Numerical Extensions to Incorporate Subgrid Corrections in an Established Storm Surge Model

Inundation models represent coastal regions with a grid of computational points, often with varying resolution of flow pathways and barriers. Models based on coarse grid solutions of shallow water equations have been improved recently via the use of subgrid corrections, which account for information (ground surface elevations, roughness characteristics) at smaller scales. In this work, numerical approaches of an established storm surge model are extended to include subgrid corrections. In an attempt to maintain continuity with existing users and results, model extensions were limited to those needed to provide basic subgrid capabilities, and included two major additions. First, a finite volume method is used to incorporate corrections to the mass and momentum equations using high-resolution ground surface elevations. Second, the no-slip condition imposed on the B-grid wet/dry interface in the model is modified to a slip condition to enable flows in channels with widths comparable to cell size. Numerical results demonstrate these numerical extensions can significantly enhance the accuracy of the model’s predictions of coastal flooding, with low additional computational cost.

A Begmohammadi, D Wirasaet, AC Poisson, JL Woodruff, JC Dietrich, D Bolster, AB Kennedy (2023). “Numerical extensions to incorporate subgrid corrections in an established storm surge model.” Coastal Engineering Journal, 65(2), 175-197, DOI: 10.1080/21664250.2022.2159290.

Storm Surge Predictions from Ocean to Subgrid Scales

The inland propagation of storm surge caused by tropical cyclones depends on large and small waterways to connect the open ocean to inland bays, estuaries, and floodplains. Numerical models for storm surge require these waterways and their surrounding topography to be resolved sufficiently, which can require millions of computational cells for flooding simulations on a large (ocean scale) computational domain, leading to higher demands for computational resources and longer wall-clock times for simulations. Alternatively, the governing shallow water equations can be modified to introduce subgrid corrections that allow coarser and cheaper simulations with comparable accuracy. In this study, subgrid corrections are extended for the first time to simulations at the ocean scale. Higher-level corrections are included for bottom friction and advection, and look-up tables are optimized for large model domains. Via simulations of tides, storm surge, and coastal flooding due to Hurricane Matthew in 2016, the improvements in water level prediction accuracy due to subgrid corrections are evaluated at 218 observation locations throughout 1500 km of coast along the South Atlantic Bight. The accuracy of the subgrid model with relatively coarse spatial resolution (ERMS = 0.41 m) is better than that of a conventional model with relatively fine spatial resolution (ERMS = 0.67 m). By running on the coarsened subgrid model, we improved the accuracy over efficiency curve for the model, and as a result, the computational expense of the simulation was decreased by a factor of 13.

JL Woodruff, JC Dietrich, D Wirasaet, AB Kennedy, D Bolster (2023). “Storm surge predictions from ocean to subgrid scales.” Natural Hazards, 117, 2989–3019, DOI: 10.1007/s11069-023-05975-2.

Poster: EWC Symposium 2023

JS Knowles, JC Dietrich. “Storm Surge Predictions at Hyperlocal Sites“. Environmental, Water Resources, and Coastal Engineering Research Symposium, North Carolina State University, 10 March 2023.

Storm Surge Predictions at Hyperlocal Sites

TA Cuevas López, BJ Tucker, JC Dietrich. “Toward Prediction of High-resolution Maps of Hurricane-driven Coastal Flooding using Deep Learning“. Environmental, Water Resources, and Coastal Engineering Research Symposium, North Carolina State University, 10 March 2023.

Toward Prediction of High-resolution Maps of Hurricane-driven Coastal Flooding using Deep Learning

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News: Preparing for a Changing Climate

2023/01/11 – UDaily, University of Delaware
UD civil engineers lead research to examine models for coastal readiness at U.S. military bases

University of Delaware civil engineers are leading a multi-institutional effort to identify the best models to calculate flood risk at coastal military installations where climate change threatens to increase the risk of flood damage from sea level rise and storm surge.

The four-year project, which launched in mid-2022 and will run through spring 2025, is funded by a $2.2 million grant from the U.S. Department of Defense (DoD). Project partners include faculty and students from the Netherlands, North Carolina State University, the University of South Alabama, Texas A&M and the United States Geological Survey (USGS).

“The goal is to provide guidance to the DoD about the strengths and weaknesses of each model in comparison. They’re all going to have things they’re good with and things they struggle with,” Dietrich said. Those comparisons will help the agencies decide what types of models they want to use to get what types of information — depending on how much time, effort and funding they want to commit.

There’s also a goal of reducing cost and building smarter models, he said.

“If we are able to improve our predictions at very specific sites along the coast, we also can have better predictions at other specific sites along the coast, like someone’s house or a bridge or other infrastructure,” Dietrich said.

Jessica assists with Field Research

MS student Jessica Gorski assisted fellow NOPP collaborators at University of Georgia with field work on Jekyll Island, Georgia. The team surveyed a large portion of the beach and deployed several sensors to measure wave runup. Jekyll Island experienced significant storm-driven erosion during the 2022 Hurricane Season and these elevation data points will be used to evaluate the erosional impact of two recent storms (Hurricanes Ian and Nicole).

Jessica Gorski surveying beach transects on Jekyll Island, Georgia.