To better predict long-term flooding risk, scientists at the Department of Energy’s Oak Ridge National Laboratory developed a 3D modeling framework that captures the complex dynamics of water as it flows across the landscape. The framework seeks to provide valuable insights into which communities are most vulnerable as the climate changes, and was developed for a project that’s assessing climate risk and mitigation pathways for an urban area along the Southeast Texas coast.
The modeling framework offers a powerful tool for urban planning by providing robust estimates of both frequent and rare flood events. By modeling the physical processes that transform rainfall into runoff, the framework accounts for factors such as land cover, soil properties and slope of the land. These elements, incorporated alongside population density data, provide a unique perspective on flood risk across vast areas such as river basins. This comprehensive approach is detailed in a study published in the Journal of Hydrology.
The modeling capability was developed for the Southeast Texas Urban Integrated Field Laboratory, or IFL, a DOE project using multidisciplinary science to inform pathways for climate resilience in the Beaumont-Port Arthur, Texas, region. The area is home to the nation’s largest oil refinery and is a major industrial center for the United States. Its proximity to the Gulf Coast makes the region vulnerable to flooding and land subsidence — the gradual sinking of the ground over time — with additional stressors from population density and pollution presenting multiple challenges to local decision-makers.
“This new state-of-the-art model not only estimates the streamflow magnitude of rare events such as a 100-year flood, but it also quantifies its associated flood depth, allowing us to assess the impact on the population directly,” said Gabriel Perez, who co-led the work as a postdoctoral researcher in ORNL’s Watershed Systems Modeling group and is currently an assistant professor at Oklahoma State University. “That’s a very unique framework that can help us better understand how flood risk is evolving due to climate change and urbanization.”
In developing such a model, “it becomes much more important to rely on the underlying physics of flooding because those are true throughout time, as opposed to a model that’s calibrated under today’s conditions and might not be right in tomorrow’s climate or tomorrow’s cities,” said Ethan Coon, project co-lead, senior R&D staff, and principal investigator for ORNL’s research for the Southeast Texas Urban IFL.
The new framework incorporates the Amanzi-ATS software, an integrated surface-subsurface hydrological model developed by ORNL, Los Alamos National Laboratory, Lawrence Berkeley National Laboratory and Pacific Northwest Natural Laboratory. Amanzi-ATS provides a holistic view of hydrological systems. It captures subsurface flows, considering complex geology and soil properties, and accounts for unique topography, including the representation of water infrastructure and disturbances such as changes in land use by accelerated urbanization. Results may identify new flood-prone areas, specifically pinpointing risks for area populations.