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Canopy‐snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time‐lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind‐Temperature (Roesch et al., 2001,https://doi.org/10.1007/s003820100153), which includes wind‐dependent and temperature‐dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009,https://doi.org/10.1029/2008wr007042), and a time‐dependent unloading parameterization, termed Exponential‐Decay (Hedstrom & Pomeroy, 1998,https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4). Wind‐Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind‐Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential‐Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology.

Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest‐snow variability has on streamflow. To test how much forest‐controlled snow variability influences streamflow, a tiling parameterization based on classifications from high‐resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest‐snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north‐ and south‐facing forest edges. This tiled parameterization was used to test where and when detailed forest‐snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non‐tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late‐season streamflow decreased by only 3%–4% compared to the non‐tiled model. In Jemez, NM, the tiled model decreased late‐season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late‐season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north‐facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late‐season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest‐SWE variability had an effect on late‐season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.