Natural Hazards Review

This paper reviews the state of the art in using benefit–cost analysis (BCA) to inform earthquake risk reduction decisions by building owners and policymakers. The goal is to provide a roadmap for the application and future development of BCA methods and tools for earthquake risk reduction. Our review covers three earthquake risk reduction measures: adopting up-to-date building codes for new construction, designing new buildings to exceed code requirements, and retrofitting deficient existing buildings. We highlight the factors that influence the cost-effectiveness of building design and retrofit, as well as tactics for increasing the cost-effectiveness of risk reduction strategies. We also present BCA results, methods, and data sources used in the literature to help researchers and practitioners design and conduct a reliable and robust BCA study. In the process, we develop a set of opportunities and challenges for applying BCA to new areas of research, as well as key gaps and limitations in current BCA approaches, including further investigation of above-code design, incorporation of code implementation and enforcement into BCA, quantification of environmental benefits of seismic retrofits, and optimization of seismic retrofits with energy upgrades. Overall, our review provides practical guidance and useful insights into BCA with the goal of increasing the earthquake resilience and economic efficiency of buildings in the United States.

China’s rural regions grapple with considerable challenges concerning infrastructure and disaster prevention capabilities. Addressing these will be pivotal in the nation’s future endeavors toward disaster mitigation and development. This research categorizes China’s comprehensive rural disaster reduction policies (RCDRPs) spanning from 1949 to 2021 into distinct evolutionary phases, using quantitative characteristics. Utilizing sociotechnical configuration analysis, we visually chart the progression of policy focal points and draw preliminary lessons from them. Key insights include: (1) the RCDRPs from 1949 to 2021 can be segregated into three distinct phases; (2) there’s been a noticeable rise in the RCDRPs jointly promulgated by multiple governmental departments, and the interconnectedness with policymaker networks has thickened, indicating increased synergy amongst governmental bodies; and (3) the primary thrust of RCDRPs has transitioned from postdisaster recuperation to predisaster safeguarding and from a primary emphasis on disaster management to a broader approach to holistic disaster reduction. Drawing upon these insights, this study employs the crisis management framework—encompassing prevention, preparation, response, and recovery—to shed light on potential directions of future disaster prevention and mitigation policies in China. The aim is to align these with the overarching vision of urban–rural integrated development, thereby advancing emergency governance modernization in China’s countryside.

Abstract

There are a series of gentle Piedmont slopes with a unique special stratigraphic structure in Ningzhen area of China, which manifest as clay on the surface and the underlying being primarily gravel, exhibiting a binary structure similar to fluvial facies. The number of landslides has increased significantly on gentle Piedmont slopes in this area in recent years due to an increase in extreme rainfall events. Medium to large creeping landslides are dominant, and the sliding surface is between the clay and gravel layers. The main reason is the formation of a water cushion effect due to the unique stratigraphic structure. In this paper, based on previous research, we conducted a model test to verify the influence of the water cushion effect on gentle Piedmont slopes. Meanwhile, we derived a stability equation based on the static equilibrium for gentle Piedmont slopes under three conditions and proposed two methods to derive an early warning value of the pressure head. The rationality of the early warning value was verified by comparing it with measurements. The research results provide important theoretical and technical guidance for early warning and prediction of landslides on gentle Piedmont slopes.

Practical Applications

The stability equation of landslides is crucial in adequately mitigating hazards, which can provide guidelines for landslide prone areas to avoid hazards in the future. Therefore, landslide warning and prediction is of the utmost significance to ensure the safety of human life, mitigate the negative impacts on the economy, and prevent landslide hazards in gentle Piedmont slopes. Government agencies, policymakers, local authorities, and urban planners can use stability equations and early warning values to plan effective landslide monitoring and prevention measures, and make wise decisions on land-use zoning and development. By identifying areas with high landslide susceptibility through early warning values, constructing buildings and engaging in human activities in hazardous zones can be avoided, reducing the risk of damage and loss during potential landslide events. In addition, landslide warning and prediction play a crucial role in disaster risk management of landslides in gentle Piedmont slopes. Authorities can use these warning values to prioritize areas for early measures. Residents can benefit from the results by being made aware of landslide-prone areas on their property. Consequently, this study is a scientific basis and technical support for the early warning and prediction of landslides on gentle Piedmont slopes in the Ningzhen area, China.

Abstract

The impact of earthquake-induced landslides is a global concern, particularly in the Himalayas and its nearby region, leading to substantial socioeconomic losses. High seismicity along with intense rainfall and steep slopes make the Himalayan region more vulnerable to these landslides. Existing research on regional scale analysis has mainly focused on studying landslides triggered by rainfall or earthquakes separately, with limited studies considering the combined effects of both. The objective of this research is to develop a GIS-based data integration approach for mapping shallow translational landslide susceptibility at regional scale, considering the combined effect of earthquakes and rainfall. The methodology utilizes a combination of the pseudostatic approach and hydrological models, taking advantage of publicly available topographic, seismic, geological, and remote sensing data and requiring basic geotechnical properties obtained from conventional tests to determine factor of safety (FoS). The approach has been implemented along an important and strategic highway corridor in the Indian state of Sikkim, located in the Eastern Himalayan region. The FoS distribution maps developed using the proposed approach have been validated against the reported landslides triggered in the study area due to the 2011 moment magnitude (${\mathrm{M}}_{\mathrm{w}}$) 6.9 Sikkim earthquake. The proposed approach performed well in terms of prediction, with a success rate of 74.75%. Finally, the FoS distribution maps are generated for different scenarios. These maps provide significant insights that can be utilized by local governments, transportation agencies, and policymakers to proactively identify high-risk zones and develop early warning systems to mitigate the risk.

Practical Applications

The Himalayan region has experienced a surge in tourist activity and infrastructure development in the past decade. However, the steep slopes, coupled with high seismic activity and heavy monsoonal rainfall, make it naturally prone to landslides. Landslides triggered by earthquakes can further worsen the effects of the seismic activity by damaging infrastructure, including roads, leading to challenges in rescue operations, disruptions in the supply of essential commodities, and inconvenience to travelers. The occurrence of earthquake during the monsoonal season can significantly increase the likelihood of landslides. To address this issue, a methodology has been proposed to identify areas prone to landslides caused by combined action of rainfall and earthquakes, focusing on a strategic highway in the Eastern Himalayas. By considering different scenarios of rainfall and earthquakes, various maps have been created which highlights the high-risk zones. These maps serve as valuable guides, indicating where it’s unsafe to construct infrastructure and which highway sections are more susceptible to landslides. Equipped with this information, policy makers can construct more robust roads, plan alternative routes, ensure better connectivity, and provide early warnings to minimize the disruptive effects of landslides on people and infrastructure.

As glaciers recede in response to global climate change, significant growth of glacial lakes is being witnessed in the Himalayan region. Owing to their high elevations, rapid growth, and their occurrence behind loosely consolidated glacial moraine deposits, these lakes pose a significant threat of glacial lake outburst floods (GLOFs). This study focused on tracing the glacial lake evolution in the Shyok catchment of the Upper Indus Basin over a 30-year period (1992–2022) and carrying out a GLOF hazard assessment. Multitemporal medium- and high-resolution satellite imagery was used to inventorize, detect spatiotemporal change in, and classify glacial lakes with areas more than $0.001{\mathrm{km}}^2$, as well as to extract various lake characteristics. The GLOF hazard assessment was carried out using a multicriteria approach based on the analytical hierarchy process (AHP). We found significant increases in the total number, from 143 (1992) to 240 (2022), and combined area, from $8.45\pm 0.02{\mathrm{km}}^2$ (1992) to $13.26\pm 0.04{\mathrm{km}}^2$ (2022), of glacial lakes. Out of 63 glacial lakes with areas $>0.05{\mathrm{km}}^2$, four lakes were classified as very-high-hazard lakes, with GLOF susceptibility indices above 0.75, and 12 were classified as high-hazard lakes, with GLOF susceptibilities indices of 0.66–0.75. These hazardous lakes have had a high growth rate owing to increasing temperature trends in the region. Our findings also suggest that the GLOF hazard of these lakes is going to continue increasing as a result of lake expansion and enhanced exposure to landslides and avalanches. The identification of potentially dangerous glacial lakes and assessing their GLOF hazard will help prioritize future research and develop better mitigation strategies for potential future disasters in the area.

Disaster recovery depends heavily on the support of social networks and the resources they can generate. Yet such support is difficult to measure and assess. This paper reviews existing quantitative approaches to measure social capital within a disaster context. The article addresses (1) how is social capital conceptualized in the disaster literature? and (2) what social capital measures have been used based on existing conceptual frameworks (e.g., bonding, bridging, and linking)? We review how social capital has been defined and what properties of social capital make it important in the disaster planning contexts. Then we explore and assess existing approaches used to measure social capital while offering suggestions for potential improvements. These potential improvements to social capital indices will capture and measure social capital in a more comprehensive way and have a more solid theoretical basis based on the social capital and disaster resilience literature. Each type of social capital is expected to have different consequences and effects on disaster planning processes and phases; therefore, it is important to distinguish and measure social capital variables in a holistic way for policymakers and practitioners to make use of social capital information in preparing for disasters.

Individuals often underestimate disaster risk and overestimate their own preparedness, even for disasters they have experienced. Considering the varied effects of prior disaster exposure on preparedness, the psychological trauma literature can inform models of preparedness among hazard survivors. Posttraumatic growth (PTG), or positive changes after a stressor, could explain how some, but not all, disaster survivors come to value and engage in preparedness. University students and employees ($N=\mathrm{1,113}$) in a tornado-prone region of the United States were surveyed from 2015 to 2016 about preparedness for tornadoes, structural fires, and large-scale disease outbreaks. For each hazard type, they reported perceived likelihood, self-efficacy responding to the hazard, how much they valued preparedness, and their prior exposure to the hazard. Tornado survivors ($N=661$) were further asked how many tornadoes they had experienced, the severity of the impact (e.g., injured, lost electricity), and their perceived PTG from tornadoes. Comparing each variable by hazard type, perceived likelihood, and prior exposure were greater for tornadoes compared with disease and fire. For tornado survivors, PTG mediated the effect of tornado severity on both preparedness and valuing preparedness. In conclusion, PTG is a process by which disaster experiences result in increased disaster preparedness. Simply experiencing a disaster, even a severe one, does not necessarily instill disaster preparedness as a value; however, positive change in recovery from a severe disaster involves caring more about preparedness and becoming increasingly prepared. Enhanced disaster preparedness is not an inevitable outcome of disaster exposure, but it is a potential outcome of psychological recovery from disaster trauma. Integrating the present findings, the preparedness literature, and the psychological trauma literature, we propose an integrated individual-level disaster resilience cycle model.

Abstract

This study examines patterns of interorganizational collaboration for hazard mitigation in metropolitan regions. Metropolitan regions whose populations reside in vulnerable places may experience extreme events. Facilitation of collective actions among organizations is critical for building resilient urban communities. Although the literature highlights the importance of interorganizational collaboration in disaster management, patterns of interorganizational collaboration for hazard mitigation in urban settings has been underexamined. Building on an institutional collective action framework, this study argues that hazard mitigation efforts tend to be localized. We tested this hypothesis using survey data collected in the Busan and Ulsan metropolitan regions, where natural disasters are problematic. An exponential random graph model confirmed the presence of a bonding strategy, which builds trust with municipalities and organizations within the region and creates interorganizational collaboration. We found that local public agencies tend to collaborate with organizations in the same jurisdictions or at higher levels of government.

Practical Applications

In the context of emergency management, hazard mitigation plays a crucial role in building resilient communities. Public agencies implement hazard mitigation actions by collaborating with other agencies and groups. Local agencies may require financial or logistical support from regional or national governments to effectively implement hazard mitigation actions. Agencies can foster collaborative relationships and strengthen their commitment to hazard mitigation efforts through regular interactions and by forming collaborative groups. Furthermore, collaboration among agencies within a region allows for shared interests in hazard mitigation and the strengthening of commitments. These strategies will help public agencies to implement hazard mitigation actions that utilize resources from a broad range of organizations.

Abstract

This study employed deep learning to analyze a substantial data set of 109.13 million COVID-19-related microblogs, leading to the construction of a specialized risk perception indicator dictionary. Employing this dictionary, we were able to capture the dynamic fluctuations in risk perception within online communities across various cities in real time. This approach highlighted the varying intensities of public response to the evolving crisis during the isolation and normalization stages of the pandemic. We observed that COVID-19-related transmission threat and information uncertainty significantly influenced public risk perception at different stages of the pandemic. Innovatively, our study quantifies public psychological resilience within online communities by examining the equilibrium between public risk perception and objective COVID-19-related risks. This equilibrium is conceptualized as the alignment of public perception with the evolving reality of COVID-19 threat and information. We investigated psychological resilience in two dimensions: adaptability, indicated by the extent of deviation from this equilibrium, and agility, reflected in the rate at which equilibrium is reestablished. Our study not only unveils new insights into the intricate relationship among public risk perception, the evolving risks, and psychological resilience but also offers empirical evidence to inform risk management strategies in online communities at different stages of a crisis.

Practical Applications

This research provides essential insights into how public perception and emotional responses during health crises like COVID-19 can be monitored and analyzed through social media data. By utilizing advanced analytical methods, including natural language processing (NLP) and panel vector error correction (PVEC) modeling, the study successfully quantified the psychological resilience of online communities. These methods allow for the real-time assessment of how communities adapt and respond to evolving risks, such as transmission threats and information uncertainty. For policymakers and crisis managers, these findings offer valuable tools for understanding public sentiment during different phases of a crisis. This understanding is crucial for shaping effective communication strategies and tailoring public health responses to specific community needs. The study’s methodology is adaptable across different cultural contexts and can be applied to various crisis scenarios, making it a versatile tool in global crisis management. The study’s approach demonstrates the potential of using social media analytics not only for academic research but also for practical, real-time crisis management. By providing a framework for measuring and interpreting public risk perception and resilience, the research aids in the development of more responsive and effective public health strategies. Overall, this research contributes significantly to both theory and practice in the field of crisis management, offering novel methods for assessing community resilience and guiding decision-making during public health emergencies.

A tsunami is a sequence of powerful waves or surges primarily resulting from underwater earthquakes. Creating tsunami vulnerability maps is of utmost importance to develop effective strategies for mitigating potential damage caused by future tsunamis. This article focuses on areas (Indian subcontinent) severely affected by the 2004 Indian Ocean tsunami. Utilizing geographic information system (GIS)-based tools, the authors employed geospatial cell-based modeling and used a multicriteria decision-making tool, an analytical hierarchy process (AHP), to develop the final inundation map. Using the digital elevation model and Environmental Systems Research Institute (ESRI) land-cover data, the authors created the elevation, slope, coastal proximity, flow accumulation, and land-use land-cover (parameters) map. Areas with a higher risk of tsunami impact are predominantly situated along the coastline with a descending terrain. The presence of waterways and lower elevations intensifies the impact of tsunamis. Regions classified under very high or high vulnerability are more likely to be inundated by the tsunami. The final vulnerability map of Nagapattinam shows that $166.455{\mathrm{km}}^2$ of the region is highly vulnerable to a tsunami. Similarly, Kanyakumari, Cuddalore, Karaikal, and Chennai show $37.6516{\mathrm{km}}^2$, $245.641{\mathrm{km}}^2$, $35.7128{\mathrm{km}}^2$, and $35.6306{\mathrm{km}}^2$, respectively, under very high vulnerability. The findings of this study serve as fundamental information for disaster mitigation and urban planning in coastal regions. The research introduces a novel approach to assess areas susceptible to tsunami inundation, utilizing a vulnerability map generated through remote sensing and spatial multicriteria analysis. Furthermore, the parameters employed closely resemble those used in actual inundation mapping, adding to the practicality and reliability of the results. Village maps of the selected area are superimposed on the final vulnerability map to understand the villages vulnerable to tsunamis better. The final vulnerability maps can be used for strategic mitigation during an actual event and to be prepared for future disasters.

This article aims to build interdisciplinary understanding about modern hazard communication by investigating visual information dissemination, diffusion, and response leading up to and during a weather-related disaster. The study analyzes data from online social media posts by authoritative sources during Hurricane Harvey, focusing on forecast and warning tweets containing hurricane risk imagery. The research integrates quantitative and qualitative analysis of tweets, retweets, and replies to explore how the roles of different information content and sources evolved with Harvey’s threat. Building on the work of Mileti and other warning scholars, the results illustrate the complexity of dynamic multisource, multimessage, and multihazard forecast and warning situations, including hurricanes. In such situations, people can engage in different hazard response processes simultaneously and near-continuously, as they are exposed to, attend to, and make sense of an evolving, heterogeneous collection of available information. The analysis also finds that during Harvey, authoritative sources used a mix of tweeting strategies to disseminate and amplify emerging information. Different types of sources led the creation of forecast and warning content at different times, with other sources playing complementary roles in communicating potentially important or salient content to broader audiences. Overall, the study provides updated models of hazard warning communication and response and associated processes such as milling, along with new methodological approaches for utilizing social media and other online data to understand these processes. In addition to these theoretical and methodological contributions, the analysis points to opportunities for the National Weather Service and others to improve tropical cyclone risk communication.

Seismic activity on a tunnel damages the tunnel support systems. The extent of the tunnel damage depends on the soil type, the magnitude of the earthquake acceleration, and the tunnel cover depth. Hence, analyzing the stress induced by the seismic event from the surrounding ground on the tunnel facilitates a safe tunnel design. Based on the pseudostatic method, this study examined the seismic stability of square and rectangular tunnels placed in cohesive-frictional soil. The tunnel collapse load was found using the lower-bound theorem of limit analysis in combination with the finite-element method. From the distribution of stresses along the periphery, the normal stress at each tunnel node was calculated, and the maximum of stresses was reported as the support pressure. Thus, the systems safeguarding the tunnel against devastating lateral earthquake forces are expected to offer the ultimate resistance equal to the maximum normal stress on the tunnel periphery. With the increase in tunnel cover depth, aspect ratio, seismic acceleration coefficients, and a decrease in soil cohesion and friction angle, the support pressure was noted to enhance. The distribution of normal stresses around the tunnel periphery depends on the tunnel geometry, the soil’s shear strength parameters, and the magnitude of earthquake acceleration. For a square tunnel, the magnitude of stress was maximum on the walls, followed by the roof and base, implying that collapse will be more prone from the side walls. However, the rectangular tunnels are noted to be susceptible to collapse from the roof, followed by walls and base.

The intensity of an atmospheric river (AR) is only one of the factors influencing the damage it will cause. We use random forest models fit to hazard, exposure, and vulnerability data at different spatial and temporal scales in California to predict the probability that a given AR event will cause flood damage, as measured by National Flood Insurance Program (NFIP) claims. We first demonstrate the usefulness of data-driven models and interpretable machine learning to identify and describe drivers of AR flood damage. Hazard features, particularly measures of AR intensity such as total precipitation, increase the probability of damage with increasing values up to a threshold point, after which the probability of damage saturates. Although hazard is generally the most important risk dimension across all models, exposure and vulnerability contribute up to a third of the explanatory power. Exposure and variability features generally increase the probability of damage with increasing values, apart from a few instances which can be explained by physical intuition, but tend to affect the probability of damage less for the largest AR events. Comparisons between random forest models at different spatial and temporal scales showed general agreement. We then examine limitations inherent in publicly available exposure, vulnerability, and loss data, focusing on the difference in temporal resolution between variables from different risk dimensions and discrepancies between NFIP claims and total flood losses, and describe how those limitations may affect the model results. Overall, the application of interpretable machine learning to understand the contributions of exposure and vulnerability to AR-driven flood risk has identified potential community risk drivers and strategies for resilience, but the results must be considered in the context of the data that produced them.

Resilient governance is essential to coping with complicated emergencies such as COVID-19. Existing studies have explored the public health system’s governance capacity and resilient issues. Although local governments play critical roles in responding to epidemics, few studies have examined the local government’s resilient governance capacity. This study develops a conceptual framework to assess local government’s resilient governance capacity toward epidemics from preparedness, awareness, response, learning, and coordination dimensions. Wuhan, Xi’an, Shijiazhuang, and Hangzhou were selected as cases to collect questionnaire survey data on local resilient governance capacity. The full-permutation polygon synthetic indicator method (FPPSI) is used to assess the performance of case cities. The study found that Hangzhou is ranked the highest, with Wuhan second, Xi’an third, and Shijiazhuang last in the comprehensive performance. Coordination is found vital for resilient governance. The governments have weaknesses in preparedness capacity, coordination capacity, and learning capacity. It is necessary to strengthen the balance between epidemic prevention and control and socioeconomic development. Comprehensive resilient governance strategies have also been proposed to guide governmental practice. This study can provide references for local governments to cope with future epidemic events.

This paper proposes a flood vulnerability index utilizing publicly available data that can be applied to different spatial scales. The index enables the development of an appropriate flood risk management strategy, tailored to those factors of vulnerability that have the greatest impact on the overall flood vulnerability of a given place. Currently, following a shift in the approach to flood protection, vulnerability has become a critical element of disaster mitigation. This index is an important contribution to the existing body of research as it can be applied to flood risk management strategies overcoming the ambiguity of definitions of vulnerability and the dynamic nature of temporal and spatial scales of analysis. The proposed flood vulnerability index is a multifaceted approach, encompassing physical, social, economic, environmental, and institutional attributes. In order to create the index, the literature on flood vulnerability was reviewed, including definitions, research methods, as well as the factors and constituents that are major components of vulnerability. The proposed indicator can be used by policy-makers responsible for flood risk management, especially in EU countries, in order to minimize the negative consequences of floods. Compared to other methods used (i.e., disaster loss estimation or modeling), the flood vulnerability index is easy to use when comparing different areas.

The selection of disaster-inducing factors and the construction of assessment models are two important stages in landslide susceptibility assessment. Existing research has neglected the influence of time-varying dynamic factors on landslide susceptibility. On the contrary, the best assessment model that fits the characteristics of disaster-pregnant environment in a certain area needs to be determined through a large number of comparative studies. In this paper, Boshan District, China was taken as the research area, and the assessment factor combination of static disaster-inducing factors and the change value of dynamic disaster-inducing factors in the current year was constructed. Landslide susceptibility modeling was carried out by random forest (RF), logistic regression (LR), support vector machine (SVM), stacking ensemble and convolutional neural network (CNN), and the assessment model with the highest accuracy was determined based on the area under the curve (AUC) method. Based on the geodetector, the interpretation degree of land use to landslide susceptibility and the relationship between the change information of dynamic disaster-inducing factors and the spatial distribution of landslide susceptibility were analyzed. The results show that CNN outperforms other models in the performance of modeling, with an AUC value of 0.920. The extremely high sensitive zones for landslides in the Boshan District were mainly distributed in the northwest, south, and east regions. The overall explanatory power of land use on landslide susceptibility shows a declining trend, suggesting that the spatial layout of land use in the Boshan District has been optimized. The rapidly growing population has destroyed the geological and hydrological environment and increased the probability of landslide susceptibility; Multiple acute changes of the normalized difference vegetation index (NDVI) in the opposite direction were more likely to trigger landslides, so it is not suitable to carry out logging, planting and other work repeatedly in a short time. This research can provide a theoretical foundation for land use planning and landslide prevention policies in the Boshan District.

Disaster management in Pakistan has been merely focusing on managing medical, food, and rehabilitation resources, but not so much on the humanitarian ethics during the relief efforts. The National Disaster Management Framework in Pakistan focuses on documenting the ethical approaches but fails to practically incorporate them when administering relief and recovery services in Pakistan. There is not so much knowledge, awareness, and practical implications about standards of conduct, which could guide the disaster management team and social service providers about actions and behaviors conforming to morals and professional values throughout the disaster recovery phase. This study is one of the first to explore the ethical issues faced by disaster survivors after relief providers respond to their needs in the posthumanitarian fields of Pakistan. It aims to identify the gaps that exist in ethical practices required in disaster management, particularly in the relief and recovery stages of humanitarian crises in Pakistan. This study gathered data from flood victims in the Pakistani cities of Sambhrial, Hafizabad, and Rajanpur. The data collection activities were funded by the Humanitarian Innovation Initiative, Brown University, and conducted in collaboration with the Pakistan Red Crescent Society (PRCS). Our results indicate that relief workers are trained technically but not on the humanitarian ethics in terms of handling humanitarian populations on equitable and rights-based disaster management grounds.

In order to study the technology of emergency disposal and provide insights into the landslide dam breach and rescue processes, a hyperbolic erosion dam breach model was used to predict the peak discharges of 10.11 and 11.03 Baige barrier lakes in the upper reach of the Jinsha River in northwest of China on October and November 2018. The results showed peak discharge values of 10,000 and $\mathrm{44,370}{\mathrm{m}}^3/\mathrm{s}$ in the 10.11 and 11.03 Baige barrier lakes, respectively. Therefore, it is proposed to conduct a series of emergency disposals of the 11.03 barrier lake, including manual excavation of a drainage channel, demolition of the cofferdam at the Suwalong Hydropower Station under construction, and discharge of the Liyuan Hydropower Station Reservoir in operation. For the 11.03 barrier lake, the obtained results showed that: (1) the excavation of a 13.5 m deep drainage channel reduced the peak discharge by 24%, which is consistent with the predicted peak discharge of $\mathrm{34,400}{\mathrm{m}}^3/\mathrm{s}$. (2) The demolition of the cofferdam of the Suwalong Station under construction avoids superimposing and magnification of the flood peak. Indeed, in the absence of this measure, the peak discharge might be 1.6 times its original size. The demolition of the cofferdam reduced the impact of peak discharge on downstream areas. (3) The Liyuan Reservoir effectively decreased the peak discharge by 37%, thereby reducing the flood control pressure of the downstream cascade stations and minimizing the flood disaster-related losses through the joint dispatch of the cascade reservoirs. (4) The joint emergency response mechanisms, such as joint interministerial consultation, central-local government coordination, emergency response social mobilization, and joint dispatch of cascade reservoirs, were important and effective in the Baige landslide dams disposal. The present paper provides a systematic back analysis and further insights into the rescue process, key technologies, and emergency measures for the successive barrier lakes. It also provides a reference for emergency rescue of similar landslide dams and risk control of cascade hydropower stations in river basins.

Abstract

Hurricane Elsa was the first hurricane to impact the island of Barbados in more than 60 years. Global warming is expected to increase the number of intense hurricanes in the Atlantic Ocean, which present a greater risk of future devastating hurricane impacts on the island. This study investigates the meteorological conditions and rapid intensification of Elsa between June 27 and July 3, 2021, by using meteorological data sets and results from the Weather Research and Forecasting model. The study also uses damage assessment data to analyze wind damage caused to residential homes as Elsa passed over Barbados on July 2. Unusually warm sea surface temperatures for June/early July, and a strong North Atlantic Subtropical High that was positioned anomalously close to the Caribbean islands, contributed to Elsa’s rapid intensification and its track across the Atlantic, respectively. It was also found that most reported damages on the island involved the complete or partial removal of roofs and were concentrated in and around the capital city Bridgetown, which is most likely due to the high concentration of poorly constructed houses in this area. Therefore, there is a need to improve the building codes of houses to ensure that they withstand strong hurricane winds. It was recommended that the implementation of a mandatory building code in addition to the provision of subsidies for low-income persons to improve their homes could aid with this issue. Furthermore, the paper highlights deficiencies in weather models in predicting the genesis and rapid intensification of Elsa, which highlights a need for improvements.

Practical Applications

After more than 60 years without hurricanes in Barbados, Hurricane Elsa passed over Barbados on July 2, 2021, causing damage to the built environment on the island. Elsa was rated as a Category 1 hurricane. Unusually warm sea surface temperatures for June/early July and a strong high-pressure system that was positioned anomalously close to the Caribbean islands were responsible for Elsa’s formation and trajectory over Barbados. Elsa exhibited rapid intensification—an increase in 1-min sustained wind speeds of more than $15\mathrm{m}{\mathrm{s}}^{-1}$ in 24 h—but this intensification was not properly forecasted by weather prediction models. Most reported damages on the island involved the complete or partial removal of roofs. The highest concentration of damages was in and around Bridgetown. Barbados has a building code, but adherence to it is not mandatory. The enforcement of a mandatory building code would increase resilience of Barbados to hurricanes, but it would also require government subsidies for persons who cannot afford to build their houses up to code. It is imperative that the government and population take early action to prepare themselves before any devastating hurricane disasters in the future.