State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy

摘要

This paper presents a study on the optimal setup for discharge assimilationwithin a spatially distributed hydrological model. The Ensemble Kalman filter(EnKF) is employed to update the grid-based distributed states of such anhourly spatially distributed version of the HBV-96 model. By using aphysically based model for the routing, the time delay and attenuation aremodelled more realistically. The discharge and states at a given time stepare assumed to be dependent on the previous time step only (Markov property).Synthetic and real world experiments are carried out for the Upper Ourthe(1600 km2), a relatively quickly responding catchment in the BelgianArdennes. We assess the impact on the forecasted discharge of (1) varioussets of the spatially distributed discharge gauges and (2) the filteringfrequency. The results show that the hydrological forecast at the catchmentoutlet is improved by assimilating interior gauges. This augmentation of theobservation vector improves the forecast more than increasing the updatingfrequency. In terms of the model states, the EnKF procedure is found tomainly change the pdfs of the two routing model storages, even when theuncertainty in the discharge simulations is smaller than the definedobservation uncertainty.