Collaborative Research: Understanding Hybrid Green-Gray Coastal Infrastructure Processes and Performance Uncertainties for Flood Hazard Mitigation

点击翻译按钮获取中文摘要

The vulnerability of shore regions to coastal flooding is increasing. Coastal communities need resilient and sustainable adaptation alternatives to mitigate damage and protect lives during hazard events. Conventional structural (gray) methods (i.e., bulkheads, revetments) have been implemented to stabilize and protect coastlines. Natural (green) and hybrid green-gray solutions have gained attention as effective alternatives. Green methods may provide ecological, economic, and cultural co-benefits in addition to protecting new development or expanding the service life of legacy infrastructure near developed coastlines. However, a fundamental lack of understanding of the performance and associated uncertainty of green and hybrid infrastructure limits these systems’ broad implementation. In this project, the investigators will develop a framework to quantify the response of hybrid natural-structural systems to water hazards. A targeted large scale physical model investigation and numerical model campaign will focus on two system types common to southern Florida: mangrove + bulkhead and mangrove + revetment. The project will leverage the expertise of nature-based engineering, ecology, and biology experts and stakeholders from government, industry, and research institutions. A Research Coordination and Advisory Network (RCAN) will be created to inform experimental design and disseminate project outcomes. The investigators will leverage data from previous field investigations characterizing mangrove geometric and mechanical properties and inherent variability to inform the construction of a large scale physical model and targeted numerical model simulations. These data will allow the investigators to disaggregate system component effects on hydraulic response and to validate and compare model limits. The validated numerical models will be used to investigate the expected performance of hybrid systems over a range of incident hydrodynamic conditions, vegetation configurations, and structural geometries. This work will enable quantification, with propagated uncertainties, of wave response to hybrid vegetation-structural systems, including temporal variations such as time to system maturity and expected future conditions (e.g., relative sea level rise). Fundamental processes affecting wave transformation through these systems will be identified and synthesized to inform the design of these systems for enhanced coastal resilience. The project will expand fundamental understanding of wave interaction with natural and hybrid systems through two approaches: (1) Identify and parameterize fundamental interactions among incident wave and surge conditions, bathymetry, emergent vegetation, and subsequent overtopping of coastal bulkheads and revetments; and (2) Quantify interaction uncertainties to enable stochastic approaches for assessing the range of expected performance in hybrid coastal systems. By identifying fundamental relationships between incident wave conditions, surge level, vegetation, and structural details, the investigators will determine performance metrics for hazard reduction (wave overtopping reduction, wave force reduction) as a function of structural geometry (crest elevation, slope, permeability), vegetation characteristics (width, density, emergence), and environmental parameters (surge level, wave height, wave period). Numerical models validated by targeted physical model tests will be extrapolated to other hydrodynamic conditions and vegetation/structural configurations to determine exceedance probabilities of performance metric thresholds and sensitivity to system geometry and epistemic and aleatory uncertainties. The RCAN will bring together domain experts in engineering, ecology, and policy to inform the project and broadly disseminate project outcomes, with the goal of catalyzing the successful implementation of research findings into practice. Student training will be integrated throughout the project through opportunities to engage with the RCAN and contribute to physical and numerical modeling efforts.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.