Abstract
Abstract. Snowmelt is a major fresh water resource, and quantifying snowmelt and its variability under climate change is necessary for the planning and management of water resources. Spatiotemporal changes in snow properties in China have drawn wide attention in recent decades; however, country-wide assessments of snowmelt are lacking. Using precipitation and temperature data with a high spatial resolution (0.5′; approximately 1 km), this study calculated the monthly snowmelt in China for the 1951–2017 period, using a simple temperature index model, and the model outputs were validated using snowfall, snow depth, snow cover extent and snow water equivalent. Precipitation and temperature scenarios developed from five CMIP5 models were used to predict future snowmelt in China under three different representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5 and RCP8.5). The results show that the mean annual snowmelt in China from 1951 to 2017 is 2.41×1011 m3 yr−1. The mean annual snowmelt values in Northern Xinjiang, Northeast China and the Tibetan Plateau – China's three main stable snow cover regions – are 0.18×1011, 0.42×1011 and 1.15×1011 m3 yr−1, respectively. From 1951 to 2017, the snowmelt increased significantly in the Tibetan Plateau and decreased significantly in northern, central and southeastern China. In the whole of China, there was a decreasing trend in snowmelt, but this was not statistically significant. The mean annual snowmelt runoff ratios are generally more than 10 % in almost all third-level basins in West China, more than 5 % in third-level basins in North and Northeast China and less than 2 % in third-level basins in South China. From 1951 to 2017, the annual snowmelt runoff ratios decreased in most third-level basins in China. Under RCP2.6, RCP4.5 and RCP8.5, the projected snowmelt in China in the near future (2011–2040; mid-future –2041–2070; far future – 2071–2099) may decrease by 10.4 % (15.8 %; 13.9 %), 12.0 % (17.9 %; 21.1 %) and 11.7 % (24.8 %; 36.5 %) compared to the reference period (1981–2010), respectively. Most of the projected mean annual snowmelt runoff ratios in third-level basins in different future periods are lower than those in the reference period. Low temperature regions can tolerate more warming, and the snowmelt change in these regions is mainly influenced by precipitation; however, the snowmelt change in warm regions is more sensitive to temperature increases. The spatial variability in snowmelt changes may lead to regional differences in the impact of snowmelt on water supply.
Highlights
Snow properties have changed significantly under the ongoing warming of the global climate, and variations in snow cover exert strong feedbacks on the climate system due to its high albedo and low thermal conductivity, as well as the high latent heat of the phase change (Zhang and Ma, 2018; Pulliainen et al, 2020; Vano, 2020; You et al, 2020)
Among China’s three main stable snow cover regions, the most accurate snowfall simulation is obtained for Northeast China, followed by North Xinjiang and the Tibetan Plateau
Li et al (2020) used temperature thresholds to calculate the snowfall in the Tianshan mountains of Central Asia and obtained a mean R2 value between the simulated snowfall and the snowfall observed at 27 meteorological stations of 0.61; in this study, the mean R2 value at 50 stations in Xinjiang is 0.39
Summary
Snow properties have changed significantly under the ongoing warming of the global climate, and variations in snow cover exert strong feedbacks on the climate system due to its high albedo and low thermal conductivity, as well as the high latent heat of the phase change (Zhang and Ma, 2018; Pulliainen et al, 2020; Vano, 2020; You et al, 2020). Climate warming has resulted in smaller snowfall/precipitation ratios and an earlier onset of snowmelt and slower snowmelt rates (Berghuijs et al, 2014; Musselman et al, 2017; Barnhart et al, 2020). Determining the amount of snowmelt and its variability under climate change is important for the planning and management of water resources, such as agricultural water management, flood forecasting, reservoir operations and the design of hydraulic structures (Barnhart et al, 2020; Qin et al, 2020)
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