Robustness of the holistic seismic risk evaluation in urban centers using the USRi

The Urban Seismic Risk index (USRi) published in a previous article (Carreño et al., Nat Hazards 40:137–172, 2007) is a composite indicator that measures risk from an integrated perspective and guides decision-making for identifying the main interdisciplinary factors of vulnerability to be reduced or intervened. The first step of the method is the evaluation of the potential physical damage (hard approach) as a result of the convolution of the seismic hazard with the physical vulnerability of buildings and infrastructure. Subsequently, a set of social context conditions that aggravate the physical effects is also considered (soft approach). According to this procedure, the physical risk index is evaluated for each unit of analysis from existing loss scenarios, whereas the total risk index is obtained by multiplying the former index by an impact factor using an aggravating coefficient, based on variables associated with the socio-economic conditions of each unit of analysis. The USRi has been developed using the underlying holistic and multi-hazard approach of the Urban Risk Index framework proposed for the evaluation of disaster risk in different megacities worldwide. This article presents the sensitivity analysis of the index to different parameters such as input data, weights and transformation functions used for the scaling or normalization of variables. This analysis has been performed using the Monte Carlo simulation to validate the robustness of this composite indicator, understanding as robustness how the cities maintain the ranking as well as predefined risk level ranges, when compared with the deterministic results of risk. Results are shown for different cities of the world.     

Integrated risk assessment method of waterlog disaster in Huaihe River Basin of China

Regional waterlog disaster integrated risk system, affected by natural, social, and economic systems and its combination relationship, is a complex system with certain structure and function. Waterlog disaster integrated risk results from the combined effects of regional environment, impact factors, vulnerability, and disaster-reducing capability of flood hazards in the drainage area. Waterlog disaster integrated risk system can be divided into four subsystems of hazard, vulnerability, disaster-reducing capability, and disaster conditions. Evaluation indexes are selected using fuzzy analytic hierarchy process method, and the evaluation index system is established. Then, the waterlog disaster integrated risk evaluation model is proposed based on set pair analysis method. Taking Huaihe river in Anhui Province of China as the typical area in this study, the results show that the proposed approach is able to obtain the spatial distribution characteristics of waterlog hazard, vulnerability, mitigation capabilities, and integrated disaster risk within the study area. From the quantitative point of view, identification of the areas with high flood risk can provide a scientific basis for the flood management and technical support.

     

Integrated risk assessment method of waterlog disaster in Huaihe River Basin of China

Regional waterlog disaster integrated risk system, affected by natural, social, and economic systems and its combination relationship, is a complex system with certain structure and function. Waterlog disaster integrated risk results from the combined effects of regional environment, impact factors, vulnerability, and disaster-reducing capability of flood hazards in the drainage area. Waterlog disaster integrated risk system can be divided into four subsystems of hazard, vulnerability, disaster-reducing capability, and disaster conditions. Evaluation indexes are selected using fuzzy analytic hierarchy process method, and the evaluation index system is established. Then, the waterlog disaster integrated risk evaluation model is proposed based on set pair analysis method. Taking Huaihe river in Anhui Province of China as the typical area in this study, the results show that the proposed approach is able to obtain the spatial distribution characteristics of waterlog hazard, vulnerability, mitigation capabilities, and integrated disaster risk within the study area. From the quantitative point of view, identification of the areas with high flood risk can provide a scientific basis for the flood management and technical support.     

Multilayer-exposure maps as a basis for a regional vulnerability assessment for landslides: applied in Waidhofen/Ybbs,Austria

Assessments of natural hazards and risks are beneficial for sustainable planning and natural hazard risk management. On a regional scale, quantitative hazard and risk assessments are data intensive and methods developed are difficult to transfer to other regions and to analyse different periods in a given region. Such transfers could be beneficial regarding factors of global change influencing the patterns of natural hazard and risk. The aim of this study was to show the landslide exposure of different elements at risk in one map, e.g. residential buildings and critical infrastructure, as a solid basis for an in-depth analysis of vulnerability and consequent risk. This enables to overcome the data intensive assessments on a regional scale and highlights the potential hotspots for risk analysis. The study area is located in the alpine foreland in Lower Austria and comprises around 112 km². The results show the different levels of exposure, as well as how many layers of elements at risk are affected. Several exposure hotspots can be delineated throughout the study area. This allows a decision on in-depth analysis of hotspots not only by indicated locations but also by a rank resulting from the different layers of incorporated elements at risk.     

Exploring multicriteria flood vulnerability by integrating economic,social and ecological dimensions of flood risk and coping capacity: from a starting point view towards an end point view of vulnerability

In this paper, we present an approach to modelling multicriteria flood vulnerability which integrates the economic, social and ecological dimension of risk and coping capacity. We start with an existing multicriteria risk mapping approach. The term risk is used here in a way that could be called a starting point view, looking at vulnerability without considering coping capacities. We extend this approach by a multicriteria modelling of coping capacities towards an end point view of vulnerability. In doing so, we explore a way to differentiate coping capacity from flood risk in each of the dimensions of vulnerability. The approach is tested in an urban case study, the city of Leipzig, Germany. Our results show that it is possible to map multicriteria risks as well as coping capacities and relate them in a simple way. However, a detailed calculation of end point vulnerability would require more detailed knowledge on the causal relationships between risk and coping capacity criteria and their relative importance.     

A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment

A procedure for landslide risk assessment is presented. The underlying hypothesis is that statistical relationships between past landslide occurrences and conditioning variables can be used to develop landslide susceptibility, hazard and risk models. The latter require also data on past damages. Landslides occurred during the last 50 years and subsequent damages were analysed. Landslide susceptibility models were obtained by means of Spatial Data Analysis techniques and independently validated. Scenarios defined on the basis of past landslide frequency and magnitude were used to transform susceptibility into quantitative hazard models. To assess vulnerability, a detailed inventory of exposed elements (infrastructures, buildings, land resources) was carried out. Vulnerability values (0–1) were obtained by comparing damages experienced in the past by each type of element with its actual value. Quantitative risk models, with a monetary meaning, were obtained for each element by integrating landslide hazard and vulnerability models. Landslide risk models showing the expected losses for the next 50 years were thus obtained for the different scenarios. Risk values obtained are not precise predictions of future losses but rather a means to identify areas where damages are likely to be greater and require priority for mitigation actions.     

Assessment of provincial social vulnerability to natural disasters in China

Assessment of social vulnerability has been recognized as a critical step to understand natural hazard risks and to enhance effective response capabilities. Although significant achievements have been made in social vulnerability researches, little is know about the comprehensive profile of regional social vulnerability in China. In this study, the social vulnerability to natural hazards was firstly divided into socioeconomic and built environmental vulnerability. Then, using factor analysis, we identified the dominant factors that influence the provincial social vulnerability in China to natural hazards based on the socioeconomic and built environmental variables in 2000 and 2010 and explored the spatial patterns of social vulnerability. The results indicated that the provincial social vulnerability in China showed significant regional differences. The social vulnerability in the southeastern and eastern regions of China was greater than its northern and central parts over the past decade. Economic status, rural (proportion of agricultural population and percentage of workers employed in primary industries), urbanization, and age structure (children) were the dominant driving forces of variations in provincial socioeconomic vulnerability in two studied years, while lifelines and housing age could explain most of changes in built environmental vulnerability in 2000 and 2010. There were no statistically significant correlations between social vulnerability and disaster losses (p > 0.05), indicating the impact of disasters was also related to the intensity of hazards and exposure. Disaster relief funds allocated to each province of China depended more on its disaster severity than the regional integrated social vulnerability over the past decade. These findings would provide a scientific base for the policy making and implementation of disaster prevention and mitigation in China.     

Earthquake risk assessment in NE India using deep learning and geospatial analysis

Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km²), vulnerability (480.98 km²), and risk (34,586.10 km²) was estimated.     

Annual landslide risk and effectiveness of risk reduction measures in Shihmen watershed,Taiwan

Landslide risk analysis procedures in this study could evaluate annual landslide risk, and assess the effectiveness of measures. Risk analysis encompassing landslide hazard, vulnerability, and resilience capacity was used to evaluate annual landslide risk. First, landslide spatial, temporal, and area probabilities were joined to estimate annual probability of landslides with an area exceeding a certain threshold in each slope unit. Second, different elements were assigned corresponding values and vulnerabilities to calculate the expected property and life losses. Third, the resilience capacities of communities were calculated based on the scores obtained through community checklists and the weights of items, including “the participation experience of disaster prevention drill,” “real-time monitoring mechanism of community,” “autonomous monitoring of residents,” and “disaster prevention volunteer.” Finally, the annual landslide probabilities, expected losses, and resilience capacities were combined to evaluate annual landslide risk in Shihmen watershed. In addition, annual risks before and after the implementation of measures were compared to determine the benefits of measures, and subsequently benefit–cost analysis was performed. Communities with high benefit–cost ratios included Hualing, Yisheng, Siouluan, and Gaoyi. The watershed as a whole had a benefit–cost ratio far greater than 1, indicating the effectiveness of measures was greater than the investment cost. The results of factor sensitivity analysis revealed changes in vulnerabilities and mortality rates would increase the uncertainty of risk, and that raise in annual interest rates or reduction in life cycle of measures would decrease the benefit–cost ratio. However, these changes did not reverse the cost-effective inference.     

Population amount risk assessment of extreme precipitation-induced landslides based on integrated machine learning model and scenario simulation

In this study, the future landslide population amount risk (LPAR) is assessed based on integrated machine learning models (MLMs) and scenario simulation techniques in Shuicheng County, China. Firstly, multiple MLMs were selected and hyperparameters were optimized, and the generated 11 models were cross-integrated to select the best model to calculate landslide susceptibility; by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard. Using the town as the basic unit, the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways (SSPs) scenarios in each town were assessed, and then combined with the hazard to estimate the LPAR in 2050. The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment. The distribution of hazard classes is similar to susceptibility, and with an increase in precipitation, the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes. The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability, whereas the northern towns of Baohua and Qinglin are at the lowest risk class. The LPAR increased with the intensity of extreme precipitation. The LPAR differs significantly among the SSPs scenarios, with the lowest in the “fossil-fueled development (SSP5)” scenario and the highest in the “regional rivalry (SSP3)” scenario. In summary, the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability. The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.     

Probabilistic assessment of annual repair rates in pipelines and of direct economic losses in water and sewage networks: application to Manizales,Colombia

A methodology for the development of fully probabilistic seismic risk assessments on water and sewage networks is presented in this paper together with a case study for the system of Manizales, Colombia. These kinds of assessments require the development of probabilistic seismic hazard analysis, the consideration of local site effects, when relevant, the assembly of databases to identify and characterize the exposed elements and the development and assignment of vulnerability models for each type of component. For the case of Manizales, a high-resolution exposure database has been developed (element by element, segment by segment) based on the information and data provided by the owner and operator of the network, Aguas de Manizales. Losses due to earthquakes are obtained after convoluting the hazard and vulnerability inputs in a fully probabilistic manner, using the state-of-the-art methodologies incorporated in the CAPRA risk assessment module. Several risk metrics such as the loss exceedance curve, the loss exceedance probabilities for different time frames and the average annual loss are obtained for the system as a whole as well as disaggregated by component. In addition, repair rates for the pipelines were also calculated. The risk results obtained in this study have been useful for the company in designing and implementing expansion and maintenance plans that explicitly account for seismic risk mitigation issues, as well as to explore and negotiate financial protection alternatives by means of risk transfer and retention schemes, thus becoming a valuable input in the continuous development of good disaster risk management practices in this city.

     

Vulnerability and adaptive capacities to slope failure threat: a study of the Klang Valley Region

This article examines the relationship between vulnerability and adaptive capacities of urban dwellers to slope failure threats. The Klang Valley Region in Peninsular Malaysia was selected as the study area based on the increasing frequency and impact of slope failures in the last decade. The study identified, examined, and mapped 10 slope failure locations, and the vulnerability characteristics of urban dwellers staying in areas that are prone to and threatened by slope failures were described. The identified vulnerability indicators are related to factors such as (1) socio-economic status, (2) population, (3) external characteristics, (4) physical structure of dwellings, and (5) adaptation measures. Finally, the perceptions of the residents who are at risk of slope failure hazards and their inherent coping mechanisms were analyzed. A model describing the relationships among slope failure threats, vulnerability of urban dwellers, and their adaptive capacities was formulated.     

自然灾害脆弱性曲线研究进展

在全球变化与全球化背景下自然灾害风险逐年增大,灾害评估就成为风险防范的重要基础。灾害评估包括灾情估算与风险评估2个方面,而脆弱性分析是把灾害与风险研究紧密联系起来的重要桥梁。脆弱性曲线作为定量精确评估承灾体脆弱性的方法,近年来在多领域被广泛运用,成为灾情估算、风险定量分析以及风险地图编制的关键环节。从致灾因子角度综述脆弱性曲线的研究进展,重点阐述基于灾情数据、已有曲线、调查和模型的脆弱性曲线构建。研究表明脆弱性曲线构建由单曲线向多曲线库、单一参数向综合参数、单一方法向多领域综合应用发展,具有综合化和精细化的趋势。进一步开展多领域、多方法综合脆弱性曲线研究,对灾损快速评估及风险评价,防灾减灾具有重要意义。     

On the driving forces of historical changes in the fatalities of tropical cyclone disasters in China from 1951 to 2014

Tropical cyclone (TC) disasters have frequently caused casualties in the coastal areas of China. According to the statistics of dead and missing people due to TCs from 1951 to 2014, the number of fatalities has been significantly decreasing over time. However, deadly TC events have still caused great losses of life in recent years, which are characterized as significant abrupt fluctuations superimposed along the downward trend of the long-term fatality time series. The numbers of fatalities caused by TC disasters are influenced by variables such as the intensity of TC hazards, the population exposed to TCs and the vulnerability of people to TC hazards. It is thus of great significance to analyze their temporal characteristics and understand the forces driving these changes. First, the time series of the TC wind, precipitation, spatial distribution of population, fatality and disaster risk reduction (DRR) measures of China from 1951 to 2014 are reconstructed. Second, the improved power dissipation index, total precipitation, integrated intensity and index of exposed population are calculated, and the population vulnerability indices, including mean and relative fatality rates, are derived. Third, the change trend of each index is detected using the Mann–Kendall test. Finally, the main driving factors of the long-term change trend and fluctuations of the TC fatalities are analyzed by a negative binomial regression model and standard deviation statistics. It is found that the decrease in vulnerability based on the improvement in structural and non-structural measures is the main driving force of the decreases in fatalities over the past six decades. Although the total population and exposure have increased dramatically in the coastal areas of China, their contributions to the increase in the fatality risk were counteracted by the decrease in vulnerability. Abrupt and catastrophic disasters were mostly caused by TCs with hazards of high intensity that surpassed the capacity of structural measures; the lack of forecasting or early warning, as well as improper emergency response actions, may also have triggered the great loss of lives. To reduce the fatalities of future TCs, especially those that may exceed the capacity of structural measures, the enhancement of non-structural measures and the adaptation of resilience strategies should be priorities for future people-centered disaster management.

     

Probabilistic assessment of annual repair rates in pipelines and of direct economic losses in water and sewage networks: application to Manizales,Colombia

A methodology for the development of fully probabilistic seismic risk assessments on water and sewage networks is presented in this paper together with a case study for the system of Manizales, Colombia. These kinds of assessments require the development of probabilistic seismic hazard analysis, the consideration of local site effects, when relevant, the assembly of databases to identify and characterize the exposed elements and the development and assignment of vulnerability models for each type of component. For the case of Manizales, a high-resolution exposure database has been developed (element by element, segment by segment) based on the information and data provided by the owner and operator of the network, Aguas de Manizales. Losses due to earthquakes are obtained after convoluting the hazard and vulnerability inputs in a fully probabilistic manner, using the state-of-the-art methodologies incorporated in the CAPRA risk assessment module. Several risk metrics such as the loss exceedance curve, the loss exceedance probabilities for different time frames and the average annual loss are obtained for the system as a whole as well as disaggregated by component. In addition, repair rates for the pipelines were also calculated. The risk results obtained in this study have been useful for the company in designing and implementing expansion and maintenance plans that explicitly account for seismic risk mitigation issues, as well as to explore and negotiate financial protection alternatives by means of risk transfer and retention schemes, thus becoming a valuable input in the continuous development of good disaster risk management practices in this city.     

基于GIS的巴东新县城滑坡灾害风险系统

本文提出了基于GIS的滑坡灾害风险预测系统流程。并将滑坡灾害风险评价模型与GIS技术先进的图形处理和空间分析功能相结合,建立了巴东县新县城区滑坡灾害风险预测系统。系统由信息管理子系统、危险性预测子系统、易损性预测子系统、风险预测子系统四大子系统构成。系统在对相关信息进行采集、存贮、检索和管理的基础上,结合物元模型、BP模型等专业预测模型,实现了滑坡灾害危险性、易损性评价,最终取得了滑坡灾害风险分布图,为三峡库区内各县的滑坡灾害信息管理和风险预测提供了新途径。预测成果可为研究区的国土规划和移民工程的顺利实施提供依据和保障。     

Groundwater vulnerability assessment in the Melaka State of Malaysia using DRASTIC and GIS techniques

The present work attempts to interpret the groundwater vulnerability of the Melaka State in peninsular Malaysia. The state of groundwater pollution in Melaka is a critical issue particularly in respect of the increasing population, and tourism industry as well as the agricultural, industrial and commercial development. Focusing on this issue, the study illustrates the groundwater vulnerability map for the Melaka State using the DRASTIC model together with remote sensing and geographic information system (GIS). The data which correspond to the seven parameters of the model were collected and converted into thematic maps by GIS. Seven thematic maps defining the depth to water level, net recharge, aquifer media, soil media, topography, impact of vadose zone and hydraulic conductivity were generated to develop the DRASTIC map. In addition, this map was integrated with a land use map for generating the risk map to assess the effect of land use activities on the groundwater vulnerability. Three types of vulnerability zones were assigned for both DRASTIC map and risk map, namely, high, moderate and low. The DRASTIC map illustrates that an area of 11.02 % is low vulnerability, 61.53 % moderate vulnerability and 23.45 % high vulnerability, whereas the risk map indicates that 14.40 % of the area is low vulnerability, 47.34 % moderate vulnerability and 38.26 % high vulnerability in the study area. The most vulnerability area exists around Melaka, Jasin and Alor Gajah cities of the Melaka State.     

Integrated risk assessment of multi-hazards in China

Maps of population exposure, vulnerability and risk to natural hazards are useful tools for designing and implementing disaster risk mitigation programs in China. The ranking of provinces by relative risk to natural hazards would provide a metric for prioritizing risk management strategies. Using provinces as our study unit, from the perspectives of hazard exposure, susceptibility, coping capacity and adaptive capacity, this study first constructed China’s disaster risk index for five types of major natural hazards: earthquakes, floods, droughts, low temperatures/snow and gale/hail. Then, the relative risk level at the provincial scale in China was assessed. Finally, the hotspots with the highest hazard exposure, vulnerability and risk were identified. The results showed that high exposure was a significant risk driver in China, whereas high vulnerability, especially social vulnerability, amplified the risk levels. Similar to the population exposure to disasters, the relative risk levels in the southwestern, central and northeastern regions of China were significantly higher than those in the eastern, northern and western regions. The high-risk regions or hotspots of multi-hazards were concentrated in southern China (less-developed regions), while the low-risk regions were mainly distributed in the eastern coastal areas (well-developed regions). Furthermore, a nonlinear relationship existed between the disaster risk level and poverty incidence as well as per capita GDP, demonstrating that disaster losses in middle-income areas are likely to increase if economic policies are not modified to account for the rising disaster risk. These findings further indicated that research on disaster risk should focus not only on hazards and exposure but also on the vulnerability to natural disasters. Thus, reducing vulnerability and population exposure to natural hazards would be an effective measure in mitigating the disaster risk at hotspots in China.     

Property insurance against debris-flow disasters based on risk assessment and the principal–agent theory

Dongchuan City is highly threatened by debris-flow disasters originating from Shengou gully, a typical debris-flow gully along Xiaojiang River in Yunnan Province (Kang et al. 2004). Shengou gully is studied, and a hazard assessment with numerical simulation is developed using ArcGIS 9.2 software. Debris-flow numerical simulation is an important method for predicting debris-flow inundation regions, zoning debris-flow risks, and helping in the design of debris-flow control works. Meanwhile, vulnerability measurement is essential for hazard and risk research. Based on the self-organized map neural network method, we combine the six vulnerability indicators to create an integrated debris-flow vulnerability map that depicts the vulnerability levels of Dongchuan City in Shengou Basin. Based on the risk assessment (including hazard assessment and vulnerability assessment), we adopt the principal–agent theory and use the risk degree of debris flows as an important index to build the insurance model and analyze the insurance premium of debris-flow disasters in Dongchuan City. This paper discusses the model and mechanism of property insurance in debris-flow risk regions and aims to provide technical support for insurance companies to participate in disaster prevention and reduction.