基于多源空间数据的市域洪涝灾害风险评价——以山西省临汾市为例

doi:10.14088/j.cnki.issn0439-8114.2023.08.030

湖北农业科学 ›› 2023, Vol. 62 ›› Issue (8): 189-196.doi: 10.14088/j.cnki.issn0439-8114.2023.08.030

基于多源空间数据的市域洪涝灾害风险评价——以山西省临汾市为例

荀佳常¹, 聂聪², 徐栋³, 方贺⁴

1.长安大学地质工程与测绘学院,西安 710054;
2.南京工业大学测绘科学与技术学院,南京 211816;
3.北京师范大学遥感科学国家重点实验室,北京 100875;
4.浙江省气候中心,杭州 310051

收稿日期:2021-03-19 出版日期:2023-08-25 发布日期:2023-09-22
通讯作者: 徐栋（1997-）,男,江苏泰州人,博士,主要从事城市环境遥感研究,（电子信箱）xd@mail.bnu.edu.cn。
作者简介:荀佳常（1997-）,男,陕西汉中人,硕士,主要从事地质灾害研究,（电话）17362195147（电子信箱）3255410357@qq.com。
基金资助:
浙江省自然科学基金项目（LQ21D060001）; 风云卫星先行计划项目（FY-APP-2021.0105）; 浙江省气象科技计划项目（2021YB07; 2019QN19）; 新疆气象局引导性计划项目（YD202224）

Risk assessment of urban flood disasters based on multi-source spatial data——Taking Linfen City, Shanxi Province as an example

XUN Jia-chang¹, NIE Cong², XU Dong³, FANG He⁴

1. School of Geological Engineering and Surveying and Mapping, Chang’an University,Xi’an 710054, China;
2. School of Geomatics Science and Technology, Nanjing Tech University,Nanjing 211816, China;
3. State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China;
4. Zhejiang Climate Center, Hangzhou 310051, China

Received:2021-03-19 Online:2023-08-25 Published:2023-09-22

摘要/Abstract

摘要： 从自然灾害风险理论出发,结合多源空间数据（遥感数据、再分析数据、社会经济数据）,对山西省临汾市洪涝灾害的致灾因子危险性、孕灾环境敏感性、防灾减灾能力进行评价,综合考虑临汾市实际情况,最终选取15项评价指标,并采用加权综合指数法定量分析,构建洪涝灾害综合风险评价模型,探究洪涝风险分布情况。结果表明,致灾因子危险性分布呈东部、西部地区高于中部地区,且东部整体高于西部的趋势;地势低、坡度小的平原和盆地易发生洪水汇入现象,孕灾环境敏感性更高,山地、丘陵等地区孕灾环境敏感性普遍较低;城镇化水平高、人口密集地区防灾减灾能力强,河谷、山地等自然区域的防灾减灾能力则整体较弱;洪涝灾害综合风险分布整体呈由西部向东部逐渐升高的趋势,具体表现为经济水平低、降水多、径流量大的河谷、平原和盆地风险较高,临汾市超过50%的地区面临洪涝灾害风险。总体来看,洪涝灾害对临汾市的社会、经济和生态发展影响较大。

关键词: 洪涝灾害, 灾害综合风险评价, 空间数据, 遥感数据, 再分析数据, 山西省临汾市

Abstract: Based on the theory of natural disaster risk, combined with multi-source spatial data （remote sensing data, reanalysis data, socio-economic data）, the risk of disaster causing factors, the sensitivity of disaster-pregnant environment, and the ability to prevent and reduce disasters of flood and waterlogging disasters in Linfen City, Shanxi Province were evaluated. Taking into account the actual situation of Linfen City, 15 evaluation indicators were finally selected, and the weighted comprehensive index method was used for quantitative analysis, a comprehensive risk assessment model for flood disasters was built, and the distribution of flood risks was explored. The results indicated that the risk distribution of disaster causing factors was higher in the eastern and western regions than in the central region, and the overall trend was higher in the eastern region than in the western region; plains and basins with low terrain and small slopes were prone to flood influx, resulting in higher sensitivity to disaster environments. Regions such as mountainous and hilly areas generally had lower sensitivity to disaster environments; high urbanization level and densely populated areas had strong disaster prevention and reduction capabilities, while natural areas such as river valleys and mountains had overall weak disaster prevention and reduction capabilities; the overall distribution of comprehensive risk of flood and waterlogging disasters was gradually increasing from the west to the east, which was reflected in the high risk of river valleys, plains and basins with low economic level, heavy rainfall and large runoff. More than 50% of Linfen City was facing the risk of flood and waterlogging disasters. In general, the flood disaster had a great impact on the social, economic and ecological development of Linfen City.

Key words: flood disaster, comprehensive disaster risk assessment, spatial data, remote sensing data, reanalysis data, Linfen City, Shanxi Province

中图分类号:

P208

荀佳常, 聂聪, 徐栋, 方贺. 基于多源空间数据的市域洪涝灾害风险评价——以山西省临汾市为例[J]. 湖北农业科学, 2023, 62(8): 189-196.

XUN Jia-chang, NIE Cong, XU Dong, FANG He. Risk assessment of urban flood disasters based on multi-source spatial data——Taking Linfen City, Shanxi Province as an example[J]. HUBEI AGRICULTURAL SCIENCES, 2023, 62(8): 189-196.

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基于多源空间数据的市域洪涝灾害风险评价——以山西省临汾市为例

Risk assessment of urban flood disasters based on multi-source spatial data——Taking Linfen City, Shanxi Province as an example

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