Climate change is increasingly evident through the increasing frequency and intensity of natural hazards across all Earth systems, the atmosphere, hydrosphere, cryosphere, biosphere, and lithosphere. As these events continue to impact lives and economies globally, advanced monitoring and early warning systems are required to support risk reduction and disaster forecasting, response, and recovery.

We are now able to leverage an unprecedented volume of data from space-borne platforms (satellites and drones) and ground-based networks (GNSS, Buoys, AWS, ARGOS). These resources provide high-resolution spatial and temporal insights into our changing planet. Currently, significant efforts are focused on integrating Artificial Intelligence (AI) and Machine Learning (ML) to enhance the reliability and accuracy of environmental monitoring, identify early warning signals for impending disasters, and develop robust models to mitigate impacts on life and property.

We welcome scientific contributions for oral and poster presentations. We seek submissions detailing innovative tools and approaches for data integration; new satellite missions and ground-based observation techniques; or advanced modeling for early warning and disaster risk reduction.

• A - Atmospheric Sciences
• C - Cryosphere
• G - Geodesy
• H - Hydrology