Given their widespread occurrence in cold-region environments, snowdrifts have been the focus of extensive research aimed at accurately predicting their formation and spatial extent. Snowdrifts impact both human and natural systems, ranging from imposing critical snow loads on infrastructure to modifying the surface energy balance of snow-covered sea ice. Despite numerous modeling efforts in snow transport research, a standardized, widely accepted, and openly accessible approach for simulating snowdrift around obstacles remains lacking. This thesis presents snowBedFoam, an Eulerian-Lagrangian model designed to simulate aeolian snow transport and predict snowdrift formation around structures. Implemented within the OpenFOAM framework, it supports a broad set of turbulence models and boundary conditions, ensuring flexibility across diverse applications. To assess and validate its performance, the model was applied to three case studies representative of snow-covered environments: an Antarctic research station, alpine solar panels, and icebergs grounded in landfast sea ice. In each case, simulations over short timescales showed strong agreement with field observations on snow distribution. Concurrently, key drivers influencing snowdrift formation were evaluated. In the Antarctic station case, the sensitivity of snowdrifts to structural design, wind speed, and snow characteristics was analyzed; in the alpine solar farms, the effects of panel arrangement and mutual shielding were investigated; and for icebergs, the influence of size and wind conditions on snowdrift formation was specifically examined. Together, these three cases offer unique yet complementary perspectives, supporting a multifaceted understanding of snow transport dynamics around obstacles. Across all scenarios, simulations highlight the critical role of turbulent gusts and wind direction variability in accurately reproducing snow distribution. Snowbed cohesion also emerged as a critical parameter, reinforcing the need for precise surface property characterization, while snowdrift morphology was strongly influenced by fine surface geometry, with minor edges and contours influencing local deposition. In parallel, group effects between obstacles -such as wind shielding by adjacent structures- significantly altered local snow accumulation, often amplifying deposition on individual units. These insights point to several key directions for future research: integrating dynamic surface evolution, improving the parameterization of snow and aerodynamic processes, and expanding short-term empirical datasets to enable rigorous model validation. Addressing these challenges is essential for advancing a next-generation snowdrift model that combines accuracy and adaptability across a wide range of cold-region applications. Overall, these findings highlight the model's ability to capture the complex interplay between airflow, surface geometry, and snowpack properties that shape snowdrift formation. They also demonstrate its potential to inform infrastructure design, optimize renewable energy layouts, and support research in extreme polar environments - making snowBedFoam a valuable asset for both scientific and engineering applications.