Middle-term　and　long-term　streamflow　forecasting　is　of　great　significance　for　water　resources　planning　and　management,　cascade　reservoirs　optimal　operation,　agriculture　and　hydro-power　generation.　In　this　work,　a　framework　was　proposed　which　integrates　least　absolute　shrinkage　and　selection　operator　(lasso),　DBN　and　bootstrap　to　improve　the　performance　and　the　stability　of　streamflow　forecasting　with　the　lead-time　of　one　month.　Lasso　helps　to　screen　the　appropriate　predictors　for　the　DBN　model,　and　the　DBN　model　simulates　the　complex　relationship　between　the　selection　predictors　and　streamflow,　and　then　bootstrap　with　the　DBN　model　contributes　to　evaluate　the　uncertainty.　The　Three-River　Headwaters　Region　(TRHR)　was　taken　as　a　case　study.　The　results　indicated　that　lasso-DBN-bootstrap　model　produced　significantly　more　accurate　forecasting　results　than　the　other　three　models　and　provides　reliable　information　on　the　forecasting　uncertainty,　which　will　be　valuable　for　water　resources　management　and　planning.