
                      hydrological modelling have become more and more important
                      in hydrological science. In this study, we explore cloud
                      computing for integrating field data acquisition and
                      stochastic, physically-based hydrological modelling in a
                      data assimilation and optimisation framework as a service to
                      water resources management. For this purpose, we developed
                      an ensemble Kalman filter-based data assimilation system for
                      the fully-coupled, physically-based hydrological model
                      HydroGeoSphere, which is able to run in a cloud computing
                      environment. A synthetic data assimilation experiment based
                      on the widely used tilted V-catchment problem showed that
                      the computational overhead for the application of the data
                      assimilation platform in a cloud computing environment is
                      minimal, which makes it well-suited for practical water
                      management problems. Advantages of the cloud-based
                      implementation comprise the independence from computational
                      infrastructure and the straightforward integration of
                      cloud-based observation databases with the modelling and
                      data assimilation platform.},
      cin          = {IBG-3},
      ddc          = {690},
      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:000403512500029},
      doi          = {10.1016/j.envsoft.2017.03.011},
      url          = {https://juser.fz-juelich.de/record/829605},
}