To enhance the forecasting of wave, surge sediment transport (erosion and accretion above and below mean sea level), structure interaction and damage, we propose to heavily leverage our existing forecasting capability and experience operating the ADCIRC Prediction System (APS). We will enhance the current APS for surge, wave and inundation calculations, interface APS with the XBeach model to better represent nearshore and cross-shore wave processes and accompanying inundation and to predict sediment transport associated with the storm events. Wave and water level information from both APS and from XBeach will then be used to predict structure interaction and damage. Our efforts will focus on implementation and evaluation of our modeling system for hindcast events and/or for reanalysis of forecast events. Then we will utilize meteorological forcing to produce a daily forecast of coastal impacts beginning five days prior to landfall for at least three named hurricanes per year. These runs will be deterministic, commensurate with our approach to forecasting surge, wave and inundation from tropical cyclones in the APS. Given this experience, we are confident we will meet the forecast objectives of the NOPP community approach to model evaluation and improvement.
RA Luettich, MV Bilskie, BO Blanton, Z Cobell, DT Cox, JC Dietrich, JG Fleming, I Ginis. “Forecasting Coastal Impacts from Tropical Cyclones along the US East and Gulf Coasts using the ADCIRC Prediction System.” Department of Defense, Office of Naval Research, National Oceanographic Partnership Program (NOPP), Predicting Hurricane Coastal Impacts FY21-24, 2021/04/06 to 2025/04/05, $1,400,000 (Dietrich: $295,000).
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.
