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@ARTICLE{Viney:3602,
      author       = {Viney, N. and Bormann, H. and Breuer, L. and Bronstert, A.
                      and Croke, B. and Frede, H.-G. and Gräff, T. and Hubrechts,
                      L. and Huisman, J. A. and Jakeman, A. and Kite, G. and
                      Lanini, J. and Leavesley, G. and Lettenmaier, D. and
                      Lindström, G. and Seibert, J. and Sivapalan, M. and
                      Willems, P.},
      title        = {{A}ssesing the {I}mpact of {L}and {U}se {C}hange on
                      {H}ydrology by {E}nsemble {M}odeling ({LUCHEM}) {II}:
                      {E}nsemble {C}ombinations and {P}redictions},
      journal      = {Advances in water resources},
      volume       = {32},
      issn         = {0309-1708},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {PreJuSER-3602},
      pages        = {147 - 158},
      year         = {2009},
      note         = {This study has been supported by the German Science
                      Foundation within the scope of the Collaborative Research
                      Centre (SFB) 299. The authors thank Lu Zhang and Santosh
                      Aryal for their comments on the manuscript.},
      abstract     = {This paper reports on a project to compare predictions from
                      a range of catchment models applied to a mesoscale river
                      basin in central Germany and to assess various ensemble
                      predictions of catchment streamflow. The models encompass a
                      large range in inherent complexity and input requirements.
                      In approximate order of decreasing complexity, they are
                      DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV,
                      LASCAM and IHACRES. The models are calibrated twice using
                      different sets of input data. The two predictions from each
                      model are then combined by simple averaging to produce a
                      single-model ensemble. The 10 resulting single-model
                      ensembles are combined in various ways to produce
                      multi-model ensemble predictions. Both the single-model
                      ensembles and the multi-model ensembles are shown to give
                      predictions that are generally superior to those of their
                      respective constituent models, both during a 7-year
                      calibration period and a 9-year validation period. This
                      occurs despite a considerable disparity in performance of
                      the individual models. Even the weakest of models is shown
                      to contribute useful information to the ensembles they are
                      part of. The best model combination methods are a trimmed
                      mean (constructed using the central four or six predictions
                      each day) and a weighted mean ensemble (with weights
                      calculated from calibration performance) that places
                      relatively large weights on the better performing models.
                      Conditional ensembles. in which separate model weights are
                      used in different system states (e.g. summer and winter,
                      high and low flows) generally yield little improvement over
                      the weighted mean ensemble. However a conditional ensemble
                      that discriminates between rising and receding flows shows
                      moderate improvement. An analysis of ensemble predictions
                      shows that the best ensembles are not necessarily those
                      containing the best individual models. Conversely, it
                      appears that some models that predict well individually do
                      not necessarily combine well with other models in
                      multi-model ensembles. The reasons behind these observations
                      may relate to the effects of the weighting schemes,
                      non-stationarity of the climate series and possible
                      cross-correlations between models. Crown Copyright (C) 2008
                      Published by Elsevier Ltd. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {ICG-4},
      ddc          = {550},
      cid          = {I:(DE-Juel1)VDB793},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Water Resources},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000264512000003},
      doi          = {10.1016/j.advwatres.2008.05.006},
      url          = {https://juser.fz-juelich.de/record/3602},
}