Forecasting Hurricane Impacts on CoastS – FHICS

PI: Nederhoff, Kees (Deltares USA)
Co-PI(s): van Dongeren, Ap (Deltares Netherlands) : Passeri, Davina (University of Central Florida) : Barnard, Patrick (USGS Pacific Coastal and Marine Science Center) : Veeramony, Jay (Naval Research Lab Stennis) : Roelvink, Dano (IHE Delft Institute for Water Education)
Start Year: 2021 | Duration: 3 years
Partners: Deltares Netherlands, University of Central Florida, USGS Pacific Coastal and Marine Science Center, Naval Research Lab Stennis, IHE Delft Institute for Water Education

Project Abstract:

Over the past few decades, the meteorological community has made considerable progress in forecasting hurricanes. Concurrently, the oceanographic and coastal communities have made significant advances in understanding physical hydrodynamic processes that drive coastal impacts due to hurricanes. While operational forecasts of storm surge and waves are now common practice and fairly robust, forecasts of morphological impacts are lagging. However, as much of the important physics has been incorporated in numerical models, which at the same time have become faster due to increased computational capabilities, it is now feasible to reliably compute the hydro-morphological response due to hydro-meteo events if accurate forcing and initial conditions (such as topography, vegetation, etc.) are known.

In cooperation with US agencies such as the Office of Naval Research (ONR), Navy Research Laboratory (NRL) and United States Geological Survey (USGS), coastal processes have been incorporated in the numerical modeling package Delft3D, which has been under continuous development since the 90’s and was acquired by the U.S Navy in 2001 as a platform for operational forecasts of nearshore hydrodynamics. In the ongoing ONR-funded “Increasing Fidelity of Morphological Storm Predictions” (IFMSIP) project, the new generation Delft3D-FM (Flexible Mesh) large-scale model was used to drive detailed morphodynamic XBeach models on domains of 10 x 2 kms with which accurate hindcasts of morphodynamic change including breaching were possible (Van der Lugt et al., 2019). USGS and Deltares are currently working on probabilistic coastal response models such as a Bayesian Network to predict storm impact at low computational cost across large spatial scales based on the Parameterized Island Gaussian Fit (PIG-F) method (Mickey et al., 2019) and CoSMoS-COAST (Vitousek et al. 2017, 2020 under review), a data-assimilated, ensemble Kalman filter shoreline model to predict shoreline change due to probabilistic hurricane tracks. We propose to integrate and further develop these existing components in order to make real-time forecasts of hurricane impacts on CONUS coasts.

BAA: Predicting Hurricane Coastal Impacts of continental US (CONUS) landing hurricanes
BAA Topic: Task 4: Forecasting of wave, surge, sediment transport (erosion and accretion above and below mean sea level), structure interaction and damage.