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遥感技术与应用  2023, Vol. 38 Issue (4): 935-944    DOI: 10.11873/j.issn.1004-0323.2023.4.0935
遥感应用     
兰州市中心城区内涝时空格局和成因分析
李秋萍1(),李雪梅1,2,3(),龚志远1,秦启勇1,张博1
1.兰州交通大学 测绘与地理信息学院,甘肃 兰州 730070
2.地理国情监测技术应用国家地方联合工程研究中心,甘肃 兰州 730070
3.甘肃省地理国情监测工程实验室,甘肃 兰州 730070
Spatial and Temporal Pattern and Cause of Waterlogging in the Central Urban Area of Lanzhou
Qiuping LI1(),Xuemei LI1,2,3(),Zhiyuan GONG1,Qiyong QIN1,Bo ZHANG1
1.Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China
2.National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China
3.Gansu Provincial Engineering laboratory for National Geographic State Monitoring,Lanzhou 730070,China
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摘要:

为探究城市地区内涝灾害成因和分布特征,以兰州市中心城区为例,基于2010~2020年月平均降水数据和历史积水点资料,选取人口密度、路网密度、给排水设施距离、高程、地形起伏度、坡度、归一化植被指数和不透水面百分比数据作为探究内涝驱动因子的指标,采用水文分析、空间相关性分析和地理探测器等方法,研究内涝灾害的时空分布格局、驱动因子及交互作用关系。结果表明:①11 a间兰州市内涝灾害与降水集中期高度吻合,均在6~9月份,7~8月份主汛期内涝灾害最严重。表明内涝灾害频次分布与降水格局密切相关;②空间上兰州市积水点主要分布在城关区,其次为安宁和七里河区,西固区最少;③归一化植被指数和不透水面百分比是兰州市内涝灾害的主要驱动因子,其次是人口密度,但任意两个驱动因子的交互作用均远大于单个因子。因此,兰州市内涝灾害需要在多因素综合考虑的前提下,采取多举措进行综合治理。

关键词: 兰州市中心城区内涝成因时空分布交互作用地理探测器    
Abstract:

In order to explore the causes and distribution characteristics of waterlogging disasters in urban areas, taking the central city of Lanzhou as an example, based on the monthly average precipitation data and historical water accumulation data from 2010 to 2020, population density, road network density, water supply and drainage facilities distance, elevation, topographic relief, slope, normalized difference vegetation index, and percentage of impervious surface were selected as indicators to explore the driving factors of waterlogging, and the time-space distribution pattern, spatial correlation analysis and geographic detector of waterlogging disasters were studied. Drivers and interactions. The results show that:(1) the flood disasters in Lanzhou City during 11 years highly coincide with the precipitation concentration period, both in June-September, and the waterlogging disaster is the most serious in the main flood season in July-August, indicating that the frequency distribution of the flood disasters is closely related to the precipitation pattern; (2) In space, Lanzhou water accumulation points are mainly distributed in Chengguan District, followed by Anning and Qilihe District, and Xigu District is the least. (3) NDVI and the percentage of impervious surface are the main drivers of flooding disasters in Lanzhou, followed by population density, but the interaction of any two drivers is much greater than a single factor. Therefore, under the premise of comprehensive consideration of multi-factors, Lanzhou waterlogging disaster needs to be comprehensively managed by multi-measures.

Key words: The central urban area of Lanzhou    Causes of waterlogging    Temporal and spatial distribution    Interaction    Geodetector
收稿日期: 2022-06-11 出版日期: 2023-09-11
ZTFLH:  TU998.4  
基金资助: 国家自然科学基金项目(41761014);兰州交通大学“百名青年优秀人才培养计划”,兰州交通大学优秀平台支持(201806)
通讯作者: 李雪梅     E-mail: 1658015086@qq.com;lixuemei@lzjtu.edu.cn
作者简介: 李秋萍(1995-),女,四川南充人,硕士研究生,主要从事地质灾害研究。 E?mail: 1658015086@qq.com
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引用本文:

李秋萍,李雪梅,龚志远,秦启勇,张博. 兰州市中心城区内涝时空格局和成因分析[J]. 遥感技术与应用, 2023, 38(4): 935-944.

Qiuping LI,Xuemei LI,Zhiyuan GONG,Qiyong QIN,Bo ZHANG. Spatial and Temporal Pattern and Cause of Waterlogging in the Central Urban Area of Lanzhou. Remote Sensing Technology and Application, 2023, 38(4): 935-944.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.4.0935        http://www.rsta.ac.cn/CN/Y2023/V38/I4/935

图1  研究区 审图号:GS(2020)4619
图2  流域单元与积水点位置分布
内涝事件发生时间降水量/mm内涝事件发生时间降水量/mm
2010.09.0645.12018.07.0122.6
2012.08.21372018.07.2054
2014.07.30252018.08.2025
2015.08.08132019.06.0415
2015.09.2128.82019.06.1415.1
2016.07.1815.42019.08.2616.9
2017.08.1218.92020.08.2328.2
表1  2010~2020年内涝事件及降水量
图3  2010~2020年兰州市主城区积水点空间分布及核密度
驱动因子Pearson相关系数Sig. (2-tailed)
人口密度/(人/km2)0.291**0.004
路网密度/(km/km2)0.274**0.000
给排水设施0.193*0.050
高程/m-0.259**0.010
地形起伏度-0.118*0.050
坡度/°-0.093*0.050

归一化植被指数

不透水面百分比/%

-0.297**

0.314**

0.002

0.000

表2  内涝密度与不同驱动因子的相关分析结果
图4  影响因子分级结果
指标驱动因子分级q
一级区二级区三级区四级区五级区六级区Y
人口密度/(人/km2)63~1 4611 461~6 8506 850~1 643116 431~26 21226 212~35 59335 593~50 9630.263
路网密度/(km/km2)≤4.424.42~8.838.83~13.2313.23~17.6417.64~22.0622.06~22.640.254
给排水设施距离/m≤697697~1 2201 220~1 7421 742~2 3432 343~3 1753 175~4 9380.212
高程/m1 450~1 4941 494~1 5321 532~1 5801 580~1 6311 631~1 7021 702~1 8700.186
地形起伏度≤33~88~1414~2020~2828~760.097
坡度/°≤3.443.44~7.947.94~14.5514.55~22.2222.22~31.4831.48~67.450.010
NDVI-0.28~-0.18-0.18~-0.01-0.01~0.080.08~0.150.15~0.230.23~0.730.311
不透水面百分比/%0~67~2021~4142~6263~8687~1000.354
表3  驱动因子分级和地理探测器结果
q因子X1X2X3X4X5X6X7X8
0.263X10.263
0.254X20.2870.254
0.212X30.5840.4970.212
0.186X40.5450.4220.3480.186
0.097X50.4020.3580.3960.2440.097
0.010X60.3900.3220.3000.2760.1250.010
0.311X70.4850.4490.4260.3170.1310.2120.311
0.354X80.6130.5710.5620.5440.3970.3320.6980.354
表4  驱动因子和交互探测器结果
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