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Theoretical and Applied Climatology

, Volume 122, Issue 3–4, pp 487–496 | Cite as

Snow cover variations in Gansu, China, from 2002 to 2013

  • Xun Liu
  • Chang-Qing KeEmail author
  • Zhu-De Shao
Original Paper
  • 341 Downloads

Abstract

Gansu is an inland province located in the northwest of China with an arid to semi-arid climate and a developed animal husbandry. Snowmelt in Gansu is an important source of water for rivers and plays an important role in ecological environment and social-economic activities. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day composite snow products MOD10A2 and MYD10A2 are combined to analyse snow cover variations during the snow season (October to March) period from 2002 to 2013. We define the snow area percentage (SAP) and snow cover occurrence percentage (SCOP) to analyse the spatial and temporal characteristics of the snow cover variation in Gansu. In addition, we apply the Mann-Kendall test to verify the SAP inter-annual variation. The results indicate that the SAP in Gansu remained above 5 % with three peaks in November, December and January. SAP varies a lot in the four sub-regions of Gansu, with the highest in the Gannan Plateau sub-region and the lowest in the Longzhong Loess Plateau sub-region in most of the snow seasons examined. The SCOP is high in the southwest mountains and low in the northeast Gobi and desert. The SCOP is highly related to elevation in most of Gansu, with an exception in the high mountains. In the Hexi Desert and oasis region, the SAP significantly decreases during the snow season, particularly in February and March. We find that there are a significantly negative correlation between SCOP and temperature during the snow season and a significantly positive correlation between SCOP and precipitation in December.

Keywords

Snow Cover Shuttle Radar Topography Mission Qilian Mountain Snow Season Oasis Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is financially supported by a program from the National Nature Science Foundation (No. 41371391), a program from the National Key Technology Research and Development (No. 2012BAH28B02) and a program from the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20120091110017). Also, this work was partially supported by the Collaborative Innovation Center of Novel Software Technology and Industrialization. The MODIS snow data used in this study are obtained from the National Snow and Ice Data Center (http://nsidc.org).

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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  1. 1.Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing UniversityNanjingChina

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