城市建设对暴雨内涝空间分布的影响研究——以武汉市主城区为例

doi:10.18306/dlkxjz.2020.11.010

Abstract

Abstract:

Urban pluvial flooding is an increasing problem for cities worldwide. Urban spatial growth and development have been widely recognized as one of the most important contributors to the increasing risk of flood disasters. Using Wuhan City, China as a case, this study developed a binary Logistic model to quantify the impacts of urban development on flooding under different rainfall scenarios (regular and extreme), especially the role of land conversion from water areas. Based on historical remote sensing data, the analysis of the dynamic land use changes indicates that over 30% of water areas had been converted into urban construction lands from 1984 to 2017. The results show that the conversion of water areas severely intensified the flooding risk of the surrounding locations in the extreme rainfall scenario. In addition to this land conversion, elevation, drainage conditions, land use types, and land use of surrounding areas are also significantly correlated with urban flooding. Based on two land use and development strategies, the risk maps of future flooding were predicted under regular and extreme rainfall scenarios. These maps show that the development of the remaining vacant lands will induce even worse flooding. Adaptation strategies corresponding to different land use types are provided according to the predicted flooding risks. The model could help develop adaptation strategies and policies to cope with future urban flooding by exploring its driving factors.

Key words: urban flooding, spatial distribution, urban construction, binary Logistic model

ZHAO Liyuan, WEI Jialing. Impact of urban development on the risk of flooding: A case study of Wuhan City, China[J].PROGRESS IN GEOGRAPHY, 2020, 39(11): 1898-1908.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Fig.5

Tab.2

Indicators of urban development and their descriptive statistics"

因素分类	解释变量	数据统计分析			数据量解释
因素分类	解释变量	取值范围	平均值	标准差	数据量解释
地形地势因素	高程 ( $x 1$ )	[-5, 101]	27.338	7.899	栅格实际高程数值
地形地势因素	邻域内海拔最低 ( $x 2$ )	[0, 1]	0.103	0.305	若Moore邻域内该栅格海拔最低,值为1; 若Moore邻域内该栅格海拔非最低,值为0
排水管网条件	距排水管网距离 ( $x 3$ )	[1, 3]	1.944	0.830	0≤距离≤30 m,值为3; 30 m<距离≤100 m,值为2; 100 m<距离,值为1
地表不透水性	各类用地不透水率 ( $x 4$ )	[0, 3]	0.622	1.066	道路不透水率,值为3; 建设用地不透水率,值为2; 绿地不透水率,值为1; 水域不透水率,值为0
土地利用类型	栅格用地类型为绿地 $(x 5)$	[0, 1]	0.171	0.377	栅格为绿地类型,值为1; 栅格不是绿地类型,值为0
	邻域绿地栅格数 $(x 6)$	[1, 8]	1.360	2.704	Moore邻域中绿地栅格的个数：值为1至8的整数
	邻域水体栅格数 $(x 7)$	[1, 8]	0.721	2.106	Moore邻域中水栅格的个数：值为1至8的整数
填湖造陆因素	1984—2017年间被填占的水域栅格 $(x 8)$	[0, 2]	0.033	0.178	未填湖非水域的栅格,值为0; 水域转绿地的栅格,值为1; 水域转城市建设用地的栅格,值为2

Tab.2

Tab.3

Logistic regression results for two rainfall scenarios"

解释变量		一般情景 (24 h~95 mm)			极端情景 (24 h~249 mm)
解释变量		回归系数	t统计量	P值	回归系数	t统计量	P值
地形地势因素	高程 $(β 1)$	-0.521	-2.616	***	1.990	22.944	***
地形地势因素	邻域内海拔最低 $(β 2)$	0.188	3.924	***	0.086	4.500	***
排水管网条件	距排水管网距离 $(β 3)$	-1.378	-25.268	***	-1.385	-61.811	***
地表不透水性	各类用地不透水性 $(β 4)$	3.112	11.299	***	1.380	14.697	***
土地利用类型	栅格用地类型为绿地 $(β 5)$	-2.224	-10.984	***	-1.104	-15.798	***
	邻域绿地栅格 $(β 6)$	2.076	8.602	***	3.866	41.569	***
	邻域水体栅格数 $(β 7)$	1.865	14.908	***	1.233	26.057	***
填湖造陆因素	1984—2017年间被填占的水域栅格 $(β 8)$	-0.202	-2.601	***	0.116	5.902	***
常数项 $(β 0)$		0.683	8.177	***	0.212	6.184	***

Tab.3

Fig.6

Fig.7

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年份	建设用地/km²	绿地/km²	水体/km²
1984	122.84	397.05	170.90
2017	364.09	208.23	118.47
变化^*	+241.25	-188.82	-52.42

Impact of urban development on the risk of flooding: A case study of Wuhan City, China

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