Flood forecasting and inundation modelling are critical components of disaster risk reduction, especially under the increasing pressures of climate variability, rapid urbanization, and land-use change. Recent advances in high-resolution satellite observations, ensemble Numerical Weather Prediction (NWP) products, and expanding hydrometeorological networks provide new opportunities to enhance predictive capability and reliability. This session seeks contributions that highlight methodological innovations and practical applications in flood forecasting and floodplain inundation modelling. The session welcomes studies that integrate diverse data sources, explore multi-scale modelling strategies, and advance process-based, statistical, and hybrid machine learning/AI frameworks. Emphasis is placed on the role of data assimilation in improving forecast accuracy, reducing uncertainty, and supporting real-time decision making. Case studies demonstrating the transition from research to operations, applications in reservoir management and emergency response, and efforts to communicate probabilistic forecasts to end users are of strong interest. We also invite discussions of uncertainty quantification and the challenges of applying models across diverse hydrological and climatic settings. The session aims to bring together hydrologists, meteorologists, remote sensing specialists, and data scientists to foster cross-disciplinary dialogue and promote innovative approaches that strengthen flood risk management worldwide.