Evaluating forecasting performance for data assimilation methods: The ensemble Kalman filter, the particle filter, and the evolutionary-based assimilation

This study has applied the Sacramento Soil Moisture Accounting (SAC-SMA) model in the Spencer Creek catchment in southern Ontario, Canada to evaluate the performance of three DA methods. The methods assimilate streamflow into SAC-SMA, where the updated ensemble members are in turn applied to forecast streamflow for up to 30-day lead time after which they were compared to observation and open-loop estimates. The results showed that the increasing order of performance at assimilation stage and forecasting for short lead times of 10-day is the EnKF, the PF and the EDA. For longer lead times, the PF performs best and is preferable when forecasting for lead times beyond 10-day. The EnKF and the PF evolve members once between assimilation time steps whereas the EDA evolves members multiple times to improve parameter convergence. The high performance of the EDA illustrates that the dynamics of large ensemble members can be encapsulated into a small continuously evolved population and that these members have high assimilation and forecasting capability.