Assimilation of streamflow discharge into a continuous flood forecasting model

Abstract

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 ..