Hydrological science seeks general understanding from systems that are intrinsically heterogeneous and observed at finite scales. Yet processes and relationships identified at a point, hillslope, reach, or catchment may change, disappear, or reorganize at larger scales. Differences in climate, geology, topography, soils, vegetation, and land use can further alter both the expression and relative importance of hydrological processes. A fundamental challenge therefore remains: when, where, and why does hydrological knowledge transfer across scales and environments? This session places transferability at the center of scaling and heterogeneity in hydrology. We invite contributions that examine which hydrological relationships, process representations, parameters, signatures, and predictive structures generalize across scales and environments; which remain context dependent; and what failures of transfer reveal about unresolved processes, scale dependencies, and model assumptions. Of particular interest are studies that distinguish robust hydrological organization from apparent relationships that emerge only at particular observational scales, under specific environmental conditions, or within particular datasets. We welcome observational, experimental, theoretical, and modeling studies spanning surface and subsurface hydrology. Relevant contributions include comparative and large-sample analyses, cross-scale observations and experiments, hydrological similarity and classification, regionalization and prediction in ungauged or data-sparse systems, and explicit tests of model and parameter transferability. Process-based, statistical, machinelearning, hybrid, and physics-informed approaches are encouraged where they help identify and explain the controls on generalization rather than merely reproduce observed patterns. The goal is to move beyond documenting heterogeneity toward establishing the domains of validity of hydrological knowledge: what can be transferred, across which scales and environments, under what conditions, and with what uncertainty? Resolving these questions is essential for developing hydrological understanding and prediction that extend beyond the individual catchments, observations, datasets, and models from which they originate.