Early Warning Systems (EWS) are critical tools for safeguarding societies against the growing threat of natural hazards, particularly as climate change increases the frequency and intensity of extreme events. However, as risks become more complex—driven by multi-hazard events, compound risks, and broader systemic challenges — conventional EWS approaches must evolve. This session will focus on cutting-edge developments and novel methodologies that enhance the effectiveness and reach of EWS, with an emphasis on integrating Artificial Intelligence (AI) and fostering transdisciplinary collaboration.
This session invites contributions that explore innovative approaches to EWS across the entire warning chain, from observations, to hazard and impact forecasting, warning production, communication and decision-making. Special attention will be given to multi-hazard, compound, and complex systemic risks, and the integration of both cutting-edge technological advancements and trans-disciplinary approaches. Thus, we welcome contributions related to artificial intelligence (AI), machine learning, remote sensing, and big data analytics for the development and implementation of EWS as well as contributions that examine the integration of physical and social science, including community-based warning systems, risk perception, and communication strategies towards the goal of the UN led “Early Warnings for All” initiative. This session seeks to enhance preparedness and response by reviewing case studies, methodological advancements, and theoretical contributions, that address observational innovations for early detection of hazards, advanced weather and hazard forecasting systems, and impact-based forecasting.
By addressing both the technical and societal aspects of EWS, this session aims to foster dialogue between disciplines, ensuring that future systems are more inclusive, equitable, and effective at reducing risks in the face of a changing climate. We seek abstracts from a diverse range of fields, including climate science, meteorology, hydrology, geoscience, engineering, and social sciences including policy studies, psychology, or communication science, to explore how novel approaches can enhance the resilience of communities to multi-hazard risks.
This session invites contributions that explore innovative approaches to EWS across the entire warning chain, from observations, to hazard and impact forecasting, warning production, communication and decision-making. Special attention will be given to multi-hazard, compound, and complex systemic risks, and the integration of both cutting-edge technological advancements and trans-disciplinary approaches. Thus, we welcome contributions related to artificial intelligence (AI), machine learning, remote sensing, and big data analytics for the development and implementation of EWS as well as contributions that examine the integration of physical and social science, including community-based warning systems, risk perception, and communication strategies towards the goal of the UN led “Early Warnings for All” initiative. This session seeks to enhance preparedness and response by reviewing case studies, methodological advancements, and theoretical contributions, that address observational innovations for early detection of hazards, advanced weather and hazard forecasting systems, and impact-based forecasting.
By addressing both the technical and societal aspects of EWS, this session aims to foster dialogue between disciplines, ensuring that future systems are more inclusive, equitable, and effective at reducing risks in the face of a changing climate. We seek abstracts from a diverse range of fields, including climate science, meteorology, hydrology, geoscience, engineering, and social sciences including policy studies, psychology, or communication science, to explore how novel approaches can enhance the resilience of communities to multi-hazard risks.