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Through distributed stream network routing, it has quantitatively been shown that the relationship between flow travel time and discharge varies strongly nonlinearly with stream stage and with catchment-specific properties. Physically derived distributions of water travel times through a stream network were successfully used to parameterise the streamflow response function of a compartmental hydrological model. Predictions were found to improve compared to conventional statistically based parameterisation schemes, for most of the modelled scenarios, particularly for peakflow conditions. A Fourier spectral analysis of 55-110 years of daily discharge time series from 79 unregulated catchments in Sweden revealed that the discharge power spectral slope has gradually increased over time, with significant increases for 58 catchments. The results indicated that the catchment scaling function power spectrum had steepened in most of the catchments for which historical precipitation series were available. These results suggest that (local) land-use changes within the catchments may affect the discharge power spectra more significantly than changes in precipitation (climate change). A case study from an agriculturally intense catchment using historical (from the 1880s) and modern stream network maps revealed that the average stream network flow distance as well as average water levels were substantially diminished over the past century, while average bottom slopes increased. The study verifies the hypothesis that anthropogenic changes (determined through scenario modelling using a 1D distributed routing model) of stream network properties can have a substantial influence on the travel times through the stream networks and thus on the discharge hydrographs. The findings stress the need for a more hydrodynamically based approach to adequately describe the variation of streamflow response, especially for predictions of higher discharges. An increased physical basis of response functions can be beneficial in improving discharge predictions during conditions in which conventional parameterisation based on historical flow patterns may not be possible - for example, for extreme peak flows and during periods of nonstationary conditions, such as during periods of climate and/or land use change. / <p>QC 20150903</p>