Impact of assimilating spaceborne microwave signals for improving hydrological

26 The availability of in-situ data has been a constraining issue in hydrological prediction, 27 especially in those regions that are only sparsely monitored or completely ungauged. The 28 application of remote-sensing data, without conventional in-situ hydrological measurements, to 29 force, calibrate and update a hydrologic model is a major contribution of this study. First, a 30 rainfall-runoff hydrological model called CREST, coupled with EnSRF, is used for exceedance 31 probability-based flood prediction. Then, this advanced flood-prediction framework, with different 32 experimental designs, is forced by TRMM precipitation while Aqua AMSR-E microwave 33 brightness temperature signals is used for model calibration and data assimilation for 34 progressively improved river discharge prediction. Results indicate that solely relying on remote-35 sensing data for model forcing, parameter calibration, and state updating with EnSRF, the 36 designed framework can adequately predict flooding events. A high flow threshold was applied 37 and has further improved modeling performance, particularly in the flooding seasons, with a flood 38 warning lead-time of one day. Given the anticipated global availability of satellite-based 39