Real-time correction is the key to reducing hydrological forecasting errors. Methods of real-time correction can be classified into terminal error correction (TEC) and process error correction (PEC) methods. A state-of-the-art PEC method is the dynamic system response curve (DSRC) method which has been developed to improve flood forecasting, but its underlying assumption of error source limits the correction accuracy. This study developed a multi-source error DSRC method (MSE-DSRC) to partition the total error of forecast discharge into input-caused error and model-caused error. Based on the known quantitative relationship between rain gauge density and error division ratio, two different sources of error are corrected simultaneously. A synthetic case study was done to evaluate the ability of the MSE-DSRC method to correct the variables and parameters of the hydrological model (XAJ model). The MSE-DSRC method was found to overcome the limitations of the traditional DSRC method and have a high accuracy indexed with the Nash-Sutcliffe (N_S) efficiency increasing above 0.9. Then, real case studies in three basins of the Huaihe River (China) demonstrated that the MSE-DSRC method achieved higher accuracy and stability than did the traditional DSRC method and can be easily applied in practice, with the average improvement of N_S above 50%. Overall, the error division of the MSE-DSRC method opens the possibility for multi-variable updating and multi-source error correction, which may further improve the accuracy of flood forecasting.