Forest snow interception can account for large snow storage differences between open and forested areas. The effect of interception can also lead to significant variations in sublimation, with estimates varying from 5 to 60% of total snowfall. Most current interception models utilize canopy closure and LAI to partition interception from snowfall and calculate interception efficiency as an exponential decrease of interception efficiency with increasing precipitation. However, as demonstrated, these models can show specific deficiencies within heterogeneous canopy. Seven field areas were equipped with 1932 surveyed points within various canopy density regimes in three elevation bands surrounding Davos, Switzerland. Snow interception measurements were taken from 2012 to 2014 ($9000 samples) and compared with measurements at two open sites. The measured data indicated the presence of snow bridging from a demonstrated increase in interception efficiency as precipitation increased until a maximum was reached. As precipitation increased beyond this maximum, the data then exhibited a decrease in interception efficiency. Standard and novel canopy parameters were developed using aerial LiDAR data. These included estimates of LAI, canopy closure, distance to canopy, gap fraction, and various tree size parameters. These canopy metrics and the underlying efficiency distribution were then integrated to formulate a conceptual model based upon the snow interception measurements. This model gave a $27% increase in the r 2 (from 0.39 to 0.66) and a $40% reduction in RMSE (from 5.19 to 3.39) for both calibration and validation data sets when compared to previous models at the point scale. When upscaled to larger grid sizes, the model demonstrated further increases in performance.

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Modeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine-scale changes in structure following a disturbance. We applied a 1 m 2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire-mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction-based forest structure metrics (aspect-based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.