Emulator for Eroded Beach and Dune Profiles due to Storms

Dunes and beaches are vulnerable to erosion during storm events. Numerical models can predict beach response to storms with fidelity, but their computational costs, the domain-specific knowledge necessary to use them, and the wide range of potential future storm and beach conditions can hinder their use in forecasting storm erosion for short- and long-term horizons. We develop an emulator, which is an efficient predictive model that behaves like a numerical model, to predict the morphologic response of the subaerial beach to storms. Specific emphasis is placed on providing antecedent beach states as an input to the emulator and predicting the post-storm profile shape. Training data include beach profiles at multiple stages in a nourishment life cycle to assess if such a framework can be applied in locations that nourish as a coastal defense policy. Development and application of the emulator is focused on Nags Head, North Carolina, which nourishes its beaches to mitigate hazards of storm waves, flooding, and erosion. A high-fidelity, process-based morphodynamic model is used to train the emulator with 1250 scenarios of sea-storms and beach profiles. The post-storm beach state is emulated with a parameterized power-law function fit to the eroded portion of the subaerial profile. When the emulator was tested for a sequence of real storms from 2019, the eroded beach profiles were predicted with a skill score of 0.66. This emulator is promising for future efforts to predict storm-induced beach erosion in hazard warnings or adaptation studies.

A Gharagozlou, DL Anderson, JF Gorski, JC Dietrich (2022). “Emulator for Eroded Beach and Dune Profiles due to Storms.” Journal of Geophysical Research: Earth Surface, 127(8), e2022JF006620, DOI: 10.1029/2022JF006620.

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

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Virtual Conference: 2022 Ocean Sciences Meeting

Improved Wave Predictions with ST6 Physics and ADCIRC+SWAN

The Simulating WAves Nearshore (SWAN, Booij et al. 1999) model is used widely for predictions of waves in coastal regions. Like other spectral wave models, SWAN uses parameterizations to represent wave evolution due to sources (e.g. wind), sinks (e.g. whitecapping, bottom friction, depth-limited breaking), and resonance (e.g. quadruplet and triad wave-wave interactions). Each parameterization is based typically on observational data to represent the transfer of energy to, from, and between waves. It is necessary for each term to represent its physical process, but it is also necessary for the terms to be calibrated collectively to represent their combined effects on wave evolution. The calibrated wave predictions can then be coupled with models for circulation and coastal flooding, e.g. ADvanced CIRCulation (ADCIRC, Luettich et al. 1992).

SWAN release version 41.20 included a new “package” of wave physics (referred to as ST6 physics). This package has new parameterizations of wind input, whitecapping, swell dissipation, wind speed scaling, and other processes (Rogers et al. 2012). The ST6 physics have been adopted by other wave models (e.g. NOAA’s WaveWatch III, Liu et al. 2019), and it may become the preferred physics package for SWAN. However, because the ST6 physics package has changes to so many parameterizations, it is necessary to quantify its effects on wave predictions. Recent studies (e.g. Aydogan and Ayat 2021) have demonstrated the benefits of using the ST6 physics in the standalone version of SWAN, but its effects have not been quantified for the coupled ADCIRC+SWAN (Dietrich et al. 2011a), which is used for real-time forecasts during impending storms. Do the ST6 physics improve the ADCIRC+SWAN wave predictions?

CC Day, JC Dietrich (2022). “Improved wave predictions with ST6 Physics and ADCIRC+SWAN.” Shore & Beach, 90(1), 59-61, DOI: 10.34237/1009016.