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Social-Ecological Coastal Resilience Risk and Vulnerability to Erosion for the Ohio Lake Erie Shoreline

Authors: Kelly Siman*, , David Kramar, Minnesota State University Moorhead
Topics: Hazards and Vulnerability, Social Geography, Coastal and Marine
Keywords: Coastal Vulnerability, Socio-Economic, Coastal Ecology
Session Type: Paper
Day: 4/7/2020
Start / End Time: 2:25 PM / 3:40 PM
Room: Virtual Track 9
Presentation File: No File Uploaded

This research combines biophysical variables with socio-economic risk and vulnerability to create an overall place vulnerability index to coastal flooding and erosion for the Ohio Lake Erie shoreline at the census-tract scale. This is the first time these methods have been adapted from the maritime to the lacustrine system and and overlaid in Geographic Information Systems with political boundaries in order to facilitate scientifically-driven Ohio Lake Erie coastal resilience management and policy decisions. Overall, place-based risk and vulnerability to flooding and erosion necessitates an integrated approach that combines socio-economic, built-environment, and bio-physical characteristics. While implementing an integrated approach to managing for coastal resilience has received increased attention, with particular emphasis on oceanic sea-level rise and coastal erosion, combining these components into a statistically-driven approach for decision and policy making within the Great Lakes region has not been applied on a wide-scale. Whilst nascent models have focused on single or simplistic multivariable models to inform policy decisions, there is an increased interest of integrating socio-ecological and socio-economic variables into a statistically-driven model that can inform policy decisions to manage for Lake Erie coastal resilience. While most of the integrated methodologies are focused on the oceanic coasts at the county scale, this research, with input from Ohio Lake Erie shoreline coastal stakeholders and decision-makers, presents a model for Lake Erie-relevant variables at the higher-resolution census-tract scale.

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