风暴潮灾害脆弱性研究综述

脆弱性是自然灾害风险研究的热点,风暴潮灾害脆弱性与风暴潮自然过程强度以及沿海社会经济、人口、自然环境等因素相关。本文从风暴潮灾害脆弱性定义出发,对国内外风暴潮灾害社会脆弱性和物理脆弱性进行了回顾,重点对人口、海堤、房屋等风暴潮灾害典型承灾体物理脆弱性研究进展进行了论述,分析了风暴潮灾害脆弱性评价中存在的不确定性,探讨了风暴潮灾害脆弱性在灾害损失评估、保险及再保险、防灾减灾决策支持等领域的应用,对未来风暴潮灾害脆弱性研究提出了以下展望：①开发符合中国沿海区域风暴潮灾害特征和承灾体分布的定量化、精细化脆弱性曲线,拓展风暴潮脆弱性评价结果在保险理赔、灾害损失评估等领域应用;②气候变化背景下中国沿海面临风暴潮巨灾风险,迫切需要建立科学的基于灾害实地踏勘以及物模实验、数值模拟相结合的风暴潮灾害典型承灾体脆弱性评估方法模型。     

Typhoon insurance pricing with spatial decision support tools

In disaster insurance and reinsurance, GIS has been used to visualize and manage geospatial data and to help vulnerability and risk analysis for years. However, hazard insurance is a multidisciplinary issue that involves complex factors and uncertainty. GIS, if used alone, has limited functionality due to poor incorporation of intelligence and spatial statistics. The Spatial Decision Support System (SDSS) presented in this paper, addresses some of the deficiencies of traditional GIS, by providing powerful tools to support disaster insurance pricing that involves procedural and declarative knowledge. In the SDSS, the knowledge‐based system shell, using the open‐source CLIPS and supporting fuzziness and uncertainty, can be applied in at least three phases: hazard simulation, fuzzy comprehensive evaluation of risk, and query for insurance pricing. The libraries of statistics and spatial statistics provide a robust support for analysis of spatial factors, including spatial correlation between zones vulnerable to hazard and spatial variation of exposures. The GIS components provide sophisticated visualization and database management support for geospatial data, helping easily locate the insured points and risk zones as well as exploratory analysis of spatial data. Standard database management interfaces are used to manage other aspatial data. COM, an industry‐wide interface protocol, tightly integrates these technologies (the expert shell, GIS, spatial statistics and DBM within an integral system), and can be used to develop mixed complex algorithms in support of other COM objects. An application of typhoon insurance pricing is demonstrated with a case study in Guangdong, China. Developed as a suite of generic tools with abilities to deal with the complex problem of disaster insurance involving spatial factors and field knowledge, this prototype SDSS can also be applied to other disaster insurance and fields that involve similar spatial decision making.     

地震灾害预测和应急模拟系统的设计与应用——以永安市城市应急系统为例

城市防震减灾是地震潜在频发区提高自然灾害防治能力建设的重要方面。本文结合福建省永安市城市防震减灾信息管理系统的建设,探讨设计防震减灾数值模型,分析模型集成的关键技术,构建了地震风险评估、建筑物易损性评价、生命与财产损失估算、救援与救灾管理调度等模型和基于GIS开发的震害预测和应急模拟系统。该系统在福建省永安市的实际应用表明,通过对地震灾害预测结果的分析,可加强城市抗震中的薄弱环节,为灾区政府应急响应和制订对策提供决策支持辅助信息,从而显著提高城市防震减灾的综合能力。     

A fuzzy multiple-attribute decision-making modelling for vulnerability analysis on the basis of population information for disaster management

Research activity and published literature on the reliability and vulnerability analysis of urban areas for disaster management has grown tremendously in the recent past. Population information has played the most important role during the entire disaster management process. In this article, population information was used as the evaluation criterion, and a fuzzy multiple-attribute decision-making (MADM) approach was used to support a vulnerability analysis of the Helsinki area for disaster management. A kernel density map was produced as a result that showed the vulnerable spatial locations in the event of a disaster. Model results were first validated against the original population information kernel density maps. In the second step, the model was validated by using fuzzy set accuracy assessment and the actual domain knowledge of the rescue experts. This is a novel approach to validation, which makes it possible to see how and if computer decision-making models compare to a real decision-making process in disaster management. The validation results showed that the fuzzy model has produced a reasonably accurate result. By using fuzzy modelling, the number of vulnerable areas was reduced to a reasonable scale and compares to the actual human assessment of these areas, which allows resources to be optimised during the rescue planning and operation.     

Assessing physical vulnerability for multi-hazards using an indicator-based methodology

Globally, many built-up areas are threatened by multiple hazards which pose significant threat to humans, buildings and infrastructure. However, the analysis of the physical vulnerability towards multiple hazards is a field that still receives little attention although vulnerability analysis and assessment can contribute significantly to risk reduction efforts. Indicator-based vulnerability approaches are flexible and can be adjusted to the different hazards as well as to specific user needs. In this paper, an indicator-based vulnerability approach, the PTVA (Papathoma Tsunami Vulnerability Assessment), was further developed to be applicable in a multi-hazard context. The resulting multi-hazard version of the PTVA consists of four steps: the identification of the study area and relevant hazards as well as the acquisition of hazard information, the determination of vulnerability indicators and collection of data, the weighting of factors and vulnerability assessment and finally, the consideration of hazard interactions. After the introduction of the newly developed methodology a pilot application is carried out in the Faucon municipality located in the Barcelonnette basin, Southern French Alps. In this case study the vulnerability of buildings to debris flows, shallow landslides and river flooding for emergency planning and for general risk reduction purposes is assessed. The implementation of the methodology leads to reasonable results indicating the vulnerable buildings and supporting the priority setting of different end-users according to their objectives. The constraints of the presented methodology are: a) the fact that the method is not hazard-intensity specific, thus, vulnerability is measured in a rather qualitative and relative way and b) the high amount of data required for its performance. However, the advantage is that it is a flexible method which can be applied for the vulnerability analysis in a multi-hazard context but also it can be adjusted to the user-specific needs to support decision-making.     

A GIS-based approach to identify the spatial variability of social vulnerability to seismic hazard in Italy

This paper argues for a multidisciplinary framework to assess the relationship between environmental processes and social sciences that can be adapted to any geographic location. This includes both physical (earthquake hazard) and human (social vulnerability) dimensions in the context of disaster risk reduction. Disasters varies drastically depending on the local context. Indeed, the probability of a natural disaster having more devastating effects in one place than in another depends on the local vulnerability components of the affected society (cultural, social and economic). Therefore, there is an important correlation between the potential risk and the social resistance and resilience of a specific place, thus the disaster response varies according to the social fabric. In this context, the evaluation of social vulnerability is a crucial point in order to understand the ability of a society (studied at individual, household or community level) to anticipate, cope with, resist and recover from the impact of natural disaster events. Within this framework, the paper discusses how it is possible to integrate social vulnerability into the seismic risk analysis in Italy. Specifically, socioeconomic indicators were used to assess and mapping social vulnerability index. Afterwards, a Geographic Information System (GIS) approach was applied to identify the spatial variability of social vulnerability to seismic hazard. Through the use of a risk matrix, the classes of a social vulnerability index map were combined with those of a seismic hazard map proposed by INGV (National Institute of Geophysics and Volcanology). Finally, a qualitative social vulnerability exposure map to an earthquake hazard was produced, highlighting areas with high seismic and social vulnerability levels. Results suggest the importance of the integration of social vulnerability studies into seismic risk mitigation policies, emergency management and territorial planning to reduce the impact of disasters.     

自然灾害综合研究的新进展--脆弱性研究

文章对脆弱性概念、研究内容、脆弱性的形成和影响因素以及减轻脆弱性、减少灾害影响和损失的途径进行了综述,并讨论了从灾害孕育发生的各个环节降低脆弱性,以最大限度减轻灾害及影响意义.     

小空间尺度农业旱灾承灾体脆弱性评价初探--以湖南省常德市鼎城区双桥坪镇为例

该文探讨小空间尺度农业旱灾承灾体脆弱性评价,阐述针对不同空间尺度建立不同的旱灾承灾体脆弱性评价指标体系的必要性。基于湖南省常德市双桥坪镇的实地调查资料,构建包括耕地平坦指数、水塘灌溉指数等评价指标体系,通过比较各因素的相对重要性确定其权重,对2001年双桥坪镇16个村农业旱灾承灾体脆弱度进行评价。结果表明,该镇旱灾脆弱度较高,西部村庄的旱灾脆弱性普遍小于东部村庄。用同年的实际旱灾灾情损失资料进行验证,吻合程度较高。     

A spatial cognition-based urban building clustering approach and its applications

This article presents a spatial cognition analysis technique for automated urban building clustering based on urban morphology and Gestalt theory. The proximity graph is selected to present the urban mrphology. The proximity graph considers the local adjacency among buildings, providing a large degree of freedom in object displacement and aggregation. Then, three principles of Gestalt theories, proximity, similarity, and common directions, are considered to extract potential Gestalt building clusters. Next, the Gestalt features are further characterized with seven indicators, that is, area difference, height difference, similarity difference, orientation difference, linear arrangement difference, interval difference, and oblique degree of arrangement. A support vector machine (SVM)-based approach is employed to extract the Gestalt building clusters. This approach transforms the Gestalt cluster extraction into a supervised discrimination process. The method presents a generalized approach for clustering buildings of a given street block into groups, while maintaining the spatial pattern and adjacency of buildings during the displacement operation. In applications of urban building generalization and three-dimensional (3D) urban panoramic-like view, the method presented in this article adequately preserves the spatial patterns, distributions, and arrangements of urban buildings. Moreover, the final 3D panoramic-like views ensure the accurate appearance of important features and landscapes.     

The socio-spatial dynamics of extreme urban heat events: The case of heat-related deaths in Philadelphia

Heat is the number one weather-related cause of mortality in the United States; typically punctuated by extreme heat waves. This study examines the relationship between the spatial distribution of vulnerable populations, satellite-detected urban heat island (UHI) and heat-related mortality distributions during a 1993 extreme heat event in Philadelphia, PA. Geostatistical methods are used to compare spatial distributions of vulnerability and to determine concentration of mortality within surface UHI intensity levels. The results suggest the spatial distribution of urban poor is congruent with heat-related death. Additionally, deaths are concentrated in higher order surface UHI intensity levels. The findings suggest that surface UHI measures and population in poverty are important variables in spatially measuring risk from extreme heat events. Coupling surface UHI measures with socioeconomic indicators of vulnerability may enable creation of risk models with improved spatial specificity to assist public health professionals. This approach is demonstrated by developing a linear regression model of potential risk in Philadelphia for the 1993 extreme heat event.     

城市暴雨内涝情景模拟与灾害风险评估

Scenario modelling and the risk assessment of natural disasters is one of the hot-spots in disaster research.However,up until now,urban natural disaster risk assessments lack common procedures and programmes.This paper selects rainstorm waterlogging as a disaster to research,which is one of the most frequently occurring hazards for most cities in China.As an example,we used a small-scale integrated methodology to assess risks relating to rainstorm waterlogging hazards in the Jing’an District of Shanghai.Based on the basic concept of disaster risk,this paper applies scenario modelling to express the risk of small-scale urban rainstorm waterlogging disasters in different return periods.Through this analysis of vulnerability and exposure,we simulate different disaster scenarios and propose a comprehensive analysis method and procedure for small-scale urban storm waterlogging disaster risk assessments.A grid-based Geographical Information System (GIS) approach,including an urban terrain model,an urban rainfall model and an urban drainage model,was applied to simulate inundation area and depth.Stage-damage curves for residential buildings and contents were then generated by the loss data of waterlogging from field surveys,which were further applied to analyse vulnerability,exposure and loss assessment.Finally,the ex-ceedance probability curve for disaster damage was constructed using the damage of each simulated event and the respective exceedance probabilities.A framework was also devel-oped for coupling the waterlogging risk with the risk planning and management through the exceedance probability curve and annual average waterlogging loss.This is a new explora-tion for small-scale urban natural disaster scenario simulation and risk assessment.     

Spatial and temporal quantification of resilience at the community scale

Indicators of natural disaster resilience are factors that impact the ability to cope with and adapt to a natural disaster and climate change events. They can either contribute to or detract from resilience. Existing research has emphasized the importance of quantifying resilience in order to estimate baseline resilience and measure progress toward resilience enhancement. Previous attempts at quantification of resilience have not incorporated place-specific indicators or differential weighting of indicators for prioritization of resilience enhancement actions. Previous research efforts have also not incorporated spatial and temporal contexts when attempting to quantify resilience indicators. This research demonstrates the importance for quantifying resilience place-specific indicators, differential weighting of indicators, and the spatial and temporal contexts of indicators for resilience estimation and quantification through a case study of Sarasota County, Florida. This case study was conducted in four phases: preliminary interviews, plan review, focus group, and spatial analysis. Preliminary interviews were intended to contribute to development of research goals. The plan review process served to identify Sarasota County's planning priorities to determine possible indicators of resilience unique to Sarasota County as well as existing and planned county hazard mitigation strategies. The focus group was concerned with identifying resilience indicators through a workshop with officials from Sarasota County. The spatial analysis portion used findings from all three previous phases to demonstrate spatial patterns of resilience. This research demonstrates that although national resilience quantification metrics are useful, local scale resilience estimates appear more useful if community hazard mitigation and climate change adaptation is the primary goal.     

High-resolution spatial assessment of population vulnerability to climate change in Nepal

Current, spatially explicit, and high-resolution assessments of population vulnerability to climate change and variability in developing countries can be difficult to create due to lack of data or financial and technical capacity constraints. We propose a comparative, multiple-approach framework to assess the spatial variation of population vulnerability to climatic changes using several high-resolution variables related to climate, topography, and socioeconomic conditions with an objective to detect the spatial variability of climate vulnerability in Nepal. Nepal is one of the most vulnerable countries to the effects of climate change due to frequent climatic hazards and poor socio-economic capacity. We used a climate vulnerability index (CVI) approach to derive climate vulnerability maps at the one-kilometer resolution and test an additive and a principal components-based composite method of data aggregation. In this work, we attempt to answer three questions. 1) How do different methods of assessment inform the spatial variation of the climate vulnerability in Nepal? 2) How do different variables interact to shape climate vulnerability in Nepal? 3) What proportions of the population in Nepal are vulnerable to climatic disasters and why? Our analysis uncovered significant spatial variations in population vulnerability to climate change across Nepal, with the highest vulnerability being experienced by the High Mountain region followed by the regions in the lower elevations. We find that although the lack of adaptive capacity is the biggest cause of population vulnerability to climate change in Nepal, a resilient community is shaped by both biophysical and socioeconomic characteristics. By performing an iterative sensitivity analysis of our thirteen variables both at the aggregate level (nationally) as well as at the more disaggregated (physiographic region) level, we contribute to identifying important, multi-scalar driving factors for vulnerability that can be employed as leverage points for lowering vulnerability at different scales. After performing analyses at multiple regions, we conclude that region-specific variable selection is needed for more detailed assessments and in order to prioritize adaptation strategies at scales that go beyond the hierarchy of administrative divisions.     

灾害链灾情累积放大研究方法进展

近年来全球范围内频发的重大自然灾害事件,表明一种灾害引发一系列次生灾害的灾害链现象使得灾情通过累积放大效应而大大超过单一灾种灾情,深入研究灾害链灾情累积放大过程是有效防范巨灾风险的前提.首先,本文梳理了国内外研究中不同视角下对灾害链现象的理解,认为灾害链一般性概念中应当包含孕灾环境、致灾因子链、承灾体以及它们在时间和空间上复杂相互作用关系,只有从地理学的综合性角度出发,才能正确而完整的理解灾害链过程灾情累积放大机制.其次,按研究思路的差异,综述了当前研究灾害链灾情累积放大过程的5 类方法,包括经验地学统计方法、概率模型、复杂网络模型、灾害模拟以及多学科理论方法.从描述灾害链要素在时间和空间上复合作用的角度出发,分别讨论了它们在刻画灾害链灾情放大过程中的优势与不足.选取了影响较大、灾害引发关系典型的地震灾害链与台风灾害链,从灾种维度综述上述几种方法在实际应用中的概况及进展.最后,提出综合多种方法发展与完善灾害链灾情累积放大效应过程的动态模拟是灾害链的研究趋势,其中关键在于模拟灾害链系统各要素的时间与空间上的耦合,研究思路从“静态—描述—解释”向“动态—过程—模拟”的转变是理解灾害链、灾害系统复杂性的重要途径.     

Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression

Scientific interpretation of the relationships between agricultural landscape patterns and urbanization is important for ecological planning and management. Ordinary least squares (OLS) regression is the primary statistical method in previous studies. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between several urbanization indicators (urbanization intensity index, distance to urban centers and distance to road) and changes in metrics describing agricultural landscape patterns (total area, patch density, perimeter area ratio distribution and aggregation index) at two block scales (5 km and 10 km). Results denoted that GWR was more powerful than OLS in interpreting relationships between agricultural landscape patterns and urbanization, since GWR was characterized by higher adjust R², lower Akaike Information Criterion values and reduced spatial autocorrelations in model residuals. Character and strength of the relationships identified by GWR varied spatially. In addition, GWR results were scale-dependent and scale effects were particularly significant in three aspects: kernel bandwidth of weight determination, block scale of pattern analysis, and window size of local variance analysis. Homogeneity and heterogeneity in the relationships between agricultural landscape patterns and urbanization were subject to the coupled influences of the three scale effects. We argue that the spatially varying relationships between agricultural landscape patterns and urbanization are not accidental but nearly universal. This study demonstrated that GWR has the potential to provide references for ecological planners and managers to address agricultural landscapes issues at all scales.     

区域人地耦合系统脆弱性及其评价指标体系

从概念界定与目标定位入手,以气候变化和系统结构要素为分析框架,建立了包括敏感性、暴露性和适应性等三类指标要素和本底脆弱性、潜在脆弱性和现实脆弱性等三个评价层次的区域脆弱性评价系统,并以南方丘陵地区为例,针对泥石流、滑坡、干旱与洪涝等区域自然灾害,构建了水土流失敏感区的人地耦合系统脆弱性评价指标体系。作者认为,区域人地耦合系统脆弱性主要是针对全球气候变化扰动下与自然灾害有关的脆弱性,敏感性与易损性是其脆弱性的本质属性,敏感性、暴露性和适应性是脆弱性的系统要素。自然灾害频率指标可以作为反映灾害空间集聚性的区位暴露性指标,现实灾害度可以提供脆弱性评价因子厘定、指标权重确定、模型建立与阈值分析的结果验证。     

城市灾害社会脆弱性研究热点问题评述与展望

对城市灾害社会脆弱性研究的热点问题及趋向进行了深入的梳理和总结。研究发现,现阶段社会脆弱性研究已经成为城市灾害脆弱性的重要研究范式,但学者们在城市灾害社会脆弱性综合维度的界定、脆弱性评估方法构建、社会脆弱性与灾害种类相关性判读以及研究尺度选取等方面存在明显的争议。对这些争议进行了深入探讨,并对未来城市灾害社会脆弱性研究进行了展望,以期为中国城市防灾减灾工作提供科学参考。     

Vulnerability to environmental hazards in the Ciudad Juárez (Mexico)–El Paso (USA) metropolis: A model for spatial risk assessment in transnational context

The purpose of this paper is to present a geographic information system (GIS)-based method for mapping risk to environmental hazards. Framed by the hazards literature, the method has been developed to specifically overcome issues of data compatibility associated with transnational contexts. The approach is elaborated in reference to a project in which risk was spatially characterized, using a suite of biophysical and social indicators, for the Ciudad Juárez (Mexico)–El Paso (USA) metropolis. Results reveal clear spatial disparities in hazard vulnerability, both within and between the two cities, based on the differential allocation of selected risk factors. The case indicates that future international analyses will be advanced by the clear definition of concepts, the systematic mining of compatible variables, and the selection of valid risk indicators based on criteria that balance the need to incorporate contextual specificity with general comparability.     

风暴洪水主要承灾体脆弱性分析——黄浦江案例

脆弱性分析是自然灾害风险研究的热点问题之一。风暴洪水是上海黄浦江流域所面临的最主要自然灾害类型,历史上对该区域造成极为严重的灾害损失。通过多次灾后调查,结合前人研究成果,构建该区域7种主要承灾体经济损失脆弱性方程和人口脆弱性方程。基于前期黄浦江风暴洪水多情景危险性成果,开展实证研究,结果显示:经济损失和人口脆弱性分布自黄浦江上游地区向下游逐渐降低。最后,提出未来脆弱性研究中有待进一步完善和发展的工作。     

基于栅格的鄱阳湖生态经济区洪灾脆弱性评价

目前洪灾脆弱性研究主要是基于行政区划上的社会脆弱性评价,无法揭示评价单元内部脆弱性的空间分布。选取鄱阳湖生态经济区作为研究对象,根据人与环境系统的特点,选择影响洪灾脆弱性的15个变量,建立了基于栅格的洪灾脆弱性综合指数模型。结果表明,研究区域的洪灾脆弱性以中度脆弱为主,极度与高度脆弱区主要分布在人口密集的鄱阳湖东南与西南部的湖滨地区、主要河流的缓冲区以及土地利用类型为水田的区域。从脆弱性的3个要素(暴露度、敏感性和适应能力)的空间分布揭示了洪灾脆弱性空间分布形成的原因。基于栅格的评价结果,能够充分反映县市内部和行政边界处洪灾脆弱性的空间分布与变化情况。