Spatiotemporal dynamics: the need for an innovative approach in mountain hazard risk management

Starting with an overview on losses due to mountain hazards in the Russian Federation and the European Alps, the question is raised why a substantial number of events still are recorded—despite considerable efforts in hazard mitigation and risk reduction. The main reason for this paradox lies in a missing dynamic risk-based approach, and it is shown that these dynamics have different roots: firstly, neglecting climate change and systems dynamics, the development of hazard scenarios is based on the static approach of design events. Secondly, due to economic development and population dynamics, the elements at risk exposed are subject to spatial and temporal changes. These issues are discussed with respect to temporal and spatial demands. As a result, it is shown how risk is dynamic on a long-term and short-term scale, which has to be acknowledged in the risk concept if this concept is targeted at a sustainable development of mountain regions. A conceptual model is presented that can be used for dynamical risk assessment, and it is shown by different management strategies how this model may be converted into practice. Furthermore, the interconnectedness and interaction between hazard and risk are addressed in order to enhance prevention, the level of protection and the degree of preparedness.     

Up-scaling of impact dependent loss distributions: a hybrid convolution approach for flood risk in Europe

This paper introduces a new method to up-scale dependent loss distributions from natural hazards to higher spatial levels, explicitly incorporating their dependency structure over the aggregation process. The method is applied for flood risk in Europe. Based on this “hybrid convolution” approach, flood loss distributions for nearly all European countries are calculated and presented. Such risk-based estimates of extreme event losses are useful for determining suitable risk management strategies on various spatial levels for different risk bearers. The method is not only applicable for natural disaster risk but can be extended for other cases as well, i.e., where comonotonic risks have to be “summed up” without loss of risk information.     

The Role of Uncertainty on Alternative Disaster Management Strategies

The work presented in this paper is an outgrowth of a multi—year study at the Wharton School of the University of Pennsylvania on Managing Catastrophic Risks. We focus on the role of homeowners and insurance companies in managing the hazard from earthquake risk. Specifically, we consider alternative earthquake disaster management strategies for a typical homeowner and a small insurance company in the Oakland, California region. These strategies involve the adoption of mitigation measures and the purchase of earthquake insurance by the homeowner and the purchase of an indemnity contract (e.g., excess—of—loss reinsurance) by the insurer.

We focus on how uncertainty impacts these disaster management strategies. Specifically, we illustrate the impact of structural mitigation and risk—transfer mechanisms on the insurer's performance when there is uncertainty in the company's risk profile. This risk profile is captured through a loss exceedance probability (EP) curve, representing the probability that a certain level of monetary loss will be exceeded on an annual basis. Parameters considered in the sensitivity analysis that will shift the loss EP curve include: earthquake recurrence, ground motion attenuation, soil mapping schemes, and the exposure and vulnerability of the residential structures. The paper demonstrates how uncertainty in these parameters impacts the cost effectiveness of mitigation and reinsurance on the insurer's profitability and chances of insolvency, as well as the number of policies the insurer is willing to issue.     

A hurricane surge risk assessment framework using the joint probability method and surge response functions

Hurricane surge events have caused devastating damage in active-hurricane areas all over the world. The ability to predict surge elevations and to use this information for damage estimation is fundamental for saving lives and protecting property. In this study, we developed a framework for evaluating hurricane flood risk and identifying areas that are more prone to them. The approach is based on the joint probability method with optimal sampling (JPM-OS) using surge response functions (SRFs) (JPM-OS-SRF). Derived from a discrete set of high-fidelity storm surge simulations, SRFs are non-dimensional, physics-based empirical equations with an algebraic form, used to rapidly estimate surge as a function of hurricane parameters (i.e., central pressure, radius, forward speed, approach angle and landfall location). The advantage of an SRF-based approach is that a continuum of storm scenarios can be efficiently evaluated and used to estimate continuous probability density functions for surge extremes, producing more statistically stable surge hazard assessments without adding measurably to epistemic uncertainty. SRFs were developed along the coastline and then used to estimate maximum surge elevations with respect to a set of hurricane parameters. Integrating information such as ground elevation, property value and population with the JPM-OS-SRF allows quantification of storm surge-induced hazard impacts over the continuum of storm possibilities, yielding a framework to create the following risk-based products, which can be used to assist in hurricane hazard management and decision making: (1) expected annual loss maps; (2) flood damage versus return period relationships; and (3) affected business (e.g., number of business, number of employees) versus return period relationships. By employing several simplifying assumptions, the framework is demonstrated at three northern Gulf of Mexico study sites exhibiting similar surge hazard exposure. The framework results reveal Gulfport, MS, USA is at relatively more risk of economic loss than Corpus Christi, TX, USA, and Panama City, FL, USA. Note that economic processes are complex and very interrelated to most other human activities. Our intention here is to present a methodology to quantify the flood damage (i.e., infrastructure economic loss, number of businesses affected, number of employees in these affected businesses and sales volume in these affected businesses) but not to discuss the complex interactions of these damages with other economic activities and recovery plans.     

A hurricane surge risk assessment framework using the joint probability method and surge response functions

Hurricane surge events have caused devastating damage in active-hurricane areas all over the world. The ability to predict surge elevations and to use this information for damage estimation is fundamental for saving lives and protecting property. In this study, we developed a framework for evaluating hurricane flood risk and identifying areas that are more prone to them. The approach is based on the joint probability method with optimal sampling (JPM-OS) using surge response functions (SRFs) (JPM-OS-SRF). Derived from a discrete set of high-fidelity storm surge simulations, SRFs are non-dimensional, physics-based empirical equations with an algebraic form, used to rapidly estimate surge as a function of hurricane parameters (i.e., central pressure, radius, forward speed, approach angle and landfall location). The advantage of an SRF-based approach is that a continuum of storm scenarios can be efficiently evaluated and used to estimate continuous probability density functions for surge extremes, producing more statistically stable surge hazard assessments without adding measurably to epistemic uncertainty. SRFs were developed along the coastline and then used to estimate maximum surge elevations with respect to a set of hurricane parameters. Integrating information such as ground elevation, property value and population with the JPM-OS-SRF allows quantification of storm surge-induced hazard impacts over the continuum of storm possibilities, yielding a framework to create the following risk-based products, which can be used to assist in hurricane hazard management and decision making: (1) expected annual loss maps; (2) flood damage versus return period relationships; and (3) affected business (e.g., number of business, number of employees) versus return period relationships. By employing several simplifying assumptions, the framework is demonstrated at three northern Gulf of Mexico study sites exhibiting similar surge hazard exposure. The framework results reveal Gulfport, MS, USA is at relatively more risk of economic loss than Corpus Christi, TX, USA, and Panama City, FL, USA. Note that economic processes are complex and very interrelated to most other human activities. Our intention here is to present a methodology to quantify the flood damage (i.e., infrastructure economic loss, number of businesses affected, number of employees in these affected businesses and sales volume in these affected businesses) but not to discuss the complex interactions of these damages with other economic activities and recovery plans.

     

Climate Change and Extreme Weather: A Basis for Action

The economic and social costs of extreme weather-related events have been increasing around the globe. There is some debate over how much of this past increase has been due to social factors and how much due to changes in frequency or characteristics of extreme events. The Intergovernmental Panel on Climate Change (2001a) has affirmed that humans are having a role in changing the climate and will have a larger role in the future. Although the changes in extreme events are by their nature both difficult to detect and difficult to model, the consensus is that there will be changes in the future. Through a risk-based decision-making analysis, it is concluded that society should make the additional investments to reduce vulnerability to this increased risk.     

Probabilistic assessment of wildfire hazard and municipal watershed exposure

The occurrence of wildfires within municipal watersheds can result in significant impacts to water quality and ultimately human health and safety. In this paper, we illustrate the application of geospatial analysis and burn probability modeling to assess the exposure of municipal watersheds to wildfire. Our assessment of wildfire exposure consists of two primary components: (1) wildfire hazard, which we characterize with burn probability, fireline intensity, and a composite index, and (2) geospatial intersection of watershed polygons with spatially resolved wildfire hazard metrics. This effort enhances investigation into spatial patterns of fire occurrence and behavior and enables quantitative comparisons of exposure across watersheds on the basis of a novel, integrated measure of wildfire hazard. As a case study, we consider the municipal watersheds located on the Beaverhead-Deerlodge National Forest (BDNF) in Montana, United States. We present simulation results to highlight exposure across watersheds and generally demonstrate vast differences in fire likelihood, fire behavior, and expected area burned among the analyzed municipal watersheds. We describe how this information can be incorporated into risk-based strategic fuels management planning and across the broader wildfire management spectrum. To conclude, we discuss strengths and limitations of our approach and offer potential future expansions.     

基于变形的土石坝地震风险分析

土石坝抗震安全的设计一般立足于预防结构的倒塌,如何使土石坝结构地震破损控制在可接受的风险水平是一个值得研究的重要课题。应用地震风险分析理论,建立了土石坝地震风险分析方法,包括地震危险性分析、地震易损性分析和地震灾害损失评估3个方面。在场地地震危险性分析基础上,将基于性能的抗震设计思想应用于土石坝结构地震易损性分析中,以土石坝坝顶相对沉陷为评价指标,划分土石坝震损等级,最后结合地震经济损失分析,建立了土石坝地震风险计算模型,在技术和经济上对土石坝地震破损风险进行分析计算。以某高土石坝为例,用该模型对大坝的震害和经济损失进行了预测分析,其结论可为土石坝安全评价及投资决策等提供依据。     

Pricing weather index insurance based on artificial controlled experiment: a case study of cold temperature for early rice in Jiangxi,China

The growth of early rice is often threatened by a phenomenon known as Grain Buds Cold, a period of anomalously cold temperatures during the booting and flowering stage. As a high yield loss due to Grain Buds Cold will lead to increasing insurance premiums, quantifying the impact of weather on crop yield is crucial to the design of weather index insurance. In this study, we propose a new approach to the estimation of premium rates of Grain Buds Cold weather index insurance. A 2-year artificial controlled experiment was utilized to develop logarithmic and linear yield loss models. Additionally, incorporating 51 years of meteorological data, an information diffusion model was used to calculate the probability of different durations of Grain Buds Cold, ranging from 3 to 20 days. The results show that the pure premium rates determined by a logarithmic yield loss model exhibit lower risk and greater efficiency than those determined by a linear yield loss model. The premium rates of Grain Buds Cold weather index insurance were found to fluctuate between 7.085 and 10.151% at the county level in Jiangxi Province, while the premium rates based on the linear yield loss model were higher (ranging from 7.787 to 11.672%). Compared with common statistical methods, the artificial controlled experiment presented below provides a more robust, reliable and accurate way of analyzing the relationship between yield and a single meteorological factor. At the same time, the minimal data requirements of this experimental approach indicate that this method could be very important in regions lacking historical yield and climate data. Estimating weather index insurance accurately will help farmers address extreme cold weather risk under changing climatic conditions.     

Impact on loss/risk assessments of inter-model variability in vulnerability analysis

Fragility curves (FCs) constitute an emerging tool for the seismic risk assessment of all elements at risk. They express the probability of a structure being damaged beyond a specific damage state for a given seismic input motion parameter, incorporating the most important sources of uncertainties, that is, seismic demand, capacity and definition of damage states. Nevertheless, the implementation of FCs in loss/risk assessments introduces other important sources of uncertainty, related to the usually limited knowledge about the elements at risk (e.g., inventory, typology). In this paper, within a Bayesian framework, it is developed a general methodology to combine into a single model (Bayesian combined model, BCM) the information provided by multiple FC models, weighting them according to their credibility/applicability, and independent past data. This combination enables to efficiently capture inter-model variability (IMV) and to propagate it into risk/loss assessments, allowing the treatment of a large spectrum of vulnerability-related uncertainties, usually neglected. As case study, FCs for shallow tunnels in alluvial deposits, when subjected to transversal seismic loading, are developed with two conventional procedures, based on a quasi-static numerical approach. Noteworthy, loss/risk assessments resulting from such conventional methods show significant unexpected differences. Conventional fragilities are then combined in a Bayesian framework, in which also probability values are treated as random variables, characterized by their probability density functions. The results show that BCM efficiently projects the whole variability of input models into risk/loss estimations. This demonstrates that BCM is a suitable framework to treat IMV in vulnerability assessments, in a straightforward and explicit manner.     

A conceptual model of avalanche hazard

This conceptual model of avalanche hazard identifies the key components of avalanche hazard and structures them into a systematic, consistent workflow for hazard and risk assessments. The method is applicable to all types of avalanche forecasting operations, and the underlying principles can be applied at any scale in space or time. The concept of an avalanche problem is introduced, describing how different types of avalanche problems directly influence the assessment and management of the risk. Four sequential questions are shown to structure the assessment of avalanche hazard, namely: (1) What type of avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our objective was to develop an underpinning for qualitative hazard and risk assessments and address this knowledge gap in the avalanche forecasting literature. We used judgmental decomposition to elicit the avalanche forecasting process from forecasters and then described it within a risk-based framework that is consistent with other natural hazards disciplines.     

Earthquake risk assessment and optimal risk management strategies for Hi-Tech Fabs in Taiwan

In this paper, we build an event-based seismic hazard assessment and financial analysis model for Hi-Tech Fabs in Taiwan. As we know, the low occurrence rate, tremendous loss and high uncertainty are characteristics of earthquake disasters. To handle the above issues, the model integrates knowledge from many fields including earth science, seismology, geology, risk management, structural engineering, the insurance profession, financial engineering and facility management. The portfolio of data from the site survey indicates that the model can be used to calculate the event losses (including buildings, contents and business interruption losses); furthermore the average annual loss and loss exceeding probabilities also can be calculated. The total earthquake risk cost, which includes earthquake insurance premiums, average annual retained loss and equivalent annual retrofit cost, is defined as an indicator for selection of optimal risk management strategies.     

Integrated model for earthquake risk assessment using neural network and analytic hierarchy process:Aceh province,Indonesia

Catastrophic natural hazards,such as earthquake,pose serious threats to properties and human lives in urban areas.Therefore,earthquake risk assessment(ERA)is indispensable in disaster management.ERA is an integration of the extent of probability and vulnerability of assets.This study develops an integrated model by using the artificial neural network–analytic hierarchy process(ANN–AHP)model for constructing the ERA map.The aim of the study is to quantify urban population risk that may be caused by impending earthquakes.The model is applied to the city of Banda Aceh in Indonesia,a seismically active zone of Aceh province frequently affected by devastating earthquakes.ANN is used for probability mapping,whereas AHP is used to assess urban vulnerability after the hazard map is created with the aid of earthquake intensity variation thematic layering.The risk map is subsequently created by combining the probability,hazard,and vulnerability maps.Then,the risk levels of various zones are obtained.The validation process reveals that the proposed model can map the earthquake probability based on historical events with an accuracy of 84%.Furthermore,results show that the central and southeastern regions of the city have moderate to very high risk classifications,whereas the other parts of the city fall under low to very low earthquake risk classifications.The findings of this research are useful for government agencies and decision makers,particularly in estimating risk dimensions in urban areas and for the future studies to project the preparedness strategies for Banda Aceh.     

考虑损伤情况下结构风险损失模型的研究

在传统风险损失模型的基础上研究了由于损伤造成结构功能未充分发挥导致的风险损失,建立了包括损伤项在内的风险损失模型,是对传统风险损失模型的有益完善和补充.通过引进结构使用功能系数θ的概念来量化结构功能的发挥程度,以此建立损伤项风险的数学模型来对损伤风险损失进行计算.以一输水隧洞为例阐述了损伤风险损失的计算过程.     

A Probabilistic Modelling System for Assessing Flood Risks

In order to be economically viable, flood disaster mitigation should be based on a comprehensive assessment of the flood risk. This requires the estimation of the flood hazard (i.e. runoff and associated probability) and the consequences of flooding (i.e. property damage, damage to persons, etc.). Within the “German Research Network Natural Disasters” project, the working group on “Flood Risk Analysis” investigated the complete flood disaster chain from the triggering event down to its various consequences. The working group developed complex, spatially distributed models representing the relevant meteorological, hydrological, hydraulic, geo-technical, and socio-economic processes. In order to assess flood risk these complex deterministic models were complemented by a simple probabilistic model. The latter model consists of modules each representing one process of the flood disaster chain. Each module is a simple parameterisation of the corresponding more complex model. This ensures that the two approaches (simple probabilistic and complex deterministic) are compatible at all steps of the flood disaster chain. The simple stochastic approach allows a large number of simulation runs in a Monte Carlo framework thus providing the basis for a probabilistic risk assessment. Using the proposed model, the flood risk including an estimation of the flood damage was quantified for an example area at the river Rhine. Additionally, the important influence of upstream levee breaches on the flood risk at the lower reaches was assessed. The proposed model concept is useful for the integrated assessment of flood risks in flood prone areas, for cost-benefit assessment and risk-based design of flood protection measures and as a decision support tool for flood management.     

基于随机理论的地下水环境风险评价

针对确定性模型难以描述含水层非均质空间分布的问题,提出基于随机理论的地下水环境风险评价方法。以矩形场地地下水污染风险评价为例,采用蒙特卡罗法生成大量渗透系数随机场,模拟含水层参数各种可能的非均质空间分布,在此基础上建立场地地下水流模型与溶质运移模型,分别计算污染物在地下水中的迁移转化情况。统计大量随机模拟中污染事故发生的频率,当模拟次数足够多时,污染频率收敛于污染概率,污染风险即通过污染概率体现出来。该方法将模型参数设为满足一定分布特征的随机变量,避免了确定性方法得出的武断的评价结果,可为工厂的选址、水源地的选址等工作提供科学指导。     

Vulnerability of buildings to debris flow impact

Quantitative risk assessments (QRAs) for landslide hazards are increasingly being executed to determine an unmitigated level of risk and compare it with risk tolerance criteria set by the local or federal jurisdiction. This approach allows urban planning with a scientific underpinning and provides the tools for emergency preparedness. Debris-flow QRAs require estimates of the hazard probability, spatial and temporal probability of impact (hazard assessment) and vulnerability of the elements at risk. The vulnerability term is perhaps the most difficult to estimate confidently because (a) human death in debris flows is most commonly associated with building damage or collapse and is thus an indirect consequence and (b) the type and scale of building damage is very difficult to predict. To determine building damage, an intensity index (I _DF) was created as the product of maximum expected flow depth d and the square of the maximum flow velocity v (I _DF = dv ²). The I _DF surrogates impact force and thus correlates with building damage. Four classes of building damage were considered ranging from nuisance flood/sedimentation damage to complete destruction. Sixty-six well-documented case studies in which damage, flow depth and flow velocity were recorded or could be estimated were selected through a search of the global literature, and I _DF was plotted on a log scale against the associated damage. As expected, the individual damage classes overlap but are distinctly different in their respective distributions and group centroids. To apply this vulnerability model, flow velocity and flow depth need to be estimated for a given building location and I _DF calculated. Using the existing database, a damage probability (P _DF) can then be computed. P _DF can be applied directly to estimate the likely insurance loss or associated loss of life. The model presented here should be updated with more case studies and is therefore made openly available to international researchers who can access it at .     

单体滑坡灾害财产风险定量评估模型

目前承灾体易损性评估尚处于经验评估阶段,制约了单体滑坡灾害财产风险评估的定量化程度。为此,针对典型承灾体（建筑结构物）,提出一种单体滑坡灾害财产风险评估模型。认为财产风险是坡体失稳概率、承灾体在滑坡作用下的失效概率以及承灾体价值的乘积。避开了易损性评估,从一个新的角度来评估滑坡灾害财产风险。通过研究,验证了该模型正确反映了滑坡成灾过程中各种因素对财产风险的影响,从而为单体滑坡灾害财产风险的定量化评估提供了新的途径。另外以该模型应用研究为基础,提出承灾体安全范围的概念,对于每一个承灾体而言,将单体滑坡灾害影响范围划分为：危险区域、相对危险区域和安全区域,为建设工程选址或场地风险评估提供了实用指标,具有一定的工程实用价值。     

Determining the initial stage loss content of tectonic coal gas desorption via the Bayesian probability method

Mine gas disaster prediction and prevention are based on gas content measurement, which results in initial stage loss when determining coal gas desorption contents in engineering applications. We propose a Bayesian probability statistical method in the coal gas desorption model on the basis of constrained prior information. First, we use a self-made coal sample gas desorption device to test initial stage gas desorption data of tectonic coal and undeformed coal. Second, we calculate the initial stage loss of different coal samples with the power exponential function parameters by using Bayesian probability statistics and least squares estimation. Results show that Bayesian probability statistics and least squares estimation can be used to obtain regression and desorption coefficients, thereby illustrating the Bayesian estimation method’s validity and reliability. Given that the Bayesian probability method can apply prior information to constrain the model’s posterior parameters, it provides results that are statistically significant in the initial stage loss of coal gas desorption by connecting observation data and prior information.