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干旱区地理 ›› 2017, Vol. 40 ›› Issue (5): 967-978.

• 气候与水文 • 上一篇    下一篇

气候变化对秦岭北陂径流过程的影响机制研究——以灞河流域为例

胡胜1,2, 邱海军1,2, 宋进喜1,2, 马舒悦, 杨冬冬1,2, 裴艳茜1,2, 杨文璐1,2, 曹明明   

  1. 1 西北大学城市与环境学院, 陕西 西安 710127;
    2 西北大学地表系统与灾害研究院, 陕西 西安 710127
  • 收稿日期:2017-05-02 修回日期:2017-07-28 出版日期:2017-09-25
  • 通讯作者: 邱海军,男,博士,副教授.Email:rgbitxpl@163.com
  • 作者简介:胡胜(1988-),湖北枣阳人,博士生,主要从事水文水资源与地质灾害等方面研究.Email:hushengl98800@126.com
  • 基金资助:

    中国科学院国际合作局对外合作重点项目(131551KYSB20160002);陕西省重点科技创新团队计划项目(2014KCT-27)

Influencing mechanisms of climate change on runoff process in the north slope of Qinling Mountains:A case of the Bahe River Basin

HU Sheng1,2, QIU Hai-jun1,2, SONG Jin-xi1,2, MA Shu-yue, YANG Dong-dong1,2, PEI Yan-qian1,2, YANG Wen-lu1,2, CAO Ming-ming   

  1. 1 College of Urban and Environmental Science, Northwest University Xi'an 710127, Shaanxi, China;
    2 Institute of Earth Surface System and Hazards, Northwest University, Xi'an 710127, Shaanxi, China
  • Received:2017-05-02 Revised:2017-07-28 Online:2017-09-25

摘要: 综合运用距平分析、连续小波变换(CWT)、交叉小波变换(XWT)、小波相干(WTC)和时滞相关性分析等方法对秦岭北坡灞河流域径流与六大气象因子进行了多尺度分析。结果表明:水文气象因子在不同时频域中存在1~4个显著性周期,且都能通过95%置信水平检验;太阳黑子数8~11.4 a的显著性周期对同时间尺度年均SOI和年均MEI的连续小波能量谱具有明显的影响;交叉小波变换和小波相干分析相结合的方法在识别水文和气象要素的共振周期、相位角变化、丰枯阶段转换、时滞性、相关性、突变与显著性检验等方面具有独特的优势;同期降水是灞河流域径流形成和变化的控制性因素,年均太阳黑子数的影响作用微乎其微,月均流量滞后月均AOI0个月或8~9个月时的相关系数绝对值达到最大,滞后月均SOI2个月或7~8个月达到最大,滞后月均MEI6~8个月达到最大。

关键词: 气候变化, 径流过程, 交叉小波变换, 小波相干, 时滞相关性, 秦岭

Abstract: Runoff process is not only directly affected by local rainfall and temperature, but also closely related to southern-northern hemispheric teleconnections, such as Arctic Oscillation, Antarctic Oscillation, Southern Oscillation, El Nino, etc. Therefore, there is a great significance to investigate the influencing mechanisms of climate change on runoff process. Qinling Mountains is the dividing line between north and south China, which is also a hot spot of climate change around the world. Therefore, taking the Bahe River Basin in the north of Qinling Mountains as study area, this paper aims to reveal influencing mechanisms of climate change on runoff process, especially changes in climate factors how to lingeringly afTect runoff change, and further provide more theoretical foundation of teleconnections for hydrograph forecast. In order to achieve this, a variety of integrated methods including anomaly analysis, continuous wavelet transform, cross wavelet transform, wavelet coherence and lag correlation were applied to complete multi-time scale analysis between runoff and meteorological factors including temperature, precipitation,AOI, SOI, sunspot number and MEL Finally, we conducted the test of delayed correlation analysis. The results show that linear trend estimation demonstrates hydrometeorological factors presented a different trend of increasing or decreasing from 1959 to 2014 in the Bahe River Basin. Over the past 56 years, climate change presented a trend of warm and dry, which had a profound influence on runoff process in this basin. There were 1 to 4 significant (P<0.05) periods. It is worth noting that the 8-11.4 a significant period of sunspots number distinctly affected the continuous wavelet energy spectrum of annual average SOI and MEI at the same time scale, because their energy spectrums on the frequency band all showed a characteristic of zonal distribution. The method of combining cross wavelet transform and wavelet coherence analysis has unique advantages in iden-ferent meteorological factors on the runoff had some differences in tenns of correlation and time lag mechanisms. Precipitation in the same period was the key controlling factor of runoff fomiation and change, however, annual average sunspot number affected the runoff weakly. When average monthly flow lags monthly AOI/for 0 months or 8-9 months, the absolute value of correlation coefficient will reach the maximum; when average monthly flow lags monthly SOI for 2 months or 7-8 months, or lags monthly MEI for 6-8 months, the maximum absolute value of correlation coefficient will also appear.

Key words: climate change, runoff process, cross wavelet transfonn, wavelet coherence, lag correlation, Qinling Mountains

中图分类号: 

  • P333.1