Projections of Future Extreme Weather Losses Under Changes in Climate and Exposure

Many attempts are made to assess future changes in extreme weather events due to anthropogenic climate change, but few studies have estimated the potential change in economic losses from such events. Projecting losses is more complex as it requires insight into the change in the weather hazard but also into exposure and vulnerability of assets. This article discusses the issues involved as well as a framework for projecting future losses, and provides an overview of some state‐of‐the‐art projections. Estimates of changes in losses from cyclones and floods are given, and particular attention is paid to the different approaches and assumptions. All projections show increases in extreme weather losses due to climate change. Flood losses are generally projected to increase more rapidly than losses from tropical and extra‐tropical cyclones. However, for the period until the year 2040, the contribution from increasing exposure and value of capital at risk to future losses is likely to be equal or larger than the contribution from anthropogenic climate change. Given the fact that the occurrence of loss events also varies over time due to natural climate variability, the signal from anthropogenic climate change is likely to be lost among the other causes for changes in risk, at least during the period until 2040. More efforts are needed to arrive at a comprehensive approach that includes quantification of changes in hazard, exposure, and vulnerability, as well as adaptation effects.     

Seeing is Believing? An Examination of Perceptions of Local Weather Conditions and Climate Change Among Residents in the U.S. Gulf Coast

What role do objective weather conditions play in coastal residents’ perceptions of local climate shifts and how do these perceptions affect attitudes toward climate change? While scholars have increasingly investigated the role of weather and climate conditions on climate‐related attitudes and behaviors, they typically assume that residents accurately perceive shifts in local climate patterns. We directly test this assumption using the largest and most comprehensive survey of Gulf Coast residents conducted to date supplemented with monthly temperature data from the U.S. Historical Climatology Network and extreme weather events data from National Climatic Data Center. We find objective conditions have limited explanatory power in determining perceptions of local climate patterns. Only the 15‐ and 19‐year hurricane trends and decadal summer temperature trend have some effects on perceptions of these weather conditions, while the decadal trend of total number of extreme weather events and 15‐ and 19‐year winter temperature trends are correlated with belief in climate change. Partisan affiliation, in contrast, plays a powerful role affecting individual perceptions of changing patterns of air temperatures, flooding, droughts, and hurricanes, as well as belief in the existence of climate change and concern for future consequences. At least when it comes to changing local conditions, “seeing is not believing.” Political orientations rather than local conditions drive perceptions of local weather conditions and these perceptions—rather than objectively measured weather conditions—influence climate‐related attitudes.     

Spatially Representing Vulnerability to Extreme Rain Events Using Midwestern Farmers’ Objective and Perceived Attributes of Adaptive Capacity

Potential climate‐change‐related impacts to agriculture in the upper Midwest pose serious economic and ecological risks to the U.S. and the global economy. On a local level, farmers are at the forefront of responding to the impacts of climate change. Hence, it is important to understand how farmers and their farm operations may be more or less vulnerable to changes in the climate. A vulnerability index is a tool commonly used by researchers and practitioners to represent the geographical distribution of vulnerability in response to global change. Most vulnerability assessments measure objective adaptive capacity using secondary data collected by governmental agencies. However, other scholarship on human behavior has noted that sociocultural and cognitive factors, such as risk perceptions and perceived capacity, are consequential for modulating people's actual vulnerability. Thus, traditional assessments can potentially overlook people's subjective perceptions of changes in climate and extreme weather events and the extent to which people feel prepared to take necessary steps to cope with and respond to the negative effects of climate change. This article addresses this knowledge gap by: (1) incorporating perceived adaptive capacity into a vulnerability assessment; (2) using spatial smoothing to aggregate individual‐level vulnerabilities to the county level; and (3) evaluating the relationships among different dimensions of adaptive capacity to examine whether perceived capacity should be integrated into vulnerability assessments. The result suggests that vulnerability assessments that rely only on objective measures might miss important sociocognitive dimensions of capacity. Vulnerability indices and maps presented in this article can inform engagement strategies for improving environmental sustainability in the region.     

A Framework to Understand Extreme Space Weather Event Probability

An extreme space weather event has the potential to disrupt or damage infrastructure systems and technologies that many societies rely on for economic and social well‐being. Space weather events occur regularly, but extreme events are less frequent, with a small number of historical examples over the last 160 years. During the past decade, published works have (1) examined the physical characteristics of the extreme historical events and (2) discussed the probability or return rate of select extreme geomagnetic disturbances, including the 1859 Carrington event. Here we present initial findings on a unified framework approach to visualize space weather event probability, using a Bayesian model average, in the context of historical extreme events. We present disturbance storm time (Dst ) probability (a proxy for geomagnetic disturbance intensity) across multiple return periods and discuss parameters of interest to policymakers and planners in the context of past extreme space weather events. We discuss the current state of these analyses, their utility to policymakers and planners, the current limitations when compared to other hazards, and several gaps that need to be filled to enhance space weather risk assessments.     

Modeling of Distributed Generators Resilience Considering Lifeline Dependencies During Extreme Events

This article derives distributed generators resilience models considering lifeline dependencies during extreme events. The effects on power resilience of storage capacity, fuel delays, and fuel order placements are analyzed. Results indicate that storage capacity has an important role in improving overall power supply resilience as seen by loads. In addition, the presented models provide a quantifiable approach in evaluating fuel delivery resilience. The models facilitate studying fuel scheduling policies and local fuel storage sizing for specified resilience requirements. It is observed that tank autonomy greatly affects the flexibility in employing scheduling policies supplying fuel to generators. Resilience dependence on buffer autonomy is high during the first few days of extreme events, and this could have considerable effects on managing evacuations and rescue operations.     

双随机复合泊松损失下巨灾债券定价与数值模拟

上世纪90年代出现的巨灾债券是以规避巨灾财产损失为目的的新型非传统风险转移金融创新工具之一,在我国有良好的发展前景。本文针对巨灾风险事件呈现出周期性与不规则的上升特征,构建了BDT过程用以刻画巨灾风险的抵达过程,并基于风险中性测度技术,在随机利率环境与双随机复合泊松损失条件下,导出了巨灾债券定价公式。进而结合伦敦同业银行拆借利率数据与美国保险服务所提供的PCS损失指数估计并校正了模型参数。最后,通过数值模拟检验了利率风险与巨灾风险如何影响巨灾债券的价格,同时验证了定价模型的可行性。     

Integrating Entropy‐Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naïve Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net‐water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics‐based entropy method. The weighted indices were input into the WNB‐based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image‐based sampling and validation, cell‐by‐cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood‐related environmental hazard studies.     

Benchmarking Safety Climate in Hazardous Environments: A Longitudinal, Interorganizational Approach

Safety climate is an important element of organizational reliability. This study applied benchmarking strategies for monitoring safety climate across nine North Sea oil and gas installations that were surveyed in consecutive years. Examination of absolute changes in safety climate complemented the benchmarking approach. Discriminant function analyses (DFA) identified the elements of safety climate predictive of self-reported accidents; correlational analyses were applied to the scale scores and accident proportions across the year period. Absolute improvements were substantial, with safety climate profiles converging in the second year. Large relative improvements were also observed. DFA highlighted perceived management commitment to safety and willingness to report accidents as significant predictors of personal accident involvement. Changes in perceived management commitment to safety were closely associated with changes in safety behavior.     

Climate Change and Human Health: Estimating Avoidable Deaths and Disease

Human population health has always been central in the justification for sustainable development but nearly invisible in the United Nations Framework Convention on Climate Change negotiations. Current scientific evidence indicates that climate change will contribute to the global burden of disease through increases in diarrhoeal disease, vector-borne disease, and malnutrition, and the health impacts of extreme weather and climate events. A few studies have estimated future potential health impacts of climate change but often generate little policy-relevant information. Robust estimates of future health impacts rely on robust projections of future disease patterns. The application of a standardized and established methodology has been developed to quantify the impact of climate change in relation to different greenhouse gas emission scenarios. All health risk assessments are necessarily biased toward conservative best-estimates of health effects that are easily measured. Global, regional, and national risk assessments can take no account of irreversibility, or plausible low-probability events with potentially very high burdens on human health. There is no "safe limit" of climate change with respect to health impacts as health systems in some regions do not adequately cope with the current climate variability. Current scientific methods cannot identify global threshold health effects in order for policymakers to regulate a "tolerable" amount of climate change. We argue for the need for more research to reduce the potential impacts of climate change on human health, including the development of improved methods for quantitative risk assessment. The large uncertainty about the future effects of climate change on human population health should be a reason to reduce greenhouse gas emissions, and not a reason for inaction.     

Risk of Extreme Events Under Nonstationary Conditions

The concept of the return period is widely used in the analysis of the risk of extreme events and in engineering design. For example, a levee can be designed to protect against the 100-year flood, the flood which on average occurs once in 100 years. Use of the return period typically assumes that the probability of occurrence of an extreme event in the current or any future year is the same. However, there is evidence that potential climate change may affect the probabilities of some extreme events such as floods and droughts. In turn, this would affect the level of protection provided by the current infrastructure. For an engineering project, the risk of an extreme event in a future year could greatly exceed the average annual risk over the design life of the project. An equivalent definition of the return period under stationary conditions is the expected waiting time before failure. This paper examines how this definition can be adapted to nonstationary conditions. Designers of flood control projects should be aware that alternative definitions of the return period imply different risk under nonstationary conditions. The statistics of extremes and extreme value distributions are useful to examine extreme event risk. This paper uses a Gumbel Type I distribution to model the probability of failure under nonstationary conditions. The probability of an extreme event under nonstationary conditions depends on the rate of change of the parameters of the underlying distribution.     

Feeling at Risk Matters: Water Managers and the Decision to Use Forecasts

Experts contend that weather and climate forecasts could have an important role in risk management strategies for community water systems. Yet, most water managers make minimal use of these forecasts. This research explores the determinants of the use of weather and climate forecasts by surveying managers of community water systems in two eastern American states (South Carolina and the Susquehanna River Basin of Pennsylvania). Assessments of the reliability of weather and climate forecasts are not driving their use as water managers who find forecasts reliable are no more likely to use them than are managers who find them unreliable. Although larger systems and those depending on surface water are more likely to use forecasts for some (but not all) purposes, the strongest determinant of forecast use is risk perceptions. Water managers who expect to face problems from weather events in the next decade are much more likely to use forecasts than are water managers who expect few problems. Their expectations of future problems are closely linked with past experience: water managers who have had problems with specific types of weather events (e.g., flood emergencies) in the last 5 years are likely to expect to experience problems in the next decade. Feeling at risk, regardless of the specific source of that weather-related risk, stimulates a decision to use weather and climate forecasts.     

Wildfire Policy in Mediterranean France: How Far is it Efficient and Sustainable?

A new fire policy reinforcing aggressive fire suppression was established in Mediterranean France in response to the devastating wildfires of the 1990s, but to what extent this has changed fire activity yet remains poorly understood. For this purpose, we compared the number and location of ignitions and of burned areas between two 20‐year periods (1975–1994 vs. 1995–2014), in parallel to the changes in fuel covering, human activity promoting ignitions, and fire weather. The number of fires decreased almost continuously since 1975, but sharply after 1994, suggesting an effect of better fire prevention due to the new policy. But the major change in fire activity is a considerable reduction in fire size and burned areas after 1994, especially during summer and in the most fire‐prone places, in response to massive efforts put into fire suppression. These reductions have occurred while the covering by fuel biomass, the human pressure on ignition, and the fire weather index increased, thus making the study area more hazardous. Our results suggest that a strategy of aggressive fire suppression has great potential for counterbalancing the effects of climate changes and human activities and for controlling fire activity in the short term. However, we discuss whether such a suppression‐oriented approach is sustainable in the context of global changes, which cast new fire challenges as demonstrated by the devastative fires of 2003 and 2016. We advocate for a more comprehensive fire policy to come.     

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.     

Climate Change Beliefs and Perceptions of Weather‐Related Changes in the United Kingdom

Public perception research in different countries has suggested that real and perceived periods of high temperature strengthen people's climate change beliefs. Such findings raise questions about the climate change beliefs of people in regions with moderate climates. Relatively little is known about whether public concerns about climate change may also be associated with perceived changes in other weather‐related events, such as precipitation or flooding. We examine the relationship between perceived changes in weather‐related events and climate change beliefs among U.K. residents at a time of below‐average winter temperatures and recent flooding. National survey data (n = 1,848) revealed that heat waves and hot summers were perceived to have become less common during respondents’ lifetimes, while flooding, periods of heavy rainfall, coastal erosions, and mild winters were perceived to have increased in frequency and cold winters were perceived to be unchanged. Although perceived changes in hot‐weather‐related events were positively associated with climate change beliefs, perceived changes in wet‐weather‐related events were found to be an even stronger predictor. Self‐reported experience of “flooding in own area” and “heat‐wave discomfort” also significantly contributed to climate change beliefs. These findings highlight the importance of salient weather‐related events and experiences in the formation of beliefs about climate change. We link our findings to research in judgment and decision making, and propose that those wishing to engage with the public on the issue of climate change should not limit their focus to heat.     

Global Warming Risk Perceptions in India

Few studies have focused on global warming risk perceptions among people in poor and developing countries, who are disproportionately impacted by climate change. This analysis conducts a comprehensive assessment of global warming risk perceptions in India using a national sample survey. Consistent with cultural theory, egalitarianism was positively associated with global warming risk perceptions. In addition, perceived vulnerability and resilience to extreme weather events were also two of the strongest factors associated with global warming risk perceptions. While worry was positively associated with risk perceptions, it accounted for only a small proportion of the variance, unlike studies in developed countries. Finally, the study also collected global warming affective images. The most common responses were “don't know” or “can't say” (25%), followed by “pollution” (21%), “heat” (20%), and “nature” (16%). The study finds that the predictors of global warming risk perceptions among the Indian public are both similar and different than those in developed countries, which has important implications for climate change communication in India.     

Geographic Distributions of Extreme Weather Risk Perceptions in the United States

Weather and climate disasters pose an increasing risk to life and property in the United States. Managing this risk requires objective information about the nature of the threat and subjective information about how people perceive it. Meteorologists and climatologists have a relatively firm grasp of the historical objective risk. For example, we know which parts of the United States are most likely to experience drought, heat waves, flooding, snow or ice storms, tornadoes, and hurricanes. We know less about the geographic distribution of the perceived risks of meteorological events and trends. Do subjective perceptions align with exposure to weather risks? This question is difficult to answer because analysts have yet to develop a comprehensive and spatially consistent methodology for measuring risk perceptions across geographic areas in the United States. In this project, we propose a methodology that uses multilevel regression and poststratification to estimate extreme weather and climate risk perceptions by geographic area (i.e., region, state, forecast area, and county). Then we apply the methodology using data from three national surveys (n = 9,542). This enables us to measure, map, and compare perceptions of risk from multiple weather hazards in geographic areas across the country.     

A Risk Assessment Framework for the Socioeconomic Impacts of Electricity Transmission Infrastructure Failure Due to Space Weather: An Application to the United Kingdom

Space weather phenomena have been studied in detail in the peer‐reviewed scientific literature. However, there has arguably been scant analysis of the potential socioeconomic impacts of space weather, despite a growing gray literature from different national studies, of varying degrees of methodological rigor. In this analysis, we therefore provide a general framework for assessing the potential socioeconomic impacts of critical infrastructure failure resulting from geomagnetic disturbances, applying it to the British high‐voltage electricity transmission network. Socioeconomic analysis of this threat has hitherto failed to address the general geophysical risk, asset vulnerability, and the network structure of critical infrastructure systems. We overcome this by using a three‐part method that includes (i) estimating the probability of intense magnetospheric substorms, (ii) exploring the vulnerability of electricity transmission assets to geomagnetically induced currents, and (iii) testing the socioeconomic impacts under different levels of space weather forecasting. This has required a multidisciplinary approach, providing a step toward the standardization of space weather risk assessment. We find that for a Carrington‐sized 1‐in‐100‐year event with no space weather forecasting capability, the gross domestic product loss to the United Kingdom could be as high as £15.9 billion, with this figure dropping to £2.9 billion based on current forecasting capability. However, with existing satellites nearing the end of their life, current forecasting capability will decrease in coming years. Therefore, if no further investment takes place, critical infrastructure will become more vulnerable to space weather. Additional investment could provide enhanced forecasting, reducing the economic loss for a Carrington‐sized 1‐in‐100‐year event to £0.9 billion.     

Flood Protection Diversification to Reduce Probabilities of Extreme Losses

Recent catastrophic losses because of floods require developing resilient approaches to flood risk protection. This article assesses how diversification of a system of coastal protections might decrease the probabilities of extreme flood losses. The study compares the performance of portfolios each consisting of four types of flood protection assets in a large region of dike rings. A parametric analysis suggests conditions in which diversifications of the types of included flood protection assets decrease extreme flood losses. Increased return periods of extreme losses are associated with portfolios where the asset types have low correlations of economic risk. The effort highlights the importance of understanding correlations across asset types in planning for large‐scale flood protection. It allows explicit integration of climate change scenarios in developing flood mitigation strategy.     

基于网络视角的银行业系统性风险度量方法

网络模型已经成为研究银行系统性风险的重要方法。然而现有研究忽视了银行系统性风险的小概率特点,同时也缺少度量银行系统性风险的统一标准。为此,本文提出了基于网络模型的银行系统性风险度量方法:银行系统性风险VaR和银行系统性风险ES。首先,本文采用蒙特卡洛模拟方法,模拟银行外部冲击造成银行间网络损失的大样本。在银行间网络损失大样本中,估计银行系统性风险VaR和银行系统性风险ES。这两个测度能够捕捉到银行间网络损失的尾部特征,解决了对比随机冲击结果无法反映银行系统性风险的问题。其次,在模拟实验中,本文利用真实银行间网络结构参数,对模拟的三种银行间网络进行校准,保证了研究结论真实性和可靠性。最后,在模拟实验中发现:(1)外部冲击会引发违约传染的连锁反应,并导致银行间网络损失分布从近似正态分布转变成尖峰厚尾分布,最后变成双峰分布。(2)网络集中度越高发生违约传染连锁反应的概率越小,但是传染的破坏力会更大。(3)银行间网络的潜在传染作用会极大的放大银行系统的风险,而且违约传染效应是呈指数增长的。     

Optimal planning of the joint placement of photovoltaic panels and green roofs under climate change uncertainty

Photovoltaic (PV) panels directly convert sunlight into electricity; but, sunlight also heats the panels, negatively impacting their efficiency. Green roofs are vegetative layers grown on rooftops, mainly to provide added insulation on the roof to save energy. Green roofs also cool near-surface air temperature. Hence, the joint installation of PV panels and green roofs may potentially lead to higher efficiency of PV panels in certain climates. We develop a two-stage stochastic programming model to optimally place PV panels and green roofs under climate change uncertainty to maximize the overall profit from energy generated and saved. We calibrate the model using the literature, industry reports, and the data from different, at times conflicting, climate projections. We then conduct a case study for a mid-size city in the U.S., perform extensive sensitivity and robustness analyses and provide insights.