## 6 Conclusions  
This study assesses the efficacy of EFFs in accurately predicting streamflow and flood events using calibrated hydrological models driven by TIGGE ensemble forecasts. Two lumped models and two EPFs were employed to address uncertainties in hydrological model structures and input uncertainties, respectively. Four forecasting schemes combining different hydrological models and EPFs were analyzed to determine the most effective setup for delivering high-quality flood forecasts. The GPP method was used to correct bias and under-dispersion within raw EPFs. The performance of EFFs was assessed using both deterministic and probabilistic metrics. The results underscore the significance of applying post-processing techniques like GPP to correct EPFs to improve the reliability of flood forecasting. The application of the GPP has proven effective in maintaining reliable forecast skills for 1-7 lead days and reducing quality degradation over extended periods. A critical finding from this study is the limited improvement in flood forecasts when using a GE with a single hydrological model, which did not substantially outperform forecasts derived from a single EPF. However, the integration of MHM with a single EPF, as seen in scheme (C), markedly enhanced forecast accuracy by offering a wider range of ensemble forecasts, lower bias, and improved correlation. This suggests that uncertainty in hydrological models plays a more critical role in enhancing forecast predictability than input data uncertainty alone. Thus, addressing hydrological model uncertainty is crucial for optimizing flood forecasting systems. Nonetheless, this study has limitations, as the GPP post-processing method corrects only temporal errors and does not consider spatial corrections. Scholars suggest that addressing errors from the spatial distribution of precipitation could further improve runoff forecast accuracy [61; 62]. Moreover, given the complex terrain of the study area and lack of observational data, this study only used two lumped hydrological models. However, semi-distributed or distributed models, which better handle spatial variability in rainfall, might provide better hydrological forecasting outcomes [63]. Exploring how different hydrological models perform under varied geographic and climatic conditions in future research could provide valuable insights. In conclusion, this research advocates for the strategic combination of a single EPF with multiple hydrological models to achieve more reliable flood forecasting. This approach not only harnesses the strengths of post-processing techniques but also aligns with the practical needs of reducing computational demands in flood forecasting practices. Conceptualization, Y.X. ; Methodology, Y.X. ; Software, T.S. ; Validation, Z.Y. ; Formal Analysis, H.Q. ; Investigation, Y.X. and H.Q. ; Resources, T.P. ; Data Curation, Z.Y. and T.S. ; Writing--Original Draft, Y.X. ; Writing--Review and Editing, Y.X. and T.P. ; Project Administration, T.P. ; Funding Acquisition, T.P. All authors have read and agreed to the published version of the manuscript. This work is jointly supported by the Yangtze River Water Science Joint Research Fund (U2340201), the Natural Science Foundation of Hubei Province (Joint Fund) (2023 AFD094 and 2022 CFD129), the Open Grants of the State Key Laboratory of Severe Weather (2024 LASW-B04), the Project of Yangtze River Basin Meteorological Opening Fund (CJLY202 ZY06), and the Basic Research Fund of WHIHR (WHIHRKYYW2024). The meteorological observations are publicly archived and can be accessed through the China Meteorological Administration. Streamflow observations can be obtained from the Shuibuya Hydropower Station.