Estimation of Snowfall Deposition in Mountainous Terrain Using a Computational Fluid Dynamics Model with a Stochastic Lagrangian Particle Tracking Model

Heterogeneities in snow accumulation in mountainous terrain is in part controlled by snowfall deposition on the ground. Capturing these heterogeneities is crucial for better predicting snowmelt runoff at basin scale and for avalanche hazards. However, snow particle trajectory in mountainous terrain during winter storms is poorly understood. In this study, a computational fluid dynamics model is applied to estimate the trajectory of different hydrometeor particles over mountainous terrain during winter storms. The model solves for the Reynolds-averaged Navier-Stokes equation to simulate wind flow coupled with a stochastic Lagrangian algorithm for particle trajectory. The model was first evaluated against observed dust deposition over an isolated hill from wind tunnel studies. Hydrometeor trajectories were simulated over a large mountainous domain (about 1 km x 8 km) for different crystal types, such as dendrite or stellar crystals; for these simulations, in-situ observations from a Doppler Lidar and a disdrometer collected during the Storm and Precipitation Across the continental Divide Experiment (SPADE) are used to initialize and evaluate the model. Snowfall deposition was quantified based on terrain topography, wind direction, wind speed at the inlet of the domain, and hydrometeor type. Future work will use the results from this study to develop a simpler parametric model that can be used in atmospheric and land surface models for predicting snow deposition during winter storms on the ground.