城市景观格局对城市内涝的影响研究——以深圳市为例

doi:10.11821/dlxb201703007

Abstract

Abstract:

In recent years, frequent urban waterlogging disasters have aroused great concern. Previous studies have mainly focused on the relationship between land use and waterlogging, but there is not enough information offered in these studies to explore the effects of urban landscape patterns on waterlogging. Taking Shenzhen as an example, this paper used correlation analysis and multiple stepwise regression analysis to explore the effects of landscape pattern on urban waterlogging. As urban waterlogging disaster is a systemic problem, which is related to the water circulation within the watershed, we divided Shenzhen into 56 sub-watersheds as the study units. Then we selected data of 278 waterlogging points during the rainstorm on May 11, 2014 and calculated the waterlogging point density of each sub-watershed to characterize the waterlogging degree. This paper considered 5 landscape pattern indexes to reflect the characteristics of landscape pattern, including LPI (Largest Patch Index), COHESION (Patch Cohesion Index), DIVISION (Landscape Division Index) at class-level, and CONTAG (Contagion Index), SHDI (Shannon's Diversity Index) at landscape-level. Furthermore, other data, including land use, impervious surface percentage, fractional vegetation coverage, precipitation, topography and storm drainage considered as factors influencing urban waterlogging, were also obtained. The results showed that: (1) Among land use types, the construction land, especially residential land, has the greatest impact on the urban waterlogging disaster, whose growth can significantly increase the disaster degree; (2) At class-level metrics, the higher level of construction patches' dominance and aggregation and the lower level of landscape division, would lead to more severe waterlogging disasters in a certain area, while the impacts of landscape pattern of greenspace are in reverse; (3) At landscape-level, the regions with more complex landscape are not prone to waterlogging disaster; (4) Impervious surface percentage and other human factors have greater impact on urban waterlogging than rainfall and other natural factors. This study demonstrated that landscape pattern had significant effect on waterlogging and could provide reference for control of waterlogging and planning management of landscape pattern.

Key words: landscape pattern, land use, urban waterlogging, Shenzhen

Jiansheng WU, Puhua ZHANG. The effect of urban landscape pattern on urban waterlogging[J].Acta Geographica Sinica, 2017, 72(3): 444-456.

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数据名称	数据说明	数据来源
深圳市2014年“5·11”暴雨期间内涝点数据	共278个	深圳市防汛防旱防风指挥部
深圳市2013年土地利用数据	矢量数据	深圳市政府
深圳市2013年遥感影像数据(LANDSAT8)	时间：2013/10/5,2013/10/28 轨道号：121/044,122/044	美国地质勘探局（USGS）网站
深圳市2014年5月11日日降雨量数据	50个气象监测站点记录数据	深圳市气象局网站
深圳市DEM数据	分辨率30 m×30 m	地理空间数据云
深圳市雨水管网数据	雨水管线和雨水口分布数据	深圳市政府

尺度水平	指数名称	单位	范围	表征含义
类型水平	最大斑块面积比例(LPI)	%	0<LPI≤100	表征景观类型的优势度。
	斑块聚集度指数(COHESION)	无	0<COHESION<100	反映同一景观类型的斑块聚集程度,其值越高,斑块内聚度越高。
	景观破碎度指数(DIVISION)	无	0≤DIVISION<1	反映同一景观类型的斑块分散程度,其值等于0时,该景观类型是由单一的斑块组成的;趋近于1时,该景观类型是由多个小斑块组成的。
景观水平	蔓延度(CONTAG)	%	0<CONTAG≤100	描述不同景观类型的团聚程度或延展趋势。一般来说,高蔓延度值说明景观中的某种优势景观类型形成了良好的连接性;反之则表明景观是具有多种要素的密集格局,景观的破碎化程度较高。
景观水平	香农多样性指(SHDI)	无	0≤SHDI	表征景观整体的复杂程度,其值越大,景观整体的复杂程度越高。

变量类别	变量子类别		变量名称	变量描述
土地利用类型	4大类		绿地,建设用地,水体,裸地	每种土地利用类型在所在小流域的面积比例
土地利用类型	16小类		耕地,园地,林地,草地,商服用地,工矿仓储用地,住宅用地,公共用地,公园绿地,特殊用地,交通运输用地,河流水域,其他水域,水利设施用地,设施农用地,荒地	每种土地利用类型在所在小流域的面积比例
景观格局指数	4大类	绿地,建设用地,水体,裸地	土地利用类型_景观格局指数 (LPI,COHESION,DIVISION) 4大类_景观格局指数(CONTAG,SHDI)	各小流域类型水平及景观水平上的景观格局指数
景观格局指数	16小类	耕地,园地,林地,草地,商服用地,工矿仓储用地,住宅用地,公共用地,公园绿地,特殊用地,交通运输用地,河流水域,其他水域,水利设施用地,设施农用地,荒地	土地利用类型_景观格局指数 (LPI,COHESION,DIVISION) 16小类_景观格局指数 (CONTAG,SHDI)	各小流域类型水平及景观水平上的景观格局指数
其他变量			平均植被覆盖度,平均不透水率,平均日降雨量,平均高程,平均起伏度,平均粗糙度,雨水管网线密度,雨水口点密度	以小流域为单位进行统计

变量类别	变量子类别		变量名称(r)
土地利用类型	4大类		绿地(-0.516),建设用地(0.555),水体(-0.347)
土地利用类型	16小类		园地(-0.346),林地(-0.435),住宅用地(0.599)
景观格局指数	4大类	绿地	绿地_LPI(-0.443),绿地_COHESION(-0.434),绿地_DIVISION(0.383)
		建设用地	建设用地_LPI(0.554),建设用地_COHESION(0.375),建设用地_DIVISION(-0.587)
		水体	水体_COHESION(-0.543)
		景观水平	4大类_CONTAG(0.438),4大类_SHDI(-0.490)
	16小类	园地	园地_COHESION(-0.503)
		林地	林地_LPI(-0.356),林地_COHESION(-0.591)
		草地	草地_COHESION(-0.359)
		工矿用地	工矿用地_LPI(0.380)
		住宅用地	住宅用地_LPI(0.390),住宅用地_DIVISION(-0.397)
		公园绿地	公园绿地_COHESION(-0.377)
		特殊用地	特殊用地_LPI(0.378),特殊用地_DIVISION(-0.446)
其他变量			平均植被覆盖度(-0.495),平均不透水率(0.501),平均日降雨量(0.542), 平均起伏度(-0.407),平均粗糙度(-0.419),雨水管网线密度(0.422),雨水口点密度(0.356)

回归模型	变量	模型参数
回归模型	变量	B	Beta	t	Sig.
a	常数项	0.916	-	5.292	0.000	R²=0.712 修正R²=0.695 F=42.810 (Sig.=0.000)
	平均日降雨量	0.002	0.486	6.334	0.000
	水体_COHESION	-0.008	-0.375	-4.860	0.000
	平均不透水率	1.157	0.487	6.474	0.000
b	常数项	11.249	-	4.843	0.000	R²=0.585 修正R²=0.559 F=22.563 (Sig.=0.000)
	平均日降雨量	0.002	0.570	6.075	0.000
	平均粗糙度	-11.209	-0.457	-4.858	0.000
	园地面积比例	-0.009	-0.331	-3.539	0.001

The effect of urban landscape pattern on urban waterlogging

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