RAPID: Reconstruction of Hurricane Florence Flood Hydrographs (HF2Hs) for South Carolina's Critical Infrastructures Using Data Analytics Algorithms and In-situ Field Measurements

University Of South Carolina At Columbia, Columbia SC

Investigators

Abstract

In the wake of Hurricane Florence in South Carolina, this research aims to collect high water marks (HWMs) data across flooded/damaged critical infrastructures, and perishable images and video footage from traffic cameras and social media outlets. The investigators will then reconstruct Hurricane Florence flood hydrographs (HF2Hs) using data analytics algorithms as well as HWMs data to estimate flood elevation and inundation extent over overtopped roads and bridges. Using the eastern portion of South Carolina (SC) as a case study, this RAPID project will address the following questions: Do reconstructed flood hydrographs over critical infrastructures provide valuable insight into flooding thresholds and frequencies? If so, how? To address these questions, the team consists of members with expertise in engineering hydrology and computer sciences and engineering who are positioned to deliver the needed collecting, examining, and archiving of perishable datasets. The methodology for collecting perishable data merges the broader objectives of enhancing perishable data collection through the use of traditional (tape measure, engineer's rule, etc.) and data analytics techniques, both of which depend on the timely collection of data. The reconstructed flood hydrographs for overtopped routes/roads and bridges will help understanding of how critical infrastructures respond to hurricane-induced flooding that presents persistent widespread challenges in many regions worldwide. The collected data will benefit the development of new numerical models for flood prediction that will deal with the unique needs and concepts of the U.S.'s southeast catchments (shallow aquifer parameterization). The data analytics algorithm is targeted be flexible and scalable to collect and analyze large sets of data which will be disseminated through open-source public repositories (e.g., GitHub). The collection and integration of data is targeted to facilitate communication/ collaboration between decision makers and technically-focused institutions. This project is intended to have an immediate impact on South Carolina, a state which is very vulnerable to repeated hurricane events and is under the threat of increasing floods. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.