Understanding the dynamics of the terrestrial water cycle in the Boreal region, including groundwater, surface water, evapotranspiration, permafrost, snow, runoff, anthropogenic drivers and land-atmosphere interactions, requires insights from both observations and models. While process-based models are essential tools for simulating hydrological processes and projecting system responses under climate variability, their inherent simplifications often fail to capture the full complexity revealed by field observations. Conversely, the spatial and temporal limitations of observations highlight the essential role of models in scaling and integrating processes over broader regions. This session aims to bring together researchers working at the intersection of theory, observations, and modelling to advance our understanding of hydrological processes, carbon–water coupling, and land–atmosphere interactions in boreal regions. We invite contributions that focus on but not limited to: (i) field observations that reveal previously overlooked processes; (ii) ancillary data that are needed for hydrological, land and earth system modelling; (iii) model development or evaluation studies that identify gaps or biases when compared with empirical evidence; and (iv) interdisciplinary efforts that integrate science, policy, and practice to address stakeholder priorities and community engagement in the boreal regions. By fostering dialogue between observationalists, modellers, and stakeholders, we aim to identify critical uncertainties, share methodological innovations, and generate actionable insights for resilient water systems in the boreal regions. Contributions spanning field, experimental, remote sensing, and modelling approaches are welcome, as are case studies addressing diverse hydroclimatic and ecological settings, including those that examine carbon–water coupling. Ultimately, this session seeks to promote cross-disciplinary collaboration to advance predictive understanding of hydrological processes, feedback, and their role under climate variability.