News: Modeling Florence’s Storm Surge

2019/04/26 – NCSU College of Engineering
After the Storm

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Dr. Casey Dietrich, an assistant professor in the Department of Civil, Construction, and Environmental Engineering (CCEE), leads the Coastal and Computational Hydraulics Team and develops computational models that predict storm surge and coastal flooding. Using the model ADCIRC, the team makes predictions about how high sea waters will rise, which areas will be flooded and for how long. These predictions are made for the entire coastline, and then his team visualizes the flooding at the scales of individual buildings and coastal infrastructure. During Florence, Dietrich’s team and collaborators acted as liaisons for state emergency managers to aid their decision making.

“The models are just one data point among many, but they’re helpful in understanding hazards and used to make predictions in real time — partly to make decisions about evacuation, where to deploy resources after, safe places to put emergency vehicles and water supplies,” he said.

The state emergency managers are able to use the flooding predictions to get immediate estimates on damages, which helps communities that are figuring out how much recovery will cost.

After Hurricane Matthew in 2016, Dietrich and his colleagues improved the models’ ability to forecast encroaching water along shorelines. Post-Florence, Dietrich said the research focus is to speed up the model and allow for more permutations to see what might happen if a storm slows down or shifts direction.

Downscaling ADCIRC Flooding Inundation Extents Using Kalpana

The ADCIRC modeling system is used often to predict coastal flooding due to tropical cyclones and other storms. The model uses high resolution to represent the coastal environment, including flow pathways (inlets, man-made channels, rivers) and hydraulic controls (barrier islands, raised features). However, due to the use of large domains to represent hazards on coastlines in an entire state or multiple states, the highest resolution is typically about 20 to 50 m in coastal regions. Thus, there is a potential gap between the flooding predictions and the true flooding extents. We have developed a geospatial software to downscale the flooding extents to higher resolution.

ADCIRC vs. extrapolated water levels, plan view. This image shows the difference in prediction of flooding extents, with the pale blue portion representing the original ADCIRC flooding extents and red representing the extrapolated extents.

The following documentation is for downscaling the flooding predictions by using Kalpana. This software was created originally to view ADCIRC outputs as either ESRI shapefiles or KML files (for viewing in Google Earth). ADCIRC (the ADvanced CIRCulation model) uses finite element methods to predict water levels throughout the modeled domain. Although this model is able to provide accurate predictions in a matter of minutes, these predictions have a limited resolution and are not able to provide information at the scale of buildings, roadways, and other critical infrastructure.

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Seminar: Coffee & Viz

Seminar: Geospatial Forum

Presentation: NSF Workshop 2018

Improving Accuracy of Real-Time Storm Surge Inundation Predictions

Emergency managers rely on fast and accurate storm surge predictions from numerical models to make decisions and estimate damages during storm events. One of the challenges for such models is providing a high level of resolution along the coast without significantly increasing the computational time. Models with large domains, such as the ADvanced CIRCulation (ADCIRC) model used in this study, are accurate in predicting water levels and their variation in complex coastal regions, however their spatial resolution may limit their predictions of flooding at the scale of buildings, roadways, and critical infrastructure.

A new tool has been developed that uses Geographic Information System (GIS) scripts to enhance the resolution of maximum water level predictions at the boundary of predicted flooding using a high-resolution Digital Elevation Model (DEM). The water levels predicted by the lower resolution model are extrapolated outward to where the water would intersect with the higher resolution elevation dataset. The result is a highly-refined flooding boundary that represents inundation on scales smaller than the typical ADCIRC mesh resolution. This tool can process a 15-m DEM for all 32 coastal counties of the state of North Carolina in less than 15 minutes during a storm event.

Comparison of results using spatial building datasets showed that for a simulation of Hurricane Matthew, 2,353 buildings were predicted to be flooded in Carteret County, NC, prior to enhancing resolution and 3,298 post-enhancement, an increase of 40 percent. In Dare County, the increase was 22 percent. This dramatic increase in flooded buildings shows the importance of achieving high accuracy in floodplains, as a relatively small change in predicted flooding extent can have a substantial impact on the predicted number of flooded buildings. The validity of these results was tested via comparisons to results of an ADCIRC model with the same 15-m resolution as the DEM in Dare County. Dare County is a coastal region with widely-varying topography and land cover, and preliminary comparisons have shown that the GIS method is accurate in coastal regions with steeper slopes and less accurate in flatter, low-lying areas.

N Tull (2018). “Improving Accuracy of Real-Time Storm Surge Inundation Predictions,North Carolina State University.