Abstract

The meteorology-dependent multiscale behavior of snow depth over the Tibetan Plateau was examined via moderate-resolution datasets. By analyzing spatial variance and power spectra at specific scales (5 km, 10 km, 20 km, and 50 km) over 28 intraseasonal continuous snow cover regions, key meteorological factors affecting snow depth across different seasons were identified based upon the Kullback-Leibler distance. In spring and winter, the homogeneous power spectra highlight the significant roles of precipitation, atmospheric moisture, and temperature in snow accumulation and melt processes. Summer exhibits the highest snow depth spatial variance driven by radiation and wind dynamics. Autumn with the lowest spatial heterogeneity underscores the impact of windspeed on snow distribution through sublimation and redistribution. The alignment between spatial variance maps and spectra across these scales implies the future downscaling or upscaling without substantial loss of spatial information. These findings are essential for improving snow cover modeling and forecasting, particularly in the context of climate change, as well as for effective water resource management and climate adaptation strategies in this strategically important plateau.