Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model

A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing‐based runoff‐routing model to form the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real‐time Global Flood Monitoring System (GFMS). The GFMS uses real‐time satellite‐based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (∼12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real‐time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research‐quality and real‐time satellite precipitation products. Evaluation results are slightly better for the research‐quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ∼0.9 and the false alarm ratio is ∼0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi‐global domain. Validation using real‐time precipitation across the tropics (30°S–30°N) gives positive daily Nash‐Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. There were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input. Key Points Coupled VIC with a physically based routing model for real‐time flood estimation GFMS gives promising flood estimation with satellite‐based precipitation Evaluation indicates improvements needed in precipitation and hydrologic model