A 0.01° daily improved snow depth dataset for the Tibetan Plateau

Snowpack is highly sensitive to global warming, and an accurate understanding of the changes in snow depth (SD) is essential in analyzing the impacts of SD on both regional and global climate change. However, the application of SD datasets is limited owing to their coarse spatial resolution, especially at the basin scale. As a result, it is difficult to obtain high-quality, long-term gridded SD datasets at the kilometer scale, especially in cold and high-altitude regions. To address this issue, we combine an improved spatiotemporal downscaling algorithm and an efficient snow depletion curve method to develop a composite long-term daily 0.01° SD dataset over the Tibetan Plateau (TP) by integrating an enhanced 0.05° SD dataset and a cloud-gap-filled fractional snow cover (FSC) product. The new 0.01° SD product is evaluated against the ground-observed SD data from 90 meteorological stations during 2001–2010, indicating that the new 0.01° SD product (with a root mean square error of 1.27 cm d-1 and a mean absolute error of 0.31 cm d-1) performs better than its 0.05° old version, as well as five other widely used SD products that cover the TP region. Thus, the new SD product is used to analyze the trends in the spatial SD pattern during 2000–2018. The results indicate that the annual SD is experiencing a decreasing trend over the inner and edge regions of the TP but an increasing trend over the areas between the inner and edge regions of the TP, e.g., the northern Himalayas, and upstream of the Yellow River, Yangtze River, and Mekong River. A negative correlation between the SD changes and the air temperature changes and a positive correlation between the SD changes and the snowfall changes are found in snow-dominated regions. Notably, the correlation between the SD changes and the air temperature changes is stronger than that between the SD changes and the snowfall changes, indicating that the SD is more sensitive to changes in air temperature. The new high-quality product will provide a more accurate means for understanding the SD changes in this region and a more accurate source of SD data for use in scientific studies that relate to hydrology, meteorology, and disaster evaluation.