The data used to calibrate hydrologic models are often captured at spatial scales that differ from the model being developed. This is especially true for snow-related datasets used in watershed-scale hydrologic models. High-quality datasets like snow pillows and snow courses capture snowpack processes at a single point, which have the potential of introducing localized, site-specific effects that aren't relevant at larger scales. Conversely, regional-scale data products, like remote sensing, may be too coarse and fail to sufficiently capture local nuances. Accurate snowpack models that offer a good match to snow accumulation and melt observations improve the representation of watershed conditions and enhance predictive power when assessing future climate impacts. An approach that appropriately considers a combination of at-a-point data and regional data products is crucial for developing accurate hydrologic models across Canada._x000D_ _x000D_ A recent study in the eastern Rockies provided insights into snowpack modelling challenges and opportunities. A distributed snowpack model covering 4,000 km² was developed using various direct observations and remote sensing data. This presentation will discuss available data sources for practitioners in British Columbia and approaches to selecting and utilizing calibration, verification, and validation datasets. It will also present strategies to adapt snow data observations from various scales for use in watershed models. The presentation will illustrate how using both snow depth and snow-water equivalent (SWE) data can result in a robust calibration and provide examples of how this approach improves the representation of the hydrologic response in the study area._x000D_
