Integration of evolutionary based assimilation into Kalman-type methods for streamflow simulations in ungauged watersheds

Data assimilation (DA) has emerged as a valuable tool for the design and application of streamflow forecasting systems. But DA applications for streamflow simulations in ungauged basins are still very limited primarily because most updated ensemble members are not usually associated with converged state and model parameterizations. Other limitations include the evaluation of massive number of ensemble members, weak/unknown relationships between parameter values and predictors, and the transfer of several members from gauged watersheds to ungauged ones is computationally expensive. But the inherent dynamics of DA to account for uncertainties in model, forcing data, and imperfect observation provide an appealing approach to simulate watershed response in ungauged basins. This study proposes a DA method namely the Pareto-Particle-Ensemble Kalman Filter (ParetoParticleEnKF) to generate and archive a small number of continuously evolved members using multi-objective evolutionary strategy where these members are updated using particle and ensemble Kalman filtering methods. The archived members for gauged watersheds are combined using inverse distance weighting where they are applied to simulate watershed response in ungauged basins.