Sciences in Cold and Arid Regions ›› 2019, Vol. 11 ›› Issue (5): 340–349.

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• 收稿日期:2019-07-27 接受日期:2019-08-29 出版日期:2019-10-31 发布日期:2019-11-12

### Analysis of chaotic climatic process in the Tarim River Basin (I)

ZuHan Liu1,2()

1. 1. Key Laboratory of the Education Ministry for Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang, Jiangxi 330022, China
2. Jiangxi Province Key Laboratory for Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China
• Received:2019-07-27 Accepted:2019-08-29 Online:2019-10-31 Published:2019-11-12
• Contact: ZuHan Liu E-mail:lzh512@nit.edu.cn

Abstract:

Based on observational data obtained from 1961 to 2011 in the Tarim River Basin, China, we investigated the chaotic dynamics of temperature, precipitation, relative humidity, and evaporation. The main findings are as follow: (1) The four data series have significant chaotic and fractal behaviors, which are the result of the evolution of a nonlinear chaotic dynamic system. The climatic process in the Tarim River Basin also has deterministic and stochastic characteristics. (2) To describe the temperature, precipitation, relative humidity, and evaporation dynamics, at least three independent variables at daily scale are required; in terms of complexity, their order is evaporation > temperature > precipitation > relative humidity. (3) Their respective largest Lyapunov exponent λ 1 shows the order of their degree of complexity is relative humidity > temperature > precipitation ≈ evaporation; the maximum time scales for which the four systems can be predicted are 17 days, 17 days, 16 days, and 16 days, if calculated separately. (4) The Kolmogorov entropy K illustrates that the complexity of the nonlinear precipitation system is much greater than that of the other three systems. Both temperature and evaporation systems exhibit weaker chaotic behavior, their predictability is better, and the degree of complexity is less than that of the other two factors.

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