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Abstract

Accurate precipitation forecasting is a critical component of developing a robust flood warning system (FWS), especially in developing countries where resources can be limited. In this study, we evaluate the utility of the TIGGE multimodel ensemble meteorological forecasts over the Upper Bhima River Basin in India, and investigate their hydrological usefulness through a calibrated hydrological model and postprocessed streamflow simulations. Our approach includes the innovative use of the Bayesian model average (BMA) approach to further improve the accuracy of the simulations. The results of our study highlight the challenges faced in achieving reliable precipitation forecasts with increasing lead time. However, our findings also demonstrate that a suitable bias-correction technique can help to mitigate this issue, leading to more accurate forecasts. The BMA-based postprocessing further improves the streamflow simulations, especially during extreme events, showcasing its efficacy in flood forecasting. From the results of this study, we recommend an integrated approach to FWS development that combines improved precipitation prediction, a calibrated hydrological model and postprocessed streamflow simulations. This compound system will lead to a more reliable and operationally effective FWS, especially in areas prone to extreme events. With the increasing need for effective flood warning systems, this study holds significant implications for communities at risk of flooding.