Sequential streamflow assimilation for short-term hydrological ensemble forecasting

•We evaluate the Ensemble Kalman Filter (EnKF) of streamflow observation within an ensemble prediction system.•We analyze the impact of ensemble size in the EnKF.•EnKF show an improvement in performance and reliability over an implementation without assimilation.•Manual assimilation led to a performance similar that of EnKF but the EnKF forecasts are more reliable.•EnKF forecasts are reliable than manual and no assimilation scenarios even with small number of members.

SummaryThis paper evaluates the application of the Ensemble Kalman Filter (EnKF) for streamflow assimilation within an ensemble prediction system designed for short-term hydrological forecasting at the outlet of the au Saumon watershed. The EnKF updates three state variables of a distributed hydrological model (soil moisture in the intermediate layer, soil moisture in the deep layer, and land routing) to improve the initial conditions of the forecasts. A systematic method for the identification of the perturbation factors (ensemble generation) and for the selection of the ensemble size is discussed. EnKF results show a substantial improvement in performance and reliability over the open-loop estimates. Manual assimilation was also assessed and led to a performance similar to the EnKF; however, the EnKF forecasts are substantially more reliable. While an ensemble size of 1000 members was required to fully sample the hydrological and meteorological uncertainty, similar results are obtained in terms of skill when limiting the ensemble size to 50.