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