The complex interactions and interdependencies of hydrological and land surface processes within the Earth system pose major challenges for prediction and understanding. Machine learning has become a powerful tool for prediction across these domains, but leveraging its scientific potential goes beyond applying existing algorithms and data. It requires detailed understanding and problem-specific integration of domain knowledge with data-driven techniques to make models more interpretable and enable new process understanding. This session explores how machine learning techniques are currently used to integrate, explain, and complement physical knowledge in hydrology and land surface modeling, including studies of surface and subsurface water dynamics, soil-vegetation interactions, land-atmosphere exchanges, and eco-hydrological processes. Submissions are welcome on topics including, but not limited to:

- Explainability and transparency in data-driven hydrological and land surface modeling;
- Integration of process knowledge and machine learning;
- Domain-specific model development;
- Data assimilation and hybrid modeling approaches;
- Causal learning and inference in machine learning models;
- Data-driven equation discovery;
- Challenges and solutions for hybrid models and explainable AI.

Submissions that present methodological innovation, critically assess limitations, or demonstrate contributions to process understanding across scales are especially encouraged.