Bias-correction data of IFS rainfall forecasts for hydrological and hydraulic models to forecast flood events

Rainfall input from the Integrated Forecasting System (IFS) to improve flood forecasting quality plays an important role when it was tested for the period of 2012–2020 at Thach Han River basin in middle Central Vietnam. This study used a combination of rainfall forecast bias correction methods from the European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VarEPS) systems into a full coupling between MIKE SHE and MIKE 11. The correction results of the IFS rain data showed low bias, but the ME value was greatly reduced. The results of the comparison between MAE and RMSE pointed that the quality of rainfall forecasting improved at all forecast periods. In particular, as the difference between MAE and RMSE was significantly reduced after validation, the anomalous errors were proved to be reduced after correcting the forecasted rainfall values from IFS using BCMA. The validation and calibration results of coupling MIKE SHE and MIKE 11 based on NSE, PBIAS, and RSR resulted in a good performance. After this, three flood events in 2020 were inputted into the coupled model to assess model performance by medium-term forecast assessment criteria and it showed that the model is capable of operational forecasting flood events with the forecasting accuracy of up to 76–85%. The outcome of this study is to provide an effective tool for flood forecasters in order to support them in the process of issuing flood forecasting bulletins.