Catastrophic Natural Disasters Research Articles

Geospatial and Temporal Patterns of Natural and Man-Made (Technological) Disasters (1900–2024): Insights from Different Socio-Economic and Demographic Perspectives

This pioneering study explores the geospatial and temporal patterns of natural and human-induced disasters from 1900 to 2024, providing essential insights into their global distribution and impacts. Significant trends and disparities in disaster occurrences and their widespread consequences are revealed through the utilization of the comprehensive international EM-DAT database. The results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991–2000 and 2001–2010 periods. A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9269). The most significant increase in natural disasters occurred from 1961–1970, while man-made (technological) disasters surged substantially from 1981–1990. Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October. Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses. The most substantial economic losses were reported during the 2001–2010 period, driven by catastrophic natural disasters in Asia and North America. Also, Asia was highlighted by our research as the most disaster-prone continent, accounting for 41.75% of global events, with 61.89% of these events being natural disasters. Oceania, despite experiencing fewer total disasters, shows a remarkable 91.51% of these as natural disasters. Africa is notable for its high incidence of man-made (technological) disasters, which constitute 43.79% of the continent’s disaster events. Europe, representing 11.96% of total disasters, exhibits a balanced distribution but tends towards natural disasters at 64.54%. Examining specific countries, China, India, and the United States emerged as the countries most frequently affected by both types of disasters. The impact of these disasters has been immense, with economic losses reaching their highest during the decade of 2010–2020, largely due to natural disasters. The human toll has been equally significant, with Asia recording the most fatalities and Africa the most injuries. Pearson’s correlation analysis identified statistically significant links between socioeconomic factors and the effects of disasters. It shows that nations with higher GDP per capita and better governance quality tend to experience fewer disasters and less severe negative consequences. These insights highlight the urgent need for tailored disaster risk management strategies that address the distinct challenges and impacts in various regions. By understanding historical disaster patterns, policymakers and stakeholders can better anticipate and manage future risks, ultimately safeguarding lives and economies.

One-dimensional deep learning driven geospatial analysis for flash flood susceptibility mapping: a case study in North Central Vietnam

AbstractFlash floods rank among the most catastrophic natural disasters worldwide, inflicting severe socio-economic, environmental, and human impacts. Consequently, accurately identifying areas at potential risk is of paramount importance. This study investigates the efficacy of Deep 1D-Convolutional Neural Networks (Deep 1D-CNN) in spatially predicting flash floods, with a specific focus on the frequent tropical cyclone-induced flash floods in Thanh Hoa province, North Central Vietnam. The Deep 1D-CNN was structured with four convolutional layers, two pooling layers, one flattened layer, and two fully connected layers, employing the ADAM algorithm for optimization and Mean Squared Error (MSE) for loss calculation. A geodatabase containing 2540 flash flood locations and 12 influencing factors was compiled using multi-source geospatial data. The database was used to train and check the model. The results indicate that the Deep 1D-CNN model achieved high predictive accuracy (90.2%), along with a Kappa value of 0.804 and an AUC (Area Under the Curve) of 0.969, surpassing the benchmark models such as SVM (Support Vector Machine) and LR (Logistic Regression). The study concludes that the Deep 1D-CNN model is a highly effective tool for modeling flash floods.

Environmentally associated colour divergence does not coincide with population structure across Lesser Antillean anoles

Abstract Species distributed across heterogeneous environments may undergo local adaptation, which can be limited by the homogenizing effects of gene flow. Lesser Antillean anoles exhibit dorsal colour variation associated with dramatic shifts in environment across small spatial scales, providing an excellent system for studying the maintenance of local adaptation despite ongoing gene flow. The 1995 Soufriere Hills eruption on Montserrat provides an opportunity to understand how natural disasters may influence the evolution of insular species. Thus, our study had two main objectives. First, we collected genetic, phenotypic, and environmental data to investigate whether environmentally associated dorsal coloration reflects underlying population structure in three species of Lesser Antillean anoles. Second, we examined the short-term evolutionary consequences of volcanism by comparing pre- and posteruption samples of Anolis lividus using multilocus sequence data. We found all three species exhibited genetic isolation-by-distance but not isolation-by-environment. Furthermore, phenotypic differences within each species were not predicted by genetic distances, suggesting gene flow across colour morphs. We confirmed the repopulation of A. lividus in the impacted area and found genetic diversity equivalent to pre-eruption sampling. Our findings contribute to our understanding of local adaptation in spatially small-scale systems and the impact of catastrophic natural disasters on population structure.

The impact of disaster on cognition in geriatric population

Aging has become one of the predominant demographic issues of the 21st century both in Türkiye and globally. All countries face major challenges to ensure that the health and social systems are ready for the present development. Disasters are natural, technological, or human-induced events that cause physical, economic and social losses. Türkiye is one of the countries frequently face to the catastrophic natural disasters. During the disasters, geriatric population should be considered as being a particular vulnerable group of society. Geriatric individuals are usually more severely injured, have prolonged hospital stay, lower quality of life and have higher mortality during disaster and in post-disaster periods. However, studies on the cognitive effects of disasters are limited. It is clear that the data to be obtained from studies to be conducted in this field will be of even greater importance, if the increasing geriatric population is considered. Age, living alone, being less educated, having chronic diseases, and not working are known as being the the most related factors with cognitive decline due to the disasters. Stressful events such as disasters have short and long-term effects on different brain structures. In the literature, it can clearly be seen that the popular area for researches is the affected locations in brain due to disasters The impotance of better preparedness and community empowerenment that can improve the vulnerable populations, particularly the geriatric people with dementia, is known and more researches are needed.

MONETARY POLICY UNDER NATURAL DISASTER SHOCKS

AbstractWith climate change increasing the frequency and intensity of natural disasters, what should central banks do in response to these catastrophic events? Looking at IMF reports for 34 disaster‐years, which occurred in 16 disaster‐prone countries from 1999 to 2017, reveals lack of any systematic approach adopted by monetary authorities in response to climate shocks. Using a small‐open‐economy New‐Keynesian model with disaster shocks, we show that consistent with textbook theory, inflation targeting remains the welfare‐optimal regime. Therefore, the best strategy for monetary authorities is to resist the impulse of accommodating in response to catastrophic natural disasters, and focus on price stability.

Impact of Earthquakes on Lung Health.

Earthquakes are catastrophic natural disasters that cause extensive damage to infrastructure and disrupt the lives of millions worldwide. Beyond the immediate physical and psychological damage caused by earthquakes, these events can significantly impact respiratory health. The inhalation of dust, smoke, particulates, toxic gases, and asbestos exposure can lead to various respiratory health pathologies. These include respiratory infections, exacerbations of pre-existing respiratory diseases, chest traumas, and pulmonary and venous thromboembolism. Longitudinal studies are necessary to assess the long-term respiratory health effects in affected populations. By addressing these knowledge gaps, future mitigation strategies and preparedness measures can be developed to minimize the respiratory health impacts of earthquakes and improve the well-being of affected communities. Robust building infrastructure and comprehensive earthquake preparedness are emerging as the most important determinants for not only mitigating building collapse but also significantly reducing the potential health impacts that follow. This comprehensive review aims to provide a systematic overview of the lung health impacts of earthquakes. It highlights the need for further research to identify specific pollutants, air contaminants, and environmental factors contributing to respiratory health issues following earthquakes.

Two‐ and three‐dimensional multiphase mesh‐free particle modeling of transitional landslide with μ(I) rheology

AbstractLandslides, which are the sources of most catastrophic natural disasters, can be subaerial (dry), submerged (underwater), or semi‐submerged (transitional). Semi‐submerged or transitional landslides occur when a subaerial landslide enters water and turns to submerged condition. Predicting the behavior of such a highly dynamic multi‐phase granular flow system is challenging, mainly due to the water entry effects, such as wave impact and partial saturation (and resulted cohesion). The mesh‐free particle methods, such as the moving particle semi‐implicit (MPS) method, have proven their capabilities for the simulation of the highly dynamic multiphase systems. This study develops and evaluates a numerical model, based on the MPS particle method in combination with the μ(I) rheological model, to simulate the morphodynamic of the granular mass in semi‐submerged landslides in two and three dimensions. An algorithm is developed to consider partial saturation (and resulting cohesion) during the water entry. Comparing the numerical results with the experimental measurements shows the ability of the proposed model to accurately reproduce the morphological evolution of the granular mass, especially at the moment of water entry.

Natural emergency modeling and forecasting

The purpose of this study is to develop forecast and analytical models (FAM) for predicting the most catastrophic natural emergencies caused by floods, earthquakes and forest fires. The article discusses predictive and analytical solutions for natural hazards for urbanized areas based on the Bayesian classifiers. The result is a formalized description of models for predicting forest fires, consequences of earthquakes and floods. The novelty of the models is due to the application of a unified scientific approach - the statistical processing method based on Bayes’ theorem. In contrast to the frequency probability determined by the relative frequency of occurrence of random events over sufficiently long observations, the Bayesian probability is the main forecasting method for constructing and training neural networks. Scientific forecasting of crises and incidents based on the Bayesian method and Bayesian networks requires a large amount of up-to-date data to model natural disasters, which is typical for frequently recurring negative events. Due to the lack of statistical data, the Bayesian method is not applicable to predicting catastrophic natural disasters that occur rarely but cause significant damage.

Spatiotemporal analysis of fatal earthquakes between 1800 and 2015 at a global scale

Earthquakes are catastrophic natural disasters and along with their aftereffects, they have caused significant fatalities, injuries and economic losses throughout history, and have changed the landscape physically. There is a need to understand the distribution and associated damage patterns of earthquakes to be better prepared and to ensure mitigation of damage in the future. This study analyses the spatial and spatiotemporal trends of earthquake occurrence and associated fatality at a global scale over the 215-year period between 1800 and 2015. Spatial and spatiotemporal analyses revealed that certain countries in Asia including Türkiye, China, India, Pakistan and Indonesia suffered the most both in terms of fatality and earthquake occurrence. There were significant spatiotemporal clusters of earthquake occurrence over this time period on the southern half of Asia, Türkiye and southwest Europe and northern Africa. The findings of the study provide a spatial and spatiotemporal characterization of fatal earthquakes and improve our understanding of these patterns at the global scale. Spatial analyses covering longer time intervals at regional and global scales should be undertaken in future studies to provide a more comprehensive understanding of earthquake occurrence and associated damage patterns

The relationship between renewable energy production and CO2 emissions in 27 OECD countries: A panel cointegration and Granger non-causality approach

Human-caused CO2 emissions are the primary cause of global warming. In this regard, determining the most effective approach for lowering CO2 emissions and the collateral risk of catastrophic natural disasters is crucial. This study examines the long-run relationship between disaggregated renewable energy production and carbon dioxide (CO2) emissions per capita for a panel of 27 OECD countries from 1965 to 2020. The panel-autoregressive distributed lag (ARDL) models of the pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) were used to evaluate the relationship between CO2 emissions and energy production from biofuel, aggregated geothermal and biomass (GEOB), hydropower, nuclear, solar, and wind. As robustness checks, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and common correlated effects mean group (CCEMG) estimators were used. Then, using a generalized method of moment (GMM) framework for panel vector autoregression (PVAR), the Granger non-causality between CO2 emissions and renewable energy production was investigated. GEOB, hydropower, nuclear, solar, and wind were found to be negatively and significantly correlated with CO2 emissions. GEOB, hydropower, and solar were the most effective renewable resources in reducing CO2 emissions. Granger non-causality approach showed unidirectional causation from hydropower, solar, and wind to CO2 emissions, bidirectional causation between CO2, and biofuel and GEOB, and unidirectional causation from CO2 emissions to nuclear. The findings were consistent across different model specifications and suggested a faster transition to GEOB, hydropower, and solar energy in OECD countries to reduce CO2 emissions and enhance environmental sustainability.

TSUNAMI WAVE LOADING ON A STRUCTURAL ARRAY PARTIALLY SHELTERED BY A SEAWALL

In recent years tsunamis have been recognized as one of the most catastrophic natural disasters in the world, highlighted by the 2004 Indian Ocean tsunami and the 2011 Tohoku earthquake and tsunami. These countries affected by tsunamis like Indonesia, Thailand and Japan usually arm their coast with sea walls to provide protection for coastal urban regions; however, surveys have found during both tsunami events, several sections of breached sea walls had led to extensive damage in those coastal regions (Dalrymple and Kriebel, 2005; Sato, 2015). Previous studies have examined the sheltering effect by macroroughness to individual structures, but limited knowledge is available on the sheltering and flow concentration effect by a partially standing wall to a field of structural array. The result from this experiment will serve to provide a better understanding on the tsunami loading variation induced by different length of partial seawalls, and help prepare more accurate hazard maps for tsunami events.

Could climate change exacerbate droughts in Bangladesh in the future?

Droughts are one of the most complex, common, and catastrophic natural disasters, causing severe damage to agriculture and the economy. However, drought susceptibility must be measured and predicted in a systematic way, especially in light of potential climate change scenarios. This study aimed to predict current and future drought susceptibility in Bangladesh using historical climate data (1991–2020) and coupled model intercomparison project 6 data for three seasons: pre-monsoon, monsoon, and post-monsoon. We applied an advanced machine-learning algorithm of artificial neural network (ANN) with a genetic algorithm (GA) optimizer to predict drought-prone areas. Nine hydrological parameters–rainfall, temperature, humidity, cloud coverage, wind speed, sunshine, potential evapotranspiration, and solar radiation–were used to develop drought susceptibility maps. Receiver operating characteristic curves and statistical metrics were used to validate the models. The results of a multilayer perceptron ANN coupled with a GA-based optimizer showed that the relevant statistical measures for training and testing datasets were the root mean square error (RMSE = 0.127 and 0.160) and coefficient of determination (R2 = 0.967 and 0.949) for the pre-monsoon season, monsoon season (RMSE = 0.023 and 0.035; R2 = 0.998 and 0.997), and post-monsoon season (RMSE = 0.083 and 0.142; R2 = 0.986 and 0.959), respectively. Further, drought-prone areas in the baseline drought period of 2020 for pre-monsoon season represented 23.86%, 14.24%, 12.85%, 29.92%, and 19.13% of the total area, respectively; similarly, for monsoon corresponding values were 1.83%, 44.18%, 4.99%, 8.76%, and 40.24%; and for post-monsoon drought they were 24.43%, 20.94%, 16.04%, 37.79%, and 0.80% of the total landmass of Bangladesh. These results can help reduce future drought impacts and be of value in assisting policy responses in the country.

Influence of sanitation facilities on diarrhea prevalence among children aged below 5 years in flood-prone areas of Bangladesh: a multilevel analysis.

Although the improvement of sanitation facilities has been a major contributor to improving public health, it is not guaranteed to prevent negative health outcomes. This is especially true in areas affected by severe natural disasters, such as flooding or extreme rainfall. Previous studies have examined the association between catastrophic natural disasters and negative health outcomes. However, studies on disaster-prone areas are limited. This study focused on the impact of flood risks and examined whether the improvement of sanitation facilities would be sufficient to suppress the prevalence of diarrhea in flood-prone areas. Two secondary datasets including geodata on flood-prone areas were used for the analysis: one each was obtained from the Bangladesh Demographic and Health Survey and Bangladesh Agricultural Research Council. Two models with categorizations of sanitation facilities based on containment type and excreta flow were applied for analysis. Results showed that the severe flood-prone areas and "diffused" type of sanitation, where the feces are diffused without any containment, had significant positive associations with diarrhea prevalence; however, the interaction between them was negative. Moderate flood-prone areas had a significant positive association with diarrhea prevalence; however, the interaction with unimproved sanitation, which includes containment without clear partition from feces, was significantly negative. These findings indicate that improved sanitation or containment type of sanitation may not positively contribute to the prevention of diarrhea in these severe- and moderate-flood prone areas. The urgent need for alternative sanitation technologies should be addressed in flood-prone regions.

A growing global threat: Long-term trends show cropland exposure to flooding on the rise

Flooding is one of the most widespread and catastrophic natural disasters. The exposure of cropland to floods is directly related to the quality of cropland and food security, so it is particularly important to map the spatiotemporal evolution of this exposure, with a specific focus on longer time series and higher resolution scales. This study is the first of its kind to analyse the worldwide spatiotemporal variability of Cropland Exposure to Flooding (CEF) with the 30 m resolution of Global Land Analysis & Discovery (GLAD) dataset during 2000–2019. The findings indicate that: (1) the global CEF area increased by a total of 83,429.50 km2 or 7.75 %, from 2000 to 2019; (2) only North America's CEF showed a downward trend, and the region with the largest increase in CEF was South Asia; (3) the CEF in 23 river basins, including Ganges, Indus, Mississippi, Yangtze, and Danube, accounted for 79.88 % of the global total in 2019P; (4) in 2019P, China had the largest CEF globally, reaching 239,525.07 km2. The fastest growing CEF was India, contributing 16.36 % of the global CEF growth. The CEF of United States experienced a reduction trend; (5) two constructed indicators were used in evaluating the CEF of countries worldwide, and a total of 46 countries are considered to be at the highest level of risk, mainly in Europe and Asia. Based on these conclusions, we carried out a cold/hot spot analysis to reveal the spatial heterogeneity and possible driving factors in this phenomenon, and we offer management suggestions to limit the risks to cropland in the floodplains.

Rainfall-Runoff Modelling Using HEC-HMS Model, Remote Sensing and GIS in Middle Gujarat, India

Hydrological modeling is a widely used approach for estimating the hydrological response of a basin to precipitation. Floods are among the most catastrophic natural disasters in small urban watersheds, inflicting loss of life, massive property destruction, and a severe danger to the economy. As a result, appropriate modeling can be a useful tool in preventing and mitigating such flood hazards. Despite this, flash flood prediction remains one of the challenges of hydrological modeling in ungauged basins due to a lack of runoff observations. This study aims to calibrate and validate the rainfall-runoff transformation model for Hathmati river sub watershed in the Sabarmati River basin using HEC-HMS (Hydrologic Engineering Centre Hydrology Modeling System). For the loss rate, SCS Curve Number method was selected while Clark Unit Hydrograph and SCS unit hydrograph was used for the transform method. The model is calibrated and verified using two rainfall-runoff events from 2006 and 2007 year The model calibration and validation efficiency were verified for both methods using the Nash-Sutcliffe efficiency (NSE), The coefficient of determination (R2), and the Percent Bias (PBIAS) As a result, the model calibration and validation were found to be satisfactory with the acceptable value of NSE between 0.869 to 0.914, with R2 0.901 to 0.947 and PBIAS from 9.76 to 14.8. it is observed that the model shows a very good correlation between simulated flow and observed flow. As a result, the model can be used to forecast river flow and aid in flood mitigation efforts to lessen their effects and associated costs. Additionally, the findings of this study can serve as guidelines for future assessments of the flood risk in the study area.

Incidence of acute myocardial infarction and hurricane Katrina: Fourteen years after the storm

IntroductionHistorically, natural disasters have been known to have an effect on humankind including physical and mental health. Studies dating from the early nineteen hundreds have shown repeated associations between different catastrophic natural disasters and its effects on cardiovascular (CV)health, including increased morbidity and mortality. Knowing that these effects on CV health last sometimes up to a decade, we sought to study the effects of hurricane Katrina on incidence of acute myocardial infarctions (AMI) to see if the effects perpetuated and continued or mitigated after the first decade. MethodsOurs is a single center, retrospective observational study at TUHSC to compare the incidence of AMI, chronobiology and other demographic characteristics between the 2-year pre-Katrina and 14-year post-Katrina group. After IRB approval, patients were identified using specific ICD 9 and 10 codes. Data was collected by chart review and stored in secure password protected files. Descriptive statistics including mean, standard deviation and percentages were calculated. Statistical analysis comparing mean and standard deviations were performed using Chi-square test and t-test. ResultsThe pre-Katrina cohort saw a 0.7% incidence of AMI, whereas the post-Katrina cohort saw 3.0% incidence of AMI (p < 0.001). The post- Katrina group was also noted to have significantly higher comorbidities including diabetes, hypertension, polysubstance abuse and coronary artery disease. ConclusionsEven 14 years after the storm, there was a four-fold increase in the incidence of AMI. Additionally, psychosocial, behavioral and traditional risk factors for CAD were significantly higher more than a decade after the natural disaster as well.

Embedding Trauma Literacy Into Curriculum: An Examination of the Attitudes of Australian and New Zealand Journalism Educators

Australia and New Zealand have reputations as countries prone to catastrophic and frequent natural and man-made disasters. Therefore, it is no surprise that antipodean academics want trauma-informed education for their journalism students. This study presents the Australian-New Zealand results of a 2021 survey exploring educators’ attitudes toward embedding trauma literacy into journalism curriculum. It mirrors a survey from the UK-based Journalism Education and Trauma Research Group. The Australian-New Zealand results confirm that educators want more training to effectively embed trauma-informed reporting into their curricula. The discussion notes the availability of local, research-based teaching materials, and identifies barriers to implementation.

HEC-RAS 2D modeling for flood inundation mapping: a case study of the Krishna River Basin

Abstract Floods are catastrophic natural disasters that cause a substantial toll on human lives, infrastructure, and the economy. Structural and non-structural measures are developed for planning flood mitigation strategies. Flood inundation mapping is valuable information for decision-makers and authorities to develop flood mitigation strategies and resource allocation. This study uses the HEC-RAS 2D model for flood inundation mapping in the Krishna River Basin. Digital elevation models (DEMs) of 12.5 and 30 m resolutions were used to model the inundation map. The study also investigated the effect of change in upstream boundary data on the inundated area. The simulated results with 12.5 m resolution DEM are found in good agreement with the validation data and conform to the inundated areas with the available reports. This study proves the 2D capabilities of HEC-RAS and helps the experts with better management practices.

Sustainable Warehouse Location Selection in Humanitarian Supply Chain: Multi-Criteria Decision-Making Approach

The frequency of catastrophic natural disasters is rising, and much emphasis is being given to the Humanitarian Supply chain (HSC). The main goal of relief efforts is to get enough emergency supplies to the area hit by the disaster as quickly as possible. The decision of where to locate warehouses that will store relief supplies presents a significant obstacle for humanitarian relief organizations as they work to enhance their capacity for providing aid and their rescue plan. A non-optimal location could make the search and rescue efforts harder. More importantly, it has been seen that when these kinds of geographical sites are evaluated, social and environmental issues are not considered. This research paper aims to make humanitarian networks more accountable by determining the ideal warehouse site and considering both traditional and sustainable factors. A framework for selecting warehouses to keep relief goods was devised using the Multi-Criteria Decision Making (MCDM) approach. Best-Worst and TOPSIS (“Technique for Order Performance by Similarity to the Ideal Solution”) methods were used to rank the potential locations based on Cost, Logistics, Environmental, and Social Criteria. A research study has been done in the State of West Bengal (District Arambagh).

A new flood chronology for KwaZulu-Natal (1836–2022): the April 2022 Durban floods in historical context

ABSTRACT In April 2022, flooding and associated geohazards caused major loss of life and extensive damage in the greater Durban region and large areas of the KwaZulu-Natal (KZN) coastal zone. Heavy rainfall that triggered the flooding and mass movement events was reported in national and international media as having ‘smashed weather records’. However, no systematic and up-to-date flood record exists for KZN to allow the April 2022 floods to be viewed within their full historical context. This study presents an historical geographic account of flooding in KZN, with a particular focus on the greater Durban region. The flood record expands upon available databases held by the South African Weather Service, drawing on missionary accounts, newspapers and personal diaries to identify all significant flood events in KZN since the mid-nineteenth century. We document 53 significant flood events from 1850-1899 (average ~1.1 per annum) and 210 from 1900-2022 (average ~1.7 per annum). Within the limits of our data, we suggest that the frequency of flooding in Durban has likely doubled over the last century. Our research confirms that the April 2022 floods were likely the most catastrophic natural disaster yet recorded in KZN, in collective terms of lives lost and overall economic impact.