长江流域资源与环境 >> 2021, Vol. 30 >> Issue (4): 849-860.doi: 10.11870/cjlyzyyhj202104008

• 自然资源 • 上一篇    下一篇

嘉陵江流域降水集中度的时空变异与驱动因素研究

杨  军,张会兰*,庞建壮   


  1. (北京林业大学水土保持学院重庆缙云三峡库区森林生态系统国家定位观测研究站,北京 100083)
  • 出版日期:2021-04-20 发布日期:2021-05-17

Study on Spatial-temporal Variation and Driving Factors of Precipitation Concentration in Jialing River Basin

YANG Jun,ZHANG Hui-lan,PANG Jian-zhuang   

  1. (Three-gorges Reservoir Area (Chongqing) Forest Ecosystem Research Station, School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083,China)
  • Online:2021-04-20 Published:2021-05-17

摘要: 降水的集中分布是引发洪涝灾害和河流高含沙事件的主要因素,对降水集中度时空变异规律的认识及其主要驱动因素的辨析可为区域水资源管理和灾害预警提供依据。以长江上游最大的产沙支流嘉陵江流域为研究区,基于流域内19个气象站点1954~2018年的逐日降水数据,计算年平均降水集中度(ACI)和长期降水集中度(LCI)指数,采用Mann-Kendall趋势检验法、Sen’s slope坡度检验法和反距离权重插值法分析降水集中度指数的时空变异规律,基于相关性分析和随机森林算法探讨其主要驱动因素。结果表明:(1)嘉陵江流域的ACI和LCI值在空间上均呈现北低南高的分布格局,东南部人口密集区降水集中度大,极端降水事件发生的可能性大;而西北部山区降水集中度较小,降水较均匀,发生极端降水的可能性较小。(2)研究期间降水集中度的数值变化范围为0.53~0.83,变幅较大;19个气象站点中,15个站点降水集中度呈现上升趋势(其中8个为显著上升),4个站点呈现下降趋势(其中2个为不显著下降),各站点的趋势性差异亦与地形、气候因素密切相关。(3)通过相关性分析发现,降水集中度与流域高程具有显著的负相关关系(p<0.01);基于随机森林算法的重要性分析结果表明,对嘉陵江流域内降水集中度影响最大的气候因子为太阳黑子指数SS,重要性为17.24%~20.25%,其次为ENSO指数(MEI和SOI)。

Abstract:

The concentrated distribution of precipitation is the main cause of flood disaster and hyper-concentrated flow. The study on the spatiotemporal of precipitation concentration and the discrimination of its main driving factors can provide support for regional water resource management and disaster warning. In this study, Jialing River Basin, the largest sediment-producing tributary in the upper reaches of the Yangtze River, was taken as the research area. Based on daily precipitation of 19 meteorological stations in the basin from 1954 to 2018, the annual average precipitation concentration (ACI) and long-term precipitation concentration (LCI) were calculated. The Mann-Kendall statistical test, Sen’s slope method and inverse distance weighted interpolation method were used to study the spatiotemporal pattern of precipitation concentration. Based on correlation analysis and random forest algorithm, the main driving factors were discussed. The results show that: (1) The spatial distribution of ACI and LCI of the Jialing River Basin shows a pattern of low in north and high in south. The southeast populated area of Jialing River Basin exhibits a higher precipitation concentration, indicating that extreme precipitation events will occur more frequently; while the northwest mountainous area of Jialing River Basin exhibits a lower precipitation concentration, indicating that extreme precipitation events will occur less frequently. (2) The value of precipitation concentration in Jialing River Basin ranges from 0.53 to 0.83, with large variation range; Of the 19 meteorological stations, 15 stations show an upward trend in precipitation concentration (8 stations show a significant upward trend), and 4 stations show a downward trend (2 stations show a non-significant downward trend). The trend difference of each station is also closely related to topographical and climate factors. (3) According to correlation analysis, it is found that there is a significant negative correlation between precipitation concentration and levation (p<0.01); The importance analysis results based on the random forest algorithm show that the climatic factor has the greatest impact on the precipitation concentration in the Jialing River Basin is the sunspot index, which is 17.24%~20.25%, followed by the ENSO index (MEI and SOI).

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