Assimilation of streamflow discharge into a continuous flood forecasting model

Four state updating schemes are explored to integrate the observed discharge data into a flood forecasting model. Hourly streamflow discharge measured in the Ovens River catchment, Australia, is assimilated into the Probability Distributed Model (PDM) using the ensemble Kalman filter. The results show that the overall forecast accuracy improves when the discharge observations are integrated, mainly due to better initialisation of the model. Setting error covariance proportional to each state variable gives better results than setting error covariance as a constant value. Updating routing states of PDM affects discharge prediction instantly, while the effect of soil moisture updating results in a lagged response in discharge leading to a poorer update performance. However, during the forecast lead time, updating soil moisture results in slower degradation of the forecast accuracy, which is mainly because the soil moisture store is the only state influencing discharge volume, while the routing storages only describe the flow delay.