Category Archives: Software
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.
How will Sea Level Rise affect the Storm Surge in Norfolk, Virginia?
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.
Storm Surge Predictions at Hyperlocal Sites
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.
Toward Prediction of High-resolution Maps of Hurricane-driven Coastal Flooding using Deep Learning
Poster: Spring 2022 Conferences
JF Gorski, JC Dietrich, RA Luettich, MV Bilskie, D Passeri, RC Mickey. “Toward deterministic, dynamic model forecasts of storm-driven erosion.” 2022 Ocean Sciences Meeting, Virtual Meeting, 2 March 2022.
JF Gorski, JC Dietrich, RA Luettich, MV Bilskie, D Passeri, RC Mickey. “Toward deterministic, dynamic model forecasts of storm-driven erosion.” Environmental, Water Resources, and Coastal Engineering Research Symposium, North Carolina State University, 4 March 2022.
Toward deterministic, dynamic forecasts of storm-driven erosion
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.
Downscaling with Head Loss due to Land Cover in Kalpana
Originally developed as a tool for visualizing ADCIRC output, Kalpana has evolved to include methods for downscaling ADCIRC water elevation results. The first method, now referred to as the static method, extrapolated ADCIRC water elevations horizontally until intersecting an equivalent DEM elevation. More information about the static method and about downscaling ADCIRC results with Kalpana can be found on an earlier post.
The static method has proven to be a useful tool but incorporates minimal physics. Therefore, a new method, referred to as the head loss method, has been introduced to include energy dissipation due to land cover during overland flow events. In this page, we describe the theory of the head loss method and provide examples for how to apply it using Kalpana.
Side-view schematic of downscaling methods. A one-dimensional schematic is displayed for each of the two downscaling methods, where the top figure is the static method and the bottom is the head loss method. In the static method, the water elevations from ADCIRC (blue hatched portion) are extrapolated as a flat surface until they intersect the DEM. In the head loss method, these water elevations are extrapolated to an energy cost surface (elevation plus cumulative head loss).