An application of the Kalman filtering technique for Streamflow forecasting in the Upper Murray Basin

In [1], the IHACRES rainfall-runoff model is calibrated for the purpose of predicting streamflow discharge in ten catchments of the Upper Murray Basin using a four-hourly time step. A map and description of the basin can be found in [1,2]. The major aim of the present paper is to describe the subsequent development and testing of a four-hourly time step flow forecasting model which exploits the Kalman Filter (KF) algorithm to upgrade the IHACRES models from a simple predictive to a real-time forecasting capability. In [2], the IHACRES model and a self-adaptive filtering approach, based on the autoregressive integrated moving average (ARIMA) representation of the model residuals, were combined and utilized for forecasting daily streamflow in nine catchments of the Upper Murray Basin. Such linear filtering of the model residuals provided a considerable improvement in forecasting both low and high values of streamflow. A KF forecasting algorithm, incorporating the subdaily Upper Murray Basin IHACRES model, has been used in this second stage of the project as a tool for operational streamflow forecasting because it provides a more flexible approach and yields even better results (in terms of Nash-Sutcliffe efficiency statistics [3] and relative errors) than the ARIMA linear filtering approach.