The retrieval of detailed, co‐located snow depth and canopy cover information from airborne lidar has advanced our understanding of links between forest snow distribution and canopy structure. In this study, we present two recent high‐resolution (1 m) lidar data sets acquired in (i) a 2017 mission in the Eastern Swiss Alps and (ii) NASA's 2017 SnowEx field campaign at Grand Mesa, Colorado. Validation of derived snow depth maps against extensive manual measurements revealed a RMSE of 6 and 3 cm for plot‐level mean and standard deviation of snow depth, respectively, demonstrating that within‐stand snow distribution patterns were captured reliably. Lidar data were further processed to obtain canopy structure metrics. To this end, we developed a novel approach involving a continuous measure of local distance to canopy edge (DCE), which enabled creating spatially aggregated nondirectional and directional descriptors of the canopy structure. DCE‐based canopy metrics were correlated to mean and standard deviation of snow depth over areas representing grid‐cell sizes typical of watershed and regional model applications (20–200 m). Snow depth increased along the DCE gradient from dense canopy to the center of canopy gaps for all sites and acquisition times, while directional effects particularly evolved during the ablation season. These findings highlight the control of canopy gap distribution on snow distribution in discontinuous forests, with higher snow depths where the open fraction is concentrated in few large gaps rather than many fragmented small gaps. In these environments, dedicated canopy structure metrics such as DCE should advance spatially distributed snow modeling.

NASA's 2017 SnowEx field campaign at Grand Mesa, CO, generated Airborne Laser Scans (ALS), Terrestrial Laser Scans (TLS), and snow‐probe transects, which allowed for a comparison between snow depth measurement techniques. At six locations, comparisons between gridded ALS and TLS observations, at 1‐m resolution, had a median snow depth difference of 5 cm, root‐mean‐square difference of 16 cm, mean‐absolute difference of 10 cm, and 3‐cm difference in standard deviation. ALS generally had greater but similar snow depth values to TLS, and results were not sensitive to the gridded cell size between 0.5 and 5 m. The greatest disagreements were where snow‐off TLS scans had shrubs and high incidence angles, leading to deeper snow depths (>10 cm) from ALS than TLS. The low vegetation and oblique angles caused occlusion in the TLS data and thus produced higher snow‐off bare Earth models relative to the ALS. Furthermore, in subcanopy areas where both ALS and TLS data existed, snow depth differences were comparable to differences in the open. Meanwhile, median values from 52 snow‐probe transects and near‐coincident ALS data had a mean difference of 6 cm, root‐mean‐square difference of 8 cm, mean‐absolute difference of 7 cm, and a mean difference in the standard deviation of 1 cm. Snow depth probes had greater but similar snow depth values to ALS. Therefore, based on comparisons with TLS and snow depth probes, ALS captured snow depth magnitude with better than or equal agreement to what has been reported in previous studies and showed the ability to capture high‐resolution spatial variability.

Modeling spatiotemporal dynamics of snow in forests is challenging, as involved processes are strongly dependent on small-scale canopy properties. In this study, we explore how local canopy structure information can be integrated in a medium-complexity energy balance snow model to replicate observed snow patterns at very high spatial resolutions. Snow depth distributions simulated with the Flexible Snow Model (FSM2) were tested against extensive experimental data acquired in discontinuous subalpine forest stands in Eastern Switzerland over three winters. While the default canopy implementation in FSM2 fails to capture the observed snow depth variability, performance is considerably improved when local canopy cover fraction and hemispherical sky view fraction are additionally accounted for (30% reduction in root mean square error). However, realistic snow depth distribution patterns throughout the season are only achieved if effective temperatures of near and distant canopy elements are discerned and if a mechanism to mimic preferential deposition of snow in canopy gaps is included. We demonstrate that by diversifying the canopy structure input in order to reflect respective portions of the canopy relevant to different processes, even a simple model based on widely used process parameterizations and canopy metrics can be applied for high-resolution simulations of the sub-canopy snow cover with just a few modifications. The presented approaches could be implemented in commonly used land surface models, allowing upscaling experiments and development of sub-grid parameterizations without necessitating complex high-resolution models.

A new interception model was integrated in a snowmelt model and for the first time the spatial variability of forest snow was effectively represented due to the inclusion of new forest structure metrics. The model was tested at 1273 field points surrounding Davos, Switzerland, that feature an extremely wide range of canopy and forest structure. The behavior of the new model was compared against a widely applied interception model. Due to the inclusion of novel forest structure parameters (mean distance to canopy and total gap area) in the new model, simulated interception mimicked the horizontal layout of canopy structure, while the standard interception model yielded fairly homogeneous interception estimations even under highly heterogeneous canopy conditions. The large variance of estimated interception between points using the new model translated into significant effects on under-canopy snow water equivalent and snow depth. Precipitation conditions were also analyzed, and further differences between the models were related to storm intensity. In climates characterized by large storm events, the new interception model provides significantly higher interception estimations (i.e., lower under-canopy snow) than the standard model, whereas in climates characterized by small storms events, the new model yields lower interception estimations (i.e., higher under-canopy snow) in areas with moderately sized to large canopy gaps. Key Points:Framework to integrate a new interception model into snowmelt models Model resolves spatial distribution of under-canopy snow in heterogeneous forests Model performs well under a broad range of canopy openness