A dynamic information extraction method for areal mean rainfall error and its application in basins of different scales for flood forecasting

In this study, we proposed and tested a method based on system response curve (SRC) to extract the error information of areal mean rainfall. These extracted rainfall error are then used to update the rainfall time series data and propagated through the model to update the stream-flow forecast. The method is evaluated via synthetic and real-data cases. After our statistical analysis in different application scenarios, we found that the SRC method can effectively improve the accuracy of real-time flood forecasting when an optimal updating width is selected (the average relative error was reduced from 1.65% to 0.86%). In addition, we benchmark our results against a more conventional AR (Auto-Regression) streamflow-updating method. The average accuracy improvement of SRC method is 6.3% higher than that of AR method. More importantly, we found that the optimal updating width of SRC method is highly correlated with the average lead time of the basin, it has guiding significance for selecting the optimal updating width in practical application.