Articles | Volume 19, issue 6
Hydrol. Earth Syst. Sci., 19, 2911–2924, 2015
https://doi.org/10.5194/hess-19-2911-2015
Hydrol. Earth Syst. Sci., 19, 2911–2924, 2015
https://doi.org/10.5194/hess-19-2911-2015

Research article 23 Jun 2015

Research article | 23 Jun 2015

Operational aspects of asynchronous filtering for flood forecasting

O. Rakovec1,3, A. H. Weerts1,2, J. Sumihar2, and R. Uijlenhoet1 O. Rakovec et al.
  • 1Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, the Netherlands
  • 2Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
  • 3UFZ – Helmholtz Centre for Environmental Research, Leipzig, Germany

Abstract. This study investigates the suitability of the asynchronous ensemble Kalman filter (AEnKF) and a partitioned updating scheme for hydrological forecasting. The AEnKF requires forward integration of the model for the analysis and enables assimilation of current and past observations simultaneously at a single analysis step. The results of discharge assimilation into a grid-based hydrological model (using a soil moisture error model) for the Upper Ourthe catchment in the Belgian Ardennes show that including past predictions and observations in the data assimilation method improves the model forecasts. Additionally, we show that elimination of the strongly non-linear relation between the soil moisture storage and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting, which is evaluated using several validation measures.

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Short summary
This is the first analysis of the asynchronous ensemble Kalman filter in hydrological forecasting. The results of discharge assimilation into a hydrological model for the catchment show that including past predictions and observations in the filter improves model forecasts. Additionally, we show that elimination of the strongly non-linear relation between soil moisture and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting.