Abstract
This paper presents the extension of the previous work on the development of short-term flood forecast model using rainfall downscaled from the global NWP outputs. The proposed downscale method has considered physically based corrections to the NWP outputs for optimization of parameters used for calibration phases using artificial neural network (ANN). Downscaled rainfall was then used as inputs to the modified super tank model for runoff forecast. Model uncertainties were quantified against forecast lead-times in order to integrate forecast results into the existing alarm levels for early flood warning. Results showed that flood forecasts based on the downscaled rainfall by ANN outperformed those using multiple linear regression methods. Though it showed larger uncertainties along with the forecast lead-times, the model can provide reliable forecasts up to 18-hour ahead. It has demonstrated an added value in flood forecasting and warning practices for river basins in Central Vietnam.
