Peter Sheng, Ph.D., a professor emeritus and adjunct research professor in coastal and oceanographic engineering, led a team of scientists to assess the value of coastal wetlands for reducing flood damage of coastal communities in New Jersey, New York and Connecticut during Hurricane Sandy in 2012 and future storms.
By using a coastal surge and wave modeling system and damage analysis, Dr. Sheng’s team evaluated the structural loss in coastal communities that sustained significant impact by the category 3 hurricane. They found that wetlands in New Jersey reduced only about 8% of the total flood damage during Sandy but can reduce 52% of the total flood loss in a 100-year flood event.
“This research presents a science-based approach for estimating the value of coastal wetlands for storm-induced flood protection,” Dr. Sheng said. “It is of significant interest to coastal communities, researchers and resource managers at the state and federal level. The important message is that, to gain insight on the value of coastal wetlands we must integrate simulation results of process-based dynamic models with extensive field data and use artificial intelligence when possible.”
He adds that coastal communities will need to restore wetlands and develop resilience plans to mitigate flood damage due to increasing flood risk and its dependence on storms, wetlands and structures. The team developed a robust regression model to allow prediction of flood protection based on simulations obtained with a combination of climate, hurricane, coastal, and economic models. The model and its findings can be used to develop coastal resilience and wetland restoration plans.
Dr. Sheng says that this approach can applied to other coastal regions throughout the U.S., and the world, to facilitate wetland restoration and coastal resilience. To read his findings, please visit https://search.proquest.com/openview/eb809b51acec5e8c7841aa4ece1d797f and https://www.nature.com/articles/s41598-021-84701-z.epdf. For additional information, please contact Dr. Sheng.