TY  - JOUR
A1  - Breuer, Lutz
A1  - Bormann, Helge
A1  - Bronstert, Axel
A1  - Croke, Barry F. W.
A1  - Frede, Hans-Georg
A1  - Gräff, Thomas
A1  - Hubrechts, Lode
A1  - Kite, Geoffrey
A1  - Lanini, Jordan
A1  - Leavesley, George
A1  - Lettenmaier, Dennis P.
A1  - Lindstroem, Goeran
A1  - Seibert, Jan
A1  - Sivapalan, Mayuran
A1  - Viney, Neil R.
A1  - Willems, Patrick
T1  - Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III : scenario analysis
N2  - 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.
Y1  - 2009
UR  - http://www.sciencedirect.com/science/journal/03091708
U6  - https://doi.org/10.1016/j.advwatres.2008.06.009
SN  - 0309-1708
ER  - 
TY  - JOUR
A1  - Viney, Neil R.
A1  - Bormann, Helge
A1  - Breuer, Lutz
A1  - Bronstert, Axel
A1  - Croke, Barry F. W.
A1  - Frede, Hans-Georg
A1  - Gräff, Thomas
A1  - Hubrechts, Lode
A1  - Huisman, Johan A.
A1  - Jakeman, Anthony J.
A1  - Kite, Geoffrey W.
A1  - Lanini, Jordan
A1  - Leavesley, George
A1  - Lettenmaier, Dennis P.
A1  - Lindstroem, Goeran
A1  - Seibert, Jan
A1  - Sivapalan, Murugesu
A1  - Willems, Patrick
T1  - Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II : ensemble combinations and predictions
N2  - 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.
Y1  - 2009
UR  - http://www.sciencedirect.com/science/journal/03091708
U6  - https://doi.org/10.1016/j.advwatres.2008.05.006
SN  - 0309-1708
ER  -