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Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide reference for natural hazard mitigation. The method of named entity recognition has many challenges, such as fast change, multiple types and various forms of named entities. This can introduce difficulties in research of natural hazard named entity recognition. To address the above problem, this paper constructed a natural disaster annotated corpus for training and evaluation model, and selected and compared several deep learning methods based on word vector features. A deep learning method for natural hazard named entity recognition can automatically mine text features and reduce the dependence on manual rules. This paper compares and analyzes the deep learning models from three aspects: pretraining, feature extraction and decoding. A natural hazard named entity recognition method based on deep learning is proposed, namely XLNet-BiLSTM-CRF model. Finally, the research hotspots of natural hazards papers in the past 10 years were obtained through this model. After training, the precision of the XLNet-BilSTM-CRF model is 92.80%, the recall rate is 91.74%, and the F1-score is 92.27%. The results show that this method, which is superior to other methods, can effectively recognize natural hazard named entities. [ABSTRACT FROM AUTHOR]

The stadium is a wind-sensitive structure, and the tornado has a greater damage to the stadium. The Ward tornado generating device was used to simulate the wind load characteristics of the stadium during tornado action in order to investigate the wind load characteristics of the stadium. The results of this paper are compared with the experimental results to verify the accuracy of the simulation method. The wind load characteristics of the stadium and its most vulnerable location during a tornado are ascertained by examining the wind pressure coefficient and wind speed distribution law of the stadium's upper and lower surfaces with different swirl ratio. The influence of uniform wind and wind profile on the wind load of the stadium is explored by comparing and analyzing the wind pressure coefficient of the stadium surface with and without wind profile at the entrance of the wind field. The impact of varying ground roughness on the stadium is compared in order to ascertain how it affects the wind speed, stadium wind pressure, and vortex core radius. The vorticity of the stadium in the tornado wind field with different swirl ratios was compared and analyzed, and the influence of the stadium on the vorticity development of the wind field was explored. This study reveals the effects of swirl ratio, ground roughness and wind profile on the wind pressure, wind speed and vorticity of the stadium under tornadoes, and determines the most vulnerable location and the most unfavorable factors. The above research is helpful to provide reference for disaster prevention and mitigation of stadium structure design. [ABSTRACT FROM AUTHOR]

Joshimath has received much attention for its massive ground subsidence at the beginning of the year. Rapid urbanization and its unique geographical location may have been one of the factors contributing to the occurrence of this geological disaster. In high mountain valley areas, the complex occurrence mechanism and diverse disaster patterns of geological hazards highlight the inadequacy of manual monitoring. To address this problem, the inversion of deformation of the Joshimath surface in multiple directions can be achieved by multidimensional InSAR techniques. Therefore, in this paper, the multidimensional SBAS-InSAR technique was used to process the lift-track Sentinel-1 data from 2020 to 2023 to obtain the two-dimensional vertical and horizontal deformation rates and time series characteristics of the Joshimath ground surface. To discover the causes of deformation and its correlation with anthropogenic activities and natural disasters by analyzing the spatial and temporal evolution of surface deformation. The results show that the area with the largest cumulative deformation is located in the northeastern part of the town, with a maximum cumulative subsidence of 271.2 mm and a cumulative horizontal movement of 336.5 mm. The spatial distribution of surface deformation is based on the lower part of the hill and develops towards the upper part of the hill, showing a trend of expansion from the bottom to the top. The temporal evolution is divided into two phases: gentle to rapid, and it is tentatively concluded that the decisive factor that caused the significant change in the rate of surface deformation and the early onset of the geological subsidence hazard was triggered by the 4.7 magnitude earthquake that struck near the town on 11 September 2021. [ABSTRACT FROM AUTHOR]

Standards and Codes of Practice for designing new constructions and for assessing and strengthening existing ones are usually based on uniform hazard maps, where different Limit States (LSs) are associated with different hazard-exceedance probabilities. This approach yields non-homogeneous LS-exceedance probabilities across a territory, thus failing to achieve the goal of uniform risk throughout a territory. Such lack of uniformity stems from estimating the probability of failure using capacity and demand models. If the capacity of new constructions—or the capacity increase of strengthened existing constructions—are designed based on a prescribed hazard-exceedance probability, then the seismic risk depends on both the structure (depending on the design philosophy and corresponding design objectives), through the capacity model, and the location, through the hazard model. The aim of this study is threefold. First, it provides a seismic probability assessment formulation and a risk-targeted intensity measure based on a linear model in log–log coordinates of the hazard, under the assumption of log-normal capacity and demand. The proposed framework introduces a factor that multiplies the code hazard-based demand to account either for intentional (from design) over-capacity or for undesired (e.g., in existing constructions) under-capacity. Second, this paper shows an application to peak ground accelerations in Europe considering parameters taken from Standards and Codes of Practice. The developed framework is used to determine the risk-target levels of peak ground acceleration used for design in Europe, for both new and existing constructions. Third, the obtained target risk levels are used to determine a risk-based intensity modification factor and a risk-based mean return period modification factor, which can be readily implemented in current Standards to achieve risk-targeted design actions, with equal LS-exceedance probability across the territory. The framework is independent of the chosen hazard-based intensity measure, be it the commonly used peak ground acceleration or any other measure. The results highlight that in large areas of Europe the design peak ground acceleration should be increased to achieve the proposed seismic risk target and that this is particularly significant for existing constructions, given their larger uncertainties and typical low capacity with respect to the code hazard-based demand. [ABSTRACT FROM AUTHOR]

Understanding the location of risk to natural hazards, namely the areas of high exposure and vulnerability is a major priority that was identified by the Sendai framework for Disaster Reduction 2015–2030 in order to reach substantial reduction of disaster risk. It is also a necessary decision-making tool for disaster mitigation policy-makers in Japan and around the world. This paper successfully develops a simple methodology using only open data to build the first large-scale (whole country), fine resolution (sub-municipal level) social vulnerability analysis in the context of five different types of natural hazards (earthquake, tsunami, storm surge, flooding and landslide). The result is then compared to an indicator of exposure of population to these hazards in Japan in order to propose a representation of disaster risk. Results show that vulnerability in Japan is highly heterogeneous with urban/rural and north/south fractures. Combining the social vulnerability index with exposure analysis, results show a wide variety of spatial patterns of risk areas in Japan. [ABSTRACT FROM AUTHOR]

Threats to water supply systems have increased in number and intensity. Natural disasters such as earthquakes have caused different types of damage to water distribution networks (WDN), particularly for those with aged infrastructure. This paper investigates the resilience of an existing water distribution network under seismic hazard. An earthquake generation model coupled with a probabilistic flow-based pressure driven demand hydraulic model is investigated and applied to an existing WDN. A total of 27 earthquake scenarios and 2 repair strategies were simulated. The analysis examined hydraulic resilience metrics such as pressure, leak demand, water serviceability, and population impacted. The results show that nodal pressure drops below nominal pressure and reaches zero in some earthquake scenarios. Leak demand could reach to more than 10 m3/s within hours following an earthquake. Water serviceability drops to a low of 40% and population impacted reaches up to 90% for a 6.5 M earthquake, for example. This study highlights and quantifies vulnerabilities within the simulated WDN. The tools outlined here illustrate an approach that can: (1) ultimately help to better inform utility water safety plans, and (2) prepare proactive strategies to mitigate/repair before a hazard of this nature occurs. [ABSTRACT FROM AUTHOR]

The quantitative spatial analysis is a strong tool for the study of natural hazards and their interactions. Over the last decades, a range of techniques have been exceedingly used in spatial analysis, especially applying GIS and R software. In the present paper, the multi-hazard susceptibility maps compared in 2020 and 2021 using an array of data mining techniques, GIS tools, and Unmanned aerial vehicles. The produced maps imply the most effective morphometric parameters on collapsed pipes, gully heads, and landslides using the linear regression model. The multi-hazard maps prepared using seven classifiers of Boosted regression tree (BRT), Flexible discriminant analysis (FDA), Multivariate adaptive regression spline (MARS), Mixture discriminant analysis (MDA), Random forest (RF), Generalized linear model (GLM), and Support vector machine (SVM). The results of each model revealed that the greatest percentage of the study region was low susceptible to collapsed pipes, landslides, and gully heads, respectively. The results of the multi-hazard models represented that 52.22% and 48.18% of the study region were not susceptible to any hazards in 2020 and 2021, while 6.19% (2020) and 7.39% (2021) of the region were at the risk of all compound events. The validation results indicate the area under the receiver operating characteristic curve of all applied models was more than 0.70 for the landform susceptibility maps in 2020 and 2021. It was found where multiple events co-exist, what their potential interrelated effects are or how they interact jointly. It is the direction to take in the future to determine the combined effect of multi-hazards so that policymakers can have a better attitude toward sustainable management of environmental landscapes and support socio-economic development. [ABSTRACT FROM AUTHOR]

With global climate change and the rapid urbanization, urban flood and drought disasters are frequent and urban water supply systems are facing a sea of serious challenges. It is crucial to assess the resilience of urban water supply systems and develop corresponding disaster mitigation and improvement strategies. Urban water supply systems include many subsystems, but existing researches generally focus on a single subsystem. Therefore, this paper proposes a correlation analysis method and a factor analysis method for the resilience evaluation index system of urban water supply systems by combining each subsystem and applying grey system theory. The method can reflect the four dimensions of the water supply process (water source, water plant, supply and distribution network and users) and the five dimensions of the urban management system (society, natural environment, economy, physics and organization). Taking Qingdao as an example, a multi-level integrated evaluation model based on a cloud model is applied to simulate and analyze the resilience of Qingdao's water supply system. As a result, decision support is provided for planning and building resilience systems for urban water systems in the short and long term, based on four main factors. [ABSTRACT FROM AUTHOR]

Seismological codes of Hermann SP 48 sp for the joint inversion of radial PRFs and fundamental mode group velocity dispersion data of Rayleigh waves can be obtained from the Saint-Louis University's website. Seismological codes of Hermann SP 48 sp for the joint inversion of radial PRFs and fundamental mode group velocity dispersion data of Rayleigh waves can be obtained from the Saint-Louis University's website. These softwares are available in the user domain." now reads: "No commercial code or software has been used in the research presented in the paper. [Extracted from the article]

Arid area is very sensitive to global warming and are extremely vulnerable to climate change. Moreover, the water resources system in the arid area is fragile and will undergo tremendous changes with climate change. Therefore, the interaction of climate and hydrology in arid area has an important impact on the formation of regional microclimate and hydrological changes. Daihai Lake is a typical closed inland lake in arid area of China, and a key area for ecological protection in North China. In this paper, WRF-Hydro model is used to simulate the climate hydrological coupling situation of Daihai Basin from 1980 to 2020, and the coupling results are verified and calibrated by meteorological statistics, runoff calculation and remote sensing analysis. Based on the synopsis of climate and hydrology in the past 40 years, the causes and future trends of the hydrological elements in Daihai Basin are analyzed. Through the analysis, it is found that the interannual variation of precipitation in Daihai Basin is sharp, with 401.75 mm as the average from 1980 to 1994; and drastic fluctuations from 1995 to 2011, with a difference of nearly 400 mm between the interannual maximum and minimum; From 2012 to 2020, the fluctuation is small. Although the interannual variation of evaporation fluctuated, it showed an upward trend with a slope of 8.855 mm/year. The annual average temperature showed an obvious upward trend with a slope of 0.040 °C/year. From 1980 to 2020, the inflow of Daihai Lake shows a downward trend; Since 2013, the runoff into the lake has tended to be flat. Climate change and human activities are the decisive factors leading to the change of water quantity in Daihai, among which human activities play a greater role. Cultivated land irrigation and industrial water use are highly correlated with the lake discharge, and these two factors have a great influence on the lake discharge. If the current agricultural and industrial water consumption does not increase, Daihai still has a lifespan of nearly 120 years. If human activities do not change and any protective measures are not taken in time, under the background of global climate change, the flow of the Daihai Lake into the lake will be reduced to zero in 2025, and the Daihai Lake will completely dry up in 2031–2033. The study of climate hydrological coupling of long time series in Daihai Basin can not only make up for the lack of runoff data, but also provide the basis for water resources management, disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

During the start of the COVID-19 pandemic in 2020, lockdowns and movement restrictions were thought to negatively impact population mental health, since depression and anxiety symptoms were frequently reported. This study investigates the effect of COVID-19 mitigation measures on mental health across the United States, at county and state levels using difference-in-differences analysis. It examines the effect on mental health facility usage and the prevalence of mental illnesses, drawing on large-scale medical claims data for mental health patients joined with publicly available state- and county-specific COVID-19 cases and lockdown information. For consistency, the main focus is on two types of social distancing policies, stay-at-home and school closure orders. Results show that lockdown has significantly and causally increased the usage of mental health facilities in regions with lockdowns in comparison to regions without such lockdowns. Particularly, resource usage increased by 18% in regions with a lockdown compared to 1% decline in regions without a lockdown. Also, female populations have been exposed to a larger lockdown effect on their mental health. Diagnosis of panic disorders and reaction to severe stress significantly increased by the lockdown. Mental health was more sensitive to lockdowns than to the presence of the pandemic itself. The effects of the lockdown increased over an extended time to the end of December 2020. [ABSTRACT FROM AUTHOR]

This research was conducted on industrial agriculture in Indonesia. Risk analysis was carried out based on previous research. One source of risk was obtained, namely raw materials that did not meet specifications, which was then proposed to be mitigated by evaluating supplier performance. This activity involves a lot of data, requiring efficient and effective data storage and access. The level in the simulation layout includes analysing system needs, using problem diagrams, compiling activity diagrams, deciding subprocesses, and filtering information. The analysis is carried out by comparing the use of supply chains with Blockchain and without Blockchain, which is then obtained to determine whether there is an increase. A sequentially stored data scenario describes a situation when the transaction process is in progress and is stored sequentially according to the process that occurs. Storing data in groups explains a problem when a transaction has been completed and stored in groups with similar data, making it easier to track specific data. In this regard, a simulation will be carried out using a website, namely a blockchain demo. The design stage starts with identifying system requirements, creating use case diagrams, compiling activity diagrams, determining subprocesses, and selecting information. The simulation results obtained will be analysed to determine the feasibility of Blockchain as a means of supporting risk mitigation related to data using aspects, including security, trust, traceability, sustainability, and costs. [ABSTRACT FROM AUTHOR]

Hurricanes can trigger widespread landslides and flooding creating compound hazards and multiple risks for vulnerable populations. An example is the island of Dominica in the Caribbean, where the population lives predominantly along the coast close to sea level and is subject to storm surge, with steep topography rising behind, with a propensity for landslides and flash river flooding. The simultaneous occurrence of the multiple hazards amplifies their impacts and couples with physical and social vulnerabilities to threaten lives, livelihoods, and the environment. Neglecting compound hazards underestimates overall risk. Using a whole island macroscale, (level-I) analysis, susceptibility scenarios for hurricanes, triggered landslides, and floods were developed by incorporating physical process parameters. The susceptibilities were combined with vulnerability indicators to map spatial patterns of hurricane multi-risks in Dominica. The analysis adopted a coupled approach involving the frequency ratio (FR), analytic hierarchy process (AHP), and geographic information system (GIS). Detailed hazard modelling was done at selected sites (level-II), incorporating storm surge estimates, landslide runout simulations, and steady flow analysis for floods. High-resolution terrain data and simulation models, the Rapid Mass Movement Simulation (RAMMS) and the hydrologic engineering center's river analysis system (HEC-RAS), were employed. Ground validation confirmed reasonable agreement between projected and observed scenarios across different spatial scales. Following the United Nations Office for disaster risk reduction (UNDRR) call for the inclusion of local, traditional, and indigenous knowledge, feedback, and expert opinion to improve understanding of disaster risk, 17 interviews with local experts and 4 participatory workshops with residents were conducted, and findings were incorporated into the analysis, so as to gain insights into risk perceptions. The study's outcomes encompass projections and quantification of hurricane compound hazards, vulnerabilities, accumulated risks, and an understanding of local priorities. These findings will inform decision-making processes for risk mitigation choices and community actions by providing a new framework for multi-hazard risk assessment that is easy to implement in combining different data forms. [ABSTRACT FROM AUTHOR]

The regional multi-hazards risk assessment poses difficulties due to data access challenges, and the potential interactions between multi-hazards and social vulnerability. For better natural hazards risk perception and preparedness, it is important to study the nature-hazards risk distribution in different areas, specifically a major priority in the areas of high hazards level and social vulnerability. We propose a multi-hazards risk assessment method which considers social vulnerability into the analyzing and utilize machine learning-enabled models to solve this issue. The proposed methodology integrates three aspects as follows: (1) characterization and mapping of multi-hazards (Flooding, Wildfires, and Seismic) using five machine learning methods including Naïve Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR), Random Forest (RF), and K-Means (KM); (2) evaluation of social vulnerability with a composite index tailored for the case-study area and using machine learning models for classification; (3) risk-based quantification of spatial interaction mechanisms between multi-hazards and social vulnerability. The results indicate that RF model performs best in both hazard-related and social vulnerability datasets. The most cities at multi-hazards risk account for 34.12% of total studied cities (covering 20.80% land). Additionally, high multi-hazards level and socially vulnerable cities account for 15.88% (covering 4.92% land). This study generates a multi-hazards risk map which show a wide variety of spatial patterns and a corresponding understanding of where regional high hazards potential and vulnerable areas are. It emphasizes an urgent need to implement information-based prioritization when natural hazards coming, and effective policy measures for reducing natural-hazards risks in future. [ABSTRACT FROM AUTHOR]

In the course of underground engineering, layered slate is often encountered. Understanding the mechanical characteristics of layered slate is a prerequisite for engineering construction and disaster prevention and mitigation. As a result, at the beginning of a project, a large number of indoor tests are required, which are time-consuming and laborious. In addition, the natural joint angle of the layered slate is random, so it is extremely difficult to establish a database of the mechanical characteristics of layered slate. Hence, it is necessary to find a simple, feasible, and high-precision method to determine the Holmquist–Johnson–Cook (HJC) constitutive model parameters for naturally jointed layered slate with different dip angles. This study first determines the HJC constitutive model parameters of layered slate with five specific joint dip angles (0°, 30°, 45°, 60°, and 90°) through static tests and the split Hopkinson pressure bar (SHPB) test. Furthermore, by employing sensitivity analysis methods, the influence of key parameters of the HJC constitutive model on the dynamic peak stress of slate is determined. Among them, parameters A and B have the most significant impact on stress, exceeding 50%. Thirdly, a nonlinear fitting regression method is used to determine the HJC constitutive model parameters of naturally jointed angular slate. The relationship between the HJC model parameters and the inclination angle of slate joints is derived, and the accuracy of these parameters is verified through numerical simulation methods. The error between the numerical simulation and indoor experiments is within 10%, indicating a high level of simulation accuracy. The research findings provide a highly precise numerical simulation method for similar projects. [ABSTRACT FROM AUTHOR]

The physiological effects of vaccination against SARS-CoV-2 (COVID-19) are well documented, yet the behavioural effects not well known. Risk compensation suggests that gains in personal safety, as a result of vaccination, are offset by increases in risky behaviour, such as socialising, commuting and working outside the home. This is potentially important because transmission of SARS-CoV-2 is driven by contacts, which could be amplified by vaccine-related risk compensation. Here, we show that behaviours were overall unrelated to personal vaccination, but—adjusting for variation in mitigation policies—were responsive to the level of vaccination in the wider population: individuals in the UK were risk compensating when rates of vaccination were rising. This effect was observed across four nations of the UK, each of which varied policies autonomously. [ABSTRACT FROM AUTHOR]

Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (Vslope). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (VSlope) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (Vslope) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies. [ABSTRACT FROM AUTHOR]

The earthquake hazard associated with the Main Himalayan Thrust (MHT) is a critical issue for India and its neighbouring countries in the north. We used data from a dense seismic network in Uttarakhand, India, to model the lateral variations in the depths of MHT (2–6% drop in Vs at 12–21 km depths), Moho (a sharp increase in Vs (by ~ 0.5–0.7 km/s) at 39–50 km depths) and lithosphere (a marked decrease in Vs(~ 1–3%) at 136–178 km depths), across the Himalayan collisional front. Our joint inversion of radial PRFs and group velocity dispersion data of Rayleigh waves detects three NNE trending transverse lithospheric blocks segmenting the lithosphere in Uttarakhand Himalaya, which spatially correlate well with the northward extension of the Delhi -Haridwar Indian basement ridge, an inferred tectonic boundary and great boundary fault, respectively. Our radial receiver function imaging detects highly deformed and segmented crustal and lithospheric structures associated with three mapped transverse lithospheric blocks, suggesting a reduction in rupture lengths of future earthquakes, thereby, reducing earthquake hazards in Uttarakhand. [ABSTRACT FROM AUTHOR]

Lung cancer is the second most common cancer in Taiwan. After Taiwan implemented the Tobacco Hazards Prevention Act in 1997, smoking rates declined. However, the incidence rates of lung cancer for both sexes are still increasing, possibly due to risk factors other than smoking. We used age–period–cohort analysis to examine the secular trends of lung cancer incidence rates by histological type in Taiwan. A stabilized kriging method was employed to map these lung cancer incidence rates. Lung adenocarcinoma incidence rates increased, but lung squamous cell carcinoma incidence rates decreased, for both the sexes in recent birth cohorts, particularly in women. In Taiwan, the hotspots of lung adenocarcinoma incidence rates were in the northern, northeastern, and western coastal areas; the incidence rates increased rapidly in the western and southern coastal regions and southern mountainous regions. The high incidence rates of lung squamous cell carcinoma in men were in the southwestern and northeastern coastal areas. The incidence rates rapidly increased in the central and southern coastal and mountainous regions. For both sexes in Taiwan, lung squamous cell carcinoma incidence rates declined from 1997 to 2017, but lung adenocarcinoma increased. The increased incidence rates of lung adenocarcinoma may be related to indoor and outdoor air pollution. Some areas in Taiwan have increasing lung cancer incidence rates, including the northwestern and southern coasts and mountains, and warrant particular attention. [ABSTRACT FROM AUTHOR]

Seismic hazard maps are crucial for earthquake mitigation and mostly rely on probabilistic seismic hazard analysis (PSHA). However, the practise and value of PSHA are under debate because objective testing procedures for seismic hazard maps are scarce. We present a lacustrine turbidite record revealing 44 earthquakes over the last ~ 14 ka and use it to test seismic hazard curves in southern Austria. We derive local seismic intensities for paleo-earthquakes by applying scaling relationships between the sedimentary imprint and seismic intensity of well-documented historical earthquakes. The last ~ 2.8 ka of the record agree with a Poissonian recurrence behaviour and therefore a constant hazard rate, which is the modelling choice for standard PSHA. The lacustrine data are consistent with the intensity-frequency relationship of the local seismic hazard curve, confirming the current PSHA approach for this part of Austria. On longer timescales, distinct phases of enhanced regional seismicity occurred, indicating a potential increase of seismic hazard after large earthquakes—a factor hitherto disregarded in the PSHA of the Eastern Alps. Our new method forms an independent procedure to test hazard maps in any setting where suitable lake systems are available. [ABSTRACT FROM AUTHOR]

To prepare for upcoming extreme events, decision makers, scientists and other stakeholders require a thorough understanding of the vulnerability of the built environment to natural hazards. A vulnerability index based on building characteristics (indicators) rather than empirical data may be an alternative approach to a comprehensive physical vulnerability assessment of the building stock. The present paper focuses on the making of such an index for dynamic flooding in mountain areas demonstrating the transferability of vulnerability assessment approaches between hazard types, reducing the amount of required data and offering a tool that can be used in areas were empirical data are not available. We use data from systematically documented torrential events in the European Alps to select and weight the important indicators using an all-relevant feature selection algorithm based on random forests. The permutation-based feature selection reduced the initial number of indicators from 22 to seven, decreasing in this way the amount of required data for assessing physical vulnerability and ensuring that only relevant indicators are considered. The new Physical Vulnerability Index (PVI) may be used in the mountain areas of Europe and beyond where only few empirical data are available supporting decision-making in reducing risk to dynamic flooding. [ABSTRACT FROM AUTHOR]

Floods are the most commonly occurring natural disasters in India due to India's unique geographical location and socioeconomic conditions. Frequent flooding causes enormous loss of human lives and damage crops and public utilities. Furthermore, floods adversely affect economic development and increase the government's financial burden by increasing spending on various disaster mitigation measures. Recent empirical literature based on cross-national comparisons shows that disaster fatalities and damages are monotonically decreasing in per capita income. We challenge this view on the monotonic negative relationship between income and flood damages. We examine the non-monotonic (inverted U-shaped) relationship between per capita income and flood impact in terms of deaths, people affected, and damages due to floods in 19 major Indian states from 1980 to 2011, using Poisson and Tobit estimation methods. In particular, deaths and the population affected by floods increase with a turning point of income up to 882 US$ and 578 US$, respectively, and diminishes thereafter. Our results confirm an inverted U-shaped relationship between income and fatalities and the population affected by floods. In addition to income, we argue that government responsiveness plays an essential role in mitigating the risk of floods. We employ the fixed-effect Poisson estimation method to examine the government's role in protecting people against disaster risk, focusing on regional differences in India. Deaths from floods remain non-linear and follow the inverted U-pattern with respect to government responsiveness. However, the effect of government responsiveness on flood fatalities and flood damages is statistically insignificant. Our results further suggest that high-income states experience a lower death toll from floods. The high-income (rich) states are capable of incurring a higher threshold level of income and higher natural calamity expenditure to reduce flood fatalities and protect the population affected by floods than the low-income (poor) states. The poor states have minimal resources and face severe financial constraints to reduce the death toll from floods. From the perspective of public policy, the poor states, in particular, require an increase in income, better governance, and effective disaster management policies to mitigate flood impact. [ABSTRACT FROM AUTHOR]

Wildfire management in the US relies on a complex nationwide network of shared resources that are allocated based on regional need. While this network bolsters firefighting capacity, it may also provide pathways for transmission of infectious diseases between fire sites. In this manuscript, we review a first attempt at building an epidemiological model adapted to the interconnected fire system, with the aims of supporting prevention and mitigation efforts along with understanding potential impacts to workforce capacity. Specifically, we developed an agent-based model of COVID-19 built on historical wildland fire assignments using detailed dispatch data from 2016–2018, which form a network of firefighters dispersed spatially and temporally across the US. We used this model to simulate SARS-CoV-2 transmission under several intervention scenarios including vaccination and social distancing. We found vaccination and social distancing are effective at reducing transmission at fire incidents. Under a scenario assuming High Compliance with recommended mitigations (including vaccination), infection rates, number of outbreaks, and worker days missed are effectively negligible, suggesting the recommended interventions could successfully mitigate the risk of cascading infections between fires. Under a contrasting Low Compliance scenario, it is possible for cascading outbreaks to emerge leading to relatively high numbers of worker days missed. As the model was built in 2021 before the emergence of the Delta and Omicron variants, the modeled viral parameters and isolation/quarantine policies may have less relevance to 2022, but nevertheless underscore the importance of following basic prevention and mitigation guidance. This work could set the foundation for future modeling efforts focused on mitigating spread of infectious disease at wildland fire incidents to manage both the health of fire personnel and system capacity. [ABSTRACT FROM AUTHOR]

Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California's population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures. [ABSTRACT FROM AUTHOR]

Autonomous mobility devices such as transport, cleaning, and delivery robots, hold a massive economic and social benefit. However, their deployment should not endanger bystanders, particularly vulnerable populations such as children and older adults who are inherently smaller and fragile. This study compared the risks faced by different pedestrian categories and determined risks through crash testing involving a service robot hitting an adult and a child dummy. Results of collisions at 3.1 m/s (11.1 km/h/6.9 mph) showed risks of serious head (14%), neck (20%), and chest (50%) injuries in children, and tibia fracture (33%) in adults. Furthermore, secondary impact analysis resulted in both populations at risk of severe head injuries, namely, from falling to the ground. Our data and simulations show mitigation strategies for reducing impact injury risks below 5% by either lowering the differential speed at impact below 1.5 m/s (5.4 km/h/3.3 mph) or through the usage of absorbent materials. The results presented herein may influence the design of controllers, sensing awareness, and assessment methods for robots and small vehicles standardization, as well as, policymaking and regulations for the speed, design, and usage of these devices in populated areas. [ABSTRACT FROM AUTHOR]

Floods are among the costliest natural hazards and their consequences are expected to increase further in the future due to urbanization in flood-prone areas. It is essential that policymakers understand the factors governing the dynamics of urbanization to adopt proper disaster risk reduction techniques. Peoples' relocation preferences and their perception of flood risk (collectively called human behavior) are among the most important factors that influence urbanization in flood-prone areas. Current studies focusing on flood risk assessment do not consider the effect of human behavior on urbanization and how it may change the nature of the risk. Moreover, flood mitigation policies are implemented without considering the role of human behavior and how the community will cope with measures such as buyout, land acquisition, and relocation that are often adopted to minimize development in flood-prone regions. Therefore, such policies may either be resisted by the community or result in severe socioeconomic consequences. In this study, we present a new Agent-Based Model (ABM) to investigate the complex interaction between human behavior and urbanization and its role in creating future communities vulnerable to flood events. We identify critical factors in the decisions of households to locate or relocate and adopt policies compatible with human behavior. The results show that when people are informed about the flood risk and proper incentives are provided, the demand for housing within 500-year floodplain may be reduced as much as 15% by 2040 for the case study considered. On the contrary, if people are not informed of the risk, 29% of the housing choices will reside in floodplains. The analyses also demonstrate that neighborhood quality—influenced by accessibility to highways, education facilities, the city center, water bodies, and green spaces, respectively—is the most influential factor in peoples' decisions on where to locate. These results provide new insights that may be used to assist city planners and stakeholders in examining tradeoffs between costs and benefits of future land development in achieving sustainable and resilient cities. [ABSTRACT FROM AUTHOR]

It is of great importance to explore the future spatiotemporal dynamics of key meteorological hazard factors in Xiongan New Area, an area of great strategic significance under construction in China. Based on 6.25 km high-resolution downscaling projection data under RCP4.5 and RCP8.5 scenarios, Mann–Kendall trend and linear trend were analyzed, and then stationary generalized extreme value (GEV) and time-varying GEV methods were determined to calculate the extremes of four key meteorological hazard factors with return periods of 10, 20, 30, 50 and 100 years during the projection period 1991–2050. Results show that extremes of annual maximum daily precipitation and annual maximum amount of consecutive precipitation under two climate scenarios will not increase too much. Extremes of annual maximum temperature will increase by above 1.5 °C under RCP4.5 scenario in most grids and above 1.9 °C under RCP8.5 scenario. Extremes of annual longest consecutive high-temperature days will increase by above 0.9d under RCP4.5 scenario and above 1.6d under RCP8.5 scenario. On the whole, the hazard of flood disaster will hardly show any change up to 2050, but there will be relatively higher flood hazard in Xiongxian county and its adjacent region. All regions in Xiongan New Area will face high hazard of high-temperature disaster. [ABSTRACT FROM AUTHOR]

Infectious disease outbreaks are increasingly recognised as events that exacerbate impacts or prolong recovery following disasters. Yet, our understanding of the frequency, geography, characteristics and risk factors of post-disaster disease outbreaks globally is lacking. This limits the extent to which disease outbreak risks can be prepared for, monitored and responded to following disasters. Here, we conducted a global systematic review of post-disaster outbreaks and found that outbreaks linked to conflicts and hydrological events were most frequently reported, and most often caused by bacterial and water-borne agents. Lack of adequate WASH facilities and poor housing were commonly reported risk factors. Displacement, through infrastructure damage, can lead to risk cascades for disease outbreaks; however, displacement can also be an opportunity to remove people from danger and ultimately protect health. The results shed new light on post-disaster disease outbreaks and their risks. Understanding these risk factors and cascades, could help improve future region-specific disaster risk reduction. [ABSTRACT FROM AUTHOR]

In Alpine regions changes in seasonal climatic parameters, such as temperature, rainfall, and snow amount have already been observed. Specifically, in the South Tyrol area, meteorological observations indicate that temperatures are increasing and the number of snow days has generally diminished over time with perennial snow line now observed at higher elevations. Changes in rainfall have also been observed with more events associated with higher temperatures in the summer season. Natural hazards - mainly debris and mud flows, landslides, avalanches, rock falls, and (flash) floods - that affect this area every year, damaging population and infrastructures, are either weather or cryosphere-related. While these events have been recorded sporadically since the beginning of the 20th century, a systematic approach of their inventory has been done by local authorities since the 1990s. So far, Earth observation data has not been exploited to complete or complement existing inventories nor have they been used to investigate the influence of climate perturbation on potentially dangerous natural phenomena. The research presented here thus has three objectives: (i) analyse long time series of climate data and hazard occurrence in South Tyrol to examine if these records exhibit a coherent response of hazards to changes in climate; (ii) measure the spatio-temporal evolution of climatic and natural hazard events recorded, and (iii) explore potential relations between meteorological conditions and the hazard occurrence. In this context, in-situ and satellite-based climate data are exploited to study natural hazard triggers while the potential of Earth observation data is evaluated as a complement to the existing historical records of natural hazards. Specifically, Copernicus Sentinel-1 images are used to detect the spatio-temporal distribution of slow earth surface deformations and the results used for checking the completeness of the actual slow-moving landslide inventories. Hazard-related changes in the South Tyrolian landscape have also been analysed in relation to particular meteorological events at a regional scale, assessing trends and anomalies. Results show that: (i) satellite data are very useful to complement the existing natural hazard inventories; (ii) in-situ and satellite-based climate records show similar patterns but differ due to regional versus local variability; (iii) even in a data-rich region such as the analysed area, the overall response of natural hazard occurrence, magnitude, and frequency to change in climate variables is difficult to decipher due to the presence of multiple triggers and locally driven ground responses. However, an increase in the average annual duration of rainfall events and debris flow occurrence can be observed. [ABSTRACT FROM AUTHOR]

The northern Bahamas have experienced more frequent intense-hurricane impacts than almost anywhere else in the Atlantic since 1850 CE. In 2019, category 5 (Saffir-Simpson scale) Hurricane Dorian demonstrated the destructive potential of these natural hazards. Problematically, determining whether high hurricane activity levels remained constant through time is difficult given the short observational record (< 170 years). We present a 700-year long, near-annually resolved stratigraphic record of hurricane passage near Thatchpoint Blue Hole (TPBH) on Abaco Island, The Bahamas. Using longer sediment cores (888 cm) and more reliable age-control, this study revises and temporally expands a previous study from TPBH that underestimated the sedimentation rate. TPBH records at least 13 ≥ category 2 hurricanes per century between 1500 to 1670 CE, which exceeds the 9 ≥ category 2 hurricanes per century within 50 km of TPBH since 1850 CE. The eastern United States also experienced frequent hurricanes from 1500 to 1670 CE, but frequency was depressed elsewhere in the Atlantic Ocean. This suggests that spatial heterogeneity in Atlantic hurricane activity since 1850 CE could have persisted throughout the last millennium. This heterogeneity is impacted by climatic and stochastic forcing, but additional high-resolution paleo-hurricane reconstructions are required to assess the mechanisms that impact regional variability. [ABSTRACT FROM AUTHOR]

Many materials are produced, processed and stored as grains, while granularity of matter can be crucial in triggering potentially catastrophic geological events like landslides, avalanches and earthquakes. The response of grain assemblies to shear stress is therefore of utmost relevance to both human and natural environment. At low shear rate a granular system flows intermittently by distinct avalanches. In such state the avalanche velocity in time is expected to follow a symmetrical and universal average behavior, whose dependence on the slip size reduces to a scale factor. Analyzing data from long lasting experiments, we observe a breakdown of this scaling: While in short slips velocity shows indeed a self-similar and symmetric profile, it does not in long slips. The investigation of frictional response in these different regimes evidences that this breakdown can be traced back to the onset of a friction weakening, which is of dynamical origin and can amplify instabilities exactly in this critical state, the most frequent state for natural hazards. [ABSTRACT FROM AUTHOR]

The El Nino and Southern Oscillation (ENSO) 'diversity' has been considered as a major factor limiting its predictability, a critical need for disaster mitigation associated with the trademark climatic swings of the ENSO. Improving climate models for ENSO forecasts relies on deeper understanding of the ENSO diversity but currently at a nascent stage. Here, we show that the ENSO diversity thought previously as 'complex,' arises largely as varied contributions from three leading modes of the ENSO to a given event. The ENSO 'slow manifold' can be fully described by three leading predictable modes, a quasi-quadrennial mode (QQD), a quasi-biennial (QB) mode and a decadal modulation of the quasi-biennial (DQB). The modal description of ENSO provides a framework for understanding the predictability of and global teleconnections with the ENSO. We further demonstrate it to be a useful framework for understanding biases of climate models in simulating and predicting the ENSO. Therefore, skillful prediction of all shades of ENSO depends critically on the coupled models' ability to simulate the three modes with fidelity, providing basis for optimism for future of ENSO forecasts. [ABSTRACT FROM AUTHOR]

Working with a large temporal dataset spanning several decades often represents a challenging task, especially when the record is heterogeneous and incomplete. The use of statistical laws could potentially overcome these problems. Here we apply Benford's Law (also called the 'First-Digit Law') to the traveled distances of tropical cyclones since 1842. The record of tropical cyclones has been extensively impacted by improvements in detection capabilities over the past decades. We have found that, while the first-digit distribution for the entire record follows Benford's Law prediction, specific changes such as satellite detection have had serious impacts on the dataset. The least-square misfit measure is used as a proxy to observe temporal variations, allowing us to assess data quality and homogeneity over the entire record, and at the same time over specific periods. Such information is crucial when running climatic models and Benford's Law could potentially be used to overcome and correct for data heterogeneity and/or to select the most appropriate part of the record for detailed studies. [ABSTRACT FROM AUTHOR]