Meteorological Disasters Research Articles

Application of Morphometric Ranking Approach using Geospatial Techniques for Flash Flood Susceptibility Modelling in District Shangla, Pakistan

Every year, disaster strikes, and led to thousands of casualties and deaths around the world. A meteorological disaster such as a flash flood is a multifaceted hydro-meteorological phenomenon that can cause a huge loss of human life and can create severe economic problems. In this study, techniques based on Geographic information systems and Remote sensing were used to get the flood susceptibility map for District Shangla, Pakistan. For the susceptibility of flash floods, geo morphometric ranking model was used. Various causative factors were considered including; topography, river pattern, and flow accumulation. ALOS PALSAR digital elevation model was used for calculating the required causative factors. Eleven different sub-basins were delineated in the Shangla basin. A total of eighteen morphometric parameters were studied. The morphometric ranking approach (MRA) score was determined with a range of 1 to 5. Rank 5 represents high risk while rank 1 exhibits low risk. The results of the model were categorized into five flood vulnerability classes; very low, low, moderate, high and very high. The total population of Shangla district is 757,810 with a population density of 480 persons per sq km2, and results from this study revealed that 23 % of the total geographic area (364.11 km2) of the district is vulnerable to high flash floods.

Climate change and extreme weather disasters: evacuation stress is associated with youths' somatic complaints.

Climate-change has brought about more frequent extreme-weather events (e.g., hurricanes, floods, and wildfires) that may require families to evacuate, without knowing precisely where and when the potential disaster will strike. Recent research indicates that evacuation is stressful for families and is associated with psychological distress. Yet, little is known about the potential impact of evacuation stressors on child health. After Hurricane Irma, which led to a mass evacuation in Florida, we examined whether evacuation stressors and hurricane exposure were uniquely associated with youth somatic complaints, and whether youth psychological distress (i.e., symptoms of posttraumatic stress, anxiety, and depression) served as a potential mediating pathway between evacuation stressors, hurricane experiences, and somatic complaints. Three months after Irma, 226 mothers of youth aged 7-17 years (N=226; M age = 9.76 years; 52% boys; 31% Hispanic) living in the five southernmost Florida counties reported on evacuation stressors, hurricane-related life threat and loss/disruption, and their child's psychological distress and somatic complaints using standardized measures. Structural equation modeling revealed a good model fit (χ2 = 32.24, p = 0.003, CFI = 0.96, RMSEA = 0.08, SRMR = 0.04). Even controlling for life-threatening hurricane experiences (β = 0.26) and hurricane loss and disruption (β = 0.26), greater evacuation stressors were associated with greater symptoms of youth psychological distress (β = 0.34; p's < 0.001), and greater psychological distress was associated with more somatic complaints (β = 0.67; p < 0.001). Indirect effects revealed that evacuation stressors (p < 0.001), actual life-threatening events (p < 0.01), and loss and disruption (p < 0.01) were all uniquely and indirectly associated with youths' somatic complaints via youth psychological distress. Findings suggest that even coping with the threat of a disaster may be sufficient to prompt psychological and physical health symptoms in youth. Due in part to climate change, threats of disaster occur much more often than actual disaster exposure, especially for areas that are prone to hurricanes or wildfires. Preparing youth and families residing in vulnerable areas for potential disaster evacuation or sheltering-in-place appears critical. Encouraging families to develop Disaster Plans and teaching stress management skills may reduce both youth distress and somatic health problems.

Risk Assessment of Maize Yield Losses in Gansu Province Based on Spatial Econometric Analysis

The frequent occurrence of meteorological disasters in China has caused huge losses to agriculture. Risk assessment serves as a bridge from disaster crisis management to disaster risk management. Therefore, it is necessary to carry out a refined comprehensive risk assessment of meteorological disasters in typical areas. However, several limitations remain in the disaster loss risk research, such as too coarse resolution and too single risk indicator. Additionally, less research has examined geographical information on risk clustering and barycenter migration, as well as temporal information on the sustainability of trends. Consequently, it is significant to unearth the geographical and temporal information on disaster loss and identify the refined spatial and temporal evolution pattern of crop risk. For this reason, we evaluated the risk of corn production in Gansu Province. First, based on maize yield data, a risk evaluation index system was constructed using the characteristics of variation trends, fluctuations, and extreme values of disaster losses. Then, the spatial distribution patterns and temporal evolution characteristics of maize production risks on a county scale in Gansu Province were determined using spatial analysis and climate diagnosis technology. The results show that there is a large interdecadal fluctuation in risk. In the 1980s, 1990s, 2000s, and 2010s, the average yield reduction rates of maize in Gansu Province were −11.8%, −12.6%, −8.7%, and −8.5%, and the proportions of counties with severe yield reduction were 34.8%, 44.4%, 20.8%, and 9.7%, respectively. Second, most counties belong to medium-low or low-risk areas for maize production. High-risk counties are primarily located in eastern and southern Gansu, whereas low-risk counties are mostly found along the Hexi Corridor. Third, most risk indicators exhibit some geographical aggregation. The Jiuquan region falls within the low-low-risk aggregation zone. In contrast, the Qingyang region is a high-high aggregation zone with a gradual expansion trend. Four, each risk indicator’s geographical barycenter migrates over a complicated path, but the direction and distance vary considerably. The comprehensive risk migrates along the south-northwest-southeast trajectory, albeit at a shorter distance. Five, the proportion of counties with a medium, medium-severe, severe, and total yield reduction tended to decline. In addition, the annual precipitation is significantly or very significantly correlated with most risk indicators and the comprehensive risk level. The results can guide agricultural production processes at all levels, as well as government disaster prevention.

An experimental study on root-reinforced soil strength via a steel root analogue in unsaturated silty soil

AbstractLandslides due to catastrophic weather events, especially heavy rainfall, have risen significantly over the last several decades, causing significant damage and affecting the health and livelihoods of millions of people. Using tree roots to bio-engineer shallow slopes has been proven to be a cost-effective, sustainable measure and thus has gained increasing popularity. As slope failure often occurs under heavy precipitation, it is important to understand the mechanical interactions in the soil matrix surrounding a root to better estimate the reinforcement capacity of a root system, especially as the soil undergoes wetting from drier conditions. However, very few studies of root reinforcements have considered the effects of degree of saturation on behaviour. In this study, steel wires are used as a root analogue to explore the impact of root geometry, soil dilation and soil saturation on the pull-out behaviour of a root and three commonly used unsaturated soil strength models have been used to interpret the pull-out results. It was found that roots with larger diameter did not contribute to additional resistance. Also, a linear relationship between degree of saturation and pull-out strength was identified over a large range of suctions and one of the unsaturated soil strength models seemed to provide a more reasonable interpretation. The results will help future bioengineering slope design by improving the understanding of soil-root interface behaviour, including the effect of root diameter in slippage failure and greater emphasis on the importance of taking degree of saturation into account in unsaturated soil strength models.

The risk impacts of global natural and technological disasters

This research develops an expected risk model and estimates risk impacts of single or multiple disasters in terms of human fatality, injury, affected, and economic damage for nearly 200 nations. Both natural hazards, including geophysical, meteorological, hydrological, and technological disasters, such as industrial, transport, and structural, are considered. Relevant measures of expected risk, such as standard deviation, coefficient of variance, range, and rank are also calculated to assess a country's overall risk. Social-economic-physical factors from the World Development Index developed by the United Nations (UN) are then regressed with occurrences and risks of natural and tech disasters to seek plausible associations. The results show that (1) the model performs reasonably well in fitting observed and modeled risks and risk impacts, relatively better for natural disaster and affected people and economic damage; (2) while natural disasters are far more risky than tech ones in total risk impacts; specific risks for subgroups of natural or tech disasters vary widely in magnitude and by country; (3) high natural and/or tech risks concentrate in a small number of countries, such as China, India, Bangladesh in Asia; U.S., Mexico, Canada in North America; Turkey, Russia, France, Germany in Europe, and Algeria, Egypt, and Ethiopia in Africa, which are relatively large in population, fast in development, or advanced in industrialization; yet (4) high risk deviations per unit risk impacts reveal that many small, developing, and tech backward countries need to prepare even more for both natural and tech disasters. Finally, (5) while many country-level development factors, together with disaster occurrence, are statistically significant, only some can provide weak predictability of disaster risk impacts under robust regression. The research findings provide useful risk references to countries for resilient national policies for disaster preparation, mitigation, and recovery.

A regret theory-based multi-granularity three-way decision model with incomplete T-spherical fuzzy information and its application in forest fire management

Forest fires are an abrupt and highly destructive meteorological disaster that can occur in all regions of the world, resulting in significant ecological, economic and social losses. Moreover, the causes of forest fire disasters are usually complex, involving several uncertain factors such as temperature, relative humidity, wind speed and rainfall. All of those pose the greatest challenge to the study of forest fire management (FRM). In order to efficiently explore FRM via valid intelligent decision-making techniques, a novel model of regret theory (RT)-based multi-granularity (MG) three-way decisions (TWD) in incomplete T-spherical fuzzy (T-SF) environments has been constructed, where incomplete T-spherical fuzzy sets (T-SFSs) have been employed to describe diverse types of uncertain information in FRM, and RT-based MG TWD is conducive to analyzing multi-source T-SF information via reducing decision risks and modeling bounded rationality owned by decision-makers (DMs). Specifically, the concept of MG T-SF incomplete information systems (IISs) has been first constructed for information depictions of FRM. Then, MG T-SF IISs have been processed via the presented T-SF similarity principles for developing adjustable MG T-SF probabilistic rough sets (PRSs). Afterwards, an RT-based MG TWD approach has been built with the support of adjustable MG T-SF PRSs. Finally, a real-world FRM case analysis has been performed by using the built RT-based MG TWD approach, and extensive comparative and experimental analyses have been performed to validate the practicability of the presented methodology. To sum up, the presented methodology has simultaneously incorporated MG T-SF IISs, MG TWD and RT to model various uncertainties, valid information fusion processes and bounded rationality for FRM, which serves as a valid intelligent decision-making technique in processing incomplete and imprecise multi-source information with plentiful decision risks and regret emotions.

Microclimate Analysis of the High-Impact Weather for the Power Grid Operation in the Jibei Region of China

High-impact weather affects the safety and economic operation of power systems. In this study, to provide regional microclimate of high-impact weather for the local power grid system in the northern Heibei province (known as the Jibei region in China), ERA5-Land global reanalysis data during 1981–2020 with a 0.1° grid size (about 9 km) are adopted to analyze the climate statistics and changes of the disastrous weather that affects the power grids. The results show that there have been significant climate changes in the region, including a temperature increase of about 1 °C, evident humidity and precipitation reductions, for the Jibei region and the six sub-regions that concentrated with wind and solar energy development in the 40 years. Due to the differences in terrain, the climate changes differ significantly among the six renewable energy development regions. The main types of high-impact weather that affect the power grid in the region are heavy fog and icing events, followed by cold waves, snowstorms, and rainstorms. In general, with climate changes in the last several decades, the weather disasters in Jibei region have become more frequent. Since most high-impact weather events have a small scale, it is necessary to simulate the weather processes with high-resolution models to accurately quantify the characteristics of the weather processes that affect the power grid. Therefore, a refined regional meteorological model (with grid size of 2 km) based on four-dimensional data assimilation (JB-FDDA) is established for the Jibei region. With one year of model reanalysis data, we found that JB-FDDA can significantly improve the accuracy of the local meteorological fields, and properly depicted the details of severe weather that affect the power grid operation. This study provide an analytical approach for regional electricity meteorological disasters by using reanalysis data.

Weather Disasters, Material Losses and Income Inequality: Evidence from a Tropical, Middle-Income Country

Weather Disasters, Material Losses and Income Inequality: Evidence from a Tropical, Middle-Income Country

Spatial and Temporal Change in Meteorological Drought in Gansu Province from 1969 to 2018 Based on REOF

Meteorological drought is one of the most serious natural disasters, and its impact in arid and semi-arid areas is significant. In order to explore the temporal and spatial distribution of meteorological disasters in Gansu Province, we first calculated the standardized precipitation evapotranspiration index (SPEI) based on the monthly meteorological data from 1969 to 2018 and extracted the drought events through the theory of runs. Then, REOF rotation orthogonal decomposition was performed to divide the study area into five climatic subregions. With each subregion as the basic unit, the variation characteristics and evolution trends of drought events at different time scales were compared based on the B-G segmentation algorithm (BG-algorithm). Finally, a correlation analysis was conducted to explore the driving factors of drought events in each subregion. The main conclusions are as follows: (1) The cumulative duration of drought in the study area showed a slight increase trend (0.475 day/decade) and a 19-year main cycle. The drought intensity showed a trend of first easing and then intensifying, especially after 2000; the drought intensified significantly and showed a spatial trend of decreasing drought in the northwest and worsening drought in the southeast. (2) The cumulative contribution rate of the first five modes of REOF decomposition was 64.46%, and the study was divided into five arid subregions: the Hexi region, middle Hedong region, eastern Hedong region, Wushaoling region and western Hedong region. (3) The meteorological drought in the Hexi region has eased significantly since 1988. In the eastern, central and western parts of the Yellow River, drought intensification was observed to have occurred in different degrees (0.12/decade, 0.129/decade, and 0.072/decade). The meteorological drought in the Wuelyaling region has alleviated significantly with a watershed region formed between drought alleviation and drought intensification. (4) Seasonally, the eastern Hedong region showed a significant trend of drought in spring, but the opposite in autumn. The trend of climate drying was obvious in the spring and summer, rather than in autumn and winter. The spring drought trend is the most obvious in the middle of the Hedong region. (5) The meteorological drought in the study area was affected by local climatic factors and circulation factors, but there were significant differences in the responses of different arid subregions to these factors.

Impact and Challenges of Intelligent IoT in Meteorological Science

The abundant data in meteorological science has facilitated applying big data techniques. The data collection was achieved by researchers using different atmospheric sounding methods in the past few decades. Compared with traditional methods, such as statistical forecast approaches and numerical weather prediction, intelligent Internet of Things (IoT) technologies have attracted extensive attention in meteorological science due to their significant advantages in data processing and analysis. In addition, extreme weather events and meteorological disasters have occurred frequently around the world in recent years. Against this background, this article aims to introduce the application of intelligent IoT technologies in meteorological science and elaborate the encountered open problems as well as the challenges in the future. Along with the introduction of intelligent IoT, a comprehensive review of current studies on meteorological observation, forecast, and services with intelligent IoT is provided. Correspondingly, the impact of intelligent IoT on meteorological businesses is analyzed. Finally, as for the meteorological operations in IoT based on artificial intelligence (AI), some open research issues, countermeasures and future potential research directions are put forward.

Landslide inventory mapping derived from multispectral imagery by Support Vector Machine (SVM) algorithm

Indonesia is located right on the equator, which receives a lot of heat from the sun and rainfall. Therefore, Indonesia is prone to hydro meteorological natural disasters such as droughts, large sea waves, erosion, floods and landslides. The National Disaster Management Agency (BNPB) noted that floods are followed by landslides of the total hydro-meteorological disasters that most often occur in Indonesia. An inventory of the distribution of multi-year landslides is essential as a basis for disaster mitigation and disaster risk reduction. The research case study was carried out in an area prone to landslides around Mount Batur, Bali-Indonesia. Characteristics of areas with high rainfall and steep slopes (>45%). Detection of areas affected by landslides can be identified with multispectral remote sensing images such as Sentinel 2 Image with a spectral resolution of 13 bands and a spatial resolution ranging from 10-60 m. Data acquisition was carried out in the period 2017-2021. The Support Vector Machine (SVM) algorithm is an alternative for detecting landslide areas in this study. The result showed that the accuracy assessment of the SVM algorithm on the training and validation/testing models is more than 84%. We obtained carrying out a landslide inventory is 25.29 km2. Based on our analysis, the most extensive landslide distribution was found in Batur Village (South and Central), followed by Songan A, Sukawana, Kintamani, and Buahan Villages. This research can be used to develop the Landslide Susceptibility model so that entering the landslide inventory parameters gives good results. As well as a basis for disaster risk reduction (DRR), especially for the community, government, and tourists in this research location.

Identifying environmental variables in potential flood hazard areas using machine learning approach at Musi Banyuasin Regency, South Sumatra

Meteorological natural disasters are related to climate. Anomaly conditions in warm sea surface temperatures cause the water vapor to overflow into rain-forming clouds, gradually forming high integrated rainfall in some areas of Indonesia. High or extreme rainfall causes a hydro-meteorological disaster in the form of a flood. Musi Banyuasin Regency, South Sumatra, has a concave to flat topography, a swamp area with abundant large and small rivers prone to flood disasters. Between 2012 and 2022, the National Disaster Management Agency (BNPB) recorded 38 locations had been flooded. This study aimed to identify environmental variables that affect the potential flood hazards and areas with a high flood hazard level. This study used a maximum entropy model approach based on machine learning techniques. The model analyzed all the findings in the sample data to produce predictive information on the contributing environmental variables. The sample data was the 38 flood areas with each preliminary fact and topographic characteristic. Threat components were arranged based on environmental variables (aspect, slope, elevation, land cover, rainfall, and distance from the river). The results indicate that contribution of the average rainfall was 58%, elevation was 26.4%, slope angle was 8.6%, slope aspect was 5.8%, land cover was 1%, and river width was 0.1%. Then, the areas with high flood hazard levels were indicated in eight districts, namely Lais, Sekayu, Babat Supat, Keluang, Sungaililin, Lawang Wetan, Babatoman, and Sangadesa.

A Two-Stage Investment Decision-Making Model for Urban Rail Transit Drainage Renovation

Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change for drainage renovation, which consists of an optimal renovation sequence model and an optimal investment timing model. This study analyzes the inundation risk of each station and its node importance in the URT network and then uses a multi-attribute decision analysis (MADA) to determine the optimal renovation sequence. This study also uses a real options pricing approach to calculate the value of an option in order to defer the renovation project and determine the optimal investment timing. Then, the Beijing Urban Rail Transit (BURT) is taken as an example to conduct an empirical analysis of the proposed model. Considering the uncertainty of climate change and the complexity of the URT network, the model can obtain the optimal renovation sequence and the investment timing of each station, which is expected to provide a decision-making tool for urban governments to formulate an optimal plan that strengthens the prevention of flooding disasters.

Blame Game: How scientists got confident attributing disastrous weather to global warming.

Blame Game: How scientists got confident attributing disastrous weather to global warming.

Changes in Tropical Cyclone Disasters Over China During 2001–2020

AbstractTropical cyclones (TCs) constitute one of the major types of meteorological disasters in China, causing extensive damage. Based on the latest quality‐controlled provincial‐level TC disaster losses data over China from 2001 to 2020, the spatiotemporal variation in TC disaster and its link with TC activity in the form of prevailing track, precipitation, and intensity were investigated. Results showed that the areas impacted by TCs expanded significantly in the past decade (2011–2020) compared with the previous decade (2001–2010) throughout China, while the direct economic losses resulting from TCs did not increase significantly. Regionally, TC disaster losses increased in Northeast China but decreased in Fujian province. Causal analysis indicated that the increased influential TC (ITC) frequency and TC precipitation together led to the increase in damage for northern China. Also, more westward ITCs resulted in the increase in TC disasters for southwestern China. For southeastern China, the reduction in TC disasters was related to the northward shift in the northwestward ITC track. Although TC disasters increase with exposure, they are heavily influenced by TC track, precipitation, and intensity. Our findings suggest that more attention should be paid to TC prediction and early warning in northern China, especially Northeast China, where TC disasters are growing but there is greater vulnerability and insufficient capacity for TC prevention and mitigation.

Welfare Consequences of Sustainable Finance

Abstract We model the welfare consequences of mandates that restrict investors to hold firms with net-zero carbon emissions. To qualify for these mandates, value-maximizing firms have to accumulate decarbonization capital. Qualification lowers a firm’s required return by its decarbonization investments divided by Tobin’s q, that is, the greenium or the dividend yield shareholders forgo to address the global-warming externality. The welfare-maximizing mandate approximates the first-best solution, yielding welfare gains compared to laissez-faire by mitigating the weather disaster risks resulting from carbon emissions. Our model generates optimal transition paths for decarbonization that we use to evaluate proposed net-zero targets. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online

Mongolia Contributed More than 42% of the Dust Concentrations in Northern China in March and April 2023

Dust storms are one of the most frequent meteorological disasters in China, endangering agricultural production, transportation, air quality, and the safety of people’s lives and property. Against the backdrop of climate change, Mongolia’s contribution to China’s dust cannot be ignored in recent years. In this study, we used the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), along with dynamic dust sources and the HYSPLIT model, to analyze the contributions of different dust sources to dust concentrations in northern China in March and April 2023. The results show that the frequency of dust storms in 2023 was the highest observed in the past decade. Mongolia and the Taklimakan Desert were identified as two main dust sources contributing to northern China. Specifically, Mongolia contributed more than 42% of dust, while the Taklimakan Desert accounted for 26%. A cold high-pressure center, a cold front, and a Mongolian cyclone resulted in the transport of dust aerosols from Mongolia and the Taklimakan Desert to northern China, where they affected most parts of the region. Moreover, two machine learning methods [the XGBoost algorithm and the Synthetic Minority Oversampling Technique (SMOTE)] were used to forecast the dust storms in March 2023, based on ground observations and WRF-Chem simulations over East Asia. XGBoost-SMOTE performed well in predicting hourly PM10 concentrations in China in March 2023, with a mean absolute error of 33.8 µg m−3 and RMSE of 54.2 µg m−3.

Design of Air Quality Detection System Based on MCU

Because of the deterioration of air quality, haze weather phenomenon increased, the phenomenon of increased harm. Many regions in China incorporate the haze phenomenon into the fog as a disastrous weather warning and forecast. Collectively known as“Haze Weather.”. The haze mainly consists of PM2.5, PM10, PM0.1 and heavy metals such as nickel, arsenic, chromium and lead. There have been a number of major smog-related incidents, such as the Great Smog that killed more than 4,000 people in four days and the Beijing smog epidemic in early 2013. Therefore, the measurement of PM2.5 becomes more and more important. This design uses STC89C51 single-chip microcomputer as the control center, by GP2Y1010AU0F dust sensor to measure the air dust concentration, LCD1602 display display current air dust concentration. According to the set alarm value alarm prompt, the corresponding color indicator light is lit, the system circuit is simple, stable, high integration, easy debugging, high testing precision, has a certain practical value.

Individual emergency response and recovery: What people worried about during and after Hurricane Maria.

Hurricane Maria was a disastrous weather event that devastated Puerto Rico (PR) in September 2017. Yet, little is known about people's perceptions of this event. In this investigation, we offer insight about Hurricane Maria's impact on PR's inhabitants. More specifically, we study a sample (n = 542 responses) of individuals' worry levels through four time points during the aftermath of Hurricane Maria: their variation through time, their relationship to decision making, and if and how certain demographic variables may influence them. For these purposes, we designed and implemented the Individual Emergency Response and Recovery Questionnaire, a web-based survey that measured several aspects of the objective and subjective experiences of individuals who underwent Hurricane Maria in PR. Results of a statistical analysis using nonparametric tests show that some of the demographic variables selected as factors of interest influence the worry levels reported by respondents. Most significant results coincide with conclusions drawn by literature: that time, age group, and the level of information influence worry levels. Another key finding is that the worry level may influence individual decision-making frequency. Understanding principal factors in people's behavior and perceptions during hurricanes is crucial to help us learn how to better prepare for and respond to natural disasters in the future.

Climate change and financial systemic risk: Evidence from US banks and insurers

We study the relationship between climate change and financial systemic risk. First, we test whether, to what extent and how quickly the systemic risk of US banking and insurance sectors reacts to billion-dollar weather and climate disasters. We prove that some extreme events can exacerbate financial systemic risk and provide insights about the different timing at which the reaction of the systemic risk measures takes place. Second, we investigate through quantile regressions how the performance of green and brown market indexes affects the systemic risk of the two US financial sectors. We observe that higher levels of the green indexes reduce systemic risk more than a raise in brown indexes, with an increasing magnitude in tail conditions. A raise in the riskiness of the green indexes seems to significantly increase systemic risk, with the effect being stronger than that of an increase in the riskiness of brown indexes. Our results confirm the importance of the adoption of appropriate policies aiming at contrasting the raise in the frequency and severity of climate disasters. Our findings are also important in the perspective of the likely increase (decrease) in the exposure of financial firms towards green (brown) companies, induced by the policy decisions taken to combat climate change, and in terms of the implications for banks’ and insurers’ risk management models and procedures.