A coupled rainfall-runoff and runoff-routing model for adaptive real-time flood forecasting

This paper describes an adaptive hydrologic modelling technique for real-time flood forecasting. The modelling approach is based on a linear stochastic time-varying representation of the rainfall-runoff process and on the Muskingum routing method formulated as an optimal linear filtering problem. The most general stochastic rainfall-runoff model used for linear forecasting is known as the transfer function noise model. An on-line identification procedure based on an extension of the recursive Instrumental Variable estimator is discussed. The routing procedure, based on the Muskingum method, is written in a state-space representation. This allows real-time updating of the state and the system parameters by means of Kalman filtering. The described method is used to forecast extreme flood events for the River Ourthe (drainage basin: approx 3626 km²), one of the main tributaries of the River Meuse, Belgium. The method is compared with stationary modelling procedures and its superiority based on objective forecasting criteria is demonstrated.