Development of a fine-resolution snow depth product based on the snow cover probability for the Tibetan Plateau: Validation and spatial-temporal analyses

Abstract Accurate remotely sensed snow depth (SD) data are essential for monitoring and modeling hydrological processes in cold regions. While the available passive microwave SD data have been widely used by the community, the coarse spatial resolution (typically at 0.25°) of these data impedes the explicit representation of the hydrological processes in snow-dominated regions, especially in mountainous regions with complex terrain. To improve the spatial resolution and quality of passive microwave SD data for the Tibetan Plateau (TP), we develop a spatial-temporal downscaling method to produce a 19-year, daily 0.05° SD product by combining the existing high temporal resolution daily SD data and the high spatial resolution 8-day cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS)-based snow cover probability (SCP) data, the latter of which were produced using an advanced temporal filter algorithm. Validations against the observed SD data from 92 meteorological stations suggest that the newly-developed 0.05° SD product greatly improves upon the original 0.25° version. Based on this 0.05° SD product, we found that higher SD values are mainly distributed on the southeastern and eastern TP as well as the Himalaya and Karakoram, while much lower SD values occur on the inner TP. During 2000–2018, the TP-averaged annual SD showed a slight (p > 0.05) increasing trend because there were little changes in SD for most grids across the TP. Regarding different basins within TP, the annual SD during 2000–2018 slightly increased over most basins except for the Amu Dayra, Ganges, Brahmaputra, and Inner TP, where the basin-scale SD showed insignificant decreasing tendencies. In general, the spatial-temporal variations in the SD across the TP were very heterogeneous because SD was affected by multiple climatic factors. The newly-developed 0.05° SD product could facilitate our understanding of the hydrological processes on the TP through a more explicit representation of the gridded-based snow water information.