Hydrological ensemble forecasting using a multi-model framework

Ensemble streamflow predictions (ESP) from a single hydrological model tend to under-sample the variability needed to provide a good representation of streamflow observations. ESPs typically end up being biased and under-dispersed, making them imprecise and less valuable in an operational context. In this study, a proposed methodology based on a multi-hydrological model approach is applied to address these issues. The forecast methodology includes eight lumped hydrological models whose states were updated through data assimilation using an Ensemble Kalman Filter (EnKF) to provide initial states to the models, allowing them to better sample uncertainty in the hydrometeorological observations and reduce initial condition errors. ESP forecasts are then driven by numerical weather prediction ensemble forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) for a nine-day lead-time on five snowmelt-dominated catchments in Quebec, Canada. These eight assimilated individual ESPs are then post-processed using a quantile mapping bias correction method, after which they are combined in a larger ensemble (known as a Grand Ensemble Streamflow Prediction (GESP)) using a multi-model approach. Results indicate that the proposed methodology allowed reducing both forecast biases and under-dispersion problems for all catchments and four periods corresponding to the seasonality of the streamflow. On their own, the data assimilation (DA) and post-processing manipulations allowed shifting the hydrological ensembles to better encompass the observed streamflow. The multi-model approach further improved the forecasts by increasing their reliability. Talagrand diagrams, supported by the Kolmogorov-Smirnov test for uniformity, as well as the Average Bin Distance to Uniformity (ABDU) and the Continuous Ranked Probability Skill Score (CRPSS), were chosen as performance metrics to evaluate the robustness of the methodology. These findings suggest that it is possible to improve hydrological short-term forecast reliability through a multi-model approach in an operational forecasting context.