WARN2RESIL · Towards Resilient Communities: Understanding triggers of Multi-Hazard Early Warning Systems with Comprehensive Risk Metrics

In our interconnected and rapidly changing world, multiple natural hazards pose significant threats to human communities andinfrastructure. Natural hazards often occur simultaneously or trigger one another, such as droughts leading to wildfires orearthquakes causing landslides. Despite the interconnected nature of these hazards, policy and research traditionally focus on singlehazards in isolation, overlooking the cascading effects of multi-hazard scenarios. Recognizing this gap, Multi-Hazard Early WarningSystems (MHEWS) aims to develop a comprehensive framework to address and scale EWS for multi-hazard events. This proposal seeksto advance understanding of the interactions between different natural hazards and improve early warning systems (EWS) to accountfor these interactions. It also aims to create a robust risk metric to inform policymakers, thereby enhancing risk mitigation strategies.The state-of-the-art approach integrates artificial intelligence (AI) and digital twins with traditional monitoring systems, providing aholistic and adaptive framework. AI-driven models analyze vast datasets to offer precise predictive insights, while digital twinssimulate real-world interactions between hazards and infrastructure, allowing for better preparedness and response strategies. Theproposal outlines three main objectives: developing a theoretical framework for multi-hazard EWS, implementing multi-hazard EWSin two case studies, and creating a risk metric for multi-hazard events. By achieving these objectives, MHEWS intends to build moreresilient societies capable of effectively responding to natural disasters. This research supports global policy initiatives like the 2030Agenda for Sustainable Development and the Paris Agreement on Climate Change, promoting integrated approaches to addresssignificant societal challenges and enhance community resilience.