长江科学院院报 ›› 2017, Vol. 34 ›› Issue (5): 5-8.DOI: 10.11988/ckyyb.20160482

• 江湖泥沙与治理 • 上一篇    下一篇

多沙河流水沙变化特征小波分析

邵雪杰, 顾圣平, 曹爱武, 何海祥, 于婷婷   

  1. 河海大学 水利水电学院,南京 210098
  • 收稿日期:2016-05-19 修回日期:2016-06-26 出版日期:2017-05-01 发布日期:2017-05-17
  • 通讯作者: 顾圣平(1957-),男,江苏泰州人,教授,硕士,研究方向为水利水电系统规划与工程经济,(电话)13951893043(电子信箱)spgu@hhu.edu.cn。
  • 作者简介:邵雪杰(1992-),男,江苏南通人,硕士研究生,研究方向为水利水电系统规划与工程经济,(电话)15850603872(电子信箱)1289770074@qq.com。
  • 基金资助:
    国家“十二五”科技支撑计划项目(2013BAB06B01)

Wavelet Analysis on Flow and Sediment Variation in Sandy Rivers

SHAO Xue-jie, GU Sheng-ping, CAO Ai-wu, HE Hai-xiang, YU Ting-ting   

  1. College of Water Conservancy and Hydropower Engineering, Hohai University,Nanjing 210098, China
  • Received:2016-05-19 Revised:2016-06-26 Online:2017-05-01 Published:2017-05-17

摘要: 为了给多沙水库运行调度提供依据,降低水沙序列长度和随机性的影响,将小波变换应用于研究流域径流与含沙量的变化特性。采用db3小波对标准化径流与含沙量序列进行多分辨率分析,研究径流与含沙量变化的趋势性;选用复Morlet小波绘制出小波方差图,分析径流过程与含沙量过程存在的周期性。以崖羊山水电站所在的李仙江流域为例,针对电站坝址断面的月平均流量与含沙量序列进行小波分析,从低频重构序列的结果中可以看出该流域径流与含沙量的变化趋势;并结合当地的降雨量与水土保持状况分析,表明结果是合理的。根据小波方差图可以看出,崖羊山水电站所在流域径流过程与含沙量过程存在非常接近的显著周期,均为2 a左右,且两者变化周期具有同步性。研究结果表明小波分析是研究非平稳随机时间序列的有效方法。

关键词: 小波变换, 水沙序列, 径流, 含沙量, 时间尺度, 多分辨率分析, 小波方差

Abstract: In the aim of providing basis for the operation of sandy reservoir and reducing the influences of length and randomness of flow and sediment series, wavelet transform was adopted to analyze the variation characteristics of flow and sediment series. Standardized monthly flow and sediment series were decomposed by Multi-resolution Analysis using the db3 wavelet function, and continuous wavelet transform was used to evaluate the periodic variations of the two standardized series using the complex valued Morlet function. The flow and sediment series at the dam site of Yayangshan Hydropower Station located in Lixianjiang watershed were taken as an example. The reconstruction of the lowest frequency part revealed the trend of the flow and sediment series. According to the local rainfall and soil and water conservation, the result is considered reasonable. Wavelet variances were obtained to identify the dominant period as 2-year approximately for both flow series and sediment series and reveal the synchronization between them. The results indicate that wavelet transform is effective for nonstationary stochastic series analysis.

Key words: wavelet transform, flow and sediment series, runoff, sediment concentration, time scale, multi-resolution analysis, wavelet variances

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