Computational Enhancements for the VDOT Regional River Severe Storm Model (R2S2) Phase II

The Regional River Severe Storm (R2S2) model is an operational tool to assist Virginia Department of Transportation (VDOT) staff in allocating resources when severe storms and flooding necessitate road closures, and to assist first responders with accessing flood prone areas. It is a cutting-edge flood modeling system. In Phase I of this study, the original model was enhanced in several important ways, including automating retrieval of the model's rainfall forecast inputs, speeding up the model run time 50x, and enhancing the model's visualization and notification features for VDOT operational personnel. In this Phase II study, the accuracy of the R2S2 model output will be further evaluated; the model will be further calibrated to improve its predictive accuracy for historical flooding events, and new automated workflows (internal to R2S2) will be developed so that the model can run end-to-end with minimal human intervention during extreme weather events. These enhancements are intended to enhance the R2S2 model's usefulness to VDOT personnel actively making decisions in extreme weather events.


    • English


    • Status: Active
    • Funding: $150000
    • Contract Numbers:


    • Sponsor Organizations:

      Virginia Transportation Research Council

      530 Edgemont Road
      Charlottesville, VA  United States  22903
    • Performing Organizations:

      University of Virginia, Charlottesville

      Center for Transportation Studies
      P.O. Box 400742, Thornton Hall, D228
      Charlottesville, VA  United States  22903
    • Principal Investigators:

      Goodall, Jonathan

    • Start Date: 20170801
    • Expected Completion Date: 20181130
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01642456
    • Record Type: Research project
    • Source Agency: Virginia Department of Transportation
    • Contract Numbers: 111957
    • Files: RiP, STATEDOT
    • Created Date: Jul 28 2017 8:49AM