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径流序列的动力行为是在复杂非线性和多尺度现象综合作用下的外在表现。基于混沌理论和相空间重构理论,以金沙江和美国Umpqua河统计的日径流序列为研究对象,对不同时间尺度(日、旬和月)的径流序列,首先利用0-1混沌测试算法计算其渐进增长率,探讨径流序列混沌特性随时间尺度的变化规律,然后重构以上径流序列的相空间,分别计算关联维数、最大Lyapunov指数和Kolmogorov熵。用这3个混沌判别指标分析不同时间尺度下径流序列的混沌特性及其随时间尺度的变化规律。研究结果表明,时间尺度和径流序列非线性特征之间的关系并不明显,渐进增长率随时间尺度的增加并无明显的变化规律,嵌入维数则随时间尺度的增大呈减小趋势,最大lyapunov指数和Kolmogorov熵随着时间尺度的增加逐渐增大。
The dynamic behavior of runoff series is an external manifestation under the combined effect of complex nonlinearity and multi-scale phenomena. Based on the chaos theory and phase space reconstruction theory, the daily runoff series of the Jinsha River and the Umpqua River in the United States are taken as the research object. The runoff sequences of different time scales (day, day and month) are first calculated by the 0-1 chaos test algorithm The incremental growth rate of the runoff sequence is discussed. The chaotic characteristics of the runoff sequence with time scale are discussed. Then the phase space of the above runoff sequence is reconstructed, and the correlation dimension, the maximum Lyapunov exponent and the Kolmogorov entropy are respectively calculated. The chaotic characteristics of runoff sequences at different timescales and their variation with time scales are analyzed with the three chaotic discriminant indexes. The results show that the relationship between the time scale and the non-linear characteristics of runoff series is not obvious. Asymptotic growth rate does not change obviously with the increase of time scale, while the embedding dimension decreases with the increase of time scale. The maximum lyapunov exponent and Kolmogorov entropy increase with time scale.