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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 mult

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.

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 mult

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

Ensemble modelling, whereby predictions from several models are pooled in an attempt to improve prediction accuracy, has often been used in the climate and atmospheric sciences, but until recently, has received little attention in hydrology. One of the key aims of the ensemble approach is to reduce uncertainty in the modelled predictions. 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 produce ensemble predictions of the effects of several projected land use changes. 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. Overall, the simpler models tend to perform better in both calibration and validation, but while all models tend to show improved performance during the lessextreme validation period, this improvement is greatest for some of the more complex models. Despite the disparity in model performance, three ensemble predictions made up of various combinations of the 10 model predictions outperform all of the individual models. In calibration, the ensemble based on a multi-variable regression of all models provides the best predictions, but its prediction accuracy declines to a greater extent than all of the models in terms of both its bias and Nash-Sutcliffe efficency when used in the validation period. In the validation period, the best predictions are provided by an ensemble consisting of the daily median model predictions. The predictions of this median ensemble also improve more between calibration and validation than any of the other models, thus indicating its robustness. The calibrated models are applied to three land use change scenarios. In the scenarios, the projected patterns of land use are based on assumed average field sizes of 0.5 ha, 1.5 ha and 5.0 ha, respectively. These contrast w

To investigate relations between solute transport, soil properties, and experimental conditions, we summarize results from leaching experiments that we carried out in a range of soils, at different scales (column (0.3-1.0 m ID, 1.0 m length) and field plot scale), and using, different leaching rates (0.5-30 cm d(-1)). The lateral mixing regime and longitudinal dispersion were derived from time series of tracer concentrations at several depths in the soil. Field- and column-scale transport were similar in loam and silt loam soils. The mixing regime was related to soil morphological features, such as vertical tongues, stratification, macropores, and a water-repellent layer. The dispersion increased in all soils more than linearly with increasing leaching rate, implying that the dispersivity is not an intrinsic soil characteristic. The change of dispersivity with leaching rate was linked to the unsaturated hydraulic conductivity using a multidomain conceptualization of the pore space.