Hydro-Climatic Extremes and Compound Hazards: Integrating Remote Sensing, Artificial Intelligence, and Physical Modeling

Co-organized by ESSI1

Convener: Susanta MahatoECSECS | Co-conveners: Letícia Santos de Lima, Vicky AnandECSECS, Gabriela GesualdoECSECS, Qing HeECSECS, Marcia Nunes Macedo, Yuei-An Liou

This session provides a platform for showcasing state-of-the-art methods and techniques to assess risks associated with hydro-climatic extremes like floods, storms, landslides, and on compound dry hazards such as droughts, heatwaves, and fires. When these events are compounded, overlapping each other in time and spatial coverage, or following one another, their compounded nature generates cascading impacts on water resources, ecosystems, infrastructure, and human systems that cannot be captured by single hazard analyses alone. We aim to exchange knowledge and insights into how machine learning algorithms, data mining techniques, physical models, and the integration of satellite data can significantly enhance predictive capabilities for analyzing the societal risks associated with hydro-climatic extremes and compound hazard events. The session highlights innovative applications and real-world case studies demonstrating how these technologies can be applied for disaster risk reduction, emergency response, and climate adaptation. Through discussions on the latest methodologies and practical applications, the session will facilitate cross-disciplinary collaboration between remote sensing experts, ecologists, climate scientists, AI researchers, hydrologists, and decision makers.

Key Themes:

Processes:
Physical processes involved in hydro-climatic extremes and compound hazards (e.g., droughts-heatwaves-fires), their precondition factors, enabling mechanisms, feedbacks, emergent properties, and synergistic effects. Interaction and impact of such events in the physical system, ecosystems, and human population.

Methods & techniques:
Integration of remote sensing, data mining, and machine learning approaches to enhance the detection, monitoring, and prediction of hydro-climatic extremes and compound events. Combination of physically-based hydrological and climatological models with AI-driven simulations, as well as applications across multiple spatial and temporal scales, from local case studies to regional and global assessments.