In the United States, flooding is a leading cause of natural disasters, with congressional budget office estimates of $54 billion in loss each year. Although both urban and rural areas are highly vulnerable to flood hazards, most natural disaster resilience studies have focused primarily on urban areas, overlooking rural communities. One such area that has been overlooked are the numerous rural communities bordering the Great Lakes. These communities face unprecedented challenges due to rising water levels, particularly since 2012, which have resulted in increased coastal flood hazard. Despite their flooding risk, they continue to lack flood hazard assessments and inundation maps, exacerbating their vulnerability. The Federal Emergency Management Agency (FEMA) commonly recommend counties to use a freely available tool—called HAZUS to develop hazard mitigation plans and enhance community resilience and adaptation. However, the usage of HAZUS for rural communities is challenging due to existing data gaps that limit the analytical potential of HAZUS in these communities. Continued use of standard datasets for HAZUS analysis by rural counties could likely leave the communities underprepared for future flood events. The proposed project’s vision is to develop methods that use remote sensing data resources and citizen engagement (crowdsourcing) to address current data gaps for improved flood hazard modeling and visualization that is scalable and transferable to rural communities.
The results of the project will expand the traditional frontiers of preparedness and resilience to natural disasters by drawing on the expertise and backgrounds of investigators working at the interface of geological engineering, civil engineering, computer science, marine engineering, urban planning, social science, and remote sensing. Specifically, the proposed research will promote intellectual discovery by i) improving our understanding of remote sensing data sources and open-source processing methods to assist rural communities in addressing the data gaps in flood hazard modeling, ii) developing sustainable geospatial visualization tools for communicating hazards to communities, iii) advancing our understanding of the utility of combining remote sensing and crowdsourcing to flood hazard delineation, iv) understanding ways to incentives the crowd for greater participation and accuracy in hazard in addressing natural disasters, and v) identifying critical community resilience indicators through crowdsourcing. These advancements will lead to prepared and resilient rural communities that can effectively mitigate hazards related to lake level rise and flooding.