The meteorology-driven multiscale behavior of snow depth over the Tibetan Plateau was investigated via analyzing the spatio-temporal variability of snow depth over 28 intraseasonal continuous snow cover regions. By employing power spectra and the Kullback-Leibler (K-L) distance, the spectral similarities between snow depth and meteorological factors were examined at scales of 5 km, 10 km, 20 km, and 50 km across seasons from 2008 to 2014. Results reveal distinct seasonal and scale-dependent dynamics: in spring and winter, snow depth exhibits lower spectral variance with scale breaks around 50 km, emphasizing the critical roles of precipitation, atmospheric moisture, and temperature, with lower K-L distances at smaller scales. Summer shows the highest spatial variance, with snow depth primarily influenced by wind and radiation, as indicated by lower K-L distances at 15-45 km. Autumn demonstrates the lowest spatial heterogeneity, with windspeed driving snow redistribution at finer scales. The alignment between spatial variance maps and power spectra implies that snow depth data can be effectively downscaled or upscaled without significant 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 vital plateau.
