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

Objective

In our interconnected and rapidly changing world, multiple natural hazards pose significant threats to human communities and infrastructure. Natural hazards often occur simultaneously or trigger one another, such as droughts leading to wildfires or earthquakes causing landslides. Despite the interconnected nature of these hazards, policy and research traditionally focus on single hazards in isolation, overlooking the cascading effects of multi-hazard scenarios. Recognizing this gap, Multi-Hazard Early Warning Systems (MHEWS) aims to develop a comprehensive framework to address and scale EWS for multi-hazard events. This proposal seeks to advance understanding of the interactions between different natural hazards and improve early warning systems (EWS) to account for 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 a holistic and adaptive framework. AI-driven models analyze vast datasets to offer precise predictive insights, while digital twins simulate real-world interactions between hazards and infrastructure, allowing for better preparedness and response strategies. The proposal outlines three main objectives: developing a theoretical framework for multi-hazard EWS, implementing multi-hazard EWS in two case studies, and creating a risk metric for multi-hazard events. By achieving these objectives, MHEWS intends to build more resilient societies capable of effectively responding to natural disasters. This research supports global policy initiatives like the 2030 Agenda for Sustainable Development and the Paris Agreement on Climate Change, promoting integrated approaches to address significant societal challenges and enhance community resilience.