Extreme Disasters Research Articles

Technological innovation for urban water and rainfall extreme disaster risk management

En este trabajo se presenta una aplicación práctica del uso de las tecnologías de la información y comunicación (TIC) en la gestión de riesgos de desastres debidos a precipitaciones extremas en regiones urbanas. El objetivo es plantear, a partir del caso de estudio del deslizamiento del cerro del Chiquihuite, una de las formas en las que se puede aprovechar la información del Observatorio Hidrológico del Instituto de Ingeniería de la Universidad Nacional Autónoma de México (OH-IIUNAM) para mejorar la gestión de desastres generadas por precipitaciones extremas. En el caso de estudio, se utilizan los datos del OH-IIUNAM para el análisis temporal y espacial de las tormentas que se presentaron en la Zona Metropolitana del Valle de México entre el 1 y 9 de septiembre de 2021; también se considera el sismo que ocurrió el 7 de septiembre del mismo año. La conjunción de ambos fenómenos produjo un deslizamiento del cerro el 10 de septiembre de 2021, que provocó grandes daños en las viviendas y pérdidas humanas. En el análisis temporal realizado se encontró que las tormentas más importantes ocurrieron entre los días 1 y 8 de septiembre, destacando las tormentas de los días 6 y 7 en intensidad y precipitación acumulada. En cuanto al análisis espacial, se utilizó la interpolación IDW (interpolación de distancia inversa ponderada) para estimar la precipitación en toda la Ciudad de México (CDMX) en el periodo del 1 al 9 de septiembre, encontrándose que la zona del cerro del Chiquihuite fue una de las áreas donde cayó mayor cantidad de agua de lluvia.

A scenario study on disaster preparedness behaviours

Abstract Europe has faced several crises over recent years, including the COVID-19 pandemic and the war in Ukraine. Additionally, the climate crisis has led to an increase in the occurrence of extreme weather events and natural disasters. Getting citizens on board when it comes to being prepared for the changing risk landscape in the EU is essential in building societal resilience. The importance of the population’s preparedness to deal with disasters has also been underlined by the Commission’s identification of the Union Disaster Resilience Goals (2023/C 56/01). One of these goals focuses on increasing risk awareness and the adoption of risk prevention and preparedness measures among the population. Research has shown that simply advising people of potential risks in their region is not necessarily sufficient to motivate them to prepare. Yet, preparation is key to an adequate responses in emergency situations and improving preparedness may thus be a powerful tool to support the population’s ability to respond in times of crisis. The EC JRC’s Competence Centre on Behavioural Insights will present a study conducted in five member states (N = ∼1000 per country), which investigates the effect of envisioning a disaster situation, and therefore explicitly eliciting personal affectedness, on individuals’ self-reported preparedness intentions and preparedness behaviours. The study uses multiple measures for intentions and preparedness behaviours, including click-through to external information and an incentivised preparedness game.

Resilience‐oriented load restoration method for power‐gas‐water systems considering public safety impact

AbstractFocusing solely on enhancing the resilience of power systems at a systemic level would lead to a significant underestimation of the actual impact of extreme disasters. Equally vital is the assurance of livelihood security amidst such extreme conditions, which is crucial for the development of a truly resilient power system. Hence, this paper attempts to incorporate quantifiable metrics assessing public safety impacts into resilience enhancement works, thereby guiding the precise allocation of funds. Considering that the residents' intuitive feelings are the most direct reflection of the severity of the disaster, this paper employs the modified prospect theory to formulate functions representing residents' psychological risk perception and risk‐taking willingness to tolerate risks during disruptions in power, gas, and water supplies. Meanwhile, in order to accurately calculate the energy loss duration for each residential customer, a resilience enhancement method for post‐disaster collaborative dispatch of electricity‐gas‐water systems is proposed. With the objective of minimizing the public safety and economic impact of disasters, the optimal multi‐source collaborative emergency restoration strategy is developed. The significant necessity and efficiency of the proposed strategy are verified with exhaustive case studies. Numerical results evince the resilience enhancement by considering the livelihood security in the post‐disaster restoration stage.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Logistics planning for direct temporary disaster housing assistance under demand uncertainty

In this paper, we propose and study a framework for disaster housing logistics planning under demand uncertainty. Specifically, we utilize a two-stage chance-constrained stochastic programming model to achieve the balance between logistics operational cost and demand fulfillment especially towards extreme disaster scenarios. To do so, we incorporate two operational modalities, one for the ordinary modality and the other for the emergency modality, and the emergency modality is only allowed to be activated for a certain percentage of scenarios that is specified by the decision maker among all scenarios. The set of scenarios is generated according to a spatial regression model for characterizing the disaster housing demand based on a selected number of independent variables related to both the hazard and socioeconomic factors, which is trained offline from historical data. We conduct a numerical experiment based on Hurricane Ian, and our numerical results show the effectiveness of the proposed approach compared to some standard benchmark approaches. We also highlight the managerial insights for disaster housing logistics planning gained through this numerical experiment.

Pre-disaster allocation and post-disaster dispatch strategies of power emergency resources for resilience enhancement of distribution networks

With the frequent occurrence of large-scale power outages caused by extreme disasters, coordinated dispatch of power emergency resources (PERs) and repair crews (RCs) has gradually become an essential method to enhance the resilience of distribution networks (DNs). Previous works solve this coordinated dispatch problem in a risk-neutral approach with the assumption that the supply of power repair materials (PRMs) is still fully available after the disaster. However, growing evidence indicates that certain extreme disasters may lead to limited availability of PERs in the DN, rendering risk-neutral decision making impractical. To fill this gap, this paper proposes a resilience-oriented PERs pre-disaster allocation and post-disaster dispatch strategy in a risk-averse manner. In the pre-disaster prevention process, a two-stage distributionally robust optimization (DRO) model based on mean-CVaR is developed, considering the uncertainties of both system damage and PERs demands, along with the decision maker's preference for risk. In the post-disaster restoration process, a collaborative spatio-temporal dispatching model for PERs and RCs with the goal of minimizing outage losses is established. To tackle the computational challenges associated with PRMs allocation based on DRO, robust counterpart transformation and dual theory are utilized. Case studies are conducted using a real world modified 118-bus distribution network. In comparative analysis, five control groups are established to validate the necessity to consider the limited supply of PRMs and the risk-averse attitude of decision makers in enhancing DNs resilience.

Prediction of the impact of sea level rise in coastal areas where the tide embankments have been constructed

After the Great East Japan Earthquake in 2011, the Japanese government reconstructed coastal protection facilities to mitigate extreme disasters and coastal erosion. However, the substantial compression of coastal zones poses a challenge in coping with sea level rise, while maintaining a sustainable coastal ecosystem. To clarify the impact of environmental factors and sea level rise on coastal ecosystems in highly developed regions, this study integrated field surveys, geo-detector analysis, and coastal inundation models into the research framework. It explored the relationship between environmental factors and vegetation patterns and predicted the inundation areas and vegetation survivorship of 12 transects in the study area from 2030 to 2150. The results indicated that the distance from the tide embankment (DTE) and elevation (DEM) were the main driving factors, and the combination of the potential of hydrogen (pH) and DTE had a major impact on vegetation coverage. Notably, the distribution of vegetation was positively correlated with the width of the coastal zone, and when the width was less than 60 m, vegetation could not survive. With sea level rise, almost no vegetation will survive until 2150. Appropriate coastal width and height are conducive to maintaining the sustainability of coastal ecosystems.

Climate stress testing for mortgage default probability

Extreme natural disasters, such as tropical cyclones, have a low probability of materialising, but a high social and economic impact, including spillover to financial institutions. We propose a framework for performing a climate-stress testing exercise for the default probability of mortgage loans. We estimated a dynamic credit scoring model based on survival analysis with a relative damage index built using the wind speed of tropical cyclones. We considered scenarios involving tropical cyclone wind speeds with different return periods. We analyse a portfolio of approximately 190,000 mortgage loans granted in Louisiana, one of the US states most affected by tropical cyclones. Our findings suggest that coastline areas are most exposed to severe damage from tropical cyclones. If the geographical area is exposed to an event with a very large return period of 1-in-1,000 years, the probability of default increases by approximately nine percentage points compared to a baseline scenario in the absence of tropical cyclones. However, this finding was mitigated by the insurance coverage. This percentage increases to almost 20 percent in the absence of insurance coverage.

Optimal allocation of distributed energy resources to cater the stochastic E-vehicle loading and natural disruption in low voltage distribution grid

The everyday extreme uncertainties become the new normal for our world. Critical infrastructures like electrical power grid and transportation systems are in dire need of adaptability to dynamic changes. Moreover, stringent policies and strategies towards zero carbon emission require the heavy influx of renewable energy sources (RES) and adoption of electric transportation systems. In addition, the world has seen an increased frequency of extreme natural disasters. These events adversely impact the electrical grid, specifically the less hardened distribution grid. Hence, a resilient electrical network is the demand of the future to fulfill critical loads and charging of emergency electrical vehicles (EV). Therefore, this paper proposes a two-dimensional methodology in planning and operational phase for a resilient electric distribution grid. Initially stochastic modelling of EV load has been performed duly considering the geographical feature and commute pattern to form probability distribution functions. Thenceforth, the impact assessment of extreme natural events like earthquakes using damage state classification has been done to model the impact on distribution grid. The efficacy of the proposed methodology has been tested by simulating an urban Indian distribution grid with mapped EV on DigSILENT PowerFactory integrated with supervised learning tools on Python. Subsequently 24-h load profile before event and after event have been compared to analyze the impact.

Power supply disruptions deter electric vehicle adoption in cities in China

Electrification plays a crucial role in deep decarbonization. However, electrification and power infrastructure can cause mutual challenges. We use nationwide power outage and electric vehicle adoption data in China to provide empirical evidence on how power infrastructure failures can deter electrification. We find that when the number of power outages per district increases by 1 in a given month, the number of new electric vehicles adopted per month decreases by 0.99%. A doubling of power outages in one year on average across the nation can create a depressed adoption rate for up to a decade, implying a decline of more than $ 31.3 million per year in carbon reduction benefits from electric vehicle adoptions. This paper adds to the policy discussion of the costs of increased power outages due to extreme weather and natural disasters, and the urgency for policy to address this issue to facilitate wide adoption of electrification.

Representation of Climate Change in the Documentary Film Ano Bulan Balong (Semiotic Analysis of Roland Barthes)

Climate change is a condition characterized by changes in world climate patterns which result in erratic weather phenomena. Climate change occurs due to changes in climate variables, such as air temperature and rainfall, which occur continuously. In this research, film is the subject, film is a mass communication tool that is packaged with characters, plot and messages in it, provides information to the audience through the storyline that is conveyed and has a strong influence on issues that are developing in society, the unit of analysis of this research contains "Semiotic Analysis of Roland Barthes' Representation of Climate Change in the Documentary Film Ano Bulan Balong". The aim of this research is to determine the representation of climate change in the film Ano Bulan Balong based on Roland Barthes' stereotypes. This research uses Roland Barthes' semiotic theory, where the researcher looks for the meaning of Denotation, Connotation and Myth in several scenes in the film Ano Bulan Balong. . The results of research on the documentary film Ano Bulan Balong can be linked to environmental issues, namely climate change. Researchers group and categorize messages to convey meaning in the film Ano Bulan Balong, including; environmental message, Through semiotic analysis, Roland Barthes in this film shows that the problem currently being faced by Sumbawa is unrest triggered by environmental problems which are a factor in climate change, forest lands full of trees are starting to disappear, replaced by corn plants. Critical trees resulting from deforestation will cause extreme natural disasters.

Comprehensive evaluation and optimal management of extreme disaster risk in Chinese urban agglomerations by integrating resilience risk elements and set pair analysis

This study comprehensively assesses the extreme disaster risk faced by Chinese urban agglomerations and optimizes disaster risk management strategies to enhance their disaster resilience and sustainable development. The focus is specifically on droughts, floods, and earthquakes. The integration of resilience risk elements and set pair analysis (SPA) is employed to obtain risk assessment results for different urban agglomerations, including hazard, exposure, vulnerability, and disaster prevention and mitigation capabilities. The study explores the contributions of important indicators, such as exposure, vulnerability, and disaster prevention and mitigation capabilities, in disaster risk assessment, as well as their performance in various risk-prone urban areas. Comparative analysis of the assessment results for different urban agglomerations under various disaster types reveals varying levels of risk in droughts, floods, and earthquakes across different regions. Some areas exhibit higher hazard, exposure, or vulnerability levels, necessitating strengthened disaster prevention and mitigation measures. Regions with high exposure, low vulnerability, and strong disaster prevention and mitigation capabilities may face lower earthquake disaster risk, while regions with low exposure, high vulnerability, and weak disaster prevention and mitigation capabilities may face higher earthquake disaster risk. Moreover, the Comprehensive SPA model and Interval-valued SPA model outperform traditional SPA methods in assessing the existing flood risk level in Chinese urban agglomerations. The integrated model plays a significant role in predicting earthquake disasters, demonstrating the best predictive performance. This study provides essential reference data for urban disaster prevention and mitigation efforts, aiming to enhance urban resilience and disaster resilience capabilities.

Exploring the influence of financial development on climate physical risk: insights from China

ABSTRACT Introduction: Currently, extreme weather events and natural disasters are occurring frequently, and countries around the world are facing a variety of physical climate risks. More and more countries are attempting to embed financial tools into the process of climate risk governance. However, the financial strength of developing countries is insufficient to enhance regional resilience to physical climate risks. Moreover, existing literature mainly focuses on the impact of financial development on greenhouse gas emissions, neglecting its crucial role in adaptation. Discussion: The present study enriches people's understanding at the national level of whether and how financial development is affected by physical climate risks. Additionally, it provides significant reference for financial reforms and physical climate risk governance in other countries and regions from the perspectives of inclusive finance and green finance, utilizing econometric methods. Conclusion: (1) Financial development effectively facilitate the governance of physical climate risk. (2) Green finance and inclusive finance intensify the negative effect of financial development on physical climate risk. (3) Financial development can governance physical climate risk by bolstering technological innovation. (4) Financial development significantly promotes physical climate risk governance only in regions with large populations, resource abundance, well-developed insurance sectors, and strong governmental intervention.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Integrated Planning and Operation Dispatching of Source–Grid–Load–Storage in a New Power System: A Coupled Socio–Cyber–Physical Perspective

The coupling between modern electric power physical and cyber systems is deepening. An increasing number of users are gradually participating in power operation and control, engaging in bidirectional interactions with the grid. The evolving new power system is transforming into a highly intelligent socio–cyber–physical system, featuring increasingly intricate and expansive architectures. Demands for stable system operation are becoming more specific and rigorous. The new power system confronts significant challenges in areas like planning, dispatching, and operational maintenance. Hence, this paper aims to comprehensively explore potential synergies among various power system components from multiple viewpoints. It analyzes numerous core elements and key technologies to fully unlock the efficiency of this coupling. Our objective is to establish a solid theoretical foundation and practical strategies for the precise implementation of integrated planning and operation dispatching of source–grid–load–storage systems. Based on this, the paper first delves into the theoretical concepts of source, grid, load, and storage, comprehensively exploring new developments and emerging changes in each domain within the new power system context. Secondly, it summarizes pivotal technologies such as data acquisition, collaborative planning, and security measures, while presenting reasonable prospects for their future advancement. Finally, the paper extensively discusses the immense value and potential applications of the integrated planning and operation dispatching concept in source–grid–load–storage systems. This includes its assistance in regards to large-scale engineering projects such as extreme disaster management, facilitating green energy development in desertification regions, and promoting the construction of zero-carbon parks.

Evolution and influencing factors of coastal resilience in the East China Sea

The coastal zone serves as a crucial hub for economic and population concentration. Amid the context of high-intensity development and global climate change, uncertain risks from diverse sources—including extreme weather events (i.e., high temperatures, typhoons, and excessive precipitation), natural disasters (i.e., floods, tsunamis, landslides, and mudslides), and societal disruptions (i.e., economic crises and viral diseases)—are escalating rapidly. Enhancing coastal resilience to minimize these risk impacts is progressively becoming a mandatory requirement for the sustainable development of coastal zones. However, existing research primarily focuses on distinct disasters, the ecological environment, or specific socio-economic aspects, thereby lacking a comprehensive theoretical framework and thorough analysis of the factors influencing coastal resilience. Here, we construct a theoretical framework centered on the unique traits and processes of coastal resilience, analyze the spatiotemporal evolution characteristics of coastal resilience from a grid and administrative division standpoint, and utilize geographic detectors to determine the factors influencing coastal resilience while considering the modifiable areal unit problem (MAUP). Our findings indicate that: (1) Coastal resilience in the East China Sea (ECS) initially declined but then increased, transitioning from a lower to a medium level. Barring the pressure index, other dimensional indices had an upward trend; (2) Continuous improvements were observed in coastal resilience across different land uses. Forests, waters, and oceans demonstrated higher resilience levels than other lands, with construction land resilience developing swiftly. The effect of changes in land use types on coastal resilience showed a rapid initial increase and subsequent decrease; (3) The change pattern of coastal resilience in the ECS is mainly unchanged and slightly increased. Areas with the most drastic changes were concentrated in Shanghai, northern Zhejiang, and central Fujian, with the main change patterns continuously rising; (4) The primary factors influencing coastal resilience in the ECS included gross domestic product and infrastructure construction level. Advanced industrial structure, technological and educational prowess, and effective government management are important determinants of coastal resilience development. The significance of human factors continues to grow. Our findings offer valuable insights for optimizing national spatial planning of coastal zones, responding to internal and external impacts, achieving resilient coastal zones, and implementing a comprehensive sustainable management approach.

A post-disaster load supply restoration model for urban integrated energy systems based on multi-energy coordination

Urban integrated energy systems (UIES) have emerged as a promising solution to address the challenges of urban energy supply and consumption. However, UIES are vulnerable to extreme disasters, such as earthquakes, hurricanes, or floods, which can disrupt the energy infrastructure and lead to power outages and energy shortages. This study introduces a novel approach aimed at enhancing the resilience and load recovery capabilities of UIES in the face of extreme events. The proposed approach encompasses several key innovations, including the comprehensive coordination of local energy sources, prioritized restoration of critical loads, and a node-based modeling approach for gas and heat networks. Firstly, by fully integrating electricity, gas, and heat sources, the overall resilience of UIES is significantly improved, ensuring higher levels of load recovery even in the event of energy supply shortages. Secondly, the critical load recovery is prioritized by load classification, and the weights set can fully consider the connection status and load level of nodes to ensure priority provisioning of important node loads. In addition, the node-based modeling approach allows for accurate consideration of the flow of gas and heat in the pipe during modeling of the pipe energy storage. Finally, the case study section conducts simulation experiments with the UIES E33-G20-H6 test system to verify the effectiveness of the proposed approach and to demonstrate its potential in enhancing the post-disaster load recovery capability of UIES.

Risk Assessment and Analysis of New Power System Operation Under Extreme Disasters

Abstract In recent years, new power systems have been developing continuously, and the operational risk assessment of new power systems under extreme disaster conditions has attracted much attention. This paper uses Latin hypercube sampling technology to establish a power system failure model under extreme disasters and proposes a new power system risk assessment method that applies the power system power flow model to establish a comprehensive system risk index. In addition, taking the power grid of a certain province in eastern China as an example, a case analysis is conducted on the IEEE39 node system to verify the effectiveness of the method.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events, causing surveillance and energy loss. This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters. Initially, the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes. Subsequently, it delves into communication and data processing mechanisms, specifically focusing on central data processing (CDP), communication routers (CRs), and phasor measurement units (PMUs), to re-establish an equipment recovery model based on these data transmission methodologies. Furthermore, it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution. The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system. The findings suggest that the proposed branch- and-bound algorithm significantly augments the observational capabilities of a power system with limited resources, thereby bolstering its stability and emergency response mechanisms.

Dynamic restoration electricity price optimization method to enhance the resilience of distribution networks with multiple‐microgrids

AbstractResilience is one of the main features of smart distribution networks, and a microgrid (MG) access to the distribution network provides an effective way to improve resilience. MG and distribution network belong to different interests, so it is necessary that MGs and flexible resources are actively guided through price leverage. In this way, MGs take part in the post‐disaster restoration and enhance its resilience. Firstly, this paper proposes a dynamic restoration electricity price response mechanism after extreme disasters and constructs a power response model for loads and electric vehicles within the MGs. Secondly, the optimal scheduling model of the distribution network with multiple‐microgrids (MMG) is proposed to improve the restoration rate of critical loads (RRCL). Single microgrid achieves the largest microgrid revenue and restoration contribution, and MMG uses the power headroom index to optimize the dynamic restoration electricity price to achieve the smallest power purchase cost of distribution network. Finally, the optimal scheduling method for resilience enhancement of distribution networks with MMG considering dynamic restoration electricity price response mechanism is validated by dual microgrid access to an IEEE 33‐node distribution system. The simulation results show that the proposed optimization method effectively improves the RRCL of distribution network.