Computational Enhancements for the Virginia Department of Transportation Regional River Severe Storm (R2S2) Model

Report No: 17-R18

Published in 2017

About the report:

Climate change introduces infrastructure flooding challenges, especially for coastal regions with low topographic relief. More frequently occurring intense storms and sea level rise are two projected impacts of climate change that will lead to increased flooding risks. These changing conditions make having the ability to forecast accurately potential flooding impacts to transportation infrastructure critical. The Virginia Department of Transportation (VDOT) Hampton Roads District worked with Hassan Water Resources, PLC, a consulting firm, to create a flood forecasting model called the Regional River Severe Storm (R2S2) model for, among other purposes, flood warning applications. The model was built for watersheds within the district that cover approximately 2,230 square miles and include 493 bridges and culverts.

This report describes work by researchers at the University of Virginia to complete computational enhancements to the R2S2 model so that it might ultimately be implemented by VDOT for flood forecasting applications. Specific project tasks were to (1) design, implement, and test software for automating rainfall forecast inputs from the National Weather Service; (2) speed up the model execution using a graphics processing unit (GPU); and (3) automate the visualization of model output through an online, map-based system and automate emails of flood impacted locations to decision makers within VDOT.

Task 1 resulted in software for automating the access, download, and transform of rainfall forecast data produced by the National Weather Service High-Resolution Rapid Refresh (HRRR) model into the inputs required by the R2S2 model. The heart of the R2S2 model, and the most time-consuming part of the model, is a high-resolution hydrodynamic model called the Two-dimensional Unsteady Flow (TUFLOW) model. Task 2 resulted in speeding up the TUFLOW model by 50x from over 100 hr when the model is run using a central processing unit to just over 2 hr when using a GPU. Finally, Task 3 resulted in software for automating tasks including extracting maximum water depth from the model output for a set of bridges, creating a Google Maps–based website showing impacted bridges, and having the system notify decision makers via email of bridges at risk of being overtopped.

Disclaimer Statement:The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.


  • Mohamed M. Morsy, Gina L. O’Neil, Jonathan L. Goodall, Ph.D., P.E., Gamal Hassan, P.E.

Last updated: November 11, 2023

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