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

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8861 ◽  
Author(s):  
Wenqian Chen ◽  
Jianli Ding ◽  
Jingzhe Wang ◽  
Junyong Zhang ◽  
Zhe Zhang
Keyword(s):  
Central Asia ◽  
Snow Cover ◽  
Temporal Variations ◽  
Snow Accumulation ◽  
River Valley ◽  
Mountain Glacier ◽  
Northern Xinjiang ◽  
Seasonal Snow ◽  
Ili River Valley ◽  
Seasonal Snow Cover

Xinjiang, China, is a typical arid and semi-arid region of Central Asia that significantly lacks freshwater resources, and the surface runoff in this region is mainly supplied by mountain glacier and snow cover meltwater. Based on the above background and issues of transnational water resources between Xinjiang and Central Asia along the Silk Road Economic Belt, which were highlighted in the major strategy of “The Belt and Road”, this study analysed the spatial and temporal variations in snow cover and snow cover days in the Xinjiang region from 2001 to 2015. The study area includes four subregions: Northern Xinjiang, Southern Xinjiang, Eastern Xinjiang and the Ili River Valley. Moderate-resolution Imaging Spectroradiometer (MODIS) 8-day snow cover data were used after removing clouds by combining MOD10A2 and MYD10A2. The results showed that seasonal snow cover occurred from October to April in most regions of Xinjiang and that this snow cover consisted of two processes: snow accumulation and snow ablation. The maximum snow cover occurred in January, whereas the minimum snow cover occurred from July to August. During the seasonal snow cover period, the snowfall rates in Northern Xinjiang and the Ili River Valley were higher, while the other regions had a low snowfall probability. To study the relationship between altitude and snow cover, the normalized snow elevation correlation index (NSACI) was calculated. The NSACI showed a significant correlation between snow cover and elevation in most regions of Xinjiang and was classified into five grades. Snow cover days did not fluctuate obviously from 2001 to 2015, and a decreasing trend was observed in the four subregions except for the Ili River Valley (nonsignificant decreasing trend). We also observed a correlation between snow cover and temperature and found that the correlations between monthly snow cover and monthly temperature in the four subregions were strongly related to the underlying land type and global warming background, which also suggests that the special topography of Xinjiang greatly influences both snow cover and climate change.

scholarly journals Distribution of seasonal snow cover in central and western Himalaya

2010 ◽  
Vol 51 (54) ◽  
pp. 123-128 ◽  
Author(s):  
Anil V. Kulkarni ◽  
B.P. Rathore ◽  
S.K. Singh ◽  
Ajai
Keyword(s):  
Snow Cover ◽  
Western Himalaya ◽  
Snow Accumulation ◽  
Wide Field ◽  
Seasonal Snow ◽  
Snow Line ◽  
Field Sensor ◽  
Normalized Difference Snow Index ◽  
The Individual ◽  
Seasonal Snow Cover

AbstractIndian rivers originating in the Himalaya depend on seasonal snow-cover melt during crucial summer months. The seasonal snow cover was monitored using Advanced Wide Field Sensor (AWiFS) data of the Indian Remote Sensing Satellite (IRS) and using the Normalized Difference Snow Index (NDSI) algorithm. The investigation was carried out for a period of 3 years (2004/05, 2005/06 and 2006/07) between October and June. A total of 28 sub-basins of the Ganga and Indus river basins were monitored at intervals of 5 or 10 days. Approximately 1500 AWiFS scenes were analyzed. A combination of area–altitude distribution and snow map was used to estimate the distribution of snow cover in altitude zones for the individual basins and for the western and central Himalaya. Hypsographic curve and snow-free area was used to estimate monthly snow-line elevation. The lowest snow-line altitude in the winters of 2004/05, 2005/06 and 2006/07 was observed at 2480 ma.s.l. on 25 February 2005. In Ravi basin for the year 2004/05, snow accumulation and ablation were continuous processes throughout the winter. Even in the middle of winter, the snow area was reduced from 90% to 55%. Similar trends were observed for 2005/06 and 2007/08. In Bhaga basin, snowmelt was observed in the early part of the winter, i.e. in December, and no significant melting was observed between January and April.

Effect of seasonal snow cover on litter decomposition in alpine forest

2013 ◽  
Vol 37 (4) ◽  
pp. 296-305 ◽  
Author(s):  
Qi-Qian WU ◽  
Fu-Zhong WU ◽  
Wan-Qin YANG ◽  
Zhen-Feng XU ◽  
Wei HE ◽  
...  
Keyword(s):  
Snow Cover ◽  
Litter Decomposition ◽  
Seasonal Snow ◽  
Alpine Forest ◽  
Seasonal Snow Cover

Modelling the hypsometric seasonal snow cover using meteorological parameters

2014 ◽  
Vol 60 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Snehmani ◽  
Anshuman Bhardwaj ◽  
Mritunjay Kumar Singh ◽  
R.D. Gupta ◽  
Pawan Kumar Joshi ◽  
...  
Keyword(s):  
Snow Cover ◽  
Meteorological Parameters ◽  
Seasonal Snow ◽  
Seasonal Snow Cover

scholarly journals An outline of avalanches in the Tien Shan mountains

1992 ◽  
Vol 16 ◽  
pp. 7-10 ◽  
Author(s):  
Hu Ruji ◽  
Ma Hong ◽  
Wang Guo
Keyword(s):  
Snow Cover ◽  
Basal Layer ◽  
Liquid Water Content ◽  
Snow Accumulation ◽  
Tien Shan ◽  
Large Temperature ◽  
Snow Avalanches ◽  
Climatic Variations ◽  
Tien Shan Mountains ◽  
Seasonal Snow Cover

The seasonal snow cover in the Tien Shan mountains is characterized by low density, low liquid-water content and low temperature. It is known as typical dry snow. Large temperature gradients in the basal layer of the snow cover exist throughout the entire period of snow accumulation, and depth hoar is therefore extremely well-developed. Full-depth depth-hoar avalanches, however, seldom occur. Avalanches in the Tien Shan mountains are mostly loose snow avalanches. Although normally not large in size, they are the most dangerous type. The occurrence of hazardous avalanches shows cycles of about ten years because of periodic climatic variations.

scholarly journals Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

2018 ◽  
Vol 12 (4) ◽  
pp. 1137-1156 ◽  
Author(s):  
Paul J. Kushner ◽  
Lawrence R. Mudryk ◽  
William Merryfield ◽  
Jaison T. Ambadan ◽  
Aaron Berg ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Climate Prediction ◽  
Earth System Model ◽  
System Model ◽  
Prediction System ◽  
Earth System ◽  
Seasonal Snow ◽  
Observational Uncertainty ◽  
Seasonal Snow Cover

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

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