Aging dam infrastructure, increasing extreme weather events, and growing water demands are intensifying risks associated with dam safety worldwide. Recent dam failures and near-failure events highlight the urgent need for improved monitoring, early warning systems, and predictive modeling frameworks.

This session aims to bring together interdisciplinary research on dam safety, focusing on advances in monitoring technologies (e.g., remote sensing, IoT, and geophysical methods), data-driven modeling approaches (machine learning, hybrid models), and risk assessment frameworks under climate variability and extremes.

We particularly welcome contributions addressing failure mechanisms, structural health monitoring, flood-induced risks, sedimentation impacts, and reservoir management strategies. Studies integrating satellite observations, hydrological modeling, and real-time data assimilation are highly encouraged.

The session seeks to bridge gaps between engineering, hydrology, geosciences, and policy to develop resilient dam management strategies under future climate scenarios. Contributions from diverse geographic regions and early-career researchers are strongly encouraged.

• H - Hydrology
• S - Seismology