Accurate hydroclimatic information obtained from data sources like gauges and remotely or reanalysis is essential for hydrological modelling, climate risk assessment, and water resources management. Despite rapid growth in the data availability, still they differ in spatio-temporal resolution, uncertainty characteristics, and representativeness, limiting their direct usability.

This session focuses on recent advances that bridge these gaps by transforming multi-source hydroclimatic data into consistent, high-resolution, and decision-ready information at any spatial scale. We aim to bring together researchers in hydrology, remote sensing, climate science, and data science working at the interface between observations and actionable hydrological applications.

Topics include:

• Multi-source data fusion, integration, and harmonisation across scales
• Temporal and spatial disaggregation of satellite and reanalysis datasets
• Uncertainty quantification, bias correction, and inter-source dependence
• Data assimilation and synthetic data generation
• Machine learning and hybrid physics-informed modelling
• Applications to hydrological extremes, water resources management, and data-sparse regions
• Operational systems, platforms, and automated workflows