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RENCI Dataset Aids Storm Surge Prediction and Management


CHAPEL HILL, N.C., Dec. 19 -- Researchers at the Renaissance Computing Institute (RENCI) have compiled new data on ocean floor and coastal typography that will help, state and local emergency managers and scientists who model, predict, plan for and respond to floods and storms along the North Carolina coast.

The dataset, dubbed TopoBathy because it combines bathymetry (ocean floor topography) and coastline topography information, will be used to create more accurate storm surge model application, including storm surge forecasts and higher quality floodplain maps.

"The physical features of the earth's surface -- whether it is offshore and underwater or on land -- are very important in determining the response of the coastal ocean to strong storm events," said Rick Leuttich, director of the Institute for Marine Sciences, a University of North Carolina research center in Morehead City.

"Bathymetric and topographic features block waves and currents, dissipate energy, channel water movement and generally effect how easy it is for a strong storm to move water. Thus it is critical to create accurate databases of this information for use in modeling storm surge."

Leuttich, who collaborates with RENCI on coastal modeling, is one of the developers of the advanced circulation model (ADCIRC) storm surge model, and using the TopoBathy dataset as input for ADCIRC will allow the model to more accurately predict how deep and how far inland water will surge during a major coastal storm.

The TopoBathy dataset also will be used as input for new high-resolution coastal storm models, which RENCI is producing in order to create updated floodplain maps of coastal areas. That project, funded by the state's Division of Emergency Management in cooperation with the Federal Emergency Management Agency (FEMA) began last August with RENCI working to produce more than 1,000 tracks of possible coastal storms in order to create floodplain maps that take advantage of new high-resolution data and models and account for new coastal developments. With RENCI compiling the TopoBathy dataset this fall, RENCI's disaster research team decided to integrate this unique, multidimensional data into the floodplain models to further enhance them.

"As we began computing the models for the floodplain mapping project, it became apparent that this new dataset would greatly enhance our model result," said Ken Galluppi, RENCI's manager of emergency management and disaster research projects. "We are now running our floodplain models using the better datasets, and we will turn those models over to the state early next year for them to develop updated floodmaps for FEMA. Combining multiple terrestrial and marine data into a single dataset at this resolution is a major improvement over existing data and should improve the resulting floodplain maps."

The coastal topography data was derived from a Light Detection and Ranging (LIDAR) survey system through the state-funded North Carolina Floodplain Mapping Program. The LIDAR is mounted on an airplane and collects three-dimensional data points from light reflected off objects such as buildings, trees and the ground.

The National Geospatial Development Center, the U.S. Geological Survey, the Army Corp of Engineers, and the National Oceanic and Atmospheric Administration also provided TopoBathy data.

About RENCI

The Renaissance Computing Institute brings together computer and discipline scientists, artists, humanists, industry leaders, entrepreneurs, state leaders and educators for collaborations designed to reshape science, the economy, the state of North Carolina and the world. RENCI leverages its expertise and resources in leading edge computing, networking and data technologies to ignite innovation and find solutions to previously intractable problems. Founded in 2004 as a major collaborative venture of Duke University, North Carolina State University, the University of North Carolina at Chapel Hill and the state of North Carolina, RENCI is a statewide virtual organization. For more, see www.renci.org.

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Source: RENCI

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