Severe Natural Disasters Research Articles

Developing comprehensive resilient water systems and their driving factors for the 21st Century Society in China

More frequent and severe natural disasters triggered by climate change and the increasing domestic water consumption have placed higher demands on sustainable water services. The establishment of “Resilient Cities” provides overall guidance on future directions of water affairs. Literature reviews were designed to transform the concept of resilient cities into waterworks from the dimensions and properties. Then the concept of “Resilient Water Affairs” was put forward. Evaluation indicators of resilient water affairs were summarized based on dimensions and properties to indicate the driving factors of the resilient water system. Finally, the future trends of resilient water affairs were proposed, which provides theoretical and technical support for establishing resilient water affairs.

Post-disaster building damage assessment based on improved U-Net

When a severe natural disaster occurs, the extraction of post-disaster building damage information is one of the methods to quickly obtain disaster information. The increasingly mature high-resolution remote sensing technology provides a solid foundation for obtaining information about building damage. To address the issues with inaccurate building positioning in existing building damage assessment methods, as well as poor classification due to similar minor and major damage characteristics in building damage classification. Based on U-Net, we designed a two-stage building damage assessment network. The first stage is an independent U-Net focused on building segmentation, followed by a Siamese U-Net focused on building damage classification. The Extra Skip Connection and Asymmetric Convolution Block were used for enhancing the network's ability to segment buildings on different scales; Shuffle Attention directed the network's attention to the correlation of buildings before and after the disaster. The xBD dataset was used for training and testing in the study, and the overall performance was evaluated using a balanced F-score (F1). The improved network had an F1 of 0.8741 for localization and F1 of 0.7536 for classification. When compared to other methods, it achieved better overall performance for building damage assessment and was able to generalize to multiple disasters.

Recent Applications of Flash Flood Hazard Assessment Techniques: Case Studies from Egypt and Saudi Arabia

Flash floods are severe natural disasters due to their suddenness, frequency, and destruction. Every year, flash floods occur in Egypt and Saudi Arabia, which are located in the heart of the Middle East. Extensive areas in Egypt and Saudi Arabia are covered by mountainous topography. Intense rainfall can generate flash floods with their magnitude dependent on geomorphology and storm characteristics. Flash flood hazard assessment is a feasible way to mitigate potential damage and losses. Illumination, review, and comparison of assessment techniques of flash flood hazards represent an urgent necessity to evaluate advances and challenges in this discipline. Google-scholar and Scopus were used to collect 50 recent scientific papers, most of them published between 2011 and 2021, using the keywords: floods, hazards, assessment, Egypt, Saudi Arabia. Remote Sensing (RS) and Geographic Information systems (GIS) are the cornerstones of these studies, which include the Digital Elevation Model (DEM) and geoprocessing tools. The two items facilitate the calculation of morphometric parameters. Dividing the area and determining the hazard level were the main steps in flash flood hazard assessment. The considered studies relied on varied sets of morphometric parameters that ranged from 3 to 24 morphometric parameters. Area, ruggedness ratio, basin shape index, stream frequency, slope index, and drainage density are the common parameters in the assessment of flash flood hazards. In a flood zone, buildings made from brick have better sustainability in terms of both global warming mitigation and life cycle cost than other materials. Future studies are essential to illustrate the relationships between different morphometric parameters and flash flood hazard degree through field data and define a reliable unified set of morphometric parameters to assess the flash flood hazard degree.

Offloading of Interdependent Computing Subtasks Based on VANET in the Harsh Environment

Inter-vehicle communication is crucial in many severe weather or natural disaster situations. However, the wireless link quality is worse than the wired in that the network topology between vehicles changes frequently as vehicles move at high speeds. This also seriously affects the quality of inter-vehicle communication and corresponding services. Emerging computing-intensive service tasks like autonomous driving are extremely time-dependent, which poses large issues for resource allocation between vehicles. In particular, vehicles driving will face greater obstacles when severe weather causes a serious shortage of roadside computing resources. Therefore, we develop appropriate offloading strategies for interdependent computation tasks in the harsh environment. To reduce the overall task completion time within the cost budget limitations, we study a dynamic offloading strategy based on vehicle distribution probability and revenue discount factor, where subtasks are offloaded to the appropriate vehicle by cellular vehicle-to-everything communication. Simultaneously, for small tasks with smaller amounts of calculation, we propose a quasi-static offloading strategy based on branch and bound. Simulation results for varying amounts of calculation, vehicle arrival rates, and average speeds show that the suggested strategies can enhance revenues while getting lower time complexity. Furthermore, our solutions are more adaptable to the extremely dynamic vehicular ad hoc network environment.

Extreme Weather Preparedness for Institutions of Higher Education

BACKGROUND: On September 1, 2021, a tornado touched down at Temple University’s Ambler, PA campus. The tornado, a byproduct of Hurricane Ida, damaged most of the buildings on the campus, destroyed thousands of academic resources and killed hundreds of irreplaceable trees, plants, and natural resources. The purpose of this study was to identify needs among the Ambler campus community, document the impact of the tornado, and capture student, faculty, and staff perceptions toward disaster preparedness, climate change, and lessons learned for future disaster preparedness. METHODS: Data were collected in partnership with the Ambler campus community. A Rapid Needs Assessment, influenced by the Centers for Disease Control (CDC) CASPER Assessment, was utilized. The needs assessment was comprised of a mixed-method approach via a site visit, an online survey, and in-depth interviews. RESULTS: A total of 74 survey responses and 20 interviews were collected. Survey and interview respondents included students, faculty, and staff. Findings indicated that participants: felt unprepared for the tornado; experienced a variety of social, professional, mental, and physical impacts; and require additional training, education, and communication for future emergencies and natural disasters. CONCLUSION: As climate change continues to impact weather patterns, institutions of higher education must prepare for more frequent, more severe, and unprecedented natural disasters. It is time to ensure that (1) campuses have transparent protocols in place for the full spectrum of possible weather events, and (2) that students, faculty, and staff receive comprehensive education, training, and communication about such weather-related events and potential negative outcomes.

Strategic framework for natural disaster risk mitigation using deep learning and cost-benefit analysis

Abstract. Given trends in more frequent and severe natural disaster events, developing effective risk mitigation strategies is crucial to reduce negative economic impacts, due to the limited budget for rehabilitation. To address this need, this study aims to develop a strategic framework for natural disaster risk mitigation, highlighting two different strategic implementation processes (SIPs). SIP-1 is intended to improve the predictability of natural disaster-triggered financial losses using deep learning. To demonstrate SIP-1, SIP-1 explores deep neural networks (DNNs) that learn storm and flood insurance loss ratios associated with selected major indicators and then develops an optimal DNN model. SIP-2 underlines the risk mitigation strategy at the project level, by adopting a cost–benefit analysis method that quantifies the cost effectiveness of disaster prevention projects. In SIP-2, a case study of disaster risk reservoir projects in South Korea was adopted. The validated result of SIP-1 confirmed that the predictability of the developed DNN is more accurate and reliable than a traditional parametric model, while SIP-2 revealed that maintenance projects are economically more beneficial in the long term as the loss amount becomes smaller after 8 years, coupled with the investment in the projects. The proposed framework is unique as it provides a combinational approach to mitigating economic damages caused by natural disasters at both financial loss and project levels. This study is its first kind and will help practitioners quantify the loss from natural disasters, while allowing them to evaluate the cost effectiveness of risk reduction projects through a holistic approach.

Increased Compound Droughts and Heatwaves in a Double Pack in Central Asia

Compound droughts and heatwaves (CDHWs) are likely to cause more severe natural disasters than a single extreme event, and they have been exacerbated by rapid global warming. Based on high-resolution grid data, this study combines the daily-scale ERA5-Land dataset and the monthly-scale SPEI dataset with multiple indicators to analyze CDHWs. We calculated and analyzed the temporal and spatial modal distribution of CDHWs in Central Asia from 1981 to 2018, and in this paper, we discuss the sequence relationship between drought events, heatwave events, and CDHWs. The results show that the number of CDHWs in the study region have increased over time and expanded in terms of area, especially in eastern and southwestern Central Asia. The tsum (total frequency of CDHWs) was 0.5 times higher than the total heatwave frequency and it increased at a rate of 0.17/yr. The maximum duration of tmax (maximum duration of CDHWs in days) was 17 days. Furthermore, the occurrence rate of tmax was 96.67%, and the AH (CDHWs’ accumulated heat) had a rate of 97.78%, which, upon examination of the spatial trend pattern, accounted for the largest increase in terms of area. We also found that the TAH (CDHWs’ average temperature anomalies, SPEI < −0.5) shows obvious seasonality, with the increases in winter and spring being significantly greater than the increases in summer and autumn. The intensity of the CDHWs was stronger than that of a single extreme event, the temperature anomaly was higher than the average of 0.4–0.8 °C, and there was a north–south spatial pattern across the study region. In eastern and northwestern Central Asia, the AH and heatwaves (SPEI < −0.5) increased by 15–30 times per year on average. During the transition from the base period to the reference period, CDHWs increased by 25%, and the number of dry days prior to the CDHWs decreased by 7.35 days. The conclusion of our study can provide a theoretical basis for coping with climate change in arid zones.

Natural Disaster and Migration Trends in Flood Prone Agricultural Areas of Indonesia

ObjectivesSudden onset severe natural disasters are more closely associated with climate related migration than slower onset disasters in Indonesia. This analysis examines whether severe flooding was related to migration in the five years prior to the 5th wave of the Indonesian Family Life Survey (IFLS), and whether there were differences between agricultural versus non-agricultural households. MethodsWe present descriptive statistics of the subset of the population that reported having experienced natural disaster (3,183 (19%) households) in wave five of IFLS. Analysis is ongoing and will include statistical modelling. The main outcome is whether the household had moved. The independent variables are experienced a flood disaster that was severe, defined as causing death or injury, financial loss, or relocation, in the prior 5 years and whether the household owned land for farming. All analysis was conducted using Stata software. ResultsOverall, among disaster impacted households, 12.7% experienced a disaster that was severe enough to cause death or injury, financial loss, or relocation. Flooding was the most commonly reported disaster (45%), followed by volcano (17.6%) and earthquake (16%). Compared to those that migrated versus those that did not, 48.7% vs. 42.7% were affected by flood, respectively, and 19% vs. 36.4% owned land for farming. ConclusionsDelineating between a severe disasters and less severe disasters allows for more strategic disaster management strategies. In areas prone to severe disaster, adaptation policies aimed towards vulnerable farmers that own land may bolster response strategies. Funding SourcesNSF.

Natural disasters and CSR: Evidence from China

Using detailed natural disaster data and a sample of Chinese listed firms over the period 2011–2019, we examine the link between natural disasters and firms' corporate social responsibility (CSR) performance. We find that both the presence of a severe natural disaster and the number of disaster categories are significantly associated with higher CSR activities in the affected area. Next, we explore why firms are motivated to increase their CSR engagement in the disaster area. Our empirical evidence shows that state ownership, political connections, and institutional ownership are the main driving forces that spur CSR activities in the disaster area. Moreover, large firms and firms with high financial constraints are more motivated to increase their immediate CSR activities following natural disasters. We also find that CSR activities during the disaster period reward firms with better future accounting and stock market performance than those in the non-disaster period. Overall, our findings suggest that state ownership, political connections, institutional ownership, and future firm performance create incentives for firms to enhance CSR investments following natural disasters.

The Effect of Earthquake and Volcanic Eruption to the Nuclear Installation: A Retrospective on 2006 Yogyakarta Earthquake and Merapi Eruption

Earthquake hitting Yogyakarta, Indonesia on May 27th, 2006 that was followed by volcanic eruption of Mount Merapi had been pronounced as one of the most severe natural disasters ever occurred in the national history alongside the Tsunami happened in Aceh, Indonesia on 2004. The earthquake was caused by the activity of Opak faults which is located only ± 7 km away from Kartini nuclear reactor in the Yogyakarta province, Indonesia. This study aims to emphasize on the importance of disaster management mitigation to reduce the potential risk in future possibilities of natural disaster reoccurrence. It was concluded that retrofitting on the nuclear installation setup to withstand earthquake and volcanic eruption along with increasing people awareness and preparedness by arranging trainings on emergency response periodically are of great necessities to reduce the risk in time of disaster. Other than that, this work also described about the community service activities done by Indonesian National Nuclear Agency to help with the community revival post disasters.

Climate Change, Environmental Disasters, and Health Inequities: The Underlying Role of Structural Inequalities

We review and analyze recent literature in public health, urban planning, and disaster management to better understand the relationships between climate change, natural disasters, and root causes of health disparities in the USA. Existing scholarship establishes clear linkages between climate change and increasing occurrences and severity of natural disasters across the USA. The frequency and types of disasters vary by region and impact both short and long-term health outcomes. Current research highlights health inequities affecting lower income and minoritized communities disproportionately, but data-driven studies critically examining the role of structural inequalities in climate-induced health disparities are sparse. Adding to the body of knowledge, our conceptual framework maps how long-standing structural inequalities in policy, practice, and funding shape vulnerability of lower-income, racially and ethnically marginalized individuals. Vulnerability follows three common pathways: disparities in "exposure", "sensitivity", and "resiliency" before, during, and after a climate disaster. We recommend that future research, policy, and practice shift towards solutions that unearth and address the structural biases that cause environmental disaster and health inequities.

Rethinking Grid Governance for Texas in the Climate Change Era

Climate change has been plaguing the United States, most recently with increasingly extreme weather (Chediak & Malik, 2021). The February 2021 blackouts in Texas impacted over 4 million individuals for several days (Reuters Staff, 2021). The Texas blackouts disproportionately affected many low-income and marginalized communities (Srikanth, n.d.). In this paper, the author will analyze whether there is a lack of regulation in the energy sector. The current statutory framework provides power-sharing between federal and state governments with respect to energy regulation (Srikanth, n.d.). Energy deregulation in Texas has both advantages and disadvantages. This research will evaluate the impact of deregulation in the energy sector. In addition, the author will explain how public needs should affect the constitutional allocation of power between federal and state governments. The goal of this research is to provide historical context with which to evaluate some difficult challenges facing Texas today due to the February 2021 blackouts. Furthermore, this research will provide recommendations on how to prevent future blackout problems. This research encourages regulation reform for the transmission grid and the regional power market. Ensuring resiliency in grids is important in preventing severe natural disasters (Waseem & Manshadi, 2020). The author will argue that the federal government, Congress, Electric Reliability Council of Texas, the Public Utility Commission of Texas, and the Federal Energy Regulatory Commission should cooperate more in implementing grid regulations.

FWENet: a deep convolutional neural network for flood water body extraction based on SAR images

ABSTRACT As one of the most severe natural disasters in the world, floods caused substantial economic losses and casualties every year. Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief. Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar (SAR) images and deep learning methods is relatively low, this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network (FWENet) for flood information extraction. Then three classical semantic segmentation models (UNet, Deeplab v3 and UNet++) and two traditional water body extraction methods (Otsu global thresholding method and Object-Oriented method) were compared with the FWENet model. Furthermore, this paper analyzed the water body area change situations of Poyang Lake. The main results of this paper were as follows: Compared with other five water body extraction methods, the FWENet model achieved the highest water body extraction accuracy, its F1 score and mean intersection over union (mIoU) were 0.9871 and 0.9808, respectively. This study could guarantee the subsequent research on flood extraction based on SAR images.

Potential vorticity perspective of the genesis of a Tibetan Plateau vortex in June 2016

At midnight on 27–28 June 2016, a Tibetan Plateau (TP) Vortex (TPV) generated over the western TP that subsequently caused a downstream record-breaking rainstorm and extremely severe natural disaster. Based on reanalysis data and satellite imagery, this study investigates the formation of this TPV from a potential vorticity (PV) perspective. Results show that, in late June 2016, a remarkable circulation anomaly occurred over the TP and its peripheral area, with easterly flow in the middle and lower troposphere developing in the subtropical zone, replacing the normal westerly flow there. Its forefront merged with the southwesterly flow from the west and penetrated and converged over the western TP where the surface was warmer than normal, forming a low-level jet and downward slantwise isentropic surfaces in-situ. When the air parcel slid down the slantwise isentropic surface, its vertical relative vorticity developed owing to slantwise vorticity development associated with PV restructuring. At the same time, the penetrating southwesterly flow brought abundant water vapor to the western TP and induced increasing sub-cloud entropy and air ascent there. Low-layer cloud formed and the cloud liquid water content increased. The strong latent heat that was released in association with the formation of cloud produced strong diabatic heating near 400 hPa at night and strong PV generation below. The normal diurnal variation was interrupted and the vortex was generated near the surface. These results demonstrate that, against a favorable circulation background, both adiabatic and diabatic PV processes are crucial for TPV genesis.

Spatially resolved risk assessment of Natech in the Yangtze River Economic Belt, China

China is not only a large industrial country but also one of the countries with severe natural disasters in the world. The impact of a technical accident triggered by natural disasters (Natech) cannot be ignored. In Natech risk assessment, most research focuses on the quantitative analysis of Natech risk in small-scale level areas, such as parks or enterprises; Natech risk assessment in large-scale areas is typically at low resolution. There is a lack of systematic and high-resolution assessments on a large regional scale. In this paper, a grid Natech risk assessment method that couples the probability model and the information diffusion method is proposed. We select the Yangtze River Economic Belt (YREB) as the research area to assess its Natech risks triggered by floods, earthquakes and typhoons with a spatial resolution of 1 km × 1 km. The results show that the atmospheric risk level is higher than the water environment risk level, but their distribution trends are roughly similar. For the Natech risk triggered by floods, the high-risk areas of the atmosphere and water accounted for 3.83% and 1.73% of the YREB respectively, and were distributed along with the river network. For the Natech risk triggered by earthquakes, the high-risk areas of the atmosphere and water accounted for 0.47% and 0.08% of the YREB, respectively, and high-risk areas were mostly concentrated in north-central Sichuan and northern Yunnan. For the Natech risk triggered by typhoons, the high-risk areas of the atmosphere and water accounted for 3.52% and 2.68% of the YREB respectively, which were mostly concentrated in parts of Jiangxi and south-eastern Hunan. The spatially resolved map can help to identify the spatial characteristics and hot spots, which can provide a scientific reference for macro decision-making in environmental risk management.

Using Organigraphs to Map Disaster Risk Management Governance in the Field of Cultural Heritage

Global cultural heritage is threatened by the increasing frequency and severity of natural disasters caused by climate change. International experts emphasise the importance of managing cultural heritage sustainably as part of a paradigm shift in cultural heritage perception, understanding, and management. This paradigm shift has stimulated a need to integrate cultural heritage into pre-existing disaster risk management governance. However, there is currently a lack of robust and practical approaches to map the complex nature of disaster risk management governance. It is here considered that a shared understanding of the respective roles and responsibilities of the different organisations involved in risk management is a critical element in improving the preparedness of cultural heritage sites. The purpose of this article is to present the utility of the Organigraph technique and its main components as a tool to map governance structures, identify key stakeholders, and integrate cultural heritage experts into wider disaster risk management. The article presents a semi-empirical research approach, consisting of four iterative phases in which a series of digital workshops, semi-structured meetings, and bilateral expert meetings were used to co-produce five Organigraphs for heritage sites participating in an ongoing European Project. Our findings suggest that Organigraphs provide a valuable tool at the disposal of practitioners and academics with the potential to provide a basis for cross-national, cross-issue, and cross-scale peer learning between heritage sites. Furthermore, the technique is a valuable self-diagnostic tool to facilitate learning and proactive discussions in the preparedness phase of disaster risk management. Finally, they facilitate the co-creation of solutions through an evolving, interactive platform to integrate data-driven approaches.

Social Media Integration of Flood Data: A Vine Copula-Based Approach

Floods are the most common and among the most severe natural disasters in many countries around the world. As global warming continues to exacerbate sea level rise and extreme weather, governmental authorities and environmental agencies are facing the pressing need of timely and accurate evaluations and predictions of flood risks. Current flood forecasts are generally based on historical measurements of environmental variables at monitoring stations. In recent years, in addition to traditional data sources, large amounts of information related to floods have been made available via social media. Members of the public are constantly and promptly posting information and updates on local environmental phenomena on social media platforms. Despite the growing interest of scholars towards the usage of online data during natural disasters, the majority of studies focus exclusively on social media as a stand-alone data source, while its joint use with other type of information is still unexplored. In this paper we propose to fill this gap by integrating traditional historical information on floods with data extracted by Twitter and Google Trends. Our methodology is based on vine copulas, that allow us to capture the dependence structure among the marginals, which are modelled via appropriate time series methods, in a very flexible way. We apply our methodology to data related to three different coastal locations on the South coast of the United Kingdom (UK). The results show that our approach, based on the integration of social media data, outperforms traditional methods in terms of evaluation and prediction of flood events.

Service Restoration Through Microgrid Formation in Distribution Networks: A Review

Microgrid formation is a promising solution to enhance resiliency of distribution networks. The self-adequacy feature of a microgrid enables continuity of power supply through distributed generation (DG) units during severe faults and natural disasters. In this paper, different methods commonly used to partition a distribution network into multiple microgrids are presented, including the graph theory, heuristic rule-based algorithm, cluster-based technique, and mixed integer programming. Advantages and disadvantages of these techniques and future research directions are presented. This review provides an excellent summary on service restoration through micrgrid formation, and offers a valuable reference for researchers working on grid modernization of distribution networks.

Simultaneous compound disasters from COVID‐19 and catastrophic flooding

Simultaneous compound disasters from <scp>COVID</scp>‐19 and catastrophic flooding

Optimizing Land Use and Land Cover Allocation for Flood Mitigation Using Land Use Change and Hydrological Models with Goal Programming, Chaiyaphum, Thailand

Floods represent one of the most severe natural disasters threatening the development of human society worldwide, including in Thailand. In recent decades, Chaiyaphum province has experienced a problem with flooding almost every year. In particular, the flood in 2010 caused property damage of 495 million Baht, more than 322,000 persons were affected, and approximately 1046.4 km2 of productive agricultural area was affected. Therefore, this study examined how to optimize land use and land cover allocation for flood mitigation using land use change and hydrological models with optimization methods. This research aimed to allocate land use and land cover (LULC) to minimize the surface for flood mitigation in Mueang Chaiyaphum district, Chaiyaphum province, Thailand. The research methodology consisted of six stages: data collection and preparation, LULC classification, LULC prediction, surface runoff estimation, the optimization of LULC allocation for flood mitigation and mapping, and economic and ecosystem service value evaluation and change. According to the results of the optimization and mapping of suitable LULC allocation to minimize surface runoff for flood mitigation in dry, normal, and wet years using goal programming and the CLUE-S model, the suitable LULC allocation for flood mitigation in 2049 under a normal year could provide the highest future economic value and gain. In the meantime, the suitable LULC allocation for flood mitigation in 2049 under a drought year could provide the highest ecosystem service value and gain. Nevertheless, considering future economic and ecosystem service values and changes with surface runoff reduction, the most suitable LULC allocation for flood mitigation is a normal year. Consequently, it can be concluded that the derived results of this study can be used as primary information for flood mitigation project implementation. Additionally, the presented conceptual framework and research workflows can be used as a guideline for government agencies to examine other flood-prone areas for flood mitigation in Thailand.