资源环境科技发展态势分析平台

Based on the daily data collected from 69 meteorological stations in the Qinling-Daba Mountains (Qinba mountains) and its surrounding areas during 1960–2017. We analyzed The spatiotemporal variations of extreme precipitation events in the Qinba mountains and the relationship between the changes of extreme precipitation indices and potential factors (including geographical factors and atmospheric circulation patterns). The Mann-Kendall method and linear trend analysis were used to test the variations of extreme precipitation indices. Besides, the Pearson correlation analysis method was used to analyze the correlation between precipitation extremes and potential controlling factors (including geographic and atmospheric factors). The results showed that (1) consecutive dry days (CDD), simple daily intensity index (SDII), highest 1-day precipitation (RX1day) and very heavy rain days (R20mm), very wet days (R95P) and extremely wet days (R95P) and extremely wet days (R99P) showed an increasing trend, all other extreme precipitation indices showed decreasing trend and the change trends in all extreme indices were non-significant in annual and seasonal scales over Qinba mountains; (2) extreme precipitation increased in the eastern part of Qinba mountains while decreased in the western region; (3) there is a significant correlation between altitude, longitude, latitude and extreme precipitation; (4) the large-scale atmospheric circulation patterns also a significant impact on the spatiotemporal variations of extreme precipitation in the transition areas; the change of extreme precipitation indices is closely related to the El Niño–Southern Oscillation (ENSO) at interannual scales; (5) East Asian Summer Monsoon Index (EASMI) and the South China Sea Summer Monsoon Index (SCSSMI) show significant correlation with the extreme precipitations in summer while Arctic Oscillation (AO) show significant correlation wih the extreme indices in winter in Qinba mountains. Results of this study may help to deeper understand and predict the extreme precipitation events in Qinba mountains.