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This project examines mangrove forests as resilient, sustainable, and non-invasive shoreline protection in subtropical and tropical areas. Hydrodynamic data were collected from idealized Rhizophora mangle and Avicennia germinans forest models. The data were analyzed to identify trends between the heterogeneity of a mangrove forest and wave runup extents. Different species of mangroves have a distinctive root system which attributes to different coastal protection abilities. Shorter wave periods were most affected by mangrove configurations tested. Configurations dominated by R. mangle models resulted in overall smaller runup extents compared to configurations dominated by A. germinans models for the same wave conditions. 

Coastal and nearshore communities face increasing coastal flood hazards associated with climate change, leading to overland flow and inundation processes in the natural and built environments. As communities seek to build resilience to address these hazards, natural infrastructure (e.g., emergent vegetation) and hybrid designs have been identified for their potential to attenuate storm-driven waves and associated effects in developed nearshore regions. However, challenges remain in robustly characterizing the performance of natural systems under a range of incident hydrodynamic conditions and in bridging interdisciplinary knowledge gaps needed for successful implementation. This paper synthesizes field and laboratory results investigating the capacity of Rhizophora mangle (red mangrove) systems to mitigate wave effects. Results indicate that R. mangle forests of moderate cross-shore width have significant effects on wave transformation and load reduction in sheltered inland areas. Opportunities for future interdisciplinary collaborations are also identified. 

Coastal highways along narrow barrier islands are vulnerable to flooding due to ocean and bay-side events, which create hazardous travel conditions and may restrict access to surrounding communities. This study investigates the vulnerability of a segment of highway passing through the Pea Island National Wildlife Refuge in the Outer Banks, North Carolina, USA. Publicly available data, computational modeling, and field observations of shoreline change are synthesized to develop fragility models for roadway flooding and marsh conditions. At 99% significance, peak daily water levels and significant wave heights at nearby monitoring stations are determined as significant predictors of roadway closure due to flooding. Computational investigations of bay-side storms identify peak water levels and the buffer distance between the estuarine shoreline and the roadway as significant predictors of roadway transect flooding. To assess the vulnerability of the marsh in the buffer area, a classification scheme is proposed and used to evaluate marsh conditions due to long-term and episodic (storm) stressors. Marsh vulnerability is found to be predicted by the long-term erosion rate and distance from the shoreline to the 5 m depth contour of the nearby flood tidal channel. The results indicate the importance of erosion mitigation and marsh conservation to enhance the resilience of coastal transportation infrastructure.