Exploration of sequential streamflow assimilation in snow dominated watersheds

• EnKF assimilation of streamflow observation in sonw watersheds was evaluated.
• We analyze the impact of ensemble size in the EnKF.
• EnKF shows an improvement in performance and reliability against open-loop scenario.
• The updating of snow and soil states together improves the EnKF results.
• EnKF forecasts are more reliable than open-loop evenwith a small number of members.

This paper evaluates Ensemble Kalman filter (EnKF) sequential data assimilation on a semi-distributed hydrological model implementation on two snow-dominated watersheds, focussing strictly on snow accumulation and melt periods while assimilating streamflow for the updating of various state variables combinations. Three scenarios are explored in depth: (1) updating the three state variables that were previously identified pertinent for snow-free hydrological processes: soil moisture in the intermediate layer, soil moisture in the deep layer, and the overland routing reservoir, (2) updating the snow water equivalent, and (3) updating all of the above state variables. Inputs (precipitation and temperature) and output (streamflow) perturbation factors are first identified for each scenario, based on their performance and reliability for simulation with assimilation. The three EnKF implementations are next compared to one another and to an open-loop run, in an ensemble forecasting context. The third scenario outperforms the others in most situations and provides the largest gain in reliability. The ensemble size may also be reduced, from 1000 to 50 members, without much loss in performance or reliability.