Predicting Damage from Hurricanes Before They Make Landfall

Weather forecasting systems today predict ever more accurately where hurricanes will make landfall, but tomorrow they may also predict how much damage the hurricanes will do. Maitane Olabarrieta, Ph.D., associate professor, and Arthriya Subgranon, Ph.D., assistant professor, in the Department of Civil & Coastal Engineering within the Engineering School of Sustainable Infrastructure & Environment at the UF Herbert Wertheim College of Engineering, are the principal investigators of a multi-organizational project sponsored by the Office of Naval Research. 

Photo of Maitane Olabarrieta
Maitane Olabarrieta is an associate professor in the Department of Civil & Coastal Engineering. Her research interests cover diverse oceanographic and long-term morphodynamic evolution problems that affect our coasts.

This four-year, $1M project aims to improve capabilities to forecast coastal impacts from land-fall hurricanes, including waves, total water levels, flooding extent and duration, maximum current speeds, sediment transport, damage and losses to structures. The ability to accurately predict the impacts of extreme storms is key for coastal hazard mitigation. 

Dr. Olabarrieta will manage four sub-teams to develop a computational framework that will predict the types of damage that will be caused by the imminent landfall of hurricanes along the eastern U.S. seaboard and the Gulf of Mexico. The project will involve: 1) developing the computational architecture needed to run the coastal impact models in forecast mode, 2) running the models in real-time forecast mode, 3) verifying the results of the models, and 4) analyzing the most efficient ways of disseminating the results (and the uncertainty associated with the results) to the public.  

Coastal hazards will be forecast using the open-source, community-developed Coupled Ocean Atmosphere Waves Sediment Transport (COAWST) modeling system. The result of the modeling will be a series of static and dynamic grids that will enable prediction of gross coastal hazards at the regional scale and detailed morphological change at the local scale, where the highest impacts are predicted. COAWST will be used to forecast wave, sediment, surge, and structure response (WSSSR). This COAWST-WSSSR forecasting system will be applied to directly predict the coastal response to landfall hurricanes.   

Under the guidance of Dr. Olabarrieta and Dr. Subgranon, UF will take the lead on developing the computational architecture throughout 2021. Once the architecture is complete, the forecasting system and developed grids will be operated at each of the four institutions that are part of the overall team – the Gulf Mexico sub-team located at Louisiana State University, the FL sub-team located at UF, the East Coast sub-team located at Fathom Science in Raleigh, NC, and the Northeast sub-team located at USGS Woods Hole in Woods Hole, MA. 

“The framework will be constructed to run autonomously. When a hurricane is within five days of predicted landfall, we will begin providing a daily forecast and continue until landfall occurs,” Dr. Olabarrieta said. 

Data for storm damage predictions will be collected during hurricane seasons in years 2022-2024. In off-season periods during those years, selected storms will be hind-casted. That is, actual storm damage measurements to coastal topography, infrastructure (roads and bridges) and structures (commercial and residential) will be compared to the predictions made by the forecasting models. These comparisons will be used to determine the effectiveness and sensitivity of the forecasts. 

Results from this exemplary National Oceanographic Partnership Program project, such as the development and verification of coastal flooding, erosion and infrastructure damage forecasting systems, will be highly beneficial to other government research and coastal management programs focused on coastal hazards and risk. The results of the forecasting system will be made available via a Thematic Real-time Environmental Distributed Data Services data server, and they will be displayed through a portal. Lessons learned within this project will be disseminated in conferences and will be incorporated in the graduate classes of the principal investigators.