The Kalman filter (KF) is a mathematical power tool that is used for real-time forecasting. In this paper, we implement KF on Urban Storage Function (USF) model which is a nonlinear lumped model considering urban runoff process. USF model using KF is applied to a virtual catchment where rainfall-runoff characteristics are known. The model parameters are updated with 1-minute river discharge data by KF. The characteristics of real-time forecasting of the model using KF is discussed by comparison with the model using a particle filter. The results show that KF forecasted in a very short computation time with performs comparable to the particle filter.
