Seminar: EWC

JC Dietrich. “Machine Learning Models for Coastal Hazards Predictions, or What I Learned on My Sabbatical.” Environmental, Water Resources, and Coastal Engineering (EWC) Seminar Series, North Carolina State University, Raleigh, North Carolina, 4 Dec 2023.

Conferences: Fall 2023

TA Cuevas López, BJ Tucker, DL Anderson, JC Dietrich. “Prediction of High-resolution Maps of Storm Driven Coastal Flooding.” 3rd International Workshop on Waves, Storm Surge, and Coastal Hazards, Notre Dame, Indiana, 1-6 Oct 2023.

TA Cuevas López, BJ Tucker, DL Anderson, JC Dietrich. “Prediction of High-resolution Maps of Storm Driven Coastal Flooding.” 2023 ASBPA National Coastal Conference, Providence, Rhode Island, 11-13 Oct 2023.

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Conference: USNCCM 17

JL Woodruff, JC Dietrich, D Wirasaet, AB Kennedy, D Bolster. “Storm Surge Predictions from Ocean- to Subgrid-Scales.” 17th U.S. National Congress on Computational Mechanics, Albuquerque, New Mexico, 24 Jul 2023.

JS Knowles, JC Dietrich, JT Voight. “Storm Surge Predictions at Hyperlocal Sites.” 17th U.S. National Congress on Computational Mechanics, Albuquerque, New Mexico, 24 Jul 2023.

TA Cuevas López, BJ Tucker, JC Dietrich. “Prediction of High-Resolution Maps of Hurricane-Driven Coastal Flooding Using Machine Learning.” 17th U.S. National Congress on Computational Mechanics, Albuquerque, New Mexico, 24 Jul 2023.

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Subgrid Corrections in Storm-Driven Coastal Flooding

Coastal flooding models based on the numerical solution of the 2D shallow water equations are used widely to predict the timing and magnitude of inundation during storms, both in real-time forecasting and long-term design. Constraints on computing time, especially in forecasting, can limit the models’ spatial resolution and hence their accuracy. However, it is desirable to have fast flooding predictions that also include the best-available representation of flow pathways and barriers at the scales of critical infrastructure. This need can be addressed via subgrid corrections, which use information at smaller scales to ‘correct’ the flow variables (water levels and current velocities) averaged over the model scale.

In this dissertation, subgrid corrections have been added to the ADvanced CIRCulation (ADCIRC) model, a widely used, continuous-Galerkin finite-element based, shallow water flow model. This includes the full derivation of averaged governing equations, closure approximations, and subgrid implementation into the source code. Testing of this new model was first performed on 3 domains: an idealized winding channel, a tidally influenced bay in Massachusetts, and a regional storm surge model covering Calcasieu Lake in Southwestern Louisiana with forcing from Rita (2005). By pre-computing the averaged variables from high-resolution bathy/topo data sets, the model can represent hydraulic connectivity at smaller scales. This allows for a coarsening of the model and thus faster predictions of flooding, while also improving accuracy. The implementation permits changing a logic-based wetting and drying algorithm to a more desirable logic-less algorithm, and requires averaging correction factors on both an elemental and vertex basis. This new framework further increases efficiency of the model, and is general enough to be used in other Galerkin-based, finite-element, hydrodynamic models. It is shown that the flooding model with subgrid corrections can match the accuracy of the conventional model, while offering a 10 to 50 times increase in speed.

Next, higher level corrections to bottom friction and advection were incorporated into the subgrid model, and the framework was expanded and tested at the ocean-scale. It was hypothesized that by adding higher-level corrections to the model and applying them to ocean-scale domains, accurate predictions of storm surge at the smallest coastal scales can be obtained. To accomplish this, higher-level corrections were derived and implemented into the governing equations and extensive elevation and landcover data sets were curated to cover the South Atlantic Bight region of the U.S. Atlantic Coast. From there, the subgrid model was tested on an ocean-scale domain with tidal and meteorological forcing from Matthew (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 (RMSE = 0.41 m) is better than that of a conventional model with relatively fine spatial resolution (RMSE = 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.

Finally, subgrid corrections were systematically tested on a series of five ocean-scale meshes with minimum nearshore resolutions ranging from around 60 m on the highest resolution mesh to 1000 m on the coarsest mesh. This study aimed to find the mesh resolution that offered the best trade-off between accuracy and efficiency. The limitations of the subgrid model were explored and guidelines for future users were established. In all, it was found that the primary limitation to the subgrid model came from the aliasing of important flow-blocking features such as barrier islands in the coarsest resolution meshes. However, in areas without these features subgrid corrections can offer tremendous advantages while running on very coarse meshes.

The work completed in this dissertation moves the science of subgrid corrections forward by integrating the corrections into a widely used ocean-circulation and storm surge model. This work offers improvements to both hurricane storm surge forecasting and long-term design by allowing for reduced run-times and increased accuracy on coarsened numerical meshes.

JL Woodruff (2023). “Subgrid Corrections in Storm-Driven Coastal Flooding,” North Carolina State University.

Conference: ADCIRC Users Meeting 2023

JL Woodruff, JC Dietrich, D Wirasaet, AB Kennedy, D Bolster. “Storm Surge Predictions from Ocean- to Subgrid-Scales.” ADCIRC Users Meeting, Baton Rouge, Louisiana, 8 Jun 2023.

JS Knowles, JC Dietrich. “Storm Surge Predictions at Hyperlocal Sites.” ADCIRC Users Meeting, Baton Rouge, Louisiana, 8 Jun 2023.

TA Cuevas López, BJ Tucker, JC Dietrich. “Prediction of High-Resolution Maps of Hurricane-Driven Coastal Flooding Using Machine Learning.” ADCIRC Users Meeting, Baton Rouge, Louisiana, 8 Jun 2023.

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Johnathan Woodruff defends PhD Dissertation

We celebrate another successful defense! Johnathan defended his PhD dissertation to a mix of in-person and remote attendees, who had a lot of interest in his research on subgrid corrections for storm surge modeling. Congrats to Johnathan!

Johnathan starts his dissertation defense.

Dissertation Defense: Johnathan Woodruff

Johnathan Woodruff. “Storm-driven coastal flooding predictions from subgrid- to ocean-scales.” Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina, 19 May 2023.

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, published online, DOI: 10.1007/s11069-023-05975-2.