Spatial and Temporal Variability of Drought and Precipitation Using Cluster Analysis in Xinjiang, Northwest China

  • Yuhu Zhang
  • Pei Xie
  • Xiao PuEmail author
  • Fuqiang Xia
  • Jialin An
  • Peng Wang
  • Qiwen Mei
Original Article


Analyses of drought and precipitation require long-term historical data and reasonable drought index to ensure reliable monitoring and prediction, especially for the Xinjiang Uygur Autonomous Region, where is sensitive and vulnerable to drought disasters. Drought characteristic was expressed using the observational precipitation data and calculated precipitation threshold at 105 meteorological stations from 1979 to 2014. The results showed that the variation of drought and precipitation was not spatially uniform. Analysis in sub-areas was conducted based on mutli-statistical methods. The historical drought and precipitation situation in Xinjiang is better characterized by three clusters. Cluster 1 is the driest, cluster 2 has a clear increasing trend of precipitation, while cluster 3 is the wettest with the mean annual precipitation approximates to 300 mm. In addition, the precipitation concentration and distribution is becoming uniform, there is a faster rate of increasing precipitation in cool-season rather than in warm-season. The results provide critical support for the drought disasters management and mitigation, it also provide a base for in-depth investigation on the possible mechanisms of regional drought.


Cluster analysis Drought Precipitation threshold Xinjiang 



This research was supported by the China Clean Development Mechanism Fund (2014108, 2014092), the National key research and development program (2017YFC0406002). We are grateful to anonymous reviewers for their valuable comments on improving the manuscript.

Compliance with Ethical Standards

Conflict of Interests

The authors declare that there is no conflict of interests.


  1. Byun, H.-R., Lee, S.-J., Morid, S., Choi, K.-S., Lee, S.-M., Kim, D.-W.: Study on the periodcoties of drought in Korea. Asia-Pac. J. Atmos. Sci. 44(4), 417–441 (2008)Google Scholar
  2. Chen, Y.-N., Wang, H.-J., Wang, Z., C., Zhang, H., 2015: Characteristics of extreme climatic/hydrological events in the arid region of northwestern China, Arid Land Geo., 40(1),1–9Google Scholar
  3. Choi, Y.-W., Ahn, J.-B., Suh, M.-S., Cha, D.-H., Lee, D.-K., Hong, S.-Y., Min, S.-K., Park, S.-C., Kang, H.-Y.: Future changes in drought characteristics over South Korea using multi regional climate models with the standardized precipitation index. Asia-Pac. J. Atmos. Sci. 52(2), 209–222 (2016)CrossRefGoogle Scholar
  4. Dai, A.: Increasing drought under global warming in observations and models. Nat. Clim. Chang. 3(1), 52–58 (2012)CrossRefGoogle Scholar
  5. Dai, X.-G., Wang, P., Zhang, K.-J.: Precipitation trend and fluctuation mechanism analysis in Xinjiang nearly 60 years. Acta Phys. Sin. 62(12), 129201–129211 (2013)Google Scholar
  6. Feng, S., Huang, Y., Xu, Y.-P.: Impact of global warming on the water cycle in Xinjiang region. J. Glaciol. Geocryol. 28(4), 523–532 (2006)Google Scholar
  7. Gocic, M., Trajkovic, S.: Analysis of precipitation and drought data in Serbia over the period 1980-2010. J. Hydrol. 494, 32–42 (2013)CrossRefGoogle Scholar
  8. Hisdal, H., Stahl, K., Tallaksen, L.-M., Demuth, S.: Have stream flow droughts in Europe become more severe or frequent? Int. J. Climatol. 21, 317–333 (2001)CrossRefGoogle Scholar
  9. Hosking, J.-R.-M., Wallis, J.-R.: Regional Frequency Analysis: an Approach Based on L-Moments. Cambridge University Press, New York (1997)CrossRefGoogle Scholar
  10. Huang, J.-P., Guan, X.-D., Ji, F.: Enhanced cold season warming in semi-arid regions. Atmos. Chem. Phys. 31, 783–792 (2012)Google Scholar
  11. Huang, J., Yu, H., Guan, X., Wang, G., Guo, R.: Accelerated dryland expansion under climate change. Nat. Clim. Change. 6(2) (2015)Google Scholar
  12. Huang, J., Yu, H., Dai, A., Wei, Y., Kang, L.: Drylands face potential threat under 2°C global warming target. Nat. Clim. Chang. 7(6), 417:422–417:422 (2017). CrossRefGoogle Scholar
  13. IPCC, 2014: Summary for Policymakers. Climate Change 2014: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate ChangeGoogle Scholar
  14. Kendall, M.-G.: Rank Correlation Methods. Griffin, London, UK (1975)Google Scholar
  15. Kim, M.-K., Kim, S., Kim, J., Heo, J., Park, J.-S., Kwon, W.-T., Suh, M.-S.: Statistical downscaling for daily precipitation in Korea using combined PRISM, RCM, and quantile mapping: part 1, methodology and evaluation in historical simulation. Asia-Pac. J. Atmos. Sci. 52(2), 79–89 (2016)CrossRefGoogle Scholar
  16. Luis, M.-D., Gonzalez-Hidalgo, J.-C., Brunetti, M., Longares, L.-A.: Precipitation concentration changes in Spain 1946-2005. Natur. Hazards Earth Syst. Sci. 11(5), 1259–1265 (2011)CrossRefGoogle Scholar
  17. Mann, H.-B.: Nonparametric tests against trend. Econometrica. 13, 245–259 (1945)CrossRefGoogle Scholar
  18. Mergenthaler, M.-J.: Nonparametrics: statistical methods based on ranks. Technometrics. 21, 272–273 (1979)CrossRefGoogle Scholar
  19. Michiels, P., Gabriels, D., Hartmann, R.: Using the seasonal and temporal precipitation concentration index for characterizing monthly rainfall distribution in Spain. Catena. 19, 43–58 (1992)CrossRefGoogle Scholar
  20. Oliver, J.-E.: Monthly precipitation distribution: a comparative index. Prof. Geogr. 32, 300–309 (1980)CrossRefGoogle Scholar
  21. Sheffield, J., wood, E.-F., Roderick, M.-L.: Little change in global drought over the past 60 years. Nature. 491, 435–438 (2012)CrossRefGoogle Scholar
  22. Shi, Y., Shen, Y., Li, D., Zhang, G., Ding, Y., Hu, R., Kang, E.: Discussion on the present climate change from warm dry to warm wet in Northwest China. Quantern. Sci. 23, 152–164 (2002)Google Scholar
  23. Sneyers, R.: On the statistical analysis of series of observations. J. Biol. Chem. 258, (1991)Google Scholar
  24. Sönmez İ. 2009: Determination of the proper site spacing density: correlation, power spectrum, and true field error variance approaches, VDM Verlag Dr. Müller Aktiengesellschaft & Co. KG Publishing, Saarbrücken, Germany, 126Google Scholar
  25. Wang, H., Chen, Y., Pan, Y., Li, W.: Spatial and temporal variability of drought in the arid region of China and its relationships to teleconnection indices. J. Hydrol. 523, 283–296 (2015)CrossRefGoogle Scholar
  26. Wilhite, D.-A., Glantz, M.-H.: Understanding the drought phenomenon: the role of definitions. Water Int. 10, 111–120 (1985)CrossRefGoogle Scholar
  27. Yevjevich, V.-M.: An objective approach to definitions and investigations of continental hydrologic droughts. J. Hydrol. 23, 25 (1967)Google Scholar
  28. Yoo, J., Kwon, H.-H., Kim, T.-W., Ahn, J.-H.: Drought frequency analysis using cluster analysis and bivariate probability distribution. J. Hydrol. 420-421, 102–111 (2012)CrossRefGoogle Scholar
  29. Zhang, Q., Li, J., Singh, V.-P., Bai, Y.: SPI-based evaluation of drought events in Xinjiang, China. Nat. Hazards. 64(1), 481–492 (2012a)CrossRefGoogle Scholar
  30. Zhang, Q., Singh, V.-P., Li, J., Jiang, F., Bai, Y.: Spatio-temporal variations of precipitation extremes in Xinjiang, China. J. Hydrol. 434-435, 7–18 (2012b)CrossRefGoogle Scholar
  31. Zhou, T.-J., Li, L.-J., Li, H.-M., Bao, Q.: Progress in climate change attribution and projection studies. Chin. J. Atmosph. Sci. 32(4), 906–922 (2008)Google Scholar

Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2019

Authors and Affiliations

  • Yuhu Zhang
    • 1
  • Pei Xie
    • 1
  • Xiao Pu
    • 1
    Email author
  • Fuqiang Xia
    • 2
  • Jialin An
    • 3
  • Peng Wang
    • 4
  • Qiwen Mei
    • 5
  1. 1.College of Resource Environment & TourismCapital Normal UniversityBeijingChina
  2. 2.Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqiChina
  3. 3.College of biological & medical engineeringBeihang UniversityBeijingChina
  4. 4.Faculty of Civil Engineering and MechanicsJiangsu UniversityZhenjiangChina
  5. 5.College of Environmental EngineeringUniversity of SeoulSeoulSouth Korea

Personalised recommendations