Modeling Data from Hurricane Impacts to Natural and Hybrid Infrastructure

CEE/MES Professor Qin Jim Chen, in collaboration with Navid Jafari from Louisiana State University, is leading a $765k NSF grant for “Integrated Numerical Modeling and Field Observations of Hurricane Impacts to Natural and Hybrid Infrastructure.” This NSF award resulted from their rapid deployment of sensors in response to Hurricane Laura in 2020.

Abstract Source: NSF

The protection of coastal communities, economic hubs, and infrastructure networks is critical to our national security and long-term prosperity. Natural infrastructure consisting of barrier islands, dunes, wetlands, mangroves, and reefs has been promoted to mitigate coastal flooding and erosion. However, the collective effectiveness of natural elements and how they work together with hard infrastructure as a hybrid system to provide flood risk reduction remain unknown. This project harnesses the unique hydrodynamic, eco-geomorphic, and geotechnical data collected from rapid field campaigns of Hurricanes Laura and Delta (2020) on the Louisiana Chenier Plain to fill this key knowledge gap. The research integrates morphodynamic modeling of coastal wetlands with field-based hydrodynamic, eco-geomorphic, and geotechnical measurements to assess and predict the performance of natural and hybrid infrastructure subject to hurricane impacts. The project will leverage existing outreach programs and regional partnerships to disseminate research results. The research team will share the modeling tools and new understanding developed with the coastal and geotechnical research communities, as well as practicing engineers and resource managers. The project provides unique opportunities to cross-train undergraduate and graduate students in both coastal and geotechnical engineering areas at two institutions and develop both field observation and numerical modeling skills.

Effective coastal hazard mitigation requires integrative field observations and numerical modeling to characterize dynamic coastal processes at appropriate space and time scales. This project integrates overland flow and morphodynamic modeling of coastal wetlands with hydrodynamic, geomorphic, ecological, and geotechnical measurements to transform our understanding of the response and recovery of natural and hybrid infrastructure impacted by hurricanes. The project harnesses the unique data collected during hurricanes to (i) advance the understanding of the spatiotemporal variation in overland flow, storm surge, and wave attenuation (flood protection) provided by hybrid infrastructure; (ii) evaluate the efficacy of numerical models to predict coastal erosion and sediment transport across a natural landscape; and (iii) explore the role of geotechnical properties, stratigraphy, and vegetation biomechanical properties in controlling the magnitude of shoreline retreat and vegetation uprooting by hurricanes. The comprehensive field, laboratory, and remote-sensing data collection, complemented by numerical modeling, will identify and evaluate root and soil properties and their roles in uprooting and erosion processes. The novel field testing will enable successful collection of observations that were previously challenging or impossible to quantify. It is anticipated that the integrative approach and modeling tools will be applicable to other coastlines for evaluating natural and hybrid infrastructure performance for coastal resilience.

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.

Related Faculty: Qin Jim Chen

Related Departments:Civil & Environmental Engineering