COAWST WSSSR Coupled Ocean Atmosphere Waves Sediment Transport Waves, Sediment, Surge and Structure Response Forecasting System
PI: Olabarrieta Lizaso, Maitane (University of Florida)
Co-PI(s): Subgranon, Athriya (University of Florida) : He, Ruoying (Fathom Science LLC) : Hegermiller, Christie (US Geological Survey) : Sherwood, Christopher (US Geological Survey) : Warner, John (US Geological Survey) : Xue, Z. George (Louisiana State University) : Zambon, Joseph (Fathom Science LLC)
Start Year: 2021 | Duration: 4 years
Partners: University of Florida, Fathom Science LLC, US Geological Survey, Louisiana State University
The ability to accurately forecast coastal change from large storms such as hurricanes is necessary to characterize military battlespace and civilian hazards. Important oceanic and terrestrial parameters include wave characteristics (height, period, and direction), water levels, currents, coastal morphologic response, and damage to infrastructure. Numerical models of coastal atmosphere-ocean processes and morphological change resolve complex physics and can make detailed forecasts. The fidelity of these types of models, when hindcasting past extreme storm events, depends on several factors, including atmospheric forecast uncertainty,
model physics, open boundary conditions, model spatial resolution, and coastal elevation and land use.
Forecasting the coastal impacts of extreme storms poses technical and scientific challenges. Given the urgent societal need for accurate coastal impact forecasting systems, it is imperative that we 1) develop the computational architecture needed to run these types of models in forecast mode, 2) run the coastal impact models in real-time forecast mode, 3) verify the results of the model, and 4) analyze the most efficient ways of disseminating the results and the uncertainty associated with these results to the public.
We will develop a real-time forecasting system to meet these needs, based on the Coupled Ocean Atmosphere Waves Sediment Transport (COAWST) framework to predict the coastal impacts from landfall hurricanes, including waves, total water levels, flooding extent and
duration, maximum current speeds, sediment transport (erosion and accretion above and below mean sea level), integrated hydrographs, structure interaction, and damage. The drivers for the forecasting system will be written in Python. The system is conceptualized such that it is easily transferable within the community and allows further nesting for specific applications and the inclusion of new grids. A series of static and dynamic grids 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.
Results from this exemplary NOPP project (e.g. development/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 (NOAA, FEMA, USGS, NSF, NPS, and many other agencies).