Coastal weather events, including hurricanes, are increasing both in frequency and intensity. This project was motivated by two recent events that impacted the Atlantic Coast - the passage of Hurricane Joaquin in October of 2015, and the passage of Hurricane Matthew in October of 2016. Flooding impacts were felt from Florida, to Georgia, to the Carolinas. Both storms resulted in massive inland flooding, underscoring the need for local and state governments, emergency management teams, and decision-makers to effectively plan and respond to incoming storms. This project focused on the design and implementation of sensing, modeling, and forecasting infrastructure to improve inland flood forecasting at state and local levels in advance of incoming tropical storms. The objective was to provide governments and citizens with more accurate and timely flood forecasts to support emergency planning efforts - and ultimately, to save lives. 

Traditional forecasting capacity for weather-induced coastal water levels is poor. Consider the water level forecast for Charleston during the passage of Hurricane Matthew. The forecast was inaccurate, off by as much as ~2ft below the observed water level in key areas. These coastal forecasting errors propagate within the forecasting model when forecasting upstream flooding. Hence, accurate forecasting of inland river flooding, which is dependent on coastal water level forecasting accuracy, is lacking in existing models. The flood observation and warning system initiated under this award will enable more accurate and timely identification of inland floods. The system comprises a suite of interlinked numerical simulations that rely on observed data collected through novel sensing devices - smart drifters. 

The smart drifters developed as part of this project are low-cost, rapidly deployable wireless sensors that collect information on surface water dynamics (e.g., surface water velocity, turbulence). This data is essential in calibrating the flood forecasting model. Clusters of these devices are intended to be deployed in canals, rivers, and streams in advance of incoming storms. The collected surface water data is then used to calibrate the modeling system to achieve a high degree of forecasting accuracy. The effort involved mechanical design, electrical design, software design, and field experimentation, both within Florida canals and the Savannah River in South Carolina. The team has also collected initial photogrammetric data of inland flows in Florida canals using UAVs. 

Recent demographic trends reported through the United Nations and NOAA indicate that the world’s coastal population is growing at an unprecedented pace. Nearly half of the world’s population lives within 100 miles of a coast, with the U.S. population following suit. In the past four decades, the U.S. coastal population grew by 45%, with more than half of the U.S. population now living in coastal counties. In the coming decades, coastal states’ urban centers are expected to continue to grow, giving way to a future where 87% of Americans share 8.1% of the nation's land. The resulting population density presents significant challenges to local and state governments in preparing for and managing the impacts of extreme weather events. The ability of state and local governments to efficiently prepare for and respond to such events is critical in mitigating potential financial and human impacts. This project supported significant progress toward the development of forecasting infrastructure to support these activities at local and state scales. While the system will be calibrated to accommodate coastal systems spanning Florida, Georgia, South Carolina, and North Carolina, the resulting infrastructure will be adaptable to most coastal systems, including the Great Lakes, Alaska, and Hawaii.

Last Modified: 05/31/2018
Modified by: Jason O Hallstrom