Trends and variability in snowmelt in China under climate change

Abstract

Abstract. Snowmelt is a major fresh water resource, and quantifying snowmelt and its variability under climate change is necessary for 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 seconds, 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 pathways (RCP) scenarios (RCP2.6, RCP4.5 and RCP8.5). The results showed that the mean annual snowmelt in China from 1951 to 2017 was 2.41 × 1011 m3. The mean annual snowmelts in Northern Xinjiang, Northeast China, and the Tibetan Plateau – China’s three main stable snow cover regions – were 0.18 × 1011 m3, 0.42 × 1011 m3 and 1.15 × 1011 m3, respectively. From 1951 to 2017, the snowmelt increased significantly in the Tibetan Plateau and decreased significantly in North, Central and Southeast 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 were 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 2030s (2050s, 2090s) may decrease by 13.4 % (16.3 %, 13.8 %), 19.1 % (19.8 %, 22.5 %), 17.1 % (24.7 %, 42.8 %) compared with the historical period (1951–2017), respectively. Most of the projected mean annual snowmelt runoff ratios in third-level basins in different decades (2030s, 2050s and 2090s) were lower than those in the historical 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 of snowmelt changes may lead to regional differences in the impact of snowmelt on water supply.

Climate-environment interactions under global warming

“…Based on high spatial resolution precipitation and temperature data, Yang et al (2022a) produced a monthly snowmelt dataset for China (SMT-Y) using a simple temperature index model. Because snowmelt is difficult to measure directly, the calculated snowmelt was verified using snowfall, snow depth, snow cover extent and snow water equivalent data, which indicated that is reliable (Yang et al, 2022b).…”

Assessing the Sustainability of Urban Agriculture in Shanghai’s Nine Agriculture Districts: A Decadal Analysis (2010–2020)

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