Improving hydropower inflow forecasts by assimilating snow data

Accurate long-term inflow forecasts are essential for optimal planning of hydropower production. In snow-rich regions, where spring snowmelt is often the largest reservoir of water, inflow forecasts may be improved by assimilating snow observations to achieve more accurate initial states for the hydrological models prior to the prognosis. In this study, we test whether an ensemble Kalman based approach is useful for this purpose for a mountainous catchment in Norway. For 15 years, annual snow observations near peak accumulation at three locations were assimilated into a distributed hydrological model. After the update, the model was run for a 4-month forecasting period with inflows compared to a base case scenario that omitted the snow observations. The assimilation framework improved the forecasts in several years, and in two of the years, the improvement was very large compared to the base case simulation. At the same time, the filter did not degrade the forecasts largely, indicating that though the updating might slightly degrade performance in some years, it maintains the potential for large improvements in others. Thus, the framework proposed here is a viable method for improving snow-related deficiencies in the initial states, which translates to better forecasts.