Reliable hydrological information is essential for water-resource management, disaster risk reduction, ecosystem protection, and climate adaptation. However, hydrological prediction remains challenging in many regions due to sparse monitoring networks, limited observations, and rapidly changing hydroclimatic conditions. Advances in Earth observation, artificial intelligence (AI), digital technologies, and computational methods are transforming the field, enabling a shift beyond traditional Prediction in Ungauged Basins (PUB) toward a broader concept of hydrological intelligence that integrates observations, process understanding, modelling, and data-driven approaches.

This session invites interdisciplinary contributions that advance hydrological prediction and intelligence in data-scarce and poorly monitored catchments. We welcome methodological, observational, and applied studies that improve predictive capability, transferability, interpretability, uncertainty characterisation, and operational utility.

Topics include hydrological prediction and forecasting, early-warning systems, regionalisation and transfer learning, AI and machine learning, foundation models, explainable and trustworthy AI, hybrid and physics-informed modelling, digital twins, data assimilation, model calibration, uncertainty analysis, climate-change impacts and adaptation, groundwater-surface water interactions, ecohydrology, socio-hydrology, citizen science, and open hydrological science.

Particular emphasis is placed on integrating Earth observation products, environmental data streams, and emerging computational techniques to improve prediction, enhance process understanding, and support decision-making under uncertainty. Contributions spanning local to global scales are encouraged.

By bringing together researchers from hydrology, hydro informatics, Earth observation, and environmental data science, the session aims to foster collaboration and accelerate the development of next-generation hydrological intelligence systems for robust prediction, adaptive water management, and climate resilience in an increasingly uncertain and data-constrained world.