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.
Predicting building damage as a function of hurricane hazards, building attributes, and the interaction between hazard and building attributes is a key to understanding how significant interaction reflects variation hazard intensity effect on damage based on building attribute levels. This paper develops multihazard hurricane fragility models for wood structure homes considering interaction between hazard and building attributes. Fragility models are developed for ordered categorical damage states (DS) and binary collapse / no collapse. Exterior physical damage and building attributes from rapid assessment in coastal Mississippi following Hurricane Katrina (2005), high-resolution numerical hindcast hazard intensities from the Simulating WAves Nearshore and ADvanced CIRCulation (SWAN+ADCIRC) models, and base flood elevation values are used as model input. Leave-one-out cross-validation (LOOCV) is used to evaluate model prediction accuracy. Eleven and forty-nine combinations of global damage response variables and main explanatory variables, respectively, were investigated and evaluated. Of these models, one DS and one collapse model met the rejection criteria. These models were refitted considering interaction terms. Maximum 3-s gust wind speed and maximum significant wave height were found to be factors that significantly affect damage. The interaction between maximum significant wave height and number of stories was the significant interaction term for the DS and collapse models. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of being in a higher rather than in a lower damage state for DS model were found to be 1.95 times greater for one- rather than for two-story buildings. For every 0.3 m (0.98 ft) increase in maximum significant wave height, the estimated odds of collapse were found to be 2.23 times greater for one- rather than for two-story buildings. Model prediction accuracy was 84% and 91% for DS and collapse models, respectively. This paper does not consider the full hazard intensity experienced in Hurricane Katrina; rather, it focuses on single-family homes in a defined study area subjected to wind, wave, and storm surge hazards. Thus, the findings of this paper are not applicable for events with hazards that exceed those experienced in the study area, from which the models were derived.