Summary: Three years after their initial release, the United Nations Office for Disaster Risk Reduction (UNDRR) and the International Science Council (ISC) are undertaking a review of the UNDRR/ISC Hazard Information Profiles (HIPs) ahead of the UNDRR Global Platform for Disaster Risk Reduction that will take place in June 2025. These HIPs provide an authoritative reference on the scope, name, and definitions of hazards of relevance to the Sendai Framework for Disaster Risk Reduction. The HIPs were hailed as ‘groundbreaking’ in the Report of the Midterm Review of the Sendai Framework in 2023 and continue to provide extensive information to various stakeholders across different sectors, including disaster risk reduction planning, monitoring, training, and research. They are widely utilized by intergovernmental bodies, national governments, disaster management agencies, statistical offices, private sectors, and academic institutions, fostering a more comprehensive and unified approach to disaster risk monitoring, recording, and planning. For example, the World Health Organization (WHO) and the International Organization for Migration (IOM) have incorporated these profiles in their reference systems and are employing them in some of their trainings globally. Additionally, UNDRR uses these profiles for monitoring disasters, with the HIPs supporting a new hazardous event and disaster losses and damages tracking system developed by UNDRR, UNDP, and WMO in partnership with many. Many other stakeholders use them as foundational tools for disaster planning and response efforts, research, and teaching. In this review cycle, particular emphasis will be placed on the ‘multi-hazard context’, aiming to enhance understanding of the interplay between different hazards, which can result in cascading, compound, and complex events. This will facilitate the utilization of the profiles for multi-hazard risk assessment and early warning systems. Leveraging the latest advancements in machine learning, efforts have been made to make the HIPs more machine actionable, thereby expanding their usability and applications
Murray et al. (Sun,) studied this question.