地理学报 ›› 2021, Vol. 76 ›› Issue (5): 1148-1162.doi: 10.11821/dlxb202105008

• 气候变化与地表过程 • 上一篇    下一篇

中国暴雨洪涝灾情时空格局及影响因素

胡畔1,2,3,4(), 陈波1,2,3,4, 史培军1,2,3,5()   

  1. 1.北京师范大学地表过程与资源生态国家重点实验室,北京100875
    2.北京师范大学环境演变与自然灾害教育部重点实验室,北京100875
    3.应急管理部-教育部减灾与应急管理研究院,北京100875
    4.北京师范大学地理科学学部,北京100875
    5.青海省人民政府-北京师范大学高原科学与可持续发展研究院,西宁810008
  • 收稿日期:2019-08-07 修回日期:2021-04-29 出版日期:2021-05-25 发布日期:2021-07-25
  • 通讯作者: 史培军(1959-), 男, 陕西靖边人, 教授, 研究方向为综合灾害风险研究与防范。E-mail: spj@bnu.edu.cn
  • 作者简介:胡畔(1994-), 女, 湖北荆门人, 硕士生, 研究方向为洪水灾害。E-mail: hupan@mail.bnu.edu.cn
  • 基金资助:
    国家重点研发计划(2016YFA0602404);国家自然科学基金项目(41621061)

Spatiotemporal patterns and influencing factors of rainstorm-induced flood disasters in China

HU Pan1,2,3,4(), CHEN Bo1,2,3,4, SHI Peijun1,2,3,5()   

  1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China
    2. Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing 100875, China
    3. Academy of Disaster Reduction and Emergency Management Ministry of Emergency Management & Ministry of Education, Beijing 100875, China
    4. Faculty of Geographical Science, BNU, Beijing 100875, China
    5. Academy of Plateau Science and Sustainability People's Government of Qinghai Province & Beijing Normal University, Xining 810008, China
  • Received:2019-08-07 Revised:2021-04-29 Published:2021-05-25 Online:2021-07-25
  • Supported by:
    National Key Research and Development Program(2016YFA0602404);National Natural Science Foundation of China(41621061)

摘要:

暴雨洪涝灾害给中国造成了巨大人口和经济损失。本文通过对中国气象灾情普查数据的分析,结合小时降水数据、统计年鉴等资料,研究了1984—2007年间中国极端降水和暴雨洪涝灾情时空分异特征。在此基础上,采用地理探测器研究了中国暴雨洪涝灾情时空格局的影响因素。结果表明,研究时段内全国极端降雨指标没有一致的变化趋势。长江、珠江及东南沿海等流域暴雨洪涝频次显著增加,但因灾人口死亡率下降,表明设防能力的提升对人口损失的缓减影响明显;西北地区因灾死亡人数和人口受灾率、经济损失等均增加,表明设防能力薄弱;因暴雨洪涝受灾人口贡献率最高的因子是气象致灾因素,又因暴雨洪涝死亡人口贡献率最高的是地理等孕灾环境和社会经济等承灾体因素,故暴雨洪涝直接经济损失贡献率最高的是地理等孕灾环境因素。该研究可为洪水灾情的影响因素定量化分析提供理论参考。

关键词: 暴雨, 暴雨洪涝, 灾情, 时空格局, 地理探测器, 影响因素, 中国

Abstract:

Understanding the influencing factors and controls of rainstorm-induced floods, which have caused tremendous losses of human lives and national economy, is a pressing need for flood risk management in China. Based on the meteorological disaster census data of counties in China, hourly precipitation data at 2420 stations, statistical yearbook, terrain data and other data, the authors (1) investigated the spatiotemporal pattern of flood impacts in China over the period from 1984 to 2007 using trend analysis techniques and (2) explored the driving factors of the spatiotemporal pattern by adopting the geospatial statistical analysis tool (Geodetector). This study considered the spatiotemporal patterns and their interplays among county-level flood impacts (i.e., flood-induced mortality rate, proportion of population affected, and economic loss in percentage), disaster-formative environmental factors (i.e., population density, urban population percentages, average elevation, river density, average slope, and average distance to the seashore), and extreme precipitation characteristics (i.e., annual average volume and duration of extreme rainfall). The results show that: (1) there were no consistent temporal trends of extreme rainfall characteristics over the study period across China. (2) The frequencies of flood disasters in the Yangtze and Pearl rivers and southeast coastal areas increased significantly, but the casualties over these regions decreased. (3) Flood-induced casualties, proportion of population affected and economic loss in percentage increased in Northwest China; and meteorological factors, disaster-formative environment factors such as geographical conditions and social economy, and geographical conditions contribute mostly to the proportion of population affected, flood-induced death and economic loss in percentage. These results indicate that more attention should be paid to improving the flood control capacity of small or medium-sized cities in the inland river basins, especially in Northwest China, and we should recognize the important roles that disaster-formative environment plays in triggering flood losses.

Key words: extreme rainfall, rainstorm-induced flood, flood disaster, spatiotemporal pattern, Geodetector, influencing factor, China