In view of the increasing non-stationary of monthly runoff series under changing environment, the limitation of ordinary learning of traditional runoff forecasting models, and the accurate runoff forecasting is facing new challenges, the research on forecasting consistent with the changing characteristics of the runoff series has been carried out. Based on Bagging and Boosting integrated learning algorithms, three integrated learning models, namely random forest (RF), gradient boosting decision tree (GBDT) and light gradient boosting machine (LightGBM), are constructed. Integrated with elastic net (EN) and variational mode decomposition (VMD), combined forecasting models of VMD-EN-RF, VMD-EN-GBDT and VMD-EN-LightGBM non-stationary monthly runoff are established. The measured monthly runoff of the Yellow River Basin is taken as the research object to evaluate the uncertainty of the forecasting results. The results show that the single ensemble learning model can provide reliable forecasting results and is suitable for complex monthly runoff forecasting. The prediction performance of the integrated learning model integrating VMD and EN is significantly improved compared with the single integrated learning model. The Nash efficiency coefficient is increased by 15%~20%, and the root mean square error is reduced by 30%~40%. The ensemble learning model built based on Boosting is better than that built based on Bagging. Among them, the prediction effect of VMD-EN-LightGBM within 3-month forecast period is better than that of VMD-EN-RF and VMD-EN-GBDT, and the coverage rate is higher than 90% in the 90% confidence interval, showing good performance.