Disturbances driven by climate variability and extreme events (e.g., wildfires, floods) are reshaping ecosystems worldwide. Observations from in situ measurements, long-term monitoring, and remote sensing have been central to assessing systems responses to environmental changes and disturbances. At the same time, advances in data-driven methods, process-based models, and hybrid approaches including artificial intelligence (AI) and machine learning (ML) are creating new opportunities to integrate diverse datasets and improve understanding of complex hydrological and biogeochemical dynamics. We invite contributions that leverage data, models, and AI/ML across spatial and temporal scales to advance understanding of processes such as streamflow dynamics, water and nutrient storage and partitioning, flood risk, sediment and solute transport, and ecosystem and biogeochemical responses to disturbance and global change. This session aims to bring together interdisciplinary perspectives on using novel hybrid methodologies for improving understanding and predicting responses in water, soil, and biological systems under changing environmental conditions.

• GC - Global Environmental Change