Coastal Flooding & Solutions, Workshop Case Studies
A coupled flooding modeling framework for assessing infrastructure resilience along Southeast Texas Coast
Location: Southeast Texas, Jefferson and Orange Counties
Submitted By: Yu Zhang - UT Arlington, Associate Professor
Project URLs: https://hydromet.uta.edu/noaa-csi-2019/
The southeast Texas coast is known for its high vulnerability to fluvial floods, storm surges, and sometimes compound flooding events where storm surge is immediately followed by high riverine discharge. Flooding poses a perennial threat to regional infrastructures, and this threat is likely to magnify with a rising sea level and warming sea surface temperatures. In a project funded by NOAA Climate Program Office, a team of researchers from UTA and Lamar University created a riverine-coastal coupled flood modeling system for a region extending from the Gulf of Mexico to the downstream portions of Lower Neches River and Sabine River. The coupled modeling system was built by connecting the National Water Model (NWM) with a 2-D hydrodynamic model Delft-3D, and it allows the ingest of NWM discharge simulations into the latter’s domain. The system was used to retrospectively simulate three major tropical storm events, i.e.,Hurricanes Rita (2005), Ike (2008), and Harvey (2017), with a focus given to potential amplification of flood magnitude due to interactions between riverine flow and tides. The preliminary results indicate that the compounding effects were the most pronounced during Rita, when Sabine River happened to experience moderate discharge when the storm surge occurred. Scenarios of climate changes are introduced wherein historical storm events undergo adjustments before being used to drive the coupled modeling system to assess future flooding risks.
1) A coupled modeling system is built for the southeast Texas coast; 2) Climate scenarios are introduce to assess future flooding risks; and 3) A clearer understanding of impacts of fluvial-storm surge interactions during past events.
Sustained funding; high-resolution wind data for historical events; confidence in future climate scenarios
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