CPP-05. Seasonal evolution of subgrid snow variability for mountainous terrain

Abstract
Subgrid variability of snow can strongly affect hydrological simulations and land-atmosphere interactions yet remains a significant modeling challenge. Studying the characteristics of subgrid variability from observations can guide the design of models representing snow processes. However, previous studies only focused on snapshots of spatial distributions of snow due to the lack of continuous high-resolution spatial data over long periods of time. In contrast, this study investigates the seasonal evolution of snow subgrid variability at different scales based on continuous high-resolution modeled snow data (daily, 50-m) with periodic assimilation of lidar-derived snow depths for the Tuolumne River Basin in California. The time series of descriptive statistics of subgrid variability within 20-km, 10-km, 3-km, and 1-km squares were calculated and analyzed from this data. The results reveal characteristics of the seasonal evolution of snow subgrid variability and provide knowledge for the development of improved large-scale snow models that consider small-scale snow processes.