Abstract/Summary

In this study, the distributed catchment-scale model (DiCaSM) has been applied on six catchments across the UK, with the catchment areas varying from 150 km2 to over 300 km2 . Given that river flows are of great importance in terms of water supply and for ecosystem services, the river flows were selected to study the uncertainty level in predicting the river flows. The hydrological model was calibrated over a short period of time, and validated over a longer period. For most of the studied catchments, the Nash-Sutcliffe efficiency (NSE) factor, used as indicator of goodness of fit during the model calibration period, was above 0.90, while for the validation test, all the studied catchments showed a NSE of above 0.80 over the entire study period (approx.1961-2012). The Generalized Likelihood Uncertainty Estimation (GLUE) methodology was applied. The uncertainty analysis supported the model efficiency results well. The observed river flows were within the predicted bounds/envelope of 5% and 95% percentiles. These predicted river flows bounds contained above 70% of the observed river flows as expressed by the Containment Ratio (CR). In addition to CR ratio, other uncertainty indices, S, T, B, RB, D, RD and R-factor of uncertainty level in the predicted river flows were also quantified and indicated that the model parameters and predicted river flow have acceptable levels of uncertainty. The GLUE methodology showed lower uncertainty in predicted river flows when increasing the time scale from daily to monthly to seasonal river flows with the lowest uncertainty associated with annual flows. The findings of the study have broader implications for hydrologists, climatologists, and water authorities to study the future impacts of climate and land use changes on water resources availability.