- Real-time supercomputer flood modeling could save property and lives
- HPC could super-size an existing small-scale flood alert system to city- or county-wide
- Understanding how regional hydrologic systems interconnect improves flood resilience
During Hurricane Harvey in 2017, torrential, unprecedented rainfall and flooding occurred in Houston, Texas. Bayous and creeks, man-made channels, and flood control reservoirs simply couldn’t keep up with the stalled hurricane’s endless downpours on this coastal plain that is flat as a pancake. Land here drains slowly even on a good day.
But ongoing hydrology research at Rice University has the potential to increase Houston’s flood resilience with the use of high-performance computing (HPC). The research addresses inland flooding with a flood alert system that analyzes Brays Bayou, a small Houston watershed. If combined with HPC, the flood alert system could be scaled to the city or county level to provide critical flood alerts for millions of people.
Led by Rice University engineering professor Dr. Phil Bedient, the Severe Storm Prediction, Education and Evacuation from Disasters (SSPEED) Center studies and helps solve the region’s severe storm issues, chief among them inland flooding. One factor in improving flood resilience is public access to accurate and timely environmental data, such as flood alerts.
Over the last 20 years, Dr. Bedient has developed a Flood Alert System for the Texas Medical Center (TMC). The current version of the Flood Alert System, FAS4, uses real-time radar-rainfall data, rain-gauge data, hydrologic and hydraulic modeling, and bayou cameras to predict flood conditions and timing. An hour’s warning provides enough time to move people and property to safety and makes a big difference for homeowners, businesses, and institutions.
FAS4 uses real-time rainfall data collected from the National Weather Service’s NEXRAD radar. During a storm, adjusted rainfall data is sent every five minutes to the FAS4 server hosted at Rice’s Primary Data Center. FAS4 then predicts peak flow levels on Brays Bayou using a distributed, physics-based hydrologic model.
Bedient says FAS4 is “old technology” built for only one watershed. But FAS4 works well and it could be used as a prototype to develop a county-wide flood alert system. Such a system, operating real-time on a supercomputer during a flood, would not only allow more accurate updates, but would arm many more people with reliable, just-in-time environmental intelligence and enable them to make informed decisions about their safety.
Scaling the system to county-wide would be a big undertaking. Brays Bayou is one watershed covering 129 square miles; Harris County has 22 watersheds covering about 2,500 square miles. Each watershed would require individual modeling that would then be connected to create one big flood alert system.
This modeled connection is important because the bayous in Harris County are hydrologically linked. Simply running 22 flood alert systems on separate computers wouldn’t account for these hydrological connections.
Another benefit of modeling the 22 watersheds together on a supercomputer is that the modeling would significantly increase our knowledge of how flooding really happens in the Houston area. HPC provides better spatial information and faster computing runs that would give the public faster and better information during a flood.
The SSPEED Center has also led research on storm surge in Galveston Bay. Unlike bayou flooding or overland flow, which are caused by rain, storm surge is caused by tropical storms or hurricanes pushing sea water onshore.
In 2008, Hurricane Ike, a Category 2 storm, came ashore between the Bolivar Peninsula and Galveston Island. As a result of the storm’s track, the highest surge of 16-18 feet was focused on Bolivar, where almost all structures were demolished. Galveston Bay experienced some storm surge damage but was spared the worst.
Galveston Bay and the Houston Ship Channel are home to one of the largest petrochemical complexes in the world, where tremendous amounts of gasoline, plastics, and military-grade jet fuel are produced at low elevations vulnerable to storm surge. The Houston Ship Channel is one of the world’s busiest shipping lanes and is lined with above ground storage tanks. Consequently, the regional, state and even national economy are also vulnerable to storm surge impacts in Galveston Bay.
Since 2009, the SSPEED Center has been studying a regional storm surge protection system called Houston-Galveston Area Protection System. Working with Dr. Clint Dawson from the University of Texas at Austin, the SSPEED team studied historical storms to establish baseline storm surges. They then used the Stampede supercomputer to model a variety of hurricane sizes and paths and identify a reasonable worst-case storm for the Houston-Galveston region.
In this scenario, the highest storm surge would be directed at Galveston Bay and the Houston Ship Channel. The model showed a storm surge of 23-24 feet on the west side of the bay and almost 25 feet in the ship channel. Such a surge would cause significant damage to the petrochemical complex, the ship channel, and western Galveston Bay population centers.
As a result, the SSPEED Center developed a storm surge mitigation strategy called the Galveston Bay Park/Mid-Bay Alternative and then modeled its effectiveness. The Mid-Bay alternative consists of an in-bay berm system that runs along the ship channel, gate structures for small craft, a large navigation gate for ship traffic, and levees and elevated roads. Supercomputer modeling revealed that western Galveston Bay would be much more protected with the plan in place.
Increasing knowledge of regional hydrologic systems with supercomputing is vital to improving Houston’s flood resilience. Armed with a better understanding of the science of how individual watersheds work, how inland flooding occurs, how storm surge behaves at the coast, and how compound flooding puts coastal residents at increased risk, Houston—and everywhere else—can be better prepared for floods and hurricanes.