Welcome to the CCHT! We develop computational models for wind waves and coastal circulation, and then apply these models to high-resolution simulations of ocean behavior. Our goals are to understand how coastlines are threatened during storms, how materials are transported in the coastal environment, and how to communicate these hazard risks for use in decision support. Our research spans the disciplines of coastal engineering, numerical methods, computational mathematics, and high-performance computing.

In this web site, we share our research progress, from development to application, and from coding to publishing. Learn more about What We Do and how to Join Our Team.

Subgrid Modeling for Compound Flooding in Coastal Systems

Compound flooding, the concurrence of multiple flooding mechanisms such as storm surge, heavy rainfall, and riverine flooding, poses a significant threat to coastal communities. To mitigate the impacts of compound flooding, forecasts must represent the variability of flooding drivers over a wide range of spatial scales while remaining timely. One approach to develop these forecasts is through subgrid corrections, which utilize information at smaller scales to “correct” water levels and current velocities averaged over the model scale. Recent studies have shown that subgrid models can improve both accuracy and efficiency; however, existing models are not able to account for the dynamic interactions of hydrologic and hydrodynamic drivers and their contributions to flooding along the smallest flow pathways when using a coarse resolution. Here, we have developed a solver called CoaSToRM (Coastal Subgrid Topography Research Model) with subgrid corrections to compute compound flooding in coastal systems resulting from fluvial, pluvial, tidal, and wind-driven processes. A key contribution is the model’s ability to enforce all flood drivers and use the subgrid corrections to improve the accuracy of the coarse-resolution simulation. The model is validated for Hurricane Eta 2020 in Tampa Bay, showing improved prediction accuracy with subgrid corrections at 42 locations. Subgrid models with coarse resolutions (R2 = 0.70, 0.73, 0.77 for 3-, 1.5-, 0.75-km grids) outperform standard counterparts (R2 = 0.03, 0.14, 0.26). A 3-km subgrid simulation runs roughly 50 times faster than a 0.75-km subgrid simulation, with similar accuracy.

A Begmohammadi, D Wirasaet, N Lin, JC Dietrich, D Bolster, AB Kennedy (2023). “Subgrid Modeling for Compound Flooding in Coastal Systems.” Coastal Engineering Journal, online, DOI: 10.1080/21664250.2024.2373482.

Tomás & Molly get their Diplomas!

The CCHT celebrated the graduation of Tomás Cuevas López and Molly McKenna!

Tomás is now coastal scientist with DHI, but he worked remotely in Raleigh through the semester. Molly finished her BS and will pursue an MS degree and continue work in our DHS project. It was great to celebrate them at the graduation ceremony. We are proud of them!

Casey Dietrich, Molly McKenna, and Tomás Cuevas López after the graduation ceremony.

Brandon wins Outstanding Senior Award for Scholarly Achievement

Undergraduate student Brandon Tucker won an Outstanding Senior Award for Scholarly Achievement, which recognizes exceptional academic performance including participation in undergraduate research. Brandon was among four outstanding seniors recognized by our department.

[H]he continued undergraduate research as part of the Coastal and Computational Hydraulics Team with Associate Professor Casey Dietrich and graduate student Tomás [Cuevas] López. The project was related to predictions of coastal flooding due to hurricanes.

“I helped run more than 1 million CPU hours of hurricane models to train our machine-learning model called Concorde, which can predict storm-driven flooding in seconds,” Tucker explained. “I also created detailed Python examples for Kalpana, an intermediary model used by researchers across the country.”

“This was a lot of work,” Dietrich emphasized. “Each hurricane simulation can take several hours on a parallel computing cluster and generate gigabytes of data, and so it took about two months to complete the simulations. It would have taken much longer without Brandon’s help and creativity. He wrote scripts to automate the process to submit, monitor, and archive the simulations, and he contributed to a post-processing visualization script. His documentation and examples are now shared widely with all users of the software. Brandon is strong at the technical skills of computing and programming, but he also sees the larger picture and looks for ways to contribute.”

After graduation, Brandon will pursue a Master of Civil Engineering at CCEE with a focus on transportation systems, while also completing a Graduate Certification in City Design from the College of Design.

Congratulations to Brandon!

Posters: Summer 2024 Conferences

Prediction of Peak Water Levels during Tropical Cyclones with Deep Learning

Storm-driven flooding is a severe hazard for coastal communities and regions. Computational models can predict the combined effects of tides, winds, and flooding due to tropical cyclones, including in real-time, but requirements for the models’ runtime make it challenging to consider simulations of the full range of storm uncertainty. To address this problem, researchers have developed neural networks, trained on libraries of storm surge simulations, to predict ensembles of coastal flooding in seconds. However, existing neural networks do not consider the interaction between storm surge and astronomical tides nor storms of any duration. Moreover, they are trained on datasets tailored to represent only extreme conditions. We aim to develop a neural network to predict the peak total water levels for any storm at specific locations along the North Carolina coast.

In this research, we implemented a neural network to predict peak values for total water level (tides and storm surge) at multiple stations, considering astronomical tides and storm tracks of any duration as inputs. To create the training library, we simulated 1,813 synthetic tropical cyclones based on historical data in the North Atlantic Ocean, with a specific focus on storms that affect North Carolina. These simulations used a full-physics hydrodynamic model with variable spatial resolution of about 50 m near the coast. The outputs were downscaled to grayscale images with a higher and constant resolution of 15 m, enhancing the flood predictions by considering small-scale topographic features, and then used as training data for the neural network. The many-to-one deep learning model predicts a single peak total water level in time at multiple locations in space using time series of the offshore astronomical tide and track parameters as inputs. We used the model to make probabilistic predictions of peak total water levels for observed and perturbed tracks of several historical storms that affected North Carolina.

We showed that the neural network performed well (with errors ranging from 8 to 43 cm) in predicting peak total water levels at nine locations in North Carolina. We applied the neural network to make probabilistic predictions of peak total water levels for observed and perturbed tracks of historical storms. For each storm, the neural network predicted at nine stations for 101 storm scenarios (the true/historical storm and 100 perturbations) in less than 10 seconds. The performance for the observed historical storms was similar to those obtained in process-based simulations, but with a significant gain in computational runtime.

TA Cuevas López (2024). “Prediction of Peak Water Levels during Tropical Cyclones with Deep Learning,” North Carolina State University.

News: Oceans and Human Health Center

2024/03/19 – NCSU Civil, Construction, and Environmental Engineering
CCEE faculty to advance understanding of toxic algae blooms, protect human health as part of new NSF, NIEHS Center at NC State


Obenour will lead a project with Dietrich and Natalie Nelson (Department of Biological and Agricultural Engineering) focused on the development of models to predict the transport of cyanotoxins — toxins produced by cyanobacteria released in algae blooms — in coastal environments. The models will focus on coastal North Carolina, especially the estuaries and sounds where freshwaters mix with saline waters. With the models, researchers will evaluate where cyanotoxins may collect and where they may originate. They will also evaluate scenarios of future climate, such as how changes in temperature, river flows, and sea levels may affect the transport of cyanotoxin.

According to Obenour, “the research will protect public health by identifying cyanotoxin hotspots and by informing management actions to reduce cyanotoxin risks in the future.”

2024/02/28 – NCSU College of Sciences
NC State Receives $6.9 Million From NSF, NIEHS to Fund New Oceans and Human Health Center


NC C-CAPE will carry out three research projects. The goal of the first project is to understand the dynamics of harmful algal blooms and learn more about the presence and distribution of microcystin — a liver toxin — across the Pamlico-Albemarle Sound System, the country’s largest lagoonal estuary. They will then link spatiotemporal patterns to the contamination of seafood. The second project will define how microcystin mixtures influence mechanisms of liver toxicity in regulatory-relevant mammalian models and at-risk human populations. In the third project, researchers will work to predict microcystin distributions in water and seafood based on various environmental controls — and assess exposure risk in a changing climate. They will do so by integrating diverse data sets and coastal circulation modeling within a probabilistic modeling framework.