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TA Cuevas López, BJ Tucker, JC Dietrich. “Prediction of High-Resolution Maps of Hurricane-Driven Coastal Flooding Using Machine Learning.” 17th U.S. National Congress on Computational Mechanics, Albuquerque, New Mexico, 24 Jul 2023.
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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.
This research builds upon a process that uses maximum water elevation output from the Advanced Circulation (ADCIRC) model and downscales these results to a finer resolution by extrapolating the water levels to small-scale topography. This downscaling process is referred to as the static method. The method was originally designed for use in North Carolina (NC), where results from an ADCIRC model designed specifically for NC were downscaled to a set of NC topographical data. By joining the static method with an ADCIRC output visualization tool, the downscaling process is now able to run faster with the same level of accuracy and can run on any ADCIRC model with downscaling data from any geographical region or given resolution. This process is used to provide extra guidance to emergency managers and decision makers during hurricanes.
The downscaling process is also improved by adding physics using the slopes method and the head loss method. The slopes method incorporates the slopes of the water levels produced by ADCIRC, rather than only the value of the water level. By interpolating ADCIRC output water elevation points into a smooth surface, slopes of this surface can be used to influence the elevations of downscaled water levels. The head loss method adds friction loss due to variations in the ground surface based on land cover types and friction associated with each type. As water travels over any surface, head loss, or a loss in energy, occurs at different rates depending on the surface roughness. This rudimentary hydrologic principle is applied to increase the accuracy of the downscaling process at minimal cost. The downscaling methods are applied for results from an ADCIRC simulation used in real-time forecasting, and then compared with results from an ADCIRC simulation with 10 times more resolution in Carteret County, NC. The static method tends to over-estimate the flood extents, and the slopes method is similar. However, the head loss method generates a downscaled flooding extent that is a close match to the predictions from the higher-resolution, full-physics model.
By improving the accuracy of downscaling methods at minimal computational cost and expanding the applicability of these downscaling methods, these methods can be used by emergency managers to provide a better estimation of flooding extents while simulating storm events.