Detection of precipitation variability based on entropy over nearly 50 years in Xinjiang, northwestern China
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Based on precipitation data of 53 meteorological stations from 1960 to 2008, the entropy method was used to analyze spatial variability of precipitation in Xinjiang, China, over monthly, seasonal, and annual timescales. The spatial distribution of precipitation variability was significantly affected by topography and was zonal on all timescales. The nonparametric Mann-Kendall test was used to analyze changes in the distributions. A precipitation concentration index was developed to categorize the variability of annual precipitation. Summer variability contributed less to annual variability than that of other seasons. Various months contributed to annual mean variability differently across the years. Overall, the variability of precipitation was shown to increase north of Xinjiang, especially in mountainous regions, where the increase was statistically significant (P = 0.05). South of Xinjiang, the variability increased only slightly, consistent with the distribution of precipitation.
KeywordsEntropy Tarim Basin Monthly Precipitation Tianshan Mountain Precipitation Variability
The study was also supported by the Major National Science Research Program (973 Program) (No. 2013CBA01806), National Natural Science Foundation of China (No. 41361013 and 31300388), State Key Laboratory of Cryosphere Open Fund (SKLCS 2012–10), and Lanzhou City University Ph.D. Research Fund (LZCU-BS2013-09 and LZCU-BS2013-12). The authors are grateful to the two anonymous reviewers for their very useful suggestions and comments.
- Chen YN, Xu CC, Hao XM, Li WH, Chen YP, Zhu CG, Ye ZX (2009) Fifty-year climate change and its effect on annual runoff in the Tarim River Basin, China. Quat Int 208(1–2):53–61Google Scholar
- De Luis M, González-Hidalgo JC, Raventós J, Sánchez JR, Cortina J (1997) Distribución espacial de la concentración y agresividad de la lluvia en el territorio de la Comunidad Valenciana. Cuaternarioy Geomorfologia 11(3–4):33–44Google Scholar
- Houghton JT, Ding YH, Griggs DJ, Noguer M, Van der Linden PJ, Dai X, Maskelh K, Johson CA (eds) (2001) Climate change 2001: the scientific basis. Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
- Kendall MG (1975) Rank Correlation Methods. Griffin, London, 202Google Scholar
- Mann HB (1945) Non-parametric test against trend. Econometrika 13, 245–259Google Scholar
- Polikar R (1999) In: Mastorakis, N. (Ed.), The story of wavelets, in physics and modern topics in mechanical and electrical engineering. World Scientific and Engineering Society Press, pp. 192–197Google Scholar