Abstract
Dunes play a crucial role in shielding inland properties from erosion caused by storm surge and waves along coastlines. Given the amplified effects of human-induced climate change and rising sea levels, safeguarding and reviving these dunes is now crucial for maintaining coastal resilience and enhancing management practices. To support these initiatives, various models have been employed to forecast the vulnerability of dunes in different beach sections based on multiple factors. Our goal is to reveal the susceptibility of Southwest Florida's coastal dunes before Hurricane Ian using a "breach susceptibility model" developed by Sontro (2003). Sontro's model evaluates dune vulnerability by considering a variety of factors including dune width, dune height, dune slope, and the presence or absence of vegetation. To validate the model's efficacy, LiDAR data from 2018, prior to Ian, were used to extract Sontro's factors, and vulnerability scores were then retrospectively calculated for a variety of beach segments. We currently are analyzing the post-Ian digital elevation models to quantify and classify Hurricane Ian's impacts along the same beach segments to determine if those segments with greater vulnerability scores were impacted more severely by the storm. The effectiveness of the Sontro method will then be tested for its usefulness as a decision-making tool for restoration. Early findings suggest promise in the model's capabilities but highlight the need to incorporate variations in surge height, which can differ significantly across regional spatial scales.
