C2H2 EAGER: Site-specific Climate Risks to South Pacific Health and Health Infrastructure.

About This Grant

Island chains, like Hawaii, the Virgin Islands, U.S. Pacific Island territories, and others are frequently affected by extreme weather events that pose serious threat to public health systems and the infrastructure that supports them. Many inhabited islands in the Pacific Ocean and Caribbean are too small to be resolved in global climate models; thus, island decision makers often lack site-specific data needed to make informed decisions about current and future risks to their populations due to extreme weather events. This research explores the integration of high-resolution geographic and weather information to support planning for the protection and continuous operation of healthcare facilities for impacted populations. Using the island nation of Fiji as a pilot/testbed, this project combines state-of-the-art advances in machine learning and geospatial modeling and data on health facility access, infrastructure, and condition. The goal is better prediction of impacts on island chains of climate-driven hazards related to wind and rain/flooding. Key outcomes will be a decision-support platform to help health officials and practitioners assess and prepare for weather-related health infrastructure risks. It will also advance modeling capabilities in integrating health and weather data. Broader impacts will be improving the ability of island chains with far flung populations to respond to the health implication of weather-related disasters. The work also directly engages atmospheric scientists with island chain health officials and decision makers, stakeholders which rarely work together. This research develops cutting-edge, machine-learning-based, modeling tools for tropical cyclone weather events and integrates them with climate health vulnerability assessments. This will yield risk projections for health infrastructure for island chain populations. This project uses Fiji as a pilot/testbed, playing off already established relations between the science team and island health officials and decision makers. Research will involve development of computationally intensive generative tools that: (a) emulate tropical cyclone impacts, (b) downscale climate model data, and (c) statistically categorize extreme events. Researchers are part of a technical advisory group to the South Pacific Community which provides them with access to health facility information allowing the combination of that data with projections of cyclone frequency, intensity, and landfall. This will help decision makers better protect against damage and loss of health operations during tropical cyclone events. Projections will be embedded in a user-friend decision-support tool that allows visualization and analysis of high-risk areas and the distribution of climate risks to health infrastructure. The tools will be developed to ensure accessibility for health/medical practitioners and decision makers. They will combine machine-learning-enabled risk projections with empirical health infrastructure vulnerability data which can be used in other locations to develop similar contextually informed extreme weather-health data for island populations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.