The seasonal snow cover has manifold impacts and feedbacks affecting water availability, vegetation patterns, ecosystems, and weather. Accurately modeling current and future snow covers is challenged by the need for modeling techniques that represent the complex and interacting controlling processes at appropriate spatial scales. This session will bring together researchers working on developing and applying snow models that collectively advance our understanding of system functionality. We solicit contributions related to modeling seasonal snow that focus on snowmelt and other physical snowpack processes (e.g., avalanching), and particularly welcome abstracts on snow cover interactions with the atmosphere, topography, vegetation and soil, operational snow models, data assimilation, subgrid schemes in coarse scale applications, modular model experiments, model evaluation and validation, and sensitivities to model parameters and forcing data. We also welcome contributions that use data-driven approaches as a way to improve the representation of uncertain processes in snowpack models