长江科学院院报 ›› 2023, Vol. 40 ›› Issue (7): 80-87.DOI: 10.11988/ckyyb.20220249

• 水灾害 • 上一篇    下一篇

重庆市山洪灾害时空演变特征及影响因素分析

张乾柱1, 卢阳1, 严同金2, 谢谦2, 赵姹1, 胡月1   

  1. 1.长江科学院 重庆分院,重庆 400026;
    2.重庆市水利局 水旱灾害防御中心,重庆 401147
  • 收稿日期:2022-03-15 修回日期:2022-05-20 出版日期:2023-07-01 发布日期:2023-07-12
  • 通讯作者: 卢 阳(1982-),男,湖北汉川人,正高级工程师,博士,主要从事山地灾害形成演化机理与减灾技术研究。E-mail: crsrily@163.com
  • 作者简介:张乾柱(1989-),男,山东菏泽人,高级工程师,博士,主要从事全球环境变化、灾害防治与流域物质循环的研究。E-mail: qianzhuzhang@163.com
  • 基金资助:
    武汉市2022年度知识创新专项-曙光计划项目(2022020801020245);中央级公益性科研院所基本科研业务费项目(CKSF2023299/CQ;CKSF2021744/TB);国家重点研发计划项目(2021YFE0111900)

Temporal and Spatial Evolution Characteristics and Influencing Factors of Mountain Torrents in Chongqing

ZHANG Qian-zhu1, LU Yang1, YAN Tong-jin2, XIE Qian2, ZHAO Cha1, HU Yue1   

  1. 1. Chongqing Branch,Changjiang River Scientific Research Institute,Chongqing 400026,China;
    2. Flood and Drought Disaster Control Office of Chongqing,Chongqing Water Conservancy Burea,Chongqing 401147,China
  • Received:2022-03-15 Revised:2022-05-20 Published:2023-07-01 Online:2023-07-12

摘要: 基于2013—2015年和2016—2019年2个阶段历史山洪灾害调查成果,分析了重庆市境内有历史记录的831次山洪灾害时空分布特征,利用ArcGis中的重力模型、标准差椭圆模型研究山洪灾害空间变化规律,并通过地理探测器模型开展了驱动因素分析。分析和研究结果表明:①从山洪灾害频次历年分布来看,重庆市历史山洪灾害大体分为3个阶段:受历史山洪灾害文献记录所限,1926—1977年和1977—2006年期间分别为灾害发生的低频期和低频波动期;随着极端暴雨事件和社会经济快速发展,2006—2017年期间为灾害发生的高频波动期,同时小波分析显示灾害频次呈3.7 a的周期变化。②从山洪灾害造成的死亡、失踪人员来看,2000年以后,尽管灾害频次不断上升,但因灾死亡、失踪人数趋于平稳,体现了山洪灾害防治措施发挥了效益。③5月份重庆地区灾害重心在渝东南彭水、武隆一带,6月份灾害重心向北迁移,进入7月份以后,灾害重心向渝西地区迁移,随后8—9月份,灾害逐渐转向渝东北。④地理探测器模型分析显示,河网密度和高程因子对山洪灾害解释程度较好,各因子两两叠加后,解释力均呈非线性增强。重庆市历史山洪灾害规律的研究成果有助于认清重庆市山洪灾害时空演变格局及驱动因素,可为山洪灾害防治工作提供技术支撑与理论指导。

关键词: 山洪灾害, 调查成果, 重力模型, 标准差椭圆模型, 地理探测器模型, 时空演变特征, 重庆市

Abstract: Based on investigations of mountain torrents in 2013-2015 and 2016-2019, this study analyzes the temporal and spatial characteristics of 831 historical mountain torrent disasters in Chongqing. The spatial changes of mountain torrent disasters are researched using an ArcGIS gravity model and the standard deviation ellipse model, while the driving factors are identified using the geographic detector model. The frequency of disasters can be divided into three stages based on statistical analysis of disaster frequency over time: a low-frequency period from 1926 to 1977, a low-frequency fluctuation period from 1977 to 2006, and a high-frequency fluctuation period from 2006 to 2017, which is related to literature records, rainfall conditions, and social and economic development. Wavelet analysis shows that the mountain flood disasters in recent years change periodically every 3.7 years. Despite of increased frequency of disasters since 2000, the number of deaths and missing persons due to disasters has stabilized, reflecting the effectiveness of mountain torrent prevention and control measures. The disasters concentrated in the Pengshui and Wulong areas in Southeast Chongqing in May and moved northward in June. After July, the focus of disasters moved westward to west Chongqing and then gradually forward to northeast Chongqing from August to September. The geographic detector model analysis shows that the river network density and elevation factors have a significant impact on mountain flood disasters. After the superposition of each factor, the release force increases non-linearly. This study comprehensively summarizes the historical patterns of mountain torrents in Chongqing, providing important insights into the temporal and spatial evolution pattern of mountain torrents and their driving factors in Chongqing. The results could provide technical support and theoretical guidance for mountain torrent prevention and control.

Key words: mountain torrent disaster, survey results, gravity model, standard deviation ellipse model, geographic detector model, temporal and spatial evolution characteristics, Chongqing

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