Floods are among the most devastating natural hazards, driven by complex and interconnected physical processes. These processes vary across catchments and seasons, making it critical to understand the specific drivers of flood events, including extreme rainfall, land use change, river dynamics, atmospheric conditions such as hurricanes, and human interventions. As natural variability continues to reshape flood behavior, advancing catchment-specific knowledge is essential for improving prediction and preparedness, particularly in under-resourced regions.

This session invites contributions that enhance understanding of the physical processes behind flooding and demonstrate how improved process-scale insights can strengthen flood forecasting, impact assessment, and risk management.

We welcome contributions in areas including, but not limited to:

1. Advancements in understanding flood generation processes
2. Incorporating physical knowledge to improve flood forecasting
3. Physics-informed statistical or machine learning models.
4. Studies on riverine, flash, coastal, urban, and hurricane-induced floods
5. Innovative approaches for flood modeling in data-limited environments.