Articles | Volume 16, issue 9
Hydrol. Earth Syst. Sci., 16, 3435–3449, 2012

Special issue: Latest advances and developments in data assimilation for...

Hydrol. Earth Syst. Sci., 16, 3435–3449, 2012

Research article 24 Sep 2012

Research article | 24 Sep 2012

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

O. Rakovec1, A. H. Weerts2, P. Hazenberg1,*, P. J. J. F. Torfs1, and R. Uijlenhoet1 O. Rakovec et al.
  • 1Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, The Netherlands
  • 2Deltares, P.O. Box 177, 2600 MH, Delft, The Netherlands
  • *now at: Atmospheric Sciences Department, The University of Arizona, Tucson, AZ, USA

Abstract. This paper presents a study on the optimal setup for discharge assimilation within a spatially distributed hydrological model. The Ensemble Kalman filter (EnKF) is employed to update the grid-based distributed states of such an hourly spatially distributed version of the HBV-96 model. By using a physically based model for the routing, the time delay and attenuation are modelled more realistically. The discharge and states at a given time step are 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 Belgian Ardennes. We assess the impact on the forecasted discharge of (1) various sets of the spatially distributed discharge gauges and (2) the filtering frequency. The results show that the hydrological forecast at the catchment outlet is improved by assimilating interior gauges. This augmentation of the observation vector improves the forecast more than increasing the updating frequency. In terms of the model states, the EnKF procedure is found to mainly change the pdfs of the two routing model storages, even when the uncertainty in the discharge simulations is smaller than the defined observation uncertainty.