地理学报 ›› 2019, Vol. 74 ›› Issue (7): 1374-1391.doi: 10.11821/dlxb201907008

• 气候变化与地表过程 • 上一篇    下一篇

西南地区山洪灾害时空分布特征及其影响因素

熊俊楠1,2,李进1,程维明2(),周成虎2,郭良3,4,张晓蕾3,4,王楠2,李伟1   

  1. 1.西南石油大学土木工程与建筑学院,成都 610500
    2.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    3.中国水利水电科学研究院,北京 100038
    4.水利部防洪抗旱减灾工程技术研究中心,北京 100038
  • 收稿日期:2018-10-15 修回日期:2019-05-19 出版日期:2019-07-25 发布日期:2019-07-23
  • 通讯作者: 程维明 E-mail:chengwm@lreis.ac.cn
  • 作者简介:熊俊楠(1981-), 男, 四川南充人, 博士后, 副教授, 主要从事地理信息系统与灾害风险分析方面的研究。E-mail: neu_xjn@163.com
  • 基金资助:
    中国科学院战略性先导科技专项(XDA20030302);水科院全国山洪灾害调查评价项目(SHZH-IWHR-57);中国地质调查项目(DD20190637);数字福建自然灾害监测大数据研究所开放课题(NDMBD2018003);西南石油大学科技创新团队项目(2017CXTD09)

Spatial-temporal distribution and the influencing factors of mountain flood disaster in southwest China

XIONG Junnan1,2,LI Jin1,CHENG Weiming2(),ZHOU Chenghu2,GUO Liang3,4,ZHANG Xiaolei3,4,WANG Nan2,LI Wei1   

  1. 1.School of Civil Engineering and Architecture, SWPU, Chengdu 610500, China
    2.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic and Natural Resources Research, CAS, Beijing 100101, China
    3.China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    4.Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038, China;
  • Received:2018-10-15 Revised:2019-05-19 Online:2019-07-25 Published:2019-07-23
  • Contact: CHENG Weiming E-mail:chengwm@lreis.ac.cn
  • Supported by:
    Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20030302);National Mountain Flood Disaster Survey and Evaluation Project of IWHR(SHZH-IWHR-57);China Geological Survey Project(DD20190637);The Open Fund of Big Data Institute of Digital Natural Disaster Monitoring in Fujian(NDMBD2018003);Scientific and Technological Innovation Team Project of Southwest Petroleum University(2017CXTD09)

摘要:

山洪灾害时空特征和影响因素是山洪评估与管理的重要内容。根据1960-2015年中国西南地区历史山洪资料,采用线性回归、标准差椭圆、空间自相关和Logistic回归模型,深入分析了西南地区山洪灾害时空分布特征及其影响因素。结果表明:① 西南地区年度山洪灾害频次呈指数增长,年际变化呈现出稳定(1960-1980年)、缓慢波动增加(1981-1998年)、快速增加(1999-2015年)3个阶段;月际特征明显,山洪主要发生在每年6-8月,尤以7月频次最高;② 西南地区山洪灾害空间差异性显著,灾害高密度区主要集中于滇中高原地区、四川盆地和周边山地单元,山洪灾害数量分布呈显著的空间正相关,空间集聚特征明显(Moran's I指数为0.127、Z = 5.784、P = 0.007);③ 西南地区历史灾害点的重心在年内存在明显的向正西方向移动的趋势,年内标准差椭圆转角均逐渐弱化,长轴逐渐变长,短轴逐渐变短;④ 降雨因子对山洪的影响度最高,人类活动因子次之,地表环境因子最低,降雨因子中1 h降雨量对山洪的影响最强,优势比值达到3.654。研究结果可为西南地区山洪灾害形成机理、监测预警研究,实施防灾减灾措施等提供科技支撑。

关键词: 西南地区, 山洪灾害, 时空格局, Logistic回归

Abstract:

Studies on spatial-temporal characteristics and influencing factors of flash floods are key contents to the assessment and management of flash flood. Based on the historic flash flood data in southwest China from 1960 to 2015, the spatial and temporal distribution of flash flood and its influencing factors were analyzed by using the linear regression model, standard deviation ellipse, spatial autocorrelation, and logistic regression model. The results show that: (1) The annual frequency of flash floods in southwest China shows an exponential growth trend, and the annual variation reveals a stable, slow and rapid increase fluctuation during 1960-1980, 1981-1998 and 1999-2015, respectively; the monthly characteristics are obvious and precipitation is mainly concentrated in June, July and August. (2) The obvious spatial difference of flash flood is observed in the study area, and the high-density areas are mainly concentrated in central Yunnan plateau, Sichuan Basin and their surrounding mountainous areas. The flash flood distribution has a significant spatial positive correlation and spatial agglomeration (Moran's I index is 0.127, Z = 5.784, P = 0.007). (3) The gravity center of historical disasters has a distinct trend of moving to the west during the year. The standard deviation ellipse angle gradually weakens, the long axis gradually lengthens, and the short axis gradually shortens. (4) Rainfall factors have the maximum impact on flash floods, followed by human activity factors, and the minimal is surface environmental factors. Among the rainfall factors, the one-hour rainfall has the strongest impact on flash floods, and its dominant ratio reached 3.654. The results can provide techinical support for research on forming mechanism, monitoring and early warning, and implementation of disaster prevention and mitigation measures of flash floods.

Key words: southwest China, flash flood, spatial-temporal pattern, logistic regression