Rainfall is the primary cause for devastating floods across the world. Advances in flood monitoring/forecasting have been constrained by the difficulty of measuring rainfall continuously over space and time. In many countries, satellite-based precipitation estimation may be the best source of rainfall data due to insufficient ground networks, long delays in data transmission and absence of data sharing along many trans-boundary river basins. In this study, remotely sensed precipitation from the NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) operational system (near real-time precipitation at a spatio-temporal resolution of 3 hours and 0.25o x 0.25o) is used to monitor extreme precipitation events. Then these data are ingested into a macro-scale hydrological model which is parameterized using spatially distributed elevation, soil and land cover datasets available globally from remote sensing sources. Preliminary results appear reasonable in terms of location and frequency of events. Case studies of this experimental system are evaluated with surface runoff data and other river monitoring systems, such as Dartmouth Flood Observatory's “Surface Water Watch” array of river reaches that are measured daily via other satellite remote sensing data. Progress in this area can lead to an integrated, space-borne flood detection/monitoring system for disaster management, response, and mitigation activities around the globe.