ABSTRACT

Floods often lead to significant loss of life and property. This project will focus on understanding how watershed conditions and atmospheric and oceanic conditions cause monthly flooding over the Southcentral and Southeast US region (SESC) as the region is vulnerable to flooding throughout the year. Given the increasing strength and frequency of tropical storms (2020 set a record with 30 named storms in the Atlantic and 12 making landfall in the SESC), this project will enhance the scientific understanding of flood hazards and will also better inform the wider community of forecasters and decision makers. For instance, five recent major hurricanes ? Matthew (2016), Harvey (2017), Irma (2017), Florence (2018), and Ida (2021) ? led to catastrophic flooding over the SESC causing major challenges in preparedness and recovery for local, state and federal agencies. The project team will engage with climate offices, National Environmental Modeling and Analysis Center (NEMAC) at UNC, Asheville and National Centers for Environmental Information (NCEI). The project team will also collaborate with faculty and graduate students from minority-serving institutions as part of the summer internship program at NC State University. Results from the project will be disseminated through peer reviewed publications, seminars and workshops.

The objective of this proposal is to improve understanding of monthly flood dynamics over the SESC US by (a) quantifying the shift in climatology, (b) identifying their moisture delivery pathways, and (c) attributing the sources (land surface, atmosphere and ocean) that modulate their spatiotemporal variability. The project team will examine the shift in climatology and interannual variability of monthly daily/3-day maximum streamflow in Hydroclimatic Data Network (HCDN) basins over the SESC. The principal investigators will also use a variety of observed and reanalysis data, climatic indices, as well as statistical and physical (numerical weather prediction) models. The shift in climatology will be quantified using optimal climate normal and ?hinge fit? methodologies, and the organizational and moisture delivery patterns will be analyzed using multiple Lagrangian particle tracking models. Attribution of sources related to monthly flood climatology will be quantified using rigorous statistical techniques (e.g., random forest, hinge fit) and through physical modeling. A Bayesian Hierarchical Model (BHM) will combine the contribution from five identified sources ? 1) shift in climatology and sources of moisture transport, 2) teleconnections, 3) atmospheric rivers (ARs), 4) tropical cyclones (TCs), and 5) initial land-surface conditions ? that influence flood processes over multiple spatial scales for explaining the observed monthly flood variability in the SESC region.

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.

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