Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models

This paper aims to investigate the effect of uncertainty originating from model inputs, parameters and initial conditions on 10 day ensemble low flow forecasts. Two hydrological models, GR4J and HBV, are applied to the Moselle River and performance in the calibration, validation and forecast periods, and the effect of different uncertainty sources on the quality of low flow forecasts are compared. The forecasts are generated by using meteorological ensemble forecasts as input to GR4J and HBV. The ensembles provided the uncertainty range for the model inputs. The Generalized Likelihood Uncertainty Estimation (GLUE) approach is used to estimate parameter uncertainty. The quality of the probabilistic low flow forecasts has been assessed by the relative confidence interval, reliability and hit/false alarm rates. The daily observed low flows are mostly captured by the 90% confidence interval for both models. However, GR4J usually overestimates low flows whereas HBV is prone to underestimate them, particularly when the parameter uncertainty is included in the forecasts. The total uncertainty in GR4J outputs is higher than in HBV. The forecasts issued by HBV incorporating input uncertainty resulted in the most reliable forecast distribution. The parameter uncertainty was the main reason reducing the number of hits. The number of false alarms in GR4J is twice the number of false alarms in HBV when considering all uncertainty sources. The results of this study showed that the parameter uncertainty has the largest effect whereas the input uncertainty had the smallest effect on the medium range low flow forecasts.