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Title: 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 ( 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. Validationmore » 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. Finally, there were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.« less
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  1. Earth System Science Interdisciplinary Center, University of Maryland, College Park Maryland USA; NASA Goddard Space Flight Center, Greenbelt Maryland USA
  2. NASA Goddard Space Flight Center, Greenbelt Maryland USA
  3. Pacific Northwest National Laboratory, Richland Washington USA
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0043-1397; KP1703020
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Water Resources Research; Journal Volume: 50; Journal Issue: 3
American Geophysical Union (AGU)
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
Country of Publication:
United States
flood modelling, flood detection, VIC, DRT, DRIVE, TRMM