Author Archives: Casey Dietrich
News: NC State on the Coast
NC State faculty and students are helping to keep coastal communities healthy through the North Carolina Center for Coastal Algae, People and Environment
NC C-CAPE was featured on the NC State homepage. Lots of information and quotes from folks in the center, including great photos of our colleagues in the field and laboratory. It is fun to contribute to such a large, meaningful research effort.
“In the past few months, we’ve officially started to sample as NC C-CAPE,” [Barrett] Rose said. “It was a shock to see the magnitude of how much we were actually studying. It went from a small pilot study to a huge center effort.”
Data collection and analysis is only the first part of the work NC C-CAPE seeks to do. While harmful algal blooms are common in fresh waters across the U.S. and the world, major data gaps around the issue exist. [Astrid] Schnetzer’s data will inform NC C-CAPE’s other two projects, which focus on predicting the health risks of toxic algal blooms on mammals and humans, as well as considering how factors like climate change will affect future toxin levels in water and seafood.
“The most exciting aspect of NC C-CAPE for me is that the research doesn’t end where my expertise ends,” said Schnetzer. “What we learn from the field about algal toxins is handed to the next team to look at the bigger picture on the ecosystem level and in connection to human health.”
Wind and Rain Compound with Tides to Cause Frequent and Unexpected Coastal Floods
Subgrid Modeling for Compound Flooding in Coastal Systems
Tomás & Molly get their Diplomas!
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!
Brandon wins Outstanding Senior Award for Scholarly Achievement
[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!
Prediction of Peak Water Levels during Tropical Cyclones with Deep Learning
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.