Flooding from heavy rainfall and storm surges can cause severe damage, leading to loss of life, environmental impacts, and economic setbacks. This is a major issue for rural areas in the Great Lakes region, including Indigenous and post-industrial communities. Many of these areas lack updated flood maps, complicating flood planning and response. The Federal Emergency Management Agency (FEMA) flood maps are the primary source of flood risk information in the United States, but many rural counties, including those in the Upper Peninsula of Michigan, remain unmapped. Flood risk models typically incorporate a series of steps, including hydrological models or frequency analysis to estimate flood discharge magnitude, hydrodynamic models to map flood inundation, and damage functions to assess risk. Despite the low disaster resilience of rural communities in the Great Lakes region to flooding, flood mitigation efforts have been hindered by the lack of data and tools for understanding flood risks.

Our project developed the Rural Hazard Resilience Tools (RHRT) to help these communities better prepare for and respond to flooding. The RHRT includes a tool to assess flood risk using simple models that need minimal data, a web app for community members to share photos and information about flood events, and a geospatial platform to visualize flood risks, critical infrastructure, and community resilience indicators. The flood risk assessment tool utilizes a simplified modeling approach with the Height Above the Nearest Drainage (HAND) model and synthetic rating curves (SRC) for approximate flood inundation mapping with minimal input data (e.g., digital elevation models (DEM)) and computational resources. It also uses the Simulating WAves Nearshore (SWAN) model for coastal flood inundation modeling, USGS regional regression equations for estimating peak discharge, and depth-damage functions of the HAZUS-MH flood model for estimating losses due to building-level impacts. The crowdsourced data collection application is an example of citizen science engagement that allows community members and the public to upload photos of flood inundation. This helps address current data gaps, improve flood hazard modeling by validating the HAND-based flood model, and enhances automated flood information extraction using machine learning algorithms. The geospatial visualization platform is designed to disseminate flood risk information to rural communities and help decision-making authorities, emergency service professionals, and other community leaders make more informed decisions for flood risk mitigation. This methodological framework of the RHRT is cost-effective, less resource-intensive, and easy to implement, making these tools transferable across multiple spatial scales.

The context-specific community resilience indicators proposed in this research provide a holistic view of resilience to extreme events in rural counties of Upper Penninsula. The proposed indicators were developed using both expert and public perspectives on resilience. Data related to these indicators is hosted on the visualization tool. This data can inform decision-making regarding resource allocations in this area.

The project emphasizes the importance of integrating local knowledge with scientific data. Involving community members in the data collection process improves the modeling process and fosters a sense of ownership and empowerment. This participatory approach ensures solutions are practical and tailored to community needs. Leveraging low-cost technologies, such as smartphone apps and social media platforms significantly enhances data collection and dissemination, helping communities stay informed and prepared for challenges during floods.

This project has paved the way for more funding to support climate-related research in the Western Upper Peninsula. Project members have applied for a grant through the Department of Energy to create a Climate Resiliency Center at Michigan Technological University. This center will expand the project’s scope both regionally and add updated features to RHRT. It will also serve as a hub for initiatives enhancing regional climate resilience, including energy modeling and climate data collection and analysis.

The involvement of graduate students in the analysis of water level change in Lake Superior has had significant impacts on teaching and educational experiences. Application of theoretical knowledge to real-world scenarios bridged the gap between classroom learning and practical implementation. This project integrated advanced research methodologies into our curriculum, enriching student’s educational experiences. The challenges faced by the students during the project became valuable case studies in subsequent courses, making lessons more relatable and understandable for students.

In summary, developing the RHRT is a milestone in transforming rural communities in the Great Lakes region into disaster-resilient and risk-informed societies. These tools provide comrehensive data and insights, enabling better understanding and mitigation of flood risks. They support informed decision-making for land use and development, integrating resilience into planning processes. During disasters, these tools can aid in efficient response efforts by identifying high-risk areas and critical infrastructure requiring immediate attention. In the recovery phase, they help prioritize rebuilding efforts and resource allocation to enhance future resilience. The RHRT project exemplifies how interdisciplinary collaboration and community engagement can lead to innovative solutions for complex societal challenges.

Last Modified: 11/26/2024
Modified by: Pengfei Xue