The development of digital twins for Earth systems, such as Destination Earth, is revolutionizing our approach to understanding and managing risks associated with climate change and natural hazards. These advanced simulations enable us to integrate diverse datasets, providing a comprehensive view of climate dynamics and human-environment interactions. By combining predictive models with real-time observations, digital twins offer unprecedented opportunities to make predictive Earth observation, explore "what if" scenarios, simulate hazard cascades, and test various adaptation strategies.
This session is dedicated to exploring the role of digital twins in enhancing our capacity for hazard prediction, risk assessment, and anticipatory mitigation action. We seek contributions that demonstrate how digital twins can be used to generate synthetic data, refine predictive models, and provide actionable insights for disaster risk management. We are particularly interested in studies that highlight the synergies between digital twin technology and other AI-driven tools, such as predictive analytics and machine learning, in improving operational outcomes. By focusing on the intersection of digital twins and predictive modeling, this session aims to foster cross-disciplinary dialogue on how these converging technologies can accelerate resilience to climate-related risks and natural hazards, including in a variety of impact sectors (energy, food, etc…). We invite researchers and practitioners to share their insights and developments in this rapidly evolving field, paving the way for more robust and effective preparedness and disaster response strategies.