摘要 : Hydrological ensemble forecasting plays a critical role in decision-making and water resource management. The skill of an ensemble forecasting system is limited by input data, model parameters and model structure, and the precisio... 展开 Hydrological ensemble forecasting plays a critical role in decision-making and water resource management. The skill of an ensemble forecasting system is limited by input data, model parameters and model structure, and the precision of the system depends mainly on the ensemble size. The main contradiction is to ensure the forecast performance and ensemble size under considering the uncertainties of inputs and models. In this study, a hybrid decomposition-based multi-model and multi-parameter (DMP) ensemble streamflow forecast method is pro-posed. The proposed method couples the signal decomposition and Artificial Intelligence (AI) forecast method in order to provide a more efficient and effective streamflow forecast. The decomposition method is used to reduce the uncertainty of inputs for the forecast model, and AI models are used to improve the performance of fore-casting. The results illustrate that the DMP ensemble streamflow forecast method not only extracts the charac-teristic periodic term and trend term of hydrological series but also improves the forecasting accuracy and reduces the ensemble forecast uncertainty. At the same time, it greatly expands the ensemble size which solves the problem of insufficient ensemble size in order to be convenient for further water resources analysis and decision-making. The findings also show that high-frequency subseries are highly sensitive to ensemble forecast, and it is recommended that more than 2 layers of high-frequency series should be used for ensemble forecast. The proposed approach in this study is more suitable for nonlinear and non-stationary hydrological series forecasting, and it is of reference significance for real reservoir operation. 收起