Multi-Hazard Early Warning Systems in the Sendai Framework for Disaster Risk Reduction: Achievements, Gaps, and Future Directions - Maryam Rokhideh; Carina Fearnley; Mirianna Budimir (International Journal of Disaster Risk Science)
Abstract Bringing together global efforts to enhance the implementation of warnings in managing vulnerabilities, hazards, risks, and disasters is essential to saving lives and for long-term vulnerability reduction. Ten years into the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), there has been a renewed focus on warnings following the 2022 announcement by the United Nations Secretary-General of the five-year goal of Early Warnings for All. Delivering on Target G of the SFDRR has subsequently generated significant outcomes, however substantial gaps remain with implementing effective early warning systems (EWS). This article charts the policy evolution of warnings within the UN context and outlines the progress and remaining gaps of EWS in the SFDRR to date. Three key gaps that hinder the effective delivery of SFDRR and beyond are identified: (1) the need for common understanding of warning processes and terminology, such as multi-hazard EWS, and further elucidation of indicators used to measure and chart progress; (2) the need to mobilize and strengthen existing EWS, many of which are not formally recognized yet do the work of warnings across actors and entities, especially in fragile or resource-poor contexts; and (3) the need to foster collaboration between the multitude of actors and approaches involved in all forms of warnings, including people-centered warnings to address diversity and inclusivity, and integrate top-down and bottom-up approaches across sectors. Significant barriers to working across the numerous silos (institutional, geographical, political, and scientific) must be overcome to generate effective people-centered multi-hazard EWS to support disaster risk reduction in the future. Recommendations on how to fill these gaps in future frameworks are provided, to support people-centered, integrated warnings for all.