地理学报 ›› 2017, Vol. 72 ›› Issue (3): 444-456.doi: 10.11821/dlxb201703007

• 气候变化与地表过程 • 上一篇    下一篇

城市景观格局对城市内涝的影响研究——以深圳市为例

吴健生1,2(), 张朴华1   

  1. 1. 北京大学城市规划与设计学院 城市人居环境科学与技术重点实验室,深圳 518055
    2. 北京大学城市与环境学院 地表过程与模拟教育部重点实验室,北京 100871
  • 收稿日期:2016-09-12 修回日期:2016-12-13 出版日期:2017-03-15 发布日期:2017-05-03
  • 作者简介:

    作者简介:吴健生(1965-), 男, 湖南新化人, 教授, 博士生导师, 主要研究方向为景观生态与土地利用。E-mail: wujs@pkusz.edu.cn

  • 基金资助:
    国家自然科学基金项目(41330747)

The effect of urban landscape pattern on urban waterlogging

Jiansheng WU1,2(), Puhua ZHANG1   

  1. 1. Key Laboratory for Urban Habitat Environmental Science and Technology, School of Urban Planning and Design, Peking University, Shenzhen 518055, Guangdong, China
    2. Laboratory of Earth Surface Processes of Ministry of Education, College of Urban and Environment Science, Peking University, Beijing 100871, China
  • Received:2016-09-12 Revised:2016-12-13 Online:2017-03-15 Published:2017-05-03
  • Supported by:
    National Natural Science Foundation of China, No.41330747

摘要:

近年来,城市内涝问题频发,引发了强烈关注。现有有关城市内涝问题的研究多关注土地利用与城市内涝的关系,而对于城市景观格局对城市内涝影响的研究则不多。本文以内涝问题严重的深圳市为研究区,利用深圳市2014年5月11日暴雨期间内涝点数据(共278个),选取类型水平上的最大斑块面积比例(Largest Patch Index, LPI)、斑块聚集度(Patch Cohesion Index, PCI)、景观破碎度(Landscape Division Index, DIVISION)、景观水平的蔓延度(Contagion Index, CONTAG)、香农多样性指数(Shannon's Diversity Index, SHDI)共5个景观指数,并结合土地利用类型、不透水率、植被覆盖度、降雨量、地形地势及雨水管网密度等内涝影响因子,运用相关分析和多元逐步回归分析,探究深圳市景观格局对内涝的影响。结果表明:① 土地利用类型中,住宅用地等建设用地对内涝灾害影响最大,其面积比例的增加会加剧区域内涝程度;② 城市各类型景观格局特征中,建设用地斑块的优势度、聚集度越高,景观破碎化程度越低,区域的内涝程度越高,绿地的景观格局对城市内涝的影响与建设用地相反;③ 城市整体景观特征中,景观整体越复杂越不易引发内涝灾害;④ 不透水率等人为因素比降雨量等自然因素对城市内涝的影响程度大。本研究的结果可以使人关注到地表景观格局对内涝的重要作用,为内涝治理和景观格局的规划管理提供参考和借鉴。

关键词: 景观格局, 土地利用, 城市内涝, 深圳市

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