城市暴雨积涝灾害风险突增效应研究进展

doi:10.18306/dlkxjz.2016.09.003

Abstract

Abstract:

Due to the frequent occurrence of floods in urban areas, it is imperative to investigate their causes and determine whether they are mainly the result of global climate change or urbanization induced by human activities. Existing research on the impact of urbanization on urban floods focuses on hazards assessment as well as sensitivity tests. They are critical to risk reduction and severe weather early warning. Precipitation intensity downwind or over cities may be enhanced. Meanwhile the increased areal coverage of impervious surfaces in urban areas can alter the natural hydrologic response. In order to reveal the spatiotemporal characteristics of elevated flood risks in urban areas, by examining published work on the topics, we put forward the following thoughts on future research direction. The first is to use high spatial resolution observation data to determine the distribution of hazards induced by rainstorms in urban areas. The second is to develop suitable hydrological models using high resolution urban land surface data for simulating the hydroclimatic sensitivity of watersheds. The third is to combine rainstorm hazards and sensitivity in risk assessment to reveal the discrepancy of flood risks in urban areas, as well as effects induced by urbanization. Risk assessment identifies flood risks in urban areas and can be useful for risk management, which is essential in risk mitigation.

Key words: flash flood, risk assessment, hydrological model, research progress

Haibo HU. Research progress of surging urban flood risks[J].PROGRESS IN GEOGRAPHY, 2016, 35(9): 1075-1086.

Figures/Tables 6

Tab.1

Characteristics of regular and irregular observation data and their suitability for urban flood hazards research"

资料类型	基本特征	不确定性特征及满足研究需求情况	文献来源
气候站资料	空间分辨率受站点分布密度的影响,总体上较粗,需插值到格点上。	插值会产生较大偏差,不确定性较大。单独使用很难满足研究需求,从日降雨量等资料中很难提取降水持续时间等信息。	Prudhome et al, 1999
地基雷达定量降水估计(QPE)	雷达数据的网格大小可达1 km×1 km,分辨率较高。	探测误差及雨量反演时(不正确ZR关系)可产生数据不确定性;可单独使用,用自动站雨量资料订正后效果更好,可减少数据不确定性。能提取雨量、雨强及降水持续时间等信息。	Sun et al, 2000;Sharif et al, 2006
自动站雨量计资料	自动站点分布较密,可反映降水在空间上的细微差别;数据的时空分辨率都较好;使用时需要插值,局部会产生一定的偏差,部分站的系统误差大。	同样在空间插值上会产生不确定性。在分布较密地区可单独使用,主要用于订正雷达定量降水估计(QPE);能提取时空信息;一些站点的数据质量不太理想,出现的系统误差不容易被发觉及剔除。	Yu et al, 2010
闪电定位资料	大致体现雷暴的激烈程度,可以网格化后使用。	因探测仪及组网性能,在空间定位及探测效率上产生较大的不确定性。其推导出的闪电密度分布可用于辅助分析城市化对暴雨过程的影响。	Stallins et al, 2008; Hu et al, 2014
TRMM卫星遥感资料	TRMM卫星的雷达反演降水(Rain Rate)资料的格点大小为0.25?×0.25?,分辨率很低,但能完整观测较大尺度的雷暴天气过程。	会忽略2.3%的近地面降水,但与地基雷达探测结果基本一致。分辨率不高,很难单独用于城市暴雨危险性研究。	Shepherd et al, 2002

Tab.1

Fig.1

Tab.2

General rainstorm hazard assessment models"

方法	简介	特点	文献来源
统一雨量量纲法	首先计算降水持续时间D(单位为h)的雨量值(R)和日降雨量( $R d$ )间的定量转换： $R d = 4.216 R D - 0.475$ ,在得到不同降雨时段内的等效日雨量后,利用下式计算致灾因子危险性指数： $H = 0, P < p ′ H = Exp (P / p ′), P > p ′$ 式中：P为降水量; $p ′$ 为累计降水阈值;H为降水过程所引起的危险性指数;设定日雨量或等效日雨量超过50 mm才有影响,即 $p ′$ =50 mm。	方法基本上定量化,为下一步的风险评估方程提供输入参数。该方法需叠加脆弱性、敏感性及风险暴露因子等来综合评估暴雨灾害风险;综合了雨量及降雨持续时间信息。	扈海波等, 2013;Hu, 2015
分位数法	建立每个格点的雨量序列,计算格点的90%或99%的分位数值,以此判断所在格点的暴雨洪涝危险性大小,同时还可建立时间序列做Mann-Kendall (M-K)分析,检验格点的雨量增强趋势。	比较常用的极端天气及气候分析方法,能较好表现暴雨危险性的时空变化特征,但未较好体现与灾害性后果之间的关联;未作概率密度分析。	Krishnamurth et al, 2009
极限阈值法(POT)	用皮尔逊-通用普拉图(Poisson-generalized Pareto)概率密度函数计算超越概率,确定暴雨危险性的阈值,用最大似然法作参数估计,可借助Matlab等软件完成。Beguería等(2005)还利用“地形”等为影响因子的回归方程来估算暴雨危险指数。	较常用的极端气象要素值分析方法,用超越概率(重现期)确定暴雨危险性阈值。	Beguería et al, 2005
历史灾情法	基于危险性越大,灾情期望损失越大的原则,统计历史灾害损失的情况,确定危险性指数。Meyer等(2009)运用这一方法得出历史灾情损失曲线与极端暴雨出现概率呈自然指数形式的非线性递减的规律。	可直接由灾害期望损失来反映灾害风险,并可判定对应的极端暴雨雨量的阈值,对历史灾情资料的数据质量有很高的要求。	Meyer et al, 2009
洪涝指数模型(Ind-ex Flood Model)	采用通用极值(Generalized Extreme Value, GEV)的概率分布型函数,用累计函数(CDF)计算超越概率。其中洪涝指标可采用GEV的位置参数 $ξ (s)$ 、统计均值及中位数。	确定连续性区域的危险性指数,不用针对单个格点进行统计;采用的GEV分布型较合适极端暴雨的雨量分布。	Buonomo et al, 2007; Goubanova et al, 2007; Hanel et al, 2009
Copulas模型方法	Copulas模型非常适合暴雨危险性特征分析,其关键是建立雨强和降雨持续时间的概率关联来推算暴雨危险性指数。Copulas运用GEV分布型来建立雨强和持续时间的概率分布函数,然后建立关联,而二者之间的关联主要有：Frank's族方法(Frank, 1979; Nelson, 1999);Gumbel族方法(Bonazzi et al, 2012);Clayton、AMH、 FGM、 A12和A14 copulas族(Genest et al, 2007)。	该方法可基于多维变量来计算暴雨危险性,有比较成熟的理论和方法体系;通常采用雨量和降雨持续时间的统计关联来建立二维信息的暴雨概率密度方程;方法比较客观并可定量化。	Nelsen, 1999; Genest et al, 2007; Bonazzi et al, 2012

Tab.2

Tab.3

Existing hydrology models and their ability to meet the needs of urban flood risk assessment"

模型	技术特征	满足研究需求情况	文献来源
SWMM	美国环境保护署(EPA)开发的城市地表水文模型。基于汇水区(Watershed)计算,需事先给出汇水区域及汇流路径,然后计算汇流量(Runoff)及水深	精度高,但工作量大,尤其是汇水区域的划分。如模拟的区域范围小,仅涉及几个小流域面(Catchment),还能基本完成。但要完成一个城市区域范围内所有汇水斑块的划分较为困难。运用矢量格式的排水管线或其他数据,如汇流路径由计算机辅助生成。对数据的要求较高。	Hsu et al, 2000; Rossman, 2010
Aquacycle	同SWMM	同SWMM	Yu et al, 2010
天津沥涝模型	同SWMM	同SWMM	解以扬等, 2005
GSSHA Model	美国工程兵团(Army Corps)开发使用的分布式物理网格化地表水文分析模型(Gridded Surface/Subsurface Hydrologic Analysis Model, GSSHA),该模型考虑土地利用类型及水文参数的空间分布,包括排水能力等。模型在网格基础上用de St-Venant方程计算二维地表积水及一维管道排水。网格精度可达30 m分辨率或更高。	能使用ANC(Auto-NowCasting,短临预报)的QPE(Quantitative Precipitation Estimation,定量降水估计)和(Quantitative Precipitation Forecast,定量降水预报)作强迫。能够较好地模拟城市水文过程。分析城市不透水地表组成、城市管网排水能力等因素对汇流点的流量影响。需要概化城市排水管网资料,可以叠加比较详细的城市排水地物目标,比如排水井/口、涵洞等的分布数据。	Ogden et al, 2000; Sharif et al, 2006
其他(SWAT、TOPMODEL等)	这类非分布式水文模型(半分布式或集中式)能较好地模拟水流过程和部分水文反应过程(Franchini et al, 1991; Johnson et al, 2003)。	SWAT没有子流域范围内水文反应的水流量,因此不能决定不同地表表面(Variable Source Areas, VSAs)上的径流量(Arnold et al, 2005)。而集中式水文模型TOPMODEL能给出大尺度上不同地表表面饱和面积,但不能给出土壤湿度及地表径流,尤其是城市地表的径流状况(Beven, 1997)。	Easton et al, 2007; Beven, 1997V

Tab.3

Tab.4

Fig.2

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选择方法	A₁	b	c	n
年多样本法	14.688	13.8	0.761	0.739
年最大值法	11.442	12.6	0.891	0.708

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Research progress of surging urban flood risks

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