Widespread Disaster Research Articles

Advancing annual global mean surface temperature prediction to 2 months lead using physics based strategy

Interannual global mean surface temperature (GMST) forecast provides critical insights into the economic and societal implications of climate variability. The pronounced GMST elevation in 2023–2024 indicates that the Earth may have accumulated enough heat to cause widespread disasters, underscoring the necessity for establishing accurate short-term GMST predictions to offer timely and sustainable public service. However, capturing high-frequency annual variability (ANV) component of GMST poses challenges due to its susceptibility to intraseasonal-to-interannual (ISI) noises, particularly across the Northern Hemisphere’s mid-to-high latitudes. Averaging these ISI variations in November and December effectively enhances signal clarity, especially over oceans, and masks unpredictable noises on land. By forecasting the average GMST for November and December to extract ANV predictability, a strategy for annual GMST prediction was established. This approach successfully advanced precise GMST hindcasts by up to 2-months during 1980–2022, exceeding performance of existing climate models and boosting early warning for interannual GMST shifts.

The Role of Forensic Anthropology in Mass Disasters

The Function of Criminal Justice in situations involving widespread disasters, forensic anthropology is essential to the identification and handling of human remains. Forensic anthropologists play a crucial role in victim identification procedures following natural disasters, terrorist attacks, and major accidents. Their expertise guarantees precise and considerate treatment of the corpses. Their proficiency encompasses not only the retrieval and scrutiny of skeletal remains but also the amalgamation of anthropological information with further forensic proof, such DNA analysis and dental records. This review looks at the various ways that forensic anthropology contributes to mass catastrophe response. The use of skeletal analysis techniques to ascertain the age, sex, ancestry, and stature of unidentified remains is one of the key areas of study. Trauma analysis is a tool used by forensic anthropologists to distinguish between pre-, peri-, and postmortem injuries. This helps them piece together the events leading up to the death. The field of forensic anthropology plays a vital role in mass disaster response by offering essential knowledge that facilitates precise identification and enhances the overall effectiveness of disaster management initiatives. As methods and procedures are developed further, forensic anthropologists will be better equipped to handle the difficulties of victim identification in such difficult situations. The assessment also looks at the logistical difficulties that arise in cases of major disasters, such as the requirement for quick and well-planned recovery efforts, collaboration with other forensic specialties, and handling substantial amounts of remains.

Research progress and visualization of underground coal fire detection methods.

Underground coal fires are a widespread disaster prevailing in major coal-producing countries globally, posing serious threats to the ecological environment and restricting the safe exploitation of coal mines. The accuracy of underground coal fire detection directly affects the effectiveness of fire control engineering. In this study, we searched 426 articles from the Web of Science database within 2002-2022 as the data foundation and visualized the research contents of the underground coal fire field using VOSviewer and CiteSpace. The results reveal that the investigation of "underground coal fire detection techniques" is currently the focal area of research in this field. Additionally, the "underground coal fire multi-information fusion inversion detection methods" are considered to be the future research trend. Moreover, we reviewed the strengths and weaknesses of various single-indicator inversion detection methods, including the temperature method, gas and radon method, natural potential method, magnetic method, electric method, remote sensing, and geological radar method. Furthermore, we conducted an analysis of the advantages of the multi-information fusion inversion detection methods, which possesses high precision and wide applicability for detecting coal fires, while highlighting the challenges in handling diverse data sources. It is our hope that the research results presented in this paper will provide valuable insights and ideas for researchers involved in the detection and practical research of underground coal fires.

Home-Based Emergency Preparedness for Families of Children and Youth With Special Healthcare Needs: A Scoping Review.

Children and youth with special healthcare needs (CYSHCN) are at disproportionate risk of harm from widespread disasters and from life-safety emergencies. These risks may be mitigated by providing preparedness training and support to family caregivers. We conducted a scoping review to identify and map the scholarly literature on home-focused preparedness of families with CYSHCN. Our search strategy yielded 22 relevant articles; 13 pertained to life-safety emergencies, 5 centered on widespread disasters, and 4 addressed preparedness on multiple scales. Approaches to measure or attempt to improve emergency preparedness levels in CYSHCN and their families were diverse and included interviews and focus groups; didactic, video-based, or side-by-side instruction; simulated medical crises; and provisioning of emergency kits. For the studies that involved an intervention (n=15, 68%), several proxy indicators of preparedness were used, including caregiver knowledge, skill, or comfort level with managing emergencies that could affect their CYSHCN; completion of preparedness tasks; and reduction in adverse clinical outcomes. Despite the varied methodologies, prevailing themes in the studies were that family caregivers of CYSHCN felt underprepared for emergencies and disasters, desired training to improve their preparedness at home, and benefited from such trainings, at least in the short term, across domains of self-efficacy, skill, and health outcomes of their CYSHCN. Although more research is needed to compare preparedness interventions and evaluate the durability of these interventions in larger, more diverse samples of CYSHCN and their families, our findings support incorporating preparedness training into preventive care encounters and the hospital-to-home transition.

Inflation Surge: Impact of COVID-19 Pandemic and Ukraine Conflict

In recent years, people have faced the fastest spreading and most widespread disaster in human history, the COVID-19 pandemic. Undoubtedly, the most direct factor affecting the effectiveness of epidemic prevention and control is the level of science and technology, especially medical science and technology. However, the level of public governance, especially public health governance, as well as cultural traditions and living habits also have a major impact on epidemic prevention and control. In 2022, Russia and Ukraine also broke out a military. Due to the impact of the epidemic and the conflict between Russia and Ukraine, the global economy has been gradually brought serious impact, inflation and other problems have also emerged. As one of the important indicators to measure the performance of the macroeconomy, the fluctuation of the inflation rate will be closely watched by the general public and the government departments that specify the macroeconomic policy. In this paper, the causes and consequences of inflation will be studied by discussing the effects of the novel coronavirus epidemic and the Russia-Ukraine conflict on the global consumer price index (CPI) and the economic trade between the three major economies.

Gut microbiota and synbiotic foods: Unveiling the relationship in COVID-19 perspective.

The Coronavirus disease 2019 (COVID-19) has spread across the globe and is causing widespread disaster. The impact of gut microbiota on lung disease has been widely documented. Diet, environment, and genetics all play a role in shaping the gut microbiota, which can influence the immune system. Improving the gut microbiota profile through customized diet, nutrition, and supplementation has been shown to boost immunity, which could be one of the preventative methods for reducing the impact of various diseases. Poor nutritional status is frequently linked to inflammation and oxidative stress, both of which can affect the immune system. This review emphasizes the necessity of maintaining an adequate level of important nutrients to effectively minimize inflammation and oxidative stress, moreover to strengthen the immune system during the COVID-19 severity. Furthermore, the purpose of this review is to present information and viewpoints on the use of probiotics, prebiotics, and synbiotics as adjuvants for microbiota modification and its effects on COVID-19 prevention and treatment.

ASSISTED LIVING ADMINISTRATORS’ JOB SATISFACTION, WORK STRESSORS, AND INTENT TO LEAVE DURING THE COVID-19 PANDEMIC

Abstract This study examines how job satisfaction in six sub-scales and selected stressors and demographic covariates influenced assisted living administrators’ (ALF) intentions to quit during the COVID-19 pandemic. Quantitative and qualitative data were collected from 103 ALF administrators as part of a national study of long-term care administrators’ intent to quit during the COVID-19 pandemic funded by the Foundation of the National Association of Long-Term Care Administrator Boards in Washington, DC. Descriptive statistics were collected for the sample, and correlations between variables were examined, as well as responses from 3 open-ended questions that were coded for analysis. Although generally satisfied, roughly 41 percent of ALF administrators reported that they were intending to quit. Qualitative data suggested that job satisfaction was influenced by a more nuanced interpretation of job characteristics and work environment intrinsic factors such as adequacy of staffing and resources, changing regulations during the COVID-19 pandemic, and external supports such as family and friends. Given the limited research on the impact of the COVID-19 pandemic on assisted living communities and their administrators, the results of this study can help to inform policies and strategies for providing support for this segment of long-term services and its workforce during widespread disasters.

Mainstreaming displacement in development policies: An analysis of Solomon Islands and Vanuatu approaches

AbstractPacific Small Island Developing States are uniquely vulnerable to climate disasters due to their geography and capacity to address disaster, displacement and development challenges. Climate disasters often trigger internal displacement. Long‐term recovery responses are ad hoc and unsustainable, often resulting in protracted displacement that undermines development progress. As displacement poses cross‐sectoral challenges, a development policy approach encourages integrated, durable solutions for internally displaced persons and host communities. This article advocates for mainstreaming displacement into development policy rather than treating it as a distinct policy issue. The article outlines the role of international law in guiding national displacement practices and analyse the policy experiences of Vanuatu and the Solomon Islands. Vanuatu has experienced widespread disaster displacement events, and the Solomon Islands have seen communities permanently relocate due to climate change. The article examines governance and institutional barriers inhibiting effective policy design and implementation. It concludes that mainstreaming displacement into development policies will promote integrated, inclusive and sustainable solutions for protracted displacement.

All Creatures Safe and Sound: The Social Landscape of Pets in Disasters

All Creatures Safe and Sound: The Social Landscape of Pets in Disasters

MORTALITY OF LIGHTNING HAZARD AND ITS MANAGEMENT IN INDIA

India is the most disaster - prone country in the world. Nearly 85% geographical area and more than 50 million people of India are in vulnerable state by one or multiple hazard. Lightning, an extreme event, has emerged as a major weather-related hazard with changing climatic conditions of tropical and sub-tropical countries. It is a massive electrostatic discharge caused by the circulation of warm moisture-filled air through unbalanced electric field in the atmosphere, accompanied by the loud sound of thunder. It is hazardous which affects lives, livelihoods and property. Every year more than 2,500 people in our country die due to lightning. In this paper an attempt has been made to analyse the spatio-temporal variations of lightning mortality in Indian context for last five decades. Assessment of lightning-mortality statistics and its comparison with other major natural disasters reveal that lightning ranks number one amongst all natural disasters in India. On the basis of available secondary data, the geographical distribution of lightning incidents along with the States which have the most lightning casualties have been identified in this study. Though lightning is a widespread disaster in India in terms of mortality and losses, but it has drawn a little attention. In this paper, I have tried to highlight the neglected dimensions of disaster compensation acts and rules related to lightning. This is the high time for reorientation of perceptions on disaster insurances, government funding and management for the people affected by lightning. The findings of this study will help such unnoticed and unrecognised problems.

Comparing Convolutional Neural Network and Machine Learning Models in Landslide Susceptibility Mapping: A Case Study in Wenchuan County

Landslides are one of the most widespread disasters and threaten people’s lives and properties in many areas worldwide. Landslide susceptibility mapping (LSM) plays a crucial role in the evaluation and extenuation of risk. To date, a large number of machine learning approaches have been applied to LSM. Of late, a high-level convolutional neural network (CNN) has been applied with the intention of raising the forecast precision of LSM. The primary contribution of the research was to present a model which was based on the CNN for LSM and methodically compare its capability with the traditional machine learning approaches, namely, support vector machine (SVM), logistic regression (LR), and random forest (RF). Subsequently, we used this model in the Wenchuan region, where a catastrophic earthquake happened on 12 May 2008 in China. There were 405 valuable landslides in the landslide inventory, which were divided into a training set (283 landslides) and validation set (122 landslides). Furthermore, 11 landslide causative factors were selected as the model’s input, and each model’s output was reclassified into five intervals according to the sensitivity. We also evaluated the model’s performance by the receiver operating characteristic (ROC) curve and several statistical metrics, such as precision, recall, F1-score, and other measures. The results indicated that the CNN-based methods achieved the best performance, with the success-rate curve (SRC) and prediction-rate curve (PRC) approaches reaching 93.14% and 91.81%, respectively. The current research indicated that the approach based on the CNN for LSM had both outstanding goodness-of-fit and excellent prediction capability. Generally, the LSM in our research is capable of advancing the ability to assess landslide susceptibility.

Olfactory and Taste Disorders in Patients Suffering from Covid-19, a Review of Literature.

Corona virus epidemic has caused a widespread disaster around the world. In studies, there are pieces of evidence of olfactory and taste dysfunction in patients with Covid-19. These symptoms occur independently or can be associated with other symptoms such as dry cough. The mechanism of the above-mentioned disorders and their clinical features in patients are not yet known. The rate of incidence of olfactory dysfunction in patients has been varied from 29.64% to 75.23% and the rate of incidence of taste dysfunction among the people can be different from 20.46% to 68.95%. Therefore, clinicians including ENT specialists and dentists should pay attention to the symptoms of anosmia and ageusia in these patients to prevent delayed diagnosis and inappropriate treatments. In this review article, data have been collected by searching the available articles in the domestic and foreign journals using databases such as PubMed, PubMed Central, Medline, EBSCO, Google Scholar, and Embase with key words of Anosmia, Ageusia, Dysgeusia, Covid-19, and Coronavirus from 2019 to 2020. Among the relevant references, 38 authoritative articles were chosen. The data showed that it seems olfactory and taste function disorders are the obvious symptoms of Covid-19, which can occur independently or with other symptoms, but the pathogenesis is not well specified yet. Therefore, further studies are required to achieve a reliable result in this area.

Universal Level Designations for Hospitalized Pediatric Patients in Evacuation.

Children comprise approximately 22% of the population in the United States.1 In a widespread disaster such as a hurricane, pandemic, wildfire or major earthquake, children are at least proportionately affected to their share of the population, if not more so. They also have unique vulnerabilities including physical, mental, and developmental differences from adults, which make them more prone to adverse health effects of disasters.2-4 There are about 5000 pediatric critical care beds and 23 000 neonatal intensive care beds out of 900 000 total hospital beds in the United States.5 While no mechanism exists to consistently track pediatric acute care beds nationally (especially in real time), a previous study6 showed a 7% decline in pediatric medical-surgical beds between 2002 and 2011. This study also estimated there are about 30 000 acute care pediatric beds nationally. Finding appropriate hospital resources for the provision of care for pediatric disaster victims is an important concern for those charged with triaging patients in a major event.

Characterization of global wildfire burned area spatiotemporal patterns and underlying climatic causes

Wildfires are widespread disasters and are concurrently influenced by global climatic drivers. Due to the widespread and far-reaching influence of climatic drivers, separate regional wildfires may have similar climatic cause mechanisms. Determining a suite of global climatic drivers that explain most of the variations in different homogeneous wildfire regions will be of great significance for wildfire management, wildfire prediction, and global wildfire climatology. Therefore, this study first identified spatiotemporally homogeneous regions of burned area worldwide during 2001–2019 using a distinct empirical orthogonal function. Eight patterns with different spatiotemporal characteristics were identified. Then, the relationships between major burned area patterns and sixteen global climatic drivers were quantified based on wavelet analysis. The most significant global climatic drivers that strongly impacted each of the eight major wildfire patterns were identified. The most significant combinations of hotspots and climatic drivers were Atlantic multidecadal Oscillation-East Pacific/North Pacific Oscillation (EP/NP)-Pacific North American Pattern (PNA) with the pattern around Ukraine and Kazakhstan, El Niño/Southern Oscillation-Arctic Oscillation (AO)-East Atlantic/Western Russia Pattern (EA/WR) with the pattern in Australia, and PNA-AO-Polar/Eurasia Pattern-EA/WR with the pattern in Brazil. Overall, these results provide a reference for predicting wildfire and understanding wildfire homogeneity.

Numerical Modeling the Flood and Pollutant Transport Processes in Residential Areas with Different Land Use Types

Large-scale flooding causes widespread disaster, and harmful pollutant concentration in water following flood affects public safety and the environment. In this study, a numerical model for solving the 2D shallow water equations and the solute transport equation is proposed to simulate overland flood and pollutant transport caused by floods. The present model is verified by comparing the predictions with the analytical solutions and simulation results; sufficiently high computational accuracy is achieved. The model is also used to simulate flood inundation and pollution spread in the area of Hun and Taizi Lane (HTL) in China due to river dike breaches; the results show that the coupling model has excellent performance for simulating the flooding process and the temporal and spatial distribution of pollutants in urban or rural areas. We use remote sensing techniques to acquire the land coverage in the area of HTL based on Landsat TM satellites. The impacts of changed land use on mitigation of flooding waves and pollutant spread are investigated; the results indicate that the land cover changes have an obvious influence on the evolution process of flood waves and pollutant transport in the study areas, where the transport of pollutants is very dynamic during flood inundation in HTL area. Furthermore, the motion of pollutants considering anisotropic diffusion is more reasonable than that due to isotropic dispersion in simulating pollutant transport associated with the flood in urban or farmland environments.

Defining Homogeneous Drought Zones Based on Soil Moisture across Japan and Teleconnections with Large-Scale Climate Signals

Abstract Droughts are widespread disasters worldwide and are concurrently influenced by multiple large-scale climate signals. This is particularly true over Japan, where drought has strong heterogeneity due to multiple factors such as monsoon, topography, and ocean circulations. Regional heterogeneity poses challenges for drought prediction and management. To overcome this difficulty, this study provides a comprehensive analysis of teleconnection between climate signals and homogeneous drought zones over Japan. First, droughts are characterized by simulated soil moisture from a land surface model during 1958–2012. The Mclust toolkit, distinct empirical orthogonal function, and wavelet coherence analysis are used, respectively, to investigate the homogeneous drought zone, principal component of each homogeneous zone, and teleconnection between climate signals and drought. Results indicate that nine homogeneous drought zones with different characteristics are defined and quantified. Among these nine zones, zone 1 is dominated by extreme drought events. Zones 2 and 6 are typical representatives of spring droughts, whereas zone 7 is wet for most of the period. The Hokkaido region is divided into wetter zone 4 and drier zone 9. Zones 3, 5, and 8 are distinguished by the topography. The analyses also reveal almost all nine zones have a high level of homogeneity, with more than 60% explained variance. Also, these nine zones are dominated by different large-scale climate signals: the Arctic Oscillation has the strongest impact on zones 1, 7, and 8; the influence of the North Atlantic Oscillation on zones 3, 4, and 6 is significant; zones 2 and 9 are both dominated by the Pacific decadal oscillation; and El Niño–Southern Oscillation dominates zone 5. The results will be valuable for drought management and drought prevention.

High-Resolution LiDAR Digital Elevation Model Referenced Landslide Slide Observation with Differential Interferometric Radar, GNSS, and Underground Measurements

The area of Taiwan is 70% hillsides. In addition, the topography fluctuates wildly, and it is active in earthquakes and young orogenic movements. Landslides are a widespread disaster in Taiwan. However, landslides are not a disaster until someone enters the mountain area for development. Therefore, landslide displacement monitoring is the primary task of this study. Potential landslide areas with mostly slate geological conditions were selected as candidate sites in this study. The slate bedding in this area is approximately 30 to 75 degrees toward the southeast, which means that creep may occur due to gravity deformation caused by high-angle rock formation strikes. In addition, because the research site is located in a densely vegetated area, the data noise is very high, and it is not easy to obtain good results. This study chose ESA Sentinel-1 data for analysis and 1-m LiDAR DEM as reference elevation. The 1-m LiDAR DEM with high accuracy can help to detect more complex deformation from DInSAR. The Sentinel-1 series of satellites have a regular revisit period. In addition, the farm areas of roads, bridges, and buildings in the study area provided enough reflections to produce good coherence. Sentinel-1 images from March 2017 to June 2021 were analyzed, obtaining slope deformation and converting it to the vertical direction. Deformation derived from SAR is compared with other measurements, including GNSS and underground slope inclinometer. The SBAS solution process provides more DInSAR pairs to overcome the problem of tremendous noise and has increased accuracy. Moreover, the SBAS method’s parameter modification derives more candidate points in the vegetated area. The vertical deformation comparison between the GNSS installation location and the ascending SBAS solution’s vertical deformation is consistent. Moreover, the reliable facing of the slope toward the SAR satellite is discussed. Due to the limitations of the GNSS stations, this study proposes a method to convert the observed deformation from the slope inclinometer and convert it to vertical deformation. The displacement of the slope indicator is originally a horizontal displacement. It is assumed that it is fixed at the farthest underground, and the bottom-to-top movement is integrated with depth. The results show that the proposed equation to convert horizontal to vertical displacement fits well in this condition. The activity of landslides within the LiDAR digital elevation model identified as scars is also mapped.

Americans' distress early in the COVID-19 pandemic: Protective resources and coping strategies.

The rapid emergence of the coronavirus disease 2019 (COVID-19) pandemic in the United States has dramatically altered daily life and taken a toll on Americans' physical, mental, social, and financial well-being. Based on previous widespread disasters, future high prevalence of short- and long-term adverse mental health consequences are anticipated. Studies of COVID-19 outside the United States indicated moderately high levels of distress, but we have little information regarding Americans' distress nor the factors associated with relative distress or adjustment during this unprecedented time. This study represents the first national view of Americans' distress during the massive disruption of COVID-19 and identifies levels of stress exposure, protective psychosocial resources, and coping strategies. Data were collected April 7-9, 2020 from an online platform, using best practices for ensuring high-quality data; 1,015 completed respondents are included ([53.9%] women; average age = 38.9 years; mostly White [82.4%] and non-Hispanic [91.5%]). Respondents' locations ranged across the United States, from 18.5% in the Northeast to 37.8% in the South. Fairly high levels of stress exposure and peritraumatic and general distress (depression, anxiety, and stress) were reported. Emotion regulation skills along with active and distraction coping emerged as the strongest predictors of lower distress levels. These results identify potential targets for online mental health interventions-focusing on engaging in adaptive emotion regulation and coping (e.g., through telehealth mental health first aid)-during the pandemic to offset the likely rise in distress over the months ahead. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Validation of Unit 1 of the Fukushima Daiichi Nuclear Power Plant during its Accident

Ten years have passed since the Great East Japan Earthquake and the subsequent accident at the Fukushima Daiichi nuclear power plant (NPP) that occurred on March 11, 2011. The earthquake and tsunami caused significant loss of lives and widespread disaster in Japan. Several reports have been published on the nuclear accident; however, the original data released at the beginning of the accident were written in Japanese, and some of these documents are no longer accessible. Some of the scenarios pertaining to the accident have become standardized theories, and these scenarios may be passed down to future generations with different descriptions, which may not fully describe the actual occurrences. To prevent future nuclear accidents, the accident at Fukushima Daiichi must be properly understood and analyzed.

Evaluation of landslide susceptibility based on the occurrence mechanism of landslide: a case study in Yuan' an county, China

Landslide is one of the most serious and widespread disasters in natural disasters, which seriously endangers the lives and property of residents. Based on the occurrence mechanism of landslide, this paper uses the analytic hierarchy process-comprehensive index (AHP-CI) model and takes Yuan'an County in China as an example to study and evaluate the susceptibility characteristics of landslides in this area. The geological conditions in Yuan’ an County are complex, and the landslide is extremely serious. This paper will discuss separately from the inherent factors and the inducing factors that lead to the occurrence of landslides. Combined with the geological environment conditions of Yuan' an County, 5 inherent factors, including the slope, slope structure, rock and soil characteristics, geological structure, and the lithology were selected as evaluation indexes in this evaluation. Based on the spatial superposition function of GIS, the landslide susceptibility in Yuan' an county was evaluated and zoned. And in the chapter of analysis and discussion, the influence mechanism of two induced factors of rainfall and human activities on the occurrence of landslide was discussed. Since the inherent factors could not be easily changed, but the induced factors could be controlled artificially, so according to the evaluation results, scientific prevention and control measures could be put forward for different grades of landslide prone areas in the study area. This paper studied the evaluation of landslide susceptibility based on the occurrence mechanism of landslide, which has good practicability and can provide important reference for the evaluation and prevention of landslide susceptibility in other areas.