In an effort to combat the effects of climate change, a team of civil engineers from the University of Delaware are spearheading a project to identify the most effective models for calculating flood risk at coastal military installations. The project, which launched in spring 2022 and will run through spring 2026, is being funded by a $2.2 million grant from the U.S. Department of Defense (DoD).
The team, which includes faculty and students from institutions such as the Netherlands, North Carolina State University, the University of South Alabama, Texas A&M, and the United States Geological Survey (USGS), will be exploring numerical models that calculate total water levels in the face of sea level rise, tides, wind-induced surge, waves, and other environmental variables. The goal is to determine which approaches not only perform the best but are also the most cost-effective.
“Many military installations are located along the coast, and they can’t be easily relocated. They need to be protected,” said Jack Puleo, chair of UD’s Department of Civil and Environmental Engineering and project lead. “To do that, we need to understand what the flooding risk is.”
The team of researchers will apply their work to three military sites: the Virginia-based Naval Station Norfolk on the Atlantic Coast, Tyndall Air Force Base on the Gulf Coast of Florida, and the Ronald Reagan Ballistic Missile Defense Test Site on the Marshall Islands in the Pacific Ocean. The goal is to be able to accurately predict what kind of flooding or damage a certain site might experience during a hurricane impact, for example, when there’s been another foot of sea level rise.
“But it’s not just getting wet that’s important,” Puleo said. “It’s about flooding duration and depth. If a prediction says there will be 1 inch of water on a roadway, maybe you don’t care as much. But if it says you’ll have 1 foot of water for multiple tidal cycles, that’s important to know. It could hamper critical services and evacuation.”
The research has the potential to have broader implications for coastal communities, as the findings could identify which applications work best in which settings. Running high-fidelity models is not cheap or easy, so understanding which models are the most effective and cost-efficient is crucial.
The team will be testing a wide range of predictive models, from those that handle basic calculations to those that can produce highly localized results. Combining these models with witness accounts and existing data will help researchers “tease out the importance of knowing the fine details,” Puleo said. The question is how much information is really needed to make accurate predictions that could help these military installations become more resilient in the face of a changing climate, especially along the coast. It’s also about timing: the models have to be able to process information quickly enough so that there’s time for a response, such as moving assets out of the way if necessary.
The military is particularly interested in learning about the best options as there can be a steep cost associated with running the higher-end models. On the other hand, there could also be a steep cost with responding to an event that never happens if the model’s prediction doesn’t play out, or the opposite if an event turns unexpectedly catastrophic and there’s no time to respond. The team aims to provide a roadmap for when to use which model and what it will cost computationally or resource-wise to be able to do that.