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From 3 to 8 May 2026, Vienna once again became the world capital of geosciences. The EGU General Assembly 2026, organised by the European Geosciences Union, Europe’s leading organisation for Earth, planetary, and space science research, brought together more than 20,000 scientists from around the world for the continent’s largest geoscience conference.

At this year’s General Assembly, several partners from the GeoHazard pillar of the GANANA project presented the latest advances of the project, highlighting how High-Performance Computing (HPC) and Artificial Intelligence can improve forecasting, modelling, and rapid response for geophysical hazards.

The project’s work and latest advancements were presented during the session “Leveraging AI & HPC for Natural Hazard Resilience: From Enhanced Detection, Forecasting, and Modelling in Time-Critical Scenarios to Inform Climate-Adaptive Response”, organised by GANANA collaborators Marisol Monterrubio-Velasco, Nishtha Srivastava, Yogesh Singh, and Jorge Macías. The session explored the role of scalable, adaptive, and AI-enhanced computing approaches across the entire natural hazard management cycle, from early detection and warning to modelling, impact forecasting, and decision support.

From earthquakes, tsunamis, and floods to wildfires, volcanic eruptions, and extreme weather events, the integration of HPC, predictive modelling, and intelligent systems is enabling faster and more effective operational frameworks and emergency response services. Several EU-funded initiatives, including GANANA, showcased how advanced computing technologies are transforming hazard preparedness and resilience strategies.

The GANANA presentation, titled “Physics-Based and AI-Driven HPC Workflows for Geophysical Hazards in the GANANA Project”, was led by Natalia Zamora together with collaborators Nishtha Srivastava, Carlos Sánchez, Leonardo Mingari, Arnau Folch, Jorge Macías, Rosa M. Badia, and Josep de la Puente. The presentation showcased how exascale-ready workflows can support near-real-time earthquake simulations, tsunami forecasting, smoke dispersion modelling, and AI-based seismic monitoring. It also emphasized the exchange of HPC technologies and expertise between Europe and India to strengthen disaster preparedness and cascading-hazard response capabilities.

During the presentation, the team introduced GANANA’s high-level framework and shared initial results across three core geophysical hazard domains: earthquakes, tsunamis, and wildfire spread and smoke dispersion. A defining feature of the project is its structured and bidirectional exchange of codes, expertise, and operational practices between Europe and India, enabling the adaptation, validation, and deployment of advanced HPC technologies across different geographical and institutional contexts.

A key aspect of the project is also its cascading-hazard framework. Preliminary demonstrators show that this collaborative exchange significantly improves model performance, interoperability, and time-to-solution, while fostering capacity building and shared ownership of advanced HPC tools. GANANA therefore demonstrates how sustained international collaboration can transform mature exascale-ready codes into scalable, user-oriented systems for geophysical hazard forecasting and early warning.