Operational hydrological data assimilation with the Retrospective Ensemble Kalman Filter: use of observed discharge to update past and present model states for flow forecasts
Abstract. This paper describes the use of the Retrospective Ensemble Kalman Filter (REnKF) to assimilate streamflow data in an operational flow forecasting system of seven catchments in New Zealand. The REnKF updates past and present model states (soil water, aquifer and surface storages), with lags up to the concentration time of the catchment, to improve model initial conditions and hence flow forecasts. To our knowledge, this is the first time the REnKF has been applied in an operational setting, for a distributed model running over large catchments. We found the REnKF overcame instabilities in the standard EnKF which were associated with the natural lag time between upstream catchment wetness and flow at the gauging locations. The forecast system performance was correspondingly improved in terms of Nash Sutcliffe score and bounding of the measured flow by the model ensemble. We present descriptions of filter design parameters and explanations and examples of filter behaviour. The paper provides information and guidance valuable for other groups wishing to apply the REnKF for operational forecasting.