Welcome to the CCHT! We develop computational models for wind waves and coastal circulation, and then apply these models to high-resolution simulations of ocean behavior. Our goals are to understand how coastlines are threatened during storms, how materials are transported in the coastal environment, and how to communicate these hazard risks for use in decision support. Our research spans the disciplines of coastal engineering, numerical methods, computational mathematics, and high-performance computing.
In this web site, we share our research progress, from development to application, and from coding to publishing. Learn more about What We Do and how to Join Our Team.
As coastal circulation models have evolved to predict storm-induced flooding, they must include progressively more overland regions that are normally dry, to where now it is possible for more than half of the domain to be needed in none or only some of the computations. While this evolution has improved real-time forecasting and long-term mitigation of coastal flooding, it poses a problem for parallelization in an HPC environment, especially for static paradigms in which the workload is balanced only at the start of the simulation. In this study, a dynamic rebalancing of computational work is developed for a finite-element-based, shallow-water, ocean circulation model of extensive overland flooding. The implementation has a low overhead cost, and we demonstrate a realistic hurricane-forced coastal flooding simulation can achieve peak speed-ups near 45% over the static case, thus operating now at 80−90% efficiency.
During coastal storms, forecasters and researchers use numerical models to predict the magnitude and extent of coastal flooding. These models must represent the large regions that may be affected by a storm, and thus, they can be computationally costly and may not use the highest geospatial resolution. However, predicted flood extents can be downscaled (by increasing resolution) as a post-processing step. Existing downscaling methods use either a static extrapolation of the flooding as a flat surface, or rely on subsequent simulations with nested, full-physics models at higher resolution. This research explores a middle way, in which the downscaling includes simplified physics to improve accuracy. Using results from a state-of-the-art model, we downscale its flood predictions with three methods: (1) static, in which the water surface elevations are extrapolated horizontally until they intersect the ground surface; (2) slopes, in which the gradient of the water surface is used; and (3) head loss, which accounts for energy losses due to land cover characteristics. The downscaling methods are then evaluated for forecasts and hindcasts of Hurricane Florence (2018), which caused widespread flooding in North Carolina. The static and slopes methods tend to over-estimate the flood extents. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from a higher-resolution, full-physics model. These results are encouraging for the use of these downscaling methods to support decision-making during coastal storms.
