Ensemble streamflow probability prediction at the sub-seasonal to seasonal (S2S) timescale

Sub-seasonal to seasonal (S2S) weather forecasting is widely regarded as a big challenge because of less predictability than short-term and seasonal forecasts, which significantly impedes the streamflow forecasts at the same time scale. In this study, we propose an integrated numerical and statistical approach to improve the accuracy of S2S streamflow forecasting based on a physically based hydrological model, i.e., the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model with a Bayesian joint probability (BJP) model. The DRIVE model provides S2S streamflow simulations by leveraging physical processes, while the BJP model could mitigate the issue of over-fitted flood peaks and partially correct under-fitted flows. The main strategy to reduce the streamflow prediction uncertainty is through optimizing the integration of hydrological model simulations and statistical predictions. We applied the integrated DRIVE-BJP model to the Pear River Basin during the 2020-2022 time period and performed the validation according to observations at 24 hydrological stations located within the river basin. The results show the proposed ensemble approach yields significant improvements compared to the single BJP or DRIVE model. This study of fusion of the DRIVE and BJP models to enhance the sub-seasonal flood prediction, showing promising practical values in flood early warning and water resource management.

Key words: S2S, ensemble streamflow forecast, DRIVE model, BJP model, Pearl River basin