Hurricane Harvey satellite image
A Russian ROSCOSMOS satellite image collected on August 31, 2017, shows extensive flooding along the Brazos River in Brazoria County near the communities of Rosharon and Angleton. Credit International Charter for Space & Major Disasters

When Hurricane Harvey made landfall on the Texas coast last August, a large number of coastal communities were devastated, prompting thousands of rescues and leaving many without homes.

As engineers and scientists anticipate an increase in high-impact storms like Harvey, the demand for real-time numerical forecast models and sensor observations to assist and search and rescue operations becomes more vital than ever. Learn how Texas ASE/EM researchers, in conjunction with UT Austin’s Center for Space Research (CSR), put their work to practice when Harvey hit the coast and their plans to mitigate future storms.

Tracking the Storm

Before Harvey even made landfall, researchers at the Center for Space Research were tracking the storm as it made its way into the Gulf of Mexico. Partnered with the State Operations Center and led by UT researcher Gordon Wells, the MAGIC (Mid-America Geospatial Information Center) team activated the United Nations Charter for Space and Major Disasters in order to access image collections from eleven international remote sensing satellites.

Additionally, Texas Engineering alumna Yunling Lou, project manager for the NASA JPL’s UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), teamed up with MAGIC, flying their aerial radar over the impact region as it began to flood rivers and communities. This first-ever use of of an aerial synthetic aperture radar in a regional flood disaster provided researchers with more detailed images than standard radar satellite imagery. These efforts, combined with aerial photography data shared by the Texas Civil Air Patrol and Military, allowed UT researchers to follow the storm impacts better than any in United States history, according to Wells.

Predicting the Storm Surge

While the MAGIC team was tracking the storm, Texas ASE/EM professor Clint Dawson’s Computational Hydraulics Research group was busy running high-resolution storm surge forecast models for Harvey on the Lonestar5 supercomputer at the Texas Advanced Computing Center. These models, which measure rising water levels with unprecedented accuracy, helped predict the storm surge once the hurricane made landfall, down to the neighborhood level. They also provided guidance for agencies like the Texas Division of Emergency Management, TxDOT, NOAA and the National Hurricane Center when making decisions such as declaring a state of emergency, where to conduct search-and-clear and issuing evacuation orders.

Hurricane Harvey storm surge model

Maximum water levels predicted by the Advanced Circulation (ADCIRC) Surge Guidance System during Hurricane Harvey, from Advisory 18 issued by the National Hurricane Center. Color contours represent feet of water above sea level. The geographic area depicted is the coastal bend region of Texas, including Port Aransas and Rockport, where the hurricane made landfall. Credit: UT Austin Computational Hydraulics Research Group

Communicating for Aid and Rescue

Once Harvey made landfall, Texas Engineering researchers provided emergency relief during the Harvey disaster when CSR functioned as a hub of communication for NOAA and other government institutions. Through MAGIC’s use of satellite-based and aerial remote sensing technology, the team provided real-time space-based data of Harvey’s aftermath and coordinated relief efforts with government agencies, enabling quicker search and rescue efforts and delivering aid where needed.

Preparing for Future Storms

Using what they learned from Hurricane Harvey, Texas ASE/EM researchers are already preparing to mitigate the destruction of future storms. Aerospace engineering assistant professor Jingyi “Ann” Chen is working with Gordon Wells at CSR to aggregate data from satellite remote sensing imagery to assess the ecological aftermath of the storm as well as develop new methods to compile data faster during a natural disaster.

The goal of Chen’s work is to better understand complex ecological systems on the earth’s surface in order to prepare for the next natural disaster and prevent the worst-case level of devastation. This research will allow decision makers to access a wide range of terrestrial data faster and better evaluate their options when it comes to situations like planning evacuation routes.