Snow data assimilation for seasonal streamflow supply prediction in mountainous basins

Accurately predicting the seasonal streamflow supply (SSS), i.e., the inflow into a reservoir accumulated during the snowmelt season (April to August), is critical to operating hydroelectric dams and avoiding hydrology-related hazard. Such forecasts generally involve numerical models that simulate the hydrological evolution of a basin. The operational department of the French electric company Electricite de France (EDF) implements a semi-distributed model and has carried out such forecasts for several decades on about 50 basins. However, both scarce observation data and oversimplified physics representation may lead to significant forecast errors. Data assimilation has been shown to be beneficial for improving predictions in various hydrological applications, yet very few have addressed the seasonal streamflow supply prediction problem. More specifically, the assimilation of snow observations, though available in various forms, has been rarely studied, despite the possible sensitivity of the streamflow supply to snow stock. This is the goal of the present paper. In three mountainous basins, a series of four ensemble data assimilation experiments - assimilating (i) the streamflow (Q) alone, (ii) Q and fractional snow cover (FSC) data, (iii) Q and local cosmic ray snow sensor (CRS) data and (iv) all the data combined - is compared to the climatologic ensemble and an ensemble of free simulations. The experiments compare the accuracy of the estimated streamflows during the reanalysis (or assimilation) period September to March, during the forecast period April to August, and the SSS estimation. The results show that Q assimilation notably improves streamflow estimations during both reanalysis and the forecast period. Also, the additional combination of CRS and FSC data to the assimilation further ameliorates the SSS prediction in two of the three basins. In the last basin, the experiments highlight a poor representativity of the CRS observations during some years and reveal the need for an enhanced observation system.