中文摘要：

径流时间序列混沌特性识别的常用方法是基于相空间重构的关联维数法、最大Lyapunov指数法和Kolmogorov熵法。引入一种新的时间序列混沌特性识别方法：0-1混沌测试方法。该方法直接应用于时间序列不需要相空间重构，并且通过量化指标Kc是否接近于0或1来识别时间序列的混沌特性。以Logistic映射生成的序列、金沙江流域和美国Umpqua河多年日径流序列为研究对象，首先利用0-1混沌测试方法进行了混沌特性识别和判定；然后基于相空间重构，运用相空间重构、伪最近邻点法、关联维数方法、最大Lyapunov指数法和Kolmogorov熵5种非线性研究方法分析了这两列径流时间序列混沌特性。研究结果表明0-1混沌测试方法简单有效。以上方法交互验证了该两列径流时间序列存在低维混沌特性。

英文摘要：

The commonly used methods for the identification of chaotic characteristic of runoff time series are the cor- relation dimension method, the largest Lyapunov exponent method and Kolmogorov entropy method, which are based on the phase space reconstruction. Recently, a novel test approach for chaos detection in time series named zero-one （0-1） test has been proposed. This method applies directly to time series data ; therefore, the phase space reconstruc- tion is not required. Moreover, the non-chaotic and chaotic motions can be decided by means of the parameters K ap- proaching asymptotically either to zero or one. Case studies of Logistic map, daily runoff series of Jinsha River in Chi- na and Umpqua River in America are implemented. The chaotic characteristics are identified and verified by using the 0-1 test algorithm. Then, based on the phase space reconstruction, five nonlinear dynamic methods are employed： （1） phase space reconstruction; （2） the false Nearest Neighbor （FNN） algorithm; （3） correlation dimension method; （4） Lyapunov exponent method; and, （5） Kolmogorov entropy. The comparative results show the effectiveness and reli- ability of the 0-1 test algorithm. The results from these methods provide cross-verification and confirmation of the exist- ence of a mild low-dimensional chaos in the two daily runoff time series.