Environmental modeling studies aim to infer the impacts on environmental variables that are caused by natural and human-induced changes in environmental systems. Changes in environmental systems are typically implemented as discrete scenarios in environmental models to simulate environmental variables under changing conditions. The scenario development of a model input usually involves several data sources and perhaps other models, that are potential sources of uncertainty. The setup and the parametrization of the implemented environmental model are additional sources of uncertainty for the simulation of environmental variables. Yet, to draw well-informed conclusions from the model simulations it is essential to identify the dominant sources of uncertainty. In two Austrian impact studies the eco-hydrological model Soil and Water Assessment Tool (SWAT) was applied to simulate discharge and nitrate-nitrogen (NO 3 − -N) loads under future changing conditions. For both catchments the SWAT model was set up with different spatial aggregations and non-unique model parameter sets were identified that adequately reproduced observations of discharge and NO 3 − -N loads. We developed scenarios of future changes for land use, point source emissions, and climate and implemented the scenario realizations in the different SWAT model setups with different model parametrizations, which resulted in 7000 combinations of scenarios and model setups for both catchments. With all model combinations we simulated daily discharge and NO 3 − -N loads at the catchment outlets. In both case studies we employed global sensitivity analysis (GSA) to identify the impact of the scenario inputs, the model setup and the parametrization on the simulation of discharge and NO 3 − -N loads. We accompanied the GSA with a visual analysis of the simulation outputs and their associated uncertainties that resulted from the simulations of the 7000 SWAT model combinations. We present visualizations of the results of the GSA and the simulation uncertainty bands that proved to be powerful diagnostic tools in this study. Based on the GSA we identified climate change and the model parametrization to be the most sensitive model inputs for the simulation of discharge and NO 3 − -N loads in both case studies. In contrast, the impact of the model setup on the simulation of discharge and NO 3 − -N loads was low and the changes in land use and point source emissions were found to have the least impact on the simulated discharge and NO 3 − -N loads. Additionally, the visual analysis of the uncertainty bands illustrated that the precipitation of the climate scenarios dominated the changes in simulation outputs, rather than changes in air temperature in both case studies.