Flow Updating in Real-Time Flood Forecasting Based on Runoff Correction by a Dynamic System Response Curve

Abstract

In order to improve the accuracy of real-time flood forecasting, a new accurate and efficient real-time flood forecasting error correction method based on a dynamic system response curve (DSRC) is developed. The dynamic system response curve was introduced into the flood forecasting error correction to establish the dynamic error feedback updating model tracing the source of the error. In this study, the flow concentration of the Xinanjiang (XAJ) model is generalized into a system. The physical basis of the system response curve is the flow concentration of the hydrological model. The theoretical basis of the concept is the differential of the system response function of the runoff time series. Based on the observed and calculated discharge, the calculated runoff series was corrected using least-squares estimation, and then the flow was recalculated with the corrected runoff. The Xinanjiang model was selected to calculate runoff. The method was tested in both an ideal scenario and in a real case study. The proposed method was applied to 26 floods in the Wangjiaba basin. The ratio of qualified flood increased from 65.4 to 92.3% after correction by the DSRC. Comparison with the second-order autoregressive error forecast model [AR(2)] shows that the method can improve the forecasting results effectively. The method has a simple structure, the performance indices will not deteriorate as the forecasting period (i.e., lead time) increases, and the method does not increase the number of model parameters.