Category Archives: Kalpana
We celebrate another successful defense! Tomás defended his MS thesis to a packed conference room of faculty, students, and family members, who had a lot of interest in his research on deep neural networks for storm surge predictions. Congrats to Tomás!
Thesis Defense: Tomás Cuevas López
Seminar: EWC
Conferences: Fall 2023
Poster: Undergraduate Research Symposium 2023
JT Voight, JS Knowles, TA Cuevas López, JC Dietrich. “How will Sea Level Rise affect the Storm Surge in Norfolk, Virginia?” Undergraduate Research Symposium, North Carolina State University, 27 July 2023.
Conference: USNCCM 17
Poster: EWC Symposium 2023
JS Knowles, JC Dietrich. “Storm Surge Predictions at Hyperlocal Sites“. Environmental, Water Resources, and Coastal Engineering Research Symposium, North Carolina State University, 10 March 2023.
TA Cuevas López, BJ Tucker, JC Dietrich. “Toward Prediction of High-resolution Maps of Hurricane-driven Coastal Flooding using Deep Learning“. Environmental, Water Resources, and Coastal Engineering Research Symposium, North Carolina State University, 10 March 2023.
Downscaling of Real-Time Coastal Flooding Predictions for Decision Support
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.