@article{VineyBormannBreueretal.2009,
  author    = {Viney, Neil R. and Bormann, Helge and Breuer, Lutz and Bronstert, Axel and Croke, Barry F. W. and Frede, Hans-Georg and Gr{\"a}ff, Thomas and Hubrechts, Lode and Huisman, Johan A. and Jakeman, Anthony J. and Kite, Geoffrey W. and Lanini, Jordan and Leavesley, George and Lettenmaier, Dennis P. and Lindstroem, Goeran and Seibert, Jan and Sivapalan, Murugesu and Willems, Patrick},
  title     = {Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II : ensemble combinations and predictions},
  issn      = {0309-1708},
  doi       = {10.1016/j.advwatres.2008.05.006},
  year      = {2009},
  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.},
  language  = {en}
}
@article{BreuerBormannBronstertetal.2009,
  author    = {Breuer, Lutz and Bormann, Helge and Bronstert, Axel and Croke, Barry F. W. and Frede, Hans-Georg and Gr{\"a}ff, Thomas and Hubrechts, Lode and Kite, Geoffrey and Lanini, Jordan and Leavesley, George and Lettenmaier, Dennis P. and Lindstroem, Goeran and Seibert, Jan and Sivapalan, Mayuran and Viney, Neil R. and Willems, Patrick},
  title     = {Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III : scenario analysis},
  issn      = {0309-1708},
  doi       = {10.1016/j.advwatres.2008.06.009},
  year      = {2009},
  abstract  = {An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90\% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions.},
  language  = {en}
}
@article{BreuerWillemsBormannetal.2009,
  author    = {Breuer, Lutz and Willems, Patrick and Bormann, Helge and Bronstert, Axel and Croke, Barry and Frede, Hans Georg and Gr{\"a}ff, Thomas and Hubrechts, Lode and Kite, Geoffrey and Lanini, Jordan and Leavesley, George and Lettenmaier, Dennis P. and Lindstroem, Goeran and Seibert, Jan and Sivapalan, Mayuran and Viney, Neil R.},
  title     = {Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) : I: model intercomparison with current land use},
  issn      = {0309-1708},
  doi       = {10.1016/j.advwatres.2008.10.003},
  year      = {2009},
  abstract  = {This paper introduces the project on 'Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM)' that aims at investigating the envelope of predictions on changes in hydrological fluxes due to land use change. As part of a series of four papers, this paper outlines the motivation and setup of LUCHEM, and presents a model intercomparison for the present-day simulation results. Such an intercomparison provides a valuable basis to investigate the effects of different model structures on model predictions and paves the ground for the analysis of the performance of multi-model ensembles and the reliability of the scenario predictions in companion papers. in this study, we applied a set of 10 lumped, semi-lumped and fully distributed hydrological models that have been previously used in land use change studies to the low mountainous Dill catchment. Germany. Substantial differences in model performance were observed with Nash-Sutcliffe efficiencies ranging from 0.53 to 0.92. Differences in model performance were attributed to (1) model input data, (2) model calibration and (3) the physical basis of the models. The models were applied with two sets of input data: an original and a homogenized data set. This homogenization of precipitation, temperature and leaf area index was performed to reduce the variation between the models. Homogenization improved the comparability of model simulations and resulted in a reduced average bias, although some variation in model data input remained. The effect of the physical differences between models on the long-term water balance was mainly attributed to differences in how models represent evapotranspiration. Semi-lumped and lumped conceptual models slightly outperformed the fully distributed and physically based models. This was attributed to the automatic model calibration typically used for this type of models. Overall, however, we conclude that there was no superior model if several measures of model performance are considered and that all models are suitable to participate in further multi-model ensemble set-ups and land use change scenario investigations.},
  language  = {en}
}