Snow depth variability across the Qinghai Plateau and its influencing factors during 1980–2018

Abstract

As a crucial component of the climate system, snow cover plays an important role on surface energy budgets, hydrological cycles and socioeconomic development. This paper investigated the spatiotemporal patterns of snow depth across the Qinghai Plateau (QP) during 1980–2018 based on passive microwave (PMW) satellite observation and reanalysis products. The study identified the relationships between the changes in PMW snow depth and topographic features and revealed the impact of climatic variables (air temperature, precipitation and wind speed) and large‐scale atmospheric circulations on observed snow depth variability. The results show that the spatial pattern of snow depth climatology is similar in all datasets, except in terms of magnitude, with high values in the southern and southeastern parts of the QP and low values in the eastern and northwestern parts. Average snow depth correlates positively with precipitation, elevation and slope, and negatively with air temperature and wind speed. The long‐term trends in snow depth vary with the season and the datasets. The PMW snow depth across the QP shows a significant annual (−0.125 cm·decades−1, p < .10) and spring (−0.184 cm·decades−1, p < .05) negative trend, while snow depth for ERA5 and MERRA2 does not present significant trends. Air temperature dominates total snow depth variation over the QP, explaining 43.85, 24.88 and 47.28% of annual, winter and spring PMW snow depth variations, which significantly affects snow depth variations in most parts of the QP, yet the effects of precipitation and wind speed on snow depth variation exhibit significant regional differences, and atmospheric circulations (e.g., AMO) also have a remarkable controlling effect on some localized areas.

Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications

“…For instance, in the Greater Khingan Mountains, an increase of SWE/SD and, for the Hengduan, Kunlun, and the Chinese part of the Himalayan Mountains, a decrease in SWE/SD was observed. However, for other regions such as the Changbai, Lesser Khingan, and Tianshan Mountains, the studies partly show conflicting results, which, however, can be attributed to varying input data, the experimental design, as well as the study domain [80,100,196,197,211,241]. For most other regions in the Northern Hemisphere, the studies are not in agreement or the observed changes are not significant.…”

“…For instance, the decline in SWE/SD is stronger in North America than in Eurasia [13,121,124,125,132]. In addition, many studies observe a stronger decrease in spring than in winter [13,100,124,240]. In addition, remote sensing-based products show a greater decrease than other products (models or in situ) [100,125,132,241].…”

“…Such frequently examined areas, which are not only covered as part of large-scale studies, but examined specifically, are for instance the Great Plains in North America [73,[82][83][84][85][86][87], sometimes as part of studies covering Central North America [46,75,[88][89][90][91][92], or the USA [93][94][95][96][97][98][99]. Another often-applied study area is the Tibetan Plateau in Asia [100][101][102][103][104][105], also sometimes as part of studies conducted over High-Mountain Asia (HMA) [106][107][108][109]. Also often examined is the Mackenzie-Basin in Canada [110][111][112][113][114] or Northwestern China [63,[115][116][117][118][119][120].…”

Temperature-dominated spatiotemporal variability in snow phenology on the Tibetan Plateau from 2002 to 2022

“…There are typical interannual variations in the SP on the TP. Ground-measured measurements and remote sensing data have been extensively used in recent decades to better understand these changes (Ma et al, 2022;Notarnicola, 2020). Remote sensing is beneficial in high-elevation areas where few ground-measured measurements are obtainable (Huang et al, 2022a).…”

“…However, SP is also closely associated with other meteorological factors, such as wind and humidity. But their quantitative influence on SP on the TP has not been discussed further (Ma et al, 2022;Xie et al, 2017). Topographic characteristics (Guo et al, 2022) and vegetation greenness (Qi et al, 2021) are also considered responsible for SP.…”