Extreme Weather Risk Research Articles

Research on Extreme Weather Risk Management Based on Gray Prediction and Multi-Criteria Evaluation Models

This study investigates the growing challenges faced by the insurance industry due to extreme weather events and climate change, with a focus on the widening global insurance gap. We propose a novel dual-model approach to address these complex issues. First, we develop a Gray Prediction-Based Risk Assessment Framework (GPRAF) that integrates multidimensional data on natural disasters, economic development, population dynamics, and agricultural trends. This framework enables insurance companies to make more informed underwriting decisions in the context of increasing climate-related risks. Through case studies in Japan and Mexico, we demonstrate the practical applications and implications of our approach. Our research contributes to the growing body of literature on climate risk management in the insurance sector and provides valuable insights for policymakers and industry practitioners. By bridging the gap between risk assessment and community preservation needs, this study offers a holistic perspective on adapting insurance strategies to the challenges of climate change while preserving community values.

Tail risk spillover network among green bond, energy and agricultural markets under extreme weather scenarios

This paper explores the tail risk spillover patterns among eight US green bond, energy, and agricultural commodity markets conditional on different extreme weather conditions, using a proposed conditional tail Diebold Yilmaz spillover network. The results of the empirical analysis, conducted using data from 2013 to 2023, indicates that, firstly, as weather conditions transit from normal to extreme risk state, the complexity and magnitude of tail risk spillovers increase, particularly affecting the relationships between different markets and sectors. Under most extreme weather risk scenarios, the energy market predominantly acts as a risk transmitter. Conversely, the agricultural market, more often emerges as a risk receiver. Secondly, of all the extreme weather scenarios considered, the US commodity markets achieve lowest in total risk spillover (TRS) in the extreme snow weather scenarios and this is the only case that is almost no more than the unconditional weather scenario over the sample period. Finally, the tail returns of most US commodity markets are more sensitive to tail movements in extreme total precipitation and extreme runoff weather conditions.

More than Magnitude: Towards a multidimensional understanding of unprecedented weather to better support disaster management

The 1900 Galveston Texas Hurricane, the 2021 Pacific Northwest heatwave, and the 2023 Tropical Cyclone Freddy were all events that were unprecedented in diverse ways and had severe humanitarian impacts. Understanding past and future risk of unprecedented weather is an emerging question across climate science disciplines but use of this research by the humanitarian sector has been limited. This cross-disciplinary paper is an effort by climate scientists and humanitarian practitioners to address this gap. For it, we combined narrative and scoping literature reviews with structured practitioner engagement to develop a working definition and typology of unprecedented weather through a disaster management lens. We qualitatively coded over 400 peer-reviewed articles to highlight the current state of research on unprecedented weather, and then discussed these findings in a workshop with 48 humanitarian practitioners. Our results show that, while analyses of past and future unprecedented weather often focus on the magnitude of such events, extreme weather can be unprecedented in many other dimensions, all which have significant implications for early warning, anticipatory action, and disaster response planning. We conclude with a call for more imagination and diversity in research on extreme weather risks, and for closer collaboration between climate scientists and disaster managers to design and answer questions that matter for humanitarian outcomes.

Research on Multi-objective Location Planning of Real Estate Based on Extreme Weather

This study aims to solve the problem of multi-objective site selection planning of real estate under the influence of extreme weather. We use meteorological data and historical disaster records to build an extreme weather risk assessment model, calculate the risk weights of various regions, and build a multi-objective real estate site selection planning model that comprehensively considers regional risks, such as transportation infrastructure and environment Using ARCGIS spatial visualization technology, we visually displayed weighted maps and final location selection results of each target in the form of maps. Chongqing City was taken as an example to demonstrate the application process of the model. The optimal results of real estate location selection in Chongqing were distributed in (28 96 N, 108 94 E), (30 19 N, 105 84 E), (28 95 N, 108 94 E) nearby This study innovatively takes the risk of extreme weather into account, which enricfies the theoretical and methodological research on real estate location.

Nitrogen facilitation was maintained in sown Mediterranean forage mixtures despite drought stress conditions with concurrent general benefits upon plant aboveground water status and yield

There is an urge to optimise the sustainability and resilience of grasslands, especially in light of the extreme weather risks posed by climate change. Plant sown diversity could contribute to mitigating these risks by improving resource use efficiency as well as reducing reliance on fertilisation. We established a field experiment to determine forage swards’ performance in terms of water use, nitrogen (N) use, and dry matter (DM) yield in a semi-arid Mediterranean area which faced a severe drought period. Most variables were tested over three growing seasons, whilst DM yield had an extra year of sampling in which two levels of N fertilisation were tested. Following the Generalised Diversity-Interactions modelling approach, we manipulated the composition and evenness of forage swards including a grass (Festuca arundinacea), a legume (Medicago sativa), and a non-legume forb (Cichorium intybus). Communities were grown as monocultures or mixtures of three species according to a simplex design, which allowed us to estimate separately the two components of the diversity effect: the individual species effects and that due to species interactions. Models were tested both with sown proportions (Pi0) and realised proportions (PiR). The Pi0 models indicated a major beneficial effect of mixtures in contrast to the average monoculture performance for all studied variables. In contrast, PiR models revealed that some of the effects in Pi0 models are indeed explained by the growing proportion of M. sativa over time. However, beneficial interaction effects are still found in PiR models, especially concerning N2 fixation (with δ15N as proxy), which was maintained regardless of the drought stress level experienced by the swards. All things considered, mixed swards merit attention as a valuable nature-based solution that can optimise N management (by boosting N2 input through fixation) as well as water use. This would result in well-nurtured swards with a better ability to cope with drought stress and maintain productivity.

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

Assessing Extreme Weather Risks for Insurance Underwriting with ARIMA and Entropy Weight Method: Insights from Brazil and India

Heatwave attribution based on reliable operational weather forecasts

The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2–50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10–50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.

Extreme Weather Risk Assessment and Insurance Decision Making Based on ARIMA Model and Visual Analysis

Extreme Weather Risk Assessment and Insurance Decision Making Based on ARIMA Model and Visual Analysis

Weather, credit, and economic fluctuations: Evidence from China

We constructed an Extreme Weather Index to measure extreme weather risks in China. Analyzing macroeconomic data through a structural vector auto-regression model suggests that a negative weather shock leads to persistently low GDP and credit obtained by non-financial firms. In our regression analysis of a panel of firms listed in China, the negative effects of weather shocks on firm level loans were statistically and practically significant. Further analysis suggests that credit risk and expectations are two important impact channels. A high existing credit risk or low confidence among firm managers, amplifies the negative effects of extreme weather on loans.

Smart Contract Application in Resisting Extreme Weather Risks for the Prefabricated Construction Supply Chain: Prototype Exploration and Assessment

Smart Contract Application in Resisting Extreme Weather Risks for the Prefabricated Construction Supply Chain: Prototype Exploration and Assessment

Correction: Wong, M.C.S.; Ho, H.M. A Framework for Integrating Extreme Weather Risk, Probability of Default, and Loss Given Default for Residential Mortgage Loans. Sustainability 2023, 15, 11808

The authors would like to make the following corrections about the published paper [...]

Predictive modeling of severe weather impact on individuals and populations using Machine Learning

In this work, Machine Learning (ML) techniques are used to develop tools capable of accurately predicting the impact of severe weather events. We use readily accessible predictors, including daily meteorological data, basic demographics, geographic and terrain features, along with the number of daily meteorological incidents reported to the emergency services. The model was built using disaggregated data from the 947 municipalities from the region of Catalonia between January 1, 2015 and June 30, 2021. Catalonia’s region is situated in the northeastern part of Spain along the Mediterranean Basin, and frequently experiences storms with strong winds and intense rainfall. In 2020, such weather events resulted in 64 injuries and damages amounting to approximately 70 million USD. The ML-based model presented in this study shows a predictive capacity for extreme weather risk superior to that of the daily meteorological warning system of the Meteorological Service of Catalonia. In addition, the model has been used to estimate how urbanization modifies the extreme weather impact. Given the expected increase in the intensity, frequency, and duration of extreme weather events in the context of global warming, the methodology presented in this work could be helpful in developing tools to assist emergency service managers and policy makers in making rapid and effective decisions.

Extreme Weather Risk to Military Operations in a Changing Climate

History demonstrates harsh weather conditions introduce uncertainty and risk into warfare. James L Regens examines the implications of extreme weather risk in a changing climate for military operations within and across the six domains of warfare and, therefore, for strategy. Because it is imperative that operations and campaigns are feasible meteorologically, reliably forecasting the frequency and intensity of extreme weather is one of the most important challenges posed by climate change for the US and allied militaries. That knowledge, combined with understanding extreme weather’s impacts on human performance, equipment and installations, is essential for conducting operations and informing strategy.◼

Research on Extreme Weather Risk Assessment Model Based on Entropy Weight Method and Cluster Analysis Method

Research on Extreme Weather Risk Assessment Model Based on Entropy Weight Method and Cluster Analysis Method

How do firms perceive and react to extreme weather risk in their supply bases?

Extreme weather events occur frequently and severely as a part of worldwide climate change. Such extreme weather events are macro supply chain risks that pose significant threats to the global supply chain. However, the current literature has provided a limited understanding of how this supply chain risk is changing firms' perception and behavior in global sourcing. In this study, we examined how a supply base's extreme weather risk affects a buying firm's trade risk perception and the sourcing value. We conducted a two-way fixed-effect regression analysis with a panel data set of U.S.-listed manufacturers and their supply bases. Our findings suggest that when the supply base has a higher level of extreme weather risk, the buying firm perceives a higher trade risk and a lower sourcing value with this supply base. We also found that a higher degree of flexibility, indicated by supply flexibility, production flexibility, and inventory slack, can mitigate the buying firm's concern caused by the supply base's extreme weather risk. Our study contributes to the literature on extreme weather, flexibility, and supply chain risk management. We also provide suggestions for supply chain practitioners and policymakers.

A Framework for Integrating Extreme Weather Risk, Probability of Default, and Loss Given Default for Residential Mortgage Loans

This paper considers a hypothetical case in which a bank wants to build a routine climate stress test exercise on residential mortgage loans. The bank has regularly updated the probability of default (PD) and loss given default (LGD) on each residential mortgage loan under the internal-rating-based (IRB) approach of Basel II/III. Additionally, the bank estimates the stressed PD and stressed LGD associated with a predetermined extreme weather event. Using simulation techniques, this paper shows that the loss of the bank’s residential mortgage portfolio can reach a median of around 36% of the portfolio value. This remarkable loss comes from the effects of default correlation and property damage. Banks should pay more attention to such impacts of extreme weather events.

A spatial analysis of power-dependent medical equipment and extreme weather risk in the southeastern United States

Extreme weather events endanger critical health infrastructure, and many individuals rely on infrastructure to meet their basic needs, such as in-home heating, water, and electric-powered medical devices. The purpose of this study is to identify spatially explicit at-risk populations for power outages due to these extreme weather events. To accomplish this, we used the HHS emPOWER Emergency Planning Dataset, which was created to help public health authorities plan for and address the needs of communities. Using Geographic Information Systems (GIS), we overlay emPOWER data with the frequency of disaster declarations for extreme weather events, including wildfires, hurricanes, tornadoes, and ice storms. Through our analysis, we identified vulnerable areas for high rates of disasters and electricity-dependent durable medical equipment (DME) to be located in communities along the coast. We also found a higher concentration of DME in rural areas compared to urban areas. In addition, we found least privileged economic locations are disproportionately vulnerable to power outages in the southeastern United States due to their high concentration of DME individuals. Results will inform public health officials where to target interventions to ensure continuity of care for vulnerable populations during power outages at the community level.

Risk Perception and Preparation for Storm Surge Flooding: A Virtual Workshop with Visualization and Stakeholder Interaction

Abstract Many factors shape public perceptions of extreme weather risk; understanding these factors is important to encourage preparedness. This article describes a novel workshop designed to encourage individual and community decision-making about predicted storm surge flooding. Over 160 U.S. college students participated in this 4-h experience. Distinctive features included 1) two kinds of visualizations, standard weather forecasting graphics versus 3D computer graphics visualization; 2) narrative about a fictitious storm, role-play, and guided discussion of participants’ concerns; and 3) use of an “ethical matrix,” a collective decision-making tool that elicits diverse perspectives based on the lived experiences of diverse stakeholders. Participants experienced a narrative about a hurricane with potential for devastating storm surge flooding on a fictitious coastal college campus. They answered survey questions before, at key points during, and after the narrative, interspersed with forecasts leading to predicted storm landfall. During facilitated breakout groups, participants role-played characters and filled out an ethical matrix. Discussing the matrix encouraged consideration of circumstances impacting evacuation decisions. Participants’ comments suggest several components may have influenced perceptions of personal risk, risks to others, the importance of monitoring weather, and preparing for emergencies. Surprisingly, no differences between the standard forecast graphics versus the immersive, hyperlocal visualizations were detected. Overall, participants’ comments indicate the workshop increased appreciation of others’ evacuation and preparation challenges.

Impact of risk-taking on enterprise value under extreme temperature: From the perspectives of external and internal governance

Climate change leads to the frequent occurrence of extreme weather, which has become an important risk factor for enterprises. Based on panel data of Chinese A-share manufacturing enterprises from 2000 to 2019, this study examines the impact and influence mechanism of extreme temperature on risk-taking and enterprise value. The results reveal that risk-taking level increases under extreme temperatures but does not enhance enterprise value. Further analysis shows that the endowment effect of risk-taking level plays an important role: when the existing risk-taking is below the optimal level, the extreme temperature increases the enterprises’ risk-taking, which then increases the market value; otherwise, the extreme temperature reduces risk-taking and has a negative impact on enterprise value. The mechanism test reveals that external supervision by market participants aggravates the negative market reaction to an increase in risk-taking under extreme temperatures and reduces enterprise value, whereas good internal governance of enterprises enhances their market value. This study expands the literature on the effect of extreme weather on the risk behavior of listed companies, provides micro-empirical evidence that extreme weather risks transmit to the capital market and impair enterprise value, and serves as an empirical reference for enterprise climate risk management.

EDITORIAL — CASCADE-NET – Increasing Civil Society’s Capacity to Deal with Changing Extreme Weather Risk: Negotiating Dichotomies in Theory and Practice

EDITORIAL — CASCADE-NET – Increasing Civil Society’s Capacity to Deal with Changing Extreme Weather Risk: Negotiating Dichotomies in Theory and Practice