It is generally acknowledged in the environmental sciences that the choice of a computational model impacts the research results. In this study of a flood and drought event in the Swiss Thur basin, we show that modeling decisions during the model configuration, beyond the model choice, also impact the model results. In our carefully designed experiment we investigated four modeling decisions in ten nested basins: the spatial resolution of the model, the spatial representation of the forcing data, the calibration period, and the performance metric. The flood characteristics were mainly affected by the performance metric, whereas the drought characteristics were mainly affected by the calibration period. The results could be related to the processes that triggered the particular events studied. The impact of the modeling decisions on the simulations did, however, vary among the investigated sub-basins. In spite of the limitations of this study, our findings have important implications for the understanding and quantification of uncertainty in any hydrological or even environmental model. Modeling decisions during model configuration introduce subjectivity from the modeler. Multiple working hypotheses during model configuration can provide insights on the impact of such subjective modeling decisions.

The transfer of parameter sets over different temporal and spatial resolutions is common practice in many large-domain hydrological modelling studies. The degree to which parameters are transferable across temporal and spatial resolutions is an indicator of how well spatial and temporal variability is represented in the models. A large degree of transferability may well indicate a poor representation of such variability in the employed models. To investigate parameter transferability over resolution in time and space we have set up a study in which the Variable Infiltration Capacity (VIC) model for the Thur basin in Switzerland was run with four different spatial resolutions (1 km × 1 km, 5 km × 5 km, 10 km × 10 km, lumped) and evaluated for three relevant temporal resolutions (hour, day, month), both applied with uniform and distributed forcing. The model was run 3150 times using the Hierarchical Latin Hypercube Sample and the best 1 % of the runs was selected as behavioural. The overlap in behavioural sets for different spatial and temporal resolutions was used as an indicator of parameter transferability. A key result from this study is that the overlap in parameter sets for different spatial resolutions was much larger than for different temporal resolutions, also when the forcing was applied in a distributed fashion. This result suggests that it is easier to transfer parameters across different spatial resolutions than across different temporal resolutions. However, the result also indicates a substantial underestimation in the spatial variability represented in the hydrological simulations, suggesting that the high spatial transferability may occur because the current generation of large-domain models has an inadequate representation of spatial variability and hydrologic connectivity. The results presented in this paper provide a strong motivation to further investigate and substantially improve the representation of spatial and temporal variability in large-domain hydrological models.