In this paper, taking railway flood in Xinjiang Line of New Eurasian Continental Bridge as an example, the dynamics mechanism of railway flood has been studied by using Chaotic Theory. During study, some nonlinear fea...In this paper, taking railway flood in Xinjiang Line of New Eurasian Continental Bridge as an example, the dynamics mechanism of railway flood has been studied by using Chaotic Theory. During study, some nonlinear features of railway flood, such as correlation dimension D2 and Kolomogorov entropy K, are analyzed based on time-series of railway flood in Xinjiang Line of New Eurasian Continental Bridge. Results show: time-series distribution of railway flood has some characteristics of Chaos dynamic system, and the variation of railway flood frequency is a definite low-dimension Chaotic attractor. The average length of Tp(Tp = 15d) calculated in this paper, which shows the time of forecasting by this Chaotic dynamic system, is close to the reality.展开更多
文摘降水是水热循环以及气候变化研究的重要环节,降水资料的准确与否直接影响流域尺度的水文过程研究。本文基于2000—2015年天山南坡阿克苏河流域气象站点观测降水数据,对比分析了Tropical Rainfall Measuring Mission(TRMM)降水数据集和Global Land Data Assimilation System(GLDAS)两种具有代表性的降水格网数据集在阿克苏河流域的适用性。结果表明:TRMM3B43数据在阿克苏河流域的整体表现优于GLDAS-2数据。两种数据的精度在月尺度上表现最优,相关系数分别为0.938和0.901,通过了0.01的显著性检验;在季节尺度,TRMM3B43数据各季节与站点插值的拟合度要优于GLDAS-2数据,但二者均呈现出高估冷季降水而低估暖季降水的趋势;在年尺度上,两种数据表现较差。在空间分布上,两种数据类型均能够反映出阿克苏河流域降水自西北向东南递减的空间分布趋势。并且两种数据在平原区的表现均优于山区,低估高海拔地区降水而高估低海拔地区的降水。
基金Supponed by world Bank Project (Disaster Reduction in china) (NoA3) and"Xibuzhiguang"Project(Prevention of Railway Flood in xinjiang Line of New Eurasian Continental Bridge)(98013010)
文摘In this paper, taking railway flood in Xinjiang Line of New Eurasian Continental Bridge as an example, the dynamics mechanism of railway flood has been studied by using Chaotic Theory. During study, some nonlinear features of railway flood, such as correlation dimension D2 and Kolomogorov entropy K, are analyzed based on time-series of railway flood in Xinjiang Line of New Eurasian Continental Bridge. Results show: time-series distribution of railway flood has some characteristics of Chaos dynamic system, and the variation of railway flood frequency is a definite low-dimension Chaotic attractor. The average length of Tp(Tp = 15d) calculated in this paper, which shows the time of forecasting by this Chaotic dynamic system, is close to the reality.
