Analysis of Precipitation Variation and Trend Forecast in Huashan Area of China
Science Discovery
Volume 4, Issue 3, June 2016, Pages: 183-188
Received: Jul. 1, 2016; Published: Jul. 5, 2016
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Authors
Hou Kai, Environmental Science and Engineering, Chang'an University College, Shanxi Xi'an, China
Li Yabin, Environmental Science and Engineering, Chang'an University College, Shanxi Xi'an, China
Qian Hui, Environmental Science and Engineering, Chang'an University College, Shanxi Xi'an, China
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Abstract
In order to mitigate the adverse effects of drought, floods and other natural disasters on agriculture, and to provide a reliable climate foundation for agricultural structure adjustment, spatial and temporal variation of precipitation in Huashan Mountain needs further research and exploration. This paper based on rainfall data from 1964 to 2013 Shaanxi Huashan station (57046) weather station, using regression analysis and Mann-Kendall nonparametric test, systematically and comprehensively analyzed spatial and temporal variation of precipitation in Huashan Mountain for nearly 50 years. The results showed that: from 1964 to 2013, the mean annual precipitation in Huashan area of Shaanxi Province generally presented a declining trend, and declined significantly. The average monthly rainfall of Huashan area in summer and autumn was higher than the rainfall in spring and winter over the last 50 years. As to the rainy month changes, rainfall from March to April and September to October represented significantly declining in Huashan area of Shaanxi Province, but from January to February, May to August, and November to December rainfall decreased slowly. In spring and autumn, variation trend of rainfall was clear in downward trend; while in summer and winter, variation trend of rainfall is not obvious in downward trend as well.
Keywords
Regression Analysis, Mann-Kendall Test, Precipitation, Trend Test, Variation
To cite this article
Hou Kai, Li Yabin, Qian Hui, Analysis of Precipitation Variation and Trend Forecast in Huashan Area of China, Science Discovery. Vol. 4, No. 3, 2016, pp. 183-188. doi: 10.11648/j.sd.20160403.15
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