Real-time error correction method combined with combination flood forecasting technique for improving the accuracy of flood forecasting

- We combine the flood error correction method and the multi-model composition approach together.
- The combined methods perform better than the single error correction or multi-model composition method.
- The proposed GRCM method is found to be the most effective method.

SummaryFlood forecasting has been recognized as one of the most important and reliable ways for flood management. It is therefore necessary to improve the reliability and accuracy of the flood forecasting model. Flood error correction (FEC) and multi-model composition (MC) methods are two effective ways to enhance the model performance. The current focus seems to be on either of these two methods. In this study, we combine these two methods and propose three combined methods, namely flood error correction together with multi-model composition method (FEC-MC), multi-model composition method together with flood error correction (MC-FEC), and global real-time combination method (GRCM). The Three Gorge Reservoir (TGR) and Jinsha River are selected as case studies. First, the flood error correction method and multi-model composition techniques are used separately. Then, the three combined methods are employed. The performances of the five models are compared using the root-mean-square error (RMSE), Nash-Sutcliffe efficiency R2, and qualified rate α. Results show that the combined methods perform better than the single FEC and MC methods. The proposed GRCM method is found to be the most effective method for improving the accuracy of discharge predicted by the flood forecasting model.