Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period

Ming, Xiaodong; Xü, Wei; Li, Ying; Du, Jinzhou; Liu, Baoyin; Shi, Peijun

doi:10.1007/s00477-014-0935-y

Cited by 62 publications

(33 citation statements)

References 29 publications

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“…Despite the significant advances in defining and formulating vulnerability [21][22][23][24] , there still remains disparities in interpretation of vulnerability between the risk/hazard research and human-environmental research communities. However, both communities acknowledge that vulnerability is composed of exposure, sensitivity, and response or resilience to the compound effects of both human and environmental systems [25][26][27][28] .…”

mentioning

confidence: 99%

A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States

Khajehei

Ahmadalipour

Shao

et al. 2020

Sci Rep

102

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Flash flood is among the most catastrophic natural hazards which causes disruption in the environment and societies. Flash flood is mainly initiated by intense rainfall, and due to its rapid onset (within six hours of rainfall), taking action for effective response is challenging. Building resilience to flash floods require understanding of the socio-economic characteristics of the societies and their vulnerability to these extreme events. This study provides a comprehensive assessment of socio-economic vulnerability to flash floods and investigates the main characteristics of flash flood hazard, i.e. frequency, duration, severity, and magnitude. A socio-economic vulnerability index is developed at the county level across the Contiguous United States (CONUS). For this purpose, an ensemble of social and economic variables from the US Census and the Bureau of Economic Analysis were analyzed. Then, the coincidence of socioeconomic vulnerability and flash flood hazard were investigated to identify the critical and non-critical regions. Results show that the southwest U.S. experienced severe flash flooding with high magnitude, whereas the Northern Great Plains experience lower severity and frequency. Critical counties (highvulnerable-hotspot) are mostly located in the southern and southwestern parts of the U.S. The majority of counties in the Northern Great Plains indicate a non-critical status.

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mentioning

confidence: 99%

A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States

Khajehei

Ahmadalipour

Shao

et al. 2020

Sci Rep

102

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“…CC BY 4.0 License. been studied both globally (Martius et al, 2016) and locally (Johansson and Chen, 2003;Ming et al, 2015). These two hazards are often associated with different types of storms (Dowdy and Catto, 2017) and in particular cyclones (Ming et al, 2015;Raveh-Rubin and Wernli, 2016).…”

Section: Rain and Wind Gusts At Heathrow Airport (Asymptotic Independmentioning

confidence: 99%

“…been studied both globally (Martius et al, 2016) and locally (Johansson and Chen, 2003;Ming et al, 2015). These two hazards are often associated with different types of storms (Dowdy and Catto, 2017) and in particular cyclones (Ming et al, 2015;Raveh-Rubin and Wernli, 2016). In South England, these two hazards are mostly associated with mid-latitude cyclones in the winter season and thunderstorms in summer season (Hawkes, 2008;Anderson and Klugmann, 2014;Webb and Elsom, 2016;515 Hendry et al, 2019).…”

Section: Rain and Wind Gusts At Heathrow Airport (Asymptotic Independmentioning

confidence: 99%

Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios

Tilloy¹,

Malamud²,

Winter³

et al. 2020

Preprint

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Abstract. Estimating risks generated by multi-hazard scenarios remains a challenge for practitioners. Here we evaluate the efficacy of bivariate extreme modelling approaches by fitting six distinct stochastic models to synthetic datasets. The properties of the synthetic datasets (marginal distributions, tail dependence structure) are chosen to match bivariate time series of environmental variables. The six models are copulas (one non-parametric, one semi-parametric, four parametric). We build 60 distinct synthetic datasets based on different parameters of log-normal margins and two different copulas. We contrast the model strengths (model flexibility) and weaknesses (poorer fits to the data). We find that no one model fits our synthetic data for all parameters, but rather a range of models are more appropriate To highlight the benefits of the systematic modelling framework developed, we consider the following environmental data: (i) daily precipitation and maximum wind gust in London, UK; (ii) daily mean temperature and wildfire number in Porto district, Portugal. In both cases there is good agreement in the estimation of bivariate return periods between models selected from the systematic framework developed in this study. Within this framework, we have explored a way to model multi-hazard events and identify the most efficient models for a given set of synthetic data and hazard sets.

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“…From the perspective of risk reduction, a multiple hazard is modeled using joint probability distribution of hazards for a specific area (Ming et al., ). Resilience to multiple concurrent independent hazards is always assessed by analyzing the joint occurrence rate and weighted sum of single hazard resilience independently for a specific duration.…”

Section: Literature Reviewmentioning

confidence: 99%

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Kong

Simonović́

2019

Risk Analysis

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Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two-layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.

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Quantitative multi-hazard risk assessment with vulnerability surface and hazard joint return period

Cited by 62 publications

References 29 publications

A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States

A Place-based Assessment of Flash Flood Hazard and Vulnerability in the Contiguous United States

Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

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