通过对安康站1950—2014年逐月降水量的趋势进行分析,发现其降水存在周期变化。基于自相关函数法、C-C关联积分法来确定安康站降水的非线性系统的延迟时间、嵌入维数后,对降水序列进行了相空间重构,并利用G-P关联维法以及最大Lyapunov指数法进行混沌特征识别。结果显示:采用G-P关联维算法分析安康站1950—2014年降水时间序列并不能得到其存在混沌特性的结果,但最大Lyapunov指数法显示其存在混沌性;基于现有780个月份降水数据,可预报的最大时间长度为7个月。研究结果可为当地和下游地区的径流预报提供科学支撑。
Abstract
By analyzing the monthly precipitation trend of Ankang Station from 1950 to 2014, we found that the precipitation at Ankang changes periodically. Having determined the delay time and embedded dimension of the nonlinear system of precipitation at Ankang using autocorrelation function method and C-C correlation integral method, we reconstructed the phase space of the precipitation series, and identified the chaotic characteristics of precipitation by using the G-P correlation dimension method and the maximum Lyapunov exponent method. Results reveal that the G-P correlation dimension algorithm indicates no chaos, while the maximum Lyapunov exponent method suggests chaos. With the existing 780 monthly precipitation data, we can forecast up to seven months of precipitation. The research finding offers scientific support for the runoff forecast in Hanjiang River and its downstream areas.
关键词
降水量特征 /
混沌理论 /
C-C法 /
G-P关联维法 /
预报时间尺度 /
汉江上游安康站
Key words
precipitation characteristics /
chaos theory /
C-C method /
G-P correlation dimension /
forecast time scale /
Ankang Station in upper Hanjiang River
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基金
国家重点研发计划项目(2018YFC0407900)
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