Assimilating　either　soil　moisture　or　streamflow　individually　has　been　well　demonstrated　to　enhance　the　simulation　performance　of　hydrological　models.　However,　the　runoff　routing　process　may　introduce　a　lag　between　soil　moisture　and　outlet　discharge,　presenting　challenges　in　simultaneously　assimilating　the　two　types　of　observations　into　a　hydrological　model.　The　asynchronous　ensemble　Kalman　filter　(AEnKF),　an　adaptation　of　the　ensemble　Kalman　filter　(EnKF),　is　capable　of　utilizing　observations　from　both　the　assimilation　moment　and　the　preceding　periods,　thus　holding　potential　to　address　this　challenge.　Our　study　first　merges　soil　moisture　data　collected　from　field　soil　moisture　monitoring　sites　with　China　Meteorological　Administration　Land　Data　Assimilation　System　(CLDAS)　soil　moisture　data.　We　then　employ　the　AEnKF,　equipped　with　improved　error　models,　to　assimilate　both　the　observed　outlet　discharge　and　the　merged　soil　moisture　data　into　the　Xin＇anjiang　model.　This　process　updates　the　state　variables　of　the　model,　aiming　to　enhance　real-time　flood　forecasting　performance.　Tests　involving　both　synthetic　and　real-world　cases　demonstrates　that　assimilation　of　these　two　types　of　observations　simultaneously　substantially　reduces　the　accumulation　of　past　errors　in　the　initial　conditions　at　the　start　of　the　forecast,　thereby　aiding　in　elevating　the　accuracy　of　flood　forecasting.　Moreover,　the　AEnKF　with　the　enhanced　error　model　consistently　yields　greater　forecasting　accuracy　across　various　lead　times　compared　to　the　standard　EnKF.