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

The aim of this paper is to investigate the effect of uncertainty originating from model inputs, parameters and initial conditions on 10 day ensemble low flow forecasts. Two hydrological models, namely 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 ECMWF meteorological ensemble forecasts as input to the GR4J and HBV models. The ensembles, each consisting of 51 members, provided the uncertainty range for the model inputs. The Generalised Likelihood Uncertainty Estimation (GLUE) approach is used to estimate parameter uncertainty. Part of the GLUE behavioural parameter set, containing parameters directly linked to storage estimates, and the observed discharge on the forecast issue day are used to update the model storages and reflect the uncertainty from the initial conditions. 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 captured by the 90% confidence interval for both models most of the time. However, the GR4J model usually overestimates low flows whereas HBV is prone to underestimate low flows. This is particularly the case if the parameter uncertainty is included in the forecasts. The total uncertainty in the GR4J model outputs is higher than in the HBV model outputs. The forecasts issued by the HBV model incorporating input uncertainty resulted in the most reliable forecast distribution. The number of hits is about equal for both models if only the input uncertainty is considered. The parameter uncertainty was the main reason reducing the number of hits. The number of false alarms in GR4J model is twice the number of false alarms in HBV when considering all uncertainty sources. The results of this study, in general, showed the parameter uncertainty has the largest effect whereas the input uncertainty had the smallest effect on the medium range low flow forecasts