Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015

Chen, Wenqian; Ding, Jianli; Wang, Jingzhe; Zhang, Junyong; Zhang, Zhe

doi:10.7717/peerj.8861

Cited by 15 publications

(18 citation statements)

References 41 publications

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“…The amount of snow cover is large in winter but little distribution in high altitude mountainous areas in summer. Chen et al [43] and Li et al [68] analyzed the relationship between snow cover and altitude, and showed that the distribution of snow cover had a clear positive correlation with altitude, we also obtained an correlation coefficient 0.76 in northern Xinjiang, China. Based on multivariate data fusion method among MODIS version 5 snow products, the advanced microwave scanning radiometer for earth observing system daily snow water equivalent products and IMS products, the analyses of the snow cover changes in northern hemisphere show that SCD of Altay Mountain and Tianshan Mountain is significantly reduced [32].…”

Section: Discussionsupporting

confidence: 67%

“…Yang et al [42] analyzed the change of snow cover in northern Xinjiang with site data, showing that snow cover increased from 1961 to 2017. Chen et al [43] used MODIS 8-day synthetic product to analyze the correlation between snow cover and elevation in Xinjiang, China, and there are some other studies about the snow cover variation in Tianshan Mountains [44][45][46]. However, systematic analysis of the spatial and temporal variation of snow cover in northern Xinjiang, China, is still rare, and current analyses are mainly based on the site data and MODIS version 5 snow products.…”

Section: Introductionmentioning

confidence: 99%

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A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

et al. 2020

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Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.

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Section: Discussionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

et al. 2020

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“…Northern Xinjiang is located in the TCZ, and snowmelt is an important source of fresh water in this region (Chen et al, 2020;Wu et al, 2021). This study showed that between 1951 and 2017 the mean annual snowmelt runoff ratios in the third-level basins in Northern Xinjiang were above 10% and exceeded 30% in some basins.…”

Section: Northern Xinjiangmentioning

confidence: 73%

Trends and variability in snowmelt in China under climate change

Yang

Chen

et al. 2021

Preprint

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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.

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“…2 of 8 typical glaciers in the middle Tianshan mountains retreated at a mean rate of < 5 m/year between 1963 and 1986, and between 1986 and 2000, seven of those glaciers retreated at a mean rate of 10–15 m/year ( Sun & W, 2006 ). The snow cover area in Xinjiang has declined significantly in recent years ( Chen, Li & Hui, 2017 ; Haidi, Zhongqin & Mingjun, 2018 ). The water surface area, water depth, and water volume of lakes at high-altitudes may see a decline in the future as mountain glaciers and snow continue to melt.…”

Section: Discussionmentioning

confidence: 99%

Lake water volume fluctuations in response to climate change in Xinjiang, China from 2002 to 2018

Wufu

Wang

Chen

et al. 2020

PeerJ

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Climate change has a global impact on the water cycle and its spatial patterns, and these impacts are more pronounced in eco-fragile regions. Arid regions are significantly affected by human activities like farming, and climate change, which influences lake water volumes, especially in different latitudes. This study integrates radar altimetry data from 2002 to 2018 with optical remote sensing images to analyze changes in the lake areas, levels, and volumes at different altitudes in Xinjiang, China. We analyzed changes in lake volumes in March, June, and October and studied their causes. The results showed large changes in the surface areas, levels, and volumes of lakes at different altitudes. During 2002–2010, the lakes in low- and medium-altitude areas were shrinking but lakes in high altitude areas were expanding. Monthly analysis revealed more diversified results: the lake water levels and volumes tended to decrease in March (−0.10 m/year, 37.55×108 m3) and increase in June (0.03 m/year, 3.48×108 m3) and October (0.04 m/year, 26.90×108 m3). The time series lake water volume data was reconstructed for 2011 to 2018 based on the empirical model and the total lake water volume showed a slightly increasing trend during this period (71.35×108 m3). We hypothesized that changes in lake water at high altitudes were influenced by temperature-induced glacial snow melt and lake water in low- to medium-altitude areas was most influenced by human activities like agricultural irrigation practices.

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Temporal and spatial variability in snow cover over the Xinjiang Uygur Autonomous Region, China, from 2001 to 2015

Cited by 15 publications

References 41 publications

A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling

Trends and variability in snowmelt in China under climate change

Lake water volume fluctuations in response to climate change in Xinjiang, China from 2002 to 2018

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