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Application of Ensemble Kalman Filter for flood forecasting in Australian rivers

Abstract There is a growing interest in understanding the uncertainty in flood forecasting and the resulting flood warnings. This is borne out of the fact that the processes involved in flood forecasting have inherent uncertainties in them. The procedure used in flood forecasting consists of a number of steps. The first step is rainfall measurement and forecasting rainfall during a flood event. The rainfall is then transformed into flow using a combined water balance and runoff-routing model. There are uncertainties associated with rainfall measurement/forecasting, model (conceptualisation and parameters) and flow measurements. All these uncertainties contribute to the uncertainty in the resulting flood forecasts. The Ensemble Kalman Filter (EnKF) enables all these uncertainties to be combined in a systematic way and it has been used by a number of researchers in the past. In this paper, the EnKF with state and parameter updating is used with the Probability Distributed Moisture model to forecast flood events in six rivers located in different parts of Australia. The results showed that the quality of forecasts deteriorated with lead time greater than 6 hours and the peak discharge magnitudes were underestimated. Of the two variations used, state updating performed better than parameter updating.