Projects

As natural hazards are becoming more frequent, insurance companies are withdrawing from high-risk areas in the United States, leaving homeowners without adequate financial protection. While parametric insurance presents a potential solution by offering rapid payouts based on predefined hazard triggers, its adoption by communities and households remains limited due to multiple factors, including potential misalignment between what payouts are provided and community expectations. This study aims to explore the feasibility of reciprocal parametric insurance at the community level, working with households, business owners, and city planners in Sarasota, FL.

Accessible hazard risk information enables individuals and communities to make critical adaptive or mitigative decisions. However, most disaster risk resources are designed for the average person (e.g., white, middle class, with postsecondary education), leaving marginalized communities—who perceive and respond to hazard information differently—underprepared and vulnerable. This project will develop and implement a community needs assessment and an interactive risk information framework to address the preparedness and risk information needs of marginalized communities. Findings will help governments refine strategies and centralize information through an equity-focused GIS platform, offering tailored emergency preparedness resources to communities and officials. The initiative aims to enhance the adaptive capacity of governments and strengthen resilience at the community level for tens of millions of Americans, with the potential for broader impact at a global scale.

Climate change is linked to more intense rainfall leading to more frequent flooding, but we do not understand how populations, especially rural populations, who are repeatedly exposed to such flooding are impacted over the long run. The gap in understanding of the long-term consequences of recurrent flooding on rural populations is partly due to how disasters are quantified and aggregated, where governments often report large disasters that occur in urban areas and cause greater direct losses. This project will approach the challenge of understanding the long-term, cumulative impacts of recurrent flooding associated with climate change on rural populations through a multilevel convergencebased framework that leverages social science-based methods to inform engineering research, and vice versa.

This project supports research focused on understanding population mobility subject to repeated flooding in regions that are historically unprepared to cope with such events. Repeated, low-attention flood disasters do not receive widespread media coverage compared to larger, catastrophic ones. Low-attention flood events are currently understudied, but their cumulative impacts are likely to compound underlying causes of risk, inequality, and poverty. Furthermore, there is not a good understanding of how they contribute to people's decisions to evacuate, return, or permanently move. By filling the knowledge gap, this study aims to better inform local and regional policymakers responsible for designing policies for mitigation strategies and aid distribution before, during, and after these events.

The Himalayan Climate Data Field Lab is a month-long, flexible unconference that will gather scholars, practitioners, activists, community leaders, and storytellers to examine the ways that climate change data and information infrastructures shape adaptation and mitigation in the Himalayan region. Join the Field Lab to co-design, test and produce new ideas, analytic tools, maps, sensing technologies, data protocols, artistic pieces and communication products that address climate change and its impacts, with the aim of creating a more equitable and pluralistic data landscape in the Himalayan region.

A community-curated, open-access, evolving platform that aggregates global landslide inventories and related geospatial data, providing detailed metadata for each dataset. The platform serves as a central hub for high-quality, globally sourced landslide and geospatial data, supporting the development and benchmarking of reliable, scalable, and generalizable AI models.