A robust real-time flood forecasting method based on error estimation for reservoirs

Abstract

The observed discharge, an important input for flood forecasting systems, can significantly affect the accuracy of forecasting results. Since the reservoir inflow is not measured directly but calculated based on the observed reservoir stage and the reservoir outflow, it always contains gross errors causing some inflow to be outliers. In this study, a robust error estimation method for real-time flood forecasting has been developed for reservoirs’ data processing. The method differs from the conventional flood forecasting method, in that it represses the gross errors by a robust loss function based on the real error distribution of measurements. Furthermore, a fluctuation coefficient has been proposed to quantify the degree of fluctuation of a jagged reservoir inflow. The performance of the method is evaluated by both synthetic data and real cases. By using floods generated synthetically by an ideal model in which the true values and errors are known, the method is shown to be efficient as theoretically expected. The results show that the method is efficient and universally applicable in different cases. And the degree of forecast improvement is positively related to the fluctuation coefficient of the floods. The more severe the fluctuation of the flood hydrograph, the more robust the method.