The predictive capacity of a physically based snow model to simulate point-scale, subcanopy snowmelt dynamics is evaluated in a mixed conifer forest, southern Sierra Nevada, California. Three model scenarios each providing varying levels of canopy structure detail were tested. Simulations of three water years initialized at locations of 24 ultrasonic snow depth sensors were evaluated against observations of snow water equivalent (SWE), snow disappearance date, and volumetric soil water content. When canopy model parameters canopy openness and effective leaf area index were obtained from satellite and literature-based sources, respectively, the model was unable to resolve the variable subcanopy snowmelt dynamics. When canopy parameters were obtained from hemispherical photos, the improvements were not statistically significant. However, when the model was modified to accept photo-derived time-varying direct beam canopy transmissivity, the error in the snow disappearance date was reduced by as much as one week and positive and negative biases in melt-season SWE and snow cover duration were significantly reduced. Errors in the timing of soil meltwater fluxes were reduced by 11 days on average. The optimum aggregated temporal model resolution of direct beam canopy transmissivity was determined to be 30 min; hourly averages performed no better than the bulk canopy scenarios and finer time steps did not increase overall model accuracy. The improvements illustrate the important contribution of direct shortwave radiation to subcanopy snowmelt and confirm the known nonlinear melt behavior of snow cover. © 2012. American Geophysical Union. All Rights Reserved.