Hydropower forecasting using data assimilation

Accurate hydropower forecasting relies on reliable simulation of hydrological processes such as streamflow, soil moisture, snow dynamics, and reservoir inflow, which are subject to significant uncertainties arising from model structure, parameterization, and forcing data. Data assimilation has emerged as a key approach to improve hydropower forecasting by systematically integrating observations into hydrological and land surface models, thereby correcting model states and reducing forecast errors over different temporal scales.

In this comparison, several hydropower forecasting studies using data assimilation are analyzed based on a common set of properties. These include the assimilated model (e.g., distributed hydrological models, rainfall–runoff models), the data assimilation technique employed (such as Ensemble Kalman Filter, Particle Filter, or control-theory-based methods), the data sources and observations assimilated (e.g., streamflow, soil moisture, snow water equivalent), and the evaluation metrics used to quantify forecast improvements. The comparison highlights how different assimilation strategies, model structures, and updated state variables influence hydrological forecast accuracy and, consequently, hydropower forecasting performance across diverse climatic and geographic contexts.