Snow is an essential source of water in the Western United States (WUS), supplying 60-80% of annual streamflow to semi-arid regions like the Upper Colorado River. Numerous studies have noted a decline in snowpack in recent decades, with lower-to-middle elevation areas experiencing the greatest losses. Topography has an important role in shaping snowpack variability. While the relationship between elevation and snow has been studied extensively in literature, the landscape-scale variability of snowpack across both elevation and aspect has been less explored due primarily to data limitations. Here, we develop a stratified regression framework to assess the relative influence of meteorological drivers (precipitation, temperature and radiation) on annual peak snow water equivalent (SWE). We investigated how terrain characteristics-specifically elevation and aspect-affect snowpack dynamics across 40 basins located across Southern, Middle, Northern and Canadian Rocky Mountains ecoregion. Preliminary analyses indicate that aspect-stratified regression models explain a larger portion of the variability in peak SWE, with R-Sqaured values ranging from approximately 0.50 to 0.85. However, for basins exhibiting a higher overall elevational range, particularly those above 2000m, the elevation-stratified model demonstrates a consistently stronger performance, with the coefficient of determination increasing with elevation (R-Sqaured > 0.55). Across most models, precipitation was the dominant driver of SWE, while the magnitude and statistical significance of the temperature and radiation coefficients were dominant at middle to lower elevations. We aim to assess the feasibility of using data-driven models to study snowpack. By refining our understanding of how terrain influences the meteorological drivers of snowpack, we also seek to support research efforts that are focused on the vulnerability of habitat for snow-adapted species.