干旱气象 ›› 2025, Vol. 43 ›› Issue (5): 745-758.DOI: 10.11755/j.issn.1006-7639-2025-05-0745

• 论文 • 上一篇    下一篇

近62 a甘肃极端降水特征及其关键影响因子

王鑫1(), 杨金虎2(), 王朋岭3, 黄鹏程1, 卢国阳1, 胡婕1   

  1. 1.兰州区域气候中心,甘肃 兰州 730020
    2.中国气象局兰州干旱气象研究所,甘肃 兰州 730020
    3.国家气候中心,北京 100081
  • 收稿日期:2025-04-22 修回日期:2025-09-11 出版日期:2025-10-31 发布日期:2025-11-09
  • 通讯作者: 杨金虎(1974—),男,甘肃会宁人,研究员,主要从事干旱气候变化及影响研究。E-mail:yjh740701@163.com
  • 作者简介:王鑫(1992—),男,甘肃和政人,工程师,主要从事干旱气候变化及影响研究。E-mail:wangx_10@lzu.edu.cn
  • 基金资助:
    甘肃省科技重大专项(25ZDFA011);国家自然科学基金面上项目(42375039);中国气象局气候变化专项(QBZZ202510);甘肃省青年科技基金计划项目(21JR7RA709);甘肃省气象局项目(Ms2023-13);甘肃省气象局项目(2425rczx-C-QNYXRC-01);干旱气象科学研究基金项目(IAM202302)

Spatial-temporal variation of extreme precipitation and its key influencing factors in Gansu Province over the past 62 years

WANG Xin1(), YANG Jinhu2(), WANG Pengling3, HUANG Pengcheng1, LU Guoyang1, HU Jie1   

  1. 1. Lanzhou Regional Climate Center, Lanzhou 730020, China
    2. Lanzhou Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China
    3. National Climate Center, Beijing 100081, China
  • Received:2025-04-22 Revised:2025-09-11 Online:2025-10-31 Published:2025-11-09

摘要:

全面认识极端降水时空特征、探究其关键影响因子,有助于更好地防御极端降水带来的不利影响。利用1961—2022年甘肃58个国家基本气象站均一化逐日降水数据,选取12个极端降水指数,分析甘肃极端降水时空特征;并运用地理探测器,量化大尺度气候因子对极端降水的贡献率。结果表明:1)近62 a甘肃持续干燥和湿润日数呈减少趋势,其余表征极端降水强度、频次的指数以不显著上升为主,强降水事件频次上升速率最大,达2.38 次·(10 a)-1;河西地区极端降水呈显著增多增强趋势,并主要在2010年前后突变增多;甘肃强降水事件出现在3—11月,以7月和8月最多最强,强降水呈增多增强趋势的月份居多,且以6月上升速率最大。2)极端降水指数呈上升趋势的站点主要出现在河西地区大部、兰州大部、白银中北部、临夏、陇东地区东南部和陇南南部。3)热带印度洋全区一致海温模态和东部型ENSO指数分别对河西(29%)和河东(33%)地区极端降水贡献率最大;热带印度洋海温增暖有利于河西地区极端降水增多增强,而东部型厄尔尼诺事件不利于河东地区极端降水发生发展;此外,双因子交互作用对极端降水的贡献率明显大于单因子作用。

关键词: 甘肃, 极端降水, 时空特征, 大尺度气候因子

Abstract:

A comprehensive understanding of the spatio-temporal characteristics of extreme precipitation and exploring its key influencing factors can help better defend against the adverse effects of extreme precipitation. Based on the standardized daily precipitation data from 58 national meteorological stations in Gansu Province from 1961 to 2022, the spatio-temporal characteristics of extreme precipitation in Gansu Province were analyzed by using 12 extreme precipitation indices, and the contribution rate of large-scale climate factors to extreme precipitation was quantified using the Geodetector. The results are as follows: 1) In the past 62 years, the consecutive dry days (CDD) and consecutive wet days (CWD) in Gansu Province showed a decreasing trend, while the other indices representing intensity and frequency of extreme precipitation showed mainly insignificant increases. The frequency of extreme precipitation events had the highest increasing rate of 2.38 times·(10 a)-1. The extreme precipitation presented a significant increasing and intensifying trend, with an abrupt change detected around 2010 in the Hexi region. The extreme precipitation events in Gansu Province occurs from March to November, especially in July and August. The months with an increasing and strengthening trend of extreme precipitation are predominant, and in June the rate of increase is the maximum. 2) The stations where the extreme precipitation indices showed an increasing trend are mainly located in the most of Hexi region and Lanzhou, the central and northern parts of Baiyin, Linxia, the southeastern part of Longdong region, and the southern part of Longnan. 3) The Indian Ocean Basin-Wide Index and Nino Eastern Pacific index contribute the most to extreme precipitations in the Hexi region (29%) and the Hedong region (33%), respectively. The warming of sea surface temperature in the tropical Indian Ocean is conducive to an increase and intensification of extreme precipitation in the Hexi region, while the Eastern Pacific El Niño event is unfavorable for the occurrence and development of extreme precipitation in the Hedong region. Moreover, the contribution rate of two-factor interaction to extreme precipitation is significantly greater than that of single-factor action.

Key words: Gansu Province, extreme precipitation, spatial-temporal variation, large-scale climate factors

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